pax_global_header00006660000000000000000000000064147574034460014530gustar00rootroot0000000000000052 comment=8db0b3812e2032544c1fc00d02d4256cab043f3d mir_eval-0.8.2/000077500000000000000000000000001475740344600133355ustar00rootroot00000000000000mir_eval-0.8.2/.codespell_ignore_list000066400000000000000000000000321475740344600177010ustar00rootroot00000000000000nce fpr shepard dum theis mir_eval-0.8.2/.github/000077500000000000000000000000001475740344600146755ustar00rootroot00000000000000mir_eval-0.8.2/.github/environment-docs.yml000066400000000000000000000002251475740344600207110ustar00rootroot00000000000000name: docs channels: - conda-forge - defaults dependencies: - pip - numpy >=1.15.4 - scipy >=1.4.0 - matplotlib-base>=3.3.0 - numpydoc mir_eval-0.8.2/.github/environment-lint.yml000066400000000000000000000004021475740344600207240ustar00rootroot00000000000000name: lint channels: - conda-forge - defaults dependencies: # required - pip - bandit - codespell - flake8 - pytest - pydocstyle # Dependencies for velin - numpydoc>=1.1.0 - sphinx>=5.1.0 - pygments - black - pip: - velin mir_eval-0.8.2/.github/environment-minimal.yml000066400000000000000000000002611475740344600214070ustar00rootroot00000000000000name: test channels: - conda-forge - defaults dependencies: - pip - numpy ==1.15.4 - scipy ==1.4.0 - matplotlib-base==3.3.0 - pytest - pytest-cov - pytest-mpl mir_eval-0.8.2/.github/environment.yml000066400000000000000000000002611475740344600177630ustar00rootroot00000000000000name: test channels: - conda-forge - defaults dependencies: - pip - numpy >=1.15.4 - scipy >=1.4.0 - matplotlib-base>=3.3.0 - pytest - pytest-cov - pytest-mpl mir_eval-0.8.2/.github/workflows/000077500000000000000000000000001475740344600167325ustar00rootroot00000000000000mir_eval-0.8.2/.github/workflows/docs-ci.yml000066400000000000000000000041031475740344600207740ustar00rootroot00000000000000name: CI Docs on: push: branches: - main pull_request: branches: - main concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: True jobs: setup: name: "Doc environment setup" runs-on: ${{ matrix.os }} strategy: fail-fast: true matrix: include: - os: ubuntu-latest python-version: "3.8" channel-priority: "flexible" envfile: ".github/environment-docs.yml" steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Cache conda uses: actions/cache@v4 env: # Increase this value to reset cache if etc/example-environment.yml has not changed CACHE_NUMBER: 0 with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-${{ matrix.python-version }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles( matrix.envfile ) }} - name: Install Conda environment uses: conda-incubator/setup-miniconda@v3 with: auto-update-conda: false python-version: ${{ matrix.python-version }} add-pip-as-python-dependency: true auto-activate-base: false activate-environment: docs channel-priority: ${{ matrix.channel-priority }} environment-file: ${{ matrix.envfile }} use-only-tar-bz2: false # IMPORTANT: This needs to be set for caching to work properly! - name: Conda info shell: bash -l {0} run: | conda info -a conda list - name: Install mir_eval shell: bash -l {0} run: python -m pip install --upgrade-strategy only-if-needed -e .[docs] - name: Build docs shell: bash -l {0} working-directory: docs run: make html - name: Link checking id: linkcheck shell: bash -l {0} working-directory: docs run: make linkcheck mir_eval-0.8.2/.github/workflows/lint_python.yml000066400000000000000000000036761475740344600220400ustar00rootroot00000000000000name: lint_python on: [pull_request, push] jobs: lint_python: name: "Lint and code analysis" runs-on: ubuntu-latest strategy: fail-fast: true matrix: include: - os: ubuntu-latest python-version: "3.11" channel-priority: "flexible" envfile: ".github/environment-lint.yml" steps: - uses: actions/checkout@v4 with: fetch-depth: 0 - name: Cache conda uses: actions/cache@v4 env: CACHE_NUMBER: 0 with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-${{ matrix.python-version }}-conda-${{ env.CACHE_NUMBER }}-${{ hashFiles( matrix.envfile ) }} - name: Install conda environmnent uses: conda-incubator/setup-miniconda@v3 with: auto-update-conda: false python-version: ${{ matrix.python-version }} add-pip-as-python-dependency: true auto-activate-base: false activate-environment: lint # mamba-version: "*" channel-priority: ${{ matrix.channel-priority }} environment-file: ${{ matrix.envfile }} use-only-tar-bz2: false - name: Conda info shell: bash -l {0} run: | conda info -a conda list - name: Spell check package shell: bash -l {0} run: codespell --ignore-words .codespell_ignore_list mir_eval - name: Security check shell: bash -l {0} run: bandit --recursive --skip B101,B110 . - name: Style check package shell: bash -l {0} run: python -m flake8 mir_eval - name: Format check package shell: bash -l {0} run: python -m black --check mir_eval - name: Format check tests shell: bash -l {0} run: python -m black --check tests - name: Docstring check shell: bash -l {0} run: python -m velin --check mir_eval - name: Docstring style check shell: bash -l {0} run: python -m pydocstyle mir_eval mir_eval-0.8.2/.github/workflows/release.yml000066400000000000000000000016471475740344600211050ustar00rootroot00000000000000name: Publish Python 🐍 distributions 📦 to PyPI on: release: types: [created] jobs: pypi-publish: name: Upload release to PyPI runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/mir_eval permissions: id-token: write # IMPORTANT: this permission is mandatory for trusted publishing steps: - uses: actions/checkout@v4 - name: Set up Python 3.12 uses: actions/setup-python@v5 with: python-version: 3.12 - name: Install pypa/build run: >- python -m pip install build --user - name: Build a binary wheel and a source tarball run: >- python -m build --sdist --wheel --outdir dist/ . - name: Publish package distributions to PyPI if: startsWith(github.ref, 'refs/tags') uses: pypa/gh-action-pypi-publish@release/v1 mir_eval-0.8.2/.github/workflows/test.yml000066400000000000000000000052631475740344600204420ustar00rootroot00000000000000name: Test Python code on: pull_request: branches: - main push: branches: - main concurrency: group: ${{ github.workflow }}-${{ github.ref }} cancel-in-progress: True jobs: test: strategy: fail-fast: false matrix: os: [ubuntu-latest] python-version: ["3.8", "3.10", "3.11", "3.12"] channel-priority: [strict] envfile: [".github/environment.yml"] include: - python-version: "3.13" os: macos-latest - python-version: "3.13" os: windows-latest - python-version: "3.13" os: ubuntu-latest channel-priority: flexible - os: ubuntu-latest python-version: "3.7" envfile: ".github/environment-minimal.yml" channel-priority: "flexible" name: "Minimal dependencies" runs-on: ${{ matrix.os }} steps: - uses: actions/checkout@v4 - name: Cache conda packages uses: actions/cache@v4 with: path: ~/conda_pkgs_dir key: ${{ runner.os }}-${{ matrix.python-version }}-${{ hashFiles( matrix.envfile ) }} - name: Create conda environment uses: conda-incubator/setup-miniconda@v3 with: python-version: ${{ matrix.python-version }} auto-activate-base: false channel-priority: ${{ matrix.channel-priority }} environment-file: ${{ matrix.envfile }} # Disabling bz2 to get more recent dependencies. # NOTE: this breaks cache support, so CI will be slower. use-only-tar-bz2: False # IMPORTANT: This needs to be set for caching to work properly! - name: Install package in development mode shell: bash -l {0} run: python -m pip install --upgrade-strategy=only-if-needed -e .[display,tests] - name: Log installed packages for debugging shell: bash -l {0} run: | conda info -a conda list - name: Show libraries in the system on which NumPy was built shell: bash -l {0} run: python -c "import numpy; numpy.show_config()" - name: Show libraries in the system on which SciPy was built shell: bash -l {0} run: python -c "import scipy; scipy.show_config()" - name: Run unit tests shell: bash -l {0} run: pytest working-directory: tests - name: Upload coverage to Codecov uses: codecov/codecov-action@v5 with: token: ${{ secrets.CODECOV_TOKEN }} files: ./coverage.xml flags: unittests env_vars: OS,PYTHON name: codecov-umbrella fail_ci_if_error: true verbose: true mir_eval-0.8.2/.gitignore000066400000000000000000000007021475740344600153240ustar00rootroot00000000000000*.py[co] # Packages *.egg *.egg-info dist build eggs parts bin var sdist develop-eggs .installed.cfg # Installer logs pip-log.txt # Unit test / coverage reports .coverage .tox #Translations *.mo #Mr Developer .mr.developer.cfg # OS generated files # .DS_Store .DS_Store? ._* .Spotlight-V100 .Trashes ehthumbs.db Thumbs.db # Vim *.swp # pycharm .idea/* # docs docs/_build/* # matplotlib tests tests/result_images/* # coverage coverage.xml mir_eval-0.8.2/.readthedocs.yaml000066400000000000000000000011541475740344600165650ustar00rootroot00000000000000# Read the Docs configuration file # See https://docs.readthedocs.io/en/stable/config-file/v2.html for details # Required version: 2 # Set the OS, Python version, and other tools you might need build: os: ubuntu-24.04 tools: python: "3.12" # Build documentation in the "docs/" directory with Sphinx sphinx: configuration: docs/conf.py # Optionally, but recommended, # declare the Python requirements required to build your documentation # See https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html python: install: - method: pip path: . extra_requirements: - docs mir_eval-0.8.2/LICENSE.txt000066400000000000000000000020671475740344600151650ustar00rootroot00000000000000The MIT License (MIT) Copyright (c) 2014 Colin Raffel Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. mir_eval-0.8.2/README.rst000066400000000000000000000026751475740344600150360ustar00rootroot00000000000000.. image:: https://anaconda.org/conda-forge/mir_eval/badges/version.svg :target: https://anaconda.org/conda-forge/mir_eval .. image:: https://img.shields.io/pypi/v/mir_eval.svg :target: https://pypi.python.org/pypi/mir_eval .. image:: https://github.com/mir-evaluation/mir_eval/actions/workflows/test.yml/badge.svg :target: https://github.com/mir-evaluation/mir_eval/actions/workflows/test.yml .. image:: https://codecov.io/gh/mir-evaluation/mir_eval/graph/badge.svg?token=OzRL3aW7TX :target: https://codecov.io/gh/mir-evaluation/mir_eval .. image:: https://img.shields.io/pypi/l/mir_eval.svg :target: https://github.com/mir-evaluation/mir_eval/blob/main/LICENSE.txt mir_eval ======== Python library for computing common heuristic accuracy scores for various music/audio information retrieval/signal processing tasks. Documentation, including installation and usage information: https://mir-evaluation.github.io/mir_eval/ Dependencies: * `Scipy/Numpy `_ * `decorator `_ If you use mir_eval in a research project, please cite the following paper: Colin Raffel, Brian McFee, Eric J. Humphrey, Justin Salamon, Oriol Nieto, Dawen Liang, and Daniel P. W. Ellis, "`mir_eval: A Transparent Implementation of Common MIR Metrics `_", Proceedings of the 15th International Conference on Music Information Retrieval, 2014. mir_eval-0.8.2/docs/000077500000000000000000000000001475740344600142655ustar00rootroot00000000000000mir_eval-0.8.2/docs/Makefile000066400000000000000000000151621475740344600157320ustar00rootroot00000000000000# 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) . # the i18n builder cannot share the environment and doctrees with the others I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) . .PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest 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 " 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)" 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/mir_eval.qhcp" @echo "To view the help file:" @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/mir_eval.qhc" devhelp: $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp @echo @echo "Build finished." @echo "To view the help file:" @echo "# mkdir -p $$HOME/.local/share/devhelp/mir_eval" @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/mir_eval" @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. The message catalogs are in $(BUILDDIR)/locale." changes: $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes @echo @echo "The overview file is in $(BUILDDIR)/changes." linkcheck: $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck @echo @echo "Link check complete; look for any errors in the above output " \ "or in $(BUILDDIR)/linkcheck/output.txt." doctest: $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest @echo "Testing of doctests in the sources finished, look at the " \ "results in $(BUILDDIR)/doctest/output.txt." xml: $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml @echo @echo "Build finished. The XML files are in $(BUILDDIR)/xml." pseudoxml: $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml @echo @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." mir_eval-0.8.2/docs/api/000077500000000000000000000000001475740344600150365ustar00rootroot00000000000000mir_eval-0.8.2/docs/api/alignment.rst000066400000000000000000000002341475740344600175450ustar00rootroot00000000000000mir_eval.alignment ================== .. automodule:: mir_eval.alignment :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/beat.rst000066400000000000000000000002151475740344600165010ustar00rootroot00000000000000mir_eval.beat ============= .. automodule:: mir_eval.beat :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/chord.rst000066400000000000000000000002201475740344600166610ustar00rootroot00000000000000mir_eval.chord ============== .. automodule:: mir_eval.chord :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/display.rst000066400000000000000000000002251475740344600172340ustar00rootroot00000000000000mir_eval.display ================ .. automodule:: mir_eval.display :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/hierarchy.rst000066400000000000000000000002341475740344600175450ustar00rootroot00000000000000mir_eval.hierarchy ================== .. automodule:: mir_eval.hierarchy :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/index.rst000066400000000000000000000005401475740344600166760ustar00rootroot00000000000000API Reference ============= Below is the reference documentation for all **mir_eval** submodules: .. toctree:: :maxdepth: 2 :titlesonly: alignment beat chord melody multipitch onset pattern segment hierarchy separation tempo transcription transcription_velocity key util io sonify display mir_eval-0.8.2/docs/api/io.rst000066400000000000000000000002071475740344600161760ustar00rootroot00000000000000mir_eval.io =========== .. automodule:: mir_eval.io :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/key.rst000066400000000000000000000002121475740344600163530ustar00rootroot00000000000000mir_eval.key ============ .. automodule:: mir_eval.key :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/melody.rst000066400000000000000000000002231475740344600170560ustar00rootroot00000000000000mir_eval.melody =============== .. automodule:: mir_eval.melody :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/multipitch.rst000066400000000000000000000002371475740344600177540ustar00rootroot00000000000000mir_eval.multipitch =================== .. automodule:: mir_eval.multipitch :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/onset.rst000066400000000000000000000002201475740344600167120ustar00rootroot00000000000000mir_eval.onset ============== .. automodule:: mir_eval.onset :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/pattern.rst000066400000000000000000000002261475740344600172450ustar00rootroot00000000000000mir_eval.pattern ================ .. automodule:: mir_eval.pattern :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/segment.rst000066400000000000000000000002261475740344600172320ustar00rootroot00000000000000mir_eval.segment ================ .. automodule:: mir_eval.segment :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/separation.rst000066400000000000000000000002371475740344600177370ustar00rootroot00000000000000mir_eval.separation =================== .. automodule:: mir_eval.separation :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/sonify.rst000066400000000000000000000002231475740344600170740ustar00rootroot00000000000000mir_eval.sonify =============== .. automodule:: mir_eval.sonify :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/tempo.rst000066400000000000000000000002201475740344600167060ustar00rootroot00000000000000mir_eval.tempo ============== .. automodule:: mir_eval.tempo :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/transcription.rst000066400000000000000000000002501475740344600204640ustar00rootroot00000000000000mir_eval.transcription ====================== .. automodule:: mir_eval.transcription :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/transcription_velocity.rst000066400000000000000000000003031475740344600224010ustar00rootroot00000000000000mir_eval.transcription_velocity =============================== .. automodule:: mir_eval.transcription_velocity :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/api/util.rst000066400000000000000000000002151475740344600165430ustar00rootroot00000000000000mir_eval.util ============= .. automodule:: mir_eval.util :members: :undoc-members: :show-inheritance: :member-order: bysource mir_eval-0.8.2/docs/changes.rst000066400000000000000000000236271475740344600164410ustar00rootroot00000000000000Changes ======= v0.8.2 ------ - `#418`_: Fixed a bug in time-frequency sonification with single-interval inputs. .. _#418: https://github.com/mir-evaluation/mir_eval/pull/418 v0.8.1 ------ - `#410`_: Fixed several errors with time-frequency sonification - `#412`_: Removed unused import of deprecated scipy submodule - `#416`_: `mir_eval.io` routines now support `pathlib.Path` (and generally `os.Pathlike`) inputs as well as strings or open file descriptors. - `#416`_: `mir_eval.io.load_wav` is deprecated and will be removed in v0.9.' .. _#410: https://github.com/mir-evaluation/mir_eval/pull/410 .. _#412: https://github.com/mir-evaluation/mir_eval/pull/412 .. _#416: https://github.com/mir-evaluation/mir_eval/pull/416 v0.8 ---- - `#397`_: Removed invalid chord types from `chord.QUALITIES` - `#390`_: Migrate from personal to organization account - `#388`_: Update to packaging workflow - `#387`_: Various modernizations - `#385`_: Fixed import error - `#384`_: Rename `testing` to `tests` - `#382`_: Deprecated the source separation module - `#380`_: Matplotlib support modernization - `#379`_: Support nans in pitch contour sonification - `#378`_: Improved efficiency in chord validation - `#374`_: Fixed length calculation error in `sonify.time_frequency` - `#370`_: Test and CI modernization - `#367`_: Added interpolation method to docs for `resample_multipitch` - `#361`_: Added PyPI package release workflow - `#360`_: Fixed broken link - `#359`_: Remove support for python 2 - `#356`_, `#357`_, `#358`_: Migrate testing to github actions - `#355`_: Optimize and fix sonification - `#353`_: Set numerical precision, fix support for modern numpy .. _#397: https://github.com/mir-evaluation/mir_eval/pull/397 .. _#390: https://github.com/mir-evaluation/mir_eval/pull/390 .. _#388: https://github.com/mir-evaluation/mir_eval/pull/388 .. _#387: https://github.com/mir-evaluation/mir_eval/pull/387 .. _#385: https://github.com/mir-evaluation/mir_eval/pull/385 .. _#384: https://github.com/mir-evaluation/mir_eval/pull/384 .. _#382: https://github.com/mir-evaluation/mir_eval/pull/382 .. _#380: https://github.com/mir-evaluation/mir_eval/pull/380 .. _#379: https://github.com/mir-evaluation/mir_eval/pull/379 .. _#378: https://github.com/mir-evaluation/mir_eval/pull/378 .. _#374: https://github.com/mir-evaluation/mir_eval/pull/374 .. _#370: https://github.com/mir-evaluation/mir_eval/pull/370 .. _#367: https://github.com/mir-evaluation/mir_eval/pull/367 .. _#361: https://github.com/mir-evaluation/mir_eval/pull/361 .. _#360: https://github.com/mir-evaluation/mir_eval/pull/360 .. _#359: https://github.com/mir-evaluation/mir_eval/pull/359 .. _#356: https://github.com/mir-evaluation/mir_eval/pull/356 .. _#357: https://github.com/mir-evaluation/mir_eval/pull/357 .. _#358: https://github.com/mir-evaluation/mir_eval/pull/358 .. _#355: https://github.com/mir-evaluation/mir_eval/pull/355 .. _#353: https://github.com/mir-evaluation/mir_eval/pull/353 v0.7 ---- - `#334`_: Support notation for unknown/ambiguous key or mode - `#343`_: Add suite of alignment metrics .. _#334: https://github.com/mir-evaluation/mir_eval/pull/334 .. _#343: https://github.com/mir-evaluation/mir_eval/pull/343 v0.6 ---- - `#297`_: Return 0 when no overlap in transcription_velocity - `#299`_: Allow one reference tempo and both estimate tempi to be zero - `#301`_: Allow zero tolerance in tempo, but issue a warning - `#302`_: Loosen separation test tolerance - `#305`_: Use toarray instead of todense for sparse matrices - `#307`_: Use tuple index in chord.rotate_bitmap_to_root - `#309`_: Require matplotlib <3 for testing - `#312`_: Fix raw chroma accuracy for unvoiced estimates - `#320`_: Add comment support to io methods - `#323`_: Fix interpolation in sonify.time_frequency - `#324`_: Add generalized melody metrics - `#327`_: Stop testing 2.7 - `#328`_: Cast n_voiced to int in display.multipitch .. _#297: https://github.com/mir-evaluation/mir_eval/pull/297 .. _#299: https://github.com/mir-evaluation/mir_eval/pull/299 .. _#301: https://github.com/mir-evaluation/mir_eval/pull/301 .. _#302: https://github.com/mir-evaluation/mir_eval/pull/302 .. _#305: https://github.com/mir-evaluation/mir_eval/pull/305 .. _#307: https://github.com/mir-evaluation/mir_eval/pull/307 .. _#309: https://github.com/mir-evaluation/mir_eval/pull/309 .. _#312: https://github.com/mir-evaluation/mir_eval/pull/312 .. _#320: https://github.com/mir-evaluation/mir_eval/pull/320 .. _#323: https://github.com/mir-evaluation/mir_eval/pull/323 .. _#324: https://github.com/mir-evaluation/mir_eval/pull/324 .. _#327: https://github.com/mir-evaluation/mir_eval/pull/327 .. _#328: https://github.com/mir-evaluation/mir_eval/pull/328 v0.5 ---- - `#222`_: added int cast for inferred length in sonify.clicks - `#225`_: improved t-measures and l-measures - `#227`_: added marginal flag to segment.nce - `#234`_: update display to use matplotlib 2 - `#236`_: force integer division in beat.pscore - `#240`_: fix unit tests for source separation - `#242`_: use regexp in chord label validation - `#245`_: add labeled interval formatter to display - `#247`_: do not sonify negative amplitudes in time_frequency - `#249`_: support gaps in util.interpolate_intervals - `#252`_: add modulo and length arguments to chord.scale_degree_to_bitmap - `#254`_: fix bss_eval_images single-frame fallback documentation - `#255`_: fix crackle in sonify.time_frequency - `#258`_: make util.match_events faster - `#259`_: run pep8 check after nosetests - `#263`_: add measures for chord over- and under-segmentation - `#266`_: add amplitude parameter to sonify.pitch_contour - `#268`_: update display tests to support mpl2.1 - `#277`_: update requirements and fix deprecations - `#279`_: isolate matplotlib side effects - `#282`_: remove evaluator scripts - `#283`_: add transcription eval with velocity .. _#222: https://github.com/mir-evaluation/mir_eval/pull/222 .. _#225: https://github.com/mir-evaluation/mir_eval/pull/225 .. _#227: https://github.com/mir-evaluation/mir_eval/pull/227 .. _#234: https://github.com/mir-evaluation/mir_eval/pull/234 .. _#236: https://github.com/mir-evaluation/mir_eval/pull/236 .. _#240: https://github.com/mir-evaluation/mir_eval/pull/240 .. _#242: https://github.com/mir-evaluation/mir_eval/pull/242 .. _#245: https://github.com/mir-evaluation/mir_eval/pull/245 .. _#247: https://github.com/mir-evaluation/mir_eval/pull/247 .. _#249: https://github.com/mir-evaluation/mir_eval/pull/249 .. _#252: https://github.com/mir-evaluation/mir_eval/pull/252 .. _#254: https://github.com/mir-evaluation/mir_eval/pull/254 .. _#255: https://github.com/mir-evaluation/mir_eval/pull/255 .. _#258: https://github.com/mir-evaluation/mir_eval/pull/258 .. _#259: https://github.com/mir-evaluation/mir_eval/pull/259 .. _#263: https://github.com/mir-evaluation/mir_eval/pull/263 .. _#266: https://github.com/mir-evaluation/mir_eval/pull/266 .. _#268: https://github.com/mir-evaluation/mir_eval/pull/268 .. _#277: https://github.com/mir-evaluation/mir_eval/pull/277 .. _#279: https://github.com/mir-evaluation/mir_eval/pull/279 .. _#282: https://github.com/mir-evaluation/mir_eval/pull/282 .. _#283: https://github.com/mir-evaluation/mir_eval/pull/283 v0.4 ---- - `#189`_: expanded transcription metrics - `#195`_: added pitch contour sonification - `#196`_: added the `display` submodule - `#203`_: support unsorted segment intervals - `#205`_: correction in documentation for `sonify.time_frequency` - `#208`_: refactored file/buffer loading - `#210`_: added `io.load_tempo` - `#212`_: added frame-wise blind-source separation evaluation - `#218`_: speed up `melody.resample_melody_series` when times are equivalent .. _#189: https://github.com/mir-evaluation/mir_eval/issues/189 .. _#195: https://github.com/mir-evaluation/mir_eval/issues/195 .. _#196: https://github.com/mir-evaluation/mir_eval/issues/196 .. _#203: https://github.com/mir-evaluation/mir_eval/issues/203 .. _#205: https://github.com/mir-evaluation/mir_eval/issues/205 .. _#208: https://github.com/mir-evaluation/mir_eval/issues/208 .. _#210: https://github.com/mir-evaluation/mir_eval/issues/210 .. _#212: https://github.com/mir-evaluation/mir_eval/issues/212 .. _#218: https://github.com/mir-evaluation/mir_eval/pull/218 v0.3 ---- - `#170`_: implemented transcription metrics - `#173`_: fixed a bug in chord sonification - `#175`_: filter_kwargs passes through `**kwargs` - `#181`_: added key detection metrics .. _#170: https://github.com/mir-evaluation/mir_eval/issues/170 .. _#173: https://github.com/mir-evaluation/mir_eval/issues/173 .. _#175: https://github.com/mir-evaluation/mir_eval/issues/175 .. _#181: https://github.com/mir-evaluation/mir_eval/issues/181 v0.2 ---- - `#103`_: incomplete files passed to `melody.evaluate` should warn - `#109`_: `STRICT_BASS_INTERVALS` is now an argument to `chord.encode` - `#122`_: improved handling of corner cases in beat tracking - `#136`_: improved test coverage - `#138`_: PEP8 compliance - `#139`_: converted documentation to numpydoc style - `#147`_: fixed a rounding error in segment intervals - `#150`_: `sonify.chroma` and `sonify.chords` pass `kwargs` to `time_frequecy` - `#151`_: removed `labels` support from `util.boundaries_to_intervals` - `#159`_: fixed documentation error in `chord.tetrads` - `#160`_: fixed documentation error in `util.intervals_to_samples` .. _#103: https://github.com/mir-evaluation/mir_eval/issues/103 .. _#109: https://github.com/mir-evaluation/mir_eval/issues/109 .. _#122: https://github.com/mir-evaluation/mir_eval/issues/122 .. _#136: https://github.com/mir-evaluation/mir_eval/issues/136 .. _#138: https://github.com/mir-evaluation/mir_eval/issues/138 .. _#139: https://github.com/mir-evaluation/mir_eval/issues/139 .. _#147: https://github.com/mir-evaluation/mir_eval/issues/147 .. _#150: https://github.com/mir-evaluation/mir_eval/issues/150 .. _#151: https://github.com/mir-evaluation/mir_eval/issues/151 .. _#159: https://github.com/mir-evaluation/mir_eval/issues/159 .. _#160: https://github.com/mir-evaluation/mir_eval/issues/160 v0.1 ---- - Initial public release. mir_eval-0.8.2/docs/conf.py000066400000000000000000000217231475740344600155710ustar00rootroot00000000000000# # mir_eval documentation build configuration file, created by # sphinx-quickstart on Thu May 8 15:55:45 2014. # # 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 sys.path.insert(0, os.path.abspath("..")) from mir_eval import __version__ as release version = release # 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 = '1.0' # 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.imgmath", "numpydoc", ] # 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 = "mir_eval" copyright = "2014, Colin Raffel et al." # 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 = "0.7" # The full version, including alpha/beta/rc tags. #release = "0.7" # 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 = ["_build"] # 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 = [] # If true, keep warnings as "system message" paragraphs in the built documents. # keep_warnings = False # -- 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 = "default" html_theme = "sphinx_rtd_theme" # 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 = { "navigation_depth": 4, "collapse_navigation": False, } # 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 = None # 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"] # Add any extra paths that contain custom files (such as robots.txt or # .htaccess) here, relative to this directory. These files are copied # directly to the root of the documentation. # html_extra_path = [] # 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' # 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 = {} html_sidebars = { '**': [ 'globaltoc.html', # shows the full, global TOC from the master toctree 'localtoc.html', # also shows the local toctree (optional) 'relations.html', 'sourcelink.html', 'searchbox.html', ] } # 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 = "mir_evaldoc" # -- 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, or own class]). latex_documents = [ ( "index", "mir_eval.tex", "mir\\_eval Documentation", "Colin Raffel et al.", "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", "mir_eval", "mir_eval Documentation", ["Colin Raffel et al."], 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", "mir_eval", "mir_eval Documentation", "Colin Raffel et al.", "mir_eval", "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' # If true, do not generate a @detailmenu in the "Top" node's menu. # texinfo_no_detailmenu = False autodoc_member_order = "bysource" linkcheck_allow_unauthorized = True linkcheck_ignore = [ # Sphinx has problems with the anchor on this one, but it does work 'http://www.music-ir.org/mirex/wiki/2015:Multiple_Fundamental_Frequency_Estimation_%26_Tracking_Results_-_MIREX_Dataset#Task_2:Note_Tracking_.28NT.29' ] mir_eval-0.8.2/docs/index.rst000066400000000000000000000100751475740344600161310ustar00rootroot00000000000000.. mir_eval documentation master file mir_eval Documentation ====================== **mir_eval** is a Python library which provides a transparent, standardized, and straightforward way to evaluate Music Information Retrieval systems. If you use **mir_eval** in a research project, please cite the following paper: C. Raffel, B. McFee, E. J. Humphrey, J. Salamon, O. Nieto, D. Liang, and D. P. W. Ellis, `"mir_eval: A Transparent Implementation of Common MIR Metrics" `_, Proceedings of the 15th International Conference on Music Information Retrieval, 2014. Installation ============ The simplest way to install **mir_eval** is via ``pip``: .. code-block:: console python -m pip install mir_eval If you use `conda` packages, **mir_eval** is available on conda-forge: .. code-block:: console conda install -c conda-forge mir_eval Alternatively, you can install from source: .. code-block:: console python setup.py install If you don't use Python and want to get started as quickly as possible, you might consider using `Anaconda `_ which makes it easy to install a Python environment which can run **mir_eval**. Using mir_eval ============== Once installed, you can import **mir_eval** in your code: .. code-block:: python import mir_eval For example, to evaluate beat tracking: .. code-block:: python reference_beats = mir_eval.io.load_events('reference_beats.txt') estimated_beats = mir_eval.io.load_events('estimated_beats.txt') scores = mir_eval.beat.evaluate(reference_beats, estimated_beats) At the end of execution, ``scores`` will be a dict containing scores for all of the metrics implemented in `mir_eval.beat`. The keys are metric names and values are the scores achieved. You can also load in the data, do some preprocessing, and call specific metric functions from the appropriate submodule like so: .. code-block:: python reference_beats = mir_eval.io.load_events('reference_beats.txt') estimated_beats = mir_eval.io.load_events('estimated_beats.txt') # Crop out beats before 5s, a common preprocessing step reference_beats = mir_eval.beat.trim_beats(reference_beats) estimated_beats = mir_eval.beat.trim_beats(estimated_beats) # Compute the F-measure metric and store it in f_measure f_measure = mir_eval.beat.f_measure(reference_beats, estimated_beats) Alternatively, you can use the evaluator scripts which allow you to run evaluation from the command line, without writing any code. These scripts are are available here: https://github.com/craffel/mir_evaluators API Reference ============= The structure of the **mir_eval** Python module is as follows: Each MIR task for which evaluation metrics are included in **mir_eval** is given its own submodule, and each metric is defined as a separate function in each submodule. Every metric function includes detailed documentation, example usage, input validation, and references to the original paper which defined the metric (see the subsections below). The task submodules also all contain a function ``evaluate()``, which takes as input reference and estimated annotations and returns a dictionary of scores for all of the metrics implemented (for casual users, this is the place to start). Finally, each task submodule also includes functions for common data pre-processing steps. **mir_eval** also includes the following additional submodules: * :mod:`mir_eval.io` which contains convenience functions for loading in task-specific data from common file formats * :mod:`mir_eval.util` which includes miscellaneous functionality shared across the submodules * :mod:`mir_eval.sonify` which implements some simple methods for synthesizing annotations of various formats for "evaluation by ear". * :mod:`mir_eval.display` which provides functions for plotting annotations for various tasks. Detailed API documentation for each submodule is available in the API Reference section. See the :doc:`API Reference ` for full details. .. toctree:: :caption: mir_eval :maxdepth: 2 api/index changes mir_eval-0.8.2/mir_eval/000077500000000000000000000000001475740344600151335ustar00rootroot00000000000000mir_eval-0.8.2/mir_eval/__init__.py000066400000000000000000000007701475740344600172500ustar00rootroot00000000000000#!/usr/bin/env python """Top-level module for mir_eval""" # Import all submodules (for each task) from . import alignment from . import beat from . import chord from . import io from . import onset from . import segment from . import separation from . import util from . import sonify from . import melody from . import multipitch from . import pattern from . import tempo from . import hierarchy from . import transcription from . import transcription_velocity from . import key __version__ = "0.8.2" mir_eval-0.8.2/mir_eval/alignment.py000066400000000000000000000341111475740344600174630ustar00rootroot00000000000000""" Alignment models are given a sequence of events along with a piece of audio, and then return a sequence of timestamps, with one timestamp for each event, indicating the position of this event in the audio. The events are listed in order of occurrence in the audio, so that output timestamps have to be monotonically increasing. Evaluation usually involves taking the series of predicted and ground truth timestamps and comparing their distance, usually on a pair-wise basis, e.g. taking the median absolute error in seconds. Conventions ----------- Timestamps should be provided in the form of a 1-dimensional array of onset times in seconds in increasing order. Metrics ------- * :func:`mir_eval.alignment.absolute_error`: Median absolute error and average absolute error * :func:`mir_eval.alignment.percentage_correct`: Percentage of correct timestamps, where a timestamp is counted as correct if it lies within a certain tolerance window around the ground truth timestamp * :func:`mir_eval.alignment.pcs`: Percentage of correct segments: Percentage of overlap between predicted segments and ground truth segments, where segments are defined by (start time, end time) pairs * :func:`mir_eval.alignment.perceptual_metric`: metric based on human synchronicity perception as measured in the paper "User-centered evaluation of lyrics to audio alignment", N. Lizé-Masclef, A. Vaglio, M. Moussallam, ISMIR 2021 References ---------- .. [#lizemasclef2021] N. Lizé-Masclef, A. Vaglio, M. Moussallam. "User-centered evaluation of lyrics to audio alignment", International Society for Music Information Retrieval (ISMIR) conference, 2021. .. [#mauch2010] M. Mauch, F: Hiromasa, M. Goto. "Lyrics-to-audio alignment and phrase-level segmentation using incomplete internet-style chord annotations", Frontiers in Proceedings of the Sound Music Computing Conference (SMC), 2010. .. [#dzhambazov2017] G. Dzhambazov. "Knowledge-Based Probabilistic Modeling For Tracking Lyrics In Music Audio Signals", PhD Thesis, 2017. .. [#fujihara2011] H. Fujihara, M. Goto, J. Ogata, H. Okuno. "LyricSynchronizer: Automatic synchronization system between musical audio signals and lyrics", IEEE Journal of Selected Topics in Signal Processing, VOL. 5, NO. 6, 2011 """ import collections from typing import Optional import numpy as np from scipy.stats import skewnorm from mir_eval.util import filter_kwargs def validate(reference_timestamps: np.ndarray, estimated_timestamps: np.ndarray): """Check that the input annotations to a metric look like valid onset time arrays, and throws helpful errors if not. Parameters ---------- reference_timestamps : np.ndarray reference timestamp locations, in seconds estimated_timestamps : np.ndarray estimated timestamp locations, in seconds """ # We need to have 1D numpy arrays if not isinstance(reference_timestamps, np.ndarray): raise ValueError( "Reference timestamps need to be a numpy array, but got" f" {type(reference_timestamps)}" ) if not isinstance(estimated_timestamps, np.ndarray): raise ValueError( "Estimated timestamps need to be a numpy array, but got" f" {type(estimated_timestamps)}" ) if reference_timestamps.ndim != 1: raise ValueError( "Reference timestamps need to be a one-dimensional vector, but got" f" {reference_timestamps.ndim} dimensions" ) if estimated_timestamps.ndim != 1: raise ValueError( "Estimated timestamps need to be a one-dimensional vector, but got" f" {estimated_timestamps.ndim} dimensions" ) # If reference or estimated timestamps are empty, cannot compute metric if reference_timestamps.size == 0: raise ValueError("Reference timestamps are empty.") if estimated_timestamps.size != reference_timestamps.size: raise ValueError( "Number of timestamps must be the same in prediction and ground" f" truth, but found {estimated_timestamps.size} in prediction and" f" {reference_timestamps.size} in ground truth" ) # Check monotonicity if not np.all(reference_timestamps[1:] - reference_timestamps[:-1] >= 0): raise ValueError("Reference timestamps are not monotonically increasing!") if not np.all(estimated_timestamps[1:] - estimated_timestamps[:-1] >= 0): raise ValueError("Estimated timestamps are not monotonically increasing!") # Check positivity (need for correct PCS metric calculation) if not np.all(reference_timestamps >= 0): raise ValueError("Reference timestamps can not be below 0!") if not np.all(estimated_timestamps >= 0): raise ValueError("Estimated timestamps can not be below 0!") def absolute_error(reference_timestamps, estimated_timestamps): """Compute the absolute deviations between estimated and reference timestamps, and then returns the median and average over all events Examples -------- >>> reference_timestamps = mir_eval.io.load_events('reference.txt') >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt') >>> mae, aae = mir_eval.align.absolute_error(reference_onsets, estimated_timestamps) Parameters ---------- reference_timestamps : np.ndarray reference timestamps, in seconds estimated_timestamps : np.ndarray estimated timestamps, in seconds Returns ------- mae : float Median absolute error aae: float Average absolute error """ validate(reference_timestamps, estimated_timestamps) deviations = np.abs(reference_timestamps - estimated_timestamps) return np.median(deviations), np.mean(deviations) def percentage_correct(reference_timestamps, estimated_timestamps, window=0.3): """Compute the percentage of correctly predicted timestamps. A timestamp is predicted correctly if its position doesn't deviate more than the window parameter from the ground truth timestamp. Examples -------- >>> reference_timestamps = mir_eval.io.load_events('reference.txt') >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt') >>> pc = mir_eval.align.percentage_correct(reference_onsets, estimated_timestamps, window=0.2) Parameters ---------- reference_timestamps : np.ndarray reference timestamps, in seconds estimated_timestamps : np.ndarray estimated timestamps, in seconds window : float Window size, in seconds (Default value = .3) Returns ------- pc : float Percentage of correct timestamps """ validate(reference_timestamps, estimated_timestamps) deviations = np.abs(reference_timestamps - estimated_timestamps) return np.mean(deviations <= window) def percentage_correct_segments( reference_timestamps, estimated_timestamps, duration: Optional[float] = None ): """Calculate the percentage of correct segments (PCS) metric. It constructs segments out of predicted and estimated timestamps separately out of each given timestamp vector and calculates the percentage of overlap between correct segments compared to the total duration. WARNING: This metrics behaves differently depending on whether "duration" is given! If duration is not given (default case), the computation follows the MIREX lyrics alignment challenge 2020. For a timestamp vector with entries (t1,t2, ... tN), segments with the following (start, end) boundaries are created: (t1, t2), ... (tN-1, tN). After the segments are created, the overlap between the reference and estimated segments is determined and divided by the total duration, which is the distance between the first and last timestamp in the reference. If duration is given, the segment boundaries are instead (0, t1), (t1, t2), ... (tN, duration). The overlap is computed in the same way, but then divided by the duration parameter given to this function. This method follows the original paper [#fujihara2011] more closely, where the metric was proposed. As a result, this variant of the metrics punishes cases where the first estimated timestamp is too early or the last estimated timestamp is too late, whereas the MIREX variant does not. On the other hand, the MIREX metric is invariant to how long the eventless beginning and end parts of the audio are, which might be a desirable property. Examples -------- >>> reference_timestamps = mir_eval.io.load_events('reference.txt') >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt') >>> pcs = mir_eval.align.percentage_correct_segments(reference_timestamps, estimated_timestamps) Parameters ---------- reference_timestamps : np.ndarray reference timestamps, in seconds estimated_timestamps : np.ndarray estimated timestamps, in seconds duration : float Optional. Total duration of audio (seconds). WARNING: Metric is computed differently depending on whether this is provided or not - see documentation above! Returns ------- pcs : float Percentage of time where ground truth and predicted segments overlap """ validate(reference_timestamps, estimated_timestamps) if duration is not None: duration = float(duration) if duration <= 0: raise ValueError( f"Positive duration needs to be provided, but got {duration}" ) if np.max(reference_timestamps) > duration: raise ValueError( "Expected largest reference timestamp" f"{np.max(reference_timestamps)} to not be " f"larger than duration {duration}" ) if np.max(estimated_timestamps) > duration: raise ValueError( "Expected largest estimated timestamp " f"{np.max(estimated_timestamps)} to not be " f"larger than duration {duration}" ) ref_starts = np.concatenate([[0], reference_timestamps]) ref_ends = np.concatenate([reference_timestamps, [duration]]) est_starts = np.concatenate([[0], estimated_timestamps]) est_ends = np.concatenate([estimated_timestamps, [duration]]) else: # MIREX lyrics alignment 2020 style: # Ignore regions before start and after end reference timestamp duration = reference_timestamps[-1] - reference_timestamps[0] if duration <= 0: raise ValueError( f"Reference timestamps are all identical, can not compute PCS" f" metric!" ) ref_starts = reference_timestamps[:-1] ref_ends = reference_timestamps[1:] est_starts = estimated_timestamps[:-1] est_ends = estimated_timestamps[1:] overlap_starts = np.maximum(ref_starts, est_starts) overlap_ends = np.minimum(ref_ends, est_ends) overlap_duration = np.sum(np.maximum(overlap_ends - overlap_starts, 0)) return overlap_duration / duration def karaoke_perceptual_metric(reference_timestamps, estimated_timestamps): """Metric based on human synchronicity perception as measured in the paper "User-centered evaluation of lyrics to audio alignment" [#lizemasclef2021] The parameters of this function were tuned on data collected through a user Karaoke-like experiment It reflects human judgment of how "synchronous" lyrics and audio stimuli are perceived in that setup. Beware that this metric is non-symmetrical and by construction it is also not equal to 1 at 0. Examples -------- >>> reference_timestamps = mir_eval.io.load_events('reference.txt') >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt') >>> score = mir_eval.align.karaoke_perceptual_metric(reference_onsets, estimated_timestamps) Parameters ---------- reference_timestamps : np.ndarray reference timestamps, in seconds estimated_timestamps : np.ndarray estimated timestamps, in seconds Returns ------- perceptual_score : float Perceptual score, averaged over all timestamps """ validate(reference_timestamps, estimated_timestamps) offsets = estimated_timestamps - reference_timestamps # Score offsets using a certain skewed normal distribution skewness = 1.12244251 localisation = -0.22270315 scale = 0.29779424 normalisation_factor = 1.6857 perceptual_scores = (1.0 / normalisation_factor) * skewnorm.pdf( offsets, skewness, loc=localisation, scale=scale ) return np.mean(perceptual_scores) def evaluate(reference_timestamps, estimated_timestamps, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> reference_timestamps = mir_eval.io.load_events('reference.txt') >>> estimated_timestamps = mir_eval.io.load_events('estimated.txt') >>> duration = max(np.max(reference_timestamps), np.max(estimated_timestamps)) + 10 >>> scores = mir_eval.align.evaluate(reference_onsets, estimated_timestamps, duration) Parameters ---------- reference_timestamps : np.ndarray reference timestamp locations, in seconds estimated_timestamps : np.ndarray estimated timestamp locations, in seconds **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all metrics scores = collections.OrderedDict() scores["pc"] = filter_kwargs( percentage_correct, reference_timestamps, estimated_timestamps, **kwargs ) scores["mae"], scores["aae"] = absolute_error( reference_timestamps, estimated_timestamps ) scores["pcs"] = filter_kwargs( percentage_correct_segments, reference_timestamps, estimated_timestamps, **kwargs, ) scores["perceptual"] = karaoke_perceptual_metric( reference_timestamps, estimated_timestamps ) return scores mir_eval-0.8.2/mir_eval/beat.py000066400000000000000000000746601475740344600164350ustar00rootroot00000000000000r""" The aim of a beat detection algorithm is to report the times at which a typical human listener might tap their foot to a piece of music. As a result, most metrics for evaluating the performance of beat tracking systems involve computing the error between the estimated beat times and some reference list of beat locations. Many metrics additionally compare the beat sequences at different metric levels in order to deal with the ambiguity of tempo. Based on the methods described in: Matthew E. P. Davies, Norberto Degara, and Mark D. Plumbley. "Evaluation Methods for Musical Audio Beat Tracking Algorithms", Queen Mary University of London Technical Report C4DM-TR-09-06 London, United Kingdom, 8 October 2009. See also the Beat Evaluation Toolbox: https://code.soundsoftware.ac.uk/projects/beat-evaluation/ Conventions ----------- Beat times should be provided in the form of a 1-dimensional array of beat times in seconds in increasing order. Typically, any beats which occur before 5s are ignored; this can be accomplished using :func:`mir_eval.beat.trim_beats()`. Metrics ------- * :func:`mir_eval.beat.f_measure`: The F-measure of the beat sequence, where an estimated beat is considered correct if it is sufficiently close to a reference beat * :func:`mir_eval.beat.cemgil`: Cemgil's score, which computes the sum of Gaussian errors for each beat * :func:`mir_eval.beat.goto`: Goto's score, a binary score which is 1 when at least 25\% of the estimated beat sequence closely matches the reference beat sequence * :func:`mir_eval.beat.p_score`: McKinney's P-score, which computes the cross-correlation of the estimated and reference beat sequences represented as impulse trains * :func:`mir_eval.beat.continuity`: Continuity-based scores which compute the proportion of the beat sequence which is continuously correct * :func:`mir_eval.beat.information_gain`: The Information Gain of a normalized beat error histogram over a uniform distribution """ import numpy as np import collections from . import util import warnings # The maximum allowable beat time MAX_TIME = 30000.0 def trim_beats(beats, min_beat_time=5.0): """Remove beats before min_beat_time. A common preprocessing step. Parameters ---------- beats : np.ndarray Array of beat times in seconds. min_beat_time : float Minimum beat time to allow (Default value = 5.) Returns ------- beats_trimmed : np.ndarray Trimmed beat array. """ # Remove beats before min_beat_time return beats[beats >= min_beat_time] def validate(reference_beats, estimated_beats): """Check that the input annotations to a metric look like valid beat time arrays, and throws helpful errors if not. Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray estimated beat times, in seconds """ # If reference or estimated beats are empty, # warn because metric will be 0 if reference_beats.size == 0: warnings.warn("Reference beats are empty.") if estimated_beats.size == 0: warnings.warn("Estimated beats are empty.") for beats in [reference_beats, estimated_beats]: util.validate_events(beats, MAX_TIME) def _get_reference_beat_variations(reference_beats): """Return metric variations of the reference beats Parameters ---------- reference_beats : np.ndarray beat locations in seconds Returns ------- reference_beats : np.ndarray Original beat locations off_beat : np.ndarray 180 degrees out of phase from the original beat locations double : np.ndarray Beats at 2x the original tempo half_odd : np.ndarray Half tempo, odd beats half_even : np.ndarray Half tempo, even beats """ # Create annotations at twice the metric level interpolated_indices = np.arange(0, reference_beats.shape[0] - 0.5, 0.5) original_indices = np.arange(0, reference_beats.shape[0]) double_reference_beats = np.interp( interpolated_indices, original_indices, reference_beats ) # Return metric variations: # True, off-beat, double tempo, half tempo odd, and half tempo even return ( reference_beats, double_reference_beats[1::2], double_reference_beats, reference_beats[::2], reference_beats[1::2], ) def f_measure(reference_beats, estimated_beats, f_measure_threshold=0.07): """Compute the F-measure of correct vs incorrectly predicted beats. "Correctness" is determined over a small window. Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> f_measure = mir_eval.beat.f_measure(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray estimated beat times, in seconds f_measure_threshold : float Window size, in seconds (Default value = 0.07) Returns ------- f_score : float The computed F-measure score """ validate(reference_beats, estimated_beats) # When estimated beats are empty, no beats are correct; metric is 0 if estimated_beats.size == 0 or reference_beats.size == 0: return 0.0 # Compute the best-case matching between reference and estimated locations matching = util.match_events(reference_beats, estimated_beats, f_measure_threshold) precision = float(len(matching)) / len(estimated_beats) recall = float(len(matching)) / len(reference_beats) return util.f_measure(precision, recall) def cemgil(reference_beats, estimated_beats, cemgil_sigma=0.04): """Cemgil's score, computes a gaussian error of each estimated beat. Compares against the original beat times and all metrical variations. Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> cemgil_score, cemgil_max = mir_eval.beat.cemgil(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds cemgil_sigma : float Sigma parameter of gaussian error windows (Default value = 0.04) Returns ------- cemgil_score : float Cemgil's score for the original reference beats cemgil_max : float The best Cemgil score for all metrical variations """ validate(reference_beats, estimated_beats) # When estimated beats are empty, no beats are correct; metric is 0 if estimated_beats.size == 0 or reference_beats.size == 0: return 0.0, 0.0 # We'll compute Cemgil's accuracy for each variation accuracies = [] for reference_beats in _get_reference_beat_variations(reference_beats): accuracy = 0 # Cycle through beats for beat in reference_beats: # Find the error for the closest beat to the reference beat beat_diff = np.min(np.abs(beat - estimated_beats)) # Add gaussian error into the accuracy accuracy += np.exp(-(beat_diff**2) / (2.0 * cemgil_sigma**2)) # Normalize the accuracy accuracy /= 0.5 * (estimated_beats.shape[0] + reference_beats.shape[0]) # Add it to our list of accuracy scores accuracies.append(accuracy) # Return raw accuracy with non-varied annotations # and maximal accuracy across all variations return accuracies[0], np.max(accuracies) def goto( reference_beats, estimated_beats, goto_threshold=0.35, goto_mu=0.2, goto_sigma=0.2 ): """Calculate Goto's score, a binary 1 or 0 depending on some specific heuristic criteria Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> goto_score = mir_eval.beat.goto(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds goto_threshold : float Threshold of beat error for a beat to be "correct" (Default value = 0.35) goto_mu : float The mean of the beat errors in the continuously correct track must be less than this (Default value = 0.2) goto_sigma : float The std of the beat errors in the continuously correct track must be less than this (Default value = 0.2) Returns ------- goto_score : float Either 1.0 or 0.0 if some specific criteria are met """ validate(reference_beats, estimated_beats) # When estimated beats are empty, no beats are correct; metric is 0 if estimated_beats.size == 0 or reference_beats.size == 0: return 0.0 # Error for each beat beat_error = np.ones(reference_beats.shape[0]) # Flag for whether the reference and estimated beats are paired paired = np.zeros(reference_beats.shape[0]) # Keep track of Goto's three criteria goto_criteria = 0 for n in range(1, reference_beats.shape[0] - 1): # Get previous inner-reference-beat-interval previous_interval = 0.5 * (reference_beats[n] - reference_beats[n - 1]) # Window start - in the middle of the current beat and the previous window_min = reference_beats[n] - previous_interval # Next inter-reference-beat-interval next_interval = 0.5 * (reference_beats[n + 1] - reference_beats[n]) # Window end - in the middle of the current beat and the next window_max = reference_beats[n] + next_interval # Get estimated beats in the window beats_in_window = np.logical_and( (estimated_beats >= window_min), (estimated_beats < window_max) ) # False negative/positive if beats_in_window.sum() == 0 or beats_in_window.sum() > 1: paired[n] = 0 beat_error[n] = 1 else: # Single beat is paired! paired[n] = 1 # Get offset of the estimated beat and the reference beat offset = estimated_beats[beats_in_window] - reference_beats[n] # Scale by previous or next interval if offset < 0: beat_error[n] = offset[0] / previous_interval else: beat_error[n] = offset[0] / next_interval # Get indices of incorrect beats incorrect_beats = np.flatnonzero(np.abs(beat_error) > goto_threshold) # All beats are correct (first and last will be 0 so always correct) if incorrect_beats.shape[0] < 3: # Get the track of correct beats track = beat_error[incorrect_beats[0] + 1 : incorrect_beats[-1] - 1] goto_criteria = 1 else: # Get the track of maximal length track_len = np.max(np.diff(incorrect_beats)) track_start = np.flatnonzero(np.diff(incorrect_beats) == track_len)[0] # Is the track length at least 25% of the song? if track_len - 1 > 0.25 * (reference_beats.shape[0] - 2): goto_criteria = 1 start_beat = incorrect_beats[track_start] end_beat = incorrect_beats[track_start + 1] track = beat_error[start_beat : end_beat + 1] # If we have a track if goto_criteria: # Are mean and std of the track less than the required thresholds? if np.mean(np.abs(track)) < goto_mu and np.std(track, ddof=1) < goto_sigma: goto_criteria = 3 # If all criteria are met, score is 100%! return 1.0 * (goto_criteria == 3) def p_score(reference_beats, estimated_beats, p_score_threshold=0.2): """Get McKinney's P-score. Based on the autocorrelation of the reference and estimated beats Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> p_score = mir_eval.beat.p_score(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds p_score_threshold : float Window size will be ``p_score_threshold*np.median(inter_annotation_intervals)``, (Default value = 0.2) Returns ------- correlation : float McKinney's P-score """ validate(reference_beats, estimated_beats) # Warn when only one beat is provided for either estimated or reference, # report a warning if reference_beats.size == 1: warnings.warn( "Only one reference beat was provided, so beat intervals" " cannot be computed." ) if estimated_beats.size == 1: warnings.warn( "Only one estimated beat was provided, so beat intervals" " cannot be computed." ) # When estimated or reference beats have <= 1 beats, can't compute the # metric, so return 0 if estimated_beats.size <= 1 or reference_beats.size <= 1: return 0.0 # Quantize beats to 10ms sampling_rate = int(1.0 / 0.010) # Shift beats so that the minimum in either sequence is zero offset = min(estimated_beats.min(), reference_beats.min()) estimated_beats = np.array(estimated_beats - offset) reference_beats = np.array(reference_beats - offset) # Get the largest time index end_point = np.int64( np.ceil(np.max([np.max(estimated_beats), np.max(reference_beats)])) ) # Make impulse trains with impulses at beat locations reference_train = np.zeros(end_point * sampling_rate + 1) beat_indices = np.ceil(reference_beats * sampling_rate).astype(np.int64) reference_train[beat_indices] = 1.0 estimated_train = np.zeros(end_point * sampling_rate + 1) beat_indices = np.ceil(estimated_beats * sampling_rate).astype(np.int64) estimated_train[beat_indices] = 1.0 # Window size to take the correlation over # defined as .2*median(inter-annotation-intervals) annotation_intervals = np.diff(np.flatnonzero(reference_train)) win_size = int(np.round(p_score_threshold * np.median(annotation_intervals))) # Get full correlation train_correlation = np.correlate(reference_train, estimated_train, "full") # Get the middle element - note we are rounding down on purpose here middle_lag = train_correlation.shape[0] // 2 # Truncate to only valid lags (those corresponding to the window) start = middle_lag - win_size end = middle_lag + win_size + 1 train_correlation = train_correlation[start:end] # Compute and return the P-score n_beats = np.max([estimated_beats.shape[0], reference_beats.shape[0]]) return np.sum(train_correlation) / n_beats def continuity( reference_beats, estimated_beats, continuity_phase_threshold=0.175, continuity_period_threshold=0.175, ): """Get metrics based on how much of the estimated beat sequence is continually correct. Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> CMLc, CMLt, AMLc, AMLt = mir_eval.beat.continuity(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds continuity_phase_threshold : float Allowable ratio of how far is the estimated beat can be from the reference beat (Default value = 0.175) continuity_period_threshold : float Allowable distance between the inter-beat-interval and the inter-annotation-interval (Default value = 0.175) Returns ------- CMLc : float Correct metric level, continuous accuracy CMLt : float Correct metric level, total accuracy (continuity not required) AMLc : float Any metric level, continuous accuracy AMLt : float Any metric level, total accuracy (continuity not required) """ validate(reference_beats, estimated_beats) # Warn when only one beat is provided for either estimated or reference, # report a warning if reference_beats.size == 1: warnings.warn( "Only one reference beat was provided, so beat intervals" " cannot be computed." ) if estimated_beats.size == 1: warnings.warn( "Only one estimated beat was provided, so beat intervals" " cannot be computed." ) # When estimated or reference beats have <= 1 beats, can't compute the # metric, so return 0 if estimated_beats.size <= 1 or reference_beats.size <= 1: return 0.0, 0.0, 0.0, 0.0 # Accuracies for each variation continuous_accuracies = [] total_accuracies = [] # Get accuracy for each variation for reference_beats in _get_reference_beat_variations(reference_beats): # Annotations that have been used n_annotations = np.max([reference_beats.shape[0], estimated_beats.shape[0]]) used_annotations = np.zeros(n_annotations) # Whether or not we are continuous at any given point beat_successes = np.zeros(n_annotations) for m in range(estimated_beats.shape[0]): # Is this beat correct? beat_success = 0 # Get differences for this beat beat_differences = np.abs(estimated_beats[m] - reference_beats) # Get nearest annotation index nearest = np.argmin(beat_differences) min_difference = beat_differences[nearest] # Have we already used this annotation? if used_annotations[nearest] == 0: # Is this the first beat or first annotation? # If so, look forward. if m == 0 or nearest == 0: # How far is the estimated beat from the reference beat, # relative to the inter-annotation-interval? if nearest + 1 < reference_beats.shape[0]: reference_interval = ( reference_beats[nearest + 1] - reference_beats[nearest] ) else: # Special case when nearest + 1 is too large - use the # previous interval instead reference_interval = ( reference_beats[nearest] - reference_beats[nearest - 1] ) # Handle this special case when beats are not unique if reference_interval == 0: if min_difference == 0: phase = 1 else: phase = np.inf else: phase = np.abs(min_difference / reference_interval) # How close is the inter-beat-interval # to the inter-annotation-interval? if m + 1 < estimated_beats.shape[0]: estimated_interval = estimated_beats[m + 1] - estimated_beats[m] else: # Special case when m + 1 is too large - use the # previous interval estimated_interval = estimated_beats[m] - estimated_beats[m - 1] # Handle this special case when beats are not unique if reference_interval == 0: if estimated_interval == 0: period = 0 else: period = np.inf else: period = np.abs(1 - estimated_interval / reference_interval) if ( phase < continuity_phase_threshold and period < continuity_period_threshold ): # Set this annotation as used used_annotations[nearest] = 1 # This beat is matched beat_success = 1 # This beat/annotation is not the first else: # How far is the estimated beat from the reference beat, # relative to the inter-annotation-interval? reference_interval = ( reference_beats[nearest] - reference_beats[nearest - 1] ) phase = np.abs(min_difference / reference_interval) # How close is the inter-beat-interval # to the inter-annotation-interval? estimated_interval = estimated_beats[m] - estimated_beats[m - 1] reference_interval = ( reference_beats[nearest] - reference_beats[nearest - 1] ) period = np.abs(1 - estimated_interval / reference_interval) if ( phase < continuity_phase_threshold and period < continuity_period_threshold ): # Set this annotation as used used_annotations[nearest] = 1 # This beat is matched beat_success = 1 # Set whether this beat is matched or not beat_successes[m] = beat_success # Add 0s at the begnning and end # so that we at least find the beginning/end of the estimated beats beat_successes = np.append(np.append(0, beat_successes), 0) # Where is the beat not a match? beat_failures = np.nonzero(beat_successes == 0)[0] # Take out those zeros we added beat_successes = beat_successes[1:-1] # Get the continuous accuracy as the longest track of successful beats longest_track = np.max(np.diff(beat_failures)) - 1 continuous_accuracy = longest_track / (1.0 * beat_successes.shape[0]) continuous_accuracies.append(continuous_accuracy) # Get the total accuracy - all sequences total_accuracy = np.sum(beat_successes) / (1.0 * beat_successes.shape[0]) total_accuracies.append(total_accuracy) # Grab accuracy scores return ( continuous_accuracies[0], total_accuracies[0], np.max(continuous_accuracies), np.max(total_accuracies), ) def information_gain(reference_beats, estimated_beats, bins=41): """Get the information gain - K-L divergence of the beat error histogram to a uniform histogram Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> reference_beats = mir_eval.beat.trim_beats(reference_beats) >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> estimated_beats = mir_eval.beat.trim_beats(estimated_beats) >>> information_gain = mir_eval.beat.information_gain(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds bins : int Number of bins in the beat error histogram (Default value = 41) Returns ------- information_gain_score : float Entropy of beat error histogram """ validate(reference_beats, estimated_beats) # If an even number of bins is provided, # there will be no bin centered at zero, so warn the user. if not bins % 2: warnings.warn( "bins parameter is even, " "so there will not be a bin centered at zero." ) # Warn when only one beat is provided for either estimated or reference, # report a warning if reference_beats.size == 1: warnings.warn( "Only one reference beat was provided, so beat intervals" " cannot be computed." ) if estimated_beats.size == 1: warnings.warn( "Only one estimated beat was provided, so beat intervals" " cannot be computed." ) # When estimated or reference beats have <= 1 beats, can't compute the # metric, so return 0 if estimated_beats.size <= 1 or reference_beats.size <= 1: return 0.0 # Get entropy for reference beats->estimated beats # and estimated beats->reference beats forward_entropy = _get_entropy(reference_beats, estimated_beats, bins) backward_entropy = _get_entropy(estimated_beats, reference_beats, bins) # Pick the larger of the entropies norm = np.log2(bins) if forward_entropy > backward_entropy: # Note that the beat evaluation toolbox does not normalize information_gain_score = (norm - forward_entropy) / norm else: information_gain_score = (norm - backward_entropy) / norm return information_gain_score def _get_entropy(reference_beats, estimated_beats, bins): """Compute the entropy of the beat error histogram. This is a helper function for the information gain metric, and needs to be run twice: once backwards, once forwards. Parameters ---------- reference_beats : np.ndarray reference beat times, in seconds estimated_beats : np.ndarray query beat times, in seconds bins : int Number of bins in the beat error histogram Returns ------- entropy : float Entropy of beat error histogram """ beat_error = np.zeros(estimated_beats.shape[0]) for n in range(estimated_beats.shape[0]): # Get index of closest annotation to this beat beat_distances = estimated_beats[n] - reference_beats closest_beat = np.argmin(np.abs(beat_distances)) absolute_error = beat_distances[closest_beat] # If the first annotation is closest... if closest_beat == 0: # Inter-annotation interval - space between first two beats interval = 0.5 * (reference_beats[1] - reference_beats[0]) # If last annotation is closest... if closest_beat == (reference_beats.shape[0] - 1): interval = 0.5 * (reference_beats[-1] - reference_beats[-2]) else: if absolute_error < 0: # Closest annotation is the one before the current beat # so look at previous inner-annotation-interval start = reference_beats[closest_beat] end = reference_beats[closest_beat - 1] interval = 0.5 * (start - end) else: # Closest annotation is the one after the current beat # so look at next inner-annotation-interval start = reference_beats[closest_beat + 1] end = reference_beats[closest_beat] interval = 0.5 * (start - end) # The actual error of this beat beat_error[n] = 0.5 * absolute_error / interval # Put beat errors in range (-.5, .5) beat_error = np.mod(beat_error + 0.5, -1) + 0.5 # Note these are slightly different the beat evaluation toolbox # (they are uniform) histogram_bin_edges = np.linspace(-0.5, 0.5, bins + 1) # Get the histogram raw_bin_values = np.histogram(beat_error, histogram_bin_edges)[0] # Turn into a proper probability distribution raw_bin_values = raw_bin_values / (1.0 * np.sum(raw_bin_values)) # Set zero-valued bins to 1 to make the entropy calculation well-behaved raw_bin_values[raw_bin_values == 0] = 1 # Calculate entropy return -np.sum(raw_bin_values * np.log2(raw_bin_values)) def evaluate(reference_beats, estimated_beats, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> reference_beats = mir_eval.io.load_events('reference.txt') >>> estimated_beats = mir_eval.io.load_events('estimated.txt') >>> scores = mir_eval.beat.evaluate(reference_beats, estimated_beats) Parameters ---------- reference_beats : np.ndarray Reference beat times, in seconds estimated_beats : np.ndarray Query beat times, in seconds **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Trim beat times at the beginning of the annotations reference_beats = util.filter_kwargs(trim_beats, reference_beats, **kwargs) estimated_beats = util.filter_kwargs(trim_beats, estimated_beats, **kwargs) # Now compute all the metrics scores = collections.OrderedDict() # F-Measure scores["F-measure"] = util.filter_kwargs( f_measure, reference_beats, estimated_beats, **kwargs ) # Cemgil scores["Cemgil"], scores["Cemgil Best Metric Level"] = util.filter_kwargs( cemgil, reference_beats, estimated_beats, **kwargs ) # Goto scores["Goto"] = util.filter_kwargs( goto, reference_beats, estimated_beats, **kwargs ) # P-Score scores["P-score"] = util.filter_kwargs( p_score, reference_beats, estimated_beats, **kwargs ) # Continuity metrics ( scores["Correct Metric Level Continuous"], scores["Correct Metric Level Total"], scores["Any Metric Level Continuous"], scores["Any Metric Level Total"], ) = util.filter_kwargs(continuity, reference_beats, estimated_beats, **kwargs) # Information gain scores["Information gain"] = util.filter_kwargs( information_gain, reference_beats, estimated_beats, **kwargs ) return scores mir_eval-0.8.2/mir_eval/chord.py000066400000000000000000001620401475740344600166070ustar00rootroot00000000000000r""" Chord estimation algorithms produce a list of intervals and labels which denote the chord being played over each timespan. They are evaluated by comparing the estimated chord labels to some reference, usually using a mapping to a chord subalphabet (e.g. minor and major chords only, all triads, etc.). There is no single 'right' way to compare two sequences of chord labels. Embracing this reality, every conventional comparison rule is provided. Comparisons are made over the different components of each chord (e.g. G:maj(6)/5): the root (G), the root-invariant active semitones as determined by the quality shorthand (maj) and scale degrees (6), and the bass interval (5). This submodule provides functions both for comparing a sequences of chord labels according to some chord subalphabet mapping and for using these comparisons to score a sequence of estimated chords against a reference. Conventions ----------- A sequence of chord labels is represented as a list of strings, where each label is the chord name based on the syntax of [#harte2010towards]_. Reference and estimated chord label sequences should be of the same length for comparison functions. When converting the chord string into its constituent parts, * Pitch class counting starts at C, e.g. C:0, D:2, E:4, F:5, etc. * Scale degree is represented as a string of the diatonic interval, relative to the root note, e.g. 'b6', '#5', or '7' * Bass intervals are represented as strings * Chord bitmaps are positional binary vectors indicating active pitch classes and may be absolute or relative depending on context in the code. If no chord is present at a given point in time, it should have the label 'N', which is defined in the variable ``mir_eval.chord.NO_CHORD``. Metrics ------- * :func:`mir_eval.chord.root`: Only compares the root of the chords. * :func:`mir_eval.chord.majmin`: Only compares major, minor, and "no chord" labels. * :func:`mir_eval.chord.majmin_inv`: Compares major/minor chords, with inversions. The bass note must exist in the triad. * :func:`mir_eval.chord.mirex`: A estimated chord is considered correct if it shares *at least* three pitch classes in common. * :func:`mir_eval.chord.thirds`: Chords are compared at the level of major or minor thirds (root and third), For example, both ('A:7', 'A:maj') and ('A:min', 'A:dim') are equivalent, as the third is major and minor in quality, respectively. * :func:`mir_eval.chord.thirds_inv`: Same as above, with inversions (bass relationships). * :func:`mir_eval.chord.triads`: Chords are considered at the level of triads (major, minor, augmented, diminished, suspended), meaning that, in addition to the root, the quality is only considered through #5th scale degree (for augmented chords). For example, ('A:7', 'A:maj') are equivalent, while ('A:min', 'A:dim') and ('A:aug', 'A:maj') are not. * :func:`mir_eval.chord.triads_inv`: Same as above, with inversions (bass relationships). * :func:`mir_eval.chord.tetrads`: Chords are considered at the level of the entire quality in closed voicing, i.e. spanning only a single octave; extended chords (9's, 11's and 13's) are rolled into a single octave with any upper voices included as extensions. For example, ('A:7', 'A:9') are equivalent but ('A:7', 'A:maj7') are not. * :func:`mir_eval.chord.tetrads_inv`: Same as above, with inversions (bass relationships). * :func:`mir_eval.chord.sevenths`: Compares according to MIREX "sevenths" rules; that is, only major, major seventh, seventh, minor, minor seventh and no chord labels are compared. * :func:`mir_eval.chord.sevenths_inv`: Same as above, with inversions (bass relationships). * :func:`mir_eval.chord.overseg`: Computes the level of over-segmentation between estimated and reference intervals. * :func:`mir_eval.chord.underseg`: Computes the level of under-segmentation between estimated and reference intervals. * :func:`mir_eval.chord.seg`: Computes the minimum of over- and under-segmentation between estimated and reference intervals. References ---------- .. [#harte2010towards] C. Harte. Towards Automatic Extraction of Harmony Information from Music Signals. PhD thesis, Queen Mary University of London, August 2010. """ import numpy as np import warnings import collections import re from mir_eval import util BITMAP_LENGTH = 12 NO_CHORD = "N" NO_CHORD_ENCODED = -1, np.array([0] * BITMAP_LENGTH), -1 X_CHORD = "X" X_CHORD_ENCODED = -1, np.array([-1] * BITMAP_LENGTH), -1 class InvalidChordException(Exception): r"""Exception class for suspect / invalid chord labels""" def __init__(self, message="", chord_label=None): self.message = message self.chord_label = chord_label self.name = self.__class__.__name__ super().__init__(message) # --- Chord Primitives --- def _pitch_classes(): r"""Map from pitch class (str) to semitone (int).""" pitch_classes = ["C", "D", "E", "F", "G", "A", "B"] semitones = [0, 2, 4, 5, 7, 9, 11] return {c: s for c, s in zip(pitch_classes, semitones)} def _scale_degrees(): r"""Map scale degrees (str) to semitones (int).""" degrees = ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13"] semitones = [0, 2, 4, 5, 7, 9, 11, 12, 14, 16, 17, 19, 21] return {d: s for d, s in zip(degrees, semitones)} # Maps pitch classes (strings) to semitone indexes (ints). PITCH_CLASSES = _pitch_classes() def pitch_class_to_semitone(pitch_class): r"""Convert a pitch class to semitone. Parameters ---------- pitch_class : str Spelling of a given pitch class, e.g. 'C#', 'Gbb' Returns ------- semitone : int Semitone value of the pitch class. """ semitone = 0 for idx, char in enumerate(pitch_class): if char == "#" and idx > 0: semitone += 1 elif char == "b" and idx > 0: semitone -= 1 elif idx == 0: semitone = PITCH_CLASSES.get(char) else: raise InvalidChordException( "Pitch class improperly formed: %s" % pitch_class ) return semitone % 12 # Maps scale degrees (strings) to semitone indexes (ints). SCALE_DEGREES = _scale_degrees() def scale_degree_to_semitone(scale_degree): r"""Convert a scale degree to semitone. Parameters ---------- scale_degree : str Spelling of a relative scale degree, e.g. 'b3', '7', '#5' Returns ------- semitone : int Relative semitone of the scale degree, wrapped to a single octave Raises ------ InvalidChordException if `scale_degree` is invalid. """ semitone = 0 offset = 0 if scale_degree.startswith("#"): offset = scale_degree.count("#") scale_degree = scale_degree.strip("#") elif scale_degree.startswith("b"): offset = -1 * scale_degree.count("b") scale_degree = scale_degree.strip("b") semitone = SCALE_DEGREES.get(scale_degree, None) if semitone is None: raise InvalidChordException( "Scale degree improperly formed: {}, expected one of {}.".format( scale_degree, list(SCALE_DEGREES.keys()) ) ) return semitone + offset def scale_degree_to_bitmap(scale_degree, modulo=False, length=BITMAP_LENGTH): """Create a bitmap representation of a scale degree. Note that values in the bitmap may be negative, indicating that the semitone is to be removed. Parameters ---------- scale_degree : str Spelling of a relative scale degree, e.g. 'b3', '7', '#5' modulo : bool, default=True If a scale degree exceeds the length of the bit-vector, modulo the scale degree back into the bit-vector; otherwise it is discarded. length : int, default=12 Length of the bit-vector to produce Returns ------- bitmap : np.ndarray, in [-1, 0, 1], len=`length` Bitmap representation of this scale degree. """ sign = 1 if scale_degree.startswith("*"): sign = -1 scale_degree = scale_degree.strip("*") edit_map = [0] * length sd_idx = scale_degree_to_semitone(scale_degree) if sd_idx < length or modulo: edit_map[sd_idx % length] = sign return np.array(edit_map) # Maps quality strings to bitmaps, corresponding to relative pitch class # semitones, i.e. vector[0] is the tonic. QUALITIES = { # 1 2 3 4 5 6 7 "maj": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], "min": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], "aug": [1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0], "dim": [1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0], "sus4": [1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0], "sus2": [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0], "7": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "maj7": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], "min7": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0], "minmaj7": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1], "maj6": [1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0], "min6": [1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0], "dim7": [1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0], "hdim7": [1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0], "maj9": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], "min9": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0], "9": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "min11": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0], "11": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "maj13": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], "min13": [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0], "13": [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0], "1": [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], "5": [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], "": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], } def quality_to_bitmap(quality): """Return the bitmap for a given quality. Parameters ---------- quality : str Chord quality name. Returns ------- bitmap : np.ndarray Bitmap representation of this quality (12-dim). """ if quality not in QUALITIES: raise InvalidChordException( "Unsupported chord quality shorthand: '%s' " "Did you mean to reduce extended chords?" % quality ) return np.array(QUALITIES[quality]) # Maps extended chord qualities to the subset above, translating additional # voicings to extensions as a set of scale degrees (strings). # TODO(ejhumphrey): Revisit how minmaj7's are mapped. This is how TMC did it, # but MMV handles it like a separate quality (rather than an add7). EXTENDED_QUALITY_REDUX = { "minmaj7": ("min", {"7"}), "maj9": ("maj7", {"9"}), "min9": ("min7", {"9"}), "9": ("7", {"9"}), "b9": ("7", {"b9"}), "#9": ("7", {"#9"}), "11": ("7", {"9", "11"}), "#11": ("7", {"9", "#11"}), "13": ("7", {"9", "11", "13"}), "b13": ("7", {"9", "11", "b13"}), "min11": ("min7", {"9", "11"}), "maj13": ("maj7", {"9", "11", "13"}), "min13": ("min7", {"9", "11", "13"}), } def reduce_extended_quality(quality): """Map an extended chord quality to a simpler one, moving upper voices to a set of scale degree extensions. Parameters ---------- quality : str Extended chord quality to reduce. Returns ------- base_quality : str New chord quality. extensions : set Scale degrees extensions for the quality. """ return EXTENDED_QUALITY_REDUX.get(quality, (quality, set())) # --- Chord Label Parsing --- # This monster regexp is pulled from the JAMS chord namespace, # which is in turn derived from the context-free grammar of # Harte et al., 2005. CHORD_RE = re.compile( r"""^((N|X)|(([A-G](b*|#*))((:(maj|min|dim|aug|1|5|sus2|sus4|maj6|min6|7|maj7|min7|dim7|hdim7|minmaj7|aug7|9|maj9|min9|11|maj11|min11|13|maj13|min13)(\((\*?((b*|#*)([1-9]|1[0-3]?))(,\*?((b*|#*)([1-9]|1[0-3]?)))*)\))?)|(:\((\*?((b*|#*)([1-9]|1[0-3]?))(,\*?((b*|#*)([1-9]|1[0-3]?)))*)\)))?((/((b*|#*)([1-9]|1[0-3]?)))?)?))$""" ) # nopep8 def validate_chord_label(chord_label): """Test for well-formedness of a chord label. Parameters ---------- chord_label : str Chord label to validate. """ if not CHORD_RE.match(chord_label): raise InvalidChordException("Invalid chord label: " "{}".format(chord_label)) pass def split(chord_label, reduce_extended_chords=False): """Parse a chord label into its four constituent parts: - root - quality shorthand - scale degrees - bass Note: Chords lacking quality AND interval information are major. - If a quality is specified, it is returned. - If an interval is specified WITHOUT a quality, the quality field is empty. Some examples:: 'C' -> ['C', 'maj', {}, '1'] 'G#:min(*b3,*5)/5' -> ['G#', 'min', {'*b3', '*5'}, '5'] 'A:(3)/6' -> ['A', '', {'3'}, '6'] Parameters ---------- chord_label : str A chord label. reduce_extended_chords : bool Whether to map the upper voicings of extended chords (9's, 11's, 13's) to semitone extensions. (Default value = False) Returns ------- chord_parts : list Split version of the chord label. """ chord_label = str(chord_label) validate_chord_label(chord_label) if chord_label == NO_CHORD: return [chord_label, "", set(), ""] bass = "1" if "/" in chord_label: chord_label, bass = chord_label.split("/") scale_degrees = set() omission = False if "(" in chord_label: chord_label, scale_degrees = chord_label.split("(") omission = "*" in scale_degrees scale_degrees = scale_degrees.strip(")") scale_degrees = {i.strip() for i in scale_degrees.split(",")} # Note: Chords lacking quality AND added interval information are major. # If a quality shorthand is specified, it is returned. # If an interval is specified WITHOUT a quality, the quality field is # empty. # Intervals specifying omissions MUST have a quality. if omission and ":" not in chord_label: raise InvalidChordException( "Intervals specifying omissions MUST have a quality." ) quality = "" if scale_degrees else "maj" if ":" in chord_label: chord_root, quality_name = chord_label.split(":") # Extended chords (with ":"s) may not explicitly have Major qualities, # so only overwrite the default if the string is not empty. if quality_name: quality = quality_name.lower() else: chord_root = chord_label if reduce_extended_chords: quality, addl_scale_degrees = reduce_extended_quality(quality) scale_degrees.update(addl_scale_degrees) return [chord_root, quality, scale_degrees, bass] def join(chord_root, quality="", extensions=None, bass=""): r"""Join the parts of a chord into a complete chord label. Parameters ---------- chord_root : str Root pitch class of the chord, e.g. 'C', 'Eb' quality : str Quality of the chord, e.g. 'maj', 'hdim7' (Default value = '') extensions : list Any added or absent scaled degrees for this chord, e.g. ['4', '\*3'] (Default value = None) bass : str Scale degree of the bass note, e.g. '5'. (Default value = '') Returns ------- chord_label : str A complete chord label. """ chord_label = chord_root if quality or extensions: chord_label += ":%s" % quality if extensions: chord_label += "(%s)" % ",".join(extensions) if bass and bass != "1": chord_label += "/%s" % bass validate_chord_label(chord_label) return chord_label # --- Chords to Numerical Representations --- def encode(chord_label, reduce_extended_chords=False, strict_bass_intervals=False): """Translate a chord label to numerical representations for evaluation. Parameters ---------- chord_label : str Chord label to encode. reduce_extended_chords : bool Whether to map the upper voicings of extended chords (9's, 11's, 13's) to semitone extensions. (Default value = False) strict_bass_intervals : bool Whether to require that the bass scale degree is present in the chord. (Default value = False) Returns ------- root_number : int Absolute semitone of the chord's root. semitone_bitmap : np.ndarray, dtype=int 12-dim vector of relative semitones in the chord spelling. bass_number : int Relative semitone of the chord's bass note, e.g. 0=root, 7=fifth, etc. """ if chord_label == NO_CHORD: return NO_CHORD_ENCODED if chord_label == X_CHORD: return X_CHORD_ENCODED chord_root, quality, scale_degrees, bass = split( chord_label, reduce_extended_chords=reduce_extended_chords ) root_number = pitch_class_to_semitone(chord_root) bass_number = scale_degree_to_semitone(bass) % 12 semitone_bitmap = quality_to_bitmap(quality) semitone_bitmap[0] = 1 for scale_degree in scale_degrees: semitone_bitmap += scale_degree_to_bitmap(scale_degree, reduce_extended_chords) semitone_bitmap = (semitone_bitmap > 0).astype(np.int64) if not semitone_bitmap[bass_number] and strict_bass_intervals: raise InvalidChordException( "Given bass scale degree is absent from this chord: " "%s" % chord_label, chord_label, ) else: semitone_bitmap[bass_number] = 1 return root_number, semitone_bitmap, bass_number def encode_many(chord_labels, reduce_extended_chords=False): """Translate a set of chord labels to numerical representations for sane evaluation. Parameters ---------- chord_labels : list Set of chord labels to encode. reduce_extended_chords : bool Whether to map the upper voicings of extended chords (9's, 11's, 13's) to semitone extensions. (Default value = False) Returns ------- root_number : np.ndarray, dtype=int Absolute semitone of the chord's root. interval_bitmap : np.ndarray, dtype=int 12-dim vector of relative semitones in the given chord quality. bass_number : np.ndarray, dtype=int Relative semitones of the chord's bass notes. """ num_items = len(chord_labels) roots, basses = np.zeros([2, num_items], dtype=np.int64) semitones = np.zeros([num_items, 12], dtype=np.int64) local_cache = dict() for i, label in enumerate(chord_labels): result = local_cache.get(label, None) if result is None: result = encode(label, reduce_extended_chords) local_cache[label] = result roots[i], semitones[i], basses[i] = result return roots, semitones, basses def rotate_bitmap_to_root(bitmap, chord_root): """Circularly shift a relative bitmap to its absolute pitch classes. For clarity, the best explanation is an example. Given 'G:Maj', the root and quality map are as follows:: root=5 quality=[1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0] # Relative chord shape After rotating to the root, the resulting bitmap becomes:: abs_quality = [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1] # G, B, and D Parameters ---------- bitmap : np.ndarray, shape=(12,) Bitmap of active notes, relative to the given root. chord_root : int Absolute pitch class number. Returns ------- bitmap : np.ndarray, shape=(12,) Absolute bitmap of active pitch classes. """ bitmap = np.asarray(bitmap) assert bitmap.ndim == 1, "Currently only 1D bitmaps are supported." idxs = list(np.nonzero(bitmap)) idxs[-1] = (idxs[-1] + chord_root) % 12 abs_bitmap = np.zeros_like(bitmap) abs_bitmap[tuple(idxs)] = 1 return abs_bitmap def rotate_bitmaps_to_roots(bitmaps, roots): """Circularly shift a relative bitmaps to absolute pitch classes. See :func:`rotate_bitmap_to_root` for more information. Parameters ---------- bitmaps : np.ndarray, shape=(N, 12) Bitmap of active notes, relative to the given root. roots : np.ndarray, shape=(N,) Absolute pitch class number. Returns ------- bitmap : np.ndarray, shape=(N, 12) Absolute bitmaps of active pitch classes. """ abs_bitmaps = [] for bitmap, chord_root in zip(bitmaps, roots): abs_bitmaps.append(rotate_bitmap_to_root(bitmap, chord_root)) return np.asarray(abs_bitmaps) # --- Comparison Routines --- def validate(reference_labels, estimated_labels): """Check that the input annotations to a comparison function look like valid chord labels. Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. """ N = len(reference_labels) M = len(estimated_labels) if N != M: raise ValueError( "Chord comparison received different length lists: " "len(reference)=%d\tlen(estimates)=%d" % (N, M) ) for labels in [reference_labels, estimated_labels]: for chord_label in labels: validate_chord_label(chord_label) # When either label list is empty, warn the user if len(reference_labels) == 0: warnings.warn("Reference labels are empty") if len(estimated_labels) == 0: warnings.warn("Estimated labels are empty") def weighted_accuracy(comparisons, weights): """Compute the weighted accuracy of a list of chord comparisons. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> # Here, we're using the "thirds" function to compare labels >>> # but any of the comparison functions would work. >>> comparisons = mir_eval.chord.thirds(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- comparisons : np.ndarray List of chord comparison scores, in [0, 1] or -1 weights : np.ndarray Weights (not necessarily normalized) for each comparison. This can be a list of interval durations Returns ------- score : float Weighted accuracy """ N = len(comparisons) # There should be as many weights as comparisons if weights.shape[0] != N: raise ValueError( "weights and comparisons should be of the same" " length. len(weights) = {} but len(comparisons)" " = {}".format(weights.shape[0], N) ) if (weights < 0).any(): raise ValueError("Weights should all be positive.") if np.sum(weights) == 0: warnings.warn("No nonzero weights, returning 0") return 0 # Find all comparison scores which are valid valid_idx = comparisons >= 0 # If no comparable chords were provided, warn and return 0 if valid_idx.sum() == 0: warnings.warn( "No reference chords were comparable " "to estimated chords, returning 0." ) return 0 # Remove any uncomparable labels comparisons = comparisons[valid_idx] weights = weights[valid_idx] # Normalize the weights total_weight = float(np.sum(weights)) normalized_weights = np.asarray(weights, dtype=float) / total_weight # Score is the sum of all weighted comparisons return np.sum(comparisons * normalized_weights) def thirds(reference_labels, estimated_labels): """Compare chords along root & third relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.thirds(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones = encode_many(reference_labels, False)[:2] est_roots, est_semitones = encode_many(estimated_labels, False)[:2] eq_roots = ref_roots == est_roots eq_thirds = ref_semitones[:, 3] == est_semitones[:, 3] comparison_scores = (eq_roots * eq_thirds).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def thirds_inv(reference_labels, estimated_labels): """Score chords along root, third, & bass relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.thirds_inv(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones, ref_bass = encode_many(reference_labels, False) est_roots, est_semitones, est_bass = encode_many(estimated_labels, False) eq_root = ref_roots == est_roots eq_bass = ref_bass == est_bass eq_third = ref_semitones[:, 3] == est_semitones[:, 3] comparison_scores = (eq_root * eq_third * eq_bass).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def triads(reference_labels, estimated_labels): """Compare chords along triad (root & quality to #5) relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.triads(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones = encode_many(reference_labels, False)[:2] est_roots, est_semitones = encode_many(estimated_labels, False)[:2] eq_roots = ref_roots == est_roots eq_semitones = np.all(np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1) comparison_scores = (eq_roots * eq_semitones).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def triads_inv(reference_labels, estimated_labels): """Score chords along triad (root, quality to #5, & bass) relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.triads_inv(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones, ref_bass = encode_many(reference_labels, False) est_roots, est_semitones, est_bass = encode_many(estimated_labels, False) eq_roots = ref_roots == est_roots eq_basses = ref_bass == est_bass eq_semitones = np.all(np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1) comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def tetrads(reference_labels, estimated_labels): """Compare chords along tetrad (root & full quality) relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.tetrads(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones = encode_many(reference_labels, False)[:2] est_roots, est_semitones = encode_many(estimated_labels, False)[:2] eq_roots = ref_roots == est_roots eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1) comparison_scores = (eq_roots * eq_semitones).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def tetrads_inv(reference_labels, estimated_labels): """Compare chords along tetrad (root, full quality, & bass) relationships. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.tetrads_inv(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones, ref_bass = encode_many(reference_labels, False) est_roots, est_semitones, est_bass = encode_many(estimated_labels, False) eq_roots = ref_roots == est_roots eq_basses = ref_bass == est_bass eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1) comparison_scores = (eq_roots * eq_semitones * eq_basses).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def root(reference_labels, estimated_labels): """Compare chords according to roots. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.root(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0], or -1 if the comparison is out of gamut. """ validate(reference_labels, estimated_labels) ref_roots, ref_semitones = encode_many(reference_labels, False)[:2] est_roots = encode_many(estimated_labels, False)[0] comparison_scores = (ref_roots == est_roots).astype(np.float64) # Ignore 'X' chords comparison_scores[np.any(ref_semitones < 0, axis=1)] = -1.0 return comparison_scores def mirex(reference_labels, estimated_labels): """Compare chords along MIREX rules. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.mirex(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0] """ validate(reference_labels, estimated_labels) # TODO(?): Should this be an argument? min_intersection = 3 ref_data = encode_many(reference_labels, False) ref_chroma = rotate_bitmaps_to_roots(ref_data[1], ref_data[0]) est_data = encode_many(estimated_labels, False) est_chroma = rotate_bitmaps_to_roots(est_data[1], est_data[0]) eq_chroma = (ref_chroma * est_chroma).sum(axis=-1) # Chroma matching for set bits comparison_scores = (eq_chroma >= min_intersection).astype(np.float64) # No-chord matching; match -1 roots, SKIP_CHORDS dropped next no_root = np.logical_and(ref_data[0] == -1, est_data[0] == -1) comparison_scores[no_root] = 1.0 # Skip chords where the number of active semitones `n` is # 0 < n < `min_intersection`. ref_semitone_count = (ref_data[1] > 0).sum(axis=1) skip_idx = np.logical_and( ref_semitone_count > 0, ref_semitone_count < min_intersection ) # Also ignore 'X' chords. np.logical_or(skip_idx, np.any(ref_data[1] < 0, axis=1), skip_idx) comparison_scores[skip_idx] = -1.0 return comparison_scores def majmin(reference_labels, estimated_labels): """Compare chords along major-minor rules. Chords with qualities outside Major/minor/no-chord are ignored. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.majmin(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0], or -1 if the comparison is out of gamut. """ validate(reference_labels, estimated_labels) maj_semitones = np.array(QUALITIES["maj"][:8]) min_semitones = np.array(QUALITIES["min"][:8]) ref_roots, ref_semitones, _ = encode_many(reference_labels, False) est_roots, est_semitones, _ = encode_many(estimated_labels, False) eq_root = ref_roots == est_roots eq_quality = np.all(np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1) comparison_scores = (eq_root * eq_quality).astype(np.float64) # Test for Major / Minor / No-chord is_maj = np.all(np.equal(ref_semitones[:, :8], maj_semitones), axis=1) is_min = np.all(np.equal(ref_semitones[:, :8], min_semitones), axis=1) is_none = np.logical_and(ref_roots < 0, np.all(ref_semitones == 0, axis=1)) # Only keep majors, minors, and Nones (NOR) comparison_scores[(is_maj + is_min + is_none) == 0] = -1 # Disable chords that disrupt this quality (apparently) # ref_voicing = np.all(np.equal(ref_qualities[:, :8], # ref_notes[:, :8]), axis=1) # comparison_scores[ref_voicing == 0] = -1 # est_voicing = np.all(np.equal(est_qualities[:, :8], # est_notes[:, :8]), axis=1) # comparison_scores[est_voicing == 0] = -1 return comparison_scores def majmin_inv(reference_labels, estimated_labels): """Compare chords along major-minor rules, with inversions. Chords with qualities outside Major/minor/no-chord are ignored, and the bass note must exist in the triad (bass in [1, 3, 5]). Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.majmin_inv(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0], or -1 if the comparison is out of gamut. """ validate(reference_labels, estimated_labels) maj_semitones = np.array(QUALITIES["maj"][:8]) min_semitones = np.array(QUALITIES["min"][:8]) ref_roots, ref_semitones, ref_bass = encode_many(reference_labels, False) est_roots, est_semitones, est_bass = encode_many(estimated_labels, False) eq_root_bass = (ref_roots == est_roots) * (ref_bass == est_bass) eq_semitones = np.all(np.equal(ref_semitones[:, :8], est_semitones[:, :8]), axis=1) comparison_scores = (eq_root_bass * eq_semitones).astype(np.float64) # Test for Major / Minor / No-chord is_maj = np.all(np.equal(ref_semitones[:, :8], maj_semitones), axis=1) is_min = np.all(np.equal(ref_semitones[:, :8], min_semitones), axis=1) is_none = np.logical_and(ref_roots < 0, np.all(ref_semitones == 0, axis=1)) # Only keep majors, minors, and Nones (NOR) comparison_scores[(is_maj + is_min + is_none) == 0] = -1 # Disable inversions that are not part of the quality valid_inversion = np.ones(ref_bass.shape, dtype=bool) bass_idx = ref_bass >= 0 valid_inversion[bass_idx] = ref_semitones[bass_idx, ref_bass[bass_idx]] comparison_scores[valid_inversion == 0] = -1 return comparison_scores def sevenths(reference_labels, estimated_labels): """Compare chords along MIREX 'sevenths' rules. Chords with qualities outside [maj, maj7, 7, min, min7, N] are ignored. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.sevenths(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0], or -1 if the comparison is out of gamut. """ validate(reference_labels, estimated_labels) seventh_qualities = ["maj", "min", "maj7", "7", "min7", ""] valid_semitones = np.array([QUALITIES[name] for name in seventh_qualities]) ref_roots, ref_semitones = encode_many(reference_labels, False)[:2] est_roots, est_semitones = encode_many(estimated_labels, False)[:2] eq_root = ref_roots == est_roots eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1) comparison_scores = (eq_root * eq_semitones).astype(np.float64) # Test for reference chord inclusion is_valid = np.array( [ np.all(np.equal(ref_semitones, semitones), axis=1) for semitones in valid_semitones ] ) # Drop if NOR comparison_scores[np.sum(is_valid, axis=0) == 0] = -1 return comparison_scores def sevenths_inv(reference_labels, estimated_labels): """Compare chords along MIREX 'sevenths' rules. Chords with qualities outside [maj, maj7, 7, min, min7, N] are ignored. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> est_intervals, est_labels = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, ref_intervals.min(), ... ref_intervals.max(), mir_eval.chord.NO_CHORD, ... mir_eval.chord.NO_CHORD) >>> (intervals, ... ref_labels, ... est_labels) = mir_eval.util.merge_labeled_intervals( ... ref_intervals, ref_labels, est_intervals, est_labels) >>> durations = mir_eval.util.intervals_to_durations(intervals) >>> comparisons = mir_eval.chord.sevenths_inv(ref_labels, est_labels) >>> score = mir_eval.chord.weighted_accuracy(comparisons, durations) Parameters ---------- reference_labels : list, len=n Reference chord labels to score against. estimated_labels : list, len=n Estimated chord labels to score against. Returns ------- comparison_scores : np.ndarray, shape=(n,), dtype=float Comparison scores, in [0.0, 1.0], or -1 if the comparison is out of gamut. """ validate(reference_labels, estimated_labels) seventh_qualities = ["maj", "min", "maj7", "7", "min7", ""] valid_semitones = np.array([QUALITIES[name] for name in seventh_qualities]) ref_roots, ref_semitones, ref_basses = encode_many(reference_labels, False) est_roots, est_semitones, est_basses = encode_many(estimated_labels, False) eq_roots_basses = (ref_roots == est_roots) * (ref_basses == est_basses) eq_semitones = np.all(np.equal(ref_semitones, est_semitones), axis=1) comparison_scores = (eq_roots_basses * eq_semitones).astype(np.float64) # Test for Major / Minor / No-chord is_valid = np.array( [ np.all(np.equal(ref_semitones, semitones), axis=1) for semitones in valid_semitones ] ) comparison_scores[np.sum(is_valid, axis=0) == 0] = -1 # Disable inversions that are not part of the quality valid_inversion = np.ones(ref_basses.shape, dtype=bool) bass_idx = ref_basses >= 0 valid_inversion[bass_idx] = ref_semitones[bass_idx, ref_basses[bass_idx]] comparison_scores[valid_inversion == 0] = -1 return comparison_scores def directional_hamming_distance(reference_intervals, estimated_intervals): """Compute the directional hamming distance between reference and estimated intervals as defined by [#harte2010towards]_ and used for MIREX 'OverSeg', 'UnderSeg' and 'MeanSeg' measures. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> overseg = 1 - mir_eval.chord.directional_hamming_distance( ... ref_intervals, est_intervals) >>> underseg = 1 - mir_eval.chord.directional_hamming_distance( ... est_intervals, ref_intervals) >>> seg = min(overseg, underseg) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2), dtype=float Reference chord intervals to score against. estimated_intervals : np.ndarray, shape=(m, 2), dtype=float Estimated chord intervals to score against. Returns ------- directional hamming distance : float directional hamming distance between reference intervals and estimated intervals. """ util.validate_intervals(estimated_intervals) util.validate_intervals(reference_intervals) # make sure chord intervals do not overlap if ( len(reference_intervals) > 1 and (reference_intervals[:-1, 1] > reference_intervals[1:, 0]).any() ): raise ValueError("Chord Intervals must not overlap") est_ts = np.unique(estimated_intervals.flatten()) seg = 0.0 for start, end in reference_intervals: dur = end - start between_start_end = est_ts[(est_ts >= start) & (est_ts < end)] seg_ts = np.hstack([start, between_start_end, end]) seg += dur - np.diff(seg_ts).max() return seg / (reference_intervals[-1, 1] - reference_intervals[0, 0]) def overseg(reference_intervals, estimated_intervals): """Compute the MIREX 'OverSeg' score. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> score = mir_eval.chord.overseg(ref_intervals, est_intervals) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2), dtype=float Reference chord intervals to score against. estimated_intervals : np.ndarray, shape=(m, 2), dtype=float Estimated chord intervals to score against. Returns ------- oversegmentation score : float Comparison score, in [0.0, 1.0], where 1.0 means no oversegmentation. """ return 1 - directional_hamming_distance(reference_intervals, estimated_intervals) def underseg(reference_intervals, estimated_intervals): """Compute the MIREX 'UnderSeg' score. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> score = mir_eval.chord.underseg(ref_intervals, est_intervals) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2), dtype=float Reference chord intervals to score against. estimated_intervals : np.ndarray, shape=(m, 2), dtype=float Estimated chord intervals to score against. Returns ------- undersegmentation score : float Comparison score, in [0.0, 1.0], where 1.0 means no undersegmentation. """ return 1 - directional_hamming_distance(estimated_intervals, reference_intervals) def seg(reference_intervals, estimated_intervals): """Compute the MIREX 'MeanSeg' score. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> score = mir_eval.chord.seg(ref_intervals, est_intervals) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2), dtype=float Reference chord intervals to score against. estimated_intervals : np.ndarray, shape=(m, 2), dtype=float Estimated chord intervals to score against. Returns ------- segmentation score : float Comparison score, in [0.0, 1.0], where 1.0 means perfect segmentation. """ return min( underseg(reference_intervals, estimated_intervals), overseg(reference_intervals, estimated_intervals), ) def merge_chord_intervals(intervals, labels): """ Merge consecutive chord intervals if they represent the same chord. Parameters ---------- intervals : np.ndarray, shape=(n, 2), dtype=float Chord intervals to be merged, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. labels : list, shape=(n,) Chord labels to be merged, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. Returns ------- merged_ivs : np.ndarray, shape=(k, 2), dtype=float Merged chord intervals, k <= n """ roots, semitones, basses = encode_many(labels, True) merged_ivs = [] prev_rt = None prev_st = None prev_ba = None for s, e, rt, st, ba in zip( intervals[:, 0], intervals[:, 1], roots, semitones, basses ): if rt != prev_rt or (st != prev_st).any() or ba != prev_ba: prev_rt, prev_st, prev_ba = rt, st, ba merged_ivs.append([s, e]) else: merged_ivs[-1][-1] = e return np.array(merged_ivs) def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs): """Compute weighted accuracy for all comparison functions for the given reference and estimated annotations. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> scores = mir_eval.chord.evaluate(ref_intervals, ref_labels, ... est_intervals, est_labels) Parameters ---------- ref_intervals : np.ndarray, shape=(n, 2) Reference chord intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. ref_labels : list, shape=(n,) reference chord labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. est_intervals : np.ndarray, shape=(m, 2) estimated chord intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. est_labels : list, shape=(m,) estimated chord labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Append or crop estimated intervals so their span is the same as reference est_intervals, est_labels = util.adjust_intervals( est_intervals, est_labels, ref_intervals.min(), ref_intervals.max(), NO_CHORD, NO_CHORD, ) # use merged intervals for segmentation evaluation merged_ref_intervals = merge_chord_intervals(ref_intervals, ref_labels) merged_est_intervals = merge_chord_intervals(est_intervals, est_labels) # Adjust the labels so that they span the same intervals intervals, ref_labels, est_labels = util.merge_labeled_intervals( ref_intervals, ref_labels, est_intervals, est_labels ) # Convert intervals to durations (used as weights) durations = util.intervals_to_durations(intervals) # Store scores for each comparison function scores = collections.OrderedDict() scores["thirds"] = weighted_accuracy(thirds(ref_labels, est_labels), durations) scores["thirds_inv"] = weighted_accuracy( thirds_inv(ref_labels, est_labels), durations ) scores["triads"] = weighted_accuracy(triads(ref_labels, est_labels), durations) scores["triads_inv"] = weighted_accuracy( triads_inv(ref_labels, est_labels), durations ) scores["tetrads"] = weighted_accuracy(tetrads(ref_labels, est_labels), durations) scores["tetrads_inv"] = weighted_accuracy( tetrads_inv(ref_labels, est_labels), durations ) scores["root"] = weighted_accuracy(root(ref_labels, est_labels), durations) scores["mirex"] = weighted_accuracy(mirex(ref_labels, est_labels), durations) scores["majmin"] = weighted_accuracy(majmin(ref_labels, est_labels), durations) scores["majmin_inv"] = weighted_accuracy( majmin_inv(ref_labels, est_labels), durations ) scores["sevenths"] = weighted_accuracy(sevenths(ref_labels, est_labels), durations) scores["sevenths_inv"] = weighted_accuracy( sevenths_inv(ref_labels, est_labels), durations ) scores["underseg"] = underseg(merged_ref_intervals, merged_est_intervals) scores["overseg"] = overseg(merged_ref_intervals, merged_est_intervals) scores["seg"] = min(scores["overseg"], scores["underseg"]) return scores mir_eval-0.8.2/mir_eval/display.py000066400000000000000000000740461475740344600171650ustar00rootroot00000000000000"""Display functions""" from collections import defaultdict from weakref import WeakKeyDictionary import numpy as np from scipy.signal import spectrogram import matplotlib as mpl from matplotlib.patches import Rectangle from matplotlib.ticker import FuncFormatter, MultipleLocator from matplotlib.ticker import Formatter from matplotlib.colors import LinearSegmentedColormap, LogNorm, ColorConverter from matplotlib.transforms import Bbox, TransformedBbox from .melody import freq_to_voicing from .util import midi_to_hz, hz_to_midi # This dictionary is used to track mir_eval-specific attributes # attached to matplotlib axes __AXMAP = WeakKeyDictionary() def __get_axes(ax=None, fig=None): """Get or construct the target axes object for a new plot. Parameters ---------- ax : matplotlib.pyplot.axes, optional If provided, return this axes object directly. fig : matplotlib.figure.Figure, optional The figure to query for axes. By default, uses the current figure `plt.gcf()`. Returns ------- ax : matplotlib.pyplot.axes An axis handle on which to draw the segmentation. If none is provided, a new set of axes is created. new_axes : bool If `True`, the axis object was newly constructed. If `False`, the axis object already existed. """ new_axes = False if ax is None: if fig is None: import matplotlib.pyplot as plt fig = plt.gcf() if not fig.get_axes(): new_axes = True ax = fig.gca() # Create a storage bucket for this axes in case we need it if ax not in __AXMAP: __AXMAP[ax] = dict() return ax, new_axes def segments( intervals, labels, base=None, height=None, text=False, text_kw=None, ax=None, prop_cycle=None, **kwargs, ): """Plot a segmentation as a set of disjoint rectangles. Parameters ---------- intervals : np.ndarray, shape=(n, 2) segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. base : number The vertical position of the base of the rectangles. By default, this will be the bottom of the plot. height : number The height of the rectangles. By default, this will be the top of the plot (minus ``base``). .. note:: If either `base` or `height` are provided, both must be provided. text : bool If true, each segment's label is displayed in its upper-left corner text_kw : dict If ``text == True``, the properties of the text object can be specified here. See ``matplotlib.pyplot.Text`` for valid parameters ax : matplotlib.pyplot.axes An axis handle on which to draw the segmentation. If none is provided, a new set of axes is created. prop_cycle : cycle.Cycler An optional property cycle object to specify style properties. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to pass to ``matplotlib.patches.Rectangle``. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ if text_kw is None: text_kw = dict() text_kw.setdefault("va", "top") text_kw.setdefault("clip_on", True) text_kw.setdefault("bbox", dict(boxstyle="round", facecolor="white")) # Make sure we have a numpy array intervals = np.atleast_2d(intervals) seg_def_style = dict(linewidth=1) ax, new_axes = __get_axes(ax=ax) if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] if new_axes: ax.set_yticks([]) if base is None and height is None: # If neither are provided, we'll use axes coordinates to span the figure base, height = 0, 1 transform = ax.get_xaxis_transform() elif base is not None and height is not None: # If both are provided, we'll use data coordinates transform = None else: raise ValueError("When specifying base or height, both must be provided.") seg_map = dict() for lab in labels: if lab in seg_map: continue try: properties = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter properties = next(prop_iter) style = { k: v for k, v in properties.items() if k in ["color", "facecolor", "edgecolor", "linewidth"] } # Swap color -> facecolor here so we preserve edgecolor on rects style.setdefault("facecolor", style["color"]) style.pop("color", None) seg_map[lab] = seg_def_style.copy() seg_map[lab].update(style) seg_map[lab].update(kwargs) seg_map[lab]["label"] = lab for ival, lab in zip(intervals, labels): rect = ax.axvspan(ival[0], ival[1], ymin=base, ymax=height, **seg_map[lab]) seg_map[lab].pop("label", None) if text: ann = ax.annotate( lab, xy=(ival[0], height), xycoords=transform, xytext=(8, -10), textcoords="offset points", **text_kw, ) ann.set_clip_path(rect) return ax def labeled_intervals( intervals, labels, label_set=None, base=None, height=None, extend_labels=True, ax=None, tick=True, prop_cycle=None, **kwargs, ): """Plot labeled intervals with each label on its own row. Parameters ---------- intervals : np.ndarray, shape=(n, 2) segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. label_set : list An (ordered) list of labels to determine the plotting order. If not provided, the labels will be inferred from ``ax.get_yticklabels()``. If no ``yticklabels`` exist, then the sorted set of unique values in ``labels`` is taken as the label set. base : np.ndarray, shape=(n,), optional Vertical positions of each label. By default, labels are positioned at integers ``np.arange(len(labels))``. height : scalar or np.ndarray, shape=(n,), optional Height for each label. If scalar, the same value is applied to all labels. By default, each label has ``height=1``. extend_labels : bool If ``False``, only values of ``labels`` that also exist in ``label_set`` will be shown. If ``True``, all labels are shown, with those in `labels` but not in `label_set` appended to the top of the plot. A horizontal line is drawn to indicate the separation between values in or out of ``label_set``. ax : matplotlib.pyplot.axes An axis handle on which to draw the intervals. If none is provided, a new set of axes is created. tick : bool If ``True``, sets tick positions and labels on the y-axis. prop_cycle : cycle.Cycler An optional property cycle object to specify style properties. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to pass to `matplotlib.collection.PolyCollection`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ # Get the axes handle ax, new_axes = __get_axes(ax=ax) if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] # Make sure we have a numpy array intervals = np.atleast_2d(intervals) if label_set is None: # If we have non-empty pre-existing tick labels, use them # If none of the label strings have content, treat it as empty label_set = __AXMAP[ax].get("labels", []) else: label_set = list(label_set) # Put additional labels at the end, in order extended = False if extend_labels: ticks = label_set + sorted(set(labels) - set(label_set)) if ticks != label_set and len(label_set) > 0: extended = True elif label_set: ticks = label_set else: ticks = sorted(set(labels)) # Push the ticks up into the axmap __AXMAP[ax]["labels"] = ticks style = dict(linewidth=1) try: properties = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter properties = next(prop_iter) style = { k: v for k, v in properties.items() if k in ["color", "facecolor", "edgecolor", "linewidth"] } # Swap color -> facecolor here so we preserve edgecolor on rects style.setdefault("facecolor", style["color"]) style.pop("color", None) style.update(kwargs) if base is None: base = np.arange(len(ticks)) if height is None: height = 1 if np.isscalar(height): height = height * np.ones_like(base) seg_y = dict() for ybase, yheight, lab in zip(base, height, ticks): seg_y[lab] = (ybase, yheight) xvals = defaultdict(list) for ival, lab in zip(intervals, labels): if lab not in seg_y: continue xvals[lab].append((ival[0], ival[1] - ival[0])) for lab in seg_y: ax.broken_barh(xvals[lab], seg_y[lab], **style) # Pop the label after the first time we see it, so we only get # one legend entry style.pop("label", None) # Draw a line separating the new labels from pre-existing labels if extended: ax.axhline(len(label_set), color="k", alpha=0.5) if tick: ax.grid(True, axis="y") ax.set_yticks([]) ax.set_yticks(base) ax.set_yticklabels(ticks, va="bottom") ax.yaxis.set_major_formatter(IntervalFormatter(base, ticks)) return ax class IntervalFormatter(Formatter): """Ticker formatter for labeled interval plots. Parameters ---------- base : array-like of int The base positions of each label ticks : array-like of string The labels for the ticks """ def __init__(self, base, ticks): self._map = {int(k): v for k, v in zip(base, ticks)} def __call__(self, x, pos=None): """Map the input position to its corresponding interval label""" return self._map.get(int(x), "") def hierarchy(intervals_hier, labels_hier, levels=None, ax=None, **kwargs): """Plot a hierarchical segmentation Parameters ---------- intervals_hier : list of np.ndarray A list of segmentation intervals. Each element should be an n-by-2 array of segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. Segmentations should be ordered by increasing specificity. labels_hier : list of list-like A list of segmentation labels. Each element should be a list of labels for the corresponding element in `intervals_hier`. levels : list of string Each element ``levels[i]`` is a label for the ```i`` th segmentation. This is used in the legend to denote the levels in a segment hierarchy. ax : matplotlib.pyplot.axes An axis handle on which to draw the intervals. If none is provided, a new set of axes is created. **kwargs Additional keyword arguments to `labeled_intervals`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ # This will break if a segment label exists in multiple levels if levels is None: levels = list(range(len(intervals_hier))) # Get the axes handle ax, _ = __get_axes(ax=ax) # Count the pre-existing patches n_patches = len(ax.patches) for ints, labs, key in zip(intervals_hier[::-1], labels_hier[::-1], levels[::-1]): labeled_intervals(ints, labs, label=key, ax=ax, **kwargs) return ax def events( times, labels=None, base=None, height=None, ax=None, text_kw=None, prop_cycle=None, **kwargs, ): """Plot event times as a set of vertical lines Parameters ---------- times : np.ndarray, shape=(n,) event times, in the format returned by :func:`mir_eval.io.load_events` or :func:`mir_eval.io.load_labeled_events`. labels : list, shape=(n,), optional event labels, in the format returned by :func:`mir_eval.io.load_labeled_events`. base : number The vertical position of the base of the line. By default, this will be the bottom of the plot. height : number The height of the lines. By default, this will be the top of the plot (minus `base`). .. note:: If either `base` or `height` are provided, both must be provided. ax : matplotlib.pyplot.axes An axis handle on which to draw the segmentation. If none is provided, a new set of axes is created. text_kw : dict If `labels` is provided, the properties of the text objects can be specified here. See `matplotlib.pyplot.Text` for valid parameters prop_cycle : cycle.Cycler An optional property cycle object to specify style properties. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to pass to `matplotlib.pyplot.vlines`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ if text_kw is None: text_kw = dict() text_kw.setdefault("va", "top") text_kw.setdefault("clip_on", True) text_kw.setdefault("bbox", dict(boxstyle="round", facecolor="white")) # make sure we have an array for times times = np.asarray(times) # Get the axes handle ax, new_axes = __get_axes(ax=ax) if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] if base is None and height is None: # If neither are provided, we'll use axes coordinates to span the figure base, height = 0, 1 transform = ax.get_xaxis_transform() elif base is not None and height is not None: # If both are provided, we'll use data coordinates transform = None else: raise ValueError("When specifying base or height, both must be provided.") # Advance the property iterator if we can, restart it if we must try: properties = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter properties = next(prop_iter) style = { k: v for k, v in properties.items() if k in ["color", "linestyle", "linewidth"] } style.update(kwargs) # If the user provided 'colors', don't override it with 'color' if "colors" in style: style.pop("color", None) lines = ax.vlines(times, base, base + height, transform=transform, **style) if labels: for path, lab in zip(lines.get_paths(), labels): ax.annotate( lab, xy=(path.vertices[0][0], height), xycoords=transform, xytext=(8, -10), textcoords="offset points", **text_kw, ) if new_axes: ax.set_yticks([]) return ax def pitch( times, frequencies, midi=False, unvoiced=False, ax=None, prop_cycle=None, **kwargs ): """Visualize pitch contours Parameters ---------- times : np.ndarray, shape=(n,) Sample times of frequencies frequencies : np.ndarray, shape=(n,) frequencies (in Hz) of the pitch contours. Voicing is indicated by sign (positive for voiced, non-positive for non-voiced). midi : bool If `True`, plot on a MIDI-numbered vertical axis. Otherwise, plot on a linear frequency axis. unvoiced : bool If `True`, unvoiced pitch contours are plotted and indicated by transparency. Otherwise, unvoiced pitch contours are omitted from the display. ax : matplotlib.pyplot.axes An axis handle on which to draw the pitch contours. If none is provided, a new set of axes is created. prop_cycle : cycle.Cycler An optional property cycle object to specify style properties. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to `matplotlib.pyplot.plot`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ ax, _ = __get_axes(ax=ax) if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] times = np.asarray(times) # First, segment into contiguously voiced contours frequencies, voicings = freq_to_voicing(np.asarray(frequencies, dtype=np.float64)) voicings = voicings.astype(bool) # Here are all the change-points v_changes = 1 + np.flatnonzero(voicings[1:] != voicings[:-1]) v_changes = np.unique(np.concatenate([[0], v_changes, [len(voicings)]])) # Set up arrays of slices for voiced and unvoiced regions v_slices, u_slices = [], [] for start, end in zip(v_changes, v_changes[1:]): idx = slice(start, end) # A region is voiced if its starting sample is voiced # It's unvoiced if none of the samples in the region are voiced. if voicings[start]: v_slices.append(idx) elif frequencies[idx].all(): u_slices.append(idx) # Now we just need to plot the contour try: style = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter style = next(prop_iter) style.update(kwargs) if midi: idx = frequencies > 0 frequencies[idx] = hz_to_midi(frequencies[idx]) # Tick at integer midi notes ax.yaxis.set_minor_locator(MultipleLocator(1)) for idx in v_slices: ax.plot(times[idx], frequencies[idx], **style) style.pop("label", None) # Plot the unvoiced portions if unvoiced: style["alpha"] = style.get("alpha", 1.0) * 0.5 for idx in u_slices: ax.plot(times[idx], frequencies[idx], **style) return ax def multipitch( times, frequencies, midi=False, unvoiced=False, ax=None, prop_cycle=None, **kwargs ): """Visualize multiple f0 measurements Parameters ---------- times : np.ndarray, shape=(n,) Sample times of frequencies frequencies : list of np.ndarray frequencies (in Hz) of the pitch measurements. Voicing is indicated by sign (positive for voiced, non-positive for non-voiced). `times` and `frequencies` should be in the format produced by :func:`mir_eval.io.load_ragged_time_series` midi : bool If `True`, plot on a MIDI-numbered vertical axis. Otherwise, plot on a linear frequency axis. unvoiced : bool If `True`, unvoiced pitches are plotted and indicated by transparency. Otherwise, unvoiced pitches are omitted from the display. ax : matplotlib.pyplot.axes An axis handle on which to draw the pitch contours. If none is provided, a new set of axes is created. prop_cycle : cycle.Cycler An optional property cycle object to specify style properties. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to `plt.scatter`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ # Get the axes handle ax, _ = __get_axes(ax=ax) if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] # Set up a style for the plot try: style_voiced = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter style_voiced = next(prop_iter) style_voiced.update(kwargs) style_unvoiced = style_voiced.copy() style_unvoiced.pop("label", None) style_unvoiced["alpha"] = style_unvoiced.get("alpha", 1.0) * 0.5 # We'll collect all times and frequencies first, then plot them voiced_times = [] voiced_freqs = [] unvoiced_times = [] unvoiced_freqs = [] for t, freqs in zip(times, frequencies): if not len(freqs): continue freqs, voicings = freq_to_voicing(np.asarray(freqs, dtype=np.float64)) # Discard all 0-frequency measurements idx = freqs > 0 freqs = freqs[idx] voicings = voicings[idx].astype(bool) if midi: freqs = hz_to_midi(freqs) n_voiced = sum(voicings) voiced_times.extend([t] * int(n_voiced)) voiced_freqs.extend(freqs[voicings]) unvoiced_times.extend([t] * (len(freqs) - n_voiced)) unvoiced_freqs.extend(freqs[~voicings]) # Plot the voiced frequencies ax.scatter(voiced_times, voiced_freqs, **style_voiced) # Plot the unvoiced frequencies if unvoiced: ax.scatter(unvoiced_times, unvoiced_freqs, **style_unvoiced) # Tick at integer midi notes if midi: ax.yaxis.set_minor_locator(MultipleLocator(1)) return ax def piano_roll(intervals, pitches=None, midi=None, ax=None, **kwargs): """Plot a quantized piano roll as intervals Parameters ---------- intervals : np.ndarray, shape=(n, 2) timing intervals for notes pitches : np.ndarray, shape=(n,), optional pitches of notes (in Hz). midi : np.ndarray, shape=(n,), optional pitches of notes (in MIDI numbers). At least one of ``pitches`` or ``midi`` must be provided. ax : matplotlib.pyplot.axes An axis handle on which to draw the intervals. If none is provided, a new set of axes is created. **kwargs Additional keyword arguments to :func:`labeled_intervals`. Returns ------- ax : matplotlib.pyplot.axes._subplots.AxesSubplot A handle to the (possibly constructed) plot axes """ if midi is None: if pitches is None: raise ValueError("At least one of `midi` or `pitches` " "must be provided.") midi = hz_to_midi(pitches) scale = np.arange(128) ax = labeled_intervals( intervals, np.round(midi).astype(int), label_set=scale, tick=False, ax=ax, **kwargs, ) # Minor tick at each semitone ax.yaxis.set_minor_locator(MultipleLocator(1)) return ax def separation( sources, fs=22050, labels=None, alpha=0.75, ax=None, rasterized=True, edgecolors="None", shading="gouraud", prop_cycle=None, **kwargs, ): """Source-separation visualization Parameters ---------- sources : np.ndarray, shape=(nsrc, nsampl) A list of waveform buffers corresponding to each source fs : number > 0 The sampling rate labels : list of strings An optional list of descriptors corresponding to each source alpha : float in [0, 1] Maximum alpha (opacity) of spectrogram values. ax : matplotlib.pyplot.axes An axis handle on which to draw the spectrograms. If none is provided, a new set of axes is created. rasterized : bool If `True`, the spectrogram is rasterized. edgecolors : str or None The color of the edges of the spectrogram patches. Set to "None" (default) to disable edge coloring. shading : str The shading method to use for the spectrogram. See `matplotlib.pyplot.pcolormesh` for valid options. prop_cycle : cycle.Cycler An optional property cycle object to specify colors for each signal. If not provided, the default property cycler will be retrieved from matplotlib. **kwargs Additional keyword arguments to ``scipy.signal.spectrogram`` Returns ------- ax The axis handle for this plot """ # Get the axes handle ax, new_axes = __get_axes(ax=ax) # Make sure we have at least two dimensions sources = np.atleast_2d(sources) if labels is None: labels = [f"Source {_:d}" for _ in range(len(sources))] kwargs.setdefault("scaling", "spectrum") # The cumulative spectrogram across sources # is used to establish the reference power # for each individual source cumspec = None specs = [] for i, src in enumerate(sources): freqs, times, spec = spectrogram(src, fs=fs, **kwargs) specs.append(spec) if cumspec is None: cumspec = spec.copy() else: cumspec += spec ref_max = cumspec.max() ref_min = ref_max * 1e-6 color_conv = ColorConverter() if prop_cycle is None: __AXMAP[ax].setdefault("prop_cycle", mpl.rcParams["axes.prop_cycle"]) __AXMAP[ax].setdefault("prop_iter", iter(mpl.rcParams["axes.prop_cycle"])) elif "prop_iter" not in __AXMAP[ax]: __AXMAP[ax]["prop_cycle"] = prop_cycle __AXMAP[ax]["prop_iter"] = iter(prop_cycle) prop_cycle = __AXMAP[ax]["prop_cycle"] prop_iter = __AXMAP[ax]["prop_iter"] for i, spec in enumerate(specs): # For each source, grab a new color from the cycler # Then construct a colormap that interpolates from # [transparent white -> new color] # Advance the property iterator if we can, restart it if we must try: properties = next(prop_iter) except StopIteration: prop_iter = iter(prop_cycle) __AXMAP[ax]["prop_iter"] = prop_iter properties = next(prop_iter) color = color_conv.to_rgba(properties["color"], alpha=alpha) cmap = LinearSegmentedColormap.from_list( labels[i], [(1.0, 1.0, 1.0, 0.0), color] ) ax.pcolormesh( times, freqs, spec, cmap=cmap, norm=LogNorm(vmin=ref_min, vmax=ref_max), rasterized=rasterized, edgecolors=edgecolors, shading=shading, ) # Attach a 0x0 rect to the axis with the corresponding label # This way, it will show up in the legend ax.add_patch( Rectangle((times.min(), freqs.min()), 0, 0, color=color, label=labels[i]) ) return ax def __ticker_midi_note(x, pos): """Format midi notes for ticker decoration. Inputs x are interpreted as midi numbers, and converted to [NOTE][OCTAVE]+[cents]. """ NOTES = ["C", "C#", "D", "D#", "E", "F", "F#", "G", "G#", "A", "A#", "B"] cents = float(np.mod(x, 1.0)) if cents >= 0.5: cents = cents - 1.0 x = x + 0.5 idx = int(x % 12) octave = int(x / 12) - 1 if cents == 0: return f"{NOTES[idx]:s}{octave:2d}" return f"{NOTES[idx]:s}{octave:2d}{int(cents * 100):+02d}" def __ticker_midi_hz(x, pos): """Format midi pitches for ticker decoration. Inputs x are interpreted as midi numbers, and converted to Hz. """ return f"{midi_to_hz(x):g}" def ticker_notes(ax=None): """Set the y-axis of the given axes to MIDI notes Parameters ---------- ax : matplotlib.pyplot.axes The axes handle to apply the ticker. By default, uses the current axes handle. """ ax, _ = __get_axes(ax=ax) ax.yaxis.set_major_formatter(FMT_MIDI_NOTE) # Get the tick labels and reset the vertical alignment for tick in ax.yaxis.get_ticklabels(): tick.set_verticalalignment("baseline") def ticker_pitch(ax=None): """Set the y-axis of the given axes to MIDI frequencies Parameters ---------- ax : matplotlib.pyplot.axes The axes handle to apply the ticker. By default, uses the current axes handle. """ ax, _ = __get_axes(ax=ax) ax.yaxis.set_major_formatter(FMT_MIDI_HZ) # Instantiate ticker objects; we don't need more than one of each FMT_MIDI_NOTE = FuncFormatter(__ticker_midi_note) FMT_MIDI_HZ = FuncFormatter(__ticker_midi_hz) mir_eval-0.8.2/mir_eval/hierarchy.py000066400000000000000000000563721475740344600175000ustar00rootroot00000000000000# CREATED:2015-09-16 14:46:47 by Brian McFee """Evaluation criteria for hierarchical structure analysis. Hierarchical structure analysis seeks to annotate a track with a nested decomposition of the temporal elements of the piece, effectively providing a kind of "parse tree" of the composition. Unlike the flat segmentation metrics defined in :mod:`mir_eval.segment`, which can only encode one level of analysis, hierarchical annotations expose the relationships between short segments and the larger compositional elements to which they belong. Conventions ----------- Annotations are assumed to take the form of an ordered list of segmentations. As in the :mod:`mir_eval.segment` metrics, each segmentation itself consists of an n-by-2 array of interval times, so that the ``i`` th segment spans time ``intervals[i, 0]`` to ``intervals[i, 1]``. Hierarchical annotations are ordered by increasing specificity, so that the first segmentation should contain the fewest segments, and the last segmentation contains the most. Metrics ------- * :func:`mir_eval.hierarchy.tmeasure`: Precision, recall, and F-measure of triplet-based frame accuracy for boundary detection. * :func:`mir_eval.hierarchy.lmeasure`: Precision, recall, and F-measure of triplet-based frame accuracy for segment labeling. References ---------- .. [#mcfee2015] Brian McFee, Oriol Nieto, and Juan P. Bello. "Hierarchical evaluation of segment boundary detection", International Society for Music Information Retrieval (ISMIR) conference, 2015. .. [#mcfee2017] Brian McFee, Oriol Nieto, Morwaread Farbood, and Juan P. Bello. "Evaluating hierarchical structure in music annotations", Frontiers in Psychology, 2017. """ import collections import itertools import warnings import numpy as np import scipy.sparse from . import util from .segment import validate_structure def _round(t, frame_size): """Round a time-stamp to a specified resolution. Equivalent to ``t - np.mod(t, frame_size)``. Examples -------- >>> _round(53.279, 0.1) 53.2 >>> _round(53.279, 0.25) 53.25 Parameters ---------- t : number or ndarray The time-stamp to round frame_size : number > 0 The resolution to round to Returns ------- t_round : number The rounded time-stamp """ return t - np.mod(t, float(frame_size)) def _hierarchy_bounds(intervals_hier): """Compute the covered time range of a hierarchical segmentation. Parameters ---------- intervals_hier : list of ndarray A hierarchical segmentation, encoded as a list of arrays of segment intervals. Returns ------- t_min : float t_max : float The minimum and maximum times spanned by the annotation """ boundaries = list(itertools.chain(*list(itertools.chain(*intervals_hier)))) return min(boundaries), max(boundaries) def _align_intervals(int_hier, lab_hier, t_min=0.0, t_max=None): """Align a hierarchical annotation to span a fixed start and end time. Parameters ---------- int_hier : list of list of intervals lab_hier : list of list of str Hierarchical segment annotations, encoded as a list of list of intervals (int_hier) and list of list of strings (lab_hier) t_min : None or number >= 0 The minimum time value for the segmentation t_max : None or number >= t_min The maximum time value for the segmentation Returns ------- intervals_hier : list of list of intervals labels_hier : list of list of str `int_hier` `lab_hier` aligned to span `[t_min, t_max]`. """ return [ list(_) for _ in zip( *[ util.adjust_intervals( np.asarray(ival), labels=lab, t_min=t_min, t_max=t_max ) for ival, lab in zip(int_hier, lab_hier) ] ) ] def _lca(intervals_hier, frame_size): """Compute the (sparse) least-common-ancestor (LCA) matrix for a hierarchical segmentation. For any pair of frames ``(s, t)``, the LCA is the deepest level in the hierarchy such that ``(s, t)`` are contained within a single segment at that level. Parameters ---------- intervals_hier : list of ndarray An ordered list of segment interval arrays. The list is assumed to be ordered by increasing specificity (depth). frame_size : number The length of the sample frames (in seconds) Returns ------- lca_matrix : scipy.sparse.csr_matrix A sparse matrix such that ``lca_matrix[i, j]`` contains the depth of the deepest segment containing frames ``i`` and ``j``. """ frame_size = float(frame_size) # Figure out how many frames we need n_start, n_end = _hierarchy_bounds(intervals_hier) n = int((_round(n_end, frame_size) - _round(n_start, frame_size)) / frame_size) # Initialize the LCA matrix lca_matrix = scipy.sparse.lil_matrix((n, n), dtype=np.uint8) for level, intervals in enumerate(intervals_hier, 1): for ival in (_round(np.asarray(intervals), frame_size) / frame_size).astype( int ): idx = slice(ival[0], ival[1]) lca_matrix[idx, idx] = level return lca_matrix.tocsr() def _meet(intervals_hier, labels_hier, frame_size): """Compute the (sparse) least-common-ancestor (LCA) matrix for a hierarchical segmentation. For any pair of frames ``(s, t)``, the LCA is the deepest level in the hierarchy such that ``(s, t)`` are contained within a single segment at that level. Parameters ---------- intervals_hier : list of ndarray An ordered list of segment interval arrays. The list is assumed to be ordered by increasing specificity (depth). labels_hier : list of list of str ``labels_hier[i]`` contains the segment labels for the ``i``th layer of the annotations frame_size : number The length of the sample frames (in seconds) Returns ------- meet_matrix : scipy.sparse.csr_matrix A sparse matrix such that ``meet_matrix[i, j]`` contains the depth of the deepest segment label containing both ``i`` and ``j``. """ frame_size = float(frame_size) # Figure out how many frames we need n_start, n_end = _hierarchy_bounds(intervals_hier) n = int((_round(n_end, frame_size) - _round(n_start, frame_size)) / frame_size) # Initialize the meet matrix meet_matrix = scipy.sparse.lil_matrix((n, n), dtype=np.uint8) for level, (intervals, labels) in enumerate(zip(intervals_hier, labels_hier), 1): # Encode the labels at this level lab_enc = util.index_labels(labels)[0] # Find unique agreements int_agree = np.triu(np.equal.outer(lab_enc, lab_enc)) # Map intervals to frame indices int_frames = (_round(intervals, frame_size) / frame_size).astype(int) # For each intervals i, j where labels agree, update the meet matrix for seg_i, seg_j in zip(*np.where(int_agree)): idx_i = slice(*list(int_frames[seg_i])) idx_j = slice(*list(int_frames[seg_j])) meet_matrix[idx_i, idx_j] = level if seg_i != seg_j: meet_matrix[idx_j, idx_i] = level return scipy.sparse.csr_matrix(meet_matrix) def _gauc(ref_lca, est_lca, transitive, window): """Generalized area under the curve (GAUC) This function computes the normalized recall score for correctly ordering triples ``(q, i, j)`` where frames ``(q, i)`` are closer than ``(q, j)`` in the reference annotation. Parameters ---------- ref_lca : scipy.sparse est_lca : scipy.sparse The least common ancestor matrices for the reference and estimated annotations transitive : bool If True, then transitive comparisons are counted, meaning that ``(q, i)`` and ``(q, j)`` can differ by any number of levels. If False, then ``(q, i)`` and ``(q, j)`` can differ by exactly one level. window : number or None The maximum number of frames to consider for each query. If `None`, then all frames are considered. Returns ------- score : number [0, 1] The percentage of reference triples correctly ordered by the estimation. Raises ------ ValueError If ``ref_lca`` and ``est_lca`` have different shapes """ # Make sure we have the right number of frames if ref_lca.shape != est_lca.shape: raise ValueError( "Estimated and reference hierarchies " "must have the same shape." ) # How many frames? n = ref_lca.shape[0] # By default, the window covers the entire track if window is None: window = n # Initialize the score score = 0.0 # Iterate over query frames num_frames = 0 for query in range(n): # Find all pairs i,j such that ref_lca[q, i] > ref_lca[q, j] results = slice(max(0, query - window), min(n, query + window)) ref_score = ref_lca[query, results] est_score = est_lca[query, results] # Densify the results ref_score = ref_score.toarray().squeeze() est_score = est_score.toarray().squeeze() # Don't count the query as a result # when query < window, query itself is the index within the slice # otherwise, query is located at the center of the slice, window # (this also holds when the slice goes off the end of the array.) idx = min(query, window) ref_score = np.concatenate((ref_score[:idx], ref_score[idx + 1 :])) est_score = np.concatenate((est_score[:idx], est_score[idx + 1 :])) inversions, normalizer = _compare_frame_rankings( ref_score, est_score, transitive=transitive ) if normalizer: score += 1.0 - inversions / float(normalizer) num_frames += 1 # Normalize by the number of frames counted. # If no frames are counted, take the convention 0/0 -> 0 if num_frames: score /= float(num_frames) else: score = 0.0 return score def _count_inversions(a, b): """Count the number of inversions in two numpy arrays: # points i, j where a[i] >= b[j] Parameters ---------- a, b : np.ndarray, shape=(n,) (m,) The arrays to be compared. This implementation is optimized for arrays with many repeated values. Returns ------- inversions : int The number of detected inversions """ a, a_counts = np.unique(a, return_counts=True) b, b_counts = np.unique(b, return_counts=True) inversions = 0 i = 0 j = 0 while i < len(a) and j < len(b): if a[i] < b[j]: i += 1 elif a[i] >= b[j]: inversions += np.sum(a_counts[i:]) * b_counts[j] j += 1 return inversions def _compare_frame_rankings(ref, est, transitive=False): """Compute the number of ranking disagreements in two lists. Parameters ---------- ref : np.ndarray, shape=(n,) est : np.ndarray, shape=(n,) Reference and estimate ranked lists. `ref[i]` is the relevance score for point `i`. transitive : bool If true, all pairs of reference levels are compared. If false, only adjacent pairs of reference levels are compared. Returns ------- inversions : int The number of pairs of indices `i, j` where `ref[i] < ref[j]` but `est[i] >= est[j]`. normalizer : float The total number of pairs (i, j) under consideration. If transitive=True, then this is |{(i,j) : ref[i] < ref[j]}| If transitive=False, then this is |{i,j) : ref[i] +1 = ref[j]}| """ idx = np.argsort(ref) ref_sorted = ref[idx] est_sorted = est[idx] # Find the break-points in ref_sorted levels, positions, counts = np.unique( ref_sorted, return_index=True, return_counts=True ) positions = list(positions) positions.append(len(ref_sorted)) index = collections.defaultdict(lambda: slice(0)) ref_map = collections.defaultdict(lambda: 0) for level, cnt, start, end in zip(levels, counts, positions[:-1], positions[1:]): index[level] = slice(start, end) ref_map[level] = cnt # Now that we have values sorted, apply the inversion-counter to # pairs of reference values if transitive: level_pairs = itertools.combinations(levels, 2) else: level_pairs = [(i, i + 1) for i in levels] level_pairs, lcounter = itertools.tee(level_pairs) normalizer = float(sum([ref_map[i] * ref_map[j] for (i, j) in lcounter])) if normalizer == 0: return 0, 0.0 inversions = 0 for level_1, level_2 in level_pairs: inversions += _count_inversions( est_sorted[index[level_1]], est_sorted[index[level_2]] ) return inversions, float(normalizer) def validate_hier_intervals(intervals_hier): """Validate a hierarchical segment annotation. Parameters ---------- intervals_hier : ordered list of segmentations Raises ------ ValueError If any segmentation does not span the full duration of the top-level segmentation. If any segmentation does not start at 0. """ # Synthesize a label array for the top layer. label_top = util.generate_labels(intervals_hier[0]) boundaries = set(util.intervals_to_boundaries(intervals_hier[0])) for level, intervals in enumerate(intervals_hier[1:], 1): # Make sure this level is consistent with the root label_current = util.generate_labels(intervals) validate_structure(intervals_hier[0], label_top, intervals, label_current) # Make sure all previous boundaries are accounted for new_bounds = set(util.intervals_to_boundaries(intervals)) if boundaries - new_bounds: warnings.warn( "Segment hierarchy is inconsistent " "at level {:d}".format(level) ) boundaries |= new_bounds def tmeasure( reference_intervals_hier, estimated_intervals_hier, transitive=False, window=15.0, frame_size=0.1, beta=1.0, ): """Compute the tree measures for hierarchical segment annotations. Parameters ---------- reference_intervals_hier : list of ndarray ``reference_intervals_hier[i]`` contains the segment intervals (in seconds) for the ``i`` th layer of the annotations. Layers are ordered from top to bottom, so that the last list of intervals should be the most specific. estimated_intervals_hier : list of ndarray Like ``reference_intervals_hier`` but for the estimated annotation transitive : bool whether to compute the t-measures using transitivity or not. window : float > 0 size of the window (in seconds). For each query frame q, result frames are only counted within q +- window. frame_size : float > 0 length (in seconds) of frames. The frame size cannot be longer than the window. beta : float > 0 beta parameter for the F-measure. Returns ------- t_precision : number [0, 1] T-measure Precision t_recall : number [0, 1] T-measure Recall t_measure : number [0, 1] F-beta measure for ``(t_precision, t_recall)`` Raises ------ ValueError If either of the input hierarchies are inconsistent If the input hierarchies have different time durations If ``frame_size > window`` or ``frame_size <= 0`` """ # Compute the number of frames in the window if frame_size <= 0: raise ValueError( "frame_size ({:.2f}) must be a positive " "number.".format(frame_size) ) if window is None: window_frames = None else: if frame_size > window: raise ValueError( "frame_size ({:.2f}) cannot exceed " "window ({:.2f})".format(frame_size, window) ) window_frames = int(_round(window, frame_size) / frame_size) # Validate the hierarchical segmentations validate_hier_intervals(reference_intervals_hier) validate_hier_intervals(estimated_intervals_hier) # Build the least common ancestor matrices ref_lca = _lca(reference_intervals_hier, frame_size) est_lca = _lca(estimated_intervals_hier, frame_size) # Compute precision and recall t_recall = _gauc(ref_lca, est_lca, transitive, window_frames) t_precision = _gauc(est_lca, ref_lca, transitive, window_frames) t_measure = util.f_measure(t_precision, t_recall, beta=beta) return t_precision, t_recall, t_measure def lmeasure( reference_intervals_hier, reference_labels_hier, estimated_intervals_hier, estimated_labels_hier, frame_size=0.1, beta=1.0, ): """Compute the tree measures for hierarchical segment annotations. Parameters ---------- reference_intervals_hier : list of ndarray ``reference_intervals_hier[i]`` contains the segment intervals (in seconds) for the ``i`` th layer of the annotations. Layers are ordered from top to bottom, so that the last list of intervals should be the most specific. reference_labels_hier : list of list of str ``reference_labels_hier[i]`` contains the segment labels for the ``i`` th layer of the annotations estimated_intervals_hier : list of ndarray estimated_labels_hier : list of ndarray Like ``reference_intervals_hier`` and ``reference_labels_hier`` but for the estimated annotation frame_size : float > 0 length (in seconds) of frames. The frame size cannot be longer than the window. beta : float > 0 beta parameter for the F-measure. Returns ------- l_precision : number [0, 1] L-measure Precision l_recall : number [0, 1] L-measure Recall l_measure : number [0, 1] F-beta measure for ``(l_precision, l_recall)`` Raises ------ ValueError If either of the input hierarchies are inconsistent If the input hierarchies have different time durations If ``frame_size > window`` or ``frame_size <= 0`` """ # Compute the number of frames in the window if frame_size <= 0: raise ValueError( "frame_size ({:.2f}) must be a positive " "number.".format(frame_size) ) # Validate the hierarchical segmentations validate_hier_intervals(reference_intervals_hier) validate_hier_intervals(estimated_intervals_hier) # Build the least common ancestor matrices ref_meet = _meet(reference_intervals_hier, reference_labels_hier, frame_size) est_meet = _meet(estimated_intervals_hier, estimated_labels_hier, frame_size) # Compute precision and recall l_recall = _gauc(ref_meet, est_meet, True, None) l_precision = _gauc(est_meet, ref_meet, True, None) l_measure = util.f_measure(l_precision, l_recall, beta=beta) return l_precision, l_recall, l_measure def evaluate( ref_intervals_hier, ref_labels_hier, est_intervals_hier, est_labels_hier, **kwargs ): r"""Compute all hierarchical structure metrics for the given reference and estimated annotations. Examples -------- A toy example with two two-layer annotations >>> ref_i = [[[0, 30], [30, 60]], [[0, 15], [15, 30], [30, 45], [45, 60]]] >>> est_i = [[[0, 45], [45, 60]], [[0, 15], [15, 30], [30, 45], [45, 60]]] >>> ref_l = [ ['A', 'B'], ['a', 'b', 'a', 'c'] ] >>> est_l = [ ['A', 'B'], ['a', 'a', 'b', 'b'] ] >>> scores = mir_eval.hierarchy.evaluate(ref_i, ref_l, est_i, est_l) >>> dict(scores) {'T-Measure full': 0.94822745804853459, 'T-Measure reduced': 0.8732458222764804, 'T-Precision full': 0.96569179094693058, 'T-Precision reduced': 0.89939075137018787, 'T-Recall full': 0.93138358189386117, 'T-Recall reduced': 0.84857799953694923} A more realistic example, using SALAMI pre-parsed annotations >>> def load_salami(filename): ... "load SALAMI event format as labeled intervals" ... events, labels = mir_eval.io.load_labeled_events(filename) ... intervals = mir_eval.util.boundaries_to_intervals(events)[0] ... return intervals, labels[:len(intervals)] >>> ref_files = ['data/10/parsed/textfile1_uppercase.txt', ... 'data/10/parsed/textfile1_lowercase.txt'] >>> est_files = ['data/10/parsed/textfile2_uppercase.txt', ... 'data/10/parsed/textfile2_lowercase.txt'] >>> ref = [load_salami(fname) for fname in ref_files] >>> ref_int = [seg[0] for seg in ref] >>> ref_lab = [seg[1] for seg in ref] >>> est = [load_salami(fname) for fname in est_files] >>> est_int = [seg[0] for seg in est] >>> est_lab = [seg[1] for seg in est] >>> scores = mir_eval.hierarchy.evaluate(ref_int, ref_lab, ... est_hier, est_lab) >>> dict(scores) {'T-Measure full': 0.66029225561405358, 'T-Measure reduced': 0.62001868041578034, 'T-Precision full': 0.66844764668949885, 'T-Precision reduced': 0.63252297209957919, 'T-Recall full': 0.6523334654992341, 'T-Recall reduced': 0.60799919710921635} Parameters ---------- ref_intervals_hier : list of list-like ref_labels_hier : list of list of str est_intervals_hier : list of list-like est_labels_hier : list of list of str Hierarchical annotations are encoded as an ordered list of segmentations. Each segmentation itself is a list (or list-like) of intervals (\*_intervals_hier) and a list of lists of labels (\*_labels_hier). **kwargs additional keyword arguments to the evaluation metrics. Returns ------- scores : OrderedDict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. T-measures are computed in both the "full" (``transitive=True``) and "reduced" (``transitive=False``) modes. Raises ------ ValueError Thrown when the provided annotations are not valid. """ # First, find the maximum length of the reference _, t_end = _hierarchy_bounds(ref_intervals_hier) # Pre-process the intervals to match the range of the reference, # and start at 0 ref_intervals_hier, ref_labels_hier = _align_intervals( ref_intervals_hier, ref_labels_hier, t_min=0.0, t_max=None ) est_intervals_hier, est_labels_hier = _align_intervals( est_intervals_hier, est_labels_hier, t_min=0.0, t_max=t_end ) scores = collections.OrderedDict() # Force the transitivity setting kwargs["transitive"] = False ( scores["T-Precision reduced"], scores["T-Recall reduced"], scores["T-Measure reduced"], ) = util.filter_kwargs(tmeasure, ref_intervals_hier, est_intervals_hier, **kwargs) kwargs["transitive"] = True ( scores["T-Precision full"], scores["T-Recall full"], scores["T-Measure full"], ) = util.filter_kwargs(tmeasure, ref_intervals_hier, est_intervals_hier, **kwargs) ( scores["L-Precision"], scores["L-Recall"], scores["L-Measure"], ) = util.filter_kwargs( lmeasure, ref_intervals_hier, ref_labels_hier, est_intervals_hier, est_labels_hier, **kwargs ) return scores mir_eval-0.8.2/mir_eval/io.py000066400000000000000000000544671475740344600161340ustar00rootroot00000000000000"""Functions for loading annotations from files in different formats.""" import contextlib import numpy as np import re import warnings import scipy.io.wavfile from . import util from . import key from . import tempo @contextlib.contextmanager def _open(file_or_path, **kwargs): """Either open a file handle, or use an existing file-like object. If `file_or_path` has the `read` attribute, it will return `file_or_path`. Otherwise, it will attempt to open the file at the specified location. """ if hasattr(file_or_path, "read"): yield file_or_path else: try: with open(file_or_path, **kwargs) as file_desc: yield file_desc except TypeError as exc: raise IOError(f"Invalid file-or-path object: {file_or_path}") from exc def load_delimited(filename, converters, delimiter=r"\s+", comment="#"): r"""Load data from an annotation file where columns are delimited. The number of columns is inferred from the length of the provided converters list. Examples -------- >>> # Load in a one-column list of event times (floats) >>> load_delimited('events.txt', [float]) >>> # Load in a list of labeled events, separated by commas >>> load_delimited('labeled_events.csv', [float, str], ',') Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file converters : list of functions Each entry in column ``n`` of the file will be cast by the function ``converters[n]``. delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- columns : tuple of lists Each list in this tuple corresponds to values in one of the columns in the file. """ # Initialize list of empty lists n_columns = len(converters) columns = tuple(list() for _ in range(n_columns)) # Create re object for splitting lines splitter = re.compile(delimiter) # And one for comments if comment is None: commenter = None else: commenter = re.compile(f"^{comment}") # Note: we do io manually here for two reasons. # 1. The csv module has difficulties with unicode, which may lead # to failures on certain annotation strings # # 2. numpy's text loader does not handle non-numeric data # with _open(filename, mode="r") as input_file: for row, line in enumerate(input_file, 1): # Skip commented lines if comment is not None and commenter.match(line): continue # Split each line using the supplied delimiter data = splitter.split(line.strip(), n_columns - 1) # Throw a helpful error if we got an unexpected # of columns if n_columns != len(data): raise ValueError( "Expected {} columns, got {} at " "{}:{:d}:\n\t{}".format(n_columns, len(data), filename, row, line) ) for value, column, converter in zip(data, columns, converters): # Try converting the value, throw a helpful error on failure try: converted_value = converter(value) except: raise ValueError( "Couldn't convert value {} using {} " "found at {}:{:d}:\n\t{}".format( value, converter.__name__, filename, row, line ) ) column.append(converted_value) # Sane output if n_columns == 1: return columns[0] else: return columns def load_events(filename, delimiter=r"\s+", comment="#"): r"""Import time-stamp events from an annotation file. The file should consist of a single column of numeric values corresponding to the event times. This is primarily useful for processing events which lack duration, such as beats or onsets. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- event_times : np.ndarray array of event times (float) """ # Use our universal function to load in the events events = load_delimited(filename, [float], delimiter=delimiter, comment=comment) events = np.array(events) # Validate them, but throw a warning in place of an error try: util.validate_events(events) except ValueError as error: warnings.warn(error.args[0]) return events def load_labeled_events(filename, delimiter=r"\s+", comment="#"): r"""Import labeled time-stamp events from an annotation file. The file should consist of two columns; the first having numeric values corresponding to the event times and the second having string labels for each event. This is primarily useful for processing labeled events which lack duration, such as beats with metric beat number or onsets with an instrument label. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- event_times : np.ndarray array of event times (float) labels : list of str list of labels """ # Use our universal function to load in the events events, labels = load_delimited( filename, [float, str], delimiter=delimiter, comment=comment ) events = np.array(events) # Validate them, but throw a warning in place of an error try: util.validate_events(events) except ValueError as error: warnings.warn(error.args[0]) return events, labels def load_intervals(filename, delimiter=r"\s+", comment="#"): r"""Import intervals from an annotation file. The file should consist of two columns of numeric values corresponding to start and end time of each interval. This is primarily useful for processing events which span a duration, such as segmentation, chords, or instrument activation. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- intervals : np.ndarray, shape=(n_events, 2) array of event start and end times """ # Use our universal function to load in the events starts, ends = load_delimited( filename, [float, float], delimiter=delimiter, comment=comment ) # Stack into an interval matrix intervals = np.array([starts, ends]).T # Validate them, but throw a warning in place of an error try: util.validate_intervals(intervals) except ValueError as error: warnings.warn(error.args[0]) return intervals def load_labeled_intervals(filename, delimiter=r"\s+", comment="#"): r"""Import labeled intervals from an annotation file. The file should consist of three columns: Two consisting of numeric values corresponding to start and end time of each interval and a third corresponding to the label of each interval. This is primarily useful for processing events which span a duration, such as segmentation, chords, or instrument activation. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- intervals : np.ndarray, shape=(n_events, 2) array of event start and end time labels : list of str list of labels """ # Use our universal function to load in the events starts, ends, labels = load_delimited( filename, [float, float, str], delimiter=delimiter, comment=comment ) # Stack into an interval matrix intervals = np.array([starts, ends]).T # Validate them, but throw a warning in place of an error try: util.validate_intervals(intervals) except ValueError as error: warnings.warn(error.args[0]) return intervals, labels def load_time_series(filename, delimiter=r"\s+", comment="#"): r"""Import a time series from an annotation file. The file should consist of two columns of numeric values corresponding to the time and value of each sample of the time series. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- times : np.ndarray array of timestamps (float) values : np.ndarray array of corresponding numeric values (float) """ # Use our universal function to load in the events times, values = load_delimited( filename, [float, float], delimiter=delimiter, comment=comment ) times = np.array(times) values = np.array(values) return times, values def load_patterns(filename): """Load the patterns contained in the filename and puts them into a list of patterns, each pattern being a list of occurrence, and each occurrence being a list of (onset, midi) pairs. The input file must be formatted as described in MIREX 2013: http://www.music-ir.org/mirex/wiki/2013:Discovery_of_Repeated_Themes_%26_Sections Parameters ---------- filename : str or `os.Pathlike` The input file path containing the patterns of a given piece using the MIREX 2013 format. Returns ------- pattern_list : list The list of patterns, containing all their occurrences, using the following format:: onset_midi = (onset_time, midi_number) occurrence = [onset_midi1, ..., onset_midiO] pattern = [occurrence1, ..., occurrenceM] pattern_list = [pattern1, ..., patternN] where ``N`` is the number of patterns, ``M[i]`` is the number of occurrences of the ``i`` th pattern, and ``O[j]`` is the number of onsets in the ``j``'th occurrence. E.g.:: occ1 = [(0.5, 67.0), (1.0, 67.0), (1.5, 67.0), (2.0, 64.0)] occ2 = [(4.5, 65.0), (5.0, 65.0), (5.5, 65.0), (6.0, 62.0)] pattern1 = [occ1, occ2] occ1 = [(10.5, 67.0), (11.0, 67.0), (11.5, 67.0), (12.0, 64.0), (12.5, 69.0), (13.0, 69.0), (13.5, 69.0), (14.0, 67.0), (14.5, 76.0), (15.0, 76.0), (15.5, 76.0), (16.0, 72.0)] occ2 = [(18.5, 67.0), (19.0, 67.0), (19.5, 67.0), (20.0, 62.0), (20.5, 69.0), (21.0, 69.0), (21.5, 69.0), (22.0, 67.0), (22.5, 77.0), (23.0, 77.0), (23.5, 77.0), (24.0, 74.0)] pattern2 = [occ1, occ2] pattern_list = [pattern1, pattern2] """ # List with all the patterns pattern_list = [] # Current pattern, which will contain all occs pattern = [] # Current occurrence, containing (onset, midi) occurrence = [] with _open(filename, mode="r") as input_file: for line in input_file.readlines(): if "pattern" in line: if occurrence != []: pattern.append(occurrence) if pattern != []: pattern_list.append(pattern) occurrence = [] pattern = [] continue if "occurrence" in line: if occurrence != []: pattern.append(occurrence) occurrence = [] continue string_values = line.split(",") onset_midi = (float(string_values[0]), float(string_values[1])) occurrence.append(onset_midi) # Add last occurrence and pattern to pattern_list if occurrence != []: pattern.append(occurrence) if pattern != []: pattern_list.append(pattern) return pattern_list @util.deprecated(version="0.8.1", version_removed="0.9.0") def load_wav(path, mono=True): """Load a .wav file as a numpy array using ``scipy.io.wavfile``. .. warning:: This function is deprecatred in mir_eval 0.8.1 and will be removed in 0.9.0. We recommend using a dedicated audio IO library such as `soundfile` instead. Parameters ---------- path : str or `os.Pathlike` Path to a .wav file mono : bool If the provided .wav has more than one channel, it will be converted to mono if ``mono=True``. (Default value = True) Returns ------- audio_data : np.ndarray Array of audio samples, normalized to the range [-1., 1.] fs : int Sampling rate of the audio data """ fs, audio_data = scipy.io.wavfile.read(path) # Make float in range [-1, 1] if audio_data.dtype == "int8": audio_data = audio_data / float(2**8) elif audio_data.dtype == "int16": audio_data = audio_data / float(2**16) elif audio_data.dtype == "int32": audio_data = audio_data / float(2**24) else: raise ValueError("Got unexpected .wav data type " "{}".format(audio_data.dtype)) # Optionally convert to mono if mono and audio_data.ndim != 1: audio_data = audio_data.mean(axis=1) return audio_data, fs def load_valued_intervals(filename, delimiter=r"\s+", comment="#"): r"""Import valued intervals from an annotation file. The file should consist of three columns: Two consisting of numeric values corresponding to start and end time of each interval and a third, also of numeric values, corresponding to the value of each interval. This is primarily useful for processing events which span a duration and have a numeric value, such as piano-roll notes which have an onset, offset, and a pitch value. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- intervals : np.ndarray, shape=(n_events, 2) Array of event start and end times values : np.ndarray, shape=(n_events,) Array of values """ # Use our universal function to load in the events starts, ends, values = load_delimited( filename, [float, float, float], delimiter=delimiter, comment=comment ) # Stack into an interval matrix intervals = np.array([starts, ends]).T # Validate them, but throw a warning in place of an error try: util.validate_intervals(intervals) except ValueError as error: warnings.warn(error.args[0]) # return values as np.ndarray values = np.array(values) return intervals, values def load_key(filename, delimiter=r"\s+", comment="#"): r"""Load key labels from an annotation file. The file should consist of two string columns: One denoting the key scale degree (semitone), and the other denoting the mode (major or minor). The file should contain only one row. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- key : str Key label, in the form ``'(key) (mode)'`` """ # Use our universal function to load the key and mode strings scale, mode = load_delimited( filename, [str, str], delimiter=delimiter, comment=comment ) if len(scale) != 1: raise ValueError("Key file should contain only one line.") scale, mode = scale[0], mode[0] # Join with a space key_string = f"{scale} {mode}" # Validate them, but throw a warning in place of an error try: key.validate_key(key_string) except ValueError as error: warnings.warn(error.args[0]) return key_string def load_tempo(filename, delimiter=r"\s+", comment="#"): r"""Load tempo estimates from an annotation file in MIREX format. The file should consist of three numeric columns: the first two correspond to tempo estimates (in beats-per-minute), and the third denotes the relative confidence of the first value compared to the second (in the range [0, 1]). The file should contain only one row. Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- tempi : np.ndarray, non-negative The two tempo estimates weight : float [0, 1] The relative importance of ``tempi[0]`` compared to ``tempi[1]`` """ # Use our universal function to load the key and mode strings t1, t2, weight = load_delimited( filename, [float, float, float], delimiter=delimiter, comment=comment ) weight = weight[0] tempi = np.concatenate([t1, t2]) if len(t1) != 1: raise ValueError("Tempo file should contain only one line.") # Validate them, but throw a warning in place of an error try: tempo.validate_tempi(tempi) except ValueError as error: warnings.warn(error.args[0]) if not 0 <= weight <= 1: raise ValueError(f"Invalid weight: {weight}") return tempi, weight def load_ragged_time_series( filename, dtype=float, delimiter=r"\s+", header=False, comment="#" ): r"""Load data from a delimited time series annotation file with a variable number of columns. This function assumes that column 0 contains time stamps and columns 1 through n contain values. n may be variable from time stamp to time stamp. Examples -------- >>> # Load a ragged list of tab-delimited multi-f0 midi notes >>> times, vals = load_ragged_time_series('multif0.txt', dtype=int, delimiter='\t') >>> # Load a raggled list of space delimited multi-f0 values with a header >>> times, vals = load_ragged_time_series('labeled_events.csv', header=True) Parameters ---------- filename : str or `os.Pathlike` Path to the annotation file dtype : function Data type to apply to values columns. delimiter : str Separator regular expression. By default, lines will be split by any amount of whitespace. header : bool Indicates whether a header row is present or not. By default, assumes no header is present. comment : str or None Comment regular expression. Any lines beginning with this string or pattern will be ignored. Setting to `None` disables comments. Returns ------- times : np.ndarray array of timestamps (float) values : list of np.ndarray list of arrays of corresponding values """ # Initialize empty lists times = [] values = [] # Create re object for splitting lines splitter = re.compile(delimiter) # And one for comments if comment is None: commenter = None else: commenter = re.compile(f"^{comment}") if header: start_row = 1 else: start_row = 0 with _open(filename, mode="r") as input_file: for row, line in enumerate(input_file, start_row): # If this is a comment line, skip it if comment is not None and commenter.match(line): continue # Split each line using the supplied delimiter data = splitter.split(line.strip()) try: converted_time = float(data[0]) except (TypeError, ValueError) as exe: raise ValueError( "Couldn't convert value {} using {} " "found at {}:{:d}:\n\t{}".format( data[0], float.__name__, filename, row, line ) ) from exe times.append(converted_time) # cast values to a numpy array. time stamps with no values are cast # to an empty array. try: converted_value = np.array(data[1:], dtype=dtype) except (TypeError, ValueError) as exe: raise ValueError( "Couldn't convert value {} using {} " "found at {}:{:d}:\n\t{}".format( data[1:], dtype.__name__, filename, row, line ) ) from exe values.append(converted_value) return np.array(times), values mir_eval-0.8.2/mir_eval/key.py000066400000000000000000000153551475740344600163060ustar00rootroot00000000000000""" Key Detection involves determining the underlying key (distribution of notes and note transitions) in a piece of music. Key detection algorithms are evaluated by comparing their estimated key to a ground-truth reference key and reporting a score according to the relationship of the keys. Conventions ----------- Keys are represented as strings of the form ``'(key) (mode)'``, e.g. ``'C# major'`` or ``'Fb minor'``. The case of the key is ignored. Note that certain key strings are equivalent, e.g. ``'C# major'`` and ``'Db major'``. The mode may only be specified as either ``'major'`` or ``'minor'``, no other mode strings will be accepted. Metrics ------- * :func:`mir_eval.key.weighted_score`: Heuristic scoring of the relation of two keys. """ import collections from . import util KEY_TO_SEMITONE = { "c": 0, "c#": 1, "db": 1, "d": 2, "d#": 3, "eb": 3, "e": 4, "f": 5, "f#": 6, "gb": 6, "g": 7, "g#": 8, "ab": 8, "a": 9, "a#": 10, "bb": 10, "b": 11, "x": None, } def validate_key(key): """Check that a key is well-formatted, e.g. in the form ``'C# major'``. The Key can be 'X' if it is not possible to categorize the Key and mode can be 'other' if it can't be categorized as major or minor. Parameters ---------- key : str Key to verify """ if len(key.split()) != 2 and not (len(key.split()) and key.lower() == "x"): raise ValueError("'{}' is not in the form '(key) (mode)' " "or 'X'".format(key)) if key.lower() != "x": key, mode = key.split() if key.lower() == "x": raise ValueError( "Mode {} is invalid; 'X' (Uncategorized) " "doesn't have mode".format(mode) ) if key.lower() not in KEY_TO_SEMITONE: raise ValueError( "Key {} is invalid; should be e.g. D or C# or Eb or " "X (Uncategorized)".format(key) ) if mode not in ["major", "minor", "other"]: raise ValueError( f"Mode '{mode}' is invalid; must be 'major', 'minor' or 'other'" ) def validate(reference_key, estimated_key): """Check that the input annotations to a metric are valid key strings and throws helpful errors if not. Parameters ---------- reference_key : str Reference key string. estimated_key : str Estimated key string. """ for key in [reference_key, estimated_key]: validate_key(key) def split_key_string(key): """Split a key string (of the form, e.g. ``'C# major'``), into a tuple of ``(key, mode)`` where ``key`` is is an integer representing the semitone distance from C. Parameters ---------- key : str String representing a key. Returns ------- key : int Number of semitones above C. mode : str String representing the mode. """ if key.lower() != "x": key, mode = key.split() else: mode = None return KEY_TO_SEMITONE[key.lower()], mode def weighted_score(reference_key, estimated_key): """Compute a heuristic score which is weighted according to the relationship of the reference and estimated key, as follows: +------------------------------------------------------+-------+ | Relationship | Score | +------------------------------------------------------+-------+ | Same key and mode | 1.0 | +------------------------------------------------------+-------+ | Estimated key is a perfect fifth above reference key | 0.5 | +------------------------------------------------------+-------+ | Relative major/minor (same key signature) | 0.3 | +------------------------------------------------------+-------+ | Parallel major/minor (same key) | 0.2 | +------------------------------------------------------+-------+ | Other | 0.0 | +------------------------------------------------------+-------+ Examples -------- >>> ref_key = mir_eval.io.load_key('ref.txt') >>> est_key = mir_eval.io.load_key('est.txt') >>> score = mir_eval.key.weighted_score(ref_key, est_key) Parameters ---------- reference_key : str Reference key string. estimated_key : str Estimated key string. Returns ------- score : float Score representing how closely related the keys are. """ validate(reference_key, estimated_key) reference_key, reference_mode = split_key_string(reference_key) estimated_key, estimated_mode = split_key_string(estimated_key) # If keys are the same, return 1. if reference_key == estimated_key and reference_mode == estimated_mode: return 1.0 # If reference or estimated key are x and they are not the same key # then the result is 'Other'. if reference_key is None or estimated_key is None: return 0.0 # If keys are the same mode and a perfect fifth (differ by 7 semitones) if estimated_mode == reference_mode and (estimated_key - reference_key) % 12 == 7: return 0.5 # Estimated key is relative minor of reference key (9 semitones) if ( estimated_mode != reference_mode == "major" and (estimated_key - reference_key) % 12 == 9 ): return 0.3 # Estimated key is relative major of reference key (3 semitones) if ( estimated_mode != reference_mode == "minor" and (estimated_key - reference_key) % 12 == 3 ): return 0.3 # If keys are in different modes and parallel (same key name) if estimated_mode != reference_mode and reference_key == estimated_key: return 0.2 # Otherwise return 0 return 0.0 def evaluate(reference_key, estimated_key, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> ref_key = mir_eval.io.load_key('reference.txt') >>> est_key = mir_eval.io.load_key('estimated.txt') >>> scores = mir_eval.key.evaluate(ref_key, est_key) Parameters ---------- reference_key : str Reference key string. estimated_key : str Estimated key string. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all metrics scores = collections.OrderedDict() scores["Weighted Score"] = util.filter_kwargs( weighted_score, reference_key, estimated_key ) return scores mir_eval-0.8.2/mir_eval/melody.py000066400000000000000000000755461475740344600170170ustar00rootroot00000000000000# CREATED:2014-03-07 by Justin Salamon """ Melody extraction algorithms aim to produce a sequence of frequency values corresponding to the pitch of the dominant melody from a musical recording. For evaluation, an estimated pitch series is evaluated against a reference based on whether the voicing (melody present or not) and the pitch is correct (within some tolerance). For a detailed explanation of the measures please refer to: J. Salamon, E. Gomez, D. P. W. Ellis and G. Richard, "Melody Extraction from Polyphonic Music Signals: Approaches, Applications and Challenges", IEEE Signal Processing Magazine, 31(2):118-134, Mar. 2014. and: G. E. Poliner, D. P. W. Ellis, A. F. Ehmann, E. Gomez, S. Streich, and B. Ong. "Melody transcription from music audio: Approaches and evaluation", IEEE Transactions on Audio, Speech, and Language Processing, 15(4):1247-1256, 2007. For an explanation of the generalized measures (using non-binary voicings), please refer to: R. Bittner and J. Bosch, "Generalized Metrics for Single-F0 Estimation Evaluation", International Society for Music Information Retrieval Conference (ISMIR), 2019. Conventions ----------- Melody annotations are assumed to be given in the format of a 1d array of frequency values which are accompanied by a 1d array of times denoting when each frequency value occurs. In a reference melody time series, a frequency value of 0 denotes "unvoiced". In a estimated melody time series, unvoiced frames can be indicated either by 0 Hz or by a negative Hz value - negative values represent the algorithm's pitch estimate for frames it has determined as unvoiced, in case they are in fact voiced. Metrics are computed using a sequence of reference and estimated pitches in cents and voicing arrays, both of which are sampled to the same timebase. The function :func:`mir_eval.melody.to_cent_voicing` can be used to convert a sequence of estimated and reference times and frequency values in Hz to voicing arrays and frequency arrays in the format required by the metric functions. By default, the convention is to resample the estimated melody time series to the reference melody time series' timebase. Metrics ------- * :func:`mir_eval.melody.voicing_measures`: Voicing measures, including the recall rate (proportion of frames labeled as melody frames in the reference that are estimated as melody frames) and the false alarm rate (proportion of frames labeled as non-melody in the reference that are mistakenly estimated as melody frames) * :func:`mir_eval.melody.raw_pitch_accuracy`: Raw Pitch Accuracy, which computes the proportion of melody frames in the reference for which the frequency is considered correct (i.e. within half a semitone of the reference frequency) * :func:`mir_eval.melody.raw_chroma_accuracy`: Raw Chroma Accuracy, where the estimated and reference frequency sequences are mapped onto a single octave before computing the raw pitch accuracy * :func:`mir_eval.melody.overall_accuracy`: Overall Accuracy, which computes the proportion of all frames correctly estimated by the algorithm, including whether non-melody frames where labeled by the algorithm as non-melody """ import numpy as np import scipy.interpolate import collections import warnings from . import util def validate_voicing(ref_voicing, est_voicing): """Check that voicing inputs to a metric are in the correct format. Parameters ---------- ref_voicing : np.ndarray Reference voicing array est_voicing : np.ndarray Estimated voicing array """ if ref_voicing.size == 0: warnings.warn("Reference voicing array is empty.") if est_voicing.size == 0: warnings.warn("Estimated voicing array is empty.") if ref_voicing.sum() == 0: warnings.warn("Reference melody has no voiced frames.") if est_voicing.sum() == 0: warnings.warn("Estimated melody has no voiced frames.") # Make sure they're the same length if ref_voicing.shape[0] != est_voicing.shape[0]: raise ValueError( "Reference and estimated voicing arrays should " "be the same length." ) for voicing in [ref_voicing, est_voicing]: # Make sure voicing is between 0 and 1 if np.logical_or(voicing < 0, voicing > 1).any(): raise ValueError("Voicing arrays must be between 0 and 1.") def validate(ref_voicing, ref_cent, est_voicing, est_cent): """Check that voicing and frequency arrays are well-formed. To be used in conjunction with :func:`mir_eval.melody.validate_voicing` Parameters ---------- ref_voicing : np.ndarray Reference voicing array ref_cent : np.ndarray Reference pitch sequence in cents est_voicing : np.ndarray Estimated voicing array est_cent : np.ndarray Estimate pitch sequence in cents """ if ref_cent.size == 0: warnings.warn("Reference frequency array is empty.") if est_cent.size == 0: warnings.warn("Estimated frequency array is empty.") # Make sure they're the same length if ( ref_voicing.shape[0] != ref_cent.shape[0] or est_voicing.shape[0] != est_cent.shape[0] or ref_cent.shape[0] != est_cent.shape[0] ): raise ValueError( "All voicing and frequency arrays must have the " "same length." ) def hz2cents(freq_hz, base_frequency=10.0): """Convert an array of frequency values in Hz to cents. 0 values are left in place. Parameters ---------- freq_hz : np.ndarray Array of frequencies in Hz. base_frequency : float Base frequency for conversion. (Default value = 10.0) """ freq_cent = np.zeros(freq_hz.shape[0]) freq_nonz_ind = np.flatnonzero(freq_hz) normalized_frequency = np.abs(freq_hz[freq_nonz_ind]) / base_frequency freq_cent[freq_nonz_ind] = 1200.0 * np.log2(normalized_frequency) return freq_cent def freq_to_voicing(frequencies, voicing=None): """Convert from an array of frequency values to frequency array + voice/unvoiced array Parameters ---------- frequencies : np.ndarray Array of frequencies. A frequency <= 0 indicates "unvoiced". voicing : np.ndarray Array of voicing values. (Default value = None) Default None, which means the voicing is inferred from `frequencies`: - frames with frequency <= 0.0 are considered "unvoiced" - frames with frequency > 0.0 are considered "voiced" If specified, `voicing` is used as the voicing array, but frequencies with value 0 are forced to have 0 voicing. - Voicing inferred by negative frequency values is ignored. Returns ------- frequencies : np.ndarray Array of frequencies, all >= 0. voiced : np.ndarray Array of voicings between 0 and 1, same length as frequencies, which indicates voiced or unvoiced """ if voicing is not None: voicing[frequencies == 0] = 0 else: voicing = (frequencies > 0).astype(float) return np.abs(frequencies), voicing def constant_hop_timebase(hop, end_time): """Generate a time series from 0 to ``end_time`` with times spaced ``hop`` apart Parameters ---------- hop : float Spacing of samples in the time series end_time : float Time series will span ``[0, end_time]`` Returns ------- times : np.ndarray Generated timebase """ # Compute new timebase. Rounding/linspace is to avoid float problems. end_time = np.round(end_time, 10) times = np.linspace( 0, hop * int(np.floor(end_time / hop)), int(np.floor(end_time / hop)) + 1 ) times = np.round(times, 10) return times def resample_melody_series(times, frequencies, voicing, times_new, kind="linear"): """Resamples frequency and voicing time series to a new timescale. Maintains any zero ("unvoiced") values in frequencies. If ``times`` and ``times_new`` are equivalent, no resampling will be performed. Parameters ---------- times : np.ndarray Times of each frequency value frequencies : np.ndarray Array of frequency values, >= 0 voicing : np.ndarray Array which indicates voiced or unvoiced. This array may be binary or have continuous values between 0 and 1. times_new : np.ndarray Times to resample frequency and voicing sequences to kind : str kind parameter to pass to scipy.interpolate.interp1d. (Default value = 'linear') Returns ------- frequencies_resampled : np.ndarray Frequency array resampled to new timebase voicing_resampled : np.ndarray Voicing array resampled to new timebase """ # If the timebases are already the same, no need to interpolate if times.shape == times_new.shape and np.allclose(times, times_new): return frequencies, voicing # Warn when the delta between the original times is not constant, # unless times[0] == 0. and frequencies[0] == frequencies[1] (see logic at # the beginning of to_cent_voicing) if not ( np.allclose(np.diff(times), np.diff(times).mean()) or ( np.allclose(np.diff(times[1:]), np.diff(times[1:]).mean()) and frequencies[0] == frequencies[1] ) ): warnings.warn( "Non-uniform timescale passed to resample_melody_series. Pitch " "will be linearly interpolated, which will result in undesirable " "behavior if silences are indicated by missing values. Silences " "should be indicated by nonpositive frequency values." ) # Round to avoid floating point problems times = np.round(times, 10) times_new = np.round(times_new, 10) # Add in an additional sample if we'll be asking for a time too large if times_new.max() > times.max(): times = np.append(times, times_new.max()) frequencies = np.append(frequencies, 0) voicing = np.append(voicing, 0) # We need to fix zero transitions if interpolation is not zero or nearest if kind != "zero" and kind != "nearest": # Fill in zero values with the last reported frequency # to avoid erroneous values when resampling frequencies_held = np.array(frequencies) for n, frequency in enumerate(frequencies[1:]): if frequency == 0: frequencies_held[n + 1] = frequencies_held[n] # Linearly interpolate frequencies frequencies_resampled = scipy.interpolate.interp1d( times, frequencies_held, kind )(times_new) # Retain zeros frequency_mask = scipy.interpolate.interp1d(times, frequencies, "zero")( times_new ) frequencies_resampled *= frequency_mask != 0 else: frequencies_resampled = scipy.interpolate.interp1d(times, frequencies, kind)( times_new ) # Use nearest-neighbor for voicing if it was used for frequencies # if voicing is not binary, use linear interpolation is_binary_voicing = np.all( np.logical_or(np.equal(voicing, 0), np.equal(voicing, 1)) ) if kind == "nearest" or (kind == "linear" and not is_binary_voicing): voicing_resampled = scipy.interpolate.interp1d(times, voicing, kind)(times_new) # otherwise, always use zeroth order else: voicing_resampled = scipy.interpolate.interp1d(times, voicing, "zero")( times_new ) return frequencies_resampled, voicing_resampled def to_cent_voicing( ref_time, ref_freq, est_time, est_freq, est_voicing=None, ref_reward=None, base_frequency=10.0, hop=None, kind="linear", ): """Convert reference and estimated time/frequency (Hz) annotations to sampled frequency (cent)/voicing arrays. A zero frequency indicates "unvoiced". If est_voicing is not provided, a negative frequency indicates: "Predicted as unvoiced, but if it's voiced, this is the frequency estimate". If it is provided, negative frequency values are ignored, and the voicing from est_voicing is directly used. Parameters ---------- ref_time : np.ndarray Time of each reference frequency value ref_freq : np.ndarray Array of reference frequency values est_time : np.ndarray Time of each estimated frequency value est_freq : np.ndarray Array of estimated frequency values est_voicing : np.ndarray Estimate voicing confidence. Default None, which means the voicing is inferred from est_freq: - frames with frequency <= 0.0 are considered "unvoiced" - frames with frequency > 0.0 are considered "voiced" ref_reward : np.ndarray Reference voicing reward. Default None, which means all frames are weighted equally. base_frequency : float Base frequency in Hz for conversion to cents (Default value = 10.) hop : float Hop size, in seconds, to resample, default None which means use ref_time kind : str kind parameter to pass to scipy.interpolate.interp1d. (Default value = 'linear') Returns ------- ref_voicing : np.ndarray Resampled reference voicing array ref_cent : np.ndarray Resampled reference frequency (cent) array est_voicing : np.ndarray Resampled estimated voicing array est_cent : np.ndarray Resampled estimated frequency (cent) array """ # Check if missing sample at time 0 and if so add one if ref_time[0] > 0: ref_time = np.insert(ref_time, 0, 0) ref_freq = np.insert(ref_freq, 0, ref_freq[0]) if ref_reward is not None: ref_reward = np.insert(ref_reward, 0, ref_reward[0]) if est_time[0] > 0: est_time = np.insert(est_time, 0, 0) est_freq = np.insert(est_freq, 0, est_freq[0]) if est_voicing is not None: est_voicing = np.insert(est_voicing, 0, est_voicing[0]) # Get separated frequency array and voicing array ref_freq, ref_voicing = freq_to_voicing(ref_freq, ref_reward) est_freq, est_voicing = freq_to_voicing(est_freq, est_voicing) # convert both sequences to cents ref_cent = hz2cents(ref_freq, base_frequency) est_cent = hz2cents(est_freq, base_frequency) # If we received a hop, use it to resample both if hop is not None: # Resample to common time base ref_cent, ref_voicing = resample_melody_series( ref_time, ref_cent, ref_voicing, constant_hop_timebase(hop, ref_time.max()), kind, ) est_cent, est_voicing = resample_melody_series( est_time, est_cent, est_voicing, constant_hop_timebase(hop, est_time.max()), kind, ) # Otherwise, only resample estimated to the reference time base else: est_cent, est_voicing = resample_melody_series( est_time, est_cent, est_voicing, ref_time, kind ) # ensure the estimated sequence is the same length as the reference len_diff = ref_cent.shape[0] - est_cent.shape[0] if len_diff >= 0: est_cent = np.append(est_cent, np.zeros(len_diff)) est_voicing = np.append(est_voicing, np.zeros(len_diff)) else: est_cent = est_cent[: ref_cent.shape[0]] est_voicing = est_voicing[: ref_voicing.shape[0]] return (ref_voicing, ref_cent, est_voicing, est_cent) def voicing_recall(ref_voicing, est_voicing): """Compute the voicing recall given two voicing indicator sequences, one as reference (truth) and the other as the estimate (prediction). The sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> recall = mir_eval.melody.voicing_recall(ref_v, est_v) Parameters ---------- ref_voicing : np.ndarray Reference boolean voicing array est_voicing : np.ndarray Estimated boolean voicing array Returns ------- vx_recall : float Voicing recall rate, the fraction of voiced frames in ref indicated as voiced in est """ if ref_voicing.size == 0 or est_voicing.size == 0: return 0.0 ref_indicator = (ref_voicing > 0).astype(float) if np.sum(ref_indicator) == 0: return 1 return np.sum(est_voicing * ref_indicator) / np.sum(ref_indicator) def voicing_false_alarm(ref_voicing, est_voicing): """Compute the voicing false alarm rates given two voicing indicator sequences, one as reference (truth) and the other as the estimate (prediction). The sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> false_alarm = mir_eval.melody.voicing_false_alarm(ref_v, est_v) Parameters ---------- ref_voicing : np.ndarray Reference boolean voicing array est_voicing : np.ndarray Estimated boolean voicing array Returns ------- vx_false_alarm : float Voicing false alarm rate, the fraction of unvoiced frames in ref indicated as voiced in est """ if ref_voicing.size == 0 or est_voicing.size == 0: return 0.0 ref_indicator = (ref_voicing == 0).astype(float) if np.sum(ref_indicator) == 0: return 0 return np.sum(est_voicing * ref_indicator) / np.sum(ref_indicator) def voicing_measures(ref_voicing, est_voicing): """Compute the voicing recall and false alarm rates given two voicing indicator sequences, one as reference (truth) and the other as the estimate (prediction). The sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> recall, false_alarm = mir_eval.melody.voicing_measures(ref_v, ... est_v) Parameters ---------- ref_voicing : np.ndarray Reference boolean voicing array est_voicing : np.ndarray Estimated boolean voicing array Returns ------- vx_recall : float Voicing recall rate, the fraction of voiced frames in ref indicated as voiced in est vx_false_alarm : float Voicing false alarm rate, the fraction of unvoiced frames in ref indicated as voiced in est """ validate_voicing(ref_voicing, est_voicing) vx_recall = voicing_recall(ref_voicing, est_voicing) vx_false_alm = voicing_false_alarm(ref_voicing, est_voicing) return vx_recall, vx_false_alm def raw_pitch_accuracy(ref_voicing, ref_cent, est_voicing, est_cent, cent_tolerance=50): """Compute the raw pitch accuracy given two pitch (frequency) sequences in cents and matching voicing indicator sequences. The first pitch and voicing arrays are treated as the reference (truth), and the second two as the estimate (prediction). All 4 sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> raw_pitch = mir_eval.melody.raw_pitch_accuracy(ref_v, ref_c, ... est_v, est_c) Parameters ---------- ref_voicing : np.ndarray Reference voicing array. When this array is non-binary, it is treated as a 'reference reward', as in (Bittner & Bosch, 2019) ref_cent : np.ndarray Reference pitch sequence in cents est_voicing : np.ndarray Estimated voicing array est_cent : np.ndarray Estimate pitch sequence in cents cent_tolerance : float Maximum absolute deviation in cents for a frequency value to be considered correct (Default value = 50) Returns ------- raw_pitch : float Raw pitch accuracy, the fraction of voiced frames in ref_cent for which est_cent provides a correct frequency values (within cent_tolerance cents). """ validate_voicing(ref_voicing, est_voicing) validate(ref_voicing, ref_cent, est_voicing, est_cent) # When input arrays are empty, return 0 by special case # If there are no voiced frames in reference, metric is 0 if ( ref_voicing.size == 0 or ref_voicing.sum() == 0 or ref_cent.size == 0 or est_cent.size == 0 ): return 0.0 # Raw pitch = the number of voiced frames in the reference for which the # estimate provides a correct frequency value (within cent_tolerance cents) # NB: voicing estimation is ignored in this measure nonzero_freqs = np.logical_and(est_cent != 0, ref_cent != 0) if sum(nonzero_freqs) == 0: return 0.0 freq_diff_cents = np.abs(ref_cent - est_cent)[nonzero_freqs] correct_frequencies = freq_diff_cents < cent_tolerance rpa = np.sum(ref_voicing[nonzero_freqs] * correct_frequencies) / np.sum(ref_voicing) return rpa def raw_chroma_accuracy( ref_voicing, ref_cent, est_voicing, est_cent, cent_tolerance=50 ): """Compute the raw chroma accuracy given two pitch (frequency) sequences in cents and matching voicing indicator sequences. The first pitch and voicing arrays are treated as the reference (truth), and the second two as the estimate (prediction). All 4 sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> raw_chroma = mir_eval.melody.raw_chroma_accuracy(ref_v, ref_c, ... est_v, est_c) Parameters ---------- ref_voicing : np.ndarray Reference voicing array. When this array is non-binary, it is treated as a 'reference reward', as in (Bittner & Bosch, 2019) ref_cent : np.ndarray Reference pitch sequence in cents est_voicing : np.ndarray Estimated voicing array est_cent : np.ndarray Estimate pitch sequence in cents cent_tolerance : float Maximum absolute deviation in cents for a frequency value to be considered correct (Default value = 50) Returns ------- raw_chroma : float Raw chroma accuracy, the fraction of voiced frames in ref_cent for which est_cent provides a correct frequency values (within cent_tolerance cents), ignoring octave errors """ validate_voicing(ref_voicing, est_voicing) validate(ref_voicing, ref_cent, est_voicing, est_cent) # When input arrays are empty, return 0 by special case # If there are no voiced frames in reference, metric is 0 if ( ref_voicing.size == 0 or ref_voicing.sum() == 0 or ref_cent.size == 0 or est_cent.size == 0 ): return 0.0 # # Raw chroma = same as raw pitch except that octave errors are ignored. nonzero_freqs = np.logical_and(est_cent != 0, ref_cent != 0) if sum(nonzero_freqs) == 0: return 0.0 freq_diff_cents = np.abs(ref_cent - est_cent)[nonzero_freqs] octave = 1200.0 * np.floor(freq_diff_cents / 1200 + 0.5) correct_chroma = np.abs(freq_diff_cents - octave) < cent_tolerance rca = np.sum(ref_voicing[nonzero_freqs] * correct_chroma) / np.sum(ref_voicing) return rca def overall_accuracy(ref_voicing, ref_cent, est_voicing, est_cent, cent_tolerance=50): """Compute the overall accuracy given two pitch (frequency) sequences in cents and matching voicing indicator sequences. The first pitch and voicing arrays are treated as the reference (truth), and the second two as the estimate (prediction). All 4 sequences must be of the same length. Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> (ref_v, ref_c, ... est_v, est_c) = mir_eval.melody.to_cent_voicing(ref_time, ... ref_freq, ... est_time, ... est_freq) >>> overall_accuracy = mir_eval.melody.overall_accuracy(ref_v, ref_c, ... est_v, est_c) Parameters ---------- ref_voicing : np.ndarray Reference voicing array. When this array is non-binary, it is treated as a 'reference reward', as in (Bittner & Bosch, 2019) ref_cent : np.ndarray Reference pitch sequence in cents est_voicing : np.ndarray Estimated voicing array est_cent : np.ndarray Estimate pitch sequence in cents cent_tolerance : float Maximum absolute deviation in cents for a frequency value to be considered correct (Default value = 50) Returns ------- overall_accuracy : float Overall accuracy, the total fraction of correctly estimates frames, where provides a correct frequency values (within cent_tolerance). """ validate_voicing(ref_voicing, est_voicing) validate(ref_voicing, ref_cent, est_voicing, est_cent) # When input arrays are empty, return 0 by special case if ( ref_voicing.size == 0 or est_voicing.size == 0 or ref_cent.size == 0 or est_cent.size == 0 ): return 0.0 nonzero_freqs = np.logical_and(est_cent != 0, ref_cent != 0) freq_diff_cents = np.abs(ref_cent - est_cent)[nonzero_freqs] correct_frequencies = freq_diff_cents < cent_tolerance ref_binary = (ref_voicing > 0).astype(float) n_frames = float(len(ref_voicing)) if np.sum(ref_voicing) == 0: ratio = 0.0 else: ratio = np.sum(ref_binary) / np.sum(ref_voicing) accuracy = ( ( ratio * np.sum( ref_voicing[nonzero_freqs] * est_voicing[nonzero_freqs] * correct_frequencies ) ) + np.sum((1.0 - ref_binary) * (1.0 - est_voicing)) ) / n_frames return accuracy def evaluate( ref_time, ref_freq, est_time, est_freq, est_voicing=None, ref_reward=None, **kwargs ): """Evaluate two melody (predominant f0) transcriptions, where the first is treated as the reference (ground truth) and the second as the estimate to be evaluated (prediction). Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_time_series('est.txt') >>> scores = mir_eval.melody.evaluate(ref_time, ref_freq, ... est_time, est_freq) Parameters ---------- ref_time : np.ndarray Time of each reference frequency value ref_freq : np.ndarray Array of reference frequency values est_time : np.ndarray Time of each estimated frequency value est_freq : np.ndarray Array of estimated frequency values est_voicing : np.ndarray Estimate voicing confidence. Default None, which means the voicing is inferred from est_freq: - frames with frequency <= 0.0 are considered "unvoiced" - frames with frequency > 0.0 are considered "voiced" ref_reward : np.ndarray Reference pitch estimation reward. Default None, which means all frames are weighted equally. