diff --git a/.gitattributes b/.gitattributes deleted file mode 100644 index 36f74eb5..00000000 --- a/.gitattributes +++ /dev/null @@ -1 +0,0 @@ -fretbursts/_version.py export-subst diff --git a/.github/workflows/build_wheel.yml b/.github/workflows/build_wheel.yml new file mode 100644 index 00000000..ae7c1766 --- /dev/null +++ b/.github/workflows/build_wheel.yml @@ -0,0 +1,32 @@ +name: Build + +on: + push: + branches: + - master + pull_request: + branches: + - master + +jobs: + build_wheels: + name: Build wheels on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-22.04, windows-2022, macos-13, macos-14] + steps: + - uses: actions/checkout@v4 + + - name: Build wheels + uses: pypa/cibuildwheel@v2.18.1 + env: + CIBW_SKIP: "pp*" + # CIBW_TEST_REQUIRES: pytest, numpy == 1.20.1, matplotlib, scipy, pandas, tables, numba, seaborn, lmfit, phconvert + # CIBW_BEFORE_TEST: python -m pip install pytest + # CIBW_TEST_COMMAND: python -m pytest {package}/tests + + - uses: actions/upload-artifact@v4 + with: + name: cibw-wheels-${{ matrix.os }}-${{ strategy.job-index }} + path: ./wheelhouse/*.whl diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml index 600d133e..87a1d5b8 100644 --- a/.github/workflows/tests.yml +++ b/.github/workflows/tests.yml @@ -1,59 +1,85 @@ name: Tests -on: - push: - branch: - - gitactions - pull_request: - branch: - - gitactions +on: [push, pull_request] jobs: build: - runs-on: ${{matrix.os}} strategy: matrix: - os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ["3.6", "3.7", "3.8", "3.9", "3.10"] - + os: [ubuntu-22.04, windows-latest, macos-latest] + python-version: ["3.7", "3.8", "3.12"] + exclude: + - os: macOS-latest + python-version: "3.7" + - os: windows-latest + python-version: "3.7" steps: - - uses: actions/checkout@v3 - - name: Setup Python ${{matrix.python-version}} - uses: actions/setup-python@v4 + - uses: actions/checkout@v4 + with: + persist-credentials: false + fetch-depth: 0 + # - name: Setup Python ${{matrix.python-version}} + # uses: actions/setup-python@v4 + # with: + # python-version: ${{matrix.python-version}} + - uses: conda-incubator/setup-miniconda@v3 with: - python-version: ${{matrix.python-version}} + auto-update-conda: true + activate-environment: test + channels: conda-forge + python-version: ${{ matrix.python-version }} + - name: Upgrade pip + shell: bash -l {0} + run: python -m pip install --upgrade pip + - name: MacOS install hdf5 dependencies + if: runner.os == 'macOS' + run: | + brew install hdf5 + export HDF5_DIR=/usr/local/ + export BLOSC_DIR=/usr/local/ + - name: Install Dependencies + shell: bash -l {0} + run: | + conda install cython numpy numba nbconvert pytest jupyter scipy pandas matplotlib pytables phconvert lmfit pybroom seaborn setuptools build pyqt + - name: Install project + shell: bash -l {0} + run: | + python -m pip install . - name: Download files Unix if: runner.os != 'Windows' + shell: bash -l {0} run: | + cd notebooks + mkdir data + cd data wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 wget -N https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5 wget -N https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5 wget -N https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5 wget -N https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5 + cd ../.. - name: Downlaod files Windows if: runner.os == 'Windows' + shell: bash -l {0} run: | - curl.exe --output 2182604/12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 - curl.exe --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 - curl.exe --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5 - curl.exe --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5 - curl.exe --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5 - curl.exe --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5 - - name: Upgrade pip - run: python -m pip install --upgrade pip - - name: Windows 3.6 Oddities - if: matrix.python-version == 3.6 && runner.os == 'Windows' - run: python -m pip install pywinpty==1.1.6 - - name: Install Dependencies - run: | - python -m pip install pytest cython numpy scipy pandas matplotlib seaborn - python -m pip install jupyter nbconvert lmfit phconvert pybroom - - name: Install project - run: | - python setup.py sdist - python -m pip install . + cd notebooks + mkdir data + cd data + curl.exe -L --output 12d_New_30p_320mW_steer_3.hdf5 --url http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 + curl.exe -L --output 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 --url http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 + curl.exe -L --output HP3_TE150_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE150_SPC630.hdf5 + curl.exe -L --output HP3_TE200_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE200_SPC630.hdf5 + curl.exe -L --output HP3_TE250_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE250_SPC630.hdf5 + curl.exe -L --output HP3_TE300_SPC630.hdf5 --url https://zenodo.org/record/5902313/files/HP3_TE300_SPC630.hdf5 + cd .. + cd .. + - name: Test project + shell: bash -l {0} run: | - python fretbursts/tests/nbrun.py notebooks + cd notebooks + python nbrun.py . + cd .. + cd tests python -m pytest diff --git a/.gitignore b/.gitignore index 80276882..aa59db03 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ burstsearch/build/ docs/source/_themes/ *ipynb_checkpoints* +fretbursts/_version.py *build/ *egg-info/ @@ -30,5 +31,6 @@ notebooks/out notebooks/wip .cache *.csv +*.mat _* docs/source/modules/generated diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 7dfcf5df..00000000 --- a/.travis.yml +++ /dev/null @@ -1,47 +0,0 @@ -language: python -sudo: required -dist: xenial - -python: - - "3.6" - - "3.7" - - "3.8" - - "3.9" - - "3.10" - -services: - - xvfb - -before_install: - - wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - chmod +x miniconda.sh - - ./miniconda.sh -b - - export PATH=/home/travis/miniconda3/bin:$PATH - -install: - - conda create -n conda_test_env --yes python=$TRAVIS_PYTHON_VERSION - - source activate conda_test_env - - conda install --yes scipy pandas matplotlib cython numba pytest nbconvert ipykernel ipywidgets seaborn - - conda config --append channels conda-forge - - conda install --yes lmfit - - conda install --yes phconvert - - pip install pybroom - - python setup.py build - - pip install pybroom - - pip install . - - rm -rf build/ - -before_script: - - mkdir notebooks/data - - cd notebooks/data - - wget -N http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 - - wget -N http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 - - cd ../.. - -script: - - python -Wd fretbursts/tests/importtest.py - - py.test -v - - cd notebooks - - python ../fretbursts/tests/nbrun.py --exclude-list dev/exclude-py27.txt . - -sudo: false diff --git a/LongDescription.md b/LongDescription.md new file mode 100644 index 00000000..851a51d1 --- /dev/null +++ b/LongDescription.md @@ -0,0 +1,20 @@ +FRETBursts +========== + +**FRETBursts** is a software toolkit for burst analysis of confocal +single-molecule FRET (smFRET) measurements. It can analyze both single-spot +and multi-spot smFRET data with or without alternating laser excitation (ALEX). + +For more info please refer to: + +- **FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET** + *Ingargiola et. al.* (2016). PLoS ONE doi: `10.1371/journal.pone.0160716 <10.1371/journal.pone.0160716>`__. + + +Quick links: + +- `FRETBursts Homepage `_ +- `FRETBursts Reference Documentation `_ +- `FRETBursts Tutorials `_ + +See also `Release Notes `__. diff --git a/MANIFEST.in b/MANIFEST.in index da275dc2..6006776d 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,2 @@ -include versioneer.py -include fretbursts/_version.py include LICENSE.txt include README.md diff --git a/appveyor.yml b/appveyor.yml deleted file mode 100644 index e9c26341..00000000 --- a/appveyor.yml +++ /dev/null @@ -1,58 +0,0 @@ -build: false - -environment: - matrix: - - - PYTHON: "C:\\Python36-x64" - PYTHON_VERSION: "3.6" - PYTHON_ARCH: "64" - MINICONDA: C:\Miniconda36-x64 - - - PYTHON: "C:\\Python37-x64" - PYTHON_VERSION: "3.7" - PYTHON_ARCH: "64" - MINICONDA: C:\Miniconda37-x64 - -init: - - "ECHO %PYTHON% %PYTHON_VERSION% %PYTHON_ARCH% %MINICONDA%" - -install: - - "set PATH=%MINICONDA%;%MINICONDA%\\Scripts;%PATH%" - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - - conda info -a - - conda config --append channels conda-forge - - "conda create -q -n test-environment python=%PYTHON_VERSION% pip scipy pandas matplotlib lmfit cython numba nbconvert pytest ipykernel ipywidgets seaborn terminado" - - activate test-environment - - conda install phconvert - - python -m pip install --upgrade pip - - pip install pybroom - - python --version - - cd %APPVEYOR_BUILD_FOLDER% - - dir - - build.cmd python setup.py build - - pip install . - - python setup.py clean --all - -before_test: - - cd %APPVEYOR_BUILD_FOLDER%\notebooks - - mkdir data - - cd data - - dir - - ps: wget http://files.figshare.com/2182604/12d_New_30p_320mW_steer_3.hdf5 -OutFile 12d_New_30p_320mW_steer_3.hdf5 - - ps: wget http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 -OutFile 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 - -test_script: - - cd %APPVEYOR_BUILD_FOLDER% - - python -Wd fretbursts/tests/importtest.py - - py.test -v - - cd %APPVEYOR_BUILD_FOLDER%\notebooks - - python ../fretbursts/tests/nbrun.py --exclude-list dev/exclude-py27.txt . - -after_test: - - cd %APPVEYOR_BUILD_FOLDER% - - python setup.py bdist_wheel - -artifacts: - # bdist_wheel puts your built wheel in the dist directory - - path: dist\* diff --git a/docs/source/absolute_beginner.rst b/docs/source/absolute_beginner.rst index 4844d2e1..b379d39f 100644 --- a/docs/source/absolute_beginner.rst +++ b/docs/source/absolute_beginner.rst @@ -6,8 +6,9 @@ Getting started for the absolute python beginner Before running FRETBursts you need to install a python distribution that includes the Jupyter/IPython Notebook application. -You