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. References ---------- .. [#] J. Salamon, E. Gomez, D. P. W. Ellis and G. Richard, "Melody Extraction from Polyphonic Music Signals: Approaches, Applications and Challenges", IEEE Signal Processing Magazine, 31(2):118-134, Mar. 2014. .. [#] G. E. Poliner, D. P. W. Ellis, A. F. Ehmann, E. Gomez, S. Streich, and B. Ong. "Melody transcription from music audio: Approaches and evaluation", IEEE Transactions on Audio, Speech, and Language Processing, 15(4):1247-1256, 2007. .. [#] R. Bittner and J. Bosch, "Generalized Metrics for Single-F0 Estimation Evaluation", International Society for Music Information Retrieval Conference (ISMIR), 2019. """ # Convert to reference/estimated voicing/frequency (cent) arrays (ref_voicing, ref_cent, est_voicing, est_cent) = util.filter_kwargs( to_cent_voicing, ref_time, ref_freq, est_time, est_freq, est_voicing, ref_reward, **kwargs ) # Compute metrics scores = collections.OrderedDict() scores["Voicing Recall"] = util.filter_kwargs( voicing_recall, ref_voicing, est_voicing, **kwargs ) scores["Voicing False Alarm"] = util.filter_kwargs( voicing_false_alarm, ref_voicing, est_voicing, **kwargs ) scores["Raw Pitch Accuracy"] = util.filter_kwargs( raw_pitch_accuracy, ref_voicing, ref_cent, est_voicing, est_cent, **kwargs ) scores["Raw Chroma Accuracy"] = util.filter_kwargs( raw_chroma_accuracy, ref_voicing, ref_cent, est_voicing, est_cent, **kwargs ) scores["Overall Accuracy"] = util.filter_kwargs( overall_accuracy, ref_voicing, ref_cent, est_voicing, est_cent, **kwargs ) return scores mir_eval-0.8.2/mir_eval/multipitch.py000066400000000000000000000411451475740344600176740ustar00rootroot00000000000000""" The goal of multiple f0 (multipitch) estimation and tracking is to identify all of the active fundamental frequencies in each time frame in a complex music signal. Conventions ----------- Multipitch estimates are represented by a timebase and a corresponding list of arrays of frequency estimates. Frequency estimates may have any number of frequency values, including 0 (represented by an empty array). Time values are in units of seconds and frequency estimates are in units of Hz. The timebase of the estimate time series should ideally match the timebase of the reference time series, but if this is not the case, the estimate time series is resampled using a nearest neighbor interpolation to match the estimate. Time values in the estimate time series that are outside of the range of the reference time series are given null (empty array) frequencies. By default, a frequency is "correct" if it is within 0.5 semitones of a reference frequency. Frequency values are compared by first mapping them to log-2 semitone space, where the distance between semitones is constant. Chroma-wrapped frequency values are computed by taking the log-2 frequency values modulo 12 to map them down to a single octave. A chroma-wrapped frequency estimate is correct if it's single-octave value is within 0.5 semitones of the single-octave reference frequency. The metrics are based on those described in [#poliner2007]_ and [#bay2009]_. Metrics ------- * :func:`mir_eval.multipitch.metrics`: Precision, Recall, Accuracy, Substitution, Miss, False Alarm, and Total Error scores based both on raw frequency values and values mapped to a single octave (chroma). References ---------- .. [#poliner2007] G. E. Poliner, and D. P. W. Ellis, "A Discriminative Model for Polyphonic Piano Transription", EURASIP Journal on Advances in Signal Processing, 2007(1):154-163, Jan. 2007. .. [#bay2009] Bay, M., Ehmann, A. F., & Downie, J. S. (2009). Evaluation of Multiple-F0 Estimation and Tracking Systems. In ISMIR (pp. 315-320). """ import numpy as np import collections import scipy.interpolate from . import util import warnings MAX_TIME = 30000.0 # The maximum allowable time stamp (seconds) MAX_FREQ = 5000.0 # The maximum allowable frequency (Hz) MIN_FREQ = 20.0 # The minimum allowable frequency (Hz) def validate(ref_time, ref_freqs, est_time, est_freqs): """Check that the time and frequency inputs are well-formed. Parameters ---------- ref_time : np.ndarray reference time stamps in seconds ref_freqs : list of np.ndarray reference frequencies in Hz est_time : np.ndarray estimate time stamps in seconds est_freqs : list of np.ndarray estimated frequencies in Hz """ util.validate_events(ref_time, max_time=MAX_TIME) util.validate_events(est_time, max_time=MAX_TIME) if ref_time.size == 0: warnings.warn("Reference times are empty.") if ref_time.ndim != 1: raise ValueError("Reference times have invalid dimension") if len(ref_freqs) == 0: warnings.warn("Reference frequencies are empty.") if est_time.size == 0: warnings.warn("Estimated times are empty.") if est_time.ndim != 1: raise ValueError("Estimated times have invalid dimension") if len(est_freqs) == 0: warnings.warn("Estimated frequencies are empty.") if ref_time.size != len(ref_freqs): raise ValueError("Reference times and frequencies have unequal " "lengths.") if est_time.size != len(est_freqs): raise ValueError("Estimate times and frequencies have unequal " "lengths.") for freq in ref_freqs: util.validate_frequencies( freq, max_freq=MAX_FREQ, min_freq=MIN_FREQ, allow_negatives=False ) for freq in est_freqs: util.validate_frequencies( freq, max_freq=MAX_FREQ, min_freq=MIN_FREQ, allow_negatives=False ) def resample_multipitch(times, frequencies, target_times): """Resamples multipitch time series to a new timescale using nearest neighbor interpolation. Values in ``target_times`` outside the range of ``times`` return no pitch estimate. Parameters ---------- times : np.ndarray Array of time stamps frequencies : list of np.ndarray List of np.ndarrays of frequency values target_times : np.ndarray Array of target time stamps Returns ------- frequencies_resampled : list of numpy arrays Frequency list of lists resampled to new timebase """ if target_times.size == 0: return [] if times.size == 0: return [np.array([])] * len(target_times) n_times = len(frequencies) # scipy's interpolate doesn't handle ragged arrays. Instead, we interpolate # the frequency index and then map back to the frequency values. # This only works because we're using a nearest neighbor interpolator! frequency_index = np.arange(0, n_times) # times are already ordered so assume_sorted=True for efficiency # since we're interpolating the index, fill_value is set to the first index # that is out of range. We handle this in the next line. new_frequency_index = scipy.interpolate.interp1d( times, frequency_index, kind="nearest", bounds_error=False, assume_sorted=True, fill_value=n_times, )(target_times) # create array of frequencies plus additional empty element at the end for # target time stamps that are out of the interpolation range freq_vals = frequencies + [np.array([])] # map interpolated indices back to frequency values frequencies_resampled = [freq_vals[i] for i in new_frequency_index.astype(int)] return frequencies_resampled def frequencies_to_midi(frequencies, ref_frequency=440.0): """Convert frequencies to continuous MIDI values. Parameters ---------- frequencies : list of np.ndarray Original frequency values ref_frequency : float reference frequency in Hz. Returns ------- frequencies_midi : list of np.ndarray Continuous MIDI frequency values. """ return [69.0 + 12.0 * np.log2(freqs / ref_frequency) for freqs in frequencies] def midi_to_chroma(frequencies_midi): """Wrap MIDI frequencies to a single octave (chroma). Parameters ---------- frequencies_midi : list of np.ndarray Continuous MIDI note frequency values. Returns ------- frequencies_chroma : list of np.ndarray Midi values wrapped to one octave. """ return [np.mod(freqs, 12) for freqs in frequencies_midi] def compute_num_freqs(frequencies): """Compute the number of frequencies for each time point. Parameters ---------- frequencies : list of np.ndarray Frequency values Returns ------- num_freqs : np.ndarray Number of frequencies at each time point. """ return np.array([f.size for f in frequencies]) def compute_num_true_positives(ref_freqs, est_freqs, window=0.5, chroma=False): """Compute the number of true positives in an estimate given a reference. A frequency is correct if it is within a quartertone of the correct frequency. Parameters ---------- ref_freqs : list of np.ndarray reference frequencies (MIDI) est_freqs : list of np.ndarray estimated frequencies (MIDI) window : float Window size, in semitones chroma : bool If True, computes distances modulo n. If True, ``ref_freqs`` and ``est_freqs`` should be wrapped modulo n. Returns ------- true_positives : np.ndarray Array the same length as ref_freqs containing the number of true positives. """ n_frames = len(ref_freqs) true_positives = np.zeros((n_frames,)) for i, (ref_frame, est_frame) in enumerate(zip(ref_freqs, est_freqs)): if chroma: # match chroma-wrapped frequency events matching = util.match_events( ref_frame, est_frame, window, distance=util._outer_distance_mod_n ) else: # match frequency events within tolerance window in semitones matching = util.match_events(ref_frame, est_frame, window) true_positives[i] = len(matching) return true_positives def compute_accuracy(true_positives, n_ref, n_est): """Compute accuracy metrics. Parameters ---------- true_positives : np.ndarray Array containing the number of true positives at each time point. n_ref : np.ndarray Array containing the number of reference frequencies at each time point. n_est : np.ndarray Array containing the number of estimate frequencies at each time point. Returns ------- precision : float ``sum(true_positives)/sum(n_est)`` recall : float ``sum(true_positives)/sum(n_ref)`` acc : float ``sum(true_positives)/sum(n_est + n_ref - true_positives)`` """ true_positive_sum = float(true_positives.sum()) n_est_sum = n_est.sum() if n_est_sum > 0: precision = true_positive_sum / n_est.sum() else: warnings.warn("Estimate frequencies are all empty.") precision = 0.0 n_ref_sum = n_ref.sum() if n_ref_sum > 0: recall = true_positive_sum / n_ref.sum() else: warnings.warn("Reference frequencies are all empty.") recall = 0.0 acc_denom = (n_est + n_ref - true_positives).sum() if acc_denom > 0: acc = true_positive_sum / acc_denom else: acc = 0.0 return precision, recall, acc def compute_err_score(true_positives, n_ref, n_est): """Compute error score metrics. Parameters ---------- true_positives : np.ndarray Array containing the number of true positives at each time point. n_ref : np.ndarray Array containing the number of reference frequencies at each time point. n_est : np.ndarray Array containing the number of estimate frequencies at each time point. Returns ------- e_sub : float Substitution error e_miss : float Miss error e_fa : float False alarm error e_tot : float Total error """ n_ref_sum = float(n_ref.sum()) if n_ref_sum == 0: warnings.warn("Reference frequencies are all empty.") return 0.0, 0.0, 0.0, 0.0 # Substitution error e_sub = (np.min([n_ref, n_est], axis=0) - true_positives).sum() / n_ref_sum # compute the max of (n_ref - n_est) and 0 e_miss_numerator = n_ref - n_est e_miss_numerator[e_miss_numerator < 0] = 0 # Miss error e_miss = e_miss_numerator.sum() / n_ref_sum # compute the max of (n_est - n_ref) and 0 e_fa_numerator = n_est - n_ref e_fa_numerator[e_fa_numerator < 0] = 0 # False alarm error e_fa = e_fa_numerator.sum() / n_ref_sum # total error e_tot = (np.max([n_ref, n_est], axis=0) - true_positives).sum() / n_ref_sum return e_sub, e_miss, e_fa, e_tot def metrics(ref_time, ref_freqs, est_time, est_freqs, **kwargs): """Compute multipitch metrics. All metrics are computed at the 'macro' level such that the frame true positive/false positive/false negative rates are summed across time and the metrics are computed on the combined values. Examples -------- >>> ref_time, ref_freqs = mir_eval.io.load_ragged_time_series( ... 'reference.txt') >>> est_time, est_freqs = mir_eval.io.load_ragged_time_series( ... 'estimated.txt') >>> metris_tuple = mir_eval.multipitch.metrics( ... ref_time, ref_freqs, est_time, est_freqs) Parameters ---------- ref_time : np.ndarray Time of each reference frequency value ref_freqs : list of np.ndarray List of np.ndarrays of reference frequency values est_time : np.ndarray Time of each estimated frequency value est_freqs : list of np.ndarray List of np.ndarrays of estimate frequency values **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- precision : float Precision (TP/(TP + FP)) recall : float Recall (TP/(TP + FN)) accuracy : float Accuracy (TP/(TP + FP + FN)) e_sub : float Substitution error e_miss : float Miss error e_fa : float False alarm error e_tot : float Total error precision_chroma : float Chroma precision recall_chroma : float Chroma recall accuracy_chroma : float Chroma accuracy e_sub_chroma : float Chroma substitution error e_miss_chroma : float Chroma miss error e_fa_chroma : float Chroma false alarm error e_tot_chroma : float Chroma total error """ validate(ref_time, ref_freqs, est_time, est_freqs) # resample est_freqs if est_times is different from ref_times if est_time.size != ref_time.size or not np.allclose(est_time, ref_time): warnings.warn( "Estimate times not equal to reference times. " "Resampling to common time base." ) est_freqs = resample_multipitch(est_time, est_freqs, ref_time) # convert frequencies from Hz to continuous midi note number ref_freqs_midi = frequencies_to_midi(ref_freqs) est_freqs_midi = frequencies_to_midi(est_freqs) # compute chroma wrapped midi number ref_freqs_chroma = midi_to_chroma(ref_freqs_midi) est_freqs_chroma = midi_to_chroma(est_freqs_midi) # count number of occurrences n_ref = compute_num_freqs(ref_freqs_midi) n_est = compute_num_freqs(est_freqs_midi) # compute the number of true positives true_positives = util.filter_kwargs( compute_num_true_positives, ref_freqs_midi, est_freqs_midi, **kwargs ) # compute the number of true positives ignoring octave mistakes true_positives_chroma = util.filter_kwargs( compute_num_true_positives, ref_freqs_chroma, est_freqs_chroma, chroma=True, **kwargs ) # compute accuracy metrics precision, recall, accuracy = compute_accuracy(true_positives, n_ref, n_est) # compute error metrics e_sub, e_miss, e_fa, e_tot = compute_err_score(true_positives, n_ref, n_est) # compute accuracy metrics ignoring octave mistakes precision_chroma, recall_chroma, accuracy_chroma = compute_accuracy( true_positives_chroma, n_ref, n_est ) # compute error metrics ignoring octave mistakes e_sub_chroma, e_miss_chroma, e_fa_chroma, e_tot_chroma = compute_err_score( true_positives_chroma, n_ref, n_est ) return ( precision, recall, accuracy, e_sub, e_miss, e_fa, e_tot, precision_chroma, recall_chroma, accuracy_chroma, e_sub_chroma, e_miss_chroma, e_fa_chroma, e_tot_chroma, ) def evaluate(ref_time, ref_freqs, est_time, est_freqs, **kwargs): """Evaluate two multipitch (multi-f0) transcriptions, where the first is treated as the reference (ground truth) and the second as the estimate to be evaluated (prediction). Examples -------- >>> ref_time, ref_freq = mir_eval.io.load_ragged_time_series('ref.txt') >>> est_time, est_freq = mir_eval.io.load_ragged_time_series('est.txt') >>> scores = mir_eval.multipitch.evaluate(ref_time, ref_freq, ... est_time, est_freq) Parameters ---------- ref_time : np.ndarray Time of each reference frequency value ref_freqs : list of np.ndarray List of np.ndarrays of reference frequency values est_time : np.ndarray Time of each estimated frequency value est_freqs : list of np.ndarray List of np.ndarrays of estimate frequency values **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ scores = collections.OrderedDict() ( scores["Precision"], scores["Recall"], scores["Accuracy"], scores["Substitution Error"], scores["Miss Error"], scores["False Alarm Error"], scores["Total Error"], scores["Chroma Precision"], scores["Chroma Recall"], scores["Chroma Accuracy"], scores["Chroma Substitution Error"], scores["Chroma Miss Error"], scores["Chroma False Alarm Error"], scores["Chroma Total Error"], ) = util.filter_kwargs(metrics, ref_time, ref_freqs, est_time, est_freqs, **kwargs) return scores mir_eval-0.8.2/mir_eval/onset.py000066400000000000000000000104211475740344600166330ustar00rootroot00000000000000""" The goal of an onset detection algorithm is to automatically determine when notes are played in a piece of music. The primary method used to evaluate onset detectors is to first determine which estimated onsets are "correct", where correctness is defined as being within a small window of a reference onset. Based in part on this script: https://github.com/CPJKU/onset_detection/blob/master/onset_evaluation.py Conventions ----------- Onsets should be provided in the form of a 1-dimensional array of onset times in seconds in increasing order. Metrics ------- * :func:`mir_eval.onset.f_measure`: Precision, Recall, and F-measure scores based on the number of estimated onsets which are sufficiently close to reference onsets. """ import collections from . import util import warnings # The maximum allowable beat time MAX_TIME = 30000.0 def validate(reference_onsets, estimated_onsets): """Check that the input annotations to a metric look like valid onset time arrays, and throws helpful errors if not. Parameters ---------- reference_onsets : np.ndarray reference onset locations, in seconds estimated_onsets : np.ndarray estimated onset locations, in seconds """ # If reference or estimated onsets are empty, warn because metric will be 0 if reference_onsets.size == 0: warnings.warn("Reference onsets are empty.") if estimated_onsets.size == 0: warnings.warn("Estimated onsets are empty.") for onsets in [reference_onsets, estimated_onsets]: util.validate_events(onsets, MAX_TIME) def f_measure(reference_onsets, estimated_onsets, window=0.05): """Compute the F-measure of correct vs incorrectly predicted onsets. "Correctness" is determined over a small window. Examples -------- >>> reference_onsets = mir_eval.io.load_events('reference.txt') >>> estimated_onsets = mir_eval.io.load_events('estimated.txt') >>> F, P, R = mir_eval.onset.f_measure(reference_onsets, ... estimated_onsets) Parameters ---------- reference_onsets : np.ndarray reference onset locations, in seconds estimated_onsets : np.ndarray estimated onset locations, in seconds window : float Window size, in seconds (Default value = .05) Returns ------- f_measure : float 2*precision*recall/(precision + recall) precision : float (# true positives)/(# true positives + # false positives) recall : float (# true positives)/(# true positives + # false negatives) """ validate(reference_onsets, estimated_onsets) # If either list is empty, return 0s if reference_onsets.size == 0 or estimated_onsets.size == 0: return 0.0, 0.0, 0.0 # Compute the best-case matching between reference and estimated onset # locations matching = util.match_events(reference_onsets, estimated_onsets, window) precision = float(len(matching)) / len(estimated_onsets) recall = float(len(matching)) / len(reference_onsets) # Compute F-measure and return all statistics return util.f_measure(precision, recall), precision, recall def evaluate(reference_onsets, estimated_onsets, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> reference_onsets = mir_eval.io.load_events('reference.txt') >>> estimated_onsets = mir_eval.io.load_events('estimated.txt') >>> scores = mir_eval.onset.evaluate(reference_onsets, ... estimated_onsets) Parameters ---------- reference_onsets : np.ndarray reference onset locations, in seconds estimated_onsets : np.ndarray estimated onset locations, in seconds **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all metrics scores = collections.OrderedDict() (scores["F-measure"], scores["Precision"], scores["Recall"]) = util.filter_kwargs( f_measure, reference_onsets, estimated_onsets, **kwargs ) return scores mir_eval-0.8.2/mir_eval/pattern.py000066400000000000000000000552251475740344600171730ustar00rootroot00000000000000""" Pattern discovery involves the identification of musical patterns (i.e. short fragments or melodic ideas that repeat at least twice) both from audio and symbolic representations. The metrics used to evaluate pattern discovery systems attempt to quantify the ability of the algorithm to not only determine the present patterns in a piece, but also to find all of their occurrences. Based on the methods described here: T. Collins. MIREX task: Discovery of repeated themes & sections. http://www.music-ir.org/mirex/wiki/2013:Discovery_of_Repeated_Themes_&_Sections, 2013. Conventions ----------- The input format can be automatically generated by calling :func:`mir_eval.io.load_patterns`. This format is a list of a list of tuples. The first list collections patterns, each of which is a list of occurrences, and each occurrence is a list of MIDI onset tuples of ``(onset_time, mid_note)`` A pattern is a list of occurrences. The first occurrence must be the prototype of that pattern (i.e. the most representative of all the occurrences). An occurrence is a list of tuples containing the onset time and the midi note number. Metrics ------- * :func:`mir_eval.pattern.standard_FPR`: Strict metric in order to find the possibly transposed patterns of exact length. This is the only metric that considers transposed patterns. * :func:`mir_eval.pattern.establishment_FPR`: Evaluates the amount of patterns that were successfully identified by the estimated results, no matter how many occurrences they found. In other words, this metric captures how the algorithm successfully *established* that a pattern repeated at least twice, and this pattern is also found in the reference annotation. * :func:`mir_eval.pattern.occurrence_FPR`: Evaluation of how well an estimation can effectively identify all the occurrences of the found patterns, independently of how many patterns have been discovered. This metric has a threshold parameter that indicates how similar two occurrences must be in order to be considered equal. In MIREX, this evaluation is run twice, with thresholds .75 and .5. * :func:`mir_eval.pattern.three_layer_FPR`: Aims to evaluate the general similarity between the reference and the estimations, combining both the establishment of patterns and the retrieval of its occurrences in a single F1 score. * :func:`mir_eval.pattern.first_n_three_layer_P`: Computes the three-layer precision for the first N patterns only in order to measure the ability of the algorithm to sort the identified patterns based on their relevance. * :func:`mir_eval.pattern.first_n_target_proportion_R`: Computes the target proportion recall for the first N patterns only in order to measure the ability of the algorithm to sort the identified patterns based on their relevance. """ import numpy as np from . import util import warnings import collections def _n_onset_midi(patterns): """Compute the number of onset_midi objects in a pattern Parameters ---------- patterns A list of patterns using the format returned by :func:`mir_eval.io.load_patterns()` Returns ------- n_onsets : int Number of onsets within the pattern. """ return len([o_m for pat in patterns for occ in pat for o_m in occ]) def validate(reference_patterns, estimated_patterns): """Check that the input annotations to a metric look like valid pattern lists, and throws helpful errors if not. Parameters ---------- reference_patterns : list The reference patterns using the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format """ # Warn if pattern lists are empty if _n_onset_midi(reference_patterns) == 0: warnings.warn("Reference patterns are empty.") if _n_onset_midi(estimated_patterns) == 0: warnings.warn("Estimated patterns are empty.") for patterns in [reference_patterns, estimated_patterns]: for pattern in patterns: if len(pattern) <= 0: raise ValueError( "Each pattern must contain at least one " "occurrence." ) for occurrence in pattern: for onset_midi in occurrence: if len(onset_midi) != 2: raise ValueError( "The (onset, midi) tuple must " "contain exactly 2 elements." ) def _occurrence_intersection(occ_P, occ_Q): """Compute the intersection between two occurrences. Parameters ---------- occ_P : list of tuples (onset, midi) pairs representing the reference occurrence. occ_Q : list second list of (onset, midi) tuples Returns ------- S : set Set of the intersection between occ_P and occ_Q. """ set_P = {tuple(onset_midi) for onset_midi in occ_P} set_Q = {tuple(onset_midi) for onset_midi in occ_Q} return set_P & set_Q # Return the intersection def _compute_score_matrix(P, Q, similarity_metric="cardinality_score"): """Compute the score matrix between the patterns P and Q. Parameters ---------- P : list Pattern containing a list of occurrences. Q : list Pattern containing a list of occurrences. similarity_metric : str A string representing the metric to be used when computing the similarity matrix. Accepted values: - "cardinality_score": Count of the intersection between occurrences. (Default value = "cardinality_score") Returns ------- sm : np.array The score matrix between P and Q using the similarity_metric. """ sm = np.zeros((len(P), len(Q))) # The score matrix for iP, occ_P in enumerate(P): for iQ, occ_Q in enumerate(Q): if similarity_metric == "cardinality_score": denom = float(np.max([len(occ_P), len(occ_Q)])) # Compute the score sm[iP, iQ] = len(_occurrence_intersection(occ_P, occ_Q)) / denom # TODO: More scores: 'normalised matching socre' else: raise ValueError( "The similarity metric (%s) can only be: " "'cardinality_score'." ) return sm def standard_FPR(reference_patterns, estimated_patterns, tol=1e-5): """Compute the standard F1 Score, Precision and Recall. This metric checks if the prototype patterns of the reference match possible translated patterns in the prototype patterns of the estimations. Since the sizes of these prototypes must be equal, this metric is quite restrictive and it tends to be 0 in most of 2013 MIREX results. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> F, P, R = mir_eval.pattern.standard_FPR(ref_patterns, est_patterns) Parameters ---------- reference_patterns : list The reference patterns using the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format tol : float Tolerance level when comparing reference against estimation. Default parameter is the one found in the original matlab code by Tom Collins used for MIREX 2013. (Default value = 1e-5) Returns ------- f_measure : float The standard F1 Score precision : float The standard Precision recall : float The standard Recall """ validate(reference_patterns, estimated_patterns) nP = len(reference_patterns) # Number of patterns in the reference nQ = len(estimated_patterns) # Number of patterns in the estimation k = 0 # Number of patterns that match # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 # Find matches of the prototype patterns for ref_pattern in reference_patterns: P = np.asarray(ref_pattern[0]) # Get reference prototype for est_pattern in estimated_patterns: Q = np.asarray(est_pattern[0]) # Get estimation prototype if len(P) != len(Q): continue # Check transposition given a certain tolerance if len(P) == len(Q) == 1 or np.max(np.abs(np.diff(P - Q, axis=0))) < tol: k += 1 break # Compute the standard measures precision = k / float(nQ) recall = k / float(nP) f_measure = util.f_measure(precision, recall) return f_measure, precision, recall def establishment_FPR( reference_patterns, estimated_patterns, similarity_metric="cardinality_score" ): """Compute the establishment F1 Score, Precision and Recall. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> F, P, R = mir_eval.pattern.establishment_FPR(ref_patterns, ... est_patterns) Parameters ---------- reference_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format similarity_metric : str A string representing the metric to be used when computing the similarity matrix. Accepted values: - "cardinality_score": Count of the intersection between occurrences. (Default value = "cardinality_score") Returns ------- f_measure : float The establishment F1 Score precision : float The establishment Precision recall : float The establishment Recall """ validate(reference_patterns, estimated_patterns) nP = len(reference_patterns) # Number of elements in reference nQ = len(estimated_patterns) # Number of elements in estimation S = np.zeros((nP, nQ)) # Establishment matrix # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 for iP, ref_pattern in enumerate(reference_patterns): for iQ, est_pattern in enumerate(estimated_patterns): s = _compute_score_matrix(ref_pattern, est_pattern, similarity_metric) S[iP, iQ] = np.max(s) # Compute scores precision = np.mean(np.max(S, axis=0)) recall = np.mean(np.max(S, axis=1)) f_measure = util.f_measure(precision, recall) return f_measure, precision, recall def occurrence_FPR( reference_patterns, estimated_patterns, thres=0.75, similarity_metric="cardinality_score", ): """Compute the occurrence F1 Score, Precision and Recall. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> F, P, R = mir_eval.pattern.occurrence_FPR(ref_patterns, ... est_patterns) Parameters ---------- reference_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format thres : float How similar two occurrences must be in order to be considered equal (Default value = .75) similarity_metric : str A string representing the metric to be used when computing the similarity matrix. Accepted values: - "cardinality_score": Count of the intersection between occurrences. (Default value = "cardinality_score") Returns ------- f_measure : float The occurrence F1 Score precision : float The occurrence Precision recall : float The occurrence Recall """ validate(reference_patterns, estimated_patterns) # Number of elements in reference nP = len(reference_patterns) # Number of elements in estimation nQ = len(estimated_patterns) # Occurrence matrix with Precision and recall in its last dimension O_PR = np.zeros((nP, nQ, 2)) # Index of the values that are greater than the specified threshold rel_idx = np.empty((0, 2), dtype=int) # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 for iP, ref_pattern in enumerate(reference_patterns): for iQ, est_pattern in enumerate(estimated_patterns): s = _compute_score_matrix(ref_pattern, est_pattern, similarity_metric) if np.max(s) >= thres: O_PR[iP, iQ, 0] = np.mean(np.max(s, axis=0)) O_PR[iP, iQ, 1] = np.mean(np.max(s, axis=1)) rel_idx = np.vstack((rel_idx, [iP, iQ])) # Compute the scores if len(rel_idx) == 0: precision = 0 recall = 0 else: P = O_PR[:, :, 0] precision = np.mean(np.max(P[np.ix_(rel_idx[:, 0], rel_idx[:, 1])], axis=0)) R = O_PR[:, :, 1] recall = np.mean(np.max(R[np.ix_(rel_idx[:, 0], rel_idx[:, 1])], axis=1)) f_measure = util.f_measure(precision, recall) return f_measure, precision, recall def three_layer_FPR(reference_patterns, estimated_patterns): """Three Layer F1 Score, Precision and Recall. As described by Meridith. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> F, P, R = mir_eval.pattern.three_layer_FPR(ref_patterns, ... est_patterns) Parameters ---------- reference_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format Returns ------- f_measure : float The three-layer F1 Score precision : float The three-layer Precision recall : float The three-layer Recall """ validate(reference_patterns, estimated_patterns) def compute_first_layer_PR(ref_occs, est_occs): """Compute the first layer Precision and Recall values given the set of occurrences in the reference and the set of occurrences in the estimation. Parameters ---------- ref_occs est_occs Returns ------- precision recall """ # Find the length of the intersection between reference and estimation s = len(_occurrence_intersection(ref_occs, est_occs)) # Compute the first layer scores precision = s / float(len(ref_occs)) recall = s / float(len(est_occs)) return precision, recall def compute_second_layer_PR(ref_pattern, est_pattern): """Compute the second layer Precision and Recall values given the set of occurrences in the reference and the set of occurrences in the estimation. Parameters ---------- ref_pattern est_pattern Returns ------- precision recall """ # Compute the first layer scores F_1 = compute_layer(ref_pattern, est_pattern) # Compute the second layer scores precision = np.mean(np.max(F_1, axis=0)) recall = np.mean(np.max(F_1, axis=1)) return precision, recall def compute_layer(ref_elements, est_elements, layer=1): """Compute the F-measure matrix for a given layer. The reference and estimated elements can be either patterns or occurrences, depending on the layer. For layer 1, the elements must be occurrences. For layer 2, the elements must be patterns. Parameters ---------- ref_elements est_elements layer (Default value = 1) Returns ------- F : F-measure for the given layer """ if layer != 1 and layer != 2: raise ValueError("Layer (%d) must be an integer between 1 and 2" % layer) nP = len(ref_elements) # Number of elements in reference nQ = len(est_elements) # Number of elements in estimation F = np.zeros((nP, nQ)) # F-measure matrix for the given layer for iP in range(nP): for iQ in range(nQ): if layer == 1: func = compute_first_layer_PR elif layer == 2: func = compute_second_layer_PR # Compute layer scores precision, recall = func(ref_elements[iP], est_elements[iQ]) F[iP, iQ] = util.f_measure(precision, recall) return F # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 # Compute the second layer (it includes the first layer) F_2 = compute_layer(reference_patterns, estimated_patterns, layer=2) # Compute the final scores (third layer) precision_3 = np.mean(np.max(F_2, axis=0)) recall_3 = np.mean(np.max(F_2, axis=1)) f_measure_3 = util.f_measure(precision_3, recall_3) return f_measure_3, precision_3, recall_3 def first_n_three_layer_P(reference_patterns, estimated_patterns, n=5): """First n three-layer precision. This metric is basically the same as the three-layer FPR but it is only applied to the first n estimated patterns, and it only returns the precision. In MIREX and typically, n = 5. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> P = mir_eval.pattern.first_n_three_layer_P(ref_patterns, ... est_patterns, n=5) Parameters ---------- reference_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format n : int Number of patterns to consider from the estimated results, in the order they appear in the matrix (Default value = 5) Returns ------- precision : float The first n three-layer Precision """ validate(reference_patterns, estimated_patterns) # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 # Get only the first n patterns from the estimated results fn_est_patterns = estimated_patterns[: min(len(estimated_patterns), n)] # Compute the three-layer scores for the first n estimated patterns F, P, R = three_layer_FPR(reference_patterns, fn_est_patterns) return P # Return the precision only def first_n_target_proportion_R(reference_patterns, estimated_patterns, n=5): """First n target proportion establishment recall metric. This metric is similar is similar to the establishment FPR score, but it only takes into account the first n estimated patterns and it only outputs the Recall value of it. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> R = mir_eval.pattern.first_n_target_proportion_R( ... ref_patterns, est_patterns, n=5) Parameters ---------- reference_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` estimated_patterns : list The estimated patterns in the same format n : int Number of patterns to consider from the estimated results, in the order they appear in the matrix. (Default value = 5) Returns ------- recall : float The first n target proportion Recall. """ validate(reference_patterns, estimated_patterns) # If no patterns were provided, metric is zero if _n_onset_midi(reference_patterns) == 0 or _n_onset_midi(estimated_patterns) == 0: return 0.0, 0.0, 0.0 # Get only the first n patterns from the estimated results fn_est_patterns = estimated_patterns[: min(len(estimated_patterns), n)] F, P, R = establishment_FPR(reference_patterns, fn_est_patterns) return R def evaluate(ref_patterns, est_patterns, **kwargs): """Load data and perform the evaluation. Examples -------- >>> ref_patterns = mir_eval.io.load_patterns("ref_pattern.txt") >>> est_patterns = mir_eval.io.load_patterns("est_pattern.txt") >>> scores = mir_eval.pattern.evaluate(ref_patterns, est_patterns) Parameters ---------- ref_patterns : list The reference patterns in the format returned by :func:`mir_eval.io.load_patterns()` est_patterns : list The estimated patterns in the same format **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() # Standard scores scores["F"], scores["P"], scores["R"] = util.filter_kwargs( standard_FPR, ref_patterns, est_patterns, **kwargs ) # Establishment scores scores["F_est"], scores["P_est"], scores["R_est"] = util.filter_kwargs( establishment_FPR, ref_patterns, est_patterns, **kwargs ) # Occurrence scores # Force these values for thresh kwargs["thresh"] = 0.5 scores["F_occ.5"], scores["P_occ.5"], scores["R_occ.5"] = util.filter_kwargs( occurrence_FPR, ref_patterns, est_patterns, **kwargs ) kwargs["thresh"] = 0.75 scores["F_occ.75"], scores["P_occ.75"], scores["R_occ.75"] = util.filter_kwargs( occurrence_FPR, ref_patterns, est_patterns, **kwargs ) # Three-layer scores scores["F_3"], scores["P_3"], scores["R_3"] = util.filter_kwargs( three_layer_FPR, ref_patterns, est_patterns, **kwargs ) # First Five Patterns scores # Set default value of n if "n" not in kwargs: kwargs["n"] = 5 scores["FFP"] = util.filter_kwargs( first_n_three_layer_P, ref_patterns, est_patterns, **kwargs ) scores["FFTP_est"] = util.filter_kwargs( first_n_target_proportion_R, ref_patterns, est_patterns, **kwargs ) return scores mir_eval-0.8.2/mir_eval/segment.py000066400000000000000000001333261475740344600171570ustar00rootroot00000000000000# CREATED:2013-08-13 12:02:42 by Brian McFee """ Evaluation criteria for structural segmentation fall into two categories: boundary annotation and structural annotation. Boundary annotation is the task of predicting the times at which structural changes occur, such as when a verse transitions to a refrain. Metrics for boundary annotation compare estimated segment boundaries to reference boundaries. Structural annotation is the task of assigning labels to detected segments. The estimated labels may be arbitrary strings - such as A, B, C, - and they need not describe functional concepts. Metrics for structural annotation are similar to those used for clustering data. Conventions ----------- Both boundary and structural annotation metrics require two dimensional arrays with two columns, one for boundary start times and one for boundary end times. Structural annotation further require lists of reference and estimated segment labels which must have a length which is equal to the number of rows in the corresponding list of boundary edges. In both tasks, we assume that annotations express a partitioning of the track into intervals. The function :func:`mir_eval.util.adjust_intervals` can be used to pad or crop the segment boundaries to span the duration of the entire track. Metrics ------- * :func:`mir_eval.segment.detection`: An estimated boundary is considered correct if it falls within a window around a reference boundary [#turnbull2007]_ * :func:`mir_eval.segment.deviation`: Computes the median absolute time difference from a reference boundary to its nearest estimated boundary, and vice versa [#turnbull2007]_ * :func:`mir_eval.segment.pairwise`: For classifying pairs of sampled time instants as belonging to the same structural component [#levy2008]_ * :func:`mir_eval.segment.rand_index`: Clusters reference and estimated annotations and compares them by the Rand Index * :func:`mir_eval.segment.ari`: Computes the Rand index, adjusted for chance * :func:`mir_eval.segment.nce`: Interprets sampled reference and estimated labels as samples of random variables :math:`Y_R, Y_E` from which the conditional entropy of :math:`Y_R` given :math:`Y_E` (Under-Segmentation) and :math:`Y_E` given :math:`Y_R` (Over-Segmentation) are estimated [#lukashevich2008]_ * :func:`mir_eval.segment.mutual_information`: Computes the standard, normalized, and adjusted mutual information of sampled reference and estimated segments * :func:`mir_eval.segment.vmeasure`: Computes the V-Measure, which is similar to the conditional entropy metrics, but uses the marginal distributions as normalization rather than the maximum entropy distribution [#rosenberg2007]_ References ---------- .. [#turnbull2007] Turnbull, D., Lanckriet, G. R., Pampalk, E., & Goto, M. A Supervised Approach for Detecting Boundaries in Music Using Difference Features and Boosting. In ISMIR (pp. 51-54). .. [#levy2008] Levy, M., & Sandler, M. Structural segmentation of musical audio by constrained clustering. IEEE transactions on audio, speech, and language processing, 16(2), 318-326. .. [#lukashevich2008] Lukashevich, H. M. Towards Quantitative Measures of Evaluating Song Segmentation. In ISMIR (pp. 375-380). .. [#rosenberg2007] Rosenberg, A., & Hirschberg, J. V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure. In EMNLP-CoNLL (Vol. 7, pp. 410-420). """ import collections import warnings import numpy as np import scipy.stats import scipy.sparse import scipy.special from . import util def validate_boundary(reference_intervals, estimated_intervals, trim): """Check that the input annotations to a segment boundary estimation metric (i.e. one that only takes in segment intervals) look like valid segment times, and throws helpful errors if not. Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. trim : bool will the start and end events be trimmed? """ if trim: # If we're trimming, then we need at least 2 intervals min_size = 2 else: # If we're not trimming, then we only need one interval min_size = 1 if len(reference_intervals) < min_size: warnings.warn("Reference intervals are empty.") if len(estimated_intervals) < min_size: warnings.warn("Estimated intervals are empty.") for intervals in [reference_intervals, estimated_intervals]: util.validate_intervals(intervals) def validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ): """Check that the input annotations to a structure estimation metric (i.e. one that takes in both segment boundaries and their labels) look like valid segment times and labels, and throws helpful errors if not. Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. """ for intervals, labels in [ (reference_intervals, reference_labels), (estimated_intervals, estimated_labels), ]: util.validate_intervals(intervals) if intervals.shape[0] != len(labels): raise ValueError("Number of intervals does not match number " "of labels") # Check only when intervals are non-empty if intervals.size > 0: # Make sure intervals start at 0 if not np.allclose(intervals.min(), 0.0): raise ValueError("Segment intervals do not start at 0") if reference_intervals.size == 0: warnings.warn("Reference intervals are empty.") if estimated_intervals.size == 0: warnings.warn("Estimated intervals are empty.") # Check only when intervals are non-empty if reference_intervals.size > 0 and estimated_intervals.size > 0: if not np.allclose(reference_intervals.max(), estimated_intervals.max()): raise ValueError("End times do not match") def detection( reference_intervals, estimated_intervals, window=0.5, beta=1.0, trim=False ): """Boundary detection hit-rate. A hit is counted whenever an reference boundary is within ``window`` of a estimated boundary. Note that each boundary is matched at most once: this is achieved by computing the size of a maximal matching between reference and estimated boundary points, subject to the window constraint. Examples -------- >>> ref_intervals, _ = mir_eval.io.load_labeled_intervals('ref.lab') >>> est_intervals, _ = mir_eval.io.load_labeled_intervals('est.lab') >>> # With 0.5s windowing >>> P05, R05, F05 = mir_eval.segment.detection(ref_intervals, ... est_intervals, ... window=0.5) >>> # With 3s windowing >>> P3, R3, F3 = mir_eval.segment.detection(ref_intervals, ... est_intervals, ... window=3) >>> # Ignoring hits for the beginning and end of track >>> P, R, F = mir_eval.segment.detection(ref_intervals, ... est_intervals, ... window=0.5, ... trim=True) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. window : float > 0 size of the window of 'correctness' around ground-truth beats (in seconds) (Default value = 0.5) beta : float > 0 weighting constant for F-measure. (Default value = 1.0) trim : boolean if ``True``, the first and last boundary times are ignored. Typically, these denote start (0) and end-markers. (Default value = False) Returns ------- precision : float precision of estimated predictions recall : float recall of reference reference boundaries f_measure : float F-measure (weighted harmonic mean of ``precision`` and ``recall``) """ validate_boundary(reference_intervals, estimated_intervals, trim) # Convert intervals to boundaries reference_boundaries = util.intervals_to_boundaries(reference_intervals) estimated_boundaries = util.intervals_to_boundaries(estimated_intervals) # Suppress the first and last intervals if trim: reference_boundaries = reference_boundaries[1:-1] estimated_boundaries = estimated_boundaries[1:-1] # If we have no boundaries, we get no score. if len(reference_boundaries) == 0 or len(estimated_boundaries) == 0: return 0.0, 0.0, 0.0 matching = util.match_events(reference_boundaries, estimated_boundaries, window) precision = float(len(matching)) / len(estimated_boundaries) recall = float(len(matching)) / len(reference_boundaries) f_measure = util.f_measure(precision, recall, beta=beta) return precision, recall, f_measure def deviation(reference_intervals, estimated_intervals, trim=False): """Compute the median deviations between reference and estimated boundary times. Examples -------- >>> ref_intervals, _ = mir_eval.io.load_labeled_intervals('ref.lab') >>> est_intervals, _ = mir_eval.io.load_labeled_intervals('est.lab') >>> r_to_e, e_to_r = mir_eval.boundary.deviation(ref_intervals, ... est_intervals) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_intervals` or :func:`mir_eval.io.load_labeled_intervals`. trim : boolean if ``True``, the first and last intervals are ignored. Typically, these denote start (0.0) and end-of-track markers. (Default value = False) Returns ------- reference_to_estimated : float median time from each reference boundary to the closest estimated boundary estimated_to_reference : float median time from each estimated boundary to the closest reference boundary """ validate_boundary(reference_intervals, estimated_intervals, trim) # Convert intervals to boundaries reference_boundaries = util.intervals_to_boundaries(reference_intervals) estimated_boundaries = util.intervals_to_boundaries(estimated_intervals) # Suppress the first and last intervals if trim: reference_boundaries = reference_boundaries[1:-1] estimated_boundaries = estimated_boundaries[1:-1] # If we have no boundaries, we get no score. if len(reference_boundaries) == 0 or len(estimated_boundaries) == 0: return np.nan, np.nan dist = np.abs(np.subtract.outer(reference_boundaries, estimated_boundaries)) estimated_to_reference = np.median(dist.min(axis=0)) reference_to_estimated = np.median(dist.min(axis=1)) return reference_to_estimated, estimated_to_reference def pairwise( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, beta=1.0, ): """Frame-clustering segmentation evaluation by pair-wise agreement. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> precision, recall, f = mir_eval.structure.pairwise(ref_intervals, ... ref_labels, ... est_intervals, ... est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) beta : float > 0 beta value for F-measure (Default value = 1.0) Returns ------- precision : float > 0 Precision of detecting whether frames belong in the same cluster recall : float > 0 Recall of detecting whether frames belong in the same cluster f : float > 0 F-measure of detecting whether frames belong in the same cluster """ validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ) # Check for empty annotations. Don't need to check labels because # validate_structure makes sure they're the same size as intervals if reference_intervals.size == 0 or estimated_intervals.size == 0: return 0.0, 0.0, 0.0 # Generate the cluster labels y_ref = util.intervals_to_samples( reference_intervals, reference_labels, sample_size=frame_size )[-1] y_ref = util.index_labels(y_ref)[0] # Map to index space y_est = util.intervals_to_samples( estimated_intervals, estimated_labels, sample_size=frame_size )[-1] y_est = util.index_labels(y_est)[0] # Build the reference label agreement matrix agree_ref = np.equal.outer(y_ref, y_ref) # Count the unique pairs n_agree_ref = (agree_ref.sum() - len(y_ref)) / 2.0 # Repeat for estimate agree_est = np.equal.outer(y_est, y_est) n_agree_est = (agree_est.sum() - len(y_est)) / 2.0 # Find where they agree matches = np.logical_and(agree_ref, agree_est) n_matches = (matches.sum() - len(y_ref)) / 2.0 precision = n_matches / n_agree_est recall = n_matches / n_agree_ref f_measure = util.f_measure(precision, recall, beta=beta) return precision, recall, f_measure def rand_index( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, beta=1.0, ): """(Non-adjusted) Rand index. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> rand_index = mir_eval.structure.rand_index(ref_intervals, ... ref_labels, ... est_intervals, ... est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) beta : float > 0 beta value for F-measure (Default value = 1.0) Returns ------- rand_index : float > 0 Rand index """ validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ) # Check for empty annotations. Don't need to check labels because # validate_structure makes sure they're the same size as intervals if reference_intervals.size == 0 or estimated_intervals.size == 0: return 0.0, 0.0, 0.0 # Generate the cluster labels y_ref = util.intervals_to_samples( reference_intervals, reference_labels, sample_size=frame_size )[-1] y_ref = util.index_labels(y_ref)[0] # Map to index space y_est = util.intervals_to_samples( estimated_intervals, estimated_labels, sample_size=frame_size )[-1] y_est = util.index_labels(y_est)[0] # Build the reference label agreement matrix agree_ref = np.equal.outer(y_ref, y_ref) # Repeat for estimate agree_est = np.equal.outer(y_est, y_est) # Find where they agree matches_pos = np.logical_and(agree_ref, agree_est) # Find where they disagree matches_neg = np.logical_and(~agree_ref, ~agree_est) n_pairs = len(y_ref) * (len(y_ref) - 1) / 2.0 n_matches_pos = (matches_pos.sum() - len(y_ref)) / 2.0 n_matches_neg = matches_neg.sum() / 2.0 rand = (n_matches_pos + n_matches_neg) / n_pairs return rand def _contingency_matrix(reference_indices, estimated_indices): """Compute the contingency matrix of a true labeling vs an estimated one. Parameters ---------- reference_indices : np.ndarray Array of reference indices estimated_indices : np.ndarray Array of estimated indices Returns ------- contingency_matrix : np.ndarray Contingency matrix, shape=(#reference indices, #estimated indices) .. note:: Based on sklearn.metrics.cluster.contingency_matrix """ ref_classes, ref_class_idx = np.unique(reference_indices, return_inverse=True) est_classes, est_class_idx = np.unique(estimated_indices, return_inverse=True) n_ref_classes = ref_classes.shape[0] n_est_classes = est_classes.shape[0] # Using coo_matrix is faster than histogram2d return scipy.sparse.coo_matrix( (np.ones(ref_class_idx.shape[0]), (ref_class_idx, est_class_idx)), shape=(n_ref_classes, n_est_classes), dtype=np.int64, ).toarray() def _adjusted_rand_index(reference_indices, estimated_indices): """Compute the Rand index, adjusted for change. Parameters ---------- reference_indices : np.ndarray Array of reference indices estimated_indices : np.ndarray Array of estimated indices Returns ------- ari : float Adjusted Rand index .. note:: Based on sklearn.metrics.cluster.adjusted_rand_score """ n_samples = len(reference_indices) ref_classes = np.unique(reference_indices) est_classes = np.unique(estimated_indices) # Special limit cases: no clustering since the data is not split; # or trivial clustering where each document is assigned a unique cluster. # These are perfect matches hence return 1.0. if ( ref_classes.shape[0] == est_classes.shape[0] == 1 or ref_classes.shape[0] == est_classes.shape[0] == 0 or (ref_classes.shape[0] == est_classes.shape[0] == len(reference_indices)) ): return 1.0 contingency = _contingency_matrix(reference_indices, estimated_indices) # Compute the ARI using the contingency data sum_comb_c = sum( scipy.special.comb(n_c, 2, exact=1) for n_c in contingency.sum(axis=1) ) sum_comb_k = sum( scipy.special.comb(n_k, 2, exact=1) for n_k in contingency.sum(axis=0) ) sum_comb = sum( scipy.special.comb(n_ij, 2, exact=1) for n_ij in contingency.flatten() ) prod_comb = (sum_comb_c * sum_comb_k) / float(scipy.special.comb(n_samples, 2)) mean_comb = (sum_comb_k + sum_comb_c) / 2.0 return (sum_comb - prod_comb) / (mean_comb - prod_comb) def ari( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, ): """Compute the Adjusted Rand Index (ARI) for frame clustering segmentation evaluation. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> ari_score = mir_eval.structure.ari(ref_intervals, ref_labels, ... est_intervals, est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) Returns ------- ari_score : float > 0 Adjusted Rand index between segmentations. """ validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ) # Check for empty annotations. Don't need to check labels because # validate_structure makes sure they're the same size as intervals if reference_intervals.size == 0 or estimated_intervals.size == 0: return 0.0, 0.0, 0.0 # Generate the cluster labels y_ref = util.intervals_to_samples( reference_intervals, reference_labels, sample_size=frame_size )[-1] y_ref = util.index_labels(y_ref)[0] # Map to index space y_est = util.intervals_to_samples( estimated_intervals, estimated_labels, sample_size=frame_size )[-1] y_est = util.index_labels(y_est)[0] return _adjusted_rand_index(y_ref, y_est) def _mutual_info_score(reference_indices, estimated_indices, contingency=None): """Compute the mutual information between two sequence labelings. Parameters ---------- reference_indices : np.ndarray Array of reference indices estimated_indices : np.ndarray Array of estimated indices contingency : np.ndarray Pre-computed contingency matrix. If None, one will be computed. (Default value = None) Returns ------- mi : float Mutual information .. note:: Based on sklearn.metrics.cluster.mutual_info_score """ if contingency is None: contingency = _contingency_matrix(reference_indices, estimated_indices).astype( float ) contingency_sum = np.sum(contingency) pi = np.sum(contingency, axis=1) pj = np.sum(contingency, axis=0) outer = np.outer(pi, pj) nnz = contingency != 0.0 # normalized contingency contingency_nm = contingency[nnz] log_contingency_nm = np.log(contingency_nm) contingency_nm /= contingency_sum # log(a / b) should be calculated as log(a) - log(b) for # possible loss of precision log_outer = -np.log(outer[nnz]) + np.log(pi.sum()) + np.log(pj.sum()) mi = ( contingency_nm * (log_contingency_nm - np.log(contingency_sum)) + contingency_nm * log_outer ) return mi.sum() def _entropy(labels): """Calculate the entropy for a labeling. Parameters ---------- labels : list-like List of labels. Returns ------- entropy : float Entropy of the labeling. .. note:: Based on sklearn.metrics.cluster.entropy """ if len(labels) == 0: return 1.0 label_idx = np.unique(labels, return_inverse=True)[1] pi = np.bincount(label_idx).astype(np.float64) pi = pi[pi > 0] pi_sum = np.sum(pi) # log(a / b) should be calculated as log(a) - log(b) for # possible loss of precision return -np.sum((pi / pi_sum) * (np.log(pi) - np.log(pi_sum))) def _adjusted_mutual_info_score(reference_indices, estimated_indices): """Compute the mutual information between two sequence labelings, adjusted for chance. Parameters ---------- reference_indices : np.ndarray Array of reference indices estimated_indices : np.ndarray Array of estimated indices Returns ------- ami : float <= 1.0 Mutual information .. note:: Based on sklearn.metrics.cluster.adjusted_mutual_info_score and sklearn.metrics.cluster.expected_mutual_info_score """ n_samples = len(reference_indices) ref_classes = np.unique(reference_indices) est_classes = np.unique(estimated_indices) # Special limit cases: no clustering since the data is not split. # This is a perfect match hence return 1.0. if ( ref_classes.shape[0] == est_classes.shape[0] == 1 or ref_classes.shape[0] == est_classes.shape[0] == 0 ): return 1.0 contingency = _contingency_matrix(reference_indices, estimated_indices).astype( float ) # Calculate the MI for the two clusterings mi = _mutual_info_score( reference_indices, estimated_indices, contingency=contingency ) # The following code is based on # sklearn.metrics.cluster.expected_mutual_information R, C = contingency.shape N = float(n_samples) a = np.sum(contingency, axis=1).astype(np.int32) b = np.sum(contingency, axis=0).astype(np.int32) # There are three major terms to the EMI equation, which are multiplied to # and then summed over varying nij values. # While nijs[0] will never be used, having it simplifies the indexing. nijs = np.arange(0, max(np.max(a), np.max(b)) + 1, dtype="float") # Stops divide by zero warnings. As its not used, no issue. nijs[0] = 1 # term1 is nij / N term1 = nijs / N # term2 is log((N*nij) / (a * b)) == log(N * nij) - log(a * b) # term2 uses the outer product log_ab_outer = np.log(np.outer(a, b)) # term2 uses N * nij log_Nnij = np.log(N * nijs) # term3 is large, and involved many factorials. Calculate these in log # space to stop overflows. gln_a = scipy.special.gammaln(a + 1) gln_b = scipy.special.gammaln(b + 1) gln_Na = scipy.special.gammaln(N - a + 1) gln_Nb = scipy.special.gammaln(N - b + 1) gln_N = scipy.special.gammaln(N + 1) gln_nij = scipy.special.gammaln(nijs + 1) # start and end values for nij terms for each summation. start = np.array([[v - N + w for w in b] for v in a], dtype="int") start = np.maximum(start, 1) end = np.minimum(np.resize(a, (C, R)).T, np.resize(b, (R, C))) + 1 # emi itself is a summation over the various values. emi = 0 for i in range(R): for j in range(C): for nij in range(start[i, j], end[i, j]): term2 = log_Nnij[nij] - log_ab_outer[i, j] # Numerators are positive, denominators are negative. gln = ( gln_a[i] + gln_b[j] + gln_Na[i] + gln_Nb[j] - gln_N - gln_nij[nij] - scipy.special.gammaln(a[i] - nij + 1) - scipy.special.gammaln(b[j] - nij + 1) - scipy.special.gammaln(N - a[i] - b[j] + nij + 1) ) term3 = np.exp(gln) emi += term1[nij] * term2 * term3 # Calculate entropy for each labeling h_true, h_pred = _entropy(reference_indices), _entropy(estimated_indices) ami = (mi - emi) / (max(h_true, h_pred) - emi) return ami def _normalized_mutual_info_score(reference_indices, estimated_indices): """Compute the mutual information between two sequence labelings, adjusted for chance. Parameters ---------- reference_indices : np.ndarray Array of reference indices estimated_indices : np.ndarray Array of estimated indices Returns ------- nmi : float <= 1.0 Normalized mutual information .. note:: Based on sklearn.metrics.cluster.normalized_mutual_info_score """ ref_classes = np.unique(reference_indices) est_classes = np.unique(estimated_indices) # Special limit cases: no clustering since the data is not split. # This is a perfect match hence return 1.0. if ( ref_classes.shape[0] == est_classes.shape[0] == 1 or ref_classes.shape[0] == est_classes.shape[0] == 0 ): return 1.0 contingency = _contingency_matrix(reference_indices, estimated_indices).astype( float ) contingency = np.array(contingency, dtype="float") # Calculate the MI for the two clusterings mi = _mutual_info_score( reference_indices, estimated_indices, contingency=contingency ) # Calculate the expected value for the mutual information # Calculate entropy for each labeling h_true, h_pred = _entropy(reference_indices), _entropy(estimated_indices) nmi = mi / max(np.sqrt(h_true * h_pred), 1e-10) return nmi def mutual_information( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, ): """Frame-clustering segmentation: mutual information metrics. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> mi, ami, nmi = mir_eval.structure.mutual_information(ref_intervals, ... ref_labels, ... est_intervals, ... est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) Returns ------- MI : float > 0 Mutual information between segmentations AMI : float Adjusted mutual information between segmentations. NMI : float > 0 Normalize mutual information between segmentations """ validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ) # Check for empty annotations. Don't need to check labels because # validate_structure makes sure they're the same size as intervals if reference_intervals.size == 0 or estimated_intervals.size == 0: return 0.0, 0.0, 0.0 # Generate the cluster labels y_ref = util.intervals_to_samples( reference_intervals, reference_labels, sample_size=frame_size )[-1] y_ref = util.index_labels(y_ref)[0] # Map to index space y_est = util.intervals_to_samples( estimated_intervals, estimated_labels, sample_size=frame_size )[-1] y_est = util.index_labels(y_est)[0] # Mutual information mutual_info = _mutual_info_score(y_ref, y_est) # Adjusted mutual information adj_mutual_info = _adjusted_mutual_info_score(y_ref, y_est) # Normalized mutual information norm_mutual_info = _normalized_mutual_info_score(y_ref, y_est) return mutual_info, adj_mutual_info, norm_mutual_info def nce( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, beta=1.0, marginal=False, ): """Frame-clustering segmentation: normalized conditional entropy Computes cross-entropy of cluster assignment, normalized by the max-entropy. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> S_over, S_under, S_F = mir_eval.structure.nce(ref_intervals, ... ref_labels, ... est_intervals, ... est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) beta : float > 0 beta for F-measure (Default value = 1.0) marginal : bool If `False`, normalize conditional entropy by uniform entropy. If `True`, normalize conditional entropy by the marginal entropy. (Default value = False) Returns ------- S_over Over-clustering score: - For `marginal=False`, ``1 - H(y_est | y_ref) / log(|y_est|)`` - For `marginal=True`, ``1 - H(y_est | y_ref) / H(y_est)`` If `|y_est|==1`, then `S_over` will be 0. S_under Under-clustering score: - For `marginal=False`, ``1 - H(y_ref | y_est) / log(|y_ref|)`` - For `marginal=True`, ``1 - H(y_ref | y_est) / H(y_ref)`` If `|y_ref|==1`, then `S_under` will be 0. S_F F-measure for (S_over, S_under) """ validate_structure( reference_intervals, reference_labels, estimated_intervals, estimated_labels ) # Check for empty annotations. Don't need to check labels because # validate_structure makes sure they're the same size as intervals if reference_intervals.size == 0 or estimated_intervals.size == 0: return 0.0, 0.0, 0.0 # Generate the cluster labels y_ref = util.intervals_to_samples( reference_intervals, reference_labels, sample_size=frame_size )[-1] y_ref = util.index_labels(y_ref)[0] # Map to index space y_est = util.intervals_to_samples( estimated_intervals, estimated_labels, sample_size=frame_size )[-1] y_est = util.index_labels(y_est)[0] # Make the contingency table: shape = (n_ref, n_est) contingency = _contingency_matrix(y_ref, y_est).astype(float) # Normalize by the number of frames contingency = contingency / len(y_ref) # Compute the marginals p_est = contingency.sum(axis=0) p_ref = contingency.sum(axis=1) # H(true | prediction) = sum_j P[estimated = j] * # sum_i P[true = i | estimated = j] log P[true = i | estimated = j] # entropy sums over axis=0, which is true labels true_given_est = p_est.dot(scipy.stats.entropy(contingency, base=2)) pred_given_ref = p_ref.dot(scipy.stats.entropy(contingency.T, base=2)) if marginal: # Normalize conditional entropy by marginal entropy z_ref = scipy.stats.entropy(p_ref, base=2) z_est = scipy.stats.entropy(p_est, base=2) else: z_ref = np.log2(contingency.shape[0]) z_est = np.log2(contingency.shape[1]) score_under = 0.0 if z_ref > 0: score_under = 1.0 - true_given_est / z_ref score_over = 0.0 if z_est > 0: score_over = 1.0 - pred_given_ref / z_est f_measure = util.f_measure(score_over, score_under, beta=beta) return score_over, score_under, f_measure def vmeasure( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=0.1, beta=1.0, ): """Frame-clustering segmentation: v-measure Computes cross-entropy of cluster assignment, normalized by the marginal-entropy. This is equivalent to `nce(..., marginal=True)`. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> # Trim or pad the estimate to match reference timing >>> (ref_intervals, ... ref_labels) = mir_eval.util.adjust_intervals(ref_intervals, ... ref_labels, ... t_min=0) >>> (est_intervals, ... est_labels) = mir_eval.util.adjust_intervals( ... est_intervals, est_labels, t_min=0, t_max=ref_intervals.max()) >>> V_precision, V_recall, V_F = mir_eval.structure.vmeasure(ref_intervals, ... ref_labels, ... est_intervals, ... est_labels) Parameters ---------- reference_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. reference_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. estimated_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. frame_size : float > 0 length (in seconds) of frames for clustering (Default value = 0.1) beta : float > 0 beta for F-measure (Default value = 1.0) Returns ------- V_precision Over-clustering score: ``1 - H(y_est | y_ref) / H(y_est)`` If `|y_est|==1`, then `V_precision` will be 0. V_recall Under-clustering score: ``1 - H(y_ref | y_est) / H(y_ref)`` If `|y_ref|==1`, then `V_recall` will be 0. V_F F-measure for (V_precision, V_recall) """ return nce( reference_intervals, reference_labels, estimated_intervals, estimated_labels, frame_size=frame_size, beta=beta, marginal=True, ) def evaluate(ref_intervals, ref_labels, est_intervals, est_labels, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> (ref_intervals, ... ref_labels) = mir_eval.io.load_labeled_intervals('ref.lab') >>> (est_intervals, ... est_labels) = mir_eval.io.load_labeled_intervals('est.lab') >>> scores = mir_eval.segment.evaluate(ref_intervals, ref_labels, ... est_intervals, est_labels) Parameters ---------- ref_intervals : np.ndarray, shape=(n, 2) reference segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. ref_labels : list, shape=(n,) reference segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. est_intervals : np.ndarray, shape=(m, 2) estimated segment intervals, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. est_labels : list, shape=(m,) estimated segment labels, in the format returned by :func:`mir_eval.io.load_labeled_intervals`. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Adjust timespan of estimations relative to ground truth ref_intervals, ref_labels = util.adjust_intervals( ref_intervals, labels=ref_labels, t_min=0.0 ) est_intervals, est_labels = util.adjust_intervals( est_intervals, labels=est_labels, t_min=0.0, t_max=ref_intervals.max() ) # Now compute all the metrics scores = collections.OrderedDict() # Boundary detection # Force these values for window kwargs["window"] = 0.5 ( scores["Precision@0.5"], scores["Recall@0.5"], scores["F-measure@0.5"], ) = util.filter_kwargs(detection, ref_intervals, est_intervals, **kwargs) kwargs["window"] = 3.0 ( scores["Precision@3.0"], scores["Recall@3.0"], scores["F-measure@3.0"], ) = util.filter_kwargs(detection, ref_intervals, est_intervals, **kwargs) # Boundary deviation scores["Ref-to-est deviation"], scores["Est-to-ref deviation"] = util.filter_kwargs( deviation, ref_intervals, est_intervals, **kwargs ) # Pairwise clustering ( scores["Pairwise Precision"], scores["Pairwise Recall"], scores["Pairwise F-measure"], ) = util.filter_kwargs( pairwise, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) # Rand index scores["Rand Index"] = util.filter_kwargs( rand_index, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) # Adjusted rand index scores["Adjusted Rand Index"] = util.filter_kwargs( ari, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) # Mutual information metrics ( scores["Mutual Information"], scores["Adjusted Mutual Information"], scores["Normalized Mutual Information"], ) = util.filter_kwargs( mutual_information, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) # Conditional entropy metrics ( scores["NCE Over"], scores["NCE Under"], scores["NCE F-measure"], ) = util.filter_kwargs( nce, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) # V-measure metrics scores["V Precision"], scores["V Recall"], scores["V-measure"] = util.filter_kwargs( vmeasure, ref_intervals, ref_labels, est_intervals, est_labels, **kwargs ) return scores mir_eval-0.8.2/mir_eval/separation.py000066400000000000000000001124111475740344600176520ustar00rootroot00000000000000""" .. warning:: The mir_eval.separation module is deprecated in mir_eval version 0.8, and will be removed. We recommend that you migrate your code to use an alternative package such as sigsep-museval https://sigsep.github.io/sigsep-mus-eval/ Source separation algorithms attempt to extract recordings of individual sources from a recording of a mixture of sources. Evaluation methods for source separation compare the extracted sources from reference sources and attempt to measure the perceptual quality of the separation. See also the bss_eval MATLAB toolbox: http://bass-db.gforge.inria.fr/bss_eval/ Conventions ----------- An audio signal is expected to be in the format of a 1-dimensional array where the entries are the samples of the audio signal. When providing a group of estimated or reference sources, they should be provided in a 2-dimensional array, where the first dimension corresponds to the source number and the second corresponds to the samples. Metrics ------- * :func:`mir_eval.separation.bss_eval_sources`: Computes the bss_eval_sources metrics from bss_eval, which optionally optimally match the estimated sources to the reference sources and measure the distortion and artifacts present in the estimated sources as well as the interference between them. * :func:`mir_eval.separation.bss_eval_sources_framewise`: Computes the bss_eval_sources metrics on a frame-by-frame basis. * :func:`mir_eval.separation.bss_eval_images`: Computes the bss_eval_images metrics from bss_eval, which includes the metrics in :func:`mir_eval.separation.bss_eval_sources` plus the image to spatial distortion ratio. * :func:`mir_eval.separation.bss_eval_images_framewise`: Computes the bss_eval_images metrics on a frame-by-frame basis. References ---------- .. [#vincent2006performance] Emmanuel Vincent, Rémi Gribonval, and Cédric Févotte, "Performance measurement in blind audio source separation," IEEE Trans. on Audio, Speech and Language Processing, 14(4):1462-1469, 2006. """ import numpy as np import scipy.fftpack from scipy.linalg import toeplitz from scipy.signal import fftconvolve import collections import itertools import warnings from . import util # The maximum allowable number of sources (prevents insane computational load) MAX_SOURCES = 100 def validate(reference_sources, estimated_sources): """Check that the input data to a metric are valid, and throws helpful errors if not. Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing estimated sources """ if reference_sources.shape != estimated_sources.shape: raise ValueError( "The shape of estimated sources and the true " "sources should match. reference_sources.shape " "= {}, estimated_sources.shape " "= {}".format(reference_sources.shape, estimated_sources.shape) ) if reference_sources.ndim > 3 or estimated_sources.ndim > 3: raise ValueError( "The number of dimensions is too high (must be less " "than 3). reference_sources.ndim = {}, " "estimated_sources.ndim " "= {}".format(reference_sources.ndim, estimated_sources.ndim) ) if reference_sources.size == 0: warnings.warn( "reference_sources is empty, should be of size " "(nsrc, nsample). sdr, sir, sar, and perm will all " "be empty np.ndarrays" ) elif _any_source_silent(reference_sources): raise ValueError( "All the reference sources should be non-silent (not " "all-zeros), but at least one of the reference " "sources is all 0s, which introduces ambiguity to the" " evaluation. (Otherwise we can add infinitely many " "all-zero sources.)" ) if estimated_sources.size == 0: warnings.warn( "estimated_sources is empty, should be of size " "(nsrc, nsample). sdr, sir, sar, and perm will all " "be empty np.ndarrays" ) elif _any_source_silent(estimated_sources): raise ValueError( "All the estimated sources should be non-silent (not " "all-zeros), but at least one of the estimated " "sources is all 0s. Since we require each reference " "source to be non-silent, having a silent estimated " "source will result in an underdetermined system." ) if ( estimated_sources.shape[0] > MAX_SOURCES or reference_sources.shape[0] > MAX_SOURCES ): raise ValueError( "The supplied matrices should be of shape (nsrc," " nsampl) but reference_sources.shape[0] = {} and " "estimated_sources.shape[0] = {} which is greater " "than mir_eval.separation.MAX_SOURCES = {}. To " "override this check, set " "mir_eval.separation.MAX_SOURCES to a " "larger value.".format( reference_sources.shape[0], estimated_sources.shape[0], MAX_SOURCES ) ) def _any_source_silent(sources): """Return true if the parameter sources has any silent first dimensions""" return np.any( np.all(np.sum(sources, axis=tuple(range(2, sources.ndim))) == 0, axis=1) ) @util.deprecated(version="0.8", version_removed="0.9") def bss_eval_sources(reference_sources, estimated_sources, compute_permutation=True): """ Ordering and measurement of the separation quality for estimated source signals in terms of filtered true source, interference and artifacts. The decomposition allows a time-invariant filter distortion of length 512, as described in Section III.B of [#vincent2006performance]_. Passing ``False`` for ``compute_permutation`` will improve the computation performance of the evaluation; however, it is not always appropriate and is not the way that the BSS_EVAL Matlab toolbox computes bss_eval_sources. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> # source >>> (sdr, sir, sar, ... perm) = mir_eval.separation.bss_eval_sources(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing true sources (must have same shape as estimated_sources) estimated_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing estimated sources (must have same shape as reference_sources) compute_permutation : bool, optional compute permutation of estimate/source combinations (True by default) Returns ------- sdr : np.ndarray, shape=(nsrc,) vector of Signal to Distortion Ratios (SDR) sir : np.ndarray, shape=(nsrc,) vector of Source to Interference Ratios (SIR) sar : np.ndarray, shape=(nsrc,) vector of Sources to Artifacts Ratios (SAR) perm : np.ndarray, shape=(nsrc,) vector containing the best ordering of estimated sources in the mean SIR sense (estimated source number ``perm[j]`` corresponds to true source number ``j``). Note: ``perm`` will be ``[0, 1, ..., nsrc-1]`` if ``compute_permutation`` is ``False``. References ---------- .. [#] Emmanuel Vincent, Shoko Araki, Fabian J. Theis, Guido Nolte, Pau Bofill, Hiroshi Sawada, Alexey Ozerov, B. Vikrham Gowreesunker, Dominik Lutter and Ngoc Q.K. Duong, "The Signal Separation Evaluation Campaign (2007-2010): Achievements and remaining challenges", Signal Processing, 92, pp. 1928-1936, 2012. """ # make sure the input is of shape (nsrc, nsampl) if estimated_sources.ndim == 1: estimated_sources = estimated_sources[np.newaxis, :] if reference_sources.ndim == 1: reference_sources = reference_sources[np.newaxis, :] validate(reference_sources, estimated_sources) # If empty matrices were supplied, return empty lists (special case) if reference_sources.size == 0 or estimated_sources.size == 0: return np.array([]), np.array([]), np.array([]), np.array([]) nsrc = estimated_sources.shape[0] # does user desire permutations? if compute_permutation: # compute criteria for all possible pair matches sdr = np.empty((nsrc, nsrc)) sir = np.empty((nsrc, nsrc)) sar = np.empty((nsrc, nsrc)) for jest in range(nsrc): for jtrue in range(nsrc): s_true, e_spat, e_interf, e_artif = _bss_decomp_mtifilt( reference_sources, estimated_sources[jest], jtrue, 512 ) sdr[jest, jtrue], sir[jest, jtrue], sar[jest, jtrue] = _bss_source_crit( s_true, e_spat, e_interf, e_artif ) # select the best ordering perms = list(itertools.permutations(list(range(nsrc)))) mean_sir = np.empty(len(perms)) dum = np.arange(nsrc) for i, perm in enumerate(perms): mean_sir[i] = np.mean(sir[perm, dum]) popt = perms[np.argmax(mean_sir)] idx = (popt, dum) return (sdr[idx], sir[idx], sar[idx], np.asarray(popt)) else: # compute criteria for only the simple correspondence # (estimate 1 is estimate corresponding to reference source 1, etc.) sdr = np.empty(nsrc) sir = np.empty(nsrc) sar = np.empty(nsrc) for j in range(nsrc): s_true, e_spat, e_interf, e_artif = _bss_decomp_mtifilt( reference_sources, estimated_sources[j], j, 512 ) sdr[j], sir[j], sar[j] = _bss_source_crit(s_true, e_spat, e_interf, e_artif) # return the default permutation for compatibility popt = np.arange(nsrc) return (sdr, sir, sar, popt) @util.deprecated(version="0.8", version_removed="0.9") def bss_eval_sources_framewise( reference_sources, estimated_sources, window=30 * 44100, hop=15 * 44100, compute_permutation=False, ): """Framewise computation of bss_eval_sources Please be aware that this function does not compute permutations (by default) on the possible relations between reference_sources and estimated_sources due to the dangers of a changing permutation. Therefore (by default), it assumes that ``reference_sources[i]`` corresponds to ``estimated_sources[i]``. To enable computing permutations please set ``compute_permutation`` to be ``True`` and check that the returned ``perm`` is identical for all windows. NOTE: if ``reference_sources`` and ``estimated_sources`` would be evaluated using only a single window or are shorter than the window length, the result of :func:`mir_eval.separation.bss_eval_sources` called on ``reference_sources`` and ``estimated_sources`` (with the ``compute_permutation`` parameter passed to :func:`mir_eval.separation.bss_eval_sources`) is returned. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> # source >>> (sdr, sir, sar, ... perm) = mir_eval.separation.bss_eval_sources_framewise( reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing true sources (must have the same shape as ``estimated_sources``) estimated_sources : np.ndarray, shape=(nsrc, nsampl) matrix containing estimated sources (must have the same shape as ``reference_sources``) window : int, optional Window length for framewise evaluation (default value is 30s at a sample rate of 44.1kHz) hop : int, optional Hop size for framewise evaluation (default value is 15s at a sample rate of 44.1kHz) compute_permutation : bool, optional compute permutation of estimate/source combinations for all windows (False by default) Returns ------- sdr : np.ndarray, shape=(nsrc, nframes) vector of Signal to Distortion Ratios (SDR) sir : np.ndarray, shape=(nsrc, nframes) vector of Source to Interference Ratios (SIR) sar : np.ndarray, shape=(nsrc, nframes) vector of Sources to Artifacts Ratios (SAR) perm : np.ndarray, shape=(nsrc, nframes) vector containing the best ordering of estimated sources in the mean SIR sense (estimated source number ``perm[j]`` corresponds to true source number ``j``). Note: ``perm`` will be ``range(nsrc)`` for all windows if ``compute_permutation`` is ``False`` """ # make sure the input is of shape (nsrc, nsampl) if estimated_sources.ndim == 1: estimated_sources = estimated_sources[np.newaxis, :] if reference_sources.ndim == 1: reference_sources = reference_sources[np.newaxis, :] validate(reference_sources, estimated_sources) # If empty matrices were supplied, return empty lists (special case) if reference_sources.size == 0 or estimated_sources.size == 0: return np.array([]), np.array([]), np.array([]), np.array([]) nsrc = reference_sources.shape[0] nwin = int(np.floor((reference_sources.shape[1] - window + hop) / hop)) # if fewer than 2 windows would be evaluated, return the sources result if nwin < 2: result = bss_eval_sources( reference_sources, estimated_sources, compute_permutation ) return [np.expand_dims(score, -1) for score in result] # compute the criteria across all windows sdr = np.empty((nsrc, nwin)) sir = np.empty((nsrc, nwin)) sar = np.empty((nsrc, nwin)) perm = np.empty((nsrc, nwin)) # k iterates across all the windows for k in range(nwin): win_slice = slice(k * hop, k * hop + window) ref_slice = reference_sources[:, win_slice] est_slice = estimated_sources[:, win_slice] # check for a silent frame if not _any_source_silent(ref_slice) and not _any_source_silent(est_slice): sdr[:, k], sir[:, k], sar[:, k], perm[:, k] = bss_eval_sources( ref_slice, est_slice, compute_permutation ) else: # if we have a silent frame set results as np.nan sdr[:, k] = sir[:, k] = sar[:, k] = perm[:, k] = np.nan return sdr, sir, sar, perm @util.deprecated(version="0.8", version_removed="0.9") def bss_eval_images(reference_sources, estimated_sources, compute_permutation=True): """Compute the bss_eval_images function from the BSS_EVAL Matlab toolbox. Ordering and measurement of the separation quality for estimated source signals in terms of filtered true source, interference and artifacts. This method also provides the ISR measure. The decomposition allows a time-invariant filter distortion of length 512, as described in Section III.B of [#vincent2006performance]_. Passing ``False`` for ``compute_permutation`` will improve the computation performance of the evaluation; however, it is not always appropriate and is not the way that the BSS_EVAL Matlab toolbox computes bss_eval_images. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> # source >>> (sdr, isr, sir, sar, ... perm) = mir_eval.separation.bss_eval_images(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl, nchan) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl, nchan) matrix containing estimated sources compute_permutation : bool, optional compute permutation of estimate/source combinations (True by default) Returns ------- sdr : np.ndarray, shape=(nsrc,) vector of Signal to Distortion Ratios (SDR) isr : np.ndarray, shape=(nsrc,) vector of source Image to Spatial distortion Ratios (ISR) sir : np.ndarray, shape=(nsrc,) vector of Source to Interference Ratios (SIR) sar : np.ndarray, shape=(nsrc,) vector of Sources to Artifacts Ratios (SAR) perm : np.ndarray, shape=(nsrc,) vector containing the best ordering of estimated sources in the mean SIR sense (estimated source number ``perm[j]`` corresponds to true source number ``j``). Note: ``perm`` will be ``(1,2,...,nsrc)`` if ``compute_permutation`` is ``False``. References ---------- .. [#] Emmanuel Vincent, Shoko Araki, Fabian J. Theis, Guido Nolte, Pau Bofill, Hiroshi Sawada, Alexey Ozerov, B. Vikrham Gowreesunker, Dominik Lutter and Ngoc Q.K. Duong, "The Signal Separation Evaluation Campaign (2007-2010): Achievements and remaining challenges", Signal Processing, 92, pp. 1928-1936, 2012. """ # make sure the input has 3 dimensions # assuming input is in shape (nsampl) or (nsrc, nsampl) estimated_sources = np.atleast_3d(estimated_sources) reference_sources = np.atleast_3d(reference_sources) # we will ensure input doesn't have more than 3 dimensions in validate validate(reference_sources, estimated_sources) # If empty matrices were supplied, return empty lists (special case) if reference_sources.size == 0 or estimated_sources.size == 0: return np.array([]), np.array([]), np.array([]), np.array([]), np.array([]) # determine size parameters nsrc = estimated_sources.shape[0] nsampl = estimated_sources.shape[1] nchan = estimated_sources.shape[2] # does the user desire permutation? if compute_permutation: # compute criteria for all possible pair matches sdr = np.empty((nsrc, nsrc)) isr = np.empty((nsrc, nsrc)) sir = np.empty((nsrc, nsrc)) sar = np.empty((nsrc, nsrc)) for jest in range(nsrc): for jtrue in range(nsrc): s_true, e_spat, e_interf, e_artif = _bss_decomp_mtifilt_images( reference_sources, np.reshape(estimated_sources[jest], (nsampl, nchan), order="F"), jtrue, 512, ) ( sdr[jest, jtrue], isr[jest, jtrue], sir[jest, jtrue], sar[jest, jtrue], ) = _bss_image_crit(s_true, e_spat, e_interf, e_artif) # select the best ordering perms = list(itertools.permutations(list(range(nsrc)))) mean_sir = np.empty(len(perms)) dum = np.arange(nsrc) for i, perm in enumerate(perms): mean_sir[i] = np.mean(sir[perm, dum]) popt = perms[np.argmax(mean_sir)] idx = (popt, dum) return (sdr[idx], isr[idx], sir[idx], sar[idx], np.asarray(popt)) else: # compute criteria for only the simple correspondence # (estimate 1 is estimate corresponding to reference source 1, etc.) sdr = np.empty(nsrc) isr = np.empty(nsrc) sir = np.empty(nsrc) sar = np.empty(nsrc) Gj = [0] * nsrc # prepare G matrices with zeroes G = np.zeros(1) for j in range(nsrc): # save G matrix to avoid recomputing it every call s_true, e_spat, e_interf, e_artif, Gj_temp, G = _bss_decomp_mtifilt_images( reference_sources, np.reshape(estimated_sources[j], (nsampl, nchan), order="F"), j, 512, Gj[j], G, ) Gj[j] = Gj_temp sdr[j], isr[j], sir[j], sar[j] = _bss_image_crit( s_true, e_spat, e_interf, e_artif ) # return the default permutation for compatibility popt = np.arange(nsrc) return (sdr, isr, sir, sar, popt) @util.deprecated(version="0.8", version_removed="0.9") def bss_eval_images_framewise( reference_sources, estimated_sources, window=30 * 44100, hop=15 * 44100, compute_permutation=False, ): """Framewise computation of bss_eval_images Please be aware that this function does not compute permutations (by default) on the possible relations between ``reference_sources`` and ``estimated_sources`` due to the dangers of a changing permutation. Therefore (by default), it assumes that ``reference_sources[i]`` corresponds to ``estimated_sources[i]``. To enable computing permutations please set ``compute_permutation`` to be ``True`` and check that the returned ``perm`` is identical for all windows. NOTE: if ``reference_sources`` and ``estimated_sources`` would be evaluated using only a single window or are shorter than the window length, the result of ``bss_eval_images`` called on ``reference_sources`` and ``estimated_sources`` (with the ``compute_permutation`` parameter passed to ``bss_eval_images``) is returned Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated >>> # source >>> (sdr, isr, sir, sar, ... perm) = mir_eval.separation.bss_eval_images_framewise( reference_sources, ... estimated_sources, window, .... hop) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl, nchan) matrix containing true sources (must have the same shape as ``estimated_sources``) estimated_sources : np.ndarray, shape=(nsrc, nsampl, nchan) matrix containing estimated sources (must have the same shape as ``reference_sources``) window : int Window length for framewise evaluation hop : int Hop size for framewise evaluation compute_permutation : bool, optional compute permutation of estimate/source combinations for all windows (False by default) Returns ------- sdr : np.ndarray, shape=(nsrc, nframes) vector of Signal to Distortion Ratios (SDR) isr : np.ndarray, shape=(nsrc, nframes) vector of source Image to Spatial distortion Ratios (ISR) sir : np.ndarray, shape=(nsrc, nframes) vector of Source to Interference Ratios (SIR) sar : np.ndarray, shape=(nsrc, nframes) vector of Sources to Artifacts Ratios (SAR) perm : np.ndarray, shape=(nsrc, nframes) vector containing the best ordering of estimated sources in the mean SIR sense (estimated source number perm[j] corresponds to true source number j) Note: perm will be range(nsrc) for all windows if compute_permutation is False """ # make sure the input has 3 dimensions # assuming input is in shape (nsampl) or (nsrc, nsampl) estimated_sources = np.atleast_3d(estimated_sources) reference_sources = np.atleast_3d(reference_sources) # we will ensure input doesn't have more than 3 dimensions in validate validate(reference_sources, estimated_sources) # If empty matrices were supplied, return empty lists (special case) if reference_sources.size == 0 or estimated_sources.size == 0: return np.array([]), np.array([]), np.array([]), np.array([]) nsrc = reference_sources.shape[0] nwin = int(np.floor((reference_sources.shape[1] - window + hop) / hop)) # if fewer than 2 windows would be evaluated, return the images result if nwin < 2: result = bss_eval_images( reference_sources, estimated_sources, compute_permutation ) return [np.expand_dims(score, -1) for score in result] # compute the criteria across all windows sdr = np.empty((nsrc, nwin)) isr = np.empty((nsrc, nwin)) sir = np.empty((nsrc, nwin)) sar = np.empty((nsrc, nwin)) perm = np.empty((nsrc, nwin)) # k iterates across all the windows for k in range(nwin): win_slice = slice(k * hop, k * hop + window) ref_slice = reference_sources[:, win_slice, :] est_slice = estimated_sources[:, win_slice, :] # check for a silent frame if not _any_source_silent(ref_slice) and not _any_source_silent(est_slice): sdr[:, k], isr[:, k], sir[:, k], sar[:, k], perm[:, k] = bss_eval_images( ref_slice, est_slice, compute_permutation ) else: # if we have a silent frame set results as np.nan sdr[:, k] = sir[:, k] = sar[:, k] = perm[:, k] = np.nan return sdr, isr, sir, sar, perm def _bss_decomp_mtifilt(reference_sources, estimated_source, j, flen): """Decomposition of an estimated source image into four components representing respectively the true source image, spatial (or filtering) distortion, interference and artifacts, derived from the true source images using multichannel time-invariant filters. """ nsampl = estimated_source.size # decomposition # true source image s_true = np.hstack((reference_sources[j], np.zeros(flen - 1))) # spatial (or filtering) distortion e_spat = ( _project(reference_sources[j, np.newaxis, :], estimated_source, flen) - s_true ) # interference e_interf = _project(reference_sources, estimated_source, flen) - s_true - e_spat # artifacts e_artif = -s_true - e_spat - e_interf e_artif[:nsampl] += estimated_source return (s_true, e_spat, e_interf, e_artif) def _bss_decomp_mtifilt_images( reference_sources, estimated_source, j, flen, Gj=None, G=None ): """Decomposition of an estimated source image into four components representing respectively the true source image, spatial (or filtering) distortion, interference and artifacts, derived from the true source images using multichannel time-invariant filters. Adapted version to work with multichannel sources. Improved performance can be gained by passing Gj and G parameters initially as all zeros. These parameters store the results from the computation of the G matrix in _project_images and then return them for subsequent calls to this function. This only works when not computing permutations. """ nsampl = np.shape(estimated_source)[0] nchan = np.shape(estimated_source)[1] # are we saving the Gj and G parameters? saveg = Gj is not None and G is not None # decomposition # true source image s_true = np.hstack( ( np.reshape(reference_sources[j], (nsampl, nchan), order="F").transpose(), np.zeros((nchan, flen - 1)), ) ) # spatial (or filtering) distortion if saveg: e_spat, Gj = _project_images( reference_sources[j, np.newaxis, :], estimated_source, flen, Gj ) else: e_spat = _project_images( reference_sources[j, np.newaxis, :], estimated_source, flen ) e_spat = e_spat - s_true # interference if saveg: e_interf, G = _project_images(reference_sources, estimated_source, flen, G) else: e_interf = _project_images(reference_sources, estimated_source, flen) e_interf = e_interf - s_true - e_spat # artifacts e_artif = -s_true - e_spat - e_interf e_artif[:, :nsampl] += estimated_source.transpose() # return Gj and G only if they were passed in if saveg: return (s_true, e_spat, e_interf, e_artif, Gj, G) else: return (s_true, e_spat, e_interf, e_artif) def _project(reference_sources, estimated_source, flen): """Least-squares projection of estimated source on the subspace spanned by delayed versions of reference sources, with delays between 0 and flen-1 """ nsrc = reference_sources.shape[0] nsampl = reference_sources.shape[1] # computing coefficients of least squares problem via FFT ## # zero padding and FFT of input data reference_sources = np.hstack((reference_sources, np.zeros((nsrc, flen - 1)))) estimated_source = np.hstack((estimated_source, np.zeros(flen - 1))) n_fft = int(2 ** np.ceil(np.log2(nsampl + flen - 1.0))) sf = scipy.fftpack.fft(reference_sources, n=n_fft, axis=1) sef = scipy.fftpack.fft(estimated_source, n=n_fft) # inner products between delayed versions of reference_sources G = np.zeros((nsrc * flen, nsrc * flen)) for i in range(nsrc): for j in range(nsrc): ssf = sf[i] * np.conj(sf[j]) ssf = np.real(scipy.fftpack.ifft(ssf)) ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen]) G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T # inner products between estimated_source and delayed versions of # reference_sources D = np.zeros(nsrc * flen) for i in range(nsrc): ssef = sf[i] * np.conj(sef) ssef = np.real(scipy.fftpack.ifft(ssef)) D[i * flen : (i + 1) * flen] = np.hstack((ssef[0], ssef[-1:-flen:-1])) # Computing projection # Distortion filters try: C = np.linalg.solve(G, D).reshape(flen, nsrc, order="F") except np.linalg.linalg.LinAlgError: C = np.linalg.lstsq(G, D)[0].reshape(flen, nsrc, order="F") # Filtering sproj = np.zeros(nsampl + flen - 1) for i in range(nsrc): sproj += fftconvolve(C[:, i], reference_sources[i])[: nsampl + flen - 1] return sproj def _project_images(reference_sources, estimated_source, flen, G=None): """Least-squares projection of estimated source on the subspace spanned by delayed versions of reference sources, with delays between 0 and flen-1. Passing G as all zeros will populate the G matrix and return it so it can be passed into the next call to avoid recomputing G (this will only works if not computing permutations). """ nsrc = reference_sources.shape[0] nsampl = reference_sources.shape[1] nchan = reference_sources.shape[2] reference_sources = np.reshape( np.transpose(reference_sources, (2, 0, 1)), (nchan * nsrc, nsampl), order="F" ) # computing coefficients of least squares problem via FFT ## # zero padding and FFT of input data reference_sources = np.hstack( (reference_sources, np.zeros((nchan * nsrc, flen - 1))) ) estimated_source = np.hstack( (estimated_source.transpose(), np.zeros((nchan, flen - 1))) ) n_fft = int(2 ** np.ceil(np.log2(nsampl + flen - 1.0))) sf = scipy.fftpack.fft(reference_sources, n=n_fft, axis=1) sef = scipy.fftpack.fft(estimated_source, n=n_fft) # inner products between delayed versions of reference_sources if G is None: saveg = False G = np.zeros((nchan * nsrc * flen, nchan * nsrc * flen)) for i in range(nchan * nsrc): for j in range(i + 1): ssf = sf[i] * np.conj(sf[j]) ssf = np.real(scipy.fftpack.ifft(ssf)) ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen]) G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T else: # avoid recomputing G (only works if no permutation is desired) saveg = True # return G if np.all(G == 0): # only compute G if passed as 0 G = np.zeros((nchan * nsrc * flen, nchan * nsrc * flen)) for i in range(nchan * nsrc): for j in range(i + 1): ssf = sf[i] * np.conj(sf[j]) ssf = np.real(scipy.fftpack.ifft(ssf)) ss = toeplitz(np.hstack((ssf[0], ssf[-1:-flen:-1])), r=ssf[:flen]) G[i * flen : (i + 1) * flen, j * flen : (j + 1) * flen] = ss G[j * flen : (j + 1) * flen, i * flen : (i + 1) * flen] = ss.T # inner products between estimated_source and delayed versions of # reference_sources D = np.zeros((nchan * nsrc * flen, nchan)) for k in range(nchan * nsrc): for i in range(nchan): ssef = sf[k] * np.conj(sef[i]) ssef = np.real(scipy.fftpack.ifft(ssef)) D[k * flen : (k + 1) * flen, i] = np.hstack( (ssef[0], ssef[-1:-flen:-1]) ).transpose() # Computing projection # Distortion filters try: C = np.linalg.solve(G, D).reshape(flen, nchan * nsrc, nchan, order="F") except np.linalg.linalg.LinAlgError: C = np.linalg.lstsq(G, D)[0].reshape(flen, nchan * nsrc, nchan, order="F") # Filtering sproj = np.zeros((nchan, nsampl + flen - 1)) for k in range(nchan * nsrc): for i in range(nchan): sproj[i] += fftconvolve(C[:, k, i].transpose(), reference_sources[k])[ : nsampl + flen - 1 ] # return G only if it was passed in if saveg: return sproj, G else: return sproj def _bss_source_crit(s_true, e_spat, e_interf, e_artif): """Measurement of the separation quality for a given source in terms of filtered true source, interference and artifacts. """ # energy ratios s_filt = s_true + e_spat sdr = _safe_db(np.sum(s_filt**2), np.sum((e_interf + e_artif) ** 2)) sir = _safe_db(np.sum(s_filt**2), np.sum(e_interf**2)) sar = _safe_db(np.sum((s_filt + e_interf) ** 2), np.sum(e_artif**2)) return (sdr, sir, sar) def _bss_image_crit(s_true, e_spat, e_interf, e_artif): """Measurement of the separation quality for a given image in terms of filtered true source, spatial error, interference and artifacts. """ # energy ratios sdr = _safe_db(np.sum(s_true**2), np.sum((e_spat + e_interf + e_artif) ** 2)) isr = _safe_db(np.sum(s_true**2), np.sum(e_spat**2)) sir = _safe_db(np.sum((s_true + e_spat) ** 2), np.sum(e_interf**2)) sar = _safe_db(np.sum((s_true + e_spat + e_interf) ** 2), np.sum(e_artif**2)) return (sdr, isr, sir, sar) def _safe_db(num, den): """Properly handle the potential +Inf db SIR, instead of raising a RuntimeWarning. Only denominator is checked because the numerator can never be 0. """ if den == 0: return np.inf return 10 * np.log10(num / den) @util.deprecated(version="0.8", version_removed="0.9") def evaluate(reference_sources, estimated_sources, **kwargs): """Compute all metrics for the given reference and estimated signals. NOTE: This will always compute :func:`mir_eval.separation.bss_eval_images` for any valid input and will additionally compute :func:`mir_eval.separation.bss_eval_sources` for valid input with fewer than 3 dimensions. Examples -------- >>> # reference_sources[n] should be an ndarray of samples of the >>> # n'th reference source >>> # estimated_sources[n] should be the same for the n'th estimated source >>> scores = mir_eval.separation.evaluate(reference_sources, ... estimated_sources) Parameters ---------- reference_sources : np.ndarray, shape=(nsrc, nsampl[, nchan]) matrix containing true sources estimated_sources : np.ndarray, shape=(nsrc, nsampl[, nchan]) matrix containing estimated sources **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() sdr, isr, sir, sar, perm = util.filter_kwargs( bss_eval_images, reference_sources, estimated_sources, **kwargs ) scores["Images - Source to Distortion"] = sdr.tolist() scores["Images - Image to Spatial"] = isr.tolist() scores["Images - Source to Interference"] = sir.tolist() scores["Images - Source to Artifact"] = sar.tolist() scores["Images - Source permutation"] = perm.tolist() sdr, isr, sir, sar, perm = util.filter_kwargs( bss_eval_images_framewise, reference_sources, estimated_sources, **kwargs ) scores["Images Frames - Source to Distortion"] = sdr.tolist() scores["Images Frames - Image to Spatial"] = isr.tolist() scores["Images Frames - Source to Interference"] = sir.tolist() scores["Images Frames - Source to Artifact"] = sar.tolist() scores["Images Frames - Source permutation"] = perm.tolist() # Verify we can compute sources on this input if reference_sources.ndim < 3 and estimated_sources.ndim < 3: sdr, sir, sar, perm = util.filter_kwargs( bss_eval_sources_framewise, reference_sources, estimated_sources, **kwargs ) scores["Sources Frames - Source to Distortion"] = sdr.tolist() scores["Sources Frames - Source to Interference"] = sir.tolist() scores["Sources Frames - Source to Artifact"] = sar.tolist() scores["Sources Frames - Source permutation"] = perm.tolist() sdr, sir, sar, perm = util.filter_kwargs( bss_eval_sources, reference_sources, estimated_sources, **kwargs ) scores["Sources - Source to Distortion"] = sdr.tolist() scores["Sources - Source to Interference"] = sir.tolist() scores["Sources - Source to Artifact"] = sar.tolist() scores["Sources - Source permutation"] = perm.tolist() return scores mir_eval-0.8.2/mir_eval/sonify.py000066400000000000000000000310151475740344600170140ustar00rootroot00000000000000""" Methods which sonify annotations for "evaluation by ear". All functions return a raw signal at the specified sampling rate. """ import numpy as np import scipy.signal from numpy.lib.stride_tricks import as_strided from scipy.interpolate import interp1d from . import util from . import chord def clicks(times, fs, click=None, length=None): """Return a signal with the signal 'click' placed at each specified time Parameters ---------- times : np.ndarray times to place clicks, in seconds fs : int desired sampling rate of the output signal click : np.ndarray click signal, defaults to a 1 kHz blip length : int desired number of samples in the output signal, defaults to ``times.max()*fs + click.shape[0] + 1`` Returns ------- click_signal : np.ndarray Synthesized click signal """ # Create default click signal if click is None: # 1 kHz tone, 100ms click = np.sin(2 * np.pi * np.arange(fs * 0.1) * 1000 / (1.0 * fs)) # Exponential decay click *= np.exp(-np.arange(fs * 0.1) / (fs * 0.01)) # Set default length if length is None: length = int(times.max() * fs + click.shape[0] + 1) # Pre-allocate click signal click_signal = np.zeros(length) # Place clicks for time in times: # Compute the boundaries of the click start = int(time * fs) end = start + click.shape[0] # Make sure we don't try to output past the end of the signal if start >= length: break if end >= length: click_signal[start:] = click[: length - start] break # Normally, just add a click here click_signal[start:end] = click return click_signal def time_frequency( gram, frequencies, times, fs, function=np.sin, length=None, n_dec=1, threshold=0.01 ): r"""Reverse synthesis of a time-frequency representation of a signal Parameters ---------- gram : np.ndarray ``gram[n, m]`` is the magnitude of ``frequencies[n]`` from ``times[m]`` to ``times[m + 1]`` Non-positive magnitudes are interpreted as silence. frequencies : np.ndarray array of size ``gram.shape[0]`` denoting the frequency (in Hz) of each row of gram times : np.ndarray, shape= ``(gram.shape[1],)`` or ``(gram.shape[1], 2)`` Either the start time (in seconds) of each column in the gram, or the time interval (in seconds) corresponding to each column. fs : int desired sampling rate of the output signal function : function function to use to synthesize notes, should be :math:`2\pi`-periodic length : int desired number of samples in the output signal, defaults to ``times[-1]*fs`` n_dec : int the number of decimals used to approximate each sonfied frequency. Defaults to 1 decimal place. Higher precision will be slower. threshold : float optimizes synthesis to only occur for frequencies that have a linear magnitude of at least one element in gram above the given threshold. Returns ------- output : np.ndarray synthesized version of the piano roll """ # Convert times to intervals if necessary time_converted = False if times.ndim == 1: # Convert to intervals times = np.hstack((times[:-1, np.newaxis], times[1:, np.newaxis])) # We'll need this to keep track of whether we should pad an interval on time_converted = True # Default value for length if length is None: length = int(np.max(times) * fs) last_time_in_secs = float(length) / fs if time_converted and times.shape[0] != gram.shape[1]: times = np.vstack((times, [np.max(times), last_time_in_secs])) if times.shape[0] != gram.shape[1]: raise ValueError( f"times.shape={times.shape} is incompatible with gram.shape={gram.shape}" ) if frequencies.shape[0] != gram.shape[0]: raise ValueError( f"frequencies.shape={frequencies.shape} is incompatible with gram.shape={gram.shape}" ) padding = [0, 0] stacking = [] if times.min() > 0: # We need to pad a silence column on to gram at the beginning padding[0] = 1 stacking.append([0, times.min()]) stacking.append(times) if times.max() < last_time_in_secs: # We need to pad a silence column onto gram at the end padding[1] = 1 stacking.append([times.max(), last_time_in_secs]) gram = np.pad(gram, ((0, 0), padding), mode="constant") times = np.vstack(stacking) # Identify the time intervals that have some overlap with the duration idx = np.logical_and(times[:, 1] >= 0, times[:, 0] <= last_time_in_secs) gram = gram[:, idx] times = np.clip(times[idx], 0, last_time_in_secs) n_times = times.shape[0] # Threshold the tfgram to remove negative values gram = np.maximum(gram, 0) # Pre-allocate output signal output = np.zeros(length) if gram.shape[1] == 0: # There are no time intervals to process, so return # the empty signal. return output # Discard frequencies below threshold freq_keep = np.max(gram, axis=1) >= threshold gram = gram[freq_keep, :] frequencies = frequencies[freq_keep] # Interpolate the values in gram over the time grid. if n_times > 1: interpolator = interp1d( times[:, 0] * fs, gram[:, :n_times], kind="previous", bounds_error=False, fill_value=(gram[:, 0], gram[:, -1]), ) signal = interpolator(np.arange(length)) else: # NOTE: This is a special case where there is only one time interval. # scipy 1.10 and above handle this case directly with the interp1d above, # but older scipy's do not. This is a workaround for that. # # In the 0.9 release, we can bump the minimum scipy to 1.10 and remove this signal = np.tile(gram[:, 0], (1, length)) for n, frequency in enumerate(frequencies): # Get a waveform of length samples at this frequency wave = _fast_synthesize(frequency, n_dec, fs, function, length) # Use a two-cycle ramp to smooth over transients period = 2 * int(fs / frequency) filter = np.ones(period) / period signal_n = scipy.signal.convolve(signal[n], filter, mode="same") # Mix the signal into the output output[:] += wave[: len(signal_n)] * signal_n # Normalize, but only if there's non-zero values norm = np.abs(output).max() if norm >= np.finfo(output.dtype).tiny: output /= norm return output def _fast_synthesize(frequency, n_dec, fs, function, length): """Efficiently synthesize a signal. Generate one cycle, and simulate arbitrary repetitions using array indexing tricks. """ # hack so that we can ensure an integer number of periods and samples # rounds frequency to 1st decimal, s.t. 10 * frequency will be an int frequency = np.round(frequency, n_dec) # Generate 10*frequency periods at this frequency # Equivalent to n_samples = int(n_periods * fs / frequency) # n_periods = 10*frequency is the smallest integer that guarantees # that n_samples will be an integer, since assuming 10*frequency # is an integer n_samples = int(10.0**n_dec * fs) short_signal = function(2.0 * np.pi * np.arange(n_samples) * frequency / fs) # Calculate the number of loops we need to fill the duration n_repeats = int(np.ceil(length / float(short_signal.shape[0]))) # Simulate tiling the short buffer by using stride tricks long_signal = as_strided( short_signal, shape=(n_repeats, len(short_signal)), strides=(0, short_signal.itemsize), ) # Use a flatiter to simulate a long 1D buffer return long_signal.flat def pitch_contour( times, frequencies, fs, amplitudes=None, function=np.sin, length=None, kind="linear" ): r"""Sonify a pitch contour. Parameters ---------- times : np.ndarray time indices for each frequency measurement, in seconds frequencies : np.ndarray frequency measurements, in Hz. Non-positive measurements or NaNs will be interpreted as un-voiced samples. fs : int desired sampling rate of the output signal amplitudes : np.ndarray amplitude measurements, nonnegative defaults to ``np.ones((length,))`` function : function function to use to synthesize notes, should be :math:`2\pi`-periodic length : int desired number of samples in the output signal, defaults to ``max(times)*fs`` kind : str Interpolation mode for the frequency and amplitude values. See: ``scipy.interpolate.interp1d`` for valid settings. Returns ------- output : np.ndarray synthesized version of the pitch contour """ fs = float(fs) if length is None: length = int(times.max() * fs) # Squash the negative frequencies. # wave(0) = 0, so clipping here will un-voice the corresponding instants frequencies = np.maximum(frequencies, 0.0) # Convert nans to zeros to unvoice frequencies = np.nan_to_num(frequencies, copy=False) # Build a frequency interpolator f_interp = interp1d( times * fs, 2 * np.pi * frequencies / fs, kind=kind, fill_value=0.0, bounds_error=False, copy=False, ) # Estimate frequency at sample points f_est = f_interp(np.arange(length)) if amplitudes is None: a_est = np.ones((length,)) else: # build an amplitude interpolator a_interp = interp1d( times * fs, amplitudes, kind=kind, fill_value=0.0, bounds_error=False, copy=False, ) a_est = a_interp(np.arange(length)) # Sonify the waveform return a_est * function(np.cumsum(f_est)) def chroma(chromagram, times, fs, **kwargs): """Reverse synthesis of a chromagram (semitone matrix) Parameters ---------- chromagram : np.ndarray, shape=(12, times.shape[0]) Chromagram matrix, where each row represents a semitone [C->Bb] i.e., ``chromagram[3, j]`` is the magnitude of D# from ``times[j]`` to ``times[j + 1]`` times : np.ndarray, shape=(len(chord_labels),) or (len(chord_labels), 2) Either the start time of each column in the chromagram, or the time interval corresponding to each column. fs : int Sampling rate to synthesize audio data at **kwargs Additional keyword arguments to pass to :func:`mir_eval.sonify.time_frequency` Returns ------- output : np.ndarray Synthesized chromagram """ # We'll just use time_frequency with a Shepard tone-gram # To create the Shepard tone-gram, we copy the chromagram across 7 octaves n_octaves = 7 # starting from C2 base_note = 24 # and weight each octave by a normal distribution # The normal distribution has mean 72 (one octave above middle C) # and std 6 (one half octave) mean = 72 std = 6 notes = np.arange(12 * n_octaves) + base_note shepard_weight = np.exp(-((notes - mean) ** 2.0) / (2.0 * std**2.0)) # Copy the chromagram matrix vertically n_octaves times gram = np.tile(chromagram.T, n_octaves).T # This fixes issues if the supplied chromagram is int type gram = gram.astype(float) # Apply Sheppard weighting gram *= shepard_weight.reshape(-1, 1) # Compute frequencies frequencies = 440.0 * (2.0 ** ((notes - 69) / 12.0)) return time_frequency(gram, frequencies, times, fs, **kwargs) def chords(chord_labels, intervals, fs, **kwargs): """Synthesizes chord labels Parameters ---------- chord_labels : list of str List of chord label strings. intervals : np.ndarray, shape=(len(chord_labels), 2) Start and end times of each chord label fs : int Sampling rate to synthesize at **kwargs Additional keyword arguments to pass to :func:`mir_eval.sonify.time_frequency` Returns ------- output : np.ndarray Synthesized chord labels """ util.validate_intervals(intervals) # Convert from labels to chroma roots, interval_bitmaps, _ = chord.encode_many(chord_labels) chromagram = np.array( [ np.roll(interval_bitmap, root) for (interval_bitmap, root) in zip(interval_bitmaps, roots) ] ).T return chroma(chromagram, intervals, fs, **kwargs) mir_eval-0.8.2/mir_eval/tempo.py000066400000000000000000000121021475740344600166250ustar00rootroot00000000000000""" The goal of a tempo estimation algorithm is to automatically detect the tempo of a piece of music, measured in beats per minute (BPM). See http://www.music-ir.org/mirex/wiki/2014:Audio_Tempo_Estimation for a description of the task and evaluation criteria. Conventions ----------- Reference and estimated tempi should be positive, and provided in ascending order as a numpy array of length 2. The weighting value from the reference must be a float in the range [0, 1]. Metrics ------- * :func:`mir_eval.tempo.detection`: Relative error, hits, and weighted precision of tempo estimation. """ import warnings import numpy as np import collections from . import util def validate_tempi(tempi, reference=True): """Check that there are two non-negative tempi. For a reference value, at least one tempo has to be greater than zero. Parameters ---------- tempi : np.ndarray length-2 array of tempo, in bpm reference : bool indicates a reference value """ if tempi.size != 2: raise ValueError("tempi must have exactly two values") if not np.all(np.isfinite(tempi)) or np.any(tempi < 0): raise ValueError(f"tempi={tempi} must be non-negative numbers") if reference and np.all(tempi == 0): raise ValueError( "reference tempi={} must have one" " value greater than zero".format(tempi) ) def validate(reference_tempi, reference_weight, estimated_tempi): """Check that the input annotations to a metric look like valid tempo annotations. Parameters ---------- reference_tempi : np.ndarray reference tempo values, in bpm reference_weight : float perceptual weight of slow vs fast in reference estimated_tempi : np.ndarray estimated tempo values, in bpm """ validate_tempi(reference_tempi, reference=True) validate_tempi(estimated_tempi, reference=False) if reference_weight < 0 or reference_weight > 1: raise ValueError("Reference weight must lie in range [0, 1]") def detection(reference_tempi, reference_weight, estimated_tempi, tol=0.08): """Compute the tempo detection accuracy metric. Parameters ---------- reference_tempi : np.ndarray, shape=(2,) Two non-negative reference tempi reference_weight : float > 0 The relative strength of ``reference_tempi[0]`` vs ``reference_tempi[1]``. estimated_tempi : np.ndarray, shape=(2,) Two non-negative estimated tempi. tol : float in [0, 1]: The maximum allowable deviation from a reference tempo to count as a hit. ``|est_t - ref_t| <= tol * ref_t`` (Default value = 0.08) Returns ------- p_score : float in [0, 1] Weighted average of recalls: ``reference_weight * hits[0] + (1 - reference_weight) * hits[1]`` one_correct : bool True if at least one reference tempo was correctly estimated both_correct : bool True if both reference tempi were correctly estimated Raises ------ ValueError If the input tempi are ill-formed If the reference weight is not in the range [0, 1] If ``tol < 0`` or ``tol > 1``. """ validate(reference_tempi, reference_weight, estimated_tempi) if tol < 0 or tol > 1: raise ValueError( "invalid tolerance {}: must lie in the range " "[0, 1]".format(tol) ) if tol == 0.0: warnings.warn("A tolerance of 0.0 may not " "lead to the results you expect.") hits = [False, False] for i, ref_t in enumerate(reference_tempi): if ref_t > 0: # Compute the relative error for this reference tempo f_ref_t = float(ref_t) relative_error = np.min(np.abs(ref_t - estimated_tempi) / f_ref_t) # Count the hits hits[i] = relative_error <= tol p_score = reference_weight * hits[0] + (1.0 - reference_weight) * hits[1] one_correct = bool(np.max(hits)) both_correct = bool(np.min(hits)) return p_score, one_correct, both_correct def evaluate(reference_tempi, reference_weight, estimated_tempi, **kwargs): """Compute all metrics for the given reference and estimated annotations. Parameters ---------- reference_tempi : np.ndarray, shape=(2,) Two non-negative reference tempi reference_weight : float > 0 The relative strength of ``reference_tempi[0]`` vs ``reference_tempi[1]``. estimated_tempi : np.ndarray, shape=(2,) Two non-negative estimated tempi. **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all metrics scores = collections.OrderedDict() ( scores["P-score"], scores["One-correct"], scores["Both-correct"], ) = util.filter_kwargs( detection, reference_tempi, reference_weight, estimated_tempi, **kwargs ) return scores mir_eval-0.8.2/mir_eval/transcription.py000066400000000000000000001071511475740344600204110ustar00rootroot00000000000000""" The aim of a transcription algorithm is to produce a symbolic representation of a recorded piece of music in the form of a set of discrete notes. There are different ways to represent notes symbolically. Here we use the piano-roll convention, meaning each note has a start time, a duration (or end time), and a single, constant, pitch value. Pitch values can be quantized (e.g. to a semitone grid tuned to 440 Hz), but do not have to be. Also, the transcription can contain the notes of a single instrument or voice (for example the melody), or the notes of all instruments/voices in the recording. This module is instrument agnostic: all notes in the estimate are compared against all notes in the reference. There are many metrics for evaluating transcription algorithms. Here we limit ourselves to the most simple and commonly used: given two sets of notes, we count how many estimated notes match the reference, and how many do not. Based on these counts we compute the precision, recall, f-measure and overlap ratio of the estimate given the reference. The default criteria for considering two notes to be a match are adopted from the `MIREX Multiple fundamental frequency estimation and tracking, Note Tracking subtask (task 2) `_: "This subtask is evaluated in two different ways. In the first setup , a returned note is assumed correct if its onset is within +-50ms of a reference note and its F0 is within +- quarter tone of the corresponding reference note, ignoring the returned offset values. In the second setup, on top of the above requirements, a correct returned note is required to have an offset value within 20% of the reference note's duration around the reference note's offset, or within 50ms whichever is larger." In short, we compute precision, recall, f-measure and overlap ratio, once without taking offsets into account, and the second time with. For further details see Salamon, 2013 (page 186), and references therein: Salamon, J. (2013). Melody Extraction from Polyphonic Music Signals. Ph.D. thesis, Universitat Pompeu Fabra, Barcelona, Spain, 2013. IMPORTANT NOTE: the evaluation code in ``mir_eval`` contains several important differences with respect to the code used in MIREX 2015 for the Note Tracking subtask on the Su dataset (henceforth "MIREX"): 1. ``mir_eval`` uses bipartite graph matching to find the optimal pairing of reference notes to estimated notes. MIREX uses a greedy matching algorithm, which can produce sub-optimal note matching. This will result in ``mir_eval``'s metrics being slightly higher compared to MIREX. 2. MIREX rounds down the onset and offset times of each note to 2 decimal points using ``new_time = 0.01 * floor(time*100)``. ``mir_eval`` rounds down the note onset and offset times to 4 decinal points. This will bring our metrics down a notch compared to the MIREX results. 3. In the MIREX wiki, the criterion for matching offsets is that they must be within ``0.2 * ref_duration`` **or 0.05 seconds from each other, whichever is greater** (i.e. ``offset_dif <= max(0.2 * ref_duration, 0.05)``. The MIREX code however only uses a threshold of ``0.2 * ref_duration``, without the 0.05 second minimum. Since ``mir_eval`` does include this minimum, it might produce slightly higher results compared to MIREX. This means that differences 1 and 3 bring ``mir_eval``'s metrics up compared to MIREX, whilst 2 brings them down. Based on internal testing, overall the effect of these three differences is that the Precision, Recall and F-measure returned by ``mir_eval`` will be higher compared to MIREX by about 1%-2%. Finally, note that different evaluation scripts have been used for the Multi-F0 Note Tracking task in MIREX over the years. In particular, some scripts used ``<`` for matching onsets, offsets, and pitch values, whilst the others used ``<=`` for these checks. ``mir_eval`` provides both options: by default the latter (``<=``) is used, but you can set ``strict=True`` when calling :func:`mir_eval.transcription.precision_recall_f1_overlap()` in which case ``<`` will be used. The default value (``strict=False``) is the same as that used in MIREX 2015 for the Note Tracking subtask on the Su dataset. Conventions ----------- Notes should be provided in the form of an interval array and a pitch array. The interval array contains two columns, one for note onsets and the second for note offsets (each row represents a single note). The pitch array contains one column with the corresponding note pitch values (one value per note), represented by their fundamental frequency (f0) in Hertz. Metrics ------- * :func:`mir_eval.transcription.precision_recall_f1_overlap`: The precision, recall, F-measure, and Average Overlap Ratio of the note transcription, where an estimated note is considered correct if its pitch, onset and (optionally) offset are sufficiently close to a reference note. * :func:`mir_eval.transcription.onset_precision_recall_f1`: The precision, recall and F-measure of the note transcription, where an estimated note is considered correct if its onset is sufficiently close to a reference note's onset. That is, these metrics are computed taking only note onsets into account, meaning two notes could be matched even if they have very different pitch values. * :func:`mir_eval.transcription.offset_precision_recall_f1`: The precision, recall and F-measure of the note transcription, where an estimated note is considered correct if its offset is sufficiently close to a reference note's offset. That is, these metrics are computed taking only note offsets into account, meaning two notes could be matched even if they have very different pitch values. """ import numpy as np import collections from . import util import warnings # The number of decimals to keep for onset/offset threshold checks N_DECIMALS = 4 def validate(ref_intervals, ref_pitches, est_intervals, est_pitches): """Check that the input annotations to a metric look like time intervals and a pitch list, and throws helpful errors if not. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz """ # Validate intervals validate_intervals(ref_intervals, est_intervals) # Make sure intervals and pitches match in length if not ref_intervals.shape[0] == ref_pitches.shape[0]: raise ValueError("Reference intervals and pitches have different " "lengths.") if not est_intervals.shape[0] == est_pitches.shape[0]: raise ValueError("Estimated intervals and pitches have different " "lengths.") # Make sure all pitch values are positive if ref_pitches.size > 0 and np.min(ref_pitches) <= 0: raise ValueError("Reference contains at least one non-positive pitch " "value") if est_pitches.size > 0 and np.min(est_pitches) <= 0: raise ValueError("Estimate contains at least one non-positive pitch " "value") def validate_intervals(ref_intervals, est_intervals): """Check that the input annotations to a metric look like time intervals, and throws helpful errors if not. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) """ # If reference or estimated notes are empty, warn if ref_intervals.size == 0: warnings.warn("Reference notes are empty.") if est_intervals.size == 0: warnings.warn("Estimated notes are empty.") # Validate intervals util.validate_intervals(ref_intervals) util.validate_intervals(est_intervals) def match_note_offsets( ref_intervals, est_intervals, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, ): """Compute a maximum matching between reference and estimated notes, only taking note offsets into account. Given two note sequences represented by ``ref_intervals`` and ``est_intervals`` (see :func:`mir_eval.io.load_valued_intervals`), we seek the largest set of correspondences ``(i, j)`` such that the offset of reference note ``i`` has to be within ``offset_tolerance`` of the offset of estimated note ``j``, where ``offset_tolerance`` is equal to ``offset_ratio`` times the reference note's duration, i.e. ``offset_ratio * ref_duration[i]`` where ``ref_duration[i] = ref_intervals[i, 1] - ref_intervals[i, 0]``. If the resulting ``offset_tolerance`` is less than ``offset_min_tolerance`` (50 ms by default) then ``offset_min_tolerance`` is used instead. Every reference note is matched against at most one estimated note. Note there are separate functions :func:`match_note_onsets` and :func:`match_notes` for matching notes based on onsets only or based on onset, offset, and pitch, respectively. This is because the rules for matching note onsets and matching note offsets are different. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) offset_ratio : float > 0 The ratio of the reference note's duration used to define the ``offset_tolerance``. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or 0.05 (50 ms), whichever is greater. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See ``offset_ratio`` description for an explanation of how the offset tolerance is determined. strict : bool If ``strict=False`` (the default), threshold checks for offset matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). Returns ------- matching : list of tuples A list of matched reference and estimated notes. ``matching[i] == (i, j)`` where reference note ``i`` matches estimated note ``j``. """ # set the comparison function if strict: cmp_func = np.less else: cmp_func = np.less_equal # check for offset matches offset_distances = np.abs( np.subtract.outer(ref_intervals[:, 1], est_intervals[:, 1]) ) # Round distances to a target precision to avoid the situation where # if the distance is exactly 50ms (and strict=False) it erroneously # doesn't match the notes because of precision issues. offset_distances = np.around(offset_distances, decimals=N_DECIMALS) ref_durations = util.intervals_to_durations(ref_intervals) offset_tolerances = np.maximum(offset_ratio * ref_durations, offset_min_tolerance) offset_hit_matrix = cmp_func(offset_distances, offset_tolerances.reshape(-1, 1)) # check for hits hits = np.where(offset_hit_matrix) # Construct the graph input # Flip graph so that 'matching' is a list of tuples where the first item # in each tuple is the reference note index, and the second item is the # estimated note index. G = {} for ref_i, est_i in zip(*hits): if est_i not in G: G[est_i] = [] G[est_i].append(ref_i) # Compute the maximum matching matching = sorted(util._bipartite_match(G).items()) return matching def match_note_onsets(ref_intervals, est_intervals, onset_tolerance=0.05, strict=False): """Compute a maximum matching between reference and estimated notes, only taking note onsets into account. Given two note sequences represented by ``ref_intervals`` and ``est_intervals`` (see :func:`mir_eval.io.load_valued_intervals`), we see the largest set of correspondences ``(i,j)`` such that the onset of reference note ``i`` is within ``onset_tolerance`` of the onset of estimated note ``j``. Every reference note is matched against at most one estimated note. Note there are separate functions :func:`match_note_offsets` and :func:`match_notes` for matching notes based on offsets only or based on onset, offset, and pitch, respectively. This is because the rules for matching note onsets and matching note offsets are different. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). strict : bool If ``strict=False`` (the default), threshold checks for onset matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). Returns ------- matching : list of tuples A list of matched reference and estimated notes. ``matching[i] == (i, j)`` where reference note ``i`` matches estimated note ``j``. """ # set the comparison function if strict: cmp_func = np.less else: cmp_func = np.less_equal # check for onset matches onset_distances = np.abs( np.subtract.outer(ref_intervals[:, 0], est_intervals[:, 0]) ) # Round distances to a target precision to avoid the situation where # if the distance is exactly 50ms (and strict=False) it erroneously # doesn't match the notes because of precision issues. onset_distances = np.around(onset_distances, decimals=N_DECIMALS) onset_hit_matrix = cmp_func(onset_distances, onset_tolerance) # find hits hits = np.where(onset_hit_matrix) # Construct the graph input # Flip graph so that 'matching' is a list of tuples where the first item # in each tuple is the reference note index, and the second item is the # estimated note index. G = {} for ref_i, est_i in zip(*hits): if est_i not in G: G[est_i] = [] G[est_i].append(ref_i) # Compute the maximum matching matching = sorted(util._bipartite_match(G).items()) return matching def match_notes( ref_intervals, ref_pitches, est_intervals, est_pitches, onset_tolerance=0.05, pitch_tolerance=50.0, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, ): """Compute a maximum matching between reference and estimated notes, subject to onset, pitch and (optionally) offset constraints. Given two note sequences represented by ``ref_intervals``, ``ref_pitches``, ``est_intervals`` and ``est_pitches`` (see :func:`mir_eval.io.load_valued_intervals`), we seek the largest set of correspondences ``(i, j)`` such that: 1. The onset of reference note ``i`` is within ``onset_tolerance`` of the onset of estimated note ``j``. 2. The pitch of reference note ``i`` is within ``pitch_tolerance`` of the pitch of estimated note ``j``. 3. If ``offset_ratio`` is not ``None``, the offset of reference note ``i`` has to be within ``offset_tolerance`` of the offset of estimated note ``j``, where ``offset_tolerance`` is equal to ``offset_ratio`` times the reference note's duration, i.e. ``offset_ratio * ref_duration[i]`` where ``ref_duration[i] = ref_intervals[i, 1] - ref_intervals[i, 0]``. If the resulting ``offset_tolerance`` is less than 0.05 (50 ms), 0.05 is used instead. 4. If ``offset_ratio`` is ``None``, note offsets are ignored, and only criteria 1 and 2 are taken into consideration. Every reference note is matched against at most one estimated note. This is useful for computing precision/recall metrics for note transcription. Note there are separate functions :func:`match_note_onsets` and :func:`match_note_offsets` for matching notes based on onsets only or based on offsets only, respectively. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). pitch_tolerance : float > 0 The tolerance for an estimated note's pitch deviating from the reference note's pitch, in cents. Default is 50.0 (50 cents). offset_ratio : float > 0 or None The ratio of the reference note's duration used to define the offset_tolerance. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or 0.05 (50 ms), whichever is greater. If ``offset_ratio`` is set to ``None``, offsets are ignored in the matching. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See offset_ratio description for an explanation of how the offset tolerance is determined. Note: this parameter only influences the results if ``offset_ratio`` is not ``None``. strict : bool If ``strict=False`` (the default), threshold checks for onset, offset, and pitch matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). Returns ------- matching : list of tuples A list of matched reference and estimated notes. ``matching[i] == (i, j)`` where reference note ``i`` matches estimated note ``j``. """ # set the comparison function if strict: cmp_func = np.less else: cmp_func = np.less_equal # check for onset matches onset_distances = np.abs( np.subtract.outer(ref_intervals[:, 0], est_intervals[:, 0]) ) # Round distances to a target precision to avoid the situation where # if the distance is exactly 50ms (and strict=False) it erroneously # doesn't match the notes because of precision issues. onset_distances = np.around(onset_distances, decimals=N_DECIMALS) onset_hit_matrix = cmp_func(onset_distances, onset_tolerance) # check for pitch matches pitch_distances = np.abs( 1200 * np.subtract.outer(np.log2(ref_pitches), np.log2(est_pitches)) ) pitch_hit_matrix = cmp_func(pitch_distances, pitch_tolerance) # check for offset matches if offset_ratio is not None if offset_ratio is not None: offset_distances = np.abs( np.subtract.outer(ref_intervals[:, 1], est_intervals[:, 1]) ) # Round distances to a target precision to avoid the situation where # if the distance is exactly 50ms (and strict=False) it erroneously # doesn't match the notes because of precision issues. offset_distances = np.around(offset_distances, decimals=N_DECIMALS) ref_durations = util.intervals_to_durations(ref_intervals) offset_tolerances = np.maximum( offset_ratio * ref_durations, offset_min_tolerance ) offset_hit_matrix = cmp_func(offset_distances, offset_tolerances.reshape(-1, 1)) else: offset_hit_matrix = True # check for overall matches note_hit_matrix = onset_hit_matrix * pitch_hit_matrix * offset_hit_matrix hits = np.where(note_hit_matrix) # Construct the graph input # Flip graph so that 'matching' is a list of tuples where the first item # in each tuple is the reference note index, and the second item is the # estimated note index. G = {} for ref_i, est_i in zip(*hits): if est_i not in G: G[est_i] = [] G[est_i].append(ref_i) # Compute the maximum matching matching = sorted(util._bipartite_match(G).items()) return matching def precision_recall_f1_overlap( ref_intervals, ref_pitches, est_intervals, est_pitches, onset_tolerance=0.05, pitch_tolerance=50.0, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, beta=1.0, ): """Compute the Precision, Recall and F-measure of correct vs incorrectly transcribed notes, and the Average Overlap Ratio for correctly transcribed notes (see :func:`average_overlap_ratio`). "Correctness" is determined based on note onset, pitch and (optionally) offset: an estimated note is assumed correct if its onset is within +-50ms of a reference note and its pitch (F0) is within +- quarter tone (50 cents) of the corresponding reference note. If ``offset_ratio`` is ``None``, note offsets are ignored in the comparison. Otherwise, on top of the above requirements, a correct returned note is required to have an offset value within 20% (by default, adjustable via the ``offset_ratio`` parameter) of the reference note's duration around the reference note's offset, or within ``offset_min_tolerance`` (50 ms by default), whichever is larger. Examples -------- >>> ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals( ... 'reference.txt') >>> est_intervals, est_pitches = mir_eval.io.load_valued_intervals( ... 'estimated.txt') >>> (precision, ... recall, ... f_measure) = mir_eval.transcription.precision_recall_f1_overlap( ... ref_intervals, ref_pitches, est_intervals, est_pitches) >>> (precision_no_offset, ... recall_no_offset, ... f_measure_no_offset) = ( ... mir_eval.transcription.precision_recall_f1_overlap( ... ref_intervals, ref_pitches, est_intervals, est_pitches, ... offset_ratio=None)) Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). pitch_tolerance : float > 0 The tolerance for an estimated note's pitch deviating from the reference note's pitch, in cents. Default is 50.0 (50 cents). offset_ratio : float > 0 or None The ratio of the reference note's duration used to define the offset_tolerance. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or ``offset_min_tolerance`` (0.05 by default, i.e. 50 ms), whichever is greater. If ``offset_ratio`` is set to ``None``, offsets are ignored in the evaluation. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See ``offset_ratio`` description for an explanation of how the offset tolerance is determined. Note: this parameter only influences the results if ``offset_ratio`` is not ``None``. strict : bool If ``strict=False`` (the default), threshold checks for onset, offset, and pitch matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). beta : float > 0 Weighting factor for f-measure (default value = 1.0). Returns ------- precision : float The computed precision score recall : float The computed recall score f_measure : float The computed F-measure score avg_overlap_ratio : float The computed Average Overlap Ratio score """ validate(ref_intervals, ref_pitches, est_intervals, est_pitches) # When reference notes are empty, metrics are undefined, return 0's if len(ref_pitches) == 0 or len(est_pitches) == 0: return 0.0, 0.0, 0.0, 0.0 matching = match_notes( ref_intervals, ref_pitches, est_intervals, est_pitches, onset_tolerance=onset_tolerance, pitch_tolerance=pitch_tolerance, offset_ratio=offset_ratio, offset_min_tolerance=offset_min_tolerance, strict=strict, ) precision = float(len(matching)) / len(est_pitches) recall = float(len(matching)) / len(ref_pitches) f_measure = util.f_measure(precision, recall, beta=beta) avg_overlap_ratio = average_overlap_ratio(ref_intervals, est_intervals, matching) return precision, recall, f_measure, avg_overlap_ratio def average_overlap_ratio(ref_intervals, est_intervals, matching): """Compute the Average Overlap Ratio between a reference and estimated note transcription. Given a reference and corresponding estimated note, their overlap ratio (OR) is defined as the ratio between the duration of the time segment in which the two notes overlap and the time segment spanned by the two notes combined (earliest onset to latest offset): >>> OR = ((min(ref_offset, est_offset) - max(ref_onset, est_onset)) / ... (max(ref_offset, est_offset) - min(ref_onset, est_onset))) The Average Overlap Ratio (AOR) is given by the mean OR computed over all matching reference and estimated notes. The metric goes from 0 (worst) to 1 (best). Note: this function assumes the matching of reference and estimated notes (see :func:`match_notes`) has already been performed and is provided by the ``matching`` parameter. Furthermore, it is highly recommended to validate the intervals (see :func:`validate_intervals`) before calling this function, otherwise it is possible (though unlikely) for this function to attempt a divide-by-zero operation. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) matching : list of tuples A list of matched reference and estimated notes. ``matching[i] == (i, j)`` where reference note ``i`` matches estimated note ``j``. Returns ------- avg_overlap_ratio : float The computed Average Overlap Ratio score """ ratios = [] for match in matching: ref_int = ref_intervals[match[0]] est_int = est_intervals[match[1]] overlap_ratio = (min(ref_int[1], est_int[1]) - max(ref_int[0], est_int[0])) / ( max(ref_int[1], est_int[1]) - min(ref_int[0], est_int[0]) ) ratios.append(overlap_ratio) if len(ratios) == 0: return 0 else: return np.mean(ratios) def onset_precision_recall_f1( ref_intervals, est_intervals, onset_tolerance=0.05, strict=False, beta=1.0 ): """Compute the Precision, Recall and F-measure of note onsets: an estimated onset is considered correct if it is within +-50ms of a reference onset. Note that this metric completely ignores note offset and note pitch. This means an estimated onset will be considered correct if it matches a reference onset, even if the onsets come from notes with completely different pitches (i.e. notes that would not match with :func:`match_notes`). Examples -------- >>> ref_intervals, _ = mir_eval.io.load_valued_intervals( ... 'reference.txt') >>> est_intervals, _ = mir_eval.io.load_valued_intervals( ... 'estimated.txt') >>> (onset_precision, ... onset_recall, ... onset_f_measure) = mir_eval.transcription.onset_precision_recall_f1( ... ref_intervals, est_intervals) Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). strict : bool If ``strict=False`` (the default), threshold checks for onset matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). beta : float > 0 Weighting factor for f-measure (default value = 1.0). Returns ------- precision : float The computed precision score recall : float The computed recall score f_measure : float The computed F-measure score """ validate_intervals(ref_intervals, est_intervals) # When reference notes are empty, metrics are undefined, return 0's if len(ref_intervals) == 0 or len(est_intervals) == 0: return 0.0, 0.0, 0.0 matching = match_note_onsets( ref_intervals, est_intervals, onset_tolerance=onset_tolerance, strict=strict ) onset_precision = float(len(matching)) / len(est_intervals) onset_recall = float(len(matching)) / len(ref_intervals) onset_f_measure = util.f_measure(onset_precision, onset_recall, beta=beta) return onset_precision, onset_recall, onset_f_measure def offset_precision_recall_f1( ref_intervals, est_intervals, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, beta=1.0, ): """Compute the Precision, Recall and F-measure of note offsets: an estimated offset is considered correct if it is within +-50ms (or 20% of the ref note duration, which ever is greater) of a reference offset. Note that this metric completely ignores note onsets and note pitch. This means an estimated offset will be considered correct if it matches a reference offset, even if the offsets come from notes with completely different pitches (i.e. notes that would not match with :func:`match_notes`). Examples -------- >>> ref_intervals, _ = mir_eval.io.load_valued_intervals( ... 'reference.txt') >>> est_intervals, _ = mir_eval.io.load_valued_intervals( ... 'estimated.txt') >>> (offset_precision, ... offset_recall, ... offset_f_measure) = mir_eval.transcription.offset_precision_recall_f1( ... ref_intervals, est_intervals) Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) offset_ratio : float > 0 or None The ratio of the reference note's duration used to define the offset_tolerance. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or ``offset_min_tolerance`` (0.05 by default, i.e. 50 ms), whichever is greater. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See ``offset_ratio`` description for an explanation of how the offset tolerance is determined. strict : bool If ``strict=False`` (the default), threshold checks for onset matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). beta : float > 0 Weighting factor for f-measure (default value = 1.0). Returns ------- precision : float The computed precision score recall : float The computed recall score f_measure : float The computed F-measure score """ validate_intervals(ref_intervals, est_intervals) # When reference notes are empty, metrics are undefined, return 0's if len(ref_intervals) == 0 or len(est_intervals) == 0: return 0.0, 0.0, 0.0 matching = match_note_offsets( ref_intervals, est_intervals, offset_ratio=offset_ratio, offset_min_tolerance=offset_min_tolerance, strict=strict, ) offset_precision = float(len(matching)) / len(est_intervals) offset_recall = float(len(matching)) / len(ref_intervals) offset_f_measure = util.f_measure(offset_precision, offset_recall, beta=beta) return offset_precision, offset_recall, offset_f_measure def evaluate(ref_intervals, ref_pitches, est_intervals, est_pitches, **kwargs): """Compute all metrics for the given reference and estimated annotations. Examples -------- >>> ref_intervals, ref_pitches = mir_eval.io.load_valued_intervals( ... 'reference.txt') >>> est_intervals, est_pitches = mir_eval.io.load_valued_intervals( ... 'estimate.txt') >>> scores = mir_eval.transcription.evaluate(ref_intervals, ref_pitches, ... est_intervals, est_pitches) Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() # Precision, recall and f-measure taking note offsets into account kwargs.setdefault("offset_ratio", 0.2) orig_offset_ratio = kwargs["offset_ratio"] if kwargs["offset_ratio"] is not None: ( scores["Precision"], scores["Recall"], scores["F-measure"], scores["Average_Overlap_Ratio"], ) = util.filter_kwargs( precision_recall_f1_overlap, ref_intervals, ref_pitches, est_intervals, est_pitches, **kwargs ) # Precision, recall and f-measure NOT taking note offsets into account kwargs["offset_ratio"] = None ( scores["Precision_no_offset"], scores["Recall_no_offset"], scores["F-measure_no_offset"], scores["Average_Overlap_Ratio_no_offset"], ) = util.filter_kwargs( precision_recall_f1_overlap, ref_intervals, ref_pitches, est_intervals, est_pitches, **kwargs ) # onset-only metrics ( scores["Onset_Precision"], scores["Onset_Recall"], scores["Onset_F-measure"], ) = util.filter_kwargs( onset_precision_recall_f1, ref_intervals, est_intervals, **kwargs ) # offset-only metrics kwargs["offset_ratio"] = orig_offset_ratio if kwargs["offset_ratio"] is not None: ( scores["Offset_Precision"], scores["Offset_Recall"], scores["Offset_F-measure"], ) = util.filter_kwargs( offset_precision_recall_f1, ref_intervals, est_intervals, **kwargs ) return scores mir_eval-0.8.2/mir_eval/transcription_velocity.py000066400000000000000000000413771475740344600223360ustar00rootroot00000000000000""" Transcription evaluation, as defined in :mod:`mir_eval.transcription`, does not take into account the velocities of reference and estimated notes. This submodule implements a variant of :func:`mir_eval.transcription.precision_recall_f1_overlap` which additionally considers note velocity when determining whether a note is correctly transcribed. This is done by defining a new function :func:`mir_eval.transcription_velocity.match_notes` which first calls :func:`mir_eval.transcription.match_notes` to get a note matching based on onset, offset, and pitch. Then, we follow the evaluation procedure described in [#hawthorne2018onsets]_ to test whether an estimated note should be considered correct: 1. Reference velocities are re-scaled to the range [0, 1]. 2. A linear regression is performed to estimate global scale and offset parameters which minimize the L2 distance between matched estimated and (rescaled) reference notes. 3. The scale and offset parameters are used to rescale estimated velocities. 4. An estimated/reference note pair which has been matched according to the onset, offset, and pitch is further only considered correct if the rescaled velocities are within a predefined threshold, defaulting to 0.1. :func:`mir_eval.transcription_velocity.match_notes` is used to define a new variant :func:`mir_eval.transcription_velocity.precision_recall_f1_overlap` which considers velocity. Conventions ----------- This submodule follows the conventions of :mod:`mir_eval.transcription` and additionally requires velocities to be provided as MIDI velocities in the range [0, 127]. Metrics ------- * :func:`mir_eval.transcription_velocity.precision_recall_f1_overlap`: The precision, recall, F-measure, and Average Overlap Ratio of the note transcription, where an estimated note is considered correct if its pitch, onset, velocity and (optionally) offset are sufficiently close to a reference note. References ---------- .. [#hawthorne2018onsets] Curtis Hawthorne, Erich Elsen, Jialin Song, Adam Roberts, Ian Simon, Colin Raffel, Jesse Engel, Sageev Oore, and Douglas Eck, "Onsets and Frames: Dual-Objective Piano Transcription", Proceedings of the 19th International Society for Music Information Retrieval Conference, 2018. """ import collections import numpy as np from . import transcription from . import util def validate( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, ): """Check that the input annotations have valid time intervals, pitches, and velocities, and throws helpful errors if not. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz ref_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of reference notes est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz est_velocities : np.ndarray, shape=(m,) Array of MIDI velocities (i.e. between 0 and 127) of estimated notes """ transcription.validate(ref_intervals, ref_pitches, est_intervals, est_pitches) # Check that velocities have the same length as intervals/pitches if not ref_velocities.shape[0] == ref_pitches.shape[0]: raise ValueError( "Reference velocities must have the same length as " "pitches and intervals." ) if not est_velocities.shape[0] == est_pitches.shape[0]: raise ValueError( "Estimated velocities must have the same length as " "pitches and intervals." ) # Check that the velocities are positive if ref_velocities.size > 0 and np.min(ref_velocities) < 0: raise ValueError("Reference velocities must be positive.") if est_velocities.size > 0 and np.min(est_velocities) < 0: raise ValueError("Estimated velocities must be positive.") def match_notes( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, onset_tolerance=0.05, pitch_tolerance=50.0, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, velocity_tolerance=0.1, ): """Match notes, taking note velocity into consideration. This function first calls :func:`mir_eval.transcription.match_notes` to match notes according to the supplied intervals, pitches, onset, offset, and pitch tolerances. The velocities of the matched notes are then used to estimate a slope and intercept which can rescale the estimated velocities so that they are as close as possible (in L2 sense) to their matched reference velocities. Velocities are then normalized to the range [0, 1]. A estimated note is then further only considered correct if its velocity is within ``velocity_tolerance`` of its matched (according to pitch and timing) reference note. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz ref_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of reference notes est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz est_velocities : np.ndarray, shape=(m,) Array of MIDI velocities (i.e. between 0 and 127) of estimated notes onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). pitch_tolerance : float > 0 The tolerance for an estimated note's pitch deviating from the reference note's pitch, in cents. Default is 50.0 (50 cents). offset_ratio : float > 0 or None The ratio of the reference note's duration used to define the offset_tolerance. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or 0.05 (50 ms), whichever is greater. If ``offset_ratio`` is set to ``None``, offsets are ignored in the matching. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See offset_ratio description for an explanation of how the offset tolerance is determined. Note: this parameter only influences the results if ``offset_ratio`` is not ``None``. strict : bool If ``strict=False`` (the default), threshold checks for onset, offset, and pitch matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). velocity_tolerance : float > 0 Estimated notes are considered correct if, after rescaling and normalization to [0, 1], they are within ``velocity_tolerance`` of a matched reference note. Returns ------- matching : list of tuples A list of matched reference and estimated notes. ``matching[i] == (i, j)`` where reference note ``i`` matches estimated note ``j``. """ # Compute note matching as usual using standard transcription function matching = transcription.match_notes( ref_intervals, ref_pitches, est_intervals, est_pitches, onset_tolerance, pitch_tolerance, offset_ratio, offset_min_tolerance, strict, ) # Rescale reference velocities to the range [0, 1] min_velocity, max_velocity = np.min(ref_velocities), np.max(ref_velocities) # Make the smallest possible range 1 to avoid divide by zero velocity_range = max(1, max_velocity - min_velocity) ref_velocities = (ref_velocities - min_velocity) / float(velocity_range) # Convert matching list-of-tuples to array for fancy indexing matching = np.array(matching) # When there is no matching, return an empty list if matching.size == 0: return [] # Grab velocities for matched notes ref_matched_velocities = ref_velocities[matching[:, 0]] est_matched_velocities = est_velocities[matching[:, 1]] # Find slope and intercept of line which produces best least-squares fit # between matched est and ref velocities slope, intercept = np.linalg.lstsq( np.vstack([est_matched_velocities, np.ones(len(est_matched_velocities))]).T, ref_matched_velocities, rcond=None, )[0] # Re-scale est velocities to match ref est_matched_velocities = slope * est_matched_velocities + intercept # Compute the absolute error of (rescaled) estimated velocities vs. # normalized reference velocities. Error will be in [0, 1] velocity_diff = np.abs(est_matched_velocities - ref_matched_velocities) # Check whether each error is within the provided tolerance velocity_within_tolerance = velocity_diff < velocity_tolerance # Only keep matches whose velocity was within the provided tolerance matching = matching[velocity_within_tolerance] # Convert back to list-of-tuple format matching = [tuple(_) for _ in matching] return matching def precision_recall_f1_overlap( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, onset_tolerance=0.05, pitch_tolerance=50.0, offset_ratio=0.2, offset_min_tolerance=0.05, strict=False, velocity_tolerance=0.1, beta=1.0, ): """Compute the Precision, Recall and F-measure of correct vs incorrectly transcribed notes, and the Average Overlap Ratio for correctly transcribed notes (see :func:`mir_eval.transcription.average_overlap_ratio`). "Correctness" is determined based on note onset, velocity, pitch and (optionally) offset. An estimated note is considered correct if 1. Its onset is within ``onset_tolerance`` (default +-50ms) of a reference note 2. Its pitch (F0) is within +/- ``pitch_tolerance`` (default one quarter tone, 50 cents) of the corresponding reference note 3. Its velocity, after normalizing reference velocities to the range [0, 1] and globally rescaling estimated velocities to minimize L2 distance between matched reference notes, is within ``velocity_tolerance`` (default 0.1) the corresponding reference note 4. If ``offset_ratio`` is ``None``, note offsets are ignored in the comparison. Otherwise, on top of the above requirements, a correct returned note is required to have an offset value within `offset_ratio`` (default 20%) of the reference note's duration around the reference note's offset, or within ``offset_min_tolerance`` (default 50 ms), whichever is larger. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz ref_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of reference notes est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz est_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of estimated notes onset_tolerance : float > 0 The tolerance for an estimated note's onset deviating from the reference note's onset, in seconds. Default is 0.05 (50 ms). pitch_tolerance : float > 0 The tolerance for an estimated note's pitch deviating from the reference note's pitch, in cents. Default is 50.0 (50 cents). offset_ratio : float > 0 or None The ratio of the reference note's duration used to define the offset_tolerance. Default is 0.2 (20%), meaning the ``offset_tolerance`` will equal the ``ref_duration * 0.2``, or ``offset_min_tolerance`` (0.05 by default, i.e. 50 ms), whichever is greater. If ``offset_ratio`` is set to ``None``, offsets are ignored in the evaluation. offset_min_tolerance : float > 0 The minimum tolerance for offset matching. See ``offset_ratio`` description for an explanation of how the offset tolerance is determined. Note: this parameter only influences the results if ``offset_ratio`` is not ``None``. strict : bool If ``strict=False`` (the default), threshold checks for onset, offset, and pitch matching are performed using ``<=`` (less than or equal). If ``strict=True``, the threshold checks are performed using ``<`` (less than). velocity_tolerance : float > 0 Estimated notes are considered correct if, after rescaling and normalization to [0, 1], they are within ``velocity_tolerance`` of a matched reference note. beta : float > 0 Weighting factor for f-measure (default value = 1.0). Returns ------- precision : float The computed precision score recall : float The computed recall score f_measure : float The computed F-measure score avg_overlap_ratio : float The computed Average Overlap Ratio score """ validate( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, ) # When reference notes are empty, metrics are undefined, return 0's if len(ref_pitches) == 0 or len(est_pitches) == 0: return 0.0, 0.0, 0.0, 0.0 matching = match_notes( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, onset_tolerance, pitch_tolerance, offset_ratio, offset_min_tolerance, strict, velocity_tolerance, ) precision = float(len(matching)) / len(est_pitches) recall = float(len(matching)) / len(ref_pitches) f_measure = util.f_measure(precision, recall, beta=beta) avg_overlap_ratio = transcription.average_overlap_ratio( ref_intervals, est_intervals, matching ) return precision, recall, f_measure, avg_overlap_ratio def evaluate( ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, **kwargs ): """Compute all metrics for the given reference and estimated annotations. Parameters ---------- ref_intervals : np.ndarray, shape=(n,2) Array of reference notes time intervals (onset and offset times) ref_pitches : np.ndarray, shape=(n,) Array of reference pitch values in Hertz ref_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of reference notes est_intervals : np.ndarray, shape=(m,2) Array of estimated notes time intervals (onset and offset times) est_pitches : np.ndarray, shape=(m,) Array of estimated pitch values in Hertz est_velocities : np.ndarray, shape=(n,) Array of MIDI velocities (i.e. between 0 and 127) of estimated notes **kwargs Additional keyword arguments which will be passed to the appropriate metric or preprocessing functions. Returns ------- scores : dict Dictionary of scores, where the key is the metric name (str) and the value is the (float) score achieved. """ # Compute all the metrics scores = collections.OrderedDict() # Precision, recall and f-measure taking note offsets into account kwargs.setdefault("offset_ratio", 0.2) if kwargs["offset_ratio"] is not None: ( scores["Precision"], scores["Recall"], scores["F-measure"], scores["Average_Overlap_Ratio"], ) = util.filter_kwargs( precision_recall_f1_overlap, ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, **kwargs ) # Precision, recall and f-measure NOT taking note offsets into account kwargs["offset_ratio"] = None ( scores["Precision_no_offset"], scores["Recall_no_offset"], scores["F-measure_no_offset"], scores["Average_Overlap_Ratio_no_offset"], ) = util.filter_kwargs( precision_recall_f1_overlap, ref_intervals, ref_pitches, ref_velocities, est_intervals, est_pitches, est_velocities, **kwargs ) return scores mir_eval-0.8.2/mir_eval/util.py000066400000000000000000000720271475740344600164720ustar00rootroot00000000000000""" Useful functionality required across the task submodules, such as preprocessing, validation, and common computations. """ import os import inspect import warnings from decorator import decorator import numpy as np def index_labels(labels, case_sensitive=False): """Convert a list of string identifiers into numerical indices. Parameters ---------- labels : list of strings, shape=(n,) A list of annotations, e.g., segment or chord labels from an annotation file. case_sensitive : bool Set to True to enable case-sensitive label indexing (Default value = False) Returns ------- indices : list, shape=(n,) Numerical representation of ``labels`` index_to_label : dict Mapping to convert numerical indices back to labels. ``labels[i] == index_to_label[indices[i]]`` """ label_to_index = {} index_to_label = {} # If we're not case-sensitive, if not case_sensitive: labels = [str(s).lower() for s in labels] # First, build the unique label mapping for index, s in enumerate(sorted(set(labels))): label_to_index[s] = index index_to_label[index] = s # Remap the labels to indices indices = [label_to_index[s] for s in labels] # Return the converted labels, and the inverse mapping return indices, index_to_label def generate_labels(items, prefix="__"): """Given an array of items (e.g. events, intervals), create a synthetic label for each event of the form '(label prefix)(item number)' Parameters ---------- items : list-like A list or array of events or intervals prefix : str This prefix will be prepended to all synthetically generated labels (Default value = '__') Returns ------- labels : list of str Synthetically generated labels """ return [f"{prefix}{n}" for n in range(len(items))] def intervals_to_samples(intervals, labels, offset=0, sample_size=0.1, fill_value=None): """Convert an array of labeled time intervals to annotated samples. Parameters ---------- intervals : np.ndarray, shape=(n, d) An array of time intervals, as returned by :func:`mir_eval.io.load_intervals()` or :func:`mir_eval.io.load_labeled_intervals()`. The ``i`` th interval spans time ``intervals[i, 0]`` to ``intervals[i, 1]``. labels : list, shape=(n,) The annotation for each interval offset : float > 0 Phase offset of the sampled time grid (in seconds) (Default value = 0) sample_size : float > 0 duration of each sample to be generated (in seconds) (Default value = 0.1) fill_value : type(labels[0]) Object to use for the label with out-of-range time points. (Default value = None) Returns ------- sample_times : list list of sample times sample_labels : list array of labels for each generated sample Notes ----- Intervals will be rounded down to the nearest multiple of ``sample_size``. """ # Round intervals to the sample size num_samples = int(np.floor(intervals.max() / sample_size)) sample_indices = np.arange(num_samples, dtype=np.float32) sample_times = (sample_indices * sample_size + offset).tolist() sampled_labels = interpolate_intervals(intervals, labels, sample_times, fill_value) return sample_times, sampled_labels def interpolate_intervals(intervals, labels, time_points, fill_value=None): """Assign labels to a set of points in time given a set of intervals. Time points that do not lie within an interval are mapped to `fill_value`. Parameters ---------- intervals : np.ndarray, shape=(n, 2) An array of time intervals, as returned by :func:`mir_eval.io.load_intervals()`. The ``i`` th interval spans time ``intervals[i, 0]`` to ``intervals[i, 1]``. Intervals are assumed to be disjoint. labels : list, shape=(n,) The annotation for each interval time_points : array_like, shape=(m,) Points in time to assign labels. These must be in non-decreasing order. fill_value : type(labels[0]) Object to use for the label with out-of-range time points. (Default value = None) Returns ------- aligned_labels : list Labels corresponding to the given time points. Raises ------ ValueError If `time_points` is not in non-decreasing order. """ # Verify that time_points is sorted time_points = np.asarray(time_points) if np.any(time_points[1:] < time_points[:-1]): raise ValueError("time_points must be in non-decreasing order") aligned_labels = [fill_value] * len(time_points) starts = np.searchsorted(time_points, intervals[:, 0], side="left") ends = np.searchsorted(time_points, intervals[:, 1], side="right") for start, end, lab in zip(starts, ends, labels): aligned_labels[start:end] = [lab] * (end - start) return aligned_labels def sort_labeled_intervals(intervals, labels=None): """Sort intervals, and optionally, their corresponding labels according to start time. Parameters ---------- intervals : np.ndarray, shape=(n, 2) The input intervals labels : list, optional Labels for each interval Returns ------- intervals_sorted or (intervals_sorted, labels_sorted) Labels are only returned if provided as input """ idx = np.argsort(intervals[:, 0]) intervals_sorted = intervals[idx] if labels is None: return intervals_sorted else: return intervals_sorted, [labels[_] for _ in idx] def f_measure(precision, recall, beta=1.0): """Compute the f-measure from precision and recall scores. Parameters ---------- precision : float in (0, 1] Precision recall : float in (0, 1] Recall beta : float > 0 Weighting factor for f-measure (Default value = 1.0) Returns ------- f_measure : float The weighted f-measure """ if precision == 0 and recall == 0: return 0.0 return (1 + beta**2) * precision * recall / ((beta**2) * precision + recall) def intervals_to_boundaries(intervals, q=5): """Convert interval times into boundaries. Parameters ---------- intervals : np.ndarray, shape=(n_events, 2) Array of interval start and end-times q : int Number of decimals to round to. (Default value = 5) Returns ------- boundaries : np.ndarray Interval boundary times, including the end of the final interval """ return np.unique(np.ravel(np.round(intervals, decimals=q))) def boundaries_to_intervals(boundaries): """Convert an array of event times into intervals Parameters ---------- boundaries : list-like List-like of event times. These are assumed to be unique timestamps in ascending order. Returns ------- intervals : np.ndarray, shape=(n_intervals, 2) Start and end time for each interval """ if not np.allclose(boundaries, np.unique(boundaries)): raise ValueError("Boundary times are not unique or not ascending.") intervals = np.asarray(list(zip(boundaries[:-1], boundaries[1:]))) return intervals def adjust_intervals( intervals, labels=None, t_min=0.0, t_max=None, start_label="__T_MIN", end_label="__T_MAX", ): """Adjust a list of time intervals to span the range ``[t_min, t_max]``. Any intervals lying completely outside the specified range will be removed. Any intervals lying partially outside the specified range will be cropped. If the specified range exceeds the span of the provided data in either direction, additional intervals will be appended. If an interval is appended at the beginning, it will be given the label ``start_label``; if an interval is appended at the end, it will be given the label ``end_label``. Parameters ---------- intervals : np.ndarray, shape=(n_events, 2) Array of interval start and end-times labels : list, len=n_events or None List of labels (Default value = None) t_min : float or None Minimum interval start time. (Default value = 0.0) t_max : float or None Maximum interval end time. (Default value = None) start_label : str or float or int Label to give any intervals appended at the beginning (Default value = '__T_MIN') end_label : str or float or int Label to give any intervals appended at the end (Default value = '__T_MAX') Returns ------- new_intervals : np.ndarray Intervals spanning ``[t_min, t_max]`` new_labels : list List of labels for ``new_labels`` """ # When supplied intervals are empty and t_max and t_min are supplied, # create one interval from t_min to t_max with the label start_label if t_min is not None and t_max is not None and intervals.size == 0: return np.array([[t_min, t_max]]), [start_label] # When intervals are empty and either t_min or t_max are not supplied, # we can't append new intervals elif (t_min is None or t_max is None) and intervals.size == 0: raise ValueError("Supplied intervals are empty, can't append new" " intervals") if t_min is not None: # Find the intervals that end at or after t_min first_idx = np.argwhere(intervals[:, 1] >= t_min) if len(first_idx) > 0: # If we have events below t_min, crop them out if labels is not None: labels = labels[first_idx[0, 0] :] # Clip to the range (t_min, +inf) intervals = intervals[first_idx[0, 0] :] intervals = np.maximum(t_min, intervals) if intervals.min() > t_min: # Lowest boundary is higher than t_min: # add a new boundary and label intervals = np.vstack(([t_min, intervals.min()], intervals)) if labels is not None: labels.insert(0, start_label) if t_max is not None: # Find the intervals that begin after t_max last_idx = np.argwhere(intervals[:, 0] > t_max) if len(last_idx) > 0: # We have boundaries above t_max. # Trim to only boundaries <= t_max if labels is not None: labels = labels[: last_idx[0, 0]] # Clip to the range (-inf, t_max) intervals = intervals[: last_idx[0, 0]] intervals = np.minimum(t_max, intervals) if intervals.max() < t_max: # Last boundary is below t_max: add a new boundary and label intervals = np.vstack((intervals, [intervals.max(), t_max])) if labels is not None: labels.append(end_label) return intervals, labels def adjust_events(events, labels=None, t_min=0.0, t_max=None, label_prefix="__"): """Adjust the given list of event times to span the range ``[t_min, t_max]``. Any event times outside of the specified range will be removed. If the times do not span ``[t_min, t_max]``, additional events will be added with the prefix ``label_prefix``. Parameters ---------- events : np.ndarray Array of event times (seconds) labels : list or None List of labels (Default value = None) t_min : float or None Minimum valid event time. (Default value = 0.0) t_max : float or None Maximum valid event time. (Default value = None) label_prefix : str Prefix string to use for synthetic labels (Default value = '__') Returns ------- new_times : np.ndarray Event times corrected to the given range. """ if t_min is not None: first_idx = np.argwhere(events >= t_min) if len(first_idx) > 0: # We have events below t_min # Crop them out if labels is not None: labels = labels[first_idx[0, 0] :] events = events[first_idx[0, 0] :] if events[0] > t_min: # Lowest boundary is higher than t_min: # add a new boundary and label events = np.concatenate(([t_min], events)) if labels is not None: labels.insert(0, "%sT_MIN" % label_prefix) if t_max is not None: last_idx = np.argwhere(events > t_max) if len(last_idx) > 0: # We have boundaries above t_max. # Trim to only boundaries <= t_max if labels is not None: labels = labels[: last_idx[0, 0]] events = events[: last_idx[0, 0]] if events[-1] < t_max: # Last boundary is below t_max: add a new boundary and label events = np.concatenate((events, [t_max])) if labels is not None: labels.append("%sT_MAX" % label_prefix) return events, labels def intersect_files(flist1, flist2): """Return the intersection of two sets of filepaths, based on the file name (after the final '/') and ignoring the file extension. Examples -------- >>> flist1 = ['/a/b/abc.lab', '/c/d/123.lab', '/e/f/xyz.lab'] >>> flist2 = ['/g/h/xyz.npy', '/i/j/123.txt', '/k/l/456.lab'] >>> sublist1, sublist2 = mir_eval.util.intersect_files(flist1, flist2) >>> print sublist1 ['/e/f/xyz.lab', '/c/d/123.lab'] >>> print sublist2 ['/g/h/xyz.npy', '/i/j/123.txt'] Parameters ---------- flist1 : list first list of filepaths flist2 : list second list of filepaths Returns ------- sublist1 : list subset of filepaths with matching stems from ``flist1`` sublist2 : list corresponding filepaths from ``flist2`` """ def fname(abs_path): """Return the filename given an absolute path. Parameters ---------- abs_path Returns ------- filename """ return os.path.splitext(os.path.split(abs_path)[-1])[0] fmap = {fname(f): f for f in flist1} pairs = [list(), list()] for f in flist2: if fname(f) in fmap: pairs[0].append(fmap[fname(f)]) pairs[1].append(f) return pairs def merge_labeled_intervals(x_intervals, x_labels, y_intervals, y_labels): r"""Merge the time intervals of two sequences. Parameters ---------- x_intervals : np.ndarray Array of interval times (seconds) x_labels : list or None List of labels y_intervals : np.ndarray Array of interval times (seconds) y_labels : list or None List of labels Returns ------- new_intervals : np.ndarray New interval times of the merged sequences. new_x_labels : list New labels for the sequence ``x`` new_y_labels : list New labels for the sequence ``y`` """ align_check = [ x_intervals[0, 0] == y_intervals[0, 0], x_intervals[-1, 1] == y_intervals[-1, 1], ] if False in align_check: raise ValueError( "Time intervals do not align; did you mean to call " "'adjust_intervals()' first?" ) time_boundaries = np.unique(np.concatenate([x_intervals, y_intervals], axis=0)) output_intervals = np.array([time_boundaries[:-1], time_boundaries[1:]]).T x_labels_out, y_labels_out = [], [] x_label_range = np.arange(len(x_labels)) y_label_range = np.arange(len(y_labels)) for t0, _ in output_intervals: x_idx = x_label_range[(t0 >= x_intervals[:, 0])] x_labels_out.append(x_labels[x_idx[-1]]) y_idx = y_label_range[(t0 >= y_intervals[:, 0])] y_labels_out.append(y_labels[y_idx[-1]]) return output_intervals, x_labels_out, y_labels_out def _bipartite_match(graph): """Find maximum cardinality matching of a bipartite graph (U,V,E). The input format is a dictionary mapping members of U to a list of their neighbors in V. The output is a dict M mapping members of V to their matches in U. Parameters ---------- graph : dictionary : left-vertex -> list of right vertices The input bipartite graph. Each edge need only be specified once. Returns ------- matching : dictionary : right-vertex -> left vertex A maximal bipartite matching. """ # Adapted from: # # Hopcroft-Karp bipartite max-cardinality matching and max independent set # David Eppstein, UC Irvine, 27 Apr 2002 # initialize greedy matching (redundant, but faster than full search) matching = {} for u in graph: for v in graph[u]: if v not in matching: matching[v] = u break while True: # structure residual graph into layers # pred[u] gives the neighbor in the previous layer for u in U # preds[v] gives a list of neighbors in the previous layer for v in V # unmatched gives a list of unmatched vertices in final layer of V, # and is also used as a flag value for pred[u] when u is in the first # layer preds = {} unmatched = [] pred = {u: unmatched for u in graph} for v in matching: del pred[matching[v]] layer = list(pred) # repeatedly extend layering structure by another pair of layers while layer and not unmatched: new_layer = {} for u in layer: for v in graph[u]: if v not in preds: new_layer.setdefault(v, []).append(u) layer = [] for v in new_layer: preds[v] = new_layer[v] if v in matching: layer.append(matching[v]) pred[matching[v]] = v else: unmatched.append(v) # did we finish layering without finding any alternating paths? if not unmatched: unlayered = {} for u in graph: for v in graph[u]: if v not in preds: unlayered[v] = None return matching def recurse(v): """Recursively search backward through layers to find alternating paths. recursion returns true if found path, false otherwise """ if v in preds: L = preds[v] del preds[v] for u in L: if u in pred: pu = pred[u] del pred[u] if pu is unmatched or recurse(pu): matching[v] = u return True return False for v in unmatched: recurse(v) def _outer_distance_mod_n(ref, est, modulus=12): """Compute the absolute outer distance modulo n. Using this distance, d(11, 0) = 1 (modulo 12) Parameters ---------- ref : np.ndarray, shape=(n,) Array of reference values. est : np.ndarray, shape=(m,) Array of estimated values. modulus : int The modulus. 12 by default for octave equivalence. Returns ------- outer_distance : np.ndarray, shape=(n, m) The outer circular distance modulo n. """ ref_mod_n = np.mod(ref, modulus) est_mod_n = np.mod(est, modulus) abs_diff = np.abs(np.subtract.outer(ref_mod_n, est_mod_n)) return np.minimum(abs_diff, modulus - abs_diff) def match_events(ref, est, window, distance=None): """Compute a maximum matching between reference and estimated event times, subject to a window constraint. Given two lists of event times ``ref`` and ``est``, we seek the largest set of correspondences ``(ref[i], est[j])`` such that ``distance(ref[i], est[j]) <= window``, and each ``ref[i]`` and ``est[j]`` is matched at most once. This is useful for computing precision/recall metrics in beat tracking, onset detection, and segmentation. Parameters ---------- ref : np.ndarray, shape=(n,) Array of reference values est : np.ndarray, shape=(m,) Array of estimated values window : float > 0 Size of the window. distance : function function that computes the outer distance of ref and est. By default uses ``|ref[i] - est[j]|`` Returns ------- matching : list of tuples A list of matched reference and event numbers. ``matching[i] == (i, j)`` where ``ref[i]`` matches ``est[j]``. """ if distance is not None: # Compute the indices of feasible pairings hits = np.where(distance(ref, est) <= window) else: hits = _fast_hit_windows(ref, est, window) # Construct the graph input G = {} for ref_i, est_i in zip(*hits): if est_i not in G: G[est_i] = [] G[est_i].append(ref_i) # Compute the maximum matching matching = sorted(_bipartite_match(G).items()) return matching def _fast_hit_windows(ref, est, window): """Fast calculation of windowed hits for time events. Given two lists of event times ``ref`` and ``est``, and a tolerance window, computes a list of pairings ``(i, j)`` where ``|ref[i] - est[j]| <= window``. This is equivalent to, but more efficient than the following: >>> hit_ref, hit_est = np.where(np.abs(np.subtract.outer(ref, est)) ... <= window) Parameters ---------- ref : np.ndarray, shape=(n,) Array of reference values est : np.ndarray, shape=(m,) Array of estimated values window : float >= 0 Size of the tolerance window Returns ------- hit_ref : np.ndarray hit_est : np.ndarray indices such that ``|hit_ref[i] - hit_est[i]| <= window`` """ ref = np.asarray(ref) est = np.asarray(est) ref_idx = np.argsort(ref) ref_sorted = ref[ref_idx] left_idx = np.searchsorted(ref_sorted, est - window, side="left") right_idx = np.searchsorted(ref_sorted, est + window, side="right") hit_ref, hit_est = [], [] for j, (start, end) in enumerate(zip(left_idx, right_idx)): hit_ref.extend(ref_idx[start:end]) hit_est.extend([j] * (end - start)) return hit_ref, hit_est def validate_intervals(intervals): """Check that an (n, 2) interval ndarray is well-formed, and raises errors if not. Parameters ---------- intervals : np.ndarray, shape=(n, 2) Array of interval start/end locations. """ # Validate interval shape if intervals.ndim != 2 or intervals.shape[1] != 2: raise ValueError( "Intervals should be n-by-2 numpy ndarray, " "but shape={}".format(intervals.shape) ) # Make sure no times are negative if (intervals < 0).any(): raise ValueError("Negative interval times found") # Make sure all intervals have strictly positive duration if (intervals[:, 1] <= intervals[:, 0]).any(): raise ValueError("All interval durations must be strictly positive") def validate_events(events, max_time=30000.0): """Check that a 1-d event location ndarray is well-formed, and raises errors if not. Parameters ---------- events : np.ndarray, shape=(n,) Array of event times max_time : float If an event is found above this time, a ValueError will be raised. (Default value = 30000.) """ # Make sure no event times are huge if (events > max_time).any(): raise ValueError( "An event at time {} was found which is greater than " "the maximum allowable time of max_time = {} (did you" " supply event times in " "seconds?)".format(events.max(), max_time) ) # Make sure event locations are 1-d np ndarrays if events.ndim != 1: raise ValueError( "Event times should be 1-d numpy ndarray, " "but shape={}".format(events.shape) ) # Make sure event times are increasing if (np.diff(events) < 0).any(): raise ValueError("Events should be in increasing order.") def validate_frequencies(frequencies, max_freq, min_freq, allow_negatives=False): """Check that a 1-d frequency ndarray is well-formed, and raises errors if not. Parameters ---------- frequencies : np.ndarray, shape=(n,) Array of frequency values max_freq : float If a frequency is found above this pitch, a ValueError will be raised. (Default value = 5000.) min_freq : float If a frequency is found below this pitch, a ValueError will be raised. (Default value = 20.) allow_negatives : bool Whether or not to allow negative frequency values. """ # If flag is true, map frequencies to their absolute value. if allow_negatives: frequencies = np.abs(frequencies) # Make sure no frequency values are huge if (np.abs(frequencies) > max_freq).any(): raise ValueError( "A frequency of {} was found which is greater than " "the maximum allowable value of max_freq = {} (did " "you supply frequency values in " "Hz?)".format(frequencies.max(), max_freq) ) # Make sure no frequency values are tiny if (np.abs(frequencies) < min_freq).any(): raise ValueError( "A frequency of {} was found which is less than the " "minimum allowable value of min_freq = {} (did you " "supply frequency values in " "Hz?)".format(frequencies.min(), min_freq) ) # Make sure frequency values are 1-d np ndarrays if frequencies.ndim != 1: raise ValueError( "Frequencies should be 1-d numpy ndarray, " "but shape={}".format(frequencies.shape) ) def has_kwargs(function): r"""Determine whether a function has \*\*kwargs. Parameters ---------- function : callable The function to test Returns ------- True if function accepts arbitrary keyword arguments. False otherwise. """ sig = inspect.signature(function) for param in list(sig.parameters.values()): if param.kind == param.VAR_KEYWORD: return True return False def filter_kwargs(_function, *args, **kwargs): r"""Given a function and args and keyword args to pass to it, call the function but using only the keyword arguments which it accepts. This is equivalent to redefining the function with an additional \*\*kwargs to accept slop keyword args. If the target function already accepts \*\*kwargs parameters, no filtering is performed. Parameters ---------- _function : callable Function to call. Can take in any number of args or kwargs *args **kwargs Arguments and keyword arguments to _function. """ if has_kwargs(_function): return _function(*args, **kwargs) # Get the list of function arguments func_code = _function.__code__ function_args = func_code.co_varnames[: func_code.co_argcount] # Construct a dict of those kwargs which appear in the function filtered_kwargs = {} for kwarg, value in list(kwargs.items()): if kwarg in function_args: filtered_kwargs[kwarg] = value # Call the function with the supplied args and the filtered kwarg dict return _function(*args, **filtered_kwargs) def intervals_to_durations(intervals): """Convert an array of n intervals to their n durations. Parameters ---------- intervals : np.ndarray, shape=(n, 2) An array of time intervals, as returned by :func:`mir_eval.io.load_intervals()`. The ``i`` th interval spans time ``intervals[i, 0]`` to ``intervals[i, 1]``. Returns ------- durations : np.ndarray, shape=(n,) Array of the duration of each interval. """ validate_intervals(intervals) return np.abs(np.diff(intervals, axis=-1)).flatten() def hz_to_midi(freqs): """Convert Hz to MIDI numbers Parameters ---------- freqs : number or ndarray Frequency/frequencies in Hz Returns ------- midi : number or ndarray MIDI note numbers corresponding to input frequencies. Note that these may be fractional. """ return 12.0 * (np.log2(freqs) - np.log2(440.0)) + 69.0 def midi_to_hz(midi): """Convert MIDI numbers to Hz Parameters ---------- midi : number or ndarray MIDI notes Returns ------- freqs : number or ndarray Frequency/frequencies in Hz corresponding to `midi` """ return 440.0 * (2.0 ** ((midi - 69.0) / 12.0)) def deprecated(*, version, version_removed): """Mark a function as deprecated. Using the decorated (old) function will result in a warning. """ def __wrapper(func, *args, **kwargs): """Warn the user, and then proceed.""" warnings.warn( f"{func.__module__}.{func.__name__}\n\tDeprecated as of mir_eval version {version}." f"\n\tIt will be removed in mir_eval version {version_removed}.", category=FutureWarning, stacklevel=3, # Would be 2, but the decorator adds a level ) return func(*args, **kwargs) return decorator(__wrapper) mir_eval-0.8.2/setup.cfg000066400000000000000000000033311475740344600151560ustar00rootroot00000000000000[tool:pytest] addopts = --cov-report term-missing --cov mir_eval --cov-report=xml --mpl --mpl-baseline-path=baseline_images/test_display [coverage:report] show_missing = True [coverage:run] omit = mir_eval/separation.py [pydocstyle] # convention = numpy # Below is equivalent to numpy convention + D400 and D205 ignore = D107,D203,D205,D212,D213,D400,D402,D413,D415,D416,D417 [flake8] count = True statistics = True show_source = True select = E9, F63, F7, F82 [metadata] name = mir_eval version = attr: mir_eval.__version__ description = Common metrics for common audio/music processing tasks. author = Colin Raffel author_email = craffel@gmail.com url = https://github.com/mir-evaluation/mir_eval long_description = file: README.rst long_description_content_type = text/x-rst; charset=UTF-8 license = MIT python_requires = ">=3.7" classifiers = License :: OSI Approved :: MIT License Programming Language :: Python Development Status :: 5 - Production/Stable Intended Audience :: Developers Topic :: Multimedia :: Sound/Audio :: Analysis Programming Language :: Python :: 3 Programming Language :: Python :: 3.7 Programming Language :: Python :: 3.8 Programming Language :: Python :: 3.9 Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.13 [options] packages = find: keywords = audio music mir dsp install_requires = numpy >= 1.15.4 scipy >= 1.4.0 decorator [options.extras_require] display = matplotlib >= 3.3.0 docs = numpydoc sphinx_rtd_theme matplotlib >= 3.3.0 tests = matplotlib >= 3.3.0 pytest pytest-cov pytest-mpl mir_eval-0.8.2/setup.py000066400000000000000000000001051475740344600150430ustar00rootroot00000000000000from setuptools import setup if __name__ == "__main__": setup() mir_eval-0.8.2/tests/000077500000000000000000000000001475740344600144775ustar00rootroot00000000000000mir_eval-0.8.2/tests/baseline_images/000077500000000000000000000000001475740344600176065ustar00rootroot00000000000000mir_eval-0.8.2/tests/baseline_images/test_display/000077500000000000000000000000001475740344600223125ustar00rootroot00000000000000mir_eval-0.8.2/tests/baseline_images/test_display/test_display_events.png000066400000000000000000000220031475740344600271050ustar00rootroot00000000000000PNG  IHDR5 pHYsaa?i#IDATx{u0̀ "*EIP6;h5;lyT|x˰aj=FM5Ғo߾vm gLVfm>Kmv£Iήy_Vk]]&]|s߼o^ϝYkε͛Ç/=^"|ڷo߾ GS%:w]Yk5Mo<=߷ DdM}zjl$YXm}mHs֤o4iag%IC߾g?okYjۆ}WkGWפkIW~6\W{];3d{u?]J~VO|LFk`)MuY`AzT $}:>bHϞY,??=*+{ `',}/H*7|g׽WչP___*jmrygcEP$$IrFu`*ˋY]Yy|®ޫ\Kݻw N-$y~Ǔ3| 3bİ4ԯ :* $*l:%=V]ic5~:W:>y…gy&F*ÞWF@ESmꛞW^+zTSI]WYin\uf{u?] zJϞ=f…innHbH7GVεPV? nw#|#.@(̭ޚqם٬ ܰG]@X,\h<2dHƍAǃj/^g%'`6a„9餓2p}cO9N>}2xL4)/r裏 '9s椮!⋳fW^aӟt~f̘vmˮikë!wیӻOKƍ'Xin)cǎMsss=yy .̩78lAvu̘1c-^?+Ѷp-mQ%gwަkꫯNCCC~}߽K'dO~6;C~[o_tAI.,w^ ~<_$IW2}\qyG3eʔq{ש Zfj;o璳Nmr1,9oĉΝwܞ]r?׿ε^ٳgSToOJm{m+9.\;}[lE.ҞVv~L;%>|x|liiiI}}}Z[[kmo~+wuWvm$fm?w{.y睗;yiv֝qsmsgܜ??rH=܎+agt<]8?|$9Sr뭷f6mZ/ Ǝg~XqG?sagꩧfyꩧr{r%IK r'eʔ)T*?~|͛x }QG꺷;쳳^{e7! 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3.596987109734720320e+02 3.602701939718527910e+02 3.608416769702336069e+02 3.614131599686144227e+02 3.619846429669951817e+02 3.625561259653759976e+02 3.631276089637568134e+02 3.636990919621375724e+02 3.642705749605183883e+02 3.648420579588992041e+02 3.654135409572800199e+02 3.659850239556607789e+02 mir_eval-0.8.2/tests/data/chord/000077500000000000000000000000001475740344600165075ustar00rootroot00000000000000mir_eval-0.8.2/tests/data/chord/est00.lab000066400000000000000000000157171475740344600201350ustar00rootroot00000000000000# Another comment 0.256 0.718 B:min 0.718 1.186 G:maj 1.186 1.633 G:maj 1.633 2.089 D:maj 2.089 2.542 D:maj 2.542 2.989 G:maj 2.989 3.415 G:maj 3.415 3.895 D:maj 3.895 4.369 D:maj 4.369 4.831 G:maj 4.831 5.284 G:maj 5.284 5.746 D:maj 5.746 6.199 G:maj 6.199 6.667 G:maj 6.667 7.123 G:maj 7.123 7.585 D:maj 7.585 8.071 D:maj 8.071 8.506 G:maj 8.506 8.953 G:maj 8.953 9.415 D:maj 9.415 9.883 D:maj 9.883 10.327 G:maj 10.327 10.777 G:maj 10.777 11.233 D:maj 11.233 11.695 A:min 11.695 12.166 G:maj 12.166 12.595 G:maj 12.595 13.081 D:maj 13.081 13.552 G:maj 13.552 13.999 G:maj 13.999 14.467 G:maj 14.467 14.932 D:maj 14.932 15.397 A:min 15.397 15.841 G:maj 15.841 16.306 G:maj 16.306 16.756 D:maj 16.756 17.233 G:maj 17.233 17.689 C:maj 17.689 18.142 E:min 18.142 18.589 E:maj 18.589 19.075 A:min 19.075 19.546 C:maj 19.546 20.014 C:maj 20.014 20.482 C:maj 20.482 20.947 D:maj 20.947 21.4 D:maj 21.4 21.871 D:maj 21.871 22.327 D:maj 22.327 22.78 G:maj 22.78 23.251 G:maj 23.251 23.701 G:maj 23.701 24.166 D:maj 24.166 24.628 G:maj 24.628 25.087 G:maj 25.087 25.546 G:maj 25.546 26.005 D:maj 26.005 26.482 D:maj 26.482 26.923 G:maj 26.923 27.382 G:maj 27.382 27.841 D:maj 27.841 28.303 D:maj 28.303 28.777 G:maj 28.777 29.218 G:maj 29.218 29.683 D:maj 29.683 30.133 A:min 30.133 30.592 G:maj 30.592 31.03 G:maj 31.03 31.534 D:maj 31.534 32.002 G:maj 32.002 32.431 G:maj 32.431 32.893 G:maj 32.893 33.352 D:maj 33.352 33.802 A:min 33.802 34.267 G:maj 34.267 34.714 G:maj 34.714 35.188 D:maj 35.188 35.635 G:maj 35.635 36.109 E:min 36.109 36.562 E:min 36.562 37.015 E:min 37.015 37.471 A:min 37.471 37.942 C:maj 37.942 38.41 C:maj 38.41 38.875 C:maj 38.875 39.349 A:maj 39.349 39.811 B:maj 39.811 40.27 B:maj 40.27 40.726 B:maj 40.726 41.188 B:maj 41.188 41.668 E:min 41.668 42.133 E:maj 42.133 42.595 E:min 42.595 43.048 E:min 43.048 43.531 C:maj 43.531 43.999 C:maj 43.999 44.461 C:maj 44.461 44.908 C:maj 44.908 45.376 G:maj 45.376 45.835 G:maj 45.835 46.297 G:maj 46.297 46.777 G:min 46.777 47.218 B:maj 47.218 47.683 B:maj 47.683 48.136 B:maj 48.136 48.589 B:maj 48.589 49.054 E:min 49.054 49.513 E:min 49.513 49.984 E:min 49.984 50.461 E:min 50.461 50.911 E:min 50.911 51.361 E:min 51.361 51.826 E:min 51.826 52.264 E:min 52.264 52.75 G:maj 52.75 53.212 G:maj 53.212 53.671 G:maj 53.671 54.136 G:maj 54.136 54.604 G:maj 54.604 55.066 G:maj 55.066 55.528 G:min 55.528 55.957 E:maj 55.957 56.446 C:maj 56.446 56.917 C:maj 56.917 57.373 A:min 57.373 57.835 E:min 57.835 58.303 D:maj 58.303 58.768 D:maj 58.768 59.233 D:maj 59.233 59.707 G:min 59.707 60.172 G:maj 60.172 60.625 G:maj 60.625 61.096 G:maj 61.096 61.558 G:maj 61.558 62.017 E:min 62.017 62.47 E:min 62.47 62.932 E:min 62.932 63.37 E:min 63.37 63.856 C:maj 63.856 64.321 C:maj 64.321 64.798 E:min 64.798 65.257 B:min 65.257 65.725 D:maj 65.725 66.184 D:maj 66.184 66.634 D:maj 66.634 67.093 A:min 67.093 67.573 G:maj 67.573 68.035 G:maj 68.035 68.491 D:maj 68.491 68.965 G:maj 68.965 69.433 G:maj 69.433 69.91 G:maj 69.91 70.369 D:maj 70.369 70.846 D:maj 70.846 71.305 G:maj 71.305 71.779 G:maj 71.779 72.238 D:maj 72.238 72.682 G:maj 72.682 73.162 G:maj 73.162 73.606 G:maj 73.606 74.083 D:maj 74.083 74.53 D:maj 74.53 75.001 G:maj 75.001 75.454 G:maj 75.454 75.916 D:maj 75.916 76.378 G:maj 76.378 76.84 G:maj 76.84 77.308 G:maj 77.308 77.758 D:maj 77.758 78.223 A:min 78.223 78.679 G:maj 78.679 79.132 G:maj 79.132 79.597 D:maj 79.597 80.05 E:min 80.05 80.518 E:min 80.518 80.983 E:min 80.983 81.436 E:maj 81.436 81.883 E:maj 81.883 82.357 C:maj 82.357 82.825 C:maj 82.825 83.287 C:maj 83.287 83.752 D:maj 83.752 84.217 D:maj 84.217 84.685 D:maj 84.685 85.135 D:min 85.135 85.633 E:min 85.633 86.068 G:maj 86.068 86.518 G:maj 86.518 86.98 D:maj 86.98 87.451 G:maj 87.451 87.907 G:maj 87.907 88.372 G:maj 88.372 88.831 D:maj 88.831 89.305 D:maj 89.305 89.761 G:maj 89.761 90.223 G:maj 90.223 90.688 D:maj 90.688 91.153 G:maj 91.153 91.603 G:maj 91.603 92.068 G:maj 92.068 92.527 G:maj 92.527 92.998 D:maj 92.998 93.436 G:maj 93.436 93.907 G:maj 93.907 94.36 D:maj 94.36 94.819 G:maj 94.819 95.284 G:maj 95.284 95.734 G:maj 95.734 96.208 D:maj 96.208 96.679 G:maj 96.679 97.138 G:maj 97.138 97.603 G:maj 97.603 98.065 D:maj 98.065 98.521 G:maj 98.521 98.977 E:min 98.977 99.442 E:min 99.442 99.901 E:min 99.901 100.372 A:min 100.372 100.816 C:maj 100.816 101.278 C:maj 101.278 101.737 C:maj 101.737 102.199 C:maj 102.199 102.649 D:maj 102.649 103.114 D:maj 103.114 103.561 D:maj 103.561 104.041 G:maj 104.041 104.494 G:maj 104.494 104.962 G:maj 104.962 105.418 D:maj 105.418 105.871 G:maj 105.871 106.333 G:maj 106.333 106.789 G:maj 106.789 107.254 D:maj 107.254 107.716 D:maj 107.716 108.181 G:maj 108.181 108.631 G:maj 108.631 109.084 D:maj 109.084 109.543 D:maj 109.543 110.011 G:maj 110.011 110.458 G:maj 110.458 110.917 D:maj 110.917 111.367 A:min 111.367 111.811 G:maj 111.811 112.267 G:maj 112.267 112.756 D:maj 112.756 113.218 G:maj 113.218 113.677 G:maj 113.677 114.142 G:maj 114.142 114.595 D:maj 114.595 115.054 A:min 115.054 115.516 G:maj 115.516 115.96 G:maj 115.96 116.431 D:maj 116.431 116.884 E:min 116.884 117.355 E:min 117.355 117.826 G:maj 117.826 118.285 E:maj 118.285 118.756 E:min 118.756 119.215 C:maj 119.215 119.692 C:maj 119.692 120.151 C:maj 120.151 120.625 B:maj 120.625 121.081 B:maj 121.081 121.525 B:maj 121.525 121.984 B:maj 121.984 122.455 B:maj 122.455 122.932 E:min 122.932 123.4 E:min 123.4 123.853 E:maj 123.853 124.324 D:maj 124.324 124.792 C:maj 124.792 125.263 C:maj 125.263 125.728 C:maj 125.728 126.187 C:maj 126.187 126.643 G:maj 126.643 127.105 G:maj 127.105 127.558 G:maj 127.558 128.041 G:min 128.041 128.497 B:maj 128.497 128.962 B:maj 128.962 129.415 B:maj 129.415 129.868 F:maj 129.868 130.345 E:min 130.345 130.81 E:min 130.81 131.269 E:min 131.269 131.728 E:min 131.728 132.211 E:min 132.211 132.682 E:min 132.682 133.153 E:min 133.153 133.621 N 133.621 134.083 G:maj 134.083 134.563 G:maj 134.563 135.01 G:maj 135.01 135.481 G:min 135.481 135.934 G:maj 135.934 136.399 F:maj 136.399 136.852 G:min 136.852 137.275 G:min 137.275 137.764 C:maj 137.764 138.241 C:maj 138.241 138.706 A:min 138.706 139.144 E:min 139.144 139.621 D:maj 139.621 140.113 D:maj 140.113 140.581 D:maj 140.581 141.064 N 141.064 141.526 G:maj 141.526 141.979 G:maj 141.979 142.441 G:maj 142.441 142.903 G:maj 142.903 143.362 E:min 143.362 143.821 E:min 143.821 144.289 E:maj 144.289 144.73 E:min 144.73 145.228 C:maj 145.228 145.693 C:maj 145.693 146.173 E:min 146.173 146.659 C:maj 146.659 147.106 D:maj 147.106 147.565 D:maj 147.565 148.024 D:maj 148.024 148.51 A:min 148.51 148.99 G:maj 148.99 149.437 G:maj 149.437 149.911 D:maj 149.911 150.376 A:min 150.376 150.853 G:maj 150.853 151.333 G:maj 151.333 151.783 D:maj 151.783 152.23 A:min 152.23 152.713 G:maj 152.713 153.166 G:maj 153.166 153.643 D:maj 153.643 154.12 D:maj 154.12 154.573 G:maj 154.573 155.044 G:maj 155.044 155.509 D:maj 155.509 156.004 D:maj 156.004 156.451 G:maj 156.451 156.922 G:maj 156.922 157.372 D:maj 157.372 157.843 G:maj 157.843 158.311 G:maj 158.311 158.788 G:maj 158.788 159.259 D:maj 159.259 159.733 D:maj 159.733 160.183 G:maj 160.183 160.283 Eb:maj mir_eval-0.8.2/tests/data/chord/est01.lab000066400000000000000000000015141475740344600201240ustar00rootroot000000000000000.000000 2.212063 N 2.212063 6.452063 G:maj 6.452063 12.884082 C:min 12.884082 14.680000 Bb:min 14.680000 26.776054 C:min 26.776054 27.284082 Bb:maj 27.284082 34.932063 C:min 34.932063 37.320000 Bb:maj 37.320000 51.128073 C:min 51.128073 59.996009 C:maj 59.996009 60.244082 F:maj 60.244082 61.556009 Bb:maj 61.556009 63.842086 C:maj 63.842086 66.956009 Bb:maj 66.956009 74.924082 C:min 74.924082 76.376054 A:min 76.376054 81.388027 C:min 81.388027 82.460045 A:min 82.460045 83.020045 Bb:maj 83.020045 87.832018 C:maj 87.832018 90.360000 C:min 90.360000 93.372063 Bb:maj 93.372063 94.124082 C:min 94.124082 97.468027 Bb:maj 97.468027 117.280000 C:min 117.280000 124.094059 Bb:maj 124.094059 141.666032 C:min 141.666032 144.364082 C:maj 144.364082 147.748027 C:min 147.748027 152.658050 Bb:maj 152.658050 179.452063 C:min 179.452063 183.576961 N mir_eval-0.8.2/tests/data/chord/est02.lab000066400000000000000000000245571475740344600201410ustar00rootroot000000000000000.481 0.814 B:min 0.814 1.126 B:min 1.126 1.453 D:maj 1.453 1.756 B:min 1.756 2.053 B:min 2.053 2.35 B:min 2.35 2.668 B:maj 2.668 3.007 E:maj 3.007 3.343 E:maj 3.343 3.634 A:maj 3.634 3.961 D:maj 3.961 4.267 G:maj 4.267 4.546 G:maj 4.546 4.852 D:min 4.852 5.17 B:maj 5.17 5.479 D:min 5.479 5.806 Eb:maj 5.806 6.121 E:maj 6.121 6.445 E:maj 6.445 6.766 B:maj 6.766 7.09 A:maj 7.09 7.417 C#:min 7.417 7.741 A:maj 7.741 8.059 Ab:min 8.059 8.365 B:maj 8.365 8.686 A:maj 8.686 9.016 A:maj 9.016 9.325 E:maj 9.325 9.655 B:min 9.655 9.964 B:maj 9.964 10.297 B:min 10.297 10.618 B:maj 10.618 10.945 B:maj 10.945 11.272 E:maj 11.272 11.602 E:maj 11.602 11.923 B:maj 11.923 12.262 E:maj 12.262 12.595 E:maj 12.595 12.925 E:maj 12.925 13.252 E:maj 13.252 13.579 E:maj 13.579 13.921 C#:min 13.921 14.257 C#:min 14.257 14.587 C#:maj 14.587 14.911 C#:maj 14.911 15.229 C#:min 15.229 15.577 C#:min 15.577 15.904 B:min 15.904 16.231 F:maj 16.231 16.546 A:maj 16.546 16.891 A:maj 16.891 17.224 E:maj 17.224 17.551 A:maj 17.551 17.878 A:maj 17.878 18.202 A:maj 18.202 18.523 A:maj 18.523 18.85 A:maj 18.85 19.198 B:maj 19.198 19.537 B:maj 19.537 19.864 B:maj 19.864 20.173 B:maj 20.173 20.5 B:maj 20.5 20.824 B:min 20.824 21.151 B:maj 21.151 21.472 C#:min 21.472 21.799 E:maj 21.799 22.129 E:maj 22.129 22.456 B:maj 22.456 22.786 E:maj 22.786 23.113 E:maj 23.113 23.446 E:maj 23.446 23.77 E:maj 23.77 24.097 E:maj 24.097 24.409 C#:min 24.409 24.76 C#:maj 24.76 25.09 Ab:maj 25.09 25.414 A:maj 25.414 25.744 C#:min 25.744 26.071 C#:min 26.071 26.395 B:maj 26.395 26.731 A:maj 26.731 27.055 A:maj 27.055 27.406 A:maj 27.406 27.736 E:maj 27.736 28.063 A:maj 28.063 28.393 A:maj 28.393 28.723 A:maj 28.723 29.038 A:maj 29.038 29.365 A:maj 29.365 29.707 B:min 29.707 30.043 B:maj 30.043 30.361 B:maj 30.361 30.676 B:maj 30.676 30.994 B:min 30.994 31.321 B:maj 31.321 31.642 B:maj 31.642 31.921 Eb:maj 31.921 32.275 C#:maj 32.275 32.608 C#:min 32.608 32.929 C#:min 32.929 33.256 C#:min 33.256 33.589 C#:min 33.589 33.919 C#:maj 33.919 34.219 C#:maj 34.219 34.555 C#:min 34.555 34.888 C#:maj 34.888 35.206 C#:maj 35.206 35.521 Eb:min 35.521 35.893 C#:maj 35.893 36.229 C#:min 36.229 36.565 C#:min 36.565 36.904 B:maj 36.904 37.231 B:maj 37.231 37.567 A:maj 37.567 37.888 A:maj 37.888 38.221 A:maj 38.221 38.539 A:maj 38.539 38.866 A:maj 38.866 39.196 A:maj 39.196 39.529 A:maj 39.529 39.835 B:maj 39.835 40.177 B:maj 40.177 40.495 B:maj 40.495 40.807 B:maj 40.807 41.152 B:maj 41.152 41.479 B:maj 41.479 41.815 B:maj 41.815 42.127 C#:min 42.127 42.448 Eb:maj 42.448 42.778 E:maj 42.778 43.108 E:maj 43.108 43.435 E:maj 43.435 43.753 E:maj 43.753 44.071 E:maj 44.071 44.392 E:maj 44.392 44.719 E:maj 44.719 45.04 A:maj 45.04 45.367 C#:min 45.367 45.697 C#:min 45.697 46.018 C#:min 46.018 46.33 C#:min 46.33 46.651 C#:min 46.651 46.984 C#:maj 46.984 47.302 C#:min 47.302 47.629 B:min 47.629 47.965 A:maj 47.965 48.316 A:maj 48.316 48.631 A:maj 48.631 48.946 A:maj 48.946 49.267 B:maj 49.267 49.603 B:maj 49.603 49.924 C#:maj 49.924 50.257 Eb:maj 50.257 50.578 E:maj 50.578 50.902 E:maj 50.902 51.229 E:maj 51.229 51.547 E:maj 51.547 51.862 E:maj 51.862 52.186 E:maj 52.186 52.519 E:maj 52.519 52.837 E:maj 52.837 53.158 E:maj 53.158 53.491 E:maj 53.491 53.821 E:maj 53.821 54.148 E:maj 54.148 54.484 E:maj 54.484 54.817 E:maj 54.817 55.147 B:maj 55.147 55.462 E:maj 55.462 55.789 C#:min 55.789 56.137 C#:min 56.137 56.473 Ab:maj 56.473 56.8 C#:maj 56.8 57.133 C#:min 57.133 57.451 C#:min 57.451 57.778 B:maj 57.778 58.105 B:min 58.105 58.444 A:maj 58.444 58.783 A:maj 58.783 59.101 A:maj 59.101 59.44 A:maj 59.44 59.773 A:maj 59.773 60.1 A:maj 60.1 60.415 A:maj 60.415 60.754 A:maj 60.754 61.093 B:min 61.093 61.423 B:min 61.423 61.741 Gb:maj 61.741 62.062 B:maj 62.062 62.377 B:maj 62.377 62.707 C#:min 62.707 63.031 E:maj 63.031 63.358 B:maj 63.358 63.688 E:maj 63.688 64.012 E:maj 64.012 64.345 E:maj 64.345 64.669 E:maj 64.669 65.008 E:maj 65.008 65.332 E:maj 65.332 65.656 E:maj 65.656 65.965 E:maj 65.965 66.31 C#:min 66.31 66.637 C#:min 66.637 66.967 Ab:maj 66.967 67.297 C#:maj 67.297 67.633 C#:min 67.633 67.963 C#:min 67.963 68.278 B:maj 68.278 68.605 G:maj 68.605 68.941 A:maj 68.941 69.277 A:maj 69.277 69.598 A:maj 69.598 69.916 A:maj 69.916 70.24 A:maj 70.24 70.567 A:maj 70.567 70.882 C#:min 70.882 71.203 E:maj 71.203 71.542 B:maj 71.542 71.866 B:maj 71.866 72.178 B:maj 72.178 72.484 C#:min 72.484 72.802 Eb:maj 72.802 73.12 Eb:maj 73.12 73.441 B:maj 73.441 73.762 A:maj 73.762 74.095 C#:min 74.095 74.431 C#:min 74.431 74.758 C#:min 74.758 75.085 C#:min 75.085 75.427 C#:maj 75.427 75.751 C#:maj 75.751 76.069 C#:maj 76.069 76.393 C:min 76.393 76.726 C#:maj 76.726 77.053 C#:maj 77.053 77.38 Eb:min 77.38 77.71 C#:maj 77.71 78.043 C#:min 78.043 78.376 C#:min 78.376 78.697 B:maj 78.697 79.024 B:maj 79.024 79.345 A:maj 79.345 79.672 A:maj 79.672 79.996 A:maj 79.996 80.326 A:maj 80.326 80.644 A:maj 80.644 80.974 A:maj 80.974 81.295 A:maj 81.295 81.613 A:maj 81.613 81.943 B:maj 81.943 82.27 B:maj 82.27 82.603 B:maj 82.603 82.936 B:maj 82.936 83.254 Eb:min 83.254 83.584 B:maj 83.584 83.893 C#:min 83.893 84.214 Eb:maj 84.214 84.55 E:maj 84.55 84.877 E:maj 84.877 85.195 E:maj 85.195 85.51 E:maj 85.51 85.828 E:maj 85.828 86.149 E:maj 86.149 86.47 E:maj 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84.004 84.727 Ab:maj 84.727 85.426 Ab:maj 85.426 86.131 Eb:maj 86.131 86.827 Eb:maj 86.827 87.529 Ab:maj 87.529 88.234 Ab:maj 88.234 88.951 Bb:min 88.951 89.65 Bb:maj 89.65 90.361 Bb:min 90.361 91.069 C#:maj 91.069 91.78 Ab:maj 91.78 92.476 Ab:maj 92.476 93.178 Ab:maj 93.178 93.892 Ab:maj 93.892 94.576 Ab:maj 94.576 95.293 Ab:maj 95.293 96.004 Ab:maj 96.004 96.706 Ab:maj 96.706 97.417 C#:maj 97.417 98.116 Ab:maj 98.116 98.824 C#:maj 98.824 99.526 C#:maj 99.526 100.24 Ab:maj 100.24 100.927 Ab:maj 100.927 101.632 Ab:maj 101.632 102.328 Ab:maj 102.328 103.039 N 103.039 103.732 C#:maj 103.732 104.431 C#:maj 104.431 105.121 C#:maj 105.121 105.817 Eb:maj 105.817 106.513 Ab:maj 106.513 107.215 Eb:maj 107.215 107.914 Eb:maj 107.914 108.613 Ab:maj 108.613 109.321 C#:maj 109.321 110.035 Bb:maj 110.035 110.734 Bb:min 110.734 111.43 Bb:min 111.43 112.12 F:min 112.12 112.834 Eb:maj 112.834 113.521 Eb:maj 113.521 114.226 Ab:maj 114.226 114.925 C#:min 114.925 115.636 Bb:maj 115.636 116.332 Bb:min 116.332 117.019 Bb:maj 117.019 117.733 F:min 117.733 118.432 Eb:maj 118.432 119.125 Eb:maj 119.125 119.839 Eb:maj 119.839 120.547 F:min 120.547 121.258 Ab:maj 121.258 121.966 Bb:min 121.966 122.671 Ab:maj 122.671 123.361 Ab:maj 123.361 124.072 Eb:maj 124.072 124.771 Eb:maj 124.771 125.476 Ab:maj 125.476 126.202 Ab:maj 126.202 126.913 F:min 126.913 127.612 Bb:maj 127.612 128.311 Bb:min 128.311 129.022 C#:maj 129.022 129.721 Ab:maj 129.721 130.426 Ab:maj 130.426 131.146 Ab:maj 131.146 131.836 C#:maj 131.836 132.538 Ab:maj 132.538 133.243 Ab:maj 133.243 133.954 Ab:maj 133.954 134.65 Ab:maj 134.65 135.358 Ab:maj 135.358 136.054 C#:maj 136.054 136.759 Ab:maj 136.759 137.452 C#:maj 137.452 138.166 Ab:maj 138.166 138.859 Ab:maj 138.859 139.567 Ab:maj 139.567 140.272 Ab:maj 140.272 140.98 Ab:maj 140.98 141.08 Eb:maj mir_eval-0.8.2/tests/data/chord/est06.lab000066400000000000000000000055611475740344600201370ustar00rootroot000000000000000.000000 0.024036 N 0.024036 2.164082 D:min7 2.164082 3.844082 F:maj 3.844082 5.708027 C:maj 5.708027 7.888073 G:maj 7.888073 9.316009 D:min 9.316009 11.464036 G:maj 11.464036 15.300045 C:maj 15.300045 16.976054 D:min 16.976054 18.888073 G:maj 18.888073 19.852063 C:maj 19.852063 20.324082 F:maj 20.324082 23.192018 C:maj 23.192018 24.628027 D:min 24.628027 27.028027 G:7 27.028027 30.636009 C:maj 30.636009 32.540045 D:min 32.540045 34.452063 Bb:maj 34.452063 34.680000 F:maj 34.680000 37.544036 G:maj 37.544036 40.176054 D:min7 40.176054 42.132063 F:maj 42.132063 43.788027 C:maj 43.788027 45.440000 G:maj 45.440000 46.228027 C:maj 46.228027 47.840000 D:min7 47.840000 49.760000 F:maj7 49.760000 51.184036 C:maj 51.184036 51.660045 D:min7 51.660045 53.796009 G:maj 53.796009 55.488073 D:min7 55.488073 57.408073 F:maj7 57.408073 57.656054 D:min7 57.656054 59.328073 C:maj 59.328073 60.980045 G:maj 60.980045 63.236009 D:min7 63.236009 65.048073 Bb:maj 65.048073 65.284082 F:maj 65.284082 68.904036 G:maj 68.904036 70.796009 D:min7 70.796009 72.956009 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147.228027 149.128073 G:maj 149.128073 149.844082 C:maj 149.844082 151.028027 A:min7 151.028027 152.488073 C:maj 152.488073 153.200000 A:min 153.200000 154.868027 D:min 154.868027 156.764082 Bb:maj 156.764082 160.364082 G:maj 160.364082 162.528073 D:min7 162.528073 164.416054 F:maj7 164.416054 165.852063 C:maj 165.852063 166.092063 D:min7 166.092063 167.764082 G:maj 167.764082 168.468027 C:maj 168.468027 170.132063 D:min7 170.132063 172.284082 F:maj7 172.284082 173.488073 C:maj 173.488073 173.716009 D:min 173.716009 176.084082 G:maj 176.084082 177.784036 D:min7 177.784036 179.748027 F:maj 179.748027 181.420045 C:maj 181.420045 183.276009 G:maj 183.276009 185.412063 D:min7 185.412063 187.544036 Bb:maj 187.544036 191.100045 G:maj 191.100045 191.348027 C:maj 191.348027 193.036009 D:min 193.036009 194.712018 Bb:maj 194.712018 204.848073 F:maj 204.848073 204.939320 N mir_eval-0.8.2/tests/data/chord/est07.lab000066400000000000000000000073051475740344600201360ustar00rootroot000000000000000.000000 0.116009 N 0.116009 1.928073 D#:maj 1.928073 3.572063 Ab:maj 3.572063 5.692063 D#:maj 5.692063 7.572063 Ab:maj 7.572063 8.280000 D#:maj 8.280000 10.304036 F:min 10.304036 11.268027 Ab:maj7 11.268027 12.608073 D:min7 12.608073 14.840000 Bb:maj6 14.840000 16.628027 C:min 16.628027 16.880000 F:maj 16.880000 17.112018 D#:maj 17.112018 18.472018 C:min7 18.472018 18.920091 A:min7 18.920091 19.352018 C:min7 19.352018 20.308027 F:7 20.308027 21.156009 Ab:maj7 21.156009 22.044082 Ab:maj6 22.044082 23.912018 D#:maj 23.912018 26.120000 Bb:7 26.120000 27.432018 G:7 27.432018 28.500045 C:min 28.500045 29.196009 Bb:maj 29.196009 30.956009 D#:maj 30.956009 32.748027 F:min 32.748027 34.060045 Ab:maj 34.060045 34.488073 Ab:maj6 34.488073 35.136054 D:min7 35.136054 36.668027 Bb:maj 36.668027 38.036009 D:dim 38.036009 40.120000 D#:maj 40.120000 41.740045 Ab:maj7 41.740045 42.860045 D:min7 42.860045 43.528073 Bb:maj6 43.528073 45.084082 G:maj 45.084082 50.068027 C:min 50.068027 50.744036 F:maj 50.744036 51.208073 F:7 51.208073 51.860045 Ab:maj7 51.860045 52.608073 Ab:maj 52.608073 54.484082 D#:maj 54.484082 56.060045 Bb:maj 56.060045 57.796009 G:maj 57.796009 59.116009 C:min 59.116009 59.604082 Bb:maj 59.604082 61.368073 D#:maj 61.368073 62.480000 F:min 62.480000 64.040091 Ab:maj6 64.040091 64.956009 Ab:maj 64.956009 66.764082 D#:maj 66.764082 67.192018 Ab:maj 67.192018 68.816054 D#:maj 68.816054 70.580045 Bb:maj 70.580045 72.172063 C:min 72.172063 73.784036 Ab:maj6 73.784036 75.784036 Ab:maj 75.784036 77.628027 Bb:maj 77.628027 78.844082 C:min 78.844082 81.168073 F:min 81.168073 83.156009 Ab:maj 83.156009 86.328073 C:min 86.328073 88.312018 F:7 88.312018 91.664036 Ab:maj6 91.664036 91.860045 C#:maj 91.860045 93.656054 D#:maj 93.656054 97.360000 Ab:maj6 97.360000 98.924082 Bb:maj 98.924082 100.712018 Bb:7 100.712018 102.476009 D#:maj 102.476009 104.016054 Ab:maj 104.016054 106.016054 D#:maj 106.016054 107.596009 Ab:maj 107.596009 109.628027 D#:maj 109.628027 111.404082 Ab:maj7 111.404082 112.772063 Bb:maj 112.772063 115.064036 Bb:maj6 115.064036 116.172063 C:min 116.172063 116.872018 F:7 116.872018 118.712018 Bb:maj 118.712018 119.848073 A:min 119.848073 120.736054 F:7 120.736054 122.340045 Ab:maj6 122.340045 124.156009 D#:maj 124.156009 125.956009 Bb:maj 125.956009 127.740045 G:maj 127.740045 129.552018 C:min 129.552018 131.316009 D#:maj 131.316009 133.128073 Ab:maj6 133.128073 134.944036 Ab:maj 134.944036 138.556009 Bb:maj 138.556009 140.404082 D#:maj 140.404082 142.200000 Ab:maj 142.200000 143.760000 Bb:maj 143.760000 145.812063 G:min 145.812063 147.596009 C:min 147.596009 148.500045 Bb:maj 148.500045 150.492063 C:min7 150.492063 152.992018 Ab:maj6 152.992018 154.764082 D#:maj 154.764082 156.572063 Bb:maj 156.572063 158.344036 G:maj 158.344036 160.148027 C:min 160.148027 161.924082 D#:maj 161.924082 164.156009 Ab:maj6 164.156009 165.696054 Ab:maj 165.696054 169.060045 D#:maj 169.060045 170.872018 Bb:maj 170.872018 172.628027 C:min 172.628027 174.416054 F:min 174.416054 176.224036 Ab:maj 176.224036 177.988027 Bb:maj 177.988027 179.800091 C:min 179.800091 181.556009 F:min 181.556009 183.372063 Ab:maj 183.372063 185.108027 C:min 185.108027 186.968073 Bb:maj 186.968073 188.760000 A:min 188.760000 191.984036 Ab:maj6 191.984036 192.484082 Ab:maj 192.484082 193.996009 D#:maj 193.996009 195.904036 Ab:maj6 195.904036 197.632018 Ab:maj 197.632018 201.156009 Bb:maj 201.156009 202.964082 C:min 202.964082 204.688073 Bb:maj 204.688073 206.504036 F:maj 206.504036 210.008073 Ab:maj6 210.008073 211.820045 D#:maj 211.820045 212.916009 Ab:maj7 212.916009 214.804082 Ab:maj 214.804082 217.076009 D#:maj 217.076009 218.632018 Ab:maj 218.632018 220.660045 D#:maj 220.660045 222.760000 Ab:maj 222.760000 228.996009 D#:maj 228.996009 244.970522 N mir_eval-0.8.2/tests/data/chord/est08.lab000066400000000000000000000441301475740344600201340ustar00rootroot000000000000007.76117913832 8.08743764172 E:maj 8.08743764172 8.44063492063 E:maj 8.44063492063 8.79083900227 C#:min 8.79083900227 9.13804988662 E:min 9.13804988662 9.48526077098 Gb:maj 9.48526077098 9.82349206349 N 9.82349206349 10.1766893424 N 10.1766893424 10.526893424 C#:maj 10.526893424 10.8711111111 N 10.8711111111 11.2213151927 Bb:min 11.2213151927 11.5655328798 Ab:min 11.5655328798 11.9097505669 Ab:maj 11.9097505669 12.2599546485 Ab:maj 12.2599546485 12.6071655329 Ab:min 12.6071655329 12.9603628118 Eb:min 12.9603628118 13.3075736961 Ab:maj 13.3075736961 13.6517913832 Ab:min 13.6517913832 13.9990022676 Ab:maj 13.9990022676 14.3462131519 Ab:maj 14.3462131519 14.6934240363 Ab:maj 14.6934240363 15.0406349206 Ab:maj 15.0406349206 15.3908390023 Ab:min 15.3908390023 15.7380498866 Ab:maj 15.7380498866 16.085260771 Ab:maj 16.085260771 16.4324716553 Ab:maj 16.4324716553 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2.09505668934 2.57396825397 C#:maj 2.57396825397 3.0379138322 C#:maj 3.0379138322 3.50185941043 Bb:min 3.50185941043 3.97179138322 G:maj 3.97179138322 4.44172335601 G:maj 4.44172335601 4.90566893424 C#:maj 4.90566893424 5.37260770975 Eb:maj 5.37260770975 5.84253968254 C#:maj 5.84253968254 6.31845804989 G:min 6.31845804989 6.7853968254 G:min 6.7853968254 7.25233560091 G:min 7.25233560091 7.72526077098 C#:maj 7.72526077098 8.19219954649 N 8.19219954649 8.66512471655 E:maj 8.66512471655 9.14403628118 E:maj 9.14403628118 9.60498866213 D:maj 9.60498866213 10.0719274376 G:min 10.0719274376 10.5328798186 G:min 10.5328798186 11.0028117914 G:min 11.0028117914 11.4697505669 E:maj 11.4697505669 11.9456689342 E:maj 11.9456689342 12.4185941043 D:maj 12.4185941043 12.8855328798 D:maj 12.8855328798 13.3554648526 D:maj 13.3554648526 13.8224036281 D:maj 13.8224036281 14.30430839 E:maj 14.30430839 14.7682539683 B:maj 14.7682539683 15.2292063492 D:maj 15.2292063492 15.6901587302 D:maj 15.6901587302 16.1570975057 E:maj 16.1570975057 16.6180498866 B:maj 16.6180498866 17.0790022676 D:maj 17.0790022676 17.5429478458 E:maj 17.5429478458 18.0158730159 E:maj 18.0158730159 18.4738321995 B:maj 18.4738321995 18.9407709751 D:maj 18.9407709751 19.4047165533 E:maj 19.4047165533 19.8686621315 E:maj 19.8686621315 20.335600907 Ab:maj 20.335600907 20.796553288 D:maj 20.796553288 21.2724716553 F#:min 21.2724716553 21.730430839 A:maj 21.730430839 22.2003628118 A:maj 22.2003628118 22.6553287982 A:maj 22.6553287982 23.1192743764 A:maj 23.1192743764 23.5832199546 F#:min 23.5832199546 24.0531519274 F#:min 24.0531519274 24.5260770975 F#:min 24.5260770975 24.993015873 F#:min 24.993015873 25.4659410431 A:maj 25.4659410431 25.9358730159 A:maj 25.9358730159 26.4147845805 A:maj 26.4147845805 26.8727437642 Bb:min 26.8727437642 27.3366893424 F#:min 27.3366893424 27.7946485261 F#:min 27.7946485261 28.2585941043 F#:min 28.2585941043 28.7315192744 E:maj 28.7315192744 29.2044444444 G:maj 29.2044444444 29.67138322 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E:maj 70.8487981859 71.3037641723 E:maj 71.3037641723 71.7527437642 E:maj 71.7527437642 72.2256689342 D:maj 72.2256689342 72.6896145125 D:min 72.6896145125 73.1685260771 F:maj 73.1685260771 73.629478458 A:maj 73.629478458 74.0994104308 G:maj 74.0994104308 74.5663492063 F:maj 74.5663492063 75.0183219955 C:maj 75.0183219955 75.4762811791 G:maj 75.4762811791 75.9611791383 C:maj 75.9611791383 76.4251247166 C:maj 76.4251247166 76.8920634921 C:maj 76.8920634921 77.353015873 C:maj 77.353015873 77.8199546485 A:min 77.8199546485 78.2839002268 C:maj 78.2839002268 78.7388662132 C:maj 78.7388662132 79.1818594104 D:min 79.1818594104 79.6637641723 D:min 79.6637641723 80.121723356 D:min 80.121723356 80.5886621315 D:min 80.5886621315 81.0436281179 A:min 81.0436281179 81.5075736961 C:maj 81.5075736961 81.9655328798 G:maj 81.9655328798 82.4234920635 N 82.4234920635 82.8754648526 D:min 82.8754648526 83.3483900227 C:maj 83.3483900227 83.8153287982 C:maj 83.8153287982 84.2792743764 C:maj 84.2792743764 84.7432199546 C:maj 84.7432199546 85.1951927438 C:maj 85.1951927438 85.659138322 C:maj 85.659138322 86.1081179138 E:maj 86.1081179138 86.5511111111 E:maj 86.5511111111 87.0240362812 D:maj 87.0240362812 87.4969614512 D:min 87.4969614512 87.9519274376 F:maj 87.9519274376 88.4218594104 A:min 88.4218594104 88.8828117914 G:maj 88.8828117914 89.3317913832 G:maj 89.3317913832 89.7837641723 E:min 89.7837641723 90.2387301587 G:maj 90.2387301587 90.7146485261 C:maj 90.7146485261 91.175600907 C:maj 91.175600907 91.6275736961 C:maj 91.6275736961 92.0855328798 G:maj 92.0855328798 92.5345124717 A:min 92.5345124717 92.9984580499 C:maj 92.9984580499 93.4354648526 Eb:min 93.4354648526 93.8934240363 A:min 93.8934240363 94.3693424036 D:maj 94.3693424036 94.8362811791 D:maj 94.8362811791 95.2912471655 D:min 95.2912471655 95.7492063492 A:min 95.7492063492 96.2101587302 C:maj 96.2101587302 96.6711111111 G:maj 96.6711111111 97.1200907029 E:min 97.1200907029 97.5780498866 G:maj 97.5780498866 98.0270294785 C:maj 98.0270294785 98.4879818594 C:maj 98.4879818594 98.9459410431 C:maj 98.9459410431 99.4009070295 C:maj 99.4009070295 99.8588662132 A:maj 99.8588662132 100.304852608 A:maj 100.304852608 100.759818594 A:maj 100.759818594 101.229750567 A:maj 101.229750567 101.69968254 F#:min 101.69968254 102.17260771 F#:min 102.17260771 102.642539683 F#:min 102.642539683 103.094512472 F#:min 103.094512472 103.549478458 D:maj 103.549478458 104.010430839 D:maj 104.010430839 104.474376417 D:min 104.474376417 104.941315193 D:maj 104.941315193 105.390294785 E:maj 105.390294785 105.860226757 E:maj 105.860226757 106.309206349 F#:min 106.309206349 106.773151927 E:maj 106.773151927 107.231111111 A:maj 107.231111111 107.698049887 C#:min 107.698049887 108.161995465 E:maj 108.161995465 108.625941043 A:maj 108.625941043 109.092879819 F#:min 109.092879819 109.544852608 F#:min 109.544852608 110.01478458 Gb:maj 110.01478458 110.46675737 D:maj 110.46675737 110.936689342 D:maj 110.936689342 111.403628118 D:min 111.403628118 111.855600907 D:min 111.855600907 112.313560091 F:maj 112.313560091 112.780498866 E:maj 112.780498866 113.235464853 E:maj 113.235464853 113.693424036 F#:min 113.693424036 114.157369615 E:maj 114.157369615 114.618321995 E:maj 114.618321995 115.09723356 B:maj 115.09723356 115.561179138 Ab:min 115.561179138 116.016145125 E:maj 116.016145125 116.462131519 E:maj 116.462131519 116.917097506 E:maj 116.917097506 117.369070295 F#:min 117.369070295 117.821043084 E:maj 117.821043084 118.279002268 E:maj 118.279002268 118.739954649 E:maj 118.739954649 119.203900227 D:maj 119.203900227 119.667845805 E:maj 119.667845805 120.131791383 A:maj 120.131791383 120.595736961 A:maj 120.595736961 121.053696145 A:maj 121.053696145 121.511655329 A:maj 121.511655329 121.97260771 F#:min 121.97260771 122.439546485 F#:min 122.439546485 122.909478458 F#:min 122.909478458 123.370430839 F#:min 123.370430839 123.828390023 A:maj 123.828390023 124.301315193 A:maj 124.301315193 124.762267574 A:maj 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178.840362812 179.307301587 E:maj 179.307301587 179.771247166 E:maj 179.771247166 180.235192744 E:maj 180.235192744 180.696145125 E:maj 180.696145125 181.1630839 E:maj 181.1630839 181.621043084 E:maj 181.621043084 182.079002268 E:maj 182.079002268 182.539954649 A:maj 182.539954649 183.000907029 E:maj 183.000907029 183.458866213 E:maj 183.458866213 183.919818594 E:maj 183.919818594 184.377777778 E:maj 184.377777778 184.835736961 E:maj 184.835736961 185.290702948 E:maj 185.290702948 185.748662132 E:maj 185.748662132 186.215600907 E:maj 186.215600907 186.676553288 E:maj 186.676553288 187.131519274 E:maj 187.131519274 187.592471655 E:maj 187.592471655 188.047437642 E:maj 188.047437642 188.51138322 E:maj 188.51138322 188.972335601 E:maj 188.972335601 189.436281179 E:maj 189.436281179 189.89723356 E:maj 189.89723356 190.358185941 E:maj 190.358185941 190.819138322 E:maj 190.819138322 191.277097506 E:maj 191.277097506 191.735056689 E:maj 191.735056689 192.193015873 E:maj 192.193015873 192.659954649 E:maj 192.659954649 193.123900227 E:maj 193.123900227 193.587845805 E:maj 193.587845805 194.045804989 E:maj 194.045804989 194.50675737 E:maj 194.50675737 194.964716553 E:maj 194.964716553 195.425668934 E:maj 195.425668934 195.889614512 E:maj 195.889614512 196.33260771 E:maj 196.33260771 196.796553288 E:maj 196.796553288 197.254512472 E:maj 197.254512472 197.712471655 E:maj 197.712471655 198.167437642 E:maj 198.167437642 198.628390023 E:maj 198.628390023 199.086349206 E:maj 199.086349206 199.547301587 E:maj 199.547301587 200.014240363 E:maj 200.014240363 200.475192744 E:maj 200.475192744 200.936145125 E:maj 200.936145125 201.4030839 E:maj 201.4030839 201.852063492 E:maj 201.852063492 202.31600907 E:maj 202.31600907 202.773968254 E:maj 202.773968254 203.240907029 E:maj 203.240907029 203.698866213 E:maj 203.698866213 204.159818594 E:maj 204.159818594 204.611791383 E:maj 204.611791383 205.072743764 E:maj 205.072743764 205.530702948 E:maj 205.530702948 205.991655329 E:maj 205.991655329 206.449614512 E:maj 206.449614512 206.910566893 E:maj 206.910566893 207.371519274 E:maj 207.371519274 207.829478458 E:maj 207.829478458 208.290430839 E:maj 208.290430839 208.754376417 E:maj 208.754376417 209.218321995 E:maj 209.218321995 209.676281179 E:maj 209.676281179 210.125260771 D:maj 210.125260771 210.574240363 E:maj 210.574240363 211.029206349 E:maj 211.029206349 211.484172336 E:maj 211.484172336 211.936145125 E:maj 211.936145125 212.397097506 E:maj 212.397097506 212.852063492 E:maj 212.852063492 213.310022676 E:maj 213.310022676 213.75600907 E:maj 213.75600907 214.210975057 E:maj 214.210975057 214.671927438 E:maj 214.671927438 215.126893424 E:maj 215.126893424 215.5938322 E:maj 215.5938322 216.051791383 E:maj 216.051791383 216.512743764 E:maj 216.512743764 216.967709751 E:maj 216.967709751 217.413696145 D:maj 217.413696145 217.865668934 E:maj 217.865668934 218.311655329 E:maj 218.311655329 218.769614512 E:maj 218.769614512 219.230566893 D:maj 219.230566893 219.697505669 E:maj 219.697505669 220.152471655 E:maj 220.152471655 220.607437642 E:maj 220.607437642 221.065396825 D:maj 221.065396825 221.517369615 E:maj 221.517369615 221.978321995 E:maj 221.978321995 222.454240363 E:maj 222.454240363 222.912199546 E:maj 222.912199546 223.373151927 E:maj 223.373151927 223.834104308 E:maj 223.834104308 224.292063492 E:maj 224.292063492 224.747029478 E:maj 224.747029478 225.199002268 E:maj 225.199002268 225.653968254 E:maj 225.653968254 226.105941043 E:maj 226.105941043 226.557913832 E:maj 226.557913832 227.024852608 E:maj 227.024852608 227.4738322 E:maj 227.4738322 227.931791383 E:maj 227.931791383 228.389750567 E:maj 228.389750567 228.856689342 E:maj 228.856689342 229.311655329 E:maj 229.311655329 229.778594104 E:maj 229.778594104 230.233560091 E:maj 230.233560091 230.68553288 C#:min 230.68553288 231.146485261 E:maj 231.146485261 231.607437642 E:maj 231.607437642 232.065396825 E:maj 232.065396825 232.529342404 E:maj 232.529342404 232.975328798 E:maj 232.975328798 233.436281179 E:maj 233.436281179 233.891247166 E:maj 233.891247166 234.352199546 E:maj 234.352199546 234.81015873 E:maj 234.81015873 235.274104308 E:maj 235.274104308 235.732063492 E:maj 235.732063492 236.190022676 E:maj 236.190022676 236.653968254 E:maj 236.653968254 237.10893424 E:maj 237.10893424 237.563900227 E:maj 237.563900227 238.018866213 E:maj 238.018866213 238.482811791 E:maj 238.482811791 238.937777778 E:maj 238.937777778 239.38675737 E:maj 239.38675737 239.853696145 E:maj 239.853696145 240.305668934 E:maj 240.305668934 240.763628118 E:maj 240.763628118 241.218594104 E:maj 241.218594104 241.673560091 E:maj 241.673560091 242.134512472 E:maj 242.134512472 242.592471655 E:maj 242.592471655 243.047437642 Gb:maj 243.047437642 243.51138322 E:maj 243.51138322 243.972335601 E:maj 243.972335601 244.430294785 E:maj 244.430294785 244.888253968 E:maj 244.888253968 245.358185941 E:maj 245.358185941 245.816145125 E:maj 245.816145125 246.277097506 E:maj 246.277097506 246.735056689 E:maj 246.735056689 247.201995465 E:maj 247.201995465 247.659954649 E:maj 247.659954649 248.120907029 E:maj 248.120907029 248.575873016 E:maj 248.575873016 249.036825397 E:maj 249.036825397 249.491791383 E:maj 249.491791383 249.964716553 E:maj 249.964716553 250.431655329 E:maj 250.431655329 250.886621315 E:maj 250.886621315 251.338594104 E:maj 251.338594104 251.793560091 E:maj 251.793560091 252.251519274 E:maj 252.251519274 252.715464853 E:maj 252.715464853 253.152471655 E:maj 253.152471655 253.607437642 E:maj 253.607437642 254.07138322 E:maj 254.07138322 254.523356009 E:maj 254.523356009 254.975328798 E:maj 254.975328798 255.430294785 E:maj 255.430294785 255.900226757 E:maj 255.900226757 256.358185941 E:maj 256.358185941 256.816145125 E:maj 256.816145125 257.274104308 E:maj 257.274104308 257.732063492 E:maj 257.732063492 258.193015873 E:maj 258.193015873 258.650975057 E:maj 258.650975057 259.114920635 E:maj 259.114920635 259.572879819 E:maj 259.572879819 260.039818594 E:maj 260.039818594 260.50675737 E:maj 260.50675737 260.964716553 E:maj 260.964716553 261.41968254 E:maj 261.41968254 261.877641723 E:maj 261.877641723 262.329614512 E:maj 262.329614512 262.784580499 E:maj 262.784580499 263.239546485 E:maj 263.239546485 263.68553288 E:maj 263.68553288 264.149478458 E:maj 264.149478458 264.610430839 E:maj 264.610430839 265.059410431 E:maj 265.059410431 265.526349206 E:maj 265.526349206 265.981315193 E:maj 265.981315193 266.442267574 E:maj 266.442267574 266.89723356 E:maj 266.89723356 267.352199546 E:maj 267.352199546 267.816145125 E:maj 267.816145125 268.274104308 E:maj 268.274104308 268.732063492 G:maj 268.732063492 269.19600907 E:maj 269.19600907 269.650975057 E:maj 269.650975057 270.114920635 E:maj 270.114920635 270.575873016 E:maj 270.575873016 271.039818594 E:maj 271.039818594 271.500770975 E:maj 271.500770975 271.955736961 E:maj 271.955736961 272.407709751 E:maj 272.407709751 272.868662132 E:maj 272.868662132 273.329614512 E:maj 273.329614512 273.787573696 E:maj 273.787573696 274.242539683 F#:min 274.242539683 274.706485261 E:maj 274.706485261 275.164444444 E:maj 275.164444444 275.625396825 E:maj 275.625396825 276.080362812 E:maj 276.080362812 276.535328798 E:maj 276.535328798 276.993287982 E:maj 276.993287982 277.454240363 E:maj 277.454240363 277.912199546 E:maj 277.912199546 278.364172336 E:maj 278.364172336 278.825124717 E:maj 278.825124717 279.280090703 E:maj 279.280090703 279.744036281 E:maj 279.744036281 280.204988662 E:maj 280.204988662 280.662947846 E:maj 280.662947846 281.117913832 E:maj 281.117913832 281.575873016 E:maj 281.575873016 282.036825397 C#:min 282.036825397 282.497777778 E:maj 282.497777778 282.958730159 E:maj 282.958730159 283.413696145 E:maj 283.413696145 283.865668934 E:maj 283.865668934 284.326621315 E:maj 284.326621315 284.775600907 E:maj 284.775600907 285.230566893 F#:min 285.230566893 285.688526077 E:maj 285.688526077 286.137505669 E:maj 286.137505669 286.589478458 E:maj 286.589478458 287.056417234 E:maj 287.056417234 287.520362812 E:maj 287.520362812 287.981315193 E:maj 287.981315193 288.433287982 E:maj 288.433287982 288.894240363 E:maj 288.894240363 289.343219955 E:maj 289.343219955 289.801179138 E:maj 289.801179138 290.256145125 E:maj 290.256145125 290.714104308 E:maj 290.714104308 291.172063492 E:maj 291.172063492 291.630022676 E:maj 291.630022676 292.07600907 E:maj 292.07600907 292.530975057 E:maj 292.530975057 292.985941043 E:maj 292.985941043 293.446893424 E:maj 293.446893424 293.895873016 E:maj 293.895873016 294.3538322 E:maj 294.3538322 294.805804989 E:maj 294.805804989 295.263764172 E:maj 295.263764172 295.724716553 E:maj 295.724716553 296.173696145 E:maj 296.173696145 296.634648526 E:maj 296.634648526 297.089614512 E:maj 297.089614512 297.550566893 E:maj 297.550566893 298.011519274 E:maj 298.011519274 298.475464853 E:maj 298.475464853 298.942403628 E:maj 298.942403628 299.406349206 E:maj 299.406349206 299.867301587 E:maj 299.867301587 300.33723356 E:maj 300.33723356 300.795192744 E:maj 300.795192744 301.259138322 E:maj 301.259138322 301.7230839 E:maj 301.7230839 302.193015873 E:maj 302.193015873 302.644988662 E:maj 302.644988662 303.10893424 E:maj 303.10893424 303.560907029 F#:min 303.560907029 304.02185941 E:maj 304.02185941 304.4738322 E:maj 304.4738322 304.928798186 E:maj 304.928798186 305.38675737 Gb:maj 305.38675737 305.841723356 E:maj 305.841723356 306.302675737 E:maj 306.302675737 306.754648526 E:maj 306.754648526 307.206621315 E:maj 307.206621315 307.667573696 E:maj 307.667573696 308.119546485 E:maj 308.119546485 308.580498866 E:maj 308.580498866 309.03845805 D:maj 309.03845805 309.496417234 E:maj 309.496417234 309.960362812 E:maj 309.960362812 310.418321995 E:maj 310.418321995 310.876281179 B:min 310.876281179 311.340226757 E:maj 311.340226757 311.789206349 E:maj 311.789206349 312.25015873 G:maj 312.25015873 312.714104308 E:maj 312.714104308 313.178049887 E:maj 313.178049887 313.630022676 E:maj 313.630022676 314.087981859 E:maj 314.087981859 314.54893424 E:maj 314.54893424 315.000907029 E:maj 315.000907029 315.449886621 E:maj 315.449886621 315.910839002 E:maj 315.910839002 316.362811791 E:maj 316.362811791 316.82675737 E:maj 316.82675737 317.275736961 E:maj 317.275736961 317.730702948 E:maj 317.730702948 318.188662132 E:maj 318.188662132 318.649614512 E:maj 318.649614512 319.104580499 E:maj 319.104580499 319.562539683 E:maj 319.562539683 320.023492063 E:maj 320.023492063 320.481451247 E:maj 320.481451247 320.933424036 E:maj 320.933424036 321.385396825 E:maj 321.385396825 321.840362812 F#:min 321.840362812 322.307301587 E:maj 322.307301587 322.750294785 E:maj 322.750294785 323.205260771 E:maj 323.205260771 323.660226757 E:maj 323.660226757 324.115192744 E:maj 324.115192744 324.564172336 E:maj 324.564172336 325.022131519 E:maj 325.022131519 325.474104308 C#:maj 325.474104308 325.932063492 F:min 325.932063492 326.390022676 N 326.390022676 326.844988662 N 326.844988662 327.299954649 N 327.299954649 327.751927438 F:min 327.751927438 328.206893424 E:maj 328.206893424 328.664852608 E:maj 328.664852608 329.1138322 E:maj 329.1138322 329.57478458 E:maj 329.57478458 330.032743764 E:maj 330.032743764 330.490702948 E:maj 330.490702948 330.960634921 E:maj 330.960634921 331.418594104 E:maj 331.418594104 331.870566893 D:maj 331.870566893 332.32553288 D:maj 332.32553288 332.777505669 D:min 332.777505669 333.256417234 C:maj 333.256417234 333.714376417 E:maj 333.714376417 334.172335601 B:min 334.172335601 334.627301587 F#:min 334.627301587 335.106213152 E:maj 335.106213152 335.576145125 E:maj 335.576145125 336.040090703 E:maj 336.040090703 336.477097506 F#:min 336.477097506 336.944036281 E:maj 336.944036281 337.404988662 E:maj 337.404988662 337.862947846 B:maj 337.862947846 338.326893424 D:maj 338.326893424 338.7938322 E:maj 338.7938322 339.25478458 E:maj 339.25478458 339.718730159 E:maj 339.718730159 340.17968254 Gb:maj 340.17968254 340.640634921 E:maj 340.640634921 341.095600907 E:maj 341.095600907 341.556553288 B:min 341.556553288 342.011519274 B:maj 342.011519274 342.487437642 E:maj 342.487437642 342.945396825 E:maj 342.945396825 343.406349206 E:maj 343.406349206 343.867301587 F#:min 343.867301587 344.313287982 E:maj 344.313287982 344.765260771 E:maj 344.765260771 345.235192744 Ab:min 345.235192744 345.696145125 D:maj 345.696145125 346.160090703 E:maj 346.160090703 346.630022676 E:maj 346.630022676 347.087981859 E:maj 347.087981859 347.527981859 F#:min 347.527981859 347.973968254 E:maj 347.973968254 348.434920635 E:maj 348.434920635 348.889886621 B:min 348.889886621 349.335873016 B:min 349.335873016 349.81478458 E:maj 349.81478458 350.33260771 A:maj 350.33260771 350.823492063 A:maj 350.823492063 351.314376417 N 351.314376417 351.78430839 N 351.78430839 352.269206349 N 352.269206349 352.757097506 G:min 352.757097506 353.224036281 N 353.224036281 353.723900227 Eb:maj 353.723900227 354.208798186 N 354.208798186 354.708662132 N 354.708662132 355.184580499 N 355.184580499 355.64553288 N 355.64553288 356.112471655 N 356.112471655 356.576417234 E:maj 356.576417234 357.025396825 F:min 357.025396825 357.47138322 N 357.47138322 357.932335601 N 357.932335601 358.399274376 N 358.399274376 358.863219955 N 358.863219955 359.300226757 N 359.300226757 359.776145125 N 359.776145125 360.246077098 N 360.246077098 360.692063492 N 360.692063492 361.15600907 N 361.15600907 361.634920635 Eb:maj 361.634920635 362.14675737 N 362.14675737 362.61968254 N 362.61968254 363.09260771 N 363.09260771 363.568526077 Eb:maj 363.568526077 364.035464853 N 364.035464853 364.493424036 C#:maj 364.493424036 364.960362812 Eb:min 364.960362812 365.412335601 N 365.412335601 365.870294785 D:maj 365.870294785 366.331247166 N 366.331247166 366.813151927 N 366.813151927 367.292063492 N 367.292063492 367.761995465 N 367.761995465 368.225941043 C#:maj 368.225941043 368.719818594 N 368.719818594 369.195736961 E:maj 369.195736961 369.701587302 N 369.701587302 370.168526077 N 370.168526077 370.63845805 N 370.63845805 371.11138322 Eb:maj 371.11138322 371.563356009 N 371.563356009 372.039274376 N 372.039274376 372.491247166 E:maj 372.491247166 372.946213152 N 372.946213152 373.404172336 N 373.404172336 373.880090703 N 373.880090703 374.373968254 Eb:maj 374.373968254 374.852879819 N 374.852879819 375.376689342 N 375.376689342 375.840634921 B:maj 375.840634921 376.340498866 N 376.340498866 376.807437642 N 376.807437642 377.286349206 E:maj 377.286349206 377.783219955 F:min 377.783219955 378.259138322 N 378.259138322 378.761995465 E:maj 378.761995465 379.26185941 N 379.26185941 379.764716553 N 379.764716553 380.237641723 Eb:maj 380.237641723 380.701587302 Eb:maj 380.701587302 381.177505669 N 381.177505669 381.641451247 Eb:maj 381.641451247 382.099410431 Eb:maj 382.099410431 382.563356009 N 382.563356009 383.030294785 N 383.030294785 383.494240363 N 383.494240363 383.967165533 N 383.967165533 384.455056689 Eb:min 384.455056689 384.907029478 N 384.907029478 385.376961451 N 385.376961451 385.837913832 N 385.837913832 386.322811791 N 386.322811791 386.777777778 Eb:maj 386.777777778 387.262675737 E:maj 387.262675737 387.711655329 Eb:maj 387.711655329 388.199546485 E:maj 388.199546485 388.684444444 N 388.684444444 389.154376417 N 389.154376417 389.621315193 C:maj 389.621315193 390.100226757 N 390.100226757 390.582131519 N 390.582131519 391.093968254 N 391.093968254 391.611791383 D:maj 391.611791383 391.711791383 Eb:maj mir_eval-0.8.2/tests/data/chord/output00.json000066400000000000000000000007321475740344600211040ustar00rootroot00000000000000{"thirds": 0.76314050772927455, "thirds_inv": 0.74084785691131028, "triads": 0.76314050772927455, "triads_inv": 0.74084785691131028, "tetrads": 0.74350305483862278, "tetrads_inv": 0.72121040402065839, "root": 0.80276927493052763, "mirex": 0.76314050772927455, "majmin": 0.76314050772927455, "majmin_inv": 0.74084785691131028, "sevenths": 0.74350305483862278, "sevenths_inv": 0.72121040402065839, "underseg": 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94.9712 95.6710 Db 95.6710 97.1171 Ab 97.1171 99.8927 Db 99.8927 102.6800 Ab 102.6800 105.4906 Db 105.4906 106.8667 Eb:sus4 106.8667 108.2545 Eb 108.2545 108.9776 Ab 108.9776 109.6890 Db 109.6890 111.1118 Bb:min 111.1118 112.4996 F:min 112.4996 113.8757 Eb 113.8757 114.5988 Ab 114.5988 115.3102 Db 115.3102 116.6746 Bb:min 116.6746 118.0974 F:min 118.0974 119.5319 Eb 119.5319 120.9314 F:min 120.9314 122.3308 Bb:min 122.3308 123.0655 Ab 123.0655 123.7536 Db 123.7536 125.1181 Eb 125.1181 125.8645 Ab 125.8645 126.5759 Db 126.5759 127.9637 Bb:min 127.9637 129.3748 Db 129.3748 130.1095 Ab 130.1095 130.8442 Db 130.8442 131.5323 Ab 131.5323 132.2320 Db 132.2320 132.9085 Ab 132.9085 133.6548 Db 133.6548 134.3546 Ab 134.3546 135.0776 Db 135.0776 135.7540 Ab 135.7540 136.4188 Db 136.4188 137.1418 Ab 137.1418 137.8299 Db 137.8299 138.5646 Ab 138.5646 139.2644 Db 139.2644 139.9874 Ab 139.9874 140.6638 Db 140.6638 141.3986 Ab 141.3986 142.0633 Db 142.0633 142.7864 Ab 142.7864 148.1758 N 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7.76 389.738 7.77 389.738 7.78 389.738 7.79 389.738 7.8 389.738 7.81 389.738 7.82 391.995 7.83 391.995 7.84 391.995 7.85 394.266 7.86 394.266 7.87 391.995 7.88 391.995 7.89 391.995 7.9 387.493 7.91 387.493 7.92 389.738 7.93 387.493 7.94 389.738 7.95 387.493 7.96 387.493 7.97 389.738 7.98 389.738 7.99 389.738 8 391.995 8.01 387.493 8.02 389.738 8.03 389.738 8.04 387.493 8.05 387.493 8.06 391.995 8.07 389.738 8.08 389.738 8.09 389.738 8.1 391.995 8.11 391.995 8.12 391.995 8.13 389.738 8.14 389.738 8.15 389.738 8.16 389.738 8.17 389.738 8.18 391.995 8.19 391.995 8.2 391.995 8.21 389.738 8.22 394.266 8.23 394.266 8.24 394.266 8.25 391.995 8.26 391.995 8.27 389.738 8.28 389.738 8.29 387.493 8.3 389.738 8.31 387.493 8.32 389.738 8.33 389.738 8.34 387.493 8.35 394.266 8.36 394.266 8.37 391.995 8.38 394.266 8.39 396.55 8.4 396.55 8.41 396.55 8.42 394.266 8.43 394.266 8.44 396.55 8.45 391.995 8.46 394.266 8.47 394.266 8.48 396.55 8.49 396.55 8.5 396.55 8.51 394.266 8.52 394.266 8.53 396.55 8.54 398.847 8.55 391.995 8.56 391.995 8.57 391.995 8.58 398.847 8.59 396.55 8.6 398.847 8.61 398.847 8.62 396.55 8.63 -440 8.64 -440 8.65 -440 8.66 347.217 8.67 343.229 8.68 339.286 8.69 343.229 8.7 343.229 8.71 343.229 8.72 343.229 8.73 343.229 8.74 345.217 8.75 345.217 8.76 347.217 8.77 347.217 8.78 347.217 8.79 347.217 8.8 347.217 8.81 347.217 8.82 347.217 8.83 347.217 8.84 347.217 8.85 347.217 8.86 349.228 8.87 347.217 8.88 349.228 8.89 347.217 8.9 347.217 8.91 347.217 8.92 347.217 8.93 347.217 8.94 347.217 8.95 347.217 8.96 347.217 8.97 347.217 8.98 347.217 8.99 347.217 9 349.228 9.01 349.228 9.02 349.228 9.03 349.228 9.04 349.228 9.05 349.228 9.06 347.217 9.07 345.217 9.08 347.217 9.09 347.217 9.1 349.228 9.11 347.217 9.12 347.217 9.13 347.217 9.14 349.228 9.15 347.217 9.16 347.217 9.17 347.217 9.18 349.228 9.19 349.228 9.2 349.228 9.21 349.228 9.22 349.228 9.23 349.228 9.24 349.228 9.25 349.228 9.26 347.217 9.27 349.228 9.28 349.228 9.29 349.228 9.3 349.228 9.31 347.217 9.32 347.217 9.33 347.217 9.34 347.217 9.35 347.217 9.36 349.228 9.37 349.228 9.38 349.228 9.39 349.228 9.4 349.228 9.41 349.228 9.42 349.228 9.43 349.228 9.44 347.217 9.45 347.217 9.46 347.217 9.47 349.228 9.48 347.217 9.49 347.217 9.5 347.217 9.51 347.217 9.52 347.217 9.53 347.217 9.54 347.217 9.55 347.217 9.56 349.228 9.57 349.228 9.58 351.251 9.59 349.228 9.6 349.228 9.61 349.228 9.62 351.251 9.63 349.228 9.64 349.228 9.65 349.228 9.66 347.217 9.67 345.217 9.68 345.217 9.69 347.217 9.7 345.217 9.71 345.217 9.72 345.217 9.73 345.217 9.74 347.217 9.75 347.217 9.76 349.228 9.77 349.228 9.78 349.228 9.79 349.228 9.8 349.228 9.81 351.251 9.82 349.228 9.83 349.228 9.84 349.228 9.85 349.228 9.86 349.228 9.87 347.217 9.88 347.217 9.89 347.217 9.9 345.217 9.91 345.217 9.92 347.217 9.93 347.217 9.94 345.217 9.95 345.217 9.96 347.217 9.97 349.228 9.98 349.228 9.99 349.228 10 349.228 10.01 351.251 10.02 349.228 10.03 351.251 10.04 349.228 10.05 349.228 10.06 347.217 10.07 347.217 10.08 345.217 10.09 345.217 10.1 343.229 10.11 343.229 10.12 345.217 10.13 345.217 10.14 345.217 10.15 345.217 10.16 347.217 10.17 349.228 10.18 351.251 10.19 351.251 10.2 351.251 10.21 351.251 10.22 351.251 10.23 349.228 10.24 351.251 10.25 349.228 10.26 349.228 10.27 347.217 10.28 345.217 10.29 345.217 10.3 345.217 10.31 347.217 10.32 349.228 10.33 349.228 10.34 349.228 10.35 349.228 10.36 -440 10.37 -440 10.38 -440 10.39 -440 10.4 -440 10.41 -440 10.42 -440 10.43 -440 10.44 -440 10.45 -440 10.46 -440 10.47 -440 10.48 -440 10.49 -440 10.5 -440 10.51 -440 10.52 -440 10.53 -440 10.54 -440 10.55 -440 10.56 -440 10.57 -440 10.58 -440 10.59 -440 10.6 -440 10.61 -440 10.62 -440 10.63 -440 10.64 -440 10.65 -440 10.66 -440 10.67 -440 10.68 -440 10.69 -440 10.7 -440 10.71 -440 10.72 -440 10.73 -440 10.74 -440 10.75 -440 10.76 -440 10.77 -440 10.78 -440 10.79 -440 10.8 -440 10.81 -440 10.82 -440 10.83 -440 10.84 -440 10.85 -440 10.86 -440 10.87 -440 10.88 -440 10.89 -440 10.9 -440 10.91 -440 10.92 -440 10.93 -440 10.94 -440 10.95 -440 10.96 -440 10.97 -440 10.98 -440 10.99 -440 11 -440 11.01 -220 11.02 -220 11.03 -220 11.04 -220 11.05 -220 11.06 -220 11.07 -220 11.08 -220 11.09 -220 11.1 -220 11.11 -220 11.12 -220 11.13 -220 11.14 -220 11.15 -220 11.16 -220 11.17 -220 11.18 -220 11.19 -220 11.2 -220 11.21 347.217 11.22 347.217 11.23 349.228 11.24 349.228 11.25 349.228 11.26 349.228 11.27 347.217 11.28 347.217 11.29 347.217 11.3 347.217 11.31 347.217 11.32 347.217 11.33 347.217 11.34 347.217 11.35 349.228 11.36 349.228 11.37 349.228 11.38 347.217 11.39 347.217 11.4 347.217 11.41 345.217 11.42 345.217 11.43 345.217 11.44 345.217 11.45 347.217 11.46 349.228 11.47 351.251 11.48 349.228 11.49 349.228 11.5 349.228 11.51 349.228 11.52 349.228 11.53 347.217 11.54 347.217 11.55 347.217 11.56 347.217 11.57 347.217 11.58 345.217 11.59 345.217 11.6 345.217 11.61 347.217 11.62 347.217 11.63 347.217 11.64 347.217 11.65 345.217 11.66 345.217 11.67 345.217 11.68 347.217 11.69 349.228 11.7 347.217 11.71 349.228 11.72 351.251 11.73 353.286 11.74 353.286 11.75 351.251 11.76 347.217 11.77 -220 11.78 -220 11.79 -220 11.8 -220 11.81 -220 11.82 -220 11.83 -220 11.84 -220 11.85 -220 11.86 -220 11.87 -220 11.88 -220 11.89 -220 11.9 295.366 11.91 290.292 11.92 286.957 11.93 286.957 11.94 285.305 11.95 285.305 11.96 286.957 11.97 286.957 11.98 288.62 11.99 290.292 12 291.973 12.01 293.665 12.02 291.973 12.03 291.973 12.04 291.973 12.05 291.973 12.06 291.973 12.07 291.973 12.08 291.973 12.09 291.973 12.1 291.973 12.11 293.665 12.12 293.665 12.13 293.665 12.14 293.665 12.15 293.665 12.16 291.973 12.17 291.973 12.18 291.973 12.19 291.973 12.2 293.665 12.21 293.665 12.22 293.665 12.23 293.665 12.24 291.973 12.25 291.973 12.26 291.973 12.27 291.973 12.28 291.973 12.29 291.973 12.3 291.973 12.31 291.973 12.32 291.973 12.33 293.665 12.34 293.665 12.35 293.665 12.36 293.665 12.37 291.973 12.38 291.973 12.39 291.973 12.4 295.366 12.41 295.366 12.42 293.665 12.43 293.665 12.44 293.665 12.45 293.665 12.46 291.973 12.47 293.665 12.48 293.665 12.49 293.665 12.5 293.665 12.51 293.665 12.52 293.665 12.53 293.665 12.54 293.665 12.55 293.665 12.56 293.665 12.57 291.973 12.58 291.973 12.59 293.665 12.6 293.665 12.61 293.665 12.62 293.665 12.63 293.665 12.64 293.665 12.65 293.665 12.66 293.665 12.67 293.665 12.68 293.665 12.69 293.665 12.7 293.665 12.71 293.665 12.72 291.973 12.73 293.665 12.74 293.665 12.75 293.665 12.76 293.665 12.77 293.665 12.78 293.665 12.79 293.665 12.8 293.665 12.81 293.665 12.82 293.665 12.83 293.665 12.84 293.665 12.85 293.665 12.86 293.665 12.87 293.665 12.88 291.973 12.89 293.665 12.9 293.665 12.91 293.665 12.92 293.665 12.93 291.973 12.94 293.665 12.95 293.665 12.96 293.665 12.97 293.665 12.98 293.665 12.99 293.665 13 293.665 13.01 293.665 13.02 291.973 13.03 293.665 13.04 293.665 13.05 293.665 13.06 295.366 13.07 297.077 13.08 297.077 13.09 297.077 13.1 297.077 13.11 293.665 13.12 291.973 13.13 290.292 13.14 286.957 13.15 282.028 13.16 272.421 13.17 264.666 13.18 264.666 13.19 266.199 13.2 270.852 13.21 267.741 13.22 255.65 13.23 255.65 13.24 258.621 13.25 258.621 13.26 257.131 13.27 258.621 13.28 260.119 13.29 260.119 13.3 260.119 13.31 263.141 13.32 261.626 13.33 257.131 13.34 258.621 13.35 260.119 13.36 260.119 13.37 260.119 13.38 261.626 13.39 263.141 13.4 260.119 13.41 260.119 13.42 260.119 13.43 260.119 13.44 257.131 13.45 260.119 13.46 261.626 13.47 258.621 13.48 258.621 13.49 260.119 13.5 260.119 13.51 255.65 13.52 255.65 13.53 260.119 13.54 261.626 13.55 258.621 13.56 258.621 13.57 261.626 13.58 260.119 13.59 260.119 13.6 261.626 13.61 263.141 13.62 260.119 13.63 261.626 13.64 260.119 13.65 260.119 13.66 258.621 13.67 260.119 13.68 260.119 13.69 260.119 13.7 258.621 13.71 261.626 13.72 260.119 13.73 260.119 13.74 260.119 13.75 261.626 13.76 260.119 13.77 260.119 13.78 260.119 13.79 261.626 13.8 260.119 13.81 260.119 13.82 260.119 13.83 261.626 13.84 260.119 13.85 260.119 13.86 260.119 13.87 260.119 13.88 260.119 13.89 260.119 13.9 260.119 13.91 258.621 13.92 258.621 13.93 260.119 13.94 260.119 13.95 260.119 13.96 260.119 13.97 260.119 13.98 260.119 13.99 260.119 14 260.119 14.01 260.119 14.02 258.621 14.03 258.621 14.04 260.119 14.05 260.119 14.06 260.119 14.07 260.119 14.08 260.119 14.09 260.119 14.1 260.119 14.11 261.626 14.12 263.141 14.13 261.626 14.14 263.141 14.15 263.141 14.16 261.626 14.17 261.626 14.18 261.626 14.19 261.626 14.2 260.119 14.21 260.119 14.22 260.119 14.23 258.621 14.24 258.621 14.25 260.119 14.26 258.621 14.27 260.119 14.28 260.119 14.29 261.626 14.3 261.626 14.31 263.141 14.32 263.141 14.33 263.141 14.34 263.141 14.35 263.141 14.36 263.141 14.37 261.626 14.38 261.626 14.39 263.141 14.4 260.119 14.41 260.119 14.42 260.119 14.43 260.119 14.44 258.621 14.45 260.119 14.46 258.621 14.47 258.621 14.48 260.119 14.49 260.119 14.5 261.626 14.51 261.626 14.52 261.626 14.53 263.141 14.54 263.141 14.55 261.626 14.56 261.626 14.57 263.141 14.58 260.119 14.59 260.119 14.6 261.626 14.61 258.621 14.62 257.131 14.63 260.119 14.64 260.119 14.65 257.131 14.66 255.65 14.67 260.119 14.68 260.119 14.69 258.621 14.7 260.119 14.71 264.666 14.72 263.141 14.73 261.626 14.74 263.141 14.75 264.666 14.76 261.626 14.77 263.141 14.78 263.141 14.79 260.119 14.8 258.621 14.81 261.626 14.82 260.119 14.83 255.65 14.84 257.131 14.85 260.119 14.86 258.621 14.87 257.131 14.88 260.119 14.89 261.626 14.9 260.119 14.91 261.626 14.92 263.141 14.93 264.666 14.94 263.141 14.95 263.141 14.96 263.141 14.97 261.626 14.98 261.626 14.99 263.141 15 260.119 15.01 258.621 15.02 260.119 15.03 260.119 15.04 257.131 15.05 258.621 15.06 260.119 15.07 260.119 15.08 260.119 15.09 261.626 15.1 261.626 15.11 261.626 15.12 261.626 15.13 263.141 15.14 261.626 15.15 261.626 15.16 263.141 15.17 261.626 15.18 260.119 15.19 260.119 15.2 260.119 15.21 258.621 15.22 257.131 15.23 260.119 15.24 260.119 15.25 258.621 15.26 260.119 15.27 263.141 15.28 -263.141 15.29 -261.626 15.3 -263.141 15.31 -220 15.32 -220 15.33 -220 15.34 -220 15.35 -220 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323.965 16.97 311.127 16.98 307.553 16.99 311.127 17 311.127 17.01 309.335 17.02 311.127 17.03 314.742 17.04 305.782 17.05 295.366 17.06 290.292 17.07 288.62 17.08 288.62 17.09 291.973 17.1 293.665 17.11 290.292 17.12 290.292 17.13 291.973 17.14 293.665 17.15 295.366 17.16 291.973 17.17 290.292 17.18 291.973 17.19 295.366 17.2 295.366 17.21 295.366 17.22 293.665 17.23 291.973 17.24 293.665 17.25 295.366 17.26 293.665 17.27 291.973 17.28 291.973 17.29 293.665 17.3 293.665 17.31 293.665 17.32 293.665 17.33 291.973 17.34 293.665 17.35 293.665 17.36 295.366 17.37 293.665 17.38 293.665 17.39 293.665 17.4 293.665 17.41 295.366 17.42 295.366 17.43 293.665 17.44 293.665 17.45 293.665 17.46 293.665 17.47 293.665 17.48 293.665 17.49 293.665 17.5 293.665 17.51 293.665 17.52 293.665 17.53 293.665 17.54 291.973 17.55 288.62 17.56 -297.077 17.57 -297.077 17.58 -297.077 17.59 -297.077 17.6 -297.077 17.61 -297.077 17.62 -297.077 17.63 -297.077 17.64 -295.366 17.65 -293.665 17.66 -293.665 17.67 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329.628 9.03 327.729 9.04 323.965 9.05 311.127 9.06 300.529 9.07 288.62 9.08 283.661 9.09 277.183 9.1 275.586 9.11 278.788 9.12 282.028 9.13 291.973 9.14 300.529 9.15 311.127 9.16 323.965 9.17 329.628 9.18 331.537 9.19 329.628 9.2 323.965 9.21 312.929 9.22 300.529 9.23 295.366 9.24 290.292 9.25 288.62 9.26 286.957 9.27 286.957 9.28 290.292 9.29 293.665 9.3 293.665 9.31 288.62 9.32 282.028 9.33 264.666 9.34 -440 9.35 -440 9.36 -440 9.37 -440 9.38 -440 9.39 -440 9.4 -440 9.41 -440 9.42 -440 9.43 -440 9.44 -440 9.45 -440 9.46 -440 9.47 -440 9.48 -440 9.49 -440 9.5 -440 9.51 -440 9.52 -440 9.53 -440 9.54 -440 9.55 -333.458 9.56 -331.537 9.57 -331.537 9.58 -331.537 9.59 -331.537 9.6 -331.537 9.61 -329.628 9.62 -329.628 9.63 -329.628 9.64 -329.628 9.65 -329.628 9.66 -329.628 9.67 -440 9.68 -440 9.69 -440 9.7 -440 9.71 -440 9.72 -440 9.73 -440 9.74 -440 9.75 -440 9.76 -440 9.77 -440 9.78 -440 9.79 -440 9.8 -440 9.81 -440 9.82 -440 9.83 -440 9.84 -440 9.85 -440 9.86 -440 9.87 -440 9.88 -440 9.89 -440 9.9 -440 9.91 -440 9.92 -458.155 9.93 -458.155 9.94 471.58 9.95 485.399 9.96 523.251 9.97 541.704 9.98 554.365 9.99 557.577 10 551.172 10.01 538.584 10.02 526.282 10.03 499.622 10.04 485.399 10.05 468.864 10.06 460.809 10.07 458.155 10.08 458.155 10.09 466.164 10.1 474.312 10.11 479.823 10.12 496.744 10.13 514.262 10.14 532.397 10.15 544.842 10.16 554.365 10.17 557.577 10.18 551.172 10.19 535.482 10.2 517.241 10.21 496.744 10.22 479.823 10.23 460.809 10.24 447.691 10.25 450.285 10.26 463.479 10.27 471.58 10.28 488.211 10.29 502.516 10.3 523.251 10.31 535.482 10.32 547.998 10.33 554.365 10.34 551.172 10.35 541.704 10.36 526.282 10.37 505.427 10.38 488.211 10.39 471.58 10.4 458.155 10.41 455.517 10.42 460.809 10.43 468.864 10.44 491.039 10.45 508.355 10.46 523.251 10.47 538.584 10.48 551.172 10.49 551.172 10.5 541.704 10.51 532.397 10.52 -508.355 10.53 -485.399 10.54 -468.864 10.55 -458.155 10.56 -452.893 10.57 -455.517 10.58 -463.479 10.59 -474.312 10.6 -485.399 10.61 -493.883 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437.466 12.06 455.517 12.07 468.864 12.08 479.823 12.09 482.603 12.1 479.823 12.11 477.06 12.12 466.164 12.13 458.155 12.14 445.113 12.15 434.946 12.16 427.474 12.17 425.012 12.18 427.474 12.19 432.441 12.2 440 12.21 445.113 12.22 468.864 12.23 482.603 12.24 493.883 12.25 493.883 12.26 471.58 12.27 460.809 12.28 442.549 12.29 445.113 12.3 452.893 12.31 450.285 12.32 434.946 12.33 427.474 12.34 432.441 12.35 440 12.36 450.285 12.37 466.164 12.38 479.823 12.39 482.603 12.4 479.823 12.41 471.58 12.42 463.479 12.43 450.285 12.44 442.549 12.45 432.441 12.46 417.711 12.47 412.913 12.48 405.819 12.49 405.819 12.5 408.17 12.51 415.305 12.52 422.564 12.53 434.946 12.54 445.113 12.55 460.809 12.56 474.312 12.57 482.603 12.58 488.211 12.59 491.039 12.6 485.399 12.61 474.312 12.62 463.479 12.63 452.893 12.64 432.441 12.65 422.564 12.66 405.819 12.67 403.482 12.68 403.482 12.69 405.819 12.7 417.711 12.71 432.441 12.72 450.285 12.73 474.312 12.74 491.039 12.75 511.3 12.76 514.262 12.77 511.3 12.78 493.883 12.79 477.06 12.8 463.479 12.81 450.285 12.82 427.474 12.83 417.711 12.84 410.535 12.85 408.17 12.86 412.913 12.87 422.564 12.88 437.466 12.89 447.691 12.9 463.479 12.91 471.58 12.92 479.823 12.93 485.399 12.94 482.603 12.95 463.479 12.96 -288.62 12.97 -560.807 12.98 -560.807 12.99 -560.807 13 -283.661 13.01 -288.62 13.02 -297.077 13.03 -305.782 13.04 -322.099 13.05 -335.389 13.06 -355.333 13.07 544.842 13.08 551.172 13.09 544.842 13.1 532.397 13.11 508.355 13.12 496.744 13.13 482.603 13.14 474.312 13.15 450.285 13.16 445.113 13.17 442.549 13.18 440 13.19 442.549 13.2 450.285 13.21 460.809 13.22 482.603 13.23 -333.458 13.24 -331.537 13.25 -331.537 13.26 331.537 13.27 331.537 13.28 333.458 13.29 333.458 13.3 333.458 13.31 331.537 13.32 329.628 13.33 329.628 13.34 329.628 13.35 329.628 13.36 329.628 13.37 329.628 13.38 329.628 13.39 325.841 13.4 327.729 13.41 329.628 13.42 325.841 13.43 327.729 13.44 329.628 13.45 331.537 13.46 329.628 13.47 329.628 13.48 329.628 13.49 329.628 13.5 325.841 13.51 327.729 13.52 329.628 13.53 327.729 13.54 327.729 13.55 331.537 13.56 333.458 13.57 331.537 13.58 329.628 13.59 325.841 13.6 329.628 13.61 333.458 13.62 329.628 13.63 329.628 13.64 327.729 13.65 331.537 13.66 325.841 13.67 327.729 13.68 329.628 13.69 325.841 13.7 327.729 13.71 331.537 13.72 333.458 13.73 331.537 13.74 347.217 13.75 359.461 13.76 337.332 13.77 331.537 13.78 331.537 13.79 331.537 13.8 331.537 13.81 331.537 13.82 496.744 13.83 491.039 13.84 488.211 13.85 479.823 13.86 -322.099 13.87 -437.466 13.88 -437.466 13.89 597.596 13.9 618.67 13.91 212.506 13.92 212.506 13.93 208.855 13.94 192.631 13.95 184.997 13.96 171.614 13.97 168.666 13.98 164.814 13.99 164.814 14 165.769 14.01 169.643 14.02 174.614 14.03 186.069 14.04 191.521 14.05 200.579 14.06 205.267 14.07 206.456 14.08 205.267 14.09 201.741 14.1 193.746 14.11 184.997 14.12 175.626 14.13 171.614 14.14 168.666 14.15 168.666 14.16 173.608 14.17 176.643 14.18 181.819 14.19 191.521 14.2 197.133 14.21 202.91 14.22 205.267 14.23 200.579 14.24 195.998 14.25 184.997 14.26 179.731 14.27 177.666 14.28 177.666 14.29 179.731 14.3 183.932 14.31 187.147 14.32 187.147 14.33 187.147 14.34 -220 14.35 -220 14.36 -220 14.37 -220 14.38 -220 14.39 -220 14.4 -220 14.41 -220 14.42 -220 14.43 -220 14.44 -220 14.45 -220 14.46 -220 14.47 -220 14.48 -220 14.49 -220 14.5 -220 14.51 -220 14.52 -220 14.53 -220 14.54 -220 14.55 -220 14.56 -220 14.57 -220 14.58 -220 14.59 -220 14.6 -220 14.61 314.742 14.62 312.929 14.63 316.565 14.64 320.244 14.65 323.965 14.66 323.965 14.67 322.099 14.68 320.244 14.69 318.399 14.7 312.929 14.71 311.127 14.72 316.565 14.73 320.244 14.74 323.965 14.75 323.965 14.76 325.841 14.77 325.841 14.78 325.841 14.79 323.965 14.8 320.244 14.81 316.565 14.82 314.742 14.83 312.929 14.84 312.929 14.85 311.127 14.86 309.335 14.87 314.742 14.88 322.099 14.89 323.965 14.9 329.628 14.91 335.389 14.92 343.229 14.93 347.217 14.94 345.217 14.95 343.229 14.96 339.286 14.97 331.537 14.98 323.965 14.99 320.244 15 314.742 15.01 312.929 15.02 309.335 15.03 312.929 15.04 320.244 15.05 323.965 15.06 329.628 15.07 337.332 15.08 351.251 15.09 355.333 15.1 355.333 15.11 353.286 15.12 345.217 15.13 337.332 15.14 333.458 15.15 325.841 15.16 318.399 15.17 298.798 15.18 288.62 15.19 290.292 15.2 309.335 15.21 314.742 15.22 316.565 15.23 322.099 15.24 333.458 15.25 337.332 15.26 343.229 15.27 347.217 15.28 353.286 15.29 357.391 15.3 361.544 15.31 361.544 15.32 355.333 15.33 341.252 15.34 333.458 15.35 331.537 15.36 335.389 15.37 351.251 15.38 -220 15.39 -220 15.4 -220 15.41 -220 15.42 238.53 15.43 237.156 15.44 242.699 15.45 255.65 15.46 272.421 15.47 288.62 15.48 298.798 15.49 311.127 15.5 320.244 15.51 325.841 15.52 322.099 15.53 316.565 15.54 307.553 15.55 298.798 15.56 288.62 15.57 280.403 15.58 266.199 15.59 261.626 15.6 258.621 15.61 258.621 15.62 263.141 15.63 267.741 15.64 277.183 15.65 288.62 15.66 298.798 15.67 311.127 15.68 316.565 15.69 318.399 15.7 320.244 15.71 312.929 15.72 305.782 15.73 300.529 15.74 290.292 15.75 280.403 15.76 269.292 15.77 263.141 15.78 260.119 15.79 258.621 15.8 260.119 15.81 263.141 15.82 266.199 15.83 275.586 15.84 282.028 15.85 288.62 15.86 293.665 15.87 297.077 15.88 298.798 15.89 293.665 15.9 288.62 15.91 282.028 15.92 269.292 15.93 263.141 15.94 257.131 15.95 252.714 15.96 249.811 15.97 246.942 15.98 245.519 15.99 244.105 16 246.942 16.01 252.714 16.02 260.119 16.03 264.666 16.04 263.141 16.05 244.105 16.06 246.942 16.07 252.714 16.08 245.519 16.09 229.078 16.1 223.846 16.11 221.274 16.12 217.473 16.13 216.221 16.14 216.221 16.15 218.733 16.16 222.556 16.17 225.142 16.18 229.078 16.19 229.078 16.2 226.446 16.21 226.446 16.22 223.846 16.23 220 16.24 220 16.25 220 16.26 221.274 16.27 217.473 16.28 217.473 16.29 218.733 16.3 220 16.31 220 16.32 220 16.33 220 16.34 218.733 16.35 218.733 16.36 216.221 16.37 213.737 16.38 210.065 16.39 205.267 16.4 201.741 16.41 199.424 16.42 198.275 16.43 200.579 16.44 205.267 16.45 213.737 16.46 223.846 16.47 230.405 16.48 235.79 16.49 237.156 16.5 235.79 16.51 235.79 16.52 231.739 16.53 227.758 16.54 221.274 16.55 216.221 16.56 212.506 16.57 208.855 16.58 208.855 16.59 208.855 16.6 210.065 16.61 214.975 16.62 220 16.63 226.446 16.64 230.405 16.65 234.432 16.66 237.156 16.67 237.156 16.68 237.156 16.69 234.432 16.7 230.405 16.71 226.446 16.72 220 16.73 216.221 16.74 213.737 16.75 213.737 16.76 213.737 16.77 217.473 16.78 221.274 16.79 226.446 16.8 230.405 16.81 -234.432 16.82 -235.79 16.83 -237.156 16.84 -238.53 16.85 -237.156 16.86 -234.432 16.87 -231.739 16.88 -226.446 16.89 -225.142 16.9 -226.446 16.91 -220 16.92 -220 16.93 -220 16.94 -220 16.95 -220 16.96 305.782 16.97 302.27 16.98 309.335 16.99 314.742 17 318.399 17.01 318.399 17.02 316.565 17.03 314.742 17.04 305.782 17.05 298.798 17.06 291.973 17.07 286.957 17.08 283.661 17.09 282.028 17.1 282.028 17.11 285.305 17.12 288.62 17.13 300.529 17.14 311.127 17.15 323.965 17.16 331.537 17.17 337.332 17.18 337.332 17.19 331.537 17.2 320.244 17.21 311.127 17.22 295.366 17.23 288.62 17.24 280.403 17.25 277.183 17.26 273.999 17.27 275.586 17.28 280.403 17.29 286.957 17.3 290.292 17.31 297.077 17.32 300.529 17.33 305.782 17.34 307.553 17.35 309.335 17.36 311.127 17.37 316.565 17.38 316.565 17.39 314.742 17.4 311.127 17.41 307.553 17.42 297.077 17.43 291.973 17.44 286.957 17.45 282.028 17.46 280.403 17.47 286.957 17.48 288.62 17.49 290.292 17.5 291.973 17.51 293.665 17.52 297.077 17.53 298.798 17.54 304.021 17.55 307.553 17.56 305.782 17.57 304.021 17.58 302.27 17.59 297.077 17.6 293.665 17.61 291.973 17.62 288.62 17.63 288.62 17.64 288.62 17.65 290.292 17.66 291.973 17.67 295.366 17.68 298.798 17.69 311.127 17.7 320.244 17.71 320.244 17.72 295.366 17.73 293.665 17.74 291.973 17.75 293.665 17.76 291.973 17.77 290.292 17.78 291.973 17.79 298.798 17.8 304.021 17.81 311.127 17.82 322.099 17.83 331.537 17.84 337.332 17.85 341.252 17.86 341.252 17.87 339.286 17.88 337.332 17.89 335.389 17.9 331.537 17.91 327.729 17.92 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291.953 13.456 291.422 13.462 291.275 13.468 291.788 13.473 292.458 13.479 292.716 13.485 292.425 13.491 292.040 13.497 291.920 13.502 292.215 13.508 292.510 13.514 292.806 13.520 293.101 13.526 292.992 13.531 292.436 13.537 291.381 13.543 290.815 13.549 290.924 13.555 291.408 13.560 291.874 13.566 291.958 13.572 292.492 13.578 292.801 13.584 292.998 13.589 292.681 13.595 292.315 13.601 292.368 13.607 292.732 13.613 293.096 13.619 293.124 13.624 291.686 13.630 291.516 13.636 291.854 13.642 292.191 13.648 291.180 13.653 291.685 13.659 288.768 13.665 290.367 13.671 290.283 13.677 286.955 13.682 296.659 13.688 297.672 13.694 300.000 13.700 302.328 13.706 313.795 13.711 312.377 13.717 0.000 13.723 0.000 13.729 0.000 13.735 0.000 13.740 0.000 13.746 0.000 13.752 0.000 13.758 0.000 13.764 0.000 13.769 0.000 13.775 0.000 13.781 0.000 13.787 0.000 13.793 0.000 13.798 0.000 13.804 0.000 13.810 0.000 13.816 0.000 13.822 0.000 13.827 0.000 13.833 0.000 13.839 0.000 13.845 0.000 13.851 0.000 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210.872 14.669 201.114 14.675 207.279 14.681 202.014 14.687 215.523 14.692 0.000 14.698 0.000 14.704 0.000 14.710 0.000 14.716 0.000 14.721 0.000 14.727 0.000 14.733 0.000 14.739 0.000 14.745 0.000 14.750 0.000 14.756 0.000 14.762 0.000 14.768 0.000 14.774 0.000 14.780 0.000 14.785 0.000 14.791 0.000 14.797 244.012 14.803 246.974 14.809 246.974 14.814 244.905 14.820 244.905 14.826 250.436 14.832 253.202 14.838 254.777 14.843 256.840 14.849 258.908 14.855 260.974 14.861 262.055 14.867 263.137 14.872 263.725 14.878 264.312 14.884 264.677 14.890 264.175 14.896 264.367 14.901 264.559 14.907 264.750 14.913 264.942 14.919 263.709 14.925 263.571 14.930 263.528 14.936 263.515 14.942 263.502 14.948 263.494 14.954 263.298 14.959 263.366 14.965 262.873 14.971 262.529 14.977 262.139 14.983 261.749 14.988 261.358 14.994 261.352 15.000 261.153 15.006 261.185 15.012 261.256 15.018 261.468 15.023 261.726 15.029 262.025 15.035 262.325 15.041 262.451 15.047 262.482 15.052 262.376 15.058 262.180 15.064 261.935 15.070 261.691 15.076 261.447 15.081 261.382 15.087 261.278 15.093 261.173 15.099 261.069 15.105 260.964 15.110 260.860 15.116 260.755 15.122 260.651 15.128 260.546 15.134 260.688 15.139 260.634 15.145 260.581 15.151 260.523 15.157 260.466 15.163 260.409 15.168 260.352 15.174 260.336 15.180 260.418 15.186 260.590 15.192 260.762 15.197 260.934 15.203 261.105 15.209 261.277 15.215 261.239 15.221 261.138 15.226 260.964 15.232 260.888 15.238 260.949 15.244 260.935 15.250 260.925 15.256 260.913 15.261 260.875 15.267 260.740 15.273 260.606 15.279 260.471 15.285 260.534 15.290 260.503 15.296 260.544 15.302 260.634 15.308 260.766 15.314 260.825 15.319 260.828 15.325 260.738 15.331 260.645 15.337 260.459 15.343 260.274 15.348 260.221 15.354 260.354 15.360 260.615 15.366 260.876 15.372 261.228 15.377 261.439 15.383 261.649 15.389 261.860 15.395 262.070 15.401 262.280 15.406 262.491 15.412 262.576 15.418 262.662 15.424 262.632 15.430 262.431 15.435 262.230 15.441 262.029 15.447 261.828 15.453 261.627 15.459 262.335 15.464 262.647 15.470 262.960 15.476 263.729 15.482 264.490 15.488 265.274 15.494 266.046 15.499 265.990 15.505 265.935 15.511 265.782 15.517 265.893 15.523 266.285 15.528 266.068 15.534 266.081 15.540 266.093 15.546 266.106 15.552 263.458 15.557 262.392 15.563 263.141 15.569 263.011 15.575 262.864 15.581 262.718 15.586 259.945 15.592 261.070 15.598 261.227 15.604 254.012 15.610 250.545 15.615 232.944 15.621 217.962 15.627 213.770 15.633 0.000 15.639 0.000 15.644 0.000 15.650 0.000 15.656 0.000 15.662 236.290 15.668 233.891 15.673 231.552 15.679 231.552 15.685 229.237 15.691 230.360 15.697 230.117 15.702 229.874 15.708 229.631 15.714 229.388 15.720 231.426 15.726 230.276 15.732 230.561 15.737 231.130 15.743 231.698 15.749 232.022 15.755 232.237 15.761 232.360 15.766 232.353 15.772 232.320 15.778 232.391 15.784 232.594 15.790 232.673 15.795 232.832 15.801 232.991 15.807 233.694 15.813 233.866 15.819 233.805 15.824 233.740 15.830 233.545 15.836 233.350 15.842 233.156 15.848 232.545 15.853 232.182 15.859 231.696 15.865 231.407 15.871 231.199 15.877 231.245 15.882 231.503 15.888 232.034 15.894 232.565 15.900 233.096 15.906 231.291 15.911 229.893 15.917 228.495 15.923 229.367 15.929 229.263 15.935 229.064 15.940 229.872 15.946 230.680 15.952 231.488 15.958 231.538 15.964 231.588 15.970 231.276 15.975 231.646 15.981 231.517 15.987 231.484 15.993 232.211 15.999 232.389 16.004 232.086 16.010 231.802 16.016 231.538 16.022 231.865 16.028 231.748 16.033 231.631 16.039 231.978 16.045 231.805 16.051 232.178 16.057 233.030 16.062 233.099 16.068 233.559 16.074 231.690 16.080 232.106 16.086 231.748 16.091 231.391 16.097 231.033 16.103 232.983 16.109 229.601 16.115 226.424 16.120 220.914 16.126 220.901 16.132 222.503 16.138 223.761 16.144 225.019 16.149 224.015 16.155 221.998 16.161 220.487 16.167 218.976 16.173 216.464 16.179 214.951 16.184 214.215 16.190 214.138 16.196 214.315 16.202 214.492 16.208 214.668 16.213 214.089 16.219 214.383 16.225 214.678 16.231 214.973 16.237 215.295 16.242 215.963 16.248 216.630 16.254 217.298 16.260 217.966 16.266 217.981 16.271 218.444 16.277 218.682 16.283 218.890 16.289 219.051 16.295 219.183 16.300 219.346 16.306 219.584 16.312 219.822 16.318 219.985 16.324 219.868 16.329 219.624 16.335 219.237 16.341 218.921 16.347 218.997 16.353 218.881 16.358 218.764 16.364 218.926 16.370 219.033 16.376 219.239 16.382 219.395 16.387 219.261 16.393 219.295 16.399 219.315 16.405 219.288 16.411 219.318 16.417 219.347 16.422 219.414 16.428 219.480 16.434 219.546 16.440 219.628 16.446 219.953 16.451 219.875 16.457 219.955 16.463 219.939 16.469 219.829 16.475 219.668 16.480 219.452 16.486 219.386 16.492 219.317 16.498 219.188 16.504 219.146 16.509 219.175 16.515 219.204 16.521 219.277 16.527 219.416 16.533 219.522 16.538 219.628 16.544 219.734 16.550 219.840 16.556 219.946 16.562 219.811 16.567 219.704 16.573 219.606 16.579 219.417 16.585 219.321 16.591 219.279 16.596 219.310 16.602 219.452 16.608 219.781 16.614 219.873 16.620 220.083 16.625 220.234 16.631 220.385 16.637 220.451 16.643 220.415 16.649 220.197 16.655 220.017 16.660 219.813 16.666 219.622 16.672 219.607 16.678 219.592 16.684 219.618 16.689 219.607 16.695 219.596 16.701 219.585 16.707 219.574 16.713 220.236 16.718 220.464 16.724 220.651 16.730 220.303 16.736 220.456 16.742 220.609 16.747 220.761 16.753 220.556 16.759 220.633 16.765 220.496 16.771 220.068 16.776 220.164 16.782 220.166 16.788 220.231 16.794 220.296 16.800 220.323 16.805 220.345 16.811 220.289 16.817 220.077 16.823 219.482 16.829 219.607 16.834 218.957 16.840 218.989 16.846 219.184 16.852 219.727 16.858 219.175 16.863 218.402 16.869 218.015 16.875 218.281 16.881 218.875 16.887 219.470 16.893 220.064 16.898 220.659 16.904 221.253 16.910 221.215 16.916 221.821 16.922 221.469 16.927 223.713 16.933 222.472 16.939 219.360 16.945 216.247 16.951 211.003 16.956 198.552 16.962 0.000 16.968 0.000 16.974 0.000 16.980 0.000 16.985 0.000 16.991 0.000 16.997 0.000 17.003 0.000 17.009 0.000 17.014 0.000 17.020 0.000 17.026 0.000 17.032 0.000 17.038 238.881 17.043 236.751 17.049 236.751 17.055 236.751 17.061 234.495 17.067 234.291 17.072 234.088 17.078 233.801 17.084 233.398 17.090 232.190 17.096 230.981 17.101 230.542 17.107 230.822 17.113 231.101 17.119 231.419 17.125 231.737 17.131 232.054 17.136 232.372 17.142 231.424 17.148 231.742 17.154 232.495 17.160 232.943 17.165 233.064 17.171 233.185 17.177 233.123 17.183 233.256 17.189 233.390 17.194 233.217 17.200 233.062 17.206 232.874 17.212 232.603 17.218 232.612 17.223 232.502 17.229 232.392 17.235 232.283 17.241 232.242 17.247 232.201 17.252 232.318 17.258 232.435 17.264 232.488 17.270 232.542 17.276 232.595 17.281 232.649 17.287 232.625 17.293 232.358 17.299 231.973 17.305 231.823 17.310 232.177 17.316 232.413 17.322 232.164 17.328 231.714 17.334 231.263 17.340 230.994 17.345 230.230 17.351 229.504 17.357 229.007 17.363 229.357 17.369 229.708 17.374 230.058 17.380 232.713 17.386 229.531 17.392 228.337 17.398 221.838 17.403 217.991 17.409 221.729 17.415 0.000 17.421 0.000 17.427 0.000 17.432 0.000 17.438 0.000 17.444 0.000 17.450 0.000 17.456 0.000 17.461 0.000 17.467 273.483 17.473 268.013 17.479 265.333 17.485 265.333 17.490 265.333 17.496 267.051 17.502 266.486 17.508 266.310 17.514 266.133 17.519 265.957 17.525 265.780 17.531 265.604 17.537 265.428 17.543 265.252 17.548 265.075 17.554 264.899 17.560 264.723 17.566 263.349 17.572 262.828 17.578 262.335 17.583 261.939 17.589 261.830 17.595 261.578 17.601 261.397 17.607 261.235 17.612 261.332 17.618 261.322 17.624 261.102 17.630 261.070 17.636 260.943 17.641 261.100 17.647 261.044 17.653 261.031 17.659 261.235 17.665 261.116 17.670 261.126 17.676 261.136 17.682 260.941 17.688 260.745 17.694 260.664 17.699 260.817 17.705 260.970 17.711 260.966 17.717 260.923 17.723 260.879 17.728 260.836 17.734 260.685 17.740 260.959 17.746 260.668 17.752 260.760 17.757 260.792 17.763 260.943 17.769 261.127 17.775 261.310 17.781 261.494 17.786 261.385 17.792 261.430 17.798 261.475 17.804 261.520 17.810 261.624 17.816 261.728 17.821 261.719 17.827 261.576 17.833 261.374 17.839 261.171 17.845 260.969 17.850 261.064 17.856 261.068 17.862 260.964 17.868 260.860 17.874 260.696 17.879 260.334 17.885 259.973 17.891 259.912 17.897 259.870 17.903 259.934 17.908 260.061 17.914 260.242 17.920 260.396 17.926 260.297 17.932 260.166 17.937 259.833 17.943 259.602 17.949 259.590 17.955 259.648 17.961 259.751 17.966 259.855 17.972 259.959 17.978 260.062 17.984 260.166 17.990 260.269 17.995 260.373 18.001 260.316 18.007 260.260 18.013 260.203 18.019 260.108 18.024 260.013 18.030 259.918 18.036 259.811 18.042 259.740 18.048 259.765 18.054 259.957 18.059 260.325 18.065 260.779 18.071 261.294 18.077 261.839 18.083 262.195 18.088 262.415 18.094 262.562 18.100 262.559 18.106 262.556 18.112 262.424 18.117 262.205 18.123 261.817 18.129 261.429 18.135 260.938 18.141 260.652 18.146 260.464 18.152 260.616 18.158 260.879 18.164 261.140 18.170 261.403 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261.317 18.564 261.492 18.570 261.735 18.576 261.979 18.582 262.096 18.588 262.260 18.593 262.149 18.599 261.988 18.605 261.609 18.611 261.230 18.617 260.799 18.622 260.394 18.628 260.415 18.634 260.523 18.640 260.632 18.646 260.741 18.651 260.559 18.657 260.580 18.663 260.448 18.669 260.706 18.675 260.965 18.680 261.223 18.686 261.116 18.692 261.106 18.698 261.114 18.704 261.205 18.709 261.343 18.715 261.457 18.721 261.477 18.727 261.389 18.733 261.271 18.739 261.149 18.744 261.028 18.750 261.010 18.756 260.993 18.762 260.975 18.768 260.958 18.773 261.109 18.779 261.259 18.785 261.461 18.791 261.643 18.797 261.520 18.802 261.396 18.808 261.273 18.814 261.150 18.820 260.914 18.826 260.677 18.831 260.689 18.837 260.790 18.843 260.892 18.849 261.050 18.855 261.210 18.860 261.380 18.866 261.636 18.872 261.893 18.878 262.167 18.884 262.356 18.889 262.498 18.895 262.466 18.901 262.294 18.907 261.977 18.913 261.601 18.918 261.174 18.924 261.045 18.930 261.044 18.936 261.195 18.942 261.347 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144.078 5.799 143.734 5.805 143.200 5.811 142.745 5.817 142.158 5.822 129.735 5.828 106.572 5.834 0.000 5.840 283.051 5.846 199.343 5.851 0.000 5.857 0.000 5.863 0.000 5.869 0.000 5.875 0.000 5.880 0.000 5.886 137.609 5.892 146.087 5.898 139.128 5.904 137.523 5.909 135.669 5.915 134.350 5.921 133.950 5.927 134.406 5.933 135.017 5.939 135.589 5.944 136.333 5.950 137.181 5.956 138.299 5.962 139.363 5.968 140.020 5.973 138.240 5.979 134.818 5.985 130.892 5.991 0.000 5.997 0.000 6.002 136.395 6.008 136.896 6.014 136.682 6.020 136.752 6.026 136.602 6.031 136.343 6.037 136.195 6.043 135.614 6.049 135.292 6.055 134.907 6.060 134.731 6.066 135.016 6.072 138.622 6.078 135.646 6.084 59.478 6.089 92.504 6.095 0.000 6.101 0.000 6.107 152.237 6.113 151.302 6.118 150.770 6.124 150.343 6.130 149.968 6.136 149.609 6.142 149.237 6.147 148.620 6.153 147.723 6.159 146.423 6.165 144.993 6.171 143.626 6.177 144.184 6.182 142.749 6.188 146.565 6.194 141.931 6.200 150.988 6.206 161.110 6.211 166.321 6.217 169.042 6.223 194.970 6.229 199.596 6.235 201.784 6.240 203.927 6.246 205.129 6.252 0.000 6.258 0.000 6.264 0.000 6.269 0.000 6.275 163.600 6.281 187.502 6.287 179.521 6.293 175.152 6.298 173.982 6.304 174.651 6.310 176.276 6.316 177.742 6.322 178.566 6.327 178.431 6.333 177.158 6.339 175.376 6.345 173.145 6.351 170.471 6.356 168.807 6.362 166.940 6.368 164.829 6.374 162.646 6.380 161.719 6.385 160.810 6.391 159.280 6.397 157.301 6.403 150.785 6.409 131.414 6.415 123.023 6.420 125.235 6.426 127.654 6.432 187.321 6.438 192.200 6.444 193.789 6.449 194.662 6.455 195.149 6.461 195.690 6.467 196.435 6.473 197.345 6.478 198.108 6.484 198.512 6.490 198.499 6.496 198.122 6.502 197.419 6.507 196.319 6.513 194.176 6.519 192.117 6.525 191.320 6.531 192.795 6.536 193.154 6.542 192.749 6.548 193.257 6.554 193.970 6.560 194.638 6.565 195.376 6.571 196.011 6.577 196.337 6.583 196.186 6.589 195.308 6.594 193.607 6.600 190.944 6.606 187.088 6.612 175.479 6.618 169.502 6.623 196.218 6.629 201.132 6.635 205.717 6.641 209.010 6.647 208.958 6.653 208.828 6.658 208.521 6.664 208.272 6.670 208.346 6.676 208.111 6.682 207.832 6.687 207.529 6.693 207.351 6.699 207.377 6.705 207.611 6.711 208.053 6.716 208.643 6.722 209.294 6.728 209.829 6.734 209.810 6.740 208.565 6.745 206.799 6.751 206.060 6.757 207.391 6.763 211.697 6.769 213.874 6.774 215.135 6.780 217.051 6.786 218.147 6.792 219.525 6.798 221.294 6.803 222.347 6.809 223.462 6.815 223.992 6.821 223.720 6.827 230.287 6.832 232.726 6.838 234.705 6.844 236.306 6.850 236.940 6.856 237.504 6.861 237.806 6.867 237.790 6.873 237.349 6.879 236.503 6.885 235.281 6.891 233.710 6.896 232.028 6.902 229.931 6.908 227.258 6.914 225.097 6.920 223.653 6.925 222.901 6.931 222.752 6.937 223.462 6.943 222.695 6.949 218.420 6.954 216.206 6.960 212.229 6.966 209.194 6.972 210.580 6.978 214.408 6.983 215.488 6.989 220.154 6.995 236.107 7.001 249.383 7.007 250.938 7.012 252.535 7.018 253.323 7.024 253.210 7.030 252.459 7.036 251.225 7.041 249.992 7.047 249.146 7.053 248.531 7.059 248.280 7.065 248.072 7.070 247.823 7.076 247.466 7.082 247.159 7.088 246.786 7.094 246.466 7.100 246.353 7.105 246.971 7.111 247.976 7.117 248.307 7.123 247.808 7.129 246.864 7.134 247.465 7.140 248.879 7.146 250.495 7.152 252.492 7.158 254.772 7.163 258.282 7.169 265.173 7.175 270.435 7.181 274.386 7.187 274.529 7.192 276.064 7.198 278.089 7.204 280.802 7.210 282.398 7.216 284.050 7.221 285.239 7.227 286.159 7.233 286.625 7.239 286.455 7.245 285.441 7.250 284.164 7.256 283.048 7.262 263.893 7.268 246.722 7.274 232.147 7.279 230.274 7.285 222.901 7.291 222.419 7.297 221.614 7.303 225.783 7.308 227.981 7.314 245.680 7.320 283.178 7.326 298.801 7.332 301.560 7.338 303.989 7.343 304.505 7.349 304.477 7.355 304.266 7.361 304.067 7.367 303.953 7.372 304.956 7.378 306.886 7.384 307.489 7.390 309.245 7.396 308.071 7.401 308.238 7.407 306.758 7.413 305.127 7.419 303.758 7.425 302.908 7.430 302.203 7.436 301.428 7.442 300.600 7.448 299.579 7.454 298.020 7.459 296.087 7.465 294.296 7.471 292.831 7.477 291.904 7.483 291.574 7.488 291.705 7.494 292.109 7.500 292.645 7.506 293.206 7.512 293.759 7.517 294.343 7.523 295.070 7.529 296.127 7.535 297.618 7.541 299.157 7.546 300.333 7.552 300.861 7.558 300.715 7.564 300.072 7.570 299.320 7.576 298.745 7.581 298.752 7.587 299.473 7.593 294.457 7.599 291.936 7.605 292.333 7.610 292.965 7.616 292.876 7.622 292.634 7.628 293.177 7.634 306.416 7.639 303.859 7.645 302.608 7.651 297.349 7.657 294.945 7.663 293.246 7.668 292.085 7.674 291.098 7.680 290.150 7.686 289.360 7.692 288.972 7.697 288.918 7.703 288.966 7.709 288.961 7.715 289.060 7.721 289.614 7.726 290.893 7.732 292.471 7.738 294.479 7.744 296.205 7.750 298.061 7.755 287.760 7.761 284.893 7.767 282.356 7.773 283.689 7.779 291.786 7.784 315.771 7.790 314.396 7.796 312.471 7.802 310.497 7.808 308.361 7.814 307.515 7.819 306.834 7.825 306.151 7.831 305.668 7.837 305.352 7.843 305.151 7.848 304.795 7.854 304.201 7.860 303.548 7.866 303.104 7.872 303.137 7.877 303.736 7.883 304.579 7.889 304.863 7.895 304.088 7.901 301.521 7.906 295.711 7.912 290.603 7.918 287.038 7.924 285.914 7.930 289.540 7.935 290.492 7.941 289.885 7.947 293.634 7.953 295.936 7.959 298.105 7.964 299.528 7.970 300.241 7.976 300.625 7.982 301.441 7.988 302.585 7.993 303.452 7.999 303.202 8.005 301.856 8.011 298.614 8.017 293.234 8.022 291.117 8.028 289.984 8.034 289.133 8.040 287.996 8.046 286.158 8.052 284.136 8.057 282.073 8.063 280.120 8.069 275.190 8.075 271.257 8.081 277.359 8.086 269.824 8.092 293.590 8.098 292.055 8.104 289.952 8.110 289.219 8.115 289.153 8.121 290.492 8.127 0.000 8.133 0.000 8.139 0.000 8.144 299.630 8.150 287.641 8.156 282.731 8.162 281.164 8.168 282.028 8.173 284.415 8.179 287.291 8.185 290.474 8.191 293.509 8.197 296.299 8.202 298.222 8.208 299.295 8.214 299.333 8.220 298.579 8.226 297.991 8.231 297.803 8.237 297.921 8.243 297.967 8.249 297.657 8.255 297.050 8.260 296.542 8.266 296.613 8.272 297.458 8.278 299.216 8.284 300.959 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273.259 8.707 275.258 8.713 276.364 8.719 278.815 8.725 279.158 8.731 282.658 8.737 281.651 8.742 281.315 8.748 281.110 8.754 281.847 8.760 282.850 8.766 284.060 8.771 284.546 8.777 284.642 8.783 284.563 8.789 284.306 8.795 284.221 8.800 284.373 8.806 284.837 8.812 285.412 8.818 285.710 8.824 285.761 8.829 285.694 8.835 285.493 8.841 284.843 8.847 283.590 8.853 281.939 8.858 278.838 8.864 275.505 8.870 269.543 8.876 268.463 8.882 285.295 8.887 285.802 8.893 285.992 8.899 286.599 8.905 287.213 8.911 287.996 8.916 288.735 8.922 288.909 8.928 0.000 8.934 0.000 8.940 0.000 8.945 0.000 8.951 283.278 8.957 273.872 8.963 269.669 8.969 269.663 8.975 274.705 8.980 282.240 8.986 289.827 8.992 296.922 8.998 303.530 9.004 312.528 9.009 319.057 9.015 325.948 9.021 328.752 9.027 329.863 9.033 329.550 9.038 328.090 9.044 325.266 9.050 320.381 9.056 315.189 9.062 310.780 9.067 304.717 9.073 291.779 9.079 286.581 9.085 282.857 9.091 279.762 9.096 277.803 9.102 276.823 9.108 276.927 9.114 278.038 9.120 279.978 9.125 283.327 9.131 287.786 9.137 293.975 9.143 300.079 9.149 305.774 9.154 311.400 9.160 317.593 9.166 323.844 9.172 329.429 9.178 331.644 9.183 331.888 9.189 331.615 9.195 330.009 9.201 327.539 9.207 323.315 9.213 317.272 9.218 311.323 9.224 305.243 9.230 299.404 9.236 295.451 9.242 292.499 9.247 290.514 9.253 289.240 9.259 288.252 9.265 287.708 9.271 287.767 9.276 288.730 9.282 290.469 9.288 293.152 9.294 295.104 9.300 295.700 9.305 295.162 9.311 292.700 9.317 289.765 9.323 285.745 9.329 277.762 9.334 270.112 9.340 263.289 9.346 251.794 9.352 263.437 9.358 266.072 9.363 264.189 9.369 0.000 9.375 0.000 9.381 0.000 9.387 0.000 9.392 0.000 9.398 0.000 9.404 0.000 9.410 0.000 9.416 0.000 9.421 0.000 9.427 0.000 9.433 0.000 9.439 0.000 9.445 0.000 9.451 0.000 9.456 0.000 9.462 0.000 9.468 0.000 9.474 0.000 9.480 0.000 9.485 0.000 9.491 0.000 9.497 0.000 9.503 0.000 9.509 0.000 9.514 0.000 9.520 0.000 9.526 0.000 9.532 0.000 9.538 0.000 9.543 0.000 9.549 0.000 9.555 0.000 9.561 0.000 9.567 0.000 9.572 0.000 9.578 0.000 9.584 0.000 9.590 0.000 9.596 0.000 9.601 0.000 9.607 0.000 9.613 0.000 9.619 0.000 9.625 0.000 9.630 0.000 9.636 0.000 9.642 0.000 9.648 0.000 9.654 0.000 9.660 0.000 9.665 0.000 9.671 0.000 9.677 0.000 9.683 0.000 9.689 0.000 9.694 0.000 9.700 0.000 9.706 0.000 9.712 0.000 9.718 0.000 9.723 0.000 9.729 0.000 9.735 0.000 9.741 0.000 9.747 0.000 9.752 0.000 9.758 0.000 9.764 0.000 9.770 0.000 9.776 0.000 9.781 0.000 9.787 0.000 9.793 0.000 9.799 0.000 9.805 0.000 9.810 0.000 9.816 0.000 9.822 0.000 9.828 0.000 9.834 0.000 9.839 0.000 9.845 0.000 9.851 0.000 9.857 0.000 9.863 0.000 9.868 0.000 9.874 0.000 9.880 0.000 9.886 0.000 9.892 0.000 9.898 0.000 9.903 0.000 9.909 0.000 9.915 0.000 9.921 232.727 9.927 229.457 9.932 229.684 9.938 230.250 9.944 234.973 9.950 241.421 9.956 246.087 9.961 253.637 9.967 261.130 9.973 267.909 9.979 272.439 9.985 279.455 9.990 280.099 9.996 279.928 10.002 278.290 10.008 275.627 10.014 272.150 10.019 267.654 10.025 261.543 10.031 255.800 10.037 250.007 10.043 244.367 10.048 239.577 10.054 235.868 10.060 232.963 10.066 230.607 10.072 229.194 10.077 228.888 10.083 229.679 10.089 231.208 10.095 233.082 10.101 235.217 10.106 237.372 10.112 240.192 10.118 243.381 10.124 246.696 10.130 250.345 10.136 254.714 10.141 264.212 10.147 269.144 10.153 273.287 10.159 276.385 10.165 279.363 10.170 280.457 10.176 280.556 10.182 279.238 10.188 276.407 10.194 269.008 10.199 265.658 10.205 259.473 10.211 253.318 10.217 247.089 10.223 241.855 10.228 236.257 10.234 232.838 10.240 228.873 10.246 226.785 10.252 224.964 10.257 226.927 10.263 230.060 10.269 233.282 10.275 236.743 10.281 239.869 10.286 243.516 10.292 247.316 10.298 251.869 10.304 257.729 10.310 263.138 10.315 267.498 10.321 274.934 10.327 277.104 10.333 278.359 10.339 279.050 10.344 278.523 10.350 276.681 10.356 273.325 10.362 264.885 10.368 261.601 10.374 255.262 10.379 248.563 10.385 242.926 10.391 238.382 10.397 235.102 10.403 231.500 10.408 229.382 10.414 227.520 10.420 228.436 10.426 230.673 10.432 233.181 10.437 237.314 10.443 242.904 10.449 248.037 10.455 255.301 10.461 258.975 10.466 263.529 10.472 269.046 10.478 273.441 10.484 276.349 10.490 277.563 10.495 277.676 10.501 276.875 10.507 274.034 10.513 268.623 10.519 263.038 10.524 257.603 10.530 254.424 10.536 243.218 10.542 240.107 10.548 234.280 10.553 231.174 10.559 228.872 10.565 227.918 10.571 228.309 10.577 228.699 10.582 231.287 10.588 234.300 10.594 237.216 10.600 240.054 10.606 243.733 10.612 247.213 10.617 250.021 10.623 251.607 10.629 252.806 10.635 253.549 10.641 253.568 10.646 252.668 10.652 250.430 10.658 244.971 10.664 236.593 10.670 228.552 10.675 223.861 10.681 222.355 10.687 0.000 10.693 0.000 10.699 232.759 10.704 0.000 10.710 0.000 10.716 0.000 10.722 0.000 10.728 0.000 10.733 0.000 10.739 229.222 10.745 230.972 10.751 227.758 10.757 231.229 10.762 233.548 10.768 236.870 10.774 242.249 10.780 249.171 10.786 255.855 10.791 263.230 10.797 269.726 10.803 275.428 10.809 278.126 10.815 279.236 10.820 279.194 10.826 278.265 10.832 276.913 10.838 275.317 10.844 273.297 10.850 270.316 10.855 266.697 10.861 263.026 10.867 260.253 10.873 258.359 10.879 257.294 10.884 256.768 10.890 256.773 10.896 257.349 10.902 258.627 10.908 260.964 10.913 264.418 10.919 268.373 10.925 273.623 10.931 279.402 10.937 285.356 10.942 291.433 10.948 296.999 10.954 301.859 10.960 305.531 10.966 308.161 10.971 310.297 10.977 311.287 10.983 311.207 10.989 309.968 10.995 307.765 11.000 305.429 11.006 302.856 11.012 299.952 11.018 295.682 11.024 291.297 11.029 286.492 11.035 280.778 11.041 274.302 11.047 268.949 11.053 264.627 11.059 261.217 11.064 259.459 11.070 258.638 11.076 258.655 11.082 259.520 11.088 261.434 11.093 264.041 11.099 267.507 11.105 272.636 11.111 280.546 11.117 285.422 11.122 289.987 11.128 295.368 11.134 300.364 11.140 304.876 11.146 309.170 11.151 313.174 11.157 315.536 11.163 316.390 11.169 316.005 11.175 314.642 11.180 311.896 11.186 307.831 11.192 303.246 11.198 296.223 11.204 290.708 11.209 284.387 11.215 273.976 11.221 267.037 11.227 262.362 11.233 258.952 11.238 257.257 11.244 256.414 11.250 256.675 11.256 258.011 11.262 260.401 11.267 263.834 11.273 268.269 11.279 275.900 11.285 283.502 11.291 288.976 11.297 293.093 11.302 296.800 11.308 300.314 11.314 302.532 11.320 303.897 11.326 304.642 11.331 306.472 11.337 301.438 11.343 297.420 11.349 298.057 11.355 293.587 11.360 289.730 11.366 287.755 11.372 287.350 11.378 276.530 11.384 267.193 11.389 272.997 11.395 274.627 11.401 274.890 11.407 274.631 11.413 273.664 11.418 273.005 11.424 273.330 11.430 274.844 11.436 276.764 11.442 277.847 11.447 278.015 11.453 276.841 11.459 273.656 11.465 267.843 11.471 258.348 11.476 251.973 11.482 248.205 11.488 241.346 11.494 238.403 11.500 229.298 11.505 207.417 11.511 199.750 11.517 193.189 11.523 189.814 11.529 188.123 11.535 187.318 11.540 187.587 11.546 188.707 11.552 190.933 11.558 195.547 11.564 199.832 11.569 205.566 11.575 211.998 11.581 216.225 11.587 219.406 11.593 222.017 11.598 223.167 11.604 223.735 11.610 223.634 11.616 222.843 11.622 221.057 11.627 218.287 11.633 215.012 11.639 211.557 11.645 208.270 11.651 205.303 11.656 203.370 11.662 201.734 11.668 200.366 11.674 199.188 11.680 198.232 11.685 197.690 11.691 197.684 11.697 198.274 11.703 199.505 11.709 201.096 11.714 204.060 11.720 210.155 11.726 214.722 11.732 220.267 11.738 228.193 11.743 232.445 11.749 235.807 11.755 238.570 11.761 240.785 11.767 242.072 11.773 242.343 11.778 241.549 11.784 239.597 11.790 236.493 11.796 232.395 11.802 228.731 11.807 223.113 11.813 215.591 11.819 209.166 11.825 206.340 11.831 201.712 11.836 198.707 11.842 195.796 11.848 193.426 11.854 192.024 11.860 191.678 11.865 192.326 11.871 193.838 11.877 196.379 11.883 199.938 11.889 204.267 11.894 210.561 11.900 220.913 11.906 234.909 11.912 240.182 11.918 243.646 11.923 246.619 11.929 248.538 11.935 249.615 11.941 249.659 11.947 248.583 11.952 246.404 11.958 242.662 11.964 238.479 11.970 233.240 11.976 227.595 11.981 219.218 11.987 209.172 11.993 202.704 11.999 198.168 12.005 195.667 12.011 193.991 12.016 193.336 12.022 193.398 12.028 194.486 12.034 195.584 12.040 198.513 12.045 201.397 12.051 210.335 12.057 220.673 12.063 224.991 12.069 232.487 12.074 236.441 12.080 239.158 12.086 241.224 12.092 242.250 12.098 242.479 12.103 241.911 12.109 240.584 12.115 238.477 12.121 236.211 12.127 232.368 12.132 229.932 12.138 227.750 12.144 224.598 12.150 220.581 12.156 217.480 12.161 215.359 12.167 214.184 12.173 213.848 12.179 214.101 12.185 214.762 12.190 215.806 12.196 217.281 12.202 218.993 12.208 221.048 12.214 223.819 12.220 231.292 12.225 236.936 12.231 239.427 12.237 239.102 12.243 234.478 12.249 218.922 12.254 204.277 12.260 170.983 12.266 155.878 12.272 128.969 12.278 236.027 12.283 207.934 12.289 144.206 12.295 143.597 12.301 145.136 12.307 145.944 12.312 145.475 12.318 144.617 12.324 143.422 12.330 142.307 12.336 141.951 12.341 142.556 12.347 144.043 12.353 145.928 12.359 148.219 12.365 150.466 12.370 153.627 12.376 156.854 12.382 158.892 12.388 160.217 12.394 160.785 12.399 160.746 12.405 159.777 12.411 158.534 12.417 156.815 12.423 154.934 12.428 153.197 12.434 151.325 12.440 149.453 12.446 147.055 12.452 144.495 12.458 142.192 12.463 140.439 12.469 139.045 12.475 137.595 12.481 136.311 12.487 135.568 12.492 135.349 12.498 135.538 12.504 136.073 12.510 137.039 12.516 138.357 12.521 139.832 12.527 141.655 12.533 144.069 12.539 146.920 12.545 149.384 12.550 151.703 12.556 154.548 12.562 157.250 12.568 159.575 12.574 161.581 12.579 163.049 12.585 163.967 12.591 164.059 12.597 163.411 12.603 162.175 12.608 160.571 12.614 158.646 12.620 156.513 12.626 153.829 12.632 151.113 12.637 148.542 12.643 145.268 12.649 142.190 12.655 139.958 12.661 138.111 12.666 136.357 12.672 135.041 12.678 134.301 12.684 134.256 12.690 134.791 12.696 135.702 12.701 137.159 12.707 139.416 12.713 142.893 12.719 146.882 12.725 151.007 12.730 155.075 12.736 158.748 12.742 163.093 12.748 168.254 12.754 171.190 12.759 172.820 12.765 173.410 12.771 172.829 12.777 171.281 12.783 167.264 12.788 163.483 12.794 160.022 12.800 157.056 12.806 153.909 12.812 150.713 12.817 147.521 12.823 143.663 12.829 140.969 12.835 139.155 12.841 137.667 12.846 136.742 12.852 136.276 12.858 136.380 12.864 137.057 12.870 138.256 12.875 139.962 12.881 142.817 12.887 145.826 12.893 149.039 12.899 151.885 12.904 154.137 12.910 156.863 12.916 159.170 12.922 160.829 12.928 162.225 12.934 162.496 12.939 161.573 12.945 160.081 12.951 157.570 12.957 153.483 12.963 147.376 12.968 142.935 12.974 141.809 12.980 140.496 12.986 140.460 12.992 139.969 12.997 139.649 13.003 140.235 13.009 142.647 13.015 143.814 13.021 145.932 13.026 150.031 13.032 151.106 13.038 156.279 13.044 158.755 13.050 166.279 13.055 171.023 13.061 175.040 13.067 178.738 13.073 181.829 13.079 183.838 13.084 184.265 13.090 183.809 13.096 182.731 13.102 180.688 13.108 174.466 13.113 171.550 13.119 168.940 13.125 165.418 13.131 161.897 13.137 160.107 13.142 156.781 13.148 153.563 13.154 151.436 13.160 150.041 13.166 148.784 13.172 147.861 13.177 147.309 13.183 147.193 13.189 147.470 13.195 148.069 13.201 149.077 13.206 150.553 13.212 153.554 13.218 155.907 13.224 161.606 13.230 165.848 13.235 170.709 13.241 179.849 13.247 182.976 13.253 186.131 13.259 188.462 13.264 189.751 13.270 190.002 13.276 189.546 13.282 188.151 13.288 185.404 13.293 180.466 13.299 175.479 13.305 170.492 13.311 166.498 13.317 163.779 13.322 159.499 13.328 157.526 13.334 152.621 13.340 150.624 13.346 149.077 13.351 147.849 13.357 146.967 13.363 146.569 13.369 146.691 13.375 147.332 13.380 148.404 13.386 149.876 13.392 151.827 13.398 154.830 13.404 157.822 13.410 164.371 13.415 166.497 13.421 171.505 13.427 175.498 13.433 178.699 13.439 180.008 13.444 180.298 13.450 179.904 13.456 178.843 13.462 176.905 13.468 173.431 13.473 169.788 13.479 166.383 13.485 164.438 13.491 160.285 13.497 158.119 13.502 154.476 13.508 152.189 13.514 150.226 13.520 148.785 13.526 147.872 13.531 147.541 13.537 147.722 13.543 148.364 13.549 149.358 13.555 150.683 13.560 152.349 13.566 155.350 13.572 157.445 13.578 158.212 13.584 163.478 13.589 172.637 13.595 176.535 13.601 179.615 13.607 180.612 13.613 180.758 13.619 180.112 13.624 178.320 13.630 175.012 13.636 171.614 13.642 164.402 13.648 161.567 13.653 159.370 13.659 155.261 13.665 151.645 13.671 149.746 13.677 148.313 13.682 147.043 13.688 146.329 13.694 146.107 13.700 146.515 13.706 147.563 13.711 149.069 13.717 151.655 13.723 155.003 13.729 162.576 13.735 165.785 13.740 168.829 13.746 171.505 13.752 174.959 13.758 175.870 13.764 174.372 13.769 173.050 13.775 163.048 13.781 162.490 13.787 155.133 13.793 150.711 13.798 139.222 13.804 116.619 13.810 0.000 13.816 0.000 13.822 128.015 13.827 0.000 13.833 161.444 13.839 160.498 13.845 159.520 13.851 159.222 13.857 159.352 13.862 160.123 13.868 161.697 13.874 165.433 13.880 175.455 13.886 184.725 13.891 193.213 13.897 196.509 13.903 203.503 13.909 209.103 13.915 210.846 13.920 211.320 13.926 210.393 13.932 207.347 13.938 203.083 13.944 198.706 13.949 193.297 13.955 183.988 13.961 174.052 13.967 173.485 13.973 169.954 13.978 167.201 13.984 166.048 13.990 165.397 13.996 165.371 14.002 166.007 14.007 167.222 14.013 168.815 14.019 170.810 14.025 174.734 14.031 179.647 14.036 187.027 14.042 192.858 14.048 196.417 14.054 199.721 14.060 203.618 14.065 206.314 14.071 207.299 14.077 207.316 14.083 206.275 14.089 204.435 14.095 201.576 14.100 198.205 14.106 194.708 14.112 189.852 14.118 184.253 14.124 177.941 14.129 174.906 14.135 171.701 14.141 170.326 14.147 169.699 14.153 169.435 14.158 169.911 14.164 171.199 14.170 173.176 14.176 175.793 14.182 180.446 14.187 185.284 14.193 190.222 14.199 194.577 14.205 198.328 14.211 201.164 14.216 205.034 14.222 205.678 14.228 205.186 14.234 203.374 14.240 200.484 14.245 196.577 14.251 191.626 14.257 186.478 14.263 181.675 14.269 178.726 14.274 177.758 14.280 177.884 14.286 178.068 14.292 178.970 14.298 180.845 14.303 183.452 14.309 186.207 14.315 187.600 14.321 188.759 14.327 189.218 14.333 189.254 14.338 189.133 14.344 188.862 14.350 186.482 14.356 183.156 14.362 183.582 14.367 184.880 14.373 183.592 14.379 182.916 14.385 185.470 14.391 183.156 14.396 183.127 14.402 182.926 14.408 177.881 14.414 175.806 14.420 185.993 14.425 186.406 14.431 185.523 14.437 184.221 14.443 204.035 14.449 191.378 14.454 182.289 14.460 170.593 14.466 150.883 14.472 181.543 14.478 185.148 14.483 185.265 14.489 185.447 14.495 186.632 14.501 189.242 14.507 188.393 14.512 0.000 14.518 0.000 14.524 0.000 14.530 0.000 14.536 0.000 14.541 0.000 14.547 0.000 14.553 0.000 14.559 0.000 14.565 0.000 14.571 0.000 14.576 0.000 14.582 0.000 14.588 0.000 14.594 0.000 14.600 0.000 14.605 322.803 14.611 363.462 14.617 357.509 14.623 312.664 14.629 314.417 14.634 317.931 14.640 321.594 14.646 323.678 14.652 324.748 14.658 325.704 14.663 325.182 14.669 324.233 14.675 322.798 14.681 321.210 14.687 319.503 14.692 317.670 14.698 315.655 14.704 314.095 14.710 313.447 14.716 313.865 14.721 315.286 14.727 317.255 14.733 319.742 14.739 322.047 14.745 324.040 14.750 325.153 14.756 325.854 14.762 326.237 14.768 326.488 14.774 326.553 14.780 326.326 14.785 325.698 14.791 324.654 14.797 323.296 14.803 321.926 14.809 320.405 14.814 318.597 14.820 316.581 14.826 314.800 14.832 313.318 14.838 311.797 14.843 310.664 14.849 310.200 14.855 310.313 14.861 310.979 14.867 312.019 14.872 313.681 14.878 316.099 14.884 318.795 14.890 321.889 14.896 325.111 14.901 328.319 14.907 331.213 14.913 333.887 14.919 341.452 14.925 344.472 14.930 346.071 14.936 346.914 14.942 347.269 14.948 347.223 14.954 346.279 14.959 344.178 14.965 340.679 14.971 335.842 14.977 331.128 14.983 327.236 14.988 323.261 14.994 319.323 15.000 315.584 15.006 312.731 15.012 311.180 15.018 310.379 15.023 310.423 15.029 311.119 15.035 312.560 15.041 314.561 15.047 317.006 15.052 320.962 15.058 325.624 15.064 329.466 15.070 334.047 15.076 343.004 15.081 347.852 15.087 351.428 15.093 354.500 15.099 356.393 15.105 357.139 15.110 356.742 15.116 354.487 15.122 350.271 15.128 345.474 15.134 341.063 15.139 337.432 15.145 334.040 15.151 330.731 15.157 326.895 15.163 323.104 15.168 321.877 15.174 315.503 15.180 299.493 15.186 295.247 15.192 295.765 15.197 295.879 15.203 311.095 15.209 309.711 15.215 309.238 15.221 314.972 15.226 318.288 15.232 330.017 15.238 334.598 15.244 337.898 15.250 340.554 15.256 343.300 15.261 345.712 15.267 346.701 15.273 348.973 15.279 351.683 15.285 353.376 15.290 355.171 15.296 357.789 15.302 361.283 15.308 362.422 15.314 360.786 15.319 358.290 15.325 356.579 15.331 352.855 15.337 345.468 15.343 339.990 15.348 338.306 15.354 358.835 15.360 359.877 15.366 357.916 15.372 354.614 15.377 352.338 15.383 351.109 15.389 348.194 15.395 348.243 15.401 348.608 15.406 350.215 15.412 350.146 15.418 350.469 15.424 414.087 15.430 434.553 15.435 441.344 15.441 446.662 15.447 452.692 15.453 249.563 15.459 261.313 15.464 271.842 15.470 283.710 15.476 290.345 15.482 295.695 15.488 301.802 15.494 309.181 15.499 316.582 15.505 322.297 15.511 325.076 15.517 325.518 15.523 324.400 15.528 322.152 15.534 318.972 15.540 315.113 15.546 310.182 15.552 304.125 15.557 298.208 15.563 292.307 15.569 286.802 15.575 280.935 15.581 272.515 15.586 266.785 15.592 262.969 15.598 260.943 15.604 259.798 15.610 259.462 15.615 259.977 15.621 261.369 15.627 263.659 15.633 265.777 15.639 269.578 15.644 276.561 15.650 288.010 15.656 290.859 15.662 297.295 15.668 304.263 15.673 311.331 15.679 315.690 15.685 318.433 15.691 319.914 15.697 320.347 15.702 319.835 15.708 318.416 15.714 315.835 15.720 312.902 15.726 309.282 15.732 304.667 15.737 299.401 15.743 293.551 15.749 288.498 15.755 283.285 15.761 277.481 15.766 267.722 15.772 264.868 15.778 262.347 15.784 260.811 15.790 259.958 15.795 259.839 15.801 260.424 15.807 261.801 15.813 262.655 15.819 264.659 15.824 267.468 15.830 271.635 15.836 277.475 15.842 281.836 15.848 285.623 15.853 288.681 15.859 291.744 15.865 295.542 15.871 297.827 15.877 299.372 15.882 299.583 15.888 298.333 15.894 295.824 15.900 292.336 15.906 289.137 15.911 285.073 15.917 280.397 15.923 275.034 15.929 269.500 15.935 263.653 15.940 260.217 15.946 257.623 15.952 254.943 15.958 251.671 15.964 250.383 15.970 248.787 15.975 247.706 15.981 246.952 15.987 246.515 15.993 246.116 15.999 246.194 16.004 247.262 16.010 250.032 16.016 254.579 16.022 258.939 16.028 262.173 16.033 264.039 16.039 264.473 16.045 260.973 16.051 256.958 16.057 248.756 16.062 250.832 16.068 255.486 16.074 253.708 16.080 252.035 16.086 249.937 16.091 237.822 16.097 232.190 16.103 219.011 16.109 221.177 16.115 220.060 16.120 218.564 16.126 215.698 16.132 215.821 16.138 215.522 16.144 216.190 16.149 217.945 16.155 219.704 16.161 221.933 16.167 223.426 16.173 226.141 16.179 228.293 16.184 229.885 16.190 230.722 16.196 231.045 16.202 229.991 16.208 229.080 16.213 228.819 16.219 227.600 16.225 228.665 16.231 227.696 16.237 227.033 16.242 218.295 16.248 217.029 16.254 216.695 16.260 219.952 16.266 218.306 16.271 217.692 16.277 217.467 16.283 217.696 16.289 218.181 16.295 218.808 16.300 219.521 16.306 220.051 16.312 220.287 16.318 220.269 16.324 220.130 16.329 220.052 16.335 220.035 16.341 219.970 16.347 219.754 16.353 219.001 16.358 218.186 16.364 216.998 16.370 215.153 16.376 213.040 16.382 210.976 16.387 208.946 16.393 206.754 16.399 204.565 16.405 202.813 16.411 201.081 16.417 199.842 16.422 199.227 16.428 199.321 16.434 200.242 16.440 201.895 16.446 204.526 16.451 207.955 16.457 215.256 16.463 221.208 16.469 225.820 16.475 231.120 16.480 234.401 16.486 236.405 16.492 237.323 16.498 237.407 16.504 237.084 16.509 236.219 16.515 235.140 16.521 233.453 16.527 231.527 16.533 229.170 16.538 226.555 16.544 223.772 16.550 220.152 16.556 216.865 16.562 214.210 16.567 211.906 16.573 210.270 16.579 209.151 16.585 208.502 16.591 208.376 16.596 208.889 16.602 210.124 16.608 212.146 16.614 215.000 16.620 218.102 16.625 220.862 16.631 223.789 16.637 227.032 16.643 230.416 16.649 233.252 16.655 235.275 16.660 236.836 16.666 237.864 16.672 238.414 16.678 238.415 16.684 237.994 16.689 237.007 16.695 235.441 16.701 233.471 16.707 231.081 16.713 228.324 16.718 225.122 16.724 221.969 16.730 218.419 16.736 215.830 16.742 214.530 16.747 214.015 16.753 214.033 16.759 214.435 16.765 215.240 16.771 216.638 16.776 218.812 16.782 221.111 16.788 223.697 16.794 226.248 16.800 229.242 16.805 231.818 16.811 233.933 16.817 235.728 16.823 236.971 16.829 237.879 16.834 238.471 16.840 238.721 16.846 238.631 16.852 238.197 16.858 237.498 16.863 236.585 16.869 235.270 16.875 233.482 16.881 231.420 16.887 229.582 16.893 228.571 16.898 228.891 16.904 228.017 16.910 229.438 16.916 228.527 16.922 224.567 16.927 215.943 16.933 220.750 16.939 222.196 16.945 228.492 16.951 234.981 16.956 237.206 16.962 240.199 16.968 304.397 16.974 303.432 16.980 304.313 16.985 306.834 16.991 311.174 16.997 314.987 17.003 318.668 17.009 319.758 17.014 320.055 17.020 319.506 17.026 318.161 17.032 315.875 17.038 312.674 17.043 308.202 17.049 303.926 17.055 299.961 17.061 295.682 17.067 291.724 17.072 288.102 17.078 285.101 17.084 282.885 17.090 281.434 17.096 281.209 17.101 281.816 17.107 283.354 17.113 285.562 17.119 288.102 17.125 291.441 17.131 295.481 17.136 302.222 17.142 309.968 17.148 316.523 17.154 321.390 17.160 328.299 17.165 331.975 17.171 334.783 17.177 336.675 17.183 337.950 17.189 335.579 17.194 332.183 17.200 326.453 17.206 321.117 17.212 316.394 17.218 311.990 17.223 306.944 17.229 299.105 17.235 292.672 17.241 286.384 17.247 282.780 17.252 274.526 17.258 272.869 17.264 272.118 17.270 273.226 17.276 273.725 17.281 276.499 17.287 283.798 17.293 285.839 17.299 289.562 17.305 290.157 17.310 294.899 17.316 299.718 17.322 303.322 17.328 306.338 17.334 308.788 17.340 310.873 17.345 312.079 17.351 312.487 17.357 312.653 17.363 313.189 17.369 314.599 17.374 316.698 17.380 318.179 17.386 318.636 17.392 317.926 17.398 315.973 17.403 313.530 17.409 310.986 17.415 308.335 17.421 305.531 17.427 297.475 17.432 292.040 17.438 288.826 17.444 286.447 17.450 287.824 17.456 286.498 17.461 285.829 17.467 286.164 17.473 286.530 17.479 287.304 17.485 285.630 17.490 287.516 17.496 289.202 17.502 290.670 17.508 292.323 17.514 294.391 17.519 296.551 17.525 298.592 17.531 300.367 17.537 302.285 17.543 304.923 17.548 307.050 17.554 308.002 17.560 308.036 17.566 307.577 17.572 307.066 17.578 306.369 17.583 305.166 17.589 303.392 17.595 300.674 17.601 298.289 17.607 296.342 17.612 294.667 17.618 293.114 17.624 290.397 17.630 289.664 17.636 289.102 17.641 288.747 17.647 288.698 17.653 289.080 17.659 289.820 17.665 290.737 17.670 292.282 17.676 294.858 17.682 299.629 17.688 304.326 17.694 311.713 17.699 317.154 17.705 319.992 17.711 321.013 17.717 320.591 17.723 315.156 17.728 293.733 17.734 297.841 17.740 299.562 17.746 299.170 17.752 299.491 17.757 298.929 17.763 296.199 17.769 293.931 17.775 290.158 17.781 290.150 17.786 291.507 17.792 293.351 17.798 296.842 17.804 302.090 17.810 311.866 17.816 317.543 17.821 322.380 17.827 326.002 17.833 330.380 17.839 334.127 17.845 338.183 17.850 341.601 17.856 343.478 17.862 344.354 17.868 344.396 17.874 343.705 17.879 342.487 17.885 340.885 17.891 338.826 17.897 336.580 17.903 334.367 17.908 331.897 17.914 329.040 17.920 325.372 17.926 321.834 17.932 319.120 17.937 317.291 17.943 316.116 17.949 315.646 17.955 315.975 17.961 317.154 17.966 319.465 17.972 323.184 17.978 325.922 17.984 328.157 17.990 330.302 17.995 332.350 18.001 334.314 18.007 336.395 18.013 338.283 18.019 339.678 18.024 340.346 18.030 340.350 18.036 339.772 18.042 338.799 18.048 337.586 18.054 336.158 18.059 334.447 18.065 332.195 18.071 329.350 18.077 326.713 18.083 324.323 18.088 321.500 18.094 318.294 18.100 316.404 18.106 315.510 18.112 315.282 18.117 315.642 18.123 316.486 18.129 317.945 18.135 320.489 18.141 323.571 18.146 326.792 18.152 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254.256 11.453 257.102 11.459 259.514 11.465 261.217 11.471 262.208 11.476 264.190 11.482 264.640 11.488 264.956 11.494 264.729 11.500 263.823 11.505 262.558 11.511 261.348 11.517 260.220 11.523 259.078 11.529 257.505 11.535 255.092 11.540 251.946 11.546 248.206 11.552 245.318 11.558 242.646 11.564 239.034 11.569 235.290 11.575 232.193 11.581 230.094 11.587 228.134 11.593 228.392 11.598 229.304 11.604 230.744 11.610 233.418 11.616 236.784 11.622 240.237 11.627 244.058 11.633 247.732 11.639 251.721 11.645 255.144 11.651 258.761 11.656 264.124 11.662 267.239 11.668 269.050 11.674 270.832 11.680 273.724 11.685 274.762 11.691 275.060 11.697 274.548 11.703 271.967 11.709 270.696 11.714 268.910 11.720 267.038 11.726 264.997 11.732 262.994 11.738 260.557 11.743 257.428 11.749 253.584 11.755 250.021 11.761 246.952 11.767 243.703 11.773 240.585 11.778 237.606 11.784 234.938 11.790 231.995 11.796 231.536 11.802 231.878 11.807 232.873 11.813 234.572 11.819 237.329 11.825 240.595 11.831 244.444 11.836 247.508 11.842 250.964 11.848 254.785 11.854 258.933 11.860 262.912 11.865 266.740 11.871 269.014 11.877 270.270 11.883 271.264 11.889 273.292 11.894 271.320 11.900 270.324 11.906 268.604 11.912 266.757 11.918 264.885 11.923 262.848 11.929 260.064 11.935 255.982 11.941 251.569 11.947 247.959 11.952 243.746 11.958 238.324 11.964 234.008 11.970 230.693 11.976 228.144 11.981 225.779 11.987 225.870 11.993 226.492 11.999 227.834 12.005 230.422 12.011 234.064 12.016 237.931 12.022 243.325 12.028 247.456 12.034 250.941 12.040 254.558 12.045 259.752 12.051 263.577 12.057 266.348 12.063 268.925 12.069 269.561 12.074 270.071 12.080 270.039 12.086 269.437 12.092 267.998 12.098 265.361 12.103 262.395 12.109 259.116 12.115 255.281 12.121 250.390 12.127 241.470 12.132 235.519 12.138 233.613 12.144 231.069 12.150 229.821 12.156 229.392 12.161 229.697 12.167 230.931 12.173 232.740 12.179 236.077 12.185 238.465 12.190 240.793 12.196 249.974 12.202 253.312 12.208 256.322 12.214 257.870 12.220 258.473 12.225 258.727 12.231 257.986 12.237 255.334 12.243 254.064 12.249 253.291 12.254 252.820 12.260 251.867 12.266 250.086 12.272 248.663 12.278 247.170 12.283 245.640 12.289 244.812 12.295 243.915 12.301 243.110 12.307 242.122 12.312 241.540 12.318 241.330 12.324 241.590 12.330 241.772 12.336 242.544 12.341 243.324 12.347 243.943 12.353 244.331 12.359 244.076 12.365 242.713 12.370 240.201 12.376 236.472 12.382 232.246 12.388 231.211 12.394 226.886 12.399 219.923 12.405 233.384 12.411 239.579 12.417 236.095 12.423 235.927 12.428 237.542 12.434 239.115 12.440 240.972 12.446 243.400 12.452 246.561 12.458 250.854 12.463 255.813 12.469 260.773 12.475 266.147 12.481 270.280 12.487 273.296 12.492 274.529 12.498 275.158 12.504 274.932 12.510 273.613 12.516 270.886 12.521 266.697 12.527 261.065 12.533 254.554 12.539 249.322 12.545 245.285 12.550 241.594 12.556 237.735 12.562 232.679 12.568 228.556 12.574 225.552 12.579 224.088 12.585 223.501 12.591 223.908 12.597 225.290 12.603 227.650 12.608 231.235 12.614 234.561 12.620 238.519 12.626 243.065 12.632 247.893 12.637 253.111 12.643 264.973 12.649 277.779 12.655 282.064 12.661 284.590 12.666 285.809 12.672 286.409 12.678 286.275 12.684 284.936 12.690 281.878 12.696 277.537 12.701 272.745 12.707 267.877 12.713 261.708 12.719 256.472 12.725 244.274 12.730 238.997 12.736 235.494 12.742 232.267 12.748 230.671 12.754 230.116 12.759 230.431 12.765 231.329 12.771 232.598 12.777 234.672 12.783 237.545 12.788 242.542 12.794 246.447 12.800 257.204 12.806 268.269 12.812 270.850 12.817 271.444 12.823 270.345 12.829 267.316 12.835 261.305 12.841 255.441 12.846 248.671 12.852 242.697 12.858 239.497 12.864 237.580 12.870 225.994 12.875 241.846 12.881 256.046 12.887 269.078 12.893 269.867 12.899 268.064 12.904 265.041 12.910 265.098 12.916 261.533 12.922 260.880 12.928 261.008 12.934 252.852 12.939 249.257 12.945 250.683 12.951 253.558 12.957 256.217 12.963 258.621 12.968 235.988 12.974 241.671 12.980 243.608 12.986 254.746 12.992 251.256 12.997 250.256 13.003 248.392 13.009 247.799 13.015 238.686 13.021 238.888 13.026 248.050 13.032 242.921 13.038 246.241 13.044 250.153 13.050 233.020 13.055 252.256 13.061 249.770 13.067 249.269 13.073 241.575 13.079 244.792 13.084 242.261 13.090 238.870 13.096 0.000 13.102 467.093 13.108 215.426 13.113 169.915 13.119 270.838 13.125 270.034 13.131 271.023 13.137 269.785 13.142 270.641 13.148 272.350 13.154 276.704 13.160 280.730 13.166 283.805 13.172 286.933 13.177 289.495 13.183 291.283 13.189 292.163 13.195 291.873 13.201 290.670 13.206 288.884 13.212 286.586 13.218 283.435 13.224 279.984 13.230 276.331 13.235 273.020 13.241 270.457 13.247 268.697 13.253 267.555 13.259 267.925 13.264 270.086 13.270 274.638 13.276 280.108 13.282 281.536 13.288 281.500 13.293 281.139 13.299 280.730 13.305 280.373 13.311 280.088 13.317 279.884 13.322 279.788 13.328 279.767 13.334 279.767 13.340 279.682 13.346 279.445 13.351 279.007 13.357 278.381 13.363 277.634 13.369 276.959 13.375 276.551 13.380 276.391 13.386 276.294 13.392 276.107 13.398 275.781 13.404 275.449 13.410 275.266 13.415 275.254 13.421 275.353 13.427 275.436 13.433 275.437 13.439 275.472 13.444 275.663 13.450 276.169 13.456 277.015 13.462 277.953 13.468 278.647 13.473 278.916 13.479 278.767 13.485 278.384 13.491 277.972 13.497 277.625 13.502 277.293 13.508 276.963 13.514 276.735 13.520 276.671 13.526 276.791 13.531 277.049 13.537 277.388 13.543 277.824 13.549 278.322 13.555 278.838 13.560 279.310 13.566 279.657 13.572 279.920 13.578 280.208 13.584 280.603 13.589 281.138 13.595 281.792 13.601 282.572 13.607 283.373 13.613 284.051 13.619 284.436 13.624 284.418 13.630 284.035 13.636 283.466 13.642 282.856 13.648 282.333 13.653 281.969 13.659 281.705 13.665 281.445 13.671 281.095 13.677 280.593 13.682 279.956 13.688 279.239 13.694 278.428 13.700 277.528 13.706 276.642 13.711 275.836 13.717 275.128 13.723 274.601 13.729 274.274 13.735 274.035 13.740 273.799 13.746 273.611 13.752 273.634 13.758 274.039 13.764 274.865 13.769 275.983 13.775 277.353 13.781 278.734 13.787 280.002 13.793 281.171 13.798 282.265 13.804 283.352 13.810 284.436 13.816 285.495 13.822 286.351 13.827 286.932 13.833 287.582 13.839 288.103 13.845 288.410 13.851 288.223 13.857 287.517 13.862 286.558 13.868 285.631 13.874 284.703 13.880 283.497 13.886 281.855 13.891 280.015 13.897 278.032 13.903 275.743 13.909 273.603 13.915 271.965 13.920 271.122 13.926 270.961 13.932 271.081 13.938 271.262 13.944 271.449 13.949 272.068 13.955 273.131 13.961 274.600 13.967 276.653 13.973 279.080 13.978 281.640 13.984 283.656 13.990 285.512 13.996 287.255 14.002 289.310 14.007 290.522 14.013 291.269 14.019 292.186 14.025 293.257 14.031 293.423 14.036 292.564 14.042 291.153 14.048 290.255 14.054 289.845 14.060 289.200 14.065 288.071 14.071 286.650 14.077 285.252 14.083 283.261 14.089 280.469 14.095 277.831 14.100 275.388 14.106 273.349 14.112 271.368 14.118 269.916 14.124 269.087 14.129 268.515 14.135 267.681 14.141 266.782 14.147 266.532 14.153 267.034 14.158 268.375 14.164 269.881 14.170 271.368 14.176 273.227 14.182 275.431 14.187 277.790 14.193 280.430 14.199 283.836 14.205 286.375 14.211 287.844 14.216 289.897 14.222 291.864 14.228 293.249 14.234 294.025 14.240 294.290 14.245 294.161 14.251 293.741 14.257 292.957 14.263 291.887 14.269 290.887 14.274 289.989 14.280 288.457 14.286 286.993 14.292 285.490 14.298 283.256 14.303 280.249 14.309 277.505 14.315 274.829 14.321 272.548 14.327 270.784 14.333 269.804 14.338 269.443 14.344 269.407 14.350 269.255 14.356 269.109 14.362 269.394 14.367 270.276 14.373 271.710 14.379 273.292 14.385 275.179 14.391 277.926 14.396 281.174 14.402 284.421 14.408 286.895 14.414 289.380 14.420 291.794 14.425 294.373 14.431 297.024 14.437 299.746 14.443 301.388 14.449 301.578 14.454 301.239 14.460 300.332 14.466 298.401 14.472 295.226 14.478 292.097 14.483 289.845 14.489 287.603 14.495 285.440 14.501 282.152 14.507 279.189 14.512 276.344 14.518 273.933 14.524 271.965 14.530 270.535 14.536 269.831 14.541 269.653 14.547 269.689 14.553 269.840 14.559 270.353 14.565 271.730 14.571 274.573 14.576 278.391 14.582 282.399 14.588 285.477 14.594 287.716 14.600 289.896 14.605 291.995 14.611 293.627 14.617 294.988 14.623 295.929 14.629 295.920 14.634 294.584 14.640 291.252 14.646 287.671 14.652 284.697 14.658 281.195 14.663 276.747 14.669 271.718 14.675 266.099 14.681 261.041 14.687 257.582 14.692 255.897 14.698 255.258 14.704 255.789 14.710 257.479 14.716 260.141 14.721 263.434 14.727 267.454 14.733 271.679 14.739 277.323 14.745 282.773 14.750 285.770 14.756 288.332 14.762 290.780 14.768 292.610 14.774 293.717 14.780 293.791 14.785 293.080 14.791 291.372 14.797 288.944 14.803 285.391 14.809 281.503 14.814 277.900 14.820 274.700 14.826 271.810 14.832 269.185 14.838 267.494 14.843 266.567 14.849 266.236 14.855 266.233 14.861 266.369 14.867 266.677 14.872 267.335 14.878 268.481 14.884 269.971 14.890 271.975 14.896 274.679 14.901 278.661 14.907 282.928 14.913 286.209 14.919 288.318 14.925 289.697 14.930 290.919 14.936 292.134 14.942 292.511 14.948 292.069 14.954 291.180 14.959 290.450 14.965 289.506 14.971 287.773 14.977 285.119 14.983 282.732 14.988 280.820 14.994 279.136 15.000 277.531 15.006 276.039 15.012 274.821 15.018 273.991 15.023 273.899 15.029 274.888 15.035 277.425 15.041 280.576 15.047 283.128 15.052 285.122 15.058 287.266 15.064 289.858 15.070 291.915 15.076 293.545 15.081 294.735 15.087 295.219 15.093 294.912 15.099 293.774 15.105 291.667 15.110 288.927 15.116 285.596 15.122 275.782 15.128 267.495 15.134 0.000 15.139 0.000 15.145 0.000 15.151 0.000 15.157 0.000 15.163 0.000 15.168 0.000 15.174 0.000 15.180 0.000 15.186 0.000 15.192 0.000 15.197 0.000 15.203 0.000 15.209 0.000 15.215 0.000 15.221 0.000 15.226 0.000 15.232 0.000 15.238 0.000 15.244 0.000 15.250 0.000 15.256 0.000 15.261 0.000 15.267 0.000 15.273 0.000 15.279 0.000 15.285 0.000 15.290 0.000 15.296 0.000 15.302 0.000 15.308 0.000 15.314 0.000 15.319 0.000 15.325 0.000 15.331 0.000 15.337 0.000 15.343 0.000 15.348 0.000 15.354 274.162 15.360 261.915 15.366 258.841 15.372 255.211 15.377 253.483 15.383 254.097 15.389 257.731 15.395 263.462 15.401 270.808 15.406 278.994 15.412 286.590 15.418 292.183 15.424 295.717 15.430 297.741 15.435 298.997 15.441 299.744 15.447 300.395 15.453 301.670 15.459 304.940 15.464 308.520 15.470 310.846 15.476 310.318 15.482 307.337 15.488 303.202 15.494 299.662 15.499 296.126 15.505 291.057 15.511 285.664 15.517 280.052 15.523 276.171 15.528 273.401 15.534 272.774 15.540 273.362 15.546 274.456 15.552 275.968 15.557 278.044 15.563 280.216 15.569 282.417 15.575 285.440 15.581 288.870 15.586 292.908 15.592 297.369 15.598 303.505 15.604 308.372 15.610 313.261 15.615 317.062 15.621 320.287 15.627 322.813 15.633 324.226 15.639 324.658 15.644 324.206 15.650 322.990 15.656 321.014 15.662 318.030 15.668 314.857 15.673 311.687 15.679 308.459 15.685 304.134 15.691 297.343 15.697 292.237 15.702 288.708 15.708 285.999 15.714 283.411 15.720 282.111 15.726 280.535 15.732 279.748 15.737 279.828 15.743 280.616 15.749 281.749 15.755 283.009 15.761 284.528 15.766 286.319 15.772 288.381 15.778 290.815 15.784 293.322 15.790 296.503 15.795 300.311 15.801 303.516 15.807 306.757 15.813 309.846 15.819 312.395 15.824 314.298 15.830 315.768 15.836 316.569 15.842 316.951 15.848 316.812 15.853 315.829 15.859 314.227 15.865 312.045 15.871 309.155 15.877 306.388 15.882 303.024 15.888 297.711 15.894 295.413 15.900 292.168 15.906 288.705 15.911 285.883 15.917 283.859 15.923 282.172 15.929 280.956 15.935 280.293 15.940 280.194 15.946 280.274 15.952 280.374 15.958 280.566 15.964 281.985 15.970 284.327 15.975 286.862 15.981 289.575 15.987 291.922 15.993 294.511 15.999 297.549 16.004 300.649 16.010 303.439 16.016 306.706 16.022 310.344 16.028 312.866 16.033 314.412 16.039 315.270 16.045 315.523 16.051 315.268 16.057 314.424 16.062 312.912 16.068 310.807 16.074 307.962 16.080 304.965 16.086 301.612 16.091 298.758 16.097 295.604 16.103 292.609 16.109 290.254 16.115 289.214 16.120 287.438 16.126 285.536 16.132 283.768 16.138 282.793 16.144 282.452 16.149 282.455 16.155 282.411 16.161 282.580 16.167 282.952 16.173 284.577 16.179 286.819 16.184 289.242 16.190 291.537 16.196 294.143 16.202 297.223 16.208 301.014 16.213 304.057 16.219 307.046 16.225 309.842 16.231 311.972 16.237 313.329 16.242 314.252 16.248 314.891 16.254 315.312 16.260 315.431 16.266 315.120 16.271 314.392 16.277 313.362 16.283 312.122 16.289 310.680 16.295 308.572 16.300 305.817 16.306 301.778 16.312 296.614 16.318 293.763 16.324 291.281 16.329 289.828 16.335 288.050 16.341 286.083 16.347 283.997 16.353 282.495 16.358 281.626 16.364 281.293 16.370 281.246 16.376 280.841 16.382 281.662 16.387 282.819 16.393 284.309 16.399 286.181 16.405 288.733 16.411 291.243 16.417 293.938 16.422 297.532 16.428 300.694 16.434 303.425 16.440 306.867 16.446 310.972 16.451 314.314 16.457 317.257 16.463 319.255 16.469 320.530 16.475 321.149 16.480 321.074 16.486 320.344 16.492 319.150 16.498 317.650 16.504 315.788 16.509 313.700 16.515 310.942 16.521 308.208 16.527 304.608 16.533 300.338 16.538 297.096 16.544 294.515 16.550 292.242 16.556 290.210 16.562 288.453 16.567 286.859 16.573 285.609 16.579 285.011 16.585 285.172 16.591 285.826 16.596 286.527 16.602 287.154 16.608 286.966 16.614 288.547 16.620 290.196 16.625 292.188 16.631 294.865 16.637 298.127 16.643 301.215 16.649 303.163 16.655 305.993 16.660 308.964 16.666 311.567 16.672 313.585 16.678 315.675 16.684 317.522 16.689 319.068 16.695 320.090 16.701 320.343 16.707 319.873 16.713 318.776 16.718 317.122 16.724 315.048 16.730 312.669 16.736 309.998 16.742 306.506 16.747 303.912 16.753 301.075 16.759 296.544 16.765 293.107 16.771 290.441 16.776 288.418 16.782 286.880 16.788 285.546 16.794 284.655 16.800 284.354 16.805 284.690 16.811 285.403 16.817 286.221 16.823 287.168 16.829 288.029 16.834 290.376 16.840 292.887 16.846 295.760 16.852 298.443 16.858 301.073 16.863 302.613 16.869 304.948 16.875 307.361 16.881 309.044 16.887 309.808 16.893 309.955 16.898 309.901 16.904 309.662 16.910 309.052 16.916 308.071 16.922 306.961 16.927 305.857 16.933 304.519 16.939 302.999 16.945 300.700 16.951 299.395 16.956 297.956 16.962 296.372 16.968 294.726 16.974 293.143 16.980 291.875 16.985 290.896 16.991 290.073 16.997 289.378 17.003 288.883 17.009 288.575 17.014 288.381 17.020 288.304 17.026 288.521 17.032 289.165 17.038 290.224 17.043 291.495 17.049 292.942 17.055 294.389 17.061 295.492 17.067 296.353 17.072 297.207 17.078 298.310 17.084 300.004 17.090 302.014 17.096 304.283 17.101 306.437 17.107 307.758 17.113 308.465 17.119 308.860 17.125 309.090 17.131 309.101 17.136 308.751 17.142 307.973 17.148 306.757 17.154 304.799 17.160 301.974 17.165 297.539 17.171 294.174 17.177 291.291 17.183 288.982 17.189 287.061 17.194 285.164 17.200 284.950 17.206 284.477 17.212 284.282 17.218 284.350 17.223 284.688 17.229 284.950 17.235 286.045 17.241 287.529 17.247 289.306 17.252 291.169 17.258 293.445 17.264 296.122 17.270 298.609 17.276 300.556 17.281 302.288 17.287 304.606 17.293 307.241 17.299 309.494 17.305 311.396 17.310 312.916 17.316 314.275 17.322 315.305 17.328 315.769 17.334 315.403 17.340 314.111 17.345 311.919 17.351 308.638 17.357 305.487 17.363 302.699 17.369 299.801 17.374 297.354 17.380 295.278 17.386 293.168 17.392 291.155 17.398 290.438 17.403 288.849 17.409 287.136 17.415 285.651 17.421 284.577 17.427 284.199 17.432 284.321 17.438 284.455 17.444 284.229 17.450 284.868 17.456 285.961 17.461 287.515 17.467 289.235 17.473 290.929 17.479 293.002 17.485 295.964 17.490 298.660 17.496 300.840 17.502 302.990 17.508 306.323 17.514 310.272 17.519 313.131 17.525 315.168 17.531 317.157 17.537 318.450 17.543 319.055 17.548 318.755 17.554 317.605 17.560 316.009 17.566 314.192 17.572 312.375 17.578 310.193 17.583 307.439 17.589 304.722 17.595 301.152 17.601 298.064 17.607 295.663 17.612 292.377 17.618 292.406 17.624 291.499 17.630 290.768 17.636 290.003 17.641 289.150 17.647 288.538 17.653 288.222 17.659 288.328 17.665 288.050 17.670 289.293 17.676 290.776 17.682 292.401 17.688 294.175 17.694 295.951 17.699 297.753 17.705 299.493 17.711 301.233 17.717 303.389 17.723 306.470 17.728 309.398 17.734 311.881 17.740 313.671 17.746 315.341 17.752 316.530 17.757 317.189 17.763 317.138 17.769 316.343 17.775 314.862 17.781 313.078 17.786 311.440 17.792 309.757 17.798 307.723 17.804 305.333 17.810 302.617 17.816 298.246 17.821 295.521 17.827 293.772 17.833 292.513 17.839 292.852 17.845 292.397 17.850 291.790 17.856 290.925 17.862 290.005 17.868 289.269 17.874 288.182 17.879 288.701 17.885 289.457 17.891 290.363 17.897 291.421 17.903 292.656 17.908 294.113 17.914 295.555 17.920 297.012 17.926 298.506 17.932 300.251 17.937 302.233 17.943 304.543 17.949 306.732 17.955 308.439 17.961 309.897 17.966 311.033 17.972 311.793 17.978 312.231 17.984 312.348 17.990 312.133 17.995 311.527 18.001 310.387 18.007 308.685 18.013 306.696 18.019 304.762 18.024 302.421 18.030 300.478 18.036 298.382 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0.8 2.67 0.2 2.676 0.5 2.682 0.2 2.688 1.0 2.694 0.6 2.699 0.7 2.705 0.4 2.711 0.4 2.717 1.0 2.723 0.3 2.728 0.6 2.734 0.7 2.74 0.9 2.746 1.0 2.752 1.0 2.757 0.9 2.763 0.4 2.769 0.7 2.775 0.3 2.781 0.3 2.786 0.6 2.792 0.5 2.798 1.0 2.804 1.0 2.81 0.5 2.815 0.7 2.821 0.7 2.827 0.6 2.833 0.3 2.839 0.7 2.844 0.5 2.85 0.4 2.856 0.6 2.862 0.5 2.868 0.5 2.873 0.1 2.879 0.9 2.885 0.2 2.891 0.2 2.897 0.2 2.902 0.2 2.908 0.6 2.914 0.3 2.92 0.3 2.926 0.7 2.932 0.2 2.937 0.7 2.943 0.9 2.949 0.9 2.955 0.7 2.961 0.2 2.966 0.7 2.972 0.9 2.978 0.8 2.984 0.7 2.99 0.7 2.995 0.7 3.001 0.3 3.007 0.1 3.013 0.7 3.019 0.6 3.024 0.5 3.03 0.5 3.036 0.1 3.042 0.9 3.048 0.9 3.053 0.9 3.059 0.6 3.065 0.9 3.071 0.3 3.077 0.4 3.082 0.6 3.088 0.9 3.094 0.8 3.1 1.0 3.106 0.7 3.111 0.6 3.117 0.5 3.123 0.1 3.129 0.6 3.135 0.4 3.14 0.4 3.146 0.9 3.152 1.0 3.158 0.9 3.164 0.8 3.17 0.4 3.175 0.3 3.181 0.8 3.187 0.9 3.193 0.6 3.199 0.4 3.204 0.7 3.21 0.7 3.216 1.0 3.222 0.7 3.228 0.4 3.233 1.0 3.239 0.8 3.245 0.2 3.251 1.0 3.257 0.4 3.262 0.5 3.268 0.1 3.274 0.4 3.28 0.6 3.286 0.9 3.291 0.1 3.297 0.1 3.303 0.3 3.309 0.5 3.315 1.0 3.32 0.7 3.326 0.2 3.332 1.0 3.338 0.3 3.344 0.9 3.349 0.1 3.355 0.9 3.361 0.8 3.367 0.6 3.373 0.7 3.379 0.3 3.384 0.9 3.39 0.7 3.396 0.3 3.402 0.5 3.408 0.1 3.413 0.9 3.419 0.9 3.425 0.6 3.431 1.0 3.437 0.5 3.442 0.7 3.448 0.1 3.454 0.5 3.46 0.3 3.466 0.3 3.471 0.7 3.477 0.1 3.483 0.6 3.489 0.6 3.495 0.1 3.5 0.6 3.506 0.7 3.512 0.1 3.518 0.1 3.524 0.9 3.529 0.6 3.535 0.5 3.541 0.9 3.547 1.0 3.553 0.6 3.558 0.3 3.564 0.6 3.57 0.6 3.576 0.4 3.582 1.0 3.587 0.5 3.593 0.6 3.599 0.7 3.605 0.8 3.611 0.4 3.617 0.6 3.622 0.1 3.628 0.8 3.634 0.3 3.64 1.0 3.646 0.3 3.651 0.7 3.657 0.1 3.663 0.8 3.669 0.5 3.675 0.6 3.68 0.3 3.686 0.6 3.692 0.3 3.698 0.5 3.704 1.0 3.709 0.2 3.715 0.5 3.721 0.1 3.727 0.1 3.733 0.3 3.738 1.0 3.744 0.3 3.75 0.5 3.756 0.3 3.762 0.9 3.767 0.2 3.773 0.3 3.779 0.2 3.785 0.5 3.791 0.3 3.796 0.5 3.802 1.0 3.808 0.8 3.814 0.2 3.82 0.2 3.825 0.2 3.831 0.7 3.837 0.1 3.843 1.0 3.849 1.0 3.855 1.0 3.86 0.8 3.866 0.9 3.872 1.0 3.878 0.4 3.884 0.3 3.889 0.7 3.895 0.8 3.901 0.4 3.907 0.3 3.913 1.0 3.918 0.6 3.924 0.4 3.93 0.9 3.936 0.4 3.942 0.3 3.947 0.7 3.953 1.0 3.959 0.5 3.965 1.0 3.971 0.4 3.976 0.9 3.982 0.1 3.988 0.2 3.994 0.2 4.0 0.8 4.005 0.8 4.011 0.3 4.017 0.6 4.023 0.1 4.029 0.1 4.034 0.4 4.04 0.7 4.046 0.9 4.052 0.5 4.058 0.2 4.063 0.1 4.069 0.9 4.075 0.1 4.081 0.7 4.087 0.3 4.093 1.0 4.098 0.5 4.104 0.4 4.11 1.0 4.116 0.8 4.122 0.7 4.127 0.2 4.133 0.9 4.139 0.7 4.145 0.5 4.151 0.8 4.156 0.2 4.162 1.0 4.168 0.3 4.174 0.1 4.18 0.8 4.185 0.9 4.191 1.0 4.197 0.6 4.203 0.9 4.209 0.6 4.214 0.1 4.22 0.8 4.226 0.5 4.232 0.6 4.238 0.7 4.243 0.6 4.249 0.8 4.255 1.0 4.261 0.9 4.267 0.2 4.272 0.5 4.278 0.3 4.284 0.7 4.29 0.1 4.296 0.3 4.301 0.1 4.307 0.7 4.313 0.6 4.319 0.6 4.325 1.0 4.331 0.1 4.336 0.5 4.342 0.8 4.348 1.0 4.354 1.0 4.36 0.9 4.365 0.7 4.371 0.5 4.377 1.0 4.383 0.5 4.389 1.0 4.394 0.7 4.4 1.0 4.406 0.6 4.412 0.7 4.418 0.6 4.423 0.6 4.429 0.8 4.435 0.7 4.441 0.9 4.447 0.6 4.452 0.5 4.458 0.4 4.464 0.9 4.47 0.6 4.476 0.6 4.481 0.9 4.487 0.5 4.493 0.5 4.499 1.0 4.505 0.6 4.51 0.0 4.516 0.0 4.522 0.0 4.528 0.0 4.534 0.0 4.54 0.0 4.545 0.0 4.551 0.0 4.557 0.0 4.563 0.0 4.569 0.0 4.574 0.0 4.58 0.0 4.586 0.0 4.592 0.0 4.598 0.0 4.603 0.0 4.609 0.0 4.615 0.0 4.621 0.0 4.627 0.0 4.632 0.0 4.638 0.0 4.644 0.0 4.65 0.0 4.656 0.0 4.661 0.0 4.667 0.0 4.673 0.0 4.679 0.0 4.685 0.0 4.69 0.0 4.696 0.0 4.702 0.0 4.708 0.0 4.714 0.0 4.719 0.0 4.725 0.0 4.731 0.0 4.737 0.0 4.743 0.0 4.748 0.0 4.754 0.0 4.76 0.0 4.766 0.0 4.772 0.0 4.778 0.0 4.783 0.0 4.789 0.0 4.795 0.0 4.801 0.0 4.807 0.0 4.812 0.0 4.818 0.0 4.824 0.0 4.83 0.0 4.836 0.0 4.841 0.0 4.847 0.0 4.853 0.0 4.859 0.0 4.865 0.0 4.87 0.0 4.876 0.0 4.882 0.0 4.888 0.0 4.894 0.0 4.899 0.0 4.905 0.0 4.911 0.0 4.917 0.0 4.923 0.0 4.928 0.0 4.934 0.0 4.94 0.0 4.946 0.0 4.952 0.0 4.957 0.0 4.963 0.0 4.969 0.0 4.975 0.0 4.981 0.0 4.986 0.0 4.992 0.0 4.998 0.0 5.004 0.0 5.01 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0.5 5.602 0.8 5.608 0.5 5.613 0.4 5.619 0.9 5.625 0.6 5.631 1.0 5.637 0.4 5.642 0.7 5.648 0.7 5.654 0.2 5.66 1.0 5.666 0.1 5.671 0.8 5.677 1.0 5.683 0.2 5.689 0.8 5.695 0.5 5.7 0.6 5.706 0.2 5.712 0.8 5.718 0.2 5.724 0.1 5.73 0.8 5.735 0.4 5.741 0.4 5.747 0.9 5.753 0.4 5.759 0.1 5.764 1.0 5.77 1.0 5.776 0.9 5.782 0.3 5.788 0.3 5.793 0.4 5.799 0.4 5.805 0.7 5.811 0.6 5.817 0.9 5.822 0.8 5.828 0.2 5.834 0.6 5.84 1.0 5.846 0.7 5.851 1.0 5.857 1.0 5.863 1.0 5.869 0.7 5.875 0.9 5.88 0.9 5.886 0.7 5.892 0.2 5.898 0.2 5.904 0.1 5.909 0.8 5.915 0.4 5.921 0.2 5.927 0.9 5.933 0.7 5.939 0.1 5.944 0.7 5.95 0.2 5.956 1.0 5.962 0.1 5.968 0.2 5.973 0.3 5.979 0.4 5.985 0.3 5.991 0.6 5.997 0.5 6.002 0.1 6.008 0.6 6.014 0.5 6.02 0.9 6.026 0.3 6.031 0.1 6.037 0.1 6.043 0.7 6.049 0.1 6.055 1.0 6.06 0.1 6.066 0.2 6.072 0.5 6.078 0.7 6.084 0.2 6.089 0.7 6.095 0.6 6.101 1.0 6.107 0.9 6.113 0.1 6.118 0.5 6.124 1.0 6.13 0.3 6.136 1.0 6.142 0.1 6.147 0.7 6.153 0.7 6.159 0.1 6.165 0.9 6.171 0.6 6.177 0.4 6.182 0.5 6.188 0.5 6.194 0.1 6.2 0.7 6.206 0.3 6.211 0.1 6.217 0.5 6.223 0.5 6.229 0.8 6.235 0.7 6.24 0.3 6.246 0.5 6.252 0.2 6.258 0.4 6.264 0.2 6.269 1.0 6.275 0.9 6.281 0.5 6.287 0.6 6.293 0.6 6.298 0.5 6.304 0.4 6.31 0.4 6.316 0.3 6.322 0.9 6.327 0.2 6.333 0.8 6.339 0.1 6.345 1.0 6.351 0.5 6.356 0.8 6.362 0.1 6.368 0.2 6.374 0.5 6.38 0.6 6.385 0.4 6.391 0.6 6.397 0.9 6.403 0.9 6.409 0.7 6.415 0.2 6.42 0.7 6.426 0.5 6.432 0.5 6.438 0.6 6.444 0.9 6.449 0.3 6.455 0.8 6.461 0.1 6.467 0.1 6.473 0.9 6.478 0.4 6.484 0.2 6.49 0.4 6.496 0.3 6.502 0.2 6.507 0.7 6.513 0.7 6.519 0.9 6.525 0.7 6.531 0.5 6.536 0.9 6.542 0.1 6.548 0.5 6.554 0.6 6.56 0.7 6.565 0.5 6.571 0.8 6.577 0.7 6.583 1.0 6.589 0.5 6.594 0.3 6.6 0.9 6.606 0.4 6.612 0.7 6.618 0.3 6.623 0.1 6.629 0.3 6.635 0.8 6.641 0.8 6.647 0.7 6.653 1.0 6.658 0.6 6.664 0.9 6.67 0.6 6.676 0.9 6.682 0.4 6.687 0.7 6.693 0.6 6.699 0.3 6.705 0.3 6.711 0.2 6.716 1.0 6.722 0.3 6.728 0.8 6.734 0.4 6.74 0.5 6.745 0.7 6.751 0.3 6.757 0.2 6.763 0.2 6.769 0.5 6.774 0.4 6.78 0.9 6.786 0.6 6.792 0.6 6.798 0.5 6.803 0.6 6.809 0.1 6.815 0.3 6.821 0.5 6.827 0.8 6.832 0.1 6.838 0.6 6.844 0.5 6.85 1.0 6.856 1.0 6.861 0.1 6.867 0.4 6.873 0.5 6.879 0.5 6.885 0.2 6.891 0.9 6.896 1.0 6.902 0.4 6.908 0.3 6.914 0.9 6.92 0.8 6.925 0.4 6.931 0.7 6.937 0.1 6.943 0.9 6.949 0.6 6.954 0.6 6.96 0.3 6.966 0.1 6.972 1.0 6.978 0.2 6.983 0.4 6.989 0.2 6.995 0.2 7.001 0.2 7.007 1.0 7.012 0.9 7.018 1.0 7.024 0.8 7.03 1.0 7.036 0.5 7.041 0.3 7.047 0.3 7.053 0.5 7.059 0.5 7.065 0.6 7.07 0.9 7.076 0.5 7.082 0.7 7.088 0.5 7.094 0.7 7.1 1.0 7.105 0.3 7.111 0.6 7.117 0.6 7.123 0.8 7.129 0.6 7.134 0.2 7.14 0.8 7.146 1.0 7.152 0.8 7.158 0.9 7.163 0.9 7.169 0.6 7.175 0.3 7.181 0.8 7.187 0.8 7.192 0.8 7.198 1.0 7.204 0.1 7.21 0.5 7.216 0.7 7.221 0.3 7.227 0.1 7.233 1.0 7.239 1.0 7.245 0.4 7.25 0.6 7.256 0.1 7.262 1.0 7.268 0.1 7.274 0.3 7.279 0.9 7.285 0.4 7.291 0.5 7.297 0.9 7.303 0.2 7.308 0.1 7.314 0.5 7.32 0.8 7.326 0.3 7.332 0.1 7.338 1.0 7.343 0.1 7.349 1.0 7.355 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1.0 9.12 0.6 9.125 0.2 9.131 0.7 9.137 0.1 9.143 0.2 9.149 0.8 9.154 0.8 9.16 0.7 9.166 0.4 9.172 0.2 9.178 0.9 9.183 0.7 9.189 0.9 9.195 0.8 9.201 0.9 9.207 1.0 9.213 0.2 9.218 0.1 9.224 0.8 9.23 0.4 9.236 0.2 9.242 0.1 9.247 0.3 9.253 0.5 9.259 0.8 9.265 1.0 9.271 0.4 9.276 0.2 9.282 1.0 9.288 0.7 9.294 0.5 9.3 0.9 9.305 0.1 9.311 0.5 9.317 0.3 9.323 0.8 9.329 0.8 9.334 0.7 9.34 0.8 9.346 0.6 9.352 0.7 9.358 0.3 9.363 0.3 9.369 0.4 9.375 0.1 9.381 0.7 9.387 1.0 9.392 0.9 9.398 0.5 9.404 0.1 9.41 0.1 9.416 0.5 9.421 0.9 9.427 0.6 9.433 1.0 9.439 0.1 9.445 0.4 9.451 0.7 9.456 0.3 9.462 0.5 9.468 0.9 9.474 1.0 9.48 0.7 9.485 0.7 9.491 0.4 9.497 0.1 9.503 0.9 9.509 0.4 9.514 0.6 9.52 0.6 9.526 0.7 9.532 0.1 9.538 0.9 9.543 0.6 9.549 0.5 9.555 0.4 9.561 0.1 9.567 0.6 9.572 0.2 9.578 0.3 9.584 0.8 9.59 0.1 9.596 0.6 9.601 0.9 9.607 0.5 9.613 0.9 9.619 1.0 9.625 0.3 9.63 1.0 9.636 0.4 9.642 0.6 9.648 1.0 9.654 0.9 9.66 0.7 9.665 0.8 9.671 0.7 9.677 0.9 9.683 0.7 9.689 0.1 9.694 0.9 9.7 0.7 9.706 0.4 9.712 0.3 9.718 0.2 9.723 0.6 9.729 0.1 9.735 0.8 9.741 0.9 9.747 0.3 9.752 0.1 9.758 0.8 9.764 0.1 9.77 0.4 9.776 0.3 9.781 0.1 9.787 0.7 9.793 0.8 9.799 0.8 9.805 0.7 9.81 0.1 9.816 0.6 9.822 0.8 9.828 1.0 9.834 0.8 9.839 0.6 9.845 0.4 9.851 0.6 9.857 0.7 9.863 0.1 9.868 0.5 9.874 1.0 9.88 0.5 9.886 0.2 9.892 0.8 9.898 1.0 9.903 0.2 9.909 0.8 9.915 0.7 9.921 0.3 9.927 1.0 9.932 0.8 9.938 0.4 9.944 0.5 9.95 0.9 9.956 0.7 9.961 0.3 9.967 0.5 9.973 0.2 9.979 0.2 9.985 0.8 9.99 0.1 9.996 0.4 10.002 0.9 10.008 0.1 10.014 0.3 10.019 0.9 10.025 0.9 10.031 1.0 10.037 0.3 10.043 0.1 10.048 0.9 10.054 0.7 10.06 0.2 10.066 0.9 10.072 1.0 10.077 0.7 10.083 0.7 10.089 1.0 10.095 0.2 10.101 0.4 10.106 0.3 10.112 0.1 10.118 0.6 10.124 0.3 10.13 0.7 10.136 0.9 10.141 0.4 10.147 0.6 10.153 0.9 10.159 0.5 10.165 0.2 10.17 0.2 10.176 0.1 10.182 0.9 10.188 0.9 10.194 0.7 10.199 1.0 10.205 1.0 10.211 0.6 10.217 0.5 10.223 0.9 10.228 0.9 10.234 0.6 10.24 0.2 10.246 0.7 10.252 0.9 10.257 0.3 10.263 0.8 10.269 0.7 10.275 0.9 10.281 0.2 10.286 0.4 10.292 0.9 10.298 0.3 10.304 0.4 10.31 0.7 10.315 0.1 10.321 0.4 10.327 0.8 10.333 0.1 10.339 0.7 10.344 0.8 10.35 0.6 10.356 0.5 10.362 0.3 10.368 1.0 10.374 0.6 10.379 0.0 10.385 0.0 10.391 0.0 10.397 0.0 10.403 0.0 10.408 0.0 10.414 0.0 10.42 0.0 10.426 0.0 10.432 0.0 10.437 0.0 10.443 0.0 10.449 0.0 10.455 0.0 10.461 0.0 10.466 0.0 10.472 0.0 10.478 0.0 10.484 0.0 10.49 0.0 10.495 0.0 10.501 0.0 10.507 0.0 10.513 0.0 10.519 0.0 10.524 0.0 10.53 0.0 10.536 0.0 10.542 0.0 10.548 0.0 10.553 0.0 10.559 0.0 10.565 0.0 10.571 0.0 10.577 0.0 10.582 0.0 10.588 0.0 10.594 0.0 10.6 0.0 10.606 0.0 10.612 0.0 10.617 0.0 10.623 0.0 10.629 0.0 10.635 0.0 10.641 0.0 10.646 0.0 10.652 0.0 10.658 0.0 10.664 0.0 10.67 0.0 10.675 0.0 10.681 0.0 10.687 0.0 10.693 0.0 10.699 0.0 10.704 0.0 10.71 0.0 10.716 0.0 10.722 0.0 10.728 0.0 10.733 0.0 10.739 0.0 10.745 0.0 10.751 0.0 10.757 0.0 10.762 0.0 10.768 0.0 10.774 0.0 10.78 0.0 10.786 0.0 10.791 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11.86 0.0 11.865 0.0 11.871 0.0 11.877 0.0 11.883 0.0 11.889 0.0 11.894 0.0 11.9 0.7 11.906 1.0 11.912 1.0 11.918 1.0 11.923 0.7 11.929 0.7 11.935 0.7 11.941 0.2 11.947 1.0 11.952 0.9 11.958 0.3 11.964 0.8 11.97 0.4 11.976 1.0 11.981 0.9 11.987 0.8 11.993 0.4 11.999 0.9 12.005 0.3 12.011 0.4 12.016 0.8 12.022 0.1 12.028 0.7 12.034 0.4 12.04 0.3 12.045 0.1 12.051 1.0 12.057 0.4 12.063 0.1 12.069 0.9 12.074 0.6 12.08 0.2 12.086 0.5 12.092 0.2 12.098 0.2 12.103 0.6 12.109 0.6 12.115 0.9 12.121 0.8 12.127 0.5 12.132 0.2 12.138 0.7 12.144 0.6 12.15 0.1 12.156 0.7 12.161 0.4 12.167 0.9 12.173 0.8 12.179 0.7 12.185 0.8 12.19 0.5 12.196 0.7 12.202 0.9 12.208 0.5 12.214 0.2 12.22 0.9 12.225 0.1 12.231 0.3 12.237 0.2 12.243 0.7 12.249 0.6 12.254 0.2 12.26 0.8 12.266 0.6 12.272 0.9 12.278 0.3 12.283 0.7 12.289 0.2 12.295 0.5 12.301 0.2 12.307 0.6 12.312 0.2 12.318 0.2 12.324 0.3 12.33 0.8 12.336 0.2 12.341 0.6 12.347 0.3 12.353 0.4 12.359 0.3 12.365 0.5 12.37 0.2 12.376 1.0 12.382 0.5 12.388 0.9 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0.1 12.928 0.7 12.934 0.5 12.939 0.6 12.945 0.8 12.951 0.1 12.957 0.4 12.963 0.7 12.968 0.7 12.974 0.3 12.98 0.4 12.986 1.0 12.992 0.2 12.997 0.4 13.003 0.5 13.009 0.7 13.015 0.2 13.021 0.8 13.026 0.6 13.032 0.5 13.038 0.5 13.044 0.6 13.05 0.5 13.055 0.7 13.061 0.5 13.067 0.3 13.073 0.3 13.079 0.1 13.084 0.3 13.09 0.7 13.096 0.2 13.102 0.4 13.108 1.0 13.113 0.6 13.119 0.2 13.125 0.2 13.131 0.7 13.137 0.6 13.142 0.2 13.148 1.0 13.154 0.8 13.16 0.5 13.166 0.5 13.172 0.2 13.177 0.9 13.183 0.1 13.189 0.7 13.195 0.2 13.201 0.8 13.206 0.4 13.212 0.5 13.218 0.4 13.224 0.7 13.23 0.8 13.235 0.7 13.241 0.6 13.247 0.8 13.253 0.9 13.259 0.5 13.264 0.7 13.27 0.7 13.276 0.9 13.282 0.1 13.288 0.1 13.293 0.1 13.299 1.0 13.305 0.1 13.311 0.2 13.317 0.3 13.322 0.1 13.328 0.6 13.334 1.0 13.34 0.8 13.346 1.0 13.351 0.3 13.357 0.3 13.363 0.2 13.369 0.1 13.375 0.8 13.38 0.6 13.386 0.3 13.392 0.5 13.398 0.2 13.404 0.4 13.41 0.4 13.415 0.8 13.421 0.4 13.427 0.1 13.433 0.6 13.439 1.0 13.444 0.3 13.45 0.7 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0.8 13.99 0.6 13.996 0.9 14.002 0.1 14.007 0.4 14.013 0.8 14.019 0.5 14.025 0.7 14.031 0.1 14.036 0.2 14.042 0.5 14.048 0.4 14.054 0.1 14.06 0.7 14.065 0.3 14.071 1.0 14.077 0.2 14.083 1.0 14.089 0.5 14.095 0.2 14.1 0.1 14.106 0.1 14.112 1.0 14.118 0.7 14.124 0.8 14.129 0.2 14.135 0.9 14.141 0.4 14.147 0.9 14.153 0.2 14.158 0.4 14.164 0.7 14.17 0.8 14.176 0.2 14.182 0.6 14.187 0.7 14.193 1.0 14.199 0.1 14.205 0.5 14.211 0.1 14.216 0.5 14.222 0.8 14.228 0.2 14.234 0.9 14.24 0.4 14.245 0.4 14.251 0.4 14.257 0.7 14.263 0.5 14.269 0.1 14.274 0.4 14.28 0.9 14.286 0.3 14.292 1.0 14.298 0.7 14.303 0.7 14.309 0.5 14.315 0.8 14.321 1.0 14.327 0.1 14.333 0.7 14.338 0.2 14.344 0.8 14.35 0.3 14.356 0.5 14.362 0.5 14.367 0.6 14.373 0.9 14.379 0.2 14.385 0.9 14.391 0.3 14.396 0.5 14.402 0.3 14.408 0.8 14.414 0.3 14.42 1.0 14.425 0.9 14.431 0.5 14.437 0.9 14.443 0.2 14.449 0.6 14.454 1.0 14.46 0.4 14.466 0.9 14.472 0.5 14.478 0.5 14.483 0.5 14.489 0.2 14.495 0.9 14.501 0.8 14.507 0.4 14.512 0.1 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311.13 932.33 4.93 311.13 932.33 4.94 311.13 932.33 4.95 311.13 932.33 4.96 311.13 932.33 4.97 311.13 932.33 4.98 311.13 932.33 4.99 311.13 5.00 311.13 5.01 311.13 5.02 932.33 5.03 932.33 5.04 932.33 5.05 932.33 5.06 932.33 5.07 932.33 5.08 932.33 5.09 932.33 5.10 932.33 5.11 138.59 5.12 138.59 5.13 138.59 5.14 138.59 5.15 138.59 5.16 138.59 5.17 138.59 5.18 5.19 5.20 5.21 5.22 5.23 5.24 5.25 5.26 5.27 5.28 5.29 5.30 5.31 5.32 5.33 5.34 5.35 5.36 5.37 5.38 5.39 5.40 5.41 5.42 5.43 5.44 5.45 5.46 5.47 5.48 5.49 5.50 5.51 5.52 5.53 5.54 5.55 5.56 5.57 5.58 5.59 5.60 5.61 5.62 5.63 5.64 5.65 5.66 5.67 5.68 5.69 5.70 5.71 5.72 5.73 5.74 5.75 5.76 5.77 5.78 5.79 5.80 5.81 5.82 5.83 5.84 5.85 5.86 5.87 5.88 5.89 5.90 5.91 5.92 5.93 5.94 5.95 5.96 5.97 5.98 5.99 6.00 6.01 6.02 6.03 6.04 6.05 6.06 6.07 6.08 6.09 246.94 6.10 246.94 6.11 246.94 6.12 246.94 6.13 246.94 6.14 246.94 6.15 246.94 6.16 246.94 6.17 246.94 6.18 246.94 6.19 246.94 6.20 246.94 6.21 246.94 6.22 246.94 6.23 246.94 6.24 246.94 6.25 246.94 6.26 246.94 6.27 246.94 6.28 246.94 6.29 246.94 6.30 246.94 6.31 246.94 6.32 6.33 6.34 6.35 6.36 6.37 6.38 6.39 6.40 6.41 6.42 6.43 6.44 6.45 6.46 6.47 6.48 6.49 6.50 6.51 6.52 6.53 6.54 6.55 6.56 6.57 6.58 6.59 6.60 6.61 6.62 6.63 6.64 6.65 659.26 6.66 659.26 6.67 659.26 6.68 659.26 6.69 659.26 6.70 659.26 6.71 659.26 6.72 659.26 6.73 659.26 6.74 659.26 6.75 659.26 6.76 6.77 6.78 6.79 6.80 6.81 493.88 659.26 6.82 493.88 659.26 6.83 493.88 659.26 6.84 493.88 659.26 6.85 493.88 659.26 6.86 493.88 659.26 6.87 493.88 659.26 6.88 493.88 659.26 6.89 493.88 659.26 6.90 138.59 659.26 6.91 138.59 659.26 6.92 138.59 6.93 138.59 6.94 138.59 6.95 138.59 493.88 659.26 6.96 138.59 493.88 659.26 6.97 138.59 493.88 659.26 6.98 138.59 493.88 659.26 6.99 493.88 659.26 7.00 493.88 659.26 7.01 493.88 659.26 7.02 493.88 659.26 7.03 493.88 659.26 7.04 155.56 7.05 155.56 7.06 155.56 7.07 155.56 7.08 155.56 7.09 155.56 7.10 155.56 7.11 155.56 220.00 7.12 155.56 220.00 7.13 155.56 220.00 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155.56 246.94 369.99 466.16 932.33 14.98 155.56 246.94 369.99 466.16 932.33 14.99 155.56 246.94 369.99 466.16 932.33 15.00 155.56 246.94 369.99 466.16 932.33 15.01 155.56 246.94 369.99 466.16 932.33 15.02 155.56 246.94 369.99 466.16 932.33 15.03 155.56 246.94 369.99 466.16 932.33 15.04 155.56 246.94 369.99 466.16 932.33 15.05 155.56 369.99 466.16 932.33 15.06 155.56 369.99 466.16 932.33 15.07 155.56 369.99 466.16 932.33 15.08 155.56 369.99 466.16 932.33 15.09 369.99 466.16 932.33 15.10 246.94 369.99 466.16 932.33 15.11 246.94 369.99 466.16 932.33 15.12 155.56 246.94 369.99 466.16 932.33 987.77 15.13 155.56 246.94 369.99 466.16 932.33 987.77 15.14 155.56 246.94 369.99 466.16 932.33 987.77 15.15 155.56 246.94 369.99 466.16 932.33 987.77 15.16 155.56 246.94 369.99 466.16 932.33 987.77 15.17 155.56 246.94 369.99 466.16 932.33 987.77 15.18 155.56 246.94 369.99 466.16 932.33 987.77 15.19 155.56 246.94 369.99 466.16 932.33 987.77 15.20 155.56 246.94 369.99 466.16 932.33 987.77 15.21 155.56 246.94 369.99 466.16 932.33 15.22 155.56 246.94 369.99 466.16 932.33 15.23 155.56 246.94 369.99 466.16 932.33 15.24 155.56 246.94 369.99 466.16 932.33 15.25 155.56 246.94 369.99 466.16 932.33 15.26 155.56 246.94 369.99 466.16 932.33 15.27 155.56 246.94 369.99 466.16 932.33 15.28 155.56 246.94 369.99 466.16 932.33 987.77 15.29 155.56 246.94 369.99 466.16 932.33 987.77 15.30 155.56 246.94 369.99 466.16 932.33 987.77 15.31 155.56 246.94 369.99 466.16 932.33 987.77 15.32 155.56 246.94 369.99 466.16 932.33 987.77 15.33 155.56 246.94 369.99 466.16 932.33 987.77 15.34 155.56 246.94 369.99 466.16 932.33 987.77 15.35 155.56 246.94 369.99 466.16 932.33 15.36 155.56 246.94 369.99 466.16 932.33 15.37 155.56 246.94 369.99 932.33 15.38 155.56 246.94 369.99 466.16 932.33 15.39 155.56 246.94 369.99 466.16 932.33 15.40 155.56 246.94 369.99 466.16 932.33 15.41 155.56 246.94 369.99 466.16 932.33 15.42 155.56 246.94 369.99 466.16 932.33 15.43 155.56 246.94 369.99 466.16 932.33 15.44 155.56 246.94 369.99 466.16 932.33 15.45 155.56 246.94 369.99 466.16 932.33 15.46 155.56 246.94 369.99 466.16 932.33 15.47 155.56 246.94 369.99 466.16 932.33 15.48 155.56 246.94 369.99 466.16 932.33 15.49 155.56 246.94 369.99 466.16 932.33 15.50 155.56 246.94 369.99 466.16 932.33 15.51 155.56 246.94 369.99 466.16 932.33 15.52 123.47 155.56 246.94 369.99 466.16 932.33 15.53 123.47 155.56 246.94 369.99 466.16 932.33 15.54 123.47 155.56 369.99 466.16 932.33 15.55 123.47 155.56 369.99 466.16 932.33 15.56 123.47 155.56 369.99 466.16 932.33 15.57 123.47 155.56 369.99 466.16 932.33 15.58 123.47 155.56 369.99 466.16 932.33 15.59 123.47 155.56 369.99 466.16 932.33 15.60 123.47 155.56 369.99 466.16 932.33 15.61 123.47 466.16 932.33 15.62 123.47 466.16 932.33 15.63 123.47 466.16 932.33 15.64 123.47 466.16 932.33 15.65 123.47 466.16 932.33 15.66 123.47 466.16 932.33 15.67 123.47 466.16 932.33 15.68 61.74 123.47 466.16 932.33 15.69 61.74 123.47 466.16 932.33 15.70 61.74 123.47 466.16 932.33 15.71 61.74 123.47 466.16 932.33 15.72 61.74 123.47 466.16 932.33 15.73 61.74 123.47 466.16 932.33 15.74 61.74 123.47 466.16 932.33 15.75 61.74 123.47 311.13 466.16 932.33 15.76 61.74 123.47 311.13 466.16 932.33 15.77 61.74 123.47 311.13 466.16 932.33 15.78 61.74 123.47 311.13 466.16 932.33 15.79 61.74 123.47 311.13 466.16 932.33 15.80 61.74 123.47 311.13 466.16 932.33 15.81 61.74 123.47 311.13 466.16 932.33 15.82 61.74 123.47 311.13 466.16 932.33 15.83 61.74 123.47 311.13 466.16 932.33 15.84 61.74 123.47 311.13 466.16 932.33 15.85 61.74 123.47 311.13 466.16 932.33 15.86 61.74 123.47 311.13 466.16 15.87 61.74 123.47 311.13 466.16 15.88 61.74 123.47 311.13 466.16 15.89 61.74 123.47 311.13 466.16 15.90 61.74 123.47 311.13 466.16 15.91 61.74 123.47 311.13 466.16 15.92 61.74 123.47 311.13 466.16 15.93 61.74 123.47 311.13 466.16 932.33 15.94 61.74 123.47 311.13 466.16 932.33 15.95 61.74 123.47 311.13 466.16 932.33 15.96 61.74 123.47 311.13 466.16 932.33 15.97 61.74 123.47 311.13 466.16 932.33 15.98 61.74 123.47 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415.30 830.61 19.11 123.47 246.94 311.13 415.30 830.61 19.12 123.47 246.94 311.13 415.30 830.61 19.13 123.47 246.94 311.13 415.30 830.61 19.14 123.47 246.94 311.13 415.30 19.15 123.47 246.94 311.13 415.30 19.16 123.47 246.94 311.13 415.30 19.17 123.47 246.94 311.13 415.30 19.18 123.47 311.13 415.30 19.19 123.47 311.13 415.30 19.20 123.47 311.13 415.30 19.21 123.47 311.13 415.30 19.22 123.47 311.13 415.30 19.23 123.47 311.13 19.24 123.47 311.13 19.25 123.47 830.61 19.26 123.47 830.61 19.27 123.47 830.61 19.28 123.47 830.61 19.29 123.47 830.61 19.30 123.47 415.30 830.61 19.31 123.47 415.30 830.61 19.32 123.47 415.30 830.61 19.33 123.47 415.30 830.61 19.34 123.47 415.30 19.35 123.47 415.30 19.36 123.47 415.30 19.37 123.47 415.30 19.38 123.47 415.30 19.39 123.47 415.30 19.40 123.47 415.30 19.41 123.47 415.30 19.42 123.47 138.59 415.30 19.43 123.47 138.59 415.30 19.44 123.47 138.59 19.45 123.47 138.59 19.46 123.47 138.59 19.47 123.47 138.59 19.48 123.47 138.59 19.49 123.47 138.59 19.50 123.47 138.59 19.51 123.47 138.59 415.30 19.52 123.47 138.59 415.30 19.53 123.47 138.59 329.63 415.30 19.54 123.47 138.59 329.63 415.30 19.55 123.47 138.59 329.63 415.30 19.56 123.47 138.59 329.63 415.30 19.57 123.47 138.59 329.63 415.30 19.58 123.47 138.59 329.63 415.30 19.59 123.47 138.59 329.63 415.30 19.60 123.47 138.59 329.63 415.30 19.61 123.47 138.59 329.63 415.30 19.62 123.47 329.63 415.30 19.63 123.47 329.63 415.30 659.26 19.64 123.47 329.63 415.30 659.26 19.65 123.47 329.63 415.30 659.26 19.66 123.47 329.63 415.30 659.26 19.67 277.18 329.63 415.30 659.26 1975.53 19.68 277.18 329.63 415.30 659.26 1975.53 19.69 277.18 329.63 415.30 659.26 1975.53 19.70 207.65 277.18 329.63 415.30 659.26 1975.53 19.71 207.65 277.18 329.63 415.30 659.26 1975.53 19.72 138.59 207.65 277.18 329.63 415.30 1975.53 19.73 138.59 207.65 277.18 329.63 415.30 1975.53 19.74 138.59 207.65 329.63 415.30 659.26 19.75 138.59 207.65 329.63 415.30 659.26 19.76 138.59 329.63 415.30 659.26 19.77 138.59 329.63 415.30 659.26 19.78 138.59 329.63 415.30 659.26 19.79 138.59 329.63 415.30 659.26 987.77 19.80 138.59 329.63 415.30 659.26 987.77 19.81 207.65 329.63 415.30 659.26 987.77 19.82 207.65 329.63 415.30 659.26 987.77 19.83 207.65 329.63 415.30 659.26 987.77 19.84 207.65 329.63 415.30 659.26 987.77 19.85 207.65 329.63 415.30 659.26 987.77 19.86 207.65 329.63 415.30 659.26 987.77 19.87 207.65 329.63 415.30 659.26 987.77 19.88 207.65 246.94 329.63 415.30 659.26 987.77 19.89 207.65 246.94 329.63 415.30 659.26 987.77 19.90 207.65 246.94 329.63 415.30 659.26 987.77 19.91 207.65 246.94 329.63 415.30 659.26 987.77 19.92 246.94 329.63 415.30 659.26 19.93 246.94 329.63 415.30 659.26 19.94 246.94 329.63 415.30 659.26 19.95 246.94 329.63 415.30 659.26 19.96 246.94 329.63 415.30 659.26 19.97 207.65 246.94 329.63 415.30 659.26 1318.51 19.98 207.65 246.94 329.63 415.30 659.26 1318.51 19.99 207.65 246.94 329.63 415.30 659.26 1318.51 20.00 207.65 246.94 329.63 415.30 659.26 1318.51 20.01 207.65 246.94 329.63 415.30 659.26 1318.51 20.02 207.65 246.94 329.63 415.30 659.26 1318.51 20.03 207.65 246.94 329.63 415.30 659.26 1318.51 20.04 138.59 207.65 246.94 329.63 415.30 659.26 1318.51 20.05 138.59 207.65 246.94 329.63 415.30 659.26 1318.51 20.06 138.59 207.65 246.94 329.63 415.30 659.26 20.07 138.59 207.65 246.94 329.63 415.30 659.26 20.08 138.59 207.65 246.94 329.63 415.30 659.26 20.09 138.59 207.65 246.94 329.63 415.30 659.26 20.10 138.59 207.65 246.94 329.63 415.30 659.26 20.11 207.65 246.94 329.63 415.30 659.26 20.12 207.65 246.94 329.63 415.30 659.26 20.13 207.65 246.94 329.63 415.30 659.26 20.14 207.65 246.94 329.63 415.30 659.26 20.15 207.65 246.94 329.63 415.30 659.26 20.16 207.65 246.94 329.63 415.30 659.26 20.17 207.65 246.94 329.63 415.30 659.26 20.18 207.65 246.94 329.63 415.30 659.26 20.19 207.65 246.94 329.63 415.30 659.26 20.20 207.65 246.94 329.63 415.30 659.26 20.21 207.65 246.94 329.63 415.30 659.26 1318.51 20.22 207.65 246.94 329.63 415.30 659.26 1318.51 20.23 207.65 246.94 329.63 415.30 659.26 1318.51 20.24 207.65 246.94 329.63 415.30 659.26 1318.51 20.25 207.65 246.94 329.63 415.30 659.26 1318.51 20.26 207.65 246.94 329.63 415.30 659.26 1318.51 20.27 207.65 246.94 329.63 415.30 659.26 1318.51 20.28 207.65 246.94 329.63 415.30 659.26 1318.51 20.29 207.65 246.94 329.63 415.30 659.26 1318.51 20.30 207.65 246.94 329.63 415.30 659.26 1318.51 20.31 207.65 246.94 329.63 415.30 659.26 1318.51 20.32 207.65 246.94 329.63 415.30 659.26 1318.51 20.33 207.65 246.94 329.63 415.30 659.26 1318.51 20.34 207.65 246.94 329.63 415.30 659.26 1318.51 20.35 207.65 246.94 329.63 415.30 659.26 1318.51 20.36 207.65 246.94 329.63 415.30 659.26 1318.51 20.37 207.65 246.94 329.63 415.30 659.26 1318.51 20.38 207.65 246.94 329.63 415.30 659.26 1318.51 20.39 116.54 246.94 329.63 415.30 659.26 1318.51 20.40 116.54 246.94 329.63 415.30 659.26 1318.51 20.41 116.54 415.30 659.26 1318.51 20.42 116.54 415.30 659.26 1318.51 20.43 116.54 415.30 659.26 1318.51 20.44 116.54 415.30 659.26 20.45 116.54 415.30 659.26 20.46 116.54 174.61 415.30 20.47 116.54 174.61 415.30 20.48 116.54 174.61 415.30 20.49 116.54 174.61 233.08 415.30 20.50 116.54 174.61 233.08 415.30 20.51 174.61 233.08 415.30 20.52 174.61 233.08 415.30 20.53 174.61 233.08 415.30 20.54 174.61 233.08 415.30 20.55 174.61 233.08 415.30 20.56 174.61 233.08 415.30 20.57 174.61 233.08 415.30 20.58 174.61 233.08 349.23 415.30 20.59 174.61 233.08 349.23 415.30 20.60 174.61 233.08 349.23 415.30 659.26 20.61 174.61 233.08 349.23 415.30 659.26 20.62 174.61 233.08 349.23 415.30 659.26 830.61 20.63 174.61 233.08 349.23 415.30 659.26 830.61 20.64 174.61 233.08 415.30 659.26 830.61 20.65 174.61 233.08 415.30 659.26 830.61 20.66 174.61 233.08 415.30 659.26 830.61 20.67 174.61 415.30 830.61 20.68 174.61 415.30 830.61 20.69 174.61 415.30 830.61 20.70 174.61 415.30 830.61 20.71 174.61 415.30 830.61 20.72 174.61 415.30 830.61 20.73 174.61 415.30 830.61 20.74 116.54 174.61 415.30 830.61 20.75 116.54 174.61 415.30 830.61 20.76 116.54 174.61 415.30 830.61 20.77 116.54 174.61 415.30 830.61 20.78 116.54 174.61 415.30 830.61 20.79 116.54 174.61 415.30 830.61 20.80 116.54 174.61 415.30 830.61 20.81 116.54 415.30 830.61 20.82 116.54 415.30 830.61 20.83 116.54 415.30 830.61 20.84 116.54 415.30 830.61 20.85 116.54 415.30 830.61 20.86 116.54 415.30 830.61 20.87 116.54 415.30 830.61 20.88 116.54 277.18 415.30 830.61 20.89 116.54 277.18 415.30 830.61 20.90 116.54 277.18 415.30 830.61 20.91 116.54 277.18 415.30 830.61 20.92 116.54 277.18 415.30 830.61 20.93 116.54 277.18 415.30 830.61 20.94 116.54 277.18 415.30 830.61 20.95 116.54 277.18 415.30 830.61 20.96 116.54 277.18 415.30 830.61 20.97 116.54 277.18 415.30 830.61 20.98 116.54 277.18 415.30 830.61 20.99 116.54 277.18 415.30 21.00 116.54 277.18 415.30 21.01 116.54 277.18 415.30 21.02 116.54 277.18 415.30 21.03 116.54 277.18 415.30 21.04 116.54 277.18 415.30 21.05 116.54 277.18 415.30 21.06 116.54 277.18 415.30 21.07 116.54 277.18 415.30 21.08 116.54 277.18 415.30 21.09 116.54 277.18 415.30 21.10 116.54 277.18 415.30 21.11 116.54 277.18 415.30 21.12 116.54 277.18 415.30 21.13 116.54 277.18 415.30 21.14 116.54 277.18 415.30 21.15 116.54 277.18 415.30 21.16 116.54 277.18 415.30 21.17 116.54 277.18 415.30 21.18 116.54 277.18 415.30 21.19 116.54 277.18 415.30 21.20 116.54 277.18 415.30 21.21 116.54 277.18 415.30 21.22 116.54 277.18 415.30 21.23 116.54 277.18 415.30 21.24 116.54 277.18 415.30 21.25 116.54 277.18 415.30 21.26 116.54 277.18 415.30 21.27 116.54 277.18 415.30 21.28 116.54 277.18 415.30 21.29 116.54 277.18 415.30 21.30 116.54 277.18 415.30 21.31 116.54 277.18 415.30 21.32 116.54 277.18 415.30 21.33 116.54 277.18 415.30 21.34 116.54 277.18 415.30 21.35 116.54 277.18 415.30 21.36 116.54 277.18 415.30 21.37 116.54 277.18 415.30 21.38 116.54 277.18 415.30 21.39 116.54 164.81 233.08 277.18 415.30 21.40 116.54 164.81 233.08 277.18 415.30 21.41 82.41 116.54 164.81 233.08 277.18 415.30 21.42 82.41 116.54 164.81 233.08 277.18 415.30 21.43 82.41 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311.13 329.63 659.26 27.21 246.94 311.13 329.63 659.26 27.22 246.94 311.13 329.63 659.26 27.23 246.94 311.13 329.63 659.26 27.24 246.94 311.13 329.63 659.26 27.25 246.94 311.13 329.63 659.26 27.26 246.94 311.13 329.63 659.26 27.27 246.94 311.13 329.63 659.26 27.28 246.94 311.13 329.63 659.26 27.29 246.94 311.13 329.63 659.26 27.30 246.94 311.13 329.63 659.26 27.31 246.94 311.13 329.63 659.26 27.32 246.94 311.13 329.63 659.26 27.33 246.94 311.13 329.63 659.26 27.34 246.94 311.13 329.63 659.26 27.35 246.94 311.13 329.63 659.26 27.36 103.83 246.94 311.13 329.63 659.26 27.37 103.83 246.94 311.13 329.63 659.26 27.38 103.83 246.94 311.13 329.63 659.26 27.39 103.83 246.94 311.13 329.63 659.26 27.40 103.83 246.94 311.13 329.63 659.26 27.41 103.83 246.94 311.13 329.63 659.26 27.42 103.83 246.94 311.13 329.63 659.26 27.43 103.83 246.94 311.13 329.63 659.26 27.44 103.83 246.94 311.13 329.63 659.26 27.45 103.83 246.94 311.13 329.63 659.26 27.46 103.83 246.94 311.13 329.63 659.26 27.47 103.83 246.94 311.13 329.63 659.26 27.48 103.83 246.94 311.13 329.63 659.26 27.49 103.83 246.94 311.13 329.63 27.50 103.83 246.94 311.13 329.63 27.51 103.83 246.94 311.13 329.63 27.52 103.83 246.94 311.13 329.63 27.53 103.83 246.94 311.13 329.63 27.54 98.00 103.83 246.94 311.13 329.63 27.55 98.00 103.83 246.94 311.13 329.63 27.56 98.00 103.83 246.94 329.63 27.57 98.00 103.83 233.08 246.94 329.63 27.58 98.00 103.83 233.08 246.94 329.63 27.59 98.00 233.08 246.94 329.63 27.60 98.00 233.08 246.94 329.63 27.61 98.00 233.08 329.63 27.62 98.00 233.08 329.63 27.63 98.00 233.08 329.63 27.64 98.00 233.08 329.63 27.65 98.00 233.08 329.63 27.66 49.00 98.00 329.63 27.67 49.00 98.00 329.63 27.68 49.00 98.00 329.63 659.26 27.69 49.00 98.00 329.63 659.26 27.70 49.00 98.00 329.63 659.26 27.71 49.00 98.00 329.63 659.26 27.72 49.00 98.00 329.63 659.26 27.73 49.00 98.00 329.63 659.26 27.74 49.00 98.00 329.63 659.26 27.75 49.00 98.00 329.63 27.76 49.00 98.00 329.63 27.77 49.00 98.00 27.78 49.00 98.00 233.08 27.79 49.00 98.00 233.08 27.80 49.00 98.00 233.08 27.81 49.00 98.00 233.08 27.82 49.00 98.00 233.08 27.83 49.00 98.00 233.08 27.84 49.00 98.00 233.08 27.85 49.00 98.00 233.08 329.63 27.86 49.00 98.00 233.08 329.63 27.87 49.00 233.08 329.63 27.88 49.00 233.08 329.63 27.89 49.00 233.08 329.63 27.90 49.00 233.08 329.63 27.91 49.00 233.08 329.63 27.92 49.00 233.08 329.63 27.93 49.00 233.08 329.63 27.94 49.00 27.95 49.00 27.96 49.00 329.63 27.97 49.00 329.63 27.98 49.00 329.63 27.99 49.00 329.63 28.00 49.00 329.63 28.01 49.00 329.63 28.02 49.00 329.63 28.03 49.00 233.08 329.63 28.04 49.00 233.08 329.63 28.05 49.00 233.08 329.63 28.06 49.00 233.08 329.63 28.07 49.00 233.08 329.63 28.08 49.00 98.00 233.08 329.63 622.25 28.09 49.00 98.00 233.08 329.63 622.25 28.10 49.00 98.00 233.08 622.25 28.11 49.00 98.00 233.08 622.25 28.12 49.00 98.00 233.08 622.25 28.13 49.00 98.00 233.08 622.25 28.14 49.00 98.00 233.08 622.25 28.15 49.00 98.00 233.08 622.25 28.16 49.00 98.00 233.08 622.25 28.17 49.00 98.00 233.08 622.25 28.18 49.00 98.00 233.08 622.25 28.19 49.00 98.00 233.08 28.20 49.00 98.00 233.08 28.21 49.00 98.00 233.08 28.22 49.00 98.00 233.08 622.25 28.23 49.00 98.00 233.08 622.25 28.24 49.00 98.00 233.08 622.25 28.25 49.00 98.00 233.08 622.25 28.26 49.00 98.00 233.08 622.25 28.27 49.00 98.00 233.08 622.25 28.28 49.00 98.00 233.08 622.25 28.29 49.00 98.00 233.08 622.25 28.30 49.00 98.00 233.08 622.25 28.31 49.00 98.00 233.08 622.25 28.32 49.00 98.00 233.08 622.25 28.33 49.00 98.00 233.08 622.25 28.34 49.00 98.00 233.08 622.25 28.35 49.00 98.00 233.08 622.25 28.36 49.00 98.00 233.08 622.25 28.37 49.00 98.00 233.08 622.25 28.38 49.00 98.00 233.08 622.25 28.39 49.00 98.00 233.08 622.25 28.40 98.00 233.08 622.25 28.41 98.00 233.08 622.25 28.42 98.00 233.08 622.25 28.43 98.00 233.08 622.25 28.44 98.00 233.08 622.25 28.45 98.00 233.08 311.13 622.25 28.46 98.00 233.08 311.13 622.25 28.47 49.00 98.00 233.08 311.13 622.25 28.48 49.00 98.00 233.08 311.13 622.25 28.49 49.00 98.00 233.08 311.13 622.25 28.50 49.00 98.00 233.08 311.13 622.25 28.51 49.00 98.00 233.08 311.13 622.25 28.52 49.00 98.00 233.08 311.13 622.25 28.53 49.00 98.00 233.08 311.13 622.25 28.54 49.00 98.00 311.13 622.25 28.55 49.00 98.00 311.13 622.25 28.56 49.00 98.00 311.13 622.25 28.57 49.00 98.00 311.13 622.25 28.58 49.00 98.00 311.13 622.25 28.59 49.00 98.00 28.60 49.00 98.00 28.61 49.00 98.00 28.62 49.00 98.00 28.63 98.00 28.64 98.00 622.25 28.65 98.00 622.25 28.66 622.25 28.67 622.25 28.68 246.94 622.25 28.69 246.94 622.25 28.70 103.83 246.94 622.25 28.71 103.83 246.94 622.25 28.72 103.83 246.94 622.25 28.73 103.83 246.94 622.25 28.74 103.83 246.94 622.25 28.75 103.83 246.94 622.25 28.76 103.83 246.94 622.25 28.77 103.83 622.25 28.78 103.83 622.25 28.79 103.83 622.25 28.80 103.83 246.94 622.25 28.81 103.83 246.94 622.25 28.82 103.83 246.94 622.25 28.83 103.83 246.94 622.25 28.84 103.83 246.94 622.25 28.85 103.83 246.94 622.25 28.86 103.83 246.94 622.25 28.87 103.83 246.94 622.25 28.88 103.83 246.94 622.25 28.89 103.83 246.94 311.13 622.25 28.90 103.83 246.94 311.13 622.25 28.91 103.83 246.94 311.13 622.25 28.92 103.83 246.94 311.13 622.25 28.93 103.83 311.13 622.25 28.94 103.83 311.13 622.25 28.95 103.83 311.13 622.25 28.96 103.83 311.13 622.25 28.97 103.83 311.13 622.25 28.98 103.83 311.13 622.25 28.99 103.83 311.13 622.25 29.00 103.83 311.13 622.25 29.01 103.83 311.13 622.25 29.02 103.83 311.13 622.25 29.03 103.83 622.25 29.04 103.83 622.25 29.05 103.83 622.25 1244.51 29.06 103.83 622.25 1244.51 29.07 103.83 622.25 1244.51 29.08 103.83 311.13 622.25 932.33 1244.51 29.09 103.83 311.13 622.25 932.33 1244.51 29.10 103.83 311.13 622.25 932.33 1244.51 29.11 103.83 311.13 622.25 932.33 1244.51 29.12 103.83 311.13 622.25 932.33 29.13 103.83 311.13 622.25 932.33 29.14 103.83 311.13 622.25 932.33 29.15 103.83 311.13 622.25 932.33 29.16 103.83 311.13 622.25 932.33 29.17 103.83 311.13 622.25 932.33 29.18 103.83 311.13 622.25 932.33 29.19 103.83 311.13 622.25 29.20 103.83 311.13 622.25 29.21 103.83 311.13 622.25 29.22 103.83 311.13 622.25 29.23 103.83 311.13 622.25 29.24 103.83 311.13 622.25 29.25 103.83 311.13 622.25 29.26 103.83 311.13 622.25 29.27 103.83 311.13 622.25 29.28 311.13 29.29 311.13 1244.51 29.30 311.13 1244.51 29.31 311.13 622.25 1244.51 29.32 311.13 622.25 1244.51 29.33 311.13 622.25 1244.51 29.34 311.13 622.25 1244.51 29.35 311.13 622.25 29.36 311.13 622.25 29.37 311.13 622.25 29.38 311.13 622.25 29.39 311.13 622.25 29.40 622.25 29.41 622.25 29.42 622.25 29.43 622.25 29.44 622.25 29.45 311.13 622.25 29.46 311.13 622.25 29.47 311.13 622.25 29.48 311.13 622.25 29.49 311.13 622.25 29.50 311.13 622.25 29.51 311.13 622.25 29.52 311.13 622.25 29.53 311.13 622.25 29.54 311.13 622.25 29.55 311.13 622.25 29.56 622.25 29.57 622.25 29.58 29.59 29.60 29.61 29.62 29.63 29.64 29.65 29.66 29.67 29.68 29.69 29.70 29.71 29.72 29.73 29.74 29.75 29.76 29.77 29.78 29.79 29.80 92.50 29.81 92.50 29.82 92.50 29.83 92.50 29.84 92.50 29.85 92.50 29.86 92.50 29.87 92.50 29.88 92.50 29.89 92.50 29.90 92.50 29.91 92.50 185.00 29.92 92.50 185.00 29.93 185.00 29.94 185.00 523.25 29.95 185.00 523.25 29.96 185.00 523.25 29.97 185.00 523.25 29.98 92.50 185.00 523.25 29.99 92.50 185.00 523.25 30.00 92.50 523.25 30.01 92.50 523.25 1046.50 30.02 92.50 523.25 1046.50 30.03 92.50 523.25 1046.50 30.04 92.50 523.25 1046.50 30.05 92.50 523.25 1046.50 30.06 92.50 523.25 1046.50 30.07 523.25 1046.50 30.08 523.25 1046.50 30.09 523.25 1046.50 30.10 523.25 1046.50 30.11 523.25 1046.50 30.12 523.25 1046.50 30.13 523.25 1046.50 30.14 185.00 523.25 30.15 185.00 523.25 30.16 185.00 523.25 30.17 185.00 523.25 30.18 185.00 523.25 30.19 185.00 523.25 1046.50 30.20 185.00 523.25 1046.50 30.21 185.00 220.00 523.25 1046.50 30.22 185.00 220.00 523.25 1046.50 30.23 185.00 220.00 523.25 1046.50 30.24 185.00 220.00 523.25 1046.50 30.25 185.00 220.00 523.25 1046.50 30.26 185.00 220.00 523.25 1046.50 30.27 185.00 220.00 523.25 1046.50 30.28 185.00 220.00 523.25 1046.50 30.29 185.00 220.00 523.25 1046.50 30.30 185.00 220.00 523.25 1046.50 30.31 185.00 220.00 523.25 1046.50 30.32 185.00 220.00 523.25 1046.50 30.33 220.00 523.25 1046.50 30.34 220.00 523.25 1046.50 30.35 185.00 220.00 523.25 1046.50 30.36 185.00 220.00 523.25 1046.50 30.37 185.00 220.00 523.25 1046.50 30.38 185.00 220.00 523.25 1046.50 30.39 185.00 220.00 523.25 1046.50 30.40 185.00 220.00 523.25 1046.50 30.41 185.00 220.00 523.25 1046.50 30.42 185.00 220.00 523.25 1046.50 30.43 185.00 220.00 523.25 1046.50 30.44 185.00 220.00 523.25 1046.50 30.45 185.00 220.00 523.25 1046.50 30.46 185.00 220.00 523.25 1046.50 30.47 185.00 220.00 523.25 1046.50 30.48 185.00 220.00 523.25 1046.50 30.49 220.00 369.99 523.25 1046.50 30.50 220.00 369.99 523.25 1046.50 30.51 220.00 369.99 523.25 1046.50 30.52 220.00 369.99 523.25 1046.50 30.53 220.00 369.99 523.25 1046.50 30.54 220.00 369.99 523.25 1046.50 30.55 220.00 369.99 523.25 1046.50 30.56 220.00 369.99 523.25 1046.50 30.57 220.00 369.99 523.25 1046.50 30.58 220.00 369.99 523.25 1046.50 30.59 185.00 220.00 369.99 523.25 1046.50 30.60 185.00 220.00 369.99 523.25 1046.50 30.61 185.00 220.00 369.99 523.25 1046.50 30.62 185.00 220.00 369.99 523.25 1046.50 30.63 185.00 220.00 369.99 523.25 1046.50 30.64 185.00 220.00 369.99 523.25 1046.50 30.65 185.00 220.00 523.25 1046.50 30.66 185.00 220.00 523.25 1046.50 30.67 185.00 220.00 523.25 1046.50 30.68 185.00 220.00 523.25 1046.50 30.69 185.00 220.00 523.25 1046.50 30.70 185.00 369.99 523.25 1046.50 30.71 185.00 369.99 523.25 1046.50 30.72 185.00 369.99 523.25 1046.50 30.73 185.00 369.99 523.25 1046.50 30.74 185.00 369.99 523.25 1046.50 30.75 185.00 369.99 415.30 523.25 1046.50 30.76 185.00 369.99 415.30 523.25 1046.50 30.77 185.00 415.30 523.25 1046.50 30.78 185.00 415.30 523.25 1046.50 30.79 164.81 415.30 523.25 1046.50 30.80 164.81 415.30 523.25 1046.50 30.81 164.81 415.30 523.25 1046.50 30.82 164.81 415.30 523.25 554.37 1046.50 30.83 164.81 415.30 523.25 554.37 1046.50 30.84 164.81 415.30 523.25 554.37 1046.50 30.85 164.81 415.30 523.25 554.37 1046.50 30.86 164.81 415.30 554.37 30.87 164.81 415.30 554.37 30.88 164.81 415.30 554.37 30.89 82.41 164.81 415.30 554.37 30.90 82.41 164.81 415.30 554.37 30.91 82.41 415.30 554.37 30.92 82.41 415.30 554.37 30.93 82.41 415.30 554.37 30.94 82.41 415.30 554.37 30.95 82.41 415.30 554.37 30.96 82.41 415.30 523.25 554.37 30.97 82.41 415.30 523.25 554.37 30.98 164.81 277.18 415.30 523.25 554.37 30.99 164.81 277.18 415.30 523.25 554.37 31.00 164.81 277.18 415.30 523.25 554.37 31.01 164.81 277.18 415.30 523.25 554.37 31.02 164.81 277.18 415.30 523.25 554.37 31.03 164.81 277.18 415.30 523.25 554.37 31.04 164.81 277.18 415.30 523.25 554.37 31.05 164.81 277.18 415.30 554.37 31.06 164.81 277.18 415.30 554.37 31.07 164.81 277.18 415.30 554.37 31.08 164.81 277.18 415.30 554.37 31.09 164.81 277.18 415.30 554.37 31.10 164.81 277.18 415.30 554.37 31.11 164.81 277.18 415.30 554.37 31.12 164.81 415.30 554.37 31.13 164.81 415.30 554.37 31.14 164.81 415.30 554.37 31.15 164.81 415.30 554.37 31.16 554.37 31.17 82.41 277.18 554.37 31.18 82.41 277.18 554.37 31.19 82.41 277.18 415.30 554.37 31.20 82.41 277.18 415.30 554.37 31.21 82.41 277.18 415.30 554.37 31.22 82.41 277.18 415.30 554.37 31.23 82.41 277.18 415.30 554.37 31.24 82.41 277.18 415.30 554.37 31.25 82.41 277.18 415.30 554.37 31.26 277.18 415.30 554.37 31.27 277.18 415.30 554.37 31.28 277.18 415.30 554.37 31.29 277.18 415.30 554.37 31.30 277.18 415.30 554.37 31.31 207.65 277.18 415.30 554.37 31.32 207.65 277.18 415.30 554.37 31.33 207.65 277.18 415.30 554.37 31.34 207.65 277.18 415.30 554.37 31.35 207.65 277.18 415.30 554.37 31.36 207.65 277.18 415.30 554.37 31.37 207.65 277.18 415.30 554.37 31.38 207.65 277.18 415.30 554.37 31.39 207.65 277.18 415.30 554.37 31.40 207.65 277.18 415.30 554.37 31.41 207.65 277.18 415.30 554.37 31.42 207.65 277.18 415.30 31.43 207.65 277.18 415.30 31.44 207.65 277.18 415.30 554.37 31.45 207.65 277.18 415.30 554.37 31.46 207.65 277.18 415.30 554.37 31.47 207.65 277.18 415.30 554.37 31.48 207.65 277.18 415.30 554.37 31.49 277.18 415.30 554.37 31.50 277.18 415.30 554.37 31.51 277.18 415.30 554.37 31.52 277.18 415.30 554.37 31.53 277.18 415.30 554.37 31.54 277.18 415.30 554.37 31.55 277.18 415.30 554.37 31.56 277.18 415.30 31.57 277.18 415.30 31.58 277.18 415.30 31.59 277.18 415.30 31.60 277.18 415.30 31.61 277.18 415.30 554.37 31.62 277.18 415.30 554.37 31.63 277.18 415.30 554.37 31.64 277.18 415.30 554.37 31.65 82.41 277.18 415.30 554.37 31.66 82.41 277.18 415.30 554.37 31.67 82.41 277.18 554.37 31.68 82.41 207.65 277.18 554.37 31.69 82.41 207.65 277.18 554.37 31.70 82.41 207.65 277.18 554.37 31.71 82.41 207.65 277.18 554.37 31.72 82.41 207.65 277.18 554.37 31.73 82.41 207.65 277.18 554.37 31.74 82.41 207.65 277.18 554.37 31.75 82.41 207.65 277.18 554.37 31.76 82.41 207.65 277.18 554.37 31.77 82.41 277.18 31.78 82.41 277.18 31.79 277.18 31.80 277.18 31.81 277.18 31.82 277.18 369.99 31.83 277.18 369.99 31.84 155.56 277.18 369.99 31.85 155.56 277.18 369.99 31.86 155.56 277.18 369.99 31.87 155.56 277.18 369.99 31.88 155.56 277.18 369.99 31.89 155.56 277.18 369.99 31.90 155.56 277.18 369.99 31.91 277.18 369.99 31.92 277.18 369.99 31.93 277.18 369.99 31.94 277.18 369.99 31.95 277.18 369.99 31.96 277.18 369.99 31.97 277.18 369.99 31.98 277.18 369.99 31.99 277.18 369.99 32.00 277.18 369.99 32.01 277.18 369.99 32.02 277.18 369.99 32.03 277.18 369.99 32.04 277.18 369.99 32.05 220.00 277.18 369.99 32.06 220.00 277.18 369.99 32.07 77.78 220.00 277.18 369.99 32.08 77.78 220.00 277.18 369.99 32.09 77.78 220.00 277.18 369.99 32.10 77.78 220.00 277.18 369.99 32.11 77.78 220.00 277.18 369.99 32.12 77.78 220.00 277.18 369.99 32.13 77.78 220.00 277.18 369.99 32.14 77.78 220.00 277.18 369.99 32.15 77.78 220.00 277.18 369.99 32.16 77.78 155.56 220.00 277.18 369.99 32.17 77.78 155.56 220.00 277.18 369.99 32.18 155.56 220.00 277.18 369.99 32.19 155.56 220.00 277.18 369.99 32.20 155.56 220.00 277.18 369.99 32.21 155.56 220.00 277.18 369.99 32.22 155.56 220.00 277.18 369.99 32.23 155.56 220.00 277.18 369.99 32.24 155.56 220.00 277.18 369.99 32.25 155.56 220.00 277.18 369.99 32.26 77.78 155.56 220.00 277.18 369.99 32.27 77.78 155.56 220.00 277.18 369.99 32.28 77.78 220.00 277.18 369.99 32.29 77.78 220.00 277.18 369.99 32.30 77.78 220.00 369.99 32.31 77.78 220.00 369.99 32.32 77.78 220.00 32.33 77.78 185.00 220.00 32.34 77.78 185.00 220.00 32.35 77.78 185.00 220.00 32.36 77.78 185.00 220.00 32.37 77.78 185.00 220.00 32.38 77.78 185.00 220.00 32.39 77.78 185.00 220.00 32.40 77.78 185.00 220.00 32.41 77.78 185.00 220.00 32.42 155.56 185.00 220.00 32.43 155.56 185.00 220.00 32.44 155.56 185.00 220.00 369.99 32.45 155.56 185.00 220.00 369.99 32.46 155.56 185.00 220.00 369.99 32.47 155.56 185.00 220.00 369.99 32.48 155.56 185.00 220.00 369.99 32.49 155.56 185.00 220.00 369.99 32.50 155.56 185.00 220.00 369.99 32.51 185.00 220.00 369.99 32.52 185.00 220.00 369.99 32.53 185.00 220.00 32.54 185.00 220.00 32.55 185.00 220.00 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164.81 246.94 8.78 103.83 164.81 246.94 8.79 103.83 164.81 246.94 8.80 103.83 164.81 246.94 8.81 103.83 164.81 246.94 8.82 103.83 164.81 246.94 8.83 103.83 164.81 246.94 8.84 103.83 164.81 246.94 8.85 103.83 164.81 246.94 8.86 103.83 164.81 246.94 8.87 103.83 246.94 8.88 103.83 246.94 8.89 103.83 246.94 8.90 103.83 246.94 8.91 103.83 246.94 8.92 103.83 246.94 8.93 103.83 246.94 8.94 103.83 8.95 103.83 8.96 103.83 8.97 103.83 8.98 103.83 8.99 103.83 9.00 103.83 9.01 103.83 9.02 103.83 9.03 103.83 9.04 103.83 9.05 103.83 9.06 103.83 9.07 103.83 9.08 103.83 9.09 103.83 9.10 103.83 9.11 103.83 9.12 103.83 9.13 103.83 9.14 103.83 9.15 103.83 110.00 9.16 103.83 110.00 9.17 103.83 110.00 9.18 103.83 110.00 9.19 103.83 110.00 9.20 103.83 110.00 9.21 103.83 110.00 9.22 103.83 110.00 9.23 103.83 110.00 9.24 103.83 110.00 9.25 103.83 110.00 9.26 103.83 110.00 9.27 110.00 9.28 110.00 9.29 110.00 329.63 523.25 9.30 110.00 329.63 523.25 9.31 110.00 329.63 523.25 9.32 110.00 329.63 523.25 9.33 110.00 329.63 523.25 9.34 110.00 329.63 523.25 9.35 110.00 329.63 523.25 9.36 220.00 329.63 523.25 9.37 220.00 329.63 523.25 9.38 220.00 329.63 523.25 9.39 220.00 329.63 523.25 9.40 220.00 329.63 523.25 9.41 220.00 329.63 523.25 9.42 220.00 329.63 523.25 9.43 220.00 329.63 523.25 9.44 220.00 329.63 523.25 9.45 220.00 523.25 9.46 220.00 523.25 9.47 220.00 523.25 9.48 220.00 329.63 523.25 9.49 220.00 329.63 523.25 9.50 220.00 261.63 329.63 523.25 9.51 220.00 261.63 329.63 523.25 9.52 220.00 261.63 329.63 523.25 9.53 220.00 261.63 329.63 523.25 9.54 220.00 261.63 329.63 523.25 9.55 220.00 261.63 329.63 523.25 9.56 220.00 261.63 329.63 523.25 9.57 220.00 261.63 329.63 523.25 9.58 220.00 261.63 329.63 523.25 9.59 220.00 261.63 329.63 523.25 9.60 220.00 261.63 329.63 523.25 9.61 220.00 261.63 9.62 220.00 261.63 9.63 220.00 261.63 9.64 220.00 261.63 523.25 9.65 220.00 261.63 523.25 9.66 220.00 261.63 523.25 9.67 220.00 261.63 523.25 9.68 220.00 261.63 523.25 9.69 220.00 261.63 523.25 9.70 220.00 261.63 523.25 9.71 220.00 261.63 329.63 523.25 9.72 220.00 261.63 329.63 523.25 9.73 220.00 261.63 329.63 9.74 220.00 261.63 329.63 9.75 220.00 261.63 329.63 9.76 220.00 261.63 329.63 9.77 220.00 261.63 329.63 9.78 110.00 261.63 329.63 9.79 110.00 261.63 329.63 9.80 110.00 261.63 329.63 9.81 110.00 261.63 329.63 9.82 110.00 261.63 329.63 9.83 110.00 246.94 261.63 329.63 9.84 110.00 246.94 261.63 329.63 9.85 110.00 246.94 261.63 329.63 9.86 110.00 246.94 261.63 329.63 9.87 246.94 261.63 9.88 246.94 261.63 9.89 246.94 261.63 9.90 246.94 261.63 9.91 246.94 261.63 9.92 246.94 261.63 9.93 246.94 261.63 9.94 164.81 261.63 9.95 164.81 261.63 9.96 164.81 261.63 9.97 164.81 261.63 9.98 164.81 261.63 9.99 164.81 261.63 10.00 164.81 261.63 10.01 164.81 261.63 10.02 164.81 261.63 10.03 164.81 261.63 10.04 164.81 261.63 10.05 164.81 261.63 10.06 164.81 261.63 10.07 164.81 261.63 10.08 164.81 261.63 10.09 164.81 261.63 10.10 164.81 261.63 10.11 164.81 261.63 10.12 164.81 261.63 10.13 164.81 261.63 10.14 164.81 261.63 10.15 164.81 261.63 10.16 164.81 261.63 10.17 164.81 10.18 164.81 10.19 164.81 10.20 164.81 10.21 164.81 10.22 164.81 10.23 164.81 10.24 164.81 10.25 164.81 10.26 164.81 10.27 164.81 10.28 164.81 10.29 164.81 10.30 164.81 10.31 164.81 10.32 164.81 10.33 164.81 10.34 164.81 10.35 164.81 10.36 164.81 10.37 164.81 10.38 174.61 10.39 174.61 10.40 174.61 10.41 164.81 174.61 10.42 164.81 174.61 10.43 164.81 174.61 10.44 164.81 174.61 10.45 164.81 174.61 10.46 164.81 174.61 10.47 164.81 174.61 10.48 164.81 174.61 10.49 164.81 174.61 10.50 164.81 174.61 10.51 164.81 174.61 10.52 87.31 164.81 174.61 10.53 87.31 164.81 174.61 10.54 87.31 164.81 174.61 10.55 87.31 164.81 174.61 10.56 87.31 164.81 174.61 10.57 87.31 164.81 349.23 10.58 87.31 164.81 349.23 10.59 87.31 349.23 10.60 87.31 349.23 10.61 87.31 349.23 10.62 87.31 349.23 10.63 87.31 349.23 10.64 87.31 174.61 349.23 10.65 87.31 174.61 349.23 10.66 87.31 174.61 349.23 10.67 87.31 174.61 349.23 10.68 87.31 174.61 349.23 10.69 87.31 174.61 349.23 10.70 87.31 174.61 349.23 10.71 87.31 174.61 349.23 10.72 87.31 174.61 349.23 10.73 87.31 174.61 349.23 10.74 87.31 174.61 349.23 10.75 87.31 174.61 349.23 10.76 87.31 174.61 349.23 10.77 87.31 174.61 349.23 10.78 87.31 174.61 349.23 10.79 87.31 174.61 349.23 10.80 87.31 174.61 349.23 10.81 87.31 174.61 349.23 10.82 87.31 174.61 349.23 10.83 87.31 174.61 349.23 10.84 87.31 174.61 349.23 10.85 87.31 174.61 349.23 10.86 87.31 174.61 349.23 10.87 87.31 174.61 349.23 10.88 87.31 174.61 349.23 10.89 87.31 174.61 349.23 10.90 87.31 174.61 349.23 10.91 87.31 174.61 349.23 10.92 87.31 174.61 349.23 10.93 87.31 174.61 349.23 10.94 87.31 174.61 10.95 87.31 174.61 10.96 87.31 174.61 10.97 87.31 174.61 10.98 87.31 174.61 10.99 87.31 110.00 174.61 11.00 87.31 110.00 174.61 11.01 87.31 110.00 174.61 11.02 87.31 110.00 174.61 11.03 110.00 11.04 110.00 11.05 110.00 11.06 110.00 11.07 110.00 11.08 110.00 440.00 11.09 110.00 440.00 11.10 110.00 440.00 11.11 110.00 440.00 11.12 110.00 440.00 11.13 110.00 440.00 11.14 110.00 440.00 11.15 110.00 440.00 11.16 110.00 440.00 11.17 110.00 261.63 440.00 11.18 110.00 261.63 440.00 11.19 261.63 440.00 11.20 261.63 440.00 11.21 261.63 440.00 11.22 261.63 11.23 261.63 11.24 261.63 11.25 261.63 11.26 261.63 11.27 261.63 11.28 261.63 11.29 261.63 11.30 261.63 11.31 261.63 11.32 261.63 11.33 261.63 11.34 130.81 261.63 11.35 130.81 261.63 11.36 130.81 261.63 11.37 130.81 261.63 11.38 130.81 261.63 11.39 130.81 261.63 11.40 130.81 261.63 11.41 130.81 261.63 11.42 130.81 261.63 11.43 130.81 261.63 11.44 130.81 261.63 11.45 130.81 261.63 11.46 130.81 261.63 11.47 130.81 261.63 11.48 130.81 261.63 523.25 11.49 130.81 261.63 523.25 11.50 130.81 261.63 523.25 11.51 130.81 261.63 523.25 11.52 130.81 261.63 523.25 11.53 130.81 261.63 523.25 11.54 130.81 261.63 523.25 11.55 130.81 261.63 523.25 11.56 130.81 261.63 523.25 11.57 130.81 261.63 11.58 130.81 261.63 11.59 130.81 261.63 11.60 130.81 261.63 11.61 82.41 261.63 11.62 82.41 261.63 11.63 82.41 261.63 11.64 82.41 261.63 11.65 82.41 261.63 11.66 82.41 261.63 329.63 11.67 82.41 261.63 329.63 11.68 82.41 261.63 329.63 11.69 82.41 261.63 329.63 11.70 82.41 329.63 11.71 82.41 329.63 11.72 82.41 329.63 11.73 82.41 329.63 11.74 82.41 329.63 11.75 82.41 329.63 11.76 82.41 329.63 11.77 82.41 329.63 11.78 82.41 329.63 11.79 82.41 329.63 11.80 82.41 329.63 11.81 82.41 329.63 11.82 82.41 329.63 11.83 82.41 329.63 11.84 82.41 329.63 11.85 82.41 329.63 11.86 82.41 329.63 11.87 82.41 329.63 11.88 82.41 329.63 11.89 82.41 329.63 11.90 82.41 329.63 11.91 82.41 329.63 11.92 82.41 329.63 11.93 82.41 329.63 11.94 82.41 329.63 11.95 82.41 329.63 11.96 82.41 11.97 82.41 11.98 82.41 11.99 82.41 12.00 82.41 12.01 82.41 12.02 82.41 12.03 82.41 12.04 82.41 12.05 82.41 12.06 82.41 12.07 82.41 12.08 82.41 12.09 82.41 12.10 82.41 12.11 82.41 12.12 82.41 12.13 82.41 12.14 82.41 12.15 82.41 12.16 82.41 12.17 82.41 220.00 12.18 82.41 220.00 12.19 82.41 220.00 12.20 82.41 220.00 12.21 82.41 220.00 12.22 82.41 220.00 12.23 82.41 220.00 12.24 82.41 110.00 220.00 12.25 82.41 110.00 220.00 12.26 110.00 220.00 12.27 110.00 220.00 12.28 110.00 220.00 12.29 110.00 220.00 12.30 110.00 220.00 12.31 110.00 12.32 110.00 12.33 110.00 12.34 110.00 12.35 12.36 261.63 12.37 261.63 12.38 261.63 12.39 261.63 12.40 130.81 261.63 12.41 130.81 261.63 12.42 130.81 261.63 12.43 130.81 261.63 12.44 130.81 261.63 12.45 130.81 261.63 12.46 130.81 261.63 12.47 130.81 261.63 12.48 130.81 261.63 12.49 130.81 261.63 12.50 130.81 220.00 261.63 12.51 130.81 220.00 261.63 12.52 130.81 220.00 261.63 12.53 130.81 220.00 261.63 12.54 130.81 220.00 261.63 12.55 130.81 220.00 261.63 12.56 130.81 261.63 12.57 130.81 261.63 12.58 130.81 261.63 12.59 130.81 261.63 12.60 130.81 261.63 12.61 130.81 261.63 12.62 130.81 261.63 12.63 130.81 261.63 12.64 130.81 261.63 12.65 130.81 261.63 12.66 130.81 261.63 12.67 130.81 261.63 12.68 130.81 261.63 12.69 130.81 261.63 12.70 130.81 261.63 12.71 130.81 261.63 12.72 130.81 261.63 12.73 130.81 261.63 12.74 130.81 261.63 12.75 130.81 261.63 12.76 130.81 261.63 12.77 130.81 12.78 130.81 12.79 130.81 12.80 130.81 155.56 12.81 130.81 155.56 12.82 77.78 130.81 155.56 12.83 77.78 130.81 155.56 12.84 77.78 130.81 155.56 12.85 77.78 130.81 155.56 12.86 77.78 130.81 155.56 12.87 77.78 155.56 12.88 77.78 155.56 12.89 77.78 155.56 12.90 77.78 155.56 12.91 77.78 155.56 12.92 77.78 155.56 12.93 77.78 155.56 12.94 77.78 155.56 311.13 12.95 77.78 155.56 311.13 12.96 155.56 311.13 12.97 155.56 311.13 12.98 77.78 155.56 311.13 12.99 77.78 155.56 311.13 13.00 77.78 155.56 311.13 13.01 77.78 155.56 311.13 13.02 77.78 155.56 311.13 13.03 77.78 155.56 311.13 13.04 77.78 155.56 311.13 13.05 77.78 311.13 13.06 77.78 311.13 13.07 77.78 311.13 13.08 77.78 311.13 13.09 77.78 311.13 13.10 77.78 311.13 13.11 77.78 311.13 13.12 77.78 311.13 13.13 77.78 311.13 13.14 77.78 311.13 13.15 77.78 311.13 13.16 77.78 311.13 13.17 77.78 311.13 13.18 77.78 311.13 13.19 77.78 311.13 13.20 77.78 311.13 13.21 77.78 311.13 13.22 77.78 311.13 13.23 77.78 311.13 13.24 77.78 311.13 13.25 77.78 311.13 13.26 77.78 311.13 13.27 77.78 311.13 13.28 77.78 311.13 13.29 77.78 311.13 13.30 77.78 311.13 13.31 77.78 311.13 13.32 77.78 311.13 13.33 77.78 311.13 13.34 77.78 311.13 13.35 77.78 311.13 13.36 77.78 311.13 13.37 77.78 311.13 13.38 77.78 311.13 13.39 77.78 311.13 13.40 77.78 311.13 13.41 77.78 311.13 13.42 77.78 311.13 13.43 77.78 311.13 13.44 77.78 311.13 13.45 77.78 311.13 13.46 77.78 311.13 13.47 77.78 311.13 13.48 77.78 311.13 13.49 77.78 311.13 13.50 77.78 311.13 13.51 77.78 311.13 13.52 77.78 311.13 13.53 77.78 311.13 13.54 77.78 311.13 13.55 77.78 311.13 13.56 77.78 13.57 77.78 523.25 13.58 77.78 523.25 13.59 77.78 523.25 13.60 77.78 523.25 13.61 77.78 523.25 13.62 77.78 523.25 13.63 77.78 523.25 13.64 77.78 523.25 13.65 77.78 523.25 13.66 77.78 523.25 13.67 77.78 523.25 13.68 77.78 523.25 13.69 77.78 523.25 13.70 77.78 523.25 13.71 77.78 523.25 13.72 77.78 523.25 13.73 77.78 246.94 369.99 523.25 13.74 77.78 246.94 369.99 523.25 13.75 77.78 246.94 369.99 523.25 13.76 77.78 246.94 369.99 523.25 13.77 77.78 246.94 369.99 13.78 77.78 246.94 369.99 13.79 77.78 246.94 369.99 13.80 77.78 246.94 369.99 13.81 77.78 246.94 369.99 13.82 77.78 246.94 369.99 13.83 77.78 246.94 369.99 13.84 77.78 220.00 246.94 369.99 13.85 77.78 220.00 246.94 369.99 13.86 77.78 220.00 246.94 369.99 13.87 77.78 220.00 246.94 369.99 13.88 77.78 220.00 246.94 369.99 13.89 77.78 220.00 246.94 369.99 13.90 77.78 220.00 246.94 369.99 13.91 77.78 220.00 246.94 369.99 13.92 77.78 220.00 246.94 369.99 13.93 77.78 220.00 246.94 369.99 13.94 77.78 220.00 246.94 369.99 13.95 77.78 220.00 246.94 369.99 13.96 77.78 220.00 246.94 369.99 13.97 77.78 220.00 246.94 369.99 13.98 220.00 246.94 13.99 220.00 246.94 14.00 220.00 246.94 14.01 220.00 246.94 440.00 14.02 220.00 246.94 440.00 14.03 220.00 246.94 440.00 14.04 220.00 246.94 440.00 14.05 220.00 246.94 440.00 14.06 220.00 246.94 440.00 14.07 220.00 246.94 440.00 14.08 220.00 246.94 440.00 14.09 220.00 246.94 440.00 14.10 220.00 246.94 440.00 14.11 220.00 246.94 440.00 14.12 220.00 246.94 440.00 14.13 220.00 246.94 440.00 14.14 220.00 246.94 440.00 14.15 220.00 246.94 440.00 14.16 220.00 246.94 440.00 14.17 220.00 246.94 440.00 14.18 220.00 246.94 440.00 14.19 220.00 246.94 440.00 14.20 220.00 246.94 440.00 14.21 220.00 246.94 14.22 220.00 246.94 14.23 220.00 246.94 14.24 220.00 246.94 14.25 220.00 246.94 14.26 220.00 246.94 440.00 14.27 220.00 246.94 440.00 14.28 220.00 246.94 440.00 14.29 220.00 246.94 440.00 14.30 220.00 246.94 440.00 14.31 220.00 246.94 440.00 14.32 220.00 246.94 440.00 14.33 220.00 246.94 440.00 14.34 220.00 246.94 440.00 14.35 220.00 246.94 14.36 220.00 246.94 14.37 220.00 246.94 14.38 220.00 246.94 14.39 220.00 246.94 14.40 220.00 246.94 440.00 14.41 220.00 246.94 440.00 14.42 220.00 246.94 440.00 14.43 220.00 246.94 440.00 14.44 220.00 246.94 440.00 14.45 220.00 246.94 440.00 14.46 220.00 246.94 440.00 14.47 220.00 246.94 440.00 14.48 220.00 246.94 440.00 14.49 220.00 246.94 440.00 14.50 220.00 246.94 440.00 14.51 220.00 246.94 440.00 14.52 220.00 246.94 440.00 14.53 220.00 246.94 440.00 14.54 220.00 246.94 440.00 14.55 220.00 246.94 440.00 14.56 220.00 246.94 440.00 14.57 220.00 246.94 440.00 14.58 220.00 246.94 440.00 14.59 220.00 246.94 440.00 14.60 220.00 246.94 440.00 14.61 220.00 246.94 440.00 14.62 220.00 246.94 440.00 14.63 220.00 246.94 440.00 14.64 220.00 246.94 440.00 14.65 220.00 246.94 440.00 14.66 220.00 246.94 440.00 14.67 220.00 246.94 440.00 14.68 220.00 246.94 440.00 14.69 220.00 246.94 440.00 14.70 220.00 246.94 440.00 14.71 220.00 246.94 440.00 14.72 220.00 246.94 440.00 14.73 220.00 246.94 440.00 14.74 220.00 246.94 440.00 14.75 220.00 246.94 440.00 14.76 220.00 246.94 440.00 14.77 220.00 246.94 440.00 14.78 220.00 246.94 440.00 14.79 220.00 246.94 440.00 14.80 220.00 246.94 440.00 14.81 220.00 246.94 440.00 14.82 220.00 246.94 440.00 14.83 220.00 246.94 440.00 14.84 220.00 246.94 14.85 220.00 246.94 14.86 220.00 246.94 14.87 220.00 246.94 14.88 220.00 246.94 14.89 220.00 14.90 220.00 14.91 220.00 14.92 220.00 14.93 220.00 14.94 220.00 246.94 14.95 220.00 246.94 14.96 220.00 246.94 14.97 220.00 246.94 14.98 220.00 246.94 14.99 220.00 246.94 15.00 220.00 246.94 15.01 220.00 246.94 15.02 220.00 246.94 15.03 220.00 246.94 15.04 220.00 246.94 15.05 220.00 246.94 15.06 220.00 246.94 15.07 220.00 246.94 15.08 220.00 246.94 15.09 246.94 15.10 246.94 15.11 246.94 15.12 246.94 15.13 246.94 15.14 246.94 15.15 246.94 15.16 15.17 15.18 15.19 15.20 15.21 15.22 15.23 15.24 15.25 15.26 15.27 15.28 15.29 15.30 15.31 15.32 15.33 415.30 15.34 415.30 15.35 415.30 15.36 415.30 15.37 415.30 15.38 415.30 15.39 415.30 15.40 415.30 15.41 415.30 15.42 15.43 15.44 15.45 15.46 15.47 15.48 15.49 15.50 15.51 15.52 15.53 15.54 15.55 15.56 15.57 15.58 15.59 15.60 15.61 15.62 15.63 15.64 15.65 15.66 207.65 15.67 207.65 15.68 207.65 15.69 207.65 15.70 207.65 15.71 207.65 15.72 207.65 15.73 207.65 15.74 207.65 15.75 207.65 220.00 15.76 207.65 220.00 15.77 220.00 15.78 220.00 15.79 220.00 15.80 220.00 15.81 220.00 15.82 220.00 15.83 220.00 15.84 220.00 15.85 220.00 15.86 220.00 15.87 220.00 15.88 220.00 15.89 220.00 15.90 220.00 15.91 220.00 15.92 220.00 15.93 220.00 15.94 220.00 15.95 220.00 15.96 220.00 15.97 220.00 15.98 220.00 15.99 220.00 16.00 220.00 392.00 16.01 220.00 392.00 16.02 220.00 392.00 16.03 220.00 392.00 16.04 220.00 392.00 16.05 392.00 16.06 392.00 16.07 392.00 16.08 392.00 16.09 16.10 16.11 16.12 16.13 16.14 16.15 16.16 16.17 16.18 16.19 466.16 16.20 466.16 16.21 466.16 16.22 466.16 16.23 466.16 16.24 466.16 16.25 466.16 16.26 466.16 16.27 466.16 16.28 466.16 16.29 466.16 16.30 466.16 16.31 466.16 16.32 466.16 16.33 440.00 16.34 440.00 16.35 440.00 16.36 440.00 16.37 440.00 16.38 440.00 16.39 440.00 16.40 440.00 16.41 440.00 16.42 440.00 16.43 440.00 16.44 440.00 16.45 440.00 16.46 440.00 16.47 440.00 16.48 440.00 16.49 16.50 16.51 392.00 16.52 392.00 16.53 392.00 16.54 392.00 16.55 392.00 16.56 220.00 392.00 16.57 220.00 392.00 16.58 220.00 392.00 16.59 220.00 392.00 16.60 220.00 392.00 16.61 220.00 392.00 16.62 220.00 392.00 16.63 220.00 392.00 16.64 220.00 392.00 16.65 220.00 392.00 16.66 220.00 392.00 16.67 220.00 16.68 220.00 16.69 220.00 16.70 220.00 16.71 220.00 16.72 220.00 392.00 16.73 220.00 392.00 16.74 220.00 392.00 16.75 146.83 220.00 392.00 16.76 146.83 220.00 392.00 16.77 146.83 392.00 16.78 146.83 392.00 16.79 146.83 392.00 16.80 146.83 392.00 16.81 146.83 392.00 16.82 146.83 392.00 16.83 146.83 392.00 16.84 146.83 392.00 16.85 146.83 392.00 16.86 146.83 392.00 16.87 146.83 392.00 16.88 146.83 392.00 16.89 146.83 369.99 392.00 16.90 146.83 369.99 392.00 16.91 146.83 369.99 16.92 146.83 369.99 16.93 146.83 220.00 293.66 369.99 16.94 146.83 220.00 293.66 369.99 16.95 220.00 293.66 16.96 220.00 293.66 16.97 220.00 293.66 16.98 73.42 220.00 293.66 16.99 73.42 220.00 293.66 17.00 73.42 220.00 293.66 17.01 73.42 220.00 293.66 17.02 73.42 220.00 293.66 17.03 73.42 220.00 293.66 17.04 73.42 220.00 293.66 17.05 73.42 220.00 293.66 17.06 73.42 220.00 293.66 17.07 73.42 220.00 293.66 17.08 73.42 220.00 293.66 17.09 73.42 220.00 293.66 17.10 73.42 220.00 293.66 17.11 220.00 293.66 17.12 220.00 293.66 17.13 220.00 293.66 17.14 220.00 293.66 17.15 220.00 293.66 17.16 220.00 293.66 17.17 220.00 293.66 17.18 293.66 17.19 293.66 17.20 293.66 17.21 293.66 17.22 293.66 17.23 293.66 17.24 293.66 17.25 293.66 17.26 293.66 17.27 293.66 17.28 293.66 17.29 293.66 17.30 293.66 369.99 17.31 293.66 369.99 17.32 293.66 369.99 17.33 293.66 369.99 17.34 293.66 369.99 17.35 293.66 369.99 17.36 293.66 369.99 17.37 293.66 369.99 17.38 293.66 369.99 17.39 293.66 369.99 17.40 293.66 369.99 17.41 293.66 369.99 17.42 293.66 369.99 17.43 293.66 369.99 17.44 369.99 17.45 369.99 17.46 369.99 17.47 293.66 369.99 17.48 293.66 369.99 17.49 293.66 369.99 17.50 293.66 369.99 17.51 293.66 369.99 17.52 293.66 369.99 17.53 293.66 369.99 17.54 293.66 369.99 440.00 17.55 293.66 369.99 440.00 17.56 369.99 440.00 17.57 369.99 440.00 17.58 369.99 440.00 17.59 369.99 440.00 17.60 17.61 17.62 17.63 17.64 17.65 17.66 17.67 17.68 17.69 17.70 220.00 17.71 220.00 17.72 220.00 17.73 220.00 17.74 220.00 17.75 220.00 17.76 220.00 17.77 220.00 17.78 220.00 17.79 220.00 17.80 220.00 17.81 220.00 17.82 220.00 17.83 220.00 17.84 220.00 17.85 220.00 17.86 220.00 17.87 220.00 17.88 220.00 17.89 220.00 17.90 220.00 17.91 220.00 17.92 220.00 17.93 246.94 17.94 246.94 17.95 246.94 17.96 246.94 17.97 246.94 17.98 246.94 17.99 246.94 18.00 246.94 18.01 246.94 18.02 246.94 18.03 246.94 18.04 246.94 18.05 123.47 246.94 18.06 123.47 246.94 18.07 123.47 246.94 18.08 123.47 246.94 18.09 123.47 246.94 18.10 123.47 246.94 18.11 123.47 18.12 123.47 18.13 123.47 18.14 123.47 18.15 123.47 18.16 123.47 18.17 123.47 18.18 123.47 18.19 123.47 18.20 123.47 18.21 123.47 18.22 123.47 18.23 123.47 18.24 123.47 18.25 123.47 18.26 123.47 18.27 123.47 18.28 123.47 18.29 123.47 18.30 123.47 18.31 123.47 18.32 123.47 18.33 123.47 18.34 123.47 18.35 18.36 18.37 293.66 18.38 293.66 18.39 293.66 18.40 293.66 18.41 293.66 18.42 123.47 293.66 18.43 123.47 293.66 18.44 123.47 293.66 18.45 123.47 293.66 18.46 123.47 293.66 18.47 123.47 293.66 18.48 123.47 293.66 18.49 123.47 293.66 18.50 123.47 293.66 18.51 18.52 18.53 123.47 18.54 123.47 18.55 123.47 18.56 123.47 18.57 123.47 18.58 123.47 18.59 123.47 18.60 123.47 18.61 123.47 18.62 123.47 18.63 123.47 18.64 123.47 18.65 123.47 18.66 123.47 18.67 123.47 18.68 123.47 18.69 123.47 18.70 123.47 18.71 123.47 18.72 123.47 392.00 18.73 123.47 392.00 18.74 123.47 392.00 18.75 123.47 392.00 18.76 123.47 392.00 18.77 123.47 392.00 18.78 123.47 392.00 18.79 123.47 392.00 18.80 123.47 392.00 18.81 123.47 392.00 18.82 123.47 392.00 18.83 123.47 392.00 18.84 123.47 392.00 18.85 123.47 392.00 18.86 123.47 18.87 123.47 18.88 123.47 18.89 123.47 18.90 18.91 349.23 18.92 349.23 18.93 349.23 18.94 349.23 18.95 349.23 18.96 349.23 18.97 349.23 18.98 349.23 18.99 349.23 19.00 349.23 19.01 349.23 19.02 349.23 19.03 349.23 19.04 349.23 19.05 349.23 19.06 349.23 19.07 349.23 19.08 349.23 19.09 19.10 19.11 19.12 19.13 19.14 130.81 19.15 130.81 19.16 130.81 19.17 130.81 19.18 130.81 19.19 130.81 19.20 130.81 19.21 130.81 19.22 130.81 19.23 130.81 19.24 130.81 19.25 130.81 19.26 130.81 19.27 130.81 19.28 130.81 19.29 130.81 19.30 130.81 19.31 130.81 19.32 19.33 19.34 19.35 19.36 19.37 65.41 19.38 65.41 19.39 65.41 19.40 65.41 19.41 65.41 19.42 65.41 19.43 65.41 19.44 65.41 19.45 65.41 19.46 19.47 19.48 19.49 261.63 19.50 261.63 19.51 261.63 19.52 261.63 19.53 261.63 19.54 261.63 19.55 261.63 19.56 261.63 19.57 261.63 19.58 261.63 19.59 261.63 19.60 261.63 19.61 261.63 19.62 261.63 19.63 261.63 19.64 261.63 19.65 261.63 19.66 261.63 19.67 261.63 19.68 261.63 19.69 261.63 19.70 261.63 19.71 261.63 19.72 261.63 19.73 261.63 19.74 261.63 19.75 261.63 19.76 261.63 19.77 261.63 19.78 261.63 19.79 261.63 19.80 261.63 19.81 261.63 19.82 261.63 19.83 261.63 19.84 261.63 19.85 261.63 19.86 261.63 19.87 261.63 19.88 261.63 19.89 261.63 19.90 19.91 19.92 19.93 19.94 19.95 19.96 19.97 19.98 19.99 20.00 20.01 20.02 20.03 20.04 20.05 20.06 20.07 20.08 20.09 20.10 20.11 20.12 20.13 20.14 20.15 20.16 196.00 20.17 196.00 20.18 196.00 20.19 196.00 20.20 196.00 20.21 196.00 20.22 196.00 20.23 196.00 20.24 196.00 20.25 20.26 20.27 20.28 20.29 20.30 20.31 20.32 103.83 20.33 103.83 20.34 103.83 20.35 103.83 20.36 103.83 20.37 103.83 20.38 103.83 20.39 103.83 20.40 103.83 20.41 103.83 20.42 103.83 20.43 103.83 20.44 103.83 20.45 103.83 20.46 103.83 20.47 103.83 20.48 103.83 20.49 103.83 20.50 103.83 20.51 103.83 20.52 103.83 20.53 103.83 20.54 103.83 20.55 103.83 246.94 20.56 103.83 246.94 20.57 103.83 246.94 20.58 103.83 246.94 20.59 103.83 246.94 20.60 103.83 246.94 20.61 103.83 246.94 20.62 246.94 20.63 246.94 20.64 246.94 20.65 246.94 349.23 20.66 246.94 349.23 20.67 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24.07 110.00 220.00 261.63 24.08 110.00 220.00 261.63 24.09 110.00 220.00 261.63 24.10 110.00 220.00 261.63 24.11 110.00 220.00 261.63 24.12 110.00 220.00 261.63 24.13 110.00 261.63 24.14 110.00 261.63 24.15 110.00 261.63 24.16 110.00 261.63 24.17 110.00 261.63 24.18 110.00 220.00 261.63 24.19 110.00 220.00 261.63 24.20 110.00 220.00 261.63 329.63 24.21 110.00 220.00 261.63 329.63 24.22 110.00 220.00 261.63 329.63 24.23 110.00 220.00 261.63 329.63 24.24 110.00 220.00 261.63 329.63 24.25 110.00 220.00 261.63 329.63 24.26 110.00 220.00 261.63 329.63 24.27 110.00 220.00 261.63 329.63 24.28 110.00 220.00 261.63 329.63 24.29 110.00 220.00 261.63 329.63 24.30 110.00 220.00 261.63 329.63 24.31 110.00 261.63 329.63 24.32 110.00 220.00 261.63 329.63 24.33 110.00 220.00 261.63 329.63 24.34 110.00 220.00 261.63 329.63 24.35 110.00 220.00 261.63 329.63 24.36 110.00 220.00 261.63 24.37 110.00 220.00 261.63 24.38 110.00 220.00 261.63 24.39 110.00 220.00 261.63 880.00 24.40 110.00 220.00 261.63 880.00 24.41 220.00 261.63 880.00 24.42 220.00 261.63 880.00 24.43 220.00 261.63 880.00 24.44 220.00 261.63 880.00 24.45 220.00 261.63 880.00 24.46 220.00 261.63 880.00 24.47 220.00 261.63 880.00 24.48 220.00 261.63 880.00 24.49 220.00 261.63 880.00 24.50 220.00 261.63 880.00 24.51 220.00 261.63 880.00 24.52 220.00 261.63 880.00 24.53 220.00 261.63 880.00 24.54 220.00 261.63 880.00 24.55 220.00 261.63 880.00 24.56 220.00 261.63 880.00 24.57 110.00 220.00 261.63 523.25 1046.50 24.58 110.00 220.00 261.63 523.25 1046.50 24.59 110.00 220.00 261.63 523.25 1046.50 24.60 110.00 220.00 261.63 523.25 1046.50 24.61 110.00 220.00 261.63 523.25 1046.50 24.62 110.00 220.00 261.63 523.25 1046.50 24.63 110.00 220.00 261.63 523.25 1046.50 24.64 110.00 220.00 261.63 523.25 1046.50 24.65 110.00 220.00 261.63 523.25 1046.50 24.66 110.00 220.00 261.63 329.63 523.25 24.67 110.00 220.00 261.63 329.63 523.25 24.68 220.00 261.63 329.63 523.25 24.69 220.00 261.63 329.63 523.25 24.70 220.00 261.63 329.63 523.25 24.71 220.00 261.63 329.63 523.25 24.72 220.00 261.63 329.63 523.25 24.73 220.00 261.63 329.63 523.25 1046.50 24.74 220.00 261.63 329.63 523.25 1046.50 24.75 220.00 261.63 329.63 1046.50 24.76 220.00 261.63 329.63 1046.50 24.77 220.00 261.63 329.63 1046.50 24.78 261.63 329.63 1046.50 24.79 261.63 329.63 1046.50 24.80 261.63 329.63 1046.50 24.81 261.63 329.63 1046.50 24.82 261.63 329.63 1046.50 24.83 261.63 329.63 1046.50 24.84 261.63 329.63 1046.50 24.85 261.63 329.63 1046.50 24.86 261.63 329.63 1046.50 24.87 261.63 329.63 440.00 24.88 261.63 329.63 440.00 24.89 261.63 329.63 440.00 24.90 261.63 329.63 440.00 1046.50 24.91 261.63 329.63 440.00 1046.50 24.92 261.63 329.63 440.00 1046.50 24.93 261.63 329.63 440.00 1046.50 24.94 146.83 261.63 440.00 1046.50 24.95 146.83 261.63 440.00 1046.50 24.96 146.83 261.63 440.00 1046.50 24.97 146.83 261.63 349.23 440.00 1046.50 24.98 146.83 261.63 349.23 440.00 1046.50 24.99 73.42 146.83 261.63 349.23 1046.50 25.00 73.42 146.83 261.63 349.23 1046.50 25.01 73.42 146.83 261.63 349.23 1046.50 25.02 73.42 146.83 261.63 349.23 1046.50 25.03 73.42 146.83 261.63 349.23 1046.50 25.04 73.42 146.83 261.63 349.23 1046.50 25.05 73.42 146.83 261.63 349.23 1046.50 25.06 73.42 146.83 261.63 349.23 1046.50 25.07 73.42 146.83 261.63 349.23 1046.50 25.08 73.42 146.83 261.63 349.23 1046.50 25.09 73.42 146.83 261.63 349.23 1046.50 25.10 73.42 146.83 349.23 1046.50 25.11 73.42 146.83 220.00 349.23 1046.50 25.12 73.42 146.83 220.00 349.23 1046.50 25.13 73.42 146.83 220.00 349.23 1046.50 25.14 73.42 146.83 220.00 349.23 1046.50 25.15 73.42 146.83 220.00 349.23 1046.50 25.16 73.42 146.83 220.00 349.23 1046.50 25.17 73.42 146.83 220.00 349.23 1046.50 25.18 73.42 146.83 220.00 349.23 1046.50 25.19 73.42 146.83 220.00 349.23 1046.50 25.20 73.42 146.83 220.00 349.23 1046.50 25.21 73.42 146.83 220.00 349.23 1046.50 25.22 73.42 146.83 220.00 349.23 1046.50 25.23 73.42 146.83 220.00 349.23 1046.50 25.24 73.42 146.83 220.00 349.23 1046.50 25.25 73.42 146.83 220.00 349.23 1046.50 25.26 73.42 146.83 220.00 349.23 1046.50 25.27 73.42 146.83 220.00 349.23 1046.50 25.28 73.42 146.83 220.00 349.23 1046.50 25.29 73.42 146.83 220.00 349.23 1046.50 25.30 73.42 146.83 220.00 349.23 1046.50 25.31 73.42 146.83 220.00 349.23 493.88 1046.50 25.32 73.42 146.83 220.00 349.23 493.88 1046.50 25.33 73.42 146.83 220.00 349.23 493.88 1046.50 25.34 73.42 146.83 220.00 349.23 493.88 1046.50 25.35 73.42 146.83 220.00 349.23 493.88 1046.50 25.36 73.42 146.83 220.00 349.23 493.88 1046.50 25.37 73.42 146.83 220.00 349.23 493.88 1046.50 25.38 73.42 146.83 220.00 349.23 493.88 1046.50 25.39 73.42 146.83 220.00 349.23 493.88 1046.50 25.40 73.42 146.83 349.23 493.88 1046.50 25.41 73.42 146.83 349.23 493.88 1046.50 25.42 73.42 146.83 349.23 493.88 1046.50 25.43 73.42 146.83 293.66 349.23 493.88 1046.50 25.44 73.42 146.83 293.66 349.23 493.88 1046.50 25.45 73.42 293.66 349.23 493.88 1046.50 25.46 73.42 293.66 349.23 493.88 1046.50 25.47 73.42 293.66 349.23 493.88 1046.50 25.48 73.42 293.66 349.23 493.88 1046.50 25.49 73.42 293.66 349.23 493.88 1046.50 25.50 73.42 293.66 349.23 493.88 1046.50 25.51 73.42 293.66 349.23 493.88 1046.50 25.52 73.42 293.66 493.88 1046.50 25.53 73.42 293.66 493.88 1046.50 25.54 73.42 293.66 493.88 25.55 73.42 293.66 493.88 25.56 73.42 293.66 493.88 25.57 73.42 293.66 493.88 25.58 73.42 293.66 493.88 25.59 73.42 293.66 349.23 493.88 25.60 73.42 293.66 349.23 493.88 25.61 73.42 293.66 349.23 493.88 25.62 73.42 293.66 349.23 493.88 25.63 73.42 293.66 349.23 493.88 25.64 73.42 146.83 293.66 349.23 493.88 25.65 73.42 146.83 293.66 349.23 493.88 25.66 73.42 146.83 293.66 349.23 493.88 25.67 73.42 146.83 293.66 349.23 493.88 25.68 73.42 146.83 293.66 349.23 493.88 25.69 73.42 146.83 220.00 293.66 349.23 493.88 25.70 73.42 146.83 220.00 293.66 349.23 493.88 25.71 73.42 146.83 220.00 293.66 349.23 493.88 25.72 73.42 146.83 220.00 293.66 349.23 493.88 25.73 73.42 146.83 220.00 293.66 349.23 493.88 25.74 73.42 146.83 220.00 293.66 349.23 493.88 25.75 73.42 146.83 220.00 293.66 349.23 493.88 25.76 73.42 146.83 220.00 293.66 349.23 493.88 25.77 73.42 146.83 220.00 293.66 349.23 493.88 25.78 146.83 220.00 293.66 349.23 493.88 25.79 146.83 220.00 293.66 349.23 493.88 25.80 146.83 220.00 293.66 349.23 493.88 25.81 146.83 220.00 293.66 349.23 493.88 25.82 146.83 220.00 293.66 349.23 493.88 25.83 146.83 220.00 293.66 349.23 493.88 25.84 146.83 220.00 293.66 349.23 493.88 25.85 146.83 220.00 293.66 349.23 493.88 25.86 146.83 220.00 293.66 349.23 493.88 25.87 146.83 220.00 349.23 493.88 1396.91 25.88 146.83 220.00 349.23 493.88 1396.91 25.89 146.83 220.00 349.23 493.88 1396.91 25.90 73.42 146.83 220.00 349.23 493.88 1396.91 25.91 73.42 146.83 220.00 349.23 493.88 1396.91 25.92 73.42 146.83 220.00 349.23 493.88 1396.91 25.93 73.42 146.83 220.00 349.23 493.88 1396.91 25.94 73.42 146.83 220.00 349.23 493.88 1396.91 25.95 73.42 146.83 220.00 349.23 493.88 1396.91 25.96 73.42 146.83 220.00 349.23 493.88 1396.91 25.97 73.42 146.83 220.00 349.23 493.88 1396.91 25.98 73.42 146.83 220.00 349.23 493.88 1396.91 25.99 73.42 146.83 220.00 349.23 493.88 1396.91 26.00 73.42 146.83 220.00 349.23 493.88 1396.91 26.01 73.42 146.83 220.00 349.23 493.88 1396.91 26.02 73.42 146.83 220.00 349.23 493.88 1396.91 26.03 73.42 146.83 349.23 1396.91 26.04 73.42 146.83 349.23 1396.91 26.05 146.83 349.23 1396.91 26.06 146.83 164.81 349.23 1396.91 26.07 146.83 164.81 349.23 1396.91 26.08 164.81 246.94 349.23 1396.91 26.09 164.81 246.94 349.23 1396.91 26.10 164.81 246.94 349.23 830.61 1396.91 26.11 164.81 246.94 349.23 830.61 1396.91 26.12 164.81 246.94 349.23 830.61 1396.91 26.13 82.41 164.81 246.94 349.23 830.61 1396.91 26.14 82.41 164.81 246.94 349.23 830.61 1396.91 26.15 82.41 164.81 246.94 349.23 830.61 1396.91 26.16 82.41 164.81 246.94 349.23 830.61 1396.91 26.17 82.41 164.81 246.94 349.23 1396.91 26.18 82.41 164.81 246.94 349.23 1396.91 26.19 82.41 164.81 246.94 349.23 1396.91 26.20 82.41 164.81 246.94 349.23 1396.91 26.21 82.41 164.81 246.94 349.23 1396.91 26.22 82.41 164.81 246.94 26.23 82.41 164.81 246.94 26.24 82.41 164.81 246.94 26.25 82.41 164.81 246.94 26.26 82.41 164.81 246.94 26.27 82.41 164.81 246.94 1318.51 26.28 82.41 164.81 246.94 1318.51 26.29 82.41 164.81 246.94 1318.51 26.30 82.41 164.81 246.94 1318.51 26.31 82.41 164.81 1318.51 26.32 82.41 164.81 1318.51 26.33 82.41 164.81 1318.51 26.34 82.41 164.81 349.23 1318.51 26.35 82.41 164.81 349.23 1318.51 26.36 82.41 164.81 349.23 1318.51 26.37 82.41 164.81 349.23 1318.51 26.38 82.41 164.81 349.23 1318.51 26.39 82.41 164.81 349.23 1318.51 26.40 82.41 164.81 349.23 26.41 82.41 164.81 349.23 26.42 82.41 164.81 349.23 26.43 82.41 87.31 164.81 440.00 26.44 82.41 87.31 164.81 440.00 26.45 82.41 87.31 164.81 246.94 440.00 1174.66 26.46 82.41 87.31 164.81 246.94 440.00 1174.66 26.47 82.41 87.31 164.81 246.94 440.00 1174.66 26.48 82.41 87.31 164.81 246.94 440.00 1174.66 26.49 82.41 87.31 164.81 246.94 440.00 1174.66 26.50 82.41 87.31 164.81 246.94 440.00 1174.66 26.51 82.41 87.31 164.81 246.94 440.00 1174.66 26.52 82.41 164.81 246.94 440.00 1174.66 26.53 82.41 164.81 246.94 440.00 1174.66 26.54 82.41 164.81 246.94 440.00 26.55 82.41 164.81 246.94 440.00 26.56 82.41 164.81 246.94 440.00 26.57 82.41 164.81 440.00 26.58 82.41 164.81 440.00 26.59 82.41 164.81 440.00 26.60 82.41 164.81 440.00 26.61 82.41 164.81 440.00 26.62 82.41 164.81 440.00 523.25 26.63 82.41 164.81 440.00 523.25 26.64 82.41 164.81 440.00 523.25 26.65 82.41 164.81 440.00 523.25 26.66 82.41 164.81 440.00 523.25 26.67 82.41 164.81 440.00 523.25 26.68 82.41 164.81 440.00 523.25 26.69 82.41 164.81 440.00 523.25 26.70 82.41 164.81 440.00 523.25 26.71 82.41 164.81 246.94 440.00 523.25 26.72 82.41 164.81 246.94 440.00 523.25 26.73 82.41 164.81 246.94 440.00 523.25 26.74 82.41 164.81 246.94 440.00 523.25 26.75 82.41 164.81 246.94 440.00 523.25 26.76 82.41 164.81 246.94 440.00 523.25 26.77 82.41 164.81 246.94 440.00 523.25 26.78 82.41 164.81 246.94 440.00 523.25 987.77 26.79 82.41 164.81 246.94 440.00 523.25 987.77 26.80 82.41 164.81 329.63 440.00 523.25 987.77 26.81 82.41 164.81 329.63 440.00 523.25 987.77 26.82 82.41 164.81 329.63 440.00 523.25 987.77 26.83 82.41 164.81 329.63 440.00 523.25 987.77 26.84 82.41 164.81 329.63 440.00 523.25 987.77 26.85 82.41 164.81 329.63 440.00 523.25 987.77 26.86 82.41 164.81 329.63 440.00 523.25 987.77 26.87 82.41 246.94 329.63 440.00 523.25 987.77 26.88 82.41 246.94 329.63 440.00 523.25 987.77 26.89 82.41 246.94 329.63 523.25 987.77 26.90 82.41 246.94 329.63 523.25 987.77 26.91 82.41 246.94 523.25 987.77 26.92 82.41 246.94 523.25 987.77 26.93 82.41 246.94 523.25 987.77 26.94 82.41 246.94 523.25 987.77 26.95 82.41 246.94 523.25 987.77 26.96 82.41 164.81 246.94 440.00 523.25 880.00 1760.00 26.97 82.41 164.81 246.94 440.00 523.25 880.00 1760.00 26.98 82.41 164.81 440.00 880.00 1760.00 26.99 82.41 164.81 440.00 880.00 1760.00 27.00 82.41 164.81 440.00 880.00 1760.00 27.01 164.81 440.00 830.61 880.00 1760.00 27.02 164.81 440.00 830.61 880.00 1760.00 27.03 164.81 440.00 830.61 880.00 1760.00 27.04 164.81 440.00 830.61 880.00 1760.00 27.05 164.81 440.00 830.61 880.00 27.06 164.81 440.00 830.61 880.00 27.07 164.81 440.00 830.61 880.00 27.08 82.41 164.81 440.00 830.61 880.00 27.09 82.41 164.81 440.00 830.61 880.00 27.10 82.41 164.81 440.00 830.61 880.00 27.11 82.41 164.81 440.00 830.61 880.00 27.12 82.41 164.81 440.00 830.61 27.13 82.41 164.81 440.00 830.61 27.14 82.41 164.81 440.00 830.61 27.15 82.41 164.81 246.94 329.63 440.00 830.61 27.16 82.41 164.81 246.94 329.63 440.00 830.61 27.17 164.81 246.94 329.63 440.00 830.61 27.18 164.81 246.94 329.63 440.00 830.61 27.19 164.81 246.94 329.63 440.00 830.61 27.20 164.81 246.94 329.63 440.00 830.61 2489.02 27.21 164.81 246.94 329.63 440.00 830.61 2489.02 27.22 82.41 164.81 246.94 329.63 440.00 830.61 2489.02 27.23 82.41 164.81 246.94 329.63 440.00 830.61 2489.02 27.24 82.41 164.81 329.63 440.00 830.61 2489.02 27.25 82.41 164.81 329.63 440.00 830.61 2489.02 27.26 82.41 164.81 329.63 440.00 830.61 2489.02 27.27 82.41 164.81 329.63 440.00 830.61 2489.02 27.28 82.41 164.81 329.63 440.00 830.61 2489.02 27.29 82.41 164.81 329.63 830.61 1479.98 2489.02 27.30 82.41 164.81 329.63 830.61 1479.98 2489.02 27.31 82.41 164.81 329.63 830.61 1479.98 27.32 82.41 164.81 329.63 830.61 1479.98 27.33 82.41 164.81 329.63 415.30 1479.98 27.34 82.41 164.81 329.63 415.30 1479.98 27.35 82.41 164.81 329.63 415.30 27.36 82.41 164.81 329.63 415.30 698.46 1661.22 27.37 82.41 164.81 329.63 415.30 698.46 1661.22 27.38 82.41 164.81 329.63 415.30 698.46 1661.22 27.39 82.41 164.81 329.63 415.30 698.46 1661.22 27.40 82.41 329.63 415.30 698.46 1661.22 27.41 82.41 329.63 415.30 698.46 1661.22 27.42 82.41 329.63 415.30 698.46 1661.22 27.43 82.41 329.63 415.30 698.46 1661.22 27.44 82.41 329.63 415.30 698.46 1661.22 27.45 82.41 329.63 415.30 27.46 82.41 329.63 415.30 27.47 82.41 329.63 415.30 27.48 82.41 329.63 415.30 27.49 82.41 329.63 415.30 27.50 82.41 329.63 415.30 659.26 27.51 82.41 329.63 415.30 659.26 27.52 82.41 329.63 415.30 659.26 27.53 82.41 329.63 415.30 659.26 27.54 82.41 415.30 493.88 659.26 27.55 82.41 415.30 493.88 659.26 27.56 82.41 415.30 493.88 659.26 27.57 82.41 415.30 493.88 659.26 27.58 82.41 415.30 493.88 659.26 27.59 82.41 415.30 493.88 659.26 27.60 82.41 415.30 493.88 659.26 27.61 82.41 415.30 493.88 659.26 27.62 82.41 415.30 493.88 659.26 27.63 82.41 415.30 493.88 659.26 27.64 82.41 415.30 493.88 659.26 27.65 82.41 415.30 493.88 659.26 27.66 82.41 415.30 493.88 27.67 82.41 415.30 493.88 27.68 82.41 415.30 493.88 27.69 82.41 415.30 493.88 27.70 82.41 415.30 493.88 27.71 82.41 415.30 493.88 27.72 82.41 415.30 493.88 27.73 82.41 164.81 415.30 493.88 587.33 27.74 82.41 164.81 415.30 493.88 587.33 27.75 82.41 164.81 415.30 493.88 587.33 659.26 27.76 82.41 164.81 415.30 493.88 587.33 659.26 27.77 82.41 164.81 415.30 493.88 587.33 659.26 27.78 82.41 164.81 415.30 493.88 587.33 659.26 27.79 82.41 164.81 415.30 493.88 587.33 659.26 27.80 82.41 164.81 415.30 493.88 587.33 659.26 27.81 82.41 164.81 415.30 493.88 587.33 659.26 27.82 82.41 164.81 246.94 415.30 493.88 659.26 27.83 82.41 164.81 246.94 415.30 493.88 659.26 27.84 82.41 164.81 246.94 415.30 493.88 659.26 27.85 82.41 164.81 246.94 415.30 493.88 659.26 27.86 82.41 164.81 246.94 415.30 493.88 659.26 27.87 82.41 164.81 246.94 415.30 523.25 659.26 830.61 27.88 82.41 164.81 246.94 415.30 523.25 659.26 830.61 27.89 164.81 246.94 415.30 523.25 659.26 830.61 27.90 164.81 246.94 415.30 523.25 659.26 830.61 27.91 164.81 415.30 523.25 830.61 27.92 164.81 415.30 523.25 830.61 27.93 164.81 415.30 523.25 830.61 27.94 164.81 415.30 523.25 830.61 27.95 164.81 415.30 523.25 830.61 27.96 164.81 415.30 523.25 830.61 27.97 164.81 415.30 523.25 830.61 27.98 164.81 415.30 523.25 830.61 27.99 164.81 415.30 523.25 830.61 28.00 164.81 415.30 523.25 830.61 28.01 164.81 415.30 830.61 28.02 164.81 415.30 830.61 28.03 164.81 415.30 830.61 28.04 164.81 415.30 830.61 28.05 164.81 415.30 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110.00 277.18 15.61 110.00 277.18 15.62 110.00 277.18 15.63 110.00 15.64 110.00 15.65 110.00 15.66 110.00 15.67 110.00 15.68 110.00 15.69 110.00 15.70 110.00 15.71 110.00 15.72 110.00 15.73 110.00 15.74 110.00 15.75 110.00 15.76 110.00 15.77 110.00 15.78 110.00 15.79 110.00 15.80 110.00 15.81 110.00 15.82 110.00 277.18 15.83 110.00 277.18 15.84 110.00 277.18 15.85 110.00 277.18 15.86 110.00 277.18 15.87 110.00 277.18 15.88 110.00 277.18 15.89 110.00 277.18 15.90 110.00 277.18 15.91 110.00 277.18 15.92 110.00 277.18 15.93 110.00 277.18 15.94 110.00 277.18 15.95 110.00 277.18 15.96 110.00 277.18 15.97 110.00 277.18 15.98 110.00 15.99 110.00 16.00 110.00 16.01 110.00 16.02 110.00 16.03 110.00 220.00 16.04 110.00 220.00 16.05 110.00 220.00 16.06 110.00 220.00 16.07 110.00 220.00 16.08 110.00 220.00 16.09 110.00 220.00 16.10 110.00 220.00 16.11 110.00 220.00 16.12 110.00 220.00 16.13 110.00 220.00 16.14 110.00 220.00 16.15 110.00 220.00 16.16 110.00 220.00 16.17 110.00 220.00 16.18 110.00 220.00 16.19 110.00 220.00 16.20 110.00 220.00 16.21 110.00 220.00 16.22 110.00 220.00 16.23 110.00 220.00 16.24 110.00 220.00 16.25 110.00 220.00 16.26 110.00 220.00 16.27 110.00 220.00 16.28 110.00 220.00 16.29 110.00 220.00 16.30 110.00 220.00 16.31 110.00 220.00 16.32 110.00 220.00 16.33 110.00 220.00 16.34 110.00 220.00 16.35 110.00 220.00 16.36 110.00 220.00 16.37 110.00 220.00 16.38 110.00 220.00 16.39 110.00 220.00 16.40 110.00 220.00 16.41 110.00 220.00 16.42 110.00 220.00 16.43 110.00 220.00 16.44 110.00 220.00 277.18 16.45 110.00 220.00 277.18 16.46 110.00 220.00 277.18 16.47 110.00 220.00 277.18 16.48 110.00 220.00 277.18 16.49 110.00 220.00 277.18 16.50 110.00 220.00 277.18 16.51 110.00 220.00 277.18 16.52 110.00 220.00 277.18 16.53 110.00 220.00 277.18 16.54 110.00 220.00 277.18 16.55 110.00 220.00 277.18 16.56 110.00 220.00 277.18 329.63 16.57 110.00 220.00 277.18 329.63 16.58 110.00 220.00 277.18 329.63 16.59 110.00 220.00 277.18 329.63 16.60 110.00 220.00 277.18 329.63 16.61 110.00 220.00 277.18 329.63 16.62 110.00 220.00 277.18 329.63 16.63 110.00 220.00 277.18 329.63 16.64 110.00 220.00 277.18 329.63 16.65 110.00 277.18 16.66 110.00 277.18 16.67 110.00 277.18 16.68 110.00 277.18 16.69 110.00 277.18 16.70 110.00 277.18 16.71 110.00 277.18 16.72 110.00 277.18 16.73 110.00 277.18 16.74 110.00 277.18 16.75 110.00 277.18 16.76 110.00 277.18 16.77 110.00 16.78 110.00 16.79 110.00 277.18 16.80 110.00 277.18 16.81 110.00 277.18 16.82 110.00 277.18 16.83 110.00 277.18 16.84 110.00 277.18 16.85 110.00 277.18 16.86 110.00 277.18 16.87 110.00 277.18 16.88 110.00 277.18 16.89 110.00 277.18 16.90 110.00 277.18 16.91 110.00 277.18 16.92 110.00 277.18 16.93 110.00 277.18 329.63 16.94 110.00 277.18 329.63 16.95 110.00 277.18 329.63 16.96 110.00 277.18 329.63 16.97 110.00 277.18 329.63 16.98 110.00 329.63 16.99 110.00 329.63 17.00 110.00 329.63 17.01 110.00 329.63 17.02 110.00 329.63 17.03 110.00 329.63 17.04 110.00 329.63 17.05 110.00 329.63 17.06 110.00 329.63 17.07 110.00 329.63 17.08 110.00 329.63 17.09 110.00 329.63 17.10 110.00 329.63 17.11 110.00 329.63 17.12 110.00 329.63 17.13 110.00 329.63 17.14 110.00 277.18 329.63 17.15 110.00 277.18 329.63 17.16 110.00 277.18 329.63 17.17 110.00 277.18 329.63 17.18 110.00 277.18 329.63 17.19 110.00 277.18 329.63 17.20 110.00 277.18 329.63 17.21 110.00 220.00 277.18 329.63 17.22 110.00 220.00 277.18 329.63 17.23 220.00 277.18 329.63 17.24 220.00 277.18 329.63 17.25 220.00 329.63 17.26 220.00 329.63 17.27 220.00 329.63 17.28 110.00 220.00 277.18 329.63 17.29 110.00 220.00 277.18 329.63 17.30 110.00 220.00 277.18 329.63 17.31 110.00 220.00 277.18 329.63 17.32 110.00 220.00 277.18 329.63 17.33 110.00 220.00 277.18 329.63 17.34 110.00 220.00 277.18 329.63 17.35 110.00 220.00 277.18 329.63 17.36 110.00 220.00 277.18 329.63 17.37 220.00 277.18 329.63 17.38 220.00 277.18 329.63 17.39 220.00 277.18 329.63 17.40 220.00 277.18 329.63 17.41 220.00 277.18 329.63 17.42 220.00 277.18 329.63 17.43 220.00 277.18 329.63 17.44 220.00 277.18 329.63 17.45 220.00 277.18 329.63 17.46 220.00 329.63 17.47 220.00 329.63 17.48 220.00 329.63 17.49 220.00 329.63 17.50 220.00 329.63 17.51 92.50 220.00 277.18 329.63 17.52 92.50 220.00 277.18 329.63 17.53 92.50 220.00 277.18 329.63 17.54 92.50 220.00 277.18 329.63 17.55 92.50 220.00 277.18 329.63 17.56 46.25 92.50 220.00 277.18 17.57 46.25 92.50 220.00 277.18 17.58 46.25 92.50 220.00 277.18 17.59 46.25 92.50 220.00 277.18 17.60 46.25 92.50 220.00 277.18 17.61 46.25 92.50 220.00 277.18 17.62 46.25 92.50 220.00 277.18 17.63 46.25 92.50 220.00 277.18 17.64 46.25 92.50 220.00 277.18 17.65 46.25 92.50 220.00 277.18 17.66 46.25 92.50 220.00 277.18 17.67 46.25 92.50 220.00 277.18 17.68 46.25 92.50 220.00 277.18 17.69 46.25 92.50 220.00 277.18 17.70 46.25 92.50 220.00 277.18 17.71 46.25 92.50 220.00 277.18 17.72 46.25 92.50 220.00 17.73 46.25 92.50 220.00 17.74 46.25 92.50 220.00 17.75 46.25 92.50 220.00 17.76 46.25 92.50 220.00 17.77 46.25 92.50 220.00 17.78 46.25 92.50 220.00 17.79 46.25 92.50 220.00 17.80 46.25 92.50 220.00 17.81 46.25 92.50 220.00 17.82 46.25 92.50 220.00 17.83 46.25 92.50 220.00 17.84 46.25 92.50 220.00 17.85 46.25 92.50 220.00 17.86 46.25 92.50 220.00 17.87 46.25 92.50 220.00 17.88 46.25 92.50 220.00 17.89 46.25 92.50 220.00 17.90 46.25 92.50 220.00 17.91 46.25 92.50 220.00 17.92 46.25 92.50 220.00 17.93 46.25 92.50 220.00 17.94 46.25 92.50 220.00 17.95 46.25 92.50 220.00 17.96 46.25 92.50 220.00 17.97 46.25 92.50 220.00 17.98 46.25 92.50 220.00 17.99 46.25 92.50 220.00 18.00 46.25 92.50 220.00 18.01 46.25 92.50 220.00 18.02 46.25 92.50 220.00 293.66 18.03 46.25 92.50 220.00 293.66 18.04 46.25 92.50 220.00 293.66 18.05 46.25 92.50 220.00 293.66 18.06 46.25 92.50 220.00 293.66 18.07 46.25 92.50 220.00 293.66 18.08 46.25 92.50 220.00 293.66 18.09 46.25 92.50 220.00 293.66 18.10 46.25 92.50 220.00 293.66 18.11 46.25 92.50 220.00 293.66 18.12 46.25 92.50 220.00 293.66 18.13 46.25 92.50 220.00 293.66 18.14 46.25 92.50 220.00 293.66 18.15 46.25 92.50 220.00 293.66 18.16 46.25 92.50 220.00 293.66 18.17 46.25 92.50 220.00 293.66 18.18 46.25 92.50 220.00 293.66 18.19 46.25 92.50 220.00 293.66 18.20 46.25 92.50 220.00 293.66 18.21 46.25 92.50 220.00 293.66 18.22 46.25 92.50 220.00 293.66 18.23 46.25 92.50 220.00 293.66 18.24 46.25 92.50 220.00 293.66 18.25 46.25 92.50 220.00 293.66 18.26 46.25 92.50 220.00 293.66 18.27 46.25 92.50 220.00 293.66 18.28 46.25 92.50 220.00 293.66 18.29 46.25 92.50 220.00 293.66 18.30 46.25 92.50 220.00 293.66 18.31 46.25 92.50 220.00 293.66 18.32 46.25 92.50 220.00 293.66 18.33 46.25 92.50 220.00 293.66 18.34 46.25 92.50 220.00 293.66 18.35 46.25 92.50 220.00 293.66 18.36 46.25 92.50 220.00 293.66 18.37 46.25 92.50 220.00 293.66 18.38 46.25 92.50 220.00 293.66 18.39 46.25 92.50 293.66 18.40 46.25 92.50 293.66 18.41 46.25 92.50 293.66 18.42 46.25 92.50 293.66 18.43 46.25 92.50 293.66 18.44 46.25 92.50 293.66 18.45 46.25 92.50 293.66 18.46 46.25 92.50 293.66 18.47 46.25 92.50 293.66 18.48 46.25 92.50 293.66 18.49 46.25 92.50 293.66 369.99 18.50 46.25 92.50 293.66 369.99 18.51 46.25 92.50 293.66 369.99 18.52 46.25 92.50 293.66 369.99 18.53 92.50 293.66 369.99 18.54 92.50 293.66 369.99 18.55 92.50 293.66 369.99 18.56 92.50 293.66 369.99 18.57 92.50 293.66 369.99 18.58 92.50 293.66 369.99 18.59 92.50 293.66 369.99 18.60 92.50 293.66 369.99 18.61 92.50 293.66 369.99 18.62 92.50 293.66 369.99 18.63 92.50 293.66 369.99 18.64 92.50 293.66 369.99 18.65 92.50 293.66 369.99 18.66 92.50 293.66 369.99 18.67 92.50 293.66 369.99 18.68 92.50 293.66 369.99 18.69 92.50 293.66 369.99 18.70 92.50 293.66 369.99 18.71 92.50 293.66 369.99 18.72 92.50 293.66 18.73 92.50 293.66 18.74 92.50 293.66 18.75 92.50 293.66 18.76 92.50 293.66 18.77 92.50 293.66 18.78 92.50 293.66 18.79 46.25 92.50 293.66 18.80 46.25 92.50 293.66 18.81 46.25 92.50 293.66 18.82 46.25 92.50 293.66 18.83 46.25 92.50 293.66 18.84 46.25 92.50 293.66 18.85 46.25 92.50 293.66 18.86 46.25 92.50 293.66 18.87 46.25 92.50 293.66 18.88 46.25 92.50 185.00 293.66 18.89 46.25 92.50 185.00 293.66 18.90 46.25 92.50 185.00 293.66 18.91 46.25 92.50 185.00 293.66 18.92 46.25 92.50 185.00 293.66 18.93 92.50 185.00 293.66 18.94 92.50 185.00 293.66 18.95 92.50 185.00 293.66 18.96 92.50 185.00 293.66 18.97 92.50 185.00 293.66 18.98 92.50 185.00 220.00 293.66 18.99 92.50 185.00 220.00 293.66 19.00 92.50 220.00 293.66 19.01 92.50 220.00 293.66 19.02 92.50 220.00 293.66 19.03 92.50 220.00 293.66 19.04 92.50 220.00 293.66 19.05 92.50 220.00 293.66 19.06 92.50 220.00 293.66 19.07 92.50 220.00 293.66 19.08 92.50 220.00 293.66 19.09 92.50 220.00 277.18 293.66 19.10 92.50 220.00 277.18 293.66 19.11 92.50 220.00 277.18 293.66 19.12 92.50 220.00 277.18 293.66 19.13 92.50 220.00 277.18 293.66 19.14 92.50 220.00 277.18 293.66 19.15 92.50 220.00 277.18 293.66 19.16 92.50 220.00 277.18 293.66 19.17 92.50 220.00 277.18 293.66 19.18 92.50 220.00 277.18 293.66 19.19 92.50 220.00 277.18 293.66 19.20 92.50 220.00 277.18 293.66 19.21 92.50 220.00 277.18 293.66 19.22 92.50 220.00 277.18 293.66 19.23 92.50 220.00 277.18 293.66 19.24 92.50 220.00 277.18 293.66 19.25 220.00 277.18 293.66 19.26 220.00 277.18 293.66 19.27 220.00 277.18 293.66 19.28 220.00 277.18 293.66 19.29 220.00 277.18 293.66 19.30 220.00 277.18 19.31 220.00 277.18 19.32 220.00 277.18 19.33 220.00 277.18 19.34 220.00 19.35 220.00 19.36 220.00 19.37 220.00 19.38 220.00 19.39 220.00 19.40 220.00 19.41 220.00 19.42 220.00 293.66 19.43 220.00 293.66 19.44 220.00 277.18 293.66 19.45 220.00 277.18 293.66 19.46 220.00 277.18 293.66 369.99 19.47 220.00 277.18 293.66 369.99 19.48 220.00 277.18 293.66 369.99 19.49 220.00 277.18 293.66 369.99 19.50 220.00 277.18 293.66 369.99 19.51 220.00 277.18 293.66 369.99 19.52 220.00 277.18 293.66 369.99 19.53 220.00 293.66 369.99 19.54 220.00 293.66 369.99 19.55 220.00 293.66 19.56 220.00 293.66 19.57 220.00 293.66 19.58 220.00 293.66 19.59 220.00 293.66 19.60 92.50 220.00 293.66 19.61 92.50 220.00 293.66 19.62 92.50 220.00 293.66 19.63 92.50 220.00 293.66 19.64 92.50 220.00 293.66 19.65 92.50 220.00 293.66 19.66 92.50 220.00 293.66 19.67 92.50 220.00 293.66 19.68 92.50 220.00 293.66 19.69 92.50 220.00 293.66 19.70 92.50 220.00 293.66 19.71 92.50 220.00 293.66 19.72 92.50 220.00 293.66 19.73 92.50 220.00 293.66 19.74 92.50 220.00 293.66 19.75 92.50 220.00 293.66 19.76 92.50 220.00 293.66 19.77 92.50 220.00 293.66 19.78 92.50 220.00 293.66 19.79 92.50 220.00 293.66 19.80 92.50 220.00 293.66 19.81 92.50 220.00 293.66 19.82 92.50 220.00 293.66 19.83 92.50 220.00 293.66 19.84 92.50 220.00 293.66 19.85 92.50 220.00 293.66 19.86 92.50 220.00 293.66 19.87 92.50 220.00 293.66 19.88 92.50 220.00 293.66 19.89 92.50 220.00 293.66 19.90 92.50 220.00 293.66 19.91 92.50 220.00 293.66 19.92 92.50 220.00 293.66 19.93 92.50 220.00 293.66 369.99 19.94 92.50 220.00 293.66 369.99 19.95 92.50 220.00 293.66 369.99 19.96 92.50 220.00 293.66 369.99 19.97 92.50 220.00 293.66 369.99 19.98 92.50 220.00 293.66 369.99 19.99 92.50 220.00 293.66 369.99 20.00 92.50 220.00 293.66 369.99 20.01 92.50 220.00 293.66 369.99 20.02 46.25 92.50 220.00 293.66 369.99 20.03 46.25 92.50 220.00 293.66 369.99 20.04 46.25 92.50 220.00 293.66 369.99 20.05 46.25 92.50 220.00 293.66 369.99 20.06 46.25 220.00 293.66 369.99 20.07 46.25 220.00 293.66 369.99 20.08 46.25 220.00 293.66 369.99 20.09 46.25 92.50 220.00 293.66 369.99 20.10 46.25 92.50 220.00 293.66 369.99 20.11 46.25 92.50 220.00 293.66 369.99 20.12 46.25 92.50 220.00 293.66 369.99 20.13 92.50 220.00 293.66 369.99 20.14 92.50 220.00 293.66 369.99 20.15 92.50 220.00 293.66 369.99 20.16 92.50 220.00 293.66 369.99 20.17 92.50 220.00 293.66 369.99 20.18 92.50 220.00 293.66 369.99 20.19 92.50 220.00 293.66 369.99 20.20 92.50 220.00 293.66 369.99 20.21 92.50 220.00 293.66 369.99 20.22 92.50 220.00 293.66 369.99 20.23 92.50 220.00 293.66 369.99 20.24 92.50 220.00 293.66 369.99 20.25 92.50 220.00 293.66 369.99 20.26 92.50 220.00 293.66 369.99 20.27 92.50 220.00 293.66 369.99 20.28 92.50 220.00 293.66 369.99 20.29 92.50 220.00 293.66 369.99 20.30 92.50 220.00 293.66 369.99 20.31 92.50 220.00 293.66 369.99 20.32 92.50 220.00 293.66 369.99 20.33 92.50 220.00 293.66 369.99 20.34 92.50 220.00 293.66 369.99 20.35 92.50 220.00 293.66 369.99 20.36 92.50 220.00 293.66 369.99 20.37 92.50 220.00 293.66 369.99 20.38 92.50 220.00 293.66 369.99 20.39 92.50 220.00 293.66 369.99 20.40 92.50 220.00 293.66 369.99 20.41 92.50 220.00 293.66 369.99 20.42 92.50 220.00 293.66 369.99 20.43 92.50 220.00 293.66 369.99 20.44 92.50 220.00 293.66 369.99 20.45 92.50 220.00 293.66 369.99 20.46 92.50 220.00 293.66 369.99 20.47 92.50 220.00 293.66 369.99 20.48 92.50 220.00 293.66 369.99 20.49 92.50 207.65 220.00 293.66 369.99 20.50 92.50 207.65 220.00 293.66 369.99 20.51 103.83 207.65 293.66 369.99 20.52 103.83 207.65 293.66 369.99 20.53 103.83 207.65 369.99 20.54 103.83 207.65 369.99 20.55 103.83 207.65 20.56 103.83 207.65 20.57 103.83 207.65 20.58 103.83 207.65 20.59 103.83 207.65 20.60 103.83 207.65 293.66 20.61 103.83 207.65 293.66 20.62 103.83 207.65 293.66 20.63 103.83 207.65 293.66 20.64 103.83 207.65 293.66 20.65 103.83 207.65 293.66 20.66 103.83 207.65 293.66 20.67 103.83 207.65 293.66 20.68 103.83 207.65 293.66 20.69 103.83 207.65 20.70 103.83 207.65 20.71 103.83 207.65 20.72 103.83 207.65 20.73 103.83 207.65 20.74 103.83 207.65 20.75 103.83 207.65 20.76 103.83 207.65 20.77 103.83 207.65 20.78 103.83 207.65 20.79 103.83 207.65 20.80 103.83 207.65 20.81 103.83 207.65 20.82 103.83 207.65 20.83 103.83 207.65 20.84 103.83 207.65 20.85 103.83 207.65 20.86 103.83 207.65 20.87 103.83 207.65 20.88 103.83 207.65 20.89 103.83 207.65 20.90 103.83 207.65 20.91 103.83 207.65 20.92 103.83 207.65 20.93 103.83 207.65 20.94 103.83 207.65 20.95 103.83 207.65 20.96 103.83 207.65 20.97 103.83 207.65 20.98 103.83 207.65 20.99 103.83 207.65 21.00 103.83 207.65 21.01 103.83 207.65 21.02 103.83 207.65 21.03 103.83 207.65 21.04 103.83 207.65 21.05 103.83 207.65 21.06 103.83 207.65 21.07 103.83 207.65 261.63 21.08 103.83 207.65 261.63 21.09 103.83 207.65 261.63 21.10 103.83 207.65 261.63 21.11 103.83 207.65 261.63 21.12 103.83 207.65 261.63 21.13 103.83 207.65 261.63 21.14 103.83 207.65 261.63 21.15 103.83 207.65 261.63 21.16 51.91 103.83 207.65 261.63 21.17 51.91 103.83 207.65 261.63 21.18 51.91 103.83 207.65 261.63 21.19 51.91 103.83 207.65 261.63 21.20 51.91 103.83 207.65 261.63 21.21 51.91 103.83 207.65 261.63 21.22 51.91 103.83 207.65 261.63 21.23 51.91 103.83 207.65 261.63 21.24 51.91 103.83 207.65 261.63 21.25 51.91 103.83 207.65 21.26 51.91 103.83 207.65 21.27 51.91 103.83 207.65 21.28 51.91 103.83 207.65 21.29 51.91 103.83 207.65 21.30 51.91 103.83 207.65 261.63 21.31 51.91 103.83 207.65 261.63 21.32 51.91 103.83 261.63 21.33 51.91 103.83 261.63 21.34 51.91 103.83 261.63 21.35 51.91 103.83 261.63 21.36 51.91 103.83 21.37 51.91 103.83 21.38 51.91 103.83 21.39 51.91 103.83 21.40 51.91 103.83 21.41 51.91 103.83 21.42 51.91 103.83 21.43 51.91 103.83 21.44 51.91 103.83 21.45 51.91 103.83 21.46 51.91 103.83 21.47 51.91 103.83 21.48 51.91 103.83 21.49 51.91 103.83 21.50 51.91 103.83 21.51 51.91 103.83 21.52 51.91 103.83 21.53 51.91 103.83 207.65 369.99 21.54 51.91 103.83 207.65 369.99 21.55 51.91 103.83 207.65 369.99 21.56 51.91 103.83 207.65 369.99 21.57 51.91 103.83 207.65 369.99 21.58 51.91 103.83 207.65 261.63 369.99 21.59 51.91 103.83 207.65 261.63 369.99 21.60 51.91 103.83 207.65 261.63 369.99 21.61 51.91 103.83 207.65 261.63 369.99 21.62 103.83 261.63 369.99 21.63 103.83 261.63 369.99 21.64 103.83 369.99 21.65 103.83 369.99 21.66 103.83 369.99 21.67 103.83 207.65 369.99 21.68 103.83 207.65 369.99 21.69 103.83 207.65 369.99 21.70 103.83 207.65 369.99 21.71 103.83 207.65 369.99 21.72 103.83 207.65 261.63 369.99 21.73 103.83 207.65 261.63 369.99 21.74 103.83 207.65 261.63 369.99 21.75 103.83 207.65 261.63 369.99 21.76 103.83 207.65 261.63 369.99 21.77 103.83 207.65 261.63 369.99 21.78 103.83 207.65 261.63 369.99 21.79 103.83 207.65 261.63 369.99 21.80 103.83 207.65 261.63 369.99 21.81 103.83 207.65 261.63 369.99 21.82 103.83 207.65 261.63 369.99 21.83 103.83 207.65 261.63 369.99 21.84 103.83 207.65 261.63 369.99 21.85 103.83 207.65 261.63 369.99 21.86 103.83 207.65 261.63 369.99 21.87 103.83 207.65 261.63 369.99 21.88 103.83 207.65 261.63 369.99 21.89 103.83 207.65 261.63 369.99 21.90 103.83 207.65 369.99 21.91 103.83 207.65 369.99 21.92 103.83 207.65 369.99 21.93 103.83 207.65 369.99 21.94 103.83 207.65 369.99 21.95 103.83 207.65 369.99 21.96 103.83 207.65 369.99 21.97 103.83 207.65 369.99 21.98 103.83 207.65 369.99 21.99 103.83 207.65 369.99 22.00 103.83 207.65 369.99 22.01 103.83 207.65 369.99 22.02 103.83 207.65 369.99 22.03 103.83 207.65 369.99 22.04 103.83 207.65 369.99 22.05 103.83 207.65 369.99 22.06 103.83 207.65 369.99 22.07 103.83 207.65 369.99 22.08 103.83 207.65 369.99 22.09 103.83 207.65 369.99 22.10 103.83 207.65 369.99 22.11 51.91 103.83 207.65 369.99 22.12 51.91 103.83 207.65 369.99 22.13 51.91 103.83 207.65 22.14 51.91 103.83 207.65 22.15 51.91 103.83 207.65 22.16 51.91 103.83 207.65 22.17 51.91 103.83 207.65 22.18 103.83 207.65 22.19 103.83 207.65 22.20 103.83 207.65 22.21 103.83 207.65 22.22 103.83 207.65 22.23 103.83 207.65 22.24 103.83 207.65 22.25 103.83 207.65 22.26 103.83 207.65 22.27 51.91 103.83 207.65 22.28 51.91 103.83 207.65 22.29 51.91 103.83 207.65 22.30 51.91 103.83 207.65 22.31 51.91 103.83 207.65 22.32 51.91 103.83 207.65 22.33 51.91 103.83 207.65 22.34 51.91 103.83 207.65 22.35 51.91 103.83 207.65 22.36 51.91 103.83 207.65 22.37 51.91 103.83 207.65 22.38 51.91 103.83 207.65 22.39 103.83 207.65 22.40 103.83 207.65 22.41 103.83 207.65 22.42 103.83 207.65 22.43 103.83 207.65 22.44 103.83 207.65 22.45 103.83 207.65 22.46 103.83 207.65 22.47 103.83 207.65 22.48 103.83 207.65 22.49 103.83 207.65 22.50 103.83 207.65 22.51 103.83 207.65 277.18 22.52 103.83 207.65 277.18 22.53 103.83 277.18 22.54 103.83 277.18 22.55 103.83 277.18 22.56 103.83 277.18 22.57 103.83 277.18 22.58 103.83 277.18 22.59 103.83 277.18 22.60 103.83 277.18 22.61 103.83 277.18 22.62 103.83 277.18 22.63 103.83 277.18 22.64 103.83 277.18 22.65 103.83 277.18 22.66 103.83 277.18 22.67 103.83 277.18 22.68 103.83 277.18 22.69 103.83 277.18 22.70 103.83 277.18 22.71 103.83 277.18 22.72 103.83 277.18 22.73 277.18 22.74 277.18 22.75 277.18 22.76 277.18 22.77 277.18 22.78 277.18 22.79 277.18 22.80 277.18 22.81 277.18 22.82 277.18 22.83 277.18 22.84 277.18 22.85 277.18 22.86 277.18 22.87 277.18 22.88 277.18 22.89 277.18 22.90 277.18 22.91 277.18 22.92 277.18 22.93 277.18 22.94 277.18 22.95 103.83 277.18 22.96 103.83 277.18 22.97 103.83 277.18 22.98 103.83 277.18 22.99 103.83 277.18 329.63 23.00 103.83 277.18 329.63 23.01 103.83 277.18 329.63 23.02 103.83 277.18 329.63 23.03 103.83 277.18 329.63 23.04 103.83 277.18 329.63 23.05 103.83 277.18 329.63 23.06 103.83 277.18 329.63 23.07 