can find a quick guide for installing the software and running your first -notebook here: +We recomend using Anaconda, which you can find instructions for downloading and installing here: `Anaconda installation instructions `_. + +You can find a guide for jupyter notebooks here: - |jupyter_quick_guide| @@ -31,6 +32,19 @@ The installation should take a few seconds. If you notice any error please report it by opening a new issue on the `FRETBursts GitHub Issues `_. +Alternatively create an environment from one of our yaml files where we have verified compatibility of all versions of the software: :downlaod:`frbmin.yml ` + +First download `frbmin.yml` + +Then run the following in your terminal:: + + conda env create -f frbmin.yml + conda activate frbmin + +.. note:: + You may need to replace frbmin.yml with the path to the file you downloaded. + :ref:`instalation` provides other yaml files for more complete environments. + Running FRETBursts tutorial notebook ------------------------------------ diff --git a/docs/source/conf.py b/docs/source/conf.py index 2f8fa2a0..60af79e4 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -54,8 +54,9 @@ # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../..')) import fretbursts -version = fretbursts._version.get_versions()['version'][:13] -release = version +from importlib.metadata import version as get_version +release: str = get_version("fretbursts") +version: str = ".".join(release).split('.')[:2]) import sphinx_bootstrap_theme html_theme = 'bootstrap' diff --git a/docs/source/downloads/frbcmplt.yml b/docs/source/downloads/frbcmplt.yml new file mode 100644 index 00000000..447709a3 --- /dev/null +++ b/docs/source/downloads/frbcmplt.yml @@ -0,0 +1,23 @@ +name: frbcmplt +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - importlib_metadata + - pytest=7.4.0 + - cython=3.0.10 + - ipython=8.24.0 + - jupyter=1.0.0 + - numpy=1.26.4 + - numba=0.59.1 + - pytables=3.9.2 + - matplotlib=3.8.4 + - pandas=2.2.2 + - scipy=1.13.1 + - seaborn=0.13.1 + - pyqt=5.15.9 + - lmfit=1.2.2 + - phconvert=0.9.1 + - pybroom=0.2 + diff --git a/docs/source/downloads/frbmin.yml b/docs/source/downloads/frbmin.yml new file mode 100644 index 00000000..53452bfd --- /dev/null +++ b/docs/source/downloads/frbmin.yml @@ -0,0 +1,19 @@ +name: frbmin +channels: + - conda-forge + - defaults +dependencies: + - python=3.10 + - importlib_metadata + - ipython=8.24.0 + - jupyter=1.0.0 + - numpy=1.26.4 + - pytables=3.9.2 + - matplotlib=3.8.4 + - pandas=2.2.2 + - scipy=1.13.1 + - seaborn=0.13.1 + - pyqt=5.15.9 + - lmfit=1.2.2 + - phconvert=0.9.1 + diff --git a/docs/source/installation.rst b/docs/source/installation.rst index a47769ad..4355c550 100644 --- a/docs/source/installation.rst +++ b/docs/source/installation.rst @@ -17,9 +17,8 @@ Installing latest stable version The preferred way to to install and keep FRETBursts updated is through `conda`, a package manager used by Anaconda scientific python distribution. -If you haven't done it already, please install the python3 version of -`Continuum Anaconda distribution `__ -(legacy python 2.7 works at the moment but it will be discontinued soon). +If you haven't done it already, please install the python3. We recommend using `Anaconda `_. + Then, you can install or upgrade FRETBursts with:: conda install fretbursts -c conda-forge @@ -33,6 +32,35 @@ and how to launch it please see: See also the FRETBursts documentation section: :ref:`running_fretbursts`. +Install from yaml file +---------------------- + +With anaconda, you can manage different environments, allowing specific versions to be installed ensuring compatibility. +Which packages you need will depend on your use case. + +Environments can be build from yaml files with:: + + conda env create -f + +And activate with:: + + conda activate + +Below are environment files that we have *_**verified to work** +#. Minimal environment: :download:`frbmin.yml` which will create an environment named `frbmin` which contains just the essential packages for running the notebooks +#. Complete environment :download:`frbcmpt.yml` which will create an environment named `frbcmplt` which also includes cython, testing packages and numba, which are not necesary for running FRETBursts, but can come in helpful in other circumstances + +For packages build off of FRETBursts, check their respective documentation for similar yaml files. + +To create an environemnt from a downloaded yml file (like those above) run the command in your terminal:: + + conda env create + +Then simply activate the environment with:: + + conda activate + + Alternative methods: using PIP ------------------------------ @@ -57,13 +85,13 @@ containing the fretbursts (do it only once after installing Anaconda):: conda config --append channels conda-forge -Then create a new conda environment with python 3.7 and FRETbursts:: +Then create a new conda environment with python 3.10 and FRETbursts:: - conda create -n py37-fb python=3.7 fretbursts - conda