103.83 277.18 329.63 23.08 103.83 277.18 329.63 23.09 103.83 277.18 329.63 23.10 103.83 277.18 329.63 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23.48 103.83 277.18 329.63 23.49 103.83 277.18 329.63 23.50 103.83 277.18 23.51 103.83 277.18 23.52 103.83 277.18 23.53 103.83 277.18 23.54 103.83 277.18 23.55 103.83 277.18 23.56 103.83 277.18 23.57 103.83 277.18 23.58 103.83 277.18 23.59 103.83 277.18 23.60 103.83 277.18 23.61 103.83 277.18 23.62 103.83 23.63 103.83 23.64 103.83 23.65 103.83 23.66 103.83 23.67 103.83 23.68 103.83 23.69 103.83 207.65 23.70 103.83 207.65 23.71 103.83 207.65 23.72 103.83 207.65 23.73 103.83 207.65 23.74 103.83 207.65 23.75 103.83 207.65 23.76 103.83 207.65 23.77 103.83 207.65 23.78 103.83 207.65 23.79 103.83 207.65 23.80 103.83 207.65 23.81 103.83 207.65 23.82 103.83 207.65 23.83 103.83 207.65 23.84 103.83 207.65 23.85 103.83 207.65 23.86 103.83 207.65 23.87 103.83 207.65 23.88 103.83 207.65 23.89 103.83 207.65 23.90 103.83 207.65 23.91 103.83 207.65 23.92 103.83 207.65 23.93 103.83 207.65 23.94 103.83 207.65 23.95 103.83 207.65 23.96 103.83 207.65 23.97 103.83 207.65 23.98 103.83 207.65 23.99 103.83 207.65 24.00 103.83 207.65 24.01 103.83 207.65 24.02 103.83 207.65 24.03 103.83 207.65 24.04 51.91 103.83 207.65 24.05 51.91 103.83 207.65 24.06 51.91 103.83 207.65 24.07 51.91 103.83 207.65 24.08 51.91 103.83 207.65 24.09 51.91 103.83 207.65 24.10 103.83 24.11 103.83 24.12 103.83 24.13 103.83 24.14 103.83 24.15 103.83 277.18 24.16 103.83 277.18 24.17 103.83 277.18 24.18 103.83 277.18 24.19 103.83 277.18 24.20 103.83 277.18 24.21 103.83 277.18 24.22 103.83 277.18 24.23 103.83 277.18 24.24 103.83 277.18 24.25 103.83 277.18 24.26 103.83 277.18 24.27 103.83 277.18 24.28 103.83 277.18 24.29 103.83 277.18 24.30 103.83 277.18 24.31 103.83 277.18 24.32 103.83 277.18 24.33 103.83 277.18 24.34 103.83 277.18 24.35 103.83 277.18 24.36 103.83 24.37 103.83 24.38 103.83 24.39 103.83 24.40 103.83 24.41 103.83 24.42 103.83 24.43 103.83 24.44 103.83 24.45 103.83 24.46 103.83 24.47 103.83 24.48 103.83 24.49 103.83 24.50 103.83 277.18 24.51 103.83 277.18 24.52 103.83 277.18 24.53 103.83 277.18 24.54 103.83 277.18 24.55 103.83 277.18 311.13 24.56 103.83 277.18 311.13 24.57 103.83 311.13 24.58 103.83 311.13 24.59 103.83 311.13 24.60 103.83 311.13 24.61 103.83 311.13 24.62 103.83 311.13 24.63 103.83 311.13 24.64 103.83 311.13 24.65 103.83 311.13 24.66 103.83 311.13 24.67 103.83 311.13 24.68 103.83 311.13 24.69 103.83 311.13 24.70 103.83 311.13 24.71 103.83 311.13 24.72 103.83 311.13 24.73 103.83 311.13 24.74 103.83 311.13 24.75 103.83 311.13 24.76 103.83 311.13 24.77 103.83 311.13 24.78 103.83 311.13 24.79 103.83 311.13 24.80 103.83 311.13 24.81 103.83 311.13 24.82 103.83 311.13 24.83 103.83 311.13 24.84 103.83 311.13 24.85 103.83 311.13 24.86 103.83 311.13 24.87 103.83 311.13 24.88 103.83 311.13 24.89 103.83 311.13 24.90 103.83 311.13 24.91 103.83 311.13 24.92 103.83 311.13 24.93 103.83 311.13 24.94 51.91 103.83 311.13 24.95 51.91 103.83 311.13 24.96 51.91 103.83 311.13 24.97 51.91 103.83 311.13 24.98 51.91 103.83 311.13 24.99 51.91 103.83 311.13 25.00 51.91 103.83 311.13 25.01 51.91 103.83 311.13 25.02 51.91 103.83 311.13 25.03 103.83 311.13 25.04 103.83 185.00 311.13 25.05 103.83 185.00 311.13 25.06 103.83 185.00 25.07 103.83 185.00 25.08 103.83 185.00 25.09 103.83 185.00 25.10 103.83 185.00 25.11 103.83 185.00 25.12 103.83 185.00 25.13 103.83 185.00 25.14 103.83 185.00 25.15 103.83 185.00 25.16 103.83 185.00 25.17 103.83 185.00 25.18 103.83 185.00 25.19 103.83 185.00 25.20 103.83 185.00 25.21 103.83 185.00 25.22 103.83 185.00 25.23 103.83 185.00 25.24 103.83 185.00 25.25 103.83 185.00 25.26 103.83 185.00 25.27 103.83 185.00 25.28 103.83 185.00 25.29 103.83 185.00 25.30 103.83 185.00 25.31 103.83 185.00 25.32 103.83 185.00 25.33 103.83 185.00 25.34 103.83 185.00 25.35 103.83 185.00 25.36 103.83 185.00 25.37 103.83 185.00 25.38 103.83 185.00 25.39 103.83 185.00 25.40 103.83 185.00 25.41 103.83 185.00 25.42 103.83 185.00 25.43 103.83 185.00 25.44 103.83 185.00 25.45 103.83 185.00 25.46 103.83 185.00 25.47 103.83 185.00 25.48 103.83 185.00 25.49 103.83 185.00 25.50 103.83 185.00 25.51 103.83 185.00 25.52 103.83 185.00 25.53 103.83 185.00 25.54 103.83 185.00 25.55 103.83 185.00 25.56 103.83 185.00 25.57 103.83 185.00 207.65 261.63 25.58 103.83 185.00 207.65 261.63 25.59 103.83 185.00 207.65 261.63 25.60 103.83 185.00 207.65 261.63 25.61 103.83 185.00 207.65 261.63 25.62 103.83 185.00 207.65 261.63 25.63 103.83 185.00 207.65 261.63 25.64 103.83 185.00 207.65 261.63 25.65 103.83 185.00 207.65 261.63 25.66 103.83 185.00 261.63 25.67 103.83 185.00 261.63 25.68 103.83 185.00 261.63 25.69 103.83 185.00 261.63 25.70 103.83 185.00 261.63 25.71 103.83 185.00 261.63 25.72 103.83 185.00 261.63 25.73 103.83 185.00 261.63 25.74 103.83 185.00 261.63 25.75 103.83 185.00 261.63 25.76 103.83 185.00 261.63 25.77 103.83 185.00 261.63 25.78 103.83 185.00 261.63 25.79 103.83 185.00 261.63 25.80 103.83 185.00 261.63 25.81 103.83 185.00 261.63 25.82 103.83 185.00 261.63 25.83 103.83 185.00 261.63 25.84 103.83 185.00 261.63 25.85 103.83 185.00 261.63 25.86 103.83 185.00 261.63 25.87 103.83 185.00 261.63 25.88 103.83 185.00 261.63 25.89 103.83 185.00 261.63 25.90 103.83 185.00 261.63 25.91 103.83 185.00 261.63 25.92 103.83 185.00 261.63 25.93 103.83 185.00 261.63 25.94 103.83 185.00 261.63 25.95 103.83 185.00 261.63 25.96 103.83 185.00 261.63 25.97 103.83 185.00 261.63 25.98 103.83 185.00 261.63 25.99 103.83 185.00 261.63 26.00 103.83 185.00 261.63 26.01 103.83 185.00 261.63 26.02 103.83 185.00 261.63 26.03 103.83 185.00 261.63 26.04 103.83 185.00 261.63 26.05 103.83 185.00 261.63 26.06 103.83 185.00 261.63 26.07 103.83 185.00 261.63 26.08 103.83 185.00 261.63 26.09 103.83 185.00 261.63 26.10 103.83 185.00 261.63 26.11 103.83 185.00 261.63 26.12 103.83 185.00 261.63 26.13 103.83 185.00 261.63 311.13 26.14 103.83 185.00 261.63 311.13 26.15 103.83 185.00 261.63 311.13 26.16 103.83 185.00 261.63 311.13 26.17 185.00 261.63 311.13 26.18 185.00 261.63 311.13 26.19 185.00 261.63 311.13 26.20 103.83 185.00 261.63 311.13 26.21 103.83 185.00 261.63 311.13 26.22 103.83 185.00 261.63 311.13 26.23 103.83 185.00 261.63 311.13 26.24 103.83 185.00 261.63 311.13 26.25 103.83 185.00 261.63 311.13 26.26 103.83 185.00 261.63 311.13 26.27 103.83 185.00 261.63 311.13 26.28 103.83 185.00 261.63 311.13 26.29 103.83 185.00 261.63 311.13 26.30 103.83 185.00 261.63 311.13 26.31 103.83 185.00 261.63 311.13 26.32 103.83 185.00 261.63 311.13 26.33 103.83 185.00 261.63 311.13 26.34 103.83 185.00 261.63 311.13 26.35 103.83 185.00 261.63 311.13 26.36 103.83 185.00 261.63 311.13 26.37 103.83 185.00 261.63 311.13 26.38 103.83 185.00 261.63 311.13 26.39 103.83 185.00 261.63 311.13 26.40 103.83 185.00 261.63 311.13 26.41 103.83 185.00 261.63 311.13 26.42 103.83 185.00 261.63 311.13 26.43 103.83 185.00 261.63 311.13 26.44 103.83 185.00 261.63 311.13 26.45 103.83 185.00 261.63 311.13 26.46 103.83 185.00 261.63 311.13 26.47 103.83 185.00 261.63 311.13 26.48 103.83 185.00 261.63 311.13 26.49 103.83 185.00 261.63 311.13 26.50 103.83 185.00 261.63 311.13 26.51 103.83 185.00 261.63 311.13 26.52 103.83 185.00 261.63 311.13 26.53 103.83 185.00 261.63 311.13 26.54 103.83 185.00 261.63 311.13 26.55 103.83 185.00 261.63 311.13 26.56 103.83 185.00 261.63 311.13 26.57 103.83 261.63 311.13 26.58 103.83 261.63 311.13 26.59 103.83 311.13 26.60 103.83 311.13 26.61 103.83 311.13 26.62 103.83 311.13 26.63 103.83 311.13 26.64 103.83 311.13 26.65 103.83 311.13 26.66 103.83 261.63 311.13 26.67 103.83 261.63 311.13 26.68 103.83 261.63 311.13 26.69 103.83 261.63 311.13 26.70 103.83 261.63 311.13 26.71 103.83 261.63 311.13 26.72 103.83 261.63 311.13 26.73 103.83 261.63 311.13 26.74 103.83 261.63 311.13 26.75 103.83 261.63 311.13 26.76 103.83 261.63 311.13 26.77 103.83 261.63 311.13 26.78 103.83 261.63 311.13 26.79 103.83 261.63 311.13 26.80 103.83 261.63 311.13 26.81 103.83 261.63 311.13 26.82 103.83 311.13 26.83 103.83 311.13 26.84 103.83 311.13 26.85 103.83 311.13 26.86 103.83 311.13 26.87 103.83 311.13 26.88 103.83 311.13 26.89 103.83 277.18 311.13 26.90 103.83 277.18 311.13 26.91 103.83 277.18 311.13 26.92 69.30 103.83 277.18 311.13 26.93 69.30 103.83 277.18 311.13 26.94 69.30 103.83 138.59 277.18 26.95 69.30 103.83 138.59 277.18 26.96 69.30 103.83 138.59 164.81 277.18 26.97 69.30 103.83 138.59 164.81 277.18 26.98 69.30 103.83 138.59 164.81 277.18 26.99 69.30 103.83 138.59 164.81 207.65 277.18 27.00 69.30 103.83 138.59 164.81 207.65 277.18 27.01 69.30 103.83 138.59 164.81 207.65 277.18 27.02 69.30 103.83 138.59 164.81 207.65 277.18 27.03 69.30 103.83 138.59 164.81 207.65 277.18 27.04 69.30 103.83 138.59 164.81 207.65 277.18 27.05 69.30 103.83 138.59 164.81 207.65 277.18 27.06 69.30 103.83 138.59 164.81 207.65 277.18 27.07 69.30 103.83 138.59 164.81 207.65 277.18 27.08 69.30 103.83 164.81 207.65 277.18 27.09 69.30 103.83 164.81 207.65 277.18 27.10 69.30 103.83 164.81 207.65 277.18 27.11 69.30 103.83 164.81 207.65 277.18 27.12 69.30 103.83 164.81 207.65 277.18 27.13 69.30 103.83 164.81 207.65 277.18 27.14 69.30 103.83 164.81 207.65 277.18 27.15 69.30 103.83 164.81 207.65 277.18 27.16 69.30 103.83 164.81 207.65 277.18 27.17 69.30 103.83 164.81 207.65 277.18 27.18 69.30 103.83 164.81 207.65 277.18 27.19 69.30 103.83 164.81 207.65 277.18 27.20 69.30 103.83 164.81 207.65 277.18 27.21 69.30 103.83 164.81 207.65 277.18 27.22 69.30 103.83 164.81 207.65 277.18 27.23 69.30 103.83 164.81 207.65 277.18 27.24 69.30 103.83 164.81 207.65 277.18 27.25 69.30 103.83 164.81 207.65 277.18 27.26 69.30 103.83 164.81 207.65 277.18 27.27 69.30 103.83 164.81 207.65 277.18 27.28 69.30 103.83 164.81 207.65 277.18 27.29 69.30 103.83 164.81 207.65 277.18 27.30 69.30 103.83 164.81 207.65 277.18 27.31 69.30 103.83 164.81 207.65 277.18 27.32 69.30 103.83 164.81 207.65 277.18 27.33 69.30 103.83 164.81 207.65 277.18 27.34 69.30 103.83 164.81 207.65 277.18 27.35 69.30 103.83 164.81 207.65 27.36 69.30 103.83 164.81 207.65 27.37 69.30 103.83 164.81 207.65 27.38 69.30 103.83 164.81 207.65 27.39 69.30 103.83 164.81 207.65 27.40 69.30 103.83 164.81 207.65 277.18 27.41 69.30 103.83 164.81 207.65 277.18 27.42 69.30 103.83 164.81 207.65 277.18 27.43 69.30 103.83 164.81 207.65 277.18 27.44 69.30 103.83 164.81 207.65 277.18 27.45 69.30 103.83 164.81 207.65 277.18 27.46 69.30 103.83 164.81 207.65 277.18 27.47 69.30 103.83 164.81 207.65 277.18 27.48 69.30 103.83 164.81 207.65 277.18 27.49 69.30 103.83 164.81 207.65 277.18 27.50 69.30 103.83 164.81 207.65 277.18 27.51 69.30 103.83 164.81 207.65 277.18 27.52 69.30 103.83 164.81 207.65 277.18 27.53 69.30 103.83 164.81 207.65 277.18 27.54 69.30 103.83 164.81 207.65 277.18 27.55 69.30 103.83 164.81 207.65 277.18 27.56 69.30 164.81 207.65 277.18 27.57 69.30 164.81 207.65 277.18 27.58 69.30 164.81 207.65 277.18 27.59 69.30 164.81 207.65 277.18 27.60 69.30 164.81 207.65 277.18 27.61 69.30 103.83 164.81 207.65 277.18 27.62 69.30 103.83 164.81 207.65 277.18 27.63 69.30 103.83 164.81 207.65 277.18 27.64 69.30 103.83 164.81 207.65 277.18 27.65 69.30 103.83 164.81 207.65 277.18 27.66 69.30 103.83 164.81 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34.25 65.41 207.65 261.63 311.13 415.30 34.26 65.41 207.65 261.63 311.13 415.30 34.27 65.41 207.65 261.63 311.13 415.30 34.28 65.41 207.65 261.63 311.13 415.30 34.29 65.41 207.65 261.63 311.13 415.30 34.30 65.41 207.65 261.63 311.13 415.30 34.31 65.41 207.65 261.63 311.13 415.30 34.32 65.41 130.81 207.65 261.63 311.13 415.30 34.33 65.41 130.81 207.65 261.63 311.13 415.30 34.34 65.41 130.81 207.65 261.63 311.13 415.30 34.35 65.41 130.81 207.65 261.63 311.13 415.30 34.36 65.41 130.81 207.65 261.63 311.13 415.30 34.37 65.41 130.81 207.65 261.63 311.13 415.30 34.38 65.41 130.81 207.65 261.63 311.13 415.30 34.39 65.41 130.81 207.65 261.63 311.13 369.99 415.30 34.40 65.41 130.81 207.65 261.63 311.13 369.99 415.30 34.41 65.41 130.81 207.65 261.63 311.13 369.99 415.30 34.42 65.41 130.81 207.65 261.63 311.13 369.99 415.30 34.43 65.41 130.81 207.65 261.63 311.13 369.99 415.30 34.44 65.41 207.65 261.63 311.13 369.99 415.30 34.45 65.41 207.65 261.63 311.13 369.99 415.30 34.46 65.41 207.65 261.63 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65.41 207.65 261.63 369.99 34.77 65.41 207.65 261.63 369.99 34.78 65.41 207.65 261.63 369.99 34.79 65.41 207.65 261.63 369.99 34.80 65.41 207.65 261.63 369.99 34.81 65.41 207.65 261.63 369.99 34.82 65.41 207.65 261.63 369.99 34.83 65.41 207.65 261.63 369.99 34.84 65.41 207.65 261.63 369.99 34.85 65.41 207.65 261.63 369.99 34.86 65.41 207.65 261.63 369.99 34.87 65.41 207.65 261.63 369.99 34.88 65.41 207.65 261.63 34.89 65.41 207.65 261.63 34.90 65.41 207.65 261.63 34.91 65.41 207.65 261.63 34.92 65.41 207.65 261.63 34.93 65.41 207.65 261.63 34.94 65.41 207.65 261.63 34.95 65.41 207.65 261.63 34.96 65.41 207.65 261.63 34.97 65.41 207.65 261.63 34.98 65.41 207.65 261.63 34.99 65.41 207.65 261.63 35.00 65.41 207.65 261.63 35.01 65.41 207.65 261.63 35.02 65.41 207.65 261.63 35.03 65.41 207.65 261.63 35.04 65.41 207.65 261.63 35.05 65.41 207.65 261.63 35.06 65.41 207.65 261.63 35.07 65.41 207.65 261.63 35.08 65.41 207.65 261.63 35.09 65.41 207.65 261.63 35.10 65.41 207.65 261.63 35.11 65.41 207.65 261.63 35.12 65.41 207.65 261.63 35.13 65.41 207.65 261.63 35.14 65.41 207.65 261.63 35.15 65.41 207.65 261.63 35.16 65.41 207.65 261.63 35.17 65.41 207.65 261.63 35.18 65.41 207.65 261.63 35.19 65.41 207.65 261.63 35.20 65.41 207.65 261.63 35.21 65.41 207.65 261.63 35.22 65.41 207.65 261.63 35.23 65.41 207.65 261.63 415.30 35.24 65.41 207.65 261.63 415.30 35.25 65.41 207.65 261.63 415.30 35.26 65.41 207.65 261.63 415.30 35.27 65.41 261.63 415.30 35.28 65.41 261.63 415.30 35.29 65.41 261.63 415.30 35.30 65.41 261.63 415.30 35.31 65.41 261.63 415.30 35.32 65.41 261.63 311.13 415.30 35.33 65.41 261.63 311.13 415.30 35.34 65.41 261.63 311.13 415.30 35.35 65.41 261.63 311.13 415.30 35.36 65.41 261.63 311.13 415.30 35.37 65.41 261.63 311.13 35.38 65.41 261.63 311.13 35.39 130.81 261.63 311.13 35.40 130.81 261.63 311.13 35.41 130.81 261.63 311.13 35.42 130.81 261.63 311.13 35.43 130.81 261.63 311.13 35.44 261.63 311.13 35.45 261.63 311.13 35.46 261.63 311.13 35.47 261.63 311.13 35.48 261.63 311.13 35.49 261.63 311.13 35.50 261.63 311.13 35.51 261.63 311.13 35.52 261.63 311.13 35.53 261.63 311.13 35.54 261.63 311.13 35.55 261.63 311.13 35.56 261.63 311.13 35.57 261.63 311.13 35.58 261.63 311.13 35.59 261.63 311.13 35.60 261.63 311.13 35.61 261.63 311.13 35.62 261.63 311.13 35.63 261.63 311.13 35.64 261.63 311.13 35.65 261.63 311.13 35.66 261.63 311.13 35.67 261.63 311.13 35.68 261.63 311.13 35.69 261.63 311.13 35.70 261.63 311.13 35.71 261.63 311.13 35.72 261.63 311.13 35.73 261.63 311.13 35.74 261.63 311.13 35.75 261.63 311.13 35.76 261.63 311.13 35.77 261.63 311.13 35.78 261.63 311.13 35.79 261.63 311.13 35.80 261.63 311.13 35.81 261.63 311.13 35.82 261.63 311.13 35.83 261.63 311.13 369.99 35.84 261.63 311.13 369.99 35.85 261.63 311.13 369.99 35.86 261.63 311.13 369.99 35.87 261.63 311.13 369.99 35.88 261.63 311.13 369.99 35.89 261.63 311.13 369.99 35.90 261.63 311.13 369.99 35.91 261.63 311.13 369.99 35.92 261.63 311.13 369.99 35.93 261.63 311.13 369.99 35.94 261.63 311.13 369.99 35.95 261.63 311.13 369.99 35.96 261.63 311.13 369.99 35.97 261.63 311.13 369.99 35.98 261.63 311.13 369.99 35.99 261.63 369.99 36.00 261.63 369.99 36.01 261.63 369.99 36.02 261.63 369.99 36.03 261.63 369.99 36.04 261.63 369.99 36.05 261.63 369.99 36.06 261.63 36.07 261.63 36.08 261.63 36.09 261.63 36.10 261.63 36.11 261.63 369.99 36.12 261.63 369.99 36.13 261.63 369.99 36.14 261.63 369.99 415.30 36.15 261.63 369.99 415.30 36.16 261.63 369.99 415.30 36.17 261.63 369.99 415.30 36.18 261.63 369.99 415.30 36.19 261.63 369.99 415.30 36.20 261.63 369.99 415.30 36.21 261.63 369.99 415.30 36.22 261.63 369.99 415.30 36.23 261.63 369.99 415.30 36.24 261.63 369.99 415.30 36.25 261.63 369.99 415.30 36.26 261.63 369.99 415.30 36.27 207.65 261.63 369.99 415.30 36.28 207.65 261.63 369.99 415.30 36.29 207.65 36.30 207.65 36.31 207.65 36.32 207.65 36.33 207.65 36.34 207.65 36.35 207.65 36.36 207.65 36.37 207.65 36.38 207.65 36.39 207.65 36.40 207.65 36.41 207.65 36.42 207.65 36.43 207.65 36.44 207.65 36.45 207.65 36.46 207.65 36.47 207.65 36.48 207.65 36.49 207.65 36.50 207.65 36.51 207.65 36.52 207.65 36.53 207.65 36.54 207.65 36.55 207.65 36.56 207.65 36.57 207.65 36.58 207.65 36.59 207.65 36.60 207.65 36.61 207.65 36.62 207.65 36.63 207.65 36.64 207.65 36.65 207.65 415.30 36.66 207.65 415.30 36.67 207.65 415.30 36.68 207.65 415.30 36.69 207.65 415.30 36.70 207.65 415.30 36.71 207.65 415.30 36.72 207.65 415.30 36.73 207.65 415.30 36.74 311.13 415.30 36.75 311.13 415.30 36.76 311.13 415.30 36.77 311.13 415.30 36.78 311.13 415.30 36.79 311.13 415.30 36.80 311.13 415.30 36.81 311.13 415.30 36.82 311.13 415.30 36.83 311.13 415.30 36.84 311.13 415.30 36.85 311.13 415.30 36.86 311.13 415.30 36.87 311.13 415.30 36.88 311.13 415.30 36.89 311.13 415.30 36.90 311.13 415.30 36.91 311.13 415.30 36.92 311.13 415.30 36.93 311.13 415.30 36.94 311.13 415.30 36.95 311.13 415.30 36.96 311.13 415.30 36.97 311.13 36.98 311.13 36.99 311.13 37.00 311.13 37.01 311.13 37.02 311.13 37.03 311.13 37.04 311.13 37.05 311.13 37.06 311.13 37.07 311.13 37.08 311.13 37.09 311.13 37.10 311.13 37.11 311.13 37.12 311.13 37.13 311.13 37.14 311.13 37.15 311.13 37.16 311.13 37.17 311.13 37.18 311.13 37.19 311.13 37.20 311.13 37.21 311.13 37.22 311.13 37.23 311.13 37.24 311.13 37.25 311.13 369.99 37.26 311.13 369.99 37.27 311.13 369.99 37.28 311.13 369.99 37.29 311.13 369.99 37.30 311.13 369.99 37.31 311.13 369.99 37.32 311.13 369.99 37.33 311.13 369.99 37.34 311.13 369.99 37.35 311.13 369.99 37.36 311.13 369.99 37.37 311.13 369.99 37.38 311.13 369.99 37.39 311.13 369.99 37.40 311.13 369.99 37.41 311.13 369.99 37.42 311.13 369.99 37.43 311.13 369.99 37.44 311.13 369.99 37.45 311.13 369.99 37.46 65.41 311.13 369.99 37.47 65.41 311.13 369.99 37.48 65.41 311.13 369.99 37.49 65.41 311.13 369.99 37.50 65.41 311.13 37.51 65.41 311.13 37.52 65.41 311.13 37.53 65.41 311.13 37.54 65.41 311.13 37.55 65.41 311.13 37.56 65.41 311.13 37.57 65.41 311.13 369.99 415.30 37.58 65.41 311.13 369.99 415.30 37.59 65.41 369.99 415.30 37.60 65.41 369.99 415.30 37.61 65.41 369.99 415.30 37.62 65.41 369.99 37.63 65.41 369.99 37.64 65.41 369.99 37.65 65.41 369.99 37.66 65.41 369.99 37.67 65.41 369.99 37.68 65.41 369.99 37.69 369.99 415.30 37.70 369.99 415.30 37.71 369.99 415.30 37.72 369.99 415.30 37.73 415.30 37.74 415.30 37.75 415.30 37.76 415.30 37.77 415.30 37.78 207.65 415.30 37.79 207.65 415.30 37.80 207.65 415.30 37.81 207.65 415.30 37.82 207.65 415.30 37.83 207.65 415.30 37.84 207.65 415.30 37.85 207.65 415.30 37.86 207.65 415.30 37.87 207.65 415.30 37.88 207.65 415.30 37.89 207.65 415.30 37.90 207.65 415.30 37.91 207.65 415.30 37.92 207.65 415.30 37.93 207.65 415.30 37.94 207.65 415.30 37.95 207.65 415.30 37.96 207.65 415.30 37.97 207.65 415.30 37.98 207.65 415.30 37.99 207.65 415.30 38.00 207.65 415.30 38.01 207.65 415.30 38.02 207.65 415.30 38.03 207.65 415.30 38.04 207.65 415.30 38.05 207.65 415.30 38.06 207.65 415.30 38.07 207.65 415.30 38.08 207.65 415.30 38.09 207.65 415.30 38.10 207.65 415.30 38.11 207.65 415.30 38.12 207.65 415.30 38.13 207.65 415.30 38.14 207.65 415.30 38.15 207.65 415.30 38.16 207.65 415.30 38.17 207.65 415.30 38.18 207.65 415.30 38.19 207.65 415.30 38.20 207.65 415.30 38.21 207.65 415.30 38.22 415.30 38.23 415.30 38.24 415.30 38.25 415.30 38.26 415.30 38.27 311.13 415.30 38.28 311.13 415.30 38.29 311.13 415.30 38.30 311.13 415.30 38.31 311.13 415.30 38.32 311.13 415.30 38.33 311.13 415.30 38.34 207.65 311.13 415.30 38.35 207.65 311.13 415.30 38.36 207.65 311.13 415.30 38.37 207.65 311.13 415.30 38.38 207.65 311.13 415.30 38.39 207.65 311.13 415.30 38.40 207.65 311.13 415.30 38.41 207.65 311.13 415.30 38.42 207.65 311.13 415.30 38.43 207.65 311.13 415.30 38.44 207.65 311.13 415.30 38.45 207.65 311.13 415.30 38.46 207.65 311.13 415.30 38.47 207.65 311.13 415.30 38.48 207.65 311.13 415.30 38.49 207.65 311.13 415.30 38.50 207.65 311.13 415.30 38.51 207.65 311.13 415.30 38.52 311.13 415.30 38.53 311.13 415.30 38.54 311.13 415.30 38.55 311.13 415.30 38.56 311.13 415.30 38.57 311.13 415.30 38.58 311.13 415.30 38.59 311.13 415.30 38.60 311.13 415.30 38.61 311.13 415.30 38.62 311.13 415.30 38.63 311.13 415.30 38.64 311.13 415.30 38.65 311.13 415.30 38.66 311.13 415.30 38.67 311.13 415.30 38.68 311.13 415.30 38.69 311.13 415.30 38.70 311.13 415.30 38.71 311.13 415.30 38.72 311.13 415.30 38.73 311.13 415.30 38.74 311.13 369.99 415.30 38.75 311.13 369.99 415.30 38.76 311.13 369.99 415.30 38.77 311.13 369.99 415.30 38.78 311.13 369.99 415.30 38.79 311.13 369.99 415.30 38.80 311.13 369.99 415.30 38.81 311.13 369.99 415.30 38.82 311.13 369.99 415.30 38.83 311.13 369.99 415.30 38.84 311.13 369.99 415.30 38.85 311.13 369.99 415.30 38.86 311.13 369.99 415.30 38.87 311.13 369.99 415.30 38.88 311.13 369.99 415.30 38.89 311.13 369.99 415.30 38.90 311.13 369.99 415.30 38.91 311.13 369.99 415.30 38.92 369.99 415.30 38.93 369.99 415.30 38.94 369.99 415.30 38.95 369.99 415.30 38.96 369.99 415.30 38.97 369.99 415.30 38.98 369.99 415.30 38.99 369.99 415.30 39.00 369.99 415.30 39.01 369.99 415.30 39.02 369.99 415.30 39.03 415.30 39.04 415.30 39.05 415.30 39.06 369.99 415.30 39.07 369.99 415.30 39.08 369.99 415.30 39.09 369.99 415.30 39.10 369.99 415.30 39.11 369.99 415.30 39.12 369.99 415.30 39.13 369.99 415.30 39.14 369.99 415.30 39.15 369.99 415.30 39.16 369.99 415.30 39.17 369.99 415.30 39.18 369.99 415.30 39.19 369.99 415.30 39.20 369.99 415.30 39.21 369.99 415.30 39.22 369.99 415.30 39.23 369.99 415.30 39.24 369.99 415.30 39.25 369.99 415.30 39.26 369.99 415.30 39.27 369.99 415.30 39.28 369.99 415.30 39.29 369.99 415.30 39.30 369.99 415.30 39.31 369.99 415.30 39.32 369.99 415.30 39.33 369.99 415.30 39.34 369.99 415.30 39.35 369.99 415.30 39.36 415.30 39.37 415.30 39.38 415.30 39.39 415.30 39.40 415.30 39.41 415.30 39.42 415.30 39.43 415.30 39.44 415.30 39.45 415.30 39.46 415.30 39.47 415.30 39.48 69.30 415.30 39.49 69.30 415.30 39.50 69.30 138.59 207.65 415.30 39.51 69.30 138.59 207.65 415.30 39.52 69.30 138.59 207.65 415.30 39.53 69.30 138.59 207.65 415.30 39.54 69.30 138.59 207.65 415.30 39.55 69.30 138.59 207.65 415.30 39.56 69.30 138.59 207.65 415.30 39.57 69.30 138.59 207.65 415.30 39.58 69.30 138.59 207.65 415.30 39.59 69.30 138.59 207.65 415.30 39.60 69.30 138.59 207.65 415.30 39.61 69.30 138.59 207.65 415.30 39.62 69.30 138.59 207.65 415.30 39.63 69.30 138.59 207.65 415.30 39.64 69.30 138.59 207.65 415.30 39.65 69.30 138.59 207.65 415.30 39.66 69.30 138.59 207.65 415.30 39.67 69.30 138.59 207.65 415.30 39.68 69.30 138.59 207.65 415.30 39.69 69.30 138.59 207.65 415.30 39.70 69.30 138.59 207.65 415.30 39.71 69.30 138.59 207.65 415.30 39.72 69.30 138.59 207.65 415.30 39.73 69.30 138.59 207.65 415.30 39.74 69.30 138.59 207.65 415.30 39.75 69.30 138.59 415.30 39.76 69.30 138.59 415.30 39.77 69.30 138.59 415.30 39.78 69.30 138.59 415.30 39.79 69.30 138.59 415.30 39.80 69.30 415.30 39.81 69.30 415.30 39.82 69.30 415.30 39.83 69.30 415.30 39.84 69.30 415.30 39.85 69.30 415.30 39.86 69.30 415.30 39.87 69.30 277.18 415.30 39.88 69.30 277.18 415.30 39.89 69.30 277.18 415.30 39.90 69.30 277.18 415.30 39.91 69.30 277.18 415.30 39.92 69.30 277.18 415.30 39.93 69.30 277.18 415.30 39.94 69.30 415.30 39.95 69.30 415.30 39.96 69.30 415.30 39.97 69.30 415.30 39.98 69.30 415.30 39.99 69.30 415.30 40.00 69.30 415.30 40.01 69.30 415.30 40.02 69.30 415.30 40.03 69.30 415.30 40.04 69.30 138.59 415.30 40.05 69.30 138.59 415.30 40.06 69.30 138.59 415.30 40.07 69.30 138.59 415.30 40.08 69.30 138.59 415.30 40.09 69.30 138.59 415.30 40.10 69.30 138.59 415.30 40.11 69.30 138.59 415.30 40.12 69.30 138.59 415.30 40.13 69.30 138.59 415.30 40.14 69.30 138.59 415.30 40.15 69.30 138.59 415.30 40.16 69.30 138.59 415.30 40.17 69.30 138.59 415.30 40.18 69.30 138.59 277.18 415.30 40.19 69.30 138.59 277.18 415.30 40.20 69.30 138.59 277.18 415.30 40.21 69.30 138.59 277.18 415.30 40.22 69.30 138.59 277.18 415.30 40.23 69.30 138.59 277.18 415.30 40.24 69.30 138.59 277.18 415.30 40.25 69.30 138.59 277.18 415.30 40.26 69.30 138.59 277.18 415.30 40.27 69.30 138.59 277.18 415.30 40.28 69.30 138.59 277.18 415.30 40.29 69.30 277.18 415.30 40.30 69.30 277.18 415.30 40.31 69.30 277.18 415.30 40.32 69.30 277.18 415.30 40.33 69.30 277.18 415.30 40.34 69.30 277.18 415.30 40.35 69.30 277.18 415.30 40.36 69.30 277.18 415.30 40.37 69.30 277.18 415.30 40.38 69.30 277.18 415.30 40.39 69.30 277.18 415.30 40.40 69.30 277.18 415.30 40.41 69.30 277.18 415.30 40.42 69.30 277.18 415.30 40.43 69.30 277.18 415.30 40.44 69.30 277.18 415.30 40.45 69.30 277.18 415.30 40.46 69.30 277.18 415.30 40.47 69.30 277.18 415.30 40.48 69.30 277.18 415.30 40.49 69.30 277.18 415.30 40.50 69.30 277.18 415.30 40.51 69.30 277.18 415.30 40.52 69.30 277.18 415.30 40.53 69.30 277.18 415.30 40.54 69.30 277.18 415.30 40.55 69.30 277.18 415.30 40.56 69.30 277.18 415.30 40.57 69.30 277.18 415.30 40.58 69.30 277.18 415.30 40.59 69.30 277.18 329.63 415.30 40.60 69.30 277.18 329.63 415.30 40.61 69.30 277.18 329.63 415.30 40.62 69.30 277.18 329.63 415.30 40.63 69.30 277.18 329.63 415.30 40.64 69.30 277.18 329.63 415.30 40.65 69.30 277.18 329.63 415.30 40.66 69.30 277.18 329.63 415.30 40.67 69.30 277.18 329.63 415.30 40.68 69.30 277.18 329.63 415.30 40.69 69.30 277.18 329.63 415.30 40.70 69.30 277.18 329.63 415.30 40.71 69.30 277.18 329.63 415.30 40.72 69.30 277.18 329.63 415.30 40.73 69.30 277.18 329.63 415.30 40.74 69.30 277.18 329.63 415.30 40.75 69.30 277.18 329.63 415.30 40.76 69.30 277.18 329.63 415.30 40.77 69.30 277.18 329.63 415.30 40.78 69.30 277.18 329.63 415.30 40.79 69.30 277.18 329.63 415.30 40.80 69.30 277.18 329.63 415.30 40.81 69.30 277.18 329.63 415.30 40.82 69.30 277.18 329.63 415.30 40.83 69.30 277.18 329.63 415.30 40.84 69.30 277.18 329.63 415.30 40.85 277.18 329.63 415.30 40.86 277.18 329.63 415.30 40.87 277.18 329.63 415.30 40.88 277.18 329.63 415.30 40.89 277.18 329.63 415.30 40.90 277.18 329.63 415.30 40.91 277.18 329.63 415.30 40.92 69.30 277.18 329.63 415.30 40.93 69.30 277.18 329.63 415.30 40.94 69.30 277.18 329.63 415.30 40.95 69.30 277.18 329.63 415.30 40.96 69.30 277.18 329.63 415.30 40.97 69.30 277.18 329.63 415.30 40.98 69.30 277.18 329.63 415.30 40.99 69.30 277.18 329.63 415.30 41.00 69.30 277.18 329.63 415.30 41.01 277.18 329.63 415.30 41.02 277.18 329.63 415.30 41.03 207.65 277.18 329.63 415.30 41.04 207.65 277.18 329.63 415.30 41.05 207.65 277.18 415.30 41.06 207.65 277.18 415.30 41.07 207.65 277.18 415.30 41.08 207.65 277.18 41.09 207.65 277.18 41.10 207.65 277.18 41.11 207.65 277.18 41.12 207.65 277.18 41.13 207.65 277.18 41.14 207.65 277.18 41.15 207.65 277.18 41.16 207.65 277.18 41.17 207.65 277.18 41.18 207.65 277.18 41.19 207.65 277.18 41.20 207.65 277.18 41.21 207.65 277.18 41.22 207.65 277.18 41.23 207.65 277.18 41.24 207.65 277.18 41.25 207.65 277.18 41.26 207.65 277.18 41.27 207.65 277.18 41.28 207.65 277.18 41.29 207.65 277.18 41.30 207.65 277.18 41.31 207.65 277.18 41.32 207.65 277.18 41.33 207.65 277.18 41.34 207.65 277.18 41.35 207.65 277.18 41.36 207.65 277.18 41.37 207.65 277.18 41.38 207.65 277.18 41.39 207.65 277.18 41.40 207.65 277.18 41.41 207.65 277.18 41.42 207.65 277.18 41.43 207.65 277.18 41.44 207.65 277.18 41.45 207.65 277.18 41.46 207.65 277.18 41.47 207.65 277.18 41.48 207.65 277.18 41.49 207.65 277.18 41.50 207.65 277.18 41.51 207.65 277.18 41.52 207.65 277.18 41.53 207.65 277.18 41.54 207.65 277.18 41.55 207.65 277.18 41.56 207.65 277.18 41.57 207.65 277.18 41.58 207.65 277.18 41.59 207.65 277.18 41.60 207.65 277.18 41.61 207.65 277.18 41.62 207.65 277.18 41.63 207.65 277.18 41.64 207.65 277.18 41.65 207.65 277.18 41.66 277.18 41.67 277.18 41.68 277.18 41.69 277.18 41.70 277.18 41.71 277.18 41.72 277.18 41.73 277.18 41.74 277.18 41.75 207.65 277.18 41.76 207.65 277.18 41.77 207.65 277.18 41.78 207.65 277.18 41.79 207.65 277.18 41.80 207.65 277.18 41.81 207.65 277.18 41.82 207.65 277.18 41.83 207.65 277.18 41.84 207.65 277.18 41.85 207.65 277.18 41.86 207.65 277.18 41.87 207.65 277.18 41.88 207.65 277.18 41.89 207.65 277.18 41.90 207.65 277.18 41.91 207.65 277.18 41.92 207.65 277.18 41.93 207.65 277.18 41.94 207.65 277.18 41.95 207.65 277.18 41.96 207.65 277.18 41.97 207.65 277.18 41.98 207.65 277.18 41.99 207.65 277.18 42.00 207.65 277.18 42.01 207.65 277.18 329.63 42.02 207.65 277.18 329.63 42.03 207.65 277.18 329.63 42.04 207.65 277.18 329.63 42.05 207.65 277.18 329.63 42.06 207.65 277.18 329.63 42.07 207.65 277.18 329.63 42.08 207.65 277.18 329.63 42.09 207.65 277.18 329.63 42.10 207.65 277.18 329.63 42.11 207.65 277.18 329.63 42.12 207.65 277.18 329.63 42.13 207.65 277.18 329.63 42.14 207.65 277.18 329.63 42.15 207.65 277.18 329.63 42.16 207.65 277.18 329.63 42.17 207.65 277.18 329.63 42.18 207.65 277.18 329.63 42.19 207.65 277.18 329.63 42.20 207.65 277.18 329.63 42.21 207.65 277.18 329.63 42.22 207.65 277.18 329.63 42.23 207.65 277.18 329.63 42.24 207.65 277.18 329.63 42.25 207.65 277.18 329.63 42.26 207.65 277.18 329.63 42.27 207.65 277.18 329.63 42.28 207.65 277.18 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329.63 440.00 42.65 207.65 220.00 329.63 440.00 42.66 92.50 220.00 329.63 440.00 42.67 92.50 220.00 329.63 440.00 42.68 92.50 220.00 440.00 42.69 92.50 220.00 440.00 42.70 92.50 220.00 440.00 42.71 92.50 220.00 329.63 440.00 42.72 92.50 220.00 329.63 440.00 42.73 46.25 92.50 220.00 329.63 440.00 42.74 46.25 92.50 220.00 329.63 440.00 42.75 46.25 92.50 220.00 329.63 440.00 42.76 46.25 92.50 220.00 329.63 440.00 42.77 46.25 92.50 220.00 329.63 440.00 42.78 46.25 92.50 220.00 329.63 440.00 42.79 46.25 92.50 220.00 329.63 440.00 42.80 46.25 92.50 220.00 440.00 42.81 46.25 92.50 220.00 440.00 42.82 46.25 92.50 220.00 440.00 42.83 46.25 92.50 220.00 440.00 42.84 46.25 92.50 220.00 440.00 42.85 46.25 92.50 220.00 440.00 42.86 46.25 92.50 220.00 440.00 42.87 46.25 92.50 220.00 440.00 42.88 46.25 92.50 220.00 440.00 42.89 46.25 92.50 220.00 440.00 42.90 46.25 92.50 220.00 440.00 42.91 46.25 220.00 440.00 42.92 46.25 220.00 440.00 42.93 46.25 220.00 440.00 42.94 46.25 220.00 440.00 42.95 46.25 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440.00 43.26 46.25 92.50 220.00 277.18 440.00 43.27 46.25 92.50 220.00 277.18 440.00 43.28 46.25 92.50 220.00 277.18 440.00 43.29 46.25 92.50 220.00 277.18 440.00 43.30 46.25 92.50 220.00 277.18 440.00 43.31 46.25 92.50 220.00 277.18 440.00 43.32 46.25 92.50 220.00 277.18 440.00 43.33 46.25 92.50 220.00 277.18 440.00 43.34 46.25 92.50 220.00 277.18 440.00 43.35 46.25 92.50 220.00 277.18 440.00 43.36 46.25 92.50 220.00 277.18 440.00 43.37 46.25 92.50 220.00 277.18 440.00 43.38 46.25 92.50 220.00 277.18 440.00 43.39 46.25 92.50 220.00 277.18 440.00 43.40 46.25 92.50 220.00 277.18 440.00 43.41 46.25 92.50 220.00 277.18 440.00 43.42 46.25 92.50 220.00 440.00 43.43 46.25 92.50 220.00 440.00 43.44 46.25 92.50 220.00 440.00 43.45 46.25 92.50 220.00 440.00 43.46 46.25 92.50 220.00 440.00 43.47 46.25 92.50 440.00 43.48 46.25 92.50 440.00 43.49 46.25 92.50 440.00 43.50 46.25 92.50 440.00 43.51 46.25 92.50 440.00 43.52 46.25 92.50 440.00 43.53 46.25 92.50 440.00 43.54 46.25 92.50 440.00 43.55 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124.905 311.127 15.79 124.905 311.127 15.8 124.905 314.742 15.81 124.905 314.742 15.82 124.905 314.742 15.83 124.905 314.742 15.84 124.905 314.742 15.85 124.905 314.742 15.86 124.905 314.742 15.87 124.905 314.742 15.88 124.905 15.89 124.905 15.9 124.905 15.91 124.905 15.92 124.905 15.93 124.905 15.94 124.905 15.95 124.905 15.96 124.905 314.742 15.97 124.905 314.742 15.98 124.905 314.742 15.99 124.905 314.742 16.0 124.905 314.742 16.01 124.905 314.742 16.02 124.905 314.742 16.03 124.905 314.742 16.04 126.357 314.742 16.05 126.357 314.742 16.06 126.357 314.742 16.07 126.357 314.742 16.08 126.357 311.127 16.09 126.357 311.127 16.1 124.905 311.127 16.11 124.905 311.127 16.12 124.905 311.127 16.13 124.905 311.127 16.14 124.905 314.742 16.15 124.905 314.742 16.16 124.905 314.742 16.17 124.905 314.742 16.18 124.905 314.742 16.19 124.905 314.742 16.2 124.905 314.742 16.21 124.905 314.742 16.22 124.905 314.742 16.23 124.905 314.742 16.24 124.905 314.742 16.25 124.905 314.742 16.26 124.905 314.742 16.27 124.905 314.742 16.28 124.905 314.742 16.29 124.905 314.742 16.3 124.905 307.553 16.31 124.905 307.553 16.32 124.905 16.33 124.905 16.34 124.905 16.35 124.905 16.36 318.399 124.905 16.37 318.399 124.905 16.38 318.399 124.905 16.39 318.399 124.905 16.4 318.399 124.905 16.41 318.399 124.905 16.42 124.905 314.742 16.43 124.905 314.742 16.44 124.905 314.742 16.45 124.905 314.742 16.46 124.905 314.742 16.47 124.905 314.742 16.48 124.905 314.742 16.49 124.905 314.742 16.5 124.905 314.742 16.51 124.905 314.742 16.52 124.905 314.742 16.53 124.905 314.742 16.54 83.3644 124.905 314.742 16.55 83.3644 124.905 314.742 16.56 83.3644 124.905 314.742 16.57 83.3644 124.905 314.742 16.58 83.3644 124.905 16.59 83.3644 124.905 16.6 83.3644 420.13 16.61 83.3644 420.13 16.62 83.3644 420.13 16.63 83.3644 420.13 16.64 83.3644 420.13 16.65 83.3644 420.13 16.66 83.3644 420.13 16.67 83.3644 420.13 16.68 83.3644 420.13 16.69 83.3644 420.13 16.7 83.3644 425.012 16.71 83.3644 425.012 16.72 83.3644 425.012 16.73 83.3644 425.012 16.74 83.3644 425.012 16.75 83.3644 425.012 16.76 83.3644 420.13 16.77 83.3644 420.13 16.78 83.3644 420.13 16.79 83.3644 420.13 16.8 83.3644 420.13 16.81 83.3644 420.13 16.82 83.3644 420.13 16.83 83.3644 420.13 16.84 83.3644 420.13 16.85 83.3644 420.13 16.86 83.3644 420.13 16.87 83.3644 420.13 16.88 83.3644 420.13 16.89 83.3644 420.13 16.9 83.3644 420.13 16.91 83.3644 420.13 16.92 83.3644 420.13 16.93 83.3644 420.13 16.94 83.3644 420.13 840.261 16.95 83.3644 420.13 840.261 16.96 420.13 840.261 16.97 420.13 840.261 16.98 420.13 840.261 16.99 420.13 840.261 17.0 83.3644 850.024 420.13 17.01 83.3644 850.024 420.13 17.02 83.3644 850.024 420.13 17.03 83.3644 850.024 420.13 17.04 83.3644 850.024 420.13 17.05 83.3644 850.024 420.13 17.06 83.3644 420.13 17.07 83.3644 420.13 17.08 83.3644 420.13 17.09 83.3644 420.13 17.1 83.3644 420.13 840.261 17.11 83.3644 420.13 840.261 17.12 83.3644 420.13 840.261 17.13 83.3644 420.13 840.261 17.14 83.3644 420.13 840.261 17.15 83.3644 420.13 840.261 17.16 83.3644 420.13 840.261 17.17 83.3644 420.13 840.261 17.18 83.3644 830.609 420.13 17.19 83.3644 830.609 420.13 17.2 83.3644 830.609 420.13 17.21 83.3644 830.609 420.13 17.22 83.3644 420.13 17.23 83.3644 420.13 17.24 83.3644 420.13 17.25 83.3644 420.13 17.26 83.3644 420.13 17.27 83.3644 420.13 17.28 83.3644 420.13 17.29 83.3644 420.13 17.3 83.3644 420.13 17.31 83.3644 420.13 17.32 83.3644 420.13 17.33 83.3644 420.13 17.34 83.3644 420.13 17.35 83.3644 420.13 17.36 83.3644 425.012 17.37 83.3644 425.012 17.38 83.3644 425.012 17.39 83.3644 425.012 17.4 83.3644 425.012 840.261 17.41 83.3644 425.012 840.261 17.42 83.3644 425.012 840.261 17.43 83.3644 425.012 840.261 17.44 840.261 17.45 840.261 17.46 850.024 17.47 850.024 17.48 850.024 17.49 850.024 17.5 850.024 17.51 850.024 17.52 17.53 17.54 374.294 17.55 374.294 17.56 374.294 17.57 374.294 17.58 374.294 17.59 374.294 17.6 78.6855 374.294 17.61 78.6855 374.294 17.62 78.6855 374.294 17.63 78.6855 374.294 17.64 78.6855 374.294 17.65 78.6855 374.294 17.66 78.6855 374.294 17.67 78.6855 374.294 17.68 78.6855 374.294 17.69 78.6855 374.294 17.7 78.6855 369.994 17.71 78.6855 369.994 17.72 78.6855 369.994 17.73 78.6855 369.994 17.74 78.6855 369.994 17.75 78.6855 369.994 17.76 78.6855 374.294 17.77 78.6855 374.294 17.78 78.6855 374.294 17.79 78.6855 374.294 17.8 78.6855 374.294 17.81 78.6855 374.294 17.82 78.6855 374.294 17.83 78.6855 374.294 17.84 78.6855 374.294 17.85 78.6855 374.294 17.86 78.6855 369.994 17.87 78.6855 369.994 17.88 78.6855 369.994 17.89 78.6855 369.994 17.9 78.6855 369.994 17.91 78.6855 369.994 17.92 78.6855 17.93 78.6855 17.94 78.6855 374.294 17.95 78.6855 374.294 17.96 78.6855 374.294 17.97 78.6855 374.294 17.98 78.6855 374.294 17.99 78.6855 374.294 18.0 78.6855 374.294 18.01 78.6855 374.294 18.02 78.6855 369.994 18.03 78.6855 369.994 18.04 78.6855 369.994 18.05 78.6855 369.994 18.06 78.6855 369.994 18.07 78.6855 369.994 18.08 78.6855 369.994 18.09 78.6855 369.994 18.1 78.6855 374.294 18.11 78.6855 374.294 18.12 78.6855 374.294 18.13 78.6855 374.294 18.14 374.294 249.811 18.15 374.294 249.811 18.16 374.294 249.811 18.17 374.294 249.811 18.18 374.294 249.811 18.19 374.294 249.811 18.2 249.811 369.994 18.21 249.811 369.994 18.22 249.811 369.994 18.23 249.811 369.994 18.24 249.811 369.994 18.25 249.811 369.994 18.26 374.294 18.27 374.294 18.28 374.294 18.29 374.294 18.3 374.294 18.31 374.294 18.32 374.294 18.33 374.294 18.34 374.294 18.35 374.294 18.36 374.294 18.37 374.294 18.38 374.294 18.39 374.294 18.4 374.294 18.41 374.294 18.42 374.294 18.43 374.294 18.44 124.905 374.294 18.45 124.905 374.294 18.46 124.905 374.294 18.47 124.905 374.294 18.48 378.643 124.905 18.49 378.643 124.905 18.5 124.905 18.51 124.905 18.52 124.905 314.742 18.53 124.905 314.742 18.54 124.905 314.742 18.55 124.905 314.742 18.56 124.905 314.742 18.57 124.905 314.742 18.58 124.905 314.742 18.59 124.905 314.742 18.6 124.905 314.742 18.61 124.905 314.742 18.62 124.905 314.742 18.63 124.905 314.742 18.64 318.399 124.905 18.65 318.399 124.905 18.66 124.905 18.67 124.905 18.68 124.905 18.69 124.905 18.7 124.905 18.71 124.905 18.72 124.905 18.73 124.905 18.74 124.905 18.75 124.905 18.76 124.905 18.77 124.905 18.78 124.905 18.79 124.905 18.8 124.905 18.81 124.905 18.82 124.905 420.13 18.83 124.905 420.13 18.84 420.13 123.471 18.85 420.13 123.471 18.86 420.13 123.471 18.87 420.13 123.471 18.88 420.13 123.471 18.89 420.13 123.471 18.9 124.905 420.13 18.91 124.905 420.13 18.92 124.905 420.13 18.93 124.905 420.13 18.94 124.905 314.742 18.95 124.905 314.742 18.96 124.905 314.742 18.97 124.905 314.742 18.98 314.742 123.471 18.99 314.742 123.471 19.0 314.742 123.471 19.01 314.742 123.471 19.02 314.742 123.471 19.03 314.742 123.471 19.04 314.742 123.471 19.05 314.742 123.471 19.06 311.127 123.471 19.07 311.127 123.471 19.08 124.905 311.127 19.09 124.905 311.127 19.1 124.905 311.127 19.11 124.905 311.127 19.12 124.905 19.13 124.905 19.14 124.905 19.15 124.905 19.16 124.905 19.17 124.905 19.18 124.905 19.19 124.905 19.2 124.905 19.21 124.905 19.22 124.905 19.23 124.905 19.24 124.905 19.25 124.905 19.26 124.905 19.27 124.905 19.28 124.905 19.29 124.905 19.3 124.905 19.31 124.905 19.32 123.471 19.33 123.471 19.34 123.471 19.35 123.471 19.36 123.471 19.37 123.471 19.38 124.905 19.39 124.905 19.4 124.905 19.41 124.905 19.42 19.43 19.44 19.45 19.46 277.183 19.47 277.183 19.48 277.183 19.49 277.183 19.5 277.183 19.51 277.183 19.52 277.183 19.53 277.183 19.54 277.183 19.55 277.183 19.56 283.661 420.13 19.57 283.661 420.13 19.58 420.13 19.59 420.13 19.6 333.458 420.13 19.61 333.458 420.13 19.62 333.458 415.305 19.63 333.458 415.305 19.64 333.458 415.305 19.65 333.458 415.305 19.66 333.458 415.305 19.67 333.458 415.305 19.68 333.458 425.012 19.69 333.458 425.012 19.7 333.458 425.012 19.71 333.458 425.012 19.72 333.458 425.012 19.73 333.458 425.012 19.74 333.458 415.305 19.75 333.458 415.305 19.76 329.628 415.305 19.77 329.628 415.305 19.78 415.305 19.79 415.305 19.8 420.13 19.81 420.13 19.82 420.13 19.83 420.13 19.84 333.458 420.13 19.85 333.458 420.13 19.86 333.458 425.012 19.87 333.458 425.012 19.88 333.458 425.012 19.89 333.458 425.012 19.9 333.458 425.012 19.91 333.458 425.012 19.92 333.458 415.305 19.93 333.458 415.305 19.94 252.714 329.628 415.305 19.95 252.714 329.628 415.305 19.96 252.714 329.628 415.305 19.97 252.714 329.628 415.305 19.98 252.714 329.628 420.13 19.99 252.714 329.628 420.13 20.0 333.458 252.714 420.13 666.915 20.01 333.458 252.714 420.13 666.915 20.02 333.458 252.714 420.13 666.915 20.03 333.458 252.714 420.13 666.915 20.04 249.811 420.13 666.915 20.05 249.811 420.13 666.915 20.06 333.458 651.683 249.811 420.13 20.07 333.458 651.683 249.811 420.13 20.08 333.458 651.683 249.811 420.13 20.09 333.458 651.683 249.811 420.13 20.1 333.458 651.683 249.811 420.13 20.11 333.458 651.683 249.811 420.13 20.12 659.255 333.458 252.714 420.13 20.13 659.255 333.458 252.714 420.13 20.14 659.255 333.458 252.714 420.13 20.15 659.255 333.458 252.714 420.13 20.16 659.255 333.458 420.13 212.506 20.17 659.255 333.458 420.13 212.506 20.18 333.458 425.012 666.915 212.506 20.19 333.458 425.012 666.915 212.506 20.2 333.458 425.012 666.915 212.506 20.21 333.458 425.012 666.915 212.506 20.22 333.458 425.012 210.065 666.915 20.23 333.458 425.012 210.065 666.915 20.24 333.458 210.065 666.915 420.13 20.25 333.458 210.065 666.915 420.13 20.26 333.458 210.065 420.13 20.27 333.458 210.065 420.13 20.28 333.458 420.13 20.29 333.458 420.13 20.3 333.458 420.13 20.31 333.458 420.13 20.32 1333.83 333.458 420.13 20.33 1333.83 333.458 420.13 20.34 1333.83 333.458 420.13 20.35 1333.83 333.458 420.13 20.36 1333.83 333.458 425.012 20.37 1333.83 333.458 425.012 20.38 1333.83 20.39 1333.83 20.4 1333.83 425.012 20.41 1333.83 425.012 20.42 425.012 1349.33 20.43 425.012 1349.33 20.44 425.012 20.45 425.012 20.46 420.13 20.47 420.13 20.48 420.13 20.49 420.13 20.5 420.13 20.51 420.13 20.52 425.012 20.53 425.012 20.54 425.012 20.55 425.012 20.56 425.012 20.57 425.012 20.58 425.012 20.59 425.012 20.6 420.13 20.61 420.13 20.62 420.13 20.63 420.13 20.64 420.13 20.65 420.13 20.66 420.13 20.67 420.13 20.68 425.012 20.69 425.012 20.7 425.012 20.71 425.012 20.72 425.012 20.73 425.012 20.74 420.13 20.75 420.13 20.76 420.13 20.77 420.13 20.78 420.13 20.79 420.13 20.8 420.13 20.81 420.13 20.82 420.13 20.83 420.13 20.84 425.012 20.85 425.012 20.86 425.012 20.87 425.012 20.88 425.012 20.89 425.012 20.9 277.183 420.13 20.91 277.183 420.13 20.92 277.183 420.13 20.93 277.183 420.13 20.94 277.183 420.13 20.95 277.183 420.13 20.96 280.403 420.13 20.97 280.403 420.13 20.98 280.403 420.13 20.99 280.403 420.13 21.0 280.403 420.13 21.01 280.403 420.13 21.02 280.403 420.13 21.03 280.403 420.13 21.04 280.403 420.13 21.05 280.403 420.13 21.06 280.403 21.07 280.403 21.08 280.403 21.09 280.403 21.1 280.403 21.11 280.403 21.12 280.403 21.13 280.403 21.14 280.403 21.15 280.403 21.16 280.403 21.17 280.403 21.18 280.403 21.19 280.403 21.2 280.403 21.21 280.403 21.22 280.403 21.23 280.403 21.24 280.403 21.25 280.403 21.26 280.403 21.27 280.403 21.28 280.403 21.29 280.403 21.3 280.403 420.13 21.31 280.403 420.13 21.32 280.403 420.13 21.33 280.403 420.13 21.34 280.403 420.13 21.35 280.403 420.13 21.36 420.13 21.37 420.13 21.38 420.13 21.39 420.13 21.4 425.012 21.41 425.012 21.42 425.012 21.43 425.012 21.44 21.45 21.46 233.082 21.47 233.082 21.48 233.082 21.49 233.082 21.5 233.082 21.51 233.082 21.52 233.082 21.53 233.082 21.54 233.082 21.55 233.082 21.56 233.082 21.57 233.082 21.58 230.405 21.59 230.405 21.6 230.405 21.61 230.405 21.62 230.405 21.63 230.405 21.64 233.082 21.65 233.082 21.66 233.082 21.67 233.082 21.68 233.082 21.69 233.082 21.7 233.082 21.71 233.082 21.72 233.082 21.73 233.082 21.74 233.082 21.75 233.082 21.76 233.082 21.77 233.082 21.78 233.082 21.79 233.082 21.8 83.3644 233.082 21.81 83.3644 233.082 21.82 83.3644 233.082 21.83 83.3644 233.082 21.84 83.3644 235.79 21.85 83.3644 235.79 21.86 235.79 84.3331 21.87 235.79 84.3331 21.88 235.79 84.3331 21.89 235.79 84.3331 21.9 84.3331 233.082 21.91 84.3331 233.082 21.92 369.994 84.3331 233.082 21.93 369.994 84.3331 233.082 21.94 369.994 84.3331 233.082 21.95 369.994 84.3331 233.082 21.96 369.994 233.082 21.97 369.994 233.082 21.98 369.994 233.082 21.99 369.994 233.082 22.0 374.294 233.082 22.01 374.294 233.082 22.02 374.294 233.082 22.03 374.294 233.082 22.04 374.294 233.082 22.05 374.294 233.082 22.06 374.294 233.082 22.07 374.294 233.082 22.08 369.994 233.082 22.09 369.994 233.082 22.1 369.994 233.082 22.11 369.994 233.082 22.12 369.994 233.082 22.13 369.994 233.082 22.14 374.294 233.082 22.15 374.294 233.082 22.16 374.294 235.79 22.17 374.294 235.79 22.18 374.294 235.79 22.19 374.294 235.79 22.2 374.294 22.21 374.294 22.22 374.294 22.23 374.294 22.24 369.994 233.082 22.25 369.994 233.082 22.26 369.994 233.082 22.27 369.994 233.082 22.28 369.994 233.082 22.29 369.994 233.082 22.3 369.994 233.082 22.31 369.994 233.082 22.32 374.294 233.082 22.33 374.294 233.082 22.34 374.294 233.082 22.35 374.294 233.082 22.36 233.082 22.37 233.082 22.38 233.082 22.39 233.082 22.4 22.41 22.42 22.43 22.44 78.6855 22.45 78.6855 22.46 78.6855 369.994 22.47 78.6855 369.994 22.48 78.6855 369.994 22.49 78.6855 369.994 22.5 78.6855 369.994 22.51 78.6855 369.994 22.52 78.6855 374.294 22.53 78.6855 374.294 22.54 78.6855 374.294 22.55 78.6855 374.294 22.56 78.6855 374.294 22.57 78.6855 374.294 22.58 78.6855 369.994 22.59 78.6855 369.994 22.6 78.6855 369.994 22.61 78.6855 369.994 22.62 78.6855 369.994 22.63 78.6855 369.994 22.64 78.6855 365.745 22.65 78.6855 365.745 22.66 78.6855 365.745 22.67 78.6855 365.745 22.68 78.6855 365.745 22.69 78.6855 365.745 22.7 78.6855 374.294 22.71 78.6855 374.294 22.72 78.6855 374.294 22.73 78.6855 374.294 22.74 78.6855 374.294 22.75 78.6855 374.294 22.76 78.6855 369.994 22.77 78.6855 369.994 22.78 78.6855 369.994 22.79 78.6855 369.994 22.8 78.6855 369.994 22.81 78.6855 369.994 22.82 78.6855 365.745 22.83 78.6855 365.745 22.84 78.6855 365.745 314.742 22.85 78.6855 365.745 314.742 22.86 78.6855 365.745 314.742 22.87 78.6855 365.745 314.742 22.88 78.6855 314.742 374.294 22.89 78.6855 314.742 374.294 22.9 78.6855 314.742 374.294 22.91 78.6855 314.742 374.294 22.92 78.6855 314.742 374.294 22.93 78.6855 314.742 374.294 22.94 314.742 374.294 22.95 314.742 374.294 22.96 369.994 22.97 369.994 22.98 369.994 249.811 22.99 369.994 249.811 23.0 369.994 249.811 23.01 369.994 249.811 23.02 365.745 249.811 23.03 365.745 249.811 23.04 365.745 249.811 23.05 365.745 249.811 23.06 365.745 249.811 23.07 365.745 249.811 23.08 378.643 249.811 23.09 378.643 249.811 23.1 378.643 249.811 23.11 378.643 249.811 23.12 378.643 249.811 23.13 378.643 249.811 23.14 369.994 23.15 369.994 23.16 23.17 23.18 23.19 23.2 23.21 23.22 23.23 23.24 23.25 23.26 23.27 23.28 23.29 23.3 23.31 23.32 23.33 23.34 23.35 23.36 23.37 23.38 23.39 23.4 23.41 23.42 23.43 23.44 23.45 23.46 23.47 23.48 23.49 23.5 23.51 23.52 314.742 23.53 314.742 23.54 124.905 314.742 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9.42 111.278 529.331 337.332 222.556 252.714 9.43 111.278 529.331 337.332 222.556 252.714 9.44 111.278 529.331 337.332 222.556 9.45 111.278 529.331 337.332 222.556 9.46 111.278 529.331 333.458 222.556 9.47 111.278 529.331 333.458 222.556 9.48 111.278 523.251 333.458 222.556 9.49 111.278 523.251 333.458 222.556 9.5 111.278 523.251 333.458 264.666 222.556 9.51 111.278 523.251 333.458 264.666 222.556 9.52 111.278 523.251 333.458 264.666 222.556 9.53 111.278 523.251 333.458 264.666 222.556 9.54 523.251 333.458 264.666 222.556 9.55 523.251 333.458 264.666 222.556 9.56 529.331 333.458 264.666 222.556 9.57 529.331 333.458 264.666 222.556 9.58 529.331 333.458 264.666 222.556 9.59 529.331 333.458 264.666 222.556 9.6 529.331 333.458 264.666 222.556 9.61 529.331 333.458 264.666 222.556 9.62 529.331 333.458 264.666 222.556 9.63 529.331 333.458 264.666 222.556 9.64 523.251 333.458 264.666 222.556 9.65 523.251 333.458 264.666 222.556 9.66 523.251 264.666 222.556 9.67 523.251 264.666 222.556 9.68 523.251 264.666 222.556 9.69 523.251 264.666 222.556 9.7 523.251 264.666 222.556 9.71 523.251 264.666 222.556 9.72 523.251 264.666 222.556 9.73 523.251 264.666 222.556 9.74 523.251 264.666 222.556 9.75 523.251 264.666 222.556 9.76 523.251 264.666 222.556 9.77 523.251 264.666 222.556 9.78 523.251 264.666 252.714 9.79 523.251 264.666 252.714 9.8 264.666 252.714 9.81 264.666 252.714 9.82 264.666 252.714 9.83 264.666 252.714 9.84 264.666 333.458 252.714 9.85 264.666 333.458 252.714 9.86 267.741 333.458 252.714 9.87 267.741 333.458 252.714 9.88 267.741 333.458 252.714 9.89 267.741 333.458 252.714 9.9 267.741 333.458 252.714 9.91 267.741 333.458 252.714 9.92 264.666 337.332 252.714 9.93 264.666 337.332 252.714 9.94 166.729 264.666 337.332 9.95 166.729 264.666 337.332 9.96 166.729 264.666 9.97 166.729 264.666 9.98 166.729 264.666 9.99 166.729 264.666 10.0 166.729 264.666 10.01 166.729 264.666 10.02 166.729 264.666 10.03 166.729 264.666 10.04 166.729 264.666 10.05 166.729 264.666 10.06 261.626 166.729 10.07 261.626 166.729 10.08 261.626 166.729 10.09 261.626 166.729 10.1 261.626 166.729 10.11 261.626 166.729 10.12 261.626 166.729 10.13 261.626 166.729 10.14 261.626 166.729 10.15 261.626 166.729 10.16 261.626 168.666 10.17 261.626 168.666 10.18 261.626 168.666 10.19 261.626 168.666 10.2 168.666 10.21 168.666 10.22 168.666 10.23 168.666 10.24 168.666 10.25 168.666 10.26 168.666 10.27 168.666 10.28 168.666 10.29 168.666 10.3 168.666 10.31 168.666 10.32 168.666 10.33 168.666 10.34 168.666 10.35 168.666 10.36 168.666 10.37 168.666 10.38 88.3215 168.666 10.39 88.3215 168.666 10.4 88.3215 168.666 10.41 88.3215 168.666 10.42 88.3215 168.666 10.43 88.3215 168.666 10.44 88.3215 168.666 10.45 88.3215 168.666 10.46 88.3215 168.666 10.47 88.3215 168.666 10.48 88.3215 168.666 10.49 88.3215 168.666 10.5 88.3215 10.51 88.3215 10.52 88.3215 353.286 10.53 88.3215 353.286 10.54 88.3215 353.286 10.55 88.3215 353.286 10.56 88.3215 353.286 10.57 88.3215 353.286 10.58 88.3215 353.286 10.59 88.3215 353.286 10.6 88.3215 353.286 10.61 88.3215 353.286 10.62 88.3215 353.286 10.63 88.3215 353.286 10.64 88.3215 176.643 353.286 10.65 88.3215 176.643 353.286 10.66 88.3215 176.643 353.286 10.67 88.3215 176.643 353.286 10.68 88.3215 357.391 176.643 10.69 88.3215 357.391 176.643 10.7 88.3215 357.391 176.643 10.71 88.3215 357.391 176.643 10.72 88.3215 357.391 176.643 10.73 88.3215 357.391 176.643 10.74 88.3215 176.643 353.286 10.75 88.3215 176.643 353.286 10.76 88.3215 176.643 353.286 10.77 88.3215 176.643 353.286 10.78 88.3215 176.643 353.286 10.79 88.3215 176.643 353.286 10.8 88.3215 176.643 353.286 10.81 88.3215 176.643 353.286 10.82 88.3215 176.643 353.286 10.83 88.3215 176.643 353.286 10.84 88.3215 176.643 353.286 10.85 88.3215 176.643 353.286 10.86 88.3215 176.643 353.286 10.87 88.3215 176.643 353.286 10.88 88.3215 176.643 353.286 10.89 88.3215 176.643 353.286 10.9 88.3215 176.643 353.286 10.91 88.3215 176.643 353.286 10.92 88.3215 176.643 353.286 10.93 88.3215 176.643 353.286 10.94 88.3215 176.643 353.286 10.95 88.3215 176.643 353.286 10.96 88.3215 176.643 353.286 10.97 88.3215 176.643 353.286 10.98 112.571 88.3215 176.643 10.99 112.571 88.3215 176.643 11.0 112.571 88.3215 176.643 11.01 112.571 88.3215 176.643 11.02 112.571 88.3215 11.03 112.571 88.3215 11.04 112.571 88.3215 11.05 112.571 88.3215 11.06 112.571 11.07 112.571 11.08 112.571 450.285 11.09 112.571 450.285 11.1 110 450.285 11.11 110 450.285 11.12 110 450.285 11.13 110 450.285 11.14 110 450.285 11.15 110 450.285 11.16 110 450.285 11.17 110 450.285 11.18 445.113 110 11.19 445.113 110 11.2 445.113 264.666 11.21 445.113 264.666 11.22 264.666 11.23 264.666 11.24 264.666 11.25 264.666 11.26 264.666 11.27 264.666 11.28 264.666 11.29 264.666 11.3 264.666 11.31 264.666 11.32 264.666 11.33 264.666 11.34 264.666 11.35 264.666 11.36 264.666 11.37 264.666 11.38 264.666 11.39 264.666 11.4 264.666 11.41 264.666 11.42 264.666 11.43 264.666 11.44 264.666 11.45 264.666 11.46 264.666 535.482 11.47 264.666 535.482 11.48 264.666 535.482 11.49 264.666 535.482 11.5 267.741 535.482 11.51 267.741 535.482 11.52 267.741 529.331 11.53 267.741 529.331 11.54 267.741 529.331 11.55 267.741 529.331 11.56 267.741 529.331 11.57 267.741 529.331 11.58 267.741 83.3644 11.59 267.741 83.3644 11.6 267.741 83.3644 11.61 267.741 83.3644 11.62 267.741 83.3644 11.63 267.741 83.3644 11.64 84.3331 11.65 84.3331 11.66 84.3331 11.67 84.3331 11.68 84.3331 337.332 11.69 84.3331 337.332 11.7 84.3331 337.332 11.71 84.3331 337.332 11.72 84.3331 337.332 11.73 84.3331 337.332 11.74 333.458 84.3331 11.75 333.458 84.3331 11.76 333.458 84.3331 11.77 333.458 84.3331 11.78 333.458 84.3331 11.79 333.458 84.3331 11.8 333.458 84.3331 11.81 333.458 84.3331 11.82 84.3331 337.332 11.83 84.3331 337.332 11.84 84.3331 337.332 11.85 84.3331 337.332 11.86 84.3331 337.332 11.87 84.3331 337.332 11.88 333.458 84.3331 11.89 333.458 84.3331 11.9 333.458 84.3331 11.91 333.458 84.3331 11.92 333.458 84.3331 11.93 333.458 84.3331 11.94 84.3331 11.95 84.3331 11.96 84.3331 11.97 84.3331 11.98 84.3331 11.99 84.3331 12.0 84.3331 12.01 84.3331 12.02 84.3331 12.03 84.3331 12.04 84.3331 12.05 84.3331 12.06 84.3331 12.07 84.3331 12.08 84.3331 12.09 84.3331 12.1 84.3331 12.11 84.3331 12.12 84.3331 12.13 84.3331 12.14 84.3331 12.15 84.3331 12.16 225.142 84.3331 12.17 225.142 84.3331 12.18 225.142 84.3331 12.19 225.142 84.3331 12.2 225.142 84.3331 12.21 225.142 84.3331 12.22 84.3331 222.556 12.23 84.3331 222.556 12.24 84.3331 222.556 12.25 84.3331 222.556 12.26 445.113 84.3331 222.556 12.27 445.113 84.3331 222.556 12.28 445.113 84.3331 222.556 12.29 445.113 84.3331 222.556 12.3 445.113 84.3331 222.556 12.31 445.113 84.3331 222.556 12.32 445.113 222.556 12.33 445.113 222.556 12.34 445.113 267.741 12.35 445.113 267.741 12.36 267.741 450.285 12.37 267.741 450.285 12.38 267.741 12.39 267.741 12.4 264.666 12.41 264.666 12.42 264.666 12.43 264.666 12.44 264.666 12.45 264.666 12.46 264.666 12.47 264.666 12.48 264.666 12.49 264.666 12.5 264.666 12.51 264.666 12.52 264.666 12.53 264.666 12.54 264.666 12.55 264.666 12.56 264.666 12.57 264.666 12.58 264.666 12.59 264.666 12.6 264.666 12.61 264.666 12.62 264.666 12.63 264.666 12.64 264.666 535.482 12.65 264.666 535.482 12.66 264.666 535.482 12.67 264.666 535.482 12.68 264.666 535.482 12.69 264.666 535.482 12.7 264.666 535.482 12.71 264.666 535.482 12.72 529.331 264.666 12.73 529.331 264.666 12.74 529.331 267.741 12.75 529.331 267.741 12.76 529.331 267.741 12.77 529.331 267.741 12.78 267.741 12.79 267.741 12.8 12.81 12.82 12.83 12.84 78.6855 12.85 78.6855 12.86 78.6855 12.87 78.6855 12.88 78.6855 12.89 78.6855 12.9 79.5998 12.91 79.5998 12.92 80.5247 314.742 79.5998 12.93 80.5247 314.742 79.5998 12.94 80.5247 314.742 79.5998 12.95 80.5247 314.742 79.5998 12.96 80.5247 314.742 79.5998 12.97 80.5247 314.742 79.5998 12.98 80.5247 79.5998 311.127 12.99 80.5247 79.5998 311.127 13.0 80.5247 79.5998 311.127 13.01 80.5247 79.5998 311.127 13.02 80.5247 79.5998 311.127 13.03 80.5247 79.5998 311.127 13.04 80.5247 314.742 79.5998 13.05 80.5247 314.742 79.5998 13.06 80.5247 314.742 79.5998 13.07 80.5247 314.742 79.5998 13.08 80.5247 314.742 13.09 80.5247 314.742 13.1 80.5247 314.742 13.11 80.5247 314.742 13.12 80.5247 311.127 13.13 80.5247 311.127 13.14 80.5247 311.127 13.15 80.5247 311.127 13.16 80.5247 311.127 13.17 80.5247 311.127 13.18 80.5247 311.127 13.19 80.5247 311.127 13.2 80.5247 318.399 13.21 80.5247 318.399 13.22 80.5247 318.399 13.23 80.5247 318.399 13.24 80.5247 318.399 13.25 80.5247 318.399 13.26 80.5247 314.742 13.27 80.5247 314.742 13.28 80.5247 314.742 13.29 80.5247 314.742 13.3 80.5247 314.742 13.31 80.5247 314.742 13.32 80.5247 314.742 13.33 80.5247 314.742 13.34 80.5247 314.742 13.35 80.5247 314.742 13.36 80.5247 314.742 13.37 80.5247 314.742 13.38 80.5247 314.742 13.39 80.5247 314.742 13.4 80.5247 314.742 13.41 80.5247 314.742 13.42 80.5247 314.742 13.43 80.5247 314.742 13.44 80.5247 314.742 13.45 80.5247 314.742 13.46 80.5247 314.742 13.47 80.5247 314.742 13.48 80.5247 314.742 13.49 80.5247 314.742 13.5 80.5247 314.742 13.51 80.5247 314.742 13.52 80.5247 311.127 13.53 80.5247 311.127 13.54 80.5247 13.55 80.5247 13.56 80.5247 13.57 80.5247 13.58 80.5247 535.482 13.59 80.5247 535.482 13.6 80.5247 535.482 13.61 80.5247 535.482 13.62 80.5247 535.482 13.63 80.5247 535.482 13.64 535.482 13.65 535.482 13.66 529.331 13.67 529.331 13.68 529.331 13.69 529.331 13.7 529.331 13.71 529.331 13.72 535.482 13.73 535.482 13.74 535.482 374.294 252.714 13.75 535.482 374.294 252.714 13.76 374.294 252.714 13.77 374.294 252.714 13.78 374.294 252.714 13.79 374.294 252.714 13.8 374.294 252.714 13.81 374.294 252.714 13.82 374.294 252.714 13.83 374.294 252.714 13.84 374.294 222.556 252.714 13.85 374.294 222.556 252.714 13.86 374.294 222.556 252.714 13.87 374.294 222.556 252.714 13.88 374.294 222.556 252.714 13.89 374.294 222.556 252.714 13.9 374.294 222.556 252.714 13.91 374.294 222.556 252.714 13.92 374.294 222.556 252.714 13.93 374.294 222.556 252.714 13.94 222.556 252.714 13.95 222.556 252.714 13.96 222.556 252.714 13.97 222.556 252.714 13.98 222.556 252.714 13.99 222.556 252.714 14.0 445.113 222.556 252.714 14.01 445.113 222.556 252.714 14.02 445.113 222.556 252.714 14.03 445.113 222.556 252.714 14.04 445.113 222.556 252.714 14.05 445.113 222.556 252.714 14.06 445.113 222.556 252.714 14.07 445.113 222.556 252.714 14.08 445.113 222.556 252.714 14.09 445.113 222.556 252.714 14.1 445.113 222.556 252.714 14.11 445.113 222.556 252.714 14.12 440 222.556 252.714 14.13 440 222.556 252.714 14.14 440 222.556 252.714 14.15 440 222.556 252.714 14.16 440 222.556 252.714 14.17 440 222.556 252.714 14.18 445.113 222.556 252.714 14.19 445.113 222.556 252.714 14.2 445.113 222.556 252.714 14.21 445.113 222.556 252.714 14.22 445.113 222.556 252.714 14.23 445.113 222.556 252.714 14.24 222.556 450.285 252.714 14.25 222.556 450.285 252.714 14.26 222.556 450.285 252.714 14.27 222.556 450.285 252.714 14.28 222.556 450.285 252.714 14.29 222.556 450.285 252.714 14.3 445.113 222.556 252.714 14.31 445.113 222.556 252.714 14.32 445.113 222.556 252.714 14.33 445.113 222.556 252.714 14.34 445.113 222.556 252.714 14.35 445.113 222.556 252.714 14.36 222.556 450.285 252.714 14.37 222.556 450.285 252.714 14.38 222.556 450.285 252.714 14.39 222.556 450.285 252.714 14.4 222.556 252.714 14.41 222.556 252.714 14.42 445.113 222.556 252.714 14.43 445.113 222.556 252.714 14.44 445.113 222.556 252.714 14.45 445.113 222.556 252.714 14.46 445.113 222.556 252.714 14.47 445.113 222.556 252.714 14.48 440 222.556 252.714 14.49 440 222.556 252.714 14.5 440 222.556 252.714 14.51 440 222.556 252.714 14.52 225.142 440 252.714 14.53 225.142 440 252.714 14.54 225.142 450.285 252.714 14.55 225.142 450.285 252.714 14.56 225.142 252.714 14.57 225.142 252.714 14.58 225.142 252.714 14.59 225.142 252.714 14.6 225.142 440 252.714 14.61 225.142 440 252.714 14.62 440 222.556 252.714 14.63 440 222.556 252.714 14.64 440 222.556 252.714 14.65 440 222.556 252.714 14.66 222.556 450.285 252.714 14.67 222.556 450.285 252.714 14.68 225.142 450.285 252.714 14.69 225.142 450.285 252.714 14.7 225.142 450.285 252.714 14.71 225.142 450.285 252.714 14.72 225.142 450.285 252.714 14.73 225.142 450.285 252.714 14.74 445.113 225.142 252.714 14.75 445.113 225.142 252.714 14.76 445.113 225.142 252.714 14.77 445.113 225.142 252.714 14.78 445.113 222.556 252.714 14.79 445.113 222.556 252.714 14.8 445.113 222.556 252.714 14.81 445.113 222.556 252.714 14.82 222.556 450.285 252.714 14.83 222.556 450.285 252.714 14.84 225.142 450.285 252.714 14.85 225.142 450.285 252.714 14.86 225.142 450.285 252.714 14.87 225.142 450.285 252.714 14.88 225.142 252.714 14.89 225.142 252.714 14.9 225.142 252.714 14.91 225.142 252.714 14.92 225.142 14.93 225.142 14.94 225.142 14.95 225.142 14.96 225.142 14.97 225.142 14.98 225.142 14.99 225.142 15.0 15.01 15.02 15.03 15.04 15.05 15.06 15.07 15.08 15.09 15.1 15.11 15.12 15.13 15.14 15.15 15.16 15.17 15.18 15.19 15.2 15.21 15.22 15.23 15.24 15.25 15.26 15.27 15.28 15.29 15.3 15.31 15.32 15.33 15.34 15.35 15.36 15.37 15.38 15.39 15.4 15.41 15.42 15.43 15.44 15.45 15.46 15.47 15.48 15.49 15.5 15.51 15.52 15.53 15.54 15.55 15.56 15.57 15.58 15.59 15.6 15.61 15.62 15.63 15.64 15.65 15.66 15.67 15.68 15.69 15.7 15.71 15.72 15.73 15.74 15.75 15.76 15.77 15.78 15.79 15.8 15.81 15.82 222.556 15.83 222.556 15.84 222.556 15.85 222.556 15.86 222.556 15.87 222.556 15.88 222.556 15.89 222.556 15.9 222.556 15.91 222.556 15.92 222.556 15.93 222.556 15.94 222.556 15.95 222.556 15.96 222.556 15.97 222.556 15.98 15.99 16.0 16.01 16.02 16.03 16.04 16.05 16.06 16.07 16.08 16.09 16.1 16.11 16.12 16.13 16.14 16.15 16.16 16.17 16.18 16.19 16.2 16.21 16.22 16.23 16.24 16.25 16.26 16.27 16.28 16.29 16.3 16.31 16.32 450.285 16.33 450.285 16.34 450.285 16.35 450.285 16.36 450.285 16.37 450.285 16.38 450.285 16.39 450.285 16.4 450.285 16.41 450.285 16.42 450.285 16.43 450.285 16.44 450.285 16.45 450.285 16.46 450.285 16.47 450.285 16.48 450.285 16.49 450.285 16.5 16.51 16.52 16.53 16.54 396.55 222.556 16.55 396.55 222.556 16.56 396.55 222.556 16.57 396.55 222.556 16.58 396.55 222.556 16.59 396.55 222.556 16.6 396.55 222.556 16.61 396.55 222.556 16.62 396.55 222.556 16.63 396.55 222.556 16.64 396.55 222.556 16.65 396.55 222.556 16.66 222.556 16.67 222.556 16.68 222.556 16.69 222.556 16.7 222.556 75.1322 16.71 222.556 75.1322 16.72 75.1322 16.73 75.1322 16.74 75.1322 16.75 75.1322 16.76 75.1322 16.77 75.1322 16.78 75.1322 16.79 75.1322 16.8 396.55 75.1322 16.81 396.55 75.1322 16.82 396.55 75.1322 16.83 396.55 75.1322 16.84 396.55 75.1322 16.85 396.55 75.1322 16.86 396.55 75.1322 16.87 396.55 75.1322 16.88 383.042 75.1322 16.89 383.042 75.1322 16.9 383.042 75.1322 16.91 383.042 75.1322 16.92 75.1322 16.93 75.1322 16.94 75.1322 16.95 75.1322 16.96 75.1322 16.97 75.1322 16.98 75.1322 16.99 75.1322 17.0 75.1322 17.01 75.1322 17.02 75.1322 17.03 75.1322 17.04 75.1322 17.05 75.1322 17.06 75.1322 17.07 75.1322 17.08 75.1322 17.09 75.1322 17.1 75.1322 17.11 75.1322 17.12 75.1322 297.077 17.13 75.1322 297.077 17.14 75.1322 297.077 17.15 75.1322 297.077 17.16 75.1322 297.077 17.17 75.1322 297.077 17.18 300.529 75.1322 17.19 300.529 75.1322 17.2 300.529 75.1322 17.21 300.529 75.1322 17.22 300.529 75.1322 17.23 300.529 75.1322 17.24 300.529 75.1322 17.25 300.529 75.1322 17.26 300.529 75.1322 17.27 300.529 75.1322 17.28 300.529 75.1322 17.29 300.529 75.1322 17.3 75.1322 297.077 17.31 75.1322 297.077 17.32 75.1322 297.077 17.33 75.1322 297.077 17.34 374.294 75.1322 297.077 17.35 374.294 75.1322 297.077 17.36 374.294 300.529 75.1322 17.37 374.294 300.529 75.1322 17.38 374.294 300.529 75.1322 17.39 374.294 300.529 75.1322 17.4 374.294 300.529 75.1322 17.41 374.294 300.529 75.1322 17.42 374.294 300.529 75.1322 17.43 374.294 300.529 75.1322 17.44 374.294 300.529 75.1322 17.45 374.294 300.529 75.1322 17.46 374.294 75.1322 17.47 374.294 75.1322 17.48 75.1322 17.49 75.1322 17.5 75.1322 17.51 75.1322 17.52 75.1322 17.53 75.1322 17.54 75.1322 17.55 75.1322 17.56 75.1322 17.57 75.1322 17.58 75.1322 17.59 75.1322 17.6 75.1322 17.61 75.1322 17.62 75.1322 17.63 75.1322 17.64 75.1322 17.65 75.1322 17.66 75.1322 17.67 75.1322 17.68 75.1322 17.69 75.1322 17.7 222.556 75.1322 17.71 222.556 75.1322 17.72 222.556 17.73 222.556 17.74 222.556 17.75 222.556 17.76 222.556 17.77 222.556 17.78 222.556 17.79 222.556 17.8 222.556 17.81 222.556 17.82 222.556 17.83 222.556 17.84 222.556 17.85 222.556 17.86 222.556 17.87 222.556 17.88 222.556 17.89 222.556 17.9 126.357 222.556 17.91 126.357 222.556 17.92 126.357 17.93 126.357 17.94 126.357 17.95 126.357 17.96 126.357 17.97 126.357 17.98 126.357 17.99 126.357 18.0 126.357 18.01 126.357 18.02 126.357 18.03 126.357 18.04 126.357 18.05 126.357 18.06 126.357 18.07 126.357 18.08 126.357 18.09 126.357 18.1 126.357 18.11 126.357 18.12 126.357 18.13 126.357 18.14 126.357 18.15 126.357 18.16 126.357 18.17 126.357 18.18 126.357 18.19 126.357 18.2 126.357 18.21 126.357 18.22 126.357 18.23 126.357 18.24 126.357 18.25 126.357 18.26 126.357 18.27 126.357 18.28 126.357 18.29 126.357 18.3 126.357 18.31 126.357 18.32 126.357 18.33 126.357 18.34 126.357 18.35 126.357 18.36 126.357 18.37 126.357 18.38 126.357 297.077 18.39 126.357 297.077 18.4 126.357 297.077 18.41 126.357 297.077 18.42 126.357 297.077 18.43 126.357 297.077 18.44 126.357 297.077 18.45 126.357 297.077 18.46 126.357 297.077 18.47 126.357 297.077 18.48 126.357 300.529 18.49 126.357 300.529 18.5 126.357 300.529 18.51 126.357 300.529 18.52 126.357 300.529 18.53 126.357 300.529 18.54 126.357 300.529 18.55 126.357 300.529 18.56 126.357 300.529 18.57 126.357 300.529 18.58 126.357 353.286 18.59 126.357 353.286 18.6 126.357 353.286 18.61 126.357 353.286 18.62 126.357 353.286 18.63 126.357 353.286 18.64 126.357 353.286 18.65 126.357 353.286 18.66 126.357 353.286 18.67 126.357 353.286 18.68 126.357 353.286 18.69 126.357 353.286 18.7 126.357 353.286 18.71 126.357 353.286 18.72 126.357 353.286 18.73 126.357 353.286 18.74 396.55 126.357 353.286 18.75 396.55 126.357 353.286 18.76 396.55 126.357 353.286 18.77 396.55 126.357 353.286 18.78 396.55 126.357 353.286 18.79 396.55 126.357 353.286 18.8 396.55 126.357 353.286 18.81 396.55 126.357 353.286 18.82 126.357 401.158 353.286 18.83 126.357 401.158 353.286 18.84 126.357 401.158 353.286 18.85 126.357 401.158 353.286 18.86 126.357 353.286 18.87 126.357 353.286 18.88 126.357 353.286 18.89 126.357 353.286 18.9 126.357 353.286 18.91 126.357 353.286 18.92 126.357 353.286 18.93 126.357 353.286 18.94 126.357 353.286 18.95 126.357 353.286 18.96 353.286 18.97 353.286 18.98 353.286 18.99 353.286 19.0 353.286 19.01 353.286 19.02 353.286 19.03 353.286 19.04 353.286 19.05 353.286 19.06 353.286 19.07 353.286 19.08 353.286 19.09 353.286 19.1 19.11 19.12 19.13 19.14 19.15 19.16 132.333 19.17 132.333 19.18 132.333 19.19 132.333 19.2 132.333 19.21 132.333 19.22 132.333 19.23 132.333 19.24 132.333 19.25 132.333 19.26 132.333 19.27 132.333 19.28 132.333 19.29 132.333 19.3 132.333 19.31 132.333 19.32 132.333 19.33 132.333 19.34 132.333 19.35 132.333 19.36 198.275 19.37 198.275 19.38 198.275 19.39 198.275 19.4 198.275 19.41 198.275 19.42 198.275 19.43 198.275 19.44 198.275 19.45 198.275 19.46 198.275 19.47 198.275 19.48 264.666 19.49 264.666 19.5 264.666 19.51 264.666 19.52 264.666 19.53 264.666 19.54 264.666 19.55 264.666 19.56 264.666 19.57 264.666 19.58 264.666 19.59 264.666 19.6 264.666 19.61 264.666 19.62 264.666 19.63 264.666 19.64 264.666 19.65 264.666 19.66 264.666 19.67 264.666 19.68 264.666 19.69 264.666 19.7 264.666 19.71 264.666 19.72 264.666 19.73 264.666 19.74 264.666 19.75 264.666 19.76 264.666 19.77 264.666 19.78 264.666 19.79 264.666 19.8 264.666 19.81 264.666 19.82 264.666 19.83 264.666 19.84 264.666 19.85 264.666 19.86 264.666 19.87 264.666 19.88 264.666 19.89 264.666 19.9 264.666 19.91 264.666 19.92 264.666 19.93 264.666 19.94 264.666 19.95 264.666 19.96 264.666 19.97 264.666 19.98 264.666 19.99 264.666 20.0 264.666 20.01 264.666 20.02 264.666 20.03 264.666 20.04 264.666 20.05 264.666 20.06 264.666 20.07 264.666 20.08 20.09 20.1 20.11 20.12 20.13 20.14 20.15 20.16 200.579 20.17 200.579 20.18 200.579 20.19 200.579 20.2 200.579 20.21 200.579 20.22 198.275 20.23 198.275 20.24 198.275 20.25 198.275 20.26 198.275 20.27 198.275 20.28 20.29 20.3 20.31 20.32 106.253 20.33 106.253 20.34 106.253 20.35 106.253 20.36 106.253 20.37 106.253 20.38 106.253 20.39 106.253 20.4 106.253 20.41 106.253 20.42 106.253 20.43 106.253 20.44 20.45 20.46 20.47 20.48 345.217 20.49 345.217 20.5 345.217 20.51 345.217 20.52 345.217 20.53 345.217 20.54 341.252 252.714 20.55 341.252 252.714 20.56 341.252 252.714 20.57 341.252 252.714 20.58 210.065 341.252 252.714 20.59 210.065 341.252 252.714 20.6 210.065 341.252 252.714 20.61 210.065 341.252 252.714 20.62 210.065 345.217 252.714 20.63 210.065 345.217 252.714 20.64 210.065 345.217 252.714 20.65 210.065 345.217 252.714 20.66 210.065 345.217 252.714 20.67 210.065 345.217 252.714 20.68 210.065 345.217 252.714 20.69 210.065 345.217 252.714 20.7 210.065 341.252 252.714 20.71 210.065 341.252 252.714 20.72 341.252 252.714 20.73 341.252 252.714 20.74 341.252 252.714 20.75 341.252 252.714 20.76 345.217 300.529 252.714 20.77 345.217 300.529 252.714 20.78 345.217 300.529 252.714 20.79 345.217 300.529 252.714 20.8 345.217 300.529 252.714 20.81 345.217 300.529 252.714 20.82 341.252 300.529 252.714 20.83 341.252 300.529 252.714 20.84 341.252 252.714 297.077 20.85 341.252 252.714 297.077 20.86 341.252 252.714 297.077 20.87 341.252 252.714 297.077 20.88 341.252 252.714 297.077 20.89 341.252 252.714 297.077 20.9 341.252 300.529 252.714 20.91 341.252 300.529 252.714 20.92 300.529 353.286 252.714 20.93 300.529 353.286 252.714 20.94 337.332 300.529 353.286 252.714 20.95 337.332 300.529 353.286 252.714 20.96 337.332 300.529 353.286 252.714 20.97 337.332 300.529 353.286 252.714 20.98 337.332 353.286 252.714 20.99 337.332 353.286 252.714 21.0 337.332 353.286 252.714 21.01 337.332 353.286 252.714 21.02 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529.331 222.556 24.64 445.113 267.741 529.331 222.556 24.65 445.113 267.741 529.331 222.556 24.66 267.741 529.331 222.556 24.67 267.741 529.331 222.556 24.68 267.741 529.331 222.556 24.69 267.741 529.331 222.556 24.7 267.741 529.331 222.556 24.71 267.741 529.331 222.556 24.72 267.741 529.331 333.458 222.556 24.73 267.741 529.331 333.458 222.556 24.74 267.741 529.331 333.458 222.556 24.75 267.741 529.331 333.458 222.556 24.76 267.741 535.482 333.458 24.77 267.741 535.482 333.458 24.78 267.741 535.482 333.458 24.79 267.741 535.482 333.458 24.8 535.482 333.458 264.666 24.81 535.482 333.458 264.666 24.82 529.331 333.458 264.666 24.83 529.331 333.458 264.666 24.84 333.458 264.666 24.85 333.458 264.666 24.86 337.332 264.666 24.87 337.332 264.666 24.88 76.0052 337.332 264.666 24.89 76.0052 337.332 264.666 24.9 76.0052 337.332 264.666 24.91 76.0052 337.332 264.666 24.92 76.0052 264.666 1058.66 24.93 76.0052 264.666 1058.66 24.94 445.113 76.0052 264.666 1058.66 24.95 445.113 76.0052 264.666 1058.66 24.96 445.113 76.0052 264.666 353.286 1058.66 24.97 445.113 76.0052 264.666 353.286 1058.66 24.98 445.113 76.0052 1046.5 264.666 353.286 24.99 445.113 76.0052 1046.5 264.666 353.286 25.0 445.113 1046.5 264.666 353.286 75.1322 25.01 445.113 1046.5 264.666 353.286 75.1322 25.02 1046.5 264.666 353.286 75.1322 450.285 25.03 1046.5 264.666 353.286 75.1322 450.285 25.04 264.666 353.286 75.1322 1070.96 450.285 25.05 264.666 353.286 75.1322 1070.96 450.285 25.06 264.666 353.286 75.1322 1070.96 25.07 264.666 353.286 75.1322 1070.96 25.08 353.286 75.1322 1070.96 25.09 353.286 75.1322 1070.96 25.1 1034.48 353.286 75.1322 25.11 1034.48 353.286 75.1322 25.12 1034.48 353.286 75.1322 25.13 1034.48 353.286 75.1322 25.14 1034.48 353.286 75.1322 25.15 1034.48 353.286 75.1322 25.16 353.286 75.1322 1070.96 25.17 353.286 75.1322 1070.96 25.18 353.286 75.1322 25.19 353.286 75.1322 25.2 353.286 75.1322 25.21 353.286 75.1322 25.22 353.286 75.1322 450.285 25.23 353.286 75.1322 450.285 25.24 353.286 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13.0 207.652 13.01 207.652 13.02 207.652 13.03 207.652 13.04 207.652 13.05 207.652 13.06 207.652 13.07 207.652 13.08 207.652 13.09 207.652 13.1 207.652 13.11 207.652 13.12 207.652 13.13 207.652 13.14 207.652 13.15 207.652 13.16 207.652 13.17 207.652 13.18 207.652 13.19 207.652 13.2 207.652 13.21 207.652 13.22 207.652 13.23 207.652 13.24 207.652 13.25 207.652 13.26 207.652 13.27 207.652 13.28 207.652 13.29 207.652 13.3 207.652 13.31 207.652 13.32 207.652 13.33 207.652 13.34 207.652 13.35 207.652 13.36 207.652 13.37 207.652 13.38 207.652 13.39 207.652 13.4 283.661 13.41 283.661 13.42 283.661 13.43 283.661 13.44 283.661 13.45 283.661 13.46 280.403 13.47 280.403 13.48 280.403 13.49 280.403 13.5 280.403 13.51 280.403 13.52 280.403 13.53 280.403 13.54 280.403 13.55 280.403 13.56 280.403 13.57 280.403 13.58 280.403 13.59 280.403 13.6 280.403 13.61 280.403 13.62 280.403 13.63 280.403 13.64 280.403 13.65 280.403 13.66 277.183 13.67 277.183 13.68 277.183 13.69 277.183 13.7 277.183 13.71 277.183 13.72 277.183 13.73 277.183 13.74 277.183 13.75 277.183 13.76 277.183 13.77 277.183 13.78 277.183 13.79 277.183 13.8 277.183 13.81 277.183 13.82 277.183 13.83 277.183 13.84 280.403 13.85 280.403 13.86 280.403 13.87 280.403 13.88 329.628 280.403 13.89 329.628 280.403 13.9 329.628 280.403 13.91 329.628 280.403 13.92 329.628 280.403 13.93 329.628 280.403 13.94 280.403 333.458 13.95 280.403 333.458 13.96 280.403 333.458 13.97 280.403 333.458 13.98 333.458 13.99 333.458 14.0 333.458 14.01 333.458 14.02 333.458 277.183 14.03 333.458 277.183 14.04 333.458 277.183 14.05 333.458 277.183 14.06 333.458 277.183 14.07 333.458 277.183 14.08 280.403 333.458 14.09 280.403 333.458 14.1 280.403 333.458 14.11 280.403 333.458 14.12 280.403 333.458 14.13 280.403 333.458 14.14 329.628 280.403 14.15 329.628 280.403 14.16 329.628 280.403 14.17 329.628 280.403 14.18 329.628 280.403 14.19 329.628 280.403 14.2 280.403 333.458 14.21 280.403 333.458 14.22 280.403 333.458 14.23 280.403 333.458 14.24 280.403 333.458 14.25 280.403 333.458 14.26 329.628 280.403 14.27 329.628 280.403 14.28 329.628 280.403 14.29 329.628 280.403 14.3 329.628 280.403 14.31 329.628 280.403 14.32 280.403 333.458 14.33 280.403 333.458 14.34 280.403 333.458 14.35 280.403 333.458 14.36 280.403 333.458 14.37 280.403 333.458 14.38 280.403 14.39 280.403 14.4 280.403 14.41 280.403 14.42 220 14.43 220 14.44 220 111.278 14.45 220 111.278 14.46 220 111.278 14.47 220 111.278 14.48 220 111.278 14.49 220 111.278 14.5 168.666 220 111.278 14.51 168.666 220 111.278 14.52 168.666 220 111.278 14.53 168.666 220 111.278 14.54 168.666 220 277.183 111.278 14.55 168.666 220 277.183 111.278 14.56 220 277.183 111.278 166.729 14.57 220 277.183 111.278 166.729 14.58 220 277.183 111.278 166.729 14.59 220 277.183 111.278 166.729 14.6 220 277.183 111.278 166.729 14.61 220 277.183 111.278 166.729 14.62 220 277.183 111.278 166.729 14.63 220 277.183 111.278 166.729 14.64 220 277.183 111.278 166.729 14.65 220 277.183 111.278 166.729 14.66 220 277.183 111.278 166.729 14.67 220 277.183 111.278 166.729 14.68 220 277.183 111.278 166.729 14.69 220 277.183 111.278 166.729 14.7 220 277.183 111.278 166.729 14.71 220 277.183 111.278 166.729 14.72 220 277.183 111.278 166.729 14.73 220 277.183 111.278 166.729 14.74 220 277.183 111.278 166.729 14.75 220 277.183 111.278 166.729 14.76 220 277.183 111.278 166.729 14.77 220 277.183 111.278 166.729 14.78 220 111.278 166.729 14.79 220 111.278 166.729 14.8 220 111.278 166.729 14.81 220 111.278 166.729 14.82 220 111.278 166.729 14.83 220 111.278 166.729 14.84 220 111.278 166.729 14.85 220 111.278 166.729 14.86 220 111.278 166.729 14.87 220 111.278 166.729 14.88 220 111.278 166.729 14.89 220 111.278 166.729 14.9 220 111.278 166.729 14.91 220 111.278 166.729 14.92 220 111.278 166.729 14.93 220 111.278 166.729 14.94 220 111.278 166.729 14.95 220 111.278 166.729 14.96 220 111.278 166.729 14.97 220 111.278 166.729 14.98 220 111.278 166.729 14.99 220 111.278 166.729 15.0 111.278 166.729 15.01 111.278 166.729 15.02 111.278 166.729 15.03 111.278 166.729 15.04 277.183 111.278 166.729 15.05 277.183 111.278 166.729 15.06 277.183 111.278 166.729 15.07 277.183 111.278 166.729 15.08 277.183 111.278 166.729 15.09 277.183 111.278 166.729 15.1 277.183 111.278 166.729 15.11 277.183 111.278 166.729 15.12 277.183 111.278 166.729 15.13 277.183 111.278 166.729 15.14 277.183 111.278 166.729 15.15 277.183 111.278 166.729 15.16 280.403 111.278 166.729 15.17 280.403 111.278 166.729 15.18 280.403 111.278 166.729 15.19 280.403 111.278 166.729 15.2 280.403 111.278 166.729 15.21 280.403 111.278 166.729 15.22 280.403 111.278 166.729 15.23 280.403 111.278 166.729 15.24 280.403 111.278 166.729 15.25 280.403 111.278 166.729 15.26 277.183 111.278 166.729 15.27 277.183 111.278 166.729 15.28 277.183 111.278 166.729 15.29 277.183 111.278 166.729 15.3 277.183 111.278 15.31 277.183 111.278 15.32 277.183 111.278 15.33 277.183 111.278 15.34 277.183 111.278 15.35 277.183 111.278 15.36 277.183 111.278 15.37 277.183 111.278 15.38 277.183 111.278 15.39 277.183 111.278 15.4 277.183 111.278 15.41 277.183 111.278 15.42 277.183 111.278 15.43 277.183 111.278 15.44 277.183 111.278 15.45 277.183 111.278 15.46 277.183 111.278 15.47 277.183 111.278 15.48 277.183 111.278 15.49 277.183 111.278 15.5 277.183 111.278 15.51 277.183 111.278 15.52 277.183 111.278 15.53 277.183 111.278 15.54 277.183 111.278 15.55 277.183 111.278 15.56 277.183 111.278 15.57 277.183 111.278 15.58 277.183 111.278 15.59 277.183 111.278 15.6 277.183 111.278 15.61 277.183 111.278 15.62 277.183 111.278 15.63 277.183 111.278 15.64 277.183 111.278 15.65 277.183 111.278 15.66 111.278 15.67 111.278 15.68 111.278 15.69 111.278 15.7 111.278 15.71 111.278 15.72 111.278 15.73 111.278 15.74 111.278 15.75 111.278 15.76 111.278 15.77 111.278 15.78 111.278 15.79 111.278 15.8 111.278 15.81 111.278 15.82 111.278 15.83 111.278 15.84 277.183 111.278 15.85 277.183 111.278 15.86 277.183 111.278 15.87 277.183 111.278 15.88 277.183 111.278 15.89 277.183 111.278 15.9 277.183 111.278 15.91 277.183 111.278 15.92 277.183 111.278 15.93 277.183 111.278 15.94 283.661 111.278 15.95 283.661 111.278 15.96 111.278 15.97 111.278 15.98 220 111.278 15.99 220 111.278 16.0 220 111.278 16.01 220 111.278 16.02 220 111.278 16.03 220 111.278 16.04 220 111.278 16.05 220 111.278 16.06 220 111.278 16.07 220 111.278 16.08 220 111.278 16.09 220 111.278 16.1 220 111.278 16.11 220 111.278 16.12 220 111.278 16.13 220 111.278 16.14 220 111.278 16.15 220 111.278 16.16 220 111.278 16.17 220 111.278 16.18 220 111.278 16.19 220 111.278 16.2 220 111.278 16.21 220 111.278 16.22 220 111.278 16.23 220 111.278 16.24 220 111.278 16.25 220 111.278 16.26 220 111.278 16.27 220 111.278 16.28 220 111.278 16.29 220 111.278 16.3 220 111.278 16.31 220 111.278 16.32 220 111.278 16.33 220 111.278 16.34 220 111.278 16.35 220 111.278 16.36 220 111.278 16.37 220 111.278 16.38 220 111.278 16.39 220 111.278 16.4 220 16.41 220 16.42 220 16.43 220 16.44 220 111.278 16.45 220 111.278 16.46 283.661 111.278 16.47 283.661 111.278 16.48 283.661 111.278 16.49 283.661 111.278 16.5 283.661 111.278 16.51 283.661 111.278 16.52 280.403 111.278 16.53 280.403 111.278 16.54 280.403 111.278 16.55 280.403 111.278 16.56 280.403 111.278 16.57 280.403 111.278 16.58 280.403 111.278 16.59 280.403 111.278 16.6 280.403 16.61 280.403 16.62 280.403 16.63 280.403 16.64 280.403 16.65 280.403 16.66 280.403 16.67 280.403 16.68 280.403 16.69 280.403 16.7 280.403 16.71 280.403 16.72 280.403 16.73 280.403 16.74 16.75 16.76 16.77 16.78 16.79 16.8 16.81 16.82 16.83 16.84 16.85 16.86 16.87 16.88 16.89 16.9 16.91 16.92 16.93 16.94 333.458 16.95 333.458 16.96 333.458 16.97 333.458 16.98 333.458 16.99 333.458 17.0 333.458 17.01 333.458 17.02 333.458 17.03 333.458 17.04 333.458 17.05 333.458 17.06 333.458 111.278 17.07 333.458 111.278 17.08 333.458 111.278 17.09 333.458 111.278 17.1 333.458 111.278 17.11 333.458 111.278 17.12 333.458 111.278 17.13 333.458 111.278 17.14 333.458 111.278 17.15 333.458 111.278 17.16 333.458 111.278 17.17 333.458 111.278 17.18 333.458 17.19 333.458 17.2 333.458 17.21 333.458 17.22 333.458 17.23 333.458 17.24 333.458 17.25 333.458 17.26 17.27 17.28 17.29 17.3 17.31 17.32 17.33 17.34 333.458 17.35 333.458 17.36 333.458 17.37 333.458 17.38 333.458 17.39 333.458 17.4 333.458 17.41 333.458 17.42 333.458 17.43 333.458 17.44 333.458 17.45 333.458 17.46 333.458 17.47 333.458 17.48 220 92.4986 17.49 220 92.4986 17.5 220 92.4986 17.51 220 92.4986 17.52 220 92.4986 17.53 220 92.4986 17.54 280.403 220 92.4986 17.55 280.403 220 92.4986 17.56 280.403 220 92.4986 17.57 280.403 220 92.4986 17.58 280.403 220 92.4986 17.59 280.403 220 92.4986 17.6 280.403 220 92.4986 17.61 280.403 220 92.4986 17.62 280.403 220 92.4986 17.63 280.403 220 92.4986 17.64 280.403 220 92.4986 17.65 280.403 220 92.4986 17.66 220 277.183 92.4986 17.67 220 277.183 92.4986 17.68 220 92.4986 17.69 220 92.4986 17.7 220 92.4986 17.71 220 92.4986 17.72 220 92.4986 17.73 220 92.4986 17.74 220 92.4986 17.75 220 92.4986 17.76 220 92.4986 17.77 220 92.4986 17.78 220 92.4986 17.79 220 92.4986 17.8 220 92.4986 17.81 220 92.4986 17.82 220 92.4986 17.83 220 92.4986 17.84 220 92.4986 17.85 220 92.4986 17.86 220 92.4986 17.87 220 92.4986 17.88 220 92.4986 17.89 220 92.4986 17.9 220 92.4986 17.91 220 92.4986 17.92 220 92.4986 17.93 220 92.4986 17.94 220 92.4986 17.95 220 92.4986 17.96 220 92.4986 17.97 220 92.4986 17.98 220 92.4986 17.99 220 92.4986 18.0 220 92.4986 18.01 220 92.4986 18.02 220 92.4986 18.03 220 92.4986 18.04 297.077 220 92.4986 18.05 297.077 220 92.4986 18.06 297.077 220 92.4986 18.07 297.077 220 92.4986 18.08 297.077 220 92.4986 18.09 297.077 220 92.4986 18.1 297.077 220 92.4986 18.11 297.077 220 92.4986 18.12 297.077 220 92.4986 18.13 297.077 220 92.4986 18.14 297.077 220 92.4986 18.15 297.077 220 92.4986 18.16 297.077 220 92.4986 18.17 297.077 220 92.4986 18.18 297.077 220 92.4986 18.19 297.077 220 92.4986 18.2 297.077 220 92.4986 18.21 297.077 220 92.4986 18.22 297.077 220 92.4986 18.23 297.077 220 92.4986 18.24 297.077 220 92.4986 18.25 297.077 220 92.4986 18.26 297.077 92.4986 18.27 297.077 92.4986 18.28 297.077 92.4986 18.29 297.077 92.4986 18.3 297.077 92.4986 18.31 297.077 92.4986 18.32 92.4986 18.33 92.4986 18.34 92.4986 18.35 92.4986 18.36 92.4986 18.37 92.4986 18.38 92.4986 18.39 92.4986 18.4 92.4986 18.41 92.4986 18.42 92.4986 18.43 92.4986 18.44 92.4986 18.45 92.4986 18.46 92.4986 18.47 92.4986 18.48 92.4986 18.49 92.4986 18.5 92.4986 18.51 92.4986 18.52 92.4986 18.53 92.4986 18.54 92.4986 18.55 92.4986 18.56 92.4986 18.57 92.4986 18.58 92.4986 18.59 92.4986 18.6 92.4986 18.61 92.4986 18.62 92.4986 18.63 92.4986 18.64 92.4986 18.65 92.4986 18.66 92.4986 18.67 92.4986 18.68 92.4986 18.69 92.4986 18.7 92.4986 18.71 92.4986 18.72 92.4986 18.73 92.4986 18.74 92.4986 18.75 92.4986 18.76 92.4986 18.77 92.4986 18.78 92.4986 18.79 92.4986 18.8 92.4986 18.81 92.4986 18.82 92.4986 18.83 92.4986 18.84 92.4986 18.85 92.4986 18.86 92.4986 18.87 92.4986 18.88 92.4986 18.89 92.4986 18.9 92.4986 18.91 92.4986 18.92 92.4986 18.93 92.4986 18.94 92.4986 18.95 92.4986 18.96 92.4986 18.97 92.4986 18.98 220 92.4986 18.99 220 92.4986 19.0 220 92.4986 19.01 220 92.4986 19.02 220 92.4986 19.03 220 92.4986 19.04 220 92.4986 19.05 220 92.4986 19.06 220 92.4986 19.07 220 92.4986 19.08 220 92.4986 19.09 220 92.4986 19.1 220 92.4986 19.11 220 92.4986 19.12 220 92.4986 19.13 220 92.4986 19.14 220 92.4986 19.15 220 92.4986 19.16 220 92.4986 19.17 220 92.4986 19.18 220 92.4986 19.19 220 92.4986 19.2 220 92.4986 19.21 220 92.4986 19.22 220 92.4986 19.23 220 92.4986 19.24 220 92.4986 19.25 220 92.4986 19.26 220 92.4986 19.27 220 92.4986 19.28 220 92.4986 19.29 220 92.4986 19.3 220 92.4986 19.31 220 92.4986 19.32 220 92.4986 19.33 220 92.4986 19.34 220 93.5734 19.35 220 93.5734 19.36 220 93.5734 19.37 220 93.5734 19.38 220 93.5734 19.39 220 93.5734 19.4 220 93.5734 19.41 220 93.5734 19.42 220 93.5734 19.43 220 93.5734 19.44 297.077 220 93.5734 19.45 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207.652 20.77 105.033 207.652 20.78 105.033 207.652 20.79 105.033 207.652 20.8 105.033 207.652 20.81 105.033 207.652 20.82 105.033 207.652 20.83 105.033 207.652 20.84 105.033 207.652 20.85 105.033 207.652 20.86 105.033 207.652 20.87 105.033 207.652 20.88 105.033 207.652 20.89 105.033 207.652 20.9 105.033 207.652 20.91 105.033 207.652 20.92 105.033 207.652 20.93 105.033 207.652 20.94 105.033 207.652 20.95 105.033 207.652 20.96 105.033 207.652 20.97 105.033 207.652 20.98 105.033 207.652 20.99 105.033 207.652 21.0 105.033 207.652 21.01 105.033 207.652 21.02 105.033 207.652 21.03 105.033 207.652 21.04 105.033 207.652 21.05 105.033 207.652 21.06 105.033 207.652 21.07 105.033 207.652 21.08 105.033 207.652 21.09 105.033 207.652 21.1 105.033 207.652 21.11 105.033 207.652 21.12 105.033 207.652 21.13 105.033 207.652 21.14 105.033 207.652 21.15 105.033 207.652 21.16 105.033 210.065 21.17 105.033 210.065 21.18 105.033 210.065 21.19 105.033 210.065 21.2 105.033 210.065 21.21 105.033 210.065 21.22 105.033 210.065 21.23 105.033 210.065 21.24 105.033 207.652 21.25 105.033 207.652 21.26 105.033 207.652 21.27 105.033 207.652 21.28 105.033 207.652 21.29 105.033 207.652 21.3 105.033 207.652 21.31 105.033 207.652 21.32 105.033 207.652 21.33 105.033 207.652 21.34 105.033 207.652 21.35 105.033 207.652 21.36 105.033 207.652 21.37 105.033 207.652 21.38 105.033 207.652 21.39 105.033 207.652 21.4 105.033 21.41 105.033 21.42 105.033 21.43 105.033 21.44 105.033 21.45 105.033 21.46 105.033 21.47 105.033 21.48 105.033 207.652 21.49 105.033 207.652 21.5 105.033 207.652 21.51 105.033 207.652 21.52 105.033 207.652 21.53 105.033 207.652 21.54 105.033 207.652 21.55 105.033 207.652 21.56 105.033 207.652 21.57 105.033 207.652 21.58 105.033 369.994 207.652 21.59 105.033 369.994 207.652 21.6 105.033 369.994 207.652 21.61 105.033 369.994 207.652 21.62 369.994 207.652 21.63 369.994 207.652 21.64 369.994 207.652 21.65 369.994 207.652 21.66 374.294 207.652 21.67 374.294 207.652 21.68 374.294 207.652 21.69 374.294 207.652 21.7 207.652 21.71 207.652 21.72 207.652 21.73 207.652 21.74 207.652 21.75 207.652 21.76 207.652 21.77 207.652 21.78 207.652 21.79 207.652 21.8 207.652 21.81 207.652 21.82 207.652 21.83 207.652 21.84 207.652 21.85 207.652 21.86 207.652 21.87 207.652 21.88 21.89 21.9 21.91 21.92 21.93 21.94 21.95 21.96 21.97 21.98 207.652 21.99 207.652 22.0 207.652 22.01 207.652 22.02 207.652 22.03 207.652 22.04 210.065 22.05 210.065 22.06 210.065 22.07 210.065 22.08 210.065 22.09 210.065 22.1 207.652 22.11 207.652 22.12 207.652 22.13 207.652 22.14 207.652 22.15 207.652 22.16 207.652 22.17 207.652 22.18 207.652 22.19 207.652 22.2 207.652 22.21 207.652 22.22 207.652 22.23 207.652 22.24 207.652 22.25 207.652 22.26 207.652 22.27 207.652 22.28 210.065 22.29 210.065 22.3 210.065 22.31 210.065 22.32 210.065 22.33 210.065 22.34 207.652 22.35 207.652 22.36 207.652 22.37 207.652 22.38 207.652 22.39 207.652 22.4 207.652 22.41 207.652 22.42 207.652 22.43 207.652 22.44 207.652 22.45 207.652 22.46 207.652 22.47 207.652 22.48 207.652 22.49 207.652 22.5 22.51 22.52 22.53 22.54 22.55 22.56 283.661 22.57 283.661 22.58 283.661 22.59 283.661 22.6 283.661 22.61 283.661 22.62 280.403 22.63 280.403 22.64 280.403 22.65 280.403 22.66 280.403 22.67 280.403 22.68 280.403 22.69 280.403 22.7 280.403 22.71 280.403 22.72 280.403 22.73 280.403 22.74 280.403 22.75 280.403 22.76 280.403 22.77 280.403 22.78 280.403 22.79 280.403 22.8 280.403 22.81 280.403 22.82 277.183 22.83 277.183 22.84 277.183 22.85 277.183 22.86 277.183 22.87 277.183 22.88 277.183 22.89 277.183 22.9 277.183 22.91 277.183 22.92 277.183 22.93 277.183 22.94 280.403 22.95 280.403 22.96 280.403 22.97 280.403 22.98 22.99 23.0 23.01 23.02 23.03 23.04 23.05 23.06 23.07 23.08 23.09 23.1 23.11 23.12 23.13 23.14 333.458 23.15 333.458 23.16 333.458 23.17 333.458 23.18 333.458 23.19 333.458 23.2 333.458 23.21 333.458 23.22 333.458 23.23 333.458 23.24 337.332 23.25 337.332 23.26 23.27 23.28 23.29 23.3 23.31 23.32 23.33 23.34 23.35 23.36 23.37 23.38 23.39 23.4 23.41 23.42 23.43 23.44 23.45 23.46 23.47 23.48 23.49 23.5 105.033 23.51 105.033 23.52 105.033 23.53 105.033 23.54 105.033 23.55 105.033 23.56 105.033 23.57 105.033 23.58 105.033 23.59 105.033 23.6 105.033 210.065 23.61 105.033 210.065 23.62 105.033 210.065 23.63 105.033 210.065 23.64 105.033 210.065 23.65 105.033 210.065 23.66 105.033 210.065 23.67 105.033 210.065 23.68 105.033 210.065 23.69 105.033 210.065 23.7 105.033 210.065 23.71 105.033 210.065 23.72 105.033 210.065 23.73 105.033 210.065 23.74 105.033 210.065 23.75 105.033 210.065 23.76 105.033 210.065 23.77 105.033 210.065 23.78 105.033 210.065 23.79 105.033 210.065 23.8 105.033 210.065 23.81 105.033 210.065 23.82 105.033 210.065 23.83 105.033 210.065 23.84 105.033 207.652 23.85 105.033 207.652 23.86 105.033 207.652 23.87 105.033 207.652 23.88 207.652 23.89 207.652 23.9 207.652 23.91 207.652 23.92 105.033 207.652 23.93 105.033 207.652 23.94 105.033 207.652 23.95 105.033 207.652 23.96 105.033 207.652 23.97 105.033 207.652 23.98 105.033 207.652 23.99 105.033 207.652 24.0 105.033 207.652 24.01 105.033 207.652 24.02 105.033 207.652 24.03 105.033 207.652 24.04 105.033 207.652 24.05 105.033 207.652 24.06 105.033 207.652 24.07 105.033 207.652 24.08 105.033 207.652 24.09 105.033 207.652 24.1 105.033 210.065 24.11 105.033 210.065 24.12 105.033 24.13 105.033 24.14 105.033 24.15 105.033 24.16 105.033 24.17 105.033 24.18 280.403 105.033 24.19 280.403 105.033 24.2 280.403 105.033 24.21 280.403 105.033 24.22 280.403 105.033 24.23 280.403 105.033 24.24 280.403 105.033 24.25 280.403 105.033 24.26 280.403 105.033 24.27 280.403 105.033 24.28 280.403 105.033 24.29 280.403 105.033 24.3 105.033 24.31 105.033 24.32 105.033 24.33 105.033 24.34 105.033 24.35 105.033 24.36 105.033 24.37 105.033 24.38 105.033 24.39 105.033 24.4 105.033 24.41 105.033 24.42 105.033 24.43 105.033 24.44 105.033 24.45 105.033 24.46 105.033 24.47 105.033 24.48 105.033 24.49 105.033 24.5 105.033 24.51 105.033 24.52 105.033 24.53 105.033 24.54 105.033 24.55 105.033 24.56 105.033 24.57 105.033 24.58 105.033 24.59 105.033 24.6 105.033 24.61 105.033 24.62 314.742 105.033 24.63 314.742 105.033 24.64 314.742 105.033 24.65 314.742 105.033 24.66 314.742 105.033 24.67 314.742 105.033 24.68 314.742 105.033 24.69 314.742 105.033 24.7 314.742 105.033 24.71 314.742 105.033 24.72 318.399 105.033 24.73 318.399 105.033 24.74 105.033 24.75 105.033 24.76 105.033 24.77 105.033 24.78 105.033 24.79 105.033 24.8 105.033 24.81 105.033 24.82 105.033 24.83 105.033 24.84 105.033 24.85 105.033 24.86 105.033 24.87 105.033 24.88 105.033 24.89 105.033 24.9 105.033 24.91 105.033 24.92 105.033 24.93 105.033 24.94 105.033 24.95 105.033 24.96 105.033 24.97 105.033 24.98 105.033 24.99 105.033 25.0 105.033 25.01 105.033 25.02 105.033 25.03 105.033 25.04 187.147 105.033 25.05 187.147 105.033 25.06 187.147 105.033 25.07 187.147 105.033 25.08 187.147 105.033 25.09 187.147 105.033 25.1 187.147 105.033 25.11 187.147 105.033 25.12 187.147 105.033 25.13 187.147 105.033 25.14 187.147 105.033 25.15 187.147 105.033 25.16 187.147 105.033 25.17 187.147 105.033 25.18 187.147 105.033 25.19 187.147 105.033 25.2 187.147 105.033 25.21 187.147 105.033 25.22 187.147 105.033 25.23 187.147 105.033 25.24 187.147 105.033 25.25 187.147 105.033 25.26 187.147 105.033 25.27 187.147 105.033 25.28 187.147 105.033 25.29 187.147 105.033 25.3 187.147 105.033 25.31 187.147 105.033 25.32 187.147 105.033 25.33 187.147 105.033 25.34 187.147 105.033 25.35 187.147 105.033 25.36 187.147 105.033 25.37 187.147 105.033 25.38 187.147 105.033 25.39 187.147 105.033 25.4 187.147 105.033 25.41 187.147 105.033 25.42 187.147 105.033 25.43 187.147 105.033 25.44 187.147 105.033 25.45 187.147 105.033 25.46 187.147 105.033 25.47 187.147 105.033 25.48 187.147 105.033 25.49 187.147 105.033 25.5 187.147 105.033 25.51 187.147 105.033 25.52 187.147 105.033 25.53 187.147 105.033 25.54 187.147 105.033 25.55 187.147 105.033 25.56 187.147 25.57 187.147 25.58 187.147 261.626 25.59 187.147 261.626 25.6 187.147 261.626 25.61 187.147 261.626 25.62 187.147 261.626 25.63 187.147 261.626 25.64 187.147 261.626 25.65 187.147 261.626 25.66 187.147 261.626 25.67 187.147 261.626 25.68 187.147 261.626 25.69 187.147 261.626 25.7 187.147 264.666 25.71 187.147 264.666 25.72 187.147 25.73 187.147 25.74 187.147 25.75 187.147 25.76 187.147 25.77 187.147 25.78 187.147 25.79 187.147 25.8 187.147 25.81 187.147 25.82 187.147 264.666 25.83 187.147 264.666 25.84 187.147 264.666 25.85 187.147 264.666 25.86 187.147 264.666 25.87 187.147 264.666 25.88 187.147 261.626 25.89 187.147 261.626 25.9 187.147 261.626 25.91 187.147 261.626 25.92 187.147 261.626 25.93 187.147 261.626 25.94 187.147 105.033 264.666 25.95 187.147 105.033 264.666 25.96 187.147 105.033 264.666 25.97 187.147 105.033 264.666 25.98 187.147 105.033 264.666 25.99 187.147 105.033 264.666 26.0 187.147 105.033 264.666 26.01 187.147 105.033 264.666 26.02 187.147 105.033 264.666 26.03 187.147 105.033 264.666 26.04 187.147 105.033 264.666 26.05 187.147 105.033 264.666 26.06 187.147 105.033 264.666 26.07 187.147 105.033 264.666 26.08 187.147 105.033 264.666 26.09 187.147 105.033 264.666 26.1 187.147 105.033 264.666 26.11 187.147 105.033 264.666 26.12 187.147 105.033 26.13 187.147 105.033 26.14 314.742 187.147 105.033 26.15 314.742 187.147 105.033 26.16 314.742 187.147 26.17 314.742 187.147 26.18 314.742 187.147 26.19 314.742 187.147 26.2 314.742 187.147 105.033 26.21 314.742 187.147 105.033 26.22 314.742 187.147 105.033 26.23 314.742 187.147 105.033 26.24 314.742 105.033 26.25 314.742 105.033 26.26 314.742 105.033 26.27 314.742 105.033 26.28 314.742 105.033 26.29 314.742 105.033 26.3 314.742 105.033 26.31 314.742 105.033 26.32 314.742 105.033 26.33 314.742 105.033 26.34 314.742 105.033 26.35 314.742 105.033 26.36 314.742 105.033 26.37 314.742 105.033 26.38 314.742 105.033 26.39 314.742 105.033 26.4 314.742 105.033 26.41 314.742 105.033 26.42 314.742 105.033 26.43 314.742 105.033 26.44 314.742 105.033 26.45 314.742 105.033 26.46 314.742 105.033 26.47 314.742 105.033 26.48 314.742 105.033 26.49 314.742 105.033 26.5 314.742 105.033 26.51 314.742 105.033 26.52 314.742 105.033 26.53 314.742 105.033 26.54 314.742 105.033 26.55 314.742 105.033 26.56 314.742 105.033 26.57 314.742 105.033 26.58 314.742 105.033 26.59 314.742 105.033 26.6 105.033 26.61 105.033 26.62 105.033 26.63 105.033 26.64 105.033 26.65 105.033 26.66 105.033 26.67 105.033 26.68 105.033 26.69 105.033 26.7 105.033 26.71 105.033 26.72 105.033 26.73 105.033 26.74 105.033 26.75 105.033 26.76 105.033 26.77 105.033 26.78 26.79 26.8 26.81 26.82 26.83 26.84 26.85 26.86 26.87 26.88 207.652 26.89 207.652 26.9 105.033 207.652 26.91 105.033 207.652 26.92 280.403 105.033 207.652 26.93 280.403 105.033 207.652 26.94 280.403 105.033 210.065 26.95 280.403 105.033 210.065 26.96 280.403 105.033 210.065 26.97 280.403 105.033 210.065 26.98 280.403 105.033 210.065 26.99 280.403 105.033 210.065 27.0 164.814 280.403 105.033 210.065 27.01 164.814 280.403 105.033 210.065 27.02 164.814 280.403 105.033 210.065 27.03 164.814 280.403 105.033 210.065 27.04 164.814 280.403 105.033 210.065 27.05 164.814 280.403 105.033 210.065 27.06 164.814 280.403 105.033 70.1008 210.065 27.07 164.814 280.403 105.033 70.1008 210.065 27.08 164.814 280.403 105.033 70.1008 210.065 27.09 164.814 280.403 105.033 70.1008 210.065 27.1 164.814 105.033 70.1008 210.065 277.183 27.11 164.814 105.033 70.1008 210.065 277.183 27.12 164.814 105.033 70.1008 210.065 277.183 27.13 164.814 105.033 70.1008 210.065 277.183 27.14 105.033 70.1008 166.729 210.065 277.183 27.15 105.033 70.1008 166.729 210.065 277.183 27.16 280.403 105.033 70.1008 166.729 210.065 27.17 280.403 105.033 70.1008 166.729 210.065 27.18 280.403 105.033 70.1008 166.729 210.065 27.19 280.403 105.033 70.1008 166.729 210.065 27.2 280.403 105.033 70.1008 166.729 210.065 27.21 280.403 105.033 70.1008 166.729 210.065 27.22 280.403 105.033 70.1008 166.729 207.652 27.23 280.403 105.033 70.1008 166.729 207.652 27.24 280.403 105.033 70.1008 166.729 207.652 27.25 280.403 105.033 70.1008 166.729 207.652 27.26 280.403 105.033 70.1008 166.729 207.652 27.27 280.403 105.033 70.1008 166.729 207.652 27.28 105.033 70.1008 166.729 207.652 27.29 105.033 70.1008 166.729 207.652 27.3 105.033 70.1008 166.729 207.652 27.31 105.033 70.1008 166.729 207.652 27.32 105.033 70.1008 166.729 210.065 27.33 105.033 70.1008 166.729 210.065 27.34 105.033 70.1008 166.729 210.065 27.35 105.033 70.1008 166.729 210.065 27.36 105.033 70.1008 166.729 210.065 27.37 105.033 70.1008 166.729 210.065 27.38 105.033 70.1008 166.729 210.065 27.39 105.033 70.1008 166.729 210.065 27.4 105.033 70.1008 166.729 207.652 27.41 105.033 70.1008 166.729 207.652 27.42 70.1008 207.652 27.43 70.1008 207.652 27.44 70.1008 207.652 27.45 70.1008 207.652 27.46 210.065 70.1008 27.47 210.065 70.1008 27.48 210.065 70.1008 27.49 210.065 70.1008 27.5 210.065 70.1008 27.51 210.065 70.1008 27.52 210.065 70.1008 27.53 210.065 70.1008 27.54 210.065 70.1008 27.55 210.065 70.1008 27.56 210.065 70.1008 27.57 210.065 70.1008 27.58 207.652 27.59 207.652 27.6 207.652 27.61 207.652 27.62 207.652 27.63 207.652 27.64 207.652 27.65 207.652 27.66 207.652 27.67 207.652 27.68 210.065 27.69 210.065 27.7 210.065 27.71 210.065 27.72 210.065 27.73 210.065 27.74 210.065 27.75 210.065 27.76 210.065 27.77 210.065 27.78 210.065 27.79 210.065 27.8 210.065 27.81 210.065 27.82 210.065 27.83 210.065 27.84 210.065 27.85 210.065 27.86 207.652 27.87 207.652 27.88 207.652 27.89 207.652 27.9 207.652 27.91 207.652 27.92 207.652 27.93 207.652 27.94 207.652 27.95 207.652 27.96 207.652 27.97 207.652 27.98 207.652 27.99 207.652 28.0 207.652 28.01 207.652 28.02 210.065 277.183 28.03 210.065 277.183 28.04 210.065 277.183 28.05 210.065 277.183 28.06 277.183 28.07 277.183 28.08 280.403 28.09 280.403 28.1 280.403 28.11 280.403 28.12 280.403 28.13 280.403 28.14 280.403 28.15 280.403 28.16 280.403 28.17 280.403 28.18 280.403 28.19 280.403 28.2 280.403 28.21 280.403 28.22 280.403 28.23 280.403 28.24 280.403 28.25 280.403 28.26 280.403 28.27 280.403 28.28 280.403 28.29 280.403 28.3 280.403 28.31 280.403 28.32 280.403 28.33 280.403 28.34 280.403 28.35 280.403 28.36 280.403 28.37 280.403 28.38 277.183 28.39 277.183 28.4 277.183 28.41 277.183 28.42 277.183 28.43 277.183 28.44 280.403 28.45 280.403 28.46 280.403 210.065 28.47 280.403 210.065 28.48 280.403 210.065 28.49 280.403 210.065 28.5 280.403 210.065 28.51 280.403 210.065 28.52 280.403 207.652 28.53 280.403 207.652 28.54 280.403 207.652 28.55 280.403 207.652 28.56 280.403 207.652 28.57 280.403 207.652 28.58 280.403 207.652 28.59 280.403 207.652 28.6 280.403 207.652 28.61 280.403 207.652 28.62 280.403 207.652 28.63 280.403 207.652 28.64 280.403 207.652 28.65 280.403 207.652 28.66 280.403 207.652 28.67 280.403 207.652 28.68 280.403 207.652 28.69 280.403 207.652 28.7 280.403 207.652 28.71 280.403 207.652 28.72 280.403 207.652 28.73 280.403 207.652 28.74 280.403 207.652 28.75 280.403 207.652 28.76 280.403 207.652 28.77 280.403 207.652 28.78 280.403 207.652 28.79 280.403 207.652 28.8 280.403 207.652 28.81 280.403 207.652 28.82 280.403 207.652 28.83 280.403 207.652 28.84 280.403 207.652 28.85 280.403 207.652 28.86 280.403 207.652 28.87 280.403 207.652 28.88 280.403 207.652 28.89 280.403 207.652 28.9 280.403 207.652 28.91 280.403 207.652 28.92 280.403 28.93 280.403 28.94 280.403 28.95 280.403 28.96 280.403 28.97 280.403 28.98 277.183 28.99 277.183 29.0 277.183 29.01 277.183 29.02 277.183 29.03 277.183 29.04 277.183 29.05 277.183 29.06 280.403 29.07 280.403 29.08 280.403 29.09 280.403 29.1 280.403 29.11 280.403 29.12 280.403 29.13 280.403 29.14 280.403 29.15 280.403 29.16 280.403 29.17 280.403 29.18 280.403 29.19 280.403 29.2 280.403 29.21 280.403 29.22 280.403 29.23 280.403 29.24 280.403 29.25 280.403 29.26 280.403 29.27 280.403 29.28 280.403 29.29 280.403 29.3 280.403 29.31 280.403 29.32 277.183 29.33 277.183 29.34 277.183 29.35 277.183 29.36 277.183 29.37 277.183 29.38 277.183 29.39 277.183 29.4 277.183 29.41 277.183 29.42 280.403 29.43 280.403 29.44 280.403 29.45 280.403 29.46 280.403 29.47 280.403 29.48 280.403 29.49 280.403 29.5 280.403 29.51 280.403 29.52 280.403 29.53 280.403 29.54 280.403 29.55 280.403 29.56 280.403 29.57 280.403 29.58 280.403 29.59 280.403 29.6 280.403 29.61 280.403 29.62 280.403 29.63 280.403 29.64 280.403 29.65 280.403 29.66 280.403 29.67 280.403 29.68 280.403 29.69 280.403 29.7 280.403 29.71 280.403 29.72 280.403 29.73 280.403 29.74 280.403 29.75 280.403 29.76 280.403 29.77 280.403 29.78 280.403 29.79 280.403 29.8 280.403 29.81 280.403 29.82 280.403 29.83 280.403 29.84 280.403 29.85 280.403 29.86 280.403 29.87 280.403 29.88 280.403 29.89 280.403 29.9 280.403 29.91 280.403 29.92 280.403 29.93 280.403 29.94 280.403 29.95 280.403 29.96 280.403 29.97 280.403 29.98 280.403 29.99 280.403 30.0 280.403 210.065 30.01 280.403 210.065 30.02 280.403 210.065 30.03 280.403 210.065 30.04 280.403 210.065 30.05 280.403 210.065 30.06 280.403 207.652 30.07 280.403 207.652 30.08 280.403 207.652 30.09 280.403 207.652 30.1 280.403 207.652 30.11 280.403 207.652 30.12 280.403 207.652 30.13 280.403 207.652 30.14 280.403 207.652 30.15 280.403 207.652 30.16 280.403 207.652 30.17 280.403 207.652 30.18 280.403 207.652 30.19 280.403 207.652 30.2 280.403 207.652 30.21 280.403 207.652 30.22 280.403 207.652 30.23 280.403 207.652 30.24 280.403 207.652 30.25 280.403 207.652 30.26 280.403 207.652 30.27 280.403 207.652 30.28 280.403 207.652 30.29 280.403 207.652 30.3 280.403 207.652 30.31 280.403 207.652 30.32 280.403 207.652 30.33 280.403 207.652 30.34 280.403 207.652 30.35 280.403 207.652 30.36 280.403 207.652 30.37 280.403 207.652 30.38 207.652 30.39 207.652 30.4 207.652 30.41 207.652 30.42 207.652 30.43 207.652 30.44 207.652 30.45 207.652 30.46 207.652 30.47 207.652 30.48 280.403 30.49 280.403 30.5 280.403 30.51 280.403 30.52 280.403 30.53 280.403 30.54 280.403 30.55 280.403 30.56 280.403 30.57 280.403 30.58 280.403 30.59 280.403 30.6 280.403 30.61 280.403 30.62 280.403 30.63 280.403 30.64 280.403 30.65 280.403 30.66 280.403 30.67 280.403 30.68 280.403 30.69 280.403 30.7 277.183 30.71 277.183 30.72 277.183 30.73 277.183 30.74 277.183 30.75 277.183 30.76 277.183 30.77 277.183 30.78 280.403 30.79 280.403 30.8 280.403 30.81 280.403 30.82 280.403 30.83 280.403 30.84 280.403 30.85 280.403 30.86 280.403 30.87 280.403 30.88 280.403 30.89 280.403 30.9 280.403 30.91 280.403 30.92 280.403 30.93 280.403 30.94 280.403 30.95 280.403 30.96 280.403 30.97 280.403 30.98 30.99 31.0 31.01 31.02 31.03 31.04 31.05 31.06 31.07 31.08 31.09 31.1 31.11 31.12 31.13 31.14 31.15 31.16 31.17 31.18 31.19 31.2 31.21 31.22 31.23 31.24 31.25 31.26 31.27 31.28 31.29 31.3 31.31 31.32 31.33 31.34 31.35 31.36 31.37 31.38 31.39 31.4 31.41 31.42 31.43 31.44 420.13 31.45 420.13 31.46 207.652 420.13 31.47 207.652 420.13 31.48 207.652 420.13 31.49 207.652 420.13 31.5 207.652 420.13 31.51 207.652 420.13 31.52 207.652 420.13 31.53 207.652 420.13 31.54 207.652 420.13 31.55 207.652 420.13 31.56 207.652 420.13 31.57 207.652 420.13 31.58 207.652 420.13 31.59 207.652 420.13 31.6 207.652 420.13 31.61 207.652 420.13 31.62 207.652 420.13 31.63 207.652 420.13 31.64 207.652 420.13 31.65 207.652 420.13 31.66 207.652 420.13 31.67 207.652 420.13 31.68 207.652 420.13 31.69 207.652 420.13 31.7 210.065 420.13 31.71 210.065 420.13 31.72 210.065 420.13 31.73 210.065 420.13 31.74 210.065 420.13 31.75 210.065 420.13 31.76 210.065 420.13 31.77 210.065 420.13 31.78 210.065 420.13 31.79 210.065 420.13 31.8 210.065 420.13 31.81 210.065 420.13 31.82 210.065 420.13 31.83 210.065 420.13 31.84 207.652 420.13 31.85 207.652 420.13 31.86 207.652 420.13 31.87 207.652 420.13 31.88 207.652 420.13 31.89 207.652 420.13 31.9 420.13 31.91 420.13 31.92 420.13 31.93 420.13 31.94 420.13 31.95 420.13 31.96 420.13 31.97 420.13 31.98 420.13 31.99 420.13 32.0 420.13 32.01 420.13 32.02 420.13 32.03 420.13 32.04 420.13 32.05 420.13 32.06 420.13 32.07 420.13 32.08 420.13 32.09 420.13 32.1 420.13 32.11 420.13 32.12 280.403 420.13 32.13 280.403 420.13 32.14 280.403 420.13 32.15 280.403 420.13 32.16 280.403 420.13 32.17 280.403 420.13 32.18 280.403 420.13 32.19 280.403 420.13 32.2 280.403 420.13 32.21 280.403 420.13 32.22 280.403 420.13 32.23 280.403 420.13 32.24 280.403 420.13 32.25 280.403 420.13 32.26 280.403 420.13 32.27 280.403 420.13 32.28 420.13 32.29 420.13 32.3 420.13 32.31 420.13 32.32 420.13 32.33 420.13 32.34 420.13 32.35 420.13 32.36 420.13 32.37 420.13 32.38 420.13 32.39 420.13 32.4 420.13 32.41 420.13 32.42 420.13 32.43 420.13 32.44 420.13 32.45 420.13 32.46 420.13 32.47 420.13 32.48 420.13 32.49 420.13 32.5 420.13 32.51 420.13 32.52 420.13 32.53 420.13 32.54 420.13 32.55 420.13 32.56 420.13 32.57 420.13 32.58 420.13 32.59 420.13 32.6 420.13 32.61 420.13 32.62 420.13 32.63 420.13 32.64 420.13 32.65 420.13 32.66 420.13 32.67 420.13 32.68 420.13 32.69 420.13 32.7 420.13 32.71 420.13 32.72 420.13 32.73 420.13 32.74 420.13 32.75 420.13 32.76 32.77 32.78 420.13 32.79 420.13 32.8 420.13 32.81 420.13 32.82 420.13 32.83 420.13 32.84 420.13 32.85 420.13 32.86 420.13 32.87 420.13 32.88 420.13 32.89 420.13 32.9 420.13 32.91 420.13 32.92 420.13 32.93 420.13 32.94 420.13 32.95 420.13 32.96 420.13 32.97 420.13 32.98 420.13 32.99 420.13 33.0 420.13 33.01 420.13 33.02 420.13 33.03 420.13 33.04 420.13 33.05 420.13 33.06 420.13 33.07 420.13 33.08 420.13 33.09 420.13 33.1 420.13 33.11 420.13 33.12 420.13 33.13 420.13 33.14 420.13 33.15 420.13 33.16 420.13 33.17 420.13 33.18 425.012 33.19 425.012 33.2 425.012 33.21 425.012 33.22 425.012 33.23 425.012 33.24 420.13 33.25 420.13 33.26 210.065 264.666 420.13 33.27 210.065 264.666 420.13 33.28 210.065 264.666 420.13 33.29 210.065 264.666 420.13 33.3 210.065 264.666 420.13 33.31 210.065 264.666 420.13 33.32 210.065 264.666 420.13 33.33 210.065 264.666 420.13 33.34 210.065 264.666 420.13 33.35 210.065 264.666 420.13 33.36 210.065 264.666 420.13 33.37 210.065 264.666 420.13 33.38 210.065 264.666 420.13 33.39 210.065 264.666 420.13 33.4 210.065 264.666 420.13 33.41 210.065 264.666 420.13 33.42 210.065 264.666 420.13 33.43 210.065 264.666 420.13 33.44 210.065 264.666 105.033 420.13 33.45 210.065 264.666 105.033 420.13 33.46 210.065 264.666 105.033 420.13 33.47 210.065 264.666 105.033 420.13 33.48 264.666 105.033 66.9352 420.13 33.49 264.666 105.033 66.9352 420.13 33.5 264.666 105.033 66.9352 420.13 33.51 264.666 105.033 66.9352 420.13 33.52 264.666 105.033 66.9352 420.13 33.53 264.666 105.033 66.9352 420.13 33.54 264.666 105.033 66.9352 420.13 33.55 264.666 105.033 66.9352 420.13 33.56 264.666 105.033 66.9352 420.13 33.57 264.666 105.033 66.9352 420.13 33.58 264.666 105.033 66.9352 420.13 33.59 264.666 105.033 66.9352 420.13 33.6 264.666 105.033 66.9352 420.13 33.61 264.666 105.033 66.9352 420.13 33.62 210.065 264.666 66.9352 420.13 33.63 210.065 264.666 66.9352 420.13 33.64 210.065 264.666 66.9352 420.13 33.65 210.065 264.666 66.9352 420.13 33.66 210.065 264.666 66.9352 420.13 33.67 210.065 264.666 66.9352 420.13 33.68 210.065 264.666 66.9352 420.13 33.69 210.065 264.666 66.9352 420.13 33.7 210.065 264.666 66.9352 420.13 33.71 210.065 264.666 66.9352 420.13 33.72 210.065 264.666 66.9352 420.13 33.73 210.065 264.666 66.9352 420.13 33.74 210.065 264.666 66.9352 420.13 33.75 210.065 264.666 66.9352 420.13 33.76 210.065 264.666 66.9352 420.13 33.77 210.065 264.666 66.9352 420.13 33.78 210.065 264.666 66.9352 420.13 33.79 210.065 264.666 66.9352 420.13 33.8 210.065 264.666 66.9352 420.13 33.81 210.065 264.666 66.9352 420.13 33.82 210.065 264.666 66.9352 420.13 33.83 210.065 264.666 66.9352 420.13 33.84 210.065 264.666 420.13 33.85 210.065 264.666 420.13 33.86 210.065 264.666 420.13 33.87 210.065 264.666 420.13 33.88 210.065 264.666 420.13 33.89 210.065 264.666 420.13 33.9 210.065 264.666 420.13 33.91 210.065 264.666 420.13 33.92 314.742 210.065 264.666 420.13 33.93 314.742 210.065 264.666 420.13 33.94 314.742 210.065 264.666 420.13 33.95 314.742 210.065 264.666 420.13 33.96 314.742 210.065 264.666 420.13 33.97 314.742 210.065 264.666 420.13 33.98 314.742 210.065 264.666 420.13 33.99 314.742 210.065 264.666 420.13 34.0 314.742 210.065 264.666 420.13 34.01 314.742 210.065 264.666 420.13 34.02 314.742 210.065 264.666 420.13 34.03 314.742 210.065 264.666 420.13 34.04 314.742 210.065 264.666 420.13 34.05 314.742 210.065 264.666 420.13 34.06 314.742 210.065 264.666 420.13 34.07 314.742 210.065 264.666 420.13 34.08 314.742 210.065 264.666 420.13 34.09 314.742 210.065 264.666 420.13 34.1 314.742 264.666 420.13 34.11 314.742 264.666 420.13 34.12 314.742 264.666 420.13 34.13 314.742 264.666 420.13 34.14 314.742 264.666 420.13 34.15 314.742 264.666 420.13 34.16 314.742 264.666 420.13 34.17 314.742 264.666 420.13 34.18 264.666 420.13 34.19 264.666 420.13 34.2 264.666 420.13 34.21 264.666 420.13 34.22 264.666 420.13 34.23 264.666 420.13 34.24 264.666 420.13 34.25 264.666 420.13 34.26 264.666 420.13 34.27 264.666 420.13 34.28 264.666 420.13 34.29 264.666 420.13 34.3 264.666 34.31 264.666 34.32 34.33 34.34 34.35 34.36 34.37 34.38 34.39 34.4 34.41 34.42 34.43 34.44 34.45 34.46 34.47 34.48 34.49 34.5 34.51 34.52 34.53 34.54 34.55 34.56 34.57 34.58 34.59 34.6 34.61 34.62 34.63 34.64 34.65 34.66 34.67 34.68 34.69 34.7 34.71 34.72 34.73 34.74 34.75 34.76 34.77 34.78 34.79 34.8 34.81 34.82 34.83 34.84 34.85 34.86 207.652 34.87 207.652 34.88 207.652 34.89 207.652 34.9 207.652 34.91 207.652 34.92 207.652 34.93 207.652 34.94 207.652 34.95 207.652 34.96 207.652 34.97 207.652 34.98 207.652 34.99 207.652 35.0 207.652 35.01 207.652 35.02 207.652 35.03 207.652 35.04 207.652 35.05 207.652 35.06 210.065 35.07 210.065 35.08 210.065 35.09 210.065 35.1 210.065 35.11 210.065 35.12 207.652 35.13 207.652 35.14 207.652 35.15 207.652 35.16 207.652 35.17 207.652 35.18 35.19 35.2 35.21 35.22 35.23 35.24 35.25 35.26 35.27 35.28 35.29 35.3 35.31 35.32 35.33 35.34 311.127 35.35 311.127 35.36 311.127 35.37 311.127 35.38 311.127 35.39 311.127 35.4 314.742 35.41 314.742 35.42 314.742 35.43 314.742 35.44 314.742 35.45 314.742 35.46 314.742 35.47 314.742 35.48 314.742 35.49 314.742 35.5 314.742 35.51 314.742 35.52 314.742 35.53 314.742 35.54 314.742 35.55 314.742 35.56 314.742 35.57 314.742 35.58 314.742 35.59 314.742 35.6 314.742 35.61 314.742 35.62 314.742 35.63 314.742 35.64 314.742 35.65 314.742 35.66 314.742 35.67 314.742 35.68 314.742 35.69 314.742 35.7 314.742 35.71 314.742 35.72 314.742 35.73 314.742 35.74 35.75 35.76 35.77 35.78 35.79 35.8 35.81 35.82 35.83 35.84 35.85 35.86 35.87 35.88 35.89 35.9 35.91 35.92 35.93 35.94 35.95 35.96 35.97 35.98 35.99 36.0 36.01 36.02 36.03 36.04 36.05 36.06 36.07 36.08 36.09 36.1 36.11 36.12 36.13 36.14 36.15 36.16 36.17 36.18 36.19 36.2 36.21 36.22 36.23 36.24 36.25 36.26 210.065 36.27 210.065 36.28 210.065 36.29 210.065 36.3 210.065 36.31 210.065 36.32 207.652 36.33 207.652 36.34 207.652 36.35 207.652 36.36 207.652 36.37 207.652 36.38 207.652 36.39 207.652 36.4 207.652 36.41 207.652 36.42 207.652 36.43 207.652 36.44 207.652 36.45 207.652 36.46 207.652 36.47 207.652 36.48 207.652 36.49 207.652 36.5 210.065 36.51 210.065 36.52 210.065 36.53 210.065 36.54 210.065 36.55 210.065 36.56 210.065 36.57 210.065 36.58 210.065 36.59 210.065 36.6 207.652 36.61 207.652 36.62 207.652 36.63 207.652 36.64 36.65 36.66 36.67 36.68 36.69 36.7 36.71 36.72 36.73 36.74 36.75 36.76 314.742 36.77 314.742 36.78 314.742 36.79 314.742 36.8 314.742 36.81 314.742 36.82 314.742 36.83 314.742 36.84 314.742 36.85 314.742 36.86 314.742 36.87 314.742 36.88 314.742 36.89 314.742 36.9 314.742 36.91 314.742 36.92 314.742 36.93 314.742 36.94 314.742 36.95 314.742 36.96 314.742 36.97 314.742 36.98 314.742 36.99 314.742 37.0 314.742 37.01 314.742 37.02 314.742 37.03 314.742 37.04 314.742 37.05 314.742 37.06 314.742 37.07 314.742 37.08 314.742 37.09 314.742 37.1 314.742 37.11 314.742 37.12 314.742 37.13 314.742 37.14 314.742 37.15 314.742 37.16 314.742 37.17 314.742 37.18 314.742 37.19 314.742 37.2 314.742 37.21 314.742 37.22 37.23 37.24 37.25 37.26 37.27 37.28 37.29 37.3 37.31 37.32 37.33 37.34 37.35 37.36 37.37 37.38 37.39 37.4 37.41 37.42 37.43 37.44 37.45 37.46 37.47 37.48 37.49 37.5 37.51 37.52 37.53 37.54 37.55 37.56 37.57 37.58 37.59 37.6 37.61 37.62 37.63 37.64 37.65 37.66 37.67 37.68 37.69 37.7 37.71 37.72 415.305 37.73 415.305 37.74 415.305 37.75 415.305 37.76 415.305 37.77 415.305 37.78 420.13 37.79 420.13 37.8 207.652 420.13 37.81 207.652 420.13 37.82 207.652 420.13 37.83 207.652 420.13 37.84 207.652 420.13 37.85 207.652 420.13 37.86 207.652 420.13 37.87 207.652 420.13 37.88 207.652 420.13 37.89 207.652 420.13 37.9 207.652 420.13 37.91 207.652 420.13 37.92 207.652 420.13 37.93 207.652 420.13 37.94 207.652 420.13 37.95 207.652 420.13 37.96 207.652 420.13 37.97 207.652 420.13 37.98 420.13 37.99 420.13 38.0 420.13 38.01 420.13 38.02 420.13 38.03 420.13 38.04 420.13 38.05 420.13 38.06 420.13 38.07 420.13 38.08 420.13 38.09 420.13 38.1 420.13 38.11 420.13 38.12 420.13 38.13 420.13 38.14 420.13 38.15 420.13 38.16 420.13 38.17 420.13 38.18 420.13 38.19 420.13 38.2 420.13 38.21 420.13 38.22 420.13 38.23 420.13 38.24 420.13 38.25 420.13 38.26 420.13 38.27 420.13 38.28 311.127 420.13 38.29 311.127 420.13 38.3 311.127 420.13 38.31 311.127 420.13 38.32 311.127 420.13 38.33 311.127 420.13 38.34 314.742 420.13 38.35 314.742 420.13 38.36 314.742 420.13 38.37 314.742 420.13 38.38 314.742 420.13 38.39 314.742 420.13 38.4 314.742 420.13 38.41 314.742 420.13 38.42 314.742 420.13 38.43 314.742 420.13 38.44 314.742 420.13 38.45 314.742 420.13 38.46 314.742 420.13 38.47 314.742 420.13 38.48 314.742 420.13 38.49 314.742 420.13 38.5 314.742 420.13 38.51 314.742 420.13 38.52 314.742 420.13 38.53 314.742 420.13 38.54 314.742 420.13 38.55 314.742 420.13 38.56 314.742 420.13 38.57 314.742 420.13 38.58 314.742 420.13 38.59 314.742 420.13 38.6 314.742 420.13 38.61 314.742 420.13 38.62 314.742 420.13 38.63 314.742 420.13 38.64 314.742 420.13 38.65 314.742 420.13 38.66 314.742 420.13 38.67 314.742 420.13 38.68 314.742 420.13 38.69 314.742 420.13 38.7 314.742 420.13 38.71 314.742 420.13 38.72 314.742 420.13 38.73 314.742 420.13 38.74 314.742 420.13 38.75 314.742 420.13 38.76 420.13 38.77 420.13 38.78 369.994 420.13 38.79 369.994 420.13 38.8 369.994 420.13 38.81 369.994 420.13 38.82 369.994 420.13 38.83 369.994 420.13 38.84 374.294 420.13 38.85 374.294 420.13 38.86 374.294 420.13 38.87 374.294 420.13 38.88 374.294 420.13 38.89 374.294 420.13 38.9 374.294 420.13 38.91 374.294 420.13 38.92 420.13 38.93 420.13 38.94 420.13 38.95 420.13 38.96 38.97 38.98 420.13 38.99 420.13 39.0 420.13 39.01 420.13 39.02 420.13 39.03 420.13 39.04 420.13 39.05 420.13 39.06 420.13 39.07 420.13 39.08 420.13 39.09 420.13 39.1 420.13 39.11 420.13 39.12 420.13 39.13 420.13 39.14 420.13 39.15 420.13 39.16 420.13 39.17 420.13 39.18 420.13 39.19 420.13 39.2 420.13 39.21 420.13 39.22 420.13 39.23 420.13 39.24 420.13 39.25 420.13 39.26 420.13 39.27 420.13 39.28 420.13 39.29 420.13 39.3 420.13 39.31 420.13 39.32 420.13 39.33 420.13 39.34 420.13 39.35 420.13 39.36 420.13 39.37 420.13 39.38 420.13 39.39 420.13 39.4 420.13 39.41 420.13 39.42 420.13 39.43 420.13 39.44 420.13 39.45 420.13 39.46 39.47 39.48 420.13 39.49 420.13 39.5 70.1008 420.13 39.51 70.1008 420.13 39.52 70.1008 207.652 420.13 39.53 70.1008 207.652 420.13 39.54 70.1008 207.652 420.13 39.55 70.1008 207.652 420.13 39.56 70.1008 207.652 420.13 39.57 70.1008 207.652 420.13 39.58 70.1008 207.652 420.13 39.59 70.1008 207.652 420.13 39.6 70.1008 207.652 420.13 39.61 70.1008 207.652 420.13 39.62 70.1008 207.652 420.13 39.63 70.1008 207.652 420.13 39.64 210.065 70.1008 420.13 39.65 210.065 70.1008 420.13 39.66 210.065 70.1008 420.13 39.67 210.065 70.1008 420.13 39.68 210.065 70.1008 420.13 39.69 210.065 70.1008 420.13 39.7 210.065 70.1008 420.13 39.71 210.065 70.1008 420.13 39.72 210.065 70.1008 420.13 39.73 210.065 70.1008 420.13 39.74 210.065 70.1008 420.13 39.75 210.065 70.1008 420.13 39.76 210.065 70.1008 420.13 39.77 210.065 70.1008 420.13 39.78 210.065 70.1008 420.13 39.79 210.065 70.1008 420.13 39.8 210.065 70.1008 420.13 39.81 210.065 70.1008 420.13 39.82 70.1008 420.13 39.83 70.1008 420.13 39.84 70.1008 420.13 39.85 70.1008 420.13 39.86 70.1008 420.13 39.87 70.1008 420.13 39.88 70.1008 420.13 39.89 70.1008 420.13 39.9 70.1008 420.13 39.91 70.1008 420.13 39.92 70.1008 420.13 39.93 70.1008 420.13 39.94 70.1008 420.13 39.95 70.1008 420.13 39.96 70.1008 420.13 39.97 70.1008 420.13 39.98 70.1008 420.13 39.99 70.1008 420.13 40.0 70.1008 420.13 40.01 70.1008 420.13 40.02 70.1008 420.13 40.03 70.1008 420.13 40.04 70.1008 420.13 40.05 70.1008 420.13 40.06 70.1008 420.13 40.07 70.1008 420.13 40.08 70.1008 420.13 40.09 70.1008 420.13 40.1 70.1008 420.13 40.11 70.1008 420.13 40.12 70.1008 420.13 40.13 70.1008 420.13 40.14 70.1008 420.13 40.15 70.1008 420.13 40.16 70.1008 420.13 40.17 70.1008 420.13 40.18 70.1008 420.13 40.19 70.1008 420.13 40.2 70.1008 420.13 40.21 70.1008 420.13 40.22 280.403 70.1008 420.13 40.23 280.403 70.1008 420.13 40.24 280.403 70.1008 420.13 40.25 280.403 70.1008 420.13 40.26 280.403 70.1008 420.13 40.27 280.403 70.1008 420.13 40.28 280.403 420.13 40.29 280.403 420.13 40.3 280.403 420.13 40.31 280.403 420.13 40.32 280.403 420.13 40.33 280.403 420.13 40.34 420.13 40.35 420.13 40.36 420.13 40.37 420.13 40.38 420.13 40.39 420.13 40.4 420.13 40.41 420.13 40.42 420.13 40.43 420.13 40.44 420.13 40.45 420.13 40.46 420.13 40.47 420.13 40.48 420.13 40.49 420.13 40.5 420.13 40.51 420.13 40.52 420.13 40.53 420.13 40.54 420.13 40.55 420.13 40.56 40.57 40.58 40.59 40.6 40.61 40.62 40.63 40.64 40.65 40.66 40.67 40.68 40.69 40.7 40.71 40.72 40.73 40.74 40.75 40.76 40.77 40.78 40.79 40.8 40.81 40.82 40.83 40.84 40.85 40.86 40.87 40.88 40.89 40.9 40.91 40.92 40.93 40.94 40.95 40.96 40.97 40.98 40.99 41.0 41.01 41.02 41.03 41.04 41.05 41.06 207.652 41.07 207.652 41.08 207.652 41.09 207.652 41.1 207.652 41.11 207.652 41.12 207.652 41.13 207.652 41.14 207.652 41.15 207.652 41.16 207.652 41.17 207.652 41.18 207.652 41.19 207.652 41.2 207.652 41.21 207.652 41.22 207.652 41.23 207.652 41.24 207.652 41.25 207.652 41.26 207.652 41.27 207.652 41.28 41.29 41.3 41.31 41.32 41.33 41.34 41.35 41.36 41.37 41.38 41.39 41.4 41.41 41.42 41.43 41.44 41.45 41.46 41.47 41.48 41.49 41.5 41.51 41.52 41.53 41.54 41.55 41.56 41.57 41.58 41.59 41.6 41.61 41.62 41.63 41.64 280.403 41.65 280.403 41.66 280.403 41.67 280.403 41.68 280.403 41.69 280.403 41.7 280.403 41.71 280.403 41.72 280.403 41.73 280.403 41.74 280.403 41.75 280.403 41.76 280.403 41.77 280.403 41.78 41.79 41.8 41.81 41.82 41.83 41.84 41.85 41.86 41.87 41.88 41.89 41.9 41.91 41.92 41.93 41.94 41.95 41.96 41.97 41.98 41.99 42.0 42.01 42.02 42.03 42.04 42.05 42.06 42.07 42.08 42.09 42.1 42.11 42.12 42.13 42.14 42.15 42.16 42.17 42.18 42.19 42.2 42.21 42.22 42.23 42.24 42.25 42.26 42.27 42.28 42.29 42.3 42.31 42.32 42.33 42.34 42.35 42.36 42.37 42.38 42.39 42.4 42.41 42.42 42.43 42.44 42.45 42.46 42.47 42.48 42.49 42.5 42.51 42.52 42.53 42.54 445.113 42.55 445.113 42.56 445.113 42.57 445.113 42.58 445.113 42.59 445.113 42.6 445.113 42.61 445.113 42.62 445.113 42.63 445.113 42.64 222.556 445.113 42.65 222.556 445.113 42.66 222.556 445.113 42.67 222.556 445.113 42.68 222.556 445.113 42.69 222.556 445.113 42.7 222.556 445.113 42.71 222.556 445.113 42.72 222.556 92.4986 445.113 42.73 222.556 92.4986 445.113 42.74 222.556 92.4986 445.113 42.75 222.556 92.4986 445.113 42.76 222.556 92.4986 42.77 222.556 92.4986 42.78 222.556 92.4986 42.79 222.556 92.4986 42.8 222.556 92.4986 42.81 222.556 92.4986 42.82 222.556 92.4986 42.83 222.556 92.4986 42.84 222.556 92.4986 42.85 222.556 92.4986 42.86 222.556 92.4986 42.87 222.556 92.4986 42.88 222.556 92.4986 445.113 42.89 222.556 92.4986 445.113 42.9 222.556 92.4986 445.113 42.91 222.556 92.4986 445.113 42.92 222.556 92.4986 445.113 42.93 222.556 92.4986 445.113 42.94 222.556 92.4986 445.113 42.95 222.556 92.4986 445.113 42.96 222.556 92.4986 445.113 42.97 222.556 92.4986 445.113 42.98 222.556 92.4986 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92.4986 43.41 92.4986 43.42 92.4986 43.43 92.4986 43.44 92.4986 43.45 92.4986 43.46 92.4986 43.47 92.4986 43.48 92.4986 43.49 92.4986 43.5 92.4986 43.51 92.4986 43.52 92.4986 43.53 92.4986 43.54 92.4986 43.55 92.4986 43.56 92.4986 43.57 92.4986 43.58 92.4986 43.59 92.4986 43.6 92.4986 43.61 92.4986 43.62 92.4986 43.63 92.4986 43.64 92.4986 43.65 92.4986 43.66 92.4986 43.67 92.4986 43.68 92.4986 43.69 92.4986 43.7 92.4986 43.71 92.4986 43.72 92.4986 43.73 92.4986 43.74 92.4986 43.75 92.4986 43.76 92.4986 43.77 92.4986 43.78 92.4986 43.79 92.4986 43.8 92.4986 43.81 92.4986 43.82 92.4986 43.83 92.4986 43.84 92.4986 43.85 92.4986 43.86 92.4986 43.87 92.4986 43.88 92.4986 43.89 92.4986 43.9 92.4986 43.91 92.4986 43.92 92.4986 43.93 92.4986 43.94 92.4986 43.95 92.4986 43.96 92.4986 43.97 92.4986 43.98 92.4986 43.99 92.4986 44.0 92.4986 44.01 92.4986 44.02 92.4986 44.03 92.4986 44.04 92.4986 44.05 92.4986 44.06 93.5734 220 44.07 93.5734 220 44.08 93.5734 220 44.09 93.5734 220 44.1 93.5734 220 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44.61 220 44.62 93.5734 220 44.63 93.5734 220 44.64 93.5734 220 44.65 93.5734 220 44.66 93.5734 220 44.67 93.5734 220 44.68 280.403 222.556 93.5734 44.69 280.403 222.556 93.5734 44.7 280.403 222.556 93.5734 44.71 280.403 222.556 93.5734 44.72 280.403 222.556 93.5734 44.73 280.403 222.556 93.5734 44.74 280.403 93.5734 220 44.75 280.403 93.5734 220 44.76 280.403 93.5734 220 44.77 280.403 93.5734 220 44.78 280.403 92.4986 220 44.79 280.403 92.4986 220 44.8 280.403 92.4986 220 44.81 280.403 92.4986 220 44.82 280.403 92.4986 220 44.83 280.403 92.4986 220 44.84 222.556 92.4986 44.85 222.556 92.4986 44.86 222.556 92.4986 44.87 222.556 92.4986 44.88 222.556 92.4986 44.89 222.556 92.4986 44.9 222.556 92.4986 44.91 222.556 92.4986 44.92 222.556 92.4986 44.93 222.556 92.4986 44.94 222.556 92.4986 44.95 222.556 92.4986 44.96 222.556 92.4986 44.97 222.556 92.4986 44.98 222.556 92.4986 44.99 222.556 92.4986 45.0 92.4986 45.01 92.4986 45.02 92.4986 45.03 92.4986 45.04 92.4986 45.05 92.4986 45.06 92.4986 45.07 92.4986 45.08 92.4986 45.09 92.4986 45.1 92.4986 45.11 92.4986 45.12 92.4986 45.13 92.4986 45.14 92.4986 45.15 92.4986 45.16 92.4986 45.17 92.4986 45.18 92.4986 45.19 92.4986 45.2 92.4986 45.21 92.4986 45.22 92.4986 45.23 92.4986 45.24 93.5734 45.25 93.5734 45.26 93.5734 45.27 93.5734 45.28 93.5734 45.29 93.5734 45.3 93.5734 45.31 93.5734 45.32 93.5734 45.33 93.5734 45.34 93.5734 45.35 93.5734 45.36 93.5734 45.37 93.5734 45.38 93.5734 45.39 93.5734 45.4 92.4986 45.41 92.4986 45.42 92.4986 45.43 92.4986 45.44 92.4986 45.45 92.4986 45.46 92.4986 45.47 92.4986 45.48 92.4986 45.49 92.4986 45.5 92.4986 45.51 92.4986 45.52 92.4986 45.53 92.4986 45.54 92.4986 45.55 92.4986 45.56 92.4986 45.57 92.4986 45.58 92.4986 45.59 92.4986 45.6 92.4986 45.61 92.4986 45.62 92.4986 425.012 45.63 92.4986 425.012 45.64 92.4986 425.012 45.65 92.4986 425.012 45.66 425.012 45.67 425.012 45.68 210.065 420.13 45.69 210.065 420.13 45.7 210.065 420.13 45.71 210.065 420.13 45.72 210.065 420.13 45.73 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47.91 47.92 47.93 47.94 47.95 47.96 47.97 47.98 47.99 48.0 48.01 48.02 48.03 48.04 48.05 48.06 48.07 48.08 48.09 48.1 48.11 48.12 48.13 48.14 48.15 48.16 48.17 48.18 48.19 48.2 333.458 48.21 333.458 48.22 333.458 48.23 333.458 48.24 333.458 48.25 333.458 48.26 333.458 48.27 333.458 48.28 333.458 48.29 333.458 48.3 333.458 48.31 333.458 48.32 333.458 48.33 333.458 48.34 333.458 48.35 333.458 48.36 48.37 48.38 48.39 48.4 333.458 48.41 333.458 48.42 333.458 48.43 333.458 48.44 333.458 48.45 333.458 48.46 333.458 48.47 333.458 48.48 333.458 48.49 333.458 48.5 333.458 48.51 333.458 48.52 333.458 48.53 333.458 48.54 333.458 48.55 333.458 48.56 48.57 48.58 369.994 48.59 369.994 48.6 369.994 48.61 369.994 48.62 369.994 48.63 369.994 48.64 369.994 48.65 369.994 48.66 369.994 48.67 369.994 48.68 369.994 48.69 369.994 48.7 374.294 48.71 374.294 48.72 374.294 48.73 374.294 48.74 374.294 220 124.905 48.75 374.294 220 124.905 48.76 374.294 220 124.905 48.77 374.294 220 124.905 48.78 374.294 220 124.905 48.79 374.294 220 124.905 48.8 374.294 220 124.905 48.81 374.294 220 124.905 48.82 374.294 220 124.905 48.83 374.294 220 124.905 48.84 374.294 220 124.905 48.85 374.294 220 124.905 48.86 374.294 220 124.905 48.87 374.294 220 124.905 48.88 374.294 220 124.905 48.89 374.294 220 124.905 48.9 374.294 220 124.905 48.91 374.294 220 124.905 48.92 374.294 124.905 48.93 374.294 124.905 48.94 369.994 124.905 48.95 369.994 124.905 48.96 369.994 124.905 48.97 369.994 124.905 48.98 369.994 124.905 48.99 369.994 124.905 49.0 374.294 124.905 49.01 374.294 124.905 49.02 374.294 124.905 49.03 374.294 124.905 49.04 374.294 124.905 49.05 374.294 124.905 49.06 374.294 124.905 49.07 374.294 124.905 49.08 369.994 124.905 49.09 369.994 124.905 49.1 369.994 124.905 49.11 369.994 124.905 49.12 369.994 49.13 369.994 49.14 374.294 49.15 374.294 49.16 374.294 49.17 374.294 49.18 374.294 49.19 374.294 49.2 374.294 49.21 374.294 49.22 374.294 49.23 374.294 49.24 374.294 49.25 374.294 49.26 374.294 49.27 374.294 49.28 374.294 49.29 374.294 49.3 374.294 49.31 374.294 49.32 374.294 49.33 374.294 49.34 374.294 49.35 374.294 49.36 374.294 49.37 374.294 49.38 374.294 49.39 374.294 49.4 374.294 49.41 374.294 49.42 374.294 49.43 374.294 49.44 374.294 49.45 374.294 49.46 374.294 49.47 374.294 49.48 374.294 49.49 374.294 49.5 374.294 49.51 374.294 49.52 374.294 49.53 374.294 49.54 374.294 49.55 374.294 49.56 374.294 49.57 374.294 49.58 374.294 49.59 374.294 49.6 311.127 374.294 49.61 311.127 374.294 49.62 311.127 374.294 49.63 311.127 374.294 49.64 311.127 49.65 311.127 49.66 314.742 49.67 314.742 49.68 314.742 49.69 314.742 49.7 314.742 49.71 314.742 49.72 314.742 49.73 314.742 49.74 314.742 49.75 314.742 49.76 314.742 49.77 314.742 49.78 314.742 49.79 314.742 49.8 314.742 49.81 314.742 49.82 314.742 49.83 314.742 49.84 314.742 49.85 314.742 49.86 314.742 49.87 314.742 49.88 314.742 49.89 314.742 49.9 314.742 49.91 314.742 49.92 314.742 49.93 314.742 49.94 314.742 49.95 314.742 49.96 314.742 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50.44 220 493.883 249.811 50.45 220 493.883 249.811 50.46 220 493.883 249.811 50.47 220 493.883 249.811 50.48 220 493.883 249.811 50.49 220 493.883 249.811 50.5 220 493.883 249.811 50.51 220 493.883 249.811 50.52 220 493.883 249.811 50.53 220 493.883 249.811 50.54 220 493.883 249.811 50.55 220 493.883 249.811 50.56 220 493.883 249.811 50.57 220 493.883 249.811 50.58 220 493.883 249.811 50.59 220 493.883 249.811 50.6 220 249.811 50.61 220 249.811 50.62 220 249.811 50.63 220 249.811 50.64 220 249.811 50.65 220 249.811 50.66 249.811 50.67 249.811 50.68 249.811 50.69 249.811 50.7 249.811 50.71 249.811 50.72 249.811 50.73 249.811 50.74 249.811 50.75 249.811 50.76 249.811 50.77 249.811 50.78 249.811 50.79 249.811 50.8 249.811 50.81 249.811 50.82 249.811 50.83 249.811 50.84 249.811 50.85 249.811 50.86 249.811 50.87 249.811 50.88 249.811 50.89 249.811 50.9 249.811 50.91 249.811 50.92 249.811 50.93 249.811 50.94 249.811 50.95 249.811 50.96 249.811 50.97 249.811 50.98 249.811 50.99 249.811 51.0 249.811 51.01 249.811 51.02 249.811 51.03 249.811 51.04 249.811 51.05 249.811 51.06 249.811 51.07 249.811 51.08 249.811 51.09 249.811 51.1 249.811 51.11 249.811 51.12 249.811 51.13 249.811 51.14 249.811 51.15 249.811 51.16 249.811 51.17 249.811 51.18 249.811 51.19 249.811 51.2 311.127 249.811 51.21 311.127 249.811 51.22 311.127 249.811 51.23 311.127 249.811 51.24 311.127 51.25 311.127 51.26 314.742 51.27 314.742 51.28 314.742 51.29 314.742 51.3 314.742 51.31 314.742 51.32 314.742 51.33 314.742 51.34 314.742 51.35 314.742 51.36 314.742 51.37 314.742 51.38 314.742 51.39 314.742 51.4 314.742 51.41 314.742 51.42 314.742 51.43 314.742 51.44 314.742 51.45 314.742 51.46 314.742 51.47 314.742 51.48 314.742 51.49 314.742 51.5 314.742 51.51 314.742 51.52 314.742 51.53 314.742 51.54 314.742 51.55 314.742 51.56 314.742 51.57 314.742 51.58 314.742 51.59 314.742 51.6 314.742 51.61 314.742 51.62 314.742 51.63 314.742 51.64 314.742 51.65 314.742 51.66 314.742 51.67 314.742 51.68 314.742 51.69 314.742 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246.94 932.33 987.77 4.26 185.00 932.33 4.27 185.00 932.33 4.28 185.00 932.33 4.29 185.00 415.30 830.61 932.33 4.30 185.00 415.30 830.61 932.33 4.31 185.00 415.30 830.61 932.33 4.32 185.00 415.30 830.61 932.33 4.33 185.00 415.30 830.61 932.33 4.34 185.00 415.30 830.61 932.33 4.35 185.00 415.30 830.61 932.33 4.36 185.00 415.30 830.61 932.33 4.37 185.00 415.30 830.61 932.33 4.38 185.00 415.30 830.61 932.33 4.39 185.00 415.30 830.61 932.33 4.40 185.00 415.30 830.61 932.33 4.41 185.00 415.30 830.61 932.33 4.42 185.00 415.30 830.61 932.33 4.43 185.00 415.30 830.61 932.33 4.44 185.00 415.30 830.61 932.33 4.45 185.00 415.30 830.61 932.33 4.46 185.00 415.30 830.61 932.33 4.47 185.00 246.94 415.30 830.61 932.33 4.48 185.00 246.94 415.30 830.61 932.33 4.49 185.00 246.94 415.30 493.88 830.61 932.33 4.50 185.00 246.94 415.30 493.88 830.61 932.33 4.51 185.00 246.94 493.88 830.61 932.33 4.52 185.00 246.94 493.88 830.61 932.33 4.53 185.00 246.94 493.88 830.61 932.33 4.54 185.00 246.94 493.88 830.61 932.33 4.55 185.00 246.94 493.88 830.61 932.33 4.56 185.00 246.94 493.88 622.25 830.61 932.33 4.57 185.00 246.94 493.88 622.25 830.61 4.58 185.00 246.94 493.88 622.25 830.61 4.59 185.00 246.94 493.88 622.25 830.61 4.60 185.00 246.94 493.88 622.25 830.61 4.61 185.00 246.94 493.88 622.25 830.61 4.62 185.00 246.94 493.88 622.25 830.61 4.63 185.00 246.94 493.88 622.25 830.61 4.64 185.00 246.94 493.88 622.25 830.61 4.65 185.00 246.94 622.25 830.61 4.66 185.00 622.25 830.61 4.67 185.00 622.25 830.61 4.68 185.00 622.25 830.61 4.69 185.00 622.25 4.70 185.00 622.25 4.71 185.00 622.25 4.72 185.00 587.33 622.25 4.73 185.00 587.33 622.25 4.74 185.00 587.33 622.25 4.75 185.00 587.33 622.25 4.76 185.00 587.33 622.25 4.77 185.00 587.33 622.25 4.78 185.00 587.33 622.25 4.79 185.00 587.33 622.25 4.80 185.00 587.33 622.25 4.81 185.00 587.33 622.25 4.82 185.00 587.33 622.25 4.83 185.00 587.33 622.25 4.84 185.00 587.33 622.25 4.85 185.00 587.33 622.25 4.86 185.00 587.33 4.87 185.00 587.33 4.88 185.00 587.33 4.89 185.00 587.33 4.90 185.00 587.33 4.91 185.00 587.33 4.92 185.00 587.33 4.93 185.00 587.33 4.94 185.00 587.33 4.95 587.33 4.96 587.33 4.97 587.33 4.98 587.33 4.99 587.33 5.00 587.33 5.01 587.33 5.02 587.33 5.03 587.33 5.04 587.33 5.05 587.33 5.06 587.33 5.07 587.33 5.08 587.33 5.09 587.33 5.10 587.33 5.11 587.33 622.25 5.12 587.33 622.25 5.13 587.33 622.25 5.14 246.94 587.33 622.25 5.15 246.94 587.33 622.25 5.16 246.94 587.33 622.25 5.17 246.94 587.33 622.25 5.18 246.94 587.33 622.25 5.19 246.94 587.33 622.25 5.20 246.94 587.33 622.25 5.21 246.94 587.33 622.25 5.22 246.94 622.25 5.23 185.00 246.94 622.25 5.24 185.00 246.94 622.25 5.25 185.00 246.94 622.25 5.26 185.00 246.94 622.25 5.27 185.00 246.94 622.25 5.28 185.00 246.94 622.25 5.29 185.00 622.25 5.30 185.00 622.25 5.31 185.00 622.25 5.32 185.00 622.25 5.33 185.00 622.25 5.34 185.00 622.25 5.35 185.00 622.25 5.36 185.00 622.25 5.37 622.25 5.38 622.25 5.39 622.25 5.40 5.41 415.30 5.42 415.30 5.43 415.30 5.44 185.00 415.30 5.45 185.00 415.30 5.46 185.00 415.30 5.47 185.00 415.30 5.48 185.00 415.30 5.49 185.00 415.30 5.50 185.00 415.30 5.51 185.00 415.30 5.52 185.00 415.30 5.53 185.00 415.30 5.54 185.00 415.30 5.55 185.00 415.30 5.56 185.00 415.30 5.57 185.00 415.30 5.58 185.00 415.30 5.59 185.00 415.30 5.60 415.30 5.61 415.30 5.62 207.65 415.30 5.63 207.65 415.30 5.64 207.65 415.30 5.65 207.65 415.30 5.66 207.65 415.30 5.67 207.65 415.30 5.68 207.65 415.30 5.69 207.65 369.99 415.30 5.70 185.00 207.65 369.99 415.30 5.71 185.00 207.65 369.99 415.30 5.72 185.00 369.99 415.30 5.73 185.00 369.99 415.30 5.74 185.00 369.99 415.30 5.75 185.00 369.99 415.30 5.76 185.00 369.99 415.30 5.77 185.00 369.99 415.30 5.78 185.00 369.99 415.30 5.79 185.00 369.99 415.30 5.80 185.00 369.99 415.30 5.81 185.00 369.99 415.30 5.82 185.00 369.99 415.30 5.83 185.00 369.99 415.30 5.84 185.00 369.99 5.85 185.00 369.99 5.86 185.00 369.99 5.87 185.00 369.99 5.88 185.00 369.99 5.89 185.00 369.99 5.90 185.00 369.99 5.91 185.00 369.99 622.25 5.92 185.00 369.99 622.25 5.93 185.00 369.99 622.25 5.94 185.00 369.99 622.25 5.95 185.00 369.99 622.25 5.96 185.00 369.99 622.25 5.97 185.00 369.99 622.25 5.98 185.00 369.99 622.25 5.99 369.99 622.25 6.00 369.99 622.25 6.01 369.99 622.25 6.02 369.99 622.25 6.03 369.99 622.25 6.04 369.99 622.25 6.05 369.99 622.25 659.26 6.06 369.99 622.25 659.26 6.07 369.99 622.25 659.26 6.08 369.99 622.25 659.26 6.09 369.99 659.26 6.10 369.99 659.26 6.11 659.26 6.12 659.26 6.13 207.65 659.26 6.14 207.65 659.26 6.15 207.65 659.26 6.16 207.65 659.26 6.17 207.65 659.26 6.18 207.65 659.26 6.19 207.65 392.00 659.26 6.20 207.65 392.00 659.26 6.21 207.65 392.00 659.26 6.22 207.65 392.00 659.26 6.23 207.65 392.00 659.26 6.24 207.65 392.00 659.26 6.25 392.00 659.26 6.26 392.00 659.26 6.27 392.00 659.26 6.28 392.00 659.26 6.29 392.00 659.26 6.30 392.00 659.26 6.31 392.00 659.26 6.32 392.00 659.26 6.33 392.00 659.26 6.34 392.00 659.26 6.35 392.00 659.26 6.36 659.26 6.37 659.26 6.38 659.26 6.39 659.26 6.40 415.30 659.26 6.41 415.30 659.26 6.42 415.30 659.26 6.43 415.30 659.26 6.44 415.30 659.26 6.45 415.30 659.26 6.46 415.30 659.26 6.47 415.30 659.26 6.48 415.30 659.26 6.49 415.30 659.26 6.50 415.30 659.26 6.51 415.30 659.26 6.52 415.30 659.26 6.53 415.30 659.26 6.54 415.30 622.25 659.26 6.55 415.30 622.25 659.26 6.56 622.25 659.26 6.57 622.25 659.26 6.58 622.25 659.26 6.59 622.25 659.26 6.60 622.25 6.61 622.25 6.62 622.25 6.63 622.25 6.64 622.25 6.65 622.25 6.66 622.25 6.67 622.25 6.68 622.25 6.69 622.25 6.70 622.25 6.71 622.25 6.72 622.25 6.73 622.25 6.74 622.25 739.99 6.75 207.65 622.25 739.99 6.76 207.65 622.25 739.99 6.77 207.65 622.25 739.99 6.78 207.65 622.25 739.99 6.79 207.65 622.25 739.99 6.80 103.83 207.65 311.13 622.25 739.99 6.81 103.83 207.65 311.13 622.25 739.99 6.82 103.83 207.65 311.13 554.37 622.25 739.99 6.83 103.83 207.65 311.13 554.37 622.25 739.99 6.84 103.83 207.65 311.13 554.37 622.25 739.99 6.85 103.83 207.65 311.13 554.37 622.25 739.99 6.86 103.83 207.65 311.13 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659.26 880.00 1760.00 7.70 220.00 440.00 554.37 659.26 880.00 1760.00 7.71 220.00 440.00 554.37 659.26 880.00 1760.00 7.72 220.00 440.00 554.37 659.26 880.00 1760.00 7.73 220.00 440.00 554.37 659.26 880.00 1760.00 7.74 220.00 440.00 554.37 659.26 880.00 1760.00 7.75 220.00 440.00 466.16 554.37 659.26 880.00 1318.51 7.76 220.00 440.00 466.16 554.37 622.25 659.26 880.00 1318.51 7.77 220.00 440.00 466.16 622.25 659.26 880.00 1318.51 7.78 220.00 440.00 466.16 622.25 659.26 880.00 1318.51 7.79 466.16 622.25 659.26 880.00 1318.51 7.80 466.16 622.25 659.26 880.00 1318.51 7.81 466.16 622.25 659.26 1318.51 7.82 466.16 622.25 659.26 1318.51 7.83 466.16 622.25 659.26 1318.51 7.84 466.16 622.25 659.26 1318.51 7.85 466.16 622.25 1318.51 7.86 466.16 622.25 7.87 466.16 622.25 7.88 466.16 622.25 7.89 466.16 622.25 7.90 466.16 622.25 7.91 466.16 622.25 7.92 233.08 466.16 622.25 7.93 233.08 466.16 622.25 7.94 233.08 466.16 622.25 7.95 233.08 466.16 622.25 7.96 233.08 466.16 622.25 7.97 233.08 466.16 622.25 7.98 233.08 466.16 622.25 7.99 233.08 466.16 622.25 8.00 233.08 466.16 622.25 8.01 233.08 466.16 622.25 8.02 233.08 466.16 622.25 8.03 233.08 466.16 622.25 8.04 233.08 466.16 622.25 8.05 466.16 622.25 8.06 466.16 622.25 8.07 466.16 622.25 8.08 466.16 622.25 8.09 311.13 466.16 622.25 8.10 207.65 311.13 466.16 622.25 8.11 207.65 311.13 466.16 622.25 8.12 207.65 311.13 466.16 622.25 8.13 207.65 311.13 466.16 622.25 8.14 207.65 311.13 466.16 622.25 8.15 207.65 311.13 466.16 622.25 8.16 207.65 311.13 466.16 622.25 8.17 207.65 311.13 466.16 622.25 8.18 207.65 311.13 466.16 622.25 8.19 207.65 311.13 466.16 622.25 8.20 207.65 311.13 466.16 622.25 8.21 138.59 207.65 311.13 466.16 622.25 8.22 138.59 207.65 311.13 466.16 622.25 8.23 138.59 207.65 311.13 466.16 622.25 8.24 138.59 207.65 311.13 466.16 622.25 8.25 138.59 207.65 311.13 622.25 8.26 138.59 207.65 311.13 622.25 8.27 138.59 311.13 622.25 8.28 311.13 622.25 8.29 311.13 622.25 8.30 110.00 311.13 622.25 8.31 110.00 311.13 622.25 8.32 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138.59 622.25 8.70 622.25 8.71 196.00 622.25 8.72 98.00 196.00 329.63 622.25 8.73 98.00 196.00 329.63 622.25 8.74 98.00 196.00 329.63 622.25 8.75 98.00 196.00 329.63 622.25 8.76 98.00 196.00 329.63 622.25 8.77 98.00 196.00 329.63 622.25 8.78 98.00 196.00 329.63 622.25 8.79 98.00 196.00 329.63 8.80 98.00 196.00 329.63 8.81 98.00 196.00 329.63 8.82 98.00 196.00 329.63 8.83 98.00 196.00 329.63 8.84 98.00 196.00 277.18 329.63 8.85 196.00 277.18 329.63 8.86 196.00 277.18 329.63 8.87 196.00 277.18 329.63 8.88 196.00 277.18 329.63 8.89 277.18 329.63 8.90 220.00 277.18 329.63 8.91 220.00 277.18 329.63 8.92 220.00 329.63 8.93 220.00 329.63 8.94 220.00 329.63 8.95 220.00 329.63 8.96 220.00 329.63 440.00 8.97 220.00 329.63 440.00 8.98 220.00 329.63 440.00 8.99 220.00 329.63 440.00 9.00 220.00 329.63 440.00 9.01 220.00 329.63 440.00 9.02 220.00 329.63 440.00 9.03 220.00 329.63 440.00 9.04 220.00 329.63 440.00 9.05 220.00 329.63 440.00 9.06 220.00 329.63 440.00 9.07 220.00 329.63 440.00 9.08 220.00 329.63 440.00 9.09 220.00 329.63 440.00 9.10 220.00 329.63 440.00 9.11 220.00 329.63 440.00 9.12 220.00 329.63 440.00 9.13 220.00 440.00 9.14 220.00 440.00 9.15 220.00 440.00 493.88 9.16 220.00 440.00 493.88 9.17 220.00 440.00 493.88 9.18 493.88 9.19 493.88 9.20 493.88 9.21 493.88 9.22 493.88 9.23 493.88 9.24 493.88 9.25 493.88 9.26 493.88 9.27 220.00 493.88 659.26 9.28 220.00 493.88 659.26 9.29 220.00 493.88 659.26 9.30 220.00 493.88 554.37 659.26 987.77 9.31 220.00 493.88 554.37 659.26 987.77 9.32 220.00 493.88 554.37 659.26 987.77 9.33 220.00 493.88 554.37 659.26 987.77 9.34 220.00 493.88 554.37 659.26 987.77 9.35 220.00 440.00 493.88 554.37 659.26 987.77 9.36 220.00 440.00 493.88 554.37 659.26 987.77 9.37 220.00 440.00 493.88 554.37 659.26 9.38 196.00 220.00 440.00 493.88 554.37 659.26 9.39 196.00 220.00 440.00 493.88 554.37 659.26 9.40 196.00 440.00 554.37 659.26 9.41 196.00 440.00 554.37 659.26 9.42 196.00 440.00 554.37 659.26 9.43 196.00 440.00 554.37 659.26 9.44 196.00 440.00 554.37 659.26 9.45 196.00 440.00 554.37 659.26 9.46 440.00 554.37 659.26 9.47 440.00 554.37 659.26 9.48 440.00 554.37 659.26 9.49 440.00 554.37 659.26 9.50 440.00 554.37 659.26 9.51 220.00 440.00 554.37 659.26 9.52 220.00 440.00 554.37 659.26 9.53 220.00 440.00 554.37 659.26 9.54 220.00 440.00 554.37 659.26 9.55 220.00 440.00 554.37 659.26 9.56 220.00 440.00 554.37 659.26 9.57 220.00 440.00 554.37 659.26 9.58 220.00 440.00 554.37 659.26 9.59 220.00 440.00 554.37 659.26 9.60 220.00 440.00 554.37 659.26 9.61 220.00 440.00 659.26 9.62 220.00 369.99 440.00 659.26 9.63 220.00 369.99 440.00 659.26 9.64 220.00 369.99 440.00 659.26 9.65 220.00 369.99 440.00 659.26 9.66 220.00 369.99 440.00 659.26 9.67 220.00 369.99 440.00 659.26 9.68 220.00 369.99 440.00 659.26 9.69 220.00 369.99 440.00 659.26 9.70 220.00 369.99 440.00 659.26 9.71 220.00 369.99 440.00 659.26 9.72 220.00 369.99 659.26 9.73 369.99 659.26 9.74 369.99 659.26 9.75 369.99 659.26 9.76 369.99 659.26 9.77 369.99 659.26 9.78 369.99 659.26 9.79 329.63 369.99 659.26 9.80 138.59 329.63 369.99 659.26 9.81 138.59 329.63 9.82 138.59 329.63 9.83 138.59 329.63 9.84 138.59 329.63 9.85 138.59 329.63 9.86 138.59 329.63 9.87 138.59 329.63 9.88 138.59 329.63 9.89 138.59 329.63 9.90 138.59 329.63 9.91 138.59 329.63 9.92 138.59 329.63 554.37 9.93 138.59 329.63 554.37 9.94 138.59 329.63 554.37 9.95 138.59 220.00 329.63 554.37 9.96 138.59 220.00 329.63 554.37 9.97 138.59 220.00 329.63 554.37 9.98 138.59 220.00 329.63 554.37 9.99 138.59 220.00 329.63 554.37 659.26 10.00 138.59 220.00 329.63 554.37 659.26 10.01 138.59 220.00 554.37 659.26 10.02 138.59 220.00 554.37 659.26 10.03 138.59 220.00 659.26 10.04 98.00 659.26 10.05 98.00 659.26 10.06 98.00 659.26 10.07 98.00 311.13 622.25 659.26 10.08 98.00 311.13 622.25 10.09 98.00 311.13 622.25 10.10 98.00 311.13 622.25 10.11 98.00 311.13 622.25 10.12 98.00 311.13 622.25 10.13 98.00 311.13 622.25 10.14 98.00 311.13 622.25 10.15 98.00 311.13 622.25 10.16 98.00 311.13 622.25 10.17 98.00 311.13 622.25 10.18 311.13 622.25 10.19 311.13 622.25 10.20 311.13 10.21 311.13 10.22 311.13 10.23 311.13 10.24 311.13 10.25 311.13 10.26 311.13 10.27 311.13 10.28 311.13 10.29 311.13 10.30 311.13 10.31 311.13 10.32 311.13 10.33 311.13 10.34 311.13 10.35 311.13 329.63 10.36 329.63 10.37 329.63 10.38 329.63 10.39 329.63 10.40 329.63 10.41 329.63 10.42 329.63 10.43 329.63 10.44 138.59 329.63 10.45 138.59 329.63 10.46 138.59 329.63 10.47 138.59 329.63 10.48 138.59 329.63 10.49 138.59 329.63 10.50 138.59 329.63 10.51 138.59 329.63 10.52 329.63 10.53 329.63 10.54 329.63 10.55 329.63 10.56 329.63 10.57 329.63 10.58 329.63 10.59 329.63 10.60 329.63 10.61 329.63 10.62 329.63 10.63 329.63 10.64 329.63 10.65 329.63 10.66 329.63 10.67 329.63 10.68 329.63 10.69 329.63 10.70 10.71 10.72 10.73 10.74 10.75 10.76 10.77 10.78 493.88 10.79 246.94 493.88 10.80 246.94 493.88 10.81 246.94 493.88 10.82 246.94 493.88 10.83 246.94 493.88 10.84 246.94 493.88 10.85 246.94 493.88 10.86 246.94 493.88 10.87 246.94 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246.94 369.99 493.88 11.71 246.94 369.99 493.88 554.37 11.72 246.94 369.99 493.88 554.37 11.73 246.94 369.99 493.88 554.37 11.74 246.94 369.99 493.88 554.37 11.75 246.94 493.88 554.37 11.76 246.94 493.88 554.37 11.77 185.00 246.94 493.88 554.37 11.78 185.00 246.94 493.88 554.37 11.79 185.00 246.94 493.88 554.37 11.80 185.00 246.94 493.88 554.37 11.81 185.00 246.94 493.88 554.37 11.82 185.00 246.94 493.88 554.37 11.83 185.00 246.94 493.88 554.37 11.84 185.00 246.94 493.88 554.37 11.85 185.00 246.94 493.88 554.37 11.86 185.00 246.94 493.88 554.37 11.87 185.00 246.94 493.88 554.37 11.88 185.00 246.94 493.88 554.37 11.89 185.00 246.94 493.88 554.37 11.90 185.00 246.94 493.88 554.37 11.91 185.00 246.94 493.88 554.37 11.92 185.00 246.94 493.88 554.37 11.93 185.00 246.94 493.88 554.37 11.94 92.50 185.00 246.94 493.88 554.37 11.95 92.50 185.00 246.94 493.88 554.37 11.96 92.50 185.00 246.94 493.88 554.37 11.97 92.50 185.00 246.94 493.88 554.37 11.98 92.50 185.00 246.94 493.88 554.37 11.99 92.50 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440.00 6.47 261.63 349.23 440.00 6.48 261.63 349.23 440.00 6.49 261.63 349.23 440.00 6.50 261.63 349.23 440.00 6.51 261.63 349.23 440.00 6.52 261.63 349.23 440.00 6.53 261.63 349.23 440.00 6.54 261.63 349.23 440.00 6.55 261.63 349.23 440.00 6.56 261.63 349.23 440.00 6.57 261.63 349.23 440.00 6.58 220.00 261.63 349.23 440.00 6.59 220.00 261.63 349.23 440.00 6.60 220.00 261.63 349.23 440.00 6.61 220.00 261.63 349.23 440.00 6.62 220.00 261.63 349.23 440.00 6.63 220.00 261.63 349.23 440.00 6.64 220.00 261.63 349.23 440.00 6.65 220.00 261.63 349.23 440.00 6.66 220.00 261.63 349.23 440.00 6.67 220.00 261.63 349.23 440.00 6.68 220.00 261.63 349.23 6.69 220.00 261.63 349.23 6.70 220.00 261.63 349.23 6.71 220.00 261.63 349.23 6.72 220.00 261.63 349.23 6.73 220.00 261.63 349.23 6.74 220.00 261.63 349.23 6.75 261.63 349.23 6.76 261.63 349.23 6.77 261.63 349.23 6.78 261.63 349.23 440.00 6.79 261.63 349.23 440.00 6.80 261.63 349.23 440.00 6.81 261.63 349.23 440.00 6.82 261.63 349.23 440.00 6.83 261.63 349.23 440.00 6.84 261.63 349.23 440.00 6.85 261.63 349.23 440.00 6.86 261.63 349.23 440.00 6.87 261.63 349.23 440.00 6.88 261.63 349.23 440.00 6.89 261.63 349.23 440.00 6.90 261.63 349.23 440.00 6.91 261.63 349.23 440.00 6.92 261.63 440.00 6.93 261.63 440.00 6.94 261.63 440.00 6.95 261.63 440.00 6.96 261.63 440.00 6.97 261.63 440.00 6.98 440.00 6.99 440.00 7.00 110.00 440.00 7.01 110.00 277.18 440.00 7.02 110.00 277.18 440.00 7.03 110.00 277.18 440.00 7.04 110.00 277.18 440.00 7.05 110.00 196.00 277.18 440.00 7.06 110.00 196.00 277.18 440.00 7.07 110.00 196.00 277.18 440.00 7.08 110.00 196.00 277.18 440.00 7.09 110.00 196.00 277.18 440.00 7.10 110.00 196.00 277.18 440.00 7.11 110.00 196.00 277.18 440.00 7.12 110.00 196.00 277.18 440.00 7.13 110.00 196.00 440.00 7.14 110.00 196.00 440.00 7.15 110.00 196.00 246.94 440.00 7.16 110.00 246.94 440.00 7.17 110.00 246.94 440.00 7.18 110.00 246.94 440.00 7.19 110.00 246.94 440.00 7.20 110.00 246.94 440.00 7.21 110.00 246.94 440.00 7.22 110.00 246.94 440.00 7.23 110.00 246.94 440.00 7.24 110.00 246.94 440.00 7.25 110.00 440.00 7.26 110.00 440.00 7.27 110.00 440.00 7.28 110.00 440.00 7.29 110.00 440.00 7.30 110.00 440.00 7.31 110.00 440.00 7.32 110.00 440.00 7.33 110.00 440.00 554.37 7.34 110.00 440.00 554.37 7.35 110.00 440.00 554.37 7.36 110.00 440.00 554.37 7.37 110.00 440.00 554.37 7.38 110.00 185.00 440.00 554.37 7.39 110.00 185.00 440.00 554.37 7.40 110.00 185.00 293.66 440.00 554.37 7.41 110.00 185.00 293.66 440.00 554.37 7.42 110.00 185.00 293.66 440.00 554.37 7.43 110.00 185.00 293.66 440.00 7.44 110.00 185.00 293.66 440.00 7.45 110.00 185.00 293.66 440.00 7.46 110.00 185.00 293.66 440.00 7.47 110.00 185.00 293.66 440.00 7.48 110.00 185.00 293.66 440.00 7.49 110.00 185.00 293.66 440.00 7.50 110.00 293.66 440.00 7.51 110.00 293.66 440.00 7.52 110.00 293.66 440.00 7.53 110.00 293.66 440.00 7.54 110.00 293.66 440.00 7.55 110.00 293.66 440.00 7.56 110.00 293.66 440.00 7.57 110.00 440.00 7.58 110.00 440.00 7.59 110.00 440.00 7.60 110.00 440.00 7.61 110.00 440.00 7.62 110.00 440.00 7.63 110.00 440.00 7.64 110.00 440.00 7.65 110.00 440.00 7.66 110.00 440.00 7.67 110.00 440.00 7.68 110.00 7.69 110.00 7.70 110.00 7.71 110.00 7.72 110.00 7.73 110.00 440.00 7.74 110.00 440.00 7.75 110.00 440.00 7.76 110.00 440.00 7.77 110.00 440.00 7.78 110.00 440.00 7.79 110.00 440.00 7.80 110.00 440.00 7.81 110.00 440.00 7.82 110.00 440.00 7.83 110.00 440.00 7.84 110.00 440.00 7.85 110.00 440.00 7.86 110.00 440.00 7.87 110.00 440.00 7.88 110.00 440.00 7.89 110.00 440.00 7.90 110.00 440.00 7.91 110.00 440.00 7.92 110.00 440.00 554.37 7.93 110.00 440.00 554.37 7.94 110.00 440.00 493.88 554.37 7.95 110.00 293.66 440.00 493.88 554.37 7.96 110.00 293.66 440.00 493.88 554.37 7.97 110.00 293.66 440.00 493.88 554.37 7.98 110.00 293.66 440.00 493.88 554.37 7.99 110.00 293.66 440.00 493.88 554.37 8.00 110.00 293.66 440.00 493.88 8.01 110.00 293.66 440.00 8.02 110.00 293.66 440.00 8.03 110.00 220.00 293.66 440.00 8.04 110.00 220.00 293.66 440.00 8.05 110.00 220.00 293.66 440.00 8.06 110.00 220.00 293.66 440.00 8.07 110.00 220.00 293.66 440.00 8.08 110.00 220.00 293.66 440.00 8.09 110.00 220.00 293.66 440.00 8.10 110.00 220.00 293.66 440.00 8.11 110.00 293.66 8.12 110.00 293.66 8.13 110.00 293.66 8.14 110.00 293.66 8.15 110.00 293.66 8.16 110.00 220.00 293.66 440.00 8.17 110.00 220.00 293.66 440.00 8.18 110.00 220.00 277.18 440.00 8.19 110.00 220.00 277.18 440.00 8.20 110.00 220.00 277.18 440.00 8.21 110.00 220.00 277.18 440.00 8.22 110.00 220.00 277.18 440.00 1864.66 8.23 110.00 220.00 277.18 440.00 1864.66 8.24 110.00 220.00 277.18 440.00 1864.66 8.25 110.00 220.00 277.18 440.00 1864.66 8.26 110.00 220.00 277.18 440.00 1864.66 8.27 110.00 220.00 277.18 440.00 1864.66 8.28 110.00 220.00 277.18 440.00 1864.66 8.29 110.00 220.00 277.18 440.00 1864.66 8.30 110.00 220.00 277.18 440.00 8.31 110.00 220.00 277.18 440.00 8.32 110.00 220.00 277.18 440.00 8.33 110.00 220.00 277.18 440.00 8.34 110.00 220.00 277.18 440.00 8.35 110.00 220.00 277.18 440.00 8.36 110.00 220.00 277.18 440.00 8.37 110.00 220.00 277.18 440.00 8.38 110.00 220.00 277.18 440.00 8.39 110.00 220.00 277.18 440.00 8.40 110.00 220.00 277.18 440.00 8.41 110.00 220.00 277.18 440.00 8.42 110.00 220.00 277.18 440.00 8.43 110.00 220.00 277.18 440.00 8.44 110.00 220.00 277.18 440.00 8.45 110.00 220.00 277.18 440.00 8.46 110.00 220.00 277.18 440.00 8.47 110.00 220.00 277.18 440.00 8.48 110.00 220.00 277.18 440.00 8.49 110.00 220.00 277.18 440.00 8.50 110.00 220.00 277.18 440.00 8.51 110.00 220.00 277.18 440.00 8.52 110.00 220.00 277.18 440.00 8.53 110.00 277.18 440.00 8.54 110.00 277.18 440.00 8.55 110.00 277.18 440.00 8.56 110.00 277.18 440.00 8.57 110.00 277.18 440.00 8.58 110.00 277.18 440.00 8.59 110.00 277.18 440.00 8.60 277.18 440.00 8.61 277.18 440.00 587.33 8.62 277.18 440.00 587.33 8.63 277.18 293.66 440.00 587.33 8.64 293.66 440.00 587.33 8.65 293.66 440.00 587.33 880.00 8.66 293.66 440.00 587.33 880.00 1046.50 8.67 293.66 440.00 587.33 880.00 1046.50 8.68 293.66 440.00 587.33 880.00 1046.50 8.69 293.66 440.00 587.33 880.00 1046.50 8.70 440.00 880.00 1046.50 8.71 146.83 440.00 880.00 1046.50 8.72 146.83 440.00 880.00 1046.50 8.73 146.83 440.00 880.00 1046.50 8.74 146.83 440.00 880.00 1046.50 8.75 146.83 440.00 880.00 1046.50 8.76 146.83 440.00 880.00 1046.50 8.77 146.83 440.00 880.00 1046.50 8.78 146.83 440.00 880.00 8.79 146.83 293.66 440.00 587.33 880.00 8.80 146.83 293.66 440.00 587.33 880.00 8.81 146.83 293.66 440.00 587.33 880.00 8.82 146.83 293.66 440.00 587.33 880.00 8.83 146.83 293.66 587.33 8.84 146.83 293.66 587.33 8.85 146.83 293.66 587.33 8.86 146.83 293.66 587.33 8.87 146.83 293.66 587.33 8.88 146.83 293.66 440.00 587.33 880.00 8.89 146.83 293.66 440.00 587.33 880.00 8.90 146.83 293.66 440.00 587.33 880.00 8.91 146.83 293.66 440.00 587.33 880.00 8.92 146.83 293.66 440.00 587.33 880.00 8.93 146.83 293.66 440.00 587.33 880.00 8.94 146.83 293.66 440.00 587.33 880.00 1760.00 8.95 146.83 293.66 440.00 587.33 880.00 1760.00 8.96 146.83 293.66 440.00 587.33 880.00 1760.00 8.97 146.83 293.66 440.00 587.33 880.00 1760.00 8.98 146.83 293.66 440.00 587.33 880.00 1760.00 8.99 146.83 293.66 440.00 587.33 880.00 1760.00 9.00 146.83 293.66 440.00 587.33 880.00 1760.00 9.01 146.83 293.66 440.00 587.33 880.00 1760.00 9.02 146.83 293.66 440.00 587.33 880.00 1760.00 9.03 146.83 293.66 587.33 9.04 146.83 293.66 587.33 9.05 146.83 293.66 587.33 9.06 146.83 293.66 587.33 9.07 146.83 293.66 587.33 9.08 146.83 293.66 587.33 9.09 146.83 293.66 587.33 9.10 146.83 293.66 587.33 9.11 146.83 293.66 587.33 9.12 146.83 293.66 523.25 587.33 9.13 146.83 293.66 523.25 587.33 9.14 146.83 293.66 523.25 587.33 9.15 146.83 293.66 523.25 587.33 9.16 146.83 293.66 523.25 587.33 9.17 146.83 293.66 523.25 587.33 9.18 146.83 293.66 523.25 587.33 9.19 146.83 293.66 523.25 587.33 9.20 146.83 293.66 523.25 587.33 9.21 146.83 293.66 523.25 587.33 9.22 146.83 293.66 523.25 587.33 9.23 146.83 293.66 523.25 587.33 9.24 146.83 293.66 587.33 9.25 146.83 293.66 587.33 9.26 146.83 293.66 587.33 9.27 146.83 293.66 587.33 9.28 146.83 293.66 587.33 9.29 146.83 293.66 587.33 9.30 146.83 293.66 587.33 9.31 146.83 293.66 587.33 9.32 146.83 293.66 587.33 9.33 146.83 293.66 587.33 9.34 146.83 293.66 587.33 9.35 146.83 293.66 587.33 9.36 146.83 293.66 440.00 587.33 9.37 146.83 293.66 440.00 9.38 146.83 293.66 440.00 1975.53 9.39 146.83 293.66 440.00 987.77 1975.53 9.40 293.66 440.00 659.26 987.77 1975.53 9.41 293.66 440.00 659.26 987.77 1975.53 9.42 220.00 293.66 440.00 659.26 987.77 1975.53 9.43 220.00 293.66 440.00 659.26 987.77 1975.53 9.44 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.45 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.46 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.47 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.48 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.49 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.50 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.51 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.52 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.53 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.54 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.55 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.56 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.57 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.58 220.00 293.66 440.00 659.26 987.77 1975.53 2093.00 9.59 220.00 293.66 440.00 659.26 987.77 1975.53 9.60 220.00 293.66 440.00 659.26 987.77 1975.53 9.61 220.00 293.66 440.00 659.26 987.77 1975.53 9.62 220.00 293.66 440.00 659.26 987.77 1975.53 9.63 220.00 293.66 440.00 659.26 9.64 220.00 293.66 440.00 659.26 9.65 220.00 293.66 440.00 659.26 9.66 220.00 293.66 440.00 659.26 9.67 220.00 293.66 440.00 659.26 9.68 220.00 293.66 440.00 659.26 9.69 220.00 293.66 440.00 659.26 9.70 220.00 293.66 440.00 659.26 9.71 220.00 293.66 440.00 659.26 9.72 220.00 293.66 440.00 659.26 9.73 220.00 293.66 440.00 659.26 9.74 220.00 293.66 440.00 659.26 9.75 220.00 293.66 440.00 659.26 9.76 220.00 293.66 440.00 587.33 659.26 9.77 220.00 293.66 440.00 587.33 659.26 9.78 220.00 293.66 440.00 587.33 659.26 9.79 293.66 440.00 587.33 659.26 9.80 293.66 440.00 554.37 587.33 659.26 9.81 293.66 440.00 554.37 587.33 659.26 9.82 293.66 440.00 554.37 587.33 659.26 9.83 293.66 440.00 554.37 659.26 9.84 293.66 440.00 554.37 659.26 9.85 293.66 440.00 554.37 659.26 880.00 9.86 293.66 440.00 554.37 659.26 880.00 1760.00 9.87 293.66 440.00 554.37 659.26 880.00 1760.00 9.88 293.66 440.00 554.37 659.26 880.00 1760.00 9.89 293.66 440.00 554.37 659.26 880.00 1760.00 9.90 293.66 440.00 554.37 659.26 880.00 1760.00 9.91 293.66 440.00 554.37 659.26 880.00 1760.00 9.92 440.00 554.37 659.26 880.00 1760.00 9.93 440.00 554.37 659.26 9.94 440.00 554.37 659.26 9.95 174.61 440.00 554.37 659.26 9.96 174.61 440.00 554.37 659.26 9.97 174.61 440.00 554.37 659.26 9.98 174.61 440.00 554.37 659.26 9.99 174.61 440.00 554.37 659.26 10.00 174.61 440.00 554.37 659.26 10.01 174.61 440.00 554.37 659.26 10.02 174.61 440.00 554.37 659.26 10.03 174.61 440.00 554.37 659.26 10.04 554.37 659.26 10.05 554.37 659.26 10.06 554.37 659.26 10.07 554.37 659.26 10.08 164.81 554.37 659.26 10.09 164.81 554.37 659.26 10.10 164.81 554.37 659.26 10.11 164.81 554.37 659.26 10.12 164.81 554.37 659.26 10.13 164.81 554.37 659.26 10.14 164.81 554.37 659.26 10.15 164.81 554.37 659.26 10.16 164.81 554.37 659.26 698.46 10.17 164.81 554.37 587.33 659.26 698.46 10.18 164.81 554.37 587.33 659.26 698.46 10.19 164.81 174.61 587.33 659.26 698.46 10.20 174.61 587.33 698.46 10.21 174.61 587.33 698.46 10.22 174.61 587.33 698.46 10.23 174.61 587.33 698.46 10.24 174.61 587.33 698.46 10.25 174.61 587.33 698.46 10.26 174.61 587.33 698.46 10.27 293.66 587.33 698.46 10.28 293.66 587.33 698.46 10.29 293.66 587.33 698.46 10.30 293.66 587.33 698.46 10.31 293.66 587.33 698.46 10.32 293.66 587.33 698.46 10.33 293.66 587.33 698.46 10.34 293.66 587.33 698.46 10.35 587.33 698.46 10.36 587.33 698.46 10.37 587.33 698.46 10.38 587.33 698.46 10.39 587.33 698.46 10.40 587.33 698.46 10.41 587.33 698.46 10.42 587.33 698.46 10.43 587.33 698.46 10.44 293.66 587.33 698.46 10.45 293.66 587.33 698.46 10.46 293.66 587.33 698.46 10.47 293.66 587.33 698.46 10.48 293.66 587.33 698.46 10.49 293.66 587.33 698.46 10.50 293.66 587.33 698.46 10.51 293.66 587.33 698.46 10.52 293.66 587.33 698.46 10.53 293.66 587.33 698.46 10.54 293.66 587.33 698.46 10.55 293.66 587.33 698.46 10.56 293.66 587.33 698.46 10.57 293.66 587.33 698.46 10.58 293.66 587.33 698.46 10.59 293.66 587.33 698.46 10.60 293.66 587.33 698.46 10.61 293.66 587.33 698.46 10.62 293.66 587.33 698.46 10.63 293.66 587.33 698.46 10.64 293.66 587.33 698.46 10.65 293.66 587.33 698.46 10.66 293.66 587.33 698.46 10.67 293.66 587.33 698.46 10.68 293.66 523.25 587.33 698.46 10.69 293.66 523.25 587.33 698.46 10.70 293.66 523.25 587.33 698.46 10.71 293.66 523.25 587.33 698.46 10.72 293.66 523.25 587.33 698.46 10.73 293.66 523.25 587.33 698.46 10.74 293.66 523.25 587.33 698.46 10.75 293.66 523.25 587.33 698.46 10.76 293.66 523.25 587.33 10.77 293.66 523.25 587.33 10.78 293.66 523.25 587.33 10.79 523.25 587.33 10.80 523.25 587.33 10.81 466.16 523.25 587.33 10.82 466.16 587.33 10.83 466.16 587.33 10.84 466.16 587.33 10.85 466.16 587.33 10.86 466.16 587.33 10.87 466.16 587.33 10.88 466.16 587.33 10.89 466.16 587.33 10.90 466.16 587.33 10.91 466.16 587.33 10.92 587.33 10.93 293.66 440.00 587.33 1046.50 10.94 293.66 440.00 587.33 1046.50 10.95 293.66 440.00 587.33 1046.50 10.96 220.00 293.66 440.00 587.33 659.26 1046.50 10.97 220.00 293.66 440.00 587.33 659.26 987.77 1046.50 1975.53 10.98 220.00 293.66 440.00 587.33 659.26 987.77 1046.50 1975.53 10.99 220.00 293.66 440.00 587.33 659.26 987.77 1046.50 1975.53 11.00 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 11.01 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 11.02 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 11.03 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 11.04 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.05 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.06 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.07 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.08 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.09 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.10 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.11 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.12 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.13 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.14 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.15 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.16 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.17 196.00 220.00 293.66 440.00 587.33 659.26 987.77 1975.53 2093.00 11.18 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.19 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.20 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.21 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.22 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.23 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.24 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.25 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 2093.00 11.26 196.00 220.00 440.00 587.33 659.26 987.77 1975.53 11.27 220.00 440.00 587.33 659.26 987.77 1975.53 11.28 220.00 440.00 587.33 659.26 987.77 1975.53 11.29 220.00 440.00 587.33 659.26 987.77 1975.53 11.30 220.00 440.00 554.37 587.33 659.26 987.77 1975.53 11.31 220.00 440.00 554.37 587.33 659.26 987.77 1975.53 11.32 440.00 554.37 659.26 987.77 1975.53 11.33 440.00 554.37 659.26 987.77 1975.53 11.34 440.00 554.37 659.26 987.77 1975.53 11.35 440.00 554.37 659.26 987.77 1975.53 11.36 440.00 554.37 659.26 1975.53 11.37 440.00 554.37 659.26 1975.53 11.38 440.00 554.37 659.26 1975.53 11.39 196.00 440.00 554.37 659.26 1975.53 11.40 196.00 440.00 554.37 659.26 11.41 196.00 440.00 554.37 659.26 11.42 196.00 440.00 554.37 659.26 11.43 196.00 440.00 554.37 659.26 11.44 196.00 440.00 554.37 659.26 11.45 196.00 440.00 554.37 659.26 11.46 196.00 440.00 554.37 659.26 11.47 440.00 554.37 659.26 11.48 440.00 554.37 659.26 11.49 440.00 554.37 659.26 11.50 554.37 659.26 11.51 554.37 659.26 1318.51 11.52 554.37 659.26 1318.51 11.53 554.37 659.26 1318.51 11.54 554.37 659.26 1318.51 11.55 554.37 659.26 1318.51 11.56 554.37 659.26 1318.51 11.57 554.37 659.26 1318.51 11.58 554.37 659.26 1318.51 11.59 554.37 659.26 11.60 554.37 659.26 11.61 554.37 659.26 11.62 554.37 659.26 11.63 164.81 554.37 659.26 11.64 164.81 554.37 659.26 11.65 164.81 329.63 554.37 659.26 11.66 164.81 329.63 554.37 659.26 11.67 164.81 329.63 554.37 659.26 11.68 164.81 329.63 554.37 659.26 11.69 164.81 329.63 554.37 659.26 11.70 164.81 329.63 554.37 659.26 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739.99 1479.98 12.28 164.81 493.88 523.25 739.99 1479.98 12.29 493.88 523.25 739.99 1479.98 12.30 493.88 523.25 739.99 1479.98 12.31 493.88 523.25 739.99 1479.98 12.32 493.88 523.25 739.99 1479.98 12.33 493.88 523.25 739.99 1479.98 12.34 493.88 523.25 739.99 1479.98 12.35 440.00 493.88 523.25 739.99 12.36 440.00 493.88 523.25 739.99 12.37 196.00 440.00 493.88 523.25 739.99 12.38 196.00 440.00 493.88 523.25 739.99 12.39 196.00 440.00 493.88 523.25 739.99 12.40 196.00 293.66 440.00 493.88 523.25 587.33 739.99 12.41 196.00 293.66 440.00 493.88 523.25 587.33 739.99 12.42 196.00 293.66 440.00 493.88 523.25 587.33 739.99 12.43 196.00 293.66 440.00 493.88 523.25 587.33 739.99 12.44 196.00 293.66 440.00 493.88 523.25 587.33 739.99 12.45 146.83 196.00 293.66 440.00 523.25 587.33 739.99 12.46 146.83 196.00 293.66 523.25 587.33 739.99 12.47 146.83 196.00 739.99 12.48 146.83 196.00 466.16 739.99 12.49 146.83 196.00 466.16 739.99 12.50 146.83 196.00 466.16 739.99 12.51 146.83 196.00 466.16 739.99 12.52 146.83 196.00 466.16 739.99 12.53 146.83 196.00 466.16 739.99 783.99 12.54 196.00 466.16 739.99 783.99 12.55 196.00 739.99 783.99 12.56 196.00 739.99 783.99 12.57 196.00 739.99 783.99 12.58 196.00 739.99 783.99 12.59 196.00 739.99 783.99 12.60 196.00 739.99 783.99 12.61 196.00 739.99 783.99 12.62 196.00 466.16 739.99 783.99 12.63 196.00 466.16 739.99 783.99 12.64 196.00 466.16 783.99 12.65 196.00 466.16 783.99 12.66 196.00 466.16 783.99 12.67 196.00 466.16 783.99 12.68 196.00 466.16 783.99 12.69 196.00 466.16 783.99 12.70 196.00 466.16 783.99 12.71 196.00 466.16 783.99 12.72 196.00 466.16 783.99 12.73 196.00 466.16 783.99 12.74 196.00 466.16 783.99 12.75 196.00 466.16 783.99 12.76 196.00 466.16 622.25 783.99 12.77 196.00 466.16 622.25 783.99 12.78 196.00 466.16 622.25 783.99 12.79 196.00 466.16 622.25 783.99 12.80 196.00 466.16 622.25 783.99 12.81 196.00 466.16 622.25 783.99 12.82 196.00 466.16 622.25 783.99 12.83 196.00 466.16 622.25 783.99 12.84 196.00 466.16 622.25 783.99 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1396.91 14.40 164.81 440.00 523.25 698.46 880.00 1396.91 14.41 164.81 440.00 523.25 698.46 880.00 1396.91 14.42 164.81 440.00 523.25 698.46 880.00 1396.91 14.43 164.81 440.00 523.25 698.46 880.00 1396.91 14.44 164.81 440.00 523.25 698.46 880.00 1396.91 14.45 164.81 440.00 523.25 698.46 880.00 1396.91 14.46 164.81 440.00 523.25 698.46 880.00 1396.91 14.47 440.00 523.25 698.46 880.00 1396.91 14.48 440.00 523.25 698.46 880.00 1396.91 14.49 440.00 523.25 698.46 880.00 14.50 440.00 523.25 698.46 880.00 14.51 440.00 523.25 698.46 880.00 14.52 220.00 440.00 523.25 698.46 880.00 14.53 220.00 440.00 523.25 698.46 880.00 14.54 220.00 440.00 523.25 698.46 880.00 14.55 220.00 440.00 523.25 698.46 880.00 14.56 220.00 440.00 523.25 698.46 880.00 14.57 220.00 440.00 523.25 698.46 880.00 14.58 220.00 440.00 523.25 698.46 880.00 14.59 220.00 440.00 523.25 698.46 880.00 14.60 220.00 440.00 523.25 698.46 880.00 14.61 220.00 440.00 523.25 698.46 880.00 14.62 220.00 440.00 523.25 698.46 880.00 14.63 220.00 440.00 523.25 698.46 880.00 14.64 220.00 440.00 523.25 698.46 880.00 14.65 440.00 523.25 698.46 880.00 14.66 440.00 523.25 698.46 880.00 14.67 440.00 523.25 698.46 880.00 14.68 440.00 523.25 698.46 880.00 14.69 440.00 523.25 698.46 880.00 14.70 440.00 523.25 698.46 880.00 14.71 440.00 523.25 880.00 14.72 440.00 523.25 880.00 14.73 440.00 523.25 880.00 14.74 440.00 523.25 880.00 14.75 440.00 523.25 880.00 14.76 440.00 523.25 880.00 14.77 440.00 523.25 880.00 14.78 440.00 523.25 880.00 14.79 440.00 523.25 659.26 880.00 14.80 440.00 523.25 659.26 880.00 14.81 440.00 523.25 659.26 880.00 14.82 523.25 659.26 880.00 14.83 523.25 659.26 880.00 14.84 659.26 880.00 1760.00 14.85 659.26 880.00 1760.00 14.86 659.26 880.00 1760.00 14.87 554.37 880.00 1760.00 14.88 554.37 880.00 1760.00 14.89 554.37 880.00 1760.00 14.90 554.37 880.00 1760.00 14.91 554.37 880.00 1760.00 14.92 554.37 880.00 1760.00 14.93 554.37 880.00 14.94 554.37 880.00 14.95 554.37 880.00 14.96 554.37 880.00 14.97 554.37 880.00 14.98 554.37 880.00 14.99 554.37 880.00 15.00 554.37 880.00 15.01 523.25 554.37 880.00 15.02 523.25 554.37 880.00 15.03 440.00 523.25 554.37 880.00 15.04 440.00 523.25 554.37 880.00 15.05 440.00 523.25 554.37 880.00 15.06 440.00 523.25 554.37 880.00 15.07 440.00 523.25 554.37 880.00 15.08 440.00 523.25 554.37 880.00 15.09 440.00 554.37 880.00 15.10 440.00 554.37 880.00 15.11 440.00 554.37 880.00 15.12 440.00 554.37 880.00 15.13 440.00 880.00 15.14 440.00 880.00 15.15 440.00 880.00 15.16 440.00 880.00 15.17 440.00 880.00 15.18 440.00 880.00 15.19 440.00 880.00 15.20 440.00 880.00 15.21 440.00 880.00 15.22 349.23 440.00 587.33 880.00 1046.50 15.23 349.23 440.00 587.33 880.00 1046.50 15.24 349.23 440.00 587.33 880.00 1046.50 15.25 349.23 440.00 587.33 880.00 1046.50 15.26 349.23 440.00 587.33 880.00 1046.50 15.27 349.23 440.00 587.33 880.00 1046.50 15.28 349.23 440.00 587.33 880.00 1046.50 15.29 349.23 440.00 587.33 880.00 1046.50 15.30 349.23 440.00 587.33 880.00 1046.50 15.31 349.23 440.00 587.33 880.00 15.32 349.23 440.00 587.33 880.00 15.33 440.00 587.33 880.00 15.34 440.00 587.33 880.00 15.35 440.00 587.33 880.00 15.36 220.00 329.63 440.00 587.33 880.00 15.37 220.00 329.63 440.00 587.33 880.00 15.38 220.00 329.63 440.00 587.33 880.00 15.39 220.00 329.63 440.00 587.33 880.00 15.40 220.00 329.63 440.00 587.33 880.00 15.41 220.00 329.63 440.00 587.33 880.00 15.42 220.00 329.63 440.00 587.33 880.00 15.43 220.00 329.63 440.00 587.33 880.00 15.44 220.00 329.63 440.00 587.33 880.00 15.45 440.00 587.33 880.00 15.46 440.00 587.33 880.00 15.47 440.00 587.33 880.00 15.48 293.66 440.00 587.33 880.00 15.49 293.66 440.00 587.33 880.00 15.50 293.66 440.00 587.33 880.00 15.51 293.66 587.33 880.00 15.52 293.66 587.33 880.00 15.53 220.00 293.66 587.33 880.00 15.54 220.00 293.66 587.33 880.00 15.55 220.00 293.66 587.33 880.00 15.56 220.00 293.66 440.00 587.33 880.00 15.57 220.00 293.66 440.00 587.33 880.00 15.58 220.00 293.66 440.00 587.33 880.00 15.59 220.00 293.66 440.00 587.33 880.00 15.60 220.00 293.66 440.00 587.33 880.00 15.61 220.00 293.66 440.00 587.33 880.00 15.62 220.00 293.66 329.63 440.00 587.33 880.00 15.63 220.00 329.63 440.00 587.33 880.00 15.64 220.00 329.63 440.00 587.33 880.00 15.65 220.00 329.63 440.00 587.33 880.00 15.66 220.00 329.63 440.00 587.33 880.00 15.67 220.00 329.63 440.00 587.33 880.00 15.68 220.00 329.63 440.00 587.33 880.00 15.69 220.00 329.63 440.00 587.33 880.00 15.70 220.00 329.63 440.00 587.33 880.00 15.71 220.00 329.63 440.00 587.33 880.00 15.72 220.00 329.63 440.00 587.33 880.00 15.73 220.00 329.63 440.00 587.33 880.00 15.74 220.00 329.63 440.00 587.33 880.00 15.75 220.00 329.63 440.00 587.33 880.00 15.76 220.00 329.63 440.00 587.33 880.00 15.77 220.00 329.63 440.00 587.33 880.00 15.78 220.00 329.63 440.00 587.33 880.00 15.79 220.00 329.63 440.00 587.33 880.00 15.80 220.00 329.63 440.00 587.33 880.00 15.81 220.00 329.63 440.00 587.33 880.00 15.82 220.00 329.63 440.00 587.33 880.00 15.83 220.00 329.63 440.00 587.33 880.00 15.84 220.00 329.63 440.00 587.33 880.00 15.85 220.00 329.63 440.00 587.33 880.00 15.86 220.00 329.63 440.00 587.33 880.00 15.87 220.00 329.63 440.00 587.33 880.00 15.88 220.00 329.63 440.00 587.33 880.00 15.89 220.00 329.63 440.00 587.33 880.00 15.90 220.00 329.63 440.00 587.33 880.00 15.91 220.00 329.63 440.00 587.33 880.00 15.92 220.00 329.63 440.00 587.33 880.00 15.93 220.00 329.63 440.00 587.33 880.00 15.94 220.00 329.63 440.00 587.33 880.00 15.95 220.00 329.63 440.00 587.33 880.00 15.96 220.00 329.63 440.00 587.33 880.00 15.97 220.00 329.63 440.00 587.33 880.00 15.98 220.00 329.63 440.00 587.33 880.00 15.99 220.00 329.63 440.00 587.33 880.00 16.00 220.00 329.63 440.00 587.33 880.00 16.01 220.00 329.63 440.00 587.33 880.00 16.02 220.00 329.63 440.00 587.33 880.00 16.03 220.00 329.63 440.00 587.33 880.00 16.04 220.00 329.63 440.00 587.33 880.00 16.05 220.00 329.63 440.00 587.33 880.00 16.06 220.00 329.63 440.00 587.33 880.00 16.07 220.00 329.63 440.00 554.37 587.33 880.00 16.08 220.00 329.63 440.00 554.37 587.33 880.00 16.09 220.00 329.63 440.00 554.37 587.33 880.00 16.10 220.00 329.63 440.00 554.37 587.33 880.00 16.11 220.00 329.63 440.00 554.37 587.33 880.00 16.12 220.00 329.63 440.00 554.37 587.33 880.00 16.13 220.00 329.63 440.00 554.37 587.33 880.00 16.14 220.00 329.63 440.00 554.37 587.33 880.00 16.15 220.00 329.63 440.00 554.37 587.33 880.00 16.16 220.00 329.63 440.00 554.37 880.00 16.17 220.00 329.63 440.00 554.37 880.00 16.18 220.00 329.63 440.00 554.37 880.00 16.19 220.00 329.63 440.00 554.37 880.00 16.20 220.00 329.63 440.00 554.37 880.00 16.21 220.00 329.63 440.00 554.37 880.00 16.22 220.00 329.63 440.00 554.37 880.00 16.23 220.00 329.63 440.00 554.37 880.00 16.24 220.00 329.63 440.00 554.37 880.00 16.25 220.00 329.63 440.00 554.37 880.00 16.26 220.00 329.63 440.00 554.37 880.00 16.27 220.00 329.63 440.00 554.37 880.00 16.28 220.00 329.63 440.00 554.37 880.00 16.29 220.00 329.63 440.00 554.37 880.00 16.30 220.00 329.63 440.00 554.37 880.00 16.31 220.00 329.63 440.00 554.37 880.00 16.32 220.00 329.63 440.00 554.37 880.00 16.33 220.00 329.63 440.00 554.37 880.00 16.34 220.00 329.63 440.00 554.37 880.00 16.35 220.00 329.63 440.00 554.37 880.00 16.36 220.00 329.63 440.00 554.37 880.00 16.37 220.00 329.63 440.00 554.37 880.00 16.38 220.00 329.63 440.00 554.37 880.00 16.39 220.00 329.63 440.00 554.37 880.00 16.40 220.00 329.63 440.00 554.37 880.00 16.41 220.00 329.63 440.00 554.37 880.00 16.42 220.00 329.63 440.00 554.37 880.00 16.43 220.00 329.63 440.00 554.37 880.00 16.44 220.00 329.63 440.00 554.37 880.00 16.45 220.00 329.63 440.00 554.37 880.00 16.46 220.00 329.63 440.00 554.37 880.00 16.47 220.00 329.63 440.00 554.37 880.00 16.48 220.00 329.63 440.00 554.37 880.00 16.49 220.00 329.63 440.00 554.37 880.00 16.50 220.00 329.63 440.00 554.37 880.00 16.51 220.00 329.63 440.00 554.37 880.00 16.52 220.00 329.63 440.00 554.37 880.00 16.53 220.00 329.63 440.00 554.37 880.00 16.54 220.00 329.63 440.00 554.37 587.33 880.00 16.55 220.00 329.63 440.00 554.37 587.33 880.00 16.56 220.00 329.63 440.00 554.37 587.33 880.00 16.57 220.00 329.63 440.00 554.37 587.33 880.00 16.58 220.00 293.66 329.63 440.00 554.37 587.33 880.00 16.59 220.00 293.66 329.63 440.00 554.37 587.33 880.00 16.60 220.00 293.66 329.63 440.00 554.37 587.33 880.00 16.61 220.00 293.66 329.63 440.00 554.37 587.33 16.62 220.00 293.66 329.63 440.00 554.37 587.33 16.63 220.00 293.66 329.63 440.00 554.37 587.33 16.64 220.00 293.66 329.63 440.00 554.37 587.33 16.65 220.00 293.66 329.63 440.00 554.37 587.33 16.66 220.00 293.66 329.63 554.37 587.33 16.67 220.00 293.66 329.63 587.33 16.68 220.00 293.66 329.63 587.33 16.69 220.00 293.66 329.63 587.33 16.70 220.00 293.66 329.63 587.33 16.71 220.00 293.66 329.63 587.33 16.72 220.00 293.66 329.63 587.33 16.73 220.00 293.66 329.63 587.33 16.74 220.00 293.66 329.63 587.33 16.75 220.00 293.66 329.63 587.33 16.76 293.66 329.63 587.33 16.77 293.66 329.63 587.33 16.78 293.66 329.63 587.33 16.79 293.66 587.33 16.80 293.66 587.33 16.81 293.66 587.33 16.82 293.66 587.33 16.83 293.66 587.33 16.84 293.66 587.33 16.85 293.66 587.33 16.86 293.66 587.33 16.87 293.66 587.33 16.88 293.66 587.33 16.89 293.66 440.00 587.33 16.90 293.66 440.00 587.33 16.91 293.66 440.00 587.33 16.92 293.66 440.00 587.33 16.93 293.66 440.00 587.33 16.94 293.66 440.00 587.33 16.95 220.00 293.66 440.00 587.33 16.96 220.00 293.66 440.00 587.33 16.97 220.00 293.66 440.00 587.33 16.98 220.00 293.66 440.00 587.33 16.99 220.00 293.66 587.33 17.00 220.00 293.66 587.33 17.01 220.00 293.66 587.33 17.02 220.00 293.66 587.33 17.03 220.00 293.66 587.33 17.04 220.00 293.66 587.33 17.05 220.00 293.66 587.33 17.06 293.66 587.33 17.07 293.66 587.33 17.08 293.66 587.33 17.09 293.66 587.33 17.10 293.66 587.33 17.11 293.66 587.33 17.12 293.66 587.33 17.13 293.66 587.33 17.14 293.66 587.33 17.15 293.66 587.33 17.16 293.66 587.33 17.17 293.66 587.33 17.18 293.66 523.25 587.33 17.19 293.66 523.25 587.33 17.20 293.66 523.25 587.33 17.21 293.66 523.25 587.33 17.22 293.66 523.25 587.33 17.23 293.66 523.25 587.33 17.24 293.66 523.25 587.33 17.25 293.66 523.25 587.33 17.26 293.66 523.25 587.33 17.27 293.66 523.25 587.33 17.28 293.66 523.25 17.29 293.66 523.25 17.30 293.66 523.25 17.31 293.66 523.25 17.32 523.25 17.33 466.16 523.25 17.34 466.16 523.25 17.35 466.16 523.25 17.36 466.16 523.25 17.37 466.16 523.25 17.38 466.16 523.25 17.39 466.16 523.25 17.40 466.16 523.25 17.41 466.16 523.25 17.42 466.16 523.25 17.43 466.16 523.25 17.44 466.16 523.25 17.45 440.00 466.16 523.25 17.46 440.00 466.16 17.47 440.00 466.16 17.48 440.00 17.49 440.00 17.50 440.00 17.51 440.00 17.52 440.00 17.53 440.00 17.54 440.00 17.55 440.00 17.56 440.00 17.57 440.00 17.58 440.00 17.59 440.00 17.60 440.00 17.61 440.00 17.62 440.00 17.63 440.00 17.64 440.00 17.65 440.00 17.66 440.00 17.67 440.00 17.68 440.00 17.69 440.00 17.70 440.00 17.71 440.00 17.72 440.00 17.73 440.00 17.74 440.00 17.75 440.00 17.76 440.00 17.77 440.00 17.78 440.00 17.79 440.00 17.80 440.00 17.81 440.00 17.82 440.00 17.83 138.59 440.00 17.84 138.59 440.00 17.85 138.59 440.00 17.86 138.59 440.00 17.87 138.59 440.00 17.88 138.59 440.00 17.89 138.59 440.00 17.90 138.59 440.00 17.91 138.59 440.00 17.92 138.59 440.00 17.93 138.59 440.00 17.94 138.59 440.00 17.95 138.59 440.00 17.96 138.59 440.00 17.97 440.00 17.98 440.00 17.99 440.00 18.00 440.00 18.01 440.00 18.02 440.00 18.03 440.00 18.04 440.00 18.05 440.00 18.06 440.00 18.07 110.00 440.00 18.08 110.00 440.00 18.09 110.00 138.59 440.00 18.10 110.00 138.59 440.00 18.11 110.00 138.59 440.00 18.12 110.00 138.59 440.00 18.13 110.00 138.59 440.00 18.14 110.00 138.59 440.00 18.15 110.00 138.59 440.00 18.16 110.00 138.59 440.00 18.17 110.00 138.59 440.00 18.18 110.00 138.59 440.00 18.19 110.00 138.59 440.00 18.20 110.00 138.59 440.00 18.21 110.00 138.59 440.00 18.22 110.00 440.00 18.23 110.00 698.46 18.24 110.00 698.46 18.25 698.46 18.26 698.46 18.27 698.46 18.28 698.46 18.29 698.46 18.30 698.46 18.31 698.46 18.32 698.46 18.33 698.46 18.34 698.46 18.35 698.46 18.36 698.46 18.37 146.83 698.46 18.38 146.83 698.46 18.39 146.83 698.46 18.40 146.83 698.46 18.41 146.83 698.46 18.42 146.83 698.46 18.43 146.83 698.46 18.44 698.46 18.45 698.46 18.46 698.46 18.47 698.46 18.48 698.46 18.49 98.00 698.46 18.50 98.00 698.46 18.51 98.00 698.46 18.52 98.00 698.46 18.53 98.00 698.46 18.54 98.00 698.46 18.55 98.00 698.46 18.56 98.00 698.46 18.57 98.00 698.46 18.58 698.46 18.59 698.46 18.60 698.46 18.61 698.46 18.62 698.46 18.63 293.66 698.46 18.64 293.66 698.46 18.65 293.66 698.46 18.66 293.66 698.46 18.67 293.66 698.46 18.68 293.66 698.46 18.69 293.66 698.46 18.70 293.66 698.46 18.71 293.66 698.46 18.72 293.66 698.46 18.73 293.66 349.23 698.46 18.74 293.66 349.23 698.46 18.75 293.66 349.23 698.46 18.76 293.66 349.23 698.46 18.77 349.23 698.46 18.78 349.23 698.46 18.79 349.23 698.46 18.80 349.23 698.46 18.81 698.46 18.82 698.46 18.83 523.25 698.46 18.84 523.25 698.46 18.85 523.25 698.46 18.86 523.25 698.46 18.87 293.66 523.25 698.46 18.88 293.66 523.25 698.46 18.89 293.66 523.25 698.46 18.90 293.66 523.25 698.46 18.91 293.66 523.25 698.46 18.92 293.66 523.25 698.46 18.93 293.66 523.25 698.46 18.94 293.66 523.25 18.95 293.66 523.25 18.96 146.83 293.66 523.25 18.97 146.83 466.16 523.25 18.98 146.83 466.16 523.25 18.99 146.83 466.16 523.25 19.00 146.83 466.16 523.25 19.01 146.83 466.16 523.25 19.02 146.83 466.16 523.25 19.03 146.83 466.16 523.25 19.04 466.16 523.25 19.05 293.66 466.16 19.06 293.66 466.16 19.07 293.66 466.16 19.08 293.66 440.00 19.09 293.66 440.00 19.10 293.66 440.00 19.11 293.66 440.00 19.12 440.00 19.13 440.00 19.14 440.00 19.15 440.00 19.16 440.00 19.17 440.00 19.18 440.00 19.19 440.00 19.20 440.00 19.21 440.00 19.22 440.00 19.23 440.00 19.24 440.00 19.25 440.00 19.26 440.00 19.27 440.00 19.28 440.00 19.29 440.00 19.30 440.00 19.31 220.00 329.63 440.00 19.32 220.00 329.63 440.00 19.33 220.00 329.63 440.00 19.34 220.00 329.63 440.00 19.35 220.00 329.63 440.00 19.36 220.00 329.63 440.00 19.37 220.00 329.63 440.00 19.38 220.00 329.63 440.00 19.39 220.00 329.63 440.00 19.40 220.00 329.63 440.00 19.41 220.00 329.63 440.00 19.42 220.00 329.63 440.00 19.43 329.63 440.00 19.44 440.00 19.45 440.00 19.46 440.00 19.47 440.00 19.48 440.00 19.49 440.00 19.50 440.00 19.51 440.00 19.52 440.00 19.53 440.00 19.54 440.00 19.55 329.63 440.00 19.56 329.63 440.00 880.00 19.57 329.63 440.00 880.00 19.58 329.63 440.00 880.00 19.59 329.63 440.00 880.00 19.60 329.63 440.00 880.00 19.61 329.63 440.00 880.00 19.62 329.63 440.00 880.00 19.63 329.63 440.00 880.00 19.64 329.63 440.00 880.00 19.65 329.63 440.00 880.00 19.66 329.63 440.00 880.00 19.67 329.63 440.00 880.00 19.68 110.00 329.63 440.00 880.00 19.69 110.00 220.00 329.63 440.00 880.00 19.70 110.00 220.00 329.63 440.00 880.00 19.71 110.00 220.00 440.00 19.72 110.00 220.00 440.00 19.73 110.00 220.00 440.00 19.74 110.00 220.00 440.00 19.75 110.00 220.00 440.00 19.76 110.00 220.00 440.00 19.77 110.00 220.00 329.63 440.00 19.78 110.00 220.00 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13.00 123.47 185.00 369.99 493.88 587.33 13.01 123.47 185.00 293.66 493.88 587.33 13.02 123.47 293.66 493.88 587.33 13.03 123.47 293.66 493.88 587.33 13.04 123.47 293.66 493.88 587.33 13.05 123.47 293.66 493.88 587.33 13.06 123.47 293.66 493.88 587.33 13.07 123.47 293.66 493.88 587.33 13.08 123.47 293.66 493.88 587.33 13.09 123.47 293.66 493.88 587.33 13.10 123.47 293.66 493.88 587.33 13.11 123.47 293.66 493.88 587.33 13.12 123.47 293.66 493.88 587.33 13.13 123.47 293.66 493.88 587.33 13.14 123.47 293.66 493.88 587.33 13.15 123.47 293.66 493.88 587.33 13.16 123.47 293.66 493.88 587.33 13.17 123.47 293.66 493.88 587.33 13.18 123.47 293.66 493.88 587.33 13.19 123.47 293.66 493.88 587.33 13.20 123.47 293.66 493.88 587.33 13.21 123.47 293.66 493.88 587.33 13.22 123.47 293.66 369.99 493.88 587.33 13.23 123.47 293.66 369.99 493.88 587.33 13.24 123.47 293.66 369.99 493.88 587.33 13.25 123.47 293.66 369.99 587.33 13.26 123.47 293.66 369.99 587.33 13.27 123.47 293.66 369.99 587.33 13.28 123.47 293.66 369.99 587.33 13.29 123.47 293.66 369.99 587.33 13.30 123.47 293.66 369.99 587.33 13.31 123.47 293.66 369.99 587.33 13.32 123.47 293.66 369.99 587.33 13.33 123.47 293.66 369.99 587.33 13.34 123.47 293.66 369.99 587.33 13.35 123.47 293.66 587.33 13.36 123.47 293.66 493.88 587.33 13.37 123.47 293.66 493.88 587.33 13.38 123.47 293.66 493.88 587.33 13.39 123.47 293.66 493.88 587.33 13.40 123.47 493.88 587.33 13.41 123.47 493.88 587.33 13.42 493.88 587.33 13.43 493.88 13.44 493.88 13.45 493.88 13.46 415.30 493.88 13.47 415.30 493.88 13.48 415.30 493.88 13.49 415.30 493.88 13.50 415.30 493.88 13.51 415.30 493.88 13.52 246.94 415.30 493.88 13.53 246.94 415.30 493.88 13.54 246.94 415.30 493.88 587.33 13.55 246.94 415.30 493.88 587.33 13.56 246.94 415.30 493.88 587.33 13.57 246.94 415.30 493.88 587.33 13.58 246.94 415.30 493.88 587.33 13.59 246.94 415.30 493.88 587.33 13.60 246.94 415.30 493.88 587.33 13.61 415.30 493.88 587.33 987.77 13.62 415.30 493.88 587.33 987.77 13.63 293.66 415.30 493.88 587.33 987.77 13.64 293.66 415.30 493.88 587.33 987.77 13.65 293.66 415.30 493.88 587.33 987.77 13.66 246.94 293.66 415.30 493.88 987.77 13.67 246.94 293.66 415.30 493.88 987.77 13.68 246.94 293.66 415.30 493.88 987.77 13.69 246.94 293.66 415.30 493.88 987.77 13.70 246.94 293.66 415.30 493.88 987.77 13.71 246.94 293.66 415.30 493.88 987.77 13.72 246.94 293.66 493.88 987.77 13.73 246.94 293.66 493.88 987.77 13.74 246.94 293.66 349.23 493.88 987.77 13.75 246.94 293.66 349.23 493.88 987.77 13.76 146.83 246.94 349.23 493.88 987.77 13.77 146.83 246.94 349.23 493.88 987.77 13.78 146.83 246.94 349.23 493.88 987.77 13.79 146.83 246.94 349.23 493.88 987.77 13.80 146.83 246.94 349.23 493.88 987.77 13.81 146.83 246.94 349.23 493.88 987.77 13.82 146.83 246.94 349.23 493.88 987.77 13.83 146.83 246.94 349.23 415.30 493.88 987.77 13.84 146.83 246.94 349.23 415.30 493.88 987.77 13.85 246.94 349.23 415.30 493.88 987.77 13.86 246.94 349.23 415.30 493.88 987.77 13.87 246.94 349.23 415.30 493.88 987.77 13.88 246.94 349.23 415.30 493.88 987.77 13.89 246.94 349.23 415.30 493.88 987.77 13.90 246.94 349.23 415.30 493.88 987.77 13.91 246.94 349.23 415.30 493.88 987.77 13.92 246.94 493.88 987.77 13.93 185.00 369.99 493.88 987.77 13.94 185.00 369.99 493.88 987.77 13.95 185.00 369.99 13.96 185.00 369.99 554.37 13.97 92.50 185.00 369.99 554.37 13.98 92.50 185.00 277.18 369.99 554.37 13.99 92.50 185.00 277.18 369.99 554.37 14.00 92.50 185.00 277.18 369.99 554.37 14.01 92.50 185.00 277.18 369.99 554.37 14.02 92.50 185.00 277.18 369.99 554.37 14.03 92.50 185.00 277.18 369.99 14.04 92.50 185.00 277.18 369.99 14.05 92.50 185.00 369.99 14.06 92.50 185.00 369.99 14.07 92.50 185.00 233.08 369.99 14.08 92.50 185.00 233.08 369.99 14.09 92.50 185.00 233.08 369.99 14.10 92.50 185.00 233.08 369.99 14.11 92.50 185.00 233.08 277.18 369.99 14.12 92.50 185.00 233.08 277.18 369.99 14.13 92.50 185.00 233.08 277.18 369.99 14.14 92.50 185.00 233.08 277.18 369.99 14.15 92.50 185.00 233.08 277.18 369.99 14.16 92.50 185.00 233.08 277.18 369.99 14.17 92.50 185.00 277.18 369.99 14.18 185.00 277.18 369.99 14.19 185.00 277.18 369.99 14.20 185.00 277.18 369.99 14.21 185.00 369.99 14.22 185.00 369.99 14.23 185.00 369.99 14.24 185.00 369.99 14.25 185.00 369.99 14.26 185.00 369.99 14.27 92.50 185.00 369.99 14.28 92.50 185.00 369.99 14.29 92.50 185.00 369.99 14.30 92.50 185.00 369.99 14.31 92.50 185.00 369.99 14.32 92.50 185.00 369.99 14.33 92.50 185.00 369.99 14.34 92.50 185.00 369.99 14.35 92.50 185.00 369.99 14.36 92.50 185.00 369.99 14.37 92.50 185.00 369.99 14.38 92.50 185.00 369.99 14.39 92.50 185.00 369.99 554.37 14.40 92.50 185.00 369.99 554.37 14.41 92.50 185.00 369.99 554.37 14.42 92.50 185.00 554.37 14.43 92.50 185.00 554.37 14.44 92.50 185.00 554.37 14.45 92.50 185.00 554.37 14.46 92.50 185.00 554.37 14.47 92.50 185.00 554.37 14.48 92.50 123.47 185.00 554.37 14.49 92.50 123.47 185.00 554.37 14.50 92.50 123.47 185.00 554.37 14.51 92.50 123.47 185.00 554.37 14.52 123.47 185.00 554.37 14.53 123.47 185.00 554.37 14.54 123.47 185.00 554.37 14.55 123.47 185.00 554.37 14.56 123.47 185.00 14.57 123.47 14.58 123.47 14.59 123.47 14.60 123.47 329.63 14.61 123.47 329.63 14.62 123.47 329.63 14.63 123.47 329.63 14.64 329.63 14.65 329.63 14.66 329.63 14.67 329.63 14.68 329.63 14.69 329.63 14.70 329.63 14.71 329.63 14.72 329.63 14.73 329.63 14.74 369.99 14.75 369.99 14.76 369.99 14.77 369.99 14.78 369.99 14.79 369.99 14.80 369.99 14.81 369.99 14.82 146.83 369.99 14.83 146.83 369.99 14.84 146.83 369.99 14.85 146.83 14.86 146.83 14.87 146.83 14.88 146.83 14.89 14.90 14.91 392.00 14.92 164.81 392.00 14.93 164.81 392.00 14.94 164.81 392.00 14.95 164.81 392.00 14.96 164.81 392.00 14.97 164.81 392.00 14.98 164.81 392.00 14.99 164.81 392.00 15.00 164.81 392.00 15.01 164.81 392.00 15.02 164.81 392.00 15.03 164.81 392.00 15.04 164.81 392.00 15.05 164.81 392.00 15.06 164.81 392.00 15.07 164.81 392.00 15.08 82.41 164.81 392.00 15.09 82.41 164.81 392.00 15.10 82.41 164.81 392.00 15.11 82.41 164.81 392.00 15.12 82.41 164.81 392.00 15.13 82.41 164.81 392.00 15.14 82.41 164.81 392.00 15.15 82.41 164.81 392.00 15.16 82.41 164.81 392.00 15.17 82.41 164.81 392.00 15.18 82.41 164.81 392.00 15.19 82.41 164.81 392.00 15.20 164.81 392.00 15.21 164.81 392.00 15.22 164.81 392.00 15.23 164.81 392.00 659.26 15.24 164.81 659.26 15.25 164.81 659.26 15.26 164.81 659.26 15.27 164.81 659.26 15.28 82.41 164.81 659.26 15.29 82.41 164.81 659.26 15.30 82.41 164.81 659.26 15.31 82.41 164.81 15.32 82.41 164.81 15.33 82.41 164.81 15.34 82.41 164.81 15.35 82.41 164.81 15.36 82.41 164.81 15.37 82.41 164.81 15.38 164.81 15.39 164.81 392.00 15.40 164.81 392.00 15.41 392.00 15.42 392.00 15.43 392.00 15.44 392.00 15.45 392.00 15.46 392.00 15.47 392.00 15.48 392.00 15.49 392.00 15.50 392.00 15.51 392.00 15.52 392.00 15.53 392.00 15.54 392.00 15.55 392.00 15.56 369.99 392.00 15.57 369.99 15.58 146.83 369.99 15.59 146.83 369.99 15.60 146.83 369.99 15.61 146.83 369.99 15.62 146.83 369.99 15.63 146.83 369.99 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554.37 16.25 110.00 277.18 369.99 554.37 16.26 110.00 277.18 369.99 554.37 16.27 110.00 277.18 369.99 554.37 16.28 110.00 277.18 369.99 554.37 16.29 110.00 277.18 554.37 16.30 110.00 277.18 554.37 16.31 110.00 277.18 554.37 16.32 110.00 277.18 554.37 16.33 110.00 277.18 554.37 16.34 110.00 277.18 554.37 16.35 110.00 277.18 369.99 554.37 739.99 16.36 110.00 277.18 369.99 554.37 739.99 16.37 110.00 277.18 369.99 554.37 739.99 16.38 110.00 277.18 369.99 554.37 739.99 16.39 110.00 277.18 369.99 554.37 739.99 16.40 110.00 277.18 369.99 554.37 739.99 16.41 369.99 554.37 739.99 16.42 369.99 554.37 739.99 16.43 369.99 554.37 739.99 16.44 185.00 369.99 554.37 739.99 16.45 185.00 369.99 440.00 554.37 739.99 16.46 185.00 369.99 440.00 554.37 739.99 16.47 185.00 369.99 440.00 554.37 739.99 16.48 185.00 369.99 440.00 554.37 739.99 16.49 185.00 369.99 440.00 739.99 16.50 185.00 369.99 440.00 739.99 16.51 185.00 369.99 440.00 739.99 16.52 185.00 369.99 440.00 739.99 16.53 185.00 369.99 739.99 16.54 185.00 369.99 739.99 16.55 185.00 369.99 739.99 16.56 185.00 369.99 739.99 16.57 185.00 369.99 739.99 16.58 185.00 369.99 739.99 16.59 185.00 369.99 739.99 16.60 185.00 369.99 739.99 16.61 185.00 369.99 739.99 16.62 185.00 369.99 739.99 16.63 185.00 369.99 739.99 16.64 185.00 369.99 739.99 16.65 185.00 369.99 739.99 16.66 185.00 369.99 739.99 16.67 185.00 369.99 739.99 16.68 185.00 369.99 739.99 16.69 185.00 369.99 739.99 16.70 185.00 369.99 739.99 16.71 185.00 369.99 739.99 16.72 185.00 369.99 739.99 16.73 185.00 369.99 739.99 16.74 185.00 369.99 739.99 16.75 185.00 369.99 739.99 16.76 185.00 369.99 739.99 16.77 185.00 369.99 739.99 16.78 185.00 369.99 739.99 16.79 185.00 369.99 739.99 16.80 185.00 369.99 739.99 16.81 185.00 369.99 739.99 16.82 138.59 185.00 369.99 739.99 16.83 138.59 185.00 369.99 739.99 16.84 138.59 185.00 369.99 739.99 16.85 138.59 185.00 369.99 739.99 16.86 138.59 185.00 369.99 739.99 16.87 138.59 185.00 369.99 739.99 1318.51 16.88 138.59 185.00 369.99 587.33 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17.08 123.47 185.00 293.66 369.99 587.33 1244.51 17.09 123.47 185.00 293.66 369.99 587.33 1244.51 17.10 123.47 185.00 293.66 369.99 587.33 1244.51 17.11 123.47 185.00 293.66 369.99 587.33 17.12 123.47 185.00 293.66 369.99 587.33 17.13 123.47 185.00 293.66 369.99 587.33 17.14 123.47 185.00 293.66 369.99 587.33 17.15 123.47 185.00 293.66 369.99 587.33 17.16 123.47 185.00 293.66 369.99 587.33 17.17 123.47 185.00 293.66 369.99 587.33 17.18 123.47 185.00 293.66 369.99 587.33 17.19 123.47 185.00 293.66 369.99 587.33 17.20 123.47 185.00 293.66 369.99 587.33 17.21 123.47 185.00 293.66 369.99 587.33 17.22 123.47 185.00 293.66 369.99 587.33 17.23 123.47 185.00 293.66 369.99 587.33 17.24 123.47 185.00 246.94 293.66 369.99 587.33 17.25 123.47 185.00 246.94 293.66 369.99 587.33 17.26 123.47 185.00 246.94 293.66 369.99 587.33 17.27 123.47 185.00 246.94 293.66 369.99 587.33 17.28 123.47 185.00 246.94 293.66 369.99 587.33 17.29 123.47 185.00 246.94 293.66 369.99 587.33 17.30 123.47 185.00 246.94 293.66 369.99 587.33 17.31 123.47 185.00 246.94 293.66 369.99 587.33 17.32 123.47 185.00 246.94 293.66 369.99 587.33 17.33 123.47 185.00 246.94 293.66 369.99 587.33 17.34 123.47 185.00 246.94 293.66 369.99 587.33 17.35 123.47 185.00 246.94 293.66 369.99 587.33 17.36 123.47 185.00 293.66 369.99 587.33 17.37 123.47 185.00 293.66 369.99 587.33 17.38 123.47 185.00 293.66 369.99 587.33 17.39 123.47 185.00 293.66 369.99 587.33 17.40 123.47 185.00 293.66 369.99 587.33 17.41 123.47 185.00 293.66 369.99 587.33 17.42 123.47 185.00 293.66 369.99 587.33 17.43 123.47 185.00 293.66 587.33 17.44 123.47 293.66 349.23 17.45 123.47 349.23 17.46 123.47 349.23 17.47 349.23 17.48 349.23 17.49 349.23 17.50 349.23 17.51 349.23 17.52 69.30 138.59 349.23 17.53 69.30 138.59 349.23 17.54 69.30 138.59 349.23 17.55 69.30 138.59 349.23 17.56 69.30 138.59 349.23 17.57 69.30 138.59 349.23 17.58 69.30 138.59 349.23 17.59 69.30 138.59 246.94 349.23 17.60 138.59 246.94 349.23 17.61 246.94 349.23 17.62 246.94 349.23 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246.94 587.33 1108.73 1174.66 9.28 82.41 164.81 246.94 587.33 1108.73 1174.66 9.29 82.41 164.81 246.94 587.33 1108.73 1174.66 9.30 82.41 164.81 246.94 587.33 1108.73 1174.66 9.31 82.41 164.81 246.94 587.33 1108.73 1174.66 9.32 82.41 164.81 246.94 587.33 1108.73 1174.66 9.33 82.41 164.81 246.94 587.33 1108.73 1174.66 9.34 82.41 164.81 246.94 587.33 1108.73 1174.66 9.35 82.41 164.81 246.94 587.33 1108.73 1174.66 9.36 82.41 164.81 246.94 587.33 1108.73 1174.66 9.37 82.41 164.81 246.94 587.33 1108.73 1174.66 9.38 82.41 164.81 246.94 587.33 1108.73 1174.66 9.39 82.41 164.81 246.94 587.33 1108.73 1174.66 9.40 82.41 164.81 246.94 587.33 1108.73 1174.66 9.41 82.41 164.81 246.94 587.33 1108.73 1174.66 9.42 82.41 164.81 246.94 587.33 1108.73 1174.66 9.43 82.41 164.81 246.94 587.33 1108.73 1174.66 9.44 82.41 164.81 246.94 587.33 1174.66 9.45 82.41 164.81 246.94 587.33 1174.66 9.46 82.41 164.81 246.94 587.33 1174.66 9.47 82.41 164.81 246.94 587.33 1174.66 9.48 82.41 164.81 246.94 587.33 1174.66 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123.47 196.00 329.63 392.00 493.88 587.33 987.77 10.34 82.41 98.00 123.47 196.00 329.63 392.00 493.88 587.33 987.77 10.35 82.41 98.00 123.47 196.00 329.63 392.00 493.88 587.33 987.77 10.36 82.41 98.00 123.47 196.00 329.63 392.00 493.88 587.33 987.77 10.37 82.41 98.00 123.47 196.00 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.38 82.41 98.00 123.47 196.00 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.39 82.41 98.00 123.47 196.00 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.40 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.41 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.42 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.43 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.44 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.45 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.46 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.47 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.48 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.49 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.50 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.51 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.52 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.53 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.54 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.55 82.41 123.47 246.94 329.63 392.00 493.88 587.33 783.99 987.77 10.56 82.41 123.47 246.94 329.63 392.00 493.88 783.99 987.77 10.57 82.41 123.47 246.94 329.63 392.00 493.88 783.99 987.77 10.58 82.41 123.47 246.94 329.63 392.00 493.88 783.99 987.77 10.59 82.41 123.47 246.94 329.63 392.00 493.88 987.77 10.60 82.41 123.47 246.94 329.63 392.00 493.88 987.77 10.61 82.41 123.47 246.94 329.63 392.00 493.88 987.77 10.62 82.41 123.47 246.94 329.63 392.00 493.88 987.77 10.63 82.41 123.47 246.94 329.63 493.88 987.77 10.64 82.41 123.47 246.94 329.63 493.88 987.77 10.65 82.41 196.00 246.94 329.63 493.88 987.77 10.66 82.41 196.00 246.94 329.63 493.88 987.77 10.67 82.41 196.00 246.94 329.63 493.88 987.77 10.68 196.00 246.94 329.63 493.88 987.77 10.69 196.00 246.94 329.63 493.88 987.77 10.70 196.00 246.94 329.63 493.88 987.77 10.71 98.00 196.00 246.94 329.63 493.88 659.26 987.77 10.72 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.73 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.74 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.75 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.76 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.77 98.00 196.00 246.94 293.66 329.63 493.88 659.26 987.77 10.78 98.00 196.00 246.94 293.66 329.63 493.88 987.77 10.79 98.00 196.00 293.66 329.63 493.88 987.77 10.80 98.00 196.00 293.66 329.63 493.88 987.77 10.81 98.00 196.00 293.66 329.63 493.88 987.77 10.82 98.00 196.00 293.66 329.63 493.88 587.33 987.77 10.83 98.00 196.00 293.66 329.63 493.88 587.33 987.77 10.84 196.00 293.66 493.88 554.37 587.33 987.77 10.85 196.00 554.37 587.33 987.77 10.86 196.00 554.37 587.33 987.77 10.87 196.00 554.37 587.33 987.77 10.88 196.00 554.37 587.33 987.77 10.89 196.00 554.37 587.33 987.77 10.90 196.00 554.37 587.33 987.77 10.91 196.00 554.37 987.77 10.92 196.00 987.77 10.93 196.00 493.88 987.77 10.94 196.00 493.88 987.77 10.95 196.00 493.88 987.77 10.96 196.00 493.88 987.77 10.97 196.00 493.88 987.77 10.98 196.00 493.88 987.77 10.99 196.00 493.88 987.77 1174.66 11.00 196.00 493.88 987.77 1174.66 11.01 196.00 493.88 987.77 1174.66 11.02 196.00 493.88 587.33 987.77 1174.66 11.03 196.00 493.88 587.33 987.77 1174.66 11.04 196.00 493.88 587.33 1174.66 11.05 196.00 493.88 587.33 1174.66 11.06 196.00 493.88 587.33 1174.66 11.07 196.00 493.88 587.33 1174.66 11.08 196.00 493.88 587.33 1174.66 11.09 196.00 493.88 587.33 1174.66 11.10 196.00 246.94 493.88 587.33 1174.66 11.11 246.94 493.88 1174.66 11.12 246.94 493.88 1174.66 11.13 246.94 493.88 1174.66 11.14 246.94 493.88 1174.66 11.15 246.94 493.88 1174.66 11.16 185.00 246.94 493.88 1174.66 11.17 185.00 246.94 493.88 1174.66 11.18 185.00 246.94 493.88 1174.66 11.19 185.00 246.94 493.88 1174.66 11.20 185.00 246.94 493.88 1174.66 11.21 185.00 246.94 493.88 1174.66 11.22 185.00 246.94 493.88 1174.66 11.23 185.00 246.94 493.88 1174.66 11.24 185.00 246.94 493.88 1174.66 11.25 185.00 246.94 493.88 1174.66 11.26 185.00 246.94 493.88 1174.66 11.27 185.00 246.94 493.88 1174.66 11.28 185.00 246.94 493.88 1174.66 11.29 185.00 246.94 493.88 1174.66 11.30 185.00 246.94 493.88 1174.66 11.31 185.00 246.94 493.88 1174.66 11.32 185.00 246.94 493.88 587.33 1174.66 11.33 185.00 246.94 493.88 587.33 1174.66 11.34 185.00 246.94 493.88 587.33 1174.66 11.35 185.00 246.94 493.88 587.33 987.77 1174.66 11.36 185.00 246.94 493.88 587.33 987.77 1174.66 11.37 185.00 246.94 493.88 587.33 987.77 1174.66 11.38 185.00 246.94 493.88 587.33 987.77 1174.66 11.39 185.00 246.94 493.88 587.33 987.77 1174.66 11.40 185.00 246.94 493.88 587.33 987.77 1174.66 11.41 185.00 246.94 493.88 587.33 987.77 1174.66 11.42 185.00 246.94 493.88 587.33 987.77 1174.66 11.43 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.44 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.45 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.46 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.47 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.48 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.49 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.50 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.51 123.47 185.00 246.94 493.88 587.33 987.77 1174.66 11.52 123.47 185.00 246.94 493.88 587.33 987.77 11.53 123.47 185.00 246.94 493.88 587.33 987.77 11.54 123.47 185.00 246.94 493.88 587.33 987.77 11.55 123.47 185.00 246.94 493.88 587.33 987.77 11.56 123.47 185.00 246.94 493.88 587.33 987.77 11.57 123.47 185.00 246.94 493.88 587.33 987.77 11.58 123.47 185.00 246.94 493.88 587.33 987.77 11.59 123.47 185.00 246.94 493.88 587.33 987.77 11.60 123.47 185.00 246.94 493.88 587.33 987.77 11.61 123.47 185.00 246.94 493.88 587.33 987.77 11.62 123.47 185.00 246.94 493.88 587.33 987.77 11.63 123.47 185.00 246.94 493.88 587.33 987.77 11.64 123.47 185.00 246.94 493.88 587.33 987.77 11.65 123.47 185.00 246.94 493.88 587.33 987.77 11.66 123.47 185.00 246.94 493.88 587.33 987.77 11.67 123.47 185.00 246.94 493.88 587.33 987.77 11.68 123.47 185.00 246.94 493.88 587.33 987.77 11.69 123.47 185.00 246.94 293.66 493.88 587.33 987.77 11.70 123.47 185.00 246.94 293.66 493.88 987.77 1975.53 11.71 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.72 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.73 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.74 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.75 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.76 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.77 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.78 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.79 123.47 185.00 246.94 493.88 987.77 1318.51 1975.53 11.80 123.47 185.00 246.94 493.88 987.77 1318.51 1975.53 11.81 123.47 185.00 246.94 493.88 987.77 1318.51 1975.53 11.82 123.47 185.00 246.94 493.88 987.77 1318.51 1975.53 11.83 123.47 185.00 246.94 493.88 987.77 1318.51 1975.53 11.84 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.85 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.86 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 1975.53 11.87 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.88 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.89 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.90 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.91 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.92 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.93 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.94 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.95 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.96 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.97 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.98 123.47 185.00 246.94 293.66 493.88 987.77 1318.51 11.99 123.47 185.00 246.94 293.66 1318.51 12.00 123.47 185.00 246.94 293.66 1318.51 12.01 123.47 185.00 246.94 293.66 1318.51 12.02 123.47 185.00 246.94 293.66 1318.51 12.03 123.47 185.00 246.94 293.66 1318.51 12.04 123.47 185.00 246.94 293.66 1318.51 12.05 123.47 185.00 246.94 293.66 1318.51 12.06 123.47 185.00 246.94 293.66 1318.51 12.07 123.47 185.00 246.94 293.66 1318.51 12.08 123.47 185.00 246.94 293.66 1318.51 12.09 185.00 293.66 587.33 1174.66 12.10 185.00 293.66 587.33 1174.66 12.11 185.00 293.66 587.33 1174.66 12.12 185.00 293.66 587.33 1174.66 12.13 185.00 293.66 587.33 1174.66 12.14 185.00 293.66 587.33 1174.66 12.15 185.00 246.94 293.66 587.33 1174.66 12.16 185.00 246.94 293.66 587.33 1174.66 12.17 185.00 246.94 293.66 369.99 587.33 1174.66 12.18 185.00 246.94 293.66 369.99 587.33 1174.66 12.19 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.20 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.21 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.22 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.23 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.24 185.00 246.94 293.66 369.99 440.00 587.33 1174.66 12.25 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.26 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.27 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.28 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.29 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.30 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.31 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.32 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.33 61.74 123.47 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.34 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.35 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.36 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.37 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.38 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.39 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.40 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.41 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.42 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.43 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.44 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.45 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.46 185.00 246.94 293.66 369.99 440.00 587.33 1108.73 1174.66 12.47 185.00 246.94 293.66 369.99 440.00 587.33 12.48 185.00 246.94 293.66 369.99 440.00 587.33 12.49 185.00 293.66 369.99 440.00 587.33 12.50 185.00 293.66 440.00 587.33 12.51 185.00 293.66 587.33 12.52 293.66 587.33 12.53 293.66 587.33 12.54 82.41 164.81 293.66 415.30 587.33 12.55 82.41 164.81 293.66 415.30 587.33 12.56 82.41 164.81 293.66 415.30 587.33 12.57 82.41 164.81 293.66 415.30 587.33 12.58 82.41 164.81 246.94 293.66 415.30 587.33 12.59 82.41 164.81 246.94 293.66 415.30 587.33 12.60 82.41 164.81 246.94 293.66 415.30 587.33 12.61 82.41 164.81 246.94 293.66 415.30 493.88 587.33 987.77 12.62 82.41 164.81 246.94 293.66 415.30 493.88 587.33 987.77 12.63 82.41 164.81 246.94 293.66 415.30 493.88 587.33 987.77 12.64 82.41 164.81 246.94 293.66 415.30 493.88 587.33 987.77 12.65 82.41 164.81 246.94 415.30 493.88 587.33 987.77 12.66 82.41 164.81 246.94 415.30 493.88 587.33 987.77 12.67 82.41 164.81 246.94 415.30 493.88 587.33 987.77 12.68 82.41 164.81 246.94 415.30 493.88 587.33 987.77 12.69 82.41 164.81 246.94 415.30 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493.88 739.99 987.77 13.35 164.81 329.63 369.99 493.88 739.99 987.77 13.36 164.81 329.63 369.99 493.88 739.99 987.77 1046.50 13.37 164.81 329.63 369.99 493.88 739.99 987.77 1046.50 13.38 164.81 329.63 369.99 493.88 987.77 1046.50 13.39 164.81 329.63 369.99 493.88 987.77 1046.50 13.40 164.81 329.63 369.99 493.88 987.77 1046.50 13.41 164.81 329.63 369.99 493.88 987.77 1046.50 13.42 164.81 329.63 369.99 493.88 987.77 1046.50 13.43 164.81 329.63 369.99 493.88 987.77 1046.50 13.44 164.81 329.63 369.99 493.88 987.77 1046.50 13.45 82.41 164.81 246.94 329.63 369.99 493.88 987.77 1046.50 13.46 82.41 164.81 246.94 329.63 369.99 493.88 987.77 1046.50 13.47 82.41 164.81 246.94 329.63 369.99 493.88 987.77 1046.50 13.48 82.41 164.81 246.94 329.63 369.99 493.88 987.77 1046.50 13.49 82.41 164.81 246.94 329.63 369.99 493.88 987.77 1046.50 13.50 82.41 164.81 246.94 329.63 369.99 493.88 659.26 987.77 1046.50 13.51 82.41 164.81 246.94 329.63 369.99 493.88 659.26 987.77 1046.50 13.52 82.41 164.81 246.94 329.63 369.99 493.88 659.26 987.77 1046.50 13.53 82.41 164.81 246.94 329.63 369.99 493.88 659.26 987.77 1046.50 13.54 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.55 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.56 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.57 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.58 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.59 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.60 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.61 82.41 164.81 246.94 329.63 493.88 659.26 987.77 1046.50 13.62 164.81 329.63 659.26 987.77 1046.50 13.63 277.18 329.63 659.26 987.77 1046.50 13.64 277.18 329.63 659.26 987.77 1046.50 13.65 277.18 329.63 659.26 987.77 1046.50 13.66 277.18 329.63 659.26 987.77 1046.50 13.67 277.18 329.63 659.26 1046.50 13.68 277.18 329.63 1046.50 13.69 277.18 329.63 1046.50 13.70 277.18 329.63 1046.50 13.71 277.18 329.63 1046.50 13.72 277.18 329.63 369.99 1046.50 13.73 164.81 277.18 329.63 369.99 1046.50 13.74 164.81 277.18 329.63 369.99 1046.50 13.75 164.81 277.18 329.63 369.99 1046.50 13.76 164.81 277.18 329.63 369.99 1046.50 13.77 164.81 277.18 329.63 369.99 1046.50 13.78 164.81 277.18 329.63 369.99 1760.00 13.79 164.81 277.18 329.63 369.99 1760.00 13.80 164.81 277.18 329.63 1760.00 13.81 164.81 277.18 329.63 1760.00 13.82 164.81 277.18 329.63 1760.00 13.83 164.81 277.18 329.63 1760.00 13.84 164.81 277.18 329.63 1760.00 13.85 164.81 277.18 329.63 1760.00 13.86 164.81 277.18 329.63 1760.00 13.87 164.81 277.18 329.63 1760.00 13.88 164.81 277.18 329.63 1760.00 13.89 164.81 277.18 329.63 1760.00 13.90 164.81 277.18 329.63 1760.00 13.91 164.81 277.18 329.63 1760.00 13.92 164.81 277.18 329.63 1760.00 13.93 164.81 277.18 329.63 1760.00 13.94 164.81 277.18 329.63 1760.00 13.95 164.81 277.18 329.63 1760.00 13.96 164.81 277.18 329.63 1760.00 13.97 164.81 277.18 1760.00 13.98 164.81 277.18 1760.00 13.99 164.81 277.18 880.00 1760.00 14.00 164.81 277.18 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15.18 207.65 277.18 15.19 207.65 15.20 207.65 15.21 207.65 15.22 207.65 587.33 15.23 82.41 207.65 587.33 15.24 82.41 207.65 587.33 15.25 82.41 207.65 587.33 15.26 82.41 146.83 207.65 587.33 15.27 82.41 146.83 207.65 587.33 15.28 82.41 146.83 207.65 587.33 15.29 82.41 146.83 207.65 587.33 15.30 82.41 146.83 207.65 554.37 587.33 15.31 82.41 146.83 207.65 554.37 15.32 82.41 146.83 207.65 554.37 15.33 82.41 146.83 207.65 554.37 15.34 82.41 146.83 207.65 277.18 554.37 15.35 82.41 146.83 207.65 277.18 554.37 15.36 82.41 146.83 207.65 277.18 554.37 15.37 82.41 146.83 277.18 554.37 15.38 82.41 146.83 277.18 554.37 15.39 82.41 277.18 554.37 15.40 82.41 277.18 554.37 15.41 82.41 277.18 554.37 15.42 277.18 554.37 1318.51 15.43 277.18 554.37 1318.51 15.44 277.18 554.37 1318.51 15.45 277.18 554.37 1318.51 15.46 277.18 554.37 1318.51 15.47 277.18 554.37 1318.51 15.48 277.18 554.37 1318.51 15.49 185.00 277.18 440.00 554.37 1318.51 15.50 185.00 277.18 440.00 554.37 1318.51 15.51 185.00 277.18 440.00 554.37 1318.51 15.52 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.53 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.54 