activate py37-fb + conda create -n py310-fb python=3.10 fretbursts + conda activate py310-fb conda install pyqt # optional pip install pybroom # optional - python -m ipykernel install --user --name py37-fb --display-name "Python 3.7 (FB)" + python -m ipykernel install --user --name py310-fb --display-name "Python 3.10 (FB)" The last command installs the `jupyter kernel `__ diff --git a/docs/source/releasenotes.rst b/docs/source/releasenotes.rst index e6c3002c..5573286a 100644 --- a/docs/source/releasenotes.rst +++ b/docs/source/releasenotes.rst @@ -1,6 +1,15 @@ FRETBursts Release Notes ======================== +Version 0.8.0 (Jun. 2024) +------------------------ + +- Removed support for Python 3.6, as Python 3.7 now in end of life and 3.6 not supported +- Switch to using setuptools_scm for version managemnt instead of versioneer +- Updates for newer numpy compatibility (deprecation of np.float) +- Introduce :func:`burst_plot.scatter_burst_data` function for scatter plotting (currently now used in :func:`scatter_naa_nt` and :func:`scatter_alex`) to normalize scatter ploting. +- use of :func:`burst_plot.scatter_burst_data` enables KDE density estimation with keyword argument `color_style='kde'` + Version 0.7.1 ------------- diff --git a/fretbursts/utils/examples/matplotlib_figure_mod_toolbar.py b/examples/matplotlib_figure_mod_toolbar.py similarity index 100% rename from fretbursts/utils/examples/matplotlib_figure_mod_toolbar.py rename to examples/matplotlib_figure_mod_toolbar.py diff --git a/fretbursts/utils/examples/matplotlib_fonts.py b/examples/matplotlib_fonts.py similarity index 100% rename from fretbursts/utils/examples/matplotlib_fonts.py rename to examples/matplotlib_fonts.py diff --git a/fretbursts/utils/examples/matplotlib_gui_select.py b/examples/matplotlib_gui_select.py similarity index 100% rename from fretbursts/utils/examples/matplotlib_gui_select.py rename to examples/matplotlib_gui_select.py diff --git a/fretbursts/utils/examples/mpl_gui_selection.py b/examples/mpl_gui_selection.py similarity index 100% rename from fretbursts/utils/examples/mpl_gui_selection.py rename to examples/mpl_gui_selection.py diff --git a/fretbursts/utils/examples/qt4_figure.py b/examples/qt4_figure.py similarity index 100% rename from fretbursts/utils/examples/qt4_figure.py rename to examples/qt4_figure.py diff --git a/fretbursts/utils/examples/timetrace_scroll_demo.py b/examples/timetrace_scroll_demo.py similarity index 100% rename from fretbursts/utils/examples/timetrace_scroll_demo.py rename to examples/timetrace_scroll_demo.py diff --git a/fretbursts/utils/examples/timetrace_scroll_demo2.py b/examples/timetrace_scroll_demo2.py similarity index 100% rename from fretbursts/utils/examples/timetrace_scroll_demo2.py rename to examples/timetrace_scroll_demo2.py diff --git a/fretbursts/utils/examples/timetrace_scroll_demo3.py b/examples/timetrace_scroll_demo3.py similarity index 100% rename from fretbursts/utils/examples/timetrace_scroll_demo3.py rename to examples/timetrace_scroll_demo3.py diff --git a/fretbursts/utils/examples/timetrace_scroll_pygraphqt.py b/examples/timetrace_scroll_pygraphqt.py similarity index 100% rename from fretbursts/utils/examples/timetrace_scroll_pygraphqt.py rename to examples/timetrace_scroll_pygraphqt.py diff --git a/fretbursts/__init__.py b/fretbursts/__init__.py index 018378f9..4becc5ed 100644 --- a/fretbursts/__init__.py +++ b/fretbursts/__init__.py @@ -5,12 +5,12 @@ # Antonino Ingargiola # -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions +## Citation information -## Citation information +from fretbursts._version import version as __version__ +import warnings + _CITATION = """ FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 """ @@ -25,9 +25,6 @@ def citation(bar=True): cit = ('-' * 62) + '\n' + _INFO_CITATION + ('-' * 62) print(cit) - -import warnings - try: import pandas except ImportError: diff --git a/fretbursts/_version.py b/fretbursts/_version.py deleted file mode 100644 index f3bfbd27..00000000 --- a/fretbursts/_version.py +++ /dev/null @@ -1,460 +0,0 @@ - -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. Generated by -# versioneer-0.15 (https://github.com/warner/python-versioneer) - -import errno -import os -import re -import subprocess -import sys - - -def get_keywords(): - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "$Format:%d$" - git_full = "$Format:%H$" - keywords = {"refnames": git_refnames, "full": git_full} - return keywords - - -class VersioneerConfig: - pass - - -def get_config(): - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "pep440" - cfg.tag_prefix = "" - cfg.parentdir_prefix = "fretbursts-" - cfg.versionfile_source = "fretbursts/_version.py" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - pass - - -LONG_VERSION_PY = {} -HANDLERS = {} - - -def register_vcs_handler(vcs, method): # decorator - def decorate(f): - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): - assert isinstance(commands, list) - p = None - for c in commands: - try: - dispcmd = str([c] + args) - # remember shell=False, so use git.cmd on windows, not just git - p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) - break - except EnvironmentError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None - stdout = p.communicate()[0].strip() - if sys.version_info[0] >= 3: - stdout = stdout.decode() - if p.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - return None - return stdout - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - # Source tarballs conventionally unpack into a directory that includes - # both the project name and a version string. - dirname = os.path.basename(root) - if not dirname.startswith(parentdir_prefix): - if verbose: - print("guessing rootdir is '%s', but '%s' doesn't start with " - "prefix '%s'" % (root, dirname, parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None} - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - f = open(versionfile_abs, "r") - for line in f.readlines(): - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - f.close() - except EnvironmentError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - if not keywords: - raise NotThisMethod("no keywords at all, weird") - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = set([r.strip() for r in refnames.strip("()").split(",")]) - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = set([r for r in refs if re.search(r'\d', r)]) - if verbose: - print("discarding '%s', no digits" % ",".join(refs-tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None - } - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags"} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): - # this runs 'git' from the root of the source tree. This only gets called - # if the git-archive 'subst' keywords were *not* expanded, and - # _version.py hasn't already been rewritten with a short version string, - # meaning we're inside a checked out source tree. - - if not os.path.exists(os.path.join(root, ".git")): - if verbose: - print("no .git in %s" % root) - raise NotThisMethod("no .git directory") - - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - # if there is a tag, this yields TAG-NUM-gHEX[-dirty] - # if there are no tags, this yields HEX[-dirty] (no NUM) - describe_out = run_command(GITS, ["describe", "--tags", "--dirty", - "--always", "--long"], - cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparseable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], - cwd=root) - pieces["distance"] = int(count_out) # total number of commits - - return pieces - - -def plus_or_dot(pieces): - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - # now build up version string, with post-release "local version - # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - # exceptions: - # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_pre(pieces): - # TAG[.post.devDISTANCE] . No -dirty - - # exceptions: - # 1: no tags. 0.post.devDISTANCE - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += ".post.dev%d" % pieces["distance"] - else: - # exception #1 - rendered = "0.post.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that - # .dev0 sorts backwards (a dirty tree will appear "older" than the - # corresponding clean one), but you shouldn't be releasing software with - # -dirty anyways. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_old(pieces): - # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty - # --always' - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty - # --always -long'. The distance/hash is unconditional. - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"]} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None} - - -def get_versions(): - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for i in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree"} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version"} diff --git a/fretbursts/burst_plot.py b/fretbursts/burst_plot.py index 162d440d..589ac475 100644 --- a/fretbursts/burst_plot.py +++ b/fretbursts/burst_plot.py @@ -34,18 +34,17 @@ import warnings from itertools import cycle from collections.abc import Iterable +from functools import wraps # Numeric imports import numpy as np from numpy import arange, r_ from scipy.stats import norm as norm -from scipy.stats import erlang +from scipy.stats import erlang, gaussian_kde from scipy.interpolate import UnivariateSpline # Graphics imports import matplotlib.pyplot as plt -from matplotlib.pyplot import (plot, hist, xlabel, ylabel, grid, title, legend, - gca, gcf) from matplotlib.patches import Rectangle, Ellipse from matplotlib.collections import PatchCollection, PolyCollection from matplotlib.offsetbox import AnchoredText @@ -90,6 +89,19 @@ ## # Utility functions # + +def _ax_intercept(func): + """ + Wrapper that grabs the ax keyword argument and if None or not specified, + it calls plt.gca() and adds/replaces ax argument + """ + @wraps(func) + def inner(*args, **kwargs): + if 'ax' not