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.55 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.56 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.57 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.58 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.59 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.60 92.50 185.00 220.00 277.18 440.00 554.37 659.26 1318.51 15.61 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.62 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.63 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.64 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.65 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.66 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.67 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.68 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.69 92.50 185.00 220.00 277.18 554.37 659.26 1318.51 15.70 92.50 185.00 277.18 554.37 659.26 1318.51 15.71 92.50 185.00 277.18 554.37 659.26 1318.51 15.72 92.50 185.00 277.18 554.37 659.26 1318.51 15.73 185.00 277.18 554.37 659.26 1318.51 15.74 185.00 277.18 554.37 659.26 1318.51 15.75 185.00 277.18 554.37 659.26 1318.51 15.76 185.00 277.18 554.37 659.26 1318.51 15.77 185.00 277.18 554.37 659.26 1318.51 15.78 185.00 277.18 659.26 1318.51 15.79 185.00 277.18 659.26 1318.51 15.80 185.00 277.18 659.26 1318.51 15.81 185.00 277.18 659.26 1318.51 15.82 185.00 277.18 659.26 1318.51 15.83 185.00 277.18 659.26 1318.51 15.84 185.00 277.18 659.26 1318.51 15.85 185.00 277.18 587.33 659.26 1318.51 15.86 185.00 277.18 587.33 1174.66 1318.51 15.87 185.00 277.18 587.33 1174.66 1318.51 15.88 185.00 277.18 587.33 1174.66 1318.51 15.89 185.00 277.18 587.33 1174.66 1318.51 15.90 185.00 277.18 587.33 1174.66 1318.51 15.91 185.00 277.18 587.33 1174.66 1318.51 15.92 185.00 277.18 587.33 1174.66 1318.51 15.93 185.00 277.18 587.33 1174.66 1318.51 15.94 185.00 277.18 587.33 1174.66 1318.51 15.95 185.00 277.18 587.33 1174.66 1318.51 15.96 185.00 277.18 587.33 1174.66 1318.51 15.97 185.00 277.18 587.33 1174.66 1318.51 15.98 185.00 277.18 587.33 1174.66 15.99 185.00 277.18 329.63 587.33 1174.66 16.00 185.00 277.18 329.63 587.33 1174.66 16.01 185.00 329.63 587.33 1174.66 16.02 185.00 329.63 587.33 1174.66 16.03 329.63 587.33 1174.66 16.04 329.63 587.33 1174.66 16.05 329.63 587.33 1174.66 16.06 329.63 587.33 1174.66 16.07 329.63 587.33 1174.66 16.08 329.63 587.33 1174.66 16.09 329.63 587.33 1174.66 16.10 329.63 587.33 1174.66 16.11 329.63 587.33 1174.66 16.12 329.63 587.33 1174.66 16.13 329.63 587.33 1174.66 16.14 329.63 587.33 1174.66 16.15 329.63 587.33 1174.66 16.16 329.63 587.33 1174.66 16.17 329.63 369.99 587.33 1174.66 16.18 369.99 587.33 1174.66 16.19 369.99 587.33 1174.66 16.20 220.00 293.66 369.99 587.33 1174.66 16.21 185.00 220.00 293.66 369.99 1174.66 16.22 185.00 220.00 293.66 369.99 1174.66 16.23 185.00 220.00 293.66 369.99 1174.66 16.24 185.00 220.00 293.66 369.99 1174.66 16.25 185.00 220.00 293.66 369.99 1108.73 1174.66 16.26 185.00 220.00 293.66 369.99 1108.73 1174.66 16.27 185.00 220.00 293.66 369.99 1108.73 1174.66 16.28 185.00 220.00 293.66 369.99 1108.73 1174.66 16.29 185.00 220.00 293.66 369.99 1108.73 1174.66 16.30 185.00 220.00 293.66 369.99 1108.73 1174.66 16.31 185.00 220.00 293.66 369.99 587.33 1108.73 1174.66 16.32 185.00 220.00 293.66 369.99 587.33 1108.73 1174.66 16.33 185.00 220.00 369.99 587.33 1108.73 1174.66 16.34 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.35 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.36 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.37 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.38 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.39 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.40 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.41 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.42 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.43 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.44 92.50 185.00 220.00 369.99 587.33 1108.73 1174.66 16.45 92.50 185.00 220.00 369.99 1108.73 1174.66 16.46 92.50 185.00 220.00 369.99 1108.73 1174.66 16.47 92.50 185.00 220.00 369.99 1108.73 1174.66 16.48 92.50 185.00 220.00 369.99 1108.73 1174.66 16.49 92.50 220.00 369.99 1108.73 1174.66 16.50 92.50 220.00 369.99 1108.73 1174.66 16.51 92.50 220.00 369.99 1108.73 1174.66 16.52 92.50 220.00 293.66 369.99 1108.73 1174.66 16.53 92.50 220.00 293.66 369.99 1108.73 1174.66 16.54 92.50 293.66 369.99 1108.73 1174.66 16.55 92.50 293.66 369.99 1108.73 1174.66 16.56 92.50 185.00 293.66 369.99 1108.73 1174.66 16.57 92.50 185.00 293.66 369.99 1108.73 1174.66 16.58 92.50 185.00 293.66 369.99 1108.73 1174.66 16.59 92.50 185.00 369.99 1108.73 1174.66 16.60 92.50 185.00 369.99 1108.73 1174.66 16.61 92.50 185.00 329.63 369.99 1108.73 1174.66 16.62 92.50 185.00 329.63 369.99 1108.73 1174.66 16.63 92.50 185.00 329.63 369.99 1108.73 1174.66 16.64 92.50 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.65 92.50 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.66 92.50 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.67 92.50 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.68 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.69 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.70 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.71 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.72 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.73 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.74 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.75 185.00 329.63 369.99 554.37 987.77 1108.73 1174.66 16.76 185.00 329.63 554.37 987.77 1108.73 1174.66 16.77 185.00 329.63 554.37 1108.73 1174.66 16.78 185.00 329.63 1108.73 1174.66 16.79 185.00 207.65 329.63 587.33 1108.73 1174.66 16.80 185.00 207.65 329.63 587.33 1108.73 1174.66 16.81 207.65 329.63 587.33 1108.73 1174.66 16.82 207.65 293.66 329.63 587.33 1108.73 1174.66 16.83 207.65 293.66 329.63 587.33 1108.73 1174.66 16.84 207.65 293.66 329.63 587.33 1174.66 16.85 207.65 293.66 329.63 587.33 1174.66 16.86 207.65 293.66 329.63 587.33 1174.66 16.87 207.65 293.66 587.33 1174.66 16.88 207.65 293.66 587.33 1174.66 16.89 207.65 587.33 1174.66 16.90 207.65 587.33 1174.66 16.91 587.33 1174.66 16.92 587.33 1174.66 16.93 554.37 587.33 1174.66 16.94 554.37 587.33 1174.66 16.95 554.37 587.33 1174.66 16.96 293.66 440.00 554.37 587.33 1174.66 16.97 293.66 440.00 554.37 16.98 293.66 440.00 554.37 880.00 16.99 293.66 440.00 554.37 880.00 17.00 293.66 440.00 554.37 880.00 17.01 293.66 440.00 554.37 880.00 17.02 293.66 440.00 554.37 880.00 17.03 293.66 440.00 554.37 587.33 880.00 17.04 293.66 440.00 554.37 587.33 880.00 17.05 293.66 440.00 587.33 880.00 17.06 293.66 440.00 587.33 880.00 17.07 293.66 440.00 587.33 880.00 17.08 293.66 440.00 587.33 880.00 17.09 293.66 440.00 587.33 880.00 17.10 293.66 440.00 587.33 880.00 17.11 293.66 440.00 587.33 880.00 17.12 440.00 880.00 17.13 440.00 880.00 17.14 17.15 17.16 554.37 17.17 554.37 17.18 554.37 17.19 220.00 440.00 554.37 880.00 1108.73 17.20 220.00 440.00 554.37 880.00 1108.73 17.21 220.00 440.00 554.37 880.00 1108.73 17.22 220.00 329.63 440.00 554.37 880.00 1108.73 17.23 220.00 329.63 440.00 554.37 880.00 1108.73 17.24 220.00 329.63 440.00 554.37 880.00 1108.73 17.25 220.00 329.63 440.00 554.37 880.00 1108.73 17.26 220.00 329.63 440.00 554.37 880.00 1108.73 17.27 220.00 329.63 440.00 554.37 880.00 1108.73 17.28 220.00 329.63 440.00 554.37 880.00 1108.73 17.29 220.00 329.63 440.00 554.37 880.00 1108.73 17.30 220.00 329.63 440.00 554.37 880.00 1108.73 17.31 220.00 329.63 440.00 554.37 880.00 1108.73 17.32 220.00 440.00 554.37 880.00 1108.73 1174.66 17.33 110.00 220.00 440.00 554.37 880.00 1108.73 1174.66 17.34 110.00 554.37 1108.73 1174.66 17.35 110.00 554.37 1108.73 1174.66 17.36 110.00 554.37 1108.73 1174.66 17.37 110.00 554.37 1108.73 1174.66 17.38 110.00 554.37 1108.73 1174.66 17.39 110.00 554.37 1108.73 1174.66 17.40 110.00 554.37 1108.73 1174.66 17.41 110.00 554.37 1108.73 1174.66 17.42 110.00 554.37 1108.73 1174.66 17.43 110.00 554.37 1108.73 1174.66 17.44 110.00 554.37 1108.73 1174.66 17.45 110.00 554.37 1108.73 1174.66 17.46 110.00 164.81 554.37 1108.73 1174.66 17.47 110.00 164.81 554.37 1108.73 1174.66 17.48 110.00 164.81 554.37 1108.73 1174.66 1318.51 17.49 110.00 164.81 554.37 1174.66 1318.51 17.50 110.00 164.81 554.37 1174.66 1318.51 17.51 110.00 164.81 554.37 1174.66 1318.51 17.52 110.00 164.81 554.37 1174.66 1318.51 17.53 110.00 164.81 554.37 1174.66 1318.51 17.54 110.00 164.81 1174.66 1318.51 17.55 110.00 164.81 1174.66 1318.51 17.56 110.00 164.81 1174.66 1318.51 17.57 110.00 164.81 1174.66 1318.51 17.58 110.00 164.81 1174.66 1318.51 17.59 110.00 164.81 1174.66 1318.51 17.60 110.00 164.81 1318.51 17.61 110.00 164.81 1318.51 17.62 110.00 164.81 1318.51 17.63 110.00 164.81 1318.51 17.64 110.00 164.81 1318.51 17.65 110.00 164.81 1318.51 17.66 110.00 164.81 1318.51 1479.98 17.67 110.00 164.81 1318.51 1479.98 17.68 110.00 164.81 1318.51 1479.98 17.69 110.00 164.81 1318.51 1479.98 17.70 110.00 185.00 1174.66 1318.51 1479.98 17.71 92.50 110.00 185.00 1174.66 1318.51 1479.98 17.72 92.50 185.00 1174.66 1318.51 1479.98 17.73 92.50 138.59 185.00 1174.66 1318.51 1479.98 17.74 92.50 138.59 185.00 1174.66 1318.51 1479.98 17.75 92.50 138.59 185.00 220.00 1174.66 1318.51 1479.98 17.76 92.50 138.59 185.00 220.00 1174.66 1479.98 17.77 92.50 138.59 185.00 220.00 1174.66 1479.98 17.78 92.50 138.59 185.00 220.00 1174.66 1479.98 17.79 92.50 138.59 185.00 220.00 1174.66 1479.98 17.80 92.50 138.59 185.00 220.00 1174.66 1479.98 17.81 92.50 138.59 185.00 220.00 1174.66 1479.98 17.82 92.50 138.59 185.00 220.00 1174.66 1479.98 17.83 92.50 138.59 185.00 220.00 1174.66 1479.98 17.84 92.50 138.59 185.00 220.00 1174.66 1479.98 1567.98 17.85 138.59 185.00 220.00 440.00 1479.98 1567.98 17.86 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.87 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.88 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.89 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.90 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.91 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.92 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 17.93 185.00 220.00 329.63 440.00 1479.98 1567.98 17.94 185.00 220.00 329.63 440.00 1479.98 1567.98 17.95 185.00 220.00 329.63 440.00 1479.98 1567.98 17.96 185.00 220.00 329.63 440.00 1479.98 1567.98 17.97 185.00 220.00 329.63 440.00 1479.98 1567.98 17.98 185.00 220.00 329.63 440.00 1479.98 1567.98 17.99 185.00 220.00 329.63 440.00 1479.98 1567.98 18.00 185.00 220.00 329.63 440.00 1479.98 1567.98 18.01 138.59 185.00 220.00 329.63 440.00 1479.98 1567.98 18.02 138.59 185.00 220.00 329.63 1479.98 1567.98 18.03 138.59 185.00 220.00 329.63 1479.98 1567.98 18.04 138.59 185.00 220.00 329.63 1479.98 1567.98 18.05 138.59 185.00 220.00 329.63 1479.98 1567.98 18.06 92.50 138.59 185.00 220.00 329.63 1479.98 1567.98 18.07 92.50 138.59 185.00 220.00 329.63 1479.98 1567.98 18.08 92.50 138.59 185.00 220.00 329.63 1479.98 18.09 92.50 138.59 220.00 1479.98 18.10 92.50 138.59 220.00 1479.98 18.11 92.50 138.59 220.00 369.99 739.99 1479.98 18.12 92.50 138.59 369.99 739.99 1479.98 18.13 138.59 369.99 739.99 1479.98 18.14 369.99 739.99 1479.98 18.15 110.00 369.99 739.99 1479.98 18.16 110.00 369.99 739.99 1479.98 18.17 110.00 369.99 739.99 18.18 110.00 369.99 739.99 1318.51 18.19 110.00 369.99 739.99 1318.51 18.20 110.00 369.99 739.99 1318.51 18.21 110.00 369.99 739.99 1318.51 18.22 110.00 369.99 739.99 1318.51 18.23 110.00 369.99 739.99 1318.51 18.24 110.00 369.99 739.99 1318.51 18.25 110.00 369.99 739.99 1318.51 18.26 110.00 369.99 739.99 1318.51 18.27 110.00 369.99 739.99 1318.51 18.28 369.99 739.99 1318.51 18.29 369.99 739.99 1318.51 18.30 369.99 739.99 1318.51 18.31 369.99 1318.51 18.32 369.99 1318.51 18.33 369.99 1318.51 18.34 369.99 18.35 369.99 18.36 369.99 18.37 369.99 1174.66 18.38 369.99 1174.66 18.39 110.00 369.99 1174.66 18.40 110.00 369.99 1174.66 18.41 110.00 369.99 1174.66 18.42 110.00 369.99 1174.66 18.43 110.00 369.99 1174.66 18.44 110.00 1174.66 18.45 110.00 1174.66 18.46 110.00 1174.66 18.47 110.00 1174.66 18.48 110.00 1174.66 18.49 110.00 739.99 1174.66 18.50 110.00 739.99 1174.66 18.51 110.00 739.99 1174.66 18.52 110.00 739.99 18.53 110.00 739.99 18.54 110.00 698.46 739.99 18.55 110.00 698.46 739.99 18.56 110.00 698.46 739.99 18.57 110.00 698.46 739.99 18.58 698.46 739.99 1108.73 18.59 698.46 739.99 1108.73 18.60 349.23 698.46 739.99 1108.73 18.61 349.23 698.46 1108.73 18.62 138.59 349.23 698.46 1108.73 18.63 138.59 349.23 698.46 1108.73 18.64 138.59 349.23 698.46 1108.73 18.65 138.59 349.23 698.46 1108.73 18.66 138.59 349.23 698.46 739.99 1108.73 18.67 69.30 138.59 349.23 698.46 739.99 1108.73 18.68 69.30 138.59 349.23 698.46 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1108.73 19.01 138.59 277.18 554.37 1108.73 19.02 69.30 138.59 277.18 554.37 1108.73 19.03 69.30 138.59 277.18 554.37 1108.73 19.04 69.30 138.59 277.18 554.37 1108.73 19.05 69.30 138.59 277.18 554.37 1108.73 19.06 69.30 138.59 277.18 554.37 1108.73 19.07 69.30 138.59 277.18 554.37 1108.73 19.08 69.30 138.59 277.18 554.37 1108.73 19.09 69.30 138.59 277.18 554.37 1108.73 19.10 69.30 138.59 277.18 554.37 1108.73 19.11 69.30 138.59 277.18 554.37 1108.73 19.12 69.30 138.59 277.18 554.37 1108.73 19.13 69.30 138.59 277.18 554.37 1108.73 19.14 69.30 138.59 277.18 554.37 1108.73 19.15 69.30 138.59 277.18 554.37 1108.73 19.16 69.30 138.59 277.18 554.37 1108.73 19.17 69.30 138.59 277.18 554.37 1108.73 19.18 69.30 138.59 277.18 554.37 1108.73 19.19 69.30 138.59 277.18 554.37 1108.73 19.20 69.30 138.59 277.18 554.37 1108.73 19.21 138.59 277.18 554.37 1108.73 19.22 138.59 277.18 554.37 1108.73 19.23 138.59 277.18 554.37 1108.73 19.24 138.59 277.18 554.37 1108.73 19.25 138.59 277.18 554.37 1108.73 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27.28 415.30 830.61 27.29 415.30 830.61 27.30 415.30 830.61 27.31 415.30 830.61 27.32 830.61 27.33 830.61 880.00 27.34 92.50 185.00 277.18 830.61 880.00 27.35 92.50 185.00 277.18 830.61 880.00 27.36 92.50 185.00 277.18 830.61 880.00 27.37 92.50 185.00 277.18 830.61 880.00 27.38 92.50 185.00 277.18 830.61 880.00 27.39 92.50 185.00 277.18 830.61 880.00 27.40 92.50 185.00 277.18 830.61 880.00 27.41 92.50 185.00 277.18 830.61 880.00 27.42 92.50 185.00 277.18 830.61 880.00 27.43 92.50 185.00 277.18 830.61 880.00 27.44 92.50 185.00 277.18 830.61 880.00 27.45 92.50 185.00 220.00 277.18 440.00 830.61 880.00 27.46 92.50 185.00 220.00 277.18 440.00 830.61 880.00 27.47 92.50 185.00 220.00 277.18 440.00 830.61 880.00 27.48 92.50 185.00 220.00 277.18 440.00 830.61 880.00 27.49 92.50 185.00 220.00 277.18 440.00 830.61 880.00 27.50 92.50 185.00 220.00 277.18 440.00 554.37 830.61 880.00 27.51 92.50 185.00 220.00 277.18 440.00 554.37 830.61 880.00 27.52 92.50 185.00 220.00 277.18 440.00 554.37 830.61 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415.30 311.13 2.24 415.30 246.94 415.30 311.13 2.25 415.30 246.94 415.30 311.13 2.26 415.30 246.94 415.30 311.13 2.27 415.30 246.94 415.30 311.13 2.28 415.30 246.94 415.30 311.13 2.29 415.30 246.94 415.30 311.13 2.30 415.30 246.94 415.30 311.13 2.31 415.30 246.94 415.30 311.13 2.32 415.30 246.94 415.30 311.13 2.33 415.30 246.94 415.30 311.13 2.34 415.30 246.94 415.30 311.13 2.35 415.30 246.94 415.30 311.13 2.36 415.30 246.94 415.30 311.13 2.37 415.30 246.94 415.30 311.13 2.38 415.30 246.94 415.30 311.13 2.39 415.30 246.94 415.30 311.13 2.40 415.30 246.94 415.30 311.13 2.41 415.30 246.94 415.30 311.13 2.42 415.30 246.94 415.30 311.13 2.43 415.30 246.94 415.30 311.13 2.44 415.30 246.94 415.30 311.13 2.45 415.30 246.94 415.30 311.13 2.46 415.30 246.94 415.30 311.13 2.47 415.30 246.94 415.30 311.13 2.48 415.30 246.94 415.30 311.13 2.49 415.30 246.94 415.30 311.13 2.50 415.30 246.94 415.30 311.13 2.51 415.30 246.94 415.30 311.13 2.52 415.30 246.94 415.30 311.13 311.13 2.53 415.30 246.94 415.30 311.13 311.13 2.54 415.30 246.94 415.30 311.13 311.13 2.55 415.30 246.94 415.30 311.13 311.13 2.56 415.30 246.94 415.30 311.13 311.13 2.57 415.30 246.94 415.30 311.13 311.13 2.58 415.30 246.94 415.30 311.13 311.13 2.59 246.94 415.30 311.13 311.13 2.60 246.94 415.30 311.13 311.13 2.61 246.94 415.30 311.13 311.13 2.62 246.94 415.30 311.13 311.13 2.63 246.94 415.30 311.13 311.13 2.64 246.94 415.30 311.13 311.13 2.65 246.94 415.30 311.13 311.13 2.66 246.94 415.30 311.13 311.13 2.67 246.94 415.30 311.13 311.13 2.68 246.94 415.30 311.13 311.13 2.69 246.94 415.30 311.13 311.13 2.70 246.94 415.30 311.13 311.13 2.71 246.94 415.30 311.13 311.13 2.72 246.94 415.30 311.13 311.13 2.73 246.94 415.30 311.13 311.13 2.74 246.94 415.30 311.13 311.13 2.75 246.94 415.30 311.13 311.13 2.76 246.94 415.30 311.13 311.13 2.77 246.94 415.30 311.13 311.13 2.78 246.94 415.30 311.13 311.13 2.79 246.94 415.30 311.13 311.13 2.80 246.94 415.30 311.13 311.13 2.81 246.94 415.30 311.13 311.13 2.82 246.94 415.30 311.13 311.13 2.83 246.94 415.30 311.13 311.13 2.84 246.94 415.30 311.13 311.13 2.85 246.94 415.30 311.13 311.13 2.86 246.94 415.30 311.13 311.13 2.87 246.94 415.30 311.13 311.13 2.88 246.94 415.30 311.13 311.13 2.89 246.94 415.30 311.13 311.13 2.90 246.94 415.30 311.13 311.13 2.91 246.94 415.30 311.13 311.13 2.92 246.94 415.30 311.13 311.13 2.93 246.94 415.30 311.13 311.13 2.94 246.94 415.30 311.13 311.13 2.95 246.94 415.30 311.13 311.13 2.96 246.94 415.30 311.13 311.13 2.97 246.94 415.30 311.13 311.13 2.98 246.94 415.30 311.13 311.13 246.94 2.99 246.94 415.30 311.13 311.13 246.94 3.00 246.94 415.30 311.13 311.13 246.94 3.01 246.94 415.30 311.13 311.13 246.94 493.88 3.02 246.94 415.30 311.13 246.94 493.88 3.03 246.94 415.30 311.13 246.94 493.88 3.04 246.94 415.30 311.13 246.94 493.88 3.05 246.94 415.30 311.13 246.94 493.88 3.06 246.94 415.30 311.13 246.94 493.88 3.07 246.94 311.13 246.94 493.88 3.08 246.94 311.13 246.94 493.88 3.09 246.94 311.13 246.94 493.88 3.10 246.94 311.13 246.94 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311.13 3.71 311.13 246.94 493.88 311.13 3.72 311.13 246.94 493.88 311.13 3.73 311.13 246.94 493.88 311.13 3.74 311.13 246.94 493.88 311.13 3.75 311.13 246.94 493.88 311.13 3.76 311.13 246.94 493.88 311.13 3.77 311.13 246.94 493.88 311.13 3.78 311.13 246.94 493.88 311.13 3.79 311.13 246.94 493.88 311.13 3.80 311.13 246.94 493.88 311.13 3.81 311.13 246.94 493.88 311.13 3.82 311.13 246.94 493.88 311.13 3.83 311.13 246.94 493.88 311.13 3.84 311.13 246.94 493.88 311.13 3.85 311.13 246.94 493.88 311.13 3.86 311.13 246.94 493.88 311.13 3.87 311.13 246.94 493.88 311.13 3.88 311.13 246.94 493.88 311.13 3.89 311.13 246.94 493.88 311.13 3.90 311.13 246.94 493.88 311.13 3.91 311.13 246.94 493.88 311.13 3.92 311.13 246.94 493.88 311.13 3.93 311.13 246.94 493.88 311.13 3.94 246.94 493.88 311.13 3.95 246.94 493.88 311.13 3.96 246.94 493.88 311.13 207.65 3.97 246.94 493.88 311.13 207.65 3.98 246.94 493.88 311.13 207.65 207.65 3.99 246.94 493.88 207.65 207.65 4.00 246.94 493.88 207.65 207.65 4.01 246.94 493.88 207.65 207.65 4.02 246.94 493.88 207.65 207.65 311.13 4.03 246.94 493.88 207.65 207.65 311.13 4.04 246.94 493.88 207.65 207.65 311.13 4.05 493.88 207.65 207.65 311.13 4.06 493.88 207.65 207.65 311.13 4.07 493.88 207.65 207.65 311.13 4.08 493.88 207.65 207.65 311.13 4.09 493.88 207.65 207.65 311.13 4.10 493.88 207.65 207.65 311.13 4.11 493.88 207.65 207.65 311.13 4.12 493.88 207.65 207.65 311.13 4.13 493.88 207.65 207.65 311.13 4.14 493.88 207.65 207.65 311.13 4.15 493.88 207.65 207.65 311.13 4.16 493.88 207.65 207.65 311.13 4.17 493.88 207.65 207.65 311.13 4.18 493.88 207.65 207.65 311.13 4.19 493.88 207.65 207.65 311.13 4.20 493.88 207.65 207.65 311.13 4.21 493.88 207.65 207.65 311.13 4.22 493.88 207.65 207.65 311.13 4.23 493.88 207.65 207.65 311.13 4.24 493.88 207.65 207.65 311.13 4.25 493.88 207.65 207.65 311.13 4.26 493.88 207.65 207.65 311.13 4.27 493.88 207.65 207.65 311.13 4.28 493.88 207.65 207.65 311.13 4.29 493.88 207.65 207.65 311.13 4.30 493.88 207.65 207.65 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138.59 138.59 246.94 659.26 6.10 138.59 138.59 246.94 659.26 6.11 138.59 138.59 246.94 659.26 493.88 6.12 138.59 138.59 246.94 659.26 493.88 6.13 138.59 138.59 246.94 659.26 493.88 6.14 138.59 138.59 246.94 659.26 493.88 6.15 138.59 138.59 246.94 659.26 493.88 6.16 138.59 138.59 246.94 659.26 493.88 6.17 138.59 138.59 246.94 659.26 493.88 6.18 138.59 138.59 246.94 659.26 493.88 6.19 138.59 138.59 246.94 659.26 493.88 6.20 138.59 138.59 246.94 659.26 493.88 6.21 138.59 138.59 246.94 659.26 493.88 6.22 138.59 138.59 246.94 659.26 493.88 6.23 138.59 138.59 246.94 659.26 493.88 6.24 138.59 138.59 246.94 659.26 493.88 6.25 138.59 138.59 246.94 659.26 493.88 6.26 138.59 138.59 246.94 659.26 493.88 6.27 138.59 138.59 246.94 659.26 493.88 6.28 138.59 138.59 246.94 659.26 493.88 6.29 138.59 138.59 246.94 659.26 493.88 6.30 138.59 138.59 246.94 659.26 493.88 6.31 138.59 138.59 246.94 659.26 493.88 6.32 138.59 138.59 246.94 659.26 493.88 6.33 138.59 138.59 246.94 659.26 493.88 6.34 138.59 138.59 246.94 659.26 493.88 6.35 138.59 138.59 246.94 659.26 493.88 6.36 138.59 138.59 246.94 659.26 493.88 6.37 138.59 138.59 246.94 659.26 493.88 6.38 138.59 138.59 246.94 659.26 493.88 6.39 138.59 138.59 246.94 659.26 493.88 6.40 138.59 138.59 246.94 659.26 493.88 6.41 138.59 138.59 246.94 659.26 493.88 6.42 138.59 138.59 246.94 659.26 493.88 6.43 138.59 138.59 246.94 659.26 493.88 6.44 138.59 138.59 246.94 659.26 493.88 6.45 138.59 138.59 246.94 659.26 493.88 6.46 138.59 138.59 246.94 659.26 493.88 6.47 138.59 138.59 246.94 659.26 493.88 6.48 138.59 138.59 246.94 659.26 493.88 6.49 138.59 138.59 246.94 659.26 493.88 6.50 138.59 138.59 246.94 659.26 493.88 6.51 138.59 138.59 246.94 659.26 493.88 329.63 6.52 138.59 138.59 246.94 659.26 493.88 329.63 6.53 138.59 138.59 246.94 659.26 493.88 329.63 6.54 138.59 138.59 246.94 659.26 493.88 329.63 6.55 138.59 138.59 246.94 659.26 493.88 329.63 6.56 138.59 138.59 659.26 493.88 329.63 6.57 138.59 138.59 659.26 493.88 329.63 6.58 138.59 138.59 659.26 493.88 329.63 6.59 138.59 138.59 659.26 493.88 329.63 6.60 138.59 138.59 659.26 493.88 329.63 6.61 138.59 138.59 659.26 493.88 329.63 6.62 138.59 138.59 659.26 493.88 329.63 6.63 138.59 138.59 659.26 493.88 329.63 6.64 138.59 138.59 659.26 493.88 329.63 6.65 138.59 138.59 659.26 493.88 329.63 6.66 138.59 138.59 659.26 493.88 329.63 6.67 138.59 138.59 659.26 493.88 329.63 6.68 138.59 138.59 659.26 493.88 329.63 6.69 138.59 138.59 659.26 493.88 329.63 6.70 138.59 138.59 659.26 493.88 329.63 6.71 138.59 138.59 659.26 493.88 329.63 6.72 138.59 138.59 659.26 493.88 329.63 6.73 138.59 138.59 659.26 493.88 329.63 6.74 138.59 138.59 659.26 493.88 329.63 6.75 138.59 138.59 659.26 493.88 329.63 6.76 138.59 138.59 659.26 493.88 329.63 6.77 138.59 138.59 659.26 493.88 329.63 6.78 138.59 138.59 659.26 493.88 329.63 6.79 138.59 138.59 659.26 493.88 329.63 6.80 138.59 138.59 659.26 493.88 329.63 6.81 138.59 138.59 659.26 493.88 329.63 6.82 138.59 138.59 659.26 493.88 329.63 6.83 138.59 138.59 659.26 493.88 329.63 6.84 138.59 138.59 659.26 493.88 329.63 6.85 138.59 138.59 659.26 493.88 329.63 6.86 138.59 138.59 659.26 493.88 329.63 6.87 138.59 138.59 659.26 493.88 329.63 6.88 138.59 138.59 659.26 493.88 329.63 6.89 138.59 138.59 659.26 493.88 329.63 6.90 138.59 138.59 659.26 493.88 329.63 155.56 6.91 138.59 138.59 659.26 493.88 329.63 155.56 6.92 138.59 138.59 659.26 493.88 329.63 155.56 6.93 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.94 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.95 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.96 138.59 659.26 493.88 329.63 155.56 739.99 6.97 138.59 493.88 329.63 155.56 739.99 6.98 138.59 493.88 155.56 739.99 155.56 6.99 138.59 493.88 155.56 739.99 155.56 220.00 7.00 138.59 493.88 155.56 739.99 155.56 220.00 7.01 138.59 493.88 155.56 739.99 155.56 220.00 7.02 138.59 493.88 155.56 739.99 155.56 220.00 7.03 138.59 493.88 155.56 739.99 155.56 220.00 440.00 7.04 138.59 493.88 155.56 739.99 155.56 220.00 440.00 7.05 493.88 155.56 739.99 155.56 220.00 440.00 7.06 493.88 155.56 739.99 155.56 220.00 440.00 7.07 493.88 155.56 739.99 155.56 220.00 440.00 7.08 493.88 155.56 739.99 155.56 220.00 440.00 7.09 493.88 155.56 739.99 155.56 220.00 440.00 7.10 493.88 155.56 739.99 155.56 220.00 440.00 7.11 493.88 155.56 739.99 155.56 220.00 440.00 7.12 155.56 739.99 155.56 220.00 440.00 7.13 155.56 739.99 155.56 220.00 440.00 7.14 155.56 739.99 155.56 220.00 440.00 7.15 155.56 739.99 155.56 220.00 440.00 7.16 155.56 739.99 155.56 220.00 440.00 7.17 155.56 739.99 155.56 220.00 440.00 7.18 155.56 739.99 155.56 220.00 440.00 7.19 155.56 739.99 155.56 220.00 440.00 7.20 155.56 739.99 155.56 220.00 440.00 7.21 155.56 739.99 155.56 220.00 440.00 7.22 155.56 739.99 155.56 220.00 440.00 7.23 155.56 739.99 155.56 220.00 440.00 7.24 155.56 739.99 155.56 220.00 440.00 7.25 155.56 739.99 155.56 220.00 440.00 7.26 155.56 739.99 155.56 220.00 440.00 7.27 155.56 739.99 155.56 220.00 440.00 7.28 155.56 739.99 155.56 220.00 440.00 7.29 155.56 739.99 155.56 220.00 440.00 7.30 155.56 739.99 155.56 220.00 440.00 7.31 155.56 739.99 155.56 220.00 440.00 7.32 155.56 739.99 155.56 220.00 440.00 7.33 155.56 739.99 155.56 220.00 440.00 7.34 155.56 739.99 155.56 220.00 440.00 7.35 155.56 739.99 155.56 220.00 440.00 7.36 155.56 739.99 155.56 220.00 440.00 7.37 155.56 739.99 155.56 220.00 440.00 7.38 155.56 739.99 155.56 220.00 440.00 7.39 155.56 739.99 155.56 220.00 440.00 7.40 155.56 739.99 155.56 220.00 440.00 7.41 155.56 739.99 155.56 220.00 440.00 7.42 155.56 739.99 155.56 220.00 440.00 7.43 155.56 739.99 155.56 220.00 440.00 7.44 155.56 739.99 155.56 220.00 440.00 7.45 155.56 739.99 155.56 220.00 440.00 7.46 155.56 739.99 155.56 220.00 440.00 7.47 155.56 739.99 155.56 220.00 440.00 369.99 7.48 155.56 739.99 155.56 220.00 440.00 369.99 7.49 155.56 739.99 155.56 220.00 440.00 369.99 7.50 155.56 739.99 155.56 220.00 440.00 369.99 7.51 155.56 739.99 155.56 440.00 369.99 7.52 155.56 739.99 155.56 440.00 369.99 7.53 155.56 739.99 155.56 440.00 369.99 7.54 155.56 739.99 155.56 440.00 369.99 7.55 155.56 739.99 155.56 440.00 369.99 7.56 155.56 739.99 155.56 440.00 369.99 7.57 155.56 739.99 155.56 440.00 369.99 7.58 155.56 739.99 155.56 440.00 369.99 7.59 155.56 739.99 155.56 440.00 369.99 7.60 155.56 739.99 155.56 440.00 369.99 7.61 155.56 739.99 155.56 440.00 369.99 7.62 155.56 739.99 155.56 440.00 369.99 7.63 155.56 739.99 155.56 440.00 369.99 7.64 155.56 739.99 155.56 440.00 369.99 7.65 155.56 739.99 155.56 440.00 369.99 7.66 155.56 739.99 155.56 440.00 369.99 7.67 155.56 739.99 155.56 440.00 369.99 7.68 155.56 739.99 155.56 440.00 369.99 7.69 155.56 739.99 155.56 440.00 369.99 7.70 155.56 739.99 155.56 440.00 369.99 7.71 155.56 739.99 155.56 440.00 369.99 7.72 155.56 739.99 155.56 440.00 369.99 7.73 155.56 739.99 155.56 440.00 369.99 7.74 155.56 739.99 155.56 440.00 369.99 7.75 155.56 739.99 155.56 440.00 369.99 7.76 155.56 739.99 155.56 440.00 369.99 7.77 155.56 739.99 155.56 440.00 369.99 7.78 155.56 739.99 155.56 440.00 369.99 7.79 155.56 739.99 155.56 440.00 369.99 7.80 155.56 739.99 155.56 440.00 369.99 7.81 155.56 739.99 155.56 440.00 369.99 7.82 155.56 739.99 155.56 440.00 369.99 7.83 155.56 739.99 155.56 440.00 369.99 7.84 155.56 739.99 155.56 440.00 369.99 7.85 155.56 739.99 155.56 440.00 369.99 7.86 155.56 739.99 155.56 440.00 369.99 7.87 155.56 739.99 155.56 440.00 369.99 7.88 155.56 739.99 155.56 440.00 369.99 415.30 7.89 155.56 739.99 155.56 440.00 369.99 415.30 7.90 155.56 739.99 155.56 440.00 369.99 415.30 164.81 7.91 155.56 739.99 155.56 440.00 369.99 415.30 164.81 554.37 7.92 155.56 739.99 155.56 369.99 415.30 164.81 554.37 7.93 155.56 739.99 155.56 369.99 415.30 164.81 554.37 7.94 155.56 739.99 155.56 369.99 415.30 164.81 554.37 164.81 7.95 155.56 369.99 415.30 164.81 554.37 164.81 7.96 155.56 369.99 415.30 164.81 554.37 164.81 7.97 155.56 369.99 415.30 164.81 554.37 164.81 7.98 369.99 415.30 164.81 554.37 164.81 7.99 369.99 415.30 164.81 554.37 164.81 207.65 8.00 415.30 164.81 554.37 164.81 207.65 8.01 415.30 164.81 554.37 164.81 207.65 8.02 415.30 164.81 554.37 164.81 207.65 8.03 415.30 164.81 554.37 164.81 207.65 8.04 415.30 164.81 554.37 164.81 207.65 8.05 415.30 164.81 554.37 164.81 207.65 8.06 415.30 164.81 554.37 164.81 207.65 8.07 415.30 164.81 554.37 164.81 207.65 8.08 415.30 164.81 554.37 164.81 207.65 8.09 415.30 164.81 554.37 164.81 207.65 8.10 415.30 164.81 554.37 164.81 207.65 8.11 415.30 164.81 554.37 164.81 207.65 8.12 415.30 164.81 554.37 164.81 207.65 8.13 415.30 164.81 554.37 164.81 207.65 8.14 415.30 164.81 554.37 164.81 207.65 8.15 415.30 164.81 554.37 164.81 207.65 8.16 415.30 164.81 554.37 164.81 207.65 8.17 415.30 164.81 554.37 164.81 207.65 8.18 415.30 164.81 554.37 164.81 207.65 8.19 415.30 164.81 554.37 164.81 207.65 8.20 415.30 164.81 554.37 164.81 207.65 8.21 415.30 164.81 554.37 164.81 207.65 8.22 415.30 164.81 554.37 164.81 207.65 8.23 415.30 164.81 554.37 164.81 207.65 8.24 415.30 164.81 554.37 164.81 207.65 8.25 415.30 164.81 554.37 164.81 207.65 8.26 415.30 164.81 554.37 164.81 207.65 8.27 415.30 164.81 554.37 164.81 207.65 8.28 415.30 164.81 554.37 164.81 207.65 8.29 415.30 164.81 554.37 164.81 207.65 8.30 415.30 164.81 554.37 164.81 207.65 8.31 415.30 164.81 554.37 164.81 207.65 8.32 415.30 164.81 554.37 164.81 207.65 8.33 415.30 164.81 554.37 164.81 207.65 8.34 415.30 164.81 554.37 164.81 207.65 8.35 415.30 164.81 554.37 164.81 207.65 8.36 415.30 164.81 554.37 164.81 207.65 8.37 415.30 164.81 554.37 164.81 207.65 8.38 415.30 164.81 554.37 164.81 207.65 8.39 415.30 164.81 554.37 164.81 207.65 8.40 415.30 164.81 554.37 164.81 207.65 8.41 415.30 164.81 554.37 164.81 207.65 415.30 8.42 415.30 164.81 554.37 164.81 207.65 415.30 8.43 415.30 164.81 554.37 164.81 415.30 8.44 415.30 164.81 554.37 164.81 415.30 8.45 415.30 164.81 554.37 164.81 415.30 8.46 415.30 164.81 554.37 164.81 415.30 8.47 415.30 164.81 554.37 164.81 415.30 8.48 415.30 164.81 554.37 164.81 415.30 8.49 415.30 164.81 554.37 164.81 415.30 8.50 415.30 164.81 554.37 164.81 415.30 8.51 415.30 164.81 554.37 164.81 415.30 8.52 415.30 164.81 554.37 164.81 415.30 8.53 415.30 164.81 554.37 164.81 415.30 8.54 415.30 164.81 554.37 164.81 415.30 8.55 415.30 164.81 554.37 164.81 415.30 8.56 415.30 164.81 554.37 164.81 415.30 8.57 415.30 164.81 554.37 164.81 415.30 8.58 415.30 164.81 554.37 164.81 415.30 8.59 415.30 164.81 554.37 164.81 415.30 8.60 415.30 164.81 554.37 164.81 415.30 8.61 415.30 164.81 554.37 164.81 415.30 8.62 415.30 164.81 554.37 164.81 415.30 8.63 415.30 164.81 554.37 164.81 415.30 8.64 415.30 164.81 554.37 164.81 415.30 8.65 415.30 164.81 554.37 164.81 415.30 8.66 415.30 164.81 554.37 164.81 415.30 8.67 415.30 164.81 554.37 164.81 415.30 8.68 415.30 164.81 554.37 164.81 415.30 8.69 415.30 164.81 554.37 164.81 415.30 8.70 415.30 164.81 554.37 164.81 415.30 8.71 415.30 164.81 554.37 164.81 415.30 8.72 415.30 164.81 554.37 164.81 415.30 8.73 415.30 164.81 554.37 164.81 415.30 8.74 415.30 164.81 554.37 164.81 415.30 8.75 415.30 164.81 554.37 164.81 415.30 8.76 415.30 164.81 554.37 164.81 415.30 8.77 415.30 164.81 554.37 164.81 415.30 8.78 415.30 164.81 554.37 164.81 415.30 8.79 415.30 164.81 554.37 164.81 415.30 8.80 415.30 164.81 554.37 164.81 415.30 8.81 415.30 164.81 554.37 164.81 415.30 8.82 415.30 164.81 554.37 164.81 415.30 8.83 415.30 164.81 554.37 164.81 415.30 8.84 415.30 164.81 554.37 164.81 415.30 8.85 415.30 164.81 554.37 164.81 415.30 8.86 415.30 164.81 554.37 164.81 415.30 8.87 415.30 164.81 554.37 164.81 415.30 8.88 415.30 164.81 554.37 164.81 415.30 8.89 415.30 164.81 554.37 164.81 415.30 207.65 8.90 415.30 164.81 554.37 164.81 415.30 207.65 8.91 164.81 554.37 164.81 415.30 207.65 277.18 8.92 164.81 554.37 164.81 415.30 207.65 277.18 8.93 164.81 554.37 164.81 415.30 207.65 277.18 8.94 164.81 554.37 164.81 415.30 207.65 277.18 8.95 164.81 554.37 164.81 415.30 207.65 277.18 659.26 8.96 164.81 554.37 164.81 415.30 207.65 277.18 659.26 8.97 164.81 554.37 164.81 207.65 277.18 659.26 8.98 164.81 554.37 164.81 207.65 277.18 659.26 8.99 164.81 164.81 207.65 277.18 659.26 9.00 164.81 164.81 207.65 277.18 659.26 9.01 164.81 164.81 207.65 277.18 659.26 9.02 164.81 164.81 207.65 277.18 659.26 9.03 164.81 164.81 207.65 277.18 659.26 9.04 164.81 164.81 207.65 277.18 659.26 9.05 164.81 164.81 207.65 277.18 659.26 9.06 164.81 164.81 207.65 277.18 659.26 9.07 164.81 164.81 207.65 277.18 659.26 9.08 164.81 164.81 207.65 277.18 659.26 9.09 164.81 164.81 207.65 277.18 659.26 9.10 164.81 164.81 207.65 277.18 659.26 9.11 164.81 164.81 207.65 277.18 659.26 9.12 164.81 164.81 207.65 277.18 659.26 9.13 164.81 164.81 207.65 277.18 659.26 9.14 164.81 164.81 207.65 277.18 659.26 9.15 164.81 164.81 207.65 277.18 659.26 9.16 164.81 164.81 207.65 277.18 659.26 9.17 164.81 164.81 207.65 277.18 659.26 9.18 164.81 164.81 207.65 277.18 659.26 9.19 164.81 164.81 207.65 277.18 659.26 9.20 164.81 164.81 207.65 277.18 659.26 9.21 164.81 164.81 207.65 277.18 659.26 9.22 164.81 164.81 207.65 277.18 659.26 9.23 164.81 164.81 207.65 277.18 659.26 9.24 164.81 164.81 207.65 277.18 659.26 9.25 164.81 164.81 207.65 277.18 659.26 9.26 164.81 164.81 207.65 277.18 659.26 9.27 164.81 164.81 207.65 277.18 659.26 9.28 164.81 164.81 207.65 277.18 659.26 9.29 164.81 164.81 207.65 277.18 659.26 9.30 164.81 164.81 207.65 277.18 659.26 9.31 164.81 164.81 207.65 277.18 659.26 9.32 164.81 164.81 207.65 277.18 659.26 9.33 164.81 164.81 207.65 277.18 659.26 9.34 164.81 164.81 207.65 277.18 659.26 9.35 164.81 164.81 207.65 277.18 659.26 9.36 164.81 164.81 207.65 277.18 659.26 9.37 164.81 164.81 207.65 277.18 659.26 9.38 164.81 164.81 207.65 277.18 659.26 415.30 9.39 164.81 164.81 207.65 277.18 659.26 415.30 9.40 164.81 164.81 207.65 277.18 659.26 415.30 9.41 164.81 164.81 207.65 277.18 659.26 415.30 9.42 164.81 164.81 207.65 659.26 415.30 9.43 164.81 164.81 207.65 659.26 415.30 9.44 164.81 164.81 207.65 659.26 415.30 9.45 164.81 164.81 207.65 659.26 415.30 9.46 164.81 164.81 207.65 659.26 415.30 9.47 164.81 164.81 207.65 659.26 415.30 9.48 164.81 164.81 207.65 659.26 415.30 9.49 164.81 164.81 207.65 659.26 415.30 9.50 164.81 164.81 207.65 659.26 415.30 9.51 164.81 164.81 207.65 659.26 415.30 9.52 164.81 164.81 207.65 659.26 415.30 9.53 164.81 164.81 207.65 659.26 415.30 9.54 164.81 164.81 207.65 659.26 415.30 9.55 164.81 164.81 207.65 659.26 415.30 9.56 164.81 164.81 207.65 659.26 415.30 9.57 164.81 164.81 207.65 659.26 415.30 9.58 164.81 164.81 207.65 659.26 415.30 9.59 164.81 164.81 207.65 659.26 415.30 9.60 164.81 164.81 207.65 659.26 415.30 9.61 164.81 164.81 207.65 659.26 415.30 9.62 164.81 164.81 207.65 659.26 415.30 9.63 164.81 164.81 207.65 659.26 415.30 9.64 164.81 164.81 207.65 659.26 415.30 9.65 164.81 164.81 207.65 659.26 415.30 9.66 164.81 164.81 207.65 659.26 415.30 9.67 164.81 164.81 207.65 659.26 415.30 9.68 164.81 164.81 207.65 659.26 415.30 9.69 164.81 164.81 207.65 659.26 415.30 9.70 164.81 164.81 207.65 659.26 415.30 9.71 164.81 164.81 207.65 659.26 415.30 9.72 164.81 164.81 207.65 659.26 415.30 9.73 164.81 164.81 207.65 659.26 415.30 9.74 164.81 164.81 207.65 659.26 415.30 9.75 164.81 164.81 207.65 659.26 415.30 9.76 164.81 164.81 207.65 659.26 415.30 9.77 164.81 164.81 207.65 659.26 415.30 9.78 164.81 164.81 207.65 659.26 415.30 9.79 164.81 164.81 207.65 659.26 415.30 155.56 9.80 164.81 164.81 207.65 659.26 415.30 155.56 9.81 164.81 164.81 207.65 659.26 415.30 155.56 493.88 9.82 164.81 164.81 207.65 659.26 415.30 155.56 493.88 9.83 164.81 164.81 207.65 659.26 415.30 155.56 493.88 246.94 9.84 164.81 207.65 659.26 415.30 155.56 493.88 246.94 9.85 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.86 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.87 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.88 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.89 207.65 659.26 155.56 493.88 246.94 155.56 9.90 659.26 155.56 493.88 246.94 155.56 9.91 659.26 155.56 493.88 246.94 155.56 9.92 659.26 155.56 493.88 246.94 155.56 9.93 659.26 155.56 493.88 246.94 155.56 9.94 659.26 155.56 493.88 246.94 155.56 9.95 659.26 155.56 493.88 246.94 155.56 9.96 659.26 155.56 493.88 246.94 155.56 9.97 659.26 155.56 493.88 246.94 155.56 9.98 659.26 155.56 493.88 246.94 155.56 9.99 659.26 155.56 493.88 246.94 155.56 10.00 659.26 155.56 493.88 246.94 155.56 10.01 659.26 155.56 493.88 246.94 155.56 10.02 659.26 155.56 493.88 246.94 155.56 10.03 659.26 155.56 493.88 246.94 155.56 10.04 659.26 155.56 493.88 246.94 155.56 10.05 659.26 155.56 493.88 246.94 155.56 10.06 659.26 155.56 493.88 246.94 155.56 10.07 659.26 155.56 493.88 246.94 155.56 10.08 659.26 155.56 493.88 246.94 155.56 10.09 659.26 155.56 493.88 246.94 155.56 10.10 659.26 155.56 493.88 246.94 155.56 10.11 659.26 155.56 493.88 246.94 155.56 10.12 659.26 155.56 493.88 246.94 155.56 10.13 659.26 155.56 493.88 246.94 155.56 10.14 659.26 155.56 493.88 246.94 155.56 10.15 659.26 155.56 493.88 246.94 155.56 10.16 659.26 155.56 493.88 246.94 155.56 10.17 659.26 155.56 493.88 246.94 155.56 10.18 659.26 155.56 493.88 246.94 155.56 10.19 659.26 155.56 493.88 246.94 155.56 10.20 659.26 155.56 493.88 246.94 155.56 10.21 659.26 155.56 493.88 246.94 155.56 10.22 659.26 155.56 493.88 246.94 155.56 10.23 659.26 155.56 493.88 246.94 155.56 10.24 659.26 155.56 493.88 246.94 155.56 10.25 659.26 155.56 493.88 246.94 155.56 10.26 659.26 155.56 493.88 246.94 155.56 10.27 659.26 155.56 493.88 246.94 155.56 10.28 659.26 155.56 493.88 246.94 155.56 10.29 659.26 155.56 493.88 246.94 155.56 10.30 659.26 155.56 493.88 246.94 155.56 10.31 659.26 155.56 493.88 246.94 155.56 10.32 659.26 155.56 493.88 246.94 155.56 10.33 659.26 155.56 493.88 246.94 155.56 10.34 659.26 155.56 246.94 155.56 10.35 659.26 155.56 246.94 155.56 246.94 10.36 659.26 155.56 246.94 155.56 246.94 10.37 659.26 155.56 246.94 155.56 246.94 10.38 659.26 155.56 246.94 155.56 246.94 10.39 659.26 155.56 246.94 155.56 246.94 10.40 659.26 155.56 246.94 155.56 246.94 10.41 659.26 155.56 246.94 155.56 246.94 10.42 659.26 155.56 246.94 155.56 246.94 10.43 659.26 155.56 246.94 155.56 246.94 10.44 659.26 155.56 246.94 155.56 246.94 10.45 659.26 155.56 246.94 155.56 246.94 10.46 659.26 155.56 246.94 155.56 246.94 10.47 659.26 155.56 246.94 155.56 246.94 10.48 659.26 155.56 246.94 155.56 246.94 10.49 659.26 155.56 246.94 155.56 246.94 10.50 659.26 155.56 246.94 155.56 246.94 10.51 659.26 155.56 246.94 155.56 246.94 10.52 659.26 155.56 246.94 155.56 246.94 10.53 659.26 155.56 246.94 155.56 246.94 10.54 659.26 155.56 246.94 155.56 246.94 10.55 659.26 155.56 246.94 155.56 246.94 10.56 659.26 155.56 246.94 155.56 246.94 10.57 659.26 155.56 246.94 155.56 246.94 10.58 659.26 155.56 246.94 155.56 246.94 10.59 659.26 155.56 246.94 155.56 246.94 10.60 659.26 155.56 246.94 155.56 246.94 10.61 659.26 155.56 246.94 155.56 246.94 10.62 659.26 155.56 246.94 155.56 246.94 10.63 659.26 155.56 246.94 155.56 246.94 10.64 659.26 155.56 246.94 155.56 246.94 10.65 659.26 155.56 246.94 155.56 246.94 10.66 659.26 155.56 246.94 155.56 246.94 10.67 659.26 155.56 246.94 155.56 246.94 10.68 659.26 155.56 246.94 155.56 246.94 10.69 659.26 155.56 246.94 155.56 246.94 10.70 659.26 155.56 246.94 155.56 246.94 10.71 659.26 155.56 246.94 155.56 246.94 10.72 659.26 155.56 246.94 155.56 246.94 10.73 659.26 155.56 246.94 155.56 246.94 10.74 659.26 155.56 246.94 155.56 246.94 10.75 659.26 155.56 246.94 155.56 246.94 138.59 10.76 659.26 155.56 246.94 155.56 246.94 138.59 10.77 659.26 155.56 246.94 155.56 246.94 138.59 10.78 659.26 155.56 246.94 155.56 246.94 138.59 10.79 659.26 155.56 155.56 246.94 138.59 10.80 659.26 155.56 155.56 246.94 138.59 10.81 659.26 155.56 155.56 246.94 138.59 138.59 10.82 659.26 155.56 155.56 246.94 138.59 138.59 10.83 659.26 155.56 246.94 138.59 138.59 233.08 10.84 659.26 155.56 246.94 138.59 138.59 233.08 10.85 659.26 155.56 246.94 138.59 138.59 233.08 10.86 659.26 155.56 246.94 138.59 138.59 233.08 10.87 659.26 246.94 138.59 138.59 233.08 10.88 659.26 246.94 138.59 138.59 233.08 10.89 659.26 246.94 138.59 138.59 233.08 10.90 659.26 138.59 138.59 233.08 10.91 659.26 138.59 138.59 233.08 10.92 659.26 138.59 138.59 233.08 10.93 659.26 138.59 138.59 233.08 10.94 659.26 138.59 138.59 233.08 10.95 659.26 138.59 138.59 233.08 10.96 659.26 138.59 138.59 233.08 10.97 659.26 138.59 138.59 233.08 10.98 659.26 138.59 138.59 233.08 10.99 659.26 138.59 138.59 233.08 11.00 659.26 138.59 138.59 233.08 11.01 659.26 138.59 138.59 233.08 11.02 659.26 138.59 138.59 233.08 11.03 659.26 138.59 138.59 233.08 11.04 659.26 138.59 138.59 233.08 11.05 659.26 138.59 138.59 233.08 11.06 659.26 138.59 138.59 233.08 11.07 659.26 138.59 138.59 233.08 11.08 659.26 138.59 138.59 233.08 11.09 659.26 138.59 138.59 233.08 11.10 659.26 138.59 138.59 233.08 11.11 659.26 138.59 138.59 233.08 11.12 659.26 138.59 138.59 233.08 11.13 659.26 138.59 138.59 233.08 11.14 659.26 138.59 138.59 233.08 11.15 659.26 138.59 138.59 233.08 11.16 659.26 138.59 138.59 233.08 11.17 659.26 138.59 138.59 233.08 11.18 659.26 138.59 138.59 233.08 11.19 659.26 138.59 138.59 233.08 11.20 659.26 138.59 138.59 233.08 11.21 659.26 138.59 138.59 233.08 11.22 659.26 138.59 138.59 233.08 11.23 659.26 138.59 138.59 233.08 11.24 659.26 138.59 138.59 233.08 11.25 659.26 138.59 138.59 233.08 11.26 659.26 138.59 138.59 233.08 11.27 659.26 138.59 138.59 233.08 466.16 11.28 659.26 138.59 138.59 233.08 466.16 329.63 11.29 659.26 138.59 138.59 233.08 466.16 329.63 11.30 659.26 138.59 138.59 233.08 466.16 329.63 11.31 659.26 138.59 138.59 233.08 466.16 329.63 11.32 659.26 138.59 138.59 233.08 466.16 329.63 11.33 659.26 138.59 138.59 233.08 466.16 329.63 11.34 659.26 138.59 138.59 233.08 466.16 329.63 11.35 659.26 138.59 138.59 233.08 466.16 329.63 11.36 659.26 138.59 138.59 233.08 466.16 329.63 11.37 659.26 138.59 138.59 233.08 466.16 329.63 11.38 659.26 138.59 138.59 233.08 466.16 329.63 11.39 659.26 138.59 138.59 233.08 466.16 329.63 11.40 659.26 138.59 138.59 233.08 466.16 329.63 11.41 659.26 138.59 138.59 233.08 466.16 329.63 11.42 659.26 138.59 138.59 233.08 466.16 329.63 11.43 659.26 138.59 138.59 233.08 466.16 329.63 11.44 659.26 138.59 138.59 233.08 466.16 329.63 11.45 659.26 138.59 138.59 233.08 466.16 329.63 11.46 659.26 138.59 138.59 233.08 466.16 329.63 11.47 659.26 138.59 138.59 233.08 466.16 329.63 11.48 659.26 138.59 138.59 233.08 466.16 329.63 11.49 659.26 138.59 138.59 233.08 466.16 329.63 11.50 659.26 138.59 138.59 233.08 466.16 329.63 11.51 659.26 138.59 138.59 233.08 466.16 329.63 11.52 659.26 138.59 138.59 233.08 466.16 329.63 11.53 659.26 138.59 138.59 233.08 466.16 329.63 11.54 659.26 138.59 138.59 233.08 466.16 329.63 11.55 659.26 138.59 138.59 233.08 466.16 329.63 11.56 659.26 138.59 138.59 233.08 466.16 329.63 11.57 659.26 138.59 138.59 233.08 466.16 329.63 11.58 659.26 138.59 138.59 233.08 466.16 329.63 11.59 659.26 138.59 138.59 233.08 466.16 329.63 11.60 659.26 138.59 138.59 233.08 466.16 329.63 11.61 659.26 138.59 138.59 233.08 466.16 329.63 11.62 659.26 138.59 233.08 466.16 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329.63 12.55 415.30 138.59 830.61 329.63 329.63 12.56 415.30 138.59 830.61 329.63 329.63 12.57 415.30 138.59 830.61 329.63 329.63 12.58 415.30 138.59 830.61 329.63 329.63 12.59 415.30 138.59 830.61 329.63 329.63 12.60 415.30 138.59 830.61 329.63 329.63 12.61 415.30 138.59 830.61 329.63 329.63 12.62 415.30 138.59 830.61 329.63 329.63 12.63 415.30 138.59 830.61 329.63 329.63 12.64 415.30 138.59 830.61 329.63 329.63 12.65 415.30 138.59 830.61 329.63 329.63 12.66 415.30 138.59 830.61 329.63 329.63 12.67 415.30 138.59 830.61 329.63 329.63 659.26 12.68 415.30 138.59 830.61 329.63 329.63 659.26 466.16 12.69 415.30 138.59 830.61 329.63 329.63 659.26 466.16 138.59 69.30 12.70 415.30 138.59 830.61 329.63 329.63 659.26 466.16 138.59 69.30 69.30 466.16 12.71 415.30 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 12.72 415.30 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.73 415.30 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.74 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.75 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.76 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.77 138.59 830.61 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 12.78 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.79 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.80 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.81 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.82 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.83 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.84 329.63 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.85 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.86 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.87 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.88 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.89 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.90 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.91 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.92 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.93 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.94 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.95 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.96 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.97 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.98 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 12.99 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.00 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.01 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.02 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.03 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.04 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.05 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.06 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.07 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.08 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.09 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.10 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.11 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.12 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.13 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.14 466.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.15 466.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.17 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.18 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.19 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.20 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.21 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.22 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.23 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.24 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.25 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.26 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.27 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.28 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.29 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.30 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.31 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.32 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.33 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.34 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.35 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.36 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.37 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.38 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.39 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.40 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.41 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.42 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.43 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.44 138.59 69.30 466.16 932.33 164.81 329.63 13.45 138.59 69.30 466.16 932.33 164.81 329.63 13.46 138.59 69.30 466.16 932.33 164.81 329.63 13.47 69.30 466.16 932.33 164.81 329.63 13.48 69.30 466.16 932.33 164.81 329.63 13.49 69.30 466.16 932.33 164.81 329.63 13.50 69.30 466.16 932.33 164.81 329.63 13.51 69.30 466.16 932.33 164.81 329.63 13.52 69.30 466.16 932.33 164.81 329.63 13.53 69.30 466.16 932.33 164.81 329.63 13.54 69.30 466.16 932.33 164.81 329.63 13.55 69.30 466.16 932.33 164.81 329.63 13.56 69.30 466.16 932.33 164.81 329.63 13.57 69.30 466.16 932.33 164.81 329.63 13.58 69.30 466.16 932.33 164.81 329.63 13.59 69.30 466.16 932.33 164.81 329.63 13.60 69.30 466.16 932.33 164.81 329.63 77.78 13.61 69.30 466.16 932.33 164.81 329.63 77.78 369.99 13.62 69.30 466.16 932.33 164.81 329.63 77.78 369.99 13.63 69.30 932.33 164.81 329.63 77.78 369.99 77.78 155.56 13.64 932.33 164.81 329.63 77.78 369.99 77.78 155.56 493.88 13.65 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.66 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.67 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.68 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.69 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.70 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.71 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.72 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.73 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.74 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.75 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.76 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.77 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.78 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.79 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.80 77.78 369.99 77.78 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77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.98 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.99 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.00 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.01 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.02 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.03 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.04 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.05 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.06 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.07 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.08 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.09 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.10 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.11 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 14.12 77.78 77.78 155.56 493.88 369.99 739.99 246.94 14.13 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.14 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.15 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.16 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.17 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.18 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.19 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.20 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.21 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.22 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.23 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.24 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.25 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.26 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.27 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.28 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.29 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.30 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.31 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.32 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.33 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.34 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.35 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.36 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.37 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.38 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.39 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.40 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.41 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.42 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.43 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.44 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.45 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.46 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.47 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.48 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.49 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.50 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.51 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.52 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 14.53 77.78 77.78 155.56 493.88 369.99 739.99 246.94 246.94 466.16 14.54 77.78 77.78 155.56 493.88 739.99 246.94 246.94 466.16 369.99 14.55 77.78 77.78 155.56 493.88 739.99 246.94 246.94 466.16 369.99 14.56 77.78 77.78 155.56 493.88 739.99 246.94 246.94 466.16 369.99 14.57 77.78 77.78 155.56 493.88 739.99 246.94 246.94 466.16 369.99 14.58 77.78 77.78 155.56 493.88 739.99 246.94 466.16 369.99 14.59 77.78 77.78 155.56 493.88 739.99 246.94 466.16 369.99 14.60 77.78 77.78 155.56 493.88 739.99 246.94 466.16 369.99 14.61 77.78 77.78 155.56 493.88 466.16 369.99 14.62 77.78 155.56 493.88 466.16 369.99 155.56 14.63 77.78 155.56 493.88 466.16 369.99 155.56 932.33 14.64 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.65 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.66 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.67 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.68 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.69 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.70 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.71 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.72 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.73 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.74 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.75 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.76 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.77 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.78 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.79 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.80 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.81 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.82 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.83 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.84 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.85 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.86 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.87 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.88 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.89 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.90 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.91 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.92 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.93 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.94 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.95 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.96 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.97 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.98 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 14.99 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.00 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.01 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.02 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.03 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.04 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 15.05 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 246.94 15.06 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 246.94 15.07 77.78 155.56 493.88 466.16 369.99 155.56 932.33 369.99 246.94 15.08 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.09 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.10 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.11 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.12 77.78 155.56 493.88 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77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.46 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.47 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.48 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.49 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.50 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.51 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.52 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.53 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 15.54 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.55 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.57 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 311.13 15.58 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 311.13 15.59 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 311.13 15.60 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 311.13 15.61 493.88 466.16 155.56 932.33 61.74 123.47 123.47 311.13 311.13 15.62 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.63 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.64 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.65 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.66 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.67 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.68 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.69 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.70 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.71 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.72 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.73 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.74 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.75 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61.74 123.47 123.47 311.13 311.13 15.92 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.93 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.94 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.95 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.96 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.97 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.98 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 15.99 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.00 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.01 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.02 493.88 466.16 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.03 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.04 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 16.05 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 16.06 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 16.07 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 16.08 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.09 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.10 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.11 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.12 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.13 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.14 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.15 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.16 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.17 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.18 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.19 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.20 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.21 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.22 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.23 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.24 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.25 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.26 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.27 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.28 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.29 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.30 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.31 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.32 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.33 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.34 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.35 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.36 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.37 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.38 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.39 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.40 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.41 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.42 61.74 123.47 123.47 311.13 830.61 246.94 16.43 61.74 123.47 311.13 830.61 246.94 16.44 61.74 123.47 311.13 830.61 246.94 16.45 61.74 123.47 311.13 246.94 16.46 61.74 123.47 311.13 246.94 16.47 61.74 123.47 311.13 246.94 16.48 61.74 123.47 311.13 246.94 82.41 164.81 16.49 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.50 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.51 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.52 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 830.61 16.53 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 830.61 16.54 61.74 311.13 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.55 61.74 311.13 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.56 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.57 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.58 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.59 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.60 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.61 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.62 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.63 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.64 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.65 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.66 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.67 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.68 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.69 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.70 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.71 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.72 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.73 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.74 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.75 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.76 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.77 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.78 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.79 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.80 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.81 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.82 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.83 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.84 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.85 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.86 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.87 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.88 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.89 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.90 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.91 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.92 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.93 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.95 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.96 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.97 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.98 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.99 82.41 164.81 415.30 493.88 830.61 164.81 415.30 17.00 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.01 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.02 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.03 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.04 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.05 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.06 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.07 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.08 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.09 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.10 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.11 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.12 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.13 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.14 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.15 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.16 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.17 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.18 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.19 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.20 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.21 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.22 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.23 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.24 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.25 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.26 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.27 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.28 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.29 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.30 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.31 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.32 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.33 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.34 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.35 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.36 82.41 164.81 415.30 493.88 164.81 415.30 246.94 17.37 82.41 164.81 415.30 493.88 164.81 415.30 246.94 17.38 82.41 164.81 415.30 493.88 164.81 415.30 246.94 17.39 82.41 164.81 415.30 493.88 164.81 415.30 246.94 17.40 82.41 164.81 415.30 164.81 415.30 246.94 17.41 82.41 164.81 415.30 164.81 415.30 246.94 17.42 82.41 164.81 415.30 164.81 415.30 246.94 17.43 82.41 164.81 415.30 164.81 415.30 246.94 17.44 82.41 164.81 415.30 164.81 415.30 246.94 17.45 82.41 164.81 415.30 164.81 415.30 246.94 155.56 369.99 17.46 82.41 164.81 415.30 164.81 415.30 246.94 155.56 369.99 17.47 82.41 164.81 415.30 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 17.48 82.41 164.81 415.30 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 17.49 82.41 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 17.50 82.41 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.51 82.41 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.52 82.41 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.53 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.54 164.81 415.30 246.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.55 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.56 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.57 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.58 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.59 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.60 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.61 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.62 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.63 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.64 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.65 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.66 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.67 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.68 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.69 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.70 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.71 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.72 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.73 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.74 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.75 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.76 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.77 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.78 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.79 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.80 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.81 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.82 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.83 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.84 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.85 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.86 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.87 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.88 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.89 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.90 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.91 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.92 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.93 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.95 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.96 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.97 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 17.98 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 17.99 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.00 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.01 155.56 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77.78 246.94 155.56 246.94 18.18 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.19 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.20 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.21 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.22 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.23 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.24 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.25 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.26 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.27 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.28 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.29 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.30 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.31 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.32 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.33 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.34 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.35 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.36 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.37 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.38 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 18.39 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 18.40 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 18.41 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.42 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.43 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.44 369.99 369.99 739.99 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.45 369.99 369.99 246.94 246.94 61.74 311.13 123.47 830.61 415.30 18.46 369.99 246.94 61.74 311.13 123.47 830.61 415.30 18.47 369.99 246.94 61.74 311.13 123.47 830.61 415.30 18.48 369.99 246.94 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.49 369.99 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.50 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.51 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.52 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.53 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.54 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.55 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.56 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.57 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.58 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.59 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.60 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.61 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.62 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.63 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.64 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.65 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.66 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.67 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.68 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.69 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.70 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.71 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.72 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.73 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.74 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.75 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.76 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.77 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.78 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.79 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.80 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.81 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.82 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.83 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.84 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.85 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.86 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.87 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.88 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.89 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.90 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.91 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.92 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.93 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.94 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.95 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.96 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.97 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.98 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.99 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.00 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.01 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.02 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.03 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.04 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.05 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.06 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.07 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.08 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.09 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.10 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.11 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.12 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.13 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.14 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.15 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.16 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.17 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.18 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.19 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.20 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.21 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.22 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.23 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.24 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.25 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.26 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.27 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.28 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.29 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.30 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.31 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.32 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.33 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.34 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 19.35 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 19.36 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 19.37 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 19.38 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.39 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.40 61.74 123.47 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.41 61.74 830.61 415.30 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.42 61.74 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.43 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.44 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.45 311.13 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.46 123.47 246.94 329.63 138.59 69.30 329.63 659.26 19.47 123.47 329.63 138.59 69.30 329.63 659.26 138.59 19.48 123.47 329.63 138.59 69.30 329.63 659.26 138.59 19.49 123.47 329.63 138.59 69.30 329.63 659.26 138.59 19.50 123.47 329.63 138.59 69.30 329.63 659.26 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58.27 415.30 830.61 311.13 20.74 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.75 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.76 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.77 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.78 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.79 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.80 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.81 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.82 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.83 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.84 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.85 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.86 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.87 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.88 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.89 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.90 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.91 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.92 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.93 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.95 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.96 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.97 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.98 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.99 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.00 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.01 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.02 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.03 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.04 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.05 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.06 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.07 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.08 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.09 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.10 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.11 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.12 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.13 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.14 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.15 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.16 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.17 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.18 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.19 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.20 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.21 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.22 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.23 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.24 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.25 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 21.26 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.27 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.28 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.29 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.30 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 21.31 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 21.32 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 21.33 116.54 415.30 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 233.08 21.34 116.54 415.30 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 233.08 21.35 116.54 415.30 415.30 830.61 311.13 164.81 82.41 164.81 233.08 233.08 21.36 116.54 415.30 415.30 830.61 311.13 164.81 82.41 164.81 233.08 233.08 21.37 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.38 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164.81 82.41 164.81 233.08 233.08 21.55 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.56 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.57 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.58 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.59 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.60 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.61 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.62 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.63 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.64 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.65 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.66 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.67 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.68 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.69 415.30 415.30 830.61 164.81 82.41 164.81 233.08 233.08 21.70 415.30 415.30 830.61 164.81 82.41 164.81 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22.81 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.82 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.83 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.84 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.85 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.86 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.87 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.88 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.89 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.90 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.91 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.92 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.93 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.94 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.95 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.96 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.97 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.98 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.00 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.01 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.02 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.03 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.04 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.05 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.06 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.07 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.08 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.09 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.10 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.11 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.12 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.13 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.14 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.15 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.16 369.99 77.78 155.56 739.99 246.94 155.56 246.94 23.17 369.99 77.78 155.56 739.99 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23.35 369.99 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.36 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.37 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.38 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.39 739.99 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.40 739.99 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.41 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.42 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.43 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.44 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.45 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.46 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.47 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.48 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.49 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.50 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.51 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.52 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.53 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.54 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.55 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.56 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.57 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.58 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.59 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.60 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.61 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.62 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.63 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.64 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.65 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.66 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.67 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.68 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.69 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.70 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.71 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.72 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.73 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.74 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.75 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.76 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.77 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.78 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.79 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.80 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.81 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.82 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.83 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.84 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.85 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.86 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.87 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.88 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.89 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.90 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.91 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.92 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.93 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.94 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.95 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.96 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.97 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.98 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.99 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.00 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.01 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.02 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.03 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.04 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.05 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.06 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.07 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.08 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.09 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.10 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.11 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.12 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.13 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.14 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.15 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.16 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.17 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.18 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.19 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.20 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.21 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.22 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.23 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.24 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.25 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.26 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.27 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.28 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.29 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.30 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.31 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.32 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.33 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.34 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.35 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.36 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.37 246.94 61.74 123.47 123.47 311.13 622.25 246.94 103.83 24.38 246.94 61.74 123.47 123.47 311.13 622.25 246.94 103.83 24.39 246.94 61.74 123.47 123.47 311.13 622.25 246.94 103.83 659.26 329.63 24.40 246.94 61.74 123.47 123.47 311.13 622.25 246.94 103.83 659.26 329.63 51.91 24.41 246.94 61.74 123.47 123.47 311.13 246.94 103.83 659.26 329.63 51.91 103.83 24.42 246.94 61.74 123.47 123.47 311.13 246.94 103.83 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277.18 25.94 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.95 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.96 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.97 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 25.98 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 25.99 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 26.00 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.01 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.02 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.03 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.04 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.05 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.06 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.07 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.08 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.09 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.10 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.11 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.12 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.13 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.14 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.15 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.16 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.17 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.18 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.19 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.20 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.21 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.22 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.23 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.24 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.25 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.26 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.27 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.28 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.29 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.30 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.31 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.32 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.33 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.34 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.35 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.36 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.37 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.38 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.39 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.40 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.41 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.42 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.43 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.44 554.37 116.54 329.63 58.27 116.54 277.18 164.81 659.26 26.45 554.37 116.54 329.63 58.27 116.54 277.18 164.81 659.26 103.83 26.46 116.54 329.63 58.27 116.54 277.18 164.81 659.26 103.83 51.91 103.83 246.94 329.63 26.47 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.48 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.49 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.50 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.51 329.63 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.52 329.63 116.54 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.53 329.63 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.54 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.55 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.56 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.57 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.58 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.59 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.60 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.61 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.62 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.63 659.26 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27.00 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 27.01 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 27.02 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.03 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.04 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.05 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.06 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.07 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.08 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.09 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.10 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.11 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.12 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.13 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.14 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.15 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.16 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.17 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.18 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.19 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.20 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.21 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.22 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.23 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.24 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.25 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.26 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.27 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.28 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.29 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.30 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.31 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.32 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.33 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.34 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.35 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.36 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.37 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.38 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.39 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.40 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.41 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.42 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.43 659.26 103.83 51.91 103.83 246.94 329.63 246.94 49.00 27.44 659.26 103.83 51.91 103.83 329.63 246.94 49.00 98.00 27.45 659.26 103.83 51.91 103.83 329.63 246.94 49.00 98.00 27.46 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 27.47 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 233.08 27.48 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 233.08 27.49 659.26 329.63 246.94 49.00 98.00 98.00 233.08 27.50 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.51 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.52 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.53 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.54 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.55 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.56 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.57 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.58 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.59 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.60 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.61 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.62 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.63 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.64 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.65 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.66 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.67 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.68 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.69 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.70 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.71 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.72 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.73 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.74 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.75 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.76 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.77 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.78 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.79 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.80 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.81 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.82 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.83 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.84 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.85 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.86 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.87 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.88 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.89 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.90 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.91 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.92 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.93 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.94 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.95 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.96 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.97 659.26 329.63 49.00 98.00 98.00 233.08 233.08 466.16 27.98 659.26 329.63 49.00 98.00 98.00 233.08 233.08 466.16 27.99 659.26 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.00 659.26 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.01 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.02 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.03 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.04 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.05 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.06 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.07 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.08 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.09 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.10 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.11 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.12 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.13 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.14 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.15 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.16 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.17 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.18 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.19 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.20 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.21 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.22 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.23 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.24 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.25 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.26 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.27 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.28 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.29 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.30 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.31 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.32 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.33 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.34 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.35 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.36 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.37 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.38 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.39 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.40 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.41 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.42 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.43 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.44 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.45 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.46 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.47 49.00 98.00 98.00 233.08 466.16 311.13 28.48 49.00 98.00 98.00 233.08 466.16 311.13 28.49 49.00 98.00 98.00 466.16 28.50 49.00 98.00 98.00 466.16 51.91 28.51 49.00 98.00 98.00 466.16 51.91 28.52 49.00 98.00 98.00 466.16 51.91 28.53 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 28.54 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 28.55 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 103.83 622.25 28.56 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.57 49.00 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.58 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.59 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.60 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.61 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.62 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.63 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.64 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.65 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.66 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.67 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.68 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.69 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.70 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.71 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.72 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.73 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.74 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.75 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.76 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.77 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.78 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.79 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.80 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.81 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.82 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.83 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.84 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.85 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.86 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.87 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.88 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.89 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.90 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.91 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.92 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.93 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.94 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.95 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.96 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.97 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.98 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.99 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.01 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.02 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.03 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.04 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.05 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.06 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.07 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.08 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.09 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.10 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.11 51.91 246.94 103.83 622.25 311.13 246.94 29.12 51.91 246.94 103.83 622.25 311.13 246.94 29.13 51.91 246.94 103.83 622.25 311.13 246.94 29.14 51.91 246.94 103.83 622.25 311.13 246.94 29.15 51.91 246.94 103.83 622.25 311.13 246.94 29.16 51.91 246.94 103.83 622.25 311.13 246.94 29.17 51.91 246.94 103.83 622.25 311.13 246.94 29.18 51.91 246.94 103.83 622.25 311.13 246.94 29.19 51.91 246.94 103.83 622.25 311.13 246.94 29.20 51.91 246.94 103.83 622.25 311.13 246.94 29.21 51.91 246.94 103.83 622.25 311.13 246.94 29.22 51.91 246.94 103.83 622.25 311.13 246.94 29.23 51.91 246.94 103.83 622.25 311.13 246.94 29.24 51.91 246.94 103.83 622.25 311.13 246.94 29.25 51.91 246.94 103.83 622.25 311.13 246.94 29.26 51.91 246.94 103.83 622.25 311.13 246.94 29.27 51.91 246.94 103.83 622.25 311.13 246.94 29.28 51.91 246.94 103.83 622.25 311.13 246.94 29.29 51.91 246.94 103.83 622.25 311.13 246.94 29.30 51.91 246.94 103.83 622.25 311.13 246.94 29.31 51.91 246.94 103.83 622.25 311.13 246.94 29.32 51.91 246.94 103.83 622.25 311.13 246.94 29.33 51.91 246.94 103.83 622.25 311.13 246.94 29.34 51.91 246.94 103.83 622.25 311.13 246.94 29.35 51.91 246.94 103.83 622.25 311.13 246.94 29.36 51.91 246.94 103.83 622.25 311.13 246.94 29.37 51.91 246.94 103.83 622.25 311.13 246.94 29.38 51.91 246.94 103.83 622.25 311.13 246.94 29.39 51.91 246.94 103.83 622.25 311.13 246.94 29.40 51.91 246.94 103.83 622.25 311.13 246.94 29.41 51.91 246.94 103.83 622.25 311.13 246.94 29.42 51.91 246.94 103.83 622.25 311.13 246.94 29.43 51.91 246.94 103.83 622.25 311.13 246.94 29.44 51.91 246.94 103.83 622.25 311.13 246.94 29.45 51.91 246.94 103.83 622.25 311.13 246.94 29.46 51.91 246.94 103.83 622.25 311.13 246.94 29.47 246.94 622.25 311.13 246.94 29.48 246.94 622.25 311.13 246.94 29.49 246.94 622.25 311.13 246.94 29.50 246.94 622.25 311.13 246.94 29.51 246.94 622.25 311.13 246.94 29.52 246.94 622.25 311.13 246.94 29.53 246.94 622.25 311.13 246.94 29.54 622.25 311.13 246.94 29.55 622.25 311.13 246.94 29.56 622.25 311.13 246.94 185.00 440.00 29.57 622.25 311.13 246.94 185.00 440.00 92.50 29.58 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.59 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.60 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.61 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.62 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.63 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.64 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.65 246.94 185.00 440.00 92.50 185.00 220.00 29.66 185.00 440.00 92.50 185.00 220.00 29.67 185.00 440.00 92.50 185.00 220.00 29.68 185.00 440.00 92.50 185.00 220.00 29.69 185.00 440.00 92.50 185.00 220.00 29.70 185.00 440.00 92.50 185.00 220.00 29.71 185.00 440.00 92.50 185.00 220.00 29.72 185.00 440.00 92.50 185.00 220.00 29.73 185.00 440.00 92.50 185.00 220.00 29.74 185.00 440.00 92.50 185.00 220.00 523.25 29.75 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.76 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.77 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.78 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.79 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.80 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.81 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.82 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.83 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.84 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.85 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.86 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.87 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.88 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.89 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.90 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.91 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.92 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.93 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.94 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.95 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.96 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.97 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.98 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.99 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.00 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.01 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.02 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.03 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.04 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.05 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.06 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.07 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.08 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.09 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.10 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.11 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.12 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.13 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.14 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.15 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.16 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.17 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.18 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.19 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.20 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.21 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.22 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.23 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.24 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.25 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.26 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.27 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.28 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.29 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.30 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.31 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.32 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.33 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.34 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.35 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.36 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.37 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.38 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.39 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.40 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.41 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.42 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.43 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.44 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.45 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.46 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.47 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.48 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.49 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.50 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.51 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.52 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.53 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.54 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.55 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 30.56 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.57 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.58 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.59 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.60 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.61 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.62 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.63 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.64 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 30.65 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 30.66 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 30.67 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.68 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.69 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.70 220.00 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.71 220.00 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.72 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.73 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.74 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.75 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.76 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.77 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.78 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.79 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.80 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.81 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.82 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.83 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.84 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.85 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.86 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.87 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.88 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.89 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.90 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.91 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.92 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.93 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.94 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.95 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.96 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.97 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.98 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.99 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.01 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.02 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.03 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.04 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.05 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.06 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.07 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.08 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.09 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.10 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.11 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.12 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.13 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.14 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.15 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.16 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.17 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.18 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.19 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.20 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.21 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.22 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.23 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.24 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.25 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.26 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.27 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.28 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.29 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.30 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.31 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.32 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.33 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.34 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.35 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.36 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.37 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.38 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.39 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.40 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.41 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.42 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.43 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.44 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.45 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.46 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.47 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.48 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.49 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.50 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.51 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.52 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.53 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.54 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.55 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.56 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.57 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.58 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.59 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.60 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.61 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.62 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.63 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.64 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.65 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.66 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.67 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.68 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.69 82.41 164.81 164.81 554.37 277.18 207.65 207.65 31.70 82.41 164.81 164.81 554.37 277.18 207.65 207.65 369.99 31.71 82.41 164.81 164.81 554.37 277.18 207.65 207.65 369.99 31.72 82.41 164.81 164.81 554.37 277.18 207.65 207.65 369.99 155.56 220.00 31.73 82.41 164.81 164.81 554.37 277.18 207.65 207.65 369.99 155.56 220.00 31.74 82.41 164.81 164.81 554.37 277.18 207.65 207.65 369.99 155.56 220.00 31.75 82.41 164.81 164.81 554.37 277.18 207.65 369.99 155.56 220.00 155.56 31.76 82.41 164.81 164.81 554.37 277.18 207.65 369.99 155.56 220.00 155.56 77.78 31.77 82.41 164.81 164.81 277.18 207.65 369.99 155.56 220.00 155.56 77.78 31.78 82.41 164.81 277.18 207.65 369.99 155.56 220.00 155.56 77.78 31.79 164.81 277.18 207.65 369.99 155.56 220.00 155.56 77.78 31.80 277.18 207.65 369.99 155.56 220.00 155.56 77.78 31.81 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.82 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.83 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.84 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.85 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.86 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.87 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.88 277.18 207.65 369.99 155.56 220.00 155.56 77.78 369.99 31.89 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.90 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.91 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.92 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.93 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.94 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.95 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.96 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.97 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.98 277.18 369.99 155.56 220.00 155.56 77.78 369.99 31.99 277.18 369.99 155.56 220.00 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220.00 220.00 4.36 110.00 220.00 220.00 4.37 110.00 220.00 220.00 4.38 110.00 220.00 220.00 4.39 110.00 220.00 220.00 4.40 110.00 220.00 220.00 4.41 110.00 220.00 220.00 4.42 110.00 220.00 220.00 4.43 110.00 220.00 220.00 4.44 110.00 220.00 220.00 4.45 110.00 220.00 220.00 4.46 110.00 220.00 220.00 4.47 110.00 220.00 220.00 4.48 110.00 220.00 220.00 4.49 110.00 220.00 220.00 4.50 110.00 220.00 220.00 4.51 110.00 220.00 220.00 4.52 110.00 220.00 220.00 4.53 110.00 220.00 220.00 4.54 110.00 220.00 220.00 4.55 110.00 220.00 220.00 4.56 110.00 220.00 220.00 4.57 110.00 220.00 220.00 4.58 110.00 220.00 220.00 4.59 110.00 220.00 220.00 4.60 110.00 220.00 220.00 4.61 110.00 220.00 220.00 4.62 110.00 220.00 220.00 4.63 110.00 220.00 220.00 4.64 110.00 220.00 220.00 4.65 110.00 220.00 220.00 4.66 110.00 220.00 220.00 4.67 110.00 220.00 220.00 4.68 110.00 220.00 220.00 4.69 110.00 220.00 220.00 4.70 110.00 220.00 220.00 4.71 110.00 220.00 220.00 4.72 110.00 220.00 220.00 4.73 110.00 220.00 220.00 4.74 110.00 220.00 220.00 4.75 110.00 220.00 220.00 4.76 110.00 220.00 220.00 4.77 110.00 220.00 220.00 4.78 110.00 220.00 220.00 4.79 110.00 220.00 220.00 4.80 110.00 220.00 220.00 4.81 110.00 220.00 220.00 4.82 110.00 220.00 220.00 4.83 110.00 220.00 220.00 4.84 110.00 220.00 220.00 4.85 110.00 220.00 220.00 4.86 110.00 220.00 220.00 4.87 110.00 220.00 220.00 4.88 110.00 220.00 220.00 4.89 110.00 220.00 220.00 4.90 110.00 220.00 220.00 4.91 110.00 220.00 220.00 4.92 110.00 220.00 220.00 4.93 110.00 220.00 220.00 4.94 110.00 220.00 220.00 4.95 110.00 220.00 220.00 4.96 110.00 220.00 220.00 4.97 110.00 220.00 220.00 4.98 110.00 220.00 220.00 4.99 110.00 220.00 220.00 5.00 110.00 220.00 220.00 5.01 110.00 220.00 220.00 5.02 110.00 220.00 220.00 103.83 5.03 110.00 220.00 220.00 103.83 5.04 110.00 220.00 220.00 103.83 5.05 110.00 220.00 220.00 103.83 5.06 110.00 220.00 220.00 103.83 207.65 5.07 220.00 220.00 103.83 207.65 207.65 5.08 220.00 103.83 207.65 207.65 5.09 220.00 103.83 207.65 207.65 5.10 220.00 103.83 207.65 207.65 5.11 103.83 207.65 207.65 5.12 103.83 207.65 207.65 5.13 103.83 207.65 207.65 5.14 103.83 207.65 207.65 5.15 103.83 207.65 207.65 5.16 103.83 207.65 207.65 5.17 103.83 207.65 207.65 5.18 103.83 207.65 207.65 5.19 103.83 207.65 207.65 5.20 103.83 207.65 207.65 5.21 103.83 207.65 207.65 5.22 103.83 207.65 207.65 5.23 103.83 207.65 207.65 5.24 103.83 207.65 207.65 5.25 103.83 207.65 207.65 5.26 103.83 207.65 207.65 5.27 103.83 207.65 207.65 5.28 103.83 207.65 207.65 5.29 103.83 207.65 207.65 5.30 103.83 207.65 207.65 5.31 103.83 207.65 207.65 5.32 103.83 207.65 207.65 5.33 103.83 207.65 207.65 5.34 103.83 207.65 207.65 5.35 103.83 207.65 207.65 5.36 103.83 207.65 207.65 5.37 103.83 207.65 207.65 5.38 103.83 207.65 207.65 5.39 103.83 207.65 207.65 5.40 103.83 207.65 207.65 5.41 103.83 207.65 207.65 5.42 103.83 207.65 207.65 5.43 103.83 207.65 207.65 5.44 103.83 207.65 207.65 5.45 103.83 207.65 207.65 5.46 103.83 207.65 207.65 5.47 103.83 207.65 207.65 5.48 103.83 207.65 5.49 103.83 207.65 5.50 207.65 5.51 207.65 164.81 5.52 164.81 5.53 164.81 5.54 164.81 329.63 5.55 164.81 329.63 5.56 164.81 329.63 5.57 164.81 329.63 82.41 5.58 164.81 329.63 82.41 5.59 164.81 329.63 82.41 5.60 164.81 329.63 82.41 82.41 5.61 164.81 329.63 82.41 82.41 5.62 164.81 329.63 82.41 82.41 5.63 164.81 329.63 82.41 82.41 5.64 164.81 329.63 82.41 82.41 5.65 164.81 329.63 82.41 82.41 5.66 164.81 329.63 82.41 82.41 5.67 164.81 329.63 82.41 82.41 5.68 164.81 329.63 82.41 82.41 5.69 164.81 329.63 82.41 82.41 5.70 164.81 329.63 82.41 82.41 5.71 164.81 329.63 82.41 82.41 5.72 164.81 329.63 82.41 82.41 5.73 164.81 329.63 82.41 82.41 5.74 164.81 329.63 82.41 82.41 5.75 164.81 329.63 82.41 82.41 5.76 164.81 329.63 82.41 82.41 5.77 164.81 329.63 82.41 82.41 5.78 164.81 329.63 82.41 82.41 5.79 164.81 329.63 82.41 82.41 5.80 164.81 329.63 82.41 82.41 5.81 164.81 329.63 82.41 82.41 5.82 164.81 329.63 82.41 82.41 5.83 164.81 329.63 82.41 82.41 5.84 164.81 329.63 82.41 82.41 5.85 164.81 329.63 82.41 82.41 5.86 164.81 329.63 82.41 82.41 5.87 164.81 329.63 82.41 82.41 164.81 5.88 164.81 329.63 82.41 82.41 164.81 5.89 164.81 329.63 82.41 82.41 164.81 5.90 164.81 329.63 82.41 82.41 164.81 5.91 164.81 329.63 82.41 82.41 164.81 5.92 164.81 329.63 82.41 82.41 164.81 5.93 164.81 329.63 82.41 82.41 164.81 5.94 164.81 329.63 82.41 82.41 164.81 5.95 164.81 329.63 82.41 82.41 164.81 5.96 164.81 329.63 82.41 82.41 164.81 5.97 164.81 329.63 82.41 82.41 164.81 5.98 164.81 329.63 82.41 82.41 164.81 5.99 164.81 329.63 82.41 82.41 164.81 6.00 164.81 329.63 82.41 82.41 164.81 6.01 164.81 329.63 82.41 82.41 164.81 6.02 164.81 329.63 82.41 82.41 164.81 6.03 164.81 329.63 82.41 82.41 164.81 6.04 164.81 329.63 82.41 82.41 164.81 659.26 6.05 164.81 329.63 82.41 659.26 207.65 6.06 164.81 329.63 82.41 659.26 207.65 6.07 164.81 329.63 82.41 659.26 207.65 6.08 164.81 329.63 82.41 659.26 207.65 6.09 164.81 329.63 82.41 659.26 207.65 6.10 164.81 329.63 82.41 659.26 207.65 6.11 164.81 329.63 82.41 659.26 207.65 6.12 164.81 329.63 82.41 659.26 207.65 6.13 164.81 329.63 659.26 207.65 6.14 164.81 329.63 659.26 207.65 6.15 164.81 329.63 659.26 207.65 6.16 164.81 329.63 659.26 207.65 6.17 164.81 329.63 659.26 207.65 6.18 164.81 329.63 659.26 207.65 6.19 164.81 329.63 659.26 207.65 6.20 164.81 329.63 659.26 207.65 6.21 164.81 329.63 659.26 207.65 6.22 164.81 329.63 659.26 207.65 6.23 164.81 329.63 659.26 207.65 6.24 164.81 329.63 659.26 207.65 6.25 164.81 329.63 659.26 207.65 329.63 6.26 164.81 329.63 659.26 329.63 6.27 164.81 329.63 659.26 329.63 6.28 164.81 329.63 659.26 329.63 6.29 164.81 329.63 659.26 329.63 6.30 164.81 329.63 659.26 329.63 6.31 164.81 329.63 659.26 329.63 6.32 164.81 329.63 659.26 329.63 6.33 164.81 329.63 659.26 329.63 6.34 164.81 329.63 659.26 329.63 6.35 164.81 329.63 659.26 329.63 6.36 164.81 329.63 659.26 329.63 6.37 164.81 329.63 659.26 329.63 6.38 164.81 329.63 659.26 329.63 6.39 164.81 329.63 659.26 329.63 6.40 164.81 329.63 659.26 329.63 6.41 164.81 329.63 659.26 329.63 6.42 164.81 329.63 659.26 329.63 6.43 164.81 329.63 659.26 329.63 6.44 164.81 329.63 659.26 329.63 6.45 164.81 329.63 329.63 415.30 164.81 6.46 164.81 329.63 415.30 164.81 6.47 164.81 329.63 415.30 164.81 6.48 164.81 329.63 415.30 164.81 6.49 164.81 329.63 415.30 164.81 82.41 6.50 164.81 329.63 415.30 164.81 82.41 6.51 164.81 329.63 415.30 164.81 82.41 6.52 164.81 329.63 415.30 164.81 82.41 6.53 164.81 329.63 415.30 164.81 82.41 6.54 164.81 329.63 415.30 164.81 82.41 6.55 164.81 329.63 415.30 164.81 82.41 6.56 164.81 329.63 415.30 164.81 82.41 6.57 164.81 329.63 415.30 164.81 82.41 6.58 164.81 329.63 415.30 164.81 82.41 6.59 164.81 329.63 415.30 164.81 82.41 6.60 164.81 329.63 415.30 164.81 82.41 6.61 164.81 329.63 415.30 164.81 82.41 6.62 164.81 329.63 415.30 164.81 82.41 6.63 164.81 329.63 415.30 82.41 6.64 164.81 329.63 415.30 82.41 329.63 6.65 164.81 329.63 415.30 82.41 329.63 6.66 164.81 329.63 415.30 82.41 329.63 6.67 164.81 329.63 415.30 82.41 329.63 6.68 164.81 329.63 415.30 82.41 329.63 6.69 164.81 329.63 415.30 82.41 329.63 6.70 164.81 329.63 415.30 82.41 329.63 6.71 164.81 329.63 415.30 82.41 329.63 6.72 164.81 329.63 415.30 82.41 329.63 6.73 164.81 329.63 415.30 82.41 329.63 6.74 164.81 329.63 415.30 82.41 329.63 6.75 164.81 329.63 415.30 82.41 329.63 6.76 164.81 329.63 415.30 82.41 329.63 6.77 164.81 329.63 415.30 82.41 329.63 6.78 164.81 329.63 415.30 82.41 329.63 6.79 164.81 329.63 415.30 82.41 329.63 6.80 164.81 329.63 415.30 82.41 329.63 92.50 6.81 164.81 329.63 415.30 82.41 329.63 92.50 6.82 164.81 329.63 415.30 82.41 92.50 92.50 6.83 164.81 329.63 415.30 82.41 92.50 92.50 6.84 164.81 329.63 415.30 82.41 92.50 92.50 6.85 164.81 329.63 415.30 92.50 92.50 6.86 164.81 329.63 92.50 92.50 6.87 164.81 329.63 92.50 92.50 6.88 164.81 329.63 92.50 92.50 6.89 164.81 329.63 92.50 92.50 6.90 164.81 329.63 92.50 92.50 185.00 6.91 164.81 329.63 92.50 92.50 185.00 6.92 164.81 329.63 92.50 92.50 185.00 6.93 164.81 329.63 92.50 92.50 185.00 6.94 164.81 329.63 92.50 92.50 185.00 6.95 329.63 92.50 92.50 185.00 6.96 329.63 92.50 92.50 185.00 6.97 329.63 92.50 92.50 185.00 6.98 329.63 92.50 92.50 185.00 6.99 329.63 92.50 92.50 185.00 7.00 329.63 92.50 92.50 185.00 164.81 7.01 329.63 92.50 92.50 185.00 164.81 7.02 329.63 92.50 92.50 185.00 164.81 7.03 329.63 92.50 92.50 185.00 164.81 7.04 329.63 92.50 92.50 185.00 164.81 7.05 329.63 92.50 92.50 185.00 164.81 7.06 329.63 92.50 92.50 185.00 164.81 7.07 329.63 92.50 92.50 185.00 164.81 7.08 329.63 92.50 92.50 185.00 164.81 7.09 329.63 92.50 92.50 185.00 164.81 7.10 329.63 92.50 92.50 185.00 164.81 7.11 329.63 92.50 92.50 185.00 164.81 7.12 329.63 92.50 92.50 185.00 164.81 7.13 329.63 92.50 92.50 185.00 164.81 7.14 329.63 92.50 92.50 185.00 164.81 7.15 329.63 92.50 92.50 185.00 164.81 7.16 329.63 92.50 92.50 185.00 164.81 7.17 329.63 92.50 92.50 185.00 164.81 7.18 329.63 92.50 92.50 185.00 164.81 659.26 7.19 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.20 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.21 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.22 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.23 329.63 92.50 92.50 185.00 659.26 220.00 7.24 329.63 92.50 185.00 659.26 220.00 7.25 329.63 92.50 185.00 659.26 220.00 7.26 329.63 92.50 185.00 659.26 220.00 7.27 329.63 92.50 185.00 659.26 220.00 7.28 329.63 92.50 185.00 659.26 220.00 7.29 329.63 92.50 185.00 659.26 220.00 7.30 329.63 92.50 185.00 659.26 220.00 7.31 329.63 92.50 185.00 659.26 220.00 7.32 329.63 92.50 185.00 659.26 220.00 7.33 329.63 92.50 185.00 659.26 220.00 7.34 329.63 92.50 185.00 659.26 220.00 7.35 329.63 185.00 659.26 220.00 7.36 329.63 185.00 659.26 220.00 7.37 329.63 185.00 659.26 220.00 7.38 329.63 185.00 659.26 220.00 7.39 329.63 185.00 659.26 220.00 7.40 329.63 185.00 659.26 220.00 329.63 7.41 329.63 185.00 659.26 220.00 329.63 7.42 329.63 185.00 659.26 329.63 7.43 329.63 185.00 659.26 329.63 7.44 329.63 185.00 659.26 329.63 7.45 329.63 185.00 659.26 329.63 7.46 329.63 185.00 659.26 329.63 7.47 329.63 185.00 659.26 329.63 7.48 329.63 185.00 659.26 329.63 7.49 329.63 185.00 659.26 329.63 7.50 329.63 185.00 659.26 329.63 7.51 329.63 185.00 659.26 329.63 7.52 329.63 185.00 659.26 329.63 7.53 329.63 185.00 659.26 329.63 7.54 329.63 185.00 659.26 329.63 7.55 329.63 185.00 659.26 329.63 7.56 329.63 185.00 659.26 329.63 7.57 329.63 185.00 329.63 440.00 92.50 7.58 329.63 185.00 329.63 440.00 92.50 7.59 329.63 185.00 329.63 440.00 92.50 164.81 7.60 329.63 185.00 329.63 440.00 92.50 164.81 7.61 329.63 185.00 329.63 440.00 92.50 164.81 7.62 329.63 185.00 440.00 92.50 164.81 7.63 329.63 185.00 440.00 92.50 164.81 7.64 329.63 185.00 440.00 92.50 164.81 7.65 329.63 185.00 440.00 92.50 164.81 7.66 329.63 185.00 440.00 92.50 164.81 7.67 329.63 185.00 440.00 92.50 164.81 7.68 329.63 185.00 440.00 92.50 164.81 7.69 329.63 185.00 440.00 92.50 164.81 7.70 329.63 185.00 440.00 92.50 164.81 7.71 329.63 185.00 440.00 92.50 164.81 7.72 329.63 185.00 440.00 92.50 164.81 7.73 329.63 185.00 440.00 92.50 164.81 7.74 329.63 185.00 440.00 92.50 164.81 7.75 329.63 185.00 440.00 92.50 164.81 7.76 329.63 185.00 440.00 92.50 164.81 329.63 7.77 329.63 185.00 440.00 92.50 164.81 329.63 7.78 329.63 185.00 440.00 92.50 164.81 329.63 7.79 329.63 185.00 440.00 92.50 164.81 329.63 7.80 329.63 185.00 440.00 92.50 164.81 329.63 7.81 329.63 185.00 440.00 92.50 329.63 7.82 329.63 185.00 440.00 92.50 329.63 7.83 329.63 185.00 440.00 92.50 329.63 7.84 329.63 185.00 440.00 92.50 329.63 7.85 329.63 185.00 440.00 92.50 329.63 7.86 329.63 185.00 440.00 92.50 329.63 7.87 329.63 185.00 440.00 92.50 329.63 7.88 329.63 185.00 440.00 92.50 329.63 7.89 329.63 185.00 440.00 92.50 329.63 7.90 329.63 185.00 440.00 92.50 329.63 7.91 329.63 185.00 440.00 92.50 329.63 7.92 329.63 185.00 440.00 92.50 329.63 7.93 329.63 185.00 440.00 92.50 329.63 7.94 329.63 185.00 440.00 92.50 329.63 103.83 7.95 329.63 185.00 440.00 92.50 103.83 7.96 329.63 185.00 440.00 92.50 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8.25 329.63 103.83 103.83 164.81 207.65 8.26 329.63 103.83 103.83 164.81 207.65 8.27 329.63 103.83 103.83 164.81 207.65 8.28 329.63 103.83 103.83 164.81 207.65 8.29 329.63 103.83 103.83 164.81 207.65 8.30 329.63 103.83 103.83 164.81 207.65 8.31 329.63 103.83 103.83 164.81 207.65 659.26 8.32 329.63 103.83 103.83 164.81 207.65 659.26 8.33 329.63 103.83 103.83 164.81 207.65 659.26 246.94 8.34 329.63 103.83 103.83 164.81 207.65 659.26 246.94 8.35 329.63 103.83 103.83 164.81 207.65 659.26 246.94 8.36 329.63 103.83 103.83 164.81 207.65 659.26 246.94 8.37 329.63 103.83 103.83 207.65 659.26 246.94 8.38 329.63 103.83 103.83 207.65 659.26 246.94 8.39 329.63 103.83 207.65 659.26 246.94 8.40 329.63 103.83 207.65 659.26 246.94 8.41 329.63 103.83 207.65 659.26 246.94 8.42 329.63 103.83 207.65 659.26 246.94 8.43 329.63 103.83 207.65 659.26 246.94 8.44 329.63 103.83 207.65 659.26 246.94 8.45 329.63 103.83 207.65 659.26 246.94 8.46 329.63 207.65 659.26 246.94 8.47 329.63 207.65 659.26 246.94 8.48 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9.46 329.63 523.25 110.00 110.00 220.00 164.81 9.47 329.63 523.25 110.00 110.00 220.00 164.81 9.48 329.63 523.25 110.00 110.00 220.00 164.81 9.49 329.63 523.25 110.00 110.00 220.00 164.81 9.50 329.63 523.25 110.00 110.00 220.00 164.81 261.63 9.51 329.63 523.25 110.00 110.00 220.00 164.81 261.63 9.52 329.63 523.25 110.00 110.00 220.00 164.81 261.63 9.53 329.63 523.25 110.00 110.00 220.00 261.63 9.54 329.63 523.25 110.00 110.00 220.00 261.63 9.55 329.63 523.25 110.00 110.00 220.00 261.63 9.56 329.63 523.25 110.00 110.00 220.00 261.63 9.57 329.63 523.25 110.00 110.00 220.00 261.63 9.58 329.63 523.25 110.00 110.00 220.00 261.63 9.59 329.63 523.25 110.00 110.00 220.00 261.63 9.60 329.63 523.25 110.00 220.00 261.63 9.61 329.63 523.25 110.00 220.00 261.63 9.62 329.63 523.25 110.00 220.00 261.63 9.63 329.63 523.25 110.00 220.00 261.63 9.64 329.63 523.25 110.00 220.00 261.63 9.65 329.63 523.25 220.00 261.63 9.66 329.63 523.25 220.00 261.63 9.67 329.63 523.25 220.00 261.63 9.68 329.63 523.25 220.00 261.63 9.69 329.63 523.25 220.00 261.63 9.70 329.63 523.25 220.00 261.63 9.71 329.63 523.25 220.00 261.63 329.63 9.72 329.63 523.25 220.00 261.63 329.63 9.73 329.63 523.25 220.00 261.63 329.63 9.74 329.63 523.25 220.00 329.63 9.75 329.63 523.25 220.00 329.63 9.76 329.63 523.25 220.00 329.63 9.77 329.63 523.25 220.00 329.63 9.78 329.63 523.25 220.00 329.63 9.79 329.63 523.25 220.00 329.63 9.80 329.63 523.25 220.00 329.63 246.94 9.81 329.63 523.25 220.00 329.63 246.94 9.82 329.63 523.25 329.63 246.94 9.83 329.63 523.25 329.63 246.94 9.84 329.63 523.25 329.63 246.94 9.85 329.63 523.25 329.63 246.94 9.86 329.63 523.25 329.63 246.94 9.87 329.63 523.25 329.63 246.94 9.88 329.63 523.25 329.63 246.94 9.89 329.63 523.25 246.94 164.81 9.90 329.63 523.25 246.94 164.81 9.91 329.63 523.25 246.94 164.81 9.92 329.63 523.25 246.94 164.81 9.93 329.63 523.25 246.94 164.81 9.94 329.63 523.25 246.94 164.81 9.95 329.63 523.25 246.94 164.81 9.96 329.63 246.94 164.81 9.97 329.63 246.94 164.81 9.98 329.63 246.94 164.81 9.99 329.63 246.94 164.81 10.00 329.63 246.94 164.81 10.01 329.63 246.94 164.81 10.02 246.94 164.81 10.03 246.94 164.81 10.04 246.94 164.81 10.05 246.94 164.81 261.63 10.06 246.94 164.81 261.63 10.07 261.63 10.08 261.63 10.09 261.63 329.63 10.10 261.63 329.63 10.11 261.63 329.63 10.12 261.63 329.63 10.13 261.63 329.63 10.14 261.63 329.63 10.15 261.63 329.63 10.16 261.63 329.63 10.17 261.63 329.63 10.18 261.63 329.63 10.19 261.63 329.63 10.20 261.63 329.63 10.21 261.63 329.63 10.22 261.63 10.23 261.63 10.24 261.63 10.25 261.63 10.26 261.63 10.27 261.63 10.28 261.63 10.29 261.63 10.30 261.63 87.31 10.31 261.63 87.31 10.32 261.63 87.31 10.33 261.63 87.31 349.23 10.34 261.63 87.31 349.23 10.35 261.63 87.31 349.23 174.61 10.36 261.63 87.31 349.23 174.61 10.37 261.63 87.31 349.23 174.61 10.38 261.63 87.31 349.23 174.61 10.39 261.63 87.31 349.23 174.61 10.40 261.63 87.31 349.23 174.61 10.41 261.63 87.31 349.23 174.61 10.42 261.63 87.31 349.23 174.61 10.43 261.63 87.31 349.23 174.61 10.44 261.63 87.31 349.23 174.61 174.61 10.45 261.63 87.31 349.23 174.61 174.61 10.46 261.63 87.31 349.23 174.61 174.61 10.47 261.63 87.31 349.23 174.61 174.61 10.48 261.63 87.31 349.23 174.61 174.61 10.49 261.63 87.31 349.23 174.61 174.61 87.31 10.50 87.31 349.23 174.61 174.61 87.31 87.31 10.51 87.31 349.23 174.61 174.61 87.31 87.31 10.52 87.31 349.23 174.61 174.61 87.31 87.31 10.53 87.31 349.23 174.61 174.61 87.31 87.31 10.54 87.31 349.23 174.61 174.61 87.31 87.31 10.55 87.31 349.23 174.61 174.61 87.31 87.31 10.56 87.31 349.23 174.61 174.61 87.31 87.31 10.57 87.31 349.23 174.61 174.61 87.31 87.31 10.58 87.31 349.23 174.61 174.61 87.31 87.31 10.59 87.31 349.23 174.61 174.61 87.31 87.31 10.60 87.31 349.23 174.61 174.61 87.31 87.31 10.61 87.31 349.23 174.61 174.61 87.31 87.31 10.62 87.31 349.23 174.61 174.61 87.31 87.31 10.63 87.31 349.23 174.61 174.61 87.31 87.31 10.64 87.31 349.23 174.61 174.61 87.31 87.31 10.65 87.31 349.23 174.61 174.61 87.31 87.31 10.66 87.31 349.23 174.61 174.61 87.31 87.31 10.67 87.31 349.23 174.61 174.61 87.31 87.31 10.68 87.31 349.23 174.61 174.61 87.31 87.31 10.69 87.31 349.23 174.61 174.61 87.31 87.31 10.70 87.31 349.23 174.61 174.61 87.31 87.31 10.71 87.31 349.23 174.61 174.61 87.31 87.31 10.72 87.31 349.23 174.61 174.61 87.31 87.31 10.73 87.31 349.23 174.61 174.61 87.31 87.31 10.74 87.31 349.23 174.61 174.61 87.31 87.31 10.75 87.31 349.23 174.61 174.61 87.31 87.31 10.76 87.31 349.23 174.61 174.61 87.31 87.31 10.77 87.31 349.23 174.61 174.61 87.31 87.31 10.78 87.31 349.23 174.61 174.61 87.31 87.31 10.79 87.31 349.23 174.61 174.61 87.31 87.31 10.80 87.31 349.23 174.61 174.61 87.31 87.31 10.81 87.31 349.23 174.61 174.61 87.31 87.31 10.82 87.31 349.23 174.61 174.61 87.31 87.31 10.83 87.31 349.23 174.61 174.61 87.31 87.31 10.84 87.31 349.23 174.61 174.61 87.31 87.31 10.85 87.31 349.23 174.61 174.61 87.31 87.31 10.86 87.31 349.23 174.61 174.61 87.31 87.31 10.87 87.31 349.23 174.61 174.61 87.31 87.31 10.88 87.31 349.23 174.61 174.61 87.31 87.31 10.89 87.31 349.23 174.61 174.61 87.31 87.31 110.00 10.90 349.23 174.61 174.61 87.31 87.31 110.00 10.91 349.23 174.61 174.61 87.31 87.31 110.00 220.00 440.00 10.92 349.23 174.61 174.61 87.31 87.31 110.00 220.00 440.00 10.93 349.23 174.61 174.61 87.31 87.31 110.00 220.00 440.00 10.94 349.23 174.61 174.61 87.31 87.31 110.00 220.00 440.00 10.95 349.23 174.61 174.61 87.31 87.31 110.00 220.00 440.00 220.00 10.96 174.61 174.61 87.31 87.31 110.00 220.00 440.00 220.00 10.97 174.61 87.31 87.31 110.00 220.00 440.00 220.00 10.98 174.61 87.31 87.31 110.00 220.00 440.00 220.00 10.99 174.61 87.31 87.31 110.00 220.00 440.00 220.00 11.00 87.31 87.31 110.00 220.00 440.00 220.00 11.01 87.31 87.31 110.00 220.00 440.00 220.00 110.00 11.02 87.31 87.31 110.00 220.00 440.00 220.00 110.00 11.03 87.31 87.31 110.00 220.00 440.00 220.00 110.00 11.04 87.31 110.00 220.00 440.00 220.00 110.00 11.05 87.31 110.00 220.00 440.00 220.00 110.00 110.00 11.06 87.31 110.00 220.00 440.00 220.00 110.00 110.00 11.07 87.31 110.00 220.00 440.00 220.00 110.00 110.00 11.08 110.00 220.00 440.00 220.00 110.00 110.00 11.09 110.00 220.00 440.00 220.00 110.00 110.00 11.10 110.00 220.00 440.00 220.00 110.00 110.00 11.11 110.00 220.00 440.00 220.00 110.00 110.00 11.12 110.00 220.00 440.00 220.00 110.00 110.00 130.81 11.13 110.00 220.00 440.00 220.00 110.00 110.00 130.81 11.14 220.00 440.00 220.00 110.00 110.00 130.81 11.15 220.00 440.00 220.00 110.00 110.00 130.81 261.63 11.16 220.00 440.00 220.00 110.00 110.00 130.81 261.63 523.25 11.17 440.00 220.00 110.00 110.00 130.81 261.63 523.25 11.18 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.19 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.20 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.21 110.00 110.00 130.81 261.63 523.25 261.63 11.22 110.00 110.00 130.81 261.63 523.25 261.63 11.23 110.00 110.00 130.81 261.63 523.25 261.63 130.81 11.24 110.00 110.00 130.81 261.63 523.25 261.63 130.81 11.25 110.00 130.81 261.63 523.25 261.63 130.81 11.26 130.81 261.63 523.25 261.63 130.81 130.81 11.27 130.81 261.63 523.25 261.63 130.81 130.81 11.28 130.81 261.63 523.25 261.63 130.81 130.81 11.29 130.81 261.63 523.25 261.63 130.81 130.81 11.30 130.81 261.63 523.25 261.63 130.81 130.81 11.31 130.81 261.63 523.25 261.63 130.81 130.81 11.32 130.81 261.63 523.25 261.63 130.81 130.81 11.33 130.81 261.63 523.25 261.63 130.81 130.81 11.34 130.81 261.63 523.25 261.63 130.81 130.81 11.35 130.81 261.63 523.25 261.63 130.81 130.81 11.36 130.81 261.63 523.25 261.63 130.81 130.81 11.37 130.81 261.63 523.25 261.63 130.81 130.81 11.38 130.81 261.63 523.25 261.63 130.81 130.81 11.39 130.81 261.63 523.25 261.63 130.81 130.81 11.40 130.81 261.63 523.25 261.63 130.81 130.81 11.41 130.81 261.63 523.25 261.63 130.81 130.81 11.42 130.81 261.63 523.25 261.63 130.81 130.81 11.43 130.81 261.63 523.25 261.63 130.81 130.81 11.44 130.81 261.63 523.25 261.63 130.81 130.81 11.45 130.81 261.63 523.25 261.63 130.81 130.81 11.46 130.81 261.63 523.25 261.63 130.81 130.81 11.47 130.81 261.63 523.25 261.63 130.81 130.81 11.48 130.81 523.25 261.63 130.81 130.81 11.49 130.81 523.25 261.63 130.81 130.81 11.50 130.81 523.25 261.63 130.81 130.81 11.51 130.81 523.25 261.63 130.81 130.81 11.52 523.25 261.63 130.81 130.81 82.41 11.53 523.25 261.63 130.81 130.81 82.41 11.54 261.63 130.81 130.81 82.41 11.55 261.63 130.81 130.81 82.41 11.56 130.81 130.81 82.41 164.81 11.57 130.81 130.81 82.41 164.81 11.58 130.81 130.81 82.41 164.81 329.63 11.59 130.81 130.81 82.41 164.81 329.63 11.60 130.81 130.81 82.41 164.81 329.63 164.81 11.61 130.81 130.81 82.41 164.81 329.63 164.81 11.62 130.81 82.41 164.81 329.63 164.81 82.41 11.63 130.81 82.41 164.81 329.63 164.81 82.41 82.41 11.64 82.41 164.81 329.63 164.81 82.41 82.41 11.65 82.41 164.81 329.63 164.81 82.41 82.41 11.66 82.41 164.81 329.63 164.81 82.41 82.41 11.67 82.41 164.81 329.63 164.81 82.41 82.41 11.68 82.41 164.81 329.63 164.81 82.41 82.41 11.69 82.41 164.81 329.63 164.81 82.41 82.41 11.70 82.41 164.81 329.63 164.81 82.41 82.41 11.71 82.41 164.81 329.63 164.81 82.41 82.41 11.72 82.41 164.81 329.63 164.81 82.41 82.41 11.73 82.41 164.81 329.63 164.81 82.41 82.41 11.74 82.41 164.81 329.63 164.81 82.41 82.41 11.75 82.41 164.81 329.63 164.81 82.41 82.41 11.76 82.41 164.81 329.63 164.81 82.41 82.41 11.77 82.41 164.81 329.63 164.81 82.41 82.41 11.78 82.41 164.81 329.63 164.81 82.41 82.41 11.79 82.41 164.81 329.63 164.81 82.41 82.41 11.80 82.41 164.81 329.63 164.81 82.41 82.41 11.81 82.41 164.81 329.63 164.81 82.41 82.41 11.82 82.41 164.81 329.63 164.81 82.41 82.41 11.83 82.41 164.81 329.63 164.81 82.41 82.41 11.84 82.41 164.81 329.63 164.81 82.41 82.41 11.85 82.41 164.81 329.63 164.81 82.41 82.41 11.86 82.41 164.81 329.63 164.81 82.41 82.41 11.87 82.41 164.81 329.63 164.81 82.41 82.41 11.88 82.41 164.81 329.63 164.81 82.41 82.41 11.89 82.41 164.81 329.63 164.81 82.41 82.41 11.90 82.41 164.81 329.63 164.81 82.41 82.41 11.91 82.41 164.81 329.63 164.81 82.41 82.41 11.92 82.41 164.81 329.63 164.81 82.41 82.41 11.93 82.41 164.81 329.63 164.81 82.41 82.41 11.94 82.41 164.81 329.63 164.81 82.41 82.41 11.95 82.41 164.81 329.63 164.81 82.41 82.41 11.96 82.41 164.81 329.63 164.81 82.41 82.41 11.97 82.41 164.81 329.63 164.81 82.41 82.41 11.98 82.41 164.81 329.63 164.81 82.41 82.41 11.99 82.41 164.81 329.63 164.81 82.41 82.41 12.00 82.41 164.81 329.63 164.81 82.41 82.41 12.01 82.41 164.81 329.63 164.81 82.41 82.41 12.02 82.41 164.81 329.63 164.81 82.41 82.41 12.03 82.41 164.81 329.63 164.81 82.41 82.41 12.04 82.41 164.81 329.63 164.81 82.41 82.41 12.05 82.41 164.81 329.63 164.81 82.41 82.41 12.06 82.41 164.81 329.63 164.81 82.41 82.41 12.07 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.08 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.09 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.10 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.11 82.41 164.81 329.63 164.81 82.41 82.41 220.00 110.00 12.12 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.13 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.14 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.15 329.63 164.81 82.41 82.41 220.00 110.00 12.16 329.63 164.81 82.41 82.41 220.00 110.00 110.00 12.17 329.63 164.81 82.41 82.41 220.00 110.00 110.00 220.00 12.18 329.63 164.81 82.41 82.41 220.00 110.00 110.00 220.00 12.19 329.63 164.81 82.41 220.00 110.00 110.00 220.00 440.00 110.00 12.20 329.63 164.81 82.41 220.00 110.00 110.00 220.00 440.00 110.00 12.21 329.63 164.81 220.00 110.00 110.00 220.00 440.00 110.00 12.22 329.63 164.81 220.00 110.00 110.00 220.00 440.00 110.00 12.23 329.63 220.00 110.00 110.00 220.00 440.00 110.00 12.24 220.00 110.00 110.00 220.00 440.00 110.00 12.25 220.00 110.00 110.00 220.00 440.00 110.00 12.26 220.00 110.00 110.00 220.00 440.00 110.00 12.27 220.00 110.00 110.00 220.00 440.00 110.00 12.28 220.00 110.00 110.00 220.00 440.00 110.00 12.29 220.00 110.00 110.00 220.00 440.00 110.00 12.30 220.00 110.00 110.00 220.00 440.00 110.00 261.63 12.31 220.00 110.00 110.00 220.00 440.00 110.00 261.63 12.32 110.00 110.00 220.00 440.00 110.00 261.63 12.33 110.00 110.00 220.00 440.00 110.00 261.63 12.34 110.00 220.00 440.00 110.00 261.63 12.35 110.00 220.00 440.00 110.00 261.63 130.81 130.81 12.36 110.00 220.00 440.00 110.00 261.63 130.81 130.81 12.37 220.00 440.00 110.00 261.63 130.81 130.81 12.38 220.00 440.00 110.00 261.63 130.81 130.81 523.25 12.39 220.00 110.00 261.63 130.81 130.81 523.25 12.40 220.00 110.00 261.63 130.81 130.81 523.25 12.41 220.00 110.00 261.63 130.81 130.81 523.25 261.63 130.81 12.42 220.00 261.63 130.81 130.81 523.25 261.63 130.81 12.43 261.63 130.81 130.81 523.25 261.63 130.81 12.44 261.63 130.81 130.81 523.25 261.63 130.81 12.45 261.63 130.81 130.81 523.25 261.63 130.81 12.46 261.63 130.81 130.81 523.25 261.63 130.81 12.47 261.63 130.81 130.81 523.25 261.63 130.81 12.48 261.63 130.81 130.81 523.25 261.63 130.81 12.49 261.63 130.81 130.81 523.25 261.63 130.81 12.50 261.63 130.81 130.81 523.25 261.63 130.81 12.51 261.63 130.81 130.81 523.25 261.63 130.81 12.52 261.63 130.81 130.81 523.25 261.63 130.81 12.53 261.63 130.81 130.81 523.25 261.63 130.81 12.54 261.63 130.81 130.81 523.25 261.63 130.81 12.55 261.63 130.81 130.81 523.25 261.63 130.81 12.56 261.63 130.81 130.81 523.25 261.63 130.81 12.57 261.63 130.81 130.81 523.25 261.63 130.81 12.58 261.63 130.81 130.81 523.25 261.63 130.81 12.59 261.63 130.81 130.81 523.25 261.63 130.81 12.60 261.63 130.81 130.81 523.25 261.63 130.81 12.61 261.63 130.81 130.81 523.25 261.63 130.81 12.62 261.63 130.81 130.81 523.25 261.63 130.81 12.63 261.63 130.81 130.81 523.25 261.63 130.81 12.64 261.63 130.81 130.81 523.25 261.63 130.81 12.65 261.63 130.81 130.81 523.25 261.63 130.81 12.66 261.63 130.81 130.81 523.25 261.63 130.81 12.67 261.63 130.81 130.81 523.25 261.63 130.81 12.68 261.63 130.81 130.81 523.25 261.63 130.81 12.69 261.63 130.81 130.81 523.25 261.63 130.81 12.70 261.63 130.81 130.81 523.25 261.63 130.81 12.71 261.63 130.81 130.81 261.63 130.81 12.72 261.63 130.81 130.81 130.81 12.73 261.63 130.81 130.81 130.81 12.74 261.63 130.81 130.81 12.75 261.63 130.81 130.81 155.56 77.78 12.76 261.63 130.81 130.81 155.56 77.78 77.78 311.13 12.77 130.81 130.81 155.56 77.78 77.78 311.13 12.78 130.81 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.79 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.80 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.81 155.56 77.78 77.78 311.13 77.78 155.56 12.82 155.56 77.78 77.78 311.13 77.78 155.56 12.83 155.56 77.78 77.78 311.13 77.78 155.56 12.84 155.56 77.78 77.78 311.13 77.78 155.56 12.85 155.56 77.78 77.78 311.13 77.78 155.56 12.86 155.56 77.78 77.78 311.13 77.78 155.56 12.87 155.56 77.78 77.78 311.13 77.78 155.56 12.88 155.56 77.78 77.78 311.13 77.78 155.56 12.89 155.56 77.78 77.78 311.13 77.78 155.56 12.90 155.56 77.78 77.78 311.13 77.78 155.56 12.91 155.56 77.78 77.78 311.13 77.78 155.56 12.92 155.56 77.78 77.78 311.13 77.78 155.56 12.93 155.56 77.78 77.78 311.13 77.78 155.56 12.94 155.56 77.78 77.78 311.13 77.78 155.56 12.95 155.56 77.78 77.78 311.13 77.78 155.56 12.96 155.56 77.78 77.78 311.13 77.78 155.56 12.97 155.56 77.78 77.78 311.13 77.78 155.56 12.98 155.56 77.78 77.78 311.13 77.78 155.56 12.99 155.56 77.78 77.78 311.13 77.78 155.56 13.00 155.56 77.78 77.78 311.13 77.78 155.56 13.01 155.56 77.78 77.78 311.13 77.78 155.56 13.02 155.56 77.78 77.78 311.13 77.78 155.56 13.03 155.56 77.78 77.78 311.13 77.78 155.56 13.04 155.56 77.78 77.78 311.13 77.78 155.56 13.05 155.56 77.78 77.78 311.13 77.78 155.56 13.06 155.56 77.78 77.78 311.13 77.78 155.56 13.07 155.56 77.78 77.78 311.13 77.78 155.56 13.08 155.56 77.78 77.78 311.13 77.78 155.56 13.09 155.56 77.78 77.78 311.13 77.78 155.56 13.10 155.56 77.78 77.78 311.13 77.78 155.56 13.11 155.56 77.78 77.78 311.13 77.78 155.56 13.12 155.56 77.78 77.78 311.13 77.78 155.56 13.13 155.56 77.78 77.78 311.13 77.78 155.56 13.14 155.56 77.78 77.78 311.13 77.78 155.56 13.15 155.56 77.78 77.78 311.13 77.78 155.56 13.16 155.56 77.78 77.78 311.13 77.78 155.56 13.17 155.56 77.78 77.78 311.13 77.78 155.56 13.18 155.56 77.78 77.78 311.13 77.78 155.56 13.19 155.56 77.78 77.78 311.13 77.78 155.56 13.20 155.56 77.78 77.78 311.13 77.78 155.56 13.21 155.56 77.78 77.78 311.13 77.78 155.56 13.22 155.56 77.78 77.78 311.13 77.78 155.56 13.23 155.56 77.78 77.78 311.13 77.78 155.56 13.24 155.56 77.78 77.78 311.13 77.78 155.56 13.25 155.56 77.78 77.78 311.13 77.78 155.56 13.26 155.56 77.78 77.78 311.13 77.78 155.56 13.27 155.56 77.78 77.78 311.13 77.78 155.56 13.28 155.56 77.78 77.78 311.13 77.78 155.56 13.29 155.56 77.78 77.78 311.13 77.78 155.56 13.30 155.56 77.78 77.78 311.13 77.78 155.56 13.31 155.56 77.78 77.78 311.13 77.78 155.56 13.32 155.56 77.78 77.78 311.13 77.78 13.33 155.56 77.78 77.78 311.13 77.78 13.34 155.56 77.78 77.78 77.78 261.63 13.35 155.56 77.78 77.78 77.78 261.63 13.36 155.56 77.78 77.78 77.78 261.63 523.25 13.37 77.78 77.78 77.78 261.63 523.25 13.38 77.78 77.78 77.78 261.63 523.25 13.39 77.78 77.78 77.78 261.63 523.25 13.40 77.78 77.78 77.78 261.63 523.25 13.41 77.78 77.78 77.78 261.63 523.25 13.42 77.78 77.78 77.78 261.63 523.25 13.43 77.78 77.78 77.78 261.63 523.25 13.44 77.78 77.78 77.78 261.63 523.25 13.45 77.78 77.78 77.78 261.63 523.25 13.46 77.78 77.78 77.78 261.63 523.25 13.47 77.78 77.78 77.78 261.63 523.25 13.48 77.78 77.78 77.78 261.63 523.25 13.49 77.78 77.78 77.78 261.63 523.25 13.50 77.78 77.78 77.78 261.63 523.25 13.51 77.78 77.78 77.78 261.63 523.25 13.52 77.78 77.78 77.78 261.63 523.25 13.53 77.78 77.78 77.78 261.63 523.25 13.54 77.78 77.78 77.78 261.63 523.25 13.55 77.78 77.78 77.78 261.63 523.25 13.56 77.78 77.78 77.78 261.63 523.25 246.94 13.57 77.78 77.78 77.78 261.63 523.25 246.94 493.88 13.58 77.78 77.78 77.78 261.63 523.25 246.94 493.88 13.59 77.78 77.78 261.63 246.94 493.88 369.99 13.60 77.78 77.78 246.94 493.88 369.99 261.63 13.61 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.62 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.63 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.64 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.65 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.66 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.67 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.68 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.69 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.70 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.71 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.72 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.73 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.74 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.75 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.76 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.77 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.78 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 13.79 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 440.00 13.80 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 440.00 13.81 77.78 77.78 493.88 369.99 261.63 155.56 220.00 440.00 13.82 77.78 77.78 155.56 220.00 440.00 13.83 77.78 77.78 155.56 220.00 440.00 13.84 77.78 77.78 155.56 220.00 440.00 13.85 77.78 77.78 155.56 220.00 440.00 13.86 77.78 77.78 155.56 220.00 440.00 13.87 77.78 77.78 155.56 220.00 440.00 13.88 77.78 77.78 155.56 220.00 440.00 13.89 77.78 77.78 155.56 220.00 440.00 13.90 77.78 77.78 155.56 220.00 440.00 13.91 77.78 77.78 155.56 220.00 440.00 13.92 77.78 77.78 155.56 220.00 440.00 13.93 77.78 77.78 155.56 220.00 440.00 13.94 77.78 77.78 155.56 220.00 440.00 13.95 77.78 77.78 155.56 440.00 13.96 77.78 77.78 155.56 440.00 13.97 77.78 77.78 155.56 440.00 82.41 13.98 77.78 77.78 155.56 82.41 13.99 77.78 77.78 155.56 82.41 14.00 77.78 77.78 155.56 82.41 14.01 77.78 155.56 82.41 82.41 14.02 77.78 155.56 82.41 82.41 14.03 155.56 82.41 82.41 220.00 14.04 155.56 82.41 82.41 220.00 440.00 14.05 155.56 82.41 82.41 220.00 440.00 14.06 155.56 82.41 82.41 220.00 440.00 14.07 155.56 82.41 82.41 220.00 440.00 14.08 155.56 82.41 82.41 220.00 440.00 14.09 155.56 82.41 82.41 220.00 440.00 14.10 155.56 82.41 82.41 220.00 440.00 14.11 155.56 82.41 82.41 220.00 440.00 14.12 155.56 82.41 82.41 220.00 440.00 14.13 155.56 82.41 82.41 220.00 440.00 14.14 155.56 82.41 82.41 220.00 440.00 14.15 155.56 82.41 82.41 220.00 440.00 329.63 14.16 155.56 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.17 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.18 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.19 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.20 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.21 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.22 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.23 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.24 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.25 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.26 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.27 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.28 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.29 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.30 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.31 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.32 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.33 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.34 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.35 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.36 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.37 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.38 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.39 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.40 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.41 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.42 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.43 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.44 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.45 82.41 220.00 440.00 329.63 246.94 164.81 14.46 82.41 220.00 440.00 329.63 246.94 164.81 14.47 82.41 220.00 440.00 329.63 246.94 164.81 14.48 82.41 220.00 440.00 329.63 246.94 164.81 14.49 82.41 220.00 440.00 329.63 246.94 164.81 14.50 82.41 220.00 440.00 329.63 246.94 164.81 14.51 82.41 220.00 440.00 329.63 246.94 164.81 14.52 82.41 220.00 440.00 329.63 246.94 164.81 14.53 82.41 220.00 440.00 329.63 246.94 164.81 14.54 82.41 220.00 440.00 329.63 246.94 164.81 14.55 82.41 220.00 440.00 329.63 246.94 164.81 14.56 82.41 220.00 440.00 329.63 246.94 164.81 14.57 82.41 220.00 440.00 329.63 246.94 164.81 14.58 82.41 220.00 440.00 329.63 246.94 164.81 14.59 82.41 220.00 440.00 329.63 246.94 164.81 14.60 82.41 220.00 440.00 329.63 246.94 164.81 14.61 82.41 220.00 440.00 329.63 246.94 164.81 14.62 82.41 220.00 440.00 329.63 246.94 164.81 14.63 82.41 220.00 440.00 329.63 246.94 164.81 14.64 82.41 220.00 440.00 329.63 246.94 164.81 14.65 82.41 220.00 440.00 329.63 246.94 164.81 14.66 82.41 220.00 440.00 329.63 246.94 164.81 14.67 82.41 220.00 440.00 329.63 246.94 164.81 14.68 82.41 220.00 440.00 329.63 246.94 164.81 14.69 82.41 220.00 440.00 329.63 246.94 164.81 14.70 82.41 220.00 440.00 329.63 246.94 164.81 14.71 82.41 220.00 440.00 329.63 246.94 164.81 14.72 82.41 220.00 440.00 329.63 246.94 164.81 14.73 82.41 220.00 440.00 329.63 246.94 164.81 14.74 82.41 220.00 440.00 329.63 246.94 164.81 14.75 82.41 220.00 440.00 329.63 246.94 164.81 14.76 82.41 220.00 440.00 329.63 246.94 164.81 14.77 82.41 220.00 440.00 329.63 246.94 164.81 14.78 82.41 220.00 440.00 329.63 246.94 164.81 14.79 82.41 220.00 440.00 329.63 246.94 164.81 14.80 82.41 220.00 440.00 329.63 246.94 164.81 14.81 82.41 220.00 440.00 329.63 246.94 14.82 82.41 220.00 440.00 329.63 246.94 14.83 82.41 220.00 440.00 329.63 246.94 14.84 82.41 220.00 440.00 329.63 246.94 14.85 82.41 220.00 440.00 329.63 246.94 14.86 82.41 220.00 440.00 329.63 246.94 14.87 82.41 220.00 440.00 329.63 246.94 14.88 82.41 220.00 440.00 329.63 246.94 14.89 82.41 220.00 440.00 329.63 246.94 14.90 82.41 220.00 440.00 329.63 246.94 14.91 82.41 220.00 440.00 329.63 246.94 14.92 82.41 220.00 440.00 329.63 246.94 14.93 82.41 220.00 440.00 329.63 246.94 14.94 82.41 220.00 440.00 329.63 246.94 14.95 82.41 220.00 440.00 329.63 246.94 14.96 82.41 220.00 440.00 329.63 246.94 14.97 82.41 220.00 440.00 329.63 246.94 14.98 82.41 220.00 440.00 329.63 246.94 14.99 82.41 220.00 440.00 329.63 246.94 15.00 82.41 220.00 440.00 329.63 246.94 415.30 15.01 82.41 220.00 440.00 329.63 246.94 415.30 207.65 15.02 82.41 220.00 440.00 329.63 246.94 415.30 207.65 15.03 82.41 220.00 329.63 246.94 415.30 207.65 15.04 82.41 220.00 329.63 246.94 415.30 207.65 15.05 82.41 220.00 329.63 246.94 415.30 207.65 15.06 82.41 329.63 246.94 415.30 207.65 15.07 82.41 329.63 246.94 415.30 207.65 15.08 82.41 329.63 246.94 415.30 207.65 207.65 15.09 82.41 329.63 246.94 415.30 207.65 207.65 15.10 82.41 415.30 207.65 207.65 15.11 82.41 415.30 207.65 207.65 15.12 82.41 415.30 207.65 207.65 15.13 82.41 415.30 207.65 207.65 15.14 82.41 415.30 207.65 207.65 15.15 82.41 415.30 207.65 207.65 15.16 82.41 415.30 207.65 207.65 15.17 82.41 415.30 207.65 207.65 15.18 82.41 415.30 207.65 207.65 15.19 82.41 415.30 207.65 207.65 15.20 82.41 415.30 207.65 207.65 15.21 82.41 415.30 207.65 207.65 15.22 82.41 415.30 207.65 207.65 15.23 82.41 415.30 207.65 207.65 15.24 82.41 415.30 207.65 207.65 15.25 82.41 415.30 207.65 207.65 15.26 82.41 415.30 207.65 207.65 69.30 15.27 82.41 415.30 207.65 207.65 69.30 15.28 82.41 415.30 207.65 207.65 69.30 15.29 82.41 415.30 207.65 207.65 69.30 15.30 82.41 415.30 207.65 207.65 69.30 15.31 82.41 415.30 207.65 207.65 69.30 15.32 82.41 415.30 207.65 207.65 69.30 15.33 82.41 415.30 207.65 207.65 69.30 15.34 82.41 415.30 207.65 207.65 69.30 15.35 82.41 415.30 207.65 207.65 69.30 15.36 82.41 415.30 207.65 207.65 69.30 15.37 82.41 415.30 207.65 207.65 69.30 15.38 82.41 415.30 207.65 207.65 69.30 15.39 82.41 415.30 207.65 207.65 69.30 15.40 82.41 415.30 207.65 207.65 69.30 15.41 82.41 207.65 207.65 69.30 15.42 82.41 207.65 207.65 69.30 15.43 82.41 207.65 207.65 69.30 15.44 82.41 207.65 207.65 69.30 15.45 207.65 207.65 69.30 15.46 207.65 207.65 69.30 15.47 207.65 207.65 69.30 392.00 15.48 207.65 207.65 69.30 392.00 15.49 207.65 207.65 69.30 392.00 15.50 207.65 207.65 69.30 392.00 15.51 207.65 69.30 392.00 15.52 207.65 69.30 392.00 15.53 207.65 69.30 392.00 15.54 207.65 69.30 392.00 15.55 207.65 69.30 392.00 138.59 15.56 207.65 69.30 392.00 138.59 15.57 207.65 69.30 392.00 138.59 15.58 207.65 69.30 392.00 138.59 15.59 207.65 69.30 392.00 138.59 15.60 207.65 69.30 392.00 138.59 15.61 69.30 392.00 138.59 15.62 69.30 392.00 138.59 15.63 69.30 392.00 138.59 15.64 69.30 392.00 138.59 15.65 69.30 392.00 138.59 15.66 69.30 392.00 138.59 15.67 69.30 392.00 138.59 15.68 69.30 392.00 138.59 15.69 69.30 392.00 138.59 220.00 15.70 69.30 392.00 138.59 220.00 15.71 69.30 392.00 138.59 220.00 15.72 69.30 392.00 138.59 220.00 15.73 69.30 392.00 138.59 220.00 15.74 69.30 392.00 138.59 220.00 15.75 69.30 392.00 138.59 220.00 15.76 69.30 392.00 138.59 220.00 15.77 69.30 392.00 138.59 220.00 15.78 69.30 392.00 138.59 220.00 15.79 69.30 392.00 138.59 220.00 15.80 69.30 392.00 138.59 220.00 15.81 69.30 392.00 138.59 220.00 15.82 69.30 392.00 138.59 220.00 15.83 392.00 138.59 220.00 15.84 392.00 138.59 220.00 15.85 392.00 138.59 220.00 15.86 392.00 138.59 220.00 15.87 392.00 138.59 220.00 15.88 392.00 138.59 220.00 15.89 392.00 138.59 220.00 15.90 392.00 138.59 220.00 329.63 15.91 392.00 138.59 220.00 329.63 15.92 392.00 138.59 220.00 329.63 15.93 392.00 138.59 329.63 15.94 392.00 138.59 329.63 15.95 392.00 138.59 329.63 15.96 392.00 138.59 329.63 15.97 392.00 138.59 329.63 15.98 392.00 138.59 329.63 15.99 392.00 138.59 329.63 16.00 392.00 138.59 329.63 16.01 392.00 138.59 329.63 16.02 392.00 138.59 329.63 16.03 392.00 138.59 329.63 16.04 392.00 138.59 329.63 16.05 392.00 138.59 329.63 16.06 392.00 138.59 329.63 16.07 392.00 138.59 329.63 16.08 392.00 138.59 329.63 392.00 16.09 392.00 138.59 392.00 16.10 392.00 138.59 392.00 16.11 392.00 138.59 392.00 466.16 16.12 138.59 392.00 466.16 16.13 138.59 392.00 466.16 16.14 138.59 392.00 466.16 16.15 138.59 392.00 466.16 16.16 138.59 392.00 466.16 16.17 138.59 392.00 466.16 16.18 138.59 392.00 466.16 16.19 138.59 392.00 466.16 16.20 138.59 392.00 466.16 16.21 138.59 392.00 466.16 16.22 138.59 392.00 466.16 16.23 138.59 392.00 466.16 16.24 138.59 392.00 466.16 16.25 138.59 392.00 466.16 16.26 138.59 392.00 466.16 16.27 138.59 392.00 466.16 16.28 138.59 392.00 466.16 16.29 138.59 392.00 466.16 16.30 138.59 392.00 466.16 440.00 440.00 16.31 138.59 466.16 440.00 440.00 16.32 138.59 466.16 440.00 440.00 16.33 138.59 466.16 440.00 440.00 16.34 138.59 440.00 440.00 16.35 138.59 440.00 440.00 16.36 138.59 440.00 440.00 16.37 138.59 440.00 440.00 16.38 138.59 440.00 440.00 16.39 138.59 440.00 440.00 16.40 138.59 440.00 440.00 16.41 138.59 440.00 440.00 16.42 138.59 440.00 440.00 16.43 138.59 440.00 440.00 16.44 138.59 440.00 440.00 16.45 138.59 440.00 440.00 16.46 138.59 440.00 440.00 16.47 138.59 440.00 440.00 16.48 138.59 440.00 16.49 138.59 440.00 392.00 16.50 138.59 440.00 392.00 220.00 16.51 138.59 440.00 392.00 220.00 16.52 138.59 440.00 392.00 220.00 16.53 138.59 392.00 220.00 16.54 138.59 392.00 220.00 16.55 138.59 392.00 220.00 16.56 138.59 392.00 220.00 16.57 138.59 392.00 220.00 16.58 138.59 392.00 220.00 16.59 138.59 392.00 220.00 16.60 138.59 220.00 16.61 138.59 220.00 73.42 16.62 138.59 220.00 73.42 16.63 138.59 220.00 73.42 16.64 138.59 220.00 73.42 16.65 138.59 220.00 73.42 146.83 16.66 138.59 220.00 73.42 146.83 16.67 138.59 220.00 73.42 146.83 16.68 138.59 220.00 73.42 146.83 392.00 16.69 138.59 220.00 73.42 146.83 392.00 16.70 138.59 220.00 73.42 146.83 392.00 16.71 220.00 73.42 146.83 392.00 16.72 220.00 73.42 146.83 392.00 16.73 73.42 146.83 392.00 16.74 73.42 146.83 392.00 16.75 73.42 146.83 392.00 16.76 73.42 146.83 392.00 16.77 73.42 146.83 392.00 16.78 73.42 146.83 392.00 16.79 73.42 146.83 392.00 16.80 73.42 146.83 392.00 16.81 73.42 146.83 392.00 16.82 73.42 146.83 392.00 16.83 73.42 146.83 392.00 16.84 73.42 146.83 392.00 220.00 16.85 73.42 146.83 392.00 220.00 16.86 73.42 146.83 392.00 220.00 16.87 73.42 146.83 392.00 220.00 16.88 73.42 146.83 392.00 220.00 369.99 16.89 73.42 146.83 392.00 220.00 369.99 16.90 73.42 146.83 220.00 369.99 16.91 73.42 146.83 220.00 369.99 16.92 73.42 146.83 220.00 369.99 16.93 73.42 146.83 220.00 369.99 16.94 73.42 146.83 220.00 369.99 16.95 73.42 146.83 220.00 369.99 16.96 73.42 146.83 220.00 369.99 16.97 73.42 146.83 220.00 369.99 16.98 73.42 146.83 220.00 369.99 16.99 73.42 146.83 220.00 369.99 17.00 73.42 146.83 220.00 369.99 17.01 73.42 146.83 220.00 369.99 17.02 73.42 146.83 220.00 369.99 17.03 146.83 369.99 17.04 146.83 369.99 17.05 146.83 369.99 17.06 146.83 369.99 17.07 146.83 369.99 293.66 17.08 146.83 369.99 293.66 17.09 146.83 369.99 293.66 587.33 17.10 146.83 369.99 293.66 587.33 17.11 146.83 369.99 293.66 587.33 17.12 146.83 293.66 587.33 17.13 146.83 293.66 587.33 17.14 146.83 293.66 587.33 17.15 146.83 293.66 587.33 17.16 146.83 293.66 587.33 17.17 146.83 293.66 587.33 17.18 146.83 293.66 587.33 17.19 146.83 293.66 587.33 17.20 146.83 293.66 587.33 17.21 146.83 293.66 587.33 17.22 146.83 293.66 587.33 17.23 146.83 293.66 587.33 17.24 146.83 293.66 587.33 17.25 146.83 293.66 587.33 17.26 146.83 587.33 17.27 146.83 587.33 17.28 146.83 587.33 17.29 146.83 587.33 17.30 146.83 587.33 17.31 146.83 587.33 17.32 146.83 587.33 369.99 17.33 146.83 587.33 369.99 17.34 146.83 587.33 369.99 17.35 146.83 587.33 369.99 17.36 146.83 587.33 369.99 17.37 146.83 587.33 369.99 17.38 146.83 587.33 369.99 17.39 146.83 587.33 369.99 17.40 146.83 587.33 369.99 17.41 146.83 587.33 369.99 17.42 146.83 587.33 369.99 17.43 146.83 587.33 369.99 17.44 146.83 587.33 369.99 17.45 146.83 587.33 369.99 17.46 146.83 587.33 369.99 17.47 146.83 587.33 369.99 17.48 146.83 587.33 369.99 17.49 146.83 587.33 369.99 17.50 146.83 587.33 369.99 440.00 17.51 146.83 587.33 369.99 440.00 17.52 146.83 587.33 369.99 440.00 17.53 146.83 587.33 440.00 17.54 146.83 587.33 440.00 17.55 146.83 587.33 440.00 17.56 146.83 587.33 440.00 17.57 146.83 587.33 440.00 17.58 146.83 587.33 440.00 17.59 146.83 587.33 440.00 17.60 146.83 587.33 440.00 17.61 146.83 587.33 440.00 17.62 146.83 587.33 440.00 17.63 146.83 587.33 440.00 17.64 146.83 587.33 440.00 17.65 146.83 587.33 440.00 17.66 146.83 587.33 440.00 17.67 146.83 587.33 440.00 17.68 146.83 587.33 440.00 17.69 146.83 587.33 220.00 17.70 146.83 587.33 220.00 17.71 146.83 587.33 220.00 17.72 146.83 587.33 220.00 17.73 146.83 587.33 220.00 17.74 146.83 587.33 220.00 61.74 17.75 146.83 587.33 220.00 61.74 17.76 146.83 587.33 220.00 61.74 17.77 146.83 587.33 220.00 61.74 17.78 146.83 587.33 220.00 61.74 17.79 146.83 587.33 220.00 61.74 17.80 146.83 587.33 220.00 61.74 17.81 146.83 587.33 220.00 61.74 17.82 146.83 587.33 220.00 61.74 17.83 146.83 587.33 220.00 61.74 17.84 146.83 587.33 220.00 61.74 123.47 17.85 146.83 587.33 220.00 61.74 123.47 17.86 146.83 587.33 220.00 61.74 123.47 17.87 146.83 587.33 220.00 61.74 123.47 17.88 587.33 220.00 61.74 123.47 17.89 587.33 220.00 61.74 123.47 17.90 587.33 220.00 61.74 123.47 17.91 587.33 220.00 61.74 123.47 17.92 587.33 220.00 61.74 123.47 17.93 587.33 220.00 61.74 123.47 17.94 587.33 220.00 61.74 123.47 17.95 587.33 220.00 61.74 123.47 17.96 587.33 61.74 123.47 17.97 61.74 123.47 17.98 61.74 123.47 17.99 61.74 123.47 18.00 61.74 123.47 18.01 61.74 123.47 18.02 61.74 123.47 18.03 61.74 123.47 18.04 61.74 123.47 18.05 61.74 123.47 18.06 61.74 123.47 18.07 61.74 123.47 18.08 61.74 123.47 18.09 61.74 123.47 329.63 18.10 61.74 123.47 329.63 18.11 61.74 123.47 329.63 196.00 18.12 61.74 123.47 329.63 196.00 18.13 61.74 123.47 329.63 196.00 18.14 61.74 123.47 329.63 196.00 18.15 61.74 123.47 329.63 196.00 18.16 61.74 123.47 329.63 196.00 18.17 61.74 123.47 329.63 196.00 18.18 123.47 329.63 196.00 18.19 123.47 329.63 196.00 18.20 123.47 329.63 196.00 18.21 123.47 329.63 196.00 18.22 123.47 329.63 196.00 18.23 123.47 329.63 196.00 18.24 123.47 329.63 196.00 18.25 123.47 329.63 196.00 18.26 123.47 329.63 196.00 18.27 123.47 329.63 196.00 18.28 123.47 329.63 196.00 18.29 123.47 329.63 196.00 349.23 18.30 123.47 329.63 196.00 349.23 293.66 18.31 123.47 329.63 196.00 349.23 293.66 18.32 123.47 329.63 196.00 349.23 293.66 18.33 123.47 329.63 349.23 293.66 18.34 123.47 329.63 349.23 293.66 18.35 123.47 329.63 349.23 293.66 18.36 123.47 329.63 349.23 293.66 18.37 123.47 329.63 349.23 293.66 18.38 123.47 329.63 349.23 293.66 18.39 123.47 329.63 349.23 293.66 18.40 123.47 329.63 349.23 293.66 18.41 123.47 329.63 349.23 293.66 18.42 123.47 349.23 293.66 18.43 123.47 349.23 293.66 18.44 123.47 349.23 293.66 18.45 123.47 349.23 293.66 18.46 123.47 349.23 293.66 18.47 123.47 349.23 293.66 18.48 123.47 293.66 440.00 18.49 123.47 293.66 440.00 18.50 123.47 293.66 440.00 349.23 18.51 123.47 293.66 440.00 349.23 18.52 123.47 440.00 349.23 18.53 123.47 440.00 349.23 18.54 123.47 440.00 349.23 18.55 123.47 440.00 349.23 18.56 123.47 440.00 349.23 18.57 123.47 440.00 349.23 18.58 123.47 440.00 349.23 18.59 123.47 440.00 349.23 18.60 123.47 440.00 349.23 18.61 123.47 440.00 349.23 18.62 123.47 440.00 349.23 18.63 123.47 440.00 349.23 18.64 123.47 440.00 349.23 18.65 123.47 440.00 349.23 18.66 123.47 440.00 349.23 18.67 123.47 440.00 349.23 392.00 18.68 123.47 440.00 349.23 392.00 18.69 123.47 440.00 349.23 392.00 18.70 123.47 440.00 349.23 392.00 18.71 123.47 349.23 392.00 18.72 123.47 349.23 392.00 18.73 123.47 349.23 392.00 392.00 18.74 123.47 349.23 392.00 392.00 18.75 123.47 349.23 392.00 392.00 18.76 123.47 392.00 392.00 18.77 123.47 392.00 392.00 18.78 123.47 392.00 392.00 18.79 123.47 392.00 392.00 18.80 123.47 392.00 392.00 18.81 123.47 392.00 392.00 18.82 123.47 392.00 392.00 18.83 123.47 392.00 392.00 18.84 123.47 392.00 392.00 18.85 123.47 392.00 392.00 18.86 123.47 392.00 392.00 18.87 123.47 392.00 392.00 349.23 18.88 123.47 392.00 392.00 349.23 18.89 123.47 392.00 392.00 349.23 18.90 123.47 392.00 349.23 18.91 123.47 392.00 349.23 196.00 18.92 123.47 349.23 196.00 18.93 123.47 349.23 196.00 18.94 123.47 349.23 196.00 18.95 123.47 349.23 196.00 18.96 123.47 349.23 196.00 18.97 123.47 349.23 196.00 18.98 123.47 349.23 196.00 18.99 123.47 349.23 196.00 19.00 123.47 196.00 19.01 123.47 196.00 19.02 123.47 196.00 19.03 123.47 196.00 19.04 123.47 196.00 130.81 19.05 123.47 196.00 130.81 65.41 19.06 123.47 196.00 130.81 65.41 19.07 123.47 196.00 130.81 65.41 349.23 19.08 123.47 196.00 130.81 65.41 349.23 19.09 123.47 196.00 130.81 65.41 349.23 19.10 123.47 130.81 65.41 349.23 19.11 130.81 65.41 349.23 19.12 130.81 65.41 349.23 19.13 130.81 65.41 349.23 19.14 130.81 65.41 349.23 19.15 130.81 65.41 349.23 19.16 130.81 65.41 349.23 19.17 130.81 65.41 349.23 19.18 130.81 65.41 349.23 19.19 130.81 65.41 349.23 19.20 130.81 65.41 349.23 19.21 130.81 65.41 349.23 19.22 130.81 65.41 349.23 19.23 130.81 65.41 349.23 19.24 130.81 65.41 349.23 19.25 130.81 65.41 349.23 19.26 130.81 65.41 349.23 19.27 130.81 65.41 329.63 19.28 130.81 65.41 329.63 19.29 130.81 65.41 329.63 19.30 130.81 65.41 329.63 196.00 19.31 130.81 65.41 329.63 196.00 19.32 130.81 65.41 329.63 196.00 19.33 130.81 65.41 329.63 196.00 19.34 130.81 65.41 329.63 196.00 19.35 130.81 65.41 329.63 196.00 19.36 130.81 65.41 329.63 196.00 19.37 130.81 65.41 329.63 196.00 19.38 130.81 65.41 329.63 196.00 19.39 130.81 65.41 329.63 196.00 19.40 130.81 65.41 329.63 196.00 19.41 130.81 65.41 329.63 196.00 19.42 130.81 65.41 329.63 196.00 19.43 130.81 65.41 329.63 196.00 19.44 130.81 65.41 329.63 196.00 19.45 130.81 65.41 329.63 196.00 19.46 130.81 65.41 329.63 196.00 19.47 130.81 65.41 329.63 196.00 19.48 130.81 329.63 196.00 19.49 130.81 329.63 196.00 19.50 130.81 196.00 261.63 19.51 130.81 196.00 261.63 523.25 19.52 130.81 196.00 261.63 523.25 19.53 130.81 196.00 261.63 523.25 19.54 130.81 196.00 261.63 523.25 19.55 130.81 261.63 523.25 19.56 130.81 261.63 523.25 19.57 130.81 261.63 523.25 19.58 130.81 261.63 523.25 19.59 130.81 261.63 523.25 19.60 130.81 261.63 523.25 19.61 130.81 261.63 523.25 19.62 130.81 261.63 523.25 19.63 130.81 261.63 523.25 19.64 130.81 261.63 523.25 19.65 130.81 261.63 523.25 19.66 130.81 261.63 523.25 19.67 130.81 261.63 523.25 19.68 130.81 261.63 523.25 19.69 130.81 261.63 523.25 19.70 130.81 261.63 523.25 329.63 19.71 130.81 261.63 523.25 329.63 19.72 130.81 261.63 523.25 329.63 19.73 130.81 523.25 329.63 19.74 130.81 523.25 329.63 19.75 130.81 523.25 329.63 19.76 130.81 523.25 329.63 19.77 130.81 523.25 329.63 19.78 130.81 523.25 329.63 19.79 130.81 523.25 329.63 19.80 130.81 523.25 329.63 19.81 130.81 523.25 329.63 19.82 130.81 523.25 329.63 19.83 130.81 523.25 329.63 19.84 130.81 523.25 329.63 19.85 130.81 523.25 329.63 19.86 130.81 523.25 329.63 19.87 130.81 523.25 329.63 19.88 130.81 523.25 329.63 19.89 130.81 523.25 329.63 392.00 19.90 130.81 523.25 329.63 392.00 19.91 130.81 523.25 392.00 19.92 130.81 523.25 392.00 19.93 130.81 523.25 392.00 19.94 130.81 523.25 392.00 19.95 130.81 523.25 392.00 19.96 130.81 523.25 392.00 19.97 130.81 523.25 392.00 19.98 130.81 523.25 392.00 19.99 130.81 523.25 392.00 20.00 130.81 523.25 392.00 20.01 130.81 523.25 392.00 20.02 130.81 523.25 392.00 20.03 130.81 523.25 392.00 20.04 130.81 523.25 392.00 20.05 130.81 523.25 392.00 20.06 130.81 523.25 392.00 20.07 130.81 523.25 392.00 20.08 130.81 523.25 196.00 20.09 130.81 523.25 196.00 20.10 130.81 523.25 196.00 20.11 130.81 523.25 196.00 20.12 130.81 523.25 196.00 20.13 130.81 523.25 196.00 20.14 130.81 523.25 196.00 20.15 130.81 523.25 196.00 20.16 130.81 523.25 196.00 20.17 130.81 523.25 196.00 20.18 130.81 523.25 196.00 20.19 130.81 523.25 196.00 20.20 130.81 523.25 196.00 20.21 130.81 523.25 196.00 20.22 130.81 523.25 196.00 20.23 130.81 523.25 196.00 103.83 20.24 523.25 196.00 103.83 103.83 20.25 523.25 103.83 103.83 20.26 523.25 103.83 103.83 20.27 523.25 103.83 103.83 20.28 523.25 103.83 103.83 20.29 523.25 103.83 103.83 329.63 20.30 523.25 103.83 103.83 329.63 20.31 523.25 103.83 103.83 329.63 20.32 523.25 103.83 103.83 329.63 20.33 523.25 103.83 103.83 329.63 20.34 523.25 103.83 103.83 329.63 20.35 103.83 103.83 329.63 20.36 103.83 103.83 329.63 20.37 103.83 103.83 329.63 20.38 103.83 103.83 329.63 20.39 103.83 103.83 329.63 20.40 103.83 103.83 329.63 20.41 103.83 103.83 329.63 20.42 103.83 103.83 329.63 20.43 103.83 103.83 329.63 20.44 103.83 103.83 329.63 20.45 103.83 103.83 329.63 20.46 103.83 103.83 329.63 20.47 103.83 103.83 329.63 20.48 103.83 103.83 329.63 20.49 103.83 103.83 329.63 246.94 20.50 103.83 103.83 329.63 246.94 20.51 103.83 103.83 329.63 246.94 20.52 103.83 103.83 329.63 246.94 20.53 103.83 103.83 329.63 246.94 20.54 103.83 103.83 329.63 246.94 20.55 103.83 103.83 329.63 246.94 20.56 103.83 103.83 329.63 246.94 20.57 103.83 103.83 329.63 246.94 20.58 103.83 103.83 329.63 246.94 20.59 103.83 103.83 329.63 246.94 20.60 103.83 103.83 329.63 246.94 20.61 103.83 103.83 329.63 246.94 20.62 103.83 103.83 329.63 246.94 20.63 103.83 103.83 329.63 246.94 20.64 103.83 103.83 329.63 246.94 20.65 103.83 329.63 246.94 20.66 103.83 329.63 246.94 20.67 103.83 329.63 246.94 20.68 103.83 329.63 246.94 20.69 103.83 329.63 246.94 293.66 20.70 103.83 329.63 246.94 293.66 20.71 103.83 329.63 246.94 293.66 20.72 103.83 329.63 293.66 20.73 103.83 329.63 293.66 20.74 103.83 329.63 293.66 20.75 103.83 329.63 293.66 20.76 103.83 329.63 293.66 20.77 103.83 329.63 293.66 20.78 103.83 329.63 293.66 20.79 103.83 329.63 293.66 20.80 103.83 329.63 293.66 20.81 103.83 329.63 293.66 20.82 103.83 329.63 293.66 20.83 103.83 329.63 293.66 20.84 103.83 329.63 293.66 20.85 103.83 329.63 293.66 20.86 103.83 329.63 293.66 20.87 103.83 329.63 293.66 20.88 103.83 329.63 293.66 329.63 20.89 103.83 329.63 293.66 329.63 20.90 103.83 329.63 293.66 329.63 20.91 103.83 329.63 329.63 349.23 20.92 103.83 329.63 349.23 20.93 103.83 329.63 349.23 20.94 103.83 329.63 349.23 20.95 103.83 329.63 349.23 20.96 103.83 329.63 349.23 20.97 103.83 329.63 349.23 20.98 103.83 329.63 349.23 20.99 103.83 329.63 349.23 21.00 103.83 329.63 349.23 21.01 103.83 329.63 349.23 21.02 103.83 329.63 349.23 21.03 103.83 349.23 21.04 103.83 349.23 21.05 103.83 349.23 493.88 21.06 103.83 349.23 493.88 21.07 103.83 349.23 493.88 21.08 103.83 349.23 493.88 21.09 103.83 349.23 493.88 21.10 103.83 349.23 493.88 21.11 103.83 349.23 493.88 21.12 103.83 349.23 493.88 329.63 21.13 103.83 349.23 493.88 329.63 21.14 103.83 349.23 493.88 329.63 21.15 103.83 349.23 493.88 329.63 21.16 103.83 493.88 329.63 21.17 103.83 493.88 329.63 21.18 103.83 493.88 329.63 21.19 103.83 493.88 329.63 21.20 103.83 493.88 329.63 21.21 103.83 493.88 329.63 21.22 103.83 493.88 329.63 21.23 103.83 493.88 329.63 21.24 103.83 493.88 329.63 21.25 103.83 493.88 329.63 246.94 21.26 103.83 493.88 329.63 246.94 21.27 103.83 493.88 329.63 246.94 21.28 103.83 329.63 246.94 21.29 103.83 329.63 246.94 293.66 21.30 103.83 329.63 246.94 293.66 21.31 103.83 246.94 293.66 21.32 103.83 246.94 293.66 21.33 103.83 246.94 293.66 21.34 103.83 246.94 293.66 21.35 103.83 246.94 293.66 21.36 103.83 246.94 293.66 21.37 103.83 246.94 293.66 21.38 103.83 246.94 293.66 21.39 103.83 246.94 293.66 21.40 103.83 246.94 293.66 21.41 103.83 246.94 293.66 21.42 103.83 246.94 21.43 103.83 246.94 110.00 21.44 103.83 246.94 110.00 21.45 103.83 110.00 110.00 21.46 103.83 110.00 110.00 21.47 103.83 110.00 110.00 21.48 103.83 110.00 110.00 21.49 110.00 110.00 293.66 21.50 110.00 110.00 293.66 21.51 110.00 110.00 293.66 21.52 110.00 110.00 293.66 21.53 110.00 110.00 293.66 21.54 110.00 110.00 293.66 21.55 110.00 110.00 293.66 21.56 110.00 110.00 293.66 21.57 110.00 110.00 293.66 21.58 110.00 110.00 293.66 21.59 110.00 110.00 293.66 21.60 110.00 110.00 293.66 21.61 110.00 110.00 293.66 21.62 110.00 110.00 293.66 21.63 110.00 110.00 293.66 21.64 110.00 110.00 293.66 21.65 110.00 110.00 293.66 21.66 110.00 110.00 293.66 261.63 21.67 110.00 110.00 293.66 261.63 21.68 110.00 110.00 293.66 261.63 21.69 110.00 110.00 261.63 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110.00 329.63 329.63 22.02 110.00 329.63 329.63 22.03 110.00 329.63 329.63 22.04 110.00 329.63 329.63 22.05 110.00 329.63 329.63 22.06 110.00 329.63 329.63 22.07 110.00 329.63 329.63 440.00 22.08 110.00 329.63 440.00 22.09 110.00 329.63 440.00 22.10 110.00 329.63 440.00 415.30 22.11 110.00 329.63 440.00 415.30 22.12 110.00 440.00 415.30 22.13 110.00 440.00 415.30 22.14 110.00 440.00 415.30 22.15 110.00 440.00 415.30 22.16 110.00 440.00 415.30 22.17 110.00 440.00 415.30 22.18 110.00 440.00 415.30 22.19 110.00 440.00 415.30 22.20 110.00 440.00 415.30 22.21 110.00 440.00 415.30 22.22 110.00 440.00 415.30 22.23 110.00 440.00 415.30 22.24 110.00 440.00 415.30 22.25 110.00 440.00 415.30 22.26 110.00 415.30 523.25 22.27 110.00 415.30 523.25 440.00 22.28 110.00 523.25 440.00 22.29 110.00 523.25 440.00 22.30 110.00 523.25 440.00 22.31 110.00 523.25 440.00 22.32 110.00 523.25 440.00 22.33 110.00 523.25 440.00 22.34 110.00 523.25 440.00 22.35 110.00 523.25 440.00 22.36 110.00 523.25 440.00 22.37 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123.47 277.18 12.40 61.74 123.47 277.18 12.41 61.74 123.47 277.18 12.42 61.74 123.47 277.18 12.43 61.74 123.47 277.18 12.44 61.74 123.47 277.18 12.45 61.74 123.47 277.18 12.46 61.74 123.47 277.18 12.47 61.74 123.47 277.18 329.63 12.48 61.74 123.47 277.18 329.63 12.49 61.74 123.47 277.18 329.63 12.50 61.74 123.47 277.18 329.63 12.51 61.74 123.47 329.63 12.52 61.74 123.47 329.63 12.53 61.74 123.47 329.63 12.54 61.74 123.47 329.63 12.55 61.74 123.47 329.63 12.56 61.74 123.47 329.63 12.57 61.74 123.47 329.63 12.58 61.74 123.47 329.63 12.59 61.74 123.47 329.63 12.60 61.74 123.47 329.63 12.61 61.74 123.47 329.63 12.62 61.74 123.47 329.63 12.63 61.74 123.47 329.63 12.64 61.74 123.47 329.63 12.65 61.74 123.47 329.63 12.66 61.74 123.47 329.63 12.67 61.74 123.47 329.63 12.68 61.74 123.47 329.63 12.69 61.74 123.47 329.63 12.70 61.74 123.47 329.63 12.71 61.74 123.47 329.63 12.72 61.74 123.47 329.63 12.73 61.74 123.47 329.63 12.74 61.74 123.47 329.63 12.75 61.74 123.47 329.63 12.76 61.74 123.47 329.63 12.77 61.74 123.47 329.63 12.78 61.74 123.47 329.63 12.79 61.74 123.47 329.63 12.80 61.74 123.47 329.63 12.81 61.74 123.47 329.63 12.82 61.74 123.47 329.63 12.83 61.74 123.47 329.63 12.84 61.74 123.47 329.63 12.85 61.74 123.47 329.63 12.86 61.74 123.47 329.63 12.87 61.74 123.47 329.63 12.88 61.74 123.47 329.63 12.89 61.74 123.47 329.63 12.90 61.74 123.47 329.63 12.91 61.74 123.47 329.63 12.92 61.74 123.47 329.63 12.93 61.74 123.47 329.63 12.94 61.74 123.47 329.63 12.95 61.74 123.47 329.63 12.96 61.74 123.47 207.65 12.97 61.74 123.47 207.65 12.98 61.74 123.47 207.65 12.99 61.74 123.47 207.65 13.00 61.74 123.47 207.65 13.01 61.74 123.47 207.65 13.02 61.74 123.47 207.65 13.03 61.74 123.47 207.65 13.04 61.74 123.47 207.65 13.05 61.74 123.47 207.65 13.06 61.74 123.47 207.65 13.07 61.74 123.47 207.65 13.08 61.74 123.47 207.65 13.09 61.74 123.47 207.65 13.10 61.74 123.47 207.65 13.11 61.74 123.47 207.65 13.12 61.74 123.47 207.65 13.13 61.74 123.47 207.65 13.14 61.74 123.47 207.65 13.15 61.74 123.47 207.65 13.16 61.74 123.47 207.65 13.17 61.74 123.47 207.65 13.18 61.74 123.47 207.65 13.19 61.74 123.47 207.65 13.20 61.74 123.47 207.65 13.21 61.74 123.47 207.65 13.22 61.74 123.47 207.65 13.23 61.74 123.47 207.65 13.24 61.74 123.47 207.65 13.25 61.74 123.47 207.65 13.26 61.74 123.47 207.65 13.27 61.74 123.47 207.65 13.28 61.74 123.47 207.65 13.29 61.74 123.47 207.65 13.30 61.74 123.47 207.65 13.31 61.74 123.47 207.65 13.32 61.74 123.47 207.65 13.33 61.74 123.47 207.65 13.34 61.74 123.47 207.65 13.35 61.74 123.47 207.65 13.36 61.74 123.47 207.65 13.37 61.74 123.47 207.65 13.38 61.74 123.47 207.65 13.39 61.74 123.47 207.65 277.18 13.40 61.74 123.47 207.65 277.18 13.41 61.74 123.47 207.65 277.18 13.42 61.74 123.47 207.65 277.18 13.43 61.74 123.47 207.65 277.18 13.44 61.74 123.47 207.65 277.18 13.45 61.74 123.47 207.65 277.18 13.46 61.74 123.47 277.18 13.47 61.74 123.47 277.18 13.48 61.74 123.47 277.18 13.49 61.74 123.47 277.18 13.50 61.74 123.47 277.18 13.51 61.74 123.47 277.18 13.52 61.74 123.47 277.18 13.53 61.74 123.47 277.18 13.54 61.74 123.47 277.18 13.55 61.74 123.47 277.18 13.56 61.74 123.47 277.18 13.57 61.74 123.47 277.18 13.58 61.74 123.47 277.18 13.59 61.74 123.47 277.18 13.60 61.74 123.47 277.18 13.61 61.74 123.47 277.18 13.62 61.74 123.47 277.18 13.63 61.74 123.47 277.18 13.64 61.74 123.47 277.18 13.65 61.74 123.47 277.18 13.66 61.74 123.47 277.18 13.67 61.74 123.47 277.18 13.68 61.74 123.47 277.18 13.69 61.74 123.47 277.18 13.70 61.74 123.47 277.18 13.71 61.74 123.47 277.18 13.72 61.74 123.47 277.18 13.73 61.74 123.47 277.18 13.74 61.74 123.47 277.18 13.75 61.74 123.47 277.18 13.76 61.74 123.47 277.18 13.77 61.74 123.47 277.18 13.78 61.74 123.47 277.18 13.79 61.74 123.47 277.18 13.80 61.74 277.18 13.81 61.74 277.18 13.82 61.74 277.18 13.83 61.74 277.18 13.84 61.74 277.18 13.85 61.74 277.18 13.86 277.18 13.87 277.18 13.88 277.18 13.89 277.18 13.90 277.18 329.63 13.91 277.18 329.63 13.92 277.18 329.63 13.93 277.18 329.63 13.94 277.18 329.63 13.95 329.63 13.96 329.63 13.97 329.63 13.98 329.63 13.99 329.63 14.00 329.63 14.01 329.63 14.02 329.63 14.03 329.63 14.04 329.63 14.05 329.63 14.06 329.63 14.07 329.63 14.08 329.63 14.09 329.63 14.10 329.63 14.11 329.63 14.12 329.63 14.13 329.63 14.14 329.63 14.15 329.63 14.16 329.63 14.17 329.63 14.18 329.63 14.19 329.63 14.20 329.63 14.21 329.63 14.22 329.63 14.23 329.63 14.24 329.63 14.25 329.63 14.26 329.63 14.27 329.63 14.28 329.63 14.29 329.63 14.30 329.63 14.31 329.63 14.32 329.63 14.33 329.63 14.34 329.63 14.35 329.63 14.36 329.63 14.37 329.63 14.38 329.63 14.39 329.63 14.40 329.63 14.41 14.42 220.00 14.43 220.00 55.00 110.00 14.44 220.00 55.00 110.00 14.45 220.00 55.00 110.00 14.46 220.00 55.00 110.00 14.47 220.00 55.00 110.00 14.48 220.00 55.00 110.00 14.49 220.00 55.00 110.00 14.50 220.00 55.00 110.00 14.51 220.00 55.00 110.00 14.52 220.00 55.00 110.00 14.53 220.00 55.00 110.00 14.54 220.00 55.00 110.00 14.55 220.00 55.00 110.00 14.56 220.00 55.00 110.00 14.57 220.00 55.00 110.00 14.58 220.00 55.00 110.00 14.59 220.00 55.00 110.00 14.60 220.00 55.00 110.00 14.61 220.00 55.00 110.00 14.62 220.00 55.00 110.00 14.63 220.00 55.00 110.00 14.64 220.00 55.00 110.00 14.65 220.00 55.00 110.00 14.66 220.00 55.00 110.00 14.67 220.00 55.00 110.00 14.68 220.00 55.00 110.00 14.69 220.00 55.00 110.00 14.70 220.00 55.00 110.00 14.71 220.00 55.00 110.00 14.72 220.00 55.00 110.00 14.73 220.00 55.00 110.00 14.74 220.00 55.00 110.00 14.75 220.00 55.00 110.00 14.76 220.00 55.00 110.00 14.77 220.00 55.00 110.00 14.78 220.00 55.00 110.00 14.79 220.00 55.00 110.00 14.80 220.00 55.00 110.00 14.81 220.00 55.00 110.00 14.82 220.00 55.00 110.00 14.83 220.00 55.00 110.00 14.84 220.00 55.00 110.00 14.85 220.00 55.00 110.00 14.86 220.00 55.00 110.00 14.87 220.00 55.00 110.00 14.88 220.00 55.00 110.00 14.89 220.00 55.00 110.00 14.90 220.00 55.00 110.00 14.91 220.00 55.00 110.00 14.92 220.00 55.00 110.00 14.93 220.00 55.00 110.00 14.94 220.00 55.00 110.00 14.95 220.00 55.00 110.00 14.96 220.00 55.00 110.00 14.97 220.00 55.00 110.00 14.98 220.00 55.00 110.00 14.99 220.00 55.00 110.00 277.18 15.00 220.00 55.00 110.00 277.18 15.01 220.00 55.00 110.00 277.18 15.02 220.00 55.00 110.00 277.18 15.03 220.00 55.00 110.00 277.18 15.04 220.00 55.00 110.00 277.18 15.05 220.00 55.00 110.00 277.18 15.06 220.00 55.00 110.00 277.18 15.07 55.00 110.00 277.18 15.08 55.00 110.00 277.18 15.09 55.00 110.00 277.18 15.10 55.00 110.00 277.18 15.11 55.00 110.00 277.18 15.12 55.00 110.00 277.18 15.13 55.00 110.00 277.18 15.14 55.00 110.00 277.18 15.15 55.00 110.00 277.18 15.16 55.00 110.00 277.18 15.17 55.00 110.00 277.18 15.18 55.00 110.00 277.18 15.19 55.00 110.00 277.18 15.20 55.00 110.00 277.18 15.21 55.00 110.00 277.18 15.22 55.00 110.00 277.18 15.23 55.00 110.00 277.18 15.24 55.00 110.00 277.18 15.25 55.00 110.00 277.18 15.26 55.00 110.00 277.18 15.27 55.00 110.00 277.18 15.28 55.00 110.00 277.18 15.29 55.00 110.00 277.18 15.30 55.00 110.00 277.18 15.31 55.00 110.00 277.18 15.32 55.00 110.00 277.18 15.33 55.00 110.00 277.18 15.34 55.00 110.00 277.18 15.35 55.00 110.00 277.18 15.36 55.00 110.00 277.18 15.37 55.00 110.00 277.18 15.38 55.00 110.00 277.18 15.39 55.00 110.00 277.18 15.40 55.00 110.00 277.18 15.41 55.00 110.00 277.18 15.42 55.00 110.00 277.18 15.43 55.00 110.00 277.18 15.44 55.00 110.00 277.18 15.45 55.00 110.00 277.18 15.46 55.00 110.00 277.18 15.47 55.00 110.00 277.18 15.48 55.00 110.00 277.18 15.49 55.00 110.00 277.18 329.63 15.50 55.00 110.00 277.18 329.63 15.51 55.00 110.00 277.18 329.63 15.52 55.00 110.00 329.63 15.53 55.00 110.00 329.63 15.54 55.00 110.00 329.63 15.55 55.00 110.00 329.63 15.56 55.00 110.00 329.63 15.57 55.00 110.00 329.63 15.58 55.00 110.00 329.63 15.59 55.00 110.00 329.63 15.60 55.00 110.00 329.63 15.61 55.00 110.00 329.63 15.62 55.00 110.00 329.63 15.63 55.00 110.00 329.63 15.64 55.00 110.00 329.63 15.65 55.00 110.00 329.63 15.66 55.00 110.00 329.63 15.67 55.00 110.00 329.63 15.68 55.00 110.00 329.63 15.69 55.00 110.00 329.63 15.70 55.00 110.00 329.63 15.71 55.00 110.00 329.63 15.72 55.00 110.00 329.63 15.73 55.00 110.00 329.63 15.74 55.00 110.00 329.63 15.75 55.00 110.00 329.63 15.76 55.00 110.00 329.63 15.77 55.00 110.00 329.63 15.78 55.00 110.00 329.63 15.79 55.00 110.00 329.63 15.80 55.00 110.00 329.63 15.81 55.00 110.00 329.63 15.82 55.00 110.00 329.63 15.83 55.00 110.00 329.63 15.84 55.00 110.00 329.63 15.85 55.00 110.00 329.63 15.86 55.00 110.00 329.63 15.87 55.00 110.00 329.63 15.88 55.00 110.00 329.63 15.89 55.00 110.00 329.63 15.90 55.00 110.00 329.63 15.91 55.00 110.00 329.63 15.92 55.00 110.00 329.63 15.93 55.00 110.00 329.63 15.94 55.00 110.00 329.63 15.95 55.00 110.00 329.63 15.96 55.00 110.00 329.63 15.97 55.00 110.00 329.63 15.98 55.00 110.00 329.63 15.99 55.00 110.00 329.63 220.00 16.00 55.00 110.00 329.63 220.00 16.01 55.00 110.00 329.63 220.00 16.02 55.00 110.00 329.63 220.00 16.03 55.00 110.00 329.63 220.00 16.04 55.00 110.00 220.00 16.05 55.00 110.00 220.00 16.06 55.00 110.00 220.00 16.07 55.00 110.00 220.00 16.08 55.00 110.00 220.00 16.09 55.00 110.00 220.00 16.10 55.00 110.00 220.00 16.11 55.00 110.00 220.00 16.12 55.00 110.00 220.00 16.13 55.00 110.00 220.00 16.14 55.00 110.00 220.00 16.15 55.00 110.00 220.00 16.16 55.00 110.00 220.00 16.17 55.00 110.00 220.00 16.18 55.00 110.00 220.00 16.19 55.00 110.00 220.00 16.20 55.00 110.00 220.00 16.21 55.00 110.00 220.00 16.22 55.00 110.00 220.00 16.23 55.00 110.00 220.00 16.24 55.00 110.00 220.00 16.25 55.00 110.00 220.00 16.26 55.00 110.00 220.00 16.27 55.00 110.00 220.00 16.28 55.00 110.00 220.00 16.29 55.00 110.00 220.00 16.30 55.00 110.00 220.00 16.31 55.00 110.00 220.00 16.32 55.00 110.00 220.00 16.33 55.00 110.00 220.00 16.34 55.00 110.00 220.00 16.35 55.00 110.00 220.00 16.36 55.00 110.00 220.00 16.37 55.00 110.00 220.00 16.38 55.00 110.00 220.00 16.39 55.00 110.00 220.00 16.40 55.00 110.00 220.00 16.41 55.00 110.00 220.00 16.42 55.00 110.00 220.00 16.43 55.00 110.00 220.00 16.44 55.00 110.00 220.00 16.45 55.00 110.00 220.00 16.46 55.00 110.00 220.00 16.47 55.00 110.00 220.00 277.18 16.48 55.00 110.00 220.00 277.18 16.49 55.00 110.00 220.00 277.18 16.50 55.00 110.00 220.00 277.18 16.51 55.00 110.00 220.00 277.18 16.52 55.00 110.00 220.00 277.18 16.53 55.00 110.00 220.00 277.18 16.54 55.00 110.00 277.18 16.55 55.00 110.00 277.18 16.56 55.00 110.00 277.18 16.57 55.00 110.00 277.18 16.58 55.00 110.00 277.18 16.59 55.00 110.00 277.18 16.60 55.00 110.00 277.18 16.61 55.00 110.00 277.18 16.62 55.00 110.00 277.18 16.63 55.00 110.00 277.18 16.64 55.00 110.00 277.18 16.65 55.00 110.00 277.18 16.66 55.00 110.00 277.18 16.67 55.00 110.00 277.18 16.68 55.00 110.00 277.18 16.69 55.00 110.00 277.18 16.70 55.00 110.00 277.18 16.71 55.00 110.00 277.18 16.72 55.00 110.00 277.18 16.73 55.00 110.00 277.18 16.74 55.00 110.00 277.18 16.75 55.00 110.00 277.18 16.76 55.00 110.00 277.18 16.77 55.00 110.00 277.18 16.78 55.00 110.00 277.18 16.79 55.00 110.00 277.18 16.80 55.00 110.00 277.18 16.81 55.00 110.00 277.18 16.82 55.00 110.00 277.18 16.83 55.00 110.00 277.18 16.84 55.00 110.00 277.18 16.85 55.00 110.00 277.18 16.86 55.00 110.00 277.18 16.87 55.00 110.00 277.18 16.88 55.00 110.00 277.18 16.89 55.00 110.00 277.18 16.90 55.00 110.00 277.18 16.91 55.00 110.00 277.18 16.92 55.00 110.00 277.18 16.93 55.00 277.18 16.94 55.00 277.18 16.95 55.00 277.18 16.96 55.00 277.18 16.97 55.00 277.18 329.63 16.98 277.18 329.63 16.99 277.18 329.63 17.00 277.18 329.63 17.01 329.63 17.02 329.63 17.03 329.63 17.04 329.63 17.05 329.63 17.06 329.63 17.07 329.63 17.08 329.63 17.09 329.63 17.10 329.63 17.11 329.63 17.12 329.63 17.13 329.63 17.14 329.63 17.15 329.63 17.16 329.63 17.17 329.63 17.18 329.63 17.19 329.63 17.20 329.63 17.21 329.63 17.22 329.63 17.23 329.63 17.24 329.63 17.25 329.63 17.26 329.63 17.27 329.63 17.28 329.63 17.29 329.63 17.30 329.63 17.31 329.63 17.32 329.63 17.33 329.63 17.34 329.63 17.35 329.63 17.36 329.63 17.37 329.63 17.38 329.63 17.39 329.63 17.40 329.63 17.41 329.63 17.42 329.63 17.43 329.63 17.44 329.63 17.45 329.63 17.46 329.63 17.47 329.63 92.50 17.48 329.63 92.50 46.25 17.49 92.50 46.25 220.00 17.50 92.50 46.25 220.00 17.51 92.50 46.25 220.00 17.52 92.50 46.25 220.00 17.53 92.50 46.25 220.00 17.54 92.50 46.25 220.00 17.55 92.50 46.25 220.00 17.56 92.50 46.25 220.00 17.57 92.50 46.25 220.00 17.58 92.50 46.25 220.00 17.59 92.50 46.25 220.00 17.60 92.50 46.25 220.00 17.61 92.50 46.25 220.00 17.62 92.50 46.25 220.00 17.63 92.50 46.25 220.00 17.64 92.50 46.25 220.00 17.65 92.50 46.25 220.00 17.66 92.50 46.25 220.00 17.67 92.50 46.25 220.00 17.68 92.50 46.25 220.00 17.69 92.50 46.25 220.00 17.70 92.50 46.25 220.00 17.71 92.50 46.25 220.00 17.72 92.50 46.25 220.00 17.73 92.50 46.25 220.00 17.74 92.50 46.25 220.00 17.75 92.50 46.25 220.00 17.76 92.50 46.25 220.00 17.77 92.50 46.25 220.00 17.78 92.50 46.25 220.00 17.79 92.50 46.25 220.00 17.80 92.50 46.25 220.00 17.81 92.50 46.25 220.00 17.82 92.50 46.25 220.00 17.83 92.50 46.25 220.00 17.84 92.50 46.25 220.00 17.85 92.50 46.25 220.00 17.86 92.50 46.25 220.00 17.87 92.50 46.25 220.00 17.88 92.50 46.25 220.00 17.89 92.50 46.25 220.00 17.90 92.50 46.25 220.00 17.91 92.50 46.25 220.00 17.92 92.50 46.25 220.00 17.93 92.50 46.25 220.00 17.94 92.50 46.25 220.00 17.95 92.50 46.25 220.00 17.96 92.50 46.25 220.00 17.97 92.50 46.25 220.00 17.98 92.50 46.25 220.00 17.99 92.50 46.25 220.00 18.00 92.50 46.25 220.00 18.01 92.50 46.25 220.00 18.02 92.50 46.25 220.00 18.03 92.50 46.25 220.00 18.04 92.50 46.25 220.00 18.05 92.50 46.25 220.00 293.66 18.06 92.50 46.25 220.00 293.66 18.07 92.50 46.25 220.00 293.66 18.08 92.50 46.25 220.00 293.66 18.09 92.50 46.25 220.00 293.66 18.10 92.50 46.25 220.00 293.66 18.11 92.50 46.25 220.00 293.66 18.12 92.50 46.25 220.00 293.66 18.13 92.50 46.25 220.00 293.66 18.14 92.50 46.25 220.00 293.66 18.15 92.50 46.25 293.66 18.16 92.50 46.25 293.66 18.17 92.50 46.25 293.66 18.18 92.50 46.25 293.66 18.19 92.50 46.25 293.66 18.20 92.50 46.25 293.66 18.21 92.50 46.25 293.66 18.22 92.50 46.25 293.66 18.23 92.50 46.25 293.66 18.24 92.50 46.25 293.66 18.25 92.50 46.25 293.66 18.26 92.50 46.25 293.66 18.27 92.50 46.25 293.66 18.28 92.50 46.25 293.66 18.29 92.50 46.25 293.66 18.30 92.50 46.25 293.66 18.31 92.50 46.25 293.66 18.32 92.50 46.25 293.66 18.33 92.50 46.25 293.66 18.34 92.50 46.25 293.66 18.35 92.50 46.25 293.66 18.36 92.50 46.25 293.66 18.37 92.50 46.25 293.66 18.38 92.50 46.25 293.66 18.39 92.50 46.25 293.66 18.40 92.50 46.25 293.66 18.41 92.50 46.25 293.66 18.42 92.50 46.25 293.66 18.43 92.50 46.25 293.66 18.44 92.50 46.25 293.66 18.45 92.50 46.25 293.66 18.46 92.50 46.25 293.66 18.47 92.50 46.25 293.66 18.48 92.50 46.25 293.66 18.49 92.50 46.25 293.66 18.50 92.50 46.25 293.66 18.51 92.50 46.25 293.66 18.52 92.50 46.25 293.66 18.53 92.50 46.25 293.66 369.99 18.54 92.50 46.25 293.66 369.99 18.55 92.50 46.25 293.66 369.99 18.56 92.50 46.25 369.99 18.57 92.50 46.25 369.99 18.58 92.50 46.25 369.99 18.59 92.50 46.25 369.99 18.60 92.50 46.25 369.99 18.61 92.50 46.25 369.99 18.62 92.50 46.25 369.99 18.63 92.50 46.25 369.99 18.64 92.50 46.25 369.99 18.65 92.50 46.25 369.99 18.66 92.50 46.25 369.99 18.67 92.50 46.25 369.99 18.68 92.50 46.25 369.99 18.69 92.50 46.25 369.99 18.70 92.50 46.25 369.99 18.71 92.50 46.25 369.99 18.72 92.50 46.25 369.99 18.73 92.50 46.25 369.99 18.74 92.50 46.25 369.99 18.75 92.50 46.25 369.99 18.76 92.50 46.25 369.99 18.77 92.50 46.25 369.99 18.78 92.50 46.25 369.99 18.79 92.50 46.25 369.99 18.80 92.50 46.25 369.99 18.81 92.50 46.25 369.99 18.82 92.50 46.25 369.99 18.83 92.50 46.25 369.99 18.84 92.50 46.25 369.99 18.85 92.50 46.25 369.99 18.86 92.50 46.25 369.99 18.87 92.50 46.25 369.99 18.88 92.50 46.25 369.99 18.89 92.50 46.25 369.99 18.90 92.50 46.25 369.99 18.91 92.50 46.25 369.99 18.92 92.50 46.25 369.99 18.93 92.50 46.25 369.99 18.94 92.50 46.25 369.99 18.95 92.50 46.25 369.99 18.96 92.50 46.25 369.99 18.97 92.50 46.25 369.99 18.98 92.50 46.25 369.99 18.99 92.50 46.25 369.99 19.00 92.50 46.25 369.99 220.00 19.01 92.50 46.25 369.99 220.00 19.02 92.50 46.25 369.99 220.00 19.03 92.50 46.25 369.99 220.00 19.04 92.50 46.25 369.99 220.00 19.05 92.50 46.25 369.99 220.00 19.06 92.50 46.25 220.00 19.07 92.50 46.25 220.00 19.08 92.50 46.25 220.00 19.09 92.50 46.25 220.00 19.10 92.50 46.25 220.00 19.11 92.50 46.25 220.00 19.12 92.50 46.25 220.00 19.13 92.50 46.25 220.00 19.14 92.50 46.25 220.00 19.15 92.50 46.25 220.00 19.16 92.50 46.25 220.00 19.17 92.50 46.25 220.00 19.18 92.50 46.25 220.00 19.19 92.50 46.25 220.00 19.20 92.50 46.25 220.00 19.21 92.50 46.25 220.00 19.22 92.50 46.25 220.00 19.23 92.50 46.25 220.00 19.24 92.50 46.25 220.00 19.25 92.50 46.25 220.00 19.26 92.50 46.25 220.00 19.27 92.50 46.25 220.00 19.28 92.50 46.25 220.00 19.29 92.50 46.25 220.00 19.30 92.50 46.25 220.00 19.31 92.50 46.25 220.00 19.32 92.50 46.25 220.00 19.33 92.50 46.25 220.00 19.34 92.50 46.25 220.00 19.35 92.50 46.25 220.00 19.36 92.50 46.25 220.00 19.37 92.50 46.25 220.00 19.38 92.50 46.25 220.00 19.39 92.50 46.25 220.00 19.40 92.50 46.25 220.00 19.41 92.50 46.25 220.00 19.42 92.50 46.25 220.00 19.43 92.50 46.25 220.00 19.44 92.50 46.25 220.00 293.66 19.45 92.50 46.25 220.00 293.66 19.46 92.50 46.25 220.00 293.66 19.47 92.50 46.25 220.00 293.66 19.48 92.50 46.25 220.00 293.66 19.49 92.50 46.25 220.00 293.66 19.50 92.50 46.25 220.00 293.66 19.51 92.50 46.25 293.66 19.52 92.50 46.25 293.66 19.53 92.50 46.25 293.66 19.54 92.50 46.25 293.66 19.55 92.50 46.25 293.66 19.56 92.50 46.25 293.66 19.57 92.50 46.25 293.66 19.58 92.50 46.25 293.66 19.59 92.50 46.25 293.66 19.60 92.50 46.25 293.66 19.61 92.50 46.25 293.66 19.62 92.50 46.25 293.66 19.63 92.50 46.25 293.66 19.64 92.50 46.25 293.66 19.65 92.50 46.25 293.66 19.66 92.50 46.25 293.66 19.67 92.50 46.25 293.66 19.68 92.50 46.25 293.66 19.69 92.50 46.25 293.66 19.70 92.50 46.25 293.66 19.71 92.50 46.25 293.66 19.72 92.50 46.25 293.66 19.73 92.50 46.25 293.66 19.74 92.50 46.25 293.66 19.75 92.50 46.25 293.66 19.76 92.50 46.25 293.66 19.77 92.50 46.25 293.66 19.78 92.50 46.25 293.66 19.79 92.50 46.25 293.66 19.80 92.50 46.25 293.66 19.81 92.50 46.25 293.66 19.82 92.50 46.25 293.66 19.83 92.50 46.25 293.66 19.84 92.50 46.25 293.66 19.85 92.50 46.25 293.66 19.86 92.50 46.25 293.66 19.87 92.50 46.25 293.66 19.88 92.50 46.25 293.66 19.89 92.50 46.25 293.66 19.90 92.50 46.25 293.66 19.91 92.50 293.66 19.92 92.50 293.66 19.93 92.50 293.66 19.94 92.50 293.66 19.95 92.50 293.66 369.99 19.96 92.50 293.66 369.99 19.97 92.50 293.66 369.99 19.98 92.50 293.66 369.99 19.99 92.50 369.99 20.00 92.50 369.99 20.01 92.50 369.99 20.02 92.50 369.99 20.03 369.99 20.04 369.99 20.05 369.99 20.06 369.99 20.07 369.99 20.08 369.99 20.09 369.99 20.10 369.99 20.11 369.99 20.12 369.99 20.13 369.99 20.14 369.99 20.15 369.99 20.16 369.99 20.17 369.99 20.18 369.99 20.19 369.99 20.20 369.99 20.21 369.99 20.22 369.99 20.23 369.99 20.24 369.99 20.25 369.99 20.26 369.99 20.27 369.99 20.28 369.99 20.29 369.99 20.30 369.99 20.31 369.99 20.32 369.99 20.33 369.99 20.34 369.99 20.35 369.99 20.36 369.99 20.37 369.99 20.38 369.99 20.39 369.99 20.40 369.99 20.41 369.99 20.42 369.99 20.43 369.99 20.44 369.99 51.91 20.45 369.99 51.91 103.83 20.46 369.99 51.91 103.83 20.47 369.99 51.91 103.83 20.48 369.99 51.91 103.83 20.49 51.91 103.83 207.65 20.50 51.91 103.83 207.65 20.51 51.91 103.83 207.65 20.52 51.91 103.83 207.65 20.53 51.91 103.83 207.65 20.54 51.91 103.83 207.65 20.55 51.91 103.83 207.65 20.56 51.91 103.83 207.65 20.57 51.91 103.83 207.65 20.58 51.91 103.83 207.65 20.59 51.91 103.83 207.65 20.60 51.91 103.83 207.65 20.61 51.91 103.83 207.65 20.62 51.91 103.83 207.65 20.63 51.91 103.83 207.65 20.64 51.91 103.83 207.65 20.65 51.91 103.83 207.65 20.66 51.91 103.83 207.65 20.67 51.91 103.83 207.65 20.68 51.91 103.83 207.65 20.69 51.91 103.83 207.65 20.70 51.91 103.83 207.65 20.71 51.91 103.83 207.65 20.72 51.91 103.83 207.65 20.73 51.91 103.83 207.65 20.74 51.91 103.83 207.65 20.75 51.91 103.83 207.65 20.76 51.91 103.83 207.65 20.77 51.91 103.83 207.65 20.78 51.91 103.83 207.65 20.79 51.91 103.83 207.65 20.80 51.91 103.83 207.65 20.81 51.91 103.83 207.65 20.82 51.91 103.83 207.65 20.83 51.91 103.83 207.65 20.84 51.91 103.83 207.65 20.85 51.91 103.83 207.65 20.86 51.91 103.83 207.65 20.87 51.91 103.83 207.65 20.88 51.91 103.83 207.65 20.89 51.91 103.83 207.65 20.90 51.91 103.83 207.65 20.91 51.91 103.83 207.65 20.92 51.91 103.83 207.65 20.93 51.91 103.83 207.65 20.94 51.91 103.83 207.65 20.95 51.91 103.83 207.65 20.96 51.91 103.83 207.65 20.97 51.91 103.83 207.65 20.98 51.91 103.83 207.65 20.99 51.91 103.83 207.65 21.00 51.91 103.83 207.65 21.01 51.91 103.83 207.65 21.02 51.91 103.83 207.65 21.03 51.91 103.83 207.65 261.63 21.04 51.91 103.83 207.65 261.63 21.05 51.91 103.83 207.65 261.63 21.06 51.91 103.83 207.65 261.63 21.07 51.91 103.83 207.65 261.63 21.08 51.91 103.83 207.65 261.63 21.09 51.91 103.83 261.63 21.10 51.91 103.83 261.63 21.11 51.91 103.83 261.63 21.12 51.91 103.83 261.63 21.13 51.91 103.83 261.63 21.14 51.91 103.83 261.63 21.15 51.91 103.83 261.63 21.16 51.91 103.83 261.63 21.17 51.91 103.83 261.63 21.18 51.91 103.83 261.63 21.19 51.91 103.83 261.63 21.20 51.91 103.83 261.63 21.21 51.91 103.83 261.63 21.22 51.91 103.83 261.63 21.23 51.91 103.83 261.63 21.24 51.91 103.83 261.63 21.25 51.91 103.83 261.63 21.26 51.91 103.83 261.63 21.27 51.91 103.83 261.63 21.28 51.91 103.83 261.63 21.29 51.91 103.83 261.63 21.30 51.91 103.83 261.63 21.31 51.91 103.83 261.63 21.32 51.91 103.83 261.63 21.33 51.91 103.83 261.63 21.34 51.91 103.83 261.63 21.35 51.91 103.83 261.63 21.36 51.91 103.83 261.63 21.37 51.91 103.83 261.63 21.38 51.91 103.83 261.63 21.39 51.91 103.83 261.63 21.40 51.91 103.83 261.63 21.41 51.91 103.83 261.63 21.42 51.91 103.83 261.63 21.43 51.91 103.83 261.63 21.44 51.91 103.83 261.63 21.45 51.91 103.83 261.63 21.46 51.91 103.83 261.63 21.47 51.91 103.83 261.63 21.48 51.91 103.83 261.63 21.49 51.91 103.83 261.63 21.50 51.91 103.83 261.63 369.99 21.51 51.91 103.83 261.63 369.99 21.52 51.91 103.83 261.63 369.99 21.53 51.91 103.83 369.99 21.54 51.91 103.83 369.99 21.55 51.91 103.83 369.99 21.56 51.91 103.83 369.99 21.57 51.91 103.83 369.99 21.58 51.91 103.83 369.99 21.59 51.91 103.83 369.99 21.60 51.91 103.83 369.99 21.61 51.91 103.83 369.99 21.62 51.91 103.83 369.99 21.63 51.91 103.83 369.99 21.64 51.91 103.83 369.99 21.65 51.91 103.83 369.99 21.66 51.91 103.83 369.99 21.67 51.91 103.83 369.99 21.68 51.91 103.83 369.99 21.69 51.91 103.83 369.99 21.70 51.91 103.83 369.99 21.71 51.91 103.83 369.99 21.72 51.91 103.83 369.99 21.73 51.91 103.83 369.99 21.74 51.91 103.83 369.99 21.75 51.91 103.83 369.99 21.76 51.91 103.83 369.99 21.77 51.91 103.83 369.99 21.78 51.91 103.83 369.99 21.79 51.91 103.83 369.99 21.80 51.91 103.83 369.99 21.81 51.91 103.83 369.99 21.82 51.91 103.83 369.99 21.83 51.91 103.83 369.99 21.84 51.91 103.83 369.99 21.85 51.91 103.83 369.99 21.86 51.91 103.83 369.99 21.87 51.91 103.83 369.99 21.88 51.91 103.83 369.99 21.89 51.91 103.83 369.99 21.90 51.91 103.83 369.99 21.91 51.91 103.83 369.99 21.92 51.91 103.83 369.99 21.93 51.91 103.83 369.99 21.94 51.91 103.83 369.99 21.95 51.91 103.83 369.99 21.96 51.91 103.83 369.99 21.97 51.91 103.83 369.99 21.98 51.91 103.83 369.99 21.99 51.91 103.83 369.99 22.00 51.91 103.83 369.99 207.65 22.01 51.91 103.83 369.99 207.65 22.02 51.91 103.83 369.99 207.65 22.03 51.91 103.83 369.99 207.65 22.04 51.91 103.83 207.65 22.05 51.91 103.83 207.65 22.06 51.91 103.83 207.65 22.07 51.91 103.83 207.65 22.08 51.91 103.83 207.65 22.09 51.91 103.83 207.65 22.10 51.91 103.83 207.65 22.11 51.91 103.83 207.65 22.12 51.91 103.83 207.65 22.13 51.91 103.83 207.65 22.14 51.91 103.83 207.65 22.15 51.91 103.83 207.65 22.16 51.91 103.83 207.65 22.17 51.91 103.83 207.65 22.18 51.91 103.83 207.65 22.19 51.91 103.83 207.65 22.20 51.91 103.83 207.65 22.21 51.91 103.83 207.65 22.22 51.91 103.83 207.65 22.23 51.91 103.83 207.65 22.24 51.91 103.83 207.65 22.25 51.91 103.83 207.65 22.26 51.91 103.83 207.65 22.27 51.91 103.83 207.65 22.28 51.91 103.83 207.65 22.29 51.91 103.83 207.65 22.30 51.91 103.83 207.65 22.31 51.91 103.83 207.65 22.32 51.91 103.83 207.65 22.33 51.91 103.83 207.65 22.34 51.91 103.83 207.65 22.35 51.91 103.83 207.65 22.36 51.91 103.83 207.65 22.37 51.91 103.83 207.65 22.38 51.91 103.83 207.65 22.39 51.91 103.83 207.65 22.40 51.91 103.83 207.65 22.41 51.91 103.83 207.65 22.42 51.91 103.83 207.65 22.43 51.91 103.83 207.65 22.44 51.91 103.83 207.65 22.45 51.91 103.83 207.65 22.46 51.91 103.83 207.65 22.47 51.91 103.83 207.65 277.18 22.48 51.91 103.83 207.65 277.18 22.49 51.91 103.83 207.65 277.18 22.50 51.91 103.83 207.65 277.18 22.51 51.91 103.83 207.65 277.18 22.52 51.91 103.83 207.65 277.18 22.53 51.91 103.83 277.18 22.54 51.91 103.83 277.18 22.55 51.91 103.83 277.18 22.56 51.91 103.83 277.18 22.57 51.91 103.83 277.18 22.58 51.91 103.83 277.18 22.59 51.91 103.83 277.18 22.60 51.91 103.83 277.18 22.61 51.91 103.83 277.18 22.62 51.91 103.83 277.18 22.63 51.91 103.83 277.18 22.64 51.91 103.83 277.18 22.65 51.91 103.83 277.18 22.66 51.91 103.83 277.18 22.67 51.91 103.83 277.18 22.68 51.91 103.83 277.18 22.69 51.91 103.83 277.18 22.70 51.91 103.83 277.18 22.71 51.91 103.83 277.18 22.72 51.91 103.83 277.18 22.73 51.91 103.83 277.18 22.74 51.91 103.83 277.18 22.75 51.91 103.83 277.18 22.76 51.91 103.83 277.18 22.77 51.91 103.83 277.18 22.78 51.91 103.83 277.18 22.79 51.91 103.83 277.18 22.80 51.91 103.83 277.18 22.81 51.91 103.83 277.18 22.82 51.91 103.83 277.18 22.83 51.91 103.83 277.18 22.84 51.91 103.83 277.18 22.85 51.91 103.83 277.18 22.86 51.91 103.83 277.18 22.87 51.91 103.83 277.18 22.88 51.91 103.83 277.18 22.89 51.91 103.83 277.18 22.90 51.91 103.83 277.18 22.91 51.91 103.83 277.18 22.92 51.91 103.83 277.18 22.93 51.91 103.83 277.18 22.94 51.91 103.83 277.18 329.63 22.95 51.91 103.83 277.18 329.63 22.96 51.91 103.83 277.18 329.63 22.97 51.91 103.83 277.18 329.63 22.98 51.91 103.83 277.18 329.63 22.99 51.91 103.83 329.63 23.00 51.91 103.83 329.63 23.01 51.91 103.83 329.63 23.02 51.91 103.83 329.63 23.03 51.91 103.83 329.63 23.04 103.83 329.63 23.05 103.83 329.63 23.06 103.83 329.63 23.07 103.83 329.63 23.08 329.63 23.09 329.63 23.10 329.63 23.11 329.63 23.12 329.63 23.13 329.63 23.14 329.63 23.15 329.63 23.16 329.63 23.17 329.63 23.18 329.63 23.19 329.63 23.20 329.63 23.21 329.63 23.22 329.63 23.23 329.63 23.24 329.63 23.25 329.63 23.26 329.63 23.27 329.63 23.28 329.63 23.29 329.63 23.30 329.63 23.31 329.63 23.32 329.63 23.33 329.63 23.34 329.63 23.35 329.63 23.36 329.63 23.37 329.63 23.38 329.63 23.39 329.63 23.40 329.63 23.41 329.63 23.42 329.63 23.43 329.63 23.44 329.63 23.45 329.63 23.46 329.63 23.47 329.63 51.91 207.65 103.83 23.48 329.63 51.91 207.65 103.83 23.49 329.63 51.91 207.65 103.83 23.50 329.63 51.91 207.65 103.83 23.51 51.91 207.65 103.83 23.52 51.91 207.65 103.83 23.53 51.91 207.65 103.83 23.54 51.91 207.65 103.83 23.55 51.91 207.65 103.83 23.56 51.91 207.65 103.83 23.57 51.91 207.65 103.83 23.58 51.91 207.65 103.83 23.59 51.91 207.65 103.83 23.60 51.91 207.65 103.83 23.61 51.91 207.65 103.83 23.62 51.91 207.65 103.83 23.63 51.91 207.65 103.83 23.64 51.91 207.65 103.83 23.65 51.91 207.65 103.83 23.66 51.91 207.65 103.83 23.67 51.91 207.65 103.83 23.68 51.91 207.65 103.83 23.69 51.91 207.65 103.83 23.70 51.91 207.65 103.83 23.71 51.91 207.65 103.83 23.72 51.91 207.65 103.83 23.73 51.91 207.65 103.83 23.74 51.91 207.65 103.83 23.75 51.91 207.65 103.83 23.76 51.91 207.65 103.83 23.77 51.91 207.65 103.83 23.78 51.91 207.65 103.83 23.79 51.91 207.65 103.83 23.80 51.91 207.65 103.83 23.81 51.91 207.65 103.83 23.82 51.91 207.65 103.83 23.83 51.91 207.65 103.83 23.84 51.91 207.65 103.83 23.85 51.91 207.65 103.83 23.86 51.91 207.65 103.83 23.87 51.91 207.65 103.83 23.88 51.91 207.65 103.83 23.89 51.91 207.65 103.83 23.90 51.91 207.65 103.83 23.91 51.91 207.65 103.83 23.92 51.91 207.65 103.83 23.93 51.91 207.65 103.83 23.94 51.91 207.65 103.83 23.95 51.91 207.65 103.83 23.96 51.91 207.65 103.83 23.97 51.91 207.65 103.83 23.98 51.91 207.65 103.83 23.99 51.91 207.65 103.83 24.00 51.91 207.65 103.83 24.01 51.91 207.65 103.83 24.02 51.91 207.65 103.83 24.03 51.91 207.65 103.83 24.04 51.91 207.65 103.83 24.05 51.91 207.65 103.83 24.06 51.91 207.65 103.83 24.07 51.91 207.65 103.83 24.08 51.91 207.65 103.83 24.09 51.91 207.65 103.83 24.10 51.91 207.65 103.83 24.11 51.91 207.65 103.83 24.12 51.91 207.65 103.83 277.18 24.13 51.91 207.65 103.83 277.18 24.14 51.91 207.65 103.83 277.18 24.15 51.91 207.65 103.83 277.18 24.16 51.91 207.65 103.83 277.18 24.17 51.91 207.65 103.83 277.18 24.18 51.91 103.83 277.18 24.19 51.91 103.83 277.18 24.20 51.91 103.83 277.18 24.21 51.91 103.83 277.18 24.22 51.91 103.83 277.18 24.23 51.91 103.83 277.18 24.24 51.91 103.83 277.18 24.25 51.91 103.83 277.18 24.26 51.91 103.83 277.18 24.27 51.91 103.83 277.18 24.28 51.91 103.83 277.18 24.29 51.91 103.83 277.18 24.30 51.91 103.83 277.18 24.31 51.91 103.83 277.18 24.32 51.91 103.83 277.18 24.33 51.91 103.83 277.18 24.34 51.91 103.83 277.18 24.35 51.91 103.83 277.18 24.36 51.91 103.83 277.18 24.37 51.91 103.83 277.18 24.38 51.91 103.83 277.18 24.39 51.91 103.83 277.18 24.40 51.91 103.83 277.18 24.41 51.91 103.83 277.18 24.42 51.91 103.83 277.18 24.43 51.91 103.83 277.18 24.44 51.91 103.83 277.18 24.45 51.91 103.83 277.18 24.46 51.91 103.83 277.18 24.47 51.91 103.83 277.18 24.48 51.91 103.83 277.18 24.49 51.91 103.83 277.18 24.50 51.91 103.83 277.18 24.51 51.91 103.83 277.18 24.52 51.91 103.83 277.18 24.53 51.91 103.83 277.18 24.54 51.91 103.83 277.18 24.55 51.91 103.83 277.18 24.56 51.91 103.83 277.18 24.57 51.91 103.83 277.18 24.58 51.91 103.83 277.18 24.59 51.91 103.83 277.18 311.13 24.60 51.91 103.83 277.18 311.13 24.61 51.91 103.83 311.13 24.62 51.91 103.83 311.13 24.63 51.91 103.83 311.13 24.64 51.91 103.83 311.13 24.65 51.91 103.83 311.13 24.66 51.91 103.83 311.13 24.67 51.91 103.83 311.13 24.68 51.91 103.83 311.13 24.69 51.91 103.83 311.13 24.70 51.91 103.83 311.13 24.71 51.91 103.83 311.13 24.72 51.91 103.83 311.13 24.73 51.91 103.83 311.13 24.74 51.91 103.83 311.13 24.75 51.91 103.83 311.13 24.76 51.91 103.83 311.13 24.77 51.91 103.83 311.13 24.78 51.91 103.83 311.13 24.79 51.91 103.83 311.13 24.80 51.91 103.83 311.13 24.81 51.91 103.83 311.13 24.82 51.91 103.83 311.13 24.83 51.91 103.83 311.13 24.84 51.91 103.83 311.13 24.85 51.91 103.83 311.13 24.86 51.91 103.83 311.13 24.87 51.91 103.83 311.13 24.88 51.91 103.83 311.13 24.89 51.91 103.83 311.13 24.90 51.91 103.83 311.13 24.91 51.91 103.83 311.13 24.92 51.91 103.83 311.13 24.93 51.91 103.83 311.13 24.94 51.91 103.83 311.13 24.95 51.91 103.83 311.13 24.96 51.91 103.83 311.13 24.97 51.91 103.83 311.13 24.98 51.91 103.83 311.13 24.99 51.91 103.83 311.13 25.00 51.91 103.83 311.13 25.01 51.91 103.83 311.13 25.02 51.91 103.83 311.13 25.03 51.91 103.83 311.13 25.04 51.91 103.83 311.13 25.05 51.91 103.83 311.13 25.06 51.91 103.83 311.13 25.07 51.91 103.83 311.13 25.08 51.91 103.83 311.13 25.09 51.91 103.83 311.13 185.00 25.10 51.91 103.83 185.00 25.11 51.91 103.83 185.00 25.12 51.91 103.83 185.00 25.13 51.91 103.83 185.00 25.14 51.91 103.83 185.00 25.15 51.91 103.83 185.00 25.16 51.91 103.83 185.00 25.17 51.91 103.83 185.00 25.18 51.91 103.83 185.00 25.19 51.91 103.83 185.00 25.20 51.91 103.83 185.00 25.21 51.91 103.83 185.00 25.22 51.91 103.83 185.00 25.23 51.91 103.83 185.00 25.24 51.91 103.83 185.00 25.25 51.91 103.83 185.00 25.26 51.91 103.83 185.00 25.27 51.91 103.83 185.00 25.28 51.91 103.83 185.00 25.29 51.91 103.83 185.00 25.30 51.91 103.83 185.00 25.31 51.91 103.83 185.00 25.32 51.91 103.83 185.00 25.33 51.91 103.83 185.00 25.34 51.91 103.83 185.00 25.35 51.91 103.83 185.00 25.36 51.91 103.83 185.00 25.37 51.91 103.83 185.00 25.38 51.91 103.83 185.00 25.39 51.91 103.83 185.00 25.40 51.91 103.83 185.00 25.41 51.91 103.83 185.00 25.42 51.91 103.83 185.00 25.43 51.91 103.83 185.00 25.44 51.91 103.83 185.00 25.45 51.91 103.83 185.00 25.46 51.91 103.83 185.00 25.47 51.91 103.83 185.00 25.48 51.91 103.83 185.00 25.49 51.91 103.83 185.00 25.50 51.91 103.83 185.00 25.51 51.91 103.83 185.00 25.52 51.91 103.83 185.00 25.53 51.91 103.83 185.00 25.54 51.91 103.83 185.00 25.55 51.91 103.83 185.00 261.63 25.56 51.91 103.83 185.00 261.63 25.57 51.91 103.83 185.00 261.63 25.58 51.91 103.83 185.00 261.63 25.59 51.91 103.83 185.00 261.63 25.60 51.91 103.83 185.00 261.63 25.61 51.91 103.83 261.63 25.62 51.91 103.83 261.63 25.63 51.91 103.83 261.63 25.64 51.91 103.83 261.63 25.65 51.91 103.83 261.63 25.66 51.91 103.83 261.63 25.67 51.91 103.83 261.63 25.68 51.91 103.83 261.63 25.69 51.91 103.83 261.63 25.70 51.91 103.83 261.63 25.71 51.91 103.83 261.63 25.72 51.91 103.83 261.63 25.73 51.91 103.83 261.63 25.74 51.91 103.83 261.63 25.75 51.91 103.83 261.63 25.76 51.91 103.83 261.63 25.77 51.91 103.83 261.63 25.78 51.91 103.83 261.63 25.79 51.91 103.83 261.63 25.80 51.91 103.83 261.63 25.81 51.91 103.83 261.63 25.82 51.91 103.83 261.63 25.83 51.91 103.83 261.63 25.84 51.91 103.83 261.63 25.85 51.91 103.83 261.63 25.86 51.91 103.83 261.63 25.87 51.91 103.83 261.63 25.88 51.91 103.83 261.63 25.89 51.91 103.83 261.63 25.90 51.91 103.83 261.63 25.91 51.91 103.83 261.63 25.92 103.83 261.63 25.93 103.83 261.63 25.94 103.83 261.63 25.95 103.83 261.63 25.96 103.83 261.63 25.97 103.83 261.63 25.98 103.83 261.63 25.99 103.83 261.63 26.00 103.83 261.63 26.01 261.63 26.02 261.63 26.03 261.63 26.04 261.63 26.05 261.63 26.06 261.63 311.13 26.07 261.63 311.13 26.08 261.63 311.13 26.09 311.13 26.10 311.13 26.11 311.13 26.12 311.13 26.13 311.13 26.14 311.13 26.15 311.13 26.16 311.13 26.17 311.13 26.18 311.13 26.19 311.13 26.20 311.13 26.21 311.13 26.22 311.13 26.23 311.13 26.24 311.13 26.25 311.13 26.26 311.13 26.27 311.13 26.28 311.13 26.29 311.13 26.30 311.13 26.31 311.13 26.32 311.13 26.33 311.13 26.34 311.13 26.35 311.13 26.36 311.13 26.37 311.13 26.38 311.13 26.39 311.13 26.40 311.13 26.41 311.13 26.42 311.13 26.43 311.13 26.44 311.13 26.45 311.13 26.46 311.13 26.47 311.13 26.48 311.13 26.49 311.13 26.50 311.13 26.51 311.13 26.52 311.13 26.53 311.13 26.54 311.13 26.55 311.13 26.56 311.13 26.57 311.13 26.58 311.13 26.59 311.13 26.60 311.13 26.61 311.13 26.62 311.13 26.63 311.13 26.64 311.13 26.65 311.13 26.66 311.13 26.67 311.13 26.68 311.13 26.69 311.13 26.70 311.13 26.71 311.13 26.72 311.13 26.73 26.74 69.30 26.75 69.30 138.59 164.81 26.76 69.30 138.59 164.81 26.77 69.30 138.59 164.81 103.83 26.78 69.30 138.59 164.81 103.83 26.79 69.30 138.59 164.81 103.83 26.80 69.30 138.59 164.81 103.83 26.81 69.30 138.59 164.81 103.83 26.82 69.30 138.59 164.81 103.83 26.83 69.30 138.59 164.81 103.83 26.84 69.30 138.59 164.81 103.83 26.85 69.30 138.59 164.81 103.83 26.86 69.30 138.59 164.81 103.83 26.87 69.30 138.59 164.81 103.83 26.88 69.30 138.59 164.81 103.83 26.89 69.30 138.59 164.81 103.83 26.90 69.30 138.59 164.81 103.83 26.91 69.30 138.59 164.81 103.83 26.92 69.30 138.59 164.81 103.83 26.93 69.30 138.59 164.81 103.83 26.94 69.30 138.59 164.81 103.83 26.95 69.30 138.59 164.81 103.83 26.96 69.30 138.59 164.81 103.83 26.97 69.30 138.59 164.81 103.83 26.98 69.30 138.59 164.81 103.83 26.99 69.30 138.59 164.81 103.83 27.00 69.30 138.59 164.81 103.83 27.01 69.30 138.59 164.81 103.83 27.02 69.30 138.59 164.81 103.83 27.03 69.30 138.59 164.81 103.83 27.04 69.30 138.59 164.81 103.83 27.05 69.30 138.59 164.81 103.83 27.06 69.30 138.59 164.81 103.83 27.07 69.30 138.59 164.81 103.83 27.08 69.30 138.59 164.81 103.83 27.09 69.30 138.59 164.81 103.83 27.10 69.30 138.59 164.81 103.83 27.11 69.30 138.59 164.81 103.83 27.12 69.30 138.59 164.81 103.83 27.13 69.30 138.59 164.81 103.83 27.14 69.30 138.59 164.81 103.83 27.15 69.30 138.59 164.81 103.83 27.16 69.30 138.59 164.81 103.83 27.17 69.30 138.59 164.81 103.83 27.18 69.30 138.59 164.81 103.83 27.19 69.30 138.59 164.81 103.83 27.20 69.30 138.59 164.81 103.83 27.21 69.30 138.59 164.81 103.83 27.22 69.30 138.59 164.81 103.83 27.23 69.30 138.59 164.81 103.83 27.24 69.30 138.59 164.81 103.83 27.25 69.30 138.59 164.81 103.83 27.26 69.30 138.59 164.81 103.83 27.27 69.30 138.59 164.81 103.83 27.28 69.30 138.59 164.81 103.83 27.29 69.30 138.59 164.81 103.83 27.30 69.30 138.59 164.81 103.83 27.31 69.30 138.59 164.81 103.83 27.32 69.30 138.59 164.81 103.83 27.33 69.30 138.59 164.81 103.83 27.34 69.30 138.59 164.81 103.83 27.35 69.30 138.59 164.81 103.83 27.36 69.30 138.59 164.81 103.83 27.37 69.30 138.59 164.81 103.83 27.38 69.30 138.59 164.81 103.83 27.39 69.30 138.59 164.81 103.83 27.40 69.30 138.59 164.81 103.83 27.41 69.30 138.59 164.81 103.83 27.42 69.30 138.59 164.81 103.83 27.43 69.30 138.59 164.81 103.83 27.44 69.30 138.59 164.81 103.83 27.45 69.30 138.59 164.81 103.83 27.46 69.30 138.59 164.81 103.83 27.47 69.30 138.59 164.81 103.83 27.48 69.30 138.59 164.81 103.83 27.49 69.30 138.59 164.81 103.83 207.65 27.50 69.30 138.59 164.81 103.83 207.65 27.51 69.30 138.59 164.81 103.83 207.65 27.52 69.30 138.59 164.81 103.83 207.65 27.53 69.30 138.59 164.81 103.83 207.65 27.54 69.30 138.59 164.81 103.83 207.65 27.55 69.30 138.59 164.81 103.83 207.65 27.56 69.30 138.59 164.81 103.83 207.65 27.57 69.30 138.59 164.81 103.83 207.65 27.58 69.30 138.59 103.83 207.65 27.59 69.30 138.59 103.83 207.65 27.60 69.30 138.59 103.83 207.65 27.61 69.30 138.59 103.83 207.65 27.62 69.30 138.59 103.83 207.65 27.63 69.30 138.59 103.83 207.65 27.64 69.30 138.59 103.83 207.65 27.65 69.30 138.59 103.83 207.65 27.66 69.30 138.59 103.83 207.65 27.67 69.30 138.59 103.83 207.65 27.68 69.30 138.59 103.83 207.65 27.69 69.30 138.59 103.83 207.65 27.70 69.30 138.59 103.83 207.65 27.71 69.30 138.59 103.83 207.65 27.72 69.30 138.59 103.83 207.65 27.73 69.30 138.59 103.83 207.65 27.74 69.30 138.59 103.83 207.65 27.75 69.30 138.59 103.83 207.65 27.76 69.30 138.59 103.83 207.65 27.77 69.30 138.59 103.83 207.65 27.78 69.30 138.59 103.83 207.65 27.79 69.30 138.59 103.83 207.65 27.80 69.30 138.59 103.83 207.65 27.81 69.30 138.59 103.83 207.65 27.82 69.30 138.59 103.83 207.65 27.83 69.30 138.59 103.83 207.65 27.84 69.30 138.59 103.83 207.65 27.85 69.30 138.59 103.83 207.65 27.86 69.30 138.59 103.83 207.65 27.87 69.30 138.59 103.83 207.65 27.88 69.30 138.59 103.83 207.65 27.89 69.30 138.59 103.83 207.65 27.90 69.30 138.59 103.83 207.65 27.91 69.30 138.59 103.83 207.65 27.92 69.30 138.59 103.83 207.65 27.93 69.30 138.59 103.83 207.65 27.94 69.30 138.59 103.83 207.65 27.95 69.30 138.59 103.83 207.65 27.96 69.30 138.59 103.83 207.65 27.97 69.30 138.59 103.83 207.65 27.98 69.30 138.59 103.83 207.65 27.99 69.30 138.59 103.83 207.65 28.00 69.30 138.59 103.83 207.65 28.01 69.30 138.59 103.83 207.65 277.18 28.02 69.30 138.59 103.83 207.65 277.18 28.03 69.30 138.59 103.83 207.65 277.18 28.04 69.30 138.59 103.83 207.65 277.18 28.05 69.30 138.59 103.83 277.18 28.06 69.30 138.59 103.83 277.18 28.07 69.30 138.59 103.83 277.18 28.08 69.30 138.59 103.83 277.18 28.09 69.30 138.59 103.83 277.18 28.10 69.30 138.59 103.83 277.18 28.11 69.30 138.59 103.83 277.18 28.12 69.30 138.59 103.83 277.18 28.13 69.30 138.59 103.83 277.18 28.14 69.30 138.59 103.83 277.18 28.15 69.30 138.59 103.83 277.18 28.16 69.30 138.59 103.83 277.18 28.17 69.30 138.59 103.83 277.18 28.18 69.30 138.59 103.83 277.18 28.19 69.30 138.59 103.83 277.18 28.20 69.30 138.59 103.83 277.18 28.21 69.30 138.59 103.83 277.18 28.22 69.30 138.59 103.83 277.18 28.23 69.30 138.59 103.83 277.18 28.24 69.30 138.59 103.83 277.18 28.25 69.30 138.59 103.83 277.18 28.26 69.30 138.59 103.83 277.18 28.27 69.30 138.59 103.83 277.18 28.28 69.30 138.59 103.83 277.18 28.29 69.30 138.59 103.83 277.18 28.30 69.30 138.59 103.83 277.18 28.31 69.30 138.59 103.83 277.18 28.32 69.30 138.59 103.83 277.18 28.33 69.30 138.59 103.83 277.18 28.34 69.30 138.59 103.83 277.18 28.35 69.30 138.59 103.83 277.18 28.36 69.30 138.59 103.83 277.18 28.37 69.30 138.59 103.83 277.18 28.38 69.30 138.59 103.83 277.18 28.39 69.30 138.59 103.83 277.18 28.40 69.30 138.59 103.83 277.18 28.41 69.30 138.59 103.83 277.18 28.42 69.30 138.59 103.83 277.18 28.43 69.30 138.59 103.83 277.18 28.44 69.30 138.59 103.83 277.18 28.45 69.30 138.59 103.83 277.18 28.46 69.30 138.59 103.83 277.18 28.47 69.30 138.59 103.83 277.18 28.48 69.30 138.59 103.83 277.18 28.49 69.30 138.59 103.83 277.18 28.50 69.30 138.59 103.83 277.18 28.51 69.30 138.59 103.83 277.18 207.65 28.52 69.30 138.59 103.83 277.18 207.65 28.53 69.30 138.59 103.83 207.65 28.54 69.30 138.59 103.83 207.65 28.55 69.30 138.59 103.83 207.65 28.56 69.30 138.59 103.83 207.65 28.57 69.30 138.59 103.83 207.65 28.58 69.30 138.59 103.83 207.65 28.59 69.30 138.59 103.83 207.65 28.60 69.30 138.59 103.83 207.65 28.61 69.30 138.59 103.83 207.65 28.62 69.30 138.59 103.83 207.65 28.63 69.30 138.59 103.83 207.65 28.64 69.30 138.59 103.83 207.65 28.65 69.30 138.59 103.83 207.65 28.66 69.30 138.59 103.83 207.65 28.67 69.30 138.59 103.83 207.65 28.68 69.30 138.59 103.83 207.65 28.69 69.30 138.59 103.83 207.65 28.70 69.30 138.59 103.83 207.65 28.71 69.30 138.59 103.83 207.65 28.72 69.30 138.59 103.83 207.65 28.73 69.30 138.59 103.83 207.65 28.74 69.30 138.59 103.83 207.65 28.75 69.30 138.59 103.83 207.65 28.76 69.30 138.59 103.83 207.65 28.77 69.30 138.59 103.83 207.65 28.78 69.30 138.59 103.83 207.65 28.79 69.30 138.59 103.83 207.65 28.80 69.30 138.59 103.83 207.65 28.81 69.30 138.59 103.83 207.65 28.82 69.30 138.59 103.83 207.65 28.83 69.30 138.59 103.83 207.65 28.84 69.30 138.59 103.83 207.65 28.85 69.30 138.59 103.83 207.65 28.86 69.30 138.59 103.83 207.65 28.87 69.30 138.59 103.83 207.65 28.88 69.30 138.59 103.83 207.65 28.89 69.30 138.59 103.83 207.65 28.90 69.30 138.59 103.83 207.65 28.91 69.30 138.59 103.83 207.65 28.92 69.30 138.59 103.83 207.65 28.93 69.30 138.59 103.83 207.65 28.94 69.30 138.59 103.83 207.65 28.95 69.30 138.59 103.83 207.65 28.96 69.30 138.59 103.83 207.65 28.97 69.30 138.59 103.83 207.65 28.98 69.30 138.59 103.83 207.65 28.99 69.30 138.59 103.83 207.65 29.00 69.30 138.59 103.83 207.65 277.18 29.01 69.30 138.59 103.83 207.65 277.18 29.02 69.30 138.59 103.83 207.65 277.18 29.03 69.30 138.59 103.83 207.65 277.18 29.04 69.30 138.59 103.83 207.65 277.18 29.05 69.30 138.59 103.83 207.65 277.18 29.06 69.30 138.59 103.83 277.18 29.07 69.30 138.59 103.83 277.18 29.08 69.30 138.59 103.83 277.18 29.09 69.30 138.59 103.83 277.18 29.10 69.30 138.59 103.83 277.18 29.11 69.30 138.59 103.83 277.18 29.12 69.30 138.59 103.83 277.18 29.13 69.30 138.59 103.83 277.18 29.14 69.30 138.59 103.83 277.18 29.15 69.30 138.59 103.83 277.18 29.16 69.30 138.59 103.83 277.18 29.17 69.30 138.59 103.83 277.18 29.18 69.30 138.59 103.83 277.18 29.19 69.30 138.59 103.83 277.18 29.20 69.30 138.59 103.83 277.18 29.21 69.30 138.59 103.83 277.18 29.22 69.30 138.59 103.83 277.18 29.23 69.30 138.59 103.83 277.18 29.24 69.30 138.59 103.83 277.18 29.25 69.30 138.59 103.83 277.18 29.26 69.30 138.59 103.83 277.18 29.27 69.30 138.59 103.83 277.18 29.28 69.30 138.59 103.83 277.18 29.29 69.30 138.59 103.83 277.18 29.30 69.30 138.59 103.83 277.18 29.31 69.30 138.59 103.83 277.18 29.32 69.30 138.59 103.83 277.18 29.33 69.30 138.59 103.83 277.18 29.34 69.30 138.59 103.83 277.18 29.35 69.30 138.59 103.83 277.18 29.36 69.30 138.59 103.83 277.18 29.37 69.30 138.59 103.83 277.18 29.38 69.30 138.59 103.83 277.18 29.39 69.30 138.59 103.83 277.18 29.40 69.30 138.59 103.83 277.18 29.41 69.30 138.59 103.83 277.18 29.42 69.30 138.59 103.83 277.18 29.43 69.30 138.59 103.83 277.18 29.44 69.30 138.59 103.83 277.18 29.45 69.30 138.59 103.83 277.18 29.46 69.30 138.59 103.83 277.18 29.47 69.30 138.59 103.83 277.18 29.48 69.30 138.59 103.83 277.18 29.49 69.30 138.59 103.83 277.18 329.63 29.50 69.30 138.59 103.83 277.18 329.63 29.51 69.30 138.59 103.83 277.18 329.63 29.52 69.30 138.59 103.83 329.63 29.53 69.30 138.59 103.83 329.63 29.54 69.30 138.59 103.83 329.63 29.55 69.30 138.59 103.83 329.63 29.56 69.30 138.59 103.83 329.63 29.57 69.30 138.59 103.83 329.63 29.58 69.30 138.59 103.83 329.63 29.59 69.30 138.59 103.83 329.63 29.60 69.30 138.59 103.83 329.63 29.61 69.30 138.59 103.83 329.63 29.62 69.30 138.59 103.83 329.63 29.63 69.30 138.59 103.83 329.63 29.64 69.30 138.59 103.83 329.63 29.65 69.30 138.59 103.83 329.63 29.66 69.30 138.59 103.83 329.63 29.67 69.30 138.59 103.83 329.63 29.68 69.30 138.59 103.83 329.63 29.69 69.30 138.59 103.83 329.63 29.70 69.30 138.59 103.83 329.63 29.71 69.30 138.59 103.83 329.63 29.72 69.30 138.59 103.83 329.63 29.73 69.30 138.59 103.83 329.63 29.74 69.30 138.59 103.83 329.63 29.75 69.30 138.59 103.83 329.63 29.76 69.30 138.59 103.83 329.63 29.77 69.30 138.59 103.83 329.63 29.78 69.30 138.59 103.83 329.63 29.79 69.30 138.59 103.83 329.63 29.80 69.30 138.59 103.83 329.63 29.81 69.30 138.59 103.83 329.63 29.82 69.30 138.59 103.83 329.63 29.83 69.30 138.59 103.83 329.63 29.84 69.30 138.59 103.83 329.63 29.85 69.30 138.59 103.83 329.63 29.86 69.30 138.59 103.83 329.63 29.87 69.30 138.59 103.83 329.63 29.88 69.30 138.59 103.83 329.63 29.89 69.30 138.59 103.83 329.63 29.90 69.30 138.59 103.83 329.63 29.91 69.30 138.59 103.83 329.63 29.92 69.30 138.59 103.83 329.63 29.93 69.30 138.59 103.83 329.63 29.94 69.30 138.59 103.83 329.63 29.95 69.30 138.59 103.83 329.63 29.96 69.30 138.59 103.83 329.63 29.97 69.30 138.59 103.83 329.63 29.98 69.30 138.59 103.83 329.63 29.99 69.30 138.59 103.83 329.63 207.65 30.00 69.30 138.59 103.83 329.63 207.65 30.01 69.30 138.59 103.83 329.63 207.65 30.02 69.30 138.59 103.83 207.65 30.03 69.30 138.59 103.83 207.65 30.04 69.30 138.59 103.83 207.65 30.05 69.30 138.59 103.83 207.65 30.06 69.30 138.59 103.83 207.65 30.07 69.30 138.59 103.83 207.65 30.08 69.30 138.59 103.83 207.65 30.09 69.30 138.59 103.83 207.65 30.10 69.30 138.59 103.83 207.65 30.11 69.30 138.59 103.83 207.65 30.12 69.30 138.59 103.83 207.65 30.13 69.30 138.59 103.83 207.65 30.14 69.30 138.59 103.83 207.65 30.15 69.30 138.59 103.83 207.65 30.16 69.30 138.59 103.83 207.65 30.17 69.30 138.59 103.83 207.65 30.18 69.30 138.59 103.83 207.65 30.19 69.30 138.59 103.83 207.65 30.20 69.30 138.59 103.83 207.65 30.21 69.30 138.59 103.83 207.65 30.22 69.30 138.59 103.83 207.65 30.23 69.30 138.59 103.83 207.65 30.24 69.30 138.59 103.83 207.65 30.25 69.30 138.59 103.83 207.65 30.26 69.30 138.59 103.83 207.65 30.27 69.30 138.59 103.83 207.65 30.28 69.30 138.59 103.83 207.65 30.29 69.30 138.59 103.83 207.65 30.30 69.30 138.59 103.83 207.65 30.31 69.30 138.59 103.83 207.65 30.32 69.30 138.59 103.83 207.65 30.33 69.30 138.59 103.83 207.65 30.34 69.30 138.59 103.83 207.65 30.35 69.30 138.59 103.83 207.65 30.36 69.30 138.59 103.83 207.65 30.37 69.30 138.59 103.83 207.65 30.38 69.30 138.59 103.83 207.65 30.39 69.30 138.59 103.83 207.65 30.40 69.30 138.59 103.83 207.65 30.41 69.30 138.59 103.83 207.65 30.42 69.30 138.59 103.83 207.65 30.43 69.30 138.59 103.83 207.65 30.44 69.30 138.59 103.83 207.65 30.45 69.30 138.59 103.83 207.65 30.46 69.30 138.59 103.83 207.65 30.47 69.30 138.59 103.83 207.65 30.48 69.30 138.59 103.83 207.65 277.18 30.49 69.30 138.59 103.83 207.65 277.18 30.50 69.30 138.59 103.83 207.65 277.18 30.51 69.30 138.59 103.83 207.65 277.18 30.52 69.30 138.59 103.83 207.65 277.18 30.53 69.30 138.59 103.83 207.65 277.18 30.54 69.30 138.59 103.83 207.65 277.18 30.55 69.30 138.59 103.83 207.65 277.18 30.56 69.30 138.59 103.83 277.18 30.57 69.30 138.59 103.83 277.18 30.58 69.30 138.59 103.83 277.18 30.59 69.30 138.59 103.83 277.18 30.60 69.30 138.59 103.83 277.18 30.61 69.30 138.59 103.83 277.18 30.62 69.30 138.59 103.83 277.18 30.63 69.30 138.59 103.83 277.18 30.64 69.30 138.59 103.83 277.18 30.65 69.30 138.59 103.83 277.18 30.66 69.30 138.59 103.83 277.18 30.67 69.30 138.59 103.83 277.18 30.68 69.30 138.59 103.83 277.18 30.69 69.30 138.59 103.83 277.18 30.70 69.30 138.59 103.83 277.18 30.71 69.30 138.59 103.83 277.18 30.72 69.30 138.59 103.83 277.18 30.73 69.30 138.59 103.83 277.18 30.74 69.30 138.59 103.83 277.18 30.75 69.30 138.59 103.83 277.18 30.76 69.30 138.59 103.83 277.18 30.77 69.30 138.59 103.83 277.18 30.78 69.30 138.59 103.83 277.18 30.79 69.30 138.59 103.83 277.18 30.80 69.30 138.59 103.83 277.18 30.81 69.30 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329.63 31.41 69.30 138.59 103.83 329.63 31.42 69.30 138.59 103.83 329.63 31.43 69.30 138.59 103.83 329.63 31.44 69.30 138.59 103.83 329.63 31.45 69.30 138.59 103.83 329.63 31.46 69.30 138.59 103.83 329.63 31.47 69.30 138.59 103.83 329.63 31.48 69.30 138.59 103.83 329.63 31.49 69.30 138.59 103.83 329.63 207.65 415.30 31.50 69.30 138.59 103.83 329.63 207.65 415.30 31.51 69.30 138.59 103.83 329.63 207.65 415.30 31.52 69.30 138.59 103.83 207.65 415.30 31.53 69.30 138.59 103.83 207.65 415.30 31.54 69.30 138.59 103.83 207.65 415.30 31.55 69.30 138.59 103.83 207.65 415.30 31.56 69.30 138.59 103.83 207.65 415.30 31.57 69.30 138.59 103.83 207.65 415.30 31.58 69.30 138.59 103.83 207.65 415.30 31.59 69.30 138.59 103.83 207.65 415.30 31.60 69.30 138.59 103.83 207.65 415.30 31.61 69.30 138.59 103.83 207.65 415.30 31.62 69.30 138.59 103.83 207.65 415.30 31.63 69.30 138.59 103.83 207.65 415.30 31.64 69.30 138.59 103.83 207.65 415.30 31.65 69.30 138.59 103.83 207.65 415.30 31.66 69.30 138.59 103.83 207.65 415.30 31.67 69.30 138.59 103.83 207.65 415.30 31.68 69.30 138.59 103.83 207.65 415.30 31.69 69.30 138.59 103.83 207.65 415.30 31.70 69.30 138.59 103.83 207.65 415.30 31.71 69.30 138.59 103.83 207.65 415.30 31.72 69.30 138.59 103.83 207.65 415.30 31.73 69.30 138.59 103.83 207.65 415.30 31.74 69.30 138.59 103.83 207.65 415.30 31.75 69.30 138.59 103.83 207.65 415.30 31.76 69.30 138.59 103.83 207.65 415.30 31.77 69.30 138.59 103.83 207.65 415.30 31.78 69.30 138.59 103.83 207.65 415.30 31.79 69.30 138.59 103.83 207.65 415.30 31.80 69.30 138.59 103.83 207.65 415.30 31.81 69.30 138.59 103.83 207.65 415.30 31.82 69.30 138.59 103.83 207.65 415.30 31.83 69.30 138.59 103.83 207.65 415.30 31.84 69.30 138.59 103.83 207.65 415.30 31.85 69.30 138.59 103.83 207.65 415.30 31.86 69.30 138.59 103.83 207.65 415.30 31.87 69.30 138.59 103.83 207.65 415.30 31.88 69.30 138.59 103.83 415.30 31.89 69.30 138.59 103.83 415.30 31.90 69.30 138.59 103.83 415.30 31.91 69.30 138.59 103.83 415.30 31.92 69.30 138.59 103.83 415.30 31.93 69.30 138.59 103.83 415.30 31.94 69.30 138.59 103.83 415.30 277.18 31.95 69.30 138.59 103.83 415.30 277.18 31.96 69.30 138.59 103.83 415.30 277.18 31.97 69.30 138.59 103.83 415.30 277.18 31.98 69.30 138.59 103.83 415.30 277.18 31.99 69.30 138.59 103.83 415.30 277.18 32.00 69.30 138.59 103.83 415.30 277.18 32.01 69.30 138.59 103.83 415.30 277.18 32.02 69.30 138.59 103.83 415.30 277.18 32.03 69.30 138.59 103.83 415.30 277.18 32.04 69.30 138.59 103.83 415.30 277.18 32.05 69.30 138.59 103.83 415.30 277.18 32.06 69.30 138.59 103.83 415.30 277.18 32.07 69.30 138.59 103.83 415.30 277.18 32.08 69.30 138.59 103.83 415.30 277.18 32.09 69.30 138.59 103.83 415.30 277.18 32.10 69.30 138.59 103.83 415.30 277.18 32.11 69.30 138.59 103.83 415.30 277.18 32.12 69.30 138.59 103.83 415.30 277.18 32.13 69.30 138.59 103.83 415.30 277.18 32.14 69.30 138.59 103.83 415.30 277.18 32.15 69.30 138.59 103.83 415.30 277.18 32.16 69.30 138.59 103.83 415.30 277.18 32.17 69.30 138.59 103.83 415.30 277.18 32.18 69.30 138.59 103.83 415.30 277.18 32.19 69.30 138.59 103.83 415.30 277.18 32.20 69.30 138.59 103.83 415.30 277.18 32.21 69.30 138.59 103.83 415.30 277.18 32.22 69.30 138.59 103.83 415.30 277.18 32.23 69.30 138.59 103.83 415.30 277.18 32.24 69.30 138.59 103.83 415.30 277.18 32.25 69.30 138.59 103.83 415.30 32.26 69.30 138.59 103.83 415.30 32.27 69.30 138.59 103.83 415.30 32.28 69.30 138.59 103.83 415.30 32.29 69.30 138.59 103.83 415.30 32.30 69.30 138.59 103.83 415.30 32.31 69.30 138.59 103.83 415.30 32.32 69.30 138.59 103.83 415.30 329.63 32.33 69.30 138.59 103.83 415.30 329.63 32.34 69.30 138.59 103.83 415.30 329.63 32.35 69.30 138.59 103.83 415.30 329.63 32.36 69.30 138.59 103.83 415.30 329.63 32.37 69.30 138.59 103.83 415.30 329.63 32.38 69.30 138.59 103.83 415.30 329.63 32.39 69.30 138.59 103.83 415.30 329.63 32.40 69.30 138.59 103.83 415.30 329.63 32.41 69.30 138.59 103.83 415.30 329.63 32.42 69.30 138.59 103.83 415.30 329.63 32.43 69.30 138.59 103.83 415.30 329.63 32.44 69.30 138.59 103.83 415.30 329.63 32.45 69.30 138.59 103.83 415.30 329.63 32.46 69.30 138.59 103.83 415.30 329.63 32.47 69.30 138.59 103.83 415.30 329.63 32.48 69.30 138.59 103.83 415.30 329.63 32.49 69.30 138.59 103.83 329.63 32.50 69.30 138.59 103.83 329.63 32.51 69.30 138.59 103.83 329.63 32.52 69.30 138.59 103.83 329.63 32.53 69.30 138.59 103.83 329.63 32.54 69.30 138.59 103.83 329.63 32.55 69.30 138.59 103.83 329.63 32.56 69.30 138.59 103.83 329.63 32.57 69.30 138.59 103.83 329.63 32.58 69.30 138.59 103.83 329.63 32.59 69.30 138.59 103.83 329.63 32.60 69.30 138.59 103.83 329.63 32.61 69.30 138.59 103.83 329.63 32.62 69.30 138.59 103.83 329.63 32.63 69.30 138.59 32.64 69.30 138.59 32.65 69.30 138.59 32.66 69.30 138.59 32.67 69.30 138.59 32.68 69.30 138.59 32.69 69.30 32.70 69.30 32.71 69.30 32.72 32.73 32.74 32.75 415.30 32.76 415.30 32.77 415.30 32.78 415.30 32.79 415.30 32.80 415.30 32.81 415.30 32.82 415.30 32.83 415.30 32.84 415.30 32.85 415.30 32.86 415.30 32.87 415.30 32.88 415.30 32.89 415.30 32.90 415.30 32.91 415.30 32.92 415.30 32.93 415.30 32.94 415.30 32.95 415.30 32.96 415.30 32.97 415.30 32.98 415.30 32.99 415.30 33.00 415.30 33.01 415.30 33.02 415.30 33.03 415.30 33.04 415.30 33.05 33.06 33.07 33.08 33.09 33.10 65.41 103.83 33.11 65.41 103.83 130.81 33.12 65.41 103.83 130.81 33.13 65.41 103.83 130.81 33.14 65.41 103.83 130.81 415.30 207.65 33.15 65.41 103.83 130.81 415.30 207.65 33.16 65.41 103.83 130.81 415.30 207.65 33.17 65.41 103.83 130.81 415.30 207.65 33.18 65.41 103.83 130.81 415.30 207.65 33.19 65.41 103.83 130.81 415.30 207.65 33.20 65.41 103.83 130.81 415.30 207.65 33.21 65.41 103.83 130.81 415.30 207.65 33.22 65.41 103.83 130.81 415.30 207.65 33.23 65.41 103.83 130.81 415.30 207.65 33.24 65.41 103.83 130.81 415.30 207.65 33.25 65.41 103.83 130.81 415.30 207.65 33.26 65.41 103.83 130.81 415.30 207.65 33.27 65.41 103.83 130.81 415.30 207.65 33.28 65.41 103.83 130.81 415.30 207.65 33.29 65.41 103.83 130.81 415.30 207.65 33.30 65.41 103.83 130.81 415.30 207.65 33.31 65.41 103.83 130.81 415.30 207.65 33.32 65.41 103.83 130.81 415.30 207.65 33.33 65.41 103.83 130.81 415.30 207.65 33.34 65.41 103.83 130.81 415.30 207.65 33.35 65.41 103.83 130.81 415.30 207.65 33.36 65.41 103.83 130.81 415.30 207.65 33.37 65.41 103.83 130.81 415.30 207.65 33.38 65.41 103.83 130.81 415.30 207.65 33.39 65.41 103.83 130.81 415.30 207.65 33.40 65.41 103.83 130.81 415.30 207.65 33.41 65.41 103.83 130.81 415.30 207.65 33.42 65.41 103.83 130.81 415.30 207.65 33.43 65.41 103.83 130.81 415.30 207.65 33.44 65.41 103.83 130.81 415.30 207.65 33.45 65.41 103.83 130.81 415.30 207.65 33.46 65.41 103.83 130.81 415.30 207.65 33.47 65.41 103.83 130.81 415.30 207.65 33.48 65.41 103.83 130.81 415.30 207.65 33.49 65.41 103.83 130.81 415.30 207.65 33.50 65.41 103.83 130.81 415.30 207.65 33.51 65.41 103.83 130.81 415.30 207.65 33.52 65.41 103.83 130.81 415.30 207.65 33.53 65.41 103.83 130.81 415.30 207.65 33.54 65.41 103.83 130.81 415.30 207.65 33.55 65.41 103.83 130.81 415.30 207.65 33.56 65.41 103.83 130.81 415.30 207.65 33.57 65.41 103.83 130.81 415.30 207.65 33.58 65.41 103.83 130.81 415.30 207.65 33.59 65.41 103.83 130.81 415.30 207.65 33.60 65.41 103.83 130.81 415.30 207.65 33.61 65.41 103.83 130.81 415.30 207.65 33.62 65.41 103.83 130.81 415.30 207.65 33.63 65.41 103.83 130.81 415.30 207.65 33.64 65.41 103.83 130.81 415.30 207.65 33.65 65.41 103.83 130.81 415.30 207.65 33.66 65.41 103.83 130.81 415.30 207.65 33.67 65.41 103.83 130.81 415.30 207.65 33.68 65.41 103.83 130.81 415.30 207.65 33.69 65.41 103.83 130.81 415.30 207.65 33.70 65.41 103.83 130.81 415.30 207.65 33.71 65.41 103.83 130.81 415.30 207.65 33.72 65.41 103.83 130.81 415.30 207.65 33.73 65.41 103.83 130.81 415.30 207.65 33.74 65.41 103.83 130.81 415.30 207.65 33.75 65.41 103.83 130.81 415.30 207.65 33.76 65.41 103.83 130.81 415.30 207.65 33.77 65.41 103.83 130.81 415.30 207.65 311.13 33.78 65.41 103.83 130.81 415.30 207.65 311.13 33.79 65.41 103.83 130.81 415.30 207.65 311.13 33.80 65.41 103.83 130.81 415.30 207.65 311.13 33.81 65.41 103.83 130.81 415.30 207.65 311.13 33.82 65.41 103.83 130.81 415.30 207.65 311.13 33.83 65.41 103.83 130.81 415.30 207.65 311.13 33.84 65.41 103.83 130.81 415.30 207.65 311.13 33.85 65.41 103.83 130.81 415.30 207.65 311.13 33.86 65.41 103.83 130.81 415.30 207.65 311.13 33.87 65.41 103.83 130.81 415.30 207.65 311.13 33.88 65.41 103.83 130.81 415.30 311.13 33.89 65.41 103.83 130.81 415.30 311.13 33.90 65.41 103.83 130.81 415.30 311.13 33.91 65.41 103.83 130.81 415.30 311.13 33.92 65.41 103.83 130.81 415.30 311.13 33.93 65.41 103.83 130.81 415.30 311.13 33.94 65.41 103.83 130.81 415.30 311.13 33.95 65.41 103.83 130.81 415.30 311.13 33.96 65.41 103.83 130.81 415.30 311.13 33.97 65.41 103.83 130.81 415.30 311.13 33.98 65.41 103.83 130.81 415.30 311.13 33.99 65.41 103.83 130.81 415.30 311.13 34.00 65.41 103.83 130.81 415.30 311.13 34.01 65.41 103.83 130.81 415.30 311.13 34.02 65.41 103.83 130.81 415.30 311.13 34.03 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65.41 103.83 130.81 415.30 311.13 369.99 34.29 65.41 103.83 130.81 415.30 311.13 369.99 34.30 65.41 103.83 130.81 415.30 311.13 369.99 34.31 65.41 103.83 130.81 415.30 369.99 34.32 65.41 103.83 130.81 415.30 369.99 34.33 65.41 103.83 130.81 415.30 369.99 34.34 65.41 103.83 130.81 415.30 369.99 34.35 65.41 103.83 130.81 415.30 369.99 34.36 65.41 103.83 130.81 415.30 369.99 34.37 65.41 103.83 130.81 415.30 369.99 34.38 65.41 103.83 130.81 415.30 369.99 34.39 65.41 103.83 130.81 415.30 369.99 34.40 65.41 103.83 130.81 415.30 369.99 34.41 65.41 103.83 130.81 415.30 369.99 34.42 65.41 103.83 130.81 415.30 369.99 34.43 65.41 103.83 130.81 415.30 369.99 34.44 65.41 103.83 130.81 415.30 369.99 34.45 65.41 103.83 130.81 415.30 369.99 34.46 65.41 103.83 130.81 415.30 369.99 34.47 65.41 103.83 130.81 415.30 369.99 34.48 65.41 103.83 130.81 415.30 369.99 34.49 65.41 103.83 130.81 415.30 369.99 34.50 65.41 103.83 130.81 415.30 369.99 34.51 65.41 103.83 130.81 415.30 369.99 34.52 65.41 103.83 130.81 415.30 369.99 34.53 65.41 103.83 130.81 415.30 369.99 34.54 65.41 103.83 130.81 415.30 369.99 34.55 65.41 103.83 130.81 415.30 369.99 34.56 65.41 103.83 130.81 415.30 369.99 34.57 65.41 103.83 130.81 415.30 369.99 34.58 65.41 103.83 130.81 415.30 369.99 34.59 65.41 103.83 130.81 415.30 369.99 34.60 65.41 103.83 130.81 415.30 369.99 34.61 65.41 103.83 130.81 415.30 369.99 34.62 65.41 103.83 130.81 415.30 369.99 34.63 65.41 103.83 130.81 415.30 369.99 34.64 65.41 103.83 130.81 415.30 369.99 34.65 65.41 103.83 130.81 415.30 369.99 34.66 65.41 103.83 130.81 415.30 369.99 34.67 65.41 103.83 130.81 415.30 369.99 34.68 65.41 103.83 130.81 415.30 369.99 34.69 65.41 103.83 130.81 415.30 369.99 34.70 65.41 103.83 130.81 415.30 369.99 34.71 65.41 103.83 130.81 415.30 369.99 34.72 65.41 103.83 130.81 415.30 369.99 34.73 65.41 103.83 130.81 415.30 369.99 34.74 65.41 103.83 130.81 415.30 369.99 34.75 65.41 103.83 130.81 415.30 369.99 34.76 65.41 103.83 130.81 415.30 369.99 34.77 65.41 103.83 130.81 415.30 369.99 207.65 34.78 65.41 103.83 130.81 415.30 369.99 207.65 34.79 65.41 103.83 130.81 415.30 369.99 207.65 34.80 65.41 103.83 130.81 415.30 369.99 207.65 34.81 65.41 103.83 130.81 415.30 369.99 207.65 34.82 65.41 103.83 130.81 415.30 207.65 34.83 65.41 103.83 130.81 415.30 207.65 34.84 65.41 103.83 130.81 415.30 207.65 34.85 65.41 103.83 130.81 415.30 207.65 34.86 65.41 103.83 130.81 415.30 207.65 34.87 65.41 103.83 130.81 415.30 207.65 34.88 65.41 103.83 130.81 415.30 207.65 34.89 65.41 103.83 130.81 415.30 207.65 34.90 65.41 103.83 130.81 415.30 207.65 34.91 65.41 103.83 130.81 415.30 207.65 34.92 65.41 103.83 130.81 415.30 207.65 34.93 65.41 103.83 130.81 415.30 207.65 34.94 65.41 103.83 130.81 415.30 207.65 34.95 65.41 103.83 130.81 415.30 207.65 34.96 65.41 103.83 130.81 415.30 207.65 34.97 65.41 103.83 130.81 415.30 207.65 34.98 65.41 103.83 130.81 415.30 207.65 34.99 65.41 103.83 130.81 415.30 207.65 35.00 65.41 103.83 130.81 415.30 207.65 35.01 65.41 103.83 130.81 415.30 207.65 35.02 65.41 103.83 130.81 415.30 207.65 35.03 65.41 103.83 130.81 415.30 207.65 35.04 65.41 103.83 130.81 415.30 207.65 35.05 65.41 103.83 130.81 415.30 207.65 35.06 65.41 103.83 130.81 415.30 207.65 35.07 65.41 103.83 130.81 415.30 207.65 35.08 65.41 103.83 130.81 415.30 207.65 35.09 65.41 103.83 130.81 415.30 207.65 35.10 65.41 103.83 130.81 415.30 207.65 35.11 65.41 103.83 130.81 415.30 207.65 35.12 65.41 103.83 130.81 415.30 207.65 35.13 65.41 103.83 130.81 415.30 207.65 35.14 65.41 103.83 130.81 415.30 207.65 35.15 65.41 103.83 130.81 415.30 207.65 35.16 65.41 103.83 130.81 415.30 207.65 35.17 65.41 103.83 130.81 415.30 207.65 35.18 65.41 103.83 130.81 415.30 207.65 35.19 65.41 103.83 130.81 415.30 207.65 35.20 65.41 103.83 130.81 415.30 207.65 35.21 65.41 103.83 130.81 415.30 207.65 35.22 65.41 103.83 130.81 415.30 207.65 35.23 65.41 103.83 130.81 415.30 207.65 35.24 65.41 103.83 130.81 415.30 207.65 35.25 65.41 103.83 130.81 415.30 207.65 35.26 65.41 103.83 130.81 415.30 207.65 35.27 65.41 103.83 130.81 415.30 207.65 35.28 65.41 103.83 130.81 415.30 207.65 35.29 65.41 103.83 130.81 415.30 207.65 35.30 65.41 103.83 130.81 415.30 207.65 311.13 35.31 65.41 103.83 130.81 415.30 207.65 311.13 35.32 65.41 103.83 130.81 415.30 207.65 311.13 35.33 65.41 103.83 130.81 415.30 207.65 311.13 35.34 65.41 103.83 130.81 415.30 207.65 311.13 35.35 65.41 103.83 130.81 415.30 311.13 35.36 65.41 103.83 130.81 415.30 311.13 35.37 65.41 103.83 130.81 415.30 311.13 35.38 65.41 103.83 130.81 415.30 311.13 35.39 65.41 103.83 130.81 415.30 311.13 35.40 65.41 103.83 130.81 415.30 311.13 35.41 65.41 103.83 130.81 415.30 311.13 35.42 65.41 103.83 130.81 415.30 311.13 35.43 65.41 103.83 130.81 415.30 311.13 35.44 65.41 103.83 130.81 415.30 311.13 35.45 65.41 103.83 130.81 415.30 311.13 35.46 65.41 103.83 130.81 415.30 311.13 35.47 65.41 103.83 130.81 415.30 311.13 35.48 65.41 103.83 130.81 415.30 311.13 35.49 65.41 103.83 130.81 415.30 311.13 35.50 65.41 103.83 130.81 415.30 311.13 35.51 65.41 103.83 130.81 415.30 311.13 35.52 65.41 103.83 130.81 415.30 311.13 35.53 65.41 103.83 130.81 415.30 311.13 35.54 65.41 103.83 130.81 415.30 311.13 35.55 65.41 103.83 130.81 415.30 311.13 35.56 65.41 103.83 130.81 415.30 311.13 35.57 65.41 103.83 130.81 415.30 311.13 35.58 65.41 103.83 130.81 415.30 311.13 35.59 65.41 103.83 130.81 415.30 311.13 35.60 65.41 103.83 130.81 415.30 311.13 35.61 65.41 103.83 130.81 415.30 311.13 35.62 65.41 103.83 130.81 415.30 311.13 35.63 65.41 103.83 130.81 415.30 311.13 35.64 65.41 103.83 130.81 415.30 311.13 35.65 65.41 103.83 130.81 415.30 311.13 35.66 65.41 103.83 130.81 415.30 311.13 35.67 65.41 103.83 130.81 415.30 311.13 35.68 65.41 103.83 130.81 415.30 311.13 35.69 65.41 103.83 130.81 415.30 311.13 35.70 65.41 103.83 130.81 415.30 311.13 35.71 65.41 103.83 130.81 415.30 311.13 35.72 65.41 103.83 130.81 415.30 311.13 35.73 65.41 103.83 130.81 415.30 311.13 35.74 65.41 103.83 130.81 415.30 311.13 35.75 65.41 103.83 130.81 415.30 311.13 35.76 65.41 103.83 130.81 415.30 311.13 35.77 65.41 103.83 130.81 415.30 311.13 35.78 65.41 103.83 130.81 415.30 311.13 35.79 65.41 103.83 130.81 415.30 311.13 369.99 35.80 65.41 103.83 130.81 415.30 311.13 369.99 35.81 65.41 103.83 130.81 415.30 311.13 369.99 35.82 65.41 103.83 130.81 415.30 369.99 35.83 65.41 103.83 130.81 415.30 369.99 35.84 65.41 103.83 130.81 415.30 369.99 35.85 65.41 103.83 130.81 415.30 369.99 35.86 65.41 103.83 130.81 415.30 369.99 35.87 65.41 103.83 130.81 415.30 369.99 35.88 65.41 103.83 130.81 415.30 369.99 35.89 65.41 103.83 130.81 415.30 369.99 35.90 65.41 103.83 130.81 415.30 369.99 35.91 65.41 103.83 130.81 415.30 369.99 35.92 65.41 103.83 130.81 415.30 369.99 35.93 65.41 103.83 130.81 415.30 369.99 35.94 65.41 103.83 130.81 415.30 369.99 35.95 65.41 103.83 130.81 415.30 369.99 35.96 65.41 103.83 130.81 415.30 369.99 35.97 65.41 103.83 130.81 415.30 369.99 35.98 65.41 103.83 130.81 415.30 369.99 35.99 65.41 103.83 130.81 415.30 369.99 36.00 65.41 103.83 130.81 415.30 369.99 36.01 65.41 103.83 130.81 415.30 369.99 36.02 65.41 103.83 130.81 415.30 369.99 36.03 65.41 103.83 130.81 415.30 369.99 36.04 65.41 103.83 130.81 415.30 369.99 36.05 65.41 103.83 130.81 415.30 369.99 36.06 65.41 103.83 130.81 415.30 369.99 36.07 65.41 103.83 130.81 415.30 369.99 36.08 65.41 103.83 130.81 415.30 369.99 36.09 65.41 103.83 130.81 415.30 369.99 36.10 65.41 103.83 130.81 415.30 369.99 36.11 65.41 103.83 130.81 415.30 369.99 36.12 65.41 103.83 130.81 415.30 369.99 36.13 65.41 103.83 130.81 415.30 369.99 36.14 65.41 103.83 130.81 415.30 369.99 36.15 65.41 103.83 130.81 415.30 369.99 36.16 65.41 103.83 130.81 415.30 369.99 36.17 65.41 103.83 130.81 415.30 369.99 36.18 65.41 103.83 130.81 415.30 369.99 36.19 65.41 103.83 130.81 415.30 369.99 36.20 65.41 103.83 130.81 415.30 369.99 36.21 65.41 103.83 130.81 415.30 369.99 36.22 65.41 103.83 130.81 415.30 369.99 36.23 65.41 103.83 130.81 415.30 369.99 36.24 65.41 103.83 130.81 415.30 369.99 36.25 65.41 103.83 130.81 415.30 369.99 36.26 65.41 103.83 130.81 415.30 369.99 36.27 65.41 103.83 130.81 415.30 369.99 36.28 65.41 103.83 130.81 415.30 369.99 36.29 65.41 103.83 130.81 415.30 369.99 207.65 36.30 65.41 103.83 130.81 415.30 369.99 207.65 36.31 65.41 103.83 130.81 415.30 369.99 207.65 36.32 65.41 103.83 130.81 415.30 207.65 36.33 65.41 103.83 130.81 415.30 207.65 36.34 65.41 103.83 130.81 415.30 207.65 36.35 65.41 103.83 130.81 415.30 207.65 36.36 65.41 103.83 130.81 415.30 207.65 36.37 65.41 103.83 130.81 415.30 207.65 36.38 65.41 103.83 130.81 415.30 207.65 36.39 65.41 103.83 130.81 415.30 207.65 36.40 65.41 103.83 130.81 415.30 207.65 36.41 65.41 103.83 130.81 415.30 207.65 36.42 65.41 103.83 130.81 415.30 207.65 36.43 65.41 103.83 130.81 415.30 207.65 36.44 65.41 103.83 130.81 415.30 207.65 36.45 65.41 103.83 130.81 415.30 207.65 36.46 65.41 103.83 130.81 415.30 207.65 36.47 65.41 103.83 130.81 415.30 207.65 36.48 65.41 103.83 130.81 415.30 207.65 36.49 65.41 103.83 130.81 415.30 207.65 36.50 65.41 103.83 130.81 415.30 207.65 36.51 65.41 103.83 130.81 415.30 207.65 36.52 65.41 103.83 130.81 415.30 207.65 36.53 65.41 103.83 130.81 415.30 207.65 36.54 65.41 103.83 130.81 415.30 207.65 36.55 65.41 103.83 130.81 415.30 207.65 36.56 65.41 103.83 130.81 415.30 207.65 36.57 65.41 103.83 130.81 415.30 207.65 36.58 65.41 103.83 130.81 415.30 207.65 36.59 65.41 103.83 130.81 415.30 207.65 36.60 65.41 103.83 130.81 415.30 207.65 36.61 65.41 103.83 130.81 415.30 207.65 36.62 65.41 103.83 130.81 415.30 207.65 36.63 65.41 103.83 130.81 415.30 207.65 36.64 65.41 103.83 130.81 415.30 207.65 36.65 65.41 103.83 130.81 415.30 207.65 36.66 65.41 103.83 130.81 415.30 207.65 36.67 65.41 103.83 130.81 415.30 207.65 36.68 65.41 103.83 130.81 415.30 207.65 36.69 65.41 103.83 130.81 415.30 207.65 36.70 65.41 103.83 130.81 415.30 207.65 36.71 65.41 103.83 130.81 415.30 207.65 36.72 65.41 103.83 130.81 415.30 207.65 36.73 65.41 103.83 130.81 415.30 207.65 36.74 65.41 103.83 130.81 415.30 207.65 36.75 65.41 103.83 130.81 415.30 207.65 36.76 65.41 103.83 130.81 415.30 207.65 36.77 65.41 103.83 130.81 415.30 207.65 36.78 65.41 103.83 130.81 415.30 207.65 36.79 65.41 103.83 130.81 415.30 207.65 36.80 65.41 103.83 130.81 415.30 207.65 36.81 65.41 103.83 130.81 415.30 207.65 36.82 65.41 103.83 130.81 415.30 207.65 311.13 36.83 65.41 103.83 130.81 415.30 207.65 311.13 36.84 65.41 103.83 130.81 415.30 207.65 311.13 36.85 65.41 103.83 130.81 415.30 207.65 311.13 36.86 65.41 103.83 130.81 415.30 207.65 311.13 36.87 65.41 103.83 130.81 415.30 311.13 36.88 65.41 103.83 130.81 415.30 311.13 36.89 65.41 103.83 130.81 415.30 311.13 36.90 65.41 103.83 130.81 415.30 311.13 36.91 65.41 103.83 130.81 415.30 311.13 36.92 65.41 103.83 130.81 415.30 311.13 36.93 65.41 103.83 130.81 415.30 311.13 36.94 65.41 103.83 130.81 415.30 311.13 36.95 65.41 103.83 130.81 415.30 311.13 36.96 65.41 103.83 130.81 415.30 311.13 36.97 65.41 103.83 130.81 415.30 311.13 36.98 65.41 103.83 130.81 415.30 311.13 36.99 65.41 103.83 130.81 415.30 311.13 37.00 65.41 103.83 130.81 415.30 311.13 37.01 65.41 103.83 130.81 415.30 311.13 37.02 65.41 103.83 130.81 415.30 311.13 37.03 65.41 103.83 130.81 415.30 311.13 37.04 65.41 103.83 130.81 415.30 311.13 37.05 65.41 103.83 130.81 415.30 311.13 37.06 65.41 103.83 130.81 415.30 311.13 37.07 65.41 103.83 130.81 415.30 311.13 37.08 65.41 103.83 130.81 415.30 311.13 37.09 65.41 103.83 130.81 415.30 311.13 37.10 65.41 103.83 130.81 415.30 311.13 37.11 65.41 103.83 130.81 415.30 311.13 37.12 65.41 103.83 130.81 415.30 311.13 37.13 65.41 103.83 130.81 415.30 311.13 37.14 65.41 103.83 130.81 415.30 311.13 37.15 65.41 103.83 130.81 415.30 311.13 37.16 65.41 103.83 130.81 415.30 311.13 37.17 65.41 103.83 130.81 415.30 311.13 37.18 65.41 103.83 130.81 415.30 311.13 37.19 65.41 103.83 130.81 415.30 311.13 37.20 65.41 103.83 130.81 415.30 311.13 37.21 65.41 103.83 130.81 415.30 311.13 37.22 65.41 103.83 130.81 415.30 311.13 37.23 65.41 103.83 130.81 415.30 311.13 37.24 65.41 103.83 130.81 415.30 311.13 37.25 65.41 103.83 130.81 415.30 311.13 37.26 65.41 103.83 130.81 415.30 311.13 37.27 65.41 103.83 130.81 415.30 311.13 369.99 37.28 65.41 103.83 130.81 415.30 311.13 369.99 37.29 65.41 103.83 130.81 415.30 311.13 369.99 37.30 65.41 103.83 130.81 415.30 369.99 37.31 65.41 103.83 130.81 415.30 369.99 37.32 65.41 103.83 130.81 415.30 369.99 37.33 65.41 103.83 130.81 415.30 369.99 37.34 65.41 103.83 130.81 415.30 369.99 37.35 65.41 103.83 130.81 415.30 369.99 37.36 65.41 103.83 130.81 415.30 369.99 37.37 65.41 103.83 130.81 415.30 369.99 37.38 65.41 103.83 130.81 415.30 369.99 37.39 65.41 103.83 130.81 415.30 369.99 37.40 65.41 103.83 130.81 415.30 369.99 37.41 65.41 103.83 130.81 415.30 369.99 37.42 65.41 103.83 130.81 415.30 369.99 37.43 65.41 103.83 130.81 415.30 369.99 37.44 65.41 103.83 130.81 415.30 369.99 37.45 65.41 103.83 130.81 415.30 369.99 37.46 65.41 103.83 130.81 415.30 369.99 37.47 65.41 103.83 130.81 415.30 369.99 37.48 65.41 103.83 130.81 415.30 369.99 37.49 65.41 103.83 130.81 415.30 369.99 37.50 65.41 103.83 130.81 415.30 369.99 37.51 65.41 103.83 130.81 415.30 369.99 37.52 65.41 103.83 130.81 415.30 369.99 37.53 65.41 103.83 130.81 415.30 369.99 37.54 65.41 103.83 130.81 415.30 369.99 37.55 65.41 103.83 130.81 415.30 369.99 37.56 65.41 103.83 130.81 415.30 369.99 37.57 65.41 103.83 130.81 415.30 369.99 37.58 65.41 103.83 130.81 415.30 369.99 37.59 65.41 103.83 130.81 415.30 369.99 37.60 65.41 103.83 130.81 415.30 369.99 37.61 65.41 103.83 130.81 415.30 369.99 37.62 65.41 103.83 130.81 415.30 369.99 37.63 65.41 103.83 130.81 369.99 37.64 65.41 103.83 130.81 369.99 37.65 65.41 103.83 130.81 369.99 37.66 65.41 103.83 130.81 369.99 37.67 65.41 103.83 130.81 369.99 37.68 65.41 103.83 130.81 369.99 37.69 65.41 103.83 130.81 369.99 37.70 65.41 103.83 130.81 369.99 37.71 65.41 103.83 130.81 369.99 37.72 65.41 103.83 130.81 369.99 37.73 65.41 103.83 130.81 369.99 37.74 65.41 103.83 130.81 369.99 37.75 65.41 103.83 130.81 369.99 37.76 65.41 103.83 130.81 369.99 207.65 37.77 65.41 103.83 130.81 369.99 207.65 415.30 37.78 65.41 103.83 130.81 369.99 207.65 415.30 37.79 65.41 103.83 130.81 369.99 207.65 415.30 37.80 65.41 103.83 130.81 207.65 415.30 37.81 65.41 103.83 130.81 207.65 415.30 37.82 65.41 103.83 130.81 207.65 415.30 37.83 65.41 103.83 130.81 207.65 415.30 37.84 65.41 103.83 130.81 207.65 415.30 37.85 65.41 103.83 130.81 207.65 415.30 37.86 65.41 103.83 130.81 207.65 415.30 37.87 65.41 103.83 130.81 207.65 415.30 37.88 65.41 103.83 130.81 207.65 415.30 37.89 65.41 103.83 130.81 207.65 415.30 37.90 65.41 103.83 130.81 207.65 415.30 37.91 65.41 103.83 130.81 207.65 415.30 37.92 65.41 103.83 130.81 207.65 415.30 37.93 65.41 103.83 130.81 207.65 415.30 37.94 65.41 103.83 130.81 207.65 415.30 37.95 65.41 103.83 130.81 207.65 415.30 37.96 65.41 103.83 130.81 207.65 415.30 37.97 65.41 103.83 130.81 207.65 415.30 37.98 65.41 103.83 130.81 207.65 415.30 37.99 65.41 103.83 130.81 207.65 415.30 38.00 65.41 103.83 130.81 207.65 415.30 38.01 65.41 103.83 130.81 207.65 415.30 38.02 65.41 103.83 130.81 207.65 415.30 38.03 65.41 103.83 130.81 207.65 415.30 38.04 65.41 103.83 130.81 207.65 415.30 38.05 65.41 103.83 130.81 207.65 415.30 38.06 65.41 103.83 130.81 207.65 415.30 38.07 65.41 103.83 130.81 207.65 415.30 38.08 65.41 103.83 130.81 207.65 415.30 38.09 65.41 103.83 130.81 207.65 415.30 38.10 65.41 103.83 130.81 207.65 415.30 38.11 65.41 103.83 130.81 207.65 415.30 38.12 65.41 103.83 130.81 207.65 415.30 38.13 65.41 103.83 130.81 207.65 415.30 38.14 65.41 103.83 130.81 207.65 415.30 38.15 65.41 103.83 130.81 207.65 415.30 38.16 65.41 103.83 130.81 207.65 415.30 38.17 65.41 103.83 130.81 207.65 415.30 38.18 65.41 103.83 130.81 207.65 415.30 38.19 65.41 103.83 130.81 207.65 415.30 38.20 65.41 103.83 130.81 207.65 415.30 38.21 65.41 103.83 130.81 207.65 415.30 38.22 65.41 103.83 130.81 207.65 415.30 38.23 65.41 103.83 130.81 207.65 415.30 311.13 38.24 65.41 103.83 130.81 207.65 415.30 311.13 38.25 65.41 103.83 130.81 207.65 415.30 311.13 38.26 65.41 103.83 130.81 207.65 415.30 311.13 38.27 65.41 103.83 130.81 207.65 415.30 311.13 38.28 65.41 103.83 130.81 207.65 415.30 311.13 38.29 65.41 103.83 130.81 207.65 415.30 311.13 38.30 65.41 103.83 130.81 207.65 415.30 311.13 38.31 65.41 103.83 130.81 207.65 415.30 311.13 38.32 65.41 103.83 130.81 207.65 415.30 311.13 38.33 65.41 103.83 130.81 207.65 415.30 311.13 38.34 65.41 103.83 130.81 207.65 415.30 311.13 38.35 65.41 103.83 130.81 207.65 415.30 311.13 38.36 65.41 103.83 130.81 207.65 415.30 311.13 38.37 65.41 103.83 130.81 207.65 415.30 311.13 38.38 65.41 103.83 130.81 207.65 415.30 311.13 38.39 65.41 103.83 130.81 207.65 415.30 311.13 38.40 65.41 103.83 130.81 207.65 415.30 311.13 38.41 65.41 103.83 130.81 207.65 415.30 311.13 38.42 65.41 103.83 130.81 207.65 415.30 311.13 38.43 65.41 103.83 130.81 207.65 415.30 311.13 38.44 65.41 103.83 130.81 207.65 415.30 311.13 38.45 65.41 103.83 130.81 207.65 415.30 311.13 38.46 65.41 103.83 130.81 207.65 415.30 311.13 38.47 65.41 103.83 130.81 207.65 415.30 311.13 38.48 65.41 103.83 130.81 207.65 415.30 311.13 38.49 65.41 103.83 130.81 207.65 415.30 311.13 38.50 65.41 103.83 130.81 207.65 415.30 311.13 38.51 65.41 103.83 130.81 207.65 415.30 311.13 38.52 65.41 103.83 130.81 207.65 415.30 311.13 38.53 65.41 103.83 130.81 207.65 415.30 311.13 38.54 65.41 103.83 130.81 207.65 415.30 311.13 38.55 65.41 103.83 130.81 207.65 415.30 311.13 38.56 65.41 103.83 130.81 207.65 415.30 311.13 38.57 65.41 103.83 130.81 207.65 415.30 311.13 38.58 65.41 103.83 130.81 207.65 415.30 311.13 38.59 65.41 103.83 130.81 207.65 415.30 311.13 38.60 65.41 103.83 130.81 207.65 415.30 311.13 38.61 65.41 103.83 130.81 207.65 415.30 311.13 38.62 65.41 103.83 130.81 207.65 415.30 311.13 38.63 65.41 103.83 130.81 207.65 415.30 311.13 38.64 65.41 103.83 130.81 207.65 415.30 311.13 38.65 65.41 103.83 130.81 207.65 415.30 311.13 38.66 65.41 103.83 130.81 207.65 415.30 311.13 38.67 65.41 103.83 130.81 207.65 415.30 311.13 38.68 65.41 103.83 130.81 207.65 415.30 311.13 38.69 65.41 103.83 130.81 207.65 415.30 311.13 38.70 65.41 103.83 130.81 207.65 415.30 311.13 38.71 65.41 103.83 130.81 207.65 415.30 311.13 369.99 38.72 65.41 103.83 130.81 207.65 415.30 311.13 369.99 38.73 65.41 103.83 130.81 207.65 311.13 369.99 38.74 65.41 103.83 130.81 207.65 311.13 369.99 38.75 65.41 103.83 130.81 207.65 311.13 369.99 38.76 65.41 103.83 130.81 207.65 369.99 38.77 65.41 103.83 130.81 207.65 369.99 38.78 65.41 103.83 130.81 207.65 369.99 38.79 65.41 103.83 130.81 207.65 369.99 38.80 65.41 103.83 130.81 207.65 369.99 38.81 65.41 103.83 130.81 207.65 369.99 38.82 65.41 103.83 130.81 207.65 369.99 38.83 103.83 207.65 369.99 38.84 103.83 207.65 369.99 38.85 103.83 207.65 369.99 38.86 103.83 207.65 369.99 38.87 207.65 369.99 38.88 207.65 369.99 38.89 207.65 369.99 38.90 207.65 369.99 38.91 207.65 369.99 38.92 207.65 369.99 38.93 369.99 38.94 369.99 38.95 369.99 38.96 369.99 38.97 369.99 38.98 369.99 38.99 369.99 39.00 369.99 415.30 39.01 369.99 415.30 39.02 369.99 415.30 39.03 415.30 39.04 415.30 39.05 415.30 39.06 415.30 39.07 415.30 39.08 415.30 39.09 415.30 39.10 415.30 39.11 415.30 39.12 415.30 39.13 415.30 39.14 415.30 39.15 415.30 39.16 415.30 39.17 415.30 39.18 415.30 39.19 415.30 39.20 415.30 39.21 415.30 39.22 415.30 39.23 415.30 39.24 39.25 39.26 39.27 39.28 39.29 39.30 39.31 39.32 415.30 39.33 415.30 207.65 39.34 415.30 207.65 39.35 415.30 207.65 39.36 415.30 207.65 39.37 415.30 207.65 138.59 69.30 39.38 415.30 207.65 138.59 69.30 39.39 415.30 207.65 138.59 69.30 39.40 415.30 207.65 138.59 69.30 39.41 415.30 207.65 138.59 69.30 39.42 415.30 207.65 138.59 69.30 39.43 415.30 207.65 138.59 69.30 39.44 415.30 207.65 138.59 69.30 39.45 415.30 207.65 138.59 69.30 39.46 415.30 207.65 138.59 69.30 39.47 415.30 207.65 138.59 69.30 39.48 415.30 207.65 138.59 69.30 39.49 415.30 207.65 138.59 69.30 39.50 415.30 207.65 138.59 69.30 39.51 415.30 207.65 138.59 69.30 39.52 415.30 207.65 138.59 69.30 39.53 415.30 207.65 138.59 69.30 39.54 415.30 207.65 138.59 69.30 39.55 415.30 207.65 138.59 69.30 39.56 415.30 207.65 138.59 69.30 39.57 415.30 207.65 138.59 69.30 39.58 415.30 207.65 138.59 69.30 39.59 415.30 207.65 138.59 69.30 39.60 415.30 207.65 138.59 69.30 39.61 415.30 207.65 138.59 69.30 39.62 415.30 207.65 138.59 69.30 39.63 415.30 207.65 138.59 69.30 39.64 415.30 207.65 138.59 69.30 39.65 415.30 207.65 138.59 69.30 39.66 415.30 207.65 138.59 69.30 39.67 415.30 207.65 138.59 69.30 39.68 415.30 207.65 138.59 69.30 39.69 415.30 207.65 138.59 69.30 39.70 415.30 207.65 138.59 69.30 39.71 415.30 207.65 138.59 69.30 39.72 415.30 207.65 138.59 69.30 39.73 415.30 207.65 138.59 69.30 39.74 415.30 207.65 138.59 69.30 39.75 415.30 207.65 138.59 69.30 39.76 415.30 207.65 138.59 69.30 39.77 415.30 207.65 138.59 69.30 39.78 415.30 207.65 138.59 69.30 39.79 415.30 207.65 138.59 69.30 39.80 415.30 207.65 138.59 69.30 39.81 415.30 207.65 138.59 69.30 39.82 415.30 207.65 138.59 69.30 39.83 415.30 207.65 138.59 69.30 39.84 415.30 207.65 138.59 69.30 39.85 415.30 207.65 138.59 69.30 39.86 415.30 207.65 138.59 69.30 39.87 415.30 207.65 138.59 69.30 39.88 415.30 207.65 138.59 69.30 39.89 415.30 207.65 138.59 69.30 39.90 415.30 207.65 138.59 69.30 39.91 415.30 207.65 138.59 69.30 39.92 415.30 207.65 138.59 69.30 39.93 415.30 207.65 138.59 69.30 39.94 415.30 207.65 138.59 69.30 39.95 415.30 207.65 138.59 69.30 39.96 415.30 207.65 138.59 69.30 39.97 415.30 207.65 138.59 69.30 39.98 415.30 207.65 138.59 69.30 39.99 415.30 207.65 138.59 69.30 40.00 415.30 207.65 138.59 69.30 40.01 415.30 207.65 138.59 69.30 40.02 415.30 207.65 138.59 69.30 40.03 415.30 207.65 138.59 69.30 40.04 415.30 207.65 138.59 69.30 40.05 415.30 207.65 138.59 69.30 277.18 40.06 415.30 207.65 138.59 69.30 277.18 40.07 415.30 207.65 138.59 69.30 277.18 40.08 415.30 207.65 138.59 69.30 277.18 40.09 415.30 207.65 138.59 69.30 277.18 40.10 415.30 207.65 138.59 69.30 277.18 40.11 415.30 207.65 138.59 69.30 277.18 40.12 415.30 207.65 138.59 69.30 277.18 40.13 415.30 207.65 138.59 69.30 277.18 40.14 415.30 207.65 138.59 69.30 277.18 40.15 415.30 207.65 138.59 69.30 277.18 40.16 415.30 207.65 138.59 69.30 277.18 40.17 415.30 207.65 138.59 69.30 277.18 40.18 415.30 207.65 138.59 69.30 277.18 40.19 415.30 207.65 138.59 69.30 277.18 40.20 415.30 207.65 138.59 69.30 277.18 40.21 415.30 207.65 138.59 69.30 277.18 40.22 415.30 207.65 138.59 69.30 277.18 40.23 415.30 207.65 138.59 69.30 277.18 40.24 415.30 207.65 138.59 69.30 277.18 40.25 415.30 207.65 138.59 69.30 277.18 40.26 415.30 207.65 138.59 69.30 277.18 40.27 415.30 207.65 138.59 69.30 277.18 40.28 415.30 207.65 138.59 69.30 277.18 40.29 415.30 207.65 138.59 69.30 277.18 40.30 415.30 207.65 138.59 69.30 277.18 40.31 415.30 207.65 138.59 69.30 277.18 40.32 415.30 207.65 138.59 69.30 277.18 40.33 415.30 207.65 138.59 69.30 277.18 40.34 415.30 207.65 138.59 69.30 277.18 40.35 415.30 207.65 138.59 69.30 277.18 40.36 415.30 207.65 138.59 69.30 277.18 40.37 415.30 207.65 138.59 69.30 277.18 40.38 415.30 207.65 138.59 69.30 277.18 40.39 415.30 207.65 138.59 69.30 277.18 40.40 415.30 207.65 138.59 69.30 277.18 40.41 415.30 207.65 138.59 69.30 277.18 40.42 415.30 207.65 138.59 69.30 277.18 40.43 415.30 207.65 138.59 69.30 277.18 40.44 415.30 207.65 138.59 69.30 277.18 40.45 415.30 207.65 138.59 69.30 277.18 40.46 415.30 207.65 138.59 69.30 277.18 40.47 415.30 207.65 138.59 69.30 277.18 40.48 415.30 207.65 138.59 69.30 277.18 40.49 415.30 207.65 138.59 69.30 277.18 40.50 415.30 207.65 138.59 69.30 277.18 40.51 415.30 207.65 138.59 69.30 277.18 40.52 415.30 207.65 138.59 69.30 277.18 40.53 415.30 207.65 138.59 69.30 277.18 40.54 415.30 207.65 138.59 69.30 277.18 40.55 415.30 207.65 138.59 69.30 277.18 40.56 415.30 207.65 138.59 69.30 277.18 40.57 415.30 207.65 138.59 69.30 277.18 40.58 415.30 207.65 138.59 69.30 277.18 40.59 415.30 207.65 138.59 69.30 277.18 329.63 40.60 415.30 207.65 138.59 69.30 277.18 329.63 40.61 415.30 207.65 138.59 69.30 277.18 329.63 40.62 415.30 207.65 138.59 69.30 277.18 329.63 40.63 415.30 207.65 138.59 69.30 329.63 40.64 415.30 207.65 138.59 69.30 329.63 40.65 415.30 207.65 138.59 69.30 329.63 40.66 415.30 207.65 138.59 69.30 329.63 40.67 415.30 207.65 138.59 69.30 329.63 40.68 415.30 207.65 138.59 69.30 329.63 40.69 415.30 207.65 138.59 69.30 329.63 40.70 415.30 207.65 138.59 69.30 329.63 40.71 415.30 138.59 69.30 329.63 40.72 415.30 138.59 69.30 329.63 40.73 415.30 138.59 69.30 329.63 40.74 415.30 138.59 69.30 329.63 40.75 415.30 138.59 69.30 329.63 40.76 415.30 138.59 69.30 329.63 40.77 415.30 138.59 69.30 329.63 40.78 415.30 138.59 69.30 329.63 40.79 415.30 138.59 69.30 329.63 40.80 415.30 138.59 69.30 329.63 40.81 415.30 138.59 69.30 329.63 40.82 415.30 138.59 69.30 329.63 40.83 415.30 138.59 69.30 329.63 40.84 415.30 138.59 69.30 329.63 40.85 415.30 138.59 69.30 329.63 40.86 415.30 138.59 69.30 329.63 40.87 415.30 138.59 69.30 329.63 40.88 415.30 138.59 69.30 329.63 40.89 415.30 138.59 69.30 329.63 40.90 415.30 138.59 69.30 329.63 40.91 415.30 138.59 69.30 329.63 40.92 415.30 138.59 69.30 329.63 40.93 415.30 138.59 69.30 329.63 40.94 415.30 138.59 69.30 329.63 40.95 415.30 138.59 69.30 329.63 40.96 415.30 138.59 69.30 329.63 40.97 415.30 138.59 69.30 329.63 40.98 415.30 138.59 69.30 329.63 40.99 415.30 138.59 69.30 329.63 41.00 415.30 138.59 69.30 329.63 41.01 415.30 138.59 69.30 329.63 41.02 415.30 138.59 69.30 329.63 41.03 415.30 138.59 69.30 329.63 41.04 415.30 138.59 69.30 329.63 41.05 415.30 138.59 69.30 329.63 41.06 415.30 138.59 69.30 329.63 41.07 415.30 138.59 69.30 329.63 41.08 415.30 138.59 69.30 329.63 207.65 41.09 415.30 138.59 69.30 329.63 207.65 41.10 415.30 138.59 69.30 329.63 207.65 41.11 415.30 138.59 69.30 207.65 41.12 415.30 138.59 69.30 207.65 41.13 415.30 138.59 69.30 207.65 41.14 415.30 138.59 69.30 207.65 41.15 415.30 138.59 69.30 207.65 41.16 415.30 138.59 69.30 207.65 41.17 415.30 138.59 69.30 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41.48 415.30 138.59 69.30 207.65 41.49 415.30 138.59 69.30 207.65 41.50 415.30 138.59 69.30 207.65 41.51 415.30 138.59 69.30 207.65 41.52 415.30 138.59 69.30 207.65 41.53 415.30 138.59 69.30 207.65 41.54 415.30 138.59 69.30 207.65 41.55 415.30 138.59 69.30 207.65 277.18 41.56 415.30 138.59 69.30 207.65 277.18 41.57 415.30 138.59 69.30 207.65 277.18 41.58 415.30 69.30 207.65 277.18 41.59 415.30 69.30 207.65 277.18 41.60 415.30 207.65 277.18 41.61 415.30 207.65 277.18 41.62 415.30 207.65 277.18 41.63 415.30 207.65 277.18 41.64 415.30 207.65 277.18 41.65 415.30 207.65 277.18 41.66 415.30 207.65 277.18 41.67 415.30 207.65 277.18 41.68 415.30 207.65 277.18 41.69 415.30 207.65 277.18 41.70 415.30 207.65 277.18 41.71 415.30 207.65 277.18 41.72 415.30 207.65 277.18 41.73 415.30 207.65 277.18 41.74 415.30 207.65 277.18 41.75 415.30 207.65 277.18 41.76 415.30 207.65 277.18 41.77 415.30 207.65 277.18 41.78 415.30 207.65 277.18 41.79 415.30 207.65 277.18 41.80 415.30 207.65 277.18 41.81 415.30 207.65 277.18 41.82 415.30 207.65 277.18 41.83 415.30 207.65 277.18 41.84 415.30 207.65 277.18 41.85 415.30 207.65 277.18 41.86 415.30 207.65 277.18 41.87 415.30 207.65 277.18 41.88 415.30 207.65 277.18 41.89 415.30 207.65 277.18 41.90 415.30 207.65 277.18 41.91 415.30 207.65 277.18 41.92 415.30 207.65 277.18 41.93 415.30 207.65 277.18 41.94 415.30 207.65 277.18 41.95 415.30 207.65 277.18 41.96 415.30 207.65 277.18 41.97 415.30 207.65 277.18 41.98 415.30 207.65 277.18 41.99 415.30 207.65 277.18 42.00 415.30 207.65 277.18 329.63 42.01 415.30 207.65 277.18 329.63 42.02 415.30 207.65 277.18 329.63 42.03 415.30 207.65 277.18 329.63 42.04 207.65 277.18 329.63 42.05 329.63 42.06 329.63 42.07 329.63 42.08 329.63 42.09 329.63 42.10 329.63 42.11 329.63 42.12 329.63 42.13 329.63 42.14 329.63 42.15 329.63 42.16 329.63 42.17 329.63 42.18 329.63 42.19 329.63 42.20 329.63 42.21 329.63 42.22 329.63 42.23 329.63 42.24 329.63 42.25 329.63 42.26 329.63 42.27 329.63 42.28 329.63 42.29 329.63 42.30 329.63 42.31 329.63 42.32 329.63 42.33 329.63 42.34 329.63 42.35 329.63 42.36 329.63 42.37 329.63 42.38 329.63 42.39 329.63 42.40 329.63 42.41 329.63 42.42 329.63 42.43 329.63 42.44 329.63 42.45 329.63 42.46 329.63 42.47 329.63 42.48 329.63 42.49 329.63 42.50 329.63 46.25 92.50 42.51 329.63 46.25 92.50 42.52 329.63 46.25 92.50 42.53 329.63 46.25 92.50 42.54 329.63 46.25 92.50 42.55 329.63 46.25 92.50 220.00 440.00 42.56 329.63 46.25 92.50 220.00 440.00 42.57 329.63 46.25 92.50 220.00 440.00 42.58 46.25 92.50 220.00 440.00 42.59 46.25 92.50 220.00 440.00 42.60 46.25 92.50 220.00 440.00 42.61 46.25 92.50 220.00 440.00 42.62 46.25 92.50 220.00 440.00 42.63 46.25 92.50 220.00 440.00 42.64 46.25 92.50 220.00 440.00 42.65 46.25 92.50 220.00 440.00 42.66 46.25 92.50 220.00 440.00 42.67 46.25 92.50 220.00 440.00 42.68 46.25 92.50 220.00 440.00 42.69 46.25 92.50 220.00 440.00 42.70 46.25 92.50 220.00 440.00 42.71 46.25 92.50 220.00 440.00 42.72 46.25 92.50 220.00 440.00 42.73 46.25 92.50 220.00 440.00 42.74 46.25 92.50 220.00 440.00 42.75 46.25 92.50 220.00 440.00 42.76 46.25 92.50 220.00 440.00 42.77 46.25 92.50 220.00 440.00 42.78 46.25 92.50 220.00 440.00 42.79 46.25 92.50 220.00 440.00 42.80 46.25 92.50 220.00 440.00 42.81 46.25 92.50 220.00 440.00 42.82 46.25 92.50 220.00 440.00 42.83 46.25 92.50 220.00 440.00 42.84 46.25 92.50 220.00 440.00 42.85 46.25 92.50 220.00 440.00 42.86 46.25 92.50 220.00 440.00 42.87 46.25 92.50 220.00 440.00 42.88 46.25 92.50 220.00 440.00 42.89 46.25 92.50 220.00 440.00 42.90 46.25 92.50 220.00 440.00 42.91 46.25 92.50 220.00 440.00 42.92 46.25 92.50 220.00 440.00 42.93 46.25 92.50 220.00 440.00 42.94 46.25 92.50 220.00 440.00 42.95 46.25 92.50 220.00 440.00 42.96 46.25 92.50 220.00 440.00 42.97 46.25 92.50 220.00 440.00 42.98 46.25 92.50 220.00 440.00 42.99 46.25 92.50 220.00 440.00 43.00 46.25 92.50 220.00 440.00 43.01 46.25 92.50 220.00 440.00 43.02 46.25 92.50 220.00 440.00 43.03 46.25 92.50 220.00 440.00 43.04 46.25 92.50 220.00 440.00 43.05 46.25 92.50 220.00 440.00 43.06 46.25 92.50 220.00 440.00 43.07 46.25 92.50 220.00 440.00 43.08 46.25 92.50 220.00 440.00 43.09 46.25 92.50 220.00 440.00 43.10 46.25 92.50 220.00 440.00 43.11 46.25 92.50 220.00 440.00 43.12 46.25 92.50 220.00 440.00 43.13 46.25 92.50 220.00 440.00 43.14 46.25 92.50 220.00 440.00 277.18 43.15 46.25 92.50 220.00 440.00 277.18 43.16 46.25 92.50 220.00 440.00 277.18 43.17 46.25 92.50 220.00 440.00 277.18 43.18 46.25 92.50 220.00 440.00 277.18 43.19 46.25 92.50 220.00 440.00 277.18 43.20 46.25 92.50 220.00 440.00 277.18 43.21 46.25 92.50 220.00 440.00 277.18 43.22 46.25 92.50 220.00 440.00 277.18 43.23 46.25 92.50 220.00 440.00 277.18 43.24 46.25 92.50 220.00 440.00 277.18 43.25 46.25 92.50 220.00 440.00 277.18 43.26 46.25 92.50 220.00 440.00 277.18 43.27 46.25 92.50 220.00 440.00 277.18 43.28 46.25 92.50 220.00 440.00 277.18 43.29 46.25 92.50 220.00 440.00 277.18 43.30 46.25 92.50 220.00 440.00 277.18 43.31 46.25 92.50 220.00 440.00 277.18 43.32 46.25 92.50 220.00 440.00 277.18 43.33 46.25 92.50 220.00 440.00 277.18 43.34 46.25 92.50 220.00 440.00 277.18 43.35 46.25 92.50 220.00 440.00 277.18 43.36 46.25 92.50 220.00 440.00 277.18 43.37 46.25 92.50 220.00 440.00 277.18 43.38 46.25 92.50 220.00 440.00 277.18 43.39 46.25 92.50 220.00 440.00 277.18 43.40 46.25 92.50 220.00 440.00 277.18 43.41 46.25 92.50 220.00 440.00 277.18 43.42 46.25 92.50 220.00 440.00 277.18 43.43 46.25 92.50 220.00 440.00 277.18 43.44 46.25 92.50 220.00 440.00 277.18 43.45 46.25 92.50 220.00 440.00 277.18 43.46 46.25 92.50 220.00 440.00 277.18 43.47 46.25 92.50 220.00 440.00 277.18 43.48 46.25 92.50 220.00 440.00 277.18 43.49 46.25 92.50 220.00 440.00 277.18 43.50 46.25 92.50 220.00 440.00 277.18 43.51 46.25 92.50 220.00 440.00 277.18 43.52 46.25 92.50 220.00 440.00 277.18 43.53 46.25 92.50 220.00 440.00 277.18 43.54 46.25 92.50 220.00 440.00 277.18 43.55 46.25 92.50 220.00 440.00 277.18 43.56 46.25 92.50 220.00 440.00 277.18 43.57 46.25 92.50 220.00 440.00 277.18 43.58 46.25 92.50 220.00 440.00 277.18 43.59 46.25 92.50 220.00 440.00 277.18 43.60 46.25 92.50 220.00 440.00 277.18 43.61 46.25 92.50 220.00 440.00 277.18 43.62 46.25 92.50 220.00 440.00 277.18 369.99 43.63 46.25 92.50 220.00 440.00 277.18 369.99 43.64 46.25 92.50 220.00 440.00 277.18 369.99 43.65 46.25 92.50 220.00 440.00 277.18 369.99 43.66 46.25 92.50 220.00 440.00 369.99 43.67 46.25 92.50 220.00 440.00 369.99 43.68 46.25 92.50 220.00 440.00 369.99 43.69 46.25 92.50 220.00 440.00 369.99 43.70 46.25 92.50 220.00 440.00 369.99 43.71 46.25 92.50 220.00 440.00 369.99 43.72 46.25 92.50 220.00 440.00 369.99 43.73 46.25 92.50 220.00 440.00 369.99 43.74 46.25 92.50 220.00 440.00 369.99 43.75 46.25 92.50 220.00 440.00 369.99 43.76 46.25 92.50 440.00 369.99 43.77 46.25 92.50 440.00 369.99 43.78 46.25 92.50 440.00 369.99 43.79 46.25 92.50 440.00 369.99 43.80 46.25 92.50 440.00 369.99 43.81 46.25 92.50 440.00 369.99 43.82 46.25 92.50 440.00 369.99 43.83 46.25 92.50 440.00 369.99 43.84 46.25 92.50 440.00 369.99 43.85 46.25 92.50 440.00 369.99 43.86 46.25 92.50 440.00 369.99 43.87 46.25 92.50 440.00 369.99 43.88 46.25 92.50 440.00 369.99 43.89 46.25 92.50 440.00 369.99 43.90 46.25 92.50 440.00 369.99 43.91 46.25 92.50 440.00 369.99 43.92 46.25 92.50 440.00 369.99 43.93 46.25 92.50 440.00 369.99 43.94 46.25 92.50 440.00 369.99 43.95 46.25 92.50 440.00 369.99 43.96 46.25 92.50 440.00 369.99 43.97 46.25 92.50 440.00 369.99 43.98 46.25 92.50 440.00 369.99 43.99 46.25 92.50 440.00 369.99 44.00 46.25 92.50 440.00 369.99 44.01 46.25 92.50 440.00 369.99 44.02 46.25 92.50 440.00 369.99 44.03 46.25 92.50 440.00 369.99 44.04 46.25 92.50 440.00 369.99 44.05 46.25 92.50 440.00 369.99 44.06 46.25 92.50 440.00 369.99 44.07 46.25 92.50 440.00 369.99 44.08 46.25 92.50 440.00 369.99 44.09 46.25 92.50 440.00 369.99 44.10 46.25 92.50 440.00 369.99 44.11 46.25 92.50 440.00 369.99 44.12 46.25 92.50 440.00 369.99 44.13 46.25 92.50 440.00 369.99 44.14 46.25 92.50 440.00 369.99 44.15 46.25 92.50 440.00 369.99 220.00 44.16 46.25 92.50 440.00 369.99 220.00 44.17 46.25 92.50 440.00 369.99 220.00 44.18 46.25 92.50 440.00 369.99 220.00 44.19 46.25 92.50 440.00 369.99 220.00 44.20 46.25 92.50 440.00 220.00 44.21 46.25 92.50 440.00 220.00 44.22 46.25 92.50 440.00 220.00 44.23 46.25 92.50 440.00 220.00 44.24 46.25 92.50 440.00 220.00 44.25 46.25 92.50 440.00 220.00 44.26 46.25 92.50 440.00 220.00 44.27 46.25 92.50 440.00 220.00 44.28 46.25 92.50 440.00 220.00 44.29 46.25 92.50 440.00 220.00 44.30 46.25 92.50 440.00 220.00 44.31 46.25 92.50 440.00 220.00 44.32 46.25 92.50 440.00 220.00 44.33 46.25 92.50 440.00 220.00 44.34 46.25 92.50 440.00 220.00 44.35 46.25 92.50 440.00 220.00 44.36 46.25 92.50 440.00 220.00 44.37 46.25 92.50 440.00 220.00 44.38 46.25 92.50 440.00 220.00 44.39 46.25 92.50 440.00 220.00 44.40 46.25 92.50 440.00 220.00 44.41 46.25 92.50 440.00 220.00 44.42 46.25 92.50 440.00 220.00 44.43 46.25 92.50 440.00 220.00 44.44 46.25 92.50 440.00 220.00 44.45 46.25 92.50 440.00 220.00 44.46 46.25 92.50 440.00 220.00 44.47 46.25 92.50 440.00 220.00 44.48 46.25 92.50 440.00 220.00 44.49 46.25 92.50 440.00 220.00 44.50 46.25 92.50 440.00 220.00 44.51 46.25 92.50 440.00 220.00 44.52 46.25 92.50 440.00 220.00 44.53 46.25 92.50 440.00 220.00 44.54 46.25 92.50 440.00 220.00 44.55 46.25 92.50 440.00 220.00 44.56 46.25 92.50 440.00 220.00 44.57 46.25 92.50 440.00 220.00 44.58 46.25 92.50 440.00 220.00 44.59 46.25 92.50 440.00 220.00 44.60 46.25 92.50 440.00 220.00 277.18 44.61 46.25 92.50 440.00 220.00 277.18 44.62 46.25 92.50 440.00 220.00 277.18 44.63 46.25 92.50 440.00 220.00 277.18 44.64 46.25 92.50 440.00 220.00 277.18 44.65 46.25 92.50 440.00 220.00 277.18 44.66 46.25 92.50 440.00 220.00 277.18 44.67 46.25 92.50 440.00 220.00 277.18 44.68 46.25 92.50 440.00 277.18 44.69 46.25 92.50 440.00 277.18 44.70 46.25 92.50 440.00 277.18 44.71 46.25 440.00 277.18 44.72 46.25 440.00 277.18 44.73 46.25 440.00 277.18 44.74 46.25 440.00 277.18 44.75 440.00 277.18 44.76 440.00 277.18 44.77 440.00 277.18 44.78 440.00 277.18 44.79 440.00 277.18 44.80 440.00 277.18 44.81 440.00 277.18 44.82 440.00 277.18 44.83 440.00 277.18 44.84 440.00 277.18 44.85 440.00 277.18 44.86 440.00 277.18 44.87 440.00 277.18 44.88 440.00 277.18 44.89 440.00 277.18 44.90 440.00 277.18 44.91 440.00 277.18 44.92 440.00 277.18 44.93 440.00 277.18 44.94 440.00 277.18 44.95 440.00 277.18 44.96 440.00 277.18 44.97 440.00 277.18 44.98 440.00 277.18 44.99 440.00 277.18 45.00 440.00 277.18 45.01 440.00 277.18 45.02 440.00 277.18 45.03 440.00 277.18 45.04 440.00 277.18 45.05 440.00 277.18 45.06 440.00 277.18 45.07 440.00 277.18 45.08 440.00 277.18 45.09 440.00 277.18 45.10 277.18 45.11 277.18 369.99 45.12 277.18 369.99 45.13 277.18 369.99 45.14 277.18 369.99 45.15 369.99 45.16 369.99 45.17 369.99 45.18 369.99 45.19 369.99 45.20 369.99 45.21 369.99 45.22 369.99 45.23 369.99 45.24 369.99 45.25 369.99 45.26 369.99 45.27 369.99 45.28 369.99 45.29 369.99 45.30 369.99 45.31 369.99 45.32 369.99 45.33 369.99 45.34 369.99 45.35 369.99 45.36 369.99 45.37 369.99 45.38 369.99 45.39 369.99 45.40 369.99 45.41 369.99 45.42 369.99 45.43 369.99 45.44 369.99 45.45 369.99 45.46 369.99 45.47 369.99 45.48 369.99 45.49 369.99 45.50 369.99 45.51 369.99 45.52 369.99 45.53 369.99 45.54 369.99 45.55 369.99 45.56 369.99 45.57 369.99 45.58 369.99 45.59 369.99 45.60 369.99 45.61 369.99 123.47 61.74 45.62 369.99 123.47 61.74 45.63 369.99 123.47 61.74 45.64 369.99 123.47 61.74 45.65 369.99 123.47 61.74 45.66 369.99 123.47 61.74 45.67 369.99 123.47 61.74 207.65 45.68 369.99 123.47 61.74 207.65 415.30 45.69 369.99 123.47 61.74 207.65 415.30 45.70 369.99 123.47 61.74 207.65 415.30 45.71 369.99 123.47 61.74 207.65 415.30 45.72 123.47 61.74 207.65 415.30 45.73 123.47 61.74 207.65 415.30 45.74 123.47 61.74 207.65 415.30 45.75 123.47 61.74 207.65 415.30 45.76 123.47 61.74 207.65 415.30 45.77 123.47 61.74 207.65 415.30 45.78 123.47 61.74 207.65 415.30 45.79 123.47 61.74 207.65 415.30 45.80 123.47 61.74 207.65 415.30 45.81 123.47 61.74 207.65 415.30 45.82 123.47 61.74 207.65 415.30 45.83 123.47 61.74 207.65 415.30 45.84 123.47 61.74 207.65 415.30 45.85 123.47 61.74 207.65 415.30 45.86 123.47 61.74 207.65 415.30 45.87 123.47 61.74 207.65 415.30 45.88 123.47 61.74 207.65 415.30 45.89 123.47 61.74 207.65 415.30 45.90 123.47 61.74 207.65 415.30 45.91 123.47 61.74 207.65 415.30 45.92 123.47 61.74 207.65 415.30 45.93 123.47 61.74 207.65 415.30 45.94 123.47 61.74 207.65 415.30 45.95 123.47 61.74 207.65 415.30 45.96 123.47 61.74 207.65 415.30 45.97 123.47 61.74 207.65 415.30 45.98 123.47 61.74 207.65 415.30 45.99 123.47 61.74 207.65 415.30 46.00 123.47 61.74 207.65 415.30 46.01 123.47 61.74 207.65 415.30 46.02 123.47 61.74 207.65 415.30 46.03 123.47 61.74 207.65 415.30 46.04 123.47 61.74 207.65 415.30 46.05 123.47 61.74 207.65 415.30 46.06 123.47 61.74 207.65 415.30 46.07 123.47 61.74 207.65 415.30 46.08 123.47 61.74 207.65 415.30 46.09 123.47 61.74 207.65 415.30 46.10 123.47 61.74 207.65 415.30 46.11 123.47 61.74 207.65 415.30 46.12 123.47 61.74 207.65 415.30 46.13 123.47 61.74 207.65 415.30 46.14 123.47 61.74 207.65 415.30 46.15 123.47 61.74 207.65 415.30 46.16 123.47 61.74 207.65 415.30 46.17 123.47 61.74 207.65 415.30 46.18 123.47 61.74 207.65 415.30 46.19 123.47 61.74 207.65 415.30 246.94 46.20 123.47 61.74 207.65 415.30 246.94 46.21 123.47 61.74 207.65 415.30 246.94 46.22 123.47 61.74 207.65 415.30 246.94 46.23 123.47 61.74 207.65 415.30 246.94 46.24 123.47 61.74 207.65 415.30 246.94 46.25 123.47 61.74 207.65 415.30 246.94 46.26 123.47 61.74 207.65 415.30 246.94 46.27 123.47 61.74 207.65 415.30 246.94 46.28 123.47 61.74 207.65 415.30 246.94 46.29 123.47 61.74 207.65 415.30 246.94 46.30 123.47 61.74 207.65 415.30 246.94 46.31 123.47 61.74 207.65 415.30 246.94 46.32 123.47 61.74 207.65 415.30 246.94 46.33 123.47 61.74 207.65 415.30 246.94 46.34 123.47 61.74 207.65 415.30 246.94 46.35 123.47 61.74 207.65 415.30 246.94 46.36 123.47 61.74 207.65 415.30 246.94 46.37 123.47 61.74 207.65 415.30 246.94 46.38 123.47 61.74 207.65 415.30 246.94 46.39 123.47 61.74 207.65 415.30 246.94 46.40 123.47 61.74 207.65 415.30 246.94 46.41 123.47 61.74 207.65 415.30 246.94 46.42 123.47 61.74 207.65 415.30 246.94 46.43 123.47 61.74 207.65 415.30 246.94 46.44 123.47 61.74 207.65 415.30 246.94 46.45 123.47 61.74 207.65 415.30 246.94 46.46 123.47 61.74 207.65 415.30 246.94 46.47 123.47 61.74 207.65 415.30 246.94 46.48 123.47 61.74 207.65 415.30 246.94 46.49 123.47 61.74 207.65 415.30 246.94 46.50 123.47 61.74 207.65 415.30 246.94 46.51 123.47 61.74 207.65 415.30 246.94 46.52 123.47 61.74 207.65 415.30 246.94 46.53 123.47 61.74 207.65 415.30 246.94 46.54 123.47 61.74 207.65 415.30 246.94 46.55 123.47 61.74 207.65 415.30 246.94 46.56 123.47 61.74 207.65 415.30 246.94 46.57 123.47 61.74 207.65 415.30 246.94 46.58 123.47 61.74 207.65 415.30 246.94 46.59 123.47 61.74 207.65 415.30 246.94 46.60 123.47 61.74 207.65 415.30 246.94 46.61 123.47 61.74 207.65 415.30 246.94 46.62 123.47 61.74 207.65 415.30 246.94 46.63 123.47 61.74 207.65 415.30 246.94 46.64 123.47 61.74 207.65 415.30 246.94 46.65 123.47 61.74 207.65 415.30 246.94 46.66 123.47 61.74 207.65 415.30 246.94 46.67 123.47 61.74 207.65 415.30 246.94 46.68 123.47 61.74 207.65 415.30 246.94 329.63 46.69 123.47 61.74 207.65 415.30 246.94 329.63 46.70 123.47 61.74 415.30 329.63 46.71 123.47 61.74 415.30 329.63 46.72 123.47 61.74 415.30 329.63 46.73 123.47 61.74 415.30 329.63 46.74 123.47 61.74 415.30 329.63 46.75 123.47 61.74 415.30 329.63 46.76 123.47 61.74 415.30 329.63 46.77 123.47 61.74 415.30 329.63 46.78 123.47 61.74 415.30 329.63 46.79 123.47 61.74 415.30 329.63 46.80 123.47 61.74 415.30 329.63 46.81 123.47 61.74 415.30 329.63 46.82 123.47 61.74 415.30 329.63 46.83 123.47 61.74 415.30 329.63 46.84 123.47 61.74 415.30 329.63 46.85 123.47 61.74 415.30 329.63 46.86 123.47 61.74 415.30 329.63 46.87 123.47 61.74 415.30 329.63 46.88 123.47 61.74 415.30 329.63 46.89 123.47 61.74 415.30 329.63 46.90 123.47 61.74 415.30 329.63 46.91 123.47 61.74 415.30 329.63 46.92 123.47 61.74 415.30 329.63 46.93 123.47 61.74 415.30 329.63 46.94 123.47 61.74 415.30 329.63 46.95 123.47 61.74 415.30 329.63 46.96 123.47 61.74 415.30 329.63 46.97 123.47 61.74 415.30 329.63 46.98 123.47 61.74 415.30 329.63 46.99 123.47 61.74 415.30 329.63 47.00 123.47 61.74 415.30 329.63 47.01 123.47 61.74 415.30 329.63 47.02 123.47 61.74 415.30 329.63 47.03 123.47 61.74 415.30 329.63 47.04 123.47 61.74 415.30 329.63 47.05 123.47 61.74 415.30 329.63 47.06 123.47 61.74 415.30 329.63 47.07 123.47 61.74 415.30 329.63 47.08 123.47 61.74 415.30 329.63 47.09 123.47 61.74 415.30 329.63 47.10 123.47 61.74 415.30 329.63 47.11 123.47 61.74 415.30 329.63 47.12 123.47 61.74 415.30 329.63 47.13 123.47 61.74 415.30 329.63 47.14 123.47 61.74 415.30 329.63 47.15 123.47 61.74 415.30 329.63 47.16 123.47 61.74 415.30 329.63 207.65 47.17 123.47 61.74 415.30 329.63 207.65 47.18 123.47 61.74 415.30 329.63 207.65 47.19 123.47 61.74 415.30 207.65 47.20 123.47 61.74 415.30 207.65 47.21 123.47 61.74 415.30 207.65 47.22 123.47 61.74 415.30 207.65 47.23 123.47 61.74 415.30 207.65 47.24 123.47 61.74 415.30 207.65 47.25 123.47 61.74 415.30 207.65 47.26 123.47 61.74 415.30 207.65 47.27 123.47 61.74 415.30 207.65 47.28 123.47 61.74 415.30 207.65 47.29 123.47 61.74 415.30 207.65 47.30 123.47 61.74 415.30 207.65 47.31 123.47 61.74 415.30 207.65 47.32 123.47 61.74 415.30 207.65 47.33 123.47 61.74 415.30 207.65 47.34 123.47 61.74 415.30 207.65 47.35 123.47 61.74 415.30 207.65 47.36 123.47 61.74 415.30 207.65 47.37 123.47 61.74 415.30 207.65 47.38 123.47 61.74 415.30 207.65 47.39 123.47 61.74 415.30 207.65 47.40 123.47 61.74 415.30 207.65 47.41 123.47 61.74 415.30 207.65 47.42 123.47 61.74 415.30 207.65 47.43 123.47 61.74 415.30 207.65 47.44 123.47 61.74 415.30 207.65 47.45 123.47 61.74 415.30 207.65 47.46 123.47 61.74 415.30 207.65 47.47 123.47 61.74 415.30 207.65 47.48 123.47 61.74 415.30 207.65 47.49 123.47 61.74 415.30 207.65 47.50 123.47 61.74 415.30 207.65 47.51 123.47 61.74 415.30 207.65 47.52 123.47 61.74 415.30 207.65 47.53 123.47 61.74 415.30 207.65 47.54 123.47 61.74 415.30 207.65 47.55 123.47 61.74 415.30 207.65 47.56 123.47 61.74 415.30 207.65 47.57 123.47 61.74 415.30 207.65 47.58 123.47 61.74 415.30 207.65 246.94 47.59 123.47 61.74 415.30 207.65 246.94 47.60 123.47 61.74 415.30 207.65 246.94 47.61 123.47 61.74 415.30 207.65 246.94 47.62 123.47 61.74 415.30 207.65 246.94 47.63 123.47 61.74 415.30 207.65 246.94 47.64 123.47 61.74 415.30 207.65 246.94 47.65 123.47 61.74 415.30 207.65 246.94 47.66 123.47 61.74 415.30 207.65 246.94 47.67 123.47 61.74 415.30 207.65 246.94 47.68 123.47 61.74 415.30 207.65 246.94 47.69 123.47 61.74 415.30 207.65 246.94 47.70 123.47 61.74 415.30 207.65 246.94 47.71 123.47 61.74 415.30 207.65 246.94 47.72 415.30 207.65 246.94 47.73 415.30 207.65 246.94 47.74 415.30 207.65 246.94 47.75 415.30 207.65 246.94 47.76 415.30 207.65 246.94 47.77 415.30 207.65 246.94 47.78 415.30 207.65 246.94 47.79 415.30 207.65 246.94 47.80 415.30 207.65 246.94 47.81 415.30 207.65 246.94 47.82 415.30 207.65 246.94 47.83 415.30 207.65 246.94 47.84 415.30 207.65 246.94 47.85 415.30 207.65 246.94 47.86 415.30 207.65 246.94 47.87 415.30 207.65 246.94 47.88 415.30 207.65 246.94 47.89 415.30 207.65 246.94 47.90 415.30 207.65 246.94 47.91 415.30 207.65 246.94 47.92 415.30 207.65 246.94 47.93 415.30 207.65 246.94 47.94 415.30 207.65 246.94 47.95 415.30 207.65 246.94 47.96 415.30 207.65 246.94 47.97 415.30 207.65 246.94 47.98 415.30 207.65 246.94 47.99 415.30 207.65 246.94 48.00 415.30 207.65 246.94 48.01 415.30 207.65 246.94 48.02 415.30 207.65 246.94 48.03 415.30 207.65 246.94 48.04 415.30 207.65 246.94 48.05 415.30 207.65 246.94 48.06 415.30 207.65 246.94 48.07 415.30 207.65 246.94 48.08 415.30 207.65 246.94 48.09 415.30 207.65 246.94 48.10 415.30 207.65 246.94 48.11 415.30 207.65 246.94 329.63 48.12 415.30 207.65 246.94 329.63 48.13 415.30 207.65 246.94 329.63 48.14 207.65 329.63 48.15 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1.10 493.88 415.30 329.63 415.30 1.11 493.88 415.30 329.63 415.30 1.12 493.88 415.30 329.63 415.30 1.13 493.88 415.30 329.63 415.30 1.14 493.88 415.30 329.63 415.30 329.63 1.15 493.88 415.30 329.63 415.30 329.63 1.16 493.88 415.30 329.63 415.30 329.63 1.17 493.88 415.30 329.63 415.30 329.63 1.18 493.88 415.30 329.63 415.30 329.63 1.19 493.88 415.30 329.63 415.30 329.63 1.20 493.88 415.30 329.63 415.30 329.63 554.37 1.21 493.88 329.63 415.30 329.63 554.37 1.22 493.88 329.63 415.30 329.63 554.37 1.23 493.88 329.63 415.30 329.63 554.37 1.24 493.88 329.63 415.30 329.63 554.37 1.25 493.88 329.63 415.30 329.63 554.37 1.26 493.88 329.63 415.30 329.63 554.37 1.27 493.88 329.63 415.30 329.63 554.37 1.28 329.63 415.30 329.63 554.37 1.29 329.63 415.30 329.63 554.37 1.30 329.63 415.30 329.63 554.37 1.31 329.63 415.30 329.63 554.37 1.32 329.63 415.30 329.63 554.37 1.33 329.63 415.30 329.63 554.37 1.34 329.63 415.30 329.63 554.37 1.35 329.63 415.30 329.63 554.37 1.36 329.63 415.30 329.63 554.37 1.37 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329.63 554.37 415.30 1.67 415.30 329.63 554.37 415.30 1.68 415.30 329.63 554.37 415.30 1.69 415.30 329.63 554.37 415.30 1.70 415.30 329.63 554.37 415.30 1.71 415.30 329.63 554.37 415.30 1.72 415.30 329.63 554.37 415.30 1.73 415.30 329.63 554.37 415.30 1.74 415.30 329.63 554.37 415.30 1.75 415.30 329.63 554.37 415.30 1.76 415.30 329.63 554.37 415.30 1.77 415.30 329.63 554.37 415.30 1.78 415.30 329.63 554.37 415.30 1.79 415.30 329.63 554.37 415.30 1.80 415.30 329.63 554.37 415.30 1.81 415.30 329.63 554.37 415.30 1.82 415.30 329.63 554.37 415.30 1.83 415.30 329.63 554.37 415.30 1.84 415.30 329.63 554.37 415.30 1.85 415.30 329.63 554.37 415.30 1.86 415.30 329.63 554.37 415.30 1.87 415.30 329.63 554.37 415.30 1.88 415.30 329.63 554.37 415.30 1.89 415.30 329.63 554.37 415.30 1.90 415.30 329.63 554.37 415.30 1.91 415.30 329.63 554.37 415.30 1.92 415.30 329.63 554.37 415.30 1.93 415.30 329.63 554.37 415.30 1.94 415.30 329.63 554.37 415.30 1.95 415.30 329.63 554.37 415.30 1.96 415.30 329.63 554.37 415.30 1.97 415.30 329.63 554.37 415.30 1.98 415.30 329.63 554.37 415.30 1.99 415.30 329.63 554.37 415.30 2.00 415.30 329.63 554.37 415.30 2.01 415.30 329.63 554.37 415.30 2.02 415.30 329.63 554.37 415.30 246.94 2.03 415.30 329.63 554.37 415.30 246.94 2.04 415.30 329.63 554.37 415.30 246.94 2.05 415.30 554.37 415.30 246.94 2.06 415.30 554.37 415.30 246.94 415.30 311.13 2.07 415.30 554.37 415.30 246.94 415.30 311.13 2.08 415.30 554.37 415.30 246.94 415.30 311.13 2.09 415.30 554.37 415.30 246.94 415.30 311.13 2.10 415.30 554.37 415.30 246.94 415.30 311.13 2.11 415.30 554.37 415.30 246.94 415.30 311.13 2.12 415.30 554.37 415.30 246.94 415.30 311.13 2.13 415.30 554.37 415.30 246.94 415.30 311.13 2.14 554.37 415.30 246.94 415.30 311.13 2.15 415.30 246.94 415.30 311.13 2.16 415.30 246.94 415.30 311.13 2.17 415.30 246.94 415.30 311.13 2.18 415.30 246.94 415.30 311.13 2.19 415.30 246.94 415.30 311.13 2.20 415.30 246.94 415.30 311.13 2.21 415.30 246.94 415.30 311.13 2.22 415.30 246.94 415.30 311.13 2.23 415.30 246.94 415.30 311.13 2.24 415.30 246.94 415.30 311.13 2.25 415.30 246.94 415.30 311.13 2.26 415.30 246.94 415.30 311.13 2.27 415.30 246.94 415.30 311.13 2.28 415.30 246.94 415.30 311.13 2.29 415.30 246.94 415.30 311.13 2.30 415.30 246.94 415.30 311.13 2.31 415.30 246.94 415.30 311.13 2.32 415.30 246.94 415.30 311.13 2.33 415.30 246.94 415.30 311.13 2.34 415.30 246.94 415.30 311.13 2.35 415.30 246.94 415.30 311.13 2.36 415.30 246.94 415.30 311.13 2.37 415.30 246.94 415.30 311.13 2.38 415.30 246.94 415.30 311.13 2.39 415.30 246.94 415.30 311.13 2.40 415.30 246.94 415.30 311.13 2.41 415.30 246.94 415.30 311.13 2.42 415.30 246.94 415.30 311.13 2.43 415.30 246.94 415.30 311.13 2.44 415.30 246.94 415.30 311.13 2.45 415.30 246.94 415.30 311.13 2.46 415.30 246.94 415.30 311.13 2.47 415.30 246.94 415.30 311.13 2.48 415.30 246.94 415.30 311.13 2.49 415.30 246.94 415.30 311.13 2.50 415.30 246.94 415.30 311.13 2.51 415.30 246.94 415.30 311.13 2.52 415.30 246.94 415.30 311.13 311.13 2.53 415.30 246.94 415.30 311.13 311.13 2.54 415.30 246.94 415.30 311.13 311.13 2.55 415.30 246.94 415.30 311.13 311.13 2.56 415.30 246.94 415.30 311.13 311.13 2.57 415.30 246.94 415.30 311.13 311.13 2.58 415.30 246.94 415.30 311.13 311.13 2.59 246.94 415.30 311.13 311.13 2.60 246.94 415.30 311.13 311.13 2.61 246.94 415.30 311.13 311.13 2.62 246.94 415.30 311.13 311.13 2.63 246.94 415.30 311.13 311.13 2.64 246.94 415.30 311.13 311.13 2.65 246.94 415.30 311.13 311.13 2.66 246.94 415.30 311.13 311.13 2.67 246.94 415.30 311.13 311.13 2.68 246.94 415.30 311.13 311.13 2.69 246.94 415.30 311.13 311.13 2.70 246.94 415.30 311.13 311.13 2.71 246.94 415.30 311.13 311.13 2.72 246.94 415.30 311.13 311.13 2.73 246.94 415.30 311.13 311.13 2.74 246.94 415.30 311.13 311.13 2.75 246.94 415.30 311.13 311.13 2.76 246.94 415.30 311.13 311.13 2.77 246.94 415.30 311.13 311.13 2.78 246.94 415.30 311.13 311.13 2.79 246.94 415.30 311.13 311.13 2.80 246.94 415.30 311.13 311.13 2.81 246.94 415.30 311.13 311.13 2.82 246.94 415.30 311.13 311.13 2.83 246.94 415.30 311.13 311.13 2.84 246.94 415.30 311.13 311.13 2.85 246.94 415.30 311.13 311.13 2.86 246.94 415.30 311.13 311.13 2.87 246.94 415.30 311.13 311.13 2.88 246.94 415.30 311.13 311.13 2.89 246.94 415.30 311.13 311.13 2.90 246.94 415.30 311.13 311.13 2.91 246.94 415.30 311.13 311.13 2.92 246.94 415.30 311.13 311.13 2.93 246.94 415.30 311.13 311.13 2.94 246.94 415.30 311.13 311.13 2.95 246.94 415.30 311.13 311.13 2.96 246.94 415.30 311.13 311.13 2.97 246.94 415.30 311.13 311.13 2.98 246.94 415.30 311.13 311.13 246.94 2.99 246.94 415.30 311.13 311.13 246.94 3.00 246.94 415.30 311.13 311.13 246.94 3.01 246.94 415.30 311.13 311.13 246.94 493.88 3.02 246.94 415.30 311.13 246.94 493.88 3.03 246.94 415.30 311.13 246.94 493.88 3.04 246.94 415.30 311.13 246.94 493.88 3.05 246.94 415.30 311.13 246.94 493.88 3.06 246.94 415.30 311.13 246.94 493.88 3.07 246.94 311.13 246.94 493.88 3.08 246.94 311.13 246.94 493.88 3.09 246.94 311.13 246.94 493.88 3.10 246.94 311.13 246.94 493.88 3.11 246.94 311.13 246.94 493.88 3.12 246.94 311.13 246.94 493.88 3.13 246.94 311.13 246.94 493.88 3.14 246.94 311.13 246.94 493.88 3.15 246.94 311.13 246.94 493.88 3.16 246.94 311.13 246.94 493.88 3.17 246.94 311.13 246.94 493.88 3.18 246.94 311.13 246.94 493.88 3.19 246.94 311.13 246.94 493.88 3.20 246.94 311.13 246.94 493.88 3.21 246.94 311.13 246.94 493.88 3.22 246.94 311.13 246.94 493.88 3.23 246.94 311.13 246.94 493.88 3.24 246.94 311.13 246.94 493.88 3.25 246.94 311.13 246.94 493.88 3.26 246.94 311.13 246.94 493.88 3.27 246.94 311.13 246.94 493.88 3.28 246.94 311.13 246.94 493.88 3.29 246.94 311.13 246.94 493.88 3.30 246.94 311.13 246.94 493.88 3.31 246.94 311.13 246.94 493.88 3.32 246.94 311.13 246.94 493.88 3.33 246.94 311.13 246.94 493.88 3.34 246.94 311.13 246.94 493.88 3.35 246.94 311.13 246.94 493.88 3.36 246.94 311.13 246.94 493.88 3.37 246.94 311.13 246.94 493.88 3.38 246.94 311.13 246.94 493.88 3.39 246.94 311.13 246.94 493.88 3.40 246.94 311.13 246.94 493.88 3.41 246.94 311.13 246.94 493.88 3.42 246.94 311.13 246.94 493.88 3.43 246.94 311.13 246.94 493.88 3.44 246.94 311.13 246.94 493.88 3.45 246.94 311.13 246.94 493.88 3.46 246.94 311.13 246.94 493.88 311.13 3.47 246.94 311.13 246.94 493.88 311.13 3.48 311.13 246.94 493.88 311.13 3.49 311.13 246.94 493.88 311.13 3.50 311.13 246.94 493.88 311.13 3.51 311.13 246.94 493.88 311.13 3.52 311.13 246.94 493.88 311.13 3.53 311.13 246.94 493.88 311.13 3.54 311.13 246.94 493.88 311.13 3.55 311.13 246.94 493.88 311.13 3.56 311.13 246.94 493.88 311.13 3.57 311.13 246.94 493.88 311.13 3.58 311.13 246.94 493.88 311.13 3.59 311.13 246.94 493.88 311.13 3.60 311.13 246.94 493.88 311.13 3.61 311.13 246.94 493.88 311.13 3.62 311.13 246.94 493.88 311.13 3.63 311.13 246.94 493.88 311.13 3.64 311.13 246.94 493.88 311.13 3.65 311.13 246.94 493.88 311.13 3.66 311.13 246.94 493.88 311.13 3.67 311.13 246.94 493.88 311.13 3.68 311.13 246.94 493.88 311.13 3.69 311.13 246.94 493.88 311.13 3.70 311.13 246.94 493.88 311.13 3.71 311.13 246.94 493.88 311.13 3.72 311.13 246.94 493.88 311.13 3.73 311.13 246.94 493.88 311.13 3.74 311.13 246.94 493.88 311.13 3.75 311.13 246.94 493.88 311.13 3.76 311.13 246.94 493.88 311.13 3.77 311.13 246.94 493.88 311.13 3.78 311.13 246.94 493.88 311.13 3.79 311.13 246.94 493.88 311.13 3.80 311.13 246.94 493.88 311.13 3.81 311.13 246.94 493.88 311.13 3.82 311.13 246.94 493.88 311.13 3.83 311.13 246.94 493.88 311.13 3.84 311.13 246.94 493.88 311.13 3.85 311.13 246.94 493.88 311.13 3.86 311.13 246.94 493.88 311.13 3.87 311.13 246.94 493.88 311.13 3.88 311.13 246.94 493.88 311.13 3.89 311.13 246.94 493.88 311.13 3.90 311.13 246.94 493.88 311.13 3.91 311.13 246.94 493.88 311.13 3.92 311.13 246.94 493.88 311.13 3.93 311.13 246.94 493.88 311.13 3.94 246.94 493.88 311.13 3.95 246.94 493.88 311.13 3.96 246.94 493.88 311.13 207.65 3.97 246.94 493.88 311.13 207.65 3.98 246.94 493.88 311.13 207.65 207.65 3.99 246.94 493.88 207.65 207.65 4.00 246.94 493.88 207.65 207.65 4.01 246.94 493.88 207.65 207.65 4.02 246.94 493.88 207.65 207.65 311.13 4.03 246.94 493.88 207.65 207.65 311.13 4.04 246.94 493.88 207.65 207.65 311.13 4.05 493.88 207.65 207.65 311.13 4.06 493.88 207.65 207.65 311.13 4.07 493.88 207.65 207.65 311.13 4.08 493.88 207.65 207.65 311.13 4.09 493.88 207.65 207.65 311.13 4.10 493.88 207.65 207.65 311.13 4.11 493.88 207.65 207.65 311.13 4.12 493.88 207.65 207.65 311.13 4.13 493.88 207.65 207.65 311.13 4.14 493.88 207.65 207.65 311.13 4.15 493.88 207.65 207.65 311.13 4.16 493.88 207.65 207.65 311.13 4.17 493.88 207.65 207.65 311.13 4.18 493.88 207.65 207.65 311.13 4.19 493.88 207.65 207.65 311.13 4.20 493.88 207.65 207.65 311.13 4.21 493.88 207.65 207.65 311.13 4.22 493.88 207.65 207.65 311.13 4.23 493.88 207.65 207.65 311.13 4.24 493.88 207.65 207.65 311.13 4.25 493.88 207.65 207.65 311.13 4.26 493.88 207.65 207.65 311.13 4.27 493.88 207.65 207.65 311.13 4.28 493.88 207.65 207.65 311.13 4.29 493.88 207.65 207.65 311.13 4.30 493.88 207.65 207.65 311.13 4.31 493.88 207.65 207.65 311.13 4.32 493.88 207.65 207.65 311.13 4.33 493.88 207.65 207.65 311.13 4.34 493.88 207.65 207.65 311.13 4.35 493.88 207.65 207.65 311.13 4.36 493.88 207.65 207.65 311.13 4.37 493.88 207.65 207.65 311.13 4.38 493.88 207.65 207.65 311.13 4.39 493.88 207.65 207.65 311.13 4.40 493.88 207.65 207.65 311.13 4.41 493.88 207.65 207.65 311.13 4.42 493.88 207.65 207.65 311.13 4.43 493.88 207.65 207.65 311.13 4.44 493.88 207.65 207.65 311.13 4.45 493.88 207.65 207.65 311.13 4.46 493.88 207.65 207.65 311.13 4.47 493.88 207.65 207.65 311.13 246.94 4.48 493.88 207.65 207.65 311.13 246.94 4.49 493.88 207.65 207.65 311.13 246.94 4.50 493.88 207.65 207.65 311.13 246.94 4.51 493.88 207.65 311.13 246.94 4.52 493.88 207.65 311.13 246.94 4.53 493.88 207.65 311.13 246.94 4.54 493.88 207.65 311.13 246.94 4.55 493.88 207.65 311.13 246.94 4.56 493.88 207.65 311.13 246.94 4.57 493.88 207.65 311.13 246.94 4.58 493.88 207.65 311.13 246.94 4.59 493.88 207.65 311.13 246.94 4.60 493.88 207.65 311.13 246.94 4.61 493.88 207.65 311.13 246.94 4.62 493.88 207.65 311.13 246.94 4.63 493.88 207.65 311.13 246.94 4.64 493.88 207.65 311.13 246.94 4.65 493.88 207.65 311.13 246.94 4.66 493.88 207.65 311.13 246.94 4.67 493.88 207.65 311.13 246.94 4.68 493.88 207.65 311.13 246.94 4.69 493.88 207.65 311.13 246.94 4.70 493.88 207.65 311.13 246.94 4.71 493.88 207.65 311.13 246.94 4.72 493.88 207.65 311.13 246.94 4.73 493.88 207.65 311.13 246.94 4.74 493.88 207.65 311.13 246.94 4.75 493.88 207.65 311.13 246.94 4.76 493.88 207.65 311.13 246.94 4.77 493.88 207.65 311.13 246.94 4.78 493.88 207.65 311.13 246.94 4.79 493.88 207.65 311.13 246.94 4.80 493.88 207.65 311.13 246.94 4.81 493.88 207.65 311.13 246.94 4.82 493.88 207.65 311.13 246.94 4.83 493.88 207.65 311.13 246.94 4.84 493.88 207.65 311.13 246.94 4.85 493.88 207.65 311.13 246.94 4.86 493.88 207.65 311.13 246.94 4.87 493.88 207.65 311.13 246.94 4.88 493.88 311.13 246.94 4.89 493.88 311.13 246.94 4.90 493.88 311.13 246.94 4.91 493.88 311.13 246.94 4.92 493.88 311.13 246.94 4.93 493.88 311.13 246.94 4.94 493.88 311.13 246.94 138.59 4.95 493.88 311.13 246.94 138.59 4.96 493.88 311.13 246.94 138.59 4.97 493.88 311.13 246.94 138.59 329.63 4.98 493.88 311.13 246.94 138.59 329.63 4.99 493.88 311.13 246.94 138.59 329.63 138.59 5.00 493.88 311.13 246.94 138.59 329.63 138.59 5.01 493.88 311.13 246.94 138.59 329.63 138.59 5.02 493.88 311.13 246.94 138.59 329.63 138.59 5.03 493.88 311.13 246.94 138.59 329.63 138.59 5.04 493.88 311.13 246.94 138.59 329.63 138.59 5.05 493.88 246.94 138.59 329.63 138.59 5.06 493.88 246.94 138.59 329.63 138.59 5.07 493.88 246.94 138.59 329.63 138.59 5.08 493.88 246.94 138.59 329.63 138.59 5.09 493.88 246.94 138.59 329.63 138.59 5.10 493.88 246.94 138.59 329.63 138.59 5.11 493.88 246.94 138.59 329.63 138.59 5.12 493.88 246.94 138.59 329.63 138.59 5.13 493.88 246.94 138.59 329.63 138.59 5.14 493.88 246.94 138.59 329.63 138.59 5.15 493.88 246.94 138.59 329.63 138.59 5.16 493.88 246.94 138.59 329.63 138.59 5.17 493.88 246.94 138.59 329.63 138.59 5.18 493.88 246.94 138.59 329.63 138.59 5.19 493.88 246.94 138.59 329.63 138.59 5.20 493.88 246.94 138.59 329.63 138.59 5.21 493.88 246.94 138.59 329.63 138.59 5.22 493.88 246.94 138.59 329.63 138.59 5.23 493.88 246.94 138.59 329.63 138.59 5.24 493.88 246.94 138.59 329.63 138.59 5.25 493.88 246.94 138.59 329.63 138.59 5.26 493.88 246.94 138.59 329.63 138.59 5.27 493.88 246.94 138.59 329.63 138.59 5.28 493.88 246.94 138.59 329.63 138.59 5.29 493.88 246.94 138.59 329.63 138.59 5.30 493.88 246.94 138.59 329.63 138.59 5.31 493.88 246.94 138.59 329.63 138.59 5.32 493.88 246.94 138.59 329.63 138.59 5.33 493.88 246.94 138.59 329.63 138.59 5.34 493.88 246.94 138.59 329.63 138.59 5.35 493.88 246.94 138.59 329.63 138.59 5.36 493.88 246.94 138.59 329.63 138.59 5.37 493.88 246.94 138.59 329.63 138.59 5.38 493.88 246.94 138.59 329.63 138.59 5.39 493.88 246.94 138.59 329.63 138.59 5.40 493.88 246.94 138.59 329.63 138.59 5.41 493.88 246.94 138.59 329.63 138.59 5.42 493.88 246.94 138.59 329.63 138.59 5.43 493.88 246.94 138.59 329.63 138.59 5.44 493.88 138.59 329.63 138.59 5.45 493.88 138.59 329.63 138.59 5.46 493.88 138.59 329.63 138.59 5.47 493.88 138.59 329.63 138.59 5.48 493.88 138.59 329.63 138.59 5.49 493.88 138.59 329.63 138.59 5.50 493.88 138.59 329.63 138.59 5.51 493.88 138.59 329.63 138.59 5.52 493.88 138.59 329.63 138.59 329.63 5.53 493.88 138.59 329.63 138.59 329.63 5.54 138.59 329.63 138.59 329.63 5.55 138.59 329.63 138.59 329.63 5.56 138.59 329.63 138.59 329.63 5.57 138.59 329.63 138.59 329.63 5.58 138.59 329.63 138.59 329.63 5.59 138.59 329.63 138.59 329.63 5.60 138.59 329.63 138.59 329.63 5.61 138.59 329.63 138.59 329.63 5.62 138.59 329.63 138.59 329.63 5.63 138.59 329.63 138.59 329.63 5.64 138.59 329.63 138.59 329.63 5.65 138.59 329.63 138.59 329.63 5.66 138.59 138.59 329.63 5.67 138.59 138.59 329.63 5.68 138.59 138.59 329.63 5.69 138.59 138.59 329.63 5.70 138.59 138.59 329.63 5.71 138.59 138.59 329.63 5.72 138.59 138.59 329.63 5.73 138.59 138.59 329.63 5.74 138.59 138.59 329.63 5.75 138.59 138.59 329.63 5.76 138.59 138.59 329.63 5.77 138.59 138.59 329.63 5.78 138.59 138.59 329.63 5.79 138.59 138.59 329.63 5.80 138.59 138.59 329.63 5.81 138.59 138.59 329.63 5.82 138.59 138.59 329.63 5.83 138.59 138.59 329.63 5.84 138.59 138.59 329.63 5.85 138.59 138.59 329.63 5.86 138.59 138.59 329.63 5.87 138.59 138.59 329.63 5.88 138.59 138.59 329.63 5.89 138.59 138.59 329.63 5.90 138.59 138.59 329.63 5.91 138.59 138.59 329.63 5.92 138.59 138.59 329.63 5.93 138.59 138.59 329.63 5.94 138.59 138.59 329.63 5.95 138.59 138.59 329.63 5.96 138.59 138.59 329.63 5.97 138.59 138.59 329.63 5.98 138.59 138.59 329.63 5.99 138.59 138.59 329.63 6.00 138.59 138.59 329.63 6.01 138.59 138.59 329.63 6.02 138.59 138.59 329.63 246.94 6.03 138.59 138.59 329.63 246.94 6.04 138.59 138.59 329.63 246.94 6.05 138.59 138.59 246.94 6.06 138.59 138.59 246.94 6.07 138.59 138.59 246.94 6.08 138.59 138.59 246.94 659.26 6.09 138.59 138.59 246.94 659.26 6.10 138.59 138.59 246.94 659.26 6.11 138.59 138.59 246.94 659.26 493.88 6.12 138.59 138.59 246.94 659.26 493.88 6.13 138.59 138.59 246.94 659.26 493.88 6.14 138.59 138.59 246.94 659.26 493.88 6.15 138.59 138.59 246.94 659.26 493.88 6.16 138.59 138.59 246.94 659.26 493.88 6.17 138.59 138.59 246.94 659.26 493.88 6.18 138.59 138.59 246.94 659.26 493.88 6.19 138.59 138.59 246.94 659.26 493.88 6.20 138.59 138.59 246.94 659.26 493.88 6.21 138.59 138.59 246.94 659.26 493.88 6.22 138.59 138.59 246.94 659.26 493.88 6.23 138.59 138.59 246.94 659.26 493.88 6.24 138.59 138.59 246.94 659.26 493.88 6.25 138.59 138.59 246.94 659.26 493.88 6.26 138.59 138.59 246.94 659.26 493.88 6.27 138.59 138.59 246.94 659.26 493.88 6.28 138.59 138.59 246.94 659.26 493.88 6.29 138.59 138.59 246.94 659.26 493.88 6.30 138.59 138.59 246.94 659.26 493.88 6.31 138.59 138.59 246.94 659.26 493.88 6.32 138.59 138.59 246.94 659.26 493.88 6.33 138.59 138.59 246.94 659.26 493.88 6.34 138.59 138.59 246.94 659.26 493.88 6.35 138.59 138.59 246.94 659.26 493.88 6.36 138.59 138.59 246.94 659.26 493.88 6.37 138.59 138.59 246.94 659.26 493.88 6.38 138.59 138.59 246.94 659.26 493.88 6.39 138.59 138.59 246.94 659.26 493.88 6.40 138.59 138.59 246.94 659.26 493.88 6.41 138.59 138.59 246.94 659.26 493.88 6.42 138.59 138.59 246.94 659.26 493.88 6.43 138.59 138.59 246.94 659.26 493.88 6.44 138.59 138.59 246.94 659.26 493.88 6.45 138.59 138.59 246.94 659.26 493.88 6.46 138.59 138.59 246.94 659.26 493.88 6.47 138.59 138.59 246.94 659.26 493.88 6.48 138.59 138.59 246.94 659.26 493.88 6.49 138.59 138.59 246.94 659.26 493.88 6.50 138.59 138.59 246.94 659.26 493.88 6.51 138.59 138.59 246.94 659.26 493.88 329.63 6.52 138.59 138.59 246.94 659.26 493.88 329.63 6.53 138.59 138.59 246.94 659.26 493.88 329.63 6.54 138.59 138.59 246.94 659.26 493.88 329.63 6.55 138.59 138.59 246.94 659.26 493.88 329.63 6.56 138.59 138.59 659.26 493.88 329.63 6.57 138.59 138.59 659.26 493.88 329.63 6.58 138.59 138.59 659.26 493.88 329.63 6.59 138.59 138.59 659.26 493.88 329.63 6.60 138.59 138.59 659.26 493.88 329.63 6.61 138.59 138.59 659.26 493.88 329.63 6.62 138.59 138.59 659.26 493.88 329.63 6.63 138.59 138.59 659.26 493.88 329.63 6.64 138.59 138.59 659.26 493.88 329.63 6.65 138.59 138.59 659.26 493.88 329.63 6.66 138.59 138.59 659.26 493.88 329.63 6.67 138.59 138.59 659.26 493.88 329.63 6.68 138.59 138.59 659.26 493.88 329.63 6.69 138.59 138.59 659.26 493.88 329.63 6.70 138.59 138.59 659.26 493.88 329.63 6.71 138.59 138.59 659.26 493.88 329.63 6.72 138.59 138.59 659.26 493.88 329.63 6.73 138.59 138.59 659.26 493.88 329.63 6.74 138.59 138.59 659.26 493.88 329.63 6.75 138.59 138.59 659.26 493.88 329.63 6.76 138.59 138.59 659.26 493.88 329.63 6.77 138.59 138.59 659.26 493.88 329.63 6.78 138.59 138.59 659.26 493.88 329.63 6.79 138.59 138.59 659.26 493.88 329.63 6.80 138.59 138.59 659.26 493.88 329.63 6.81 138.59 138.59 659.26 493.88 329.63 6.82 138.59 138.59 659.26 493.88 329.63 6.83 138.59 138.59 659.26 493.88 329.63 6.84 138.59 138.59 659.26 493.88 329.63 6.85 138.59 138.59 659.26 493.88 329.63 6.86 138.59 138.59 659.26 493.88 329.63 6.87 138.59 138.59 659.26 493.88 329.63 6.88 138.59 138.59 659.26 493.88 329.63 6.89 138.59 138.59 659.26 493.88 329.63 6.90 138.59 138.59 659.26 493.88 329.63 155.56 6.91 138.59 138.59 659.26 493.88 329.63 155.56 6.92 138.59 138.59 659.26 493.88 329.63 155.56 6.93 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.94 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.95 138.59 138.59 659.26 493.88 329.63 155.56 739.99 6.96 138.59 659.26 493.88 329.63 155.56 739.99 6.97 138.59 493.88 329.63 155.56 739.99 6.98 138.59 493.88 155.56 739.99 155.56 6.99 138.59 493.88 155.56 739.99 155.56 220.00 7.00 138.59 493.88 155.56 739.99 155.56 220.00 7.01 138.59 493.88 155.56 739.99 155.56 220.00 7.02 138.59 493.88 155.56 739.99 155.56 220.00 7.03 138.59 493.88 155.56 739.99 155.56 220.00 440.00 7.04 138.59 493.88 155.56 739.99 155.56 220.00 440.00 7.05 493.88 155.56 739.99 155.56 220.00 440.00 7.06 493.88 155.56 739.99 155.56 220.00 440.00 7.07 493.88 155.56 739.99 155.56 220.00 440.00 7.08 493.88 155.56 739.99 155.56 220.00 440.00 7.09 493.88 155.56 739.99 155.56 220.00 440.00 7.10 493.88 155.56 739.99 155.56 220.00 440.00 7.11 493.88 155.56 739.99 155.56 220.00 440.00 7.12 155.56 739.99 155.56 220.00 440.00 7.13 155.56 739.99 155.56 220.00 440.00 7.14 155.56 739.99 155.56 220.00 440.00 7.15 155.56 739.99 155.56 220.00 440.00 7.16 155.56 739.99 155.56 220.00 440.00 7.17 155.56 739.99 155.56 220.00 440.00 7.18 155.56 739.99 155.56 220.00 440.00 7.19 155.56 739.99 155.56 220.00 440.00 7.20 155.56 739.99 155.56 220.00 440.00 7.21 155.56 739.99 155.56 220.00 440.00 7.22 155.56 739.99 155.56 220.00 440.00 7.23 155.56 739.99 155.56 220.00 440.00 7.24 155.56 739.99 155.56 220.00 440.00 7.25 155.56 739.99 155.56 220.00 440.00 7.26 155.56 739.99 155.56 220.00 440.00 7.27 155.56 739.99 155.56 220.00 440.00 7.28 155.56 739.99 155.56 220.00 440.00 7.29 155.56 739.99 155.56 220.00 440.00 7.30 155.56 739.99 155.56 220.00 440.00 7.31 155.56 739.99 155.56 220.00 440.00 7.32 155.56 739.99 155.56 220.00 440.00 7.33 155.56 739.99 155.56 220.00 440.00 7.34 155.56 739.99 155.56 220.00 440.00 7.35 155.56 739.99 155.56 220.00 440.00 7.36 155.56 739.99 155.56 220.00 440.00 7.37 155.56 739.99 155.56 220.00 440.00 7.38 155.56 739.99 155.56 220.00 440.00 7.39 155.56 739.99 155.56 220.00 440.00 7.40 155.56 739.99 155.56 220.00 440.00 7.41 155.56 739.99 155.56 220.00 440.00 7.42 155.56 739.99 155.56 220.00 440.00 7.43 155.56 739.99 155.56 220.00 440.00 7.44 155.56 739.99 155.56 220.00 440.00 7.45 155.56 739.99 155.56 220.00 440.00 7.46 155.56 739.99 155.56 220.00 440.00 7.47 155.56 739.99 155.56 220.00 440.00 369.99 7.48 155.56 739.99 155.56 220.00 440.00 369.99 7.49 155.56 739.99 155.56 220.00 440.00 369.99 7.50 155.56 739.99 155.56 220.00 440.00 369.99 7.51 155.56 739.99 155.56 440.00 369.99 7.52 155.56 739.99 155.56 440.00 369.99 7.53 155.56 739.99 155.56 440.00 369.99 7.54 155.56 739.99 155.56 440.00 369.99 7.55 155.56 739.99 155.56 440.00 369.99 7.56 155.56 739.99 155.56 440.00 369.99 7.57 155.56 739.99 155.56 440.00 369.99 7.58 155.56 739.99 155.56 440.00 369.99 7.59 155.56 739.99 155.56 440.00 369.99 7.60 155.56 739.99 155.56 440.00 369.99 7.61 155.56 739.99 155.56 440.00 369.99 7.62 155.56 739.99 155.56 440.00 369.99 7.63 155.56 739.99 155.56 440.00 369.99 7.64 155.56 739.99 155.56 440.00 369.99 7.65 155.56 739.99 155.56 440.00 369.99 7.66 155.56 739.99 155.56 440.00 369.99 7.67 155.56 739.99 155.56 440.00 369.99 7.68 155.56 739.99 155.56 440.00 369.99 7.69 155.56 739.99 155.56 440.00 369.99 7.70 155.56 739.99 155.56 440.00 369.99 7.71 155.56 739.99 155.56 440.00 369.99 7.72 155.56 739.99 155.56 440.00 369.99 7.73 155.56 739.99 155.56 440.00 369.99 7.74 155.56 739.99 155.56 440.00 369.99 7.75 155.56 739.99 155.56 440.00 369.99 7.76 155.56 739.99 155.56 440.00 369.99 7.77 155.56 739.99 155.56 440.00 369.99 7.78 155.56 739.99 155.56 440.00 369.99 7.79 155.56 739.99 155.56 440.00 369.99 7.80 155.56 739.99 155.56 440.00 369.99 7.81 155.56 739.99 155.56 440.00 369.99 7.82 155.56 739.99 155.56 440.00 369.99 7.83 155.56 739.99 155.56 440.00 369.99 7.84 155.56 739.99 155.56 440.00 369.99 7.85 155.56 739.99 155.56 440.00 369.99 7.86 155.56 739.99 155.56 440.00 369.99 7.87 155.56 739.99 155.56 440.00 369.99 7.88 155.56 739.99 155.56 440.00 369.99 415.30 7.89 155.56 739.99 155.56 440.00 369.99 415.30 7.90 155.56 739.99 155.56 440.00 369.99 415.30 164.81 7.91 155.56 739.99 155.56 440.00 369.99 415.30 164.81 554.37 7.92 155.56 739.99 155.56 369.99 415.30 164.81 554.37 7.93 155.56 739.99 155.56 369.99 415.30 164.81 554.37 7.94 155.56 739.99 155.56 369.99 415.30 164.81 554.37 164.81 7.95 155.56 369.99 415.30 164.81 554.37 164.81 7.96 155.56 369.99 415.30 164.81 554.37 164.81 7.97 155.56 369.99 415.30 164.81 554.37 164.81 7.98 369.99 415.30 164.81 554.37 164.81 7.99 369.99 415.30 164.81 554.37 164.81 207.65 8.00 415.30 164.81 554.37 164.81 207.65 8.01 415.30 164.81 554.37 164.81 207.65 8.02 415.30 164.81 554.37 164.81 207.65 8.03 415.30 164.81 554.37 164.81 207.65 8.04 415.30 164.81 554.37 164.81 207.65 8.05 415.30 164.81 554.37 164.81 207.65 8.06 415.30 164.81 554.37 164.81 207.65 8.07 415.30 164.81 554.37 164.81 207.65 8.08 415.30 164.81 554.37 164.81 207.65 8.09 415.30 164.81 554.37 164.81 207.65 8.10 415.30 164.81 554.37 164.81 207.65 8.11 415.30 164.81 554.37 164.81 207.65 8.12 415.30 164.81 554.37 164.81 207.65 8.13 415.30 164.81 554.37 164.81 207.65 8.14 415.30 164.81 554.37 164.81 207.65 8.15 415.30 164.81 554.37 164.81 207.65 8.16 415.30 164.81 554.37 164.81 207.65 8.17 415.30 164.81 554.37 164.81 207.65 8.18 415.30 164.81 554.37 164.81 207.65 8.19 415.30 164.81 554.37 164.81 207.65 8.20 415.30 164.81 554.37 164.81 207.65 8.21 415.30 164.81 554.37 164.81 207.65 8.22 415.30 164.81 554.37 164.81 207.65 8.23 415.30 164.81 554.37 164.81 207.65 8.24 415.30 164.81 554.37 164.81 207.65 8.25 415.30 164.81 554.37 164.81 207.65 8.26 415.30 164.81 554.37 164.81 207.65 8.27 415.30 164.81 554.37 164.81 207.65 8.28 415.30 164.81 554.37 164.81 207.65 8.29 415.30 164.81 554.37 164.81 207.65 8.30 415.30 164.81 554.37 164.81 207.65 8.31 415.30 164.81 554.37 164.81 207.65 8.32 415.30 164.81 554.37 164.81 207.65 8.33 415.30 164.81 554.37 164.81 207.65 8.34 415.30 164.81 554.37 164.81 207.65 8.35 415.30 164.81 554.37 164.81 207.65 8.36 415.30 164.81 554.37 164.81 207.65 8.37 415.30 164.81 554.37 164.81 207.65 8.38 415.30 164.81 554.37 164.81 207.65 8.39 415.30 164.81 554.37 164.81 207.65 8.40 415.30 164.81 554.37 164.81 207.65 8.41 415.30 164.81 554.37 164.81 207.65 415.30 8.42 415.30 164.81 554.37 164.81 207.65 415.30 8.43 415.30 164.81 554.37 164.81 415.30 8.44 415.30 164.81 554.37 164.81 415.30 8.45 415.30 164.81 554.37 164.81 415.30 8.46 415.30 164.81 554.37 164.81 415.30 8.47 415.30 164.81 554.37 164.81 415.30 8.48 415.30 164.81 554.37 164.81 415.30 8.49 415.30 164.81 554.37 164.81 415.30 8.50 415.30 164.81 554.37 164.81 415.30 8.51 415.30 164.81 554.37 164.81 415.30 8.52 415.30 164.81 554.37 164.81 415.30 8.53 415.30 164.81 554.37 164.81 415.30 8.54 415.30 164.81 554.37 164.81 415.30 8.55 415.30 164.81 554.37 164.81 415.30 8.56 415.30 164.81 554.37 164.81 415.30 8.57 415.30 164.81 554.37 164.81 415.30 8.58 415.30 164.81 554.37 164.81 415.30 8.59 415.30 164.81 554.37 164.81 415.30 8.60 415.30 164.81 554.37 164.81 415.30 8.61 415.30 164.81 554.37 164.81 415.30 8.62 415.30 164.81 554.37 164.81 415.30 8.63 415.30 164.81 554.37 164.81 415.30 8.64 415.30 164.81 554.37 164.81 415.30 8.65 415.30 164.81 554.37 164.81 415.30 8.66 415.30 164.81 554.37 164.81 415.30 8.67 415.30 164.81 554.37 164.81 415.30 8.68 415.30 164.81 554.37 164.81 415.30 8.69 415.30 164.81 554.37 164.81 415.30 8.70 415.30 164.81 554.37 164.81 415.30 8.71 415.30 164.81 554.37 164.81 415.30 8.72 415.30 164.81 554.37 164.81 415.30 8.73 415.30 164.81 554.37 164.81 415.30 8.74 415.30 164.81 554.37 164.81 415.30 8.75 415.30 164.81 554.37 164.81 415.30 8.76 415.30 164.81 554.37 164.81 415.30 8.77 415.30 164.81 554.37 164.81 415.30 8.78 415.30 164.81 554.37 164.81 415.30 8.79 415.30 164.81 554.37 164.81 415.30 8.80 415.30 164.81 554.37 164.81 415.30 8.81 415.30 164.81 554.37 164.81 415.30 8.82 415.30 164.81 554.37 164.81 415.30 8.83 415.30 164.81 554.37 164.81 415.30 8.84 415.30 164.81 554.37 164.81 415.30 8.85 415.30 164.81 554.37 164.81 415.30 8.86 415.30 164.81 554.37 164.81 415.30 8.87 415.30 164.81 554.37 164.81 415.30 8.88 415.30 164.81 554.37 164.81 415.30 8.89 415.30 164.81 554.37 164.81 415.30 207.65 8.90 415.30 164.81 554.37 164.81 415.30 207.65 8.91 164.81 554.37 164.81 415.30 207.65 277.18 8.92 164.81 554.37 164.81 415.30 207.65 277.18 8.93 164.81 554.37 164.81 415.30 207.65 277.18 8.94 164.81 554.37 164.81 415.30 207.65 277.18 8.95 164.81 554.37 164.81 415.30 207.65 277.18 659.26 8.96 164.81 554.37 164.81 415.30 207.65 277.18 659.26 8.97 164.81 554.37 164.81 207.65 277.18 659.26 8.98 164.81 554.37 164.81 207.65 277.18 659.26 8.99 164.81 164.81 207.65 277.18 659.26 9.00 164.81 164.81 207.65 277.18 659.26 9.01 164.81 164.81 207.65 277.18 659.26 9.02 164.81 164.81 207.65 277.18 659.26 9.03 164.81 164.81 207.65 277.18 659.26 9.04 164.81 164.81 207.65 277.18 659.26 9.05 164.81 164.81 207.65 277.18 659.26 9.06 164.81 164.81 207.65 277.18 659.26 9.07 164.81 164.81 207.65 277.18 659.26 9.08 164.81 164.81 207.65 277.18 659.26 9.09 164.81 164.81 207.65 277.18 659.26 9.10 164.81 164.81 207.65 277.18 659.26 9.11 164.81 164.81 207.65 277.18 659.26 9.12 164.81 164.81 207.65 277.18 659.26 9.13 164.81 164.81 207.65 277.18 659.26 9.14 164.81 164.81 207.65 277.18 659.26 9.15 164.81 164.81 207.65 277.18 659.26 9.16 164.81 164.81 207.65 277.18 659.26 9.17 164.81 164.81 207.65 277.18 659.26 9.18 164.81 164.81 207.65 277.18 659.26 9.19 164.81 164.81 207.65 277.18 659.26 9.20 164.81 164.81 207.65 277.18 659.26 9.21 164.81 164.81 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659.26 415.30 9.46 164.81 164.81 207.65 659.26 415.30 9.47 164.81 164.81 207.65 659.26 415.30 9.48 164.81 164.81 207.65 659.26 415.30 9.49 164.81 164.81 207.65 659.26 415.30 9.50 164.81 164.81 207.65 659.26 415.30 9.51 164.81 164.81 207.65 659.26 415.30 9.52 164.81 164.81 207.65 659.26 415.30 9.53 164.81 164.81 207.65 659.26 415.30 9.54 164.81 164.81 207.65 659.26 415.30 9.55 164.81 164.81 207.65 659.26 415.30 9.56 164.81 164.81 207.65 659.26 415.30 9.57 164.81 164.81 207.65 659.26 415.30 9.58 164.81 164.81 207.65 659.26 415.30 9.59 164.81 164.81 207.65 659.26 415.30 9.60 164.81 164.81 207.65 659.26 415.30 9.61 164.81 164.81 207.65 659.26 415.30 9.62 164.81 164.81 207.65 659.26 415.30 9.63 164.81 164.81 207.65 659.26 415.30 9.64 164.81 164.81 207.65 659.26 415.30 9.65 164.81 164.81 207.65 659.26 415.30 9.66 164.81 164.81 207.65 659.26 415.30 9.67 164.81 164.81 207.65 659.26 415.30 9.68 164.81 164.81 207.65 659.26 415.30 9.69 164.81 164.81 207.65 659.26 415.30 9.70 164.81 164.81 207.65 659.26 415.30 9.71 164.81 164.81 207.65 659.26 415.30 9.72 164.81 164.81 207.65 659.26 415.30 9.73 164.81 164.81 207.65 659.26 415.30 9.74 164.81 164.81 207.65 659.26 415.30 9.75 164.81 164.81 207.65 659.26 415.30 9.76 164.81 164.81 207.65 659.26 415.30 9.77 164.81 164.81 207.65 659.26 415.30 9.78 164.81 164.81 207.65 659.26 415.30 9.79 164.81 164.81 207.65 659.26 415.30 155.56 9.80 164.81 164.81 207.65 659.26 415.30 155.56 9.81 164.81 164.81 207.65 659.26 415.30 155.56 493.88 9.82 164.81 164.81 207.65 659.26 415.30 155.56 493.88 9.83 164.81 164.81 207.65 659.26 415.30 155.56 493.88 246.94 9.84 164.81 207.65 659.26 415.30 155.56 493.88 246.94 9.85 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.86 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.87 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.88 164.81 207.65 659.26 155.56 493.88 246.94 155.56 9.89 207.65 659.26 155.56 493.88 246.94 155.56 9.90 659.26 155.56 493.88 246.94 155.56 9.91 659.26 155.56 493.88 246.94 155.56 9.92 659.26 155.56 493.88 246.94 155.56 9.93 659.26 155.56 493.88 246.94 155.56 9.94 659.26 155.56 493.88 246.94 155.56 9.95 659.26 155.56 493.88 246.94 155.56 9.96 659.26 155.56 493.88 246.94 155.56 9.97 659.26 155.56 493.88 246.94 155.56 9.98 659.26 155.56 493.88 246.94 155.56 9.99 659.26 155.56 493.88 246.94 155.56 10.00 659.26 155.56 493.88 246.94 155.56 10.01 659.26 155.56 493.88 246.94 155.56 10.02 659.26 155.56 493.88 246.94 155.56 10.03 659.26 155.56 493.88 246.94 155.56 10.04 659.26 155.56 493.88 246.94 155.56 10.05 659.26 155.56 493.88 246.94 155.56 10.06 659.26 155.56 493.88 246.94 155.56 10.07 659.26 155.56 493.88 246.94 155.56 10.08 659.26 155.56 493.88 246.94 155.56 10.09 659.26 155.56 493.88 246.94 155.56 10.10 659.26 155.56 493.88 246.94 155.56 10.11 659.26 155.56 493.88 246.94 155.56 10.12 659.26 155.56 493.88 246.94 155.56 10.13 659.26 155.56 493.88 246.94 155.56 10.14 659.26 155.56 493.88 246.94 155.56 10.15 659.26 155.56 493.88 246.94 155.56 10.16 659.26 155.56 493.88 246.94 155.56 10.17 659.26 155.56 493.88 246.94 155.56 10.18 659.26 155.56 493.88 246.94 155.56 10.19 659.26 155.56 493.88 246.94 155.56 10.20 659.26 155.56 493.88 246.94 155.56 10.21 659.26 155.56 493.88 246.94 155.56 10.22 659.26 155.56 493.88 246.94 155.56 10.23 659.26 155.56 493.88 246.94 155.56 10.24 659.26 155.56 493.88 246.94 155.56 10.25 659.26 155.56 493.88 246.94 155.56 10.26 659.26 155.56 493.88 246.94 155.56 10.27 659.26 155.56 493.88 246.94 155.56 10.28 659.26 155.56 493.88 246.94 155.56 10.29 659.26 155.56 493.88 246.94 155.56 10.30 659.26 155.56 493.88 246.94 155.56 10.31 659.26 155.56 493.88 246.94 155.56 10.32 659.26 155.56 493.88 246.94 155.56 10.33 659.26 155.56 493.88 246.94 155.56 10.34 659.26 155.56 246.94 155.56 10.35 659.26 155.56 246.94 155.56 246.94 10.36 659.26 155.56 246.94 155.56 246.94 10.37 659.26 155.56 246.94 155.56 246.94 10.38 659.26 155.56 246.94 155.56 246.94 10.39 659.26 155.56 246.94 155.56 246.94 10.40 659.26 155.56 246.94 155.56 246.94 10.41 659.26 155.56 246.94 155.56 246.94 10.42 659.26 155.56 246.94 155.56 246.94 10.43 659.26 155.56 246.94 155.56 246.94 10.44 659.26 155.56 246.94 155.56 246.94 10.45 659.26 155.56 246.94 155.56 246.94 10.46 659.26 155.56 246.94 155.56 246.94 10.47 659.26 155.56 246.94 155.56 246.94 10.48 659.26 155.56 246.94 155.56 246.94 10.49 659.26 155.56 246.94 155.56 246.94 10.50 659.26 155.56 246.94 155.56 246.94 10.51 659.26 155.56 246.94 155.56 246.94 10.52 659.26 155.56 246.94 155.56 246.94 10.53 659.26 155.56 246.94 155.56 246.94 10.54 659.26 155.56 246.94 155.56 246.94 10.55 659.26 155.56 246.94 155.56 246.94 10.56 659.26 155.56 246.94 155.56 246.94 10.57 659.26 155.56 246.94 155.56 246.94 10.58 659.26 155.56 246.94 155.56 246.94 10.59 659.26 155.56 246.94 155.56 246.94 10.60 659.26 155.56 246.94 155.56 246.94 10.61 659.26 155.56 246.94 155.56 246.94 10.62 659.26 155.56 246.94 155.56 246.94 10.63 659.26 155.56 246.94 155.56 246.94 10.64 659.26 155.56 246.94 155.56 246.94 10.65 659.26 155.56 246.94 155.56 246.94 10.66 659.26 155.56 246.94 155.56 246.94 10.67 659.26 155.56 246.94 155.56 246.94 10.68 659.26 155.56 246.94 155.56 246.94 10.69 659.26 155.56 246.94 155.56 246.94 10.70 659.26 155.56 246.94 155.56 246.94 10.71 659.26 155.56 246.94 155.56 246.94 10.72 659.26 155.56 246.94 155.56 246.94 10.73 659.26 155.56 246.94 155.56 246.94 10.74 659.26 155.56 246.94 155.56 246.94 10.75 659.26 155.56 246.94 155.56 246.94 138.59 10.76 659.26 155.56 246.94 155.56 246.94 138.59 10.77 659.26 155.56 246.94 155.56 246.94 138.59 10.78 659.26 155.56 246.94 155.56 246.94 138.59 10.79 659.26 155.56 155.56 246.94 138.59 10.80 659.26 155.56 155.56 246.94 138.59 10.81 659.26 155.56 155.56 246.94 138.59 138.59 10.82 659.26 155.56 155.56 246.94 138.59 138.59 10.83 659.26 155.56 246.94 138.59 138.59 233.08 10.84 659.26 155.56 246.94 138.59 138.59 233.08 10.85 659.26 155.56 246.94 138.59 138.59 233.08 10.86 659.26 155.56 246.94 138.59 138.59 233.08 10.87 659.26 246.94 138.59 138.59 233.08 10.88 659.26 246.94 138.59 138.59 233.08 10.89 659.26 246.94 138.59 138.59 233.08 10.90 659.26 138.59 138.59 233.08 10.91 659.26 138.59 138.59 233.08 10.92 659.26 138.59 138.59 233.08 10.93 659.26 138.59 138.59 233.08 10.94 659.26 138.59 138.59 233.08 10.95 659.26 138.59 138.59 233.08 10.96 659.26 138.59 138.59 233.08 10.97 659.26 138.59 138.59 233.08 10.98 659.26 138.59 138.59 233.08 10.99 659.26 138.59 138.59 233.08 11.00 659.26 138.59 138.59 233.08 11.01 659.26 138.59 138.59 233.08 11.02 659.26 138.59 138.59 233.08 11.03 659.26 138.59 138.59 233.08 11.04 659.26 138.59 138.59 233.08 11.05 659.26 138.59 138.59 233.08 11.06 659.26 138.59 138.59 233.08 11.07 659.26 138.59 138.59 233.08 11.08 659.26 138.59 138.59 233.08 11.09 659.26 138.59 138.59 233.08 11.10 659.26 138.59 138.59 233.08 11.11 659.26 138.59 138.59 233.08 11.12 659.26 138.59 138.59 233.08 11.13 659.26 138.59 138.59 233.08 11.14 659.26 138.59 138.59 233.08 11.15 659.26 138.59 138.59 233.08 11.16 659.26 138.59 138.59 233.08 11.17 659.26 138.59 138.59 233.08 11.18 659.26 138.59 138.59 233.08 11.19 659.26 138.59 138.59 233.08 11.20 659.26 138.59 138.59 233.08 11.21 659.26 138.59 138.59 233.08 11.22 659.26 138.59 138.59 233.08 11.23 659.26 138.59 138.59 233.08 11.24 659.26 138.59 138.59 233.08 11.25 659.26 138.59 138.59 233.08 11.26 659.26 138.59 138.59 233.08 11.27 659.26 138.59 138.59 233.08 466.16 11.28 659.26 138.59 138.59 233.08 466.16 329.63 11.29 659.26 138.59 138.59 233.08 466.16 329.63 11.30 659.26 138.59 138.59 233.08 466.16 329.63 11.31 659.26 138.59 138.59 233.08 466.16 329.63 11.32 659.26 138.59 138.59 233.08 466.16 329.63 11.33 659.26 138.59 138.59 233.08 466.16 329.63 11.34 659.26 138.59 138.59 233.08 466.16 329.63 11.35 659.26 138.59 138.59 233.08 466.16 329.63 11.36 659.26 138.59 138.59 233.08 466.16 329.63 11.37 659.26 138.59 138.59 233.08 466.16 329.63 11.38 659.26 138.59 138.59 233.08 466.16 329.63 11.39 659.26 138.59 138.59 233.08 466.16 329.63 11.40 659.26 138.59 138.59 233.08 466.16 329.63 11.41 659.26 138.59 138.59 233.08 466.16 329.63 11.42 659.26 138.59 138.59 233.08 466.16 329.63 11.43 659.26 138.59 138.59 233.08 466.16 329.63 11.44 659.26 138.59 138.59 233.08 466.16 329.63 11.45 659.26 138.59 138.59 233.08 466.16 329.63 11.46 659.26 138.59 138.59 233.08 466.16 329.63 11.47 659.26 138.59 138.59 233.08 466.16 329.63 11.48 659.26 138.59 138.59 233.08 466.16 329.63 11.49 659.26 138.59 138.59 233.08 466.16 329.63 11.50 659.26 138.59 138.59 233.08 466.16 329.63 11.51 659.26 138.59 138.59 233.08 466.16 329.63 11.52 659.26 138.59 138.59 233.08 466.16 329.63 11.53 659.26 138.59 138.59 233.08 466.16 329.63 11.54 659.26 138.59 138.59 233.08 466.16 329.63 11.55 659.26 138.59 138.59 233.08 466.16 329.63 11.56 659.26 138.59 138.59 233.08 466.16 329.63 11.57 659.26 138.59 138.59 233.08 466.16 329.63 11.58 659.26 138.59 138.59 233.08 466.16 329.63 11.59 659.26 138.59 138.59 233.08 466.16 329.63 11.60 659.26 138.59 138.59 233.08 466.16 329.63 11.61 659.26 138.59 138.59 233.08 466.16 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466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.06 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.07 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.08 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.09 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.10 659.26 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.11 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.12 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.13 466.16 138.59 69.30 69.30 466.16 932.33 164.81 13.14 466.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.15 466.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.16 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.17 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.18 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.19 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.20 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.21 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.22 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.23 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.24 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.25 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.26 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.27 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.28 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.29 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.30 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.31 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.32 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.33 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.34 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.35 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.36 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.37 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.38 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.39 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.40 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.41 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.42 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.43 138.59 69.30 69.30 466.16 932.33 164.81 329.63 13.44 138.59 69.30 466.16 932.33 164.81 329.63 13.45 138.59 69.30 466.16 932.33 164.81 329.63 13.46 138.59 69.30 466.16 932.33 164.81 329.63 13.47 69.30 466.16 932.33 164.81 329.63 13.48 69.30 466.16 932.33 164.81 329.63 13.49 69.30 466.16 932.33 164.81 329.63 13.50 69.30 466.16 932.33 164.81 329.63 13.51 69.30 466.16 932.33 164.81 329.63 13.52 69.30 466.16 932.33 164.81 329.63 13.53 69.30 466.16 932.33 164.81 329.63 13.54 69.30 466.16 932.33 164.81 329.63 13.55 69.30 466.16 932.33 164.81 329.63 13.56 69.30 466.16 932.33 164.81 329.63 13.57 69.30 466.16 932.33 164.81 329.63 13.58 69.30 466.16 932.33 164.81 329.63 13.59 69.30 466.16 932.33 164.81 329.63 13.60 69.30 466.16 932.33 164.81 329.63 77.78 13.61 69.30 466.16 932.33 164.81 329.63 77.78 369.99 13.62 69.30 466.16 932.33 164.81 329.63 77.78 369.99 13.63 69.30 932.33 164.81 329.63 77.78 369.99 77.78 155.56 13.64 932.33 164.81 329.63 77.78 369.99 77.78 155.56 493.88 13.65 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.66 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.67 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.68 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.69 932.33 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.70 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.71 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.72 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 13.73 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.74 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.75 164.81 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.76 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.77 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.78 77.78 369.99 77.78 155.56 493.88 369.99 739.99 246.94 13.79 77.78 369.99 77.78 155.56 493.88 369.99 739.99 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77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.29 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.30 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.31 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.32 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.33 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.34 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.35 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.36 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.37 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.38 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.39 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.40 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.41 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.42 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.43 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.44 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.45 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.46 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.47 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.48 77.78 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.49 155.56 493.88 466.16 155.56 932.33 369.99 246.94 15.50 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.51 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.52 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 15.53 155.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 15.54 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.55 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.56 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 15.57 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 311.13 15.58 493.88 466.16 155.56 932.33 369.99 246.94 61.74 123.47 123.47 311.13 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311.13 311.13 15.91 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.92 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.93 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.94 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.95 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.96 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.97 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 15.98 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 15.99 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.00 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.01 493.88 466.16 932.33 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.02 493.88 466.16 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.03 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 16.04 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 16.05 493.88 61.74 123.47 123.47 311.13 311.13 415.30 830.61 246.94 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123.47 123.47 311.13 415.30 830.61 246.94 16.25 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.26 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.27 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.28 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.29 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.30 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.31 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.32 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.33 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.34 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.35 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.36 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.37 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.38 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.39 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.40 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.41 61.74 123.47 123.47 311.13 415.30 830.61 246.94 16.42 61.74 123.47 123.47 311.13 830.61 246.94 16.43 61.74 123.47 311.13 830.61 246.94 16.44 61.74 123.47 311.13 830.61 246.94 16.45 61.74 123.47 311.13 246.94 16.46 61.74 123.47 311.13 246.94 16.47 61.74 123.47 311.13 246.94 16.48 61.74 123.47 311.13 246.94 82.41 164.81 16.49 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.50 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.51 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 16.52 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 830.61 16.53 61.74 123.47 311.13 246.94 82.41 164.81 415.30 493.88 830.61 16.54 61.74 311.13 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.55 61.74 311.13 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.56 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.57 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.58 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.59 246.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.60 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.61 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.62 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.63 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.64 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.65 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.66 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.67 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.68 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.69 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.70 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.71 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.72 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.73 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.74 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.75 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.76 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.77 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.78 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.79 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.80 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.81 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.82 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.83 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.84 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.85 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.86 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.87 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.88 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.89 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.90 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.91 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.92 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.93 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.94 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.95 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.96 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.97 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.98 82.41 164.81 415.30 493.88 830.61 164.81 415.30 16.99 82.41 164.81 415.30 493.88 830.61 164.81 415.30 17.00 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.01 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.02 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.03 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.04 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.05 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.06 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.07 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.08 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.09 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.10 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.11 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.12 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.13 82.41 164.81 415.30 493.88 830.61 164.81 415.30 246.94 17.14 82.41 164.81 415.30 493.88 830.61 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739.99 77.78 246.94 155.56 17.83 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.84 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.85 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.86 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.87 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.88 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.89 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.90 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.91 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.92 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.93 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.94 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.95 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.96 155.56 369.99 369.99 739.99 77.78 246.94 155.56 17.97 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 17.98 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 17.99 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.00 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.01 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.02 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.03 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.04 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.05 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.06 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.07 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.08 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.09 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.10 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.11 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.12 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.13 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.14 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.15 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.16 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.17 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.18 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.19 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.20 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.21 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.22 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.23 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.24 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.25 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.26 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.27 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.28 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.29 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.30 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.31 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.32 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.33 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.34 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.35 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.36 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.37 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 18.38 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 18.39 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 18.40 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 18.41 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.42 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.43 155.56 369.99 369.99 739.99 77.78 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.44 369.99 369.99 739.99 246.94 155.56 246.94 61.74 311.13 123.47 830.61 415.30 18.45 369.99 369.99 246.94 246.94 61.74 311.13 123.47 830.61 415.30 18.46 369.99 246.94 61.74 311.13 123.47 830.61 415.30 18.47 369.99 246.94 61.74 311.13 123.47 830.61 415.30 18.48 369.99 246.94 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.49 369.99 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.50 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.51 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.52 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.53 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.54 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.55 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.56 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.57 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.58 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.59 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.60 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.61 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.62 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.63 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.64 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.65 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.66 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.67 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.68 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.69 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.70 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.71 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.72 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.73 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.74 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.75 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.76 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.77 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.78 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.79 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.80 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.81 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.82 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.83 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.84 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.85 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.86 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.87 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.88 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.89 61.74 311.13 123.47 830.61 415.30 311.13 123.47 18.90 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.91 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.92 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.93 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.94 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.95 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.96 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.97 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.98 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 18.99 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.00 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.01 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.02 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.03 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.04 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.05 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.06 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.07 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.08 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.09 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.10 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.11 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.12 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.13 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.14 61.74 311.13 123.47 830.61 415.30 311.13 123.47 246.94 19.15 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.16 61.74 123.47 830.61 415.30 311.13 123.47 246.94 19.17 61.74 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138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.06 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.07 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.08 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.09 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.10 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.11 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.12 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.13 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.14 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.15 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.16 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.17 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.18 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.19 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.20 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.21 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.22 138.59 69.30 329.63 659.26 138.59 207.65 246.94 20.23 69.30 329.63 659.26 138.59 207.65 246.94 20.24 69.30 329.63 659.26 138.59 207.65 246.94 20.25 69.30 329.63 659.26 207.65 246.94 20.26 69.30 329.63 659.26 207.65 246.94 20.27 69.30 329.63 659.26 207.65 246.94 20.28 69.30 329.63 659.26 207.65 246.94 20.29 69.30 329.63 659.26 207.65 246.94 20.30 69.30 329.63 659.26 207.65 246.94 116.54 20.31 69.30 329.63 659.26 207.65 246.94 116.54 116.54 20.32 69.30 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 20.33 69.30 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 20.34 69.30 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 20.35 69.30 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 20.36 69.30 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 20.37 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 20.38 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.39 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.40 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.41 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.42 329.63 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.43 659.26 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.44 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.45 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.46 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.47 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.48 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.49 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.50 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.51 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.52 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.53 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.54 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.55 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.56 207.65 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.57 246.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.58 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.59 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.60 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.61 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.62 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.63 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.64 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.65 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.66 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.67 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.68 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.69 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.70 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.71 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.72 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.73 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.74 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.75 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.76 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.77 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.78 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.79 116.54 116.54 415.30 58.27 415.30 830.61 311.13 20.80 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.81 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.82 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.83 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.84 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.85 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.86 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.87 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.88 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.89 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.90 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.91 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.92 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.93 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.94 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.95 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.96 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.97 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.98 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 20.99 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.00 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.01 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.02 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.03 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.04 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.05 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.06 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.07 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.08 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.09 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.10 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.11 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.12 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.13 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.14 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.15 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.16 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.17 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.18 116.54 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.19 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.20 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.21 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.22 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.23 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.24 116.54 415.30 58.27 415.30 830.61 311.13 277.18 21.25 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 21.26 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.27 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.28 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.29 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 21.30 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 21.31 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 21.32 116.54 415.30 58.27 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 21.33 116.54 415.30 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 233.08 21.34 116.54 415.30 415.30 830.61 311.13 277.18 164.81 82.41 164.81 233.08 233.08 21.35 116.54 415.30 415.30 830.61 311.13 164.81 82.41 164.81 233.08 233.08 21.36 116.54 415.30 415.30 830.61 311.13 164.81 82.41 164.81 233.08 233.08 21.37 415.30 415.30 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369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.81 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.82 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.83 369.99 369.99 77.78 155.56 739.99 246.94 155.56 22.84 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.85 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.86 369.99 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.87 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.88 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.89 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.90 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.91 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.92 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.93 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.94 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.95 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.96 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.97 369.99 77.78 155.56 739.99 246.94 155.56 246.94 22.98 369.99 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246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.35 369.99 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.36 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.37 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 23.38 739.99 246.94 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.39 739.99 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.40 739.99 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.41 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.42 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.43 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.44 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.45 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.46 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.47 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.48 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.49 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.50 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.51 246.94 61.74 123.47 123.47 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246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.71 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.72 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.73 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.74 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.75 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.76 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.77 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.78 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.79 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.80 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.81 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.82 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.83 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.84 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.85 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.86 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.87 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.88 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.89 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.90 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.91 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.92 246.94 61.74 123.47 123.47 311.13 311.13 622.25 23.93 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.94 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.95 246.94 61.74 123.47 123.47 311.13 311.13 622.25 246.94 23.96 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.97 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.98 246.94 61.74 123.47 123.47 311.13 622.25 246.94 23.99 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.00 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.01 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.02 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.03 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.04 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.05 246.94 61.74 123.47 123.47 311.13 622.25 246.94 24.06 246.94 61.74 123.47 123.47 311.13 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329.63 246.94 25.38 103.83 659.26 51.91 103.83 329.63 246.94 25.39 103.83 659.26 51.91 103.83 329.63 246.94 25.40 103.83 659.26 51.91 103.83 329.63 246.94 25.41 103.83 51.91 103.83 329.63 246.94 25.42 103.83 51.91 103.83 329.63 246.94 554.37 277.18 25.43 103.83 51.91 103.83 329.63 246.94 554.37 277.18 25.44 103.83 51.91 103.83 329.63 246.94 554.37 277.18 25.45 103.83 51.91 103.83 329.63 246.94 554.37 277.18 25.46 51.91 103.83 329.63 246.94 554.37 277.18 25.47 51.91 103.83 329.63 246.94 554.37 277.18 116.54 329.63 25.48 51.91 103.83 246.94 554.37 277.18 116.54 329.63 25.49 51.91 103.83 246.94 554.37 277.18 116.54 329.63 58.27 116.54 25.50 51.91 103.83 246.94 554.37 277.18 116.54 329.63 58.27 116.54 25.51 51.91 246.94 554.37 277.18 116.54 329.63 58.27 116.54 25.52 246.94 554.37 277.18 116.54 329.63 58.27 116.54 25.53 246.94 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.54 246.94 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.55 246.94 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.56 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.57 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.58 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.59 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.60 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.61 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.62 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.63 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.64 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.65 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.66 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.67 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.68 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.69 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.70 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.71 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.72 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.73 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.74 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.75 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.76 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.77 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.78 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.79 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.80 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.81 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.82 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.83 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.84 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.85 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.86 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.87 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.88 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.89 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.90 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.91 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.92 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.93 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.94 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.95 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.96 554.37 277.18 116.54 329.63 58.27 116.54 277.18 25.97 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 25.98 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 25.99 554.37 277.18 116.54 329.63 58.27 116.54 277.18 164.81 26.00 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.01 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.02 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.03 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.04 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.05 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.06 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.07 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.08 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.09 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.10 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.11 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.12 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.13 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.14 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.15 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.16 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.17 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.18 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.19 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.20 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.21 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.22 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.23 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.24 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.25 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.26 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.27 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.28 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.29 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.30 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.31 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.32 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.33 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.34 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.35 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.36 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.37 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.38 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.39 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.40 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.41 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.42 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.43 554.37 116.54 329.63 58.27 116.54 277.18 164.81 26.44 554.37 116.54 329.63 58.27 116.54 277.18 164.81 659.26 26.45 554.37 116.54 329.63 58.27 116.54 277.18 164.81 659.26 103.83 26.46 116.54 329.63 58.27 116.54 277.18 164.81 659.26 103.83 51.91 103.83 246.94 329.63 26.47 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.48 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.49 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.50 116.54 329.63 58.27 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.51 329.63 116.54 277.18 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.52 329.63 116.54 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.53 329.63 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.54 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.55 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.56 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.57 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.58 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.59 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.60 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.61 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.62 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.63 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.64 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.65 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.66 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.67 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.68 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.69 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.70 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.71 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.72 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.73 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.74 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.75 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.76 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.77 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.78 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.79 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.80 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.81 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.82 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.83 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.84 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.85 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.86 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.87 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.88 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.89 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.90 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.91 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.92 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.93 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.94 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.95 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.96 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.97 659.26 103.83 51.91 103.83 246.94 329.63 329.63 26.98 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 26.99 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 27.00 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 27.01 659.26 103.83 51.91 103.83 246.94 329.63 329.63 246.94 27.02 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.03 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.04 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.05 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.06 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.07 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.08 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.09 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.10 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.11 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.12 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.13 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.14 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.15 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.16 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.17 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.18 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.19 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.20 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.21 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.22 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.23 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.24 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.25 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.26 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.27 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.28 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.29 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.30 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.31 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.32 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.33 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.34 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.35 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.36 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.37 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.38 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.39 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.40 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.41 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.42 659.26 103.83 51.91 103.83 246.94 329.63 246.94 27.43 659.26 103.83 51.91 103.83 246.94 329.63 246.94 49.00 27.44 659.26 103.83 51.91 103.83 329.63 246.94 49.00 98.00 27.45 659.26 103.83 51.91 103.83 329.63 246.94 49.00 98.00 27.46 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 27.47 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 233.08 27.48 659.26 103.83 103.83 329.63 246.94 49.00 98.00 98.00 233.08 27.49 659.26 329.63 246.94 49.00 98.00 98.00 233.08 27.50 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.51 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.52 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.53 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.54 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.55 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.56 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.57 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.58 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.59 659.26 329.63 246.94 49.00 98.00 98.00 233.08 233.08 27.60 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.61 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.62 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.63 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.64 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.65 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.66 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.67 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.68 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.69 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.70 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.71 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.72 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.73 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.74 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.75 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.76 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.77 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.78 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.79 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.80 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.81 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.82 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.83 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.84 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.85 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.86 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.87 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.88 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.89 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.90 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.91 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.92 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.93 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.94 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.95 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.96 659.26 329.63 49.00 98.00 98.00 233.08 233.08 27.97 659.26 329.63 49.00 98.00 98.00 233.08 233.08 466.16 27.98 659.26 329.63 49.00 98.00 98.00 233.08 233.08 466.16 27.99 659.26 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.00 659.26 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.01 329.63 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.02 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.03 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.04 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.05 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.06 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.07 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.08 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.09 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.10 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.11 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.12 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.13 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.14 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.15 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.16 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.17 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.18 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.19 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.20 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.21 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.22 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.23 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.24 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.25 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.26 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.27 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.28 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.29 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.30 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.31 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.32 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.33 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.34 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.35 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.36 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.37 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.38 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.39 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.40 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.41 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.42 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.43 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.44 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.45 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.46 49.00 98.00 98.00 233.08 466.16 622.25 311.13 28.47 49.00 98.00 98.00 233.08 466.16 311.13 28.48 49.00 98.00 98.00 233.08 466.16 311.13 28.49 49.00 98.00 98.00 466.16 28.50 49.00 98.00 98.00 466.16 51.91 28.51 49.00 98.00 98.00 466.16 51.91 28.52 49.00 98.00 98.00 466.16 51.91 28.53 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 28.54 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 28.55 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 103.83 622.25 28.56 49.00 98.00 98.00 466.16 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.57 49.00 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.58 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.59 98.00 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.60 98.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.61 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.62 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.63 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.64 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.65 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.66 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.67 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.68 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.69 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.70 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.71 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.72 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.73 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.74 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.75 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.76 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.77 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.78 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.79 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.80 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.81 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.82 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.83 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.84 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.85 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.86 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.87 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.88 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.89 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.90 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.91 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.92 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.93 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.94 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.95 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.96 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.97 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.98 51.91 246.94 103.83 493.88 103.83 622.25 311.13 28.99 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.00 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.01 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.02 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.03 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.04 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.05 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.06 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.07 51.91 246.94 103.83 493.88 103.83 622.25 311.13 29.08 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.09 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.10 51.91 246.94 103.83 103.83 622.25 311.13 246.94 29.11 51.91 246.94 103.83 622.25 311.13 246.94 29.12 51.91 246.94 103.83 622.25 311.13 246.94 29.13 51.91 246.94 103.83 622.25 311.13 246.94 29.14 51.91 246.94 103.83 622.25 311.13 246.94 29.15 51.91 246.94 103.83 622.25 311.13 246.94 29.16 51.91 246.94 103.83 622.25 311.13 246.94 29.17 51.91 246.94 103.83 622.25 311.13 246.94 29.18 51.91 246.94 103.83 622.25 311.13 246.94 29.19 51.91 246.94 103.83 622.25 311.13 246.94 29.20 51.91 246.94 103.83 622.25 311.13 246.94 29.21 51.91 246.94 103.83 622.25 311.13 246.94 29.22 51.91 246.94 103.83 622.25 311.13 246.94 29.23 51.91 246.94 103.83 622.25 311.13 246.94 29.24 51.91 246.94 103.83 622.25 311.13 246.94 29.25 51.91 246.94 103.83 622.25 311.13 246.94 29.26 51.91 246.94 103.83 622.25 311.13 246.94 29.27 51.91 246.94 103.83 622.25 311.13 246.94 29.28 51.91 246.94 103.83 622.25 311.13 246.94 29.29 51.91 246.94 103.83 622.25 311.13 246.94 29.30 51.91 246.94 103.83 622.25 311.13 246.94 29.31 51.91 246.94 103.83 622.25 311.13 246.94 29.32 51.91 246.94 103.83 622.25 311.13 246.94 29.33 51.91 246.94 103.83 622.25 311.13 246.94 29.34 51.91 246.94 103.83 622.25 311.13 246.94 29.35 51.91 246.94 103.83 622.25 311.13 246.94 29.36 51.91 246.94 103.83 622.25 311.13 246.94 29.37 51.91 246.94 103.83 622.25 311.13 246.94 29.38 51.91 246.94 103.83 622.25 311.13 246.94 29.39 51.91 246.94 103.83 622.25 311.13 246.94 29.40 51.91 246.94 103.83 622.25 311.13 246.94 29.41 51.91 246.94 103.83 622.25 311.13 246.94 29.42 51.91 246.94 103.83 622.25 311.13 246.94 29.43 51.91 246.94 103.83 622.25 311.13 246.94 29.44 51.91 246.94 103.83 622.25 311.13 246.94 29.45 51.91 246.94 103.83 622.25 311.13 246.94 29.46 51.91 246.94 103.83 622.25 311.13 246.94 29.47 246.94 622.25 311.13 246.94 29.48 246.94 622.25 311.13 246.94 29.49 246.94 622.25 311.13 246.94 29.50 246.94 622.25 311.13 246.94 29.51 246.94 622.25 311.13 246.94 29.52 246.94 622.25 311.13 246.94 29.53 246.94 622.25 311.13 246.94 29.54 622.25 311.13 246.94 29.55 622.25 311.13 246.94 29.56 622.25 311.13 246.94 185.00 440.00 29.57 622.25 311.13 246.94 185.00 440.00 92.50 29.58 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.59 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.60 622.25 311.13 246.94 185.00 440.00 92.50 185.00 29.61 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.62 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.63 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.64 622.25 311.13 246.94 185.00 440.00 92.50 185.00 220.00 29.65 246.94 185.00 440.00 92.50 185.00 220.00 29.66 185.00 440.00 92.50 185.00 220.00 29.67 185.00 440.00 92.50 185.00 220.00 29.68 185.00 440.00 92.50 185.00 220.00 29.69 185.00 440.00 92.50 185.00 220.00 29.70 185.00 440.00 92.50 185.00 220.00 29.71 185.00 440.00 92.50 185.00 220.00 29.72 185.00 440.00 92.50 185.00 220.00 29.73 185.00 440.00 92.50 185.00 220.00 29.74 185.00 440.00 92.50 185.00 220.00 523.25 29.75 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.76 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.77 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.78 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.79 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.80 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.81 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.82 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.83 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.84 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.85 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.86 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.87 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.88 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.89 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.90 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.91 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.92 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.93 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.94 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.95 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.96 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.97 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.98 185.00 440.00 92.50 185.00 220.00 523.25 311.13 29.99 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.00 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.01 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.02 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.03 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.04 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.05 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.06 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.07 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.08 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.09 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.10 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.11 185.00 440.00 92.50 185.00 220.00 523.25 311.13 30.12 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.13 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.14 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.15 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.16 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.17 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.18 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.19 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.20 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.21 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.22 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.23 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.24 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.25 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.26 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.27 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.28 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.29 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.30 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.31 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.32 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.33 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.34 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.35 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.36 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.37 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.38 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.39 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.40 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.41 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.42 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.43 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.44 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.45 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.46 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.47 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.48 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.49 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.50 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.51 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.52 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.53 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.54 185.00 92.50 185.00 220.00 523.25 311.13 220.00 30.55 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 30.56 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.57 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.58 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.59 185.00 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.60 92.50 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.61 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.62 185.00 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.63 220.00 523.25 311.13 220.00 82.41 164.81 164.81 30.64 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 30.65 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 30.66 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 30.67 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.68 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.69 220.00 523.25 311.13 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.70 220.00 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.71 220.00 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.72 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.73 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.74 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.75 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.76 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.77 220.