in kwargs or kwargs['ax'] is None: + kwargs['ax'] = plt.gca() + return func(*args, **kwargs) + return inner + def _normalize_kwargs(kwargs, kind='patch'): """Convert matplotlib keywords from short to long form.""" if kwargs is None: @@ -99,6 +111,9 @@ def _normalize_kwargs(kwargs, kind='patch'): long_names = dict(c='color', ls='linestyle', lw='linewidth', mec='markeredgecolor', mew='markeredgewidth', mfc='markerfacecolor', ms='markersize',) + elif kind == 'scatter': + long_names = dict(ls='linestyle', lw='linewidth', + ec='edgecolor', color='c') elif kind == 'patch': long_names = dict(c='color', ls='linestyle', lw='linewidth', ec='edgecolor', fc='facecolor',) @@ -115,40 +130,49 @@ def bsavefig(d, s): # Multi-channel plot functions # -def mch_plot_bg(d, **kwargs): +@_ax_intercept +def mch_plot_bg(d, ax=None, **kwargs): """Plot background vs channel for DA, D and A photons.""" bg = d.bg_from(Ph_sel('all')) bg_dd = d.bg_from(Ph_sel(Dex='Dem')) bg_ad = d.bg_from(Ph_sel(Dex='Aem')) - plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg], lw=2, color=blue, + ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg], lw=2, color=blue, label=' T', **kwargs) - plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_dd], color=green, lw=2, + ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_dd], color=green, lw=2, label=' D', **kwargs) - plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_ad], color=red, lw=2, + ax.plot(r_[1:d.nch+1], [b.mean()*1e-3 for b in bg_ad], color=red, lw=2, label=' A', **kwargs) - xlabel("CH"); ylabel("kcps"); grid(True); legend(loc='best') - title(d.name) + ax.set_xlabel("CH") + ax.set_ylabel("kcps") + ax.grid(True) + ax.legend(loc='best') + ax.set_title(d.name) -def mch_plot_bg_ratio(d): + +@_ax_intercept +def mch_plot_bg_ratio(d, ax=None): """Plot ratio of A over D background vs channel.""" bg_dd = d.bg_from(Ph_sel(Dex='Dem')) bg_ad = d.bg_from(Ph_sel(Dex='Aem')) - plot(r_[1:d.nch+1], - [ba.mean()/bd.mean() for bd, ba in zip(bg_dd, bg_ad)], - color=green, lw=2, label='A/D') - xlabel("CH"); ylabel("BG Ratio A/D"); grid(True) - title("BG Ratio A/D "+d.name) + ax.plot(r_[1:d.nch+1], + [ba.mean()/bd.mean() for bd, ba in zip(bg_dd, bg_ad)], + color=green, lw=2, label='A/D') + ax.set_xlabel("CH"); ax.set_ylabel("BG Ratio A/D"); ax.grid(True) + ax.set_title("BG Ratio A/D "+d.name) + -def mch_plot_bsize(d): +@_ax_intercept +def mch_plot_bsize(d, ax=None): """Plot mean burst size vs channel.""" CH = np.arange(1, d.nch+1) - plot(CH, [b.mean() for b in d.nt], color=blue, lw=2, label=' T') - plot(CH, [b.mean() for b in d.nd], color=green, lw=2, label=' D') - plot(CH, [b.mean() for b in d.na], color=red, lw=2, label=' A') - xlabel("CH"); ylabel("Mean burst size") - grid(True) - legend(loc='best') - title(d.name) + ax.plot(CH, [b.mean() for b in d.nt], color=blue, lw=2, label=' T') + ax.plot(CH, [b.mean() for b in d.nd], color=green, lw=2, label=' D') + ax.plot(CH, [b.mean() for b in d.na], color=red, lw=2, label=' A') + ax.set_xlabel("CH") + ax.set_ylabel("Mean burst size") + ax.grid(True) + ax.legend(loc='best') + ax.set_title(d.name) ## @@ -169,6 +193,7 @@ def plot_alternation_hist(d, bins=None, ax=None, **kwargs): plot_alternation = plot_alternation_hist_usalex plot_alternation(d, bins=bins, ax=ax, **kwargs) +@_ax_intercept def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0, hist_style={}, span_style={}): """Plot the us-ALEX alternation histogram for the variable `d`. @@ -176,9 +201,6 @@ def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0, This function must be called on us-ALEX data **before** calling :func:`fretbursts.loader.alex_apply_period`. """ - if ax is None: - _, ax = plt.subplots() - if bins is None: bins = 100 @@ -216,6 +238,8 @@ def plot_alternation_hist_usalex(d, bins=None, ax=None, ich=0, ax.axvspan(A_ON[0], period, color=red, **span_style_) ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), frameon=False) + +@_ax_intercept def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0, hist_style={}, span_style={}): """Plot the ns-ALEX alternation histogram for the variable `d`. @@ -223,9 +247,6 @@ def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0, This function must be called on ns-ALEX data **before** calling :func:`fretbursts.loader.alex_apply_period`. """ - if ax is None: - _, ax = plt.subplots() - if bins is None: bins = np.arange(d.nanotimes_params[ich]['tcspc_num_bins']) @@ -270,6 +291,7 @@ def plot_alternation_hist_nsalex(d, bins=None, ax=None, ich=0, # def _burst_info(d, ich, burst_index): + """Generates burst information message for the burst in data.mburst[ich][burst_index]""" burst = d.mburst[ich][burst_index] params = dict( b_index=burst_index, @@ -291,7 +313,7 @@ def _burst_info(d, ich, burst_index): return msg.format(**params) -def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999", +def _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=1e3, pmin=0, color="#999999", ytext=20): """Highlights bursts in a timetrace