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.78 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.79 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.80 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.81 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.82 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.83 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.84 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.85 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.86 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.87 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.88 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.89 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.90 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.91 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.92 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.93 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.94 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.95 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.96 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.97 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.98 82.41 164.81 164.81 554.37 415.30 277.18 207.65 30.99 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.00 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.01 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.02 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.03 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.04 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.05 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.06 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.07 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.08 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.09 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.10 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.11 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.12 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.13 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.14 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.15 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.16 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.17 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.18 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.19 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.20 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.21 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.22 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.23 82.41 164.81 164.81 554.37 415.30 277.18 207.65 31.24 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.25 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.26 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.27 82.41 164.81 164.81 554.37 415.30 277.18 207.65 207.65 31.28 82.41 164.81 164.81 554.37 277.18 207.65 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164.81 329.63 2.85 329.63 164.81 329.63 2.86 329.63 164.81 329.63 2.87 329.63 164.81 329.63 2.88 329.63 164.81 329.63 2.89 329.63 164.81 329.63 349.23 2.90 329.63 164.81 329.63 349.23 2.91 329.63 164.81 329.63 349.23 2.92 164.81 329.63 349.23 2.93 164.81 329.63 349.23 2.94 164.81 329.63 349.23 2.95 164.81 329.63 349.23 174.61 2.96 164.81 329.63 349.23 174.61 2.97 164.81 329.63 349.23 174.61 2.98 329.63 349.23 174.61 2.99 329.63 349.23 174.61 3.00 329.63 349.23 174.61 3.01 329.63 349.23 174.61 3.02 329.63 349.23 174.61 3.03 329.63 349.23 174.61 3.04 329.63 349.23 174.61 3.05 329.63 349.23 174.61 3.06 329.63 349.23 174.61 3.07 329.63 349.23 174.61 3.08 329.63 349.23 174.61 349.23 3.09 329.63 349.23 174.61 349.23 3.10 329.63 349.23 174.61 349.23 3.11 349.23 174.61 349.23 3.12 349.23 174.61 349.23 3.13 349.23 174.61 349.23 329.63 3.14 349.23 174.61 349.23 329.63 3.15 174.61 349.23 329.63 3.16 174.61 349.23 329.63 3.17 174.61 349.23 329.63 164.81 3.18 174.61 349.23 329.63 164.81 3.19 174.61 349.23 329.63 164.81 3.20 349.23 329.63 164.81 3.21 349.23 329.63 164.81 3.22 349.23 329.63 164.81 3.23 349.23 329.63 164.81 3.24 349.23 329.63 164.81 3.25 349.23 329.63 164.81 3.26 349.23 329.63 164.81 3.27 329.63 164.81 329.63 3.28 329.63 164.81 329.63 3.29 329.63 164.81 329.63 3.30 329.63 164.81 329.63 3.31 329.63 164.81 329.63 3.32 329.63 164.81 329.63 3.33 329.63 164.81 329.63 3.34 329.63 164.81 329.63 3.35 329.63 164.81 329.63 146.83 3.36 329.63 164.81 329.63 146.83 3.37 329.63 329.63 146.83 3.38 329.63 329.63 146.83 293.66 3.39 329.63 329.63 146.83 293.66 3.40 329.63 329.63 146.83 293.66 3.41 329.63 146.83 293.66 3.42 329.63 146.83 293.66 3.43 329.63 146.83 293.66 3.44 329.63 146.83 293.66 3.45 329.63 146.83 293.66 293.66 3.46 329.63 146.83 293.66 293.66 3.47 146.83 293.66 293.66 3.48 146.83 293.66 293.66 3.49 146.83 293.66 293.66 3.50 146.83 293.66 293.66 3.51 146.83 293.66 293.66 3.52 146.83 293.66 293.66 3.53 146.83 293.66 293.66 130.81 3.54 146.83 293.66 293.66 130.81 3.55 293.66 293.66 130.81 3.56 293.66 293.66 130.81 3.57 293.66 293.66 130.81 3.58 293.66 293.66 130.81 3.59 293.66 293.66 130.81 261.63 3.60 293.66 293.66 130.81 261.63 3.61 293.66 293.66 130.81 261.63 261.63 3.62 293.66 130.81 261.63 261.63 3.63 293.66 130.81 261.63 261.63 3.64 130.81 261.63 261.63 3.65 130.81 261.63 261.63 3.66 130.81 261.63 261.63 3.67 130.81 261.63 261.63 3.68 130.81 261.63 261.63 3.69 130.81 261.63 261.63 3.70 130.81 261.63 261.63 3.71 130.81 261.63 261.63 123.47 3.72 130.81 261.63 261.63 123.47 3.73 261.63 261.63 123.47 3.74 261.63 261.63 123.47 3.75 261.63 261.63 123.47 3.76 261.63 261.63 123.47 3.77 261.63 261.63 123.47 3.78 261.63 261.63 123.47 3.79 261.63 261.63 123.47 246.94 3.80 261.63 123.47 246.94 246.94 3.81 123.47 246.94 246.94 3.82 123.47 246.94 246.94 3.83 123.47 246.94 246.94 3.84 123.47 246.94 246.94 3.85 123.47 246.94 246.94 3.86 123.47 246.94 246.94 3.87 123.47 246.94 246.94 3.88 123.47 246.94 246.94 3.89 123.47 246.94 246.94 3.90 123.47 246.94 246.94 3.91 123.47 246.94 246.94 110.00 3.92 246.94 246.94 110.00 3.93 246.94 246.94 110.00 3.94 246.94 246.94 110.00 3.95 246.94 246.94 110.00 3.96 246.94 246.94 110.00 3.97 246.94 246.94 110.00 3.98 246.94 246.94 110.00 3.99 246.94 246.94 110.00 220.00 4.00 246.94 246.94 110.00 220.00 4.01 246.94 246.94 110.00 220.00 220.00 4.02 246.94 110.00 220.00 220.00 4.03 246.94 110.00 220.00 220.00 4.04 110.00 220.00 220.00 4.05 110.00 220.00 220.00 4.06 220.00 220.00 4.07 220.00 220.00 4.08 220.00 220.00 4.09 220.00 220.00 4.10 220.00 220.00 4.11 220.00 220.00 4.12 220.00 4.13 220.00 110.00 4.14 220.00 110.00 4.15 220.00 110.00 4.16 220.00 110.00 4.17 110.00 4.18 110.00 4.19 110.00 4.20 110.00 220.00 4.21 110.00 220.00 4.22 110.00 220.00 4.23 110.00 220.00 220.00 4.24 110.00 220.00 220.00 4.25 110.00 220.00 220.00 4.26 110.00 220.00 220.00 4.27 110.00 220.00 220.00 4.28 110.00 220.00 220.00 4.29 110.00 220.00 220.00 4.30 110.00 220.00 220.00 4.31 110.00 220.00 220.00 4.32 110.00 220.00 220.00 4.33 110.00 220.00 220.00 4.34 110.00 220.00 220.00 4.35 110.00 220.00 220.00 4.36 110.00 220.00 220.00 4.37 110.00 220.00 220.00 4.38 110.00 220.00 220.00 4.39 110.00 220.00 220.00 4.40 110.00 220.00 220.00 4.41 110.00 220.00 220.00 4.42 110.00 220.00 220.00 4.43 110.00 220.00 220.00 4.44 110.00 220.00 220.00 4.45 110.00 220.00 220.00 4.46 110.00 220.00 220.00 4.47 110.00 220.00 220.00 4.48 110.00 220.00 220.00 4.49 110.00 220.00 220.00 4.50 110.00 220.00 220.00 4.51 110.00 220.00 220.00 4.52 110.00 220.00 220.00 4.53 110.00 220.00 220.00 4.54 110.00 220.00 220.00 4.55 110.00 220.00 220.00 4.56 110.00 220.00 220.00 4.57 110.00 220.00 220.00 4.58 110.00 220.00 220.00 4.59 110.00 220.00 220.00 4.60 110.00 220.00 220.00 4.61 110.00 220.00 220.00 4.62 110.00 220.00 220.00 4.63 110.00 220.00 220.00 4.64 110.00 220.00 220.00 4.65 110.00 220.00 220.00 4.66 110.00 220.00 220.00 4.67 110.00 220.00 220.00 4.68 110.00 220.00 220.00 4.69 110.00 220.00 220.00 4.70 110.00 220.00 220.00 4.71 110.00 220.00 220.00 4.72 110.00 220.00 220.00 4.73 110.00 220.00 220.00 4.74 110.00 220.00 220.00 4.75 110.00 220.00 220.00 4.76 110.00 220.00 220.00 4.77 110.00 220.00 220.00 4.78 110.00 220.00 220.00 4.79 110.00 220.00 220.00 4.80 110.00 220.00 220.00 4.81 110.00 220.00 220.00 4.82 110.00 220.00 220.00 4.83 110.00 220.00 220.00 4.84 110.00 220.00 220.00 4.85 110.00 220.00 220.00 4.86 110.00 220.00 220.00 4.87 110.00 220.00 220.00 4.88 110.00 220.00 220.00 4.89 110.00 220.00 220.00 4.90 110.00 220.00 220.00 4.91 110.00 220.00 220.00 4.92 110.00 220.00 220.00 4.93 110.00 220.00 220.00 4.94 110.00 220.00 220.00 4.95 110.00 220.00 220.00 4.96 110.00 220.00 220.00 4.97 110.00 220.00 220.00 4.98 110.00 220.00 220.00 4.99 110.00 220.00 220.00 5.00 110.00 220.00 220.00 5.01 110.00 220.00 220.00 5.02 110.00 220.00 220.00 103.83 5.03 110.00 220.00 220.00 103.83 5.04 110.00 220.00 220.00 103.83 5.05 110.00 220.00 220.00 103.83 5.06 110.00 220.00 220.00 103.83 207.65 5.07 220.00 220.00 103.83 207.65 207.65 5.08 220.00 103.83 207.65 207.65 5.09 220.00 103.83 207.65 207.65 5.10 220.00 103.83 207.65 207.65 5.11 103.83 207.65 207.65 5.12 103.83 207.65 207.65 5.13 103.83 207.65 207.65 5.14 103.83 207.65 207.65 5.15 103.83 207.65 207.65 5.16 103.83 207.65 207.65 5.17 103.83 207.65 207.65 5.18 103.83 207.65 207.65 5.19 103.83 207.65 207.65 5.20 103.83 207.65 207.65 5.21 103.83 207.65 207.65 5.22 103.83 207.65 207.65 5.23 103.83 207.65 207.65 5.24 103.83 207.65 207.65 5.25 103.83 207.65 207.65 5.26 103.83 207.65 207.65 5.27 103.83 207.65 207.65 5.28 103.83 207.65 207.65 5.29 103.83 207.65 207.65 5.30 103.83 207.65 207.65 5.31 103.83 207.65 207.65 5.32 103.83 207.65 207.65 5.33 103.83 207.65 207.65 5.34 103.83 207.65 207.65 5.35 103.83 207.65 207.65 5.36 103.83 207.65 207.65 5.37 103.83 207.65 207.65 5.38 103.83 207.65 207.65 5.39 103.83 207.65 207.65 5.40 103.83 207.65 207.65 5.41 103.83 207.65 207.65 5.42 103.83 207.65 207.65 5.43 103.83 207.65 207.65 5.44 103.83 207.65 207.65 5.45 103.83 207.65 207.65 5.46 103.83 207.65 207.65 5.47 103.83 207.65 207.65 5.48 103.83 207.65 5.49 103.83 207.65 5.50 207.65 5.51 207.65 164.81 5.52 164.81 5.53 164.81 5.54 164.81 329.63 5.55 164.81 329.63 5.56 164.81 329.63 5.57 164.81 329.63 82.41 5.58 164.81 329.63 82.41 5.59 164.81 329.63 82.41 5.60 164.81 329.63 82.41 82.41 5.61 164.81 329.63 82.41 82.41 5.62 164.81 329.63 82.41 82.41 5.63 164.81 329.63 82.41 82.41 5.64 164.81 329.63 82.41 82.41 5.65 164.81 329.63 82.41 82.41 5.66 164.81 329.63 82.41 82.41 5.67 164.81 329.63 82.41 82.41 5.68 164.81 329.63 82.41 82.41 5.69 164.81 329.63 82.41 82.41 5.70 164.81 329.63 82.41 82.41 5.71 164.81 329.63 82.41 82.41 5.72 164.81 329.63 82.41 82.41 5.73 164.81 329.63 82.41 82.41 5.74 164.81 329.63 82.41 82.41 5.75 164.81 329.63 82.41 82.41 5.76 164.81 329.63 82.41 82.41 5.77 164.81 329.63 82.41 82.41 5.78 164.81 329.63 82.41 82.41 5.79 164.81 329.63 82.41 82.41 5.80 164.81 329.63 82.41 82.41 5.81 164.81 329.63 82.41 82.41 5.82 164.81 329.63 82.41 82.41 5.83 164.81 329.63 82.41 82.41 5.84 164.81 329.63 82.41 82.41 5.85 164.81 329.63 82.41 82.41 5.86 164.81 329.63 82.41 82.41 5.87 164.81 329.63 82.41 82.41 164.81 5.88 164.81 329.63 82.41 82.41 164.81 5.89 164.81 329.63 82.41 82.41 164.81 5.90 164.81 329.63 82.41 82.41 164.81 5.91 164.81 329.63 82.41 82.41 164.81 5.92 164.81 329.63 82.41 82.41 164.81 5.93 164.81 329.63 82.41 82.41 164.81 5.94 164.81 329.63 82.41 82.41 164.81 5.95 164.81 329.63 82.41 82.41 164.81 5.96 164.81 329.63 82.41 82.41 164.81 5.97 164.81 329.63 82.41 82.41 164.81 5.98 164.81 329.63 82.41 82.41 164.81 5.99 164.81 329.63 82.41 82.41 164.81 6.00 164.81 329.63 82.41 82.41 164.81 6.01 164.81 329.63 82.41 82.41 164.81 6.02 164.81 329.63 82.41 82.41 164.81 6.03 164.81 329.63 82.41 82.41 164.81 6.04 164.81 329.63 82.41 82.41 164.81 659.26 6.05 164.81 329.63 82.41 659.26 207.65 6.06 164.81 329.63 82.41 659.26 207.65 6.07 164.81 329.63 82.41 659.26 207.65 6.08 164.81 329.63 82.41 659.26 207.65 6.09 164.81 329.63 82.41 659.26 207.65 6.10 164.81 329.63 82.41 659.26 207.65 6.11 164.81 329.63 82.41 659.26 207.65 6.12 164.81 329.63 82.41 659.26 207.65 6.13 164.81 329.63 659.26 207.65 6.14 164.81 329.63 659.26 207.65 6.15 164.81 329.63 659.26 207.65 6.16 164.81 329.63 659.26 207.65 6.17 164.81 329.63 659.26 207.65 6.18 164.81 329.63 659.26 207.65 6.19 164.81 329.63 659.26 207.65 6.20 164.81 329.63 659.26 207.65 6.21 164.81 329.63 659.26 207.65 6.22 164.81 329.63 659.26 207.65 6.23 164.81 329.63 659.26 207.65 6.24 164.81 329.63 659.26 207.65 6.25 164.81 329.63 659.26 207.65 329.63 6.26 164.81 329.63 659.26 329.63 6.27 164.81 329.63 659.26 329.63 6.28 164.81 329.63 659.26 329.63 6.29 164.81 329.63 659.26 329.63 6.30 164.81 329.63 659.26 329.63 6.31 164.81 329.63 659.26 329.63 6.32 164.81 329.63 659.26 329.63 6.33 164.81 329.63 659.26 329.63 6.34 164.81 329.63 659.26 329.63 6.35 164.81 329.63 659.26 329.63 6.36 164.81 329.63 659.26 329.63 6.37 164.81 329.63 659.26 329.63 6.38 164.81 329.63 659.26 329.63 6.39 164.81 329.63 659.26 329.63 6.40 164.81 329.63 659.26 329.63 6.41 164.81 329.63 659.26 329.63 6.42 164.81 329.63 659.26 329.63 6.43 164.81 329.63 659.26 329.63 6.44 164.81 329.63 659.26 329.63 6.45 164.81 329.63 329.63 415.30 164.81 6.46 164.81 329.63 415.30 164.81 6.47 164.81 329.63 415.30 164.81 6.48 164.81 329.63 415.30 164.81 6.49 164.81 329.63 415.30 164.81 82.41 6.50 164.81 329.63 415.30 164.81 82.41 6.51 164.81 329.63 415.30 164.81 82.41 6.52 164.81 329.63 415.30 164.81 82.41 6.53 164.81 329.63 415.30 164.81 82.41 6.54 164.81 329.63 415.30 164.81 82.41 6.55 164.81 329.63 415.30 164.81 82.41 6.56 164.81 329.63 415.30 164.81 82.41 6.57 164.81 329.63 415.30 164.81 82.41 6.58 164.81 329.63 415.30 164.81 82.41 6.59 164.81 329.63 415.30 164.81 82.41 6.60 164.81 329.63 415.30 164.81 82.41 6.61 164.81 329.63 415.30 164.81 82.41 6.62 164.81 329.63 415.30 164.81 82.41 6.63 164.81 329.63 415.30 82.41 6.64 164.81 329.63 415.30 82.41 329.63 6.65 164.81 329.63 415.30 82.41 329.63 6.66 164.81 329.63 415.30 82.41 329.63 6.67 164.81 329.63 415.30 82.41 329.63 6.68 164.81 329.63 415.30 82.41 329.63 6.69 164.81 329.63 415.30 82.41 329.63 6.70 164.81 329.63 415.30 82.41 329.63 6.71 164.81 329.63 415.30 82.41 329.63 6.72 164.81 329.63 415.30 82.41 329.63 6.73 164.81 329.63 415.30 82.41 329.63 6.74 164.81 329.63 415.30 82.41 329.63 6.75 164.81 329.63 415.30 82.41 329.63 6.76 164.81 329.63 415.30 82.41 329.63 6.77 164.81 329.63 415.30 82.41 329.63 6.78 164.81 329.63 415.30 82.41 329.63 6.79 164.81 329.63 415.30 82.41 329.63 6.80 164.81 329.63 415.30 82.41 329.63 92.50 6.81 164.81 329.63 415.30 82.41 329.63 92.50 6.82 164.81 329.63 415.30 82.41 92.50 92.50 6.83 164.81 329.63 415.30 82.41 92.50 92.50 6.84 164.81 329.63 415.30 82.41 92.50 92.50 6.85 164.81 329.63 415.30 92.50 92.50 6.86 164.81 329.63 92.50 92.50 6.87 164.81 329.63 92.50 92.50 6.88 164.81 329.63 92.50 92.50 6.89 164.81 329.63 92.50 92.50 6.90 164.81 329.63 92.50 92.50 185.00 6.91 164.81 329.63 92.50 92.50 185.00 6.92 164.81 329.63 92.50 92.50 185.00 6.93 164.81 329.63 92.50 92.50 185.00 6.94 164.81 329.63 92.50 92.50 185.00 6.95 329.63 92.50 92.50 185.00 6.96 329.63 92.50 92.50 185.00 6.97 329.63 92.50 92.50 185.00 6.98 329.63 92.50 92.50 185.00 6.99 329.63 92.50 92.50 185.00 7.00 329.63 92.50 92.50 185.00 164.81 7.01 329.63 92.50 92.50 185.00 164.81 7.02 329.63 92.50 92.50 185.00 164.81 7.03 329.63 92.50 92.50 185.00 164.81 7.04 329.63 92.50 92.50 185.00 164.81 7.05 329.63 92.50 92.50 185.00 164.81 7.06 329.63 92.50 92.50 185.00 164.81 7.07 329.63 92.50 92.50 185.00 164.81 7.08 329.63 92.50 92.50 185.00 164.81 7.09 329.63 92.50 92.50 185.00 164.81 7.10 329.63 92.50 92.50 185.00 164.81 7.11 329.63 92.50 92.50 185.00 164.81 7.12 329.63 92.50 92.50 185.00 164.81 7.13 329.63 92.50 92.50 185.00 164.81 7.14 329.63 92.50 92.50 185.00 164.81 7.15 329.63 92.50 92.50 185.00 164.81 7.16 329.63 92.50 92.50 185.00 164.81 7.17 329.63 92.50 92.50 185.00 164.81 7.18 329.63 92.50 92.50 185.00 164.81 659.26 7.19 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.20 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.21 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.22 329.63 92.50 92.50 185.00 164.81 659.26 220.00 7.23 329.63 92.50 92.50 185.00 659.26 220.00 7.24 329.63 92.50 185.00 659.26 220.00 7.25 329.63 92.50 185.00 659.26 220.00 7.26 329.63 92.50 185.00 659.26 220.00 7.27 329.63 92.50 185.00 659.26 220.00 7.28 329.63 92.50 185.00 659.26 220.00 7.29 329.63 92.50 185.00 659.26 220.00 7.30 329.63 92.50 185.00 659.26 220.00 7.31 329.63 92.50 185.00 659.26 220.00 7.32 329.63 92.50 185.00 659.26 220.00 7.33 329.63 92.50 185.00 659.26 220.00 7.34 329.63 92.50 185.00 659.26 220.00 7.35 329.63 185.00 659.26 220.00 7.36 329.63 185.00 659.26 220.00 7.37 329.63 185.00 659.26 220.00 7.38 329.63 185.00 659.26 220.00 7.39 329.63 185.00 659.26 220.00 7.40 329.63 185.00 659.26 220.00 329.63 7.41 329.63 185.00 659.26 220.00 329.63 7.42 329.63 185.00 659.26 329.63 7.43 329.63 185.00 659.26 329.63 7.44 329.63 185.00 659.26 329.63 7.45 329.63 185.00 659.26 329.63 7.46 329.63 185.00 659.26 329.63 7.47 329.63 185.00 659.26 329.63 7.48 329.63 185.00 659.26 329.63 7.49 329.63 185.00 659.26 329.63 7.50 329.63 185.00 659.26 329.63 7.51 329.63 185.00 659.26 329.63 7.52 329.63 185.00 659.26 329.63 7.53 329.63 185.00 659.26 329.63 7.54 329.63 185.00 659.26 329.63 7.55 329.63 185.00 659.26 329.63 7.56 329.63 185.00 659.26 329.63 7.57 329.63 185.00 329.63 440.00 92.50 7.58 329.63 185.00 329.63 440.00 92.50 7.59 329.63 185.00 329.63 440.00 92.50 164.81 7.60 329.63 185.00 329.63 440.00 92.50 164.81 7.61 329.63 185.00 329.63 440.00 92.50 164.81 7.62 329.63 185.00 440.00 92.50 164.81 7.63 329.63 185.00 440.00 92.50 164.81 7.64 329.63 185.00 440.00 92.50 164.81 7.65 329.63 185.00 440.00 92.50 164.81 7.66 329.63 185.00 440.00 92.50 164.81 7.67 329.63 185.00 440.00 92.50 164.81 7.68 329.63 185.00 440.00 92.50 164.81 7.69 329.63 185.00 440.00 92.50 164.81 7.70 329.63 185.00 440.00 92.50 164.81 7.71 329.63 185.00 440.00 92.50 164.81 7.72 329.63 185.00 440.00 92.50 164.81 7.73 329.63 185.00 440.00 92.50 164.81 7.74 329.63 185.00 440.00 92.50 164.81 7.75 329.63 185.00 440.00 92.50 164.81 7.76 329.63 185.00 440.00 92.50 164.81 329.63 7.77 329.63 185.00 440.00 92.50 164.81 329.63 7.78 329.63 185.00 440.00 92.50 164.81 329.63 7.79 329.63 185.00 440.00 92.50 164.81 329.63 7.80 329.63 185.00 440.00 92.50 164.81 329.63 7.81 329.63 185.00 440.00 92.50 329.63 7.82 329.63 185.00 440.00 92.50 329.63 7.83 329.63 185.00 440.00 92.50 329.63 7.84 329.63 185.00 440.00 92.50 329.63 7.85 329.63 185.00 440.00 92.50 329.63 7.86 329.63 185.00 440.00 92.50 329.63 7.87 329.63 185.00 440.00 92.50 329.63 7.88 329.63 185.00 440.00 92.50 329.63 7.89 329.63 185.00 440.00 92.50 329.63 7.90 329.63 185.00 440.00 92.50 329.63 7.91 329.63 185.00 440.00 92.50 329.63 7.92 329.63 185.00 440.00 92.50 329.63 7.93 329.63 185.00 440.00 92.50 329.63 7.94 329.63 185.00 440.00 92.50 329.63 103.83 7.95 329.63 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329.63 207.65 103.83 493.88 164.81 8.75 329.63 207.65 103.83 493.88 164.81 8.76 329.63 207.65 103.83 493.88 164.81 8.77 329.63 207.65 103.83 493.88 164.81 8.78 329.63 207.65 103.83 493.88 164.81 8.79 329.63 207.65 103.83 493.88 164.81 8.80 329.63 207.65 103.83 493.88 164.81 8.81 329.63 207.65 103.83 493.88 164.81 8.82 329.63 207.65 103.83 493.88 164.81 8.83 329.63 207.65 103.83 493.88 164.81 8.84 329.63 207.65 103.83 493.88 164.81 8.85 329.63 207.65 103.83 493.88 164.81 8.86 329.63 207.65 103.83 493.88 164.81 8.87 329.63 207.65 103.83 493.88 164.81 8.88 329.63 207.65 103.83 493.88 164.81 8.89 329.63 207.65 103.83 493.88 164.81 8.90 329.63 207.65 103.83 493.88 164.81 329.63 8.91 329.63 207.65 103.83 493.88 164.81 329.63 8.92 329.63 207.65 103.83 493.88 164.81 329.63 8.93 329.63 207.65 103.83 493.88 164.81 329.63 8.94 329.63 207.65 103.83 493.88 164.81 329.63 8.95 329.63 207.65 103.83 493.88 329.63 8.96 329.63 207.65 103.83 493.88 329.63 8.97 329.63 207.65 103.83 493.88 329.63 8.98 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110.00 11.06 87.31 110.00 220.00 440.00 220.00 110.00 110.00 11.07 87.31 110.00 220.00 440.00 220.00 110.00 110.00 11.08 110.00 220.00 440.00 220.00 110.00 110.00 11.09 110.00 220.00 440.00 220.00 110.00 110.00 11.10 110.00 220.00 440.00 220.00 110.00 110.00 11.11 110.00 220.00 440.00 220.00 110.00 110.00 11.12 110.00 220.00 440.00 220.00 110.00 110.00 130.81 11.13 110.00 220.00 440.00 220.00 110.00 110.00 130.81 11.14 220.00 440.00 220.00 110.00 110.00 130.81 11.15 220.00 440.00 220.00 110.00 110.00 130.81 261.63 11.16 220.00 440.00 220.00 110.00 110.00 130.81 261.63 523.25 11.17 440.00 220.00 110.00 110.00 130.81 261.63 523.25 11.18 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.19 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.20 220.00 110.00 110.00 130.81 261.63 523.25 261.63 11.21 110.00 110.00 130.81 261.63 523.25 261.63 11.22 110.00 110.00 130.81 261.63 523.25 261.63 11.23 110.00 110.00 130.81 261.63 523.25 261.63 130.81 11.24 110.00 110.00 130.81 261.63 523.25 261.63 130.81 11.25 110.00 130.81 261.63 523.25 261.63 130.81 11.26 130.81 261.63 523.25 261.63 130.81 130.81 11.27 130.81 261.63 523.25 261.63 130.81 130.81 11.28 130.81 261.63 523.25 261.63 130.81 130.81 11.29 130.81 261.63 523.25 261.63 130.81 130.81 11.30 130.81 261.63 523.25 261.63 130.81 130.81 11.31 130.81 261.63 523.25 261.63 130.81 130.81 11.32 130.81 261.63 523.25 261.63 130.81 130.81 11.33 130.81 261.63 523.25 261.63 130.81 130.81 11.34 130.81 261.63 523.25 261.63 130.81 130.81 11.35 130.81 261.63 523.25 261.63 130.81 130.81 11.36 130.81 261.63 523.25 261.63 130.81 130.81 11.37 130.81 261.63 523.25 261.63 130.81 130.81 11.38 130.81 261.63 523.25 261.63 130.81 130.81 11.39 130.81 261.63 523.25 261.63 130.81 130.81 11.40 130.81 261.63 523.25 261.63 130.81 130.81 11.41 130.81 261.63 523.25 261.63 130.81 130.81 11.42 130.81 261.63 523.25 261.63 130.81 130.81 11.43 130.81 261.63 523.25 261.63 130.81 130.81 11.44 130.81 261.63 523.25 261.63 130.81 130.81 11.45 130.81 261.63 523.25 261.63 130.81 130.81 11.46 130.81 261.63 523.25 261.63 130.81 130.81 11.47 130.81 261.63 523.25 261.63 130.81 130.81 11.48 130.81 523.25 261.63 130.81 130.81 11.49 130.81 523.25 261.63 130.81 130.81 11.50 130.81 523.25 261.63 130.81 130.81 11.51 130.81 523.25 261.63 130.81 130.81 11.52 523.25 261.63 130.81 130.81 82.41 11.53 523.25 261.63 130.81 130.81 82.41 11.54 261.63 130.81 130.81 82.41 11.55 261.63 130.81 130.81 82.41 11.56 130.81 130.81 82.41 164.81 11.57 130.81 130.81 82.41 164.81 11.58 130.81 130.81 82.41 164.81 329.63 11.59 130.81 130.81 82.41 164.81 329.63 11.60 130.81 130.81 82.41 164.81 329.63 164.81 11.61 130.81 130.81 82.41 164.81 329.63 164.81 11.62 130.81 82.41 164.81 329.63 164.81 82.41 11.63 130.81 82.41 164.81 329.63 164.81 82.41 82.41 11.64 82.41 164.81 329.63 164.81 82.41 82.41 11.65 82.41 164.81 329.63 164.81 82.41 82.41 11.66 82.41 164.81 329.63 164.81 82.41 82.41 11.67 82.41 164.81 329.63 164.81 82.41 82.41 11.68 82.41 164.81 329.63 164.81 82.41 82.41 11.69 82.41 164.81 329.63 164.81 82.41 82.41 11.70 82.41 164.81 329.63 164.81 82.41 82.41 11.71 82.41 164.81 329.63 164.81 82.41 82.41 11.72 82.41 164.81 329.63 164.81 82.41 82.41 11.73 82.41 164.81 329.63 164.81 82.41 82.41 11.74 82.41 164.81 329.63 164.81 82.41 82.41 11.75 82.41 164.81 329.63 164.81 82.41 82.41 11.76 82.41 164.81 329.63 164.81 82.41 82.41 11.77 82.41 164.81 329.63 164.81 82.41 82.41 11.78 82.41 164.81 329.63 164.81 82.41 82.41 11.79 82.41 164.81 329.63 164.81 82.41 82.41 11.80 82.41 164.81 329.63 164.81 82.41 82.41 11.81 82.41 164.81 329.63 164.81 82.41 82.41 11.82 82.41 164.81 329.63 164.81 82.41 82.41 11.83 82.41 164.81 329.63 164.81 82.41 82.41 11.84 82.41 164.81 329.63 164.81 82.41 82.41 11.85 82.41 164.81 329.63 164.81 82.41 82.41 11.86 82.41 164.81 329.63 164.81 82.41 82.41 11.87 82.41 164.81 329.63 164.81 82.41 82.41 11.88 82.41 164.81 329.63 164.81 82.41 82.41 11.89 82.41 164.81 329.63 164.81 82.41 82.41 11.90 82.41 164.81 329.63 164.81 82.41 82.41 11.91 82.41 164.81 329.63 164.81 82.41 82.41 11.92 82.41 164.81 329.63 164.81 82.41 82.41 11.93 82.41 164.81 329.63 164.81 82.41 82.41 11.94 82.41 164.81 329.63 164.81 82.41 82.41 11.95 82.41 164.81 329.63 164.81 82.41 82.41 11.96 82.41 164.81 329.63 164.81 82.41 82.41 11.97 82.41 164.81 329.63 164.81 82.41 82.41 11.98 82.41 164.81 329.63 164.81 82.41 82.41 11.99 82.41 164.81 329.63 164.81 82.41 82.41 12.00 82.41 164.81 329.63 164.81 82.41 82.41 12.01 82.41 164.81 329.63 164.81 82.41 82.41 12.02 82.41 164.81 329.63 164.81 82.41 82.41 12.03 82.41 164.81 329.63 164.81 82.41 82.41 12.04 82.41 164.81 329.63 164.81 82.41 82.41 12.05 82.41 164.81 329.63 164.81 82.41 82.41 12.06 82.41 164.81 329.63 164.81 82.41 82.41 12.07 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.08 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.09 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.10 82.41 164.81 329.63 164.81 82.41 82.41 220.00 12.11 82.41 164.81 329.63 164.81 82.41 82.41 220.00 110.00 12.12 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.13 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.14 82.41 329.63 164.81 82.41 82.41 220.00 110.00 12.15 329.63 164.81 82.41 82.41 220.00 110.00 12.16 329.63 164.81 82.41 82.41 220.00 110.00 110.00 12.17 329.63 164.81 82.41 82.41 220.00 110.00 110.00 220.00 12.18 329.63 164.81 82.41 82.41 220.00 110.00 110.00 220.00 12.19 329.63 164.81 82.41 220.00 110.00 110.00 220.00 440.00 110.00 12.20 329.63 164.81 82.41 220.00 110.00 110.00 220.00 440.00 110.00 12.21 329.63 164.81 220.00 110.00 110.00 220.00 440.00 110.00 12.22 329.63 164.81 220.00 110.00 110.00 220.00 440.00 110.00 12.23 329.63 220.00 110.00 110.00 220.00 440.00 110.00 12.24 220.00 110.00 110.00 220.00 440.00 110.00 12.25 220.00 110.00 110.00 220.00 440.00 110.00 12.26 220.00 110.00 110.00 220.00 440.00 110.00 12.27 220.00 110.00 110.00 220.00 440.00 110.00 12.28 220.00 110.00 110.00 220.00 440.00 110.00 12.29 220.00 110.00 110.00 220.00 440.00 110.00 12.30 220.00 110.00 110.00 220.00 440.00 110.00 261.63 12.31 220.00 110.00 110.00 220.00 440.00 110.00 261.63 12.32 110.00 110.00 220.00 440.00 110.00 261.63 12.33 110.00 110.00 220.00 440.00 110.00 261.63 12.34 110.00 220.00 440.00 110.00 261.63 12.35 110.00 220.00 440.00 110.00 261.63 130.81 130.81 12.36 110.00 220.00 440.00 110.00 261.63 130.81 130.81 12.37 220.00 440.00 110.00 261.63 130.81 130.81 12.38 220.00 440.00 110.00 261.63 130.81 130.81 523.25 12.39 220.00 110.00 261.63 130.81 130.81 523.25 12.40 220.00 110.00 261.63 130.81 130.81 523.25 12.41 220.00 110.00 261.63 130.81 130.81 523.25 261.63 130.81 12.42 220.00 261.63 130.81 130.81 523.25 261.63 130.81 12.43 261.63 130.81 130.81 523.25 261.63 130.81 12.44 261.63 130.81 130.81 523.25 261.63 130.81 12.45 261.63 130.81 130.81 523.25 261.63 130.81 12.46 261.63 130.81 130.81 523.25 261.63 130.81 12.47 261.63 130.81 130.81 523.25 261.63 130.81 12.48 261.63 130.81 130.81 523.25 261.63 130.81 12.49 261.63 130.81 130.81 523.25 261.63 130.81 12.50 261.63 130.81 130.81 523.25 261.63 130.81 12.51 261.63 130.81 130.81 523.25 261.63 130.81 12.52 261.63 130.81 130.81 523.25 261.63 130.81 12.53 261.63 130.81 130.81 523.25 261.63 130.81 12.54 261.63 130.81 130.81 523.25 261.63 130.81 12.55 261.63 130.81 130.81 523.25 261.63 130.81 12.56 261.63 130.81 130.81 523.25 261.63 130.81 12.57 261.63 130.81 130.81 523.25 261.63 130.81 12.58 261.63 130.81 130.81 523.25 261.63 130.81 12.59 261.63 130.81 130.81 523.25 261.63 130.81 12.60 261.63 130.81 130.81 523.25 261.63 130.81 12.61 261.63 130.81 130.81 523.25 261.63 130.81 12.62 261.63 130.81 130.81 523.25 261.63 130.81 12.63 261.63 130.81 130.81 523.25 261.63 130.81 12.64 261.63 130.81 130.81 523.25 261.63 130.81 12.65 261.63 130.81 130.81 523.25 261.63 130.81 12.66 261.63 130.81 130.81 523.25 261.63 130.81 12.67 261.63 130.81 130.81 523.25 261.63 130.81 12.68 261.63 130.81 130.81 523.25 261.63 130.81 12.69 261.63 130.81 130.81 523.25 261.63 130.81 12.70 261.63 130.81 130.81 523.25 261.63 130.81 12.71 261.63 130.81 130.81 261.63 130.81 12.72 261.63 130.81 130.81 130.81 12.73 261.63 130.81 130.81 130.81 12.74 261.63 130.81 130.81 12.75 261.63 130.81 130.81 155.56 77.78 12.76 261.63 130.81 130.81 155.56 77.78 77.78 311.13 12.77 130.81 130.81 155.56 77.78 77.78 311.13 12.78 130.81 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.79 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.80 130.81 155.56 77.78 77.78 311.13 77.78 155.56 12.81 155.56 77.78 77.78 311.13 77.78 155.56 12.82 155.56 77.78 77.78 311.13 77.78 155.56 12.83 155.56 77.78 77.78 311.13 77.78 155.56 12.84 155.56 77.78 77.78 311.13 77.78 155.56 12.85 155.56 77.78 77.78 311.13 77.78 155.56 12.86 155.56 77.78 77.78 311.13 77.78 155.56 12.87 155.56 77.78 77.78 311.13 77.78 155.56 12.88 155.56 77.78 77.78 311.13 77.78 155.56 12.89 155.56 77.78 77.78 311.13 77.78 155.56 12.90 155.56 77.78 77.78 311.13 77.78 155.56 12.91 155.56 77.78 77.78 311.13 77.78 155.56 12.92 155.56 77.78 77.78 311.13 77.78 155.56 12.93 155.56 77.78 77.78 311.13 77.78 155.56 12.94 155.56 77.78 77.78 311.13 77.78 155.56 12.95 155.56 77.78 77.78 311.13 77.78 155.56 12.96 155.56 77.78 77.78 311.13 77.78 155.56 12.97 155.56 77.78 77.78 311.13 77.78 155.56 12.98 155.56 77.78 77.78 311.13 77.78 155.56 12.99 155.56 77.78 77.78 311.13 77.78 155.56 13.00 155.56 77.78 77.78 311.13 77.78 155.56 13.01 155.56 77.78 77.78 311.13 77.78 155.56 13.02 155.56 77.78 77.78 311.13 77.78 155.56 13.03 155.56 77.78 77.78 311.13 77.78 155.56 13.04 155.56 77.78 77.78 311.13 77.78 155.56 13.05 155.56 77.78 77.78 311.13 77.78 155.56 13.06 155.56 77.78 77.78 311.13 77.78 155.56 13.07 155.56 77.78 77.78 311.13 77.78 155.56 13.08 155.56 77.78 77.78 311.13 77.78 155.56 13.09 155.56 77.78 77.78 311.13 77.78 155.56 13.10 155.56 77.78 77.78 311.13 77.78 155.56 13.11 155.56 77.78 77.78 311.13 77.78 155.56 13.12 155.56 77.78 77.78 311.13 77.78 155.56 13.13 155.56 77.78 77.78 311.13 77.78 155.56 13.14 155.56 77.78 77.78 311.13 77.78 155.56 13.15 155.56 77.78 77.78 311.13 77.78 155.56 13.16 155.56 77.78 77.78 311.13 77.78 155.56 13.17 155.56 77.78 77.78 311.13 77.78 155.56 13.18 155.56 77.78 77.78 311.13 77.78 155.56 13.19 155.56 77.78 77.78 311.13 77.78 155.56 13.20 155.56 77.78 77.78 311.13 77.78 155.56 13.21 155.56 77.78 77.78 311.13 77.78 155.56 13.22 155.56 77.78 77.78 311.13 77.78 155.56 13.23 155.56 77.78 77.78 311.13 77.78 155.56 13.24 155.56 77.78 77.78 311.13 77.78 155.56 13.25 155.56 77.78 77.78 311.13 77.78 155.56 13.26 155.56 77.78 77.78 311.13 77.78 155.56 13.27 155.56 77.78 77.78 311.13 77.78 155.56 13.28 155.56 77.78 77.78 311.13 77.78 155.56 13.29 155.56 77.78 77.78 311.13 77.78 155.56 13.30 155.56 77.78 77.78 311.13 77.78 155.56 13.31 155.56 77.78 77.78 311.13 77.78 155.56 13.32 155.56 77.78 77.78 311.13 77.78 13.33 155.56 77.78 77.78 311.13 77.78 13.34 155.56 77.78 77.78 77.78 261.63 13.35 155.56 77.78 77.78 77.78 261.63 13.36 155.56 77.78 77.78 77.78 261.63 523.25 13.37 77.78 77.78 77.78 261.63 523.25 13.38 77.78 77.78 77.78 261.63 523.25 13.39 77.78 77.78 77.78 261.63 523.25 13.40 77.78 77.78 77.78 261.63 523.25 13.41 77.78 77.78 77.78 261.63 523.25 13.42 77.78 77.78 77.78 261.63 523.25 13.43 77.78 77.78 77.78 261.63 523.25 13.44 77.78 77.78 77.78 261.63 523.25 13.45 77.78 77.78 77.78 261.63 523.25 13.46 77.78 77.78 77.78 261.63 523.25 13.47 77.78 77.78 77.78 261.63 523.25 13.48 77.78 77.78 77.78 261.63 523.25 13.49 77.78 77.78 77.78 261.63 523.25 13.50 77.78 77.78 77.78 261.63 523.25 13.51 77.78 77.78 77.78 261.63 523.25 13.52 77.78 77.78 77.78 261.63 523.25 13.53 77.78 77.78 77.78 261.63 523.25 13.54 77.78 77.78 77.78 261.63 523.25 13.55 77.78 77.78 77.78 261.63 523.25 13.56 77.78 77.78 77.78 261.63 523.25 246.94 13.57 77.78 77.78 77.78 261.63 523.25 246.94 493.88 13.58 77.78 77.78 77.78 261.63 523.25 246.94 493.88 13.59 77.78 77.78 261.63 246.94 493.88 369.99 13.60 77.78 77.78 246.94 493.88 369.99 261.63 13.61 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.62 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.63 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.64 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.65 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.66 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.67 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.68 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.69 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.70 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.71 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.72 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.73 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.74 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.75 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.76 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.77 77.78 77.78 246.94 493.88 369.99 261.63 155.56 13.78 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 13.79 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 440.00 13.80 77.78 77.78 246.94 493.88 369.99 261.63 155.56 220.00 440.00 13.81 77.78 77.78 493.88 369.99 261.63 155.56 220.00 440.00 13.82 77.78 77.78 155.56 220.00 440.00 13.83 77.78 77.78 155.56 220.00 440.00 13.84 77.78 77.78 155.56 220.00 440.00 13.85 77.78 77.78 155.56 220.00 440.00 13.86 77.78 77.78 155.56 220.00 440.00 13.87 77.78 77.78 155.56 220.00 440.00 13.88 77.78 77.78 155.56 220.00 440.00 13.89 77.78 77.78 155.56 220.00 440.00 13.90 77.78 77.78 155.56 220.00 440.00 13.91 77.78 77.78 155.56 220.00 440.00 13.92 77.78 77.78 155.56 220.00 440.00 13.93 77.78 77.78 155.56 220.00 440.00 13.94 77.78 77.78 155.56 220.00 440.00 13.95 77.78 77.78 155.56 440.00 13.96 77.78 77.78 155.56 440.00 13.97 77.78 77.78 155.56 440.00 82.41 13.98 77.78 77.78 155.56 82.41 13.99 77.78 77.78 155.56 82.41 14.00 77.78 77.78 155.56 82.41 14.01 77.78 155.56 82.41 82.41 14.02 77.78 155.56 82.41 82.41 14.03 155.56 82.41 82.41 220.00 14.04 155.56 82.41 82.41 220.00 440.00 14.05 155.56 82.41 82.41 220.00 440.00 14.06 155.56 82.41 82.41 220.00 440.00 14.07 155.56 82.41 82.41 220.00 440.00 14.08 155.56 82.41 82.41 220.00 440.00 14.09 155.56 82.41 82.41 220.00 440.00 14.10 155.56 82.41 82.41 220.00 440.00 14.11 155.56 82.41 82.41 220.00 440.00 14.12 155.56 82.41 82.41 220.00 440.00 14.13 155.56 82.41 82.41 220.00 440.00 14.14 155.56 82.41 82.41 220.00 440.00 14.15 155.56 82.41 82.41 220.00 440.00 329.63 14.16 155.56 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.17 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.18 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.19 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.20 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.21 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.22 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.23 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.24 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.25 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.26 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.27 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.28 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.29 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.30 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.31 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.32 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.33 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.34 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.35 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.36 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.37 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.38 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.39 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.40 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.41 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.42 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.43 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.44 82.41 82.41 220.00 440.00 329.63 246.94 164.81 14.45 82.41 220.00 440.00 329.63 246.94 164.81 14.46 82.41 220.00 440.00 329.63 246.94 164.81 14.47 82.41 220.00 440.00 329.63 246.94 164.81 14.48 82.41 220.00 440.00 329.63 246.94 164.81 14.49 82.41 220.00 440.00 329.63 246.94 164.81 14.50 82.41 220.00 440.00 329.63 246.94 164.81 14.51 82.41 220.00 440.00 329.63 246.94 164.81 14.52 82.41 220.00 440.00 329.63 246.94 164.81 14.53 82.41 220.00 440.00 329.63 246.94 164.81 14.54 82.41 220.00 440.00 329.63 246.94 164.81 14.55 82.41 220.00 440.00 329.63 246.94 164.81 14.56 82.41 220.00 440.00 329.63 246.94 164.81 14.57 82.41 220.00 440.00 329.63 246.94 164.81 14.58 82.41 220.00 440.00 329.63 246.94 164.81 14.59 82.41 220.00 440.00 329.63 246.94 164.81 14.60 82.41 220.00 440.00 329.63 246.94 164.81 14.61 82.41 220.00 440.00 329.63 246.94 164.81 14.62 82.41 220.00 440.00 329.63 246.94 164.81 14.63 82.41 220.00 440.00 329.63 246.94 164.81 14.64 82.41 220.00 440.00 329.63 246.94 164.81 14.65 82.41 220.00 440.00 329.63 246.94 164.81 14.66 82.41 220.00 440.00 329.63 246.94 164.81 14.67 82.41 220.00 440.00 329.63 246.94 164.81 14.68 82.41 220.00 440.00 329.63 246.94 164.81 14.69 82.41 220.00 440.00 329.63 246.94 164.81 14.70 82.41 220.00 440.00 329.63 246.94 164.81 14.71 82.41 220.00 440.00 329.63 246.94 164.81 14.72 82.41 220.00 440.00 329.63 246.94 164.81 14.73 82.41 220.00 440.00 329.63 246.94 164.81 14.74 82.41 220.00 440.00 329.63 246.94 164.81 14.75 82.41 220.00 440.00 329.63 246.94 164.81 14.76 82.41 220.00 440.00 329.63 246.94 164.81 14.77 82.41 220.00 440.00 329.63 246.94 164.81 14.78 82.41 220.00 440.00 329.63 246.94 164.81 14.79 82.41 220.00 440.00 329.63 246.94 164.81 14.80 82.41 220.00 440.00 329.63 246.94 164.81 14.81 82.41 220.00 440.00 329.63 246.94 14.82 82.41 220.00 440.00 329.63 246.94 14.83 82.41 220.00 440.00 329.63 246.94 14.84 82.41 220.00 440.00 329.63 246.94 14.85 82.41 220.00 440.00 329.63 246.94 14.86 82.41 220.00 440.00 329.63 246.94 14.87 82.41 220.00 440.00 329.63 246.94 14.88 82.41 220.00 440.00 329.63 246.94 14.89 82.41 220.00 440.00 329.63 246.94 14.90 82.41 220.00 440.00 329.63 246.94 14.91 82.41 220.00 440.00 329.63 246.94 14.92 82.41 220.00 440.00 329.63 246.94 14.93 82.41 220.00 440.00 329.63 246.94 14.94 82.41 220.00 440.00 329.63 246.94 14.95 82.41 220.00 440.00 329.63 246.94 14.96 82.41 220.00 440.00 329.63 246.94 14.97 82.41 220.00 440.00 329.63 246.94 14.98 82.41 220.00 440.00 329.63 246.94 14.99 82.41 220.00 440.00 329.63 246.94 15.00 82.41 220.00 440.00 329.63 246.94 415.30 15.01 82.41 220.00 440.00 329.63 246.94 415.30 207.65 15.02 82.41 220.00 440.00 329.63 246.94 415.30 207.65 15.03 82.41 220.00 329.63 246.94 415.30 207.65 15.04 82.41 220.00 329.63 246.94 415.30 207.65 15.05 82.41 220.00 329.63 246.94 415.30 207.65 15.06 82.41 329.63 246.94 415.30 207.65 15.07 82.41 329.63 246.94 415.30 207.65 15.08 82.41 329.63 246.94 415.30 207.65 207.65 15.09 82.41 329.63 246.94 415.30 207.65 207.65 15.10 82.41 415.30 207.65 207.65 15.11 82.41 415.30 207.65 207.65 15.12 82.41 415.30 207.65 207.65 15.13 82.41 415.30 207.65 207.65 15.14 82.41 415.30 207.65 207.65 15.15 82.41 415.30 207.65 207.65 15.16 82.41 415.30 207.65 207.65 15.17 82.41 415.30 207.65 207.65 15.18 82.41 415.30 207.65 207.65 15.19 82.41 415.30 207.65 207.65 15.20 82.41 415.30 207.65 207.65 15.21 82.41 415.30 207.65 207.65 15.22 82.41 415.30 207.65 207.65 15.23 82.41 415.30 207.65 207.65 15.24 82.41 415.30 207.65 207.65 15.25 82.41 415.30 207.65 207.65 15.26 82.41 415.30 207.65 207.65 69.30 15.27 82.41 415.30 207.65 207.65 69.30 15.28 82.41 415.30 207.65 207.65 69.30 15.29 82.41 415.30 207.65 207.65 69.30 15.30 82.41 415.30 207.65 207.65 69.30 15.31 82.41 415.30 207.65 207.65 69.30 15.32 82.41 415.30 207.65 207.65 69.30 15.33 82.41 415.30 207.65 207.65 69.30 15.34 82.41 415.30 207.65 207.65 69.30 15.35 82.41 415.30 207.65 207.65 69.30 15.36 82.41 415.30 207.65 207.65 69.30 15.37 82.41 415.30 207.65 207.65 69.30 15.38 82.41 415.30 207.65 207.65 69.30 15.39 82.41 415.30 207.65 207.65 69.30 15.40 82.41 415.30 207.65 207.65 69.30 15.41 82.41 207.65 207.65 69.30 15.42 82.41 207.65 207.65 69.30 15.43 82.41 207.65 207.65 69.30 15.44 82.41 207.65 207.65 69.30 15.45 207.65 207.65 69.30 15.46 207.65 207.65 69.30 15.47 207.65 207.65 69.30 392.00 15.48 207.65 207.65 69.30 392.00 15.49 207.65 207.65 69.30 392.00 15.50 207.65 207.65 69.30 392.00 15.51 207.65 69.30 392.00 15.52 207.65 69.30 392.00 15.53 207.65 69.30 392.00 15.54 207.65 69.30 392.00 15.55 207.65 69.30 392.00 138.59 15.56 207.65 69.30 392.00 138.59 15.57 207.65 69.30 392.00 138.59 15.58 207.65 69.30 392.00 138.59 15.59 207.65 69.30 392.00 138.59 15.60 207.65 69.30 392.00 138.59 15.61 69.30 392.00 138.59 15.62 69.30 392.00 138.59 15.63 69.30 392.00 138.59 15.64 69.30 392.00 138.59 15.65 69.30 392.00 138.59 15.66 69.30 392.00 138.59 15.67 69.30 392.00 138.59 15.68 69.30 392.00 138.59 15.69 69.30 392.00 138.59 220.00 15.70 69.30 392.00 138.59 220.00 15.71 69.30 392.00 138.59 220.00 15.72 69.30 392.00 138.59 220.00 15.73 69.30 392.00 138.59 220.00 15.74 69.30 392.00 138.59 220.00 15.75 69.30 392.00 138.59 220.00 15.76 69.30 392.00 138.59 220.00 15.77 69.30 392.00 138.59 220.00 15.78 69.30 392.00 138.59 220.00 15.79 69.30 392.00 138.59 220.00 15.80 69.30 392.00 138.59 220.00 15.81 69.30 392.00 138.59 220.00 15.82 69.30 392.00 138.59 220.00 15.83 392.00 138.59 220.00 15.84 392.00 138.59 220.00 15.85 392.00 138.59 220.00 15.86 392.00 138.59 220.00 15.87 392.00 138.59 220.00 15.88 392.00 138.59 220.00 15.89 392.00 138.59 220.00 15.90 392.00 138.59 220.00 329.63 15.91 392.00 138.59 220.00 329.63 15.92 392.00 138.59 220.00 329.63 15.93 392.00 138.59 329.63 15.94 392.00 138.59 329.63 15.95 392.00 138.59 329.63 15.96 392.00 138.59 329.63 15.97 392.00 138.59 329.63 15.98 392.00 138.59 329.63 15.99 392.00 138.59 329.63 16.00 392.00 138.59 329.63 16.01 392.00 138.59 329.63 16.02 392.00 138.59 329.63 16.03 392.00 138.59 329.63 16.04 392.00 138.59 329.63 16.05 392.00 138.59 329.63 16.06 392.00 138.59 329.63 16.07 392.00 138.59 329.63 16.08 392.00 138.59 329.63 392.00 16.09 392.00 138.59 392.00 16.10 392.00 138.59 392.00 16.11 392.00 138.59 392.00 466.16 16.12 138.59 392.00 466.16 16.13 138.59 392.00 466.16 16.14 138.59 392.00 466.16 16.15 138.59 392.00 466.16 16.16 138.59 392.00 466.16 16.17 138.59 392.00 466.16 16.18 138.59 392.00 466.16 16.19 138.59 392.00 466.16 16.20 138.59 392.00 466.16 16.21 138.59 392.00 466.16 16.22 138.59 392.00 466.16 16.23 138.59 392.00 466.16 16.24 138.59 392.00 466.16 16.25 138.59 392.00 466.16 16.26 138.59 392.00 466.16 16.27 138.59 392.00 466.16 16.28 138.59 392.00 466.16 16.29 138.59 392.00 466.16 16.30 138.59 392.00 466.16 440.00 440.00 16.31 138.59 466.16 440.00 440.00 16.32 138.59 466.16 440.00 440.00 16.33 138.59 466.16 440.00 440.00 16.34 138.59 440.00 440.00 16.35 138.59 440.00 440.00 16.36 138.59 440.00 440.00 16.37 138.59 440.00 440.00 16.38 138.59 440.00 440.00 16.39 138.59 440.00 440.00 16.40 138.59 440.00 440.00 16.41 138.59 440.00 440.00 16.42 138.59 440.00 440.00 16.43 138.59 440.00 440.00 16.44 138.59 440.00 440.00 16.45 138.59 440.00 440.00 16.46 138.59 440.00 440.00 16.47 138.59 440.00 440.00 16.48 138.59 440.00 16.49 138.59 440.00 392.00 16.50 138.59 440.00 392.00 220.00 16.51 138.59 440.00 392.00 220.00 16.52 138.59 440.00 392.00 220.00 16.53 138.59 392.00 220.00 16.54 138.59 392.00 220.00 16.55 138.59 392.00 220.00 16.56 138.59 392.00 220.00 16.57 138.59 392.00 220.00 16.58 138.59 392.00 220.00 16.59 138.59 392.00 220.00 16.60 138.59 220.00 16.61 138.59 220.00 73.42 16.62 138.59 220.00 73.42 16.63 138.59 220.00 73.42 16.64 138.59 220.00 73.42 16.65 138.59 220.00 73.42 146.83 16.66 138.59 220.00 73.42 146.83 16.67 138.59 220.00 73.42 146.83 16.68 138.59 220.00 73.42 146.83 392.00 16.69 138.59 220.00 73.42 146.83 392.00 16.70 138.59 220.00 73.42 146.83 392.00 16.71 220.00 73.42 146.83 392.00 16.72 220.00 73.42 146.83 392.00 16.73 73.42 146.83 392.00 16.74 73.42 146.83 392.00 16.75 73.42 146.83 392.00 16.76 73.42 146.83 392.00 16.77 73.42 146.83 392.00 16.78 73.42 146.83 392.00 16.79 73.42 146.83 392.00 16.80 73.42 146.83 392.00 16.81 73.42 146.83 392.00 16.82 73.42 146.83 392.00 16.83 73.42 146.83 392.00 16.84 73.42 146.83 392.00 220.00 16.85 73.42 146.83 392.00 220.00 16.86 73.42 146.83 392.00 220.00 16.87 73.42 146.83 392.00 220.00 16.88 73.42 146.83 392.00 220.00 369.99 16.89 73.42 146.83 392.00 220.00 369.99 16.90 73.42 146.83 220.00 369.99 16.91 73.42 146.83 220.00 369.99 16.92 73.42 146.83 220.00 369.99 16.93 73.42 146.83 220.00 369.99 16.94 73.42 146.83 220.00 369.99 16.95 73.42 146.83 220.00 369.99 16.96 73.42 146.83 220.00 369.99 16.97 73.42 146.83 220.00 369.99 16.98 73.42 146.83 220.00 369.99 16.99 73.42 146.83 220.00 369.99 17.00 73.42 146.83 220.00 369.99 17.01 73.42 146.83 220.00 369.99 17.02 73.42 146.83 220.00 369.99 17.03 146.83 369.99 17.04 146.83 369.99 17.05 146.83 369.99 17.06 146.83 369.99 17.07 146.83 369.99 293.66 17.08 146.83 369.99 293.66 17.09 146.83 369.99 293.66 587.33 17.10 146.83 369.99 293.66 587.33 17.11 146.83 369.99 293.66 587.33 17.12 146.83 293.66 587.33 17.13 146.83 293.66 587.33 17.14 146.83 293.66 587.33 17.15 146.83 293.66 587.33 17.16 146.83 293.66 587.33 17.17 146.83 293.66 587.33 17.18 146.83 293.66 587.33 17.19 146.83 293.66 587.33 17.20 146.83 293.66 587.33 17.21 146.83 293.66 587.33 17.22 146.83 293.66 587.33 17.23 146.83 293.66 587.33 17.24 146.83 293.66 587.33 17.25 146.83 293.66 587.33 17.26 146.83 587.33 17.27 146.83 587.33 17.28 146.83 587.33 17.29 146.83 587.33 17.30 146.83 587.33 17.31 146.83 587.33 17.32 146.83 587.33 369.99 17.33 146.83 587.33 369.99 17.34 146.83 587.33 369.99 17.35 146.83 587.33 369.99 17.36 146.83 587.33 369.99 17.37 146.83 587.33 369.99 17.38 146.83 587.33 369.99 17.39 146.83 587.33 369.99 17.40 146.83 587.33 369.99 17.41 146.83 587.33 369.99 17.42 146.83 587.33 369.99 17.43 146.83 587.33 369.99 17.44 146.83 587.33 369.99 17.45 146.83 587.33 369.99 17.46 146.83 587.33 369.99 17.47 146.83 587.33 369.99 17.48 146.83 587.33 369.99 17.49 146.83 587.33 369.99 17.50 146.83 587.33 369.99 440.00 17.51 146.83 587.33 369.99 440.00 17.52 146.83 587.33 369.99 440.00 17.53 146.83 587.33 440.00 17.54 146.83 587.33 440.00 17.55 146.83 587.33 440.00 17.56 146.83 587.33 440.00 17.57 146.83 587.33 440.00 17.58 146.83 587.33 440.00 17.59 146.83 587.33 440.00 17.60 146.83 587.33 440.00 17.61 146.83 587.33 440.00 17.62 146.83 587.33 440.00 17.63 146.83 587.33 440.00 17.64 146.83 587.33 440.00 17.65 146.83 587.33 440.00 17.66 146.83 587.33 440.00 17.67 146.83 587.33 440.00 17.68 146.83 587.33 440.00 17.69 146.83 587.33 220.00 17.70 146.83 587.33 220.00 17.71 146.83 587.33 220.00 17.72 146.83 587.33 220.00 17.73 146.83 587.33 220.00 17.74 146.83 587.33 220.00 61.74 17.75 146.83 587.33 220.00 61.74 17.76 146.83 587.33 220.00 61.74 17.77 146.83 587.33 220.00 61.74 17.78 146.83 587.33 220.00 61.74 17.79 146.83 587.33 220.00 61.74 17.80 146.83 587.33 220.00 61.74 17.81 146.83 587.33 220.00 61.74 17.82 146.83 587.33 220.00 61.74 17.83 146.83 587.33 220.00 61.74 17.84 146.83 587.33 220.00 61.74 123.47 17.85 146.83 587.33 220.00 61.74 123.47 17.86 146.83 587.33 220.00 61.74 123.47 17.87 146.83 587.33 220.00 61.74 123.47 17.88 587.33 220.00 61.74 123.47 17.89 587.33 220.00 61.74 123.47 17.90 587.33 220.00 61.74 123.47 17.91 587.33 220.00 61.74 123.47 17.92 587.33 220.00 61.74 123.47 17.93 587.33 220.00 61.74 123.47 17.94 587.33 220.00 61.74 123.47 17.95 587.33 220.00 61.74 123.47 17.96 587.33 61.74 123.47 17.97 61.74 123.47 17.98 61.74 123.47 17.99 61.74 123.47 18.00 61.74 123.47 18.01 61.74 123.47 18.02 61.74 123.47 18.03 61.74 123.47 18.04 61.74 123.47 18.05 61.74 123.47 18.06 61.74 123.47 18.07 61.74 123.47 18.08 61.74 123.47 18.09 61.74 123.47 329.63 18.10 61.74 123.47 329.63 18.11 61.74 123.47 329.63 196.00 18.12 61.74 123.47 329.63 196.00 18.13 61.74 123.47 329.63 196.00 18.14 61.74 123.47 329.63 196.00 18.15 61.74 123.47 329.63 196.00 18.16 61.74 123.47 329.63 196.00 18.17 61.74 123.47 329.63 196.00 18.18 123.47 329.63 196.00 18.19 123.47 329.63 196.00 18.20 123.47 329.63 196.00 18.21 123.47 329.63 196.00 18.22 123.47 329.63 196.00 18.23 123.47 329.63 196.00 18.24 123.47 329.63 196.00 18.25 123.47 329.63 196.00 18.26 123.47 329.63 196.00 18.27 123.47 329.63 196.00 18.28 123.47 329.63 196.00 18.29 123.47 329.63 196.00 349.23 18.30 123.47 329.63 196.00 349.23 293.66 18.31 123.47 329.63 196.00 349.23 293.66 18.32 123.47 329.63 196.00 349.23 293.66 18.33 123.47 329.63 349.23 293.66 18.34 123.47 329.63 349.23 293.66 18.35 123.47 329.63 349.23 293.66 18.36 123.47 329.63 349.23 293.66 18.37 123.47 329.63 349.23 293.66 18.38 123.47 329.63 349.23 293.66 18.39 123.47 329.63 349.23 293.66 18.40 123.47 329.63 349.23 293.66 18.41 123.47 329.63 349.23 293.66 18.42 123.47 349.23 293.66 18.43 123.47 349.23 293.66 18.44 123.47 349.23 293.66 18.45 123.47 349.23 293.66 18.46 123.47 349.23 293.66 18.47 123.47 349.23 293.66 18.48 123.47 293.66 440.00 18.49 123.47 293.66 440.00 18.50 123.47 293.66 440.00 349.23 18.51 123.47 293.66 440.00 349.23 18.52 123.47 440.00 349.23 18.53 123.47 440.00 349.23 18.54 123.47 440.00 349.23 18.55 123.47 440.00 349.23 18.56 123.47 440.00 349.23 18.57 123.47 440.00 349.23 18.58 123.47 440.00 349.23 18.59 123.47 440.00 349.23 18.60 123.47 440.00 349.23 18.61 123.47 440.00 349.23 18.62 123.47 440.00 349.23 18.63 123.47 440.00 349.23 18.64 123.47 440.00 349.23 18.65 123.47 440.00 349.23 18.66 123.47 440.00 349.23 18.67 123.47 440.00 349.23 392.00 18.68 123.47 440.00 349.23 392.00 18.69 123.47 440.00 349.23 392.00 18.70 123.47 440.00 349.23 392.00 18.71 123.47 349.23 392.00 18.72 123.47 349.23 392.00 18.73 123.47 349.23 392.00 392.00 18.74 123.47 349.23 392.00 392.00 18.75 123.47 349.23 392.00 392.00 18.76 123.47 392.00 392.00 18.77 123.47 392.00 392.00 18.78 123.47 392.00 392.00 18.79 123.47 392.00 392.00 18.80 123.47 392.00 392.00 18.81 123.47 392.00 392.00 18.82 123.47 392.00 392.00 18.83 123.47 392.00 392.00 18.84 123.47 392.00 392.00 18.85 123.47 392.00 392.00 18.86 123.47 392.00 392.00 18.87 123.47 392.00 392.00 349.23 18.88 123.47 392.00 392.00 349.23 18.89 123.47 392.00 392.00 349.23 18.90 123.47 392.00 349.23 18.91 123.47 392.00 349.23 196.00 18.92 123.47 349.23 196.00 18.93 123.47 349.23 196.00 18.94 123.47 349.23 196.00 18.95 123.47 349.23 196.00 18.96 123.47 349.23 196.00 18.97 123.47 349.23 196.00 18.98 123.47 349.23 196.00 18.99 123.47 349.23 196.00 19.00 123.47 196.00 19.01 123.47 196.00 19.02 123.47 196.00 19.03 123.47 196.00 19.04 123.47 196.00 130.81 19.05 123.47 196.00 130.81 65.41 19.06 123.47 196.00 130.81 65.41 19.07 123.47 196.00 130.81 65.41 349.23 19.08 123.47 196.00 130.81 65.41 349.23 19.09 123.47 196.00 130.81 65.41 349.23 19.10 123.47 130.81 65.41 349.23 19.11 130.81 65.41 349.23 19.12 130.81 65.41 349.23 19.13 130.81 65.41 349.23 19.14 130.81 65.41 349.23 19.15 130.81 65.41 349.23 19.16 130.81 65.41 349.23 19.17 130.81 65.41 349.23 19.18 130.81 65.41 349.23 19.19 130.81 65.41 349.23 19.20 130.81 65.41 349.23 19.21 130.81 65.41 349.23 19.22 130.81 65.41 349.23 19.23 130.81 65.41 349.23 19.24 130.81 65.41 349.23 19.25 130.81 65.41 349.23 19.26 130.81 65.41 349.23 19.27 130.81 65.41 329.63 19.28 130.81 65.41 329.63 19.29 130.81 65.41 329.63 19.30 130.81 65.41 329.63 196.00 19.31 130.81 65.41 329.63 196.00 19.32 130.81 65.41 329.63 196.00 19.33 130.81 65.41 329.63 196.00 19.34 130.81 65.41 329.63 196.00 19.35 130.81 65.41 329.63 196.00 19.36 130.81 65.41 329.63 196.00 19.37 130.81 65.41 329.63 196.00 19.38 130.81 65.41 329.63 196.00 19.39 130.81 65.41 329.63 196.00 19.40 130.81 65.41 329.63 196.00 19.41 130.81 65.41 329.63 196.00 19.42 130.81 65.41 329.63 196.00 19.43 130.81 65.41 329.63 196.00 19.44 130.81 65.41 329.63 196.00 19.45 130.81 65.41 329.63 196.00 19.46 130.81 65.41 329.63 196.00 19.47 130.81 65.41 329.63 196.00 19.48 130.81 329.63 196.00 19.49 130.81 329.63 196.00 19.50 130.81 196.00 261.63 19.51 130.81 196.00 261.63 523.25 19.52 130.81 196.00 261.63 523.25 19.53 130.81 196.00 261.63 523.25 19.54 130.81 196.00 261.63 523.25 19.55 130.81 261.63 523.25 19.56 130.81 261.63 523.25 19.57 130.81 261.63 523.25 19.58 130.81 261.63 523.25 19.59 130.81 261.63 523.25 19.60 130.81 261.63 523.25 19.61 130.81 261.63 523.25 19.62 130.81 261.63 523.25 19.63 130.81 261.63 523.25 19.64 130.81 261.63 523.25 19.65 130.81 261.63 523.25 19.66 130.81 261.63 523.25 19.67 130.81 261.63 523.25 19.68 130.81 261.63 523.25 19.69 130.81 261.63 523.25 19.70 130.81 261.63 523.25 329.63 19.71 130.81 261.63 523.25 329.63 19.72 130.81 261.63 523.25 329.63 19.73 130.81 523.25 329.63 19.74 130.81 523.25 329.63 19.75 130.81 523.25 329.63 19.76 130.81 523.25 329.63 19.77 130.81 523.25 329.63 19.78 130.81 523.25 329.63 19.79 130.81 523.25 329.63 19.80 130.81 523.25 329.63 19.81 130.81 523.25 329.63 19.82 130.81 523.25 329.63 19.83 130.81 523.25 329.63 19.84 130.81 523.25 329.63 19.85 130.81 523.25 329.63 19.86 130.81 523.25 329.63 19.87 130.81 523.25 329.63 19.88 130.81 523.25 329.63 19.89 130.81 523.25 329.63 392.00 19.90 130.81 523.25 329.63 392.00 19.91 130.81 523.25 392.00 19.92 130.81 523.25 392.00 19.93 130.81 523.25 392.00 19.94 130.81 523.25 392.00 19.95 130.81 523.25 392.00 19.96 130.81 523.25 392.00 19.97 130.81 523.25 392.00 19.98 130.81 523.25 392.00 19.99 130.81 523.25 392.00 20.00 130.81 523.25 392.00 20.01 130.81 523.25 392.00 20.02 130.81 523.25 392.00 20.03 130.81 523.25 392.00 20.04 130.81 523.25 392.00 20.05 130.81 523.25 392.00 20.06 130.81 523.25 392.00 20.07 130.81 523.25 392.00 20.08 130.81 523.25 196.00 20.09 130.81 523.25 196.00 20.10 130.81 523.25 196.00 20.11 130.81 523.25 196.00 20.12 130.81 523.25 196.00 20.13 130.81 523.25 196.00 20.14 130.81 523.25 196.00 20.15 130.81 523.25 196.00 20.16 130.81 523.25 196.00 20.17 130.81 523.25 196.00 20.18 130.81 523.25 196.00 20.19 130.81 523.25 196.00 20.20 130.81 523.25 196.00 20.21 130.81 523.25 196.00 20.22 130.81 523.25 196.00 20.23 130.81 523.25 196.00 103.83 20.24 523.25 196.00 103.83 103.83 20.25 523.25 103.83 103.83 20.26 523.25 103.83 103.83 20.27 523.25 103.83 103.83 20.28 523.25 103.83 103.83 20.29 523.25 103.83 103.83 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24.30 110.00 220.00 329.63 659.26 329.63 24.31 110.00 220.00 329.63 659.26 329.63 24.32 110.00 220.00 329.63 659.26 329.63 24.33 110.00 220.00 329.63 659.26 329.63 24.34 110.00 220.00 329.63 659.26 329.63 24.35 110.00 220.00 329.63 659.26 329.63 880.00 24.36 110.00 220.00 329.63 659.26 329.63 880.00 440.00 24.37 110.00 220.00 329.63 659.26 329.63 880.00 440.00 24.38 110.00 220.00 329.63 880.00 440.00 24.39 110.00 220.00 329.63 880.00 440.00 24.40 110.00 220.00 329.63 880.00 440.00 24.41 110.00 220.00 329.63 880.00 440.00 24.42 110.00 220.00 329.63 880.00 440.00 24.43 110.00 220.00 329.63 880.00 440.00 24.44 110.00 220.00 329.63 880.00 440.00 24.45 110.00 220.00 329.63 880.00 440.00 24.46 110.00 220.00 329.63 880.00 440.00 24.47 110.00 220.00 329.63 880.00 440.00 24.48 110.00 220.00 329.63 880.00 440.00 24.49 110.00 220.00 329.63 880.00 440.00 24.50 110.00 220.00 329.63 880.00 440.00 24.51 110.00 220.00 329.63 880.00 440.00 24.52 110.00 220.00 329.63 880.00 440.00 24.53 110.00 220.00 329.63 880.00 440.00 24.54 110.00 220.00 329.63 880.00 440.00 1046.50 24.55 110.00 220.00 329.63 880.00 1046.50 24.56 110.00 220.00 329.63 880.00 1046.50 523.25 24.57 110.00 220.00 329.63 1046.50 523.25 24.58 220.00 329.63 1046.50 523.25 24.59 220.00 329.63 1046.50 523.25 24.60 220.00 329.63 1046.50 523.25 24.61 220.00 329.63 1046.50 523.25 24.62 220.00 329.63 1046.50 523.25 24.63 220.00 329.63 1046.50 523.25 24.64 220.00 329.63 1046.50 523.25 24.65 220.00 329.63 523.25 24.66 220.00 329.63 523.25 24.67 220.00 329.63 523.25 24.68 220.00 329.63 523.25 24.69 220.00 329.63 523.25 24.70 220.00 329.63 523.25 24.71 220.00 329.63 523.25 24.72 220.00 329.63 523.25 261.63 1046.50 24.73 220.00 329.63 523.25 261.63 1046.50 24.74 220.00 329.63 523.25 261.63 1046.50 24.75 220.00 329.63 523.25 261.63 1046.50 24.76 220.00 329.63 523.25 261.63 1046.50 24.77 220.00 329.63 261.63 1046.50 24.78 220.00 329.63 261.63 1046.50 24.79 220.00 329.63 261.63 1046.50 24.80 220.00 329.63 261.63 1046.50 24.81 220.00 329.63 261.63 1046.50 24.82 220.00 329.63 261.63 1046.50 24.83 220.00 329.63 261.63 24.84 220.00 329.63 261.63 24.85 220.00 329.63 261.63 24.86 220.00 329.63 24.87 220.00 24.88 220.00 24.89 220.00 24.90 220.00 1046.50 349.23 24.91 220.00 1046.50 349.23 24.92 220.00 1046.50 349.23 73.42 73.42 24.93 220.00 1046.50 349.23 73.42 73.42 146.83 24.94 220.00 1046.50 349.23 73.42 73.42 146.83 24.95 1046.50 349.23 73.42 73.42 146.83 24.96 1046.50 349.23 73.42 73.42 146.83 24.97 1046.50 349.23 73.42 73.42 146.83 24.98 1046.50 349.23 73.42 73.42 146.83 24.99 1046.50 349.23 73.42 73.42 146.83 25.00 1046.50 349.23 73.42 73.42 146.83 25.01 1046.50 349.23 73.42 73.42 146.83 146.83 25.02 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 25.03 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 25.04 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 25.05 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 25.06 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 25.07 1046.50 349.23 73.42 73.42 146.83 146.83 220.00 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26.23 82.41 440.00 82.41 261.63 164.81 164.81 26.24 82.41 440.00 82.41 261.63 164.81 164.81 26.25 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.26 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.27 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.28 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.29 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.30 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.31 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.32 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.33 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.34 82.41 440.00 82.41 261.63 164.81 164.81 329.63 1318.51 26.35 82.41 440.00 82.41 261.63 164.81 164.81 329.63 26.36 82.41 440.00 82.41 261.63 164.81 164.81 329.63 26.37 82.41 440.00 82.41 261.63 164.81 164.81 329.63 26.38 82.41 440.00 82.41 261.63 164.81 164.81 329.63 26.39 82.41 440.00 82.41 261.63 164.81 164.81 329.63 26.40 82.41 440.00 82.41 261.63 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207.65 277.18 12.01 61.74 123.47 207.65 277.18 12.02 61.74 123.47 207.65 277.18 12.03 61.74 123.47 207.65 277.18 12.04 61.74 123.47 207.65 277.18 12.05 61.74 123.47 207.65 277.18 12.06 61.74 123.47 277.18 12.07 61.74 123.47 277.18 12.08 61.74 123.47 277.18 12.09 61.74 123.47 277.18 12.10 61.74 123.47 277.18 12.11 61.74 123.47 277.18 12.12 61.74 123.47 277.18 12.13 61.74 123.47 277.18 12.14 61.74 123.47 277.18 12.15 61.74 123.47 277.18 12.16 61.74 123.47 277.18 12.17 61.74 123.47 277.18 12.18 61.74 123.47 277.18 12.19 61.74 123.47 277.18 12.20 61.74 123.47 277.18 12.21 61.74 123.47 277.18 12.22 61.74 123.47 277.18 12.23 61.74 123.47 277.18 12.24 61.74 123.47 277.18 12.25 61.74 123.47 277.18 12.26 61.74 123.47 277.18 12.27 61.74 123.47 277.18 12.28 61.74 123.47 277.18 12.29 61.74 123.47 277.18 12.30 61.74 123.47 277.18 12.31 61.74 123.47 277.18 12.32 61.74 123.47 277.18 12.33 61.74 123.47 277.18 12.34 61.74 123.47 277.18 12.35 61.74 123.47 277.18 12.36 61.74 123.47 277.18 12.37 61.74 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277.18 13.50 61.74 123.47 277.18 13.51 61.74 123.47 277.18 13.52 61.74 123.47 277.18 13.53 61.74 123.47 277.18 13.54 61.74 123.47 277.18 13.55 61.74 123.47 277.18 13.56 61.74 123.47 277.18 13.57 61.74 123.47 277.18 13.58 61.74 123.47 277.18 13.59 61.74 123.47 277.18 13.60 61.74 123.47 277.18 13.61 61.74 123.47 277.18 13.62 61.74 123.47 277.18 13.63 61.74 123.47 277.18 13.64 61.74 123.47 277.18 13.65 61.74 123.47 277.18 13.66 61.74 123.47 277.18 13.67 61.74 123.47 277.18 13.68 61.74 123.47 277.18 13.69 61.74 123.47 277.18 13.70 61.74 123.47 277.18 13.71 61.74 123.47 277.18 13.72 61.74 123.47 277.18 13.73 61.74 123.47 277.18 13.74 61.74 123.47 277.18 13.75 61.74 123.47 277.18 13.76 61.74 123.47 277.18 13.77 61.74 123.47 277.18 13.78 61.74 123.47 277.18 13.79 61.74 123.47 277.18 13.80 61.74 277.18 13.81 61.74 277.18 13.82 61.74 277.18 13.83 61.74 277.18 13.84 61.74 277.18 13.85 61.74 277.18 13.86 277.18 13.87 277.18 13.88 277.18 13.89 277.18 13.90 277.18 329.63 13.91 277.18 329.63 13.92 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55.00 110.00 14.56 220.00 55.00 110.00 14.57 220.00 55.00 110.00 14.58 220.00 55.00 110.00 14.59 220.00 55.00 110.00 14.60 220.00 55.00 110.00 14.61 220.00 55.00 110.00 14.62 220.00 55.00 110.00 14.63 220.00 55.00 110.00 14.64 220.00 55.00 110.00 14.65 220.00 55.00 110.00 14.66 220.00 55.00 110.00 14.67 220.00 55.00 110.00 14.68 220.00 55.00 110.00 14.69 220.00 55.00 110.00 14.70 220.00 55.00 110.00 14.71 220.00 55.00 110.00 14.72 220.00 55.00 110.00 14.73 220.00 55.00 110.00 14.74 220.00 55.00 110.00 14.75 220.00 55.00 110.00 14.76 220.00 55.00 110.00 14.77 220.00 55.00 110.00 14.78 220.00 55.00 110.00 14.79 220.00 55.00 110.00 14.80 220.00 55.00 110.00 14.81 220.00 55.00 110.00 14.82 220.00 55.00 110.00 14.83 220.00 55.00 110.00 14.84 220.00 55.00 110.00 14.85 220.00 55.00 110.00 14.86 220.00 55.00 110.00 14.87 220.00 55.00 110.00 14.88 220.00 55.00 110.00 14.89 220.00 55.00 110.00 14.90 220.00 55.00 110.00 14.91 220.00 55.00 110.00 14.92 220.00 55.00 110.00 14.93 220.00 55.00 110.00 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329.63 15.68 55.00 110.00 329.63 15.69 55.00 110.00 329.63 15.70 55.00 110.00 329.63 15.71 55.00 110.00 329.63 15.72 55.00 110.00 329.63 15.73 55.00 110.00 329.63 15.74 55.00 110.00 329.63 15.75 55.00 110.00 329.63 15.76 55.00 110.00 329.63 15.77 55.00 110.00 329.63 15.78 55.00 110.00 329.63 15.79 55.00 110.00 329.63 15.80 55.00 110.00 329.63 15.81 55.00 110.00 329.63 15.82 55.00 110.00 329.63 15.83 55.00 110.00 329.63 15.84 55.00 110.00 329.63 15.85 55.00 110.00 329.63 15.86 55.00 110.00 329.63 15.87 55.00 110.00 329.63 15.88 55.00 110.00 329.63 15.89 55.00 110.00 329.63 15.90 55.00 110.00 329.63 15.91 55.00 110.00 329.63 15.92 55.00 110.00 329.63 15.93 55.00 110.00 329.63 15.94 55.00 110.00 329.63 15.95 55.00 110.00 329.63 15.96 55.00 110.00 329.63 15.97 55.00 110.00 329.63 15.98 55.00 110.00 329.63 15.99 55.00 110.00 329.63 220.00 16.00 55.00 110.00 329.63 220.00 16.01 55.00 110.00 329.63 220.00 16.02 55.00 110.00 329.63 220.00 16.03 55.00 110.00 329.63 220.00 16.04 55.00 110.00 220.00 16.05 55.00 110.00 220.00 16.06 55.00 110.00 220.00 16.07 55.00 110.00 220.00 16.08 55.00 110.00 220.00 16.09 55.00 110.00 220.00 16.10 55.00 110.00 220.00 16.11 55.00 110.00 220.00 16.12 55.00 110.00 220.00 16.13 55.00 110.00 220.00 16.14 55.00 110.00 220.00 16.15 55.00 110.00 220.00 16.16 55.00 110.00 220.00 16.17 55.00 110.00 220.00 16.18 55.00 110.00 220.00 16.19 55.00 110.00 220.00 16.20 55.00 110.00 220.00 16.21 55.00 110.00 220.00 16.22 55.00 110.00 220.00 16.23 55.00 110.00 220.00 16.24 55.00 110.00 220.00 16.25 55.00 110.00 220.00 16.26 55.00 110.00 220.00 16.27 55.00 110.00 220.00 16.28 55.00 110.00 220.00 16.29 55.00 110.00 220.00 16.30 55.00 110.00 220.00 16.31 55.00 110.00 220.00 16.32 55.00 110.00 220.00 16.33 55.00 110.00 220.00 16.34 55.00 110.00 220.00 16.35 55.00 110.00 220.00 16.36 55.00 110.00 220.00 16.37 55.00 110.00 220.00 16.38 55.00 110.00 220.00 16.39 55.00 110.00 220.00 16.40 55.00 110.00 220.00 16.41 55.00 110.00 220.00 16.42 55.00 110.00 220.00 16.43 55.00 110.00 220.00 16.44 55.00 110.00 220.00 16.45 55.00 110.00 220.00 16.46 55.00 110.00 220.00 16.47 55.00 110.00 220.00 277.18 16.48 55.00 110.00 220.00 277.18 16.49 55.00 110.00 220.00 277.18 16.50 55.00 110.00 220.00 277.18 16.51 55.00 110.00 220.00 277.18 16.52 55.00 110.00 220.00 277.18 16.53 55.00 110.00 220.00 277.18 16.54 55.00 110.00 277.18 16.55 55.00 110.00 277.18 16.56 55.00 110.00 277.18 16.57 55.00 110.00 277.18 16.58 55.00 110.00 277.18 16.59 55.00 110.00 277.18 16.60 55.00 110.00 277.18 16.61 55.00 110.00 277.18 16.62 55.00 110.00 277.18 16.63 55.00 110.00 277.18 16.64 55.00 110.00 277.18 16.65 55.00 110.00 277.18 16.66 55.00 110.00 277.18 16.67 55.00 110.00 277.18 16.68 55.00 110.00 277.18 16.69 55.00 110.00 277.18 16.70 55.00 110.00 277.18 16.71 55.00 110.00 277.18 16.72 55.00 110.00 277.18 16.73 55.00 110.00 277.18 16.74 55.00 110.00 277.18 16.75 55.00 110.00 277.18 16.76 55.00 110.00 277.18 16.77 55.00 110.00 277.18 16.78 55.00 110.00 277.18 16.79 55.00 110.00 277.18 16.80 55.00 110.00 277.18 16.81 55.00 110.00 277.18 16.82 55.00 110.00 277.18 16.83 55.00 110.00 277.18 16.84 55.00 110.00 277.18 16.85 55.00 110.00 277.18 16.86 55.00 110.00 277.18 16.87 55.00 110.00 277.18 16.88 55.00 110.00 277.18 16.89 55.00 110.00 277.18 16.90 55.00 110.00 277.18 16.91 55.00 110.00 277.18 16.92 55.00 110.00 277.18 16.93 55.00 277.18 16.94 55.00 277.18 16.95 55.00 277.18 16.96 55.00 277.18 16.97 55.00 277.18 329.63 16.98 277.18 329.63 16.99 277.18 329.63 17.00 277.18 329.63 17.01 329.63 17.02 329.63 17.03 329.63 17.04 329.63 17.05 329.63 17.06 329.63 17.07 329.63 17.08 329.63 17.09 329.63 17.10 329.63 17.11 329.63 17.12 329.63 17.13 329.63 17.14 329.63 17.15 329.63 17.16 329.63 17.17 329.63 17.18 329.63 17.19 329.63 17.20 329.63 17.21 329.63 17.22 329.63 17.23 329.63 17.24 329.63 17.25 329.63 17.26 329.63 17.27 329.63 17.28 329.63 17.29 329.63 17.30 329.63 17.31 329.63 17.32 329.63 17.33 329.63 17.34 329.63 17.35 329.63 17.36 329.63 17.37 329.63 17.38 329.63 17.39 329.63 17.40 329.63 17.41 329.63 17.42 329.63 17.43 329.63 17.44 329.63 17.45 329.63 17.46 329.63 17.47 329.63 92.50 17.48 329.63 92.50 46.25 17.49 92.50 46.25 220.00 17.50 92.50 46.25 220.00 17.51 92.50 46.25 220.00 17.52 92.50 46.25 220.00 17.53 92.50 46.25 220.00 17.54 92.50 46.25 220.00 17.55 92.50 46.25 220.00 17.56 92.50 46.25 220.00 17.57 92.50 46.25 220.00 17.58 92.50 46.25 220.00 17.59 92.50 46.25 220.00 17.60 92.50 46.25 220.00 17.61 92.50 46.25 220.00 17.62 92.50 46.25 220.00 17.63 92.50 46.25 220.00 17.64 92.50 46.25 220.00 17.65 92.50 46.25 220.00 17.66 92.50 46.25 220.00 17.67 92.50 46.25 220.00 17.68 92.50 46.25 220.00 17.69 92.50 46.25 220.00 17.70 92.50 46.25 220.00 17.71 92.50 46.25 220.00 17.72 92.50 46.25 220.00 17.73 92.50 46.25 220.00 17.74 92.50 46.25 220.00 17.75 92.50 46.25 220.00 17.76 92.50 46.25 220.00 17.77 92.50 46.25 220.00 17.78 92.50 46.25 220.00 17.79 92.50 46.25 220.00 17.80 92.50 46.25 220.00 17.81 92.50 46.25 220.00 17.82 92.50 46.25 220.00 17.83 92.50 46.25 220.00 17.84 92.50 46.25 220.00 17.85 92.50 46.25 220.00 17.86 92.50 46.25 220.00 17.87 92.50 46.25 220.00 17.88 92.50 46.25 220.00 17.89 92.50 46.25 220.00 17.90 92.50 46.25 220.00 17.91 92.50 46.25 220.00 17.92 92.50 46.25 220.00 17.93 92.50 46.25 220.00 17.94 92.50 46.25 220.00 17.95 92.50 46.25 220.00 17.96 92.50 46.25 220.00 17.97 92.50 46.25 220.00 17.98 92.50 46.25 220.00 17.99 92.50 46.25 220.00 18.00 92.50 46.25 220.00 18.01 92.50 46.25 220.00 18.02 92.50 46.25 220.00 18.03 92.50 46.25 220.00 18.04 92.50 46.25 220.00 18.05 92.50 46.25 220.00 293.66 18.06 92.50 46.25 220.00 293.66 18.07 92.50 46.25 220.00 293.66 18.08 92.50 46.25 220.00 293.66 18.09 92.50 46.25 220.00 293.66 18.10 92.50 46.25 220.00 293.66 18.11 92.50 46.25 220.00 293.66 18.12 92.50 46.25 220.00 293.66 18.13 92.50 46.25 220.00 293.66 18.14 92.50 46.25 220.00 293.66 18.15 92.50 46.25 293.66 18.16 92.50 46.25 293.66 18.17 92.50 46.25 293.66 18.18 92.50 46.25 293.66 18.19 92.50 46.25 293.66 18.20 92.50 46.25 293.66 18.21 92.50 46.25 293.66 18.22 92.50 46.25 293.66 18.23 92.50 46.25 293.66 18.24 92.50 46.25 293.66 18.25 92.50 46.25 293.66 18.26 92.50 46.25 293.66 18.27 92.50 46.25 293.66 18.28 92.50 46.25 293.66 18.29 92.50 46.25 293.66 18.30 92.50 46.25 293.66 18.31 92.50 46.25 293.66 18.32 92.50 46.25 293.66 18.33 92.50 46.25 293.66 18.34 92.50 46.25 293.66 18.35 92.50 46.25 293.66 18.36 92.50 46.25 293.66 18.37 92.50 46.25 293.66 18.38 92.50 46.25 293.66 18.39 92.50 46.25 293.66 18.40 92.50 46.25 293.66 18.41 92.50 46.25 293.66 18.42 92.50 46.25 293.66 18.43 92.50 46.25 293.66 18.44 92.50 46.25 293.66 18.45 92.50 46.25 293.66 18.46 92.50 46.25 293.66 18.47 92.50 46.25 293.66 18.48 92.50 46.25 293.66 18.49 92.50 46.25 293.66 18.50 92.50 46.25 293.66 18.51 92.50 46.25 293.66 18.52 92.50 46.25 293.66 18.53 92.50 46.25 293.66 369.99 18.54 92.50 46.25 293.66 369.99 18.55 92.50 46.25 293.66 369.99 18.56 92.50 46.25 369.99 18.57 92.50 46.25 369.99 18.58 92.50 46.25 369.99 18.59 92.50 46.25 369.99 18.60 92.50 46.25 369.99 18.61 92.50 46.25 369.99 18.62 92.50 46.25 369.99 18.63 92.50 46.25 369.99 18.64 92.50 46.25 369.99 18.65 92.50 46.25 369.99 18.66 92.50 46.25 369.99 18.67 92.50 46.25 369.99 18.68 92.50 46.25 369.99 18.69 92.50 46.25 369.99 18.70 92.50 46.25 369.99 18.71 92.50 46.25 369.99 18.72 92.50 46.25 369.99 18.73 92.50 46.25 369.99 18.74 92.50 46.25 369.99 18.75 92.50 46.25 369.99 18.76 92.50 46.25 369.99 18.77 92.50 46.25 369.99 18.78 92.50 46.25 369.99 18.79 92.50 46.25 369.99 18.80 92.50 46.25 369.99 18.81 92.50 46.25 369.99 18.82 92.50 46.25 369.99 18.83 92.50 46.25 369.99 18.84 92.50 46.25 369.99 18.85 92.50 46.25 369.99 18.86 92.50 46.25 369.99 18.87 92.50 46.25 369.99 18.88 92.50 46.25 369.99 18.89 92.50 46.25 369.99 18.90 92.50 46.25 369.99 18.91 92.50 46.25 369.99 18.92 92.50 46.25 369.99 18.93 92.50 46.25 369.99 18.94 92.50 46.25 369.99 18.95 92.50 46.25 369.99 18.96 92.50 46.25 369.99 18.97 92.50 46.25 369.99 18.98 92.50 46.25 369.99 18.99 92.50 46.25 369.99 19.00 92.50 46.25 369.99 220.00 19.01 92.50 46.25 369.99 220.00 19.02 92.50 46.25 369.99 220.00 19.03 92.50 46.25 369.99 220.00 19.04 92.50 46.25 369.99 220.00 19.05 92.50 46.25 369.99 220.00 19.06 92.50 46.25 220.00 19.07 92.50 46.25 220.00 19.08 92.50 46.25 220.00 19.09 92.50 46.25 220.00 19.10 92.50 46.25 220.00 19.11 92.50 46.25 220.00 19.12 92.50 46.25 220.00 19.13 92.50 46.25 220.00 19.14 92.50 46.25 220.00 19.15 92.50 46.25 220.00 19.16 92.50 46.25 220.00 19.17 92.50 46.25 220.00 19.18 92.50 46.25 220.00 19.19 92.50 46.25 220.00 19.20 92.50 46.25 220.00 19.21 92.50 46.25 220.00 19.22 92.50 46.25 220.00 19.23 92.50 46.25 220.00 19.24 92.50 46.25 220.00 19.25 92.50 46.25 220.00 19.26 92.50 46.25 220.00 19.27 92.50 46.25 220.00 19.28 92.50 46.25 220.00 19.29 92.50 46.25 220.00 19.30 92.50 46.25 220.00 19.31 92.50 46.25 220.00 19.32 92.50 46.25 220.00 19.33 92.50 46.25 220.00 19.34 92.50 46.25 220.00 19.35 92.50 46.25 220.00 19.36 92.50 46.25 220.00 19.37 92.50 46.25 220.00 19.38 92.50 46.25 220.00 19.39 92.50 46.25 220.00 19.40 92.50 46.25 220.00 19.41 92.50 46.25 220.00 19.42 92.50 46.25 220.00 19.43 92.50 46.25 220.00 19.44 92.50 46.25 220.00 293.66 19.45 92.50 46.25 220.00 293.66 19.46 92.50 46.25 220.00 293.66 19.47 92.50 46.25 220.00 293.66 19.48 92.50 46.25 220.00 293.66 19.49 92.50 46.25 220.00 293.66 19.50 92.50 46.25 220.00 293.66 19.51 92.50 46.25 293.66 19.52 92.50 46.25 293.66 19.53 92.50 46.25 293.66 19.54 92.50 46.25 293.66 19.55 92.50 46.25 293.66 19.56 92.50 46.25 293.66 19.57 92.50 46.25 293.66 19.58 92.50 46.25 293.66 19.59 92.50 46.25 293.66 19.60 92.50 46.25 293.66 19.61 92.50 46.25 293.66 19.62 92.50 46.25 293.66 19.63 92.50 46.25 293.66 19.64 92.50 46.25 293.66 19.65 92.50 46.25 293.66 19.66 92.50 46.25 293.66 19.67 92.50 46.25 293.66 19.68 92.50 46.25 293.66 19.69 92.50 46.25 293.66 19.70 92.50 46.25 293.66 19.71 92.50 46.25 293.66 19.72 92.50 46.25 293.66 19.73 92.50 46.25 293.66 19.74 92.50 46.25 293.66 19.75 92.50 46.25 293.66 19.76 92.50 46.25 293.66 19.77 92.50 46.25 293.66 19.78 92.50 46.25 293.66 19.79 92.50 46.25 293.66 19.80 92.50 46.25 293.66 19.81 92.50 46.25 293.66 19.82 92.50 46.25 293.66 19.83 92.50 46.25 293.66 19.84 92.50 46.25 293.66 19.85 92.50 46.25 293.66 19.86 92.50 46.25 293.66 19.87 92.50 46.25 293.66 19.88 92.50 46.25 293.66 19.89 92.50 46.25 293.66 19.90 92.50 46.25 293.66 19.91 92.50 293.66 19.92 92.50 293.66 19.93 92.50 293.66 19.94 92.50 293.66 19.95 92.50 293.66 369.99 19.96 92.50 293.66 369.99 19.97 92.50 293.66 369.99 19.98 92.50 293.66 369.99 19.99 92.50 369.99 20.00 92.50 369.99 20.01 92.50 369.99 20.02 92.50 369.99 20.03 369.99 20.04 369.99 20.05 369.99 20.06 369.99 20.07 369.99 20.08 369.99 20.09 369.99 20.10 369.99 20.11 369.99 20.12 369.99 20.13 369.99 20.14 369.99 20.15 369.99 20.16 369.99 20.17 369.99 20.18 369.99 20.19 369.99 20.20 369.99 20.21 369.99 20.22 369.99 20.23 369.99 20.24 369.99 20.25 369.99 20.26 369.99 20.27 369.99 20.28 369.99 20.29 369.99 20.30 369.99 20.31 369.99 20.32 369.99 20.33 369.99 20.34 369.99 20.35 369.99 20.36 369.99 20.37 369.99 20.38 369.99 20.39 369.99 20.40 369.99 20.41 369.99 20.42 369.99 20.43 369.99 20.44 369.99 51.91 20.45 369.99 51.91 103.83 20.46 369.99 51.91 103.83 20.47 369.99 51.91 103.83 20.48 369.99 51.91 103.83 20.49 51.91 103.83 207.65 20.50 51.91 103.83 207.65 20.51 51.91 103.83 207.65 20.52 51.91 103.83 207.65 20.53 51.91 103.83 207.65 20.54 51.91 103.83 207.65 20.55 51.91 103.83 207.65 20.56 51.91 103.83 207.65 20.57 51.91 103.83 207.65 20.58 51.91 103.83 207.65 20.59 51.91 103.83 207.65 20.60 51.91 103.83 207.65 20.61 51.91 103.83 207.65 20.62 51.91 103.83 207.65 20.63 51.91 103.83 207.65 20.64 51.91 103.83 207.65 20.65 51.91 103.83 207.65 20.66 51.91 103.83 207.65 20.67 51.91 103.83 207.65 20.68 51.91 103.83 207.65 20.69 51.91 103.83 207.65 20.70 51.91 103.83 207.65 20.71 51.91 103.83 207.65 20.72 51.91 103.83 207.65 20.73 51.91 103.83 207.65 20.74 51.91 103.83 207.65 20.75 51.91 103.83 207.65 20.76 51.91 103.83 207.65 20.77 51.91 103.83 207.65 20.78 51.91 103.83 207.65 20.79 51.91 103.83 207.65 20.80 51.91 103.83 207.65 20.81 51.91 103.83 207.65 20.82 51.91 103.83 207.65 20.83 51.91 103.83 207.65 20.84 51.91 103.83 207.65 20.85 51.91 103.83 207.65 20.86 51.91 103.83 207.65 20.87 51.91 103.83 207.65 20.88 51.91 103.83 207.65 20.89 51.91 103.83 207.65 20.90 51.91 103.83 207.65 20.91 51.91 103.83 207.65 20.92 51.91 103.83 207.65 20.93 51.91 103.83 207.65 20.94 51.91 103.83 207.65 20.95 51.91 103.83 207.65 20.96 51.91 103.83 207.65 20.97 51.91 103.83 207.65 20.98 51.91 103.83 207.65 20.99 51.91 103.83 207.65 21.00 51.91 103.83 207.65 21.01 51.91 103.83 207.65 21.02 51.91 103.83 207.65 21.03 51.91 103.83 207.65 261.63 21.04 51.91 103.83 207.65 261.63 21.05 51.91 103.83 207.65 261.63 21.06 51.91 103.83 207.65 261.63 21.07 51.91 103.83 207.65 261.63 21.08 51.91 103.83 207.65 261.63 21.09 51.91 103.83 261.63 21.10 51.91 103.83 261.63 21.11 51.91 103.83 261.63 21.12 51.91 103.83 261.63 21.13 51.91 103.83 261.63 21.14 51.91 103.83 261.63 21.15 51.91 103.83 261.63 21.16 51.91 103.83 261.63 21.17 51.91 103.83 261.63 21.18 51.91 103.83 261.63 21.19 51.91 103.83 261.63 21.20 51.91 103.83 261.63 21.21 51.91 103.83 261.63 21.22 51.91 103.83 261.63 21.23 51.91 103.83 261.63 21.24 51.91 103.83 261.63 21.25 51.91 103.83 261.63 21.26 51.91 103.83 261.63 21.27 51.91 103.83 261.63 21.28 51.91 103.83 261.63 21.29 51.91 103.83 261.63 21.30 51.91 103.83 261.63 21.31 51.91 103.83 261.63 21.32 51.91 103.83 261.63 21.33 51.91 103.83 261.63 21.34 51.91 103.83 261.63 21.35 51.91 103.83 261.63 21.36 51.91 103.83 261.63 21.37 51.91 103.83 261.63 21.38 51.91 103.83 261.63 21.39 51.91 103.83 261.63 21.40 51.91 103.83 261.63 21.41 51.91 103.83 261.63 21.42 51.91 103.83 261.63 21.43 51.91 103.83 261.63 21.44 51.91 103.83 261.63 21.45 51.91 103.83 261.63 21.46 51.91 103.83 261.63 21.47 51.91 103.83 261.63 21.48 51.91 103.83 261.63 21.49 51.91 103.83 261.63 21.50 51.91 103.83 261.63 369.99 21.51 51.91 103.83 261.63 369.99 21.52 51.91 103.83 261.63 369.99 21.53 51.91 103.83 369.99 21.54 51.91 103.83 369.99 21.55 51.91 103.83 369.99 21.56 51.91 103.83 369.99 21.57 51.91 103.83 369.99 21.58 51.91 103.83 369.99 21.59 51.91 103.83 369.99 21.60 51.91 103.83 369.99 21.61 51.91 103.83 369.99 21.62 51.91 103.83 369.99 21.63 51.91 103.83 369.99 21.64 51.91 103.83 369.99 21.65 51.91 103.83 369.99 21.66 51.91 103.83 369.99 21.67 51.91 103.83 369.99 21.68 51.91 103.83 369.99 21.69 51.91 103.83 369.99 21.70 51.91 103.83 369.99 21.71 51.91 103.83 369.99 21.72 51.91 103.83 369.99 21.73 51.91 103.83 369.99 21.74 51.91 103.83 369.99 21.75 51.91 103.83 369.99 21.76 51.91 103.83 369.99 21.77 51.91 103.83 369.99 21.78 51.91 103.83 369.99 21.79 51.91 103.83 369.99 21.80 51.91 103.83 369.99 21.81 51.91 103.83 369.99 21.82 51.91 103.83 369.99 21.83 51.91 103.83 369.99 21.84 51.91 103.83 369.99 21.85 51.91 103.83 369.99 21.86 51.91 103.83 369.99 21.87 51.91 103.83 369.99 21.88 51.91 103.83 369.99 21.89 51.91 103.83 369.99 21.90 51.91 103.83 369.99 21.91 51.91 103.83 369.99 21.92 51.91 103.83 369.99 21.93 51.91 103.83 369.99 21.94 51.91 103.83 369.99 21.95 51.91 103.83 369.99 21.96 51.91 103.83 369.99 21.97 51.91 103.83 369.99 21.98 51.91 103.83 369.99 21.99 51.91 103.83 369.99 22.00 51.91 103.83 369.99 207.65 22.01 51.91 103.83 369.99 207.65 22.02 51.91 103.83 369.99 207.65 22.03 51.91 103.83 369.99 207.65 22.04 51.91 103.83 207.65 22.05 51.91 103.83 207.65 22.06 51.91 103.83 207.65 22.07 51.91 103.83 207.65 22.08 51.91 103.83 207.65 22.09 51.91 103.83 207.65 22.10 51.91 103.83 207.65 22.11 51.91 103.83 207.65 22.12 51.91 103.83 207.65 22.13 51.91 103.83 207.65 22.14 51.91 103.83 207.65 22.15 51.91 103.83 207.65 22.16 51.91 103.83 207.65 22.17 51.91 103.83 207.65 22.18 51.91 103.83 207.65 22.19 51.91 103.83 207.65 22.20 51.91 103.83 207.65 22.21 51.91 103.83 207.65 22.22 51.91 103.83 207.65 22.23 51.91 103.83 207.65 22.24 51.91 103.83 207.65 22.25 51.91 103.83 207.65 22.26 51.91 103.83 207.65 22.27 51.91 103.83 207.65 22.28 51.91 103.83 207.65 22.29 51.91 103.83 207.65 22.30 51.91 103.83 207.65 22.31 51.91 103.83 207.65 22.32 51.91 103.83 207.65 22.33 51.91 103.83 207.65 22.34 51.91 103.83 207.65 22.35 51.91 103.83 207.65 22.36 51.91 103.83 207.65 22.37 51.91 103.83 207.65 22.38 51.91 103.83 207.65 22.39 51.91 103.83 207.65 22.40 51.91 103.83 207.65 22.41 51.91 103.83 207.65 22.42 51.91 103.83 207.65 22.43 51.91 103.83 207.65 22.44 51.91 103.83 207.65 22.45 51.91 103.83 207.65 22.46 51.91 103.83 207.65 22.47 51.91 103.83 207.65 277.18 22.48 51.91 103.83 207.65 277.18 22.49 51.91 103.83 207.65 277.18 22.50 51.91 103.83 207.65 277.18 22.51 51.91 103.83 207.65 277.18 22.52 51.91 103.83 207.65 277.18 22.53 51.91 103.83 277.18 22.54 51.91 103.83 277.18 22.55 51.91 103.83 277.18 22.56 51.91 103.83 277.18 22.57 51.91 103.83 277.18 22.58 51.91 103.83 277.18 22.59 51.91 103.83 277.18 22.60 51.91 103.83 277.18 22.61 51.91 103.83 277.18 22.62 51.91 103.83 277.18 22.63 51.91 103.83 277.18 22.64 51.91 103.83 277.18 22.65 51.91 103.83 277.18 22.66 51.91 103.83 277.18 22.67 51.91 103.83 277.18 22.68 51.91 103.83 277.18 22.69 51.91 103.83 277.18 22.70 51.91 103.83 277.18 22.71 51.91 103.83 277.18 22.72 51.91 103.83 277.18 22.73 51.91 103.83 277.18 22.74 51.91 103.83 277.18 22.75 51.91 103.83 277.18 22.76 51.91 103.83 277.18 22.77 51.91 103.83 277.18 22.78 51.91 103.83 277.18 22.79 51.91 103.83 277.18 22.80 51.91 103.83 277.18 22.81 51.91 103.83 277.18 22.82 51.91 103.83 277.18 22.83 51.91 103.83 277.18 22.84 51.91 103.83 277.18 22.85 51.91 103.83 277.18 22.86 51.91 103.83 277.18 22.87 51.91 103.83 277.18 22.88 51.91 103.83 277.18 22.89 51.91 103.83 277.18 22.90 51.91 103.83 277.18 22.91 51.91 103.83 277.18 22.92 51.91 103.83 277.18 22.93 51.91 103.83 277.18 22.94 51.91 103.83 277.18 329.63 22.95 51.91 103.83 277.18 329.63 22.96 51.91 103.83 277.18 329.63 22.97 51.91 103.83 277.18 329.63 22.98 51.91 103.83 277.18 329.63 22.99 51.91 103.83 329.63 23.00 51.91 103.83 329.63 23.01 51.91 103.83 329.63 23.02 51.91 103.83 329.63 23.03 51.91 103.83 329.63 23.04 103.83 329.63 23.05 103.83 329.63 23.06 103.83 329.63 23.07 103.83 329.63 23.08 329.63 23.09 329.63 23.10 329.63 23.11 329.63 23.12 329.63 23.13 329.63 23.14 329.63 23.15 329.63 23.16 329.63 23.17 329.63 23.18 329.63 23.19 329.63 23.20 329.63 23.21 329.63 23.22 329.63 23.23 329.63 23.24 329.63 23.25 329.63 23.26 329.63 23.27 329.63 23.28 329.63 23.29 329.63 23.30 329.63 23.31 329.63 23.32 329.63 23.33 329.63 23.34 329.63 23.35 329.63 23.36 329.63 23.37 329.63 23.38 329.63 23.39 329.63 23.40 329.63 23.41 329.63 23.42 329.63 23.43 329.63 23.44 329.63 23.45 329.63 23.46 329.63 23.47 329.63 51.91 207.65 103.83 23.48 329.63 51.91 207.65 103.83 23.49 329.63 51.91 207.65 103.83 23.50 329.63 51.91 207.65 103.83 23.51 51.91 207.65 103.83 23.52 51.91 207.65 103.83 23.53 51.91 207.65 103.83 23.54 51.91 207.65 103.83 23.55 51.91 207.65 103.83 23.56 51.91 207.65 103.83 23.57 51.91 207.65 103.83 23.58 51.91 207.65 103.83 23.59 51.91 207.65 103.83 23.60 51.91 207.65 103.83 23.61 51.91 207.65 103.83 23.62 51.91 207.65 103.83 23.63 51.91 207.65 103.83 23.64 51.91 207.65 103.83 23.65 51.91 207.65 103.83 23.66 51.91 207.65 103.83 23.67 51.91 207.65 103.83 23.68 51.91 207.65 103.83 23.69 51.91 207.65 103.83 23.70 51.91 207.65 103.83 23.71 51.91 207.65 103.83 23.72 51.91 207.65 103.83 23.73 51.91 207.65 103.83 23.74 51.91 207.65 103.83 23.75 51.91 207.65 103.83 23.76 51.91 207.65 103.83 23.77 51.91 207.65 103.83 23.78 51.91 207.65 103.83 23.79 51.91 207.65 103.83 23.80 51.91 207.65 103.83 23.81 51.91 207.65 103.83 23.82 51.91 207.65 103.83 23.83 51.91 207.65 103.83 23.84 51.91 207.65 103.83 23.85 51.91 207.65 103.83 23.86 51.91 207.65 103.83 23.87 51.91 207.65 103.83 23.88 51.91 207.65 103.83 23.89 51.91 207.65 103.83 23.90 51.91 207.65 103.83 23.91 51.91 207.65 103.83 23.92 51.91 207.65 103.83 23.93 51.91 207.65 103.83 23.94 51.91 207.65 103.83 23.95 51.91 207.65 103.83 23.96 51.91 207.65 103.83 23.97 51.91 207.65 103.83 23.98 51.91 207.65 103.83 23.99 51.91 207.65 103.83 24.00 51.91 207.65 103.83 24.01 51.91 207.65 103.83 24.02 51.91 207.65 103.83 24.03 51.91 207.65 103.83 24.04 51.91 207.65 103.83 24.05 51.91 207.65 103.83 24.06 51.91 207.65 103.83 24.07 51.91 207.65 103.83 24.08 51.91 207.65 103.83 24.09 51.91 207.65 103.83 24.10 51.91 207.65 103.83 24.11 51.91 207.65 103.83 24.12 51.91 207.65 103.83 277.18 24.13 51.91 207.65 103.83 277.18 24.14 51.91 207.65 103.83 277.18 24.15 51.91 207.65 103.83 277.18 24.16 51.91 207.65 103.83 277.18 24.17 51.91 207.65 103.83 277.18 24.18 51.91 103.83 277.18 24.19 51.91 103.83 277.18 24.20 51.91 103.83 277.18 24.21 51.91 103.83 277.18 24.22 51.91 103.83 277.18 24.23 51.91 103.83 277.18 24.24 51.91 103.83 277.18 24.25 51.91 103.83 277.18 24.26 51.91 103.83 277.18 24.27 51.91 103.83 277.18 24.28 51.91 103.83 277.18 24.29 51.91 103.83 277.18 24.30 51.91 103.83 277.18 24.31 51.91 103.83 277.18 24.32 51.91 103.83 277.18 24.33 51.91 103.83 277.18 24.34 51.91 103.83 277.18 24.35 51.91 103.83 277.18 24.36 51.91 103.83 277.18 24.37 51.91 103.83 277.18 24.38 51.91 103.83 277.18 24.39 51.91 103.83 277.18 24.40 51.91 103.83 277.18 24.41 51.91 103.83 277.18 24.42 51.91 103.83 277.18 24.43 51.91 103.83 277.18 24.44 51.91 103.83 277.18 24.45 51.91 103.83 277.18 24.46 51.91 103.83 277.18 24.47 51.91 103.83 277.18 24.48 51.91 103.83 277.18 24.49 51.91 103.83 277.18 24.50 51.91 103.83 277.18 24.51 51.91 103.83 277.18 24.52 51.91 103.83 277.18 24.53 51.91 103.83 277.18 24.54 51.91 103.83 277.18 24.55 51.91 103.83 277.18 24.56 51.91 103.83 277.18 24.57 51.91 103.83 277.18 24.58 51.91 103.83 277.18 24.59 51.91 103.83 277.18 311.13 24.60 51.91 103.83 277.18 311.13 24.61 51.91 103.83 311.13 24.62 51.91 103.83 311.13 24.63 51.91 103.83 311.13 24.64 51.91 103.83 311.13 24.65 51.91 103.83 311.13 24.66 51.91 103.83 311.13 24.67 51.91 103.83 311.13 24.68 51.91 103.83 311.13 24.69 51.91 103.83 311.13 24.70 51.91 103.83 311.13 24.71 51.91 103.83 311.13 24.72 51.91 103.83 311.13 24.73 51.91 103.83 311.13 24.74 51.91 103.83 311.13 24.75 51.91 103.83 311.13 24.76 51.91 103.83 311.13 24.77 51.91 103.83 311.13 24.78 51.91 103.83 311.13 24.79 51.91 103.83 311.13 24.80 51.91 103.83 311.13 24.81 51.91 103.83 311.13 24.82 51.91 103.83 311.13 24.83 51.91 103.83 311.13 24.84 51.91 103.83 311.13 24.85 51.91 103.83 311.13 24.86 51.91 103.83 311.13 24.87 51.91 103.83 311.13 24.88 51.91 103.83 311.13 24.89 51.91 103.83 311.13 24.90 51.91 103.83 311.13 24.91 51.91 103.83 311.13 24.92 51.91 103.83 311.13 24.93 51.91 103.83 311.13 24.94 51.91 103.83 311.13 24.95 51.91 103.83 311.13 24.96 51.91 103.83 311.13 24.97 51.91 103.83 311.13 24.98 51.91 103.83 311.13 24.99 51.91 103.83 311.13 25.00 51.91 103.83 311.13 25.01 51.91 103.83 311.13 25.02 51.91 103.83 311.13 25.03 51.91 103.83 311.13 25.04 51.91 103.83 311.13 25.05 51.91 103.83 311.13 25.06 51.91 103.83 311.13 25.07 51.91 103.83 311.13 25.08 51.91 103.83 311.13 25.09 51.91 103.83 311.13 185.00 25.10 51.91 103.83 185.00 25.11 51.91 103.83 185.00 25.12 51.91 103.83 185.00 25.13 51.91 103.83 185.00 25.14 51.91 103.83 185.00 25.15 51.91 103.83 185.00 25.16 51.91 103.83 185.00 25.17 51.91 103.83 185.00 25.18 51.91 103.83 185.00 25.19 51.91 103.83 185.00 25.20 51.91 103.83 185.00 25.21 51.91 103.83 185.00 25.22 51.91 103.83 185.00 25.23 51.91 103.83 185.00 25.24 51.91 103.83 185.00 25.25 51.91 103.83 185.00 25.26 51.91 103.83 185.00 25.27 51.91 103.83 185.00 25.28 51.91 103.83 185.00 25.29 51.91 103.83 185.00 25.30 51.91 103.83 185.00 25.31 51.91 103.83 185.00 25.32 51.91 103.83 185.00 25.33 51.91 103.83 185.00 25.34 51.91 103.83 185.00 25.35 51.91 103.83 185.00 25.36 51.91 103.83 185.00 25.37 51.91 103.83 185.00 25.38 51.91 103.83 185.00 25.39 51.91 103.83 185.00 25.40 51.91 103.83 185.00 25.41 51.91 103.83 185.00 25.42 51.91 103.83 185.00 25.43 51.91 103.83 185.00 25.44 51.91 103.83 185.00 25.45 51.91 103.83 185.00 25.46 51.91 103.83 185.00 25.47 51.91 103.83 185.00 25.48 51.91 103.83 185.00 25.49 51.91 103.83 185.00 25.50 51.91 103.83 185.00 25.51 51.91 103.83 185.00 25.52 51.91 103.83 185.00 25.53 51.91 103.83 185.00 25.54 51.91 103.83 185.00 25.55 51.91 103.83 185.00 261.63 25.56 51.91 103.83 185.00 261.63 25.57 51.91 103.83 185.00 261.63 25.58 51.91 103.83 185.00 261.63 25.59 51.91 103.83 185.00 261.63 25.60 51.91 103.83 185.00 261.63 25.61 51.91 103.83 261.63 25.62 51.91 103.83 261.63 25.63 51.91 103.83 261.63 25.64 51.91 103.83 261.63 25.65 51.91 103.83 261.63 25.66 51.91 103.83 261.63 25.67 51.91 103.83 261.63 25.68 51.91 103.83 261.63 25.69 51.91 103.83 261.63 25.70 51.91 103.83 261.63 25.71 51.91 103.83 261.63 25.72 51.91 103.83 261.63 25.73 51.91 103.83 261.63 25.74 51.91 103.83 261.63 25.75 51.91 103.83 261.63 25.76 51.91 103.83 261.63 25.77 51.91 103.83 261.63 25.78 51.91 103.83 261.63 25.79 51.91 103.83 261.63 25.80 51.91 103.83 261.63 25.81 51.91 103.83 261.63 25.82 51.91 103.83 261.63 25.83 51.91 103.83 261.63 25.84 51.91 103.83 261.63 25.85 51.91 103.83 261.63 25.86 51.91 103.83 261.63 25.87 51.91 103.83 261.63 25.88 51.91 103.83 261.63 25.89 51.91 103.83 261.63 25.90 51.91 103.83 261.63 25.91 51.91 103.83 261.63 25.92 103.83 261.63 25.93 103.83 261.63 25.94 103.83 261.63 25.95 103.83 261.63 25.96 103.83 261.63 25.97 103.83 261.63 25.98 103.83 261.63 25.99 103.83 261.63 26.00 103.83 261.63 26.01 261.63 26.02 261.63 26.03 261.63 26.04 261.63 26.05 261.63 26.06 261.63 311.13 26.07 261.63 311.13 26.08 261.63 311.13 26.09 311.13 26.10 311.13 26.11 311.13 26.12 311.13 26.13 311.13 26.14 311.13 26.15 311.13 26.16 311.13 26.17 311.13 26.18 311.13 26.19 311.13 26.20 311.13 26.21 311.13 26.22 311.13 26.23 311.13 26.24 311.13 26.25 311.13 26.26 311.13 26.27 311.13 26.28 311.13 26.29 311.13 26.30 311.13 26.31 311.13 26.32 311.13 26.33 311.13 26.34 311.13 26.35 311.13 26.36 311.13 26.37 311.13 26.38 311.13 26.39 311.13 26.40 311.13 26.41 311.13 26.42 311.13 26.43 311.13 26.44 311.13 26.45 311.13 26.46 311.13 26.47 311.13 26.48 311.13 26.49 311.13 26.50 311.13 26.51 311.13 26.52 311.13 26.53 311.13 26.54 311.13 26.55 311.13 26.56 311.13 26.57 311.13 26.58 311.13 26.59 311.13 26.60 311.13 26.61 311.13 26.62 311.13 26.63 311.13 26.64 311.13 26.65 311.13 26.66 311.13 26.67 311.13 26.68 311.13 26.69 311.13 26.70 311.13 26.71 311.13 26.72 311.13 26.73 26.74 69.30 26.75 69.30 138.59 164.81 26.76 69.30 138.59 164.81 26.77 69.30 138.59 164.81 103.83 26.78 69.30 138.59 164.81 103.83 26.79 69.30 138.59 164.81 103.83 26.80 69.30 138.59 164.81 103.83 26.81 69.30 138.59 164.81 103.83 26.82 69.30 138.59 164.81 103.83 26.83 69.30 138.59 164.81 103.83 26.84 69.30 138.59 164.81 103.83 26.85 69.30 138.59 164.81 103.83 26.86 69.30 138.59 164.81 103.83 26.87 69.30 138.59 164.81 103.83 26.88 69.30 138.59 164.81 103.83 26.89 69.30 138.59 164.81 103.83 26.90 69.30 138.59 164.81 103.83 26.91 69.30 138.59 164.81 103.83 26.92 69.30 138.59 164.81 103.83 26.93 69.30 138.59 164.81 103.83 26.94 69.30 138.59 164.81 103.83 26.95 69.30 138.59 164.81 103.83 26.96 69.30 138.59 164.81 103.83 26.97 69.30 138.59 164.81 103.83 26.98 69.30 138.59 164.81 103.83 26.99 69.30 138.59 164.81 103.83 27.00 69.30 138.59 164.81 103.83 27.01 69.30 138.59 164.81 103.83 27.02 69.30 138.59 164.81 103.83 27.03 69.30 138.59 164.81 103.83 27.04 69.30 138.59 164.81 103.83 27.05 69.30 138.59 164.81 103.83 27.06 69.30 138.59 164.81 103.83 27.07 69.30 138.59 164.81 103.83 27.08 69.30 138.59 164.81 103.83 27.09 69.30 138.59 164.81 103.83 27.10 69.30 138.59 164.81 103.83 27.11 69.30 138.59 164.81 103.83 27.12 69.30 138.59 164.81 103.83 27.13 69.30 138.59 164.81 103.83 27.14 69.30 138.59 164.81 103.83 27.15 69.30 138.59 164.81 103.83 27.16 69.30 138.59 164.81 103.83 27.17 69.30 138.59 164.81 103.83 27.18 69.30 138.59 164.81 103.83 27.19 69.30 138.59 164.81 103.83 27.20 69.30 138.59 164.81 103.83 27.21 69.30 138.59 164.81 103.83 27.22 69.30 138.59 164.81 103.83 27.23 69.30 138.59 164.81 103.83 27.24 69.30 138.59 164.81 103.83 27.25 69.30 138.59 164.81 103.83 27.26 69.30 138.59 164.81 103.83 27.27 69.30 138.59 164.81 103.83 27.28 69.30 138.59 164.81 103.83 27.29 69.30 138.59 164.81 103.83 27.30 69.30 138.59 164.81 103.83 27.31 69.30 138.59 164.81 103.83 27.32 69.30 138.59 164.81 103.83 27.33 69.30 138.59 164.81 103.83 27.34 69.30 138.59 164.81 103.83 27.35 69.30 138.59 164.81 103.83 27.36 69.30 138.59 164.81 103.83 27.37 69.30 138.59 164.81 103.83 27.38 69.30 138.59 164.81 103.83 27.39 69.30 138.59 164.81 103.83 27.40 69.30 138.59 164.81 103.83 27.41 69.30 138.59 164.81 103.83 27.42 69.30 138.59 164.81 103.83 27.43 69.30 138.59 164.81 103.83 27.44 69.30 138.59 164.81 103.83 27.45 69.30 138.59 164.81 103.83 27.46 69.30 138.59 164.81 103.83 27.47 69.30 138.59 164.81 103.83 27.48 69.30 138.59 164.81 103.83 27.49 69.30 138.59 164.81 103.83 207.65 27.50 69.30 138.59 164.81 103.83 207.65 27.51 69.30 138.59 164.81 103.83 207.65 27.52 69.30 138.59 164.81 103.83 207.65 27.53 69.30 138.59 164.81 103.83 207.65 27.54 69.30 138.59 164.81 103.83 207.65 27.55 69.30 138.59 164.81 103.83 207.65 27.56 69.30 138.59 164.81 103.83 207.65 27.57 69.30 138.59 164.81 103.83 207.65 27.58 69.30 138.59 103.83 207.65 27.59 69.30 138.59 103.83 207.65 27.60 69.30 138.59 103.83 207.65 27.61 69.30 138.59 103.83 207.65 27.62 69.30 138.59 103.83 207.65 27.63 69.30 138.59 103.83 207.65 27.64 69.30 138.59 103.83 207.65 27.65 69.30 138.59 103.83 207.65 27.66 69.30 138.59 103.83 207.65 27.67 69.30 138.59 103.83 207.65 27.68 69.30 138.59 103.83 207.65 27.69 69.30 138.59 103.83 207.65 27.70 69.30 138.59 103.83 207.65 27.71 69.30 138.59 103.83 207.65 27.72 69.30 138.59 103.83 207.65 27.73 69.30 138.59 103.83 207.65 27.74 69.30 138.59 103.83 207.65 27.75 69.30 138.59 103.83 207.65 27.76 69.30 138.59 103.83 207.65 27.77 69.30 138.59 103.83 207.65 27.78 69.30 138.59 103.83 207.65 27.79 69.30 138.59 103.83 207.65 27.80 69.30 138.59 103.83 207.65 27.81 69.30 138.59 103.83 207.65 27.82 69.30 138.59 103.83 207.65 27.83 69.30 138.59 103.83 207.65 27.84 69.30 138.59 103.83 207.65 27.85 69.30 138.59 103.83 207.65 27.86 69.30 138.59 103.83 207.65 27.87 69.30 138.59 103.83 207.65 27.88 69.30 138.59 103.83 207.65 27.89 69.30 138.59 103.83 207.65 27.90 69.30 138.59 103.83 207.65 27.91 69.30 138.59 103.83 207.65 27.92 69.30 138.59 103.83 207.65 27.93 69.30 138.59 103.83 207.65 27.94 69.30 138.59 103.83 207.65 27.95 69.30 138.59 103.83 207.65 27.96 69.30 138.59 103.83 207.65 27.97 69.30 138.59 103.83 207.65 27.98 69.30 138.59 103.83 207.65 27.99 69.30 138.59 103.83 207.65 28.00 69.30 138.59 103.83 207.65 28.01 69.30 138.59 103.83 207.65 277.18 28.02 69.30 138.59 103.83 207.65 277.18 28.03 69.30 138.59 103.83 207.65 277.18 28.04 69.30 138.59 103.83 207.65 277.18 28.05 69.30 138.59 103.83 277.18 28.06 69.30 138.59 103.83 277.18 28.07 69.30 138.59 103.83 277.18 28.08 69.30 138.59 103.83 277.18 28.09 69.30 138.59 103.83 277.18 28.10 69.30 138.59 103.83 277.18 28.11 69.30 138.59 103.83 277.18 28.12 69.30 138.59 103.83 277.18 28.13 69.30 138.59 103.83 277.18 28.14 69.30 138.59 103.83 277.18 28.15 69.30 138.59 103.83 277.18 28.16 69.30 138.59 103.83 277.18 28.17 69.30 138.59 103.83 277.18 28.18 69.30 138.59 103.83 277.18 28.19 69.30 138.59 103.83 277.18 28.20 69.30 138.59 103.83 277.18 28.21 69.30 138.59 103.83 277.18 28.22 69.30 138.59 103.83 277.18 28.23 69.30 138.59 103.83 277.18 28.24 69.30 138.59 103.83 277.18 28.25 69.30 138.59 103.83 277.18 28.26 69.30 138.59 103.83 277.18 28.27 69.30 138.59 103.83 277.18 28.28 69.30 138.59 103.83 277.18 28.29 69.30 138.59 103.83 277.18 28.30 69.30 138.59 103.83 277.18 28.31 69.30 138.59 103.83 277.18 28.32 69.30 138.59 103.83 277.18 28.33 69.30 138.59 103.83 277.18 28.34 69.30 138.59 103.83 277.18 28.35 69.30 138.59 103.83 277.18 28.36 69.30 138.59 103.83 277.18 28.37 69.30 138.59 103.83 277.18 28.38 69.30 138.59 103.83 277.18 28.39 69.30 138.59 103.83 277.18 28.40 69.30 138.59 103.83 277.18 28.41 69.30 138.59 103.83 277.18 28.42 69.30 138.59 103.83 277.18 28.43 69.30 138.59 103.83 277.18 28.44 69.30 138.59 103.83 277.18 28.45 69.30 138.59 103.83 277.18 28.46 69.30 138.59 103.83 277.18 28.47 69.30 138.59 103.83 277.18 28.48 69.30 138.59 103.83 277.18 28.49 69.30 138.59 103.83 277.18 28.50 69.30 138.59 103.83 277.18 28.51 69.30 138.59 103.83 277.18 207.65 28.52 69.30 138.59 103.83 277.18 207.65 28.53 69.30 138.59 103.83 207.65 28.54 69.30 138.59 103.83 207.65 28.55 69.30 138.59 103.83 207.65 28.56 69.30 138.59 103.83 207.65 28.57 69.30 138.59 103.83 207.65 28.58 69.30 138.59 103.83 207.65 28.59 69.30 138.59 103.83 207.65 28.60 69.30 138.59 103.83 207.65 28.61 69.30 138.59 103.83 207.65 28.62 69.30 138.59 103.83 207.65 28.63 69.30 138.59 103.83 207.65 28.64 69.30 138.59 103.83 207.65 28.65 69.30 138.59 103.83 207.65 28.66 69.30 138.59 103.83 207.65 28.67 69.30 138.59 103.83 207.65 28.68 69.30 138.59 103.83 207.65 28.69 69.30 138.59 103.83 207.65 28.70 69.30 138.59 103.83 207.65 28.71 69.30 138.59 103.83 207.65 28.72 69.30 138.59 103.83 207.65 28.73 69.30 138.59 103.83 207.65 28.74 69.30 138.59 103.83 207.65 28.75 69.30 138.59 103.83 207.65 28.76 69.30 138.59 103.83 207.65 28.77 69.30 138.59 103.83 207.65 28.78 69.30 138.59 103.83 207.65 28.79 69.30 138.59 103.83 207.65 28.80 69.30 138.59 103.83 207.65 28.81 69.30 138.59 103.83 207.65 28.82 69.30 138.59 103.83 207.65 28.83 69.30 138.59 103.83 207.65 28.84 69.30 138.59 103.83 207.65 28.85 69.30 138.59 103.83 207.65 28.86 69.30 138.59 103.83 207.65 28.87 69.30 138.59 103.83 207.65 28.88 69.30 138.59 103.83 207.65 28.89 69.30 138.59 103.83 207.65 28.90 69.30 138.59 103.83 207.65 28.91 69.30 138.59 103.83 207.65 28.92 69.30 138.59 103.83 207.65 28.93 69.30 138.59 103.83 207.65 28.94 69.30 138.59 103.83 207.65 28.95 69.30 138.59 103.83 207.65 28.96 69.30 138.59 103.83 207.65 28.97 69.30 138.59 103.83 207.65 28.98 69.30 138.59 103.83 207.65 28.99 69.30 138.59 103.83 207.65 29.00 69.30 138.59 103.83 207.65 277.18 29.01 69.30 138.59 103.83 207.65 277.18 29.02 69.30 138.59 103.83 207.65 277.18 29.03 69.30 138.59 103.83 207.65 277.18 29.04 69.30 138.59 103.83 207.65 277.18 29.05 69.30 138.59 103.83 207.65 277.18 29.06 69.30 138.59 103.83 277.18 29.07 69.30 138.59 103.83 277.18 29.08 69.30 138.59 103.83 277.18 29.09 69.30 138.59 103.83 277.18 29.10 69.30 138.59 103.83 277.18 29.11 69.30 138.59 103.83 277.18 29.12 69.30 138.59 103.83 277.18 29.13 69.30 138.59 103.83 277.18 29.14 69.30 138.59 103.83 277.18 29.15 69.30 138.59 103.83 277.18 29.16 69.30 138.59 103.83 277.18 29.17 69.30 138.59 103.83 277.18 29.18 69.30 138.59 103.83 277.18 29.19 69.30 138.59 103.83 277.18 29.20 69.30 138.59 103.83 277.18 29.21 69.30 138.59 103.83 277.18 29.22 69.30 138.59 103.83 277.18 29.23 69.30 138.59 103.83 277.18 29.24 69.30 138.59 103.83 277.18 29.25 69.30 138.59 103.83 277.18 29.26 69.30 138.59 103.83 277.18 29.27 69.30 138.59 103.83 277.18 29.28 69.30 138.59 103.83 277.18 29.29 69.30 138.59 103.83 277.18 29.30 69.30 138.59 103.83 277.18 29.31 69.30 138.59 103.83 277.18 29.32 69.30 138.59 103.83 277.18 29.33 69.30 138.59 103.83 277.18 29.34 69.30 138.59 103.83 277.18 29.35 69.30 138.59 103.83 277.18 29.36 69.30 138.59 103.83 277.18 29.37 69.30 138.59 103.83 277.18 29.38 69.30 138.59 103.83 277.18 29.39 69.30 138.59 103.83 277.18 29.40 69.30 138.59 103.83 277.18 29.41 69.30 138.59 103.83 277.18 29.42 69.30 138.59 103.83 277.18 29.43 69.30 138.59 103.83 277.18 29.44 69.30 138.59 103.83 277.18 29.45 69.30 138.59 103.83 277.18 29.46 69.30 138.59 103.83 277.18 29.47 69.30 138.59 103.83 277.18 29.48 69.30 138.59 103.83 277.18 29.49 69.30 138.59 103.83 277.18 329.63 29.50 69.30 138.59 103.83 277.18 329.63 29.51 69.30 138.59 103.83 277.18 329.63 29.52 69.30 138.59 103.83 329.63 29.53 69.30 138.59 103.83 329.63 29.54 69.30 138.59 103.83 329.63 29.55 69.30 138.59 103.83 329.63 29.56 69.30 138.59 103.83 329.63 29.57 69.30 138.59 103.83 329.63 29.58 69.30 138.59 103.83 329.63 29.59 69.30 138.59 103.83 329.63 29.60 69.30 138.59 103.83 329.63 29.61 69.30 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33.54 65.41 103.83 130.81 415.30 207.65 33.55 65.41 103.83 130.81 415.30 207.65 33.56 65.41 103.83 130.81 415.30 207.65 33.57 65.41 103.83 130.81 415.30 207.65 33.58 65.41 103.83 130.81 415.30 207.65 33.59 65.41 103.83 130.81 415.30 207.65 33.60 65.41 103.83 130.81 415.30 207.65 33.61 65.41 103.83 130.81 415.30 207.65 33.62 65.41 103.83 130.81 415.30 207.65 33.63 65.41 103.83 130.81 415.30 207.65 33.64 65.41 103.83 130.81 415.30 207.65 33.65 65.41 103.83 130.81 415.30 207.65 33.66 65.41 103.83 130.81 415.30 207.65 33.67 65.41 103.83 130.81 415.30 207.65 33.68 65.41 103.83 130.81 415.30 207.65 33.69 65.41 103.83 130.81 415.30 207.65 33.70 65.41 103.83 130.81 415.30 207.65 33.71 65.41 103.83 130.81 415.30 207.65 33.72 65.41 103.83 130.81 415.30 207.65 33.73 65.41 103.83 130.81 415.30 207.65 33.74 65.41 103.83 130.81 415.30 207.65 33.75 65.41 103.83 130.81 415.30 207.65 33.76 65.41 103.83 130.81 415.30 207.65 33.77 65.41 103.83 130.81 415.30 207.65 311.13 33.78 65.41 103.83 130.81 415.30 207.65 311.13 33.79 65.41 103.83 130.81 415.30 207.65 311.13 33.80 65.41 103.83 130.81 415.30 207.65 311.13 33.81 65.41 103.83 130.81 415.30 207.65 311.13 33.82 65.41 103.83 130.81 415.30 207.65 311.13 33.83 65.41 103.83 130.81 415.30 207.65 311.13 33.84 65.41 103.83 130.81 415.30 207.65 311.13 33.85 65.41 103.83 130.81 415.30 207.65 311.13 33.86 65.41 103.83 130.81 415.30 207.65 311.13 33.87 65.41 103.83 130.81 415.30 207.65 311.13 33.88 65.41 103.83 130.81 415.30 311.13 33.89 65.41 103.83 130.81 415.30 311.13 33.90 65.41 103.83 130.81 415.30 311.13 33.91 65.41 103.83 130.81 415.30 311.13 33.92 65.41 103.83 130.81 415.30 311.13 33.93 65.41 103.83 130.81 415.30 311.13 33.94 65.41 103.83 130.81 415.30 311.13 33.95 65.41 103.83 130.81 415.30 311.13 33.96 65.41 103.83 130.81 415.30 311.13 33.97 65.41 103.83 130.81 415.30 311.13 33.98 65.41 103.83 130.81 415.30 311.13 33.99 65.41 103.83 130.81 415.30 311.13 34.00 65.41 103.83 130.81 415.30 311.13 34.01 65.41 103.83 130.81 415.30 311.13 34.02 65.41 103.83 130.81 415.30 311.13 34.03 65.41 103.83 130.81 415.30 311.13 34.04 65.41 103.83 130.81 415.30 311.13 34.05 65.41 103.83 130.81 415.30 311.13 34.06 65.41 103.83 130.81 415.30 311.13 34.07 65.41 103.83 130.81 415.30 311.13 34.08 65.41 103.83 130.81 415.30 311.13 34.09 65.41 103.83 130.81 415.30 311.13 34.10 65.41 103.83 130.81 415.30 311.13 34.11 65.41 103.83 130.81 415.30 311.13 34.12 65.41 103.83 130.81 415.30 311.13 34.13 65.41 103.83 130.81 415.30 311.13 34.14 65.41 103.83 130.81 415.30 311.13 34.15 65.41 103.83 130.81 415.30 311.13 34.16 65.41 103.83 130.81 415.30 311.13 34.17 65.41 103.83 130.81 415.30 311.13 34.18 65.41 103.83 130.81 415.30 311.13 34.19 65.41 103.83 130.81 415.30 311.13 34.20 65.41 103.83 130.81 415.30 311.13 34.21 65.41 103.83 130.81 415.30 311.13 34.22 65.41 103.83 130.81 415.30 311.13 34.23 65.41 103.83 130.81 415.30 311.13 34.24 65.41 103.83 130.81 415.30 311.13 34.25 65.41 103.83 130.81 415.30 311.13 34.26 65.41 103.83 130.81 415.30 311.13 34.27 65.41 103.83 130.81 415.30 311.13 34.28 65.41 103.83 130.81 415.30 311.13 369.99 34.29 65.41 103.83 130.81 415.30 311.13 369.99 34.30 65.41 103.83 130.81 415.30 311.13 369.99 34.31 65.41 103.83 130.81 415.30 369.99 34.32 65.41 103.83 130.81 415.30 369.99 34.33 65.41 103.83 130.81 415.30 369.99 34.34 65.41 103.83 130.81 415.30 369.99 34.35 65.41 103.83 130.81 415.30 369.99 34.36 65.41 103.83 130.81 415.30 369.99 34.37 65.41 103.83 130.81 415.30 369.99 34.38 65.41 103.83 130.81 415.30 369.99 34.39 65.41 103.83 130.81 415.30 369.99 34.40 65.41 103.83 130.81 415.30 369.99 34.41 65.41 103.83 130.81 415.30 369.99 34.42 65.41 103.83 130.81 415.30 369.99 34.43 65.41 103.83 130.81 415.30 369.99 34.44 65.41 103.83 130.81 415.30 369.99 34.45 65.41 103.83 130.81 415.30 369.99 34.46 65.41 103.83 130.81 415.30 369.99 34.47 65.41 103.83 130.81 415.30 369.99 34.48 65.41 103.83 130.81 415.30 369.99 34.49 65.41 103.83 130.81 415.30 369.99 34.50 65.41 103.83 130.81 415.30 369.99 34.51 65.41 103.83 130.81 415.30 369.99 34.52 65.41 103.83 130.81 415.30 369.99 34.53 65.41 103.83 130.81 415.30 369.99 34.54 65.41 103.83 130.81 415.30 369.99 34.55 65.41 103.83 130.81 415.30 369.99 34.56 65.41 103.83 130.81 415.30 369.99 34.57 65.41 103.83 130.81 415.30 369.99 34.58 65.41 103.83 130.81 415.30 369.99 34.59 65.41 103.83 130.81 415.30 369.99 34.60 65.41 103.83 130.81 415.30 369.99 34.61 65.41 103.83 130.81 415.30 369.99 34.62 65.41 103.83 130.81 415.30 369.99 34.63 65.41 103.83 130.81 415.30 369.99 34.64 65.41 103.83 130.81 415.30 369.99 34.65 65.41 103.83 130.81 415.30 369.99 34.66 65.41 103.83 130.81 415.30 369.99 34.67 65.41 103.83 130.81 415.30 369.99 34.68 65.41 103.83 130.81 415.30 369.99 34.69 65.41 103.83 130.81 415.30 369.99 34.70 65.41 103.83 130.81 415.30 369.99 34.71 65.41 103.83 130.81 415.30 369.99 34.72 65.41 103.83 130.81 415.30 369.99 34.73 65.41 103.83 130.81 415.30 369.99 34.74 65.41 103.83 130.81 415.30 369.99 34.75 65.41 103.83 130.81 415.30 369.99 34.76 65.41 103.83 130.81 415.30 369.99 34.77 65.41 103.83 130.81 415.30 369.99 207.65 34.78 65.41 103.83 130.81 415.30 369.99 207.65 34.79 65.41 103.83 130.81 415.30 369.99 207.65 34.80 65.41 103.83 130.81 415.30 369.99 207.65 34.81 65.41 103.83 130.81 415.30 369.99 207.65 34.82 65.41 103.83 130.81 415.30 207.65 34.83 65.41 103.83 130.81 415.30 207.65 34.84 65.41 103.83 130.81 415.30 207.65 34.85 65.41 103.83 130.81 415.30 207.65 34.86 65.41 103.83 130.81 415.30 207.65 34.87 65.41 103.83 130.81 415.30 207.65 34.88 65.41 103.83 130.81 415.30 207.65 34.89 65.41 103.83 130.81 415.30 207.65 34.90 65.41 103.83 130.81 415.30 207.65 34.91 65.41 103.83 130.81 415.30 207.65 34.92 65.41 103.83 130.81 415.30 207.65 34.93 65.41 103.83 130.81 415.30 207.65 34.94 65.41 103.83 130.81 415.30 207.65 34.95 65.41 103.83 130.81 415.30 207.65 34.96 65.41 103.83 130.81 415.30 207.65 34.97 65.41 103.83 130.81 415.30 207.65 34.98 65.41 103.83 130.81 415.30 207.65 34.99 65.41 103.83 130.81 415.30 207.65 35.00 65.41 103.83 130.81 415.30 207.65 35.01 65.41 103.83 130.81 415.30 207.65 35.02 65.41 103.83 130.81 415.30 207.65 35.03 65.41 103.83 130.81 415.30 207.65 35.04 65.41 103.83 130.81 415.30 207.65 35.05 65.41 103.83 130.81 415.30 207.65 35.06 65.41 103.83 130.81 415.30 207.65 35.07 65.41 103.83 130.81 415.30 207.65 35.08 65.41 103.83 130.81 415.30 207.65 35.09 65.41 103.83 130.81 415.30 207.65 35.10 65.41 103.83 130.81 415.30 207.65 35.11 65.41 103.83 130.81 415.30 207.65 35.12 65.41 103.83 130.81 415.30 207.65 35.13 65.41 103.83 130.81 415.30 207.65 35.14 65.41 103.83 130.81 415.30 207.65 35.15 65.41 103.83 130.81 415.30 207.65 35.16 65.41 103.83 130.81 415.30 207.65 35.17 65.41 103.83 130.81 415.30 207.65 35.18 65.41 103.83 130.81 415.30 207.65 35.19 65.41 103.83 130.81 415.30 207.65 35.20 65.41 103.83 130.81 415.30 207.65 35.21 65.41 103.83 130.81 415.30 207.65 35.22 65.41 103.83 130.81 415.30 207.65 35.23 65.41 103.83 130.81 415.30 207.65 35.24 65.41 103.83 130.81 415.30 207.65 35.25 65.41 103.83 130.81 415.30 207.65 35.26 65.41 103.83 130.81 415.30 207.65 35.27 65.41 103.83 130.81 415.30 207.65 35.28 65.41 103.83 130.81 415.30 207.65 35.29 65.41 103.83 130.81 415.30 207.65 35.30 65.41 103.83 130.81 415.30 207.65 311.13 35.31 65.41 103.83 130.81 415.30 207.65 311.13 35.32 65.41 103.83 130.81 415.30 207.65 311.13 35.33 65.41 103.83 130.81 415.30 207.65 311.13 35.34 65.41 103.83 130.81 415.30 207.65 311.13 35.35 65.41 103.83 130.81 415.30 311.13 35.36 65.41 103.83 130.81 415.30 311.13 35.37 65.41 103.83 130.81 415.30 311.13 35.38 65.41 103.83 130.81 415.30 311.13 35.39 65.41 103.83 130.81 415.30 311.13 35.40 65.41 103.83 130.81 415.30 311.13 35.41 65.41 103.83 130.81 415.30 311.13 35.42 65.41 103.83 130.81 415.30 311.13 35.43 65.41 103.83 130.81 415.30 311.13 35.44 65.41 103.83 130.81 415.30 311.13 35.45 65.41 103.83 130.81 415.30 311.13 35.46 65.41 103.83 130.81 415.30 311.13 35.47 65.41 103.83 130.81 415.30 311.13 35.48 65.41 103.83 130.81 415.30 311.13 35.49 65.41 103.83 130.81 415.30 311.13 35.50 65.41 103.83 130.81 415.30 311.13 35.51 65.41 103.83 130.81 415.30 311.13 35.52 65.41 103.83 130.81 415.30 311.13 35.53 65.41 103.83 130.81 415.30 311.13 35.54 65.41 103.83 130.81 415.30 311.13 35.55 65.41 103.83 130.81 415.30 311.13 35.56 65.41 103.83 130.81 415.30 311.13 35.57 65.41 103.83 130.81 415.30 311.13 35.58 65.41 103.83 130.81 415.30 311.13 35.59 65.41 103.83 130.81 415.30 311.13 35.60 65.41 103.83 130.81 415.30 311.13 35.61 65.41 103.83 130.81 415.30 311.13 35.62 65.41 103.83 130.81 415.30 311.13 35.63 65.41 103.83 130.81 415.30 311.13 35.64 65.41 103.83 130.81 415.30 311.13 35.65 65.41 103.83 130.81 415.30 311.13 35.66 65.41 103.83 130.81 415.30 311.13 35.67 65.41 103.83 130.81 415.30 311.13 35.68 65.41 103.83 130.81 415.30 311.13 35.69 65.41 103.83 130.81 415.30 311.13 35.70 65.41 103.83 130.81 415.30 311.13 35.71 65.41 103.83 130.81 415.30 311.13 35.72 65.41 103.83 130.81 415.30 311.13 35.73 65.41 103.83 130.81 415.30 311.13 35.74 65.41 103.83 130.81 415.30 311.13 35.75 65.41 103.83 130.81 415.30 311.13 35.76 65.41 103.83 130.81 415.30 311.13 35.77 65.41 103.83 130.81 415.30 311.13 35.78 65.41 103.83 130.81 415.30 311.13 35.79 65.41 103.83 130.81 415.30 311.13 369.99 35.80 65.41 103.83 130.81 415.30 311.13 369.99 35.81 65.41 103.83 130.81 415.30 311.13 369.99 35.82 65.41 103.83 130.81 415.30 369.99 35.83 65.41 103.83 130.81 415.30 369.99 35.84 65.41 103.83 130.81 415.30 369.99 35.85 65.41 103.83 130.81 415.30 369.99 35.86 65.41 103.83 130.81 415.30 369.99 35.87 65.41 103.83 130.81 415.30 369.99 35.88 65.41 103.83 130.81 415.30 369.99 35.89 65.41 103.83 130.81 415.30 369.99 35.90 65.41 103.83 130.81 415.30 369.99 35.91 65.41 103.83 130.81 415.30 369.99 35.92 65.41 103.83 130.81 415.30 369.99 35.93 65.41 103.83 130.81 415.30 369.99 35.94 65.41 103.83 130.81 415.30 369.99 35.95 65.41 103.83 130.81 415.30 369.99 35.96 65.41 103.83 130.81 415.30 369.99 35.97 65.41 103.83 130.81 415.30 369.99 35.98 65.41 103.83 130.81 415.30 369.99 35.99 65.41 103.83 130.81 415.30 369.99 36.00 65.41 103.83 130.81 415.30 369.99 36.01 65.41 103.83 130.81 415.30 369.99 36.02 65.41 103.83 130.81 415.30 369.99 36.03 65.41 103.83 130.81 415.30 369.99 36.04 65.41 103.83 130.81 415.30 369.99 36.05 65.41 103.83 130.81 415.30 369.99 36.06 65.41 103.83 130.81 415.30 369.99 36.07 65.41 103.83 130.81 415.30 369.99 36.08 65.41 103.83 130.81 415.30 369.99 36.09 65.41 103.83 130.81 415.30 369.99 36.10 65.41 103.83 130.81 415.30 369.99 36.11 65.41 103.83 130.81 415.30 369.99 36.12 65.41 103.83 130.81 415.30 369.99 36.13 65.41 103.83 130.81 415.30 369.99 36.14 65.41 103.83 130.81 415.30 369.99 36.15 65.41 103.83 130.81 415.30 369.99 36.16 65.41 103.83 130.81 415.30 369.99 36.17 65.41 103.83 130.81 415.30 369.99 36.18 65.41 103.83 130.81 415.30 369.99 36.19 65.41 103.83 130.81 415.30 369.99 36.20 65.41 103.83 130.81 415.30 369.99 36.21 65.41 103.83 130.81 415.30 369.99 36.22 65.41 103.83 130.81 415.30 369.99 36.23 65.41 103.83 130.81 415.30 369.99 36.24 65.41 103.83 130.81 415.30 369.99 36.25 65.41 103.83 130.81 415.30 369.99 36.26 65.41 103.83 130.81 415.30 369.99 36.27 65.41 103.83 130.81 415.30 369.99 36.28 65.41 103.83 130.81 415.30 369.99 36.29 65.41 103.83 130.81 415.30 369.99 207.65 36.30 65.41 103.83 130.81 415.30 369.99 207.65 36.31 65.41 103.83 130.81 415.30 369.99 207.65 36.32 65.41 103.83 130.81 415.30 207.65 36.33 65.41 103.83 130.81 415.30 207.65 36.34 65.41 103.83 130.81 415.30 207.65 36.35 65.41 103.83 130.81 415.30 207.65 36.36 65.41 103.83 130.81 415.30 207.65 36.37 65.41 103.83 130.81 415.30 207.65 36.38 65.41 103.83 130.81 415.30 207.65 36.39 65.41 103.83 130.81 415.30 207.65 36.40 65.41 103.83 130.81 415.30 207.65 36.41 65.41 103.83 130.81 415.30 207.65 36.42 65.41 103.83 130.81 415.30 207.65 36.43 65.41 103.83 130.81 415.30 207.65 36.44 65.41 103.83 130.81 415.30 207.65 36.45 65.41 103.83 130.81 415.30 207.65 36.46 65.41 103.83 130.81 415.30 207.65 36.47 65.41 103.83 130.81 415.30 207.65 36.48 65.41 103.83 130.81 415.30 207.65 36.49 65.41 103.83 130.81 415.30 207.65 36.50 65.41 103.83 130.81 415.30 207.65 36.51 65.41 103.83 130.81 415.30 207.65 36.52 65.41 103.83 130.81 415.30 207.65 36.53 65.41 103.83 130.81 415.30 207.65 36.54 65.41 103.83 130.81 415.30 207.65 36.55 65.41 103.83 130.81 415.30 207.65 36.56 65.41 103.83 130.81 415.30 207.65 36.57 65.41 103.83 130.81 415.30 207.65 36.58 65.41 103.83 130.81 415.30 207.65 36.59 65.41 103.83 130.81 415.30 207.65 36.60 65.41 103.83 130.81 415.30 207.65 36.61 65.41 103.83 130.81 415.30 207.65 36.62 65.41 103.83 130.81 415.30 207.65 36.63 65.41 103.83 130.81 415.30 207.65 36.64 65.41 103.83 130.81 415.30 207.65 36.65 65.41 103.83 130.81 415.30 207.65 36.66 65.41 103.83 130.81 415.30 207.65 36.67 65.41 103.83 130.81 415.30 207.65 36.68 65.41 103.83 130.81 415.30 207.65 36.69 65.41 103.83 130.81 415.30 207.65 36.70 65.41 103.83 130.81 415.30 207.65 36.71 65.41 103.83 130.81 415.30 207.65 36.72 65.41 103.83 130.81 415.30 207.65 36.73 65.41 103.83 130.81 415.30 207.65 36.74 65.41 103.83 130.81 415.30 207.65 36.75 65.41 103.83 130.81 415.30 207.65 36.76 65.41 103.83 130.81 415.30 207.65 36.77 65.41 103.83 130.81 415.30 207.65 36.78 65.41 103.83 130.81 415.30 207.65 36.79 65.41 103.83 130.81 415.30 207.65 36.80 65.41 103.83 130.81 415.30 207.65 36.81 65.41 103.83 130.81 415.30 207.65 36.82 65.41 103.83 130.81 415.30 207.65 311.13 36.83 65.41 103.83 130.81 415.30 207.65 311.13 36.84 65.41 103.83 130.81 415.30 207.65 311.13 36.85 65.41 103.83 130.81 415.30 207.65 311.13 36.86 65.41 103.83 130.81 415.30 207.65 311.13 36.87 65.41 103.83 130.81 415.30 311.13 36.88 65.41 103.83 130.81 415.30 311.13 36.89 65.41 103.83 130.81 415.30 311.13 36.90 65.41 103.83 130.81 415.30 311.13 36.91 65.41 103.83 130.81 415.30 311.13 36.92 65.41 103.83 130.81 415.30 311.13 36.93 65.41 103.83 130.81 415.30 311.13 36.94 65.41 103.83 130.81 415.30 311.13 36.95 65.41 103.83 130.81 415.30 311.13 36.96 65.41 103.83 130.81 415.30 311.13 36.97 65.41 103.83 130.81 415.30 311.13 36.98 65.41 103.83 130.81 415.30 311.13 36.99 65.41 103.83 130.81 415.30 311.13 37.00 65.41 103.83 130.81 415.30 311.13 37.01 65.41 103.83 130.81 415.30 311.13 37.02 65.41 103.83 130.81 415.30 311.13 37.03 65.41 103.83 130.81 415.30 311.13 37.04 65.41 103.83 130.81 415.30 311.13 37.05 65.41 103.83 130.81 415.30 311.13 37.06 65.41 103.83 130.81 415.30 311.13 37.07 65.41 103.83 130.81 415.30 311.13 37.08 65.41 103.83 130.81 415.30 311.13 37.09 65.41 103.83 130.81 415.30 311.13 37.10 65.41 103.83 130.81 415.30 311.13 37.11 65.41 103.83 130.81 415.30 311.13 37.12 65.41 103.83 130.81 415.30 311.13 37.13 65.41 103.83 130.81 415.30 311.13 37.14 65.41 103.83 130.81 415.30 311.13 37.15 65.41 103.83 130.81 415.30 311.13 37.16 65.41 103.83 130.81 415.30 311.13 37.17 65.41 103.83 130.81 415.30 311.13 37.18 65.41 103.83 130.81 415.30 311.13 37.19 65.41 103.83 130.81 415.30 311.13 37.20 65.41 103.83 130.81 415.30 311.13 37.21 65.41 103.83 130.81 415.30 311.13 37.22 65.41 103.83 130.81 415.30 311.13 37.23 65.41 103.83 130.81 415.30 311.13 37.24 65.41 103.83 130.81 415.30 311.13 37.25 65.41 103.83 130.81 415.30 311.13 37.26 65.41 103.83 130.81 415.30 311.13 37.27 65.41 103.83 130.81 415.30 311.13 369.99 37.28 65.41 103.83 130.81 415.30 311.13 369.99 37.29 65.41 103.83 130.81 415.30 311.13 369.99 37.30 65.41 103.83 130.81 415.30 369.99 37.31 65.41 103.83 130.81 415.30 369.99 37.32 65.41 103.83 130.81 415.30 369.99 37.33 65.41 103.83 130.81 415.30 369.99 37.34 65.41 103.83 130.81 415.30 369.99 37.35 65.41 103.83 130.81 415.30 369.99 37.36 65.41 103.83 130.81 415.30 369.99 37.37 65.41 103.83 130.81 415.30 369.99 37.38 65.41 103.83 130.81 415.30 369.99 37.39 65.41 103.83 130.81 415.30 369.99 37.40 65.41 103.83 130.81 415.30 369.99 37.41 65.41 103.83 130.81 415.30 369.99 37.42 65.41 103.83 130.81 415.30 369.99 37.43 65.41 103.83 130.81 415.30 369.99 37.44 65.41 103.83 130.81 415.30 369.99 37.45 65.41 103.83 130.81 415.30 369.99 37.46 65.41 103.83 130.81 415.30 369.99 37.47 65.41 103.83 130.81 415.30 369.99 37.48 65.41 103.83 130.81 415.30 369.99 37.49 65.41 103.83 130.81 415.30 369.99 37.50 65.41 103.83 130.81 415.30 369.99 37.51 65.41 103.83 130.81 415.30 369.99 37.52 65.41 103.83 130.81 415.30 369.99 37.53 65.41 103.83 130.81 415.30 369.99 37.54 65.41 103.83 130.81 415.30 369.99 37.55 65.41 103.83 130.81 415.30 369.99 37.56 65.41 103.83 130.81 415.30 369.99 37.57 65.41 103.83 130.81 415.30 369.99 37.58 65.41 103.83 130.81 415.30 369.99 37.59 65.41 103.83 130.81 415.30 369.99 37.60 65.41 103.83 130.81 415.30 369.99 37.61 65.41 103.83 130.81 415.30 369.99 37.62 65.41 103.83 130.81 415.30 369.99 37.63 65.41 103.83 130.81 369.99 37.64 65.41 103.83 130.81 369.99 37.65 65.41 103.83 130.81 369.99 37.66 65.41 103.83 130.81 369.99 37.67 65.41 103.83 130.81 369.99 37.68 65.41 103.83 130.81 369.99 37.69 65.41 103.83 130.81 369.99 37.70 65.41 103.83 130.81 369.99 37.71 65.41 103.83 130.81 369.99 37.72 65.41 103.83 130.81 369.99 37.73 65.41 103.83 130.81 369.99 37.74 65.41 103.83 130.81 369.99 37.75 65.41 103.83 130.81 369.99 37.76 65.41 103.83 130.81 369.99 207.65 37.77 65.41 103.83 130.81 369.99 207.65 415.30 37.78 65.41 103.83 130.81 369.99 207.65 415.30 37.79 65.41 103.83 130.81 369.99 207.65 415.30 37.80 65.41 103.83 130.81 207.65 415.30 37.81 65.41 103.83 130.81 207.65 415.30 37.82 65.41 103.83 130.81 207.65 415.30 37.83 65.41 103.83 130.81 207.65 415.30 37.84 65.41 103.83 130.81 207.65 415.30 37.85 65.41 103.83 130.81 207.65 415.30 37.86 65.41 103.83 130.81 207.65 415.30 37.87 65.41 103.83 130.81 207.65 415.30 37.88 65.41 103.83 130.81 207.65 415.30 37.89 65.41 103.83 130.81 207.65 415.30 37.90 65.41 103.83 130.81 207.65 415.30 37.91 65.41 103.83 130.81 207.65 415.30 37.92 65.41 103.83 130.81 207.65 415.30 37.93 65.41 103.83 130.81 207.65 415.30 37.94 65.41 103.83 130.81 207.65 415.30 37.95 65.41 103.83 130.81 207.65 415.30 37.96 65.41 103.83 130.81 207.65 415.30 37.97 65.41 103.83 130.81 207.65 415.30 37.98 65.41 103.83 130.81 207.65 415.30 37.99 65.41 103.83 130.81 207.65 415.30 38.00 65.41 103.83 130.81 207.65 415.30 38.01 65.41 103.83 130.81 207.65 415.30 38.02 65.41 103.83 130.81 207.65 415.30 38.03 65.41 103.83 130.81 207.65 415.30 38.04 65.41 103.83 130.81 207.65 415.30 38.05 65.41 103.83 130.81 207.65 415.30 38.06 65.41 103.83 130.81 207.65 415.30 38.07 65.41 103.83 130.81 207.65 415.30 38.08 65.41 103.83 130.81 207.65 415.30 38.09 65.41 103.83 130.81 207.65 415.30 38.10 65.41 103.83 130.81 207.65 415.30 38.11 65.41 103.83 130.81 207.65 415.30 38.12 65.41 103.83 130.81 207.65 415.30 38.13 65.41 103.83 130.81 207.65 415.30 38.14 65.41 103.83 130.81 207.65 415.30 38.15 65.41 103.83 130.81 207.65 415.30 38.16 65.41 103.83 130.81 207.65 415.30 38.17 65.41 103.83 130.81 207.65 415.30 38.18 65.41 103.83 130.81 207.65 415.30 38.19 65.41 103.83 130.81 207.65 415.30 38.20 65.41 103.83 130.81 207.65 415.30 38.21 65.41 103.83 130.81 207.65 415.30 38.22 65.41 103.83 130.81 207.65 415.30 38.23 65.41 103.83 130.81 207.65 415.30 311.13 38.24 65.41 103.83 130.81 207.65 415.30 311.13 38.25 65.41 103.83 130.81 207.65 415.30 311.13 38.26 65.41 103.83 130.81 207.65 415.30 311.13 38.27 65.41 103.83 130.81 207.65 415.30 311.13 38.28 65.41 103.83 130.81 207.65 415.30 311.13 38.29 65.41 103.83 130.81 207.65 415.30 311.13 38.30 65.41 103.83 130.81 207.65 415.30 311.13 38.31 65.41 103.83 130.81 207.65 415.30 311.13 38.32 65.41 103.83 130.81 207.65 415.30 311.13 38.33 65.41 103.83 130.81 207.65 415.30 311.13 38.34 65.41 103.83 130.81 207.65 415.30 311.13 38.35 65.41 103.83 130.81 207.65 415.30 311.13 38.36 65.41 103.83 130.81 207.65 415.30 311.13 38.37 65.41 103.83 130.81 207.65 415.30 311.13 38.38 65.41 103.83 130.81 207.65 415.30 311.13 38.39 65.41 103.83 130.81 207.65 415.30 311.13 38.40 65.41 103.83 130.81 207.65 415.30 311.13 38.41 65.41 103.83 130.81 207.65 415.30 311.13 38.42 65.41 103.83 130.81 207.65 415.30 311.13 38.43 65.41 103.83 130.81 207.65 415.30 311.13 38.44 65.41 103.83 130.81 207.65 415.30 311.13 38.45 65.41 103.83 130.81 207.65 415.30 311.13 38.46 65.41 103.83 130.81 207.65 415.30 311.13 38.47 65.41 103.83 130.81 207.65 415.30 311.13 38.48 65.41 103.83 130.81 207.65 415.30 311.13 38.49 65.41 103.83 130.81 207.65 415.30 311.13 38.50 65.41 103.83 130.81 207.65 415.30 311.13 38.51 65.41 103.83 130.81 207.65 415.30 311.13 38.52 65.41 103.83 130.81 207.65 415.30 311.13 38.53 65.41 103.83 130.81 207.65 415.30 311.13 38.54 65.41 103.83 130.81 207.65 415.30 311.13 38.55 65.41 103.83 130.81 207.65 415.30 311.13 38.56 65.41 103.83 130.81 207.65 415.30 311.13 38.57 65.41 103.83 130.81 207.65 415.30 311.13 38.58 65.41 103.83 130.81 207.65 415.30 311.13 38.59 65.41 103.83 130.81 207.65 415.30 311.13 38.60 65.41 103.83 130.81 207.65 415.30 311.13 38.61 65.41 103.83 130.81 207.65 415.30 311.13 38.62 65.41 103.83 130.81 207.65 415.30 311.13 38.63 65.41 103.83 130.81 207.65 415.30 311.13 38.64 65.41 103.83 130.81 207.65 415.30 311.13 38.65 65.41 103.83 130.81 207.65 415.30 311.13 38.66 65.41 103.83 130.81 207.65 415.30 311.13 38.67 65.41 103.83 130.81 207.65 415.30 311.13 38.68 65.41 103.83 130.81 207.65 415.30 311.13 38.69 65.41 103.83 130.81 207.65 415.30 311.13 38.70 65.41 103.83 130.81 207.65 415.30 311.13 38.71 65.41 103.83 130.81 207.65 415.30 311.13 369.99 38.72 65.41 103.83 130.81 207.65 415.30 311.13 369.99 38.73 65.41 103.83 130.81 207.65 311.13 369.99 38.74 65.41 103.83 130.81 207.65 311.13 369.99 38.75 65.41 103.83 130.81 207.65 311.13 369.99 38.76 65.41 103.83 130.81 207.65 369.99 38.77 65.41 103.83 130.81 207.65 369.99 38.78 65.41 103.83 130.81 207.65 369.99 38.79 65.41 103.83 130.81 207.65 369.99 38.80 65.41 103.83 130.81 207.65 369.99 38.81 65.41 103.83 130.81 207.65 369.99 38.82 65.41 103.83 130.81 207.65 369.99 38.83 103.83 207.65 369.99 38.84 103.83 207.65 369.99 38.85 103.83 207.65 369.99 38.86 103.83 207.65 369.99 38.87 207.65 369.99 38.88 207.65 369.99 38.89 207.65 369.99 38.90 207.65 369.99 38.91 207.65 369.99 38.92 207.65 369.99 38.93 369.99 38.94 369.99 38.95 369.99 38.96 369.99 38.97 369.99 38.98 369.99 38.99 369.99 39.00 369.99 415.30 39.01 369.99 415.30 39.02 369.99 415.30 39.03 415.30 39.04 415.30 39.05 415.30 39.06 415.30 39.07 415.30 39.08 415.30 39.09 415.30 39.10 415.30 39.11 415.30 39.12 415.30 39.13 415.30 39.14 415.30 39.15 415.30 39.16 415.30 39.17 415.30 39.18 415.30 39.19 415.30 39.20 415.30 39.21 415.30 39.22 415.30 39.23 415.30 39.24 39.25 39.26 39.27 39.28 39.29 39.30 39.31 39.32 415.30 39.33 415.30 207.65 39.34 415.30 207.65 39.35 415.30 207.65 39.36 415.30 207.65 39.37 415.30 207.65 138.59 69.30 39.38 415.30 207.65 138.59 69.30 39.39 415.30 207.65 138.59 69.30 39.40 415.30 207.65 138.59 69.30 39.41 415.30 207.65 138.59 69.30 39.42 415.30 207.65 138.59 69.30 39.43 415.30 207.65 138.59 69.30 39.44 415.30 207.65 138.59 69.30 39.45 415.30 207.65 138.59 69.30 39.46 415.30 207.65 138.59 69.30 39.47 415.30 207.65 138.59 69.30 39.48 415.30 207.65 138.59 69.30 39.49 415.30 207.65 138.59 69.30 39.50 415.30 207.65 138.59 69.30 39.51 415.30 207.65 138.59 69.30 39.52 415.30 207.65 138.59 69.30 39.53 415.30 207.65 138.59 69.30 39.54 415.30 207.65 138.59 69.30 39.55 415.30 207.65 138.59 69.30 39.56 415.30 207.65 138.59 69.30 39.57 415.30 207.65 138.59 69.30 39.58 415.30 207.65 138.59 69.30 39.59 415.30 207.65 138.59 69.30 39.60 415.30 207.65 138.59 69.30 39.61 415.30 207.65 138.59 69.30 39.62 415.30 207.65 138.59 69.30 39.63 415.30 207.65 138.59 69.30 39.64 415.30 207.65 138.59 69.30 39.65 415.30 207.65 138.59 69.30 39.66 415.30 207.65 138.59 69.30 39.67 415.30 207.65 138.59 69.30 39.68 415.30 207.65 138.59 69.30 39.69 415.30 207.65 138.59 69.30 39.70 415.30 207.65 138.59 69.30 39.71 415.30 207.65 138.59 69.30 39.72 415.30 207.65 138.59 69.30 39.73 415.30 207.65 138.59 69.30 39.74 415.30 207.65 138.59 69.30 39.75 415.30 207.65 138.59 69.30 39.76 415.30 207.65 138.59 69.30 39.77 415.30 207.65 138.59 69.30 39.78 415.30 207.65 138.59 69.30 39.79 415.30 207.65 138.59 69.30 39.80 415.30 207.65 138.59 69.30 39.81 415.30 207.65 138.59 69.30 39.82 415.30 207.65 138.59 69.30 39.83 415.30 207.65 138.59 69.30 39.84 415.30 207.65 138.59 69.30 39.85 415.30 207.65 138.59 69.30 39.86 415.30 207.65 138.59 69.30 39.87 415.30 207.65 138.59 69.30 39.88 415.30 207.65 138.59 69.30 39.89 415.30 207.65 138.59 69.30 39.90 415.30 207.65 138.59 69.30 39.91 415.30 207.65 138.59 69.30 39.92 415.30 207.65 138.59 69.30 39.93 415.30 207.65 138.59 69.30 39.94 415.30 207.65 138.59 69.30 39.95 415.30 207.65 138.59 69.30 39.96 415.30 207.65 138.59 69.30 39.97 415.30 207.65 138.59 69.30 39.98 415.30 207.65 138.59 69.30 39.99 415.30 207.65 138.59 69.30 40.00 415.30 207.65 138.59 69.30 40.01 415.30 207.65 138.59 69.30 40.02 415.30 207.65 138.59 69.30 40.03 415.30 207.65 138.59 69.30 40.04 415.30 207.65 138.59 69.30 40.05 415.30 207.65 138.59 69.30 277.18 40.06 415.30 207.65 138.59 69.30 277.18 40.07 415.30 207.65 138.59 69.30 277.18 40.08 415.30 207.65 138.59 69.30 277.18 40.09 415.30 207.65 138.59 69.30 277.18 40.10 415.30 207.65 138.59 69.30 277.18 40.11 415.30 207.65 138.59 69.30 277.18 40.12 415.30 207.65 138.59 69.30 277.18 40.13 415.30 207.65 138.59 69.30 277.18 40.14 415.30 207.65 138.59 69.30 277.18 40.15 415.30 207.65 138.59 69.30 277.18 40.16 415.30 207.65 138.59 69.30 277.18 40.17 415.30 207.65 138.59 69.30 277.18 40.18 415.30 207.65 138.59 69.30 277.18 40.19 415.30 207.65 138.59 69.30 277.18 40.20 415.30 207.65 138.59 69.30 277.18 40.21 415.30 207.65 138.59 69.30 277.18 40.22 415.30 207.65 138.59 69.30 277.18 40.23 415.30 207.65 138.59 69.30 277.18 40.24 415.30 207.65 138.59 69.30 277.18 40.25 415.30 207.65 138.59 69.30 277.18 40.26 415.30 207.65 138.59 69.30 277.18 40.27 415.30 207.65 138.59 69.30 277.18 40.28 415.30 207.65 138.59 69.30 277.18 40.29 415.30 207.65 138.59 69.30 277.18 40.30 415.30 207.65 138.59 69.30 277.18 40.31 415.30 207.65 138.59 69.30 277.18 40.32 415.30 207.65 138.59 69.30 277.18 40.33 415.30 207.65 138.59 69.30 277.18 40.34 415.30 207.65 138.59 69.30 277.18 40.35 415.30 207.65 138.59 69.30 277.18 40.36 415.30 207.65 138.59 69.30 277.18 40.37 415.30 207.65 138.59 69.30 277.18 40.38 415.30 207.65 138.59 69.30 277.18 40.39 415.30 207.65 138.59 69.30 277.18 40.40 415.30 207.65 138.59 69.30 277.18 40.41 415.30 207.65 138.59 69.30 277.18 40.42 415.30 207.65 138.59 69.30 277.18 40.43 415.30 207.65 138.59 69.30 277.18 40.44 415.30 207.65 138.59 69.30 277.18 40.45 415.30 207.65 138.59 69.30 277.18 40.46 415.30 207.65 138.59 69.30 277.18 40.47 415.30 207.65 138.59 69.30 277.18 40.48 415.30 207.65 138.59 69.30 277.18 40.49 415.30 207.65 138.59 69.30 277.18 40.50 415.30 207.65 138.59 69.30 277.18 40.51 415.30 207.65 138.59 69.30 277.18 40.52 415.30 207.65 138.59 69.30 277.18 40.53 415.30 207.65 138.59 69.30 277.18 40.54 415.30 207.65 138.59 69.30 277.18 40.55 415.30 207.65 138.59 69.30 277.18 40.56 415.30 207.65 138.59 69.30 277.18 40.57 415.30 207.65 138.59 69.30 277.18 40.58 415.30 207.65 138.59 69.30 277.18 40.59 415.30 207.65 138.59 69.30 277.18 329.63 40.60 415.30 207.65 138.59 69.30 277.18 329.63 40.61 415.30 207.65 138.59 69.30 277.18 329.63 40.62 415.30 207.65 138.59 69.30 277.18 329.63 40.63 415.30 207.65 138.59 69.30 329.63 40.64 415.30 207.65 138.59 69.30 329.63 40.65 415.30 207.65 138.59 69.30 329.63 40.66 415.30 207.65 138.59 69.30 329.63 40.67 415.30 207.65 138.59 69.30 329.63 40.68 415.30 207.65 138.59 69.30 329.63 40.69 415.30 207.65 138.59 69.30 329.63 40.70 415.30 207.65 138.59 69.30 329.63 40.71 415.30 138.59 69.30 329.63 40.72 415.30 138.59 69.30 329.63 40.73 415.30 138.59 69.30 329.63 40.74 415.30 138.59 69.30 329.63 40.75 415.30 138.59 69.30 329.63 40.76 415.30 138.59 69.30 329.63 40.77 415.30 138.59 69.30 329.63 40.78 415.30 138.59 69.30 329.63 40.79 415.30 138.59 69.30 329.63 40.80 415.30 138.59 69.30 329.63 40.81 415.30 138.59 69.30 329.63 40.82 415.30 138.59 69.30 329.63 40.83 415.30 138.59 69.30 329.63 40.84 415.30 138.59 69.30 329.63 40.85 415.30 138.59 69.30 329.63 40.86 415.30 138.59 69.30 329.63 40.87 415.30 138.59 69.30 329.63 40.88 415.30 138.59 69.30 329.63 40.89 415.30 138.59 69.30 329.63 40.90 415.30 138.59 69.30 329.63 40.91 415.30 138.59 69.30 329.63 40.92 415.30 138.59 69.30 329.63 40.93 415.30 138.59 69.30 329.63 40.94 415.30 138.59 69.30 329.63 40.95 415.30 138.59 69.30 329.63 40.96 415.30 138.59 69.30 329.63 40.97 415.30 138.59 69.30 329.63 40.98 415.30 138.59 69.30 329.63 40.99 415.30 138.59 69.30 329.63 41.00 415.30 138.59 69.30 329.63 41.01 415.30 138.59 69.30 329.63 41.02 415.30 138.59 69.30 329.63 41.03 415.30 138.59 69.30 329.63 41.04 415.30 138.59 69.30 329.63 41.05 415.30 138.59 69.30 329.63 41.06 415.30 138.59 69.30 329.63 41.07 415.30 138.59 69.30 329.63 41.08 415.30 138.59 69.30 329.63 207.65 41.09 415.30 138.59 69.30 329.63 207.65 41.10 415.30 138.59 69.30 329.63 207.65 41.11 415.30 138.59 69.30 207.65 41.12 415.30 138.59 69.30 207.65 41.13 415.30 138.59 69.30 207.65 41.14 415.30 138.59 69.30 207.65 41.15 415.30 138.59 69.30 207.65 41.16 415.30 138.59 69.30 207.65 41.17 415.30 138.59 69.30 207.65 41.18 415.30 138.59 69.30 207.65 41.19 415.30 138.59 69.30 207.65 41.20 415.30 138.59 69.30 207.65 41.21 415.30 138.59 69.30 207.65 41.22 415.30 138.59 69.30 207.65 41.23 415.30 138.59 69.30 207.65 41.24 415.30 138.59 69.30 207.65 41.25 415.30 138.59 69.30 207.65 41.26 415.30 138.59 69.30 207.65 41.27 415.30 138.59 69.30 207.65 41.28 415.30 138.59 69.30 207.65 41.29 415.30 138.59 69.30 207.65 41.30 415.30 138.59 69.30 207.65 41.31 415.30 138.59 69.30 207.65 41.32 415.30 138.59 69.30 207.65 41.33 415.30 138.59 69.30 207.65 41.34 415.30 138.59 69.30 207.65 41.35 415.30 138.59 69.30 207.65 41.36 415.30 138.59 69.30 207.65 41.37 415.30 138.59 69.30 207.65 41.38 415.30 138.59 69.30 207.65 41.39 415.30 138.59 69.30 207.65 41.40 415.30 138.59 69.30 207.65 41.41 415.30 138.59 69.30 207.65 41.42 415.30 138.59 69.30 207.65 41.43 415.30 138.59 69.30 207.65 41.44 415.30 138.59 69.30 207.65 41.45 415.30 138.59 69.30 207.65 41.46 415.30 138.59 69.30 207.65 41.47 415.30 138.59 69.30 207.65 41.48 415.30 138.59 69.30 207.65 41.49 415.30 138.59 69.30 207.65 41.50 415.30 138.59 69.30 207.65 41.51 415.30 138.59 69.30 207.65 41.52 415.30 138.59 69.30 207.65 41.53 415.30 138.59 69.30 207.65 41.54 415.30 138.59 69.30 207.65 41.55 415.30 138.59 69.30 207.65 277.18 41.56 415.30 138.59 69.30 207.65 277.18 41.57 415.30 138.59 69.30 207.65 277.18 41.58 415.30 69.30 207.65 277.18 41.59 415.30 69.30 207.65 277.18 41.60 415.30 207.65 277.18 41.61 415.30 207.65 277.18 41.62 415.30 207.65 277.18 41.63 415.30 207.65 277.18 41.64 415.30 207.65 277.18 41.65 415.30 207.65 277.18 41.66 415.30 207.65 277.18 41.67 415.30 207.65 277.18 41.68 415.30 207.65 277.18 41.69 415.30 207.65 277.18 41.70 415.30 207.65 277.18 41.71 415.30 207.65 277.18 41.72 415.30 207.65 277.18 41.73 415.30 207.65 277.18 41.74 415.30 207.65 277.18 41.75 415.30 207.65 277.18 41.76 415.30 207.65 277.18 41.77 415.30 207.65 277.18 41.78 415.30 207.65 277.18 41.79 415.30 207.65 277.18 41.80 415.30 207.65 277.18 41.81 415.30 207.65 277.18 41.82 415.30 207.65 277.18 41.83 415.30 207.65 277.18 41.84 415.30 207.65 277.18 41.85 415.30 207.65 277.18 41.86 415.30 207.65 277.18 41.87 415.30 207.65 277.18 41.88 415.30 207.65 277.18 41.89 415.30 207.65 277.18 41.90 415.30 207.65 277.18 41.91 415.30 207.65 277.18 41.92 415.30 207.65 277.18 41.93 415.30 207.65 277.18 41.94 415.30 207.65 277.18 41.95 415.30 207.65 277.18 41.96 415.30 207.65 277.18 41.97 415.30 207.65 277.18 41.98 415.30 207.65 277.18 41.99 415.30 207.65 277.18 42.00 415.30 207.65 277.18 329.63 42.01 415.30 207.65 277.18 329.63 42.02 415.30 207.65 277.18 329.63 42.03 415.30 207.65 277.18 329.63 42.04 207.65 277.18 329.63 42.05 329.63 42.06 329.63 42.07 329.63 42.08 329.63 42.09 329.63 42.10 329.63 42.11 329.63 42.12 329.63 42.13 329.63 42.14 329.63 42.15 329.63 42.16 329.63 42.17 329.63 42.18 329.63 42.19 329.63 42.20 329.63 42.21 329.63 42.22 329.63 42.23 329.63 42.24 329.63 42.25 329.63 42.26 329.63 42.27 329.63 42.28 329.63 42.29 329.63 42.30 329.63 42.31 329.63 42.32 329.63 42.33 329.63 42.34 329.63 42.35 329.63 42.36 329.63 42.37 329.63 42.38 329.63 42.39 329.63 42.40 329.63 42.41 329.63 42.42 329.63 42.43 329.63 42.44 329.63 42.45 329.63 42.46 329.63 42.47 329.63 42.48 329.63 42.49 329.63 42.50 329.63 46.25 92.50 42.51 329.63 46.25 92.50 42.52 329.63 46.25 92.50 42.53 329.63 46.25 92.50 42.54 329.63 46.25 92.50 42.55 329.63 46.25 92.50 220.00 440.00 42.56 329.63 46.25 92.50 220.00 440.00 42.57 329.63 46.25 92.50 220.00 440.00 42.58 46.25 92.50 220.00 440.00 42.59 46.25 92.50 220.00 440.00 42.60 46.25 92.50 220.00 440.00 42.61 46.25 92.50 220.00 440.00 42.62 46.25 92.50 220.00 440.00 42.63 46.25 92.50 220.00 440.00 42.64 46.25 92.50 220.00 440.00 42.65 46.25 92.50 220.00 440.00 42.66 46.25 92.50 220.00 440.00 42.67 46.25 92.50 220.00 440.00 42.68 46.25 92.50 220.00 440.00 42.69 46.25 92.50 220.00 440.00 42.70 46.25 92.50 220.00 440.00 42.71 46.25 92.50 220.00 440.00 42.72 46.25 92.50 220.00 440.00 42.73 46.25 92.50 220.00 440.00 42.74 46.25 92.50 220.00 440.00 42.75 46.25 92.50 220.00 440.00 42.76 46.25 92.50 220.00 440.00 42.77 46.25 92.50 220.00 440.00 42.78 46.25 92.50 220.00 440.00 42.79 46.25 92.50 220.00 440.00 42.80 46.25 92.50 220.00 440.00 42.81 46.25 92.50 220.00 440.00 42.82 46.25 92.50 220.00 440.00 42.83 46.25 92.50 220.00 440.00 42.84 46.25 92.50 220.00 440.00 42.85 46.25 92.50 220.00 440.00 42.86 46.25 92.50 220.00 440.00 42.87 46.25 92.50 220.00 440.00 42.88 46.25 92.50 220.00 440.00 42.89 46.25 92.50 220.00 440.00 42.90 46.25 92.50 220.00 440.00 42.91 46.25 92.50 220.00 440.00 42.92 46.25 92.50 220.00 440.00 42.93 46.25 92.50 220.00 440.00 42.94 46.25 92.50 220.00 440.00 42.95 46.25 92.50 220.00 440.00 42.96 46.25 92.50 220.00 440.00 42.97 46.25 92.50 220.00 440.00 42.98 46.25 92.50 220.00 440.00 42.99 46.25 92.50 220.00 440.00 43.00 46.25 92.50 220.00 440.00 43.01 46.25 92.50 220.00 440.00 43.02 46.25 92.50 220.00 440.00 43.03 46.25 92.50 220.00 440.00 43.04 46.25 92.50 220.00 440.00 43.05 46.25 92.50 220.00 440.00 43.06 46.25 92.50 220.00 440.00 43.07 46.25 92.50 220.00 440.00 43.08 46.25 92.50 220.00 440.00 43.09 46.25 92.50 220.00 440.00 43.10 46.25 92.50 220.00 440.00 43.11 46.25 92.50 220.00 440.00 43.12 46.25 92.50 220.00 440.00 43.13 46.25 92.50 220.00 440.00 43.14 46.25 92.50 220.00 440.00 277.18 43.15 46.25 92.50 220.00 440.00 277.18 43.16 46.25 92.50 220.00 440.00 277.18 43.17 46.25 92.50 220.00 440.00 277.18 43.18 46.25 92.50 220.00 440.00 277.18 43.19 46.25 92.50 220.00 440.00 277.18 43.20 46.25 92.50 220.00 440.00 277.18 43.21 46.25 92.50 220.00 440.00 277.18 43.22 46.25 92.50 220.00 440.00 277.18 43.23 46.25 92.50 220.00 440.00 277.18 43.24 46.25 92.50 220.00 440.00 277.18 43.25 46.25 92.50 220.00 440.00 277.18 43.26 46.25 92.50 220.00 440.00 277.18 43.27 46.25 92.50 220.00 440.00 277.18 43.28 46.25 92.50 220.00 440.00 277.18 43.29 46.25 92.50 220.00 440.00 277.18 43.30 46.25 92.50 220.00 440.00 277.18 43.31 46.25 92.50 220.00 440.00 277.18 43.32 46.25 92.50 220.00 440.00 277.18 43.33 46.25 92.50 220.00 440.00 277.18 43.34 46.25 92.50 220.00 440.00 277.18 43.35 46.25 92.50 220.00 440.00 277.18 43.36 46.25 92.50 220.00 440.00 277.18 43.37 46.25 92.50 220.00 440.00 277.18 43.38 46.25 92.50 220.00 440.00 277.18 43.39 46.25 92.50 220.00 440.00 277.18 43.40 46.25 92.50 220.00 440.00 277.18 43.41 46.25 92.50 220.00 440.00 277.18 43.42 46.25 92.50 220.00 440.00 277.18 43.43 46.25 92.50 220.00 440.00 277.18 43.44 46.25 92.50 220.00 440.00 277.18 43.45 46.25 92.50 220.00 440.00 277.18 43.46 46.25 92.50 220.00 440.00 277.18 43.47 46.25 92.50 220.00 440.00 277.18 43.48 46.25 92.50 220.00 440.00 277.18 43.49 46.25 92.50 220.00 440.00 277.18 43.50 46.25 92.50 220.00 440.00 277.18 43.51 46.25 92.50 220.00 440.00 277.18 43.52 46.25 92.50 220.00 440.00 277.18 43.53 46.25 92.50 220.00 440.00 277.18 43.54 46.25 92.50 220.00 440.00 277.18 43.55 46.25 92.50 220.00 440.00 277.18 43.56 46.25 92.50 220.00 440.00 277.18 43.57 46.25 92.50 220.00 440.00 277.18 43.58 46.25 92.50 220.00 440.00 277.18 43.59 46.25 92.50 220.00 440.00 277.18 43.60 46.25 92.50 220.00 440.00 277.18 43.61 46.25 92.50 220.00 440.00 277.18 43.62 46.25 92.50 220.00 440.00 277.18 369.99 43.63 46.25 92.50 220.00 440.00 277.18 369.99 43.64 46.25 92.50 220.00 440.00 277.18 369.99 43.65 46.25 92.50 220.00 440.00 277.18 369.99 43.66 46.25 92.50 220.00 440.00 369.99 43.67 46.25 92.50 220.00 440.00 369.99 43.68 46.25 92.50 220.00 440.00 369.99 43.69 46.25 92.50 220.00 440.00 369.99 43.70 46.25 92.50 220.00 440.00 369.99 43.71 46.25 92.50 220.00 440.00 369.99 43.72 46.25 92.50 220.00 440.00 369.99 43.73 46.25 92.50 220.00 440.00 369.99 43.74 46.25 92.50 220.00 440.00 369.99 43.75 46.25 92.50 220.00 440.00 369.99 43.76 46.25 92.50 440.00 369.99 43.77 46.25 92.50 440.00 369.99 43.78 46.25 92.50 440.00 369.99 43.79 46.25 92.50 440.00 369.99 43.80 46.25 92.50 440.00 369.99 43.81 46.25 92.50 440.00 369.99 43.82 46.25 92.50 440.00 369.99 43.83 46.25 92.50 440.00 369.99 43.84 46.25 92.50 440.00 369.99 43.85 46.25 92.50 440.00 369.99 43.86 46.25 92.50 440.00 369.99 43.87 46.25 92.50 440.00 369.99 43.88 46.25 92.50 440.00 369.99 43.89 46.25 92.50 440.00 369.99 43.90 46.25 92.50 440.00 369.99 43.91 46.25 92.50 440.00 369.99 43.92 46.25 92.50 440.00 369.99 43.93 46.25 92.50 440.00 369.99 43.94 46.25 92.50 440.00 369.99 43.95 46.25 92.50 440.00 369.99 43.96 46.25 92.50 440.00 369.99 43.97 46.25 92.50 440.00 369.99 43.98 46.25 92.50 440.00 369.99 43.99 46.25 92.50 440.00 369.99 44.00 46.25 92.50 440.00 369.99 44.01 46.25 92.50 440.00 369.99 44.02 46.25 92.50 440.00 369.99 44.03 46.25 92.50 440.00 369.99 44.04 46.25 92.50 440.00 369.99 44.05 46.25 92.50 440.00 369.99 44.06 46.25 92.50 440.00 369.99 44.07 46.25 92.50 440.00 369.99 44.08 46.25 92.50 440.00 369.99 44.09 46.25 92.50 440.00 369.99 44.10 46.25 92.50 440.00 369.99 44.11 46.25 92.50 440.00 369.99 44.12 46.25 92.50 440.00 369.99 44.13 46.25 92.50 440.00 369.99 44.14 46.25 92.50 440.00 369.99 44.15 46.25 92.50 440.00 369.99 220.00 44.16 46.25 92.50 440.00 369.99 220.00 44.17 46.25 92.50 440.00 369.99 220.00 44.18 46.25 92.50 440.00 369.99 220.00 44.19 46.25 92.50 440.00 369.99 220.00 44.20 46.25 92.50 440.00 220.00 44.21 46.25 92.50 440.00 220.00 44.22 46.25 92.50 440.00 220.00 44.23 46.25 92.50 440.00 220.00 44.24 46.25 92.50 440.00 220.00 44.25 46.25 92.50 440.00 220.00 44.26 46.25 92.50 440.00 220.00 44.27 46.25 92.50 440.00 220.00 44.28 46.25 92.50 440.00 220.00 44.29 46.25 92.50 440.00 220.00 44.30 46.25 92.50 440.00 220.00 44.31 46.25 92.50 440.00 220.00 44.32 46.25 92.50 440.00 220.00 44.33 46.25 92.50 440.00 220.00 44.34 46.25 92.50 440.00 220.00 44.35 46.25 92.50 440.00 220.00 44.36 46.25 92.50 440.00 220.00 44.37 46.25 92.50 440.00 220.00 44.38 46.25 92.50 440.00 220.00 44.39 46.25 92.50 440.00 220.00 44.40 46.25 92.50 440.00 220.00 44.41 46.25 92.50 440.00 220.00 44.42 46.25 92.50 440.00 220.00 44.43 46.25 92.50 440.00 220.00 44.44 46.25 92.50 440.00 220.00 44.45 46.25 92.50 440.00 220.00 44.46 46.25 92.50 440.00 220.00 44.47 46.25 92.50 440.00 220.00 44.48 46.25 92.50 440.00 220.00 44.49 46.25 92.50 440.00 220.00 44.50 46.25 92.50 440.00 220.00 44.51 46.25 92.50 440.00 220.00 44.52 46.25 92.50 440.00 220.00 44.53 46.25 92.50 440.00 220.00 44.54 46.25 92.50 440.00 220.00 44.55 46.25 92.50 440.00 220.00 44.56 46.25 92.50 440.00 220.00 44.57 46.25 92.50 440.00 220.00 44.58 46.25 92.50 440.00 220.00 44.59 46.25 92.50 440.00 220.00 44.60 46.25 92.50 440.00 220.00 277.18 44.61 46.25 92.50 440.00 220.00 277.18 44.62 46.25 92.50 440.00 220.00 277.18 44.63 46.25 92.50 440.00 220.00 277.18 44.64 46.25 92.50 440.00 220.00 277.18 44.65 46.25 92.50 440.00 220.00 277.18 44.66 46.25 92.50 440.00 220.00 277.18 44.67 46.25 92.50 440.00 220.00 277.18 44.68 46.25 92.50 440.00 277.18 44.69 46.25 92.50 440.00 277.18 44.70 46.25 92.50 440.00 277.18 44.71 46.25 440.00 277.18 44.72 46.25 440.00 277.18 44.73 46.25 440.00 277.18 44.74 46.25 440.00 277.18 44.75 440.00 277.18 44.76 440.00 277.18 44.77 440.00 277.18 44.78 440.00 277.18 44.79 440.00 277.18 44.80 440.00 277.18 44.81 440.00 277.18 44.82 440.00 277.18 44.83 440.00 277.18 44.84 440.00 277.18 44.85 440.00 277.18 44.86 440.00 277.18 44.87 440.00 277.18 44.88 440.00 277.18 44.89 440.00 277.18 44.90 440.00 277.18 44.91 440.00 277.18 44.92 440.00 277.18 44.93 440.00 277.18 44.94 440.00 277.18 44.95 440.00 277.18 44.96 440.00 277.18 44.97 440.00 277.18 44.98 440.00 277.18 44.99 440.00 277.18 45.00 440.00 277.18 45.01 440.00 277.18 45.02 440.00 277.18 45.03 440.00 277.18 45.04 440.00 277.18 45.05 440.00 277.18 45.06 440.00 277.18 45.07 440.00 277.18 45.08 440.00 277.18 45.09 440.00 277.18 45.10 277.18 45.11 277.18 369.99 45.12 277.18 369.99 45.13 277.18 369.99 45.14 277.18 369.99 45.15 369.99 45.16 369.99 45.17 369.99 45.18 369.99 45.19 369.99 45.20 369.99 45.21 369.99 45.22 369.99 45.23 369.99 45.24 369.99 45.25 369.99 45.26 369.99 45.27 369.99 45.28 369.99 45.29 369.99 45.30 369.99 45.31 369.99 45.32 369.99 45.33 369.99 45.34 369.99 45.35 369.99 45.36 369.99 45.37 369.99 45.38 369.99 45.39 369.99 45.40 369.99 45.41 369.99 45.42 369.99 45.43 369.99 45.44 369.99 45.45 369.99 45.46 369.99 45.47 369.99 45.48 369.99 45.49 369.99 45.50 369.99 45.51 369.99 45.52 369.99 45.53 369.99 45.54 369.99 45.55 369.99 45.56 369.99 45.57 369.99 45.58 369.99 45.59 369.99 45.60 369.99 45.61 369.99 123.47 61.74 45.62 369.99 123.47 61.74 45.63 369.99 123.47 61.74 45.64 369.99 123.47 61.74 45.65 369.99 123.47 61.74 45.66 369.99 123.47 61.74 45.67 369.99 123.47 61.74 207.65 45.68 369.99 123.47 61.74 207.65 415.30 45.69 369.99 123.47 61.74 207.65 415.30 45.70 369.99 123.47 61.74 207.65 415.30 45.71 369.99 123.47 61.74 207.65 415.30 45.72 123.47 61.74 207.65 415.30 45.73 123.47 61.74 207.65 415.30 45.74 123.47 61.74 207.65 415.30 45.75 123.47 61.74 207.65 415.30 45.76 123.47 61.74 207.65 415.30 45.77 123.47 61.74 207.65 415.30 45.78 123.47 61.74 207.65 415.30 45.79 123.47 61.74 207.65 415.30 45.80 123.47 61.74 207.65 415.30 45.81 123.47 61.74 207.65 415.30 45.82 123.47 61.74 207.65 415.30 45.83 123.47 61.74 207.65 415.30 45.84 123.47 61.74 207.65 415.30 45.85 123.47 61.74 207.65 415.30 45.86 123.47 61.74 207.65 415.30 45.87 123.47 61.74 207.65 415.30 45.88 123.47 61.74 207.65 415.30 45.89 123.47 61.74 207.65 415.30 45.90 123.47 61.74 207.65 415.30 45.91 123.47 61.74 207.65 415.30 45.92 123.47 61.74 207.65 415.30 45.93 123.47 61.74 207.65 415.30 45.94 123.47 61.74 207.65 415.30 45.95 123.47 61.74 207.65 415.30 45.96 123.47 61.74 207.65 415.30 45.97 123.47 61.74 207.65 415.30 45.98 123.47 61.74 207.65 415.30 45.99 123.47 61.74 207.65 415.30 46.00 123.47 61.74 207.65 415.30 46.01 123.47 61.74 207.65 415.30 46.02 123.47 61.74 207.65 415.30 46.03 123.47 61.74 207.65 415.30 46.04 123.47 61.74 207.65 415.30 46.05 123.47 61.74 207.65 415.30 46.06 123.47 61.74 207.65 415.30 46.07 123.47 61.74 207.65 415.30 46.08 123.47 61.74 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61.74 207.65 415.30 246.94 46.36 123.47 61.74 207.65 415.30 246.94 46.37 123.47 61.74 207.65 415.30 246.94 46.38 123.47 61.74 207.65 415.30 246.94 46.39 123.47 61.74 207.65 415.30 246.94 46.40 123.47 61.74 207.65 415.30 246.94 46.41 123.47 61.74 207.65 415.30 246.94 46.42 123.47 61.74 207.65 415.30 246.94 46.43 123.47 61.74 207.65 415.30 246.94 46.44 123.47 61.74 207.65 415.30 246.94 46.45 123.47 61.74 207.65 415.30 246.94 46.46 123.47 61.74 207.65 415.30 246.94 46.47 123.47 61.74 207.65 415.30 246.94 46.48 123.47 61.74 207.65 415.30 246.94 46.49 123.47 61.74 207.65 415.30 246.94 46.50 123.47 61.74 207.65 415.30 246.94 46.51 123.47 61.74 207.65 415.30 246.94 46.52 123.47 61.74 207.65 415.30 246.94 46.53 123.47 61.74 207.65 415.30 246.94 46.54 123.47 61.74 207.65 415.30 246.94 46.55 123.47 61.74 207.65 415.30 246.94 46.56 123.47 61.74 207.65 415.30 246.94 46.57 123.47 61.74 207.65 415.30 246.94 46.58 123.47 61.74 207.65 415.30 246.94 46.59 123.47 61.74 207.65 415.30 246.94 46.60 123.47 61.74 207.65 415.30 246.94 46.61 123.47 61.74 207.65 415.30 246.94 46.62 123.47 61.74 207.65 415.30 246.94 46.63 123.47 61.74 207.65 415.30 246.94 46.64 123.47 61.74 207.65 415.30 246.94 46.65 123.47 61.74 207.65 415.30 246.94 46.66 123.47 61.74 207.65 415.30 246.94 46.67 123.47 61.74 207.65 415.30 246.94 46.68 123.47 61.74 207.65 415.30 246.94 329.63 46.69 123.47 61.74 207.65 415.30 246.94 329.63 46.70 123.47 61.74 415.30 329.63 46.71 123.47 61.74 415.30 329.63 46.72 123.47 61.74 415.30 329.63 46.73 123.47 61.74 415.30 329.63 46.74 123.47 61.74 415.30 329.63 46.75 123.47 61.74 415.30 329.63 46.76 123.47 61.74 415.30 329.63 46.77 123.47 61.74 415.30 329.63 46.78 123.47 61.74 415.30 329.63 46.79 123.47 61.74 415.30 329.63 46.80 123.47 61.74 415.30 329.63 46.81 123.47 61.74 415.30 329.63 46.82 123.47 61.74 415.30 329.63 46.83 123.47 61.74 415.30 329.63 46.84 123.47 61.74 415.30 329.63 46.85 123.47 61.74 415.30 329.63 46.86 123.47 61.74 415.30 329.63 46.87 123.47 61.74 415.30 329.63 46.88 123.47 61.74 415.30 329.63 46.89 123.47 61.74 415.30 329.63 46.90 123.47 61.74 415.30 329.63 46.91 123.47 61.74 415.30 329.63 46.92 123.47 61.74 415.30 329.63 46.93 123.47 61.74 415.30 329.63 46.94 123.47 61.74 415.30 329.63 46.95 123.47 61.74 415.30 329.63 46.96 123.47 61.74 415.30 329.63 46.97 123.47 61.74 415.30 329.63 46.98 123.47 61.74 415.30 329.63 46.99 123.47 61.74 415.30 329.63 47.00 123.47 61.74 415.30 329.63 47.01 123.47 61.74 415.30 329.63 47.02 123.47 61.74 415.30 329.63 47.03 123.47 61.74 415.30 329.63 47.04 123.47 61.74 415.30 329.63 47.05 123.47 61.74 415.30 329.63 47.06 123.47 61.74 415.30 329.63 47.07 123.47 61.74 415.30 329.63 47.08 123.47 61.74 415.30 329.63 47.09 123.47 61.74 415.30 329.63 47.10 123.47 61.74 415.30 329.63 47.11 123.47 61.74 415.30 329.63 47.12 123.47 61.74 415.30 329.63 47.13 123.47 61.74 415.30 329.63 47.14 123.47 61.74 415.30 329.63 47.15 123.47 61.74 415.30 329.63 47.16 123.47 61.74 415.30 329.63 207.65 47.17 123.47 61.74 415.30 329.63 207.65 47.18 123.47 61.74 415.30 329.63 207.65 47.19 123.47 61.74 415.30 207.65 47.20 123.47 61.74 415.30 207.65 47.21 123.47 61.74 415.30 207.65 47.22 123.47 61.74 415.30 207.65 47.23 123.47 61.74 415.30 207.65 47.24 123.47 61.74 415.30 207.65 47.25 123.47 61.74 415.30 207.65 47.26 123.47 61.74 415.30 207.65 47.27 123.47 61.74 415.30 207.65 47.28 123.47 61.74 415.30 207.65 47.29 123.47 61.74 415.30 207.65 47.30 123.47 61.74 415.30 207.65 47.31 123.47 61.74 415.30 207.65 47.32 123.47 61.74 415.30 207.65 47.33 123.47 61.74 415.30 207.65 47.34 123.47 61.74 415.30 207.65 47.35 123.47 61.74 415.30 207.65 47.36 123.47 61.74 415.30 207.65 47.37 123.47 61.74 415.30 207.65 47.38 123.47 61.74 415.30 207.65 47.39 123.47 61.74 415.30 207.65 47.40 123.47 61.74 415.30 207.65 47.41 123.47 61.74 415.30 207.65 47.42 123.47 61.74 415.30 207.65 47.43 123.47 61.74 415.30 207.65 47.44 123.47 61.74 415.30 207.65 47.45 123.47 61.74 415.30 207.65 47.46 123.47 61.74 415.30 207.65 47.47 123.47 61.74 415.30 207.65 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5.78 369.99 369.99 246.94 5.79 369.99 369.99 246.94 5.80 369.99 369.99 246.94 5.81 369.99 369.99 246.94 5.82 369.99 369.99 246.94 5.83 369.99 369.99 246.94 5.84 369.99 369.99 246.94 5.85 369.99 369.99 246.94 5.86 369.99 369.99 246.94 5.87 369.99 369.99 246.94 5.88 369.99 369.99 246.94 5.89 369.99 369.99 246.94 5.90 369.99 369.99 5.91 369.99 369.99 5.92 369.99 369.99 5.93 369.99 369.99 5.94 369.99 369.99 622.25 5.95 369.99 369.99 622.25 5.96 369.99 369.99 622.25 5.97 369.99 369.99 622.25 5.98 369.99 622.25 5.99 369.99 622.25 6.00 369.99 622.25 6.01 369.99 622.25 103.83 311.13 6.02 369.99 622.25 103.83 311.13 6.03 369.99 622.25 103.83 311.13 6.04 369.99 622.25 103.83 311.13 6.05 369.99 622.25 103.83 311.13 6.06 369.99 622.25 103.83 311.13 6.07 369.99 622.25 103.83 311.13 6.08 622.25 103.83 311.13 6.09 622.25 103.83 311.13 6.10 622.25 103.83 311.13 6.11 622.25 103.83 311.13 6.12 622.25 103.83 659.26 6.13 103.83 659.26 6.14 103.83 659.26 6.15 659.26 6.16 659.26 6.17 659.26 6.18 659.26 6.19 659.26 6.20 659.26 6.21 659.26 6.22 659.26 6.23 659.26 6.24 659.26 6.25 659.26 6.26 659.26 6.27 659.26 415.30 116.54 6.28 659.26 415.30 116.54 6.29 415.30 116.54 6.30 415.30 116.54 6.31 415.30 116.54 6.32 415.30 116.54 6.33 415.30 116.54 6.34 415.30 116.54 659.26 6.35 415.30 116.54 659.26 6.36 415.30 116.54 659.26 6.37 415.30 116.54 659.26 6.38 415.30 116.54 659.26 6.39 415.30 116.54 659.26 6.40 415.30 116.54 659.26 6.41 659.26 6.42 659.26 6.43 659.26 6.44 659.26 6.45 659.26 6.46 659.26 6.47 659.26 6.48 659.26 6.49 659.26 6.50 659.26 6.51 659.26 622.25 138.59 6.52 659.26 622.25 138.59 6.53 659.26 622.25 138.59 6.54 659.26 622.25 138.59 6.55 659.26 622.25 138.59 6.56 622.25 138.59 6.57 622.25 138.59 622.25 6.58 622.25 138.59 622.25 6.59 622.25 138.59 622.25 6.60 622.25 138.59 622.25 6.61 622.25 138.59 622.25 6.62 622.25 138.59 622.25 6.63 622.25 138.59 622.25 6.64 622.25 622.25 6.65 622.25 6.66 622.25 6.67 622.25 6.68 622.25 6.69 622.25 6.70 622.25 6.71 622.25 6.72 6.73 6.74 622.25 739.99 103.83 554.37 6.75 622.25 739.99 103.83 554.37 6.76 622.25 739.99 103.83 554.37 6.77 622.25 739.99 103.83 554.37 6.78 622.25 739.99 103.83 554.37 6.79 622.25 739.99 103.83 554.37 6.80 622.25 739.99 103.83 554.37 6.81 622.25 739.99 103.83 554.37 6.82 622.25 739.99 103.83 554.37 6.83 622.25 739.99 103.83 554.37 6.84 622.25 739.99 103.83 554.37 6.85 622.25 739.99 103.83 554.37 6.86 622.25 739.99 103.83 6.87 622.25 739.99 6.88 622.25 739.99 6.89 622.25 739.99 6.90 622.25 739.99 6.91 622.25 739.99 6.92 622.25 739.99 6.93 622.25 739.99 6.94 6.95 493.88 116.54 6.96 493.88 116.54 6.97 493.88 116.54 6.98 493.88 116.54 6.99 493.88 116.54 7.00 493.88 116.54 739.99 622.25 7.01 493.88 116.54 739.99 622.25 7.02 493.88 116.54 739.99 622.25 7.03 493.88 116.54 739.99 622.25 7.04 493.88 116.54 739.99 622.25 7.05 493.88 116.54 739.99 622.25 7.06 493.88 116.54 739.99 622.25 7.07 493.88 116.54 739.99 622.25 7.08 493.88 116.54 739.99 622.25 7.09 493.88 739.99 622.25 7.10 739.99 622.25 7.11 739.99 622.25 7.12 739.99 622.25 7.13 739.99 622.25 7.14 739.99 622.25 7.15 739.99 622.25 7.16 739.99 622.25 7.17 739.99 622.25 7.18 739.99 622.25 7.19 739.99 622.25 466.16 7.20 739.99 622.25 466.16 138.59 7.21 739.99 622.25 466.16 138.59 7.22 739.99 622.25 466.16 138.59 7.23 739.99 622.25 466.16 138.59 7.24 739.99 622.25 466.16 138.59 7.25 739.99 622.25 466.16 138.59 7.26 739.99 622.25 466.16 138.59 7.27 739.99 622.25 466.16 138.59 7.28 739.99 466.16 138.59 7.29 138.59 7.30 138.59 7.31 138.59 7.32 659.26 7.33 659.26 7.34 659.26 554.37 7.35 659.26 554.37 7.36 659.26 554.37 7.37 659.26 554.37 7.38 659.26 554.37 7.39 659.26 554.37 7.40 659.26 554.37 7.41 659.26 554.37 7.42 659.26 554.37 7.43 659.26 554.37 103.83 7.44 659.26 554.37 103.83 311.13 7.45 659.26 554.37 103.83 311.13 7.46 659.26 554.37 103.83 311.13 7.47 659.26 554.37 103.83 311.13 7.48 659.26 554.37 103.83 311.13 7.49 659.26 554.37 103.83 311.13 7.50 659.26 554.37 103.83 311.13 7.51 659.26 554.37 103.83 311.13 7.52 659.26 554.37 103.83 311.13 7.53 659.26 554.37 103.83 311.13 7.54 659.26 554.37 103.83 311.13 7.55 659.26 554.37 311.13 7.56 659.26 554.37 311.13 7.57 659.26 554.37 7.58 659.26 554.37 7.59 659.26 554.37 7.60 659.26 554.37 7.61 659.26 554.37 7.62 659.26 554.37 7.63 659.26 554.37 7.64 659.26 554.37 7.65 659.26 554.37 116.54 7.66 659.26 554.37 116.54 7.67 659.26 554.37 116.54 7.68 659.26 554.37 116.54 466.16 7.69 659.26 554.37 116.54 466.16 622.25 7.70 659.26 554.37 116.54 466.16 622.25 7.71 659.26 554.37 116.54 466.16 622.25 7.72 659.26 116.54 466.16 622.25 7.73 116.54 466.16 622.25 7.74 116.54 466.16 622.25 7.75 466.16 622.25 7.76 466.16 622.25 7.77 466.16 622.25 7.78 466.16 622.25 7.79 466.16 622.25 7.80 466.16 622.25 7.81 466.16 622.25 7.82 466.16 622.25 7.83 622.25 7.84 622.25 7.85 622.25 7.86 622.25 138.59 7.87 622.25 138.59 7.88 622.25 138.59 7.89 622.25 138.59 7.90 622.25 138.59 7.91 622.25 138.59 7.92 622.25 138.59 7.93 622.25 138.59 7.94 622.25 138.59 7.95 622.25 138.59 7.96 622.25 138.59 277.18 7.97 622.25 138.59 277.18 7.98 622.25 138.59 277.18 7.99 622.25 138.59 277.18 8.00 622.25 277.18 8.01 622.25 277.18 8.02 622.25 277.18 8.03 622.25 277.18 8.04 622.25 277.18 8.05 622.25 277.18 8.06 622.25 277.18 8.07 622.25 277.18 103.83 8.08 622.25 277.18 103.83 8.09 622.25 277.18 103.83 8.10 622.25 277.18 103.83 8.11 622.25 277.18 103.83 8.12 622.25 103.83 8.13 622.25 103.83 8.14 622.25 103.83 8.15 622.25 103.83 8.16 622.25 103.83 8.17 622.25 103.83 8.18 622.25 103.83 8.19 622.25 8.20 622.25 8.21 622.25 8.22 622.25 8.23 622.25 8.24 622.25 8.25 622.25 8.26 622.25 8.27 622.25 8.28 622.25 8.29 622.25 116.54 8.30 622.25 116.54 8.31 622.25 116.54 246.94 8.32 622.25 116.54 246.94 8.33 622.25 116.54 246.94 8.34 622.25 116.54 246.94 8.35 622.25 116.54 246.94 8.36 622.25 116.54 246.94 8.37 622.25 116.54 246.94 8.38 116.54 246.94 8.39 246.94 8.40 246.94 8.41 246.94 8.42 246.94 8.43 246.94 277.18 8.44 246.94 277.18 8.45 246.94 277.18 8.46 246.94 277.18 8.47 246.94 277.18 8.48 246.94 277.18 8.49 277.18 8.50 277.18 8.51 277.18 138.59 8.52 277.18 138.59 8.53 277.18 138.59 8.54 277.18 138.59 8.55 277.18 138.59 8.56 277.18 138.59 8.57 277.18 138.59 8.58 277.18 138.59 8.59 277.18 138.59 8.60 277.18 8.61 277.18 8.62 277.18 311.13 8.63 277.18 311.13 8.64 311.13 220.00 8.65 311.13 220.00 8.66 311.13 220.00 8.67 311.13 220.00 8.68 311.13 220.00 8.69 311.13 220.00 8.70 311.13 220.00 98.00 8.71 311.13 220.00 98.00 8.72 311.13 220.00 98.00 8.73 311.13 220.00 98.00 8.74 311.13 220.00 98.00 8.75 311.13 220.00 98.00 8.76 311.13 220.00 98.00 8.77 311.13 220.00 98.00 8.78 311.13 220.00 98.00 8.79 311.13 220.00 98.00 329.63 8.80 311.13 220.00 98.00 329.63 8.81 311.13 220.00 98.00 329.63 8.82 311.13 220.00 329.63 8.83 311.13 220.00 329.63 8.84 220.00 329.63 8.85 220.00 329.63 8.86 220.00 329.63 8.87 220.00 329.63 8.88 220.00 329.63 8.89 220.00 329.63 8.90 220.00 329.63 8.91 220.00 329.63 8.92 220.00 329.63 110.00 8.93 220.00 329.63 110.00 8.94 220.00 329.63 110.00 8.95 220.00 329.63 110.00 8.96 220.00 329.63 110.00 8.97 220.00 329.63 110.00 8.98 220.00 329.63 110.00 8.99 220.00 329.63 110.00 9.00 220.00 329.63 110.00 440.00 9.01 220.00 329.63 110.00 440.00 9.02 220.00 329.63 440.00 9.03 220.00 329.63 440.00 9.04 220.00 329.63 440.00 9.05 220.00 329.63 440.00 9.06 220.00 329.63 440.00 9.07 220.00 440.00 9.08 220.00 440.00 9.09 220.00 440.00 9.10 220.00 440.00 9.11 220.00 440.00 9.12 220.00 440.00 9.13 220.00 440.00 9.14 220.00 440.00 138.59 9.15 220.00 440.00 138.59 9.16 220.00 440.00 138.59 9.17 220.00 440.00 138.59 9.18 220.00 440.00 138.59 9.19 220.00 440.00 138.59 9.20 220.00 440.00 138.59 9.21 220.00 440.00 138.59 493.88 9.22 220.00 440.00 138.59 493.88 9.23 220.00 138.59 493.88 9.24 220.00 493.88 9.25 220.00 493.88 9.26 220.00 493.88 9.27 220.00 493.88 9.28 220.00 493.88 9.29 220.00 493.88 9.30 220.00 493.88 9.31 220.00 493.88 9.32 220.00 493.88 9.33 220.00 493.88 9.34 220.00 493.88 554.37 9.35 220.00 493.88 554.37 659.26 9.36 220.00 493.88 554.37 659.26 9.37 220.00 493.88 554.37 659.26 98.00 9.38 220.00 493.88 554.37 659.26 98.00 9.39 220.00 493.88 554.37 659.26 98.00 9.40 220.00 493.88 554.37 659.26 98.00 440.00 9.41 220.00 493.88 554.37 659.26 98.00 440.00 9.42 220.00 493.88 554.37 659.26 98.00 440.00 9.43 220.00 554.37 659.26 98.00 440.00 9.44 220.00 554.37 659.26 98.00 440.00 9.45 220.00 554.37 659.26 98.00 440.00 9.46 220.00 554.37 659.26 98.00 440.00 9.47 220.00 554.37 659.26 440.00 9.48 220.00 554.37 659.26 440.00 9.49 220.00 554.37 659.26 440.00 9.50 220.00 554.37 659.26 440.00 9.51 220.00 554.37 659.26 440.00 9.52 220.00 554.37 659.26 440.00 9.53 220.00 554.37 659.26 440.00 9.54 220.00 554.37 659.26 440.00 9.55 220.00 554.37 659.26 440.00 9.56 220.00 554.37 659.26 440.00 9.57 220.00 554.37 659.26 440.00 110.00 9.58 220.00 554.37 659.26 440.00 110.00 9.59 220.00 554.37 659.26 440.00 110.00 9.60 220.00 554.37 440.00 110.00 9.61 220.00 440.00 110.00 369.99 9.62 220.00 110.00 369.99 9.63 220.00 110.00 369.99 9.64 220.00 110.00 369.99 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10.00 220.00 554.37 329.63 10.01 220.00 554.37 329.63 98.00 10.02 220.00 554.37 329.63 98.00 10.03 220.00 554.37 329.63 98.00 311.13 10.04 220.00 554.37 329.63 98.00 311.13 10.05 220.00 554.37 329.63 98.00 311.13 10.06 220.00 554.37 98.00 311.13 10.07 220.00 554.37 98.00 311.13 10.08 220.00 98.00 311.13 10.09 220.00 98.00 311.13 10.10 220.00 98.00 311.13 10.11 220.00 98.00 311.13 10.12 220.00 311.13 10.13 220.00 311.13 10.14 220.00 311.13 10.15 220.00 311.13 10.16 220.00 311.13 10.17 220.00 311.13 10.18 220.00 311.13 10.19 220.00 311.13 10.20 220.00 311.13 10.21 220.00 311.13 10.22 220.00 311.13 10.23 220.00 311.13 110.00 10.24 220.00 311.13 110.00 10.25 220.00 311.13 110.00 10.26 220.00 311.13 110.00 10.27 220.00 311.13 110.00 10.28 220.00 311.13 110.00 10.29 220.00 311.13 110.00 10.30 220.00 311.13 110.00 10.31 220.00 311.13 110.00 10.32 220.00 311.13 110.00 329.63 10.33 220.00 311.13 110.00 329.63 10.34 220.00 311.13 110.00 329.63 10.35 220.00 311.13 110.00 329.63 10.36 220.00 110.00 329.63 10.37 220.00 329.63 10.38 220.00 329.63 10.39 220.00 329.63 10.40 220.00 329.63 10.41 220.00 329.63 10.42 220.00 329.63 10.43 220.00 329.63 10.44 220.00 329.63 10.45 220.00 329.63 138.59 10.46 220.00 329.63 138.59 10.47 220.00 329.63 138.59 10.48 220.00 329.63 138.59 10.49 220.00 329.63 138.59 10.50 220.00 329.63 138.59 10.51 220.00 329.63 138.59 10.52 220.00 329.63 138.59 10.53 220.00 329.63 138.59 10.54 220.00 329.63 138.59 10.55 220.00 329.63 138.59 10.56 220.00 329.63 10.57 220.00 329.63 10.58 220.00 329.63 10.59 220.00 329.63 10.60 220.00 329.63 10.61 220.00 329.63 10.62 220.00 329.63 87.31 10.63 220.00 329.63 87.31 10.64 220.00 329.63 87.31 10.65 220.00 329.63 87.31 10.66 220.00 329.63 87.31 10.67 220.00 329.63 87.31 246.94 10.68 220.00 329.63 87.31 246.94 10.69 220.00 329.63 87.31 246.94 10.70 220.00 329.63 87.31 246.94 10.71 220.00 87.31 246.94 10.72 220.00 87.31 246.94 10.73 220.00 87.31 246.94 10.74 220.00 87.31 246.94 10.75 220.00 87.31 246.94 10.76 220.00 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293.66 329.63 220.00 110.00 1.70 293.66 329.63 220.00 110.00 1.71 293.66 329.63 220.00 110.00 1.72 293.66 329.63 220.00 110.00 1.73 293.66 329.63 220.00 110.00 1.74 293.66 329.63 220.00 110.00 1.75 293.66 329.63 220.00 110.00 1.76 293.66 329.63 220.00 110.00 1.77 293.66 329.63 220.00 110.00 1.78 293.66 329.63 220.00 110.00 1.79 293.66 329.63 220.00 110.00 1.80 293.66 329.63 220.00 110.00 1.81 293.66 329.63 220.00 110.00 1.82 293.66 329.63 220.00 1.83 293.66 329.63 220.00 1.84 293.66 329.63 220.00 1.85 293.66 329.63 220.00 1.86 293.66 329.63 220.00 1.87 293.66 329.63 220.00 1.88 293.66 329.63 220.00 1.89 293.66 329.63 220.00 1.90 293.66 329.63 220.00 98.00 1.91 293.66 329.63 220.00 98.00 1.92 293.66 329.63 220.00 98.00 1.93 293.66 329.63 220.00 98.00 1.94 329.63 220.00 98.00 277.18 1.95 329.63 220.00 98.00 277.18 1.96 329.63 220.00 98.00 277.18 1.97 329.63 220.00 277.18 1.98 329.63 220.00 277.18 1.99 329.63 220.00 277.18 2.00 329.63 220.00 277.18 2.01 329.63 220.00 277.18 2.02 329.63 277.18 2.03 329.63 277.18 2.04 329.63 277.18 87.31 2.05 329.63 277.18 87.31 2.06 329.63 277.18 87.31 2.07 329.63 277.18 87.31 2.08 329.63 277.18 87.31 2.09 329.63 277.18 87.31 2.10 329.63 277.18 87.31 2.11 329.63 277.18 2.12 329.63 277.18 2.13 329.63 277.18 2.14 329.63 277.18 2.15 329.63 277.18 2.16 329.63 277.18 82.41 2.17 329.63 277.18 82.41 2.18 329.63 277.18 82.41 2.19 329.63 277.18 82.41 2.20 329.63 277.18 82.41 2.21 277.18 82.41 2.22 277.18 82.41 2.23 277.18 2.24 277.18 2.25 277.18 2.26 2.27 2.28 349.23 2.29 349.23 2.30 349.23 73.42 2.31 349.23 73.42 2.32 349.23 73.42 2.33 349.23 73.42 293.66 220.00 2.34 349.23 73.42 293.66 220.00 2.35 349.23 73.42 293.66 220.00 2.36 73.42 293.66 220.00 2.37 73.42 293.66 220.00 2.38 73.42 293.66 220.00 2.39 73.42 293.66 2.40 73.42 293.66 2.41 73.42 293.66 2.42 73.42 293.66 2.43 73.42 293.66 2.44 73.42 293.66 2.45 73.42 293.66 2.46 73.42 293.66 2.47 73.42 293.66 2.48 73.42 293.66 2.49 73.42 293.66 2.50 73.42 293.66 2.51 73.42 293.66 2.52 73.42 293.66 2.53 73.42 293.66 2.54 73.42 293.66 2.55 73.42 293.66 2.56 73.42 293.66 2.57 73.42 293.66 2.58 73.42 293.66 2.59 73.42 293.66 2.60 73.42 293.66 2.61 73.42 293.66 2.62 73.42 293.66 2.63 73.42 293.66 2.64 73.42 293.66 2.65 73.42 293.66 2.66 73.42 293.66 2.67 73.42 293.66 2.68 73.42 293.66 293.66 293.66 2.69 73.42 293.66 293.66 293.66 2.70 73.42 293.66 293.66 293.66 2.71 73.42 293.66 293.66 293.66 2.72 73.42 293.66 293.66 293.66 2.73 73.42 293.66 293.66 293.66 2.74 73.42 293.66 293.66 293.66 2.75 293.66 293.66 2.76 293.66 2.77 293.66 2.78 293.66 2.79 293.66 2.80 293.66 261.63 2.81 293.66 261.63 2.82 293.66 261.63 2.83 293.66 261.63 2.84 293.66 261.63 2.85 293.66 261.63 2.86 293.66 261.63 2.87 293.66 261.63 2.88 293.66 261.63 2.89 293.66 2.90 293.66 2.91 293.66 2.92 293.66 233.08 2.93 293.66 233.08 2.94 293.66 233.08 2.95 293.66 233.08 2.96 293.66 233.08 2.97 293.66 233.08 2.98 293.66 233.08 2.99 293.66 3.00 293.66 3.01 293.66 3.02 293.66 3.03 293.66 3.04 293.66 110.00 220.00 3.05 293.66 110.00 220.00 3.06 293.66 110.00 220.00 3.07 293.66 110.00 220.00 3.08 293.66 110.00 220.00 3.09 293.66 110.00 220.00 3.10 293.66 110.00 220.00 329.63 3.11 293.66 110.00 220.00 329.63 3.12 293.66 110.00 220.00 329.63 3.13 293.66 110.00 220.00 329.63 3.14 293.66 110.00 220.00 329.63 3.15 293.66 110.00 220.00 329.63 3.16 293.66 110.00 220.00 329.63 3.17 293.66 110.00 220.00 329.63 3.18 293.66 110.00 220.00 329.63 3.19 293.66 110.00 220.00 329.63 3.20 293.66 110.00 220.00 329.63 3.21 293.66 110.00 220.00 329.63 3.22 293.66 110.00 220.00 329.63 3.23 293.66 110.00 220.00 329.63 3.24 293.66 110.00 220.00 329.63 3.25 293.66 110.00 220.00 329.63 3.26 293.66 110.00 220.00 329.63 3.27 293.66 110.00 220.00 329.63 3.28 293.66 110.00 220.00 329.63 3.29 293.66 110.00 220.00 329.63 3.30 293.66 110.00 220.00 329.63 3.31 293.66 110.00 220.00 329.63 3.32 293.66 110.00 220.00 329.63 3.33 293.66 110.00 220.00 329.63 3.34 293.66 110.00 220.00 329.63 3.35 293.66 110.00 220.00 329.63 3.36 293.66 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82.41 3.75 277.18 82.41 3.76 277.18 82.41 3.77 277.18 3.78 3.79 3.80 3.81 3.82 3.83 3.84 3.85 73.42 3.86 73.42 261.63 3.87 73.42 261.63 369.99 3.88 73.42 261.63 369.99 3.89 73.42 261.63 369.99 3.90 73.42 261.63 369.99 3.91 73.42 261.63 369.99 220.00 3.92 73.42 261.63 369.99 220.00 3.93 73.42 261.63 369.99 220.00 3.94 73.42 261.63 369.99 220.00 3.95 73.42 261.63 369.99 220.00 3.96 73.42 261.63 369.99 220.00 3.97 73.42 261.63 369.99 220.00 3.98 73.42 261.63 369.99 220.00 3.99 73.42 261.63 369.99 220.00 4.00 73.42 261.63 369.99 220.00 4.01 73.42 261.63 369.99 220.00 4.02 73.42 261.63 369.99 220.00 4.03 73.42 261.63 369.99 220.00 4.04 73.42 261.63 369.99 220.00 4.05 73.42 261.63 369.99 220.00 4.06 73.42 261.63 369.99 220.00 4.07 73.42 261.63 369.99 220.00 4.08 73.42 261.63 369.99 220.00 4.09 73.42 261.63 369.99 220.00 4.10 73.42 261.63 369.99 220.00 4.11 73.42 261.63 369.99 220.00 4.12 73.42 261.63 369.99 220.00 4.13 73.42 261.63 369.99 220.00 4.14 73.42 261.63 369.99 220.00 4.15 73.42 369.99 220.00 4.16 73.42 369.99 220.00 4.17 73.42 369.99 220.00 4.18 73.42 369.99 220.00 4.19 73.42 369.99 220.00 4.20 73.42 369.99 220.00 4.21 73.42 369.99 220.00 4.22 73.42 369.99 220.00 246.94 4.23 73.42 369.99 220.00 246.94 4.24 73.42 369.99 220.00 246.94 4.25 73.42 369.99 220.00 246.94 4.26 73.42 369.99 220.00 246.94 4.27 73.42 369.99 220.00 246.94 4.28 73.42 369.99 220.00 246.94 4.29 73.42 369.99 220.00 246.94 4.30 73.42 369.99 220.00 4.31 73.42 369.99 220.00 4.32 73.42 369.99 220.00 4.33 73.42 369.99 220.00 4.34 73.42 369.99 220.00 4.35 73.42 369.99 220.00 4.36 73.42 369.99 220.00 261.63 4.37 73.42 369.99 220.00 261.63 4.38 73.42 369.99 220.00 261.63 4.39 73.42 369.99 220.00 261.63 4.40 73.42 369.99 220.00 261.63 4.41 73.42 369.99 220.00 261.63 4.42 73.42 369.99 220.00 261.63 4.43 73.42 369.99 220.00 261.63 4.44 73.42 369.99 220.00 261.63 4.45 73.42 369.99 220.00 261.63 4.46 73.42 369.99 220.00 261.63 4.47 73.42 369.99 220.00 4.48 73.42 369.99 220.00 4.49 73.42 369.99 220.00 293.66 4.50 73.42 369.99 220.00 293.66 4.51 73.42 369.99 220.00 293.66 4.52 73.42 369.99 220.00 293.66 4.53 73.42 369.99 220.00 293.66 4.54 73.42 369.99 220.00 293.66 4.55 369.99 220.00 293.66 4.56 369.99 220.00 4.57 369.99 220.00 4.58 4.59 4.60 4.61 4.62 311.13 4.63 311.13 233.08 4.64 311.13 233.08 392.00 4.65 311.13 233.08 392.00 4.66 311.13 233.08 392.00 4.67 311.13 233.08 392.00 4.68 311.13 233.08 392.00 4.69 311.13 233.08 392.00 4.70 311.13 233.08 392.00 4.71 311.13 233.08 392.00 4.72 311.13 233.08 392.00 4.73 311.13 233.08 392.00 4.74 311.13 233.08 392.00 4.75 311.13 233.08 392.00 4.76 311.13 233.08 392.00 4.77 311.13 233.08 392.00 4.78 311.13 233.08 392.00 4.79 311.13 233.08 392.00 4.80 311.13 233.08 392.00 4.81 311.13 233.08 392.00 4.82 311.13 233.08 392.00 4.83 311.13 233.08 392.00 4.84 311.13 233.08 392.00 4.85 311.13 233.08 392.00 4.86 311.13 233.08 392.00 4.87 311.13 233.08 392.00 4.88 311.13 233.08 392.00 4.89 311.13 233.08 392.00 4.90 311.13 233.08 392.00 4.91 311.13 233.08 392.00 4.92 311.13 233.08 392.00 4.93 311.13 233.08 392.00 4.94 311.13 233.08 392.00 4.95 311.13 233.08 392.00 4.96 311.13 233.08 392.00 98.00 4.97 311.13 233.08 392.00 98.00 4.98 311.13 233.08 392.00 98.00 4.99 311.13 233.08 392.00 98.00 5.00 311.13 233.08 392.00 98.00 5.01 311.13 233.08 392.00 98.00 5.02 311.13 233.08 392.00 98.00 5.03 311.13 233.08 392.00 98.00 5.04 311.13 233.08 392.00 98.00 5.05 311.13 233.08 392.00 98.00 5.06 311.13 233.08 392.00 98.00 5.07 311.13 233.08 392.00 5.08 311.13 233.08 392.00 5.09 311.13 233.08 392.00 5.10 311.13 233.08 392.00 5.11 311.13 233.08 392.00 5.12 311.13 233.08 392.00 5.13 311.13 233.08 392.00 110.00 5.14 311.13 233.08 392.00 110.00 5.15 311.13 233.08 392.00 110.00 5.16 311.13 233.08 392.00 110.00 5.17 311.13 233.08 392.00 110.00 5.18 311.13 233.08 392.00 110.00 5.19 311.13 233.08 392.00 110.00 5.20 311.13 233.08 392.00 5.21 311.13 233.08 392.00 5.22 311.13 233.08 392.00 5.23 311.13 233.08 392.00 5.24 311.13 233.08 392.00 5.25 233.08 392.00 5.26 233.08 392.00 116.54 5.27 233.08 392.00 116.54 5.28 233.08 392.00 116.54 5.29 233.08 392.00 116.54 5.30 233.08 392.00 116.54 5.31 233.08 392.00 116.54 5.32 233.08 116.54 5.33 233.08 116.54 5.34 116.54 5.35 116.54 5.36 5.37 5.38 5.39 5.40 82.41 5.41 82.41 246.94 5.42 82.41 246.94 5.43 82.41 246.94 415.30 5.44 82.41 246.94 415.30 293.66 5.45 82.41 246.94 415.30 293.66 5.46 82.41 246.94 415.30 293.66 5.47 82.41 246.94 415.30 293.66 5.48 82.41 246.94 415.30 293.66 5.49 82.41 246.94 415.30 293.66 5.50 82.41 246.94 415.30 293.66 5.51 82.41 246.94 415.30 293.66 5.52 82.41 246.94 415.30 293.66 5.53 82.41 246.94 415.30 293.66 5.54 82.41 246.94 415.30 293.66 5.55 82.41 246.94 415.30 293.66 5.56 82.41 246.94 415.30 293.66 5.57 82.41 246.94 415.30 293.66 5.58 82.41 246.94 415.30 293.66 5.59 82.41 246.94 415.30 293.66 5.60 82.41 246.94 415.30 293.66 5.61 82.41 246.94 415.30 293.66 5.62 82.41 246.94 415.30 293.66 5.63 82.41 246.94 415.30 293.66 5.64 82.41 246.94 415.30 293.66 5.65 82.41 246.94 415.30 293.66 5.66 82.41 246.94 415.30 293.66 5.67 82.41 246.94 415.30 293.66 5.68 82.41 246.94 415.30 293.66 5.69 82.41 246.94 415.30 293.66 5.70 82.41 246.94 293.66 5.71 82.41 246.94 293.66 5.72 82.41 246.94 293.66 5.73 82.41 246.94 293.66 5.74 82.41 246.94 293.66 5.75 82.41 246.94 293.66 329.63 5.76 82.41 246.94 293.66 329.63 5.77 82.41 246.94 293.66 329.63 5.78 82.41 246.94 293.66 329.63 5.79 82.41 246.94 293.66 329.63 5.80 82.41 246.94 293.66 329.63 5.81 82.41 246.94 293.66 329.63 5.82 82.41 246.94 293.66 329.63 5.83 82.41 246.94 293.66 329.63 5.84 82.41 246.94 293.66 5.85 82.41 246.94 293.66 5.86 82.41 246.94 293.66 5.87 82.41 246.94 293.66 5.88 82.41 246.94 293.66 5.89 82.41 246.94 293.66 5.90 82.41 246.94 293.66 5.91 82.41 246.94 293.66 369.99 5.92 82.41 246.94 293.66 369.99 5.93 82.41 246.94 293.66 369.99 5.94 82.41 246.94 293.66 369.99 5.95 82.41 246.94 293.66 369.99 5.96 82.41 246.94 293.66 369.99 5.97 82.41 246.94 293.66 369.99 5.98 82.41 246.94 293.66 5.99 82.41 246.94 293.66 6.00 82.41 246.94 293.66 6.01 82.41 246.94 293.66 6.02 82.41 246.94 293.66 6.03 82.41 246.94 293.66 6.04 82.41 246.94 293.66 6.05 82.41 246.94 293.66 415.30 6.06 82.41 293.66 415.30 6.07 82.41 293.66 415.30 6.08 82.41 293.66 415.30 6.09 82.41 415.30 6.10 82.41 6.11 6.12 6.13 6.14 6.15 6.16 6.17 261.63 6.18 261.63 440.00 349.23 220.00 6.19 261.63 440.00 349.23 220.00 349.23 6.20 261.63 440.00 349.23 220.00 349.23 6.21 261.63 440.00 349.23 220.00 349.23 6.22 261.63 440.00 349.23 220.00 349.23 6.23 261.63 440.00 349.23 220.00 349.23 6.24 261.63 440.00 349.23 220.00 349.23 6.25 261.63 440.00 349.23 220.00 349.23 6.26 261.63 440.00 349.23 220.00 349.23 6.27 261.63 440.00 349.23 220.00 349.23 6.28 261.63 440.00 349.23 220.00 349.23 6.29 261.63 440.00 349.23 220.00 349.23 6.30 261.63 440.00 349.23 220.00 349.23 6.31 261.63 440.00 349.23 220.00 349.23 6.32 261.63 440.00 349.23 220.00 349.23 6.33 261.63 440.00 349.23 220.00 349.23 6.34 261.63 440.00 349.23 220.00 349.23 6.35 261.63 440.00 349.23 220.00 349.23 6.36 261.63 440.00 349.23 220.00 349.23 6.37 261.63 440.00 349.23 220.00 349.23 6.38 261.63 440.00 349.23 220.00 349.23 6.39 261.63 440.00 349.23 220.00 349.23 6.40 261.63 440.00 349.23 220.00 349.23 6.41 261.63 440.00 349.23 220.00 349.23 6.42 261.63 440.00 349.23 220.00 349.23 6.43 261.63 440.00 349.23 220.00 349.23 6.44 261.63 440.00 349.23 220.00 349.23 6.45 261.63 440.00 349.23 220.00 349.23 6.46 261.63 440.00 349.23 220.00 349.23 6.47 261.63 440.00 349.23 220.00 349.23 6.48 261.63 440.00 349.23 220.00 349.23 6.49 261.63 440.00 349.23 220.00 349.23 6.50 261.63 440.00 349.23 220.00 349.23 6.51 261.63 440.00 349.23 220.00 349.23 6.52 261.63 440.00 349.23 220.00 349.23 6.53 261.63 440.00 349.23 220.00 349.23 6.54 261.63 440.00 349.23 220.00 349.23 6.55 261.63 440.00 349.23 220.00 349.23 6.56 261.63 440.00 349.23 220.00 349.23 6.57 261.63 440.00 349.23 220.00 349.23 110.00 6.58 261.63 440.00 349.23 220.00 110.00 6.59 261.63 440.00 349.23 220.00 110.00 6.60 261.63 440.00 349.23 220.00 110.00 6.61 261.63 440.00 349.23 110.00 6.62 261.63 440.00 349.23 110.00 6.63 261.63 440.00 349.23 110.00 6.64 261.63 440.00 349.23 6.65 261.63 440.00 349.23 6.66 261.63 440.00 349.23 6.67 261.63 440.00 349.23 6.68 261.63 440.00 349.23 6.69 261.63 440.00 349.23 6.70 261.63 440.00 349.23 123.47 6.71 261.63 440.00 349.23 123.47 6.72 261.63 440.00 349.23 123.47 6.73 261.63 440.00 349.23 123.47 6.74 440.00 349.23 123.47 6.75 349.23 123.47 6.76 349.23 123.47 6.77 349.23 123.47 6.78 349.23 123.47 6.79 349.23 6.80 349.23 6.81 349.23 6.82 349.23 6.83 349.23 130.81 6.84 349.23 130.81 6.85 130.81 6.86 130.81 6.87 130.81 6.88 130.81 6.89 130.81 6.90 6.91 6.92 6.93 6.94 6.95 6.96 6.97 440.00 196.00 6.98 440.00 196.00 110.00 6.99 440.00 196.00 110.00 7.00 440.00 196.00 110.00 7.01 440.00 196.00 110.00 277.18 7.02 440.00 196.00 110.00 277.18 7.03 440.00 196.00 110.00 277.18 7.04 440.00 196.00 110.00 277.18 7.05 440.00 196.00 110.00 277.18 7.06 440.00 196.00 110.00 277.18 7.07 440.00 196.00 110.00 277.18 7.08 440.00 196.00 110.00 7.09 440.00 196.00 110.00 7.10 440.00 196.00 110.00 7.11 440.00 196.00 110.00 7.12 440.00 196.00 110.00 7.13 440.00 196.00 110.00 246.94 7.14 440.00 196.00 110.00 246.94 7.15 440.00 196.00 110.00 246.94 7.16 440.00 196.00 110.00 246.94 7.17 440.00 196.00 110.00 246.94 7.18 440.00 196.00 110.00 246.94 7.19 440.00 196.00 110.00 246.94 7.20 440.00 196.00 110.00 7.21 440.00 110.00 7.22 440.00 110.00 7.23 440.00 110.00 7.24 440.00 110.00 7.25 110.00 220.00 7.26 110.00 220.00 7.27 220.00 7.28 220.00 7.29 220.00 7.30 220.00 7.31 220.00 7.32 220.00 7.33 7.34 7.35 440.00 7.36 440.00 174.61 7.37 440.00 174.61 7.38 440.00 174.61 293.66 110.00 7.39 440.00 174.61 293.66 110.00 7.40 440.00 174.61 293.66 110.00 7.41 440.00 174.61 293.66 110.00 7.42 440.00 174.61 293.66 110.00 7.43 440.00 293.66 110.00 7.44 440.00 293.66 110.00 7.45 440.00 293.66 110.00 7.46 440.00 293.66 110.00 7.47 440.00 293.66 110.00 7.48 440.00 293.66 110.00 7.49 440.00 293.66 110.00 7.50 440.00 293.66 110.00 7.51 440.00 293.66 110.00 7.52 440.00 293.66 110.00 164.81 7.53 440.00 293.66 110.00 164.81 7.54 440.00 293.66 110.00 164.81 7.55 440.00 293.66 110.00 164.81 7.56 440.00 293.66 110.00 164.81 7.57 440.00 293.66 110.00 164.81 7.58 440.00 293.66 110.00 164.81 7.59 440.00 293.66 110.00 164.81 7.60 440.00 293.66 110.00 164.81 7.61 440.00 293.66 110.00 164.81 146.83 7.62 440.00 293.66 110.00 164.81 146.83 7.63 440.00 110.00 164.81 146.83 7.64 440.00 164.81 146.83 7.65 440.00 146.83 7.66 440.00 146.83 7.67 146.83 7.68 146.83 7.69 146.83 7.70 7.71 7.72 7.73 7.74 7.75 440.00 7.76 440.00 164.81 7.77 440.00 164.81 110.00 7.78 440.00 164.81 110.00 293.66 7.79 440.00 164.81 110.00 293.66 7.80 440.00 164.81 110.00 293.66 7.81 440.00 164.81 110.00 293.66 7.82 440.00 164.81 110.00 293.66 7.83 440.00 164.81 110.00 293.66 7.84 440.00 164.81 110.00 293.66 7.85 440.00 164.81 110.00 293.66 7.86 440.00 110.00 293.66 7.87 440.00 110.00 293.66 7.88 440.00 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587.33 8.75 293.66 146.83 146.83 587.33 293.66 587.33 8.76 293.66 146.83 146.83 587.33 293.66 587.33 8.77 293.66 146.83 146.83 587.33 293.66 587.33 8.78 293.66 146.83 146.83 587.33 293.66 587.33 8.79 293.66 146.83 146.83 587.33 293.66 587.33 8.80 293.66 146.83 146.83 587.33 293.66 587.33 8.81 293.66 146.83 146.83 587.33 293.66 587.33 8.82 293.66 146.83 146.83 587.33 293.66 587.33 8.83 293.66 146.83 146.83 587.33 293.66 587.33 8.84 293.66 146.83 146.83 587.33 293.66 587.33 8.85 293.66 146.83 146.83 587.33 293.66 587.33 8.86 293.66 146.83 146.83 587.33 293.66 587.33 8.87 293.66 146.83 146.83 587.33 293.66 587.33 8.88 293.66 146.83 146.83 587.33 293.66 587.33 8.89 293.66 146.83 146.83 587.33 293.66 587.33 8.90 293.66 146.83 146.83 587.33 293.66 587.33 8.91 293.66 146.83 146.83 587.33 293.66 587.33 8.92 146.83 146.83 587.33 293.66 587.33 8.93 146.83 587.33 293.66 587.33 8.94 146.83 587.33 293.66 587.33 8.95 146.83 587.33 293.66 587.33 8.96 146.83 587.33 293.66 587.33 8.97 146.83 587.33 293.66 587.33 8.98 146.83 587.33 293.66 587.33 8.99 146.83 587.33 293.66 587.33 9.00 146.83 587.33 293.66 587.33 9.01 146.83 587.33 293.66 587.33 9.02 146.83 587.33 293.66 587.33 587.33 9.03 146.83 587.33 293.66 587.33 587.33 9.04 146.83 587.33 293.66 587.33 587.33 9.05 146.83 587.33 293.66 587.33 587.33 9.06 146.83 587.33 293.66 587.33 587.33 9.07 146.83 587.33 293.66 587.33 587.33 9.08 146.83 587.33 293.66 587.33 587.33 9.09 146.83 587.33 293.66 587.33 9.10 146.83 587.33 293.66 587.33 9.11 587.33 293.66 587.33 9.12 587.33 293.66 587.33 9.13 587.33 293.66 587.33 9.14 587.33 293.66 587.33 9.15 587.33 293.66 587.33 9.16 587.33 293.66 587.33 9.17 587.33 293.66 587.33 523.25 9.18 587.33 293.66 587.33 523.25 9.19 587.33 293.66 587.33 523.25 9.20 587.33 293.66 587.33 523.25 9.21 587.33 293.66 587.33 523.25 9.22 587.33 293.66 587.33 523.25 9.23 587.33 293.66 587.33 523.25 9.24 587.33 293.66 587.33 9.25 587.33 293.66 587.33 9.26 587.33 293.66 587.33 9.27 587.33 293.66 587.33 9.28 587.33 293.66 587.33 9.29 587.33 293.66 587.33 9.30 587.33 293.66 587.33 466.16 9.31 587.33 293.66 587.33 466.16 9.32 293.66 587.33 466.16 9.33 293.66 587.33 466.16 9.34 293.66 587.33 466.16 9.35 293.66 587.33 466.16 9.36 293.66 587.33 9.37 293.66 587.33 9.38 293.66 587.33 659.26 9.39 293.66 587.33 659.26 9.40 293.66 587.33 659.26 220.00 9.41 293.66 587.33 659.26 220.00 440.00 9.42 293.66 587.33 659.26 220.00 440.00 9.43 293.66 587.33 659.26 220.00 440.00 9.44 293.66 587.33 659.26 220.00 440.00 9.45 293.66 587.33 659.26 220.00 440.00 9.46 293.66 587.33 659.26 220.00 440.00 9.47 293.66 587.33 659.26 220.00 440.00 9.48 293.66 587.33 659.26 220.00 440.00 9.49 293.66 587.33 659.26 220.00 440.00 9.50 293.66 587.33 659.26 220.00 440.00 9.51 293.66 587.33 659.26 220.00 440.00 9.52 293.66 587.33 659.26 220.00 440.00 9.53 293.66 587.33 659.26 220.00 440.00 9.54 293.66 587.33 659.26 220.00 440.00 9.55 293.66 587.33 659.26 220.00 440.00 9.56 293.66 587.33 659.26 220.00 440.00 9.57 293.66 587.33 659.26 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554.37 9.91 659.26 440.00 554.37 9.92 659.26 440.00 554.37 9.93 659.26 440.00 554.37 9.94 659.26 440.00 554.37 174.61 9.95 659.26 440.00 554.37 174.61 9.96 659.26 440.00 554.37 174.61 9.97 659.26 440.00 554.37 174.61 9.98 659.26 440.00 554.37 174.61 9.99 659.26 440.00 554.37 174.61 10.00 659.26 440.00 554.37 174.61 10.01 659.26 440.00 554.37 174.61 10.02 659.26 440.00 554.37 10.03 659.26 440.00 554.37 10.04 659.26 440.00 554.37 10.05 440.00 554.37 10.06 440.00 554.37 164.81 10.07 440.00 554.37 164.81 10.08 440.00 554.37 164.81 10.09 440.00 554.37 164.81 10.10 440.00 554.37 164.81 10.11 440.00 554.37 164.81 10.12 440.00 554.37 164.81 10.13 440.00 10.14 440.00 10.15 440.00 698.46 10.16 440.00 698.46 10.17 440.00 698.46 10.18 440.00 698.46 587.33 10.19 440.00 698.46 587.33 146.83 10.20 440.00 698.46 587.33 146.83 10.21 698.46 587.33 146.83 10.22 698.46 587.33 146.83 10.23 698.46 587.33 146.83 10.24 698.46 587.33 146.83 10.25 698.46 587.33 146.83 10.26 698.46 587.33 146.83 10.27 698.46 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11.01 587.33 220.00 659.26 440.00 11.02 587.33 220.00 659.26 440.00 11.03 587.33 220.00 659.26 440.00 11.04 587.33 220.00 659.26 440.00 11.05 587.33 220.00 659.26 440.00 11.06 587.33 220.00 659.26 440.00 11.07 587.33 220.00 659.26 440.00 11.08 587.33 220.00 659.26 440.00 11.09 587.33 220.00 659.26 440.00 11.10 587.33 220.00 659.26 440.00 11.11 587.33 220.00 659.26 440.00 11.12 587.33 220.00 659.26 440.00 11.13 587.33 220.00 659.26 440.00 11.14 587.33 220.00 659.26 440.00 11.15 220.00 659.26 440.00 11.16 220.00 659.26 440.00 11.17 220.00 659.26 440.00 11.18 220.00 659.26 440.00 11.19 220.00 659.26 440.00 11.20 220.00 659.26 440.00 11.21 220.00 659.26 440.00 11.22 220.00 659.26 440.00 11.23 659.26 440.00 11.24 659.26 440.00 11.25 659.26 440.00 11.26 659.26 440.00 11.27 659.26 440.00 11.28 659.26 440.00 11.29 659.26 440.00 11.30 659.26 440.00 11.31 659.26 440.00 11.32 659.26 440.00 11.33 659.26 440.00 196.00 11.34 659.26 440.00 196.00 11.35 659.26 440.00 196.00 554.37 11.36 659.26 440.00 196.00 554.37 11.37 659.26 440.00 196.00 554.37 11.38 659.26 440.00 196.00 554.37 11.39 659.26 440.00 196.00 554.37 11.40 659.26 440.00 554.37 11.41 659.26 440.00 554.37 11.42 659.26 440.00 554.37 11.43 659.26 440.00 554.37 11.44 659.26 440.00 554.37 11.45 659.26 440.00 554.37 11.46 659.26 440.00 554.37 11.47 659.26 440.00 554.37 174.61 11.48 659.26 440.00 554.37 174.61 11.49 659.26 440.00 554.37 174.61 11.50 659.26 554.37 174.61 11.51 659.26 554.37 174.61 11.52 659.26 554.37 174.61 11.53 659.26 554.37 174.61 11.54 659.26 554.37 174.61 11.55 554.37 11.56 554.37 11.57 554.37 11.58 554.37 11.59 554.37 164.81 11.60 554.37 164.81 11.61 554.37 164.81 11.62 554.37 164.81 11.63 554.37 164.81 11.64 164.81 11.65 164.81 11.66 164.81 11.67 164.81 11.68 11.69 739.99 11.70 739.99 523.25 11.71 739.99 523.25 11.72 739.99 523.25 11.73 739.99 523.25 146.83 440.00 11.74 739.99 523.25 146.83 440.00 11.75 739.99 523.25 146.83 440.00 11.76 739.99 523.25 146.83 440.00 11.77 739.99 523.25 146.83 440.00 11.78 739.99 523.25 146.83 440.00 11.79 739.99 523.25 146.83 440.00 11.80 739.99 523.25 146.83 440.00 11.81 739.99 523.25 146.83 440.00 11.82 739.99 523.25 146.83 440.00 11.83 739.99 523.25 146.83 440.00 11.84 739.99 523.25 146.83 440.00 11.85 739.99 523.25 146.83 440.00 11.86 739.99 523.25 146.83 440.00 11.87 739.99 523.25 146.83 440.00 11.88 739.99 523.25 146.83 440.00 11.89 739.99 523.25 146.83 440.00 11.90 739.99 523.25 146.83 440.00 11.91 739.99 523.25 146.83 440.00 11.92 739.99 523.25 146.83 440.00 11.93 739.99 523.25 146.83 440.00 11.94 739.99 523.25 146.83 440.00 11.95 739.99 523.25 146.83 440.00 11.96 739.99 523.25 146.83 440.00 11.97 739.99 523.25 146.83 440.00 11.98 739.99 523.25 146.83 440.00 11.99 739.99 146.83 440.00 12.00 739.99 146.83 440.00 12.01 739.99 146.83 440.00 12.02 739.99 146.83 440.00 12.03 739.99 146.83 440.00 12.04 739.99 146.83 440.00 12.05 739.99 146.83 440.00 12.06 739.99 146.83 440.00 12.07 739.99 146.83 440.00 493.88 12.08 739.99 146.83 440.00 493.88 12.09 739.99 146.83 440.00 493.88 12.10 739.99 146.83 440.00 493.88 12.11 739.99 146.83 440.00 493.88 12.12 739.99 146.83 440.00 493.88 12.13 739.99 146.83 440.00 493.88 12.14 739.99 146.83 440.00 493.88 12.15 739.99 146.83 440.00 12.16 739.99 146.83 440.00 12.17 739.99 146.83 440.00 12.18 739.99 440.00 12.19 739.99 440.00 12.20 739.99 440.00 12.21 739.99 440.00 12.22 739.99 440.00 523.25 12.23 739.99 440.00 523.25 12.24 739.99 440.00 523.25 12.25 739.99 440.00 523.25 12.26 739.99 440.00 523.25 12.27 739.99 440.00 523.25 12.28 739.99 440.00 523.25 12.29 739.99 440.00 523.25 12.30 739.99 440.00 523.25 12.31 739.99 440.00 12.32 739.99 440.00 12.33 739.99 440.00 12.34 739.99 440.00 12.35 739.99 440.00 12.36 739.99 440.00 587.33 12.37 739.99 440.00 587.33 12.38 739.99 440.00 587.33 12.39 739.99 587.33 12.40 739.99 587.33 12.41 739.99 587.33 12.42 739.99 12.43 12.44 12.45 12.46 12.47 622.25 12.48 622.25 466.16 12.49 622.25 466.16 98.00 783.99 12.50 622.25 466.16 98.00 783.99 12.51 622.25 466.16 98.00 783.99 12.52 622.25 466.16 98.00 783.99 12.53 622.25 466.16 98.00 783.99 12.54 622.25 466.16 98.00 783.99 12.55 622.25 466.16 98.00 783.99 12.56 622.25 466.16 98.00 783.99 12.57 622.25 466.16 98.00 783.99 12.58 622.25 466.16 98.00 783.99 12.59 622.25 466.16 98.00 783.99 12.60 622.25 466.16 98.00 783.99 12.61 622.25 466.16 98.00 783.99 12.62 622.25 466.16 98.00 783.99 12.63 622.25 466.16 98.00 783.99 12.64 622.25 466.16 98.00 783.99 12.65 622.25 466.16 98.00 783.99 12.66 622.25 466.16 98.00 783.99 12.67 622.25 466.16 98.00 783.99 12.68 622.25 466.16 98.00 783.99 12.69 622.25 466.16 98.00 783.99 12.70 622.25 466.16 98.00 783.99 12.71 622.25 466.16 98.00 783.99 12.72 622.25 466.16 98.00 783.99 12.73 622.25 466.16 98.00 783.99 12.74 622.25 466.16 98.00 783.99 12.75 622.25 466.16 98.00 783.99 12.76 622.25 466.16 98.00 783.99 12.77 622.25 466.16 98.00 783.99 12.78 622.25 466.16 783.99 12.79 622.25 466.16 783.99 12.80 622.25 466.16 783.99 12.81 622.25 466.16 783.99 12.82 622.25 466.16 783.99 12.83 622.25 466.16 783.99 12.84 622.25 466.16 783.99 12.85 622.25 466.16 783.99 12.86 622.25 466.16 783.99 12.87 622.25 466.16 783.99 12.88 622.25 466.16 783.99 12.89 622.25 466.16 783.99 12.90 622.25 466.16 783.99 196.00 12.91 622.25 466.16 783.99 196.00 12.92 622.25 466.16 783.99 196.00 12.93 622.25 466.16 783.99 196.00 12.94 622.25 466.16 783.99 196.00 12.95 622.25 466.16 783.99 196.00 12.96 622.25 466.16 783.99 196.00 12.97 622.25 466.16 783.99 12.98 622.25 466.16 783.99 12.99 622.25 466.16 783.99 13.00 622.25 466.16 783.99 13.01 622.25 466.16 783.99 13.02 622.25 466.16 783.99 13.03 622.25 466.16 783.99 13.04 622.25 466.16 783.99 220.00 13.05 622.25 466.16 783.99 220.00 13.06 622.25 466.16 783.99 220.00 13.07 622.25 466.16 783.99 220.00 13.08 622.25 466.16 783.99 220.00 13.09 622.25 466.16 783.99 220.00 13.10 622.25 783.99 13.11 622.25 783.99 13.12 622.25 783.99 13.13 622.25 783.99 13.14 622.25 783.99 13.15 622.25 783.99 233.08 13.16 622.25 783.99 233.08 13.17 622.25 233.08 13.18 233.08 13.19 233.08 13.20 233.08 13.21 233.08 13.22 233.08 13.23 830.61 13.24 830.61 13.25 830.61 13.26 830.61 587.33 13.27 830.61 587.33 13.28 830.61 587.33 493.88 13.29 830.61 587.33 493.88 13.30 830.61 587.33 493.88 13.31 830.61 587.33 493.88 164.81 13.32 830.61 587.33 493.88 164.81 13.33 830.61 587.33 493.88 164.81 13.34 830.61 587.33 493.88 164.81 13.35 830.61 587.33 493.88 164.81 13.36 830.61 587.33 493.88 164.81 13.37 830.61 587.33 493.88 164.81 13.38 830.61 587.33 493.88 164.81 13.39 830.61 587.33 493.88 164.81 13.40 830.61 587.33 493.88 164.81 13.41 830.61 587.33 493.88 164.81 13.42 830.61 587.33 493.88 164.81 13.43 830.61 587.33 493.88 164.81 13.44 830.61 587.33 493.88 164.81 13.45 830.61 587.33 493.88 164.81 13.46 830.61 587.33 493.88 164.81 13.47 830.61 587.33 493.88 164.81 13.48 830.61 587.33 493.88 164.81 13.49 830.61 587.33 493.88 164.81 13.50 830.61 587.33 493.88 164.81 13.51 830.61 587.33 493.88 164.81 13.52 830.61 587.33 493.88 164.81 13.53 830.61 587.33 493.88 164.81 13.54 587.33 493.88 164.81 13.55 587.33 493.88 164.81 13.56 587.33 493.88 164.81 13.57 587.33 493.88 164.81 13.58 587.33 493.88 164.81 13.59 587.33 493.88 164.81 13.60 587.33 493.88 164.81 13.61 587.33 493.88 164.81 13.62 587.33 493.88 164.81 13.63 587.33 493.88 164.81 13.64 587.33 493.88 164.81 659.26 13.65 587.33 493.88 164.81 659.26 13.66 587.33 493.88 164.81 659.26 13.67 587.33 493.88 164.81 659.26 13.68 587.33 493.88 164.81 659.26 13.69 587.33 493.88 164.81 659.26 13.70 587.33 493.88 164.81 659.26 13.71 587.33 493.88 164.81 659.26 13.72 587.33 493.88 164.81 13.73 587.33 493.88 164.81 13.74 587.33 493.88 164.81 13.75 587.33 493.88 164.81 13.76 587.33 493.88 164.81 13.77 587.33 493.88 164.81 13.78 587.33 493.88 164.81 13.79 587.33 493.88 164.81 13.80 587.33 493.88 164.81 739.99 13.81 587.33 493.88 164.81 739.99 13.82 587.33 493.88 164.81 739.99 13.83 587.33 493.88 164.81 739.99 13.84 587.33 493.88 164.81 739.99 13.85 587.33 493.88 164.81 739.99 13.86 587.33 493.88 164.81 13.87 587.33 493.88 164.81 13.88 587.33 493.88 164.81 13.89 587.33 493.88 13.90 587.33 493.88 13.91 587.33 493.88 830.61 13.92 587.33 493.88 830.61 13.93 493.88 830.61 13.94 493.88 830.61 13.95 493.88 830.61 13.96 493.88 830.61 13.97 493.88 13.98 493.88 13.99 14.00 14.01 14.02 14.03 110.00 880.00 14.04 110.00 880.00 14.05 110.00 880.00 440.00 523.25 14.06 110.00 880.00 440.00 523.25 14.07 110.00 880.00 440.00 523.25 14.08 110.00 880.00 440.00 523.25 14.09 110.00 880.00 440.00 523.25 14.10 110.00 880.00 440.00 523.25 14.11 110.00 880.00 440.00 523.25 14.12 110.00 880.00 440.00 523.25 14.13 110.00 880.00 440.00 523.25 14.14 110.00 880.00 440.00 523.25 14.15 110.00 880.00 440.00 523.25 14.16 110.00 880.00 440.00 523.25 698.46 14.17 110.00 880.00 440.00 523.25 698.46 14.18 110.00 880.00 440.00 523.25 698.46 14.19 110.00 880.00 440.00 523.25 698.46 14.20 110.00 880.00 440.00 523.25 698.46 14.21 110.00 880.00 440.00 523.25 698.46 14.22 110.00 880.00 440.00 523.25 698.46 14.23 110.00 880.00 440.00 523.25 698.46 14.24 110.00 880.00 440.00 523.25 698.46 14.25 110.00 880.00 440.00 523.25 698.46 14.26 110.00 880.00 440.00 523.25 698.46 14.27 110.00 880.00 440.00 523.25 698.46 14.28 110.00 880.00 440.00 523.25 698.46 14.29 110.00 880.00 440.00 523.25 698.46 14.30 110.00 880.00 440.00 523.25 698.46 14.31 110.00 880.00 440.00 523.25 698.46 14.32 110.00 880.00 440.00 523.25 698.46 14.33 110.00 880.00 440.00 523.25 698.46 14.34 110.00 880.00 440.00 523.25 698.46 14.35 110.00 880.00 440.00 523.25 698.46 14.36 110.00 880.00 440.00 523.25 698.46 14.37 880.00 440.00 523.25 698.46 14.38 880.00 440.00 523.25 698.46 14.39 880.00 440.00 523.25 698.46 14.40 880.00 440.00 523.25 698.46 14.41 880.00 440.00 523.25 698.46 14.42 880.00 440.00 523.25 698.46 14.43 880.00 440.00 523.25 698.46 14.44 880.00 440.00 523.25 698.46 220.00 14.45 880.00 440.00 523.25 698.46 220.00 14.46 440.00 523.25 698.46 220.00 14.47 440.00 523.25 698.46 220.00 14.48 440.00 523.25 698.46 220.00 14.49 523.25 698.46 220.00 14.50 523.25 698.46 14.51 698.46 14.52 698.46 14.53 698.46 14.54 698.46 14.55 14.56 14.57 14.58 246.94 14.59 246.94 14.60 246.94 14.61 246.94 14.62 246.94 14.63 246.94 14.64 14.65 14.66 14.67 261.63 14.68 261.63 14.69 261.63 14.70 261.63 14.71 261.63 14.72 261.63 14.73 261.63 14.74 261.63 14.75 261.63 14.76 14.77 14.78 14.79 554.37 14.80 554.37 14.81 554.37 392.00 14.82 554.37 392.00 14.83 554.37 392.00 220.00 14.84 554.37 392.00 220.00 880.00 14.85 554.37 392.00 220.00 880.00 14.86 392.00 220.00 880.00 14.87 392.00 220.00 880.00 14.88 392.00 220.00 880.00 14.89 392.00 220.00 880.00 14.90 392.00 220.00 880.00 14.91 392.00 220.00 880.00 14.92 392.00 220.00 880.00 14.93 392.00 220.00 880.00 14.94 392.00 220.00 880.00 14.95 392.00 220.00 880.00 493.88 14.96 392.00 220.00 880.00 493.88 14.97 392.00 220.00 880.00 493.88 14.98 392.00 220.00 880.00 493.88 14.99 392.00 220.00 880.00 493.88 15.00 392.00 220.00 880.00 493.88 15.01 392.00 220.00 880.00 493.88 15.02 392.00 220.00 880.00 15.03 392.00 220.00 880.00 15.04 392.00 880.00 15.05 392.00 880.00 15.06 392.00 880.00 15.07 392.00 880.00 15.08 392.00 880.00 440.00 15.09 392.00 880.00 440.00 15.10 392.00 440.00 15.11 392.00 440.00 15.12 440.00 15.13 440.00 15.14 15.15 15.16 15.17 15.18 15.19 15.20 880.00 15.21 880.00 15.22 880.00 587.33 220.00 349.23 15.23 880.00 587.33 220.00 349.23 15.24 880.00 587.33 220.00 349.23 15.25 880.00 587.33 220.00 349.23 15.26 880.00 587.33 220.00 349.23 15.27 880.00 587.33 220.00 349.23 15.28 880.00 587.33 220.00 349.23 15.29 880.00 587.33 220.00 349.23 15.30 880.00 587.33 220.00 15.31 880.00 587.33 220.00 15.32 880.00 587.33 220.00 15.33 880.00 587.33 220.00 15.34 880.00 587.33 220.00 15.35 880.00 587.33 220.00 15.36 880.00 587.33 220.00 329.63 15.37 880.00 587.33 220.00 329.63 15.38 880.00 587.33 220.00 329.63 15.39 880.00 587.33 220.00 329.63 15.40 880.00 587.33 220.00 329.63 15.41 880.00 587.33 220.00 329.63 15.42 880.00 587.33 220.00 329.63 15.43 880.00 587.33 220.00 15.44 880.00 587.33 220.00 15.45 880.00 587.33 220.00 15.46 880.00 587.33 220.00 15.47 880.00 587.33 220.00 15.48 880.00 587.33 220.00 293.66 15.49 587.33 220.00 293.66 15.50 220.00 293.66 15.51 220.00 293.66 15.52 293.66 15.53 293.66 15.54 293.66 15.55 293.66 15.56 15.57 15.58 15.59 587.33 15.60 587.33 15.61 587.33 220.00 15.62 587.33 220.00 329.63 880.00 15.63 587.33 220.00 329.63 880.00 15.64 587.33 220.00 329.63 880.00 15.65 587.33 220.00 329.63 880.00 15.66 587.33 220.00 329.63 880.00 15.67 587.33 220.00 329.63 880.00 15.68 587.33 220.00 329.63 880.00 15.69 587.33 220.00 329.63 880.00 15.70 587.33 220.00 329.63 880.00 15.71 587.33 220.00 329.63 880.00 15.72 587.33 220.00 329.63 880.00 15.73 587.33 220.00 329.63 880.00 15.74 587.33 220.00 329.63 880.00 15.75 587.33 220.00 329.63 880.00 15.76 587.33 220.00 329.63 15.77 587.33 220.00 329.63 15.78 587.33 220.00 329.63 15.79 587.33 220.00 329.63 15.80 587.33 220.00 329.63 15.81 587.33 220.00 329.63 15.82 587.33 220.00 329.63 15.83 587.33 220.00 329.63 15.84 587.33 220.00 329.63 15.85 587.33 220.00 329.63 15.86 587.33 220.00 329.63 15.87 220.00 329.63 15.88 220.00 329.63 15.89 220.00 329.63 15.90 220.00 329.63 15.91 329.63 15.92 15.93 15.94 15.95 15.96 880.00 15.97 880.00 15.98 880.00 15.99 880.00 16.00 880.00 554.37 16.01 880.00 554.37 16.02 880.00 554.37 329.63 16.03 880.00 554.37 329.63 220.00 16.04 880.00 554.37 329.63 220.00 16.05 554.37 329.63 220.00 16.06 329.63 220.00 16.07 329.63 220.00 16.08 329.63 220.00 16.09 329.63 220.00 16.10 329.63 220.00 16.11 329.63 220.00 16.12 329.63 220.00 16.13 329.63 220.00 16.14 329.63 220.00 16.15 329.63 220.00 16.16 329.63 220.00 16.17 329.63 220.00 16.18 329.63 220.00 16.19 329.63 220.00 16.20 329.63 220.00 16.21 329.63 220.00 16.22 329.63 220.00 16.23 329.63 220.00 16.24 329.63 220.00 16.25 329.63 220.00 16.26 329.63 220.00 16.27 329.63 220.00 16.28 329.63 16.29 329.63 16.30 329.63 16.31 329.63 16.32 16.33 16.34 16.35 16.36 16.37 16.38 16.39 16.40 16.41 293.66 16.42 293.66 16.43 293.66 16.44 293.66 293.66 16.45 293.66 293.66 16.46 293.66 293.66 587.33 16.47 293.66 293.66 587.33 349.23 220.00 16.48 293.66 293.66 587.33 349.23 220.00 16.49 293.66 293.66 587.33 349.23 220.00 16.50 293.66 587.33 349.23 220.00 16.51 293.66 587.33 349.23 220.00 16.52 293.66 587.33 349.23 16.53 293.66 587.33 16.54 293.66 587.33 16.55 293.66 587.33 16.56 293.66 587.33 16.57 293.66 587.33 16.58 293.66 587.33 16.59 293.66 587.33 16.60 293.66 587.33 16.61 293.66 587.33 293.66 16.62 293.66 587.33 293.66 16.63 293.66 587.33 293.66 349.23 220.00 16.64 293.66 587.33 293.66 349.23 220.00 16.65 293.66 587.33 349.23 220.00 16.66 293.66 587.33 349.23 220.00 16.67 293.66 587.33 349.23 220.00 16.68 293.66 587.33 16.69 293.66 587.33 16.70 293.66 587.33 16.71 293.66 587.33 16.72 293.66 587.33 16.73 587.33 16.74 587.33 16.75 587.33 293.66 16.76 587.33 293.66 16.77 587.33 293.66 16.78 587.33 293.66 220.00 349.23 16.79 587.33 293.66 220.00 349.23 16.80 587.33 293.66 220.00 349.23 16.81 587.33 220.00 349.23 16.82 587.33 220.00 349.23 16.83 587.33 146.83 16.84 587.33 146.83 16.85 587.33 146.83 16.86 587.33 146.83 16.87 587.33 146.83 293.66 16.88 587.33 146.83 293.66 16.89 587.33 146.83 293.66 16.90 587.33 146.83 293.66 349.23 220.00 16.91 587.33 146.83 293.66 349.23 220.00 16.92 587.33 146.83 293.66 349.23 220.00 16.93 587.33 146.83 349.23 220.00 16.94 587.33 146.83 349.23 220.00 16.95 587.33 146.83 349.23 16.96 587.33 146.83 16.97 587.33 146.83 16.98 587.33 146.83 16.99 587.33 146.83 17.00 587.33 146.83 17.01 146.83 293.66 17.02 146.83 293.66 17.03 146.83 293.66 17.04 146.83 293.66 349.23 220.00 17.05 146.83 349.23 220.00 17.06 146.83 349.23 220.00 17.07 146.83 17.08 146.83 17.09 146.83 523.25 17.10 146.83 523.25 17.11 146.83 523.25 17.12 146.83 523.25 17.13 146.83 523.25 17.14 146.83 523.25 17.15 146.83 523.25 293.66 17.16 146.83 293.66 17.17 146.83 293.66 220.00 349.23 17.18 146.83 293.66 220.00 349.23 17.19 146.83 220.00 349.23 17.20 146.83 349.23 17.21 146.83 466.16 17.22 146.83 466.16 17.23 146.83 466.16 17.24 146.83 466.16 17.25 146.83 466.16 17.26 146.83 466.16 220.00 293.66 17.27 146.83 466.16 220.00 293.66 17.28 146.83 466.16 220.00 293.66 349.23 17.29 146.83 220.00 349.23 17.30 146.83 220.00 349.23 17.31 146.83 220.00 17.32 146.83 17.33 146.83 17.34 146.83 17.35 146.83 440.00 17.36 146.83 440.00 17.37 146.83 440.00 17.38 146.83 440.00 329.63 220.00 17.39 146.83 440.00 329.63 220.00 17.40 146.83 440.00 329.63 220.00 392.00 17.41 146.83 440.00 329.63 220.00 392.00 17.42 146.83 440.00 220.00 392.00 17.43 440.00 220.00 392.00 17.44 440.00 17.45 440.00 17.46 440.00 17.47 440.00 17.48 440.00 17.49 440.00 17.50 440.00 329.63 17.51 440.00 329.63 17.52 440.00 329.63 17.53 440.00 329.63 220.00 17.54 440.00 329.63 220.00 17.55 440.00 329.63 17.56 440.00 329.63 17.57 440.00 17.58 440.00 17.59 440.00 17.60 440.00 17.61 440.00 17.62 440.00 329.63 17.63 440.00 329.63 17.64 440.00 329.63 392.00 17.65 440.00 392.00 220.00 17.66 440.00 392.00 220.00 17.67 440.00 220.00 17.68 440.00 17.69 440.00 17.70 440.00 138.59 17.71 440.00 138.59 17.72 440.00 138.59 17.73 440.00 138.59 17.74 440.00 138.59 329.63 17.75 440.00 138.59 329.63 17.76 440.00 138.59 329.63 392.00 220.00 17.77 440.00 138.59 329.63 392.00 220.00 17.78 440.00 329.63 392.00 220.00 17.79 440.00 392.00 220.00 17.80 440.00 392.00 220.00 17.81 440.00 17.82 440.00 17.83 440.00 17.84 440.00 17.85 440.00 123.47 17.86 440.00 123.47 17.87 440.00 123.47 329.63 17.88 440.00 123.47 329.63 17.89 440.00 123.47 329.63 220.00 392.00 17.90 440.00 123.47 329.63 220.00 392.00 17.91 440.00 123.47 220.00 392.00 17.92 440.00 123.47 220.00 392.00 17.93 440.00 123.47 220.00 17.94 440.00 123.47 17.95 440.00 123.47 17.96 440.00 123.47 110.00 17.97 440.00 123.47 110.00 17.98 440.00 123.47 110.00 17.99 440.00 123.47 110.00 18.00 440.00 110.00 18.01 440.00 110.00 329.63 18.02 440.00 110.00 329.63 18.03 440.00 110.00 329.63 392.00 18.04 440.00 110.00 329.63 392.00 18.05 110.00 392.00 18.06 110.00 392.00 18.07 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3.24 3.25 82.41 392.00 3.26 82.41 392.00 185.00 392.00 3.27 82.41 392.00 185.00 392.00 164.81 3.28 82.41 392.00 185.00 392.00 164.81 3.29 82.41 392.00 185.00 392.00 164.81 3.30 82.41 392.00 185.00 392.00 164.81 3.31 82.41 392.00 185.00 392.00 164.81 3.32 82.41 392.00 185.00 392.00 164.81 3.33 82.41 392.00 164.81 3.34 164.81 3.35 164.81 3.36 3.37 3.38 3.39 369.99 3.40 369.99 73.42 369.99 3.41 369.99 73.42 369.99 146.83 185.00 3.42 369.99 73.42 369.99 146.83 185.00 3.43 369.99 73.42 369.99 146.83 185.00 3.44 369.99 73.42 369.99 146.83 185.00 3.45 369.99 73.42 369.99 146.83 185.00 3.46 73.42 369.99 146.83 3.47 73.42 3.48 73.42 3.49 3.50 3.51 3.52 3.53 3.54 61.74 3.55 61.74 123.47 3.56 61.74 123.47 493.88 3.57 61.74 123.47 493.88 185.00 293.66 369.99 493.88 3.58 61.74 123.47 493.88 185.00 293.66 369.99 493.88 3.59 123.47 493.88 185.00 293.66 369.99 493.88 3.60 123.47 493.88 185.00 293.66 369.99 493.88 3.61 123.47 493.88 185.00 293.66 369.99 493.88 3.62 185.00 293.66 369.99 493.88 3.63 3.64 3.65 3.66 3.67 3.68 3.69 46.25 3.70 46.25 3.71 46.25 92.50 554.37 3.72 46.25 92.50 554.37 554.37 369.99 3.73 46.25 92.50 554.37 554.37 369.99 3.74 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.75 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.76 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.77 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.78 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.79 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.80 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.81 46.25 92.50 554.37 554.37 369.99 277.18 466.16 3.82 46.25 92.50 554.37 369.99 277.18 466.16 3.83 46.25 92.50 554.37 369.99 277.18 466.16 3.84 46.25 92.50 554.37 369.99 277.18 466.16 3.85 46.25 92.50 554.37 369.99 277.18 466.16 3.86 46.25 92.50 554.37 369.99 277.18 466.16 3.87 46.25 92.50 554.37 369.99 277.18 466.16 3.88 46.25 92.50 554.37 369.99 3.89 46.25 92.50 554.37 3.90 46.25 92.50 554.37 3.91 46.25 92.50 554.37 3.92 46.25 92.50 554.37 3.93 46.25 92.50 554.37 3.94 46.25 92.50 554.37 3.95 46.25 92.50 554.37 3.96 46.25 92.50 554.37 3.97 46.25 92.50 554.37 3.98 46.25 92.50 554.37 3.99 46.25 92.50 554.37 4.00 46.25 92.50 554.37 4.01 46.25 92.50 554.37 4.02 46.25 92.50 554.37 4.03 46.25 92.50 4.04 92.50 4.05 92.50 4.06 92.50 4.07 4.08 4.09 4.10 4.11 4.12 4.13 4.14 739.99 4.15 739.99 46.25 4.16 739.99 46.25 4.17 739.99 46.25 92.50 4.18 739.99 46.25 92.50 4.19 739.99 46.25 92.50 277.18 739.99 369.99 4.20 739.99 46.25 92.50 277.18 739.99 369.99 4.21 739.99 46.25 92.50 277.18 739.99 369.99 4.22 739.99 46.25 92.50 277.18 739.99 369.99 4.23 739.99 46.25 92.50 277.18 739.99 369.99 4.24 739.99 46.25 92.50 277.18 739.99 369.99 4.25 739.99 46.25 92.50 277.18 739.99 369.99 4.26 739.99 46.25 92.50 277.18 739.99 369.99 4.27 739.99 46.25 92.50 277.18 739.99 369.99 4.28 739.99 46.25 92.50 277.18 739.99 369.99 4.29 739.99 46.25 92.50 277.18 739.99 369.99 4.30 739.99 46.25 92.50 277.18 739.99 369.99 4.31 739.99 46.25 92.50 277.18 739.99 369.99 4.32 739.99 46.25 92.50 277.18 739.99 369.99 4.33 739.99 46.25 92.50 277.18 739.99 369.99 4.34 739.99 46.25 92.50 277.18 739.99 369.99 4.35 739.99 46.25 92.50 277.18 739.99 369.99 4.36 739.99 46.25 92.50 277.18 739.99 369.99 4.37 739.99 46.25 92.50 277.18 739.99 369.99 4.38 739.99 46.25 92.50 277.18 739.99 369.99 4.39 739.99 46.25 92.50 277.18 369.99 4.40 739.99 46.25 92.50 277.18 369.99 4.41 739.99 46.25 92.50 277.18 369.99 4.42 739.99 46.25 92.50 277.18 4.43 739.99 46.25 92.50 277.18 4.44 739.99 46.25 92.50 4.45 739.99 46.25 92.50 4.46 739.99 46.25 92.50 4.47 739.99 46.25 92.50 4.48 739.99 46.25 92.50 4.49 739.99 46.25 92.50 4.50 739.99 46.25 92.50 4.51 739.99 46.25 92.50 4.52 739.99 46.25 92.50 4.53 739.99 46.25 92.50 4.54 739.99 46.25 92.50 4.55 739.99 46.25 92.50 4.56 739.99 46.25 92.50 4.57 739.99 46.25 4.58 739.99 46.25 4.59 739.99 4.60 739.99 4.61 739.99 4.62 4.63 4.64 4.65 4.66 4.67 4.68 61.74 123.47 4.69 61.74 123.47 587.33 493.88 293.66 4.70 61.74 123.47 587.33 493.88 293.66 369.99 4.71 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.72 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.73 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.74 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.75 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.76 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.77 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.78 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.79 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.80 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.81 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.82 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.83 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.84 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.85 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.86 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.87 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.88 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.89 61.74 123.47 587.33 493.88 293.66 369.99 587.33 4.90 61.74 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146.83 349.23 73.42 493.88 246.94 5.23 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.24 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.25 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.26 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.27 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.28 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.29 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.30 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.31 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.32 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.33 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.34 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.35 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.36 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.37 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.38 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.39 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.40 493.88 146.83 349.23 73.42 493.88 246.94 415.30 5.41 493.88 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92.50 369.99 5.97 277.18 233.08 185.00 369.99 92.50 369.99 5.98 277.18 233.08 185.00 369.99 92.50 369.99 5.99 277.18 233.08 185.00 369.99 92.50 369.99 6.00 277.18 233.08 185.00 369.99 92.50 369.99 6.01 277.18 233.08 185.00 369.99 92.50 369.99 6.02 277.18 233.08 185.00 369.99 92.50 369.99 6.03 277.18 233.08 185.00 369.99 92.50 369.99 6.04 277.18 233.08 185.00 369.99 92.50 369.99 6.05 277.18 233.08 185.00 369.99 92.50 369.99 6.06 277.18 233.08 185.00 92.50 369.99 6.07 233.08 185.00 92.50 369.99 6.08 233.08 185.00 92.50 369.99 6.09 185.00 92.50 369.99 6.10 185.00 92.50 369.99 6.11 185.00 92.50 369.99 6.12 185.00 369.99 6.13 185.00 369.99 6.14 369.99 6.15 6.16 6.17 6.18 6.19 6.20 6.21 6.22 6.23 6.24 61.74 6.25 61.74 123.47 6.26 61.74 123.47 6.27 61.74 123.47 185.00 293.66 293.66 6.28 61.74 123.47 185.00 293.66 293.66 6.29 61.74 123.47 185.00 293.66 293.66 6.30 61.74 123.47 185.00 293.66 293.66 6.31 61.74 123.47 185.00 293.66 293.66 6.32 61.74 123.47 185.00 293.66 293.66 6.33 61.74 185.00 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6.82 82.41 164.81 185.00 392.00 392.00 6.83 82.41 164.81 185.00 392.00 392.00 6.84 82.41 164.81 185.00 392.00 392.00 6.85 82.41 164.81 185.00 392.00 392.00 6.86 82.41 164.81 185.00 392.00 392.00 6.87 82.41 164.81 185.00 392.00 392.00 6.88 82.41 164.81 185.00 392.00 392.00 6.89 82.41 164.81 185.00 392.00 392.00 6.90 82.41 164.81 185.00 392.00 392.00 6.91 82.41 164.81 185.00 392.00 392.00 6.92 82.41 164.81 185.00 392.00 392.00 6.93 82.41 164.81 185.00 392.00 392.00 6.94 82.41 164.81 185.00 392.00 392.00 6.95 82.41 164.81 185.00 392.00 392.00 6.96 82.41 164.81 185.00 392.00 392.00 6.97 82.41 164.81 185.00 392.00 392.00 6.98 82.41 164.81 185.00 392.00 392.00 6.99 82.41 164.81 185.00 392.00 392.00 7.00 82.41 164.81 185.00 392.00 392.00 7.01 82.41 164.81 392.00 392.00 7.02 82.41 164.81 392.00 392.00 7.03 392.00 392.00 7.04 392.00 392.00 7.05 392.00 7.06 392.00 7.07 392.00 7.08 392.00 7.09 392.00 7.10 7.11 7.12 369.99 73.42 7.13 369.99 73.42 185.00 146.83 7.14 369.99 73.42 185.00 146.83 7.15 369.99 73.42 185.00 146.83 369.99 7.16 369.99 73.42 185.00 146.83 369.99 7.17 369.99 73.42 185.00 146.83 369.99 7.18 369.99 73.42 146.83 369.99 7.19 73.42 146.83 369.99 7.20 73.42 146.83 369.99 7.21 146.83 369.99 7.22 7.23 7.24 7.25 7.26 7.27 7.28 7.29 392.00 82.41 7.30 392.00 82.41 164.81 185.00 7.31 392.00 82.41 164.81 185.00 7.32 392.00 82.41 164.81 185.00 7.33 392.00 82.41 164.81 185.00 392.00 7.34 392.00 82.41 164.81 185.00 392.00 7.35 392.00 82.41 164.81 392.00 7.36 392.00 82.41 164.81 392.00 7.37 82.41 164.81 392.00 7.38 82.41 392.00 7.39 392.00 7.40 392.00 7.41 7.42 7.43 7.44 185.00 7.45 185.00 73.42 369.99 146.83 369.99 7.46 185.00 73.42 369.99 146.83 369.99 7.47 185.00 73.42 369.99 146.83 369.99 7.48 185.00 73.42 369.99 146.83 369.99 7.49 185.00 73.42 369.99 146.83 369.99 7.50 185.00 73.42 369.99 146.83 369.99 7.51 185.00 73.42 369.99 146.83 369.99 7.52 185.00 73.42 146.83 7.53 73.42 146.83 7.54 7.55 7.56 7.57 7.58 293.66 7.59 293.66 7.60 293.66 493.88 123.47 7.61 293.66 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10.45 293.66 10.46 293.66 69.30 10.47 69.30 138.59 10.48 69.30 138.59 10.49 69.30 138.59 10.50 69.30 138.59 10.51 69.30 138.59 10.52 69.30 138.59 185.00 10.53 69.30 138.59 185.00 329.63 329.63 10.54 69.30 138.59 185.00 329.63 329.63 10.55 138.59 185.00 329.63 329.63 10.56 185.00 329.63 329.63 10.57 185.00 329.63 329.63 10.58 185.00 329.63 329.63 10.59 329.63 329.63 10.60 329.63 10.61 10.62 10.63 10.64 73.42 10.65 73.42 146.83 10.66 73.42 146.83 185.00 369.99 10.67 73.42 146.83 185.00 369.99 10.68 73.42 146.83 185.00 369.99 10.69 73.42 146.83 185.00 369.99 369.99 10.70 73.42 146.83 185.00 369.99 369.99 10.71 73.42 146.83 185.00 369.99 369.99 10.72 73.42 146.83 185.00 369.99 10.73 73.42 369.99 10.74 369.99 10.75 369.99 10.76 369.99 10.77 10.78 10.79 10.80 392.00 10.81 392.00 185.00 10.82 392.00 185.00 82.41 164.81 10.83 392.00 185.00 82.41 164.81 10.84 392.00 185.00 82.41 164.81 10.85 392.00 185.00 82.41 164.81 10.86 392.00 185.00 82.41 164.81 392.00 10.87 392.00 185.00 82.41 164.81 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92.50 739.99 12.55 46.25 92.50 739.99 12.56 46.25 92.50 739.99 12.57 46.25 92.50 739.99 12.58 46.25 92.50 739.99 12.59 46.25 92.50 739.99 12.60 46.25 92.50 739.99 12.61 46.25 92.50 739.99 12.62 46.25 92.50 739.99 12.63 46.25 92.50 739.99 12.64 46.25 92.50 739.99 12.65 46.25 92.50 739.99 12.66 92.50 739.99 12.67 739.99 12.68 739.99 12.69 739.99 12.70 12.71 12.72 12.73 12.74 12.75 12.76 61.74 12.77 61.74 123.47 12.78 61.74 123.47 587.33 12.79 61.74 123.47 587.33 493.88 293.66 369.99 12.80 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.81 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.82 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.83 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.84 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.85 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.86 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.87 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.88 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.89 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.90 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.91 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.92 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.93 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.94 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.95 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.96 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.97 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.98 61.74 123.47 587.33 493.88 293.66 369.99 587.33 12.99 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.00 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.01 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.02 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.03 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.04 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.05 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.06 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.07 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.08 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.09 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.10 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.11 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.12 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.13 61.74 123.47 587.33 493.88 293.66 369.99 587.33 13.14 61.74 123.47 587.33 493.88 587.33 13.15 61.74 123.47 587.33 493.88 587.33 13.16 61.74 123.47 587.33 13.17 61.74 123.47 587.33 13.18 61.74 123.47 587.33 13.19 61.74 123.47 587.33 13.20 61.74 123.47 587.33 13.21 61.74 123.47 13.22 123.47 13.23 13.24 13.25 13.26 13.27 13.28 13.29 13.30 13.31 493.88 349.23 146.83 13.32 493.88 349.23 146.83 73.42 246.94 415.30 13.33 493.88 349.23 146.83 73.42 246.94 415.30 13.34 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.35 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.36 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.37 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.38 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.39 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.40 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.41 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.42 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.43 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.44 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.45 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.46 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.47 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.48 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.49 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.50 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.51 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.52 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.53 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.54 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.55 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.56 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.57 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.58 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.59 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.60 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.61 493.88 349.23 146.83 73.42 246.94 415.30 493.88 13.62 349.23 146.83 73.42 246.94 415.30 493.88 13.63 349.23 146.83 73.42 415.30 493.88 13.64 146.83 73.42 415.30 493.88 13.65 146.83 73.42 415.30 493.88 13.66 146.83 73.42 493.88 13.67 146.83 73.42 493.88 13.68 146.83 73.42 493.88 13.69 146.83 73.42 493.88 13.70 146.83 493.88 13.71 146.83 493.88 13.72 146.83 493.88 13.73 493.88 13.74 493.88 13.75 13.76 13.77 13.78 13.79 13.80 13.81 13.82 13.83 277.18 233.08 13.84 277.18 233.08 369.99 13.85 277.18 233.08 369.99 185.00 13.86 277.18 233.08 369.99 185.00 13.87 277.18 233.08 369.99 185.00 369.99 13.88 277.18 233.08 369.99 185.00 369.99 13.89 277.18 233.08 369.99 185.00 369.99 92.50 13.90 277.18 233.08 369.99 185.00 369.99 92.50 13.91 277.18 233.08 369.99 185.00 369.99 92.50 13.92 277.18 233.08 369.99 185.00 369.99 92.50 13.93 277.18 233.08 369.99 185.00 369.99 92.50 13.94 277.18 233.08 369.99 185.00 369.99 92.50 13.95 277.18 233.08 369.99 185.00 369.99 92.50 13.96 277.18 233.08 369.99 185.00 369.99 92.50 13.97 277.18 233.08 369.99 185.00 369.99 92.50 13.98 277.18 233.08 369.99 185.00 369.99 92.50 13.99 277.18 233.08 369.99 185.00 369.99 92.50 14.00 277.18 233.08 369.99 185.00 369.99 92.50 14.01 277.18 233.08 369.99 185.00 369.99 92.50 14.02 277.18 233.08 369.99 185.00 369.99 92.50 14.03 277.18 233.08 369.99 185.00 369.99 92.50 14.04 277.18 233.08 369.99 185.00 369.99 92.50 14.05 277.18 233.08 369.99 185.00 369.99 92.50 14.06 277.18 233.08 369.99 185.00 369.99 92.50 14.07 277.18 233.08 369.99 185.00 369.99 92.50 14.08 277.18 233.08 369.99 185.00 369.99 92.50 14.09 277.18 233.08 369.99 185.00 369.99 92.50 14.10 277.18 233.08 369.99 185.00 369.99 92.50 14.11 277.18 233.08 369.99 185.00 369.99 92.50 14.12 277.18 233.08 369.99 185.00 369.99 92.50 14.13 277.18 233.08 369.99 185.00 369.99 92.50 14.14 277.18 233.08 369.99 185.00 369.99 92.50 14.15 277.18 233.08 185.00 369.99 92.50 14.16 277.18 233.08 185.00 369.99 92.50 14.17 233.08 185.00 369.99 92.50 14.18 185.00 369.99 92.50 14.19 185.00 369.99 92.50 14.20 185.00 369.99 92.50 14.21 185.00 369.99 92.50 14.22 369.99 92.50 14.23 369.99 92.50 14.24 369.99 92.50 14.25 369.99 14.26 369.99 14.27 369.99 14.28 14.29 14.30 14.31 14.32 14.33 14.34 14.35 14.36 185.00 293.66 14.37 185.00 293.66 61.74 14.38 185.00 293.66 61.74 123.47 14.39 185.00 293.66 61.74 123.47 14.40 185.00 293.66 61.74 123.47 14.41 185.00 293.66 61.74 123.47 293.66 14.42 185.00 293.66 61.74 123.47 293.66 14.43 293.66 61.74 123.47 293.66 14.44 61.74 123.47 293.66 14.45 61.74 123.47 293.66 14.46 123.47 293.66 14.47 293.66 14.48 14.49 14.50 14.51 14.52 14.53 185.00 329.63 14.54 185.00 329.63 14.55 185.00 329.63 138.59 14.56 185.00 329.63 138.59 69.30 14.57 185.00 329.63 138.59 69.30 329.63 14.58 185.00 329.63 138.59 69.30 329.63 14.59 329.63 138.59 69.30 329.63 14.60 138.59 69.30 329.63 14.61 138.59 69.30 329.63 14.62 138.59 69.30 329.63 14.63 138.59 69.30 329.63 14.64 329.63 14.65 14.66 14.67 14.68 185.00 14.69 185.00 369.99 14.70 185.00 369.99 73.42 14.71 185.00 369.99 73.42 146.83 14.72 185.00 369.99 73.42 146.83 14.73 185.00 369.99 73.42 146.83 369.99 14.74 185.00 369.99 73.42 146.83 369.99 14.75 73.42 146.83 369.99 14.76 73.42 146.83 369.99 14.77 73.42 146.83 369.99 14.78 73.42 146.83 369.99 14.79 73.42 146.83 369.99 14.80 73.42 14.81 14.82 14.83 14.84 185.00 14.85 185.00 14.86 185.00 392.00 14.87 185.00 392.00 82.41 14.88 185.00 392.00 82.41 164.81 14.89 185.00 392.00 82.41 164.81 14.90 185.00 392.00 82.41 164.81 392.00 14.91 185.00 392.00 82.41 164.81 392.00 14.92 185.00 392.00 82.41 164.81 392.00 14.93 185.00 392.00 82.41 164.81 392.00 14.94 185.00 392.00 82.41 164.81 392.00 14.95 185.00 392.00 82.41 164.81 392.00 14.96 185.00 392.00 82.41 164.81 392.00 14.97 185.00 392.00 82.41 164.81 392.00 14.98 185.00 392.00 82.41 164.81 392.00 14.99 185.00 392.00 82.41 164.81 392.00 15.00 185.00 392.00 82.41 164.81 392.00 15.01 185.00 392.00 82.41 164.81 392.00 15.02 185.00 392.00 82.41 164.81 392.00 15.03 185.00 392.00 82.41 164.81 392.00 15.04 185.00 392.00 82.41 164.81 392.00 15.05 185.00 392.00 82.41 164.81 392.00 15.06 185.00 392.00 82.41 164.81 392.00 15.07 185.00 392.00 82.41 164.81 392.00 15.08 185.00 392.00 82.41 164.81 392.00 15.09 185.00 392.00 82.41 164.81 392.00 15.10 185.00 392.00 82.41 164.81 392.00 15.11 392.00 82.41 164.81 392.00 15.12 392.00 82.41 164.81 392.00 15.13 392.00 82.41 164.81 392.00 15.14 82.41 164.81 392.00 15.15 82.41 392.00 15.16 392.00 15.17 392.00 15.18 392.00 15.19 15.20 15.21 369.99 15.22 369.99 185.00 15.23 369.99 185.00 73.42 15.24 369.99 185.00 73.42 15.25 369.99 185.00 73.42 146.83 15.26 369.99 185.00 73.42 146.83 369.99 15.27 369.99 185.00 73.42 146.83 369.99 15.28 369.99 73.42 146.83 369.99 15.29 73.42 146.83 369.99 15.30 73.42 146.83 369.99 15.31 73.42 146.83 369.99 15.32 73.42 146.83 369.99 15.33 369.99 15.34 15.35 15.36 15.37 15.38 15.39 392.00 15.40 392.00 185.00 82.41 15.41 392.00 185.00 82.41 164.81 15.42 392.00 185.00 82.41 164.81 392.00 15.43 392.00 185.00 82.41 164.81 392.00 15.44 392.00 82.41 164.81 392.00 15.45 392.00 82.41 164.81 392.00 15.46 82.41 164.81 392.00 15.47 82.41 164.81 392.00 15.48 82.41 164.81 392.00 15.49 82.41 164.81 15.50 82.41 15.51 15.52 15.53 73.42 15.54 73.42 185.00 146.83 369.99 15.55 73.42 185.00 146.83 369.99 369.99 15.56 73.42 185.00 146.83 369.99 369.99 15.57 73.42 185.00 146.83 369.99 369.99 15.58 73.42 185.00 146.83 369.99 369.99 15.59 73.42 185.00 146.83 369.99 369.99 15.60 73.42 185.00 146.83 369.99 369.99 15.61 73.42 185.00 146.83 369.99 15.62 73.42 185.00 146.83 369.99 15.63 73.42 146.83 15.64 73.42 15.65 73.42 15.66 73.42 15.67 73.42 15.68 293.66 123.47 61.74 15.69 293.66 123.47 61.74 15.70 293.66 123.47 61.74 493.88 493.88 15.71 293.66 123.47 61.74 493.88 493.88 369.99 15.72 293.66 123.47 61.74 493.88 493.88 369.99 15.73 293.66 123.47 61.74 493.88 493.88 369.99 15.74 293.66 123.47 61.74 493.88 493.88 369.99 15.75 293.66 61.74 493.88 493.88 369.99 15.76 293.66 493.88 493.88 369.99 15.77 493.88 15.78 493.88 15.79 15.80 15.81 15.82 55.00 15.83 55.00 110.00 15.84 55.00 110.00 15.85 55.00 110.00 15.86 55.00 110.00 15.87 55.00 110.00 277.18 554.37 369.99 15.88 55.00 110.00 277.18 554.37 369.99 554.37 15.89 55.00 110.00 277.18 554.37 369.99 554.37 15.90 55.00 110.00 277.18 554.37 369.99 554.37 15.91 55.00 110.00 277.18 554.37 369.99 554.37 15.92 55.00 110.00 277.18 554.37 369.99 554.37 15.93 55.00 110.00 277.18 554.37 369.99 554.37 15.94 55.00 110.00 277.18 554.37 369.99 554.37 15.95 55.00 110.00 277.18 554.37 369.99 554.37 15.96 55.00 110.00 277.18 554.37 369.99 554.37 15.97 55.00 110.00 277.18 554.37 369.99 554.37 15.98 55.00 110.00 277.18 554.37 369.99 554.37 15.99 55.00 110.00 277.18 554.37 369.99 554.37 16.00 55.00 110.00 277.18 554.37 369.99 554.37 16.01 55.00 110.00 277.18 554.37 369.99 554.37 16.02 55.00 110.00 277.18 554.37 369.99 554.37 16.03 55.00 110.00 277.18 554.37 369.99 554.37 16.04 55.00 110.00 277.18 554.37 369.99 554.37 16.05 55.00 110.00 277.18 554.37 369.99 554.37 16.06 55.00 110.00 277.18 554.37 369.99 554.37 16.07 55.00 110.00 277.18 554.37 369.99 554.37 16.08 55.00 110.00 277.18 554.37 554.37 16.09 55.00 110.00 277.18 554.37 554.37 16.10 55.00 110.00 277.18 554.37 554.37 16.11 55.00 110.00 277.18 554.37 16.12 55.00 110.00 277.18 554.37 16.13 55.00 110.00 277.18 554.37 16.14 55.00 110.00 277.18 554.37 16.15 55.00 110.00 277.18 554.37 16.16 55.00 110.00 554.37 16.17 55.00 110.00 554.37 16.18 110.00 554.37 16.19 110.00 554.37 16.20 110.00 554.37 16.21 110.00 554.37 16.22 110.00 554.37 16.23 554.37 16.24 554.37 16.25 554.37 16.26 16.27 16.28 16.29 16.30 16.31 16.32 739.99 369.99 16.33 739.99 369.99 440.00 16.34 739.99 369.99 440.00 92.50 16.35 739.99 369.99 440.00 92.50 46.25 554.37 16.36 739.99 369.99 440.00 92.50 46.25 554.37 16.37 739.99 369.99 440.00 92.50 46.25 554.37 16.38 739.99 369.99 440.00 92.50 46.25 554.37 16.39 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.40 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.41 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.42 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.43 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.44 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.45 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.46 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.47 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.48 739.99 369.99 440.00 92.50 46.25 554.37 739.99 16.49 369.99 440.00 92.50 46.25 554.37 739.99 16.50 369.99 440.00 92.50 46.25 554.37 739.99 16.51 369.99 440.00 92.50 46.25 554.37 739.99 16.52 369.99 440.00 92.50 46.25 554.37 739.99 16.53 369.99 440.00 92.50 46.25 739.99 16.54 440.00 92.50 46.25 739.99 16.55 440.00 92.50 46.25 739.99 16.56 440.00 92.50 46.25 739.99 16.57 440.00 92.50 46.25 739.99 16.58 440.00 92.50 46.25 739.99 16.59 92.50 46.25 739.99 16.60 92.50 46.25 739.99 16.61 92.50 46.25 739.99 16.62 92.50 46.25 739.99 16.63 92.50 46.25 739.99 16.64 92.50 46.25 739.99 16.65 46.25 739.99 16.66 46.25 739.99 16.67 46.25 739.99 16.68 46.25 739.99 16.69 46.25 739.99 16.70 46.25 739.99 16.71 46.25 739.99 16.72 739.99 16.73 739.99 16.74 16.75 16.76 16.77 16.78 16.79 16.80 16.81 16.82 16.83 61.74 293.66 16.84 61.74 293.66 587.33 16.85 61.74 293.66 587.33 369.99 493.88 123.47 16.86 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.87 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.88 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.89 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.90 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.91 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.92 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.93 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.94 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.95 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.96 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.97 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.98 61.74 293.66 587.33 369.99 493.88 123.47 587.33 16.99 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.00 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.01 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.02 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.03 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.04 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.05 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.06 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.07 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.08 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.09 61.74 293.66 587.33 369.99 493.88 123.47 587.33 17.10 61.74 293.66 587.33 369.99 123.47 587.33 17.11 61.74 369.99 123.47 587.33 17.12 61.74 123.47 587.33 17.13 61.74 123.47 587.33 17.14 61.74 123.47 587.33 17.15 61.74 123.47 587.33 17.16 61.74 123.47 587.33 17.17 61.74 587.33 17.18 587.33 17.19 587.33 17.20 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277.18 220.00 92.50 277.18 220.00 440.00 220.00 4.88 659.26 277.18 220.00 92.50 277.18 220.00 440.00 220.00 4.89 659.26 277.18 220.00 92.50 277.18 220.00 440.00 220.00 4.90 659.26 277.18 220.00 92.50 277.18 220.00 440.00 220.00 4.91 659.26 277.18 220.00 92.50 277.18 220.00 440.00 220.00 554.37 4.92 659.26 277.18 220.00 92.50 277.18 220.00 440.00 220.00 554.37 415.30 4.93 277.18 220.00 92.50 277.18 220.00 440.00 220.00 554.37 415.30 4.94 277.18 92.50 277.18 220.00 220.00 554.37 415.30 4.95 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 4.96 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 4.97 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 4.98 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 4.99 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.00 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.01 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.02 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.03 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.04 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.05 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.06 277.18 92.50 277.18 220.00 220.00 554.37 415.30 207.65 5.07 277.18 92.50 277.18 220.00 554.37 415.30 207.65 5.08 277.18 92.50 277.18 220.00 554.37 415.30 207.65 369.99 5.09 277.18 92.50 277.18 220.00 554.37 415.30 207.65 369.99 5.10 277.18 92.50 277.18 220.00 554.37 415.30 207.65 369.99 587.33 5.11 277.18 92.50 277.18 220.00 554.37 207.65 369.99 587.33 185.00 5.12 277.18 92.50 277.18 220.00 207.65 369.99 587.33 185.00 5.13 277.18 92.50 277.18 220.00 207.65 369.99 587.33 185.00 5.14 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.15 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.16 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.17 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.18 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.19 277.18 92.50 277.18 220.00 369.99 587.33 185.00 5.20 277.18 92.50 277.18 220.00 369.99 587.33 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11.15 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.16 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.17 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.18 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.19 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.20 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.21 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.22 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.23 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.24 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.25 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.26 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.27 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.28 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.29 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.30 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.31 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.32 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.33 246.94 293.66 493.88 1174.66 185.00 246.94 123.47 293.66 11.34 246.94 293.66 493.88 185.00 246.94 123.47 293.66 987.77 246.94 11.35 246.94 293.66 493.88 185.00 246.94 123.47 293.66 987.77 246.94 11.36 246.94 293.66 493.88 185.00 246.94 123.47 293.66 987.77 246.94 11.37 246.94 293.66 493.88 185.00 246.94 123.47 293.66 987.77 246.94 11.38 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.39 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.40 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.41 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.42 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.43 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.44 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.45 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 11.46 246.94 293.66 493.88 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123.47 293.66 987.77 246.94 11.63 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 293.66 11.64 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 293.66 11.65 246.94 293.66 493.88 246.94 123.47 293.66 987.77 246.94 293.66 11.66 293.66 493.88 246.94 123.47 293.66 987.77 246.94 293.66 11.67 293.66 493.88 246.94 123.47 293.66 987.77 246.94 293.66 11.68 293.66 246.94 123.47 293.66 987.77 246.94 293.66 11.69 293.66 246.94 123.47 293.66 987.77 246.94 293.66 11.70 293.66 246.94 123.47 293.66 987.77 246.94 293.66 1318.51 11.71 293.66 246.94 123.47 293.66 987.77 246.94 293.66 1318.51 11.72 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.73 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.74 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.75 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.76 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.77 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.78 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.79 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.80 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.81 