plot.""" b = d.mburst[i] @@ -304,7 +326,7 @@ def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999", end = bs.stop * d.clk_p R = [] width = end - start - ax = gca() + #TODO: decide how to use axvspan or other better function for b, bidx, s, w, sign, va in zip(bs, burst_indices, start, width, cycle([-1, 1]), cycle(['top', 'bottom'])): @@ -317,7 +339,7 @@ def _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=1e3, pmin=0, color="#999999", ax.add_artist(PatchCollection(R, lw=0, color=color)) -def _plot_rate_th(d, i, F, ph_sel, invert=False, scale=1, +def _plot_rate_th(d, i, F, ph_sel, ax, invert=False, scale=1, plot_style_={}, rate_th_style={}): """Plots background_rate*F as a function of time. @@ -342,7 +364,7 @@ def _plot_rate_th(d, i, F, ph_sel, invert=False, scale=1, y_rate *= scale if invert: y_rate *= -1 - plot(x_rate, y_rate, **rate_th_style_) + ax.plot(x_rate, y_rate, **rate_th_style_) def _gui_timetrace_burst_sel(d, fig, ax): @@ -361,12 +383,13 @@ def _gui_timetrace_scroll(fig): gui_status['scroll_gui'] = ScrollingToolQT(fig) +@_ax_intercept def timetrace_single(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200, ph_sel=Ph_sel('all'), invert=False, bursts=False, burst_picker=True, scroll=False, cache_bins=True, plot_style=None, show_rate_th=True, F=None, rate_th_style={}, set_ax_limits=True, - burst_color='#BBBBBB'): + burst_color='#BBBBBB', ax=None): """Plot the timetrace (histogram) of timestamps for a photon selection. See :func:`timetrace` to plot multiple photon selections (i.e. @@ -423,7 +446,7 @@ def _has_cache_for(binwidth, tmin, tmax): # Plot bursts if bursts: - _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500, + _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500, color=burst_color) # Plot timetrace @@ -434,37 +457,38 @@ def _has_cache_for(binwidth, tmin, tmax): else: plot_style_['label'] = str(ph_sel) plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) - plot(x, timetrace, **plot_style_) + ax.plot(x, timetrace, **plot_style_) # Plot burst-search rate-threshold if show_rate_th and 'bg' in d: - _plot_rate_th(d, i, F=F, ph_sel=ph_sel, invert=invert, + _plot_rate_th(d, i, F=F, ph_sel=ph_sel, ax=ax, invert=invert, scale=binwidth, plot_style_=plot_style_, rate_th_style=rate_th_style) - plt.xlabel('Time (s)') - plt.ylabel('# ph') + ax.set_xlabel('Time (s)') + ax.set_ylabel('# ph') if burst_picker and 'mburst' in d: - _gui_timetrace_burst_sel(d, gcf(), gca()) + _gui_timetrace_burst_sel(d, ax.figure, ax) if scroll: - _gui_timetrace_scroll(gcf()) + _gui_timetrace_scroll(ax.figure) if set_ax_limits: - plt.xlim(tmin, tmin + 1) + ax.set_xlim(tmin, tmin + 1) if not invert: - plt.ylim(top=100) + ax.set_ylim(top=100) else: - plt.ylim(bottom=-100) + ax.set_ylim(bottom=-100) _plot_status['timetrace_single'] = {'autoscale': False} # do not concatenate, timetrace should always be shown per channel +@_ax_intercept def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200, bursts=False, burst_picker=True, scroll=False, show_rate_th=True, F=None, rate_th_style={'label': None}, show_aa=True, legend=False, set_ax_limits=True, burst_color='#BBBBBB', plot_style=None, #dd_plot_style={}, ad_plot_style={}, aa_plot_style={} - ): + ax=None): """Plot the timetraces (histogram) of photon timestamps. Arguments: @@ -498,11 +522,12 @@ def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200, burst_color (string): string containing the the HEX RGB color to use to highlight the burst regions. plot_style (dict): matplotlib's style for the timetrace lines. + ax (mpl.axes): axis where plot will be generated """ # Plot bursts if bursts: tmin_clk, tmax_clk = tmin / d.clk_p, tmax / d.clk_p - _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500, + _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500, color=burst_color) # Plot multiple timetraces @@ -523,18 +548,19 @@ def timetrace(d, i=0, binwidth=1e-3, bins=None, tmin=0, tmax=200, tmax=tmax, ph_sel=ph_sel, invert=invert, bursts=False, burst_picker=burst_picker_list[ix], scroll=scroll_list[ix], cache_bins=True, - show_rate_th=show_rate_th, F=F, + show_rate_th=show_rate_th, F=F, ax=ax, rate_th_style=rate_th_style, set_ax_limits=set_ax_limits, plot_style=plot_style) if legend: - plt.legend(loc='best', fancybox=True) + ax.legend(loc='best', fancybox=True) +@_ax_intercept def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200, ph_sel=Ph_sel('all'), invert=False, bursts=False, burst_picker=True, scroll=False, plot_style={}, show_rate_th=True, F=None, rate_th_style={}, - set_ax_limits=True, burst_color='#BBBBBB'): + set_ax_limits=True, burst_color='#BBBBBB', ax=None): """Plot the ratetrace of timestamps for a photon selection. See :func:`ratetrace` to plot multiple photon selections (i.e. @@ -575,35 +601,36 @@ def ratetrace_single(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200, plot_style_['color'] = _ph_sel_color_dict[ph_sel] plot_style_['label'] = _ph_sel_label_dict[ph_sel] plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) - plot(times, rates, **plot_style_) + ax.plot(times, rates, **plot_style_) # Plot