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.82 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.83 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.84 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.85 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.86 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.87 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.88 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.89 293.66 246.94 123.47 293.66 246.94 293.66 1318.51 11.90 293.66 123.47 293.66 246.94 293.66 1318.51 11.91 293.66 123.47 293.66 246.94 293.66 1318.51 11.92 293.66 123.47 293.66 246.94 293.66 1318.51 11.93 293.66 123.47 293.66 246.94 293.66 1318.51 11.94 293.66 123.47 293.66 246.94 293.66 1318.51 11.95 293.66 123.47 293.66 246.94 293.66 1318.51 11.96 293.66 123.47 293.66 246.94 293.66 1318.51 11.97 293.66 123.47 293.66 246.94 293.66 1318.51 11.98 293.66 123.47 293.66 246.94 293.66 1318.51 11.99 293.66 123.47 293.66 246.94 293.66 1318.51 12.00 293.66 123.47 293.66 246.94 293.66 1318.51 12.01 293.66 123.47 293.66 246.94 293.66 1318.51 12.02 123.47 293.66 246.94 293.66 1318.51 12.03 123.47 246.94 293.66 1318.51 369.99 12.04 123.47 246.94 293.66 1318.51 369.99 880.00 12.05 123.47 246.94 1318.51 369.99 880.00 440.00 12.06 123.47 246.94 1318.51 369.99 880.00 440.00 440.00 1174.66 12.07 123.47 246.94 1318.51 369.99 880.00 440.00 440.00 1174.66 12.08 123.47 246.94 1318.51 369.99 880.00 440.00 440.00 1174.66 12.09 123.47 246.94 369.99 880.00 440.00 440.00 1174.66 12.10 123.47 369.99 880.00 440.00 440.00 1174.66 12.11 123.47 369.99 880.00 440.00 440.00 1174.66 12.12 123.47 369.99 880.00 440.00 440.00 1174.66 12.13 123.47 369.99 880.00 440.00 440.00 1174.66 12.14 123.47 369.99 880.00 440.00 440.00 1174.66 12.15 123.47 369.99 880.00 440.00 440.00 1174.66 12.16 123.47 369.99 880.00 440.00 440.00 1174.66 12.17 123.47 369.99 880.00 440.00 440.00 1174.66 12.18 123.47 369.99 880.00 440.00 440.00 1174.66 12.19 123.47 369.99 880.00 440.00 440.00 1174.66 12.20 123.47 369.99 880.00 440.00 440.00 1174.66 12.21 123.47 369.99 880.00 440.00 440.00 1174.66 12.22 123.47 369.99 880.00 440.00 440.00 1174.66 12.23 123.47 369.99 880.00 440.00 440.00 1174.66 12.24 123.47 369.99 880.00 440.00 440.00 1174.66 1108.73 12.25 123.47 369.99 880.00 440.00 440.00 1174.66 1108.73 12.26 123.47 369.99 880.00 440.00 440.00 1174.66 1108.73 12.27 123.47 369.99 880.00 440.00 440.00 1108.73 12.28 123.47 369.99 880.00 440.00 440.00 1108.73 12.29 123.47 369.99 880.00 440.00 440.00 1108.73 12.30 123.47 369.99 880.00 440.00 440.00 1108.73 12.31 123.47 369.99 880.00 440.00 440.00 1108.73 12.32 123.47 369.99 880.00 440.00 440.00 1108.73 12.33 123.47 369.99 880.00 440.00 440.00 1108.73 12.34 123.47 369.99 880.00 440.00 440.00 1108.73 12.35 123.47 369.99 880.00 440.00 440.00 1108.73 12.36 123.47 369.99 880.00 440.00 440.00 1108.73 12.37 123.47 369.99 880.00 440.00 440.00 1108.73 12.38 123.47 369.99 880.00 440.00 440.00 1108.73 12.39 123.47 369.99 880.00 440.00 440.00 1108.73 12.40 123.47 369.99 880.00 440.00 440.00 1108.73 12.41 123.47 369.99 880.00 440.00 440.00 1108.73 12.42 123.47 369.99 880.00 440.00 440.00 1108.73 164.81 587.33 12.43 123.47 369.99 880.00 440.00 440.00 1108.73 164.81 587.33 415.30 12.44 123.47 369.99 880.00 440.00 440.00 164.81 587.33 415.30 246.94 987.77 830.61 12.45 123.47 369.99 880.00 440.00 164.81 587.33 415.30 246.94 987.77 830.61 12.46 123.47 369.99 164.81 587.33 415.30 246.94 987.77 830.61 12.47 123.47 369.99 164.81 587.33 415.30 246.94 987.77 830.61 12.48 123.47 369.99 164.81 587.33 415.30 246.94 987.77 830.61 12.49 369.99 164.81 587.33 415.30 246.94 987.77 830.61 12.50 164.81 587.33 415.30 246.94 987.77 830.61 12.51 164.81 587.33 415.30 246.94 987.77 830.61 12.52 164.81 587.33 415.30 246.94 987.77 830.61 12.53 164.81 587.33 415.30 246.94 987.77 830.61 12.54 164.81 587.33 415.30 246.94 987.77 830.61 12.55 164.81 587.33 415.30 246.94 987.77 830.61 12.56 164.81 587.33 415.30 246.94 987.77 830.61 12.57 164.81 587.33 415.30 246.94 987.77 830.61 12.58 164.81 587.33 415.30 246.94 987.77 830.61 12.59 164.81 587.33 415.30 246.94 987.77 830.61 12.60 164.81 587.33 415.30 246.94 987.77 830.61 12.61 164.81 587.33 415.30 246.94 987.77 830.61 12.62 164.81 587.33 415.30 246.94 987.77 830.61 12.63 164.81 587.33 415.30 246.94 987.77 830.61 12.64 164.81 587.33 415.30 246.94 987.77 830.61 12.65 164.81 587.33 415.30 246.94 987.77 830.61 12.66 164.81 587.33 415.30 246.94 987.77 830.61 12.67 164.81 587.33 415.30 246.94 987.77 830.61 12.68 164.81 587.33 415.30 246.94 987.77 830.61 12.69 164.81 587.33 415.30 246.94 987.77 830.61 12.70 164.81 587.33 415.30 246.94 987.77 830.61 12.71 164.81 587.33 415.30 246.94 987.77 830.61 12.72 164.81 587.33 415.30 246.94 987.77 830.61 12.73 164.81 587.33 415.30 246.94 987.77 830.61 12.74 164.81 587.33 415.30 246.94 987.77 830.61 12.75 164.81 587.33 415.30 246.94 987.77 830.61 12.76 164.81 587.33 415.30 246.94 987.77 830.61 12.77 164.81 587.33 415.30 246.94 987.77 830.61 12.78 164.81 587.33 415.30 246.94 987.77 830.61 12.79 164.81 587.33 415.30 246.94 987.77 830.61 12.80 164.81 587.33 415.30 246.94 987.77 830.61 12.81 164.81 587.33 415.30 246.94 987.77 830.61 369.99 12.82 164.81 587.33 415.30 246.94 987.77 830.61 369.99 739.99 12.83 164.81 587.33 415.30 246.94 987.77 830.61 369.99 739.99 12.84 164.81 587.33 415.30 246.94 987.77 830.61 369.99 739.99 12.85 164.81 587.33 246.94 987.77 830.61 369.99 739.99 12.86 164.81 587.33 246.94 987.77 830.61 369.99 739.99 12.87 164.81 587.33 246.94 987.77 369.99 739.99 12.88 164.81 587.33 246.94 987.77 369.99 739.99 12.89 164.81 587.33 246.94 987.77 369.99 739.99 12.90 164.81 587.33 246.94 987.77 369.99 739.99 12.91 164.81 587.33 246.94 987.77 369.99 739.99 12.92 164.81 587.33 246.94 987.77 369.99 739.99 12.93 164.81 587.33 246.94 987.77 369.99 739.99 12.94 164.81 587.33 246.94 987.77 369.99 739.99 12.95 164.81 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164.81 587.33 246.94 987.77 369.99 739.99 13.17 164.81 587.33 246.94 987.77 369.99 739.99 13.18 164.81 587.33 246.94 987.77 369.99 739.99 13.19 164.81 587.33 246.94 987.77 369.99 739.99 659.26 13.20 164.81 587.33 246.94 987.77 369.99 739.99 659.26 329.63 13.21 164.81 587.33 246.94 987.77 369.99 659.26 329.63 13.22 164.81 587.33 246.94 987.77 369.99 659.26 329.63 13.23 164.81 587.33 246.94 987.77 369.99 659.26 329.63 13.24 164.81 587.33 246.94 987.77 659.26 329.63 13.25 164.81 587.33 246.94 987.77 659.26 329.63 13.26 164.81 587.33 246.94 987.77 659.26 329.63 13.27 164.81 587.33 246.94 987.77 659.26 329.63 13.28 164.81 587.33 246.94 987.77 659.26 329.63 13.29 164.81 587.33 246.94 987.77 659.26 329.63 13.30 164.81 587.33 246.94 987.77 659.26 329.63 13.31 164.81 587.33 246.94 987.77 659.26 329.63 13.32 164.81 587.33 246.94 987.77 659.26 329.63 13.33 164.81 587.33 246.94 987.77 659.26 329.63 13.34 164.81 587.33 246.94 987.77 659.26 329.63 13.35 164.81 587.33 246.94 987.77 659.26 329.63 13.36 164.81 587.33 246.94 987.77 659.26 329.63 13.37 164.81 587.33 246.94 987.77 659.26 329.63 13.38 164.81 587.33 246.94 987.77 659.26 329.63 13.39 164.81 587.33 246.94 987.77 659.26 329.63 13.40 164.81 587.33 246.94 987.77 659.26 329.63 13.41 164.81 587.33 246.94 987.77 659.26 329.63 13.42 164.81 587.33 246.94 987.77 659.26 329.63 13.43 164.81 587.33 246.94 987.77 659.26 329.63 13.44 164.81 587.33 246.94 987.77 659.26 329.63 13.45 164.81 587.33 246.94 987.77 659.26 329.63 13.46 164.81 587.33 246.94 987.77 659.26 329.63 13.47 164.81 587.33 246.94 987.77 659.26 329.63 13.48 164.81 587.33 246.94 987.77 659.26 329.63 13.49 164.81 587.33 246.94 987.77 659.26 329.63 13.50 164.81 587.33 246.94 987.77 659.26 329.63 13.51 164.81 587.33 246.94 987.77 659.26 329.63 13.52 164.81 587.33 246.94 987.77 659.26 329.63 13.53 164.81 587.33 246.94 987.77 659.26 329.63 13.54 164.81 587.33 246.94 987.77 659.26 329.63 554.37 554.37 13.55 164.81 587.33 246.94 987.77 659.26 329.63 554.37 554.37 13.56 164.81 587.33 246.94 987.77 659.26 329.63 554.37 554.37 13.57 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 13.58 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 13.59 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 277.18 13.60 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 277.18 13.61 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 277.18 13.62 164.81 246.94 659.26 329.63 554.37 554.37 1760.00 277.18 13.63 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.64 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.65 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.66 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.67 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.68 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.69 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.70 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.71 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.72 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.73 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.74 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.75 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.76 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.77 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.78 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.79 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.80 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.81 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.82 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.83 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.84 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.85 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.86 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.87 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.88 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.89 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.90 164.81 659.26 329.63 554.37 554.37 1760.00 277.18 13.91 659.26 329.63 554.37 554.37 1760.00 277.18 13.92 659.26 329.63 554.37 554.37 1760.00 277.18 13.93 659.26 329.63 554.37 554.37 1760.00 277.18 13.94 659.26 329.63 554.37 554.37 1760.00 277.18 13.95 659.26 329.63 554.37 554.37 277.18 13.96 659.26 329.63 554.37 554.37 277.18 1661.22 13.97 659.26 329.63 554.37 554.37 277.18 1661.22 493.88 13.98 659.26 329.63 554.37 554.37 277.18 1661.22 493.88 293.66 13.99 659.26 329.63 554.37 277.18 1661.22 493.88 293.66 493.88 14.00 659.26 329.63 554.37 1661.22 493.88 293.66 493.88 14.01 329.63 554.37 1661.22 493.88 293.66 493.88 14.02 329.63 554.37 1661.22 493.88 293.66 493.88 14.03 329.63 1661.22 493.88 293.66 493.88 14.04 329.63 1661.22 493.88 293.66 493.88 14.05 329.63 1661.22 493.88 293.66 493.88 14.06 329.63 1661.22 493.88 293.66 493.88 14.07 329.63 1661.22 493.88 293.66 493.88 14.08 329.63 1661.22 493.88 293.66 493.88 14.09 329.63 1661.22 493.88 293.66 493.88 14.10 329.63 1661.22 493.88 293.66 493.88 14.11 329.63 1661.22 493.88 293.66 493.88 14.12 329.63 1661.22 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14.55 493.88 293.66 587.33 69.30 164.81 1479.98 14.56 493.88 293.66 587.33 69.30 164.81 1479.98 14.57 493.88 293.66 587.33 69.30 164.81 1479.98 14.58 493.88 293.66 587.33 69.30 164.81 1479.98 14.59 493.88 293.66 587.33 69.30 164.81 1479.98 14.60 493.88 293.66 587.33 69.30 164.81 1479.98 14.61 493.88 293.66 587.33 69.30 164.81 1479.98 14.62 493.88 293.66 587.33 69.30 164.81 1479.98 14.63 493.88 293.66 587.33 69.30 164.81 1479.98 14.64 493.88 293.66 587.33 69.30 164.81 1479.98 14.65 493.88 293.66 587.33 69.30 164.81 1479.98 14.66 493.88 293.66 587.33 69.30 164.81 1479.98 14.67 493.88 293.66 587.33 69.30 164.81 1479.98 14.68 493.88 293.66 587.33 69.30 164.81 1479.98 14.69 493.88 293.66 587.33 69.30 164.81 1479.98 14.70 493.88 293.66 587.33 69.30 164.81 1479.98 14.71 493.88 293.66 587.33 69.30 164.81 1479.98 14.72 493.88 293.66 587.33 69.30 164.81 1479.98 14.73 493.88 293.66 587.33 69.30 164.81 1479.98 73.42 1318.51 185.00 14.74 493.88 293.66 587.33 69.30 164.81 73.42 1318.51 185.00 277.18 14.75 293.66 587.33 69.30 164.81 73.42 1318.51 185.00 277.18 554.37 14.76 587.33 164.81 73.42 1318.51 185.00 277.18 554.37 14.77 587.33 164.81 73.42 1318.51 185.00 277.18 554.37 14.78 587.33 73.42 1318.51 185.00 277.18 554.37 14.79 587.33 73.42 1318.51 185.00 277.18 554.37 14.80 73.42 1318.51 185.00 277.18 554.37 14.81 73.42 1318.51 185.00 277.18 554.37 14.82 73.42 1318.51 185.00 277.18 554.37 14.83 73.42 1318.51 185.00 277.18 554.37 14.84 73.42 1318.51 185.00 277.18 554.37 14.85 73.42 1318.51 185.00 277.18 554.37 14.86 73.42 1318.51 185.00 277.18 554.37 14.87 73.42 1318.51 185.00 277.18 554.37 14.88 73.42 1318.51 185.00 277.18 554.37 14.89 73.42 1318.51 185.00 277.18 554.37 14.90 73.42 1318.51 185.00 277.18 554.37 14.91 73.42 1318.51 185.00 277.18 554.37 14.92 73.42 1318.51 185.00 277.18 554.37 14.93 73.42 1318.51 185.00 277.18 554.37 14.94 73.42 1318.51 185.00 277.18 554.37 14.95 73.42 1318.51 185.00 277.18 554.37 14.96 73.42 1318.51 185.00 277.18 554.37 14.97 73.42 1318.51 185.00 277.18 554.37 14.98 73.42 1318.51 185.00 277.18 554.37 14.99 73.42 1318.51 185.00 277.18 554.37 15.00 73.42 1318.51 185.00 277.18 554.37 15.01 73.42 1318.51 185.00 277.18 554.37 15.02 73.42 1318.51 185.00 277.18 554.37 15.03 73.42 1318.51 185.00 277.18 554.37 15.04 73.42 1318.51 185.00 277.18 554.37 15.05 73.42 1318.51 185.00 277.18 554.37 15.06 73.42 1318.51 185.00 277.18 554.37 15.07 73.42 1318.51 185.00 277.18 554.37 15.08 73.42 1318.51 185.00 277.18 554.37 82.41 246.94 15.09 73.42 1318.51 185.00 277.18 554.37 82.41 246.94 15.10 73.42 1318.51 185.00 277.18 554.37 82.41 246.94 207.65 15.11 73.42 1318.51 185.00 277.18 554.37 82.41 246.94 207.65 493.88 15.12 73.42 1318.51 185.00 554.37 82.41 246.94 207.65 493.88 15.13 73.42 1318.51 185.00 554.37 82.41 246.94 207.65 493.88 15.14 73.42 1318.51 554.37 82.41 246.94 207.65 493.88 15.15 1318.51 82.41 246.94 207.65 493.88 15.16 1318.51 82.41 246.94 207.65 493.88 15.17 1318.51 82.41 246.94 207.65 493.88 15.18 1318.51 82.41 246.94 207.65 493.88 15.19 1318.51 82.41 246.94 207.65 493.88 15.20 1318.51 82.41 246.94 207.65 493.88 15.21 1318.51 82.41 246.94 207.65 493.88 15.22 1318.51 82.41 246.94 207.65 493.88 15.23 1318.51 82.41 246.94 207.65 493.88 15.24 1318.51 82.41 246.94 207.65 493.88 15.25 1318.51 82.41 246.94 207.65 493.88 15.26 1318.51 82.41 246.94 207.65 493.88 15.27 1318.51 82.41 246.94 207.65 493.88 554.37 15.28 1318.51 82.41 246.94 207.65 493.88 554.37 15.29 1318.51 82.41 246.94 207.65 554.37 277.18 15.30 1318.51 82.41 246.94 207.65 554.37 277.18 15.31 1318.51 82.41 207.65 554.37 277.18 15.32 1318.51 82.41 207.65 554.37 277.18 15.33 1318.51 82.41 207.65 554.37 277.18 15.34 1318.51 82.41 207.65 554.37 277.18 15.35 1318.51 82.41 207.65 554.37 277.18 15.36 1318.51 82.41 207.65 554.37 277.18 15.37 1318.51 82.41 207.65 554.37 277.18 15.38 1318.51 82.41 207.65 554.37 277.18 15.39 1318.51 82.41 207.65 554.37 277.18 15.40 1318.51 82.41 207.65 554.37 277.18 15.41 1318.51 82.41 207.65 554.37 277.18 15.42 1318.51 82.41 207.65 554.37 277.18 15.43 1318.51 82.41 207.65 554.37 277.18 15.44 1318.51 82.41 207.65 554.37 277.18 15.45 1318.51 82.41 554.37 277.18 220.00 15.46 1318.51 82.41 554.37 277.18 220.00 15.47 1318.51 82.41 554.37 277.18 220.00 15.48 1318.51 82.41 277.18 220.00 15.49 1318.51 82.41 277.18 220.00 440.00 92.50 220.00 15.50 1318.51 277.18 220.00 440.00 92.50 220.00 15.51 1318.51 277.18 220.00 440.00 92.50 220.00 15.52 1318.51 220.00 440.00 92.50 220.00 15.53 1318.51 220.00 440.00 92.50 220.00 15.54 1318.51 220.00 440.00 92.50 220.00 15.55 1318.51 220.00 440.00 92.50 220.00 15.56 1318.51 220.00 440.00 92.50 220.00 15.57 1318.51 220.00 440.00 92.50 220.00 15.58 1318.51 220.00 440.00 92.50 220.00 15.59 1318.51 220.00 440.00 92.50 220.00 15.60 1318.51 220.00 440.00 92.50 220.00 15.61 1318.51 220.00 440.00 92.50 220.00 15.62 1318.51 220.00 440.00 92.50 220.00 15.63 1318.51 220.00 440.00 92.50 220.00 15.64 1318.51 220.00 440.00 92.50 220.00 15.65 1318.51 220.00 440.00 92.50 220.00 15.66 1318.51 220.00 440.00 92.50 220.00 277.18 554.37 15.67 1318.51 220.00 440.00 92.50 220.00 277.18 554.37 15.68 1318.51 440.00 92.50 220.00 277.18 554.37 15.69 1318.51 92.50 220.00 277.18 554.37 15.70 1318.51 92.50 220.00 277.18 554.37 15.71 1318.51 92.50 220.00 277.18 554.37 15.72 1318.51 92.50 220.00 277.18 554.37 15.73 1318.51 92.50 220.00 277.18 554.37 15.74 1318.51 92.50 220.00 277.18 554.37 15.75 1318.51 92.50 220.00 277.18 554.37 15.76 1318.51 92.50 220.00 277.18 554.37 15.77 1318.51 92.50 220.00 277.18 554.37 15.78 1318.51 92.50 220.00 277.18 554.37 15.79 1318.51 92.50 220.00 277.18 554.37 15.80 1318.51 92.50 220.00 277.18 554.37 15.81 1318.51 92.50 220.00 277.18 554.37 293.66 15.82 1318.51 92.50 277.18 554.37 293.66 1174.66 185.00 15.83 1318.51 92.50 554.37 293.66 1174.66 185.00 587.33 15.84 1318.51 92.50 554.37 293.66 1174.66 185.00 587.33 15.85 92.50 293.66 1174.66 185.00 587.33 15.86 92.50 293.66 1174.66 185.00 587.33 15.87 92.50 293.66 1174.66 185.00 587.33 15.88 92.50 293.66 1174.66 185.00 587.33 15.89 92.50 293.66 1174.66 185.00 587.33 15.90 92.50 293.66 1174.66 185.00 587.33 15.91 92.50 293.66 1174.66 185.00 587.33 15.92 92.50 293.66 1174.66 185.00 587.33 15.93 92.50 293.66 1174.66 185.00 587.33 15.94 92.50 293.66 1174.66 185.00 587.33 15.95 92.50 293.66 1174.66 185.00 587.33 15.96 92.50 1174.66 185.00 587.33 207.65 15.97 92.50 1174.66 185.00 587.33 207.65 329.63 15.98 92.50 1174.66 185.00 207.65 329.63 659.26 15.99 92.50 1174.66 185.00 207.65 329.63 659.26 16.00 92.50 1174.66 207.65 329.63 659.26 16.01 92.50 1174.66 207.65 329.63 659.26 16.02 92.50 1174.66 207.65 329.63 659.26 16.03 92.50 1174.66 207.65 329.63 659.26 16.04 92.50 1174.66 207.65 329.63 659.26 16.05 92.50 1174.66 207.65 329.63 659.26 16.06 92.50 1174.66 207.65 329.63 659.26 16.07 92.50 1174.66 207.65 329.63 659.26 16.08 92.50 1174.66 207.65 329.63 659.26 16.09 92.50 1174.66 207.65 329.63 659.26 16.10 92.50 1174.66 207.65 329.63 659.26 16.11 92.50 1174.66 207.65 329.63 659.26 16.12 92.50 1174.66 207.65 329.63 659.26 16.13 92.50 1174.66 207.65 329.63 659.26 16.14 92.50 1174.66 207.65 329.63 659.26 16.15 92.50 1174.66 207.65 329.63 659.26 220.00 16.16 92.50 1174.66 207.65 329.63 220.00 739.99 1108.73 16.17 92.50 1174.66 207.65 329.63 220.00 739.99 1108.73 369.99 16.18 92.50 329.63 220.00 739.99 1108.73 369.99 16.19 92.50 220.00 739.99 1108.73 369.99 16.20 92.50 220.00 739.99 1108.73 369.99 16.21 92.50 220.00 739.99 1108.73 369.99 16.22 92.50 220.00 739.99 1108.73 369.99 16.23 92.50 220.00 739.99 1108.73 369.99 16.24 92.50 220.00 739.99 1108.73 369.99 16.25 92.50 220.00 739.99 1108.73 369.99 16.26 92.50 220.00 739.99 1108.73 369.99 16.27 92.50 220.00 739.99 1108.73 369.99 16.28 92.50 220.00 739.99 1108.73 369.99 16.29 92.50 220.00 739.99 1108.73 369.99 16.30 92.50 220.00 739.99 1108.73 369.99 16.31 92.50 220.00 739.99 1108.73 369.99 16.32 92.50 220.00 739.99 1108.73 369.99 16.33 92.50 220.00 739.99 1108.73 369.99 16.34 92.50 220.00 739.99 1108.73 369.99 16.35 92.50 220.00 739.99 1108.73 369.99 16.36 92.50 220.00 739.99 1108.73 369.99 16.37 92.50 220.00 739.99 1108.73 369.99 16.38 92.50 220.00 739.99 1108.73 369.99 16.39 92.50 220.00 739.99 1108.73 369.99 16.40 92.50 220.00 739.99 1108.73 369.99 16.41 92.50 220.00 739.99 1108.73 369.99 16.42 92.50 220.00 739.99 1108.73 369.99 16.43 92.50 220.00 739.99 1108.73 369.99 16.44 92.50 220.00 739.99 1108.73 369.99 16.45 92.50 220.00 739.99 1108.73 369.99 16.46 92.50 220.00 739.99 1108.73 369.99 16.47 92.50 220.00 739.99 1108.73 369.99 16.48 92.50 220.00 739.99 1108.73 369.99 16.49 92.50 220.00 739.99 1108.73 369.99 185.00 16.50 92.50 220.00 1108.73 369.99 185.00 16.51 92.50 185.00 987.77 16.52 92.50 185.00 987.77 329.63 659.26 16.53 92.50 185.00 987.77 329.63 659.26 16.54 92.50 185.00 987.77 329.63 659.26 16.55 92.50 185.00 987.77 329.63 659.26 16.56 92.50 185.00 987.77 329.63 659.26 16.57 92.50 185.00 987.77 329.63 659.26 16.58 92.50 185.00 987.77 329.63 659.26 16.59 92.50 185.00 987.77 329.63 659.26 16.60 92.50 185.00 987.77 329.63 659.26 16.61 92.50 185.00 987.77 329.63 659.26 16.62 92.50 185.00 987.77 329.63 659.26 16.63 92.50 185.00 987.77 329.63 659.26 16.64 92.50 185.00 987.77 329.63 659.26 16.65 92.50 185.00 987.77 329.63 659.26 16.66 92.50 185.00 987.77 329.63 659.26 207.65 16.67 92.50 185.00 987.77 329.63 659.26 207.65 16.68 92.50 185.00 987.77 329.63 659.26 207.65 16.69 92.50 185.00 987.77 329.63 659.26 207.65 1108.73 587.33 16.70 92.50 987.77 329.63 659.26 207.65 1108.73 587.33 293.66 16.71 92.50 329.63 659.26 207.65 1108.73 587.33 293.66 16.72 92.50 329.63 207.65 1108.73 587.33 293.66 16.73 92.50 329.63 207.65 1108.73 587.33 293.66 16.74 92.50 207.65 1108.73 587.33 293.66 16.75 92.50 207.65 1108.73 587.33 293.66 16.76 92.50 207.65 1108.73 587.33 293.66 16.77 92.50 207.65 1108.73 587.33 293.66 16.78 92.50 207.65 1108.73 587.33 293.66 16.79 92.50 207.65 1108.73 587.33 293.66 16.80 92.50 207.65 1108.73 587.33 293.66 16.81 92.50 207.65 1108.73 587.33 293.66 16.82 92.50 207.65 1108.73 587.33 293.66 16.83 92.50 207.65 1108.73 587.33 293.66 16.84 92.50 207.65 1108.73 587.33 293.66 16.85 92.50 207.65 1108.73 587.33 293.66 16.86 92.50 207.65 1108.73 587.33 293.66 220.00 16.87 92.50 1108.73 587.33 293.66 220.00 16.88 92.50 1108.73 587.33 293.66 220.00 277.18 880.00 16.89 92.50 1108.73 587.33 293.66 220.00 277.18 880.00 554.37 16.90 92.50 587.33 293.66 220.00 277.18 880.00 554.37 16.91 92.50 587.33 220.00 277.18 880.00 554.37 16.92 92.50 220.00 277.18 880.00 554.37 16.93 92.50 220.00 277.18 880.00 554.37 16.94 92.50 220.00 277.18 880.00 554.37 16.95 92.50 220.00 277.18 880.00 554.37 16.96 92.50 220.00 277.18 880.00 554.37 16.97 92.50 220.00 277.18 880.00 554.37 16.98 92.50 220.00 277.18 880.00 554.37 16.99 92.50 220.00 277.18 880.00 554.37 17.00 92.50 220.00 277.18 880.00 554.37 17.01 92.50 220.00 277.18 880.00 554.37 17.02 92.50 220.00 277.18 880.00 554.37 17.03 92.50 220.00 277.18 880.00 554.37 17.04 92.50 220.00 277.18 880.00 554.37 17.05 92.50 220.00 277.18 880.00 554.37 17.06 92.50 220.00 277.18 880.00 554.37 17.07 92.50 220.00 277.18 880.00 554.37 17.08 92.50 220.00 277.18 880.00 554.37 1108.73 17.09 92.50 220.00 277.18 880.00 554.37 1108.73 17.10 220.00 277.18 880.00 554.37 1108.73 17.11 220.00 277.18 880.00 554.37 1108.73 17.12 220.00 277.18 554.37 1108.73 17.13 220.00 277.18 554.37 1108.73 17.14 220.00 277.18 554.37 1108.73 17.15 220.00 277.18 554.37 1108.73 17.16 220.00 277.18 554.37 1108.73 17.17 220.00 277.18 554.37 1108.73 17.18 220.00 277.18 554.37 1108.73 17.19 277.18 554.37 1108.73 17.20 277.18 554.37 1108.73 17.21 277.18 554.37 1108.73 17.22 277.18 554.37 1108.73 17.23 277.18 554.37 1108.73 17.24 277.18 554.37 1108.73 17.25 277.18 554.37 1108.73 17.26 277.18 554.37 1108.73 1174.66 17.27 277.18 554.37 1108.73 1174.66 17.28 277.18 554.37 1108.73 1174.66 17.29 277.18 554.37 1174.66 17.30 277.18 554.37 1174.66 17.31 277.18 554.37 1174.66 17.32 277.18 554.37 1174.66 55.00 110.00 17.33 277.18 554.37 1174.66 55.00 110.00 17.34 277.18 554.37 1174.66 55.00 110.00 17.35 277.18 554.37 1174.66 55.00 110.00 17.36 277.18 554.37 1174.66 55.00 110.00 17.37 277.18 554.37 1174.66 55.00 110.00 17.38 277.18 554.37 1174.66 55.00 110.00 17.39 277.18 554.37 1174.66 55.00 110.00 17.40 277.18 554.37 1174.66 55.00 110.00 17.41 277.18 554.37 1174.66 55.00 110.00 17.42 277.18 554.37 1174.66 55.00 110.00 17.43 277.18 554.37 1174.66 55.00 110.00 17.44 277.18 554.37 1174.66 55.00 110.00 1318.51 17.45 277.18 554.37 55.00 110.00 1318.51 17.46 277.18 554.37 55.00 110.00 1318.51 17.47 277.18 554.37 55.00 110.00 1318.51 17.48 277.18 554.37 55.00 110.00 1318.51 17.49 277.18 554.37 55.00 110.00 1318.51 17.50 277.18 554.37 55.00 110.00 1318.51 17.51 277.18 554.37 55.00 110.00 1318.51 17.52 277.18 554.37 55.00 110.00 1318.51 17.53 277.18 554.37 55.00 110.00 1318.51 17.54 277.18 554.37 55.00 110.00 1318.51 17.55 277.18 554.37 55.00 110.00 1318.51 17.56 277.18 554.37 55.00 110.00 1318.51 17.57 277.18 554.37 1318.51 17.58 277.18 554.37 1318.51 17.59 277.18 554.37 1318.51 17.60 277.18 554.37 1318.51 17.61 277.18 554.37 1318.51 17.62 277.18 554.37 1318.51 17.63 277.18 554.37 1318.51 1479.98 17.64 277.18 554.37 1318.51 1479.98 92.50 17.65 277.18 554.37 1479.98 92.50 46.25 17.66 277.18 554.37 1479.98 92.50 46.25 17.67 277.18 554.37 1479.98 92.50 46.25 17.68 277.18 554.37 1479.98 92.50 46.25 440.00 17.69 277.18 554.37 1479.98 92.50 46.25 440.00 17.70 277.18 1479.98 92.50 46.25 440.00 17.71 277.18 1479.98 92.50 46.25 440.00 17.72 277.18 1479.98 92.50 46.25 440.00 17.73 277.18 1479.98 92.50 46.25 440.00 17.74 277.18 1479.98 92.50 46.25 440.00 17.75 277.18 1479.98 92.50 46.25 440.00 17.76 277.18 1479.98 92.50 46.25 440.00 17.77 277.18 1479.98 92.50 46.25 440.00 17.78 277.18 1479.98 92.50 46.25 440.00 17.79 277.18 1479.98 92.50 46.25 440.00 17.80 277.18 1479.98 92.50 46.25 440.00 17.81 277.18 1479.98 92.50 46.25 440.00 17.82 277.18 1479.98 92.50 46.25 440.00 17.83 277.18 1479.98 92.50 46.25 440.00 17.84 277.18 1479.98 92.50 46.25 440.00 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18.09 1318.51 110.00 55.00 739.99 369.99 18.10 1318.51 110.00 55.00 739.99 369.99 18.11 1318.51 110.00 55.00 739.99 369.99 18.12 1318.51 110.00 55.00 739.99 369.99 18.13 1318.51 110.00 55.00 739.99 369.99 18.14 1318.51 110.00 55.00 739.99 369.99 18.15 1318.51 110.00 55.00 739.99 369.99 18.16 1318.51 110.00 55.00 739.99 369.99 1174.66 18.17 1318.51 110.00 55.00 739.99 369.99 1174.66 18.18 1318.51 110.00 55.00 739.99 369.99 1174.66 18.19 110.00 55.00 739.99 369.99 1174.66 18.20 110.00 55.00 739.99 369.99 1174.66 18.21 110.00 55.00 739.99 369.99 1174.66 18.22 110.00 55.00 739.99 369.99 1174.66 18.23 110.00 55.00 739.99 369.99 1174.66 18.24 110.00 55.00 739.99 369.99 1174.66 18.25 110.00 55.00 739.99 369.99 1174.66 18.26 110.00 55.00 739.99 369.99 1174.66 18.27 110.00 55.00 739.99 369.99 1174.66 18.28 110.00 55.00 739.99 369.99 1174.66 18.29 110.00 55.00 739.99 369.99 1174.66 18.30 110.00 55.00 739.99 369.99 1174.66 18.31 110.00 55.00 739.99 369.99 1174.66 18.32 110.00 739.99 369.99 1174.66 18.33 110.00 739.99 369.99 1174.66 18.34 110.00 739.99 369.99 1174.66 18.35 110.00 739.99 369.99 1174.66 18.36 110.00 739.99 369.99 1174.66 1108.73 18.37 110.00 739.99 369.99 1174.66 1108.73 18.38 739.99 369.99 1174.66 1108.73 18.39 739.99 369.99 1108.73 18.40 739.99 369.99 1108.73 18.41 739.99 369.99 1108.73 18.42 739.99 369.99 1108.73 18.43 739.99 369.99 1108.73 18.44 739.99 369.99 1108.73 138.59 69.30 18.45 739.99 369.99 1108.73 138.59 69.30 349.23 18.46 739.99 369.99 1108.73 138.59 69.30 349.23 18.47 739.99 369.99 1108.73 138.59 69.30 349.23 698.46 18.48 739.99 369.99 1108.73 138.59 69.30 349.23 698.46 18.49 369.99 1108.73 138.59 69.30 349.23 698.46 18.50 369.99 1108.73 138.59 69.30 349.23 698.46 18.51 1108.73 138.59 69.30 349.23 698.46 18.52 1108.73 138.59 69.30 349.23 698.46 18.53 1108.73 138.59 69.30 349.23 698.46 18.54 1108.73 138.59 69.30 349.23 698.46 18.55 1108.73 138.59 69.30 349.23 698.46 18.56 1108.73 138.59 69.30 349.23 698.46 18.57 1108.73 138.59 69.30 349.23 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277.18 554.37 19.03 1108.73 138.59 69.30 349.23 277.18 554.37 19.04 1108.73 138.59 69.30 349.23 277.18 554.37 19.05 1108.73 138.59 69.30 349.23 277.18 554.37 19.06 1108.73 138.59 69.30 349.23 277.18 554.37 19.07 1108.73 138.59 69.30 349.23 277.18 554.37 19.08 1108.73 138.59 69.30 349.23 277.18 554.37 19.09 1108.73 138.59 69.30 349.23 277.18 554.37 19.10 1108.73 138.59 69.30 349.23 277.18 554.37 19.11 1108.73 138.59 69.30 349.23 277.18 554.37 19.12 1108.73 138.59 69.30 349.23 277.18 554.37 19.13 1108.73 138.59 69.30 349.23 277.18 554.37 19.14 1108.73 138.59 69.30 349.23 277.18 554.37 19.15 1108.73 138.59 69.30 349.23 277.18 554.37 19.16 1108.73 138.59 69.30 349.23 277.18 554.37 19.17 1108.73 138.59 69.30 349.23 277.18 554.37 19.18 1108.73 138.59 69.30 349.23 277.18 554.37 19.19 1108.73 138.59 69.30 349.23 277.18 554.37 19.20 1108.73 138.59 69.30 349.23 277.18 554.37 293.66 19.21 1108.73 138.59 69.30 349.23 554.37 293.66 19.22 1108.73 138.59 69.30 349.23 554.37 293.66 369.99 19.23 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880.00 24.04 138.59 220.00 369.99 880.00 24.05 138.59 220.00 369.99 880.00 24.06 138.59 220.00 369.99 880.00 24.07 138.59 220.00 369.99 880.00 24.08 138.59 220.00 369.99 880.00 24.09 138.59 220.00 369.99 880.00 24.10 138.59 220.00 369.99 880.00 24.11 138.59 220.00 369.99 880.00 24.12 138.59 220.00 369.99 880.00 24.13 138.59 220.00 369.99 880.00 24.14 138.59 220.00 369.99 880.00 24.15 138.59 220.00 369.99 880.00 24.16 138.59 220.00 369.99 880.00 24.17 138.59 220.00 369.99 880.00 24.18 138.59 220.00 369.99 880.00 830.61 24.19 138.59 220.00 369.99 880.00 830.61 24.20 138.59 220.00 369.99 830.61 24.21 138.59 220.00 369.99 830.61 277.18 24.22 138.59 220.00 369.99 830.61 277.18 24.23 369.99 830.61 277.18 554.37 24.24 369.99 830.61 277.18 554.37 24.25 369.99 830.61 277.18 554.37 24.26 369.99 830.61 277.18 554.37 24.27 369.99 830.61 277.18 554.37 24.28 369.99 830.61 277.18 554.37 24.29 369.99 830.61 277.18 554.37 24.30 369.99 830.61 277.18 554.37 24.31 369.99 830.61 277.18 554.37 24.32 369.99 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138.59 329.63 277.18 554.37 24.64 369.99 739.99 830.61 138.59 329.63 277.18 554.37 24.65 369.99 830.61 138.59 329.63 277.18 554.37 24.66 369.99 830.61 138.59 329.63 277.18 554.37 24.67 369.99 830.61 138.59 329.63 277.18 554.37 24.68 369.99 830.61 138.59 329.63 277.18 554.37 24.69 830.61 138.59 329.63 277.18 554.37 24.70 830.61 138.59 329.63 277.18 554.37 24.71 830.61 138.59 329.63 277.18 554.37 24.72 830.61 138.59 329.63 277.18 554.37 24.73 830.61 138.59 329.63 277.18 554.37 24.74 830.61 138.59 329.63 277.18 554.37 24.75 830.61 138.59 329.63 277.18 554.37 24.76 830.61 138.59 329.63 277.18 554.37 24.77 830.61 138.59 329.63 277.18 554.37 24.78 830.61 138.59 329.63 277.18 554.37 24.79 830.61 138.59 329.63 277.18 554.37 24.80 830.61 138.59 329.63 277.18 554.37 24.81 830.61 138.59 329.63 277.18 554.37 24.82 830.61 138.59 329.63 277.18 554.37 24.83 830.61 138.59 329.63 277.18 554.37 24.84 830.61 138.59 329.63 277.18 554.37 24.85 830.61 138.59 329.63 277.18 554.37 24.86 830.61 138.59 329.63 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265.000000, 64.000000 265.500000, 60.000000 266.000000, 31.000000 266.000000, 59.000000 266.000000, 65.000000 266.500000, 62.000000 267.000000, 36.000000 267.000000, 60.000000 268.000000, 48.000000 269.000000, 60.000000 270.000000, 53.000000 270.000000, 69.000000 270.500000, 67.000000 271.000000, 57.000000 271.000000, 69.000000 271.500000, 65.000000 272.000000, 60.000000 272.000000, 67.000000 272.500000, 69.000000 273.000000, 62.000000 273.000000, 70.000000 273.500000, 69.000000 274.000000, 55.000000 274.000000, 70.000000 274.500000, 67.000000 275.000000, 53.000000 275.000000, 69.000000 275.500000, 71.000000 276.000000, 52.000000 276.000000, 72.000000 276.500000, 71.000000 277.000000, 55.000000 277.000000, 72.000000 277.500000, 74.000000 278.000000, 60.000000 278.000000, 72.000000 278.500000, 70.000000 279.000000, 53.000000 279.000000, 69.000000 279.500000, 67.000000 280.000000, 57.000000 280.000000, 69.000000 280.500000, 70.000000 281.000000, 60.000000 281.000000, 69.000000 281.500000, 65.000000 occurrence3 191.000000, 61.000000 191.000000, 73.000000 191.500000, 60.000000 191.500000, 72.000000 192.000000, 58.000000 192.000000, 70.000000 192.500000, 60.000000 192.500000, 72.000000 193.000000, 61.000000 193.000000, 73.000000 193.500000, 60.000000 193.500000, 72.000000 194.000000, 58.000000 194.000000, 70.000000 194.500000, 60.000000 194.500000, 72.000000 195.000000, 58.000000 195.000000, 70.000000 195.500000, 56.000000 195.500000, 68.000000 196.000000, 55.000000 196.000000, 67.000000 196.500000, 56.000000 196.500000, 68.000000 197.000000, 55.000000 197.000000, 67.000000 197.500000, 53.000000 197.500000, 65.000000 198.000000, 52.000000 198.000000, 64.000000 198.500000, 53.000000 198.500000, 65.000000 199.000000, 55.000000 199.000000, 67.000000 199.500000, 53.000000 199.500000, 65.000000 200.000000, 56.000000 200.000000, 68.000000 200.500000, 47.000000 200.500000, 59.000000 201.000000, 48.000000 201.000000, 60.000000 202.000000, 60.000000 203.000000, 60.000000 203.000000, 68.000000 204.000000, 61.000000 204.000000, 70.000000 205.000000, 58.000000 205.000000, 67.000000 206.000000, 58.000000 206.000000, 67.000000 206.000000, 76.000000 207.000000, 60.000000 207.000000, 68.000000 207.000000, 77.000000 208.000000, 56.000000 208.000000, 65.000000 208.000000, 80.000000 209.000000, 53.000000 209.000000, 56.000000 209.000000, 84.000000 210.000000, 52.000000 210.000000, 55.000000 210.000000, 85.000000 211.000000, 55.000000 211.000000, 58.000000 211.000000, 82.000000 212.000000, 55.000000 212.000000, 58.000000 212.000000, 82.000000 213.000000, 53.000000 213.000000, 56.000000 213.000000, 84.000000 214.000000, 56.000000 214.000000, 60.000000 214.000000, 80.000000 215.000000, 56.000000 215.000000, 60.000000 215.000000, 65.000000 215.000000, 80.000000 216.000000, 55.000000 216.000000, 58.000000 216.000000, 64.000000 216.000000, 82.000000 217.000000, 58.000000 217.000000, 61.000000 217.000000, 64.000000 217.000000, 79.000000 218.000000, 58.000000 218.000000, 61.000000 218.000000, 64.000000 218.000000, 79.000000 219.000000, 59.000000 219.000000, 62.000000 219.000000, 65.000000 219.000000, 80.000000 220.000000, 68.000000 220.000000, 77.000000 221.000000, 59.000000 221.000000, 62.000000 221.000000, 68.000000 221.000000, 77.000000 222.000000, 60.000000 222.000000, 68.000000 222.000000, 77.000000 224.000000, 60.000000 224.000000, 70.000000 224.000000, 76.000000 224.000000, 79.000000 225.000000, 65.000000 225.000000, 68.000000 225.000000, 77.000000 227.000000, 75.000000 227.500000, 73.000000 228.000000, 48.000000 228.000000, 56.000000 228.000000, 65.000000 230.000000, 48.000000 230.000000, 58.000000 230.000000, 64.000000 230.000000, 67.000000 231.000000, 53.000000 231.000000, 56.000000 231.000000, 65.000000 234.000000, 36.000000 234.000000, 48.000000 234.000000, 52.000000 234.000000, 55.000000 234.000000, 60.000000 237.000000, 41.000000 237.000000, 53.000000 pattern15 occurrence1 312.000000, 64.000000 312.000000, 72.000000 312.000000, 84.000000 312.500000, 62.000000 313.000000, 64.000000 313.500000, 60.000000 314.000000, 62.000000 314.500000, 64.000000 315.000000, 65.000000 315.000000, 71.000000 315.000000, 83.000000 315.500000, 64.000000 316.000000, 65.000000 316.500000, 62.000000 317.000000, 64.000000 317.500000, 65.000000 318.000000, 67.000000 318.000000, 70.000000 318.000000, 82.000000 318.500000, 65.000000 319.000000, 67.000000 319.500000, 64.000000 319.500000, 67.000000 320.000000, 65.000000 320.000000, 69.000000 320.500000, 67.000000 320.500000, 70.000000 321.000000, 69.000000 321.000000, 72.000000 321.500000, 67.000000 321.500000, 70.000000 occurrence2 316.000000, 65.000000 316.500000, 62.000000 317.000000, 64.000000 317.500000, 65.000000 318.000000, 67.000000 318.000000, 70.000000 318.000000, 82.000000 318.500000, 65.000000 319.000000, 67.000000 319.500000, 64.000000 319.500000, 67.000000 320.000000, 65.000000 320.000000, 69.000000 320.500000, 67.000000 320.500000, 70.000000 321.000000, 69.000000 321.000000, 72.000000 321.500000, 67.000000 321.500000, 70.000000 322.000000, 69.000000 322.000000, 72.000000 322.500000, 65.000000 322.500000, 69.000000 323.000000, 67.000000 323.000000, 70.000000 323.500000, 69.000000 323.500000, 72.000000 324.000000, 70.000000 324.000000, 74.000000 324.500000, 69.000000 324.500000, 72.000000 325.000000, 70.000000 325.000000, 74.000000 325.500000, 67.000000 325.500000, 70.000000 325.500000, 76.000000 mir_eval-0.8.2/tests/data/pattern/output00.json000066400000000000000000000005441475740344600214630ustar00rootroot00000000000000{"F": 0.0, "P": 0.0, "R": 0.0, "F_est": 0.35878787878787882, "P_est": 0.44848484848484849, "R_est": 0.29898989898989897, "F_occ.5": 0.0, "P_occ.5": 0, "R_occ.5": 0, "F_occ.75": 0.0, "P_occ.75": 0, "R_occ.75": 0, "F_3": 0.16165324386492833, "P_3": 0.20206655483116043, "R_3": 0.13471103655410696, "FFP": 0.20206655483116043, "FFTP_est": 0.29898989898989897}mir_eval-0.8.2/tests/data/pattern/output01.json000066400000000000000000000006021475740344600214570ustar00rootroot00000000000000{"F": 0.28571428571428575, "P": 0.25, "R": 0.3333333333333333, "F_est": 0.25249448754612192, "P_est": 0.25927023102889601, "R_est": 0.246063877642825, "F_occ.5": 0.0, "P_occ.5": 0, "R_occ.5": 0, "F_occ.75": 0.0, "P_occ.75": 0, "R_occ.75": 0, "F_3": 0.10211154049937549, "P_3": 0.094650816870979773, "R_3": 0.11084906966815376, "FFP": 0.094650816870979773, "FFTP_est": 0.246063877642825}mir_eval-0.8.2/tests/data/pattern/output02.json000066400000000000000000000007141475740344600214640ustar00rootroot00000000000000{"F": 0.0, "P": 0.0, "R": 0.0, "F_est": 0.59059477273784466, "P_est": 0.53696061490272651, "R_est": 0.65613236703756328, "F_occ.5": 0.47732667346571606, "P_occ.5": 0.56455932565087485, "R_occ.5": 0.41344360478577541, "F_occ.75": 0.47732667346571606, "P_occ.75": 0.56455932565087485, "R_occ.75": 0.41344360478577541, "F_3": 0.36829084595436512, "P_3": 0.33484219438175999, "R_3": 0.40916375938472338, "FFP": 0.46540500984993144, "FFTP_est": 0.60838976846150139}mir_eval-0.8.2/tests/data/pattern/ref00.txt000066400000000000000000000216371475740344600205530ustar00rootroot00000000000000pattern1 occurrence1 21.0000000000, 76.0000000000 22.0000000000, 72.0000000000 23.0000000000, 77.0000000000 24.0000000000, 68.0000000000 26.5000000000, 74.0000000000 27.0000000000, 71.0000000000 27.5000000000, 76.0000000000 28.0000000000, 72.0000000000 occurrence2 81.0000000000, 81.0000000000 82.0000000000, 77.0000000000 83.0000000000, 82.0000000000 84.0000000000, 73.0000000000 86.5000000000, 79.0000000000 87.0000000000, 76.0000000000 87.5000000000, 81.0000000000 88.0000000000, 77.0000000000 occurrence3 225.0000000000, 64.0000000000 226.0000000000, 60.0000000000 227.0000000000, 65.0000000000 228.0000000000, 56.0000000000 230.5000000000, 62.0000000000 231.0000000000, 59.0000000000 231.5000000000, 64.0000000000 232.0000000000, 60.0000000000 occurrence4 259.0000000000, 55.0000000000 260.0000000000, 52.0000000000 261.0000000000, 57.0000000000 262.0000000000, 47.0000000000 264.5000000000, 53.0000000000 265.0000000000, 50.0000000000 265.5000000000, 55.0000000000 266.0000000000, 52.0000000000 occurrence5 323.0000000000, 52.0000000000 324.0000000000, 48.0000000000 325.0000000000, 53.0000000000 326.0000000000, 44.0000000000 328.5000000000, 50.0000000000 329.0000000000, 47.0000000000 329.5000000000, 52.0000000000 330.0000000000, 48.0000000000 occurrence6 49.0000000000, 81.0000000000 50.0000000000, 79.0000000000 51.0000000000, 84.0000000000 52.0000000000, 75.0000000000 54.5000000000, 81.0000000000 55.0000000000, 78.0000000000 55.5000000000, 83.0000000000 56.0000000000, 79.0000000000 occurrence7 121.0000000000, 69.0000000000 122.0000000000, 67.0000000000 123.0000000000, 72.0000000000 124.0000000000, 63.0000000000 126.5000000000, 69.0000000000 127.0000000000, 66.0000000000 127.5000000000, 71.0000000000 128.0000000000, 67.0000000000 occurrence8 226.0000000000, 60.0000000000 227.0000000000, 65.0000000000 228.0000000000, 56.0000000000 230.5000000000, 62.0000000000 231.0000000000, 59.0000000000 231.5000000000, 64.0000000000 232.0000000000, 60.0000000000 pattern2 occurrence1 36.5000000000, 84.0000000000 36.6250000000, 83.0000000000 36.7500000000, 81.0000000000 36.8750000000, 79.0000000000 37.0000000000, 77.0000000000 37.5000000000, 81.0000000000 37.6250000000, 79.0000000000 37.7500000000, 77.0000000000 37.8750000000, 76.0000000000 38.0000000000, 74.0000000000 38.1250000000, 76.0000000000 38.2500000000, 77.0000000000 38.6250000000, 76.0000000000 38.7500000000, 74.0000000000 38.8750000000, 72.0000000000 39.0000000000, 71.0000000000 39.1250000000, 72.0000000000 39.2500000000, 74.0000000000 39.6250000000, 72.0000000000 39.7500000000, 71.0000000000 39.8750000000, 69.0000000000 40.0000000000, 67.0000000000 40.7500000000, 69.0000000000 40.8750000000, 71.0000000000 41.0000000000, 71.0000000000 41.7500000000, 69.0000000000 41.8750000000, 71.0000000000 42.0000000000, 72.0000000000 occurrence2 134.5000000000, 69.0000000000 134.6250000000, 67.0000000000 134.7500000000, 65.0000000000 134.8750000000, 64.0000000000 135.0000000000, 62.0000000000 135.5000000000, 65.0000000000 135.6250000000, 64.0000000000 135.7500000000, 62.0000000000 135.8750000000, 60.0000000000 136.0000000000, 59.0000000000 136.1250000000, 60.0000000000 136.2500000000, 62.0000000000 136.6250000000, 60.0000000000 136.7500000000, 59.0000000000 136.8750000000, 57.0000000000 137.0000000000, 56.0000000000 137.1250000000, 57.0000000000 137.2500000000, 59.0000000000 137.6250000000, 57.0000000000 137.7500000000, 56.0000000000 137.8750000000, 54.0000000000 138.0000000000, 52.0000000000 138.7500000000, 54.0000000000 138.8750000000, 56.0000000000 139.0000000000, 56.0000000000 139.7500000000, 54.0000000000 139.8750000000, 56.0000000000 140.0000000000, 57.0000000000 occurrence3 162.5000000000, 76.0000000000 162.6250000000, 74.0000000000 162.7500000000, 72.0000000000 162.8750000000, 71.0000000000 163.0000000000, 69.0000000000 163.5000000000, 72.0000000000 163.6250000000, 71.0000000000 163.7500000000, 69.0000000000 163.8750000000, 67.0000000000 164.0000000000, 66.0000000000 164.1250000000, 67.0000000000 164.2500000000, 69.0000000000 164.6250000000, 67.0000000000 164.7500000000, 66.0000000000 164.8750000000, 64.0000000000 165.0000000000, 63.0000000000 165.1250000000, 64.0000000000 165.2500000000, 66.0000000000 165.6250000000, 64.0000000000 165.7500000000, 63.0000000000 165.8750000000, 61.0000000000 166.0000000000, 59.0000000000 166.7500000000, 61.0000000000 166.8750000000, 63.0000000000 167.0000000000, 63.0000000000 167.7500000000, 61.0000000000 167.8750000000, 63.0000000000 168.0000000000, 64.0000000000 occurrence4 234.5000000000, 64.0000000000 234.6250000000, 62.0000000000 234.7500000000, 60.0000000000 234.8750000000, 59.0000000000 235.0000000000, 57.0000000000 235.5000000000, 60.0000000000 235.6250000000, 59.0000000000 235.7500000000, 57.0000000000 235.8750000000, 55.0000000000 236.0000000000, 54.0000000000 236.1250000000, 55.0000000000 236.2500000000, 57.0000000000 236.6250000000, 55.0000000000 236.7500000000, 54.0000000000 236.8750000000, 52.0000000000 237.0000000000, 51.0000000000 237.1250000000, 52.0000000000 237.2500000000, 54.0000000000 237.6250000000, 52.0000000000 237.7500000000, 51.0000000000 237.8750000000, 49.0000000000 238.0000000000, 47.0000000000 238.7500000000, 49.0000000000 238.8750000000, 51.0000000000 239.0000000000, 51.0000000000 239.7500000000, 49.0000000000 239.8750000000, 51.0000000000 240.0000000000, 52.0000000000 occurrence5 290.5000000000, 57.0000000000 290.6250000000, 55.0000000000 290.7500000000, 53.0000000000 290.8750000000, 52.0000000000 291.0000000000, 50.0000000000 291.5000000000, 53.0000000000 291.6250000000, 52.0000000000 291.7500000000, 50.0000000000 291.8750000000, 48.0000000000 292.0000000000, 47.0000000000 292.1250000000, 48.0000000000 292.2500000000, 50.0000000000 292.6250000000, 48.0000000000 292.7500000000, 47.0000000000 292.8750000000, 45.0000000000 293.0000000000, 44.0000000000 293.1250000000, 45.0000000000 293.2500000000, 47.0000000000 293.6250000000, 45.0000000000 293.7500000000, 44.0000000000 293.8750000000, 42.0000000000 294.0000000000, 40.0000000000 294.7500000000, 42.0000000000 294.8750000000, 44.0000000000 295.0000000000, 44.0000000000 295.7500000000, 42.0000000000 295.8750000000, 44.0000000000 296.0000000000, 45.0000000000 occurrence6 306.5000000000, 62.0000000000 306.6250000000, 60.0000000000 306.7500000000, 58.0000000000 306.8750000000, 57.0000000000 307.0000000000, 55.0000000000 307.5000000000, 58.0000000000 307.6250000000, 57.0000000000 307.7500000000, 55.0000000000 307.8750000000, 53.0000000000 308.0000000000, 52.0000000000 308.1250000000, 53.0000000000 308.2500000000, 55.0000000000 308.6250000000, 53.0000000000 308.7500000000, 52.0000000000 308.8750000000, 50.0000000000 309.0000000000, 49.0000000000 309.1250000000, 50.0000000000 309.2500000000, 52.0000000000 309.6250000000, 50.0000000000 309.7500000000, 49.0000000000 309.8750000000, 47.0000000000 310.0000000000, 45.0000000000 310.7500000000, 47.0000000000 310.8750000000, 49.0000000000 311.0000000000, 49.0000000000 311.7500000000, 47.0000000000 311.8750000000, 49.0000000000 312.0000000000, 50.0000000000 occurrence7 312.0000000000, 50.0000000000 312.1250000000, 52.0000000000 312.2500000000, 53.0000000000 312.6250000000, 52.0000000000 312.7500000000, 50.0000000000 312.8750000000, 48.0000000000 313.0000000000, 47.0000000000 313.1250000000, 48.0000000000 313.2500000000, 50.0000000000 313.6250000000, 48.0000000000 313.7500000000, 47.0000000000 313.8750000000, 45.0000000000 314.0000000000, 43.0000000000 314.7500000000, 45.0000000000 314.8750000000, 47.0000000000 315.0000000000, 47.0000000000 315.7500000000, 45.0000000000 315.8750000000, 47.0000000000 316.0000000000, 48.0000000000 pattern3 occurrence1 248.5000000000, 53.0000000000 249.0000000000, 47.0000000000 249.5000000000, 50.0000000000 250.0000000000, 44.0000000000 250.5000000000, 47.0000000000 251.0000000000, 52.0000000000 252.0000000000, 45.0000000000 occurrence2 276.5000000000, 60.0000000000 277.0000000000, 54.0000000000 277.5000000000, 57.0000000000 278.0000000000, 51.0000000000 278.5000000000, 54.0000000000 279.0000000000, 59.0000000000 280.0000000000, 52.0000000000 occurrence3 150.5000000000, 69.0000000000 151.0000000000, 62.0000000000 151.5000000000, 65.0000000000 152.0000000000, 59.0000000000 152.5000000000, 62.0000000000 153.0000000000, 65.0000000000 occurrence4 68.5000000000, 77.0000000000 69.0000000000, 71.0000000000 69.5000000000, 74.0000000000 70.0000000000, 68.0000000000 71.0000000000, 71.0000000000 occurrence5 196.5000000000, 70.0000000000 197.0000000000, 64.0000000000 197.5000000000, 67.0000000000 198.0000000000, 61.0000000000 198.5000000000, 76.0000000000 199.0000000000, 69.0000000000 occurrence6 200.5000000000, 69.0000000000 201.0000000000, 62.0000000000 201.5000000000, 65.0000000000 202.0000000000, 59.0000000000 202.5000000000, 74.0000000000 203.0000000000, 67.0000000000 mir_eval-0.8.2/tests/data/pattern/ref01.txt000066400000000000000000000212671475740344600205530ustar00rootroot00000000000000pattern1 occurrence1 1.0000000000, 64.0000000000 2.0000000000, 60.0000000000 3.0000000000, 65.0000000000 4.0000000000, 56.0000000000 6.5000000000, 62.0000000000 7.0000000000, 59.0000000000 7.5000000000, 64.0000000000 8.0000000000, 60.0000000000 occurrence2 21.0000000000, 76.0000000000 22.0000000000, 72.0000000000 23.0000000000, 77.0000000000 24.0000000000, 68.0000000000 26.5000000000, 74.0000000000 27.0000000000, 71.0000000000 27.5000000000, 76.0000000000 28.0000000000, 72.0000000000 occurrence3 35.0000000000, 55.0000000000 36.0000000000, 52.0000000000 37.0000000000, 57.0000000000 38.0000000000, 47.0000000000 40.5000000000, 53.0000000000 41.0000000000, 50.0000000000 41.5000000000, 55.0000000000 42.0000000000, 52.0000000000 occurrence4 81.0000000000, 81.0000000000 82.0000000000, 77.0000000000 83.0000000000, 82.0000000000 84.0000000000, 73.0000000000 86.5000000000, 79.0000000000 87.0000000000, 76.0000000000 87.5000000000, 81.0000000000 88.0000000000, 77.0000000000 occurrence5 99.0000000000, 52.0000000000 100.0000000000, 48.0000000000 101.0000000000, 53.0000000000 102.0000000000, 44.0000000000 104.5000000000, 50.0000000000 105.0000000000, 47.0000000000 105.5000000000, 52.0000000000 106.0000000000, 48.0000000000 occurrence6 9.0000000000, 69.0000000000 10.0000000000, 67.0000000000 11.0000000000, 72.0000000000 12.0000000000, 63.0000000000 14.5000000000, 69.0000000000 15.0000000000, 66.0000000000 15.5000000000, 71.0000000000 16.0000000000, 67.0000000000 occurrence7 49.0000000000, 81.0000000000 50.0000000000, 79.0000000000 51.0000000000, 84.0000000000 52.0000000000, 75.0000000000 54.5000000000, 81.0000000000 55.0000000000, 78.0000000000 55.5000000000, 83.0000000000 56.0000000000, 79.0000000000 occurrence8 66.0000000000, 60.0000000000 67.0000000000, 65.0000000000 68.0000000000, 56.0000000000 70.5000000000, 62.0000000000 71.0000000000, 59.0000000000 71.5000000000, 64.0000000000 72.0000000000, 60.0000000000 pattern2 occurrence1 10.5000000000, 64.0000000000 10.6250000000, 62.0000000000 10.7500000000, 60.0000000000 10.8750000000, 59.0000000000 11.0000000000, 57.0000000000 11.5000000000, 60.0000000000 11.6250000000, 59.0000000000 11.7500000000, 57.0000000000 11.8750000000, 55.0000000000 12.0000000000, 54.0000000000 12.1250000000, 55.0000000000 12.2500000000, 57.0000000000 12.6250000000, 55.0000000000 12.7500000000, 54.0000000000 12.8750000000, 52.0000000000 13.0000000000, 51.0000000000 13.1250000000, 52.0000000000 13.2500000000, 54.0000000000 13.6250000000, 52.0000000000 13.7500000000, 51.0000000000 13.8750000000, 49.0000000000 14.0000000000, 47.0000000000 14.7500000000, 49.0000000000 14.8750000000, 51.0000000000 15.0000000000, 51.0000000000 15.7500000000, 49.0000000000 15.8750000000, 51.0000000000 16.0000000000, 52.0000000000 occurrence2 22.5000000000, 69.0000000000 22.6250000000, 67.0000000000 22.7500000000, 65.0000000000 22.8750000000, 64.0000000000 23.0000000000, 62.0000000000 23.5000000000, 65.0000000000 23.6250000000, 64.0000000000 23.7500000000, 62.0000000000 23.8750000000, 60.0000000000 24.0000000000, 59.0000000000 24.1250000000, 60.0000000000 24.2500000000, 62.0000000000 24.6250000000, 60.0000000000 24.7500000000, 59.0000000000 24.8750000000, 57.0000000000 25.0000000000, 56.0000000000 25.1250000000, 57.0000000000 25.2500000000, 59.0000000000 25.6250000000, 57.0000000000 25.7500000000, 56.0000000000 25.8750000000, 54.0000000000 26.0000000000, 52.0000000000 26.7500000000, 54.0000000000 26.8750000000, 56.0000000000 27.0000000000, 56.0000000000 27.7500000000, 54.0000000000 27.8750000000, 56.0000000000 28.0000000000, 57.0000000000 occurrence3 36.5000000000, 84.0000000000 36.6250000000, 83.0000000000 36.7500000000, 81.0000000000 36.8750000000, 79.0000000000 37.0000000000, 77.0000000000 37.5000000000, 81.0000000000 37.6250000000, 79.0000000000 37.7500000000, 77.0000000000 37.8750000000, 76.0000000000 38.0000000000, 74.0000000000 38.1250000000, 76.0000000000 38.2500000000, 77.0000000000 38.6250000000, 76.0000000000 38.7500000000, 74.0000000000 38.8750000000, 72.0000000000 39.0000000000, 71.0000000000 39.1250000000, 72.0000000000 39.2500000000, 74.0000000000 39.6250000000, 72.0000000000 39.7500000000, 71.0000000000 39.8750000000, 69.0000000000 40.0000000000, 67.0000000000 40.7500000000, 69.0000000000 40.8750000000, 71.0000000000 41.0000000000, 71.0000000000 41.7500000000, 69.0000000000 41.8750000000, 71.0000000000 42.0000000000, 72.0000000000 occurrence4 50.5000000000, 76.0000000000 50.6250000000, 74.0000000000 50.7500000000, 72.0000000000 50.8750000000, 71.0000000000 51.0000000000, 69.0000000000 51.5000000000, 72.0000000000 51.6250000000, 71.0000000000 51.7500000000, 69.0000000000 51.8750000000, 67.0000000000 52.0000000000, 66.0000000000 52.1250000000, 67.0000000000 52.2500000000, 69.0000000000 52.6250000000, 67.0000000000 52.7500000000, 66.0000000000 52.8750000000, 64.0000000000 53.0000000000, 63.0000000000 53.1250000000, 64.0000000000 53.2500000000, 66.0000000000 53.6250000000, 64.0000000000 53.7500000000, 63.0000000000 53.8750000000, 61.0000000000 54.0000000000, 59.0000000000 54.7500000000, 61.0000000000 54.8750000000, 63.0000000000 55.0000000000, 63.0000000000 55.7500000000, 61.0000000000 55.8750000000, 63.0000000000 56.0000000000, 64.0000000000 occurrence5 66.5000000000, 57.0000000000 66.6250000000, 55.0000000000 66.7500000000, 53.0000000000 66.8750000000, 52.0000000000 67.0000000000, 50.0000000000 67.5000000000, 53.0000000000 67.6250000000, 52.0000000000 67.7500000000, 50.0000000000 67.8750000000, 48.0000000000 68.0000000000, 47.0000000000 68.1250000000, 48.0000000000 68.2500000000, 50.0000000000 68.6250000000, 48.0000000000 68.7500000000, 47.0000000000 68.8750000000, 45.0000000000 69.0000000000, 44.0000000000 69.1250000000, 45.0000000000 69.2500000000, 47.0000000000 69.6250000000, 45.0000000000 69.7500000000, 44.0000000000 69.8750000000, 42.0000000000 70.0000000000, 40.0000000000 70.7500000000, 42.0000000000 70.8750000000, 44.0000000000 71.0000000000, 44.0000000000 71.7500000000, 42.0000000000 71.8750000000, 44.0000000000 72.0000000000, 45.0000000000 occurrence6 82.5000000000, 62.0000000000 82.6250000000, 60.0000000000 82.7500000000, 58.0000000000 82.8750000000, 57.0000000000 83.0000000000, 55.0000000000 83.5000000000, 58.0000000000 83.6250000000, 57.0000000000 83.7500000000, 55.0000000000 83.8750000000, 53.0000000000 84.0000000000, 52.0000000000 84.1250000000, 53.0000000000 84.2500000000, 55.0000000000 84.6250000000, 53.0000000000 84.7500000000, 52.0000000000 84.8750000000, 50.0000000000 85.0000000000, 49.0000000000 85.1250000000, 50.0000000000 85.2500000000, 52.0000000000 85.6250000000, 50.0000000000 85.7500000000, 49.0000000000 85.8750000000, 47.0000000000 86.0000000000, 45.0000000000 86.7500000000, 47.0000000000 86.8750000000, 49.0000000000 87.0000000000, 49.0000000000 87.7500000000, 47.0000000000 87.8750000000, 49.0000000000 88.0000000000, 50.0000000000 occurrence7 88.0000000000, 50.0000000000 88.1250000000, 52.0000000000 88.2500000000, 53.0000000000 88.6250000000, 52.0000000000 88.7500000000, 50.0000000000 88.8750000000, 48.0000000000 89.0000000000, 47.0000000000 89.1250000000, 48.0000000000 89.2500000000, 50.0000000000 89.6250000000, 48.0000000000 89.7500000000, 47.0000000000 89.8750000000, 45.0000000000 90.0000000000, 43.0000000000 90.7500000000, 45.0000000000 90.8750000000, 47.0000000000 91.0000000000, 47.0000000000 91.7500000000, 45.0000000000 91.8750000000, 47.0000000000 92.0000000000, 48.0000000000 pattern3 occurrence1 24.5000000000, 53.0000000000 25.0000000000, 47.0000000000 25.5000000000, 50.0000000000 26.0000000000, 44.0000000000 26.5000000000, 47.0000000000 27.0000000000, 52.0000000000 28.0000000000, 45.0000000000 occurrence2 52.5000000000, 60.0000000000 53.0000000000, 54.0000000000 53.5000000000, 57.0000000000 54.0000000000, 51.0000000000 54.5000000000, 54.0000000000 55.0000000000, 59.0000000000 56.0000000000, 52.0000000000 occurrence3 38.5000000000, 69.0000000000 39.0000000000, 62.0000000000 39.5000000000, 65.0000000000 40.0000000000, 59.0000000000 40.5000000000, 62.0000000000 41.0000000000, 65.0000000000 occurrence4 68.5000000000, 77.0000000000 69.0000000000, 71.0000000000 69.5000000000, 74.0000000000 70.0000000000, 68.0000000000 71.0000000000, 71.0000000000 occurrence5 84.5000000000, 70.0000000000 85.0000000000, 64.0000000000 85.5000000000, 67.0000000000 86.0000000000, 61.0000000000 86.5000000000, 76.0000000000 87.0000000000, 69.0000000000 occurrence6 88.5000000000, 69.0000000000 89.0000000000, 62.0000000000 89.5000000000, 65.0000000000 90.0000000000, 59.0000000000 90.5000000000, 74.0000000000 91.0000000000, 67.0000000000 mir_eval-0.8.2/tests/data/pattern/ref02.txt000066400000000000000000002270411475740344600205520ustar00rootroot00000000000000pattern1 occurrence1 -1.0000000000, 60.0000000000 -1.0000000000, 68.0000000000 0.0000000000, 61.0000000000 0.0000000000, 70.0000000000 1.0000000000, 58.0000000000 1.0000000000, 67.0000000000 2.0000000000, 53.0000000000 3.0000000000, 60.0000000000 3.0000000000, 68.0000000000 4.0000000000, 56.0000000000 4.0000000000, 65.0000000000 5.0000000000, 53.0000000000 5.0000000000, 56.0000000000 5.0000000000, 60.0000000000 5.0000000000, 65.0000000000 6.0000000000, 55.0000000000 6.0000000000, 58.0000000000 6.0000000000, 60.0000000000 6.0000000000, 64.0000000000 7.0000000000, 53.0000000000 7.0000000000, 56.0000000000 7.0000000000, 60.0000000000 7.0000000000, 65.0000000000 8.0000000000, 52.0000000000 8.0000000000, 55.0000000000 8.0000000000, 60.0000000000 8.0000000000, 67.0000000000 9.0000000000, 53.0000000000 9.0000000000, 56.0000000000 9.0000000000, 60.0000000000 9.0000000000, 70.0000000000 10.0000000000, 68.0000000000 occurrence2 11.0000000000, 63.0000000000 11.0000000000, 72.0000000000 12.0000000000, 65.0000000000 12.0000000000, 73.0000000000 13.0000000000, 61.0000000000 13.0000000000, 70.0000000000 14.0000000000, 56.0000000000 15.0000000000, 63.0000000000 15.0000000000, 72.0000000000 16.0000000000, 60.0000000000 16.0000000000, 68.0000000000 17.0000000000, 56.0000000000 17.0000000000, 60.0000000000 17.0000000000, 63.0000000000 17.0000000000, 68.0000000000 18.0000000000, 58.0000000000 18.0000000000, 61.0000000000 18.0000000000, 63.0000000000 18.0000000000, 67.0000000000 19.0000000000, 56.0000000000 19.0000000000, 60.0000000000 19.0000000000, 63.0000000000 19.0000000000, 68.0000000000 20.0000000000, 55.0000000000 20.0000000000, 58.0000000000 20.0000000000, 63.0000000000 20.0000000000, 70.0000000000 21.0000000000, 56.0000000000 21.0000000000, 60.0000000000 21.0000000000, 63.0000000000 21.0000000000, 73.0000000000 22.0000000000, 72.0000000000 occurrence3 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451.0000000000, 61.0000000000 451.0000000000, 70.0000000000 452.0000000000, 56.0000000000 453.0000000000, 63.0000000000 453.0000000000, 72.0000000000 454.0000000000, 60.0000000000 454.0000000000, 68.0000000000 455.0000000000, 56.0000000000 455.0000000000, 60.0000000000 455.0000000000, 63.0000000000 455.0000000000, 68.0000000000 456.0000000000, 58.0000000000 456.0000000000, 61.0000000000 456.0000000000, 63.0000000000 456.0000000000, 67.0000000000 457.0000000000, 56.0000000000 457.0000000000, 60.0000000000 457.0000000000, 63.0000000000 457.0000000000, 68.0000000000 458.0000000000, 55.0000000000 458.0000000000, 58.0000000000 458.0000000000, 63.0000000000 458.0000000000, 70.0000000000 459.0000000000, 56.0000000000 459.0000000000, 60.0000000000 459.0000000000, 63.0000000000 459.0000000000, 73.0000000000 460.0000000000, 72.0000000000 pattern2 occurrence1 239.0000000000, 60.0000000000 240.0000000000, 53.0000000000 240.0000000000, 69.0000000000 240.5000000000, 67.0000000000 241.0000000000, 57.0000000000 241.0000000000, 69.0000000000 241.5000000000, 65.0000000000 242.0000000000, 60.0000000000 242.0000000000, 67.0000000000 242.5000000000, 69.0000000000 243.0000000000, 62.0000000000 243.0000000000, 70.0000000000 243.5000000000, 69.0000000000 244.0000000000, 55.0000000000 244.0000000000, 70.0000000000 244.5000000000, 67.0000000000 245.0000000000, 53.0000000000 245.0000000000, 69.0000000000 245.5000000000, 71.0000000000 246.0000000000, 52.0000000000 246.0000000000, 72.0000000000 246.5000000000, 71.0000000000 247.0000000000, 55.0000000000 247.0000000000, 72.0000000000 247.5000000000, 74.0000000000 248.0000000000, 60.0000000000 248.0000000000, 72.0000000000 248.5000000000, 70.0000000000 249.0000000000, 53.0000000000 249.0000000000, 69.0000000000 249.5000000000, 67.0000000000 250.0000000000, 57.0000000000 250.0000000000, 69.0000000000 250.5000000000, 70.0000000000 251.0000000000, 60.0000000000 251.0000000000, 69.0000000000 251.5000000000, 65.0000000000 occurrence2 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280.5000000000, 70.0000000000 281.0000000000, 60.0000000000 281.0000000000, 69.0000000000 281.5000000000, 65.0000000000 occurrence3 345.0000000000, 53.0000000000 345.0000000000, 69.0000000000 345.5000000000, 67.0000000000 346.0000000000, 57.0000000000 346.0000000000, 69.0000000000 346.5000000000, 65.0000000000 347.0000000000, 60.0000000000 347.0000000000, 67.0000000000 347.5000000000, 69.0000000000 348.0000000000, 62.0000000000 348.0000000000, 70.0000000000 348.5000000000, 69.0000000000 349.0000000000, 55.0000000000 349.0000000000, 70.0000000000 349.5000000000, 67.0000000000 350.0000000000, 53.0000000000 350.0000000000, 69.0000000000 350.5000000000, 71.0000000000 351.0000000000, 52.0000000000 351.0000000000, 72.0000000000 351.5000000000, 71.0000000000 352.0000000000, 55.0000000000 352.0000000000, 72.0000000000 352.5000000000, 74.0000000000 353.0000000000, 60.0000000000 353.0000000000, 72.0000000000 353.5000000000, 70.0000000000 354.0000000000, 53.0000000000 354.0000000000, 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1}], [{o: 0, f: 6, l: "A", a: 0}, {o: 6, f: 14.8, l: "B", a: 1}, {o: 14.8, f: 26.4, l: "C", a: 0}, {o: 26.4, f: 34.8, l: "B", a: 1}, {o: 34.8, f: 46.6, l: "C", a: 0}, {o: 46.6, f: 52.6, l: "D", a: 1}, {o: 52.6, f: 58, l: "E", a: 0}, {o: 58, f: 67.2, l: "F", a: 1}, {o: 67.2, f: 85.8, l: "G", a: 0}, {o: 85.8, f: 95, l: "B", a: 1}, {o: 95, f: 107.2, l: "C", a: 0}, {o: 107.2, f: 114, l: "D", a: 1}, {o: 114, f: 119, l: "E", a: 0}, {o: 119, f: 127.8, l: "F", a: 1}, {o: 127.8, f: 133.8, l: "D", a: 0}, {o: 133.8, f: 138.4, l: "E", a: 1}, {o: 138.4, f: 147.4, l: "H", a: 0}] ]; var cf5segmentsstructmrx09000016_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/cf5segmentsstructmrx09000016_results.txt000066400000000000000000000001711475740344600302140ustar00rootroot000000000000000 struct_mrx_09_000016 0.6220 0.8514 0.6192 0.8644 0.4823 0.8386 0.3704 0.2778 0.5556 0.5926 0.4444 0.8889 0.4490 4.5000 mir_eval-0.8.2/tests/data/segment/raw_data/cf6segmentssalami000970.js000066400000000000000000000017221475740344600252670ustar00rootroot00000000000000var cf6segmentssalami000970_data = [ [{o: 0.436, f: 111.316, l: "N", a: 0}, {o: 111.316, f: 124.345, l: "A", a: 1}, {o: 124.345, f: 137.409, l: "B", a: 0}, {o: 137.409, f: 154.178, l: "C", a: 1}, {o: 154.178, f: 167.193, l: "A", a: 0}, {o: 167.193, f: 180.41, l: "B", a: 1}, {o: 180.41, f: 197.305, l: "C", a: 0}, {o: 197.305, f: 210.605, l: "A", a: 1}, {o: 210.605, f: 224.052, l: "B", a: 0}, {o: 224.052, f: 241.258, l: "C", a: 1}, {o: 241.258, f: 254.268, l: "A", a: 0}, {o: 254.268, f: 267.205, l: "B", a: 1}, {o: 267.205, f: 292.617, l: "C'", a: 0}], [{o: 0, f: 5.55, l: "N1", a: 0}, {o: 5.55, f: 17.264, l: "B", a: 1}, {o: 17.264, f: 23.116, l: "N2", a: 0}, {o: 23.116, f: 36.467, l: "B", a: 1}, {o: 36.467, f: 110.98, l: "N3", a: 0}, {o: 110.98, f: 154.076, l: "A", a: 1}, {o: 154.076, f: 197.265, l: "A", a: 0}, {o: 197.265, f: 241.232, l: "A", a: 1}, {o: 241.232, f: 291.979, l: "N5", a: 0}] ]; var cf6segmentssalami000970_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/cf6segmentssalami000970_results.txt000066400000000000000000000001621475740344600272500ustar00rootroot000000000000000 salami000970 0.6933 0.5805 0.5464 0.4956 0.6088 0.7543 0.4167 0.5000 0.3571 0.5000 0.6000 0.4286 13.0770 0.5370 mir_eval-0.8.2/tests/data/segment/raw_data/cf6segmentsstructmrx09000117.js000066400000000000000000000025521475740344600262400ustar00rootroot00000000000000var cf6segmentsstructmrx09000117_data = [ [{o: 0.2, f: 76.986, l: "Intro", a: 0}, {o: 76.986, f: 92.729, l: "Verse", a: 1}, {o: 92.729, f: 108.453, l: "Verse", a: 0}, {o: 108.453, f: 124.201, l: "Bridge", a: 1}, {o: 124.201, f: 139.933, l: "Refrain", a: 0}, {o: 139.933, f: 195.049, l: "Instrumental solo", a: 1}, {o: 195.049, f: 210.808, l: "Break", a: 0}, {o: 210.808, f: 226.512, l: "Ad Lib", a: 1}, {o: 226.512, f: 322.56, l: "Outro", a: 0}], [{o: 0, f: 3.75, l: "N1", a: 0}, {o: 3.75, f: 17.415, l: "A", a: 1}, {o: 17.415, f: 33.158, l: "A", a: 0}, {o: 33.158, f: 48.913, l: "A", a: 1}, {o: 48.913, f: 64.656, l: "A", a: 0}, {o: 64.656, f: 80.399, l: "A", a: 1}, {o: 80.399, f: 96.142, l: "A", a: 0}, {o: 96.142, f: 111.897, l: "A", a: 1}, {o: 111.897, f: 127.628, l: "A", a: 0}, {o: 127.628, f: 141.398, l: "A", a: 1}, {o: 141.398, f: 164.525, l: "N9", a: 0}, {o: 164.525, f: 180.28, l: "B", a: 1}, {o: 180.28, f: 196.023, l: "B", a: 0}, {o: 196.023, f: 207.331, l: "N10", a: 1}, {o: 207.331, f: 224.061, l: "A", a: 0}, {o: 224.061, f: 239.804, l: "A", a: 1}, {o: 239.804, f: 253.574, l: "A", a: 0}, {o: 253.574, f: 261.457, l: "N13", a: 1}, {o: 261.457, f: 277.2, l: "A", a: 0}, {o: 277.2, f: 292.943, l: "A", a: 1}, {o: 292.943, f: 308.686, l: "A", a: 0}, {o: 308.686, f: 321.991, l: "A", a: 1}] ]; var cf6segmentsstructmrx09000117_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/cf6segmentsstructmrx09000117_results.txt000066400000000000000000000001721475740344600302200ustar00rootroot000000000000000 struct_mrx_09_000117 0.8303 0.4029 0.4005 0.2658 0.8119 0.5314 0.0606 0.0435 0.1000 0.3030 0.2174 0.5000 2.9320 12.3300 mir_eval-0.8.2/tests/data/segment/raw_data/mp1segmentssalami000119.js000066400000000000000000000016501475740344600253010ustar00rootroot00000000000000var mp1segmentssalami000119_data = [ [{o: 0.041, f: 5.478, l: "Z", a: 0}, {o: 5.478, f: 24.498, l: "A", a: 1}, {o: 24.498, f: 44.068, l: "B", a: 0}, {o: 44.068, f: 63.352, l: "C", a: 1}, {o: 63.352, f: 82.6, l: "B", a: 0}, {o: 82.6, f: 101.552, l: "C", a: 1}, {o: 101.552, f: 120.82, l: "B", a: 0}, {o: 120.82, f: 149.498, l: "C", a: 1}, {o: 149.498, f: 187.963, l: "C", a: 0}, {o: 187.963, f: 263.877, l: "C", a: 1}, {o: 263.877, f: 288.16, l: "Z", a: 0}], [{o: 0, f: 32.009, l: "Seg#001", a: 0}, {o: 32.009, f: 61.652, l: "Seg#002", a: 1}, {o: 61.652, f: 100.252, l: "Seg#003", a: 0}, {o: 100.252, f: 123.652, l: "Seg#004", a: 1}, {o: 123.652, f: 159.359, l: "Seg#005", a: 0}, {o: 159.359, f: 191.094, l: "Seg#006", a: 1}, {o: 191.094, f: 217.223, l: "Seg#007", a: 0}, {o: 217.223, f: 262.742, l: "Seg#008", a: 1}, {o: 262.742, f: 288, l: "Seg#009", a: 0}] ]; var mp1segmentssalami000119_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/mp1segmentssalami000119_results.txt000066400000000000000000000001611475740344600272610ustar00rootroot000000000000000 salami000119 0.3301 0.7676 0.3200 0.7935 0.2004 0.6162 0.1818 0.2000 0.1667 0.5454 0.6000 0.5000 2.9813 2.2660 mir_eval-0.8.2/tests/data/segment/raw_data/mp1segmentsstructmrx09000084.js000066400000000000000000000012311475740344600262530ustar00rootroot00000000000000var mp1segmentsstructmrx09000084_data = [ [{o: 0, f: 34.752, l: "Part 1", a: 0}, {o: 34.752, f: 89.014, l: "Part 2", a: 1}, {o: 89.014, f: 123.498, l: "Part 3 (with complete ending)", a: 0}], [{o: 0, f: 19.522, l: "Seg#001", a: 0}, {o: 19.522, f: 31.083, l: "Seg#002", a: 1}, {o: 31.083, f: 52.39, l: "Seg#003", a: 0}, {o: 52.39, f: 69.346, l: "Seg#004", a: 1}, {o: 69.346, f: 89.58, l: "Seg#005", a: 0}, {o: 89.58, f: 96.447, l: "Seg#006", a: 1}, {o: 96.447, f: 106.017, l: "Seg#007", a: 0}, {o: 106.017, f: 121.02, l: "Seg#008", a: 1}, {o: 121.02, f: 139.833, l: "Seg#009", a: 0}] ]; var mp1segmentsstructmrx09000084_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/mp1segmentsstructmrx09000084_results.txt000066400000000000000000000001721475740344600302420ustar00rootroot000000000000000 struct_mrx_09_000084 0.4981 0.9093 0.5342 0.9009 0.3797 0.8115 0.1429 0.1000 0.2500 0.4286 0.3000 0.7500 1.5221 11.3320 mir_eval-0.8.2/tests/data/segment/raw_data/mp2segmentssalami000666.js000066400000000000000000000031571475740344600253150ustar00rootroot00000000000000var mp2segmentssalami000666_data = [ [{o: 0.372, f: 23.49, l: "A", a: 0}, {o: 23.49, f: 147.424, l: "A", 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mp2segmentsstructmrx09000132_data = [ [{o: 0.384, f: 3.686, l: "Intro", a: 0}, {o: 3.686, f: 15.372, l: "Verse", a: 1}, {o: 15.372, f: 27.121, l: "Verse", a: 0}, {o: 27.121, f: 42.51, l: "Refrain", a: 1}, {o: 42.51, f: 54.285, l: "Verse", a: 0}, {o: 54.285, f: 66.194, l: "Verse", a: 1}, {o: 66.194, f: 81.67, l: "Refrain", a: 0}, {o: 81.67, f: 93.143, l: "Verse (Guitar Solo)", a: 1}, {o: 93.143, f: 108.536, l: "Refrain", a: 0}, {o: 108.536, f: 119.267, l: "Outro (fade out)", a: 1}], [{o: 0, f: 0.499, l: "Seg#001", a: 0}, {o: 0.499, f: 3.823, l: "Seg#002", a: 1}, {o: 3.823, f: 28.706, l: "Seg#003", a: 0}, {o: 28.706, f: 42.635, l: "Seg#004", a: 1}, {o: 42.635, f: 66.801, l: "Seg#005", a: 0}, {o: 66.801, f: 79.868, l: "Seg#006", a: 1}, {o: 79.868, f: 108.669, l: "Seg#007", a: 0}, {o: 108.669, f: 117.563, l: "Seg#008", a: 1}, {o: 117.563, f: 124.552, l: "Seg#009", a: 0}] ]; var mp2segmentsstructmrx09000132_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/mp2segmentsstructmrx09000132_results.txt000066400000000000000000000001711475740344600302340ustar00rootroot000000000000000 struct_mrx_09_000132 0.6241 0.8438 0.5560 0.7901 0.4289 0.7960 0.3810 0.4000 0.3636 0.7619 0.8000 0.7273 1.5851 0.4952 mir_eval-0.8.2/tests/data/segment/raw_data/rbh3segmentssalami000025.js000066400000000000000000000016031475740344600254340ustar00rootroot00000000000000var rbh3segmentssalami000025_data = [ [{o: 0.488, f: 34.145, l: "A", a: 0}, {o: 34.145, f: 62.367, l: "B", a: 1}, {o: 62.367, f: 86.586, l: "B", a: 0}, {o: 86.586, f: 102.772, l: "C", a: 1}, {o: 102.772, f: 126.972, l: "B", a: 0}, {o: 126.972, f: 143.189, l: "C", a: 1}, {o: 143.189, f: 159.275, l: "D", a: 0}, {o: 159.275, f: 183.465, l: "B", a: 1}, {o: 183.465, f: 193.567, l: "C", a: 0}, {o: 193.567, f: 227.796, l: "C", a: 1}], [{o: 0, f: 58.249, l: "C", a: 0}, {o: 58.249, f: 74.409, l: "C", a: 1}, {o: 74.409, f: 86.529, l: "B", a: 0}, {o: 86.529, f: 112.788, 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l: "D", a: 1}, {o: 51.733, f: 62.009, l: "C", a: 0}, {o: 62.009, f: 67.272, l: "D", a: 1}, {o: 67.272, f: 88.013, l: "E", a: 0}, {o: 88.013, f: 98.093, l: "B", a: 1}, {o: 98.093, f: 108.558, l: "C", a: 0}, {o: 108.558, f: 113.773, l: "D", a: 1}, {o: 113.773, f: 124.018, l: "C", a: 0}, {o: 124.018, f: 134.292, l: "D", a: 1}, {o: 134.292, f: 144.632, l: "C", a: 0}, {o: 144.632, f: 149.704, l: "D", a: 1}, {o: 149.704, f: 170.478, l: "C", a: 0}, {o: 170.478, f: 185.469, l: "B", a: 1}], [{o: 0, f: 15.92, l: "C", a: 0}, {o: 15.92, f: 36.543, l: "D", a: 1}, {o: 36.543, f: 50.721, l: "B", a: 0}, {o: 50.721, f: 85.522, l: "D", a: 1}, {o: 85.522, f: 97.122, l: "B", a: 0}, {o: 97.122, f: 134.501, l: "D", a: 1}, {o: 134.501, f: 169.302, l: "D", a: 0}, {o: 169.302, f: 180.903, l: "C", a: 1}, {o: 180.903, f: 185.4, l: "C", a: 0}] ]; var rbh3segmentssalami000068_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/rbh3segmentssalami000068_results.txt000066400000000000000000000001611475740344600274250ustar00rootroot000000000000000 salami000068 0.6091 0.4092 0.5674 0.4452 0.7823 0.6475 0.2759 0.4000 0.2105 0.5517 0.8000 0.4210 5.4102 0.9919 mir_eval-0.8.2/tests/data/segment/raw_data/rbh4segmentsstructmrx09000020.js000066400000000000000000000016231475740344600264100ustar00rootroot00000000000000var rbh4segmentsstructmrx09000020_data = [ [{o: 0.245, f: 3.424, l: "Intro", a: 0}, {o: 3.424, f: 27.753, l: "Verse", a: 1}, {o: 27.753, f: 52.156, l: "Refrain", a: 0}, {o: 52.156, f: 75.851, l: "Verse", a: 1}, {o: 75.851, f: 100.386, l: "Refrain", a: 0}, {o: 100.386, f: 122.004, l: "Verse (instrumental)", a: 1}, {o: 122.004, f: 137.268, l: "Bridge", a: 0}, {o: 137.268, f: 161.414, l: "Refrain", a: 1}, {o: 161.414, f: 179.425, l: "Outro", a: 0}], [{o: 0, f: 10.902, l: "B", a: 0}, {o: 10.902, f: 43.431, l: "D", a: 1}, {o: 43.431, f: 51.563, l: "B", a: 0}, {o: 51.563, f: 59.695, l: "B", a: 1}, {o: 59.695, f: 94.257, l: "D", a: 0}, {o: 94.257, f: 102.389, l: "B", a: 1}, {o: 102.389, f: 120.686, l: "D", a: 0}, {o: 120.686, f: 128.818, l: "D", a: 1}, {o: 128.818, f: 155.248, l: "D", a: 0}, {o: 155.248, f: 182, l: "B", a: 1}] ]; var rbh4segmentsstructmrx09000020_seriesNames = ["Ground-truth","Prediction"]; mir_eval-0.8.2/tests/data/segment/raw_data/rbh4segmentsstructmrx09000020_results.txt000066400000000000000000000001711475740344600303710ustar00rootroot000000000000000 struct_mrx_09_000020 0.3517 0.2743 0.4005 0.2959 0.6193 0.5149 0.0952 0.0909 0.1000 0.4762 0.4546 0.5000 2.9997 6.1291 mir_eval-0.8.2/tests/data/segment/ref00.lab000066400000000000000000000003051475740344600204440ustar00rootroot000000000000000.383 7.332 Intro 7.332 27.109 Verse 27.109 47.015 Verse 47.015 67.123 Bridge 67.123 87.103 Verse (Guitar Solo) 87.103 107.649 Verse 107.649 128.114 Bridge 128.114 142.763 Outro (fade-out) mir_eval-0.8.2/tests/data/segment/ref01.lab000066400000000000000000000003661475740344600204540ustar00rootroot000000000000000.436 111.316 N 111.316 124.345 A 124.345 137.409 B 137.409 154.178 C 154.178 167.193 A 167.193 180.410 B 180.410 197.305 C 197.305 210.605 A 210.605 224.052 B 224.052 241.258 C 241.258 254.268 A 254.268 267.205 B 267.205 292.617 C' mir_eval-0.8.2/tests/data/segment/ref02.lab000066400000000000000000000003311475740344600204450ustar00rootroot000000000000000.200 76.986 Intro 76.986 92.729 Verse 92.729 108.453 Verse 108.453 124.201 Bridge 124.201 139.933 Refrain 139.933 195.049 Instrumental solo 195.049 210.808 Break 210.808 226.512 Ad Lib 226.512 322.560 Outro mir_eval-0.8.2/tests/data/segment/ref03.lab000066400000000000000000000003031475740344600204450ustar00rootroot000000000000000.041 5.478 Z 5.478 24.498 A 24.498 44.068 B 44.068 63.352 C 63.352 82.600 B 82.600 101.552 C 101.552 120.820 B 120.820 149.498 C 149.498 187.963 C 187.963 263.877 C 263.877 288.160 Z mir_eval-0.8.2/tests/data/segment/ref04.lab000066400000000000000000000001311475740344600204450ustar00rootroot000000000000000.000 34.752 Part 1 34.752 89.014 Part 2 89.014 123.498 Part 3 (with complete ending) mir_eval-0.8.2/tests/data/segment/ref05.lab000066400000000000000000000004541475740344600204560ustar00rootroot000000000000000.372 23.490 A 23.490 147.424 A 147.424 171.866 A 171.866 183.994 C 183.994 196.361 C 196.361 208.201 D 208.201 220.353 C 220.353 232.328 D 232.328 254.136 C 254.136 266.072 D 266.072 278.600 C 278.600 302.370 D 302.370 315.045 C 315.045 327.589 D 327.589 341.350 D 341.350 431.961 A mir_eval-0.8.2/tests/data/segment/ref06.lab000066400000000000000000000003611475740344600204540ustar00rootroot000000000000000.384 3.686 Intro 3.686 15.372 Verse 15.372 27.121 Verse 27.121 42.510 Refrain 42.510 54.285 Verse 54.285 66.194 Verse 66.194 81.670 Refrain 81.670 93.143 Verse (Guitar Solo) 93.143 108.536 Refrain 108.536 119.267 Outro (fade out) mir_eval-0.8.2/tests/data/segment/ref07.lab000066400000000000000000000002661475740344600204610ustar00rootroot000000000000000.488 34.145 A 34.145 62.367 B 62.367 86.586 B 86.586 102.772 C 102.772 126.972 B 126.972 143.189 C 143.189 159.275 D 159.275 183.465 B 183.465 193.567 C 193.567 227.796 C mir_eval-0.8.2/tests/data/segment/ref08.lab000066400000000000000000000004761475740344600204650ustar00rootroot000000000000000.139 5.410 A 5.410 15.581 B 15.581 25.983 C 25.983 31.045 D 31.045 41.583 C 41.583 51.733 D 51.733 62.009 C 62.009 67.272 D 67.272 88.013 E 88.013 98.093 B 98.093 108.558 C 108.558 113.773 D 113.773 124.018 C 124.018 134.292 D 134.292 144.632 C 144.632 149.704 D 149.704 170.478 C 170.478 185.469 B mir_eval-0.8.2/tests/data/segment/ref09.lab000066400000000000000000000003311475740344600204540ustar00rootroot000000000000000.245 3.424 Intro 3.424 27.753 Verse 27.753 52.156 Refrain 52.156 75.851 Verse 75.851 100.386 Refrain 100.386 122.004 Verse (instrumental) 122.004 137.268 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3p#l%@)~'"Zoaav;vs%mir_eval-0.8.2/tests/data/separation/output01.json000066400000000000000000000077141475740344600221620ustar00rootroot00000000000000{"Sources":{"Source to Distortion": [1.6357250261616694, 8.35730768374355], "Source to Interference": [2.869614536623785, 19.596668518613885], "Source to Artifact": [9.511469033244348, 8.744100747692844], "Source permutation": [0, 1]}, "Framewise": { "Source to Distortion": [ [ 5.149669474914772, 3.1488100796540404, 1.6539826780575286, 2.8748397843157396, 2.6745263013094145, 2.5542948621892525, 0.41438689453916433 ], [ 8.140962974194387, 9.168101838553662, 9.20529893957309, 8.556308362721357, 18.666441030639284, 16.815137099504994, 10.585218780016422 ] ], "Source to Interference": [ [ 7.904800707297028, 4.0570038445068395, 2.22137218148402, 3.61373944766269, 3.149070649891711, 3.1501808657513037, 1.8032413005929873 ], [ 15.402937023765805, 13.875470944863594, 16.175846921346125, 14.675806974690932, 22.988234803441774, 20.54239992419536, 15.217408852352577 ] ], "Source to Artifact": [ [ 9.08313970096718, 11.83030341933345, 12.813737665972784, 12.499974926704931, 14.239859841858657, 13.189827403144259, 8.243205007241274 ], [ 9.167978020038662, 11.135668579824056, 10.282717737529854, 9.918738648986986, 20.69256955203501, 19.24839646750305, 12.546051958209466 ] ], "Source permutation": [ [ 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 ], [ 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0 ] ], "win": 2000, "hop": 1000 }, "Images": { "Source to Distortion": [ -3.728734240907568, -0.4318201250413093 ], "Image to Spatial": [ -3.101322996790156, -0.28353454509577714 ], "Source to Interference": [ 2.8696145365946797, 19.596668515448087 ], "Source to Artifact": [ 9.511469033350544, 8.744100747708758 ], "Source permutation": [ 0, 1 ], "nchan": 2, "gain": 0.0, "reverse": false }, "Images Framewise": { "Source to Distortion": [ [ -3.610134649333453, -3.8689468446156905 ], [ -1.025984860608304, -0.3227907605130132 ] ], "Image to Spatial": [ [ -3.122461076386494, -3.20865288224359 ], [ -0.9437277119526215, -0.23800696552017314 ] ], "Source to Interference": [ [ 3.9650508040494343, 2.5015023988684137 ], [ 19.22571259353907, 20.412950345526504 ] ], "Source to Artifact": [ [ 10.543876939080949, 12.175832774712031 ], [ 11.046588452497266, 11.060465308797735 ] ], "Source permutation": [ [ 0.0, 0.0 ], [ 1.0, 1.0 ] ], "win": 3500, "hop": 3000, "nchan": 2, "gain": 0.0, "reverse": false } } mir_eval-0.8.2/tests/data/separation/output02.json000066400000000000000000000225671475740344600221660ustar00rootroot00000000000000{"Sources":{"Source to Distortion": [10.402442396290404, 11.615179897174547, 8.213820164496585], "Source to Interference": [13.688266627376017, 15.194137115835119, 10.084397177627933], "Source to Artifact": [13.335559184910213, 14.252119758894345, 13.179999062945116], "Source permutation": [2, 0, 1]}, "Framewise": { "Source to Distortion": [ [ -3.3683586251724176, -0.4258131004730029, 5.737954384287263, 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mir_eval-0.8.2/tests/data/separation/output09.json000066400000000000000000000075431475740344600221720ustar00rootroot00000000000000{"Sources":{"Source to Distortion": [6.533836035573265], "Source to Interference": [Infinity], "Source to Artifact": [6.533836035573265], "Source permutation": [0]}, "Framewise": { "Source to Distortion": [ [ 12.119934389970112, 9.839136413593899, 10.104607545126424, 14.83174440067323, 10.402492739711457, 9.962792788321892, 7.591397020480595, 6.953836345225294, 19.31013868039556, 14.789903496557441, 11.069772397512526, 10.153206550236124, 9.717806202451953, 7.908023275472441, 8.189812854799017, 18.577301858851083 ] ], "Source to Interference": [ [ Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity, Infinity ] ], "Source to Artifact": [ [ 12.119934389970112, 9.839136413593899, 10.104607545126424, 14.83174440067323, 10.402492739711457, 9.962792788321892, 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D o ؝&֟"dKb p re5|} ;OIsmLc}% T ?3OEFBJK.p|XG*\#;_ubEOYvqfqL ?ߡތ e O gsP)!>Ly%7Yh,#dN  hIw!U""f W\r}jFPR LDDr_ T&V)/19)_5bd8LI,-Z %v2 gw|WW)5-P\_yO` ,P&>O{N{i,WtoWt:%9sVP Q!F>M4Jd~ 4d*VN'?3`Z;.K M[WM?I&@*VYM-e_1]'ueFD"/a:*Ly8N:ILo^hw=!EYo AsRd2 8@D=C.{UFRfim!8#NUQ}/~s\.,PNj0#Q @QI ?W"b9l GVrKpk5gN|pTK=:=]~$)>#(aE p(Y|a ?z{+c {qf[["'` 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [ mir_eval.alignment.absolute_error, mir_eval.alignment.percentage_correct, mir_eval.alignment.percentage_correct_segments, ( lambda ref_ts, est_ts: mir_eval.alignment.percentage_correct_segments( ref_ts, est_ts, duration=max(np.max(ref_ts), np.max(est_ts)) ) ), mir_eval.alignment.karaoke_perceptual_metric, ], ) @pytest.mark.parametrize( "est_alignment, pred_alignment", [ ( np.array([[1.0, 2.0]]), np.array([[1.0, 2.0]]), ), # alignments must be 1d ndarray ( np.array([[-1.0, 2.0]]), np.array([[1.0, 2.0]]), ), # alignments must be non-negative (np.array([[2.0, 1.0]]), np.array([[1.0, 2.0]])), # alignments must be sorted ( np.array([[1.0, 2.0]]), np.array([[1.0]]), ), # alignments must have the same length ], ) def test_alignment_functions_fail(metric, est_alignment, pred_alignment): metric(est_alignment, pred_alignment) @pytest.fixture def alignment_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) reference_alignments = mir_eval.io.load_events(ref_f) estimated_alignments = mir_eval.io.load_events(est_f) return reference_alignments, estimated_alignments, expected_scores @pytest.mark.parametrize("alignment_data", file_sets, indirect=True) def test_alignment_functions(alignment_data): reference_alignments, estimated_alignments, expected_scores = alignment_data scores = mir_eval.alignment.evaluate(reference_alignments, estimated_alignments) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_beat.py000066400000000000000000000103301475740344600170200ustar00rootroot00000000000000""" Unit tests for mir_eval.beat """ import numpy as np import json import mir_eval import glob import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/beat/ref*.txt" EST_GLOB = "data/beat/est*.txt" SCORES_GLOB = "data/beat/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def beat_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) reference_beats = mir_eval.io.load_events(ref_f) estimated_beats = mir_eval.io.load_events(est_f) return reference_beats, estimated_beats, expected_scores def test_trim_beats(): # Construct dummy beat times [0., 1., ...] dummy_beats = np.arange(10, dtype=np.float64) # We expect trim_beats to remove all beats < 5s expected_beats = np.arange(5, 10, dtype=np.float64) assert np.allclose(mir_eval.beat.trim_beats(dummy_beats), expected_beats) @pytest.mark.parametrize( "metric", [ mir_eval.beat.f_measure, mir_eval.beat.cemgil, mir_eval.beat.goto, mir_eval.beat.p_score, mir_eval.beat.continuity, mir_eval.beat.information_gain, ], ) def test_beat_empty_warnings(metric): with pytest.warns(UserWarning, match="Reference beats are empty."): metric(np.array([]), np.arange(10)) with pytest.warns(UserWarning, match="Estimated beats are empty."): metric(np.arange(10), np.array([])) with pytest.warns(UserWarning, match="beats are empty."): assert np.allclose(metric(np.array([]), np.array([])), 0) @pytest.mark.parametrize( "metric", [ mir_eval.beat.f_measure, mir_eval.beat.cemgil, mir_eval.beat.goto, mir_eval.beat.p_score, mir_eval.beat.continuity, mir_eval.beat.information_gain, ], ) @pytest.mark.parametrize( "beats", [ np.array([[1.0, 2.0]]), # beats must be a 1d array np.array([1e10, 1e11]), # beats must be not huge np.array([2.0, 1.0]), # must be sorted ], ) @pytest.mark.xfail(raises=ValueError) def test_beat_fail(metric, beats): metric(beats, beats) @pytest.mark.parametrize( "metric", [ mir_eval.beat.f_measure, mir_eval.beat.cemgil, mir_eval.beat.goto, mir_eval.beat.p_score, mir_eval.beat.continuity, mir_eval.beat.information_gain, ], ) def test_beat_perfect(metric): beats = np.arange(10, dtype=np.float64) assert np.allclose(metric(beats, beats), 1) @pytest.mark.parametrize("beat_data", file_sets, indirect=True) def test_beat_functions(beat_data): reference_beats, estimated_beats, expected_scores = beat_data # Compute scores scores = mir_eval.beat.evaluate(reference_beats, estimated_beats) # Compare them assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) # Unit tests for specific behavior not covered by the above def test_goto_proportion_correct(): # This covers the case when over 75% of the beat tracking is correct, and # more than 3 beats are incorrect assert mir_eval.beat.goto( np.arange(100), np.append(np.arange(80), np.arange(80, 100) + 0.2) ) @pytest.mark.parametrize( "metric", [mir_eval.beat.p_score, mir_eval.beat.continuity, mir_eval.beat.information_gain], ) def test_warning_on_one_beat(metric): # This tests the metrics where passing only a single beat raises a warning # and returns 0 with pytest.warns(UserWarning, match="Only one reference beat"): metric(np.array([10]), np.arange(10)) with pytest.warns(UserWarning, match="Only one estimated beat"): metric(np.arange(10), np.array([10])) def test_continuity_edge_cases(): # There is some special-case logic for when there are few beats assert np.allclose( mir_eval.beat.continuity(np.array([6.0, 6.0]), np.array([6.0, 7.0])), 0.0 ) assert np.allclose( mir_eval.beat.continuity(np.array([6.0, 6.0]), np.array([6.5, 7.0])), 0.0 ) mir_eval-0.8.2/tests/test_chord.py000066400000000000000000000507571475740344600172250ustar00rootroot00000000000000""" Unit tests for mir_eval.chord """ import mir_eval import numpy as np import pytest import glob import json A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/chord/ref*.lab" EST_GLOB = "data/chord/est*.lab" SCORES_GLOB = "data/chord/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def chord_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) # Load in reference melody ref_intervals, ref_labels = mir_eval.io.load_labeled_intervals(ref_f) # Load in estimated melody est_intervals, est_labels = mir_eval.io.load_labeled_intervals(est_f) return ref_intervals, ref_labels, est_intervals, est_labels, expected_scores @pytest.mark.parametrize( "pitch, semitone", [("Gbb", 5), ("G", 7), ("G#", 8), ("Cb", 11), ("B#", 0)] ) def test_pitch_class_to_semitone_valid(pitch, semitone): assert mir_eval.chord.pitch_class_to_semitone(pitch) == semitone @pytest.mark.parametrize("pitch", ["Cab", "#C", "bG"]) @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) def test_pitch_class_to_semitone_fail(pitch): mir_eval.chord.pitch_class_to_semitone(pitch) @pytest.mark.parametrize( "degree, semitone", [ ("b7", 10), ("#3", 5), ("1", 0), ("b1", -1), ("#7", 12), ("bb5", 5), ("11", 17), ("#13", 22), ], ) def test_scale_degree_to_semitone(degree, semitone): assert mir_eval.chord.scale_degree_to_semitone(degree) == semitone @pytest.mark.parametrize("degree", ["7b", "4#", "77", "15"]) @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) def test_scale_degree_to_semitone(degree): mir_eval.chord.scale_degree_to_semitone(degree) @pytest.mark.parametrize( "degree, bitmap, modulo, length", [ ("3", [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], True, 12), ("*3", [0, 0, 0, 0, -1, 0, 0, 0, 0, 0, 0, 0], True, 12), ("b1", [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], True, 12), ("9", [0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0], True, 12), ("9", [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], False, 12), ("9", [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1], False, 15), ], ) def test_scale_degree_to_bitmap(degree, bitmap, modulo, length): assert np.allclose( mir_eval.chord.scale_degree_to_bitmap(degree, modulo=modulo, length=length), bitmap, ) @pytest.mark.parametrize( "label", ["C", "Eb:min/5", "A#:dim7", "B:maj(*1,*5)/3", "A#:sus4", "A:(9,11)"] ) def test_validate_chord_label(label): # For valid labels, calling the function without an error = pass mir_eval.chord.validate_chord_label(label) @pytest.mark.parametrize( "label", ["C::maj", "C//5", "C((4)", "C5))", "C:maj(*3/3", "Cmaj*3/3)", "asdf"] ) @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) def test_validate_bad_chord_label(label): mir_eval.chord.validate_chord_label(label) @pytest.mark.parametrize( "label, split", [ ("C", ["C", "maj", set(), "1"]), ("B:maj(*1,*3)/5", ["B", "maj", {"*1", "*3"}, "5"]), ("Ab:min/b3", ["Ab", "min", set(), "b3"]), ("N", ["N", "", set(), ""]), ("G:(3)", ["G", "", {"3"}, "1"]), ], ) def test_split(label, split): assert mir_eval.chord.split(label) == split @pytest.mark.parametrize( "label, split", [("C", ["C", "maj", set(), "1"]), ("C:minmaj7", ["C", "min", {"7"}, "1"])], ) def test_split_extended(label, split): # Test with reducing extended chords mir_eval.chord.split(label, reduce_extended_chords=True) == split @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) def test_split_fail(): # Test that an exception is raised when a chord with an omission but no # quality is supplied mir_eval.chord.split("C(*5)") # Arguments are root, quality, extensions, bass @pytest.mark.parametrize( "label, split", [ ("F#", ("F#", "", None, "")), ("F#:hdim7", ("F#", "hdim7", None, "")), ("F#:(*b3,4)", ("F#", "", ["*b3", "4"], "")), ("F#/b7", ("F#", "", None, "b7")), ("F#:(*b3,4)/b7", ("F#", "", ["*b3", "4"], "b7")), ("F#:hdim7/b7", ("F#", "hdim7", None, "b7")), ("F#:hdim7(*b3,4)/b7", ("F#", "hdim7", ["*b3", "4"], "b7")), ], ) def test_join(label, split): # Test is relying on implicit parameter ordering here: root, quality, extensions, bass assert mir_eval.chord.join(*split) == label @pytest.mark.parametrize( "bitmap, root, expected_bitmap", [ ([1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], 0, [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]), ([1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], 5, [1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0]), ( [1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0], 11, [0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1], ), ], ) def test_rotate_bitmaps_to_roots(bitmap, root, expected_bitmap): ans = mir_eval.chord.rotate_bitmaps_to_roots([bitmap], [root]) assert np.all(ans == [expected_bitmap]) @pytest.mark.parametrize( "label, e_root, e_interval, e_bass, reduce, strict", [ ("B:maj(*1,*3)/5", 11, [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], 7, False, False), ("G:dim", 7, [1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0], 0, False, False), ("C:(3)/3", 0, [1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0], 4, False, False), ("A:9/b3", 9, [1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0], 3, False, False), ("G:dim(4)/6", 7, [1, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0], 9, False, False), ("A:9", 9, [1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0], 0, True, False), ], ) def test_chord_encode(label, e_root, e_interval, e_bass, reduce, strict): root, intervals, bass = mir_eval.chord.encode( label, reduce_extended_chords=reduce, strict_bass_intervals=strict ) assert root == e_root, (root, e_root) assert np.all(intervals == e_interval), (intervals, e_interval) assert bass == e_bass, (bass, e_bass) @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) def test_chord_encode_fail(): # Non-chord bass notes *must* be explicitly named as extensions when # strict_bass_intervals == True mir_eval.chord.encode( "G:dim(4)/6", reduce_extended_chords=False, strict_bass_intervals=True ) def test_encode_many(): """Does all of the logic for checking encode_many""" labels = ["B:maj(*1,*3)/5", "B:maj(*1,*3)/5", "N", "C:min", "C:min"] expected_roots = [11, 11, -1, 0, 0] expected_intervals = [ [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], [1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0], ] expected_basses = [7, 7, -1, 0, 0] roots, intervals, basses = mir_eval.chord.encode_many(labels) assert np.all(roots == expected_roots) assert np.all(intervals == expected_intervals) assert np.all(basses == expected_basses) def __check_one_metric(metric, ref_label, est_label, score): """Checks that a metric function produces score given ref_label and est_label""" # We provide a dummy interval. We're just checking one pair # of labels at a time. assert metric([ref_label], [est_label]) == score def __check_not_comparable(metric, ref_label, est_label): """Checks that ref_label is not comparable to est_label by metric""" # Try to produce the warning with pytest.warns( UserWarning, match="No reference chords were comparable to estimated chords, returning 0.", ): score = mir_eval.chord.weighted_accuracy( metric([ref_label], [est_label]), np.array([1]) ) # And confirm that the metric is 0 assert np.allclose(score, 0) # TODO(ejhumphrey): Comparison functions lacking unit tests. # test_root() @pytest.mark.parametrize( "ref_label, est_label, score", [ ("N", "N", 1.0), ("C:maj", "N", 0.0), ("C:maj", "C:aug", 0.0), ("C:maj", "C:dim", 0.0), ("C:min", "C:dim", 0.0), ("C:maj", "C:5", 0.0), ("C:maj", "C:sus4", 0.0), ("G:min", "G:sus2", 0.0), ("C:maj", "G:maj", 0.0), ("C:min", "C:hdim7", 0.0), ("C:min", "C:min7", 1.0), ("C:maj", "C:maj6", 1.0), ("F:maj", "F:min6", 0.0), ("C:maj7", "C:minmaj7", 1.0), ("A:maj", "A:7", 1.0), ("A:maj", "A:9", 1.0), ], ) def test_mirex(ref_label, est_label, score): __check_one_metric(mir_eval.chord.mirex, ref_label, est_label, score) @pytest.mark.parametrize("ref, est", [("C:5", "C:maj"), ("X", "N")]) def test_mirex_nocomp(ref, est): __check_not_comparable(mir_eval.chord.mirex, ref, est) @pytest.mark.parametrize( "ref_label, est_label, score", [ ("N", "N", 1.0), ("C:maj", "N", 0.0), ("C:maj", "C:aug", 1.0), ("C:maj", "C:dim", 0.0), ("C:min", "C:dim", 1.0), ("C:maj", "C:sus4", 1.0), ("G:min", "G:sus2", 0.0), ("C:maj", "G:maj", 0.0), ("C:min", "C:hdim7", 1.0), ("C:min", "C:min7", 1.0), ("C:maj", "C:maj6", 1.0), ("F:maj", "F:min6", 0.0), ("C:maj", "C:minmaj7", 0.0), ("A:maj", "A:7", 1.0), ("A:maj", "A:9", 1.0), ], ) def test_thirds(ref_label, est_label, score): __check_one_metric(mir_eval.chord.thirds, ref_label, est_label, score) def test_thirds_nocomp(): __check_not_comparable(mir_eval.chord.thirds, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:maj/5", "C:sus4/5", 1.0), ("G:min", "G:min/b3", 0.0), ("C:maj", "C:maj/5", 0.0), ("C:min/b3", "C:hdim7/b3", 1.0), ("C:min", "C:dim", 1.0), ], ) def test_thirds_inv(ref_label, est_label, score): __check_one_metric(mir_eval.chord.thirds_inv, ref_label, est_label, score) def test_thirds_inv_nocomp(): __check_not_comparable(mir_eval.chord.thirds_inv, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:min", "C:min7", 1.0), ("C:maj", "C:7", 1.0), ("C:maj", "C:aug", 0.0), ("C:min", "C:dim", 0.0), ("C:maj", "C:sus2", 0.0), ("C:maj", "C:sus4", 0.0), ("G:min", "G:minmaj7", 1.0), ("C:maj", "G:maj", 0.0), ("C:min", "C:hdim7", 0.0), ("C:min", "C:min6", 1.0), ], ) def test_triads(ref_label, est_label, score): __check_one_metric(mir_eval.chord.triads, ref_label, est_label, score) def test_triads_nocomp(): __check_not_comparable(mir_eval.chord.triads, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:maj/5", "C:maj7/5", 1.0), ("G:min", "G:min7/5", 0.0), ("C:maj", "C:7/5", 0.0), ("C:min/b3", "C:min6/b3", 1.0), ("C:min/b3", "C:dim/b3", 0.0), ], ) def test_triads_inv(ref_label, est_label, score): __check_one_metric(mir_eval.chord.triads_inv, ref_label, est_label, score) def test_triads_inv_nocomp(): __check_not_comparable(mir_eval.chord.triads_inv, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:min", "C:min7", 0.0), ("C:maj", "C:maj6", 0.0), ("C:7", "C:9", 1.0), ("C:maj7", "C:maj7/5", 1.0), ("C:sus2", "C:sus2/2", 1.0), ("C:7/3", "C:11/b7", 1.0), ("G:min", "G:sus2", 0.0), ("C:maj", "G:maj", 0.0), ("C:min", "C:hdim7", 0.0), ("C:min", "C:minmaj7", 0.0), ], ) def test_tetrads(ref_label, est_label, score): __check_one_metric(mir_eval.chord.tetrads, ref_label, est_label, score) def test_tetrads_nocomp(): __check_not_comparable(mir_eval.chord.tetrads, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:maj7/5", "C:maj7/3", 0.0), ("G:min", "G:min/b3", 0.0), ("C:7/5", "C:13/5", 1.0), ("C:min/b3", "C:hdim7/b3", 0.0), ("C:min9", "C:min7", 1.0), ], ) def test_tetrads_inv(ref_label, est_label, score): __check_one_metric(mir_eval.chord.tetrads_inv, ref_label, est_label, score) def test_tetrads_inv_nocomp(): __check_not_comparable(mir_eval.chord.tetrads_inv, "X", "N") @pytest.mark.parametrize( "ref_label, est_label, score", [ ("N", "N", 1.0), ("C:maj", "N", 0.0), ("C:maj", "C:aug", 0.0), ("C:min", "C:dim", 0.0), ("G:maj7", "G", 1.0), ], ) def test_majmin(ref_label, est_label, score): __check_one_metric(mir_eval.chord.majmin, ref_label, est_label, score) @pytest.mark.parametrize("ref_label, est_label", [("C:aug", "C:maj"), ("X", "N")]) def test_majmin_nocomp(ref_label, est_label): __check_not_comparable(mir_eval.chord.majmin, ref_label, est_label) @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:maj/5", "C:sus4/5", 0.0), ("G:min", "G:min/b3", 0.0), ("C:maj/5", "C:maj/5", 1.0), ("C:min7", "C:min", 1.0), ("G:min/b3", "G:min/b3", 1.0), ("C:maj7/5", "C:maj/5", 1.0), ("C:7", "C:maj", 1.0), ], ) def test_majmin_inv(ref_label, est_label, score): __check_one_metric(mir_eval.chord.majmin_inv, ref_label, est_label, score) @pytest.mark.parametrize( "ref_label, est_label", [ ("C:hdim7/b3", "C:min/b3"), ("C:maj/4", "C:maj/4"), ("C:maj/2", "C:sus2/2"), ("X", "N"), ], ) def test_majmin_inv_nocomp(ref_label, est_label): __check_not_comparable(mir_eval.chord.majmin_inv, ref_label, est_label) @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:min", "C:min7", 0.0), ("C:maj", "C:maj6", 0.0), ("C:7", "C:9", 1.0), ("C:maj7", "C:maj7/5", 1.0), ("C:7/3", "C:11/b7", 1.0), ("G:min", "G:sus2", 0.0), ("C:maj", "G:maj", 0.0), ("C:7", "C:maj7", 0.0), ], ) def test_sevenths(ref_label, est_label, score): __check_one_metric(mir_eval.chord.sevenths, ref_label, est_label, score) @pytest.mark.parametrize( "ref_label, est_label", [("C:sus2", "C:sus2/2"), ("C:hdim7", "C:hdim7"), ("X", "N")] ) def test_sevenths_nocomp(ref_label, est_label): __check_not_comparable(mir_eval.chord.sevenths, ref_label, est_label) @pytest.mark.parametrize( "ref_label, est_label, score", [ ("C:maj7/5", "C:maj7/3", 0.0), ("G:min", "G:min/b3", 0.0), ("C:7/5", "C:13/5", 1.0), ("C:min7/b7", "C:min7/b7", 1.0), ], ) def test_sevenths_inv(ref_label, est_label, score): __check_one_metric(mir_eval.chord.sevenths_inv, ref_label, est_label, score) @pytest.mark.parametrize( "ref_label, est_label", [("C:dim7/b3", "C:dim7/b3"), ("X", "N")] ) def test_sevenths_inv_nocomp(ref_label, est_label): __check_not_comparable(mir_eval.chord.sevenths_inv, ref_label, est_label) def test_directional_hamming_distance(): ref_ivs = np.array([[0.0, 1.0], [1.0, 2.0], [2.0, 3.0]]) est_ivs = np.array([[0.0, 0.9], [0.9, 1.8], [1.8, 2.5]]) dhd_ref_to_est = (0.1 + 0.2 + 0.5) / 3.0 dhd_est_to_ref = (0.0 + 0.1 + 0.2) / 2.5 dhd = mir_eval.chord.directional_hamming_distance assert np.allclose(dhd_ref_to_est, dhd(ref_ivs, est_ivs)) assert np.allclose(dhd_est_to_ref, dhd(est_ivs, ref_ivs)) assert np.allclose(0, dhd(ref_ivs, ref_ivs)) assert np.allclose(0, dhd(est_ivs, est_ivs)) ivs_overlap_all = np.array([[0.0, 1.0], [0.9, 2.0]]) ivs_overlap_one = np.array([[0.0, 1.0], [0.9, 2.0], [2.0, 3.0]]) with pytest.raises(ValueError): dhd(ivs_overlap_all, est_ivs) with pytest.raises(ValueError): dhd(ivs_overlap_one, est_ivs) def test_segmentation_functions(): ref_ivs = np.array([[0.0, 2.0], [2.0, 2.5], [2.5, 3.2]]) est_ivs = np.array([[0.0, 3.0], [3.0, 3.5]]) true_oseg = 1.0 - 0.2 / 3.2 true_useg = 1.0 - (1.0 + 0.2) / 3.5 true_seg = min(true_oseg, true_useg) assert np.allclose(true_oseg, mir_eval.chord.overseg(ref_ivs, est_ivs)) assert np.allclose(true_useg, mir_eval.chord.underseg(ref_ivs, est_ivs)) assert np.allclose(true_seg, mir_eval.chord.seg(ref_ivs, est_ivs)) ref_ivs = np.array([[0.0, 2.0], [2.0, 2.5], [2.5, 3.2]]) est_ivs = np.array([[0.0, 2.0], [2.0, 2.5], [2.5, 3.2]]) true_oseg = 1.0 true_useg = 1.0 true_seg = 1.0 assert np.allclose(true_oseg, mir_eval.chord.overseg(ref_ivs, est_ivs)) assert np.allclose(true_useg, mir_eval.chord.underseg(ref_ivs, est_ivs)) assert np.allclose(true_seg, mir_eval.chord.seg(ref_ivs, est_ivs)) ref_ivs = np.array([[0.0, 2.0], [2.0, 2.5], [2.5, 3.2]]) est_ivs = np.array([[0.0, 3.2]]) true_oseg = 1.0 true_useg = 1 - 1.2 / 3.2 true_seg = min(true_oseg, true_useg) assert np.allclose(true_oseg, mir_eval.chord.overseg(ref_ivs, est_ivs)) assert np.allclose(true_useg, mir_eval.chord.underseg(ref_ivs, est_ivs)) assert np.allclose(true_seg, mir_eval.chord.seg(ref_ivs, est_ivs)) ref_ivs = np.array([[0.0, 2.0], [2.0, 2.5], [2.5, 3.2]]) est_ivs = np.array([[3.2, 3.5]]) true_oseg = 1.0 true_useg = 1.0 true_seg = 1.0 assert np.allclose(true_oseg, mir_eval.chord.overseg(ref_ivs, est_ivs)) assert np.allclose(true_useg, mir_eval.chord.underseg(ref_ivs, est_ivs)) assert np.allclose(true_seg, mir_eval.chord.seg(ref_ivs, est_ivs)) def test_merge_chord_intervals(): intervals = np.array([[0.0, 1.0], [1.0, 2.0], [2.0, 3], [3.0, 4.0], [4.0, 5.0]]) labels = ["C:maj", "C:(1,3,5)", "A:maj", "A:maj7", "A:maj7/3"] assert np.allclose( np.array([[0.0, 2.0], [2.0, 3], [3.0, 4.0], [4.0, 5.0]]), mir_eval.chord.merge_chord_intervals(intervals, labels), ) def test_weighted_accuracy(): # First, test for a warning on empty beats with pytest.warns(UserWarning, match="No nonzero weights, returning 0"): score = mir_eval.chord.weighted_accuracy( np.array([1, 0, 1]), np.array([0, 0, 0]) ) # And that the metric is 0 assert np.allclose(score, 0) # len(comparisons) must equal len(weights) comparisons = np.array([1, 0, 1]) weights = np.array([1, 1]) with pytest.raises(ValueError): mir_eval.chord.weighted_accuracy(comparisons, weights) # Weights must all be positive comparisons = np.array([1, 1]) weights = np.array([-1, -1]) with pytest.raises(ValueError): mir_eval.chord.weighted_accuracy(comparisons, weights) # Make sure accuracy = 1 and 0 when all comparisons are True and False resp comparisons = np.array([1, 1, 1]) weights = np.array([1, 1, 1]) score = mir_eval.chord.weighted_accuracy(comparisons, weights) assert np.allclose(score, 1) comparisons = np.array([0, 0, 0]) score = mir_eval.chord.weighted_accuracy(comparisons, weights) assert np.allclose(score, 0) @pytest.mark.parametrize("chord_data", file_sets, indirect=True) def test_chord_functions(chord_data): ref_intervals, ref_labels, est_intervals, est_labels, expected_scores = chord_data # Compute scores scores = mir_eval.chord.evaluate( ref_intervals, ref_labels, est_intervals, est_labels ) # Compare them assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) def test_quality_to_bitmap(): # Test simple case assert np.all( mir_eval.chord.quality_to_bitmap("maj") == np.array([1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0]) ) @pytest.mark.xfail(raises=mir_eval.chord.InvalidChordException) @pytest.mark.parametrize("quality", ["maj5", "2", "#7"]) def test_quality_to_bitmap_fail(quality): # Check exceptions for qualities not in the QUALITIES list mir_eval.chord.quality_to_bitmap(quality) def test_validate(): # Test that the validate function raises the appropriate errors and # warnings with pytest.warns() as w: # First, test for warnings on empty labels mir_eval.chord.validate([], []) assert len(w) == 2 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Estimated labels are empty" assert issubclass(w[-2].category, UserWarning) assert str(w[-2].message) == "Reference labels are empty" # Test that error is thrown on different-length labels with pytest.raises(ValueError): mir_eval.chord.validate([], ["C"]) @pytest.mark.parametrize("quality", list(mir_eval.chord.QUALITIES.keys())) def test_chord_qualities(quality): if len(quality) > 0: mir_eval.chord.encode(f"C:{quality}", reduce_extended_chords=True) else: mir_eval.chord.encode("C", reduce_extended_chords=True) mir_eval-0.8.2/tests/test_display.py000066400000000000000000000313431475740344600175610ustar00rootroot00000000000000#!/usr/bin/env python """Unit tests for the display module""" # For testing purposes, clobber the rcfile import matplotlib import matplotlib.pyplot as plt import numpy as np import pytest import mir_eval import mir_eval.display from mir_eval.io import load_labeled_intervals from mir_eval.io import load_valued_intervals from mir_eval.io import load_labeled_events from mir_eval.io import load_ragged_time_series from mir_eval.io import load_wav from packaging import version # Workaround to enable test skipping on older matplotlibs where we know it to be problematic MPL_VERSION = version.parse(matplotlib.__version__) OLD_MPL = not (MPL_VERSION >= version.parse("3.8.0")) # Workaround for old freetype builds with our image fixtures FT_VERSION = version.parse(matplotlib.ft2font.__freetype_version__) OLD_FT = not (FT_VERSION >= version.parse("2.10")) STYLE = "default" @pytest.mark.mpl_image_compare( baseline_images=["test_display_segment"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_segment(): plt.figure() # Load some segment data intervals, labels = load_labeled_intervals("data/segment/ref00.lab") # Plot the segments with no labels mir_eval.display.segments(intervals, labels, text=False) # Draw a legend plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_segment_text"], extensions=["png"], style=STYLE, tolerance=6, ) @pytest.mark.xfail(OLD_MPL, reason=f"matplotlib version < {MPL_VERSION}", strict=False) def test_display_segment_text(): plt.figure() # Load some segment data intervals, labels = load_labeled_intervals("data/segment/ref00.lab") # Plot the segments with no labels mir_eval.display.segments(intervals, labels, text=True) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_intervals"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_intervals(): plt.figure() # Load some chord data intervals, labels = load_labeled_intervals("data/chord/ref01.lab") # Plot the chords with nothing fancy mir_eval.display.labeled_intervals(intervals, labels) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_intervals_noextend"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_intervals_noextend(): plt.figure() # Load some chord data intervals, labels = load_labeled_intervals("data/chord/ref01.lab") # Plot the chords with nothing fancy ax = plt.axes() ax.set_yticklabels([]) mir_eval.display.labeled_intervals( intervals, labels, label_set=[], extend_labels=False, ax=ax ) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_intervals_compare"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_intervals_compare(): plt.figure() # Load some chord data ref_int, ref_labels = load_labeled_intervals("data/chord/ref01.lab") est_int, est_labels = load_labeled_intervals("data/chord/est01.lab") # Plot reference and estimates using label set extension mir_eval.display.labeled_intervals( ref_int, ref_labels, alpha=0.5, label="Reference" ) mir_eval.display.labeled_intervals(est_int, est_labels, alpha=0.5, label="Estimate") plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_intervals_compare_noextend"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_intervals_compare_noextend(): plt.figure() # Load some chord data ref_int, ref_labels = load_labeled_intervals("data/chord/ref01.lab") est_int, est_labels = load_labeled_intervals("data/chord/est01.lab") # Plot reference and estimate, but only use the reference labels mir_eval.display.labeled_intervals( ref_int, ref_labels, alpha=0.5, label="Reference" ) mir_eval.display.labeled_intervals( est_int, est_labels, extend_labels=False, alpha=0.5, label="Estimate" ) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_intervals_compare_common"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_intervals_compare_common(): plt.figure() # Load some chord data ref_int, ref_labels = load_labeled_intervals("data/chord/ref01.lab") est_int, est_labels = load_labeled_intervals("data/chord/est01.lab") label_set = list(sorted(set(ref_labels) | set(est_labels))) # Plot reference and estimate with a common label set mir_eval.display.labeled_intervals( ref_int, ref_labels, label_set=label_set, alpha=0.5, label="Reference" ) mir_eval.display.labeled_intervals( est_int, est_labels, label_set=label_set, alpha=0.5, label="Estimate" ) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_hierarchy_nolabel"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_hierarchy_nolabel(): plt.figure() # Load some chord data int0, lab0 = load_labeled_intervals("data/hierarchy/ref00.lab") int1, lab1 = load_labeled_intervals("data/hierarchy/ref01.lab") # Plot reference and estimate with a common label set mir_eval.display.hierarchy([int0, int1], [lab0, lab1]) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_hierarchy_label"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_hierarchy_label(): plt.figure() # Load some chord data int0, lab0 = load_labeled_intervals("data/hierarchy/ref00.lab") int1, lab1 = load_labeled_intervals("data/hierarchy/ref01.lab") # Plot reference and estimate with a common label set mir_eval.display.hierarchy([int0, int1], [lab0, lab1], levels=["Large", "Small"]) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_pitch_hz"], extensions=["png"], style=STYLE, tolerance=6, ) @pytest.mark.xfail(OLD_FT, reason=f"freetype version < {FT_VERSION}", strict=False) def test_display_pitch_hz(): plt.figure() ref_times, ref_freqs = load_labeled_events("data/melody/ref00.txt") est_times, est_freqs = load_labeled_events("data/melody/est00.txt") # Plot pitches on a Hz scale mir_eval.display.pitch(ref_times, ref_freqs, unvoiced=True, label="Reference") mir_eval.display.pitch(est_times, est_freqs, unvoiced=True, label="Estimate") plt.legend(loc="upper left") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_pitch_midi"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_pitch_midi(): plt.figure() times, freqs = load_labeled_events("data/melody/ref00.txt") # Plot pitches on a midi scale with note tickers mir_eval.display.pitch(times, freqs, midi=True) mir_eval.display.ticker_notes() return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_pitch_midi_hz"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_pitch_midi_hz(): plt.figure() times, freqs = load_labeled_events("data/melody/ref00.txt") # Plot pitches on a midi scale with note tickers mir_eval.display.pitch(times, freqs, midi=True) mir_eval.display.ticker_pitch() return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_multipitch_hz_unvoiced"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_multipitch_hz_unvoiced(): plt.figure() times, pitches = load_ragged_time_series("data/multipitch/est01.txt") # Plot pitches on a midi scale with note tickers mir_eval.display.multipitch(times, pitches, midi=False, unvoiced=True) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_multipitch_hz_voiced"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_multipitch_hz_voiced(): plt.figure() times, pitches = load_ragged_time_series("data/multipitch/est01.txt") mir_eval.display.multipitch(times, pitches, midi=False, unvoiced=False) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_multipitch_midi"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_multipitch_midi(): plt.figure() ref_t, ref_p = load_ragged_time_series("data/multipitch/ref01.txt") est_t, est_p = load_ragged_time_series("data/multipitch/est01.txt") # Plot pitches on a midi scale with note tickers mir_eval.display.multipitch(ref_t, ref_p, midi=True, alpha=0.5, label="Reference") mir_eval.display.multipitch(est_t, est_p, midi=True, alpha=0.5, label="Estimate") plt.legend(loc="upper left") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_piano_roll"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_piano_roll(): plt.figure() ref_t, ref_p = load_valued_intervals("data/transcription/ref04.txt") est_t, est_p = load_valued_intervals("data/transcription/est04.txt") mir_eval.display.piano_roll(ref_t, ref_p, label="Reference", alpha=0.5) mir_eval.display.piano_roll( est_t, est_p, label="Estimate", alpha=0.5, facecolor="r" ) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_piano_roll_midi"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_piano_roll_midi(): plt.figure() ref_t, ref_p = load_valued_intervals("data/transcription/ref04.txt") est_t, est_p = load_valued_intervals("data/transcription/est04.txt") ref_midi = mir_eval.util.hz_to_midi(ref_p) est_midi = mir_eval.util.hz_to_midi(est_p) mir_eval.display.piano_roll(ref_t, midi=ref_midi, label="Reference", alpha=0.5) mir_eval.display.piano_roll( est_t, midi=est_midi, label="Estimate", alpha=0.5, facecolor="r" ) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_ticker_midi_zoom"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_ticker_midi_zoom(): plt.figure() plt.plot(np.arange(3)) mir_eval.display.ticker_notes() return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_separation"], extensions=["png"], style=STYLE, tolerance=6, ) @pytest.mark.xfail(OLD_FT, reason=f"freetype version < {FT_VERSION}", strict=False) @pytest.mark.skip() def test_display_separation(): plt.figure() x0, fs = load_wav("data/separation/ref05/0.wav") x1, fs = load_wav("data/separation/ref05/1.wav") x2, fs = load_wav("data/separation/ref05/2.wav") mir_eval.display.separation([x0, x1, x2], fs=fs) return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_separation_label"], extensions=["png"], style=STYLE, tolerance=6, ) @pytest.mark.xfail(OLD_FT, reason=f"freetype version < {FT_VERSION}", strict=False) @pytest.mark.skip() def test_display_separation_label(): plt.figure() x0, fs = load_wav("data/separation/ref05/0.wav") x1, fs = load_wav("data/separation/ref05/1.wav") x2, fs = load_wav("data/separation/ref05/2.wav") mir_eval.display.separation([x0, x1, x2], fs=fs, labels=["Alice", "Bob", "Carol"]) plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_events"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_events(): plt.figure() # Load some event data beats_ref = mir_eval.io.load_events("data/beat/ref00.txt")[:30] beats_est = mir_eval.io.load_events("data/beat/est00.txt")[:30] # Plot both with labels mir_eval.display.events(beats_ref, label="reference") mir_eval.display.events(beats_est, label="estimate") plt.legend(loc="upper right") return plt.gcf() @pytest.mark.mpl_image_compare( baseline_images=["test_display_labeled_events"], extensions=["png"], style=STYLE, tolerance=6, ) def test_display_labeled_events(): plt.figure() # Load some event data beats_ref = mir_eval.io.load_events("data/beat/ref00.txt")[:10] labels = list("abcdefghijklmnop") # Plot both with labels mir_eval.display.events(beats_ref, labels) return plt.gcf() @pytest.mark.xfail(raises=ValueError) def test_display_pianoroll_nopitch_nomidi(): # Issue 214 mir_eval.display.piano_roll([[0, 1]]) mir_eval-0.8.2/tests/test_hierarchy.py000066400000000000000000000220131475740344600200640ustar00rootroot00000000000000""" Unit tests for mir_eval.hierarchy """ import glob import re import json import numpy as np import scipy.sparse import mir_eval import pytest A_TOL = 1e-12 @pytest.mark.parametrize("window", [5, 10, 15, 30, 90, None]) @pytest.mark.parametrize("frame_size", [0.1, 0.5, 1.0]) def test_tmeasure_pass(window, frame_size): # The estimate here gets none of the structure correct. ref = [[[0, 30]], [[0, 15], [15, 30]]] # convert to arrays ref = [np.asarray(_) for _ in ref] est = ref[:1] # The estimate should get 0 score here scores = mir_eval.hierarchy.tmeasure(ref, est, window=window, frame_size=frame_size) for k in scores: assert k == 0.0 # The reference should get a perfect score here scores = mir_eval.hierarchy.tmeasure(ref, ref, window=window, frame_size=frame_size) for k in scores: assert k == 1.0 def test_tmeasure_warning(): # Warn if there are missing boundaries from one layer to the next ref = [[[0, 5], [5, 10]], [[0, 10]]] ref = [np.asarray(_) for _ in ref] with pytest.warns( UserWarning, match="Segment hierarchy is inconsistent at level 1" ): mir_eval.hierarchy.tmeasure(ref, ref) def test_tmeasure_fail_span(): # Does not start at 0 ref = [[[1, 10]], [[1, 5], [5, 10]]] ref = [np.asarray(_) for _ in ref] with pytest.raises(ValueError): mir_eval.hierarchy.tmeasure(ref, ref) # Does not end at the right time ref = [[[0, 5]], [[0, 5], [5, 6]]] ref = [np.asarray(_) for _ in ref] with pytest.raises(ValueError): mir_eval.hierarchy.tmeasure(ref, ref) # Two annotaions of different shape ref = [[[0, 10]], [[0, 5], [5, 10]]] ref = [np.asarray(_) for _ in ref] est = [[[0, 15]], [[0, 5], [5, 15]]] est = [np.asarray(_) for _ in est] with pytest.raises(ValueError): mir_eval.hierarchy.tmeasure(ref, est) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "window, frame_size", [(None, -1), (None, 0), (15, -1), (15, 0), (15, 30), (30, -1), (30, 0), (30, 60)], ) def test_tmeasure_fail_frame_size(window, frame_size): ref = [[[0, 60]], [[0, 30], [30, 60]]] ref = [np.asarray(_) for _ in ref] mir_eval.hierarchy.tmeasure(ref, ref, window=window, frame_size=frame_size) @pytest.mark.parametrize("frame_size", [0.1, 0.5, 1.0]) def test_lmeasure_pass(frame_size): # The estimate here gets none of the structure correct. ref = [[[0, 30]], [[0, 15], [15, 30]]] ref_lab = [["A"], ["a", "b"]] # convert to arrays ref = [np.asarray(_) for _ in ref] est = ref[:1] est_lab = ref_lab[:1] # The estimate should get 0 score here scores = mir_eval.hierarchy.lmeasure( ref, ref_lab, est, est_lab, frame_size=frame_size ) for k in scores: assert k == 0.0 # The reference should get a perfect score here scores = mir_eval.hierarchy.lmeasure( ref, ref_lab, ref, ref_lab, frame_size=frame_size ) for k in scores: assert k == 1.0 def test_lmeasure_warning(): # Warn if there are missing boundaries from one layer to the next ref = [[[0, 5], [5, 10]], [[0, 10]]] ref = [np.asarray(_) for _ in ref] ref_lab = [["a", "b"], ["A"]] with pytest.warns( UserWarning, match="Segment hierarchy is inconsistent at level 1" ): mir_eval.hierarchy.lmeasure(ref, ref_lab, ref, ref_lab) def test_lmeasure_fail_span(): # Does not start at 0 ref = [[[1, 10]], [[1, 5], [5, 10]]] ref_lab = [["A"], ["a", "b"]] ref = [np.asarray(_) for _ in ref] with pytest.raises(ValueError): mir_eval.hierarchy.lmeasure(ref, ref_lab, ref, ref_lab) # Does not end at the right time ref = [[[0, 5]], [[0, 5], [5, 6]]] ref = [np.asarray(_) for _ in ref] with pytest.raises(ValueError): mir_eval.hierarchy.lmeasure(ref, ref_lab, ref, ref_lab) # Two annotations of different shape ref = [[[0, 10]], [[0, 5], [5, 10]]] ref = [np.asarray(_) for _ in ref] est = [[[0, 15]], [[0, 5], [5, 15]]] est = [np.asarray(_) for _ in est] with pytest.raises(ValueError): mir_eval.hierarchy.lmeasure(ref, ref_lab, est, ref_lab) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize("frame_size", [-1, 0]) def test_lmeasure_fail_frame_size(frame_size): ref = [[[0, 60]], [[0, 30], [30, 60]]] ref = [np.asarray(_) for _ in ref] ref_lab = [["A"], ["a", "b"]] mir_eval.hierarchy.lmeasure(ref, ref_lab, ref, ref_lab, frame_size=frame_size) SCORES_GLOB = "data/hierarchy/output*.json" sco_files = sorted(glob.glob(SCORES_GLOB)) @pytest.fixture def hierarchy_outcomes(request): sco_f = request.param with open(sco_f) as fdesc: expected_scores = json.load(fdesc) window = float(re.match(r".*output_w=(\d+).json$", sco_f).groups()[0]) return expected_scores, window @pytest.mark.parametrize("hierarchy_outcomes", sco_files, indirect=True) def test_hierarchy_regression(hierarchy_outcomes): expected_scores, window = hierarchy_outcomes # Hierarchy data is split across multiple lab files for these tests ref_files = sorted(glob.glob("data/hierarchy/ref*.lab")) est_files = sorted(glob.glob("data/hierarchy/est*.lab")) ref_hier = [mir_eval.io.load_labeled_intervals(_) for _ in ref_files] est_hier = [mir_eval.io.load_labeled_intervals(_) for _ in est_files] ref_ints = [seg[0] for seg in ref_hier] ref_labs = [seg[1] for seg in ref_hier] est_ints = [seg[0] for seg in est_hier] est_labs = [seg[1] for seg in est_hier] outputs = mir_eval.hierarchy.evaluate( ref_ints, ref_labs, est_ints, est_labs, window=window ) assert outputs.keys() == expected_scores.keys() for key in expected_scores: assert np.allclose(expected_scores[key], outputs[key], atol=A_TOL) def test_count_inversions(): # inversion count = |{(i, j) : a[i] >= b[j]}| a = [2, 4, 6] b = [1, 2, 3, 4] # All inversions (a, b) are: # (2, 1), (2, 2) # (4, 1), (4, 2), (4, 3), (4, 4) # (6, 1), (6, 2), (6, 3), (6, 4) assert mir_eval.hierarchy._count_inversions(a, b) == 10 # All inversions (b, a) are: # (2, 2) # (3, 2) # (4, 2), (4, 4) assert mir_eval.hierarchy._count_inversions(b, a) == 4 # And test with repetitions a = [2, 2, 4] b = [1, 2, 4, 4] # counts: (a, b) # (2, 1), (2, 2) # (2, 1), (2, 2) # (4, 1), (4, 2), (4, 4), (4, 4) assert mir_eval.hierarchy._count_inversions(a, b) == 8 # count: (b, a) # (2, 2), (2, 2) # (4, 2), (4, 2), (4, 4) # (4, 2), (4, 2), (4, 4) assert mir_eval.hierarchy._count_inversions(b, a) == 8 def test_meet(): frame_size = 1 int_hier = [ np.array([[0, 10]]), np.array([[0, 6], [6, 10]]), np.array([[0, 2], [2, 4], [4, 6], [6, 8], [8, 10]]), ] lab_hier = [["X"], ["A", "B"], ["a", "b", "a", "c", "b"]] # Target output meet_truth = np.asarray( [ [3, 3, 2, 2, 3, 3, 1, 1, 1, 1], # (XAa) [3, 3, 2, 2, 3, 3, 1, 1, 1, 1], # (XAa) [2, 2, 3, 3, 2, 2, 1, 1, 3, 3], # (XAb) [2, 2, 3, 3, 2, 2, 1, 1, 3, 3], # (XAb) [3, 3, 2, 2, 3, 3, 1, 1, 1, 1], # (XAa) [3, 3, 2, 2, 3, 3, 1, 1, 1, 1], # (XAa) [1, 1, 1, 1, 1, 1, 3, 3, 2, 2], # (XBc) [1, 1, 1, 1, 1, 1, 3, 3, 2, 2], # (XBc) [1, 1, 3, 3, 1, 1, 2, 2, 3, 3], # (XBb) [1, 1, 3, 3, 1, 1, 2, 2, 3, 3], # (XBb) ] ) meet = mir_eval.hierarchy._meet(int_hier, lab_hier, frame_size) # Is it the right type? assert isinstance(meet, scipy.sparse.csr_matrix) meet = meet.toarray() # Does it have the right shape? assert meet.shape == (10, 10) # Does it have the right value? assert np.all(meet == meet_truth) def test_compare_frame_rankings(): # number of pairs (i, j) # where ref[i] < ref[j] and est[i] >= est[j] ref = np.asarray([1, 2, 3, 3]) # ref pairs (transitive) # (1, 2), (1, 3), (1, 3), (2, 3), (2, 3) # ref pairs (non-transitive) # (1, 2), (2, 3), (2, 3) # Just count the normalizers # No self-inversions are possible from ref to itself inv, norm = mir_eval.hierarchy._compare_frame_rankings(ref, ref, transitive=True) assert inv == 0 assert norm == 5.0 inv, norm = mir_eval.hierarchy._compare_frame_rankings(ref, ref, transitive=False) assert inv == 0 assert norm == 3.0 est = np.asarray([1, 2, 1, 3]) # In the transitive case, we lose two pairs # (1, 3) and (2, 2) -> (1, 1), (2, 1) inv, norm = mir_eval.hierarchy._compare_frame_rankings(ref, est, transitive=True) assert inv == 2 assert norm == 5.0 # In the non-transitive case, we only lose one pair # because (1,3) was not counted inv, norm = mir_eval.hierarchy._compare_frame_rankings(ref, est, transitive=False) assert inv == 1 assert norm == 3.0 # Do an all-zeros test ref = np.asarray([1, 1, 1, 1]) inv, norm = mir_eval.hierarchy._compare_frame_rankings(ref, ref, transitive=True) assert inv == 0 assert norm == 0.0 mir_eval-0.8.2/tests/test_input_output.py000066400000000000000000000171441475740344600206760ustar00rootroot00000000000000"""Unit tests for input/output functions""" import numpy as np import json import mir_eval import warnings import pathlib import tempfile import pytest def test_load_delimited(): # Test for ValueError when a non-string or file handle is passed with pytest.raises(IOError): mir_eval.io.load_delimited(None, [int]) # Test for a value error when the wrong number of columns is passed with tempfile.TemporaryFile("r+") as f: f.write("10 20") f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_delimited(f, [int, int, int]) # Test for a value error on conversion failure with tempfile.TemporaryFile("r+") as f: f.write("10 a 30") f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_delimited(f, [int, int, int]) def test_load_delimited_commented(): with tempfile.TemporaryFile("r+") as f: f.write("; some comment\n10 20\n30 50") f.seek(0) col1, col2 = mir_eval.io.load_delimited(f, [int, int], comment=";") assert np.allclose(col1, [10, 30]) assert np.allclose(col2, [20, 50]) # Rewind and try with the default comment character f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_delimited(f, [int, int]) # Rewind and try with no comment support f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_delimited(f, [int, int], comment=None) def test_load_delimited_nocomment(): with tempfile.TemporaryFile("r+") as f: f.write("10 20\n30 50") f.seek(0) col1, col2 = mir_eval.io.load_delimited(f, [int, int]) assert np.allclose(col1, [10, 30]) assert np.allclose(col2, [20, 50]) # Rewind and try with a different comment char f.seek(0) col1, col2 = mir_eval.io.load_delimited(f, [int, int], comment=";") assert np.allclose(col1, [10, 30]) assert np.allclose(col2, [20, 50]) # Rewind and try with no different comment string f.seek(0) col1, col2 = mir_eval.io.load_delimited(f, [int, int], comment=None) assert np.allclose(col1, [10, 30]) assert np.allclose(col2, [20, 50]) @pytest.mark.parametrize( "file_or_path", ["data/beat/ref01.txt", pathlib.Path("data/beat/ref01.txt")] ) def test_load_delimited_file_or_path(file_or_path): # Tests the load_delimited routine with either a string or a # pathlib.Path object as the first argument data = mir_eval.io.load_delimited(file_or_path, [float]) assert len(data) == 635 def test_load_events(): # Test for a warning when invalid events are supplied with tempfile.TemporaryFile("r+") as f: with pytest.warns(UserWarning, match="Events should be in increasing order."): # Non-increasing is invalid f.write("10\n9") f.seek(0) events = mir_eval.io.load_events(f) # Make sure events were read in correctly assert np.all(events == [10, 9]) def test_load_labeled_events(): # Test for a value error when invalid labeled events are supplied with tempfile.TemporaryFile("r+") as f: with pytest.warns(UserWarning, match="Events should be in increasing order."): # Non-increasing is invalid f.write("10 a\n9 b") f.seek(0) events, labels = mir_eval.io.load_labeled_events(f) # Make sure events were read in correctly assert np.all(events == [10, 9]) # Make sure labels were read in correctly assert labels == ["a", "b"] def test_load_intervals(): # Test for a value error when invalid labeled events are supplied with tempfile.TemporaryFile("r+") as f: with pytest.warns( UserWarning, match="All interval durations must be strictly positive" ): # Non-increasing is invalid f.write("10 9\n9 10") f.seek(0) intervals = mir_eval.io.load_intervals(f) # Make sure intervals were read in correctly assert np.all(intervals == [[10, 9], [9, 10]]) def test_load_labeled_intervals(): # Test for a value error when invalid labeled events are supplied with tempfile.TemporaryFile("r+") as f: with pytest.warns( UserWarning, match="All interval durations must be strictly positive" ): # Non-increasing is invalid f.write("10 9 a\n9 10 b") f.seek(0) intervals, labels = mir_eval.io.load_labeled_intervals(f) # Make sure intervals were read in correctly assert np.all(intervals == [[10, 9], [9, 10]]) assert labels == ["a", "b"] def test_load_valued_intervals(): # Test for a value error when invalid valued events are supplied with tempfile.TemporaryFile("r+") as f: with pytest.warns( UserWarning, match="All interval durations must be strictly positive" ): # Non-increasing is invalid f.write("10 9 5\n9 10 6") f.seek(0) intervals, values = mir_eval.io.load_valued_intervals(f) # Make sure intervals were read in correctly assert np.all(intervals == [[10, 9], [9, 10]]) assert np.all(values == [5, 6]) def test_load_ragged_time_series(): # Test for ValueError when a non-string or file handle is passed with pytest.raises(IOError): mir_eval.io.load_ragged_time_series(None, float, header=False) # Test for a value error on conversion failure with tempfile.TemporaryFile("r+") as f: f.write("10 a 30") f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_ragged_time_series(f, float, header=False) # Test for a value error on invalid time stamp with tempfile.TemporaryFile("r+") as f: f.write("a 10 30") f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_ragged_time_series(f, int, header=False) # Test for a value error on invalid time stamp with header with tempfile.TemporaryFile("r+") as f: f.write("x y z\na 10 30") f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_ragged_time_series(f, int, header=True) with tempfile.TemporaryFile("r+") as f: f.write("#comment\n0 1 2\n3 4\n# comment\n5 6 7") f.seek(0) times, values = mir_eval.io.load_ragged_time_series( f, int, header=False, comment="#" ) assert np.allclose(times, [0, 3, 5]) assert np.allclose(values[0], [1, 2]) assert np.allclose(values[1], [4]) assert np.allclose(values[2], [6, 7]) # Rewind with a wrong comment string f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_ragged_time_series(f, int, header=False, comment="%") # Rewind with no comment string f.seek(0) with pytest.raises(ValueError): mir_eval.io.load_ragged_time_series(f, int, header=False, comment=None) def test_load_tempo(): # Test the tempo loader tempi, weight = mir_eval.io.load_tempo("data/tempo/ref01.lab") assert np.allclose(tempi, [60, 120]) assert weight == 0.5 @pytest.mark.xfail(raises=ValueError) def test_load_tempo_multiline(): tempi, weight = mir_eval.io.load_tempo("data/tempo/bad00.lab") @pytest.mark.xfail(raises=ValueError) def test_load_tempo_badweight(): tempi, weight = mir_eval.io.load_tempo("data/tempo/bad01.lab") def test_load_bad_tempi(): with pytest.warns(UserWarning, match="non-negative numbers"): tempi, weight = mir_eval.io.load_tempo("data/tempo/bad02.lab") mir_eval-0.8.2/tests/test_key.py000066400000000000000000000033561475740344600167070ustar00rootroot00000000000000""" Tests for mir_eval.key """ import mir_eval import pytest import glob import json import numpy as np A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/key/ref*.txt" EST_GLOB = "data/key/est*.txt" SCORES_GLOB = "data/key/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.mark.parametrize( "good_key", ["C major", "c major", "C# major", "Bb minor", "db minor", "X", "x", "C other"], ) @pytest.mark.parametrize( "bad_key", ["C maj", "Cb major", "C", "K major", "F## minor" "X other", "x minor"] ) def test_key_function_fail(good_key, bad_key): score = mir_eval.key.weighted_score(good_key, good_key) assert score == 1.0 with pytest.raises(ValueError): mir_eval.key.weighted_score(good_key, bad_key) with pytest.raises(ValueError): mir_eval.key.weighted_score(bad_key, good_key) @pytest.fixture def key_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) reference_key = mir_eval.io.load_key(ref_f) estimated_key = mir_eval.io.load_key(est_f) return reference_key, estimated_key, expected_scores @pytest.mark.parametrize("key_data", file_sets, indirect=True) def test_key_functions(key_data): reference_key, estimated_key, expected_scores = key_data # Compute scores scores = mir_eval.key.evaluate(reference_key, estimated_key) # Compare them assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_melody.py000066400000000000000000000370071475740344600174100ustar00rootroot00000000000000# CREATED: 4/15/14 9:42 AM by Justin Salamon """ Unit tests for mir_eval.melody """ import numpy as np import json import mir_eval import glob import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/melody/ref*.txt" EST_GLOB = "data/melody/est*.txt" SCORES_GLOB = "data/melody/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def melody_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) # Load in reference melody ref_time, ref_freq = mir_eval.io.load_time_series(ref_f) # Load in estimated melody est_time, est_freq = mir_eval.io.load_time_series(est_f) return ref_time, ref_freq, est_time, est_freq, expected_scores def test_hz2cents(): # Unit test some simple values hz = np.array([0.0, 10.0, 5.0, 320.0, 1420.31238974231]) # Expected cent conversion expected_cent = np.array([0.0, 0.0, -1200.0, 6000.0, 8580.0773605]) assert np.allclose(mir_eval.melody.hz2cents(hz), expected_cent) def test_freq_to_voicing(): # Unit test some simple values hz = np.array([0.0, 100.0, -132.0]) expected_hz = np.array([0.0, 100.0, 132.0]) expected_voicing = np.array([0, 1, 0]) # Check voicing conversion res_hz, res_voicing = mir_eval.melody.freq_to_voicing(hz) assert np.all(res_hz == expected_hz) assert np.all(res_voicing == expected_voicing) # Unit test some simple values where voicing is given hz = np.array([0.0, 100.0, -132.0, 0, 131.0]) voicing = np.array([0.8, 0.0, 1.0, 0.0, 0.5]) expected_hz = np.array([0.0, 100.0, 132.0, 0.0, 131.0]) expected_voicing = np.array([0.0, 0.0, 1.0, 0.0, 0.5]) # Check voicing conversion res_hz, res_voicing = mir_eval.melody.freq_to_voicing(hz, voicing=voicing) assert np.all(res_hz == expected_hz) assert np.all(res_voicing == expected_voicing) def test_constant_hop_timebase(): hop = 0.1 end_time = 0.35 expected_times = np.array([0, 0.1, 0.2, 0.3]) res_times = mir_eval.melody.constant_hop_timebase(hop, end_time) assert np.allclose(res_times, expected_times) def test_resample_melody_series(): # Check for a small example including a zero transition times = np.arange(4) / 35.0 cents = np.array([2.0, 0.0, -1.0, 1.0]) voicing = np.array([1, 0, 1, 1]) times_new = np.linspace(0, 0.08, 9) expected_cents = np.array([2.0, 2.0, 2.0, 0.0, 0.0, 0.0, -0.8, -0.1, 0.6]) expected_voicing = np.array([1, 1, 1, 0, 0, 0, 1, 1, 1]) (res_cents, res_voicing) = mir_eval.melody.resample_melody_series( times, cents, voicing, times_new ) assert np.allclose(res_cents, expected_cents) assert np.allclose(res_voicing, expected_voicing) # Check for a small example including a zero transition - nonbinary voicing times = np.arange(4) / 35.0 cents = np.array([2.0, 0.0, -1.0, 1.0]) voicing = np.array([0.8, 0.0, 0.2, 1.0]) times_new = np.linspace(0, 0.08, 9) expected_cents = np.array([2.0, 2.0, 2.0, 0.0, 0.0, 0.0, -0.8, -0.1, 0.6]) expected_voicing = np.array([0.8, 0.52, 0.24, 0.01, 0.08, 0.15, 0.28, 0.56, 0.84]) (res_cents, res_voicing) = mir_eval.melody.resample_melody_series( times, cents, voicing, times_new ) assert np.allclose(res_cents, expected_cents) assert np.allclose(res_voicing, expected_voicing) def test_resample_melody_series_same_times(): # Check the case where the time bases are identical times = np.array([0.0, 0.1, 0.2, 0.3]) times_new = np.array([0.0, 0.1, 0.2, 0.3]) cents = np.array([2.0, 0.0, -1.0, 1.0]) voicing = np.array([0, 0, 1, 1]) expected_cents = np.array([2.0, 0.0, -1.0, 1.0]) expected_voicing = np.array([False, False, True, True]) (res_cents, res_voicing) = mir_eval.melody.resample_melody_series( times, cents, voicing, times_new ) assert np.allclose(res_cents, expected_cents) assert np.allclose(res_voicing, expected_voicing) # Check the case where the time bases are identical - nonbinary voicing times = np.array([0.0, 0.1, 0.2, 0.3]) times_new = np.array([0.0, 0.1, 0.2, 0.3]) cents = np.array([2.0, 0.0, -1.0, 1.0]) voicing = np.array([0.5, 0.8, 0.9, 1.0]) expected_cents = np.array([2.0, 0.0, -1.0, 1.0]) expected_voicing = np.array([0.5, 0.8, 0.9, 1.0]) (res_cents, res_voicing) = mir_eval.melody.resample_melody_series( times, cents, voicing, times_new ) assert np.allclose(res_cents, expected_cents) assert np.allclose(res_voicing, expected_voicing) def test_to_cent_voicing(): # We'll just test a few values from one of the test annotations ref_file = sorted(glob.glob(REF_GLOB))[0] ref_time, ref_freq = mir_eval.io.load_time_series(ref_file) est_file = sorted(glob.glob(EST_GLOB))[0] est_time, est_freq = mir_eval.io.load_time_series(est_file) ref_v, ref_c, est_v, est_c = mir_eval.melody.to_cent_voicing( ref_time, ref_freq, est_time, est_freq ) # Expected values test_range = np.arange(220, 225) expected_ref_v = np.array([False, False, False, True, True]) expected_ref_c = np.array([0.0, 0.0, 0.0, 6056.8837818916609, 6028.5504583021921]) expected_est_v = np.array([False] * 5) expected_est_c = np.array([5351.3179423647571] * 5) assert np.allclose(ref_v[test_range], expected_ref_v) assert np.allclose(ref_c[test_range], expected_ref_c) assert np.allclose(est_v[test_range], expected_est_v) assert np.allclose(est_c[test_range], expected_est_c) # Test that a 0 is added to the beginning for return_item in mir_eval.melody.to_cent_voicing( np.array([1.0, 2.0]), np.array([440.0, 442.0]), np.array([1.0, 2.0]), np.array([441.0, 443.0]), ): assert len(return_item) == 3 assert return_item[0] == return_item[1] # Test custom voicings ref_time, ref_freq = mir_eval.io.load_time_series(ref_file) _, ref_reward = mir_eval.io.load_time_series("data/melody/reward00.txt") _, est_voicing = mir_eval.io.load_time_series("data/melody/voicingest00.txt") (ref_v, ref_c, est_v, est_c) = mir_eval.melody.to_cent_voicing( ref_time, ref_freq, est_time, est_freq, est_voicing=est_voicing, ref_reward=ref_reward, ) # Expected values test_range = np.arange(220, 225) expected_ref_v = np.array([0.0, 0.0, 0.0, 1.0, 0.3]) expected_ref_c = np.array([0.0, 0.0, 0.0, 6056.8837818916609, 6028.5504583021921]) expected_est_v = np.array([0.2, 0.2, 0.2, 0.2, 0.2]) expected_est_c = np.array([5351.3179423647571] * 5) assert np.allclose(ref_v[test_range], expected_ref_v) assert np.allclose(ref_c[test_range], expected_ref_c) assert np.allclose(est_v[test_range], expected_est_v) assert np.allclose(est_c[test_range], expected_est_c) # We can ignore this warning, which occurs when testing with all-zeros reward @pytest.mark.filterwarnings("ignore:Reference melody has no voiced frames") def test_continuous_voicing_metrics(): ref_time = np.array([0.0, 0.1, 0.2, 0.3]) ref_freq = np.array([440.0, 0.0, 220.0, 220.0]) est_time = np.array([0.0, 0.1, 0.2, 0.3]) est_freq = np.array([440.1, 330.0, 440.0, 330.0]) # test different estimate voicings all_est_voicing = [ np.array([1.0, 0.0, 1.0, 1.0]), # perfect np.array([0.0, 1.0, 0.0, 0.0]), # all wrong np.array([0.5, 0.5, 0.5, 0.5]), # all 0.5 np.array([0.8, 0.2, 0.8, 0.8]), # almost right np.array([0.2, 0.8, 0.2, 0.2]), # almost wrong ] all_expected = [ # perfect { "Voicing Recall": 1.0, "Voicing False Alarm": 0.0, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.5, }, # all wrong { "Voicing Recall": 0.0, "Voicing False Alarm": 1.0, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.0, }, # all 0.5 { "Voicing Recall": 0.5, "Voicing False Alarm": 0.5, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.25, }, # almost right { "Voicing Recall": 0.8, "Voicing False Alarm": 0.2, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.4, }, # almost wrong { "Voicing Recall": 0.2, "Voicing False Alarm": 0.8, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.1, }, ] for est_voicing, expected_scores in zip(all_est_voicing, all_expected): actual_scores = mir_eval.melody.evaluate( ref_time, ref_freq, est_time, est_freq, est_voicing=est_voicing ) for metric in actual_scores: assert np.isclose(actual_scores[metric], expected_scores[metric]) # test different rewards all_rewards = [ np.array([0.5, 0.5, 0.5, 0.5]), # uniform np.array([0.3, 0.3, 0.3, 0.3]), # uniform - different number np.array([0.0, 0.0, 0.0, 0.0]), # all zero np.array([1.0, 0.0, 0.0, 0.0]), # one weight np.array([1.0, 0.0, 1.0, 0.0]), # two weights np.array([1.0, 0.0, 0.5, 0.5]), # slightly generous np.array([0.1, 0.0, 0.1, 0.8]), # big penalty ] est_voicing = np.array([1.0, 0.0, 1.0, 1.0]) all_expected = [ # uniform { "Voicing Recall": 1.0, "Voicing False Alarm": 0.0, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.5, }, # uniform - different number { "Voicing Recall": 1.0, "Voicing False Alarm": 0.0, "Raw Pitch Accuracy": 1.0 / 3.0, "Raw Chroma Accuracy": 2.0 / 3.0, "Overall Accuracy": 0.5, }, # all zero { "Voicing Recall": 1.0, "Voicing False Alarm": 0.75, "Raw Pitch Accuracy": 0.0, "Raw Chroma Accuracy": 0.0, "Overall Accuracy": 0.25, }, # one weight { "Voicing Recall": 1.0, "Voicing False Alarm": 2.0 / 3.0, "Raw Pitch Accuracy": 1.0, "Raw Chroma Accuracy": 1.0, "Overall Accuracy": 0.5, }, # two weights { "Voicing Recall": 1.0, "Voicing False Alarm": 0.5, "Raw Pitch Accuracy": 0.5, "Raw Chroma Accuracy": 1.0, "Overall Accuracy": 0.5, }, # slightly generous { "Voicing Recall": 1.0, "Voicing False Alarm": 0.0, "Raw Pitch Accuracy": 0.5, "Raw Chroma Accuracy": 0.75, "Overall Accuracy": 0.625, }, # big penalty { "Voicing Recall": 1.0, "Voicing False Alarm": 0.0, "Raw Pitch Accuracy": 0.1, "Raw Chroma Accuracy": 0.2, "Overall Accuracy": 0.325, }, ] for ref_reward, expected_scores in zip(all_rewards, all_expected): actual_scores = mir_eval.melody.evaluate( ref_time, ref_freq, est_time, est_freq, est_voicing=est_voicing, ref_reward=ref_reward, ) for metric in actual_scores: assert np.isclose(actual_scores[metric], expected_scores[metric]) def test_voicing_measures_empty(): # We need a special test for voicing_measures because it only takes 2 args with pytest.warns() as w: # First, test for warnings due to empty voicing arrays score = mir_eval.melody.voicing_measures(np.array([]), np.array([])) assert len(w) == 4 assert np.all([issubclass(wrn.category, UserWarning) for wrn in w]) assert [str(wrn.message) for wrn in w] == [ "Reference voicing array is empty.", "Estimated voicing array is empty.", "Reference melody has no voiced frames.", "Estimated melody has no voiced frames.", ] # And that the metric is 0 assert np.allclose(score, 0) def test_voicing_measures_unvoiced(): with pytest.warns() as w: # Also test for a warning when the arrays have non-voiced content mir_eval.melody.voicing_measures(np.ones(10), np.zeros(10)) assert len(w) == 1 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Estimated melody has no voiced frames." @pytest.mark.xfail(raises=ValueError) def test_melody_voicing_badlength(): # ref and est voicings must be the same length mir_eval.melody.voicing_measures(np.ones(10), np.ones(11)) @pytest.mark.parametrize( "metric", [ mir_eval.melody.raw_pitch_accuracy, mir_eval.melody.raw_chroma_accuracy, mir_eval.melody.overall_accuracy, ], ) def test_melody_function_empty(metric): with pytest.warns() as w: # First, test for warnings due to empty voicing arrays score = metric(np.array([]), np.array([]), np.array([]), np.array([])) assert len(w) == 6 assert np.all([issubclass(wrn.category, UserWarning) for wrn in w]) assert [str(wrn.message) for wrn in w] == [ "Reference voicing array is empty.", "Estimated voicing array is empty.", "Reference melody has no voiced frames.", "Estimated melody has no voiced frames.", "Reference frequency array is empty.", "Estimated frequency array is empty.", ] # And that the metric is 0 assert np.allclose(score, 0) # Also test for a warning when the arrays have non-voiced content metric(np.ones(10), np.arange(10), np.zeros(10), np.arange(10)) assert len(w) == 7 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Estimated melody has no voiced frames." @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [ mir_eval.melody.raw_pitch_accuracy, mir_eval.melody.raw_chroma_accuracy, mir_eval.melody.overall_accuracy, ], ) @pytest.mark.parametrize( "ref_freq, est_freq", [(np.ones(11), np.ones(10)), (np.ones(10), np.ones(11))] ) def test_melody_badlength(metric, ref_freq, est_freq): # frequency and time must be the same length metric(np.ones(10), ref_freq, np.ones(10), est_freq) @pytest.mark.parametrize("melody_data", file_sets, indirect=True) @pytest.mark.parametrize("voicing", [False, True]) def test_melody_functions(melody_data, voicing): ref_time, ref_freq, est_time, est_freq, expected_scores = melody_data # When voicing=True, do the continuous voicing equivalence check if voicing: ref_reward = np.ones_like(ref_time) est_voicing = (est_freq >= 0).astype(float) else: ref_reward = None est_voicing = None scores = mir_eval.melody.evaluate( ref_time, ref_freq, est_time, est_freq, est_voicing=est_voicing, ref_reward=ref_reward, ) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_multipitch.py000066400000000000000000000234631475740344600203020ustar00rootroot00000000000000""" Unit tests for mir_eval.multipitch """ import numpy as np import json import mir_eval import glob import warnings import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/multipitch/ref*.txt" EST_GLOB = "data/multipitch/est*.txt" SCORES_GLOB = "data/multipitch/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def multipitch_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f_handle: expected_score = json.load(f_handle) ref_times, ref_freqs = mir_eval.io.load_ragged_time_series(ref_f) est_times, est_freqs = mir_eval.io.load_ragged_time_series(est_f) return ref_times, ref_freqs, est_times, est_freqs, expected_score def __frequencies_equal(freqs_a, freqs_b): if len(freqs_a) != len(freqs_b): return False else: equal = True for freq_a, freq_b in zip(freqs_a, freqs_b): if freq_a.size != freq_b.size: return False equal = equal and np.allclose(freq_a, freq_b, atol=A_TOL) return equal def __scores_equal(score_a, score_b): keys_a = set(list(score_a.keys())) keys_b = set(list(score_b.keys())) if keys_a != keys_b: return False else: equal = True for k in keys_a: value_a = score_a[k] value_b = score_b[k] equal = equal and np.allclose(value_a, value_b, atol=A_TOL) return equal def test_resample_multif0(): times = np.array([0.00, 0.01, 0.02, 0.03]) empty_times = np.array([]) freqs = [ np.array([200.0]), np.array([]), np.array([300.0, 400.0, 500.0]), np.array([300.0, 500.0]), ] empty_freqs = [] target_times1 = times target_times2 = np.array([0.001, 0.002, 0.01, 0.029, 0.05]) target_times3 = empty_times expected_freqs1 = freqs expected_freqs2 = [ np.array([200.0]), np.array([200.0]), np.array([]), np.array([300.0, 500.0]), np.array([]), ] expected_freqs3 = empty_freqs expected_freqs4 = [np.array([])] * 4 actual_freqs1 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times1) actual_freqs2 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times2) actual_freqs3 = mir_eval.multipitch.resample_multipitch(times, freqs, target_times3) actual_freqs4 = mir_eval.multipitch.resample_multipitch( empty_times, empty_freqs, target_times1 ) assert __frequencies_equal(actual_freqs1, expected_freqs1) assert __frequencies_equal(actual_freqs2, expected_freqs2) assert __frequencies_equal(actual_freqs3, expected_freqs3) assert __frequencies_equal(actual_freqs4, expected_freqs4) def test_frequencies_to_midi(): frequencies = [ np.array([440.0]), np.array([]), np.array([220.0, 660.0, 512.0]), np.array([300.0, 512.0]), ] expected = [ np.array([69.0]), np.array([]), np.array([57.0, 76.01955000865388, 71.623683437704088]), np.array([62.369507723654657, 71.623683437704088]), ] actual = mir_eval.multipitch.frequencies_to_midi(frequencies) assert __frequencies_equal(actual, expected) def test_midi_to_chroma(): midi_frequencies = [ np.array([69.0]), np.array([]), np.array([57.0, 76.01955000865388, 71.623683437704088]), np.array([62.369507723654657, 71.623683437704088]), ] expected = [ np.array([9.0]), np.array([]), np.array([9.0, 4.01955000865388, 11.623683437704088]), np.array([2.3695077236546567, 11.623683437704088]), ] actual = mir_eval.multipitch.midi_to_chroma(midi_frequencies) assert __frequencies_equal(actual, expected) def test_compute_num_freqs(): frequencies = [ np.array([256.0]), np.array([]), np.array([362.03867196751236, 128.0, 512.0]), np.array([300.0, 512.0]), ] expected = np.array([1, 0, 3, 2]) actual = mir_eval.multipitch.compute_num_freqs(frequencies) assert np.allclose(actual, expected, atol=A_TOL) def test_compute_num_true_positives(): ref_freqs = [ np.array([96.0, 100.0]), np.array([]), np.array([81.0]), np.array([102.0, 84.0, 108.0]), np.array([98.745824285950576, 108.0]), ] est_freqs = [ np.array([96.0]), np.array([]), np.array([200.0, 82.0]), np.array([102.0, 84.0, 108.0]), np.array([99.0, 108.0]), ] expected = np.array([1, 0, 0, 3, 2]) actual = mir_eval.multipitch.compute_num_true_positives(ref_freqs, est_freqs) assert np.allclose(actual, expected, atol=A_TOL) ref_freqs_chroma = [ np.array([0.0, 1.5]), np.array([]), np.array([2.0]), np.array([5.1, 6.0, 11.0]), np.array([11.9, 11.9]), ] est_freqs_chroma = [ np.array([0.0]), np.array([]), np.array([5.0, 2.6]), np.array([5.1, 6.0, 11.0]), np.array([0.2, 11.5]), ] expected = np.array([1, 0, 0, 3, 2]) actual = mir_eval.multipitch.compute_num_true_positives( ref_freqs_chroma, est_freqs_chroma, chroma=True ) assert np.allclose(actual, expected, atol=A_TOL) def test_accuracy_metrics(): true_positives = np.array([1, 0, 0, 3, 2]) n_ref = np.array([2, 0, 1, 3, 2]) n_est = np.array([1, 0, 2, 3, 2]) expected_precision = 0.75 expected_recall = 0.75 expected_accuracy = 0.6 ( actual_precision, actual_recall, actual_accuarcy, ) = mir_eval.multipitch.compute_accuracy(true_positives, n_ref, n_est) assert np.allclose(actual_precision, expected_precision, atol=A_TOL) assert np.allclose(actual_recall, expected_recall, atol=A_TOL) assert np.allclose(actual_accuarcy, expected_accuracy, atol=A_TOL) def test_error_score_metrics(): true_positives = np.array([1, 0, 0, 3, 2]) n_ref = np.array([2, 0, 1, 3, 2]) n_est = np.array([1, 0, 2, 3, 2]) expected_esub = 0.125 expected_emiss = 0.125 expected_efa = 0.125 expected_etot = 0.375 ( actual_esub, actual_emiss, actual_efa, actual_etot, ) = mir_eval.multipitch.compute_err_score(true_positives, n_ref, n_est) assert np.allclose(actual_esub, expected_esub, atol=A_TOL) assert np.allclose(actual_emiss, expected_emiss, atol=A_TOL) assert np.allclose(actual_efa, expected_efa, atol=A_TOL) assert np.allclose(actual_etot, expected_etot, atol=A_TOL) def unit_test_metrics(): empty_array = np.array([]) ref_time = np.array([0.0, 0.1]) ref_freqs = [np.array([201.0]), np.array([])] est_time = np.array([0.0, 0.1]) est_freqs = [np.array([200.0]), np.array([])] # ref sizes unequal with pytest.raises(ValueError): mir_eval.multipitch.metrics(np.array([0.0]), ref_freqs, est_time, est_freqs) # est sizes unequal with pytest.raises(ValueError): mir_eval.multipitch.metrics(ref_time, ref_freqs, np.array([0.0]), est_freqs) with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Test for warnings on empty values actual_score = mir_eval.multipitch.metrics( ref_time, [empty_array, empty_array], est_time, [empty_array, empty_array] ) assert len(w) == 6 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Reference frequencies are all empty." with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # Test for warnings on empty values # test all inputs empty mir_eval.multipitch.metrics(empty_array, [], empty_array, []) assert len(w) == 10 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Reference frequencies are all empty." with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # reference empty mir_eval.multipitch.metrics(empty_array, [], est_time, est_freqs) assert len(w) == 9 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Reference frequencies are all empty." with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") # estimate empty mir_eval.multipitch.metrics(ref_time, ref_freqs, empty_array, []) assert len(w) == 5 assert issubclass(w[-1].category, UserWarning) assert str(w[-1].message) == "Estimate frequencies are all empty." expected_score = ( 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ) assert np.allclose(actual_score, expected_score) # test perfect estimate ref_time = np.array([0.0, 0.1, 0.2]) ref_freqs = [np.array([201.0]), np.array([]), np.array([300.5, 87.1])] actual_score = mir_eval.multipitch.metrics(ref_time, ref_freqs, ref_time, ref_freqs) expected_score = ( 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, ) assert np.allclose(actual_score, expected_score) @pytest.mark.parametrize("multipitch_data", file_sets, indirect=True) def test_evaluate_regression(multipitch_data): ref_times, ref_freqs, est_times, est_freqs, expected_score = multipitch_data actual_score = mir_eval.multipitch.evaluate( ref_times, ref_freqs, est_times, est_freqs ) assert __scores_equal(actual_score, expected_score) mir_eval-0.8.2/tests/test_onset.py000066400000000000000000000044221475740344600172420ustar00rootroot00000000000000""" Unit tests for mir_eval.onset """ import numpy as np import pytest import json import mir_eval import glob import warnings A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/onset/ref*.txt" EST_GLOB = "data/onset/est*.txt" SCORES_GLOB = "data/onset/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def onset_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) reference_onsets = mir_eval.io.load_events(ref_f) estimated_onsets = mir_eval.io.load_events(est_f) return reference_onsets, estimated_onsets, expected_scores def test_onset_empty_warnings(): with pytest.warns(UserWarning, match="Reference onsets are empty."): mir_eval.onset.f_measure(np.array([]), np.arange(10)) with pytest.warns(UserWarning, match="Estimated onsets are empty."): mir_eval.onset.f_measure(np.arange(10), np.array([])) with pytest.warns(UserWarning, match="onsets are empty"): # Also verify that the score is 0 assert np.allclose(mir_eval.onset.f_measure(np.array([]), np.array([])), 0) @pytest.mark.xfail(raisses=ValueError) @pytest.mark.parametrize( "onsets", [ np.array([[1.0, 2.0]]), # must be 1d ndarray np.array([1e10, 1e11]), # must not be huge np.array([2.0, 1.0]), # must be sorted ], ) def test_onset_fail(onsets): mir_eval.onset.f_measure(onsets, onsets) def test_onset_match(): # Valid onsets which are the same produce a score of 1 for all metrics onsets = np.arange(10, dtype=np.float64) assert np.allclose(mir_eval.onset.f_measure(onsets, onsets), 1.0) @pytest.mark.parametrize("onset_data", file_sets, indirect=True) def test_onset_functions(onset_data): reference_onsets, estimated_onsets, expected_scores = onset_data # Compute scores scores = mir_eval.onset.evaluate(reference_onsets, estimated_onsets) # Compare them assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_pattern.py000066400000000000000000000063341475740344600175730ustar00rootroot00000000000000""" Some unit tests for the pattern discovery task. """ import numpy as np import json import mir_eval import glob import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/pattern/ref*.txt" EST_GLOB = "data/pattern/est*.txt" SCORES_GLOB = "data/pattern/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def pattern_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) reference_patterns = mir_eval.io.load_patterns(ref_f) estimated_patterns = mir_eval.io.load_patterns(est_f) return reference_patterns, estimated_patterns, expected_scores @pytest.mark.parametrize( "metric", [ mir_eval.pattern.standard_FPR, mir_eval.pattern.establishment_FPR, mir_eval.pattern.occurrence_FPR, mir_eval.pattern.three_layer_FPR, mir_eval.pattern.first_n_three_layer_P, mir_eval.pattern.first_n_target_proportion_R, ], ) def test_pattern_empty(metric): # First, test for a warning on empty pattern with pytest.warns(UserWarning, match="Reference patterns are empty"): metric([[[]]], [[[(100, 20)]]]) with pytest.warns(UserWarning, match="Estimated patterns are empty"): metric([[[(100, 20)]]], [[[]]]) with pytest.warns(UserWarning, match="patterns are empty"): # And that the metric is 0 assert np.allclose(metric([[[]]], [[[]]]), 0) @pytest.mark.parametrize( "metric", [ mir_eval.pattern.standard_FPR, mir_eval.pattern.establishment_FPR, mir_eval.pattern.occurrence_FPR, mir_eval.pattern.three_layer_FPR, mir_eval.pattern.first_n_three_layer_P, mir_eval.pattern.first_n_target_proportion_R, ], ) @pytest.mark.parametrize( "patterns", [ [[[(100, 20)]], []], # patterns must have at least one occurrence [[[(100, 20, 3)]]], # (onset, midi) tuple must contain 2 elements ], ) @pytest.mark.xfail(raises=ValueError) def test_pattern_failure(metric, patterns): metric(patterns, patterns) @pytest.mark.parametrize( "metric", [ mir_eval.pattern.standard_FPR, mir_eval.pattern.establishment_FPR, mir_eval.pattern.occurrence_FPR, mir_eval.pattern.three_layer_FPR, mir_eval.pattern.first_n_three_layer_P, mir_eval.pattern.first_n_target_proportion_R, ], ) def test_pattern_perfect(metric): # Valid patterns which are the same produce a score of 1 for all metrics patterns = [[[(100, 20), (200, 30)]]] assert np.allclose(metric(patterns, patterns), 1) @pytest.mark.parametrize("pattern_data", file_sets, indirect=True) def test_pattern_functions(pattern_data): reference_patterns, estimated_patterns, expected_scores = pattern_data # Compute scores scores = mir_eval.pattern.evaluate(reference_patterns, estimated_patterns) # Compare them assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_segment.py000066400000000000000000000155711475740344600175630ustar00rootroot00000000000000""" Unit tests for mir_eval.segment """ import numpy as np import json import mir_eval import glob import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/segment/ref*.lab" EST_GLOB = "data/segment/est*.lab" SCORES_GLOB = "data/segment/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def segment_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) # Load in an example segmentation annotation ref_intervals, ref_labels = mir_eval.io.load_labeled_intervals(ref_f) # Load in an example segmentation tracker output est_intervals, est_labels = mir_eval.io.load_labeled_intervals(est_f) return ref_intervals, ref_labels, est_intervals, est_labels, expected_scores @pytest.mark.parametrize( "metric", [mir_eval.segment.detection, mir_eval.segment.deviation] ) def test_segment_boundary_empty(metric): with pytest.warns(UserWarning, match="Reference intervals are empty"): metric(np.zeros((0, 2)), np.array([[1, 2], [2, 3]]), trim=False) with pytest.warns(UserWarning, match="Estimated intervals are empty"): metric(np.array([[1, 2], [2, 3]]), np.array([[1, 2]]), trim=True) with pytest.warns(UserWarning, match="intervals are empty"): empty_intervals = np.zeros((0, 2)) if metric == mir_eval.segment.detection: assert np.allclose(metric(empty_intervals, empty_intervals), 0) else: assert np.all(np.isnan(metric(empty_intervals, empty_intervals))) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [mir_eval.segment.detection, mir_eval.segment.deviation] ) @pytest.mark.parametrize( "intervals", [ # Now test validation function - intervals must be n by 2 np.array([1, 2, 3, 4]), # Interval boundaries must be positive np.array([[-1, 2], [2, 3]]), # Positive interval durations np.array([[2, 1], [2, 3]]), ], ) def test_segment_boundary_errors(metric, intervals): metric(intervals, intervals) def test_segment_boundary_detection_perfect(): correct_intervals = np.array([[0, 1], [1, 2]]) assert np.allclose( mir_eval.segment.detection(correct_intervals, correct_intervals), 1 ) def test_segment_boundary_deviation_perfect(): correct_intervals = np.array([[0, 1], [1, 2]]) assert np.allclose( mir_eval.segment.deviation(correct_intervals, correct_intervals), 0 ) @pytest.mark.parametrize( "metric", [ mir_eval.segment.pairwise, mir_eval.segment.rand_index, mir_eval.segment.ari, mir_eval.segment.mutual_information, mir_eval.segment.nce, mir_eval.segment.vmeasure, ], ) def test_segment_structure_empty(metric): with pytest.warns(UserWarning, match="Reference intervals are empty"): metric(np.zeros((0, 2)), [], np.array([[0, 1]]), ["foo"]) with pytest.warns(UserWarning, match="Estimated intervals are empty"): metric(np.array([[0, 1]]), ["foo"], np.zeros((0, 2)), []) with pytest.warns(UserWarning, match="intervals are empty"): empty_intervals = np.zeros((0, 2)) assert np.allclose(metric(empty_intervals, [], empty_intervals, []), 0) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [ mir_eval.segment.pairwise, mir_eval.segment.rand_index, mir_eval.segment.ari, mir_eval.segment.mutual_information, mir_eval.segment.nce, mir_eval.segment.vmeasure, ], ) @pytest.mark.parametrize( "intervals, labels", [ # Test for non-matching numbers of intervals and labels (np.array([[2, 1], [2, 3]]), ["a", "b", "c"]), # Now test validation function - intervals must be n by 2 (np.arange(4), ["a", "b", "c", "d"]), # Interval boundaries must be positive (np.array([[-1, 2], [2, 3]]), ["a", "b"]), # Positive interval durations (np.array([[2, 1], [2, 3]]), ["a", "b"]), # Number of intervals must match number of labels (np.array([[2, 1], [2, 3]]), ["a"]), # Intervals must start at 0 (np.array([[1, 2], [2, 3]]), ["a", "b"]), ], ) def test_segment_structure_fail(metric, intervals, labels): metric(intervals, labels, intervals, labels) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [ mir_eval.segment.pairwise, mir_eval.segment.rand_index, mir_eval.segment.ari, mir_eval.segment.mutual_information, mir_eval.segment.nce, mir_eval.segment.vmeasure, ], ) def test_segment_structure_end_mismatch(metric): reference_intervals = np.array([[0, 1], [1, 2]]) estimated_intervals = np.array([[0, 1], [1, 3]]) labels = ["a", "b"] metric(reference_intervals, labels, estimated_intervals, labels) @pytest.mark.parametrize( "metric", [ mir_eval.segment.pairwise, mir_eval.segment.rand_index, mir_eval.segment.ari, mir_eval.segment.mutual_information, mir_eval.segment.nce, mir_eval.segment.vmeasure, ], ) def test_segment_structure_perfect(metric): reference_intervals = np.array([[0, 1], [1, 2]]) estimated_intervals = np.array([[0, 1], [1, 2]]) labels = ["a", "b"] if metric == mir_eval.segment.mutual_information: assert np.allclose( metric(reference_intervals, labels, estimated_intervals, labels), [np.log(2), 1, 1], ) else: assert np.allclose( metric(reference_intervals, labels, estimated_intervals, labels), 1 ) @pytest.mark.parametrize("segment_data", file_sets, indirect=True) def test_segment_functions(segment_data): ref_intervals, ref_labels, est_intervals, est_labels, expected_scores = segment_data # Compute scores scores = mir_eval.segment.evaluate( ref_intervals, ref_labels, est_intervals, est_labels ) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) @pytest.mark.parametrize("segment_data", file_sets, indirect=True) def test_segment_functions_permuted(segment_data): ref_intervals, ref_labels, est_intervals, est_labels, expected_scores = segment_data # Also check with permuted references idx = np.random.permutation(np.arange(len(ref_intervals))) perm_int = ref_intervals[idx] perm_lab = [ref_labels[_] for _ in idx] scores = mir_eval.segment.evaluate(perm_int, perm_lab, est_intervals, est_labels) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_separation.py000066400000000000000000000355301475740344600202630ustar00rootroot00000000000000""" unit tests for mir_eval.separation load randomly generated source and estimated source signals and the output from BSS_eval MATLAB implementation, make sure the results from mir_eval numerically match. """ import numpy as np import mir_eval import glob import pytest import json import os A_TOL = 1e-2 REF_GLOB = "data/separation/ref*" EST_GLOB = "data/separation/est*" SCORES_GLOB = "data/separation/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) # Skip separation tests since deprecation pytest.skip(allow_module_level=True) @pytest.fixture def separation_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_results = json.load(f) expected_sources = expected_results["Sources"] expected_frames = expected_results["Framewise"] expected_images = expected_results["Images"] expected_image_frames = expected_results["Images Framewise"] # Load in example source separation data ref_sources = __load_and_stack_wavs(ref_f) est_sources = __load_and_stack_wavs(est_f) # Test inference for single source passed as single dimensional array if ref_sources.shape[0] == 1 and est_sources.shape[0] == 1: ref_sources = ref_sources[0] est_sources = est_sources[0] return ( ref_sources, est_sources, expected_sources, expected_frames, expected_images, expected_image_frames, ) @pytest.fixture(autouse=True) def seed_rng(): # Seed the RNG before each test run np.random.seed(1999) def __load_and_stack_wavs(directory): """Load all wavs in a directory and stack them vertically into a matrix""" stacked_audio_data = [] global_fs = None for f in sorted(glob.glob(os.path.join(directory, "*.wav"))): audio_data, fs = mir_eval.io.load_wav(f) assert global_fs is None or fs == global_fs global_fs = fs stacked_audio_data.append(audio_data) return np.vstack(stacked_audio_data) def __generate_multichannel(mono_sig, nchan=2, gain=1.0, reverse=False): """Turn a single channel (ie. mono) audio sample into a multichannel (e.g. stereo) Note: to achieve channels of silence pass gain=0 """ # add the channels dimension input_3d = np.atleast_3d(mono_sig) # get the desired number of channels stackin = [input_3d] * nchan # apply the gain to the new channels stackin[1:] = np.multiply(gain, stackin[1:]) if reverse: # reverse the new channels stackin[1:] = stackin[1:][:][::-1] return np.dstack(stackin) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images, mir_eval.separation.bss_eval_images_framewise, ], ) def test_empty_input(metric): if ( metric == mir_eval.separation.bss_eval_sources or metric == mir_eval.separation.bss_eval_images ): args = [np.array([]), np.array([])] elif ( metric == mir_eval.separation.bss_eval_sources_framewise or metric == mir_eval.separation.bss_eval_images_framewise ): args = [np.array([]), np.array([]), 40, 20] with pytest.warns(UserWarning, match="is empty") as record: # First, test for a warning on empty audio data metric(*args) # And that the metric returns empty arrays assert np.allclose(metric(*args), np.array([])) # These warning counters are now offset by 1 because of the deprecation message assert "reference_sources is empty" in str(record[1].message) assert "estimated_sources is empty" in str(record[2].message) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images, mir_eval.separation.bss_eval_images_framewise, ], ) def test_silent_input(metric): # Test for error when there is a silent reference/estimated source if ( metric == mir_eval.separation.bss_eval_images or metric == mir_eval.separation.bss_eval_images_framewise ): ref_sources = np.vstack( (np.zeros((1, 100, 2)), np.random.random_sample((2, 100, 2))) ) est_sources = np.vstack( (np.zeros((1, 100, 2)), np.random.random_sample((2, 100, 2))) ) else: ref_sources = np.vstack((np.zeros(100), np.random.random_sample((2, 100)))) est_sources = np.vstack((np.zeros(100), np.random.random_sample((2, 100)))) if ( metric == mir_eval.separation.bss_eval_sources or metric == mir_eval.separation.bss_eval_images ): with pytest.raises(ValueError): metric(ref_sources[:2], est_sources[1:]) with pytest.raises(ValueError): metric(ref_sources[1:], est_sources[:2]) elif ( metric == mir_eval.separation.bss_eval_sources_framewise or metric == mir_eval.separation.bss_eval_images_framewise ): with pytest.raises(ValueError): metric(ref_sources[:2], est_sources[1:], 40, 20) with pytest.raises(ValueError): metric(ref_sources[1:], est_sources[:2], 40, 20) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images_framewise, ], ) def test_partial_silence(metric): # Test for a full window of silence in reference/estimated source if metric == mir_eval.separation.bss_eval_sources_framewise: silence = np.zeros((2, 20)) sound = np.random.random_sample((2, 20)) elif metric == mir_eval.separation.bss_eval_images_framewise: silence = np.zeros((2, 20, 2)) sound = np.random.random_sample((2, 20, 2)) # test with silence in the reference results = metric( np.concatenate((sound, silence, sound), axis=1), np.concatenate((sound, sound, sound), axis=1), window=10, hop=10, ) for measure in results: for idx, source in enumerate(measure): if idx < 2 or idx > 3: assert not np.isnan(source[idx]) elif idx < 4: assert np.isnan(source[idx]) else: raise ValueError("Testing error in partial silence test") # test with silence in the estimate results = metric( np.concatenate((sound, sound, sound), axis=1), np.concatenate((sound, silence, sound), axis=1), window=10, hop=10, ) for measure in results: for idx, source in enumerate(measure): if idx < 2 or idx > 3: assert not np.isnan(source[idx]) elif idx < 4: assert np.isnan(source[idx]) else: raise ValueError("Testing error in partial silence test") @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images, mir_eval.separation.bss_eval_images_framewise, ], ) def test_incompatible_shapes(metric): # Test for error when shape is different if ( metric == mir_eval.separation.bss_eval_images or metric == mir_eval.separation.bss_eval_images_framewise ): sources_4 = np.random.random_sample((4, 100, 2)) sources_3 = np.random.random_sample((3, 100, 2)) sources_4_chan = np.random.random_sample((4, 100, 3)) else: sources_4 = np.random.random_sample((4, 100)) sources_3 = np.random.random_sample((3, 100)) if ( metric == mir_eval.separation.bss_eval_sources or metric == mir_eval.separation.bss_eval_images ): args1 = [sources_3, sources_4] args2 = [sources_4, sources_3] elif ( metric == mir_eval.separation.bss_eval_sources_framewise or metric == mir_eval.separation.bss_eval_images_framewise ): args1 = [sources_3, sources_4, 40, 20] args2 = [sources_4, sources_3, 40, 20] with pytest.raises(ValueError): metric(*args1) with pytest.raises(ValueError): metric(*args2) if ( metric == mir_eval.separation.bss_eval_images or metric == mir_eval.separation.bss_eval_images_framewise ): with pytest.raises(ValueError): metric(sources_4, sources_4_chan) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images, mir_eval.separation.bss_eval_images_framewise, ], ) def test_too_many_sources(metric): # Test for error when too many sources or references are provided many_sources = np.random.random_sample((mir_eval.separation.MAX_SOURCES * 2, 400)) if metric == mir_eval.separation.bss_eval_sources: with pytest.raises(ValueError): metric(many_sources, many_sources) elif metric == mir_eval.separation.bss_eval_sources_framewise: with pytest.raises(ValueError): metric(many_sources, many_sources, 40, 20) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images, mir_eval.separation.bss_eval_images_framewise, ], ) def test_too_many_dimensions(metric): # Test for detection of too high dimensioned images ref_sources = np.random.random_sample((4, 100, 2, 3)) est_sources = np.random.random_sample((4, 100, 2, 3)) metric(ref_sources, est_sources) @pytest.mark.parametrize( "metric", [mir_eval.separation.bss_eval_sources, mir_eval.separation.bss_eval_images], ) def test_default_permutation(metric): # Test for default permutation matrix when not computing permutation if metric == mir_eval.separation.bss_eval_sources: ref_sources = np.random.random_sample((4, 100)) est_sources = np.random.random_sample((4, 100)) elif metric == mir_eval.separation.bss_eval_images: ref_sources = np.random.random_sample((4, 100, 2)) est_sources = np.random.random_sample((4, 100, 2)) results = metric(ref_sources, est_sources, compute_permutation=False) assert np.array_equal(results[-1], np.asarray([0, 1, 2, 3])) @pytest.mark.parametrize( "metric", [ mir_eval.separation.bss_eval_sources_framewise, mir_eval.separation.bss_eval_images_framewise, ], ) def test_framewise_small_window(metric): # Test for invalid win/hop parameter detection if metric == mir_eval.separation.bss_eval_sources_framewise: ref_sources = np.random.random_sample((4, 100)) est_sources = np.random.random_sample((4, 100)) comparison_fcn = mir_eval.separation.bss_eval_sources elif metric == mir_eval.separation.bss_eval_images_framewise: ref_sources = np.random.random_sample((4, 100, 2)) est_sources = np.random.random_sample((4, 100, 2)) comparison_fcn = mir_eval.separation.bss_eval_images # Test with window larger than source length assert np.allclose( np.squeeze(metric(ref_sources, est_sources, window=120, hop=20)), comparison_fcn(ref_sources, est_sources, False), atol=A_TOL, ) # Test with hop larger than source length assert np.allclose( np.squeeze(metric(ref_sources, est_sources, window=20, hop=120)), comparison_fcn(ref_sources, est_sources, False), atol=A_TOL, ) @pytest.mark.parametrize("separation_data", file_sets, indirect=True) def test_separation_functions(separation_data): ( ref_sources, est_sources, expected_sources, expected_frames, expected_images, expected_image_frames, ) = separation_data # Compute scores scores = mir_eval.separation.evaluate( ref_sources, est_sources, window=expected_frames["win"], hop=expected_frames["hop"], ) # Compare them for key in scores: if "Sources - " in key: test_data_name = key.replace("Sources - ", "") assert np.allclose( scores[key], expected_sources[test_data_name], atol=A_TOL ) elif "Sources Frames - " in key: test_data_name = key.replace("Sources Frames - ", "") assert np.allclose(scores[key], expected_frames[test_data_name], atol=A_TOL) @pytest.mark.parametrize("separation_data", file_sets, indirect=True) def test_separation_images(separation_data): ( ref_sources, est_sources, expected_sources, expected_frames, expected_images, expected_image_frames, ) = separation_data # Compute scores with images ref_images = __generate_multichannel(ref_sources, expected_images["nchan"]) est_images = __generate_multichannel( est_sources, expected_images["nchan"], expected_images["gain"], expected_images["reverse"], ) image_scores = mir_eval.separation.evaluate(ref_images, est_images) # Compare them for key in image_scores: if "Images - " in key: test_data_name = key.replace("Images - ", "") assert np.allclose( image_scores[key], expected_images[test_data_name], atol=A_TOL ) @pytest.mark.parametrize("separation_data", file_sets, indirect=True) def test_separation_images_framewise(separation_data): ( ref_sources, est_sources, expected_sources, expected_frames, expected_images, expected_image_frames, ) = separation_data # Compute scores with images framewise ref_images = __generate_multichannel(ref_sources, expected_image_frames["nchan"]) est_images = __generate_multichannel( est_sources, expected_image_frames["nchan"], expected_image_frames["gain"], expected_image_frames["reverse"], ) imageframe_scores = mir_eval.separation.evaluate( ref_images, est_images, window=expected_image_frames["win"], hop=expected_image_frames["hop"], ) # Compare them for key in imageframe_scores: if "Images Frames - " in key: test_data_name = key.replace("Images Frames - ", "") assert np.allclose( imageframe_scores[key], expected_image_frames[test_data_name], atol=A_TOL, ) # Catch a few exceptions in the evaluate function image_scores = mir_eval.separation.evaluate(ref_images, est_images) # make sure sources is not being evaluated on images assert "Sources - Source to Distortion" not in image_scores mir_eval-0.8.2/tests/test_sonify.py000066400000000000000000000131521475740344600174210ustar00rootroot00000000000000"""Unit tests for sonification methods""" import pytest import mir_eval import numpy as np import scipy @pytest.mark.parametrize("times", [np.array([1.0]), np.arange(10.0)]) @pytest.mark.parametrize("fs", [8000, 44100]) def test_clicks(times, fs): # Test output length for a variety of parameter settings click_signal = mir_eval.sonify.clicks(times, fs) assert len(click_signal) == times.max() * fs + int(fs * 0.1) + 1 click_signal = mir_eval.sonify.clicks(times, fs, length=1000) assert len(click_signal) == 1000 click_signal = mir_eval.sonify.clicks(times, fs, click=np.zeros(1000)) assert len(click_signal) == times.max() * fs + 1000 + 1 @pytest.mark.parametrize("fs", [8000, 44100]) def test_time_frequency(fs): # Test length for different inputs signal = mir_eval.sonify.time_frequency( np.random.standard_normal((100, 1000)), np.arange(1, 101), np.linspace(0, 10, 1000), fs, ) assert len(signal) == 10 * fs # Make one longer signal = mir_eval.sonify.time_frequency( np.random.standard_normal((100, 1000)), np.arange(1, 101), np.linspace(0, 10, 1000), fs, length=fs * 11, ) assert len(signal) == 11 * fs # Make one shorter signal = mir_eval.sonify.time_frequency( np.random.standard_normal((100, 1000)), np.arange(1, 101), np.linspace(0, 10, 1000), fs, length=fs * 5, ) assert len(signal) == 5 * fs def test_time_frequency_const(): # Sonify with a single interval to hit the const interpolator s1 = mir_eval.sonify.time_frequency( np.ones((1, 1)), np.array([60]), np.array([[0, 1]]), 8000, ) # Sonify with two tiem intervals to hit the regular interpolator # but the second frequency will have no energy, so it doesn't # change the resulting signal s2 = mir_eval.sonify.time_frequency( np.array([[1, 1], [0, 0]]), np.array([60, 90]), np.array([[0, 1], [0, 1]]), 8000, ) assert np.allclose(s1, s2) def test_time_frequency_offset(): fs = 8000 # Length is 3 seconds, first interval starts at 5. # Should produce an empty signal. signal = mir_eval.sonify.time_frequency( np.random.standard_normal((100, 100)), np.arange(1, 101), np.linspace(5, 10, 100), fs, length=fs * 3, ) assert len(signal) == 3 * fs assert np.allclose(signal, 0) @pytest.mark.xfail(raises=ValueError) def test_time_frequency_badtime(): fs = 8000 gram = np.ones((10, 20)) times = np.arange(10) mir_eval.sonify.time_frequency(gram, np.arange(1, 11), times, fs) @pytest.mark.xfail(raises=ValueError) def test_time_frequency_badintervals(): fs = 8000 gram = np.ones((10, 20)) times = np.ones((11, 2)) mir_eval.sonify.time_frequency(gram, np.arange(1, 11), times, fs) @pytest.mark.xfail(raises=ValueError) def test_time_frequency_frequency(): fs = 8000 gram = np.ones((10, 20)) times = np.arange(20) mir_eval.sonify.time_frequency(gram, np.arange(1, 8), times, fs) @pytest.mark.parametrize("fs", [8000, 44100]) def test_chroma(fs): signal = mir_eval.sonify.chroma( np.random.standard_normal((12, 1000)), np.linspace(0, 10, 1000), fs ) assert len(signal) == 10 * fs signal = mir_eval.sonify.chroma( np.random.standard_normal((12, 1000)), np.linspace(0, 10, 1000), fs, length=fs * 11, ) assert len(signal) == 11 * fs @pytest.mark.parametrize("fs", [8000, 44100]) def test_chords(fs): intervals = np.array([np.arange(10), np.arange(1, 11)]).T signal = mir_eval.sonify.chords( ["C", "C:maj", "D:min7", "E:min", "C#", "C", "C", "C", "C", "C"], intervals, fs ) assert len(signal) == 10 * fs signal = mir_eval.sonify.chords( ["C", "C:maj", "D:min7", "E:min", "C#", "C", "C", "C", "C", "C"], intervals, fs, length=fs * 11, ) assert len(signal) == 11 * fs def test_chord_x(): # This test verifies that X sonifies as silence intervals = np.array([[0, 1]]) signal = mir_eval.sonify.chords(["X"], intervals, 8000) assert not np.any(signal), signal def test_pitch_contour(): # Generate some random pitch fs = 8000 times = np.linspace(0, 5, num=5 * fs, endpoint=True) noise = scipy.ndimage.gaussian_filter1d(np.random.randn(len(times)), sigma=256) freqs = 440.0 * 2.0 ** (16 * noise) amps = np.linspace(0, 1, num=5 * fs, endpoint=True) # negate a bunch of sequences idx = np.unique(np.random.randint(0, high=len(times), size=32)) for start, end in zip(idx[::2], idx[1::2]): freqs[start:end] *= -1 # Test with inferring duration x = mir_eval.sonify.pitch_contour(times, freqs, fs) assert len(x) == fs * 5 # Test with an explicit duration # This forces the interpolator to go off the end of the sampling grid, # which should result in a constant sequence in the output x = mir_eval.sonify.pitch_contour(times, freqs, fs, length=fs * 7) assert len(x) == fs * 7 assert np.allclose(x[-fs * 2 :], x[-fs * 2]) # Test with an explicit duration and a fixed offset # This forces the interpolator to go off the beginning of # the sampling grid, which should result in a constant output x = mir_eval.sonify.pitch_contour(times + 5.0, freqs, fs, length=fs * 7) assert len(x) == fs * 7 assert np.allclose(x[: fs * 5], x[0]) # Test with explicit amplitude x = mir_eval.sonify.pitch_contour(times, freqs, fs, length=fs * 7, amplitudes=amps) assert len(x) == fs * 7 assert np.allclose(x[0], 0) mir_eval-0.8.2/tests/test_tempo.py000066400000000000000000000072031475740344600172360ustar00rootroot00000000000000#!/usr/bin/env python """ Unit tests for mir_eval.tempo """ import numpy as np import mir_eval import json import glob import pytest A_TOL = 1e-12 REF_GLOB = "data/tempo/ref*.lab" EST_GLOB = "data/tempo/est*.lab" SCORES_GLOB = "data/tempo/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def tempo_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) def _load_tempi(filename): values = mir_eval.io.load_delimited(filename, [float] * 3) return np.concatenate(values[:2]), values[-1][0] reference_tempi, ref_weight = _load_tempi(ref_f) estimated_tempi, _ = _load_tempi(est_f) return reference_tempi, ref_weight, estimated_tempi, expected_scores def test_zero_tolerance_pass(): good_ref = np.array([60, 120]) good_weight = 0.5 good_est = np.array([120, 180]) zero_tol = 0.0 with pytest.warns( UserWarning, match="A tolerance of 0.0 may not lead to the results you expect" ): mir_eval.tempo.detection(good_ref, good_weight, good_est, tol=zero_tol) def test_tempo_pass(): good_ref = np.array([60, 120]) good_weight = 0.5 good_est = np.array([120, 180]) good_tol = 0.08 for good_tempo in [np.array([50, 50]), np.array([0, 50]), np.array([50, 0])]: mir_eval.tempo.detection(good_tempo, good_weight, good_est, good_tol) mir_eval.tempo.detection(good_ref, good_weight, good_tempo, good_tol) # allow both estimates to be zero mir_eval.tempo.detection(good_ref, good_weight, np.array([0, 0]), good_tol) @pytest.mark.xfail(raises=ValueError) def test_tempo_zero_ref(): # Both references cannot be zero mir_eval.tempo.detection(np.array([0.0, 0.0]), 0.5, np.array([60, 120])) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize("weight", [-1, 1.5]) def test_tempo_weight_range(weight): # Weight needs to be in the range [0, 1] mir_eval.tempo.detection(np.array([60, 120]), weight, np.array([120, 180])) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize("tol", [-1, 1.5]) def test_tempo_tol_range(tol): # Weight needs to be in the range [0, 1] mir_eval.tempo.detection(np.array([60, 120]), 0.5, np.array([120, 180]), tol=tol) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "bad_tempo", [ np.array([-1, -1]), np.array([-1, 0]), np.array([-1, 50]), np.array([0, 1, 2]), np.array([0]), ], ) def test_tempo_fail_bad_reftempo(bad_tempo): good_ref = np.array([60, 120]) good_est = np.array([120, 180]) mir_eval.tempo.detection(bad_tempo, 0.5, good_est, 0.08) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "bad_tempo", [ np.array([-1, -1]), np.array([-1, 0]), np.array([-1, 50]), np.array([0, 1, 2]), np.array([0]), ], ) def test_tempo_fail_bad_esttempo(bad_tempo): good_ref = np.array([60, 120]) good_est = np.array([120, 180]) mir_eval.tempo.detection(good_ref, 0.5, bad_tempo, 0.08) @pytest.mark.parametrize("tempo_data", file_sets, indirect=True) def test_tempo_regression(tempo_data): ref_tempi, ref_weight, est_tempi, expected_scores = tempo_data scores = mir_eval.tempo.evaluate(ref_tempi, ref_weight, est_tempi) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) mir_eval-0.8.2/tests/test_transcription.py000066400000000000000000000221461475740344600210140ustar00rootroot00000000000000# CREATED: 2/9/16 2:27 PM by Justin Salamon import mir_eval import numpy as np import glob import json import pytest A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/transcription/ref*.txt" EST_GLOB = "data/transcription/est*.txt" SCORES_GLOB = "data/transcription/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) @pytest.fixture def transcription_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) # Load in an example segmentation annotation ref_int, ref_pitch = mir_eval.io.load_valued_intervals(ref_f) # Load in estimated transcription est_int, est_pitch = mir_eval.io.load_valued_intervals(est_f) return ref_int, ref_pitch, est_int, est_pitch, expected_scores REF = np.array( [ [0.100, 0.300, 220.000], [0.300, 0.400, 246.942], [0.500, 0.600, 277.183], [0.550, 0.650, 293.665], ] ) EST = np.array( [ [0.120, 0.290, 225.000], [0.300, 0.340, 246.942], [0.500, 0.600, 500.000], [0.550, 0.600, 293.665], [0.560, 0.650, 293.665], ] ) SCORES = { "Precision": 0.4, "Recall": 0.5, "F-measure": 0.4444444444444445, "Average_Overlap_Ratio": 0.675, "Precision_no_offset": 0.6, "Recall_no_offset": 0.75, "F-measure_no_offset": 0.6666666666666665, "Average_Overlap_Ratio_no_offset": 0.5833333333333333, } ONSET_SCORES = { "Onset_Precision": 0.8, "Onset_Recall": 1.0, "Onset_F-measure": 0.8888888888888889, } OFFSET_SCORES = { "Offset_Precision": 0.6, "Offset_Recall": 0.75, "Offset_F-measure": 0.6666666666666665, } def test_match_note_offsets(): ref_int = REF[:, :2] est_int = EST[:, :2] matching = mir_eval.transcription.match_note_offsets(ref_int, est_int) assert matching == [(0, 0), (2, 2), (3, 3)] def test_match_note_offsets_strict(): ref_int = REF[:, :2] est_int = EST[:, :2] matching = mir_eval.transcription.match_note_offsets(ref_int, est_int, strict=True) assert matching == [(0, 0), (2, 2), (3, 4)] def test_match_note_onsets(): ref_int = REF[:, :2] est_int = EST[:, :2] matching = mir_eval.transcription.match_note_onsets(ref_int, est_int) assert matching == [(0, 0), (1, 1), (2, 2), (3, 3)] def test_match_note_onsets_strict(): ref_int = REF[:, :2] est_int = EST[:, :2] matching = mir_eval.transcription.match_note_onsets(ref_int, est_int, strict=True) assert matching == [(0, 0), (1, 1), (2, 2), (3, 3)] def test_match_notes(): ref_int, ref_pitch = REF[:, :2], REF[:, 2] est_int, est_pitch = EST[:, :2], EST[:, 2] matching = mir_eval.transcription.match_notes( ref_int, ref_pitch, est_int, est_pitch ) assert matching == [(0, 0), (3, 3)] matching = mir_eval.transcription.match_notes( ref_int, ref_pitch, est_int, est_pitch, offset_ratio=None ) assert matching == [(0, 0), (1, 1), (3, 3)] def test_match_notes_strict(): ref_int, ref_pitch = np.array([[0, 1]]), np.array([100]) est_int, est_pitch = np.array([[0.05, 1]]), np.array([100]) matching = mir_eval.transcription.match_notes( ref_int, ref_pitch, est_int, est_pitch, strict=True ) assert matching == [] def test_precision_recall_f1_overlap(): # load test data ref_int, ref_pitch = REF[:, :2], REF[:, 2] est_int, est_pitch = EST[:, :2], EST[:, 2] ( precision, recall, f_measure, avg_overlap_ratio, ) = mir_eval.transcription.precision_recall_f1_overlap( ref_int, ref_pitch, est_int, est_pitch ) scores_gen = np.array([precision, recall, f_measure, avg_overlap_ratio]) scores_exp = np.array( [ SCORES["Precision"], SCORES["Recall"], SCORES["F-measure"], SCORES["Average_Overlap_Ratio"], ] ) assert np.allclose(scores_exp, scores_gen, atol=A_TOL) ( precision, recall, f_measure, avg_overlap_ratio, ) = mir_eval.transcription.precision_recall_f1_overlap( ref_int, ref_pitch, est_int, est_pitch, offset_ratio=None ) scores_gen = np.array([precision, recall, f_measure, avg_overlap_ratio]) scores_exp = np.array( [ SCORES["Precision_no_offset"], SCORES["Recall_no_offset"], SCORES["F-measure_no_offset"], SCORES["Average_Overlap_Ratio_no_offset"], ] ) assert np.allclose(scores_exp, scores_gen, atol=A_TOL) def test_onset_precision_recall_f1(): # load test data ref_int = REF[:, :2] est_int = EST[:, :2] precision, recall, f_measure = mir_eval.transcription.onset_precision_recall_f1( ref_int, est_int ) scores_gen = np.array([precision, recall, f_measure]) scores_exp = np.array( [ ONSET_SCORES["Onset_Precision"], ONSET_SCORES["Onset_Recall"], ONSET_SCORES["Onset_F-measure"], ] ) assert np.allclose(scores_exp, scores_gen, atol=A_TOL) def test_offset_precision_recall_f1(): # load test data ref_int = REF[:, :2] est_int = EST[:, :2] precision, recall, f_measure = mir_eval.transcription.offset_precision_recall_f1( ref_int, est_int ) scores_gen = np.array([precision, recall, f_measure]) scores_exp = np.array( [ OFFSET_SCORES["Offset_Precision"], OFFSET_SCORES["Offset_Recall"], OFFSET_SCORES["Offset_F-measure"], ] ) assert np.allclose(scores_exp, scores_gen, atol=A_TOL) @pytest.mark.parametrize("transcription_data", file_sets, indirect=True) def test_regression(transcription_data): ref_int, ref_pitch, est_int, est_pitch, expected_scores = transcription_data scores = mir_eval.transcription.evaluate(ref_int, ref_pitch, est_int, est_pitch) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "ref_pitch, est_pitch", [(np.array([-100]), np.array([100])), (np.array([100]), np.array([-100]))], ) def test_invalid_pitch(ref_pitch, est_pitch): ref_int = np.array([[0, 1]]) mir_eval.transcription.validate(ref_int, ref_pitch, ref_int, est_pitch) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "ref_int, est_int", [ (np.array([[0, 1], [2, 3]]), np.array([[0, 1]])), (np.array([[0, 1]]), np.array([[0, 1], [2, 3]])), ], ) def test_inconsistent_int_pitch(ref_int, est_int): ref_pitch = np.array([100]) mir_eval.transcription.validate(ref_int, ref_pitch, est_int, ref_pitch) def test_empty_ref(): ref_int, ref_pitch = np.empty(shape=(0, 2)), np.array([]) est_int, est_pitch = np.array([[0, 1]]), np.array([100]) with pytest.warns(UserWarning, match="Reference notes are empty"): mir_eval.transcription.validate(ref_int, ref_pitch, est_int, est_pitch) def test_empty_est(): ref_int, ref_pitch = np.array([[0, 1]]), np.array([100]) est_int, est_pitch = np.empty(shape=(0, 2)), np.array([]) with pytest.warns(UserWarning, match="Estimated notes are empty"): mir_eval.transcription.validate(ref_int, ref_pitch, est_int, est_pitch) @pytest.mark.filterwarnings("ignore:.*notes are empty") def test_precision_recall_f1_overlap_empty(): ref_int, ref_pitch = np.empty(shape=(0, 2)), np.array([]) est_int, est_pitch = np.array([[0, 1]]), np.array([100]) ( precision, recall, f1, avg_overlap_ratio, ) = mir_eval.transcription.precision_recall_f1_overlap( ref_int, ref_pitch, est_int, est_pitch ) assert (precision, recall, f1) == (0, 0, 0) ( precision, recall, f1, avg_overlap_ratio, ) = mir_eval.transcription.precision_recall_f1_overlap( est_int, est_pitch, ref_int, ref_pitch ) assert (precision, recall, f1) == (0, 0, 0) @pytest.mark.filterwarnings("ignore:.*notes are empty") def test_onset_precision_recall_f1_empty(): ref_int = np.empty(shape=(0, 2)) est_int = np.array([[0, 1]]) precision, recall, f1 = mir_eval.transcription.onset_precision_recall_f1( ref_int, est_int ) assert (precision, recall, f1) == (0, 0, 0) precision, recall, f1 = mir_eval.transcription.onset_precision_recall_f1( est_int, ref_int ) assert (precision, recall, f1) == (0, 0, 0) @pytest.mark.filterwarnings("ignore:.*notes are empty") def test_offset_precision_recall_f1_empty(): ref_int = np.empty(shape=(0, 2)) est_int = np.array([[0, 1]]) precision, recall, f1 = mir_eval.transcription.offset_precision_recall_f1( ref_int, est_int ) assert (precision, recall, f1) == (0, 0, 0) precision, recall, f1 = mir_eval.transcription.offset_precision_recall_f1( est_int, ref_int ) assert (precision, recall, f1) == (0, 0, 0) mir_eval-0.8.2/tests/test_transcription_velocity.py000066400000000000000000000112031475740344600227220ustar00rootroot00000000000000import pytest import mir_eval import numpy as np import glob import json A_TOL = 1e-12 # Path to the fixture files REF_GLOB = "data/transcription_velocity/ref*.txt" EST_GLOB = "data/transcription_velocity/est*.txt" SCORES_GLOB = "data/transcription_velocity/output*.json" ref_files = sorted(glob.glob(REF_GLOB)) est_files = sorted(glob.glob(EST_GLOB)) sco_files = sorted(glob.glob(SCORES_GLOB)) assert len(ref_files) == len(est_files) == len(sco_files) > 0 file_sets = list(zip(ref_files, est_files, sco_files)) def _load_transcription_velocity(filename): """Loader for data in the format start, end, pitch, velocity.""" starts, ends, pitches, velocities = mir_eval.io.load_delimited( filename, [float, float, int, int] ) # Stack into an interval matrix intervals = np.array([starts, ends]).T # return pitches and velocities as np.ndarray pitches = np.array(pitches) velocities = np.array(velocities) return intervals, pitches, velocities @pytest.fixture def velocity_data(request): ref_f, est_f, sco_f = request.param with open(sco_f) as f: expected_scores = json.load(f) # Load in reference transcription ref_int, ref_pitch, ref_vel = _load_transcription_velocity(ref_f) # Load in estimated transcription est_int, est_pitch, est_vel = _load_transcription_velocity(est_f) return (ref_int, ref_pitch, ref_vel), (est_int, est_pitch, est_vel), expected_scores def test_negative_velocity(): good_i, good_p, good_v = np.array([[0, 1]]), np.array([100]), np.array([1]) bad_i, bad_p, bad_v = np.array([[0, 1]]), np.array([100]), np.array([-1]) with pytest.raises(ValueError): mir_eval.transcription_velocity.validate( bad_i, bad_p, bad_v, good_i, good_p, good_v ) with pytest.raises(ValueError): mir_eval.transcription_velocity.validate( good_i, good_p, good_v, bad_i, bad_p, bad_v ) def test_wrong_shape_velocity(): good_i, good_p, good_v = np.array([[0, 1]]), np.array([100]), np.array([1]) bad_i, bad_p, bad_v = np.array([[0, 1]]), np.array([100]), np.array([1, 2]) with pytest.raises(ValueError): mir_eval.transcription_velocity.validate( bad_i, bad_p, bad_v, good_i, good_p, good_v ) with pytest.raises(ValueError): mir_eval.transcription_velocity.validate( good_i, good_p, good_v, bad_i, bad_p, bad_v ) def test_precision_recall_f1_overlap(): # Simple unit test ref_i = np.array([[0, 1], [0.5, 0.7], [1, 2]]) ref_p = np.array([100, 110, 80]) ref_v = np.array([10, 90, 110]) est_i = np.array([[0, 1], [0.5, 0.7], [1, 2]]) est_p = np.array([100, 110, 80]) est_v = np.array([10, 70, 110]) p, r, f, o = mir_eval.transcription_velocity.precision_recall_f1_overlap( ref_i, ref_p, ref_v, est_i, est_p, est_v ) assert np.allclose((p, r, f, o), (2 / 3.0, 2 / 3.0, 2 / 3.0, 1.0)) p, r, f, o = mir_eval.transcription_velocity.precision_recall_f1_overlap( ref_i, ref_p, ref_v, est_i, est_p, est_v, velocity_tolerance=0.3 ) assert np.allclose((p, r, f, o), (1.0, 1.0, 1.0, 1.0)) # Suppressing this warning. We know the notes are empty, that's not the point. @pytest.mark.filterwarnings("ignore:.*notes are empty") def test_precision_recall_f1_overlap_empty(): good_i, good_p, good_v = np.array([[0, 1]]), np.array([100]), np.array([1]) bad_i, bad_p, bad_v = np.empty((0, 2)), np.array([]), np.array([]) p, r, f, o = mir_eval.transcription_velocity.precision_recall_f1_overlap( good_i, good_p, good_v, bad_i, bad_p, bad_v ) assert (p, r, f, o) == (0.0, 0.0, 0.0, 0.0) p, r, f, o = mir_eval.transcription_velocity.precision_recall_f1_overlap( bad_i, bad_p, bad_v, good_i, good_p, good_v ) assert (p, r, f, o) == (0.0, 0.0, 0.0, 0.0) def test_precision_recall_f1_overlap_no_overlap(): p, r, f, o = mir_eval.transcription_velocity.precision_recall_f1_overlap( np.array([[1, 2]]), np.array([1]), np.array([1]), np.array([[3, 4]]), np.array([1]), np.array([1]), ) assert (p, r, f, o) == (0.0, 0.0, 0.0, 0.0) @pytest.mark.parametrize("velocity_data", file_sets, indirect=True) def test_regression(velocity_data): ( (ref_int, ref_pitch, ref_vel), (est_int, est_pitch, est_vel), expected_scores, ) = velocity_data scores = mir_eval.transcription_velocity.evaluate( ref_int, ref_pitch, ref_vel, est_int, est_pitch, est_vel ) assert scores.keys() == expected_scores.keys() for metric in scores: assert np.allclose(scores[metric], expected_scores[metric], atol=A_TOL), metric mir_eval-0.8.2/tests/test_util.py000066400000000000000000000253021475740344600170670ustar00rootroot00000000000000"""Unit tests for utils""" import collections import pytest import numpy as np import mir_eval from mir_eval import util def test_interpolate_intervals(): """Check that an interval set is interpolated properly, with boundaries conditions and out-of-range values. """ labels = list("abc") intervals = np.array([(n, n + 1.0) for n in range(len(labels))]) time_points = [-1.0, 0.1, 0.9, 1.0, 2.3, 4.0] expected_ans = ["N", "a", "a", "b", "c", "N"] assert ( util.interpolate_intervals(intervals, labels, time_points, "N") == expected_ans ) def test_interpolate_intervals_gap(): """Check that an interval set is interpolated properly, with gaps.""" labels = list("abc") intervals = np.array([[0.5, 1.0], [1.5, 2.0], [2.5, 3.0]]) time_points = [0.0, 0.75, 1.25, 1.75, 2.25, 2.75, 3.5] expected_ans = ["N", "a", "N", "b", "N", "c", "N"] assert ( util.interpolate_intervals(intervals, labels, time_points, "N") == expected_ans ) @pytest.mark.xfail(raises=ValueError) def test_interpolate_intervals_badtime(): """Check that interpolate_intervals throws an exception if input is unordered. """ labels = list("abc") intervals = np.array([(n, n + 1.0) for n in range(len(labels))]) time_points = [-1.0, 0.1, 0.9, 0.8, 2.3, 4.0] mir_eval.util.interpolate_intervals(intervals, labels, time_points) def test_intervals_to_samples(): """Check that an interval set is sampled properly, with boundaries conditions and out-of-range values. """ labels = list("abc") intervals = np.array([(n, n + 1.0) for n in range(len(labels))]) expected_times = [0.0, 0.5, 1.0, 1.5, 2.0, 2.5] expected_labels = ["a", "a", "b", "b", "c", "c"] result = util.intervals_to_samples( intervals, labels, offset=0, sample_size=0.5, fill_value="N" ) assert result[0] == expected_times assert result[1] == expected_labels expected_times = [0.25, 0.75, 1.25, 1.75, 2.25, 2.75] expected_labels = ["a", "a", "b", "b", "c", "c"] result = util.intervals_to_samples( intervals, labels, offset=0.25, sample_size=0.5, fill_value="N" ) assert result[0] == expected_times assert result[1] == expected_labels def test_intersect_files(): """Check that two non-identical produce correct results.""" flist1 = ["/a/b/abc.lab", "/c/d/123.lab", "/e/f/xyz.lab"] flist2 = ["/g/h/xyz.npy", "/i/j/123.txt", "/k/l/456.lab"] sublist1, sublist2 = util.intersect_files(flist1, flist2) assert sublist1 == ["/e/f/xyz.lab", "/c/d/123.lab"] assert sublist2 == ["/g/h/xyz.npy", "/i/j/123.txt"] sublist1, sublist2 = util.intersect_files(flist1[:1], flist2[:1]) assert sublist1 == [] assert sublist2 == [] def test_merge_labeled_intervals(): """Check that two labeled interval sequences merge correctly.""" x_intvs = np.array([[0.0, 0.44], [0.44, 2.537], [2.537, 4.511], [4.511, 6.409]]) x_labels = ["A", "B", "C", "D"] y_intvs = np.array([[0.0, 0.464], [0.464, 2.415], [2.415, 4.737], [4.737, 6.409]]) y_labels = [0, 1, 2, 3] expected_intvs = [ [0.0, 0.44], [0.44, 0.464], [0.464, 2.415], [2.415, 2.537], [2.537, 4.511], [4.511, 4.737], [4.737, 6.409], ] expected_x_labels = ["A", "B", "B", "B", "C", "D", "D"] expected_y_labels = [0, 0, 1, 2, 2, 2, 3] new_intvs, new_x_labels, new_y_labels = util.merge_labeled_intervals( x_intvs, x_labels, y_intvs, y_labels ) assert new_x_labels == expected_x_labels assert new_y_labels == expected_y_labels assert new_intvs.tolist() == expected_intvs # Check that invalid inputs raise a ValueError y_intvs[-1, -1] = 10.0 with pytest.raises(ValueError): util.merge_labeled_intervals(x_intvs, x_labels, y_intvs, y_labels) def test_boundaries_to_intervals(): # Basic tests boundaries = np.arange(10) correct_intervals = np.array([np.arange(10 - 1), np.arange(1, 10)]).T intervals = mir_eval.util.boundaries_to_intervals(boundaries) assert np.all(intervals == correct_intervals) def test_adjust_events(): # Test appending at the end events = np.arange(1, 11) labels = [str(n) for n in range(10)] new_e, new_l = mir_eval.util.adjust_events(events, labels, 0.0, 11.0) assert new_e[0] == 0.0 assert new_l[0] == "__T_MIN" assert new_e[-1] == 11.0 assert new_l[-1] == "__T_MAX" assert np.all(new_e[1:-1] == events) assert new_l[1:-1] == labels # Test trimming new_e, new_l = mir_eval.util.adjust_events(events, labels, 0.0, 9.0) assert new_e[0] == 0.0 assert new_l[0] == "__T_MIN" assert new_e[-1] == 9.0 assert np.all(new_e[1:] == events[:-1]) assert new_l[1:] == labels[:-1] def test_bipartite_match(): # This test constructs a graph as follows: # v9 -- (u0) # v8 -- (u0, u1) # v7 -- (u0, u1, u2) # ... # v0 -- (u0, u1, ..., u9) # # This structure and ordering of this graph should force Hopcroft-Karp to # hit each algorithm/layering phase # G = collections.defaultdict(list) u_set = [f"u{_:d}" for _ in range(10)] v_set = [f"v{_:d}" for _ in range(len(u_set) + 1)] for i, u in enumerate(u_set): for v in v_set[: -i - 1]: G[v].append(u) matching = util._bipartite_match(G) # Make sure that each u vertex is matched assert len(matching) == len(u_set) # Make sure that there are no duplicate keys lhs = {k for k in matching} rhs = {matching[k] for k in matching} assert len(matching) == len(lhs) assert len(matching) == len(rhs) # Finally, make sure that all detected edges are present in G for k in matching: v = matching[k] assert v in G[k] or k in G[v] def test_outer_distance_mod_n(): ref = [1.0, 2.0, 3.0] est = [1.1, 6.0, 1.9, 5.0, 10.0] expected = np.array( [ [0.1, 5.0, 0.9, 4.0, 3.0], [0.9, 4.0, 0.1, 3.0, 4.0], [1.9, 3.0, 1.1, 2.0, 5.0], ] ) actual = mir_eval.util._outer_distance_mod_n(ref, est) assert np.allclose(actual, expected) ref = [13.0, 14.0, 15.0] est = [1.1, 6.0, 1.9, 5.0, 10.0] expected = np.array( [ [0.1, 5.0, 0.9, 4.0, 3.0], [0.9, 4.0, 0.1, 3.0, 4.0], [1.9, 3.0, 1.1, 2.0, 5.0], ] ) actual = mir_eval.util._outer_distance_mod_n(ref, est) assert np.allclose(actual, expected) def test_match_events(): ref = [1.0, 2.0, 3.0] est = [1.1, 6.0, 1.9, 5.0, 10.0] expected = [(0, 0), (1, 2)] actual = mir_eval.util.match_events(ref, est, 0.5) assert actual == expected ref = [1.0, 2.0, 3.0, 11.9] est = [1.1, 6.0, 1.9, 5.0, 10.0, 0.0] expected = [(0, 0), (1, 2), (3, 5)] actual = mir_eval.util.match_events( ref, est, 0.5, distance=mir_eval.util._outer_distance_mod_n ) assert actual == expected def test_fast_hit_windows(): ref = [1.0, 2.0, 3.0] est = [1.1, 6.0, 1.9, 5.0, 10.0] ref_fast, est_fast = mir_eval.util._fast_hit_windows(ref, est, 0.5) ref_slow, est_slow = np.where(np.abs(np.subtract.outer(ref, est)) <= 0.5) assert np.all(ref_fast == ref_slow) assert np.all(est_fast == est_slow) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "intervals", [ # Test for ValueError when interval shape is invalid np.array([[1.0], [2.5], [5.0]]), # Test for ValueError when times are negative np.array([[1.0, -2.0], [2.5, 3.0], [5.0, 6.0]]), # Test for ValueError when duration is zero np.array([[1.0, 2.0], [2.5, 2.5], [5.0, 6.0]]), # Test for ValueError when duration is negative np.array([[1.0, 2.0], [2.5, 1.5], [5.0, 6.0]]), ], ) def test_validate_intervals(intervals): mir_eval.util.validate_intervals(intervals) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "events", [ # Test for ValueError when max_time is violated np.array([100.0, 100000.0]), # Test for ValueError when events aren't 1-d arrays np.array([[1.0, 2.0], [3.0, 4.0]]), # Test for ValueError when event times are not increasing np.array([1.0, 2.0, 5.0, 3.0]), ], ) def test_validate_events(events): mir_eval.util.validate_events(events) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "freqs", [ # Test for ValueError when max_freq is violated np.array([100, 10000]), # Test for ValueError when min_freq is violated np.array([2, 200]), # Test for ValueError when events aren't 1-d arrays np.array([[100, 200], [300, 400]]), # Test for ValueError when allow_negatives is false and negative values # are passed np.array([-100, 200]), ], ) def test_validate_frequencies(freqs): mir_eval.util.validate_frequencies(freqs, 5000, 20, allow_negatives=False) @pytest.mark.xfail(raises=ValueError) @pytest.mark.parametrize( "freqs", [ # Test for ValueError when max_freq is violated and allow_negatives=True np.array([100, -100000]), # Test for ValueError when min_freq is violated and allow_negatives=True np.array([-2, 200]), ], ) def test_validate_frequencies_negative(freqs): mir_eval.util.validate_frequencies(freqs, 5000, 20, allow_negatives=True) def test_has_kwargs(): def __test(target, f): assert target == mir_eval.util.has_kwargs(f) def f1(_): return None def f2(_=5): return None def f3(*_): return None def f4(_, **kw): return None def f5(_=5, **kw): return None assert not mir_eval.util.has_kwargs(f1) assert not mir_eval.util.has_kwargs(f2) assert not mir_eval.util.has_kwargs(f3) assert mir_eval.util.has_kwargs(f4) assert mir_eval.util.has_kwargs(f5) @pytest.mark.parametrize( "x,labels,x_true,lab_true", [ ( np.asarray([[10, 20], [0, 10]]), ["a", "b"], np.asarray([[0, 10], [10, 20]]), ["b", "a"], ), ( np.asarray([[0, 10], [10, 20]]), ["b", "a"], np.asarray([[0, 10], [10, 20]]), ["b", "a"], ), ], ) def test_sort_labeled_intervals_with_labels(x, labels, x_true, lab_true): xs, ls = mir_eval.util.sort_labeled_intervals(x, labels) assert np.allclose(xs, x_true) assert ls == lab_true @pytest.mark.parametrize( "x,x_true", [ (np.asarray([[10, 20], [0, 10]]), np.asarray([[0, 10], [10, 20]])), (np.asarray([[0, 10], [10, 20]]), np.asarray([[0, 10], [10, 20]])), ], ) def test_sort_labeled_intervals_without_labels(x, x_true): xs = mir_eval.util.sort_labeled_intervals(x) assert np.allclose(xs, x_true)