burst-search rate-threshold if show_rate_th and 'bg' in d: - _plot_rate_th(d, i, F=F, scale=1e-3, ph_sel=ph_sel, invert=invert, + _plot_rate_th(d, i, F=F, ph_sel=ph_sel, ax=ax, scale=1e-3, invert=invert, plot_style_=plot_style_, rate_th_style=rate_th_style) - plt.xlabel('Time (s)') - plt.ylabel('Rate (kcps)') + ax.set_xlabel('Time (s)') + ax.set_ylabel('Rate (kcps)') if burst_picker: - _gui_timetrace_burst_sel(d, gcf(), gca()) + _gui_timetrace_burst_sel(d, ax.figure, ax) if scroll: - _gui_timetrace_scroll(gcf()) + _gui_timetrace_scroll(ax.figure) if set_ax_limits: - plt.xlim(tmin, tmin + 1) + ax.set_xlim(tmin, tmin + 1) if not invert: - plt.ylim(top=100) + ax.set_ylim(top=100) else: - plt.ylim(bottom=-100) + ax.set_ylim(bottom=-100) _plot_status['ratetrace_single'] = {'autoscale': False} # same, must be plotted per channel always +@_ax_intercept def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200, bursts=False, burst_picker=True, scroll=False, show_rate_th=True, F=None, rate_th_style={'label': None}, show_aa=True, legend=False, set_ax_limits=True, #dd_plot_style={}, ad_plot_style={}, aa_plot_style={} - burst_color='#BBBBBB'): + burst_color='#BBBBBB', ax=None): """Plot the rate timetraces of photon timestamps. Arguments: @@ -634,11 +661,12 @@ def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200, timetrace. burst_color (string): string containing the the HEX RGB color to use to highlight the burst regions. + ax (mpl.axes): axis where plot will be generated """ # Plot bursts if bursts: tmin_clk, tmax_clk = tmin / d.clk_p, tmax / d.clk_p - _plot_bursts(d, i, tmin_clk, tmax_clk, pmax=500, pmin=-500, + _plot_bursts(d, i, tmin_clk, tmax_clk, ax, pmax=500, pmin=-500, color=burst_color) # Plot multiple timetraces @@ -659,10 +687,10 @@ def ratetrace(d, i=0, m=None, max_num_ph=1e6, tmin=0, tmax=200, tmax=tmax, ph_sel=ph_sel, invert=invert, bursts=False, burst_picker=burst_picker_list[ix], scroll=scroll_list[ix], - show_rate_th=show_rate_th, F=F, + show_rate_th=show_rate_th, F=F, ax=ax, rate_th_style=rate_th_style, set_ax_limits=set_ax_limits) if legend: - plt.legend(loc='best', fancybox=True) + ax.legend(loc='best', fancybox=True) def sort_burst_sizes(sizes, levels=np.arange(1, 102, 20)): @@ -676,7 +704,8 @@ def sort_burst_sizes(sizes, levels=np.arange(1, 102, 20)): return masks # plot per channel always -def timetrace_fret(d, i=0, gamma=1., **kwargs): +@_ax_intercept +def timetrace_fret(d, i=0, gamma=1., ax=None, **kwargs): """Timetrace of burst FRET vs time. Uses `plot`.""" b = d.mburst[i] bsizes = d.burst_sizes_ich(ich=i, gamma=gamma) @@ -688,25 +717,29 @@ def timetrace_fret(d, i=0, gamma=1., **kwargs): t, E = b.start*d.clk_p, d.E[i] levels = sort_burst_sizes(bsizes) for ilev, level in enumerate(levels): - plt.plot(t[level], E[level], ms=np.sqrt((ilev+1)*15), + ax.plot(t[level], E[level], ms=np.sqrt((ilev+1)*15), **style_kwargs) - plt.plot(b.start*d.clk_p, d.E[i], '-k', alpha=0.1, lw=1) - xlabel('Time (s)'); ylabel('E') - _gui_timetrace_burst_sel(d, gcf(), gca()) + ax.plot(b.start*d.clk_p, d.E[i], '-k', alpha=0.1, lw=1) + ax.set_xlabel('Time (s)') + ax.set_ylabel('E') + _gui_timetrace_burst_sel(d, ax.figure, ax) # plot per channel always -def timetrace_fret_scatter(d, i=0, gamma=1., **kwargs): +@_ax_intercept +def timetrace_fret_scatter(d, i=0, gamma=1., ax=None, **kwargs): """Timetrace of burst FRET vs time. Uses `scatter` (slow).""" b = d.mburst[i] bsizes = d.burst_sizes_ich(ich=i, gamma=gamma) style_kwargs = dict(s=bsizes, marker='o', alpha=0.5) style_kwargs.update(**kwargs) - plt.scatter(b.start*d.clk_p, d.E[i], **style_kwargs) - xlabel('Time (s)'); ylabel('E') + ax.scatter(b.start*d.clk_p, d.E[i], **style_kwargs) + ax.set_xlabel('Time (s)') + ax.set_ylabel('E') # plot per channel always -def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False): +@_ax_intercept +def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False, ax=None): """Timetrace of background rates.""" bg = d.bg_from(Ph_sel('all')) bg_dd = d.bg_from(Ph_sel(Dex='Dem')) @@ -715,29 +748,30 @@ def timetrace_bg(d, i=0, nolegend=False, ncol=2, plot_style={}, show_da=False): plot_style_ = dict(linewidth=2, marker='o', markersize=6) plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) label = "T: %d cps" % d.bg_mean[Ph_sel('all')][i] - plot(t, 1e-3 * bg[i], color='k', label=label, **plot_style_) + ax.plot(t, 1e-3 * bg[i], color='k', label=label, **plot_style_) label = "DD: %d cps" % d.bg_mean[Ph_sel(Dex='Dem')][i] - plot(t, 1e-3 * bg_dd[i], color=green, label=label, **plot_style_) + ax.plot(t, 1e-3 * bg_dd[i], color=green, label=label, **plot_style_) label = "AD: %d cps" % d.bg_mean[Ph_sel(Dex='Aem')][i] - plot(t, 1e-3 * bg_ad[i], color=red, label=label, **plot_style_) + ax.plot(t, 1e-3 * bg_ad[i], color=red, label=label, **plot_style_) if d.alternated: bg_aa = d.bg_from(Ph_sel(Aex='Aem')) label = "AA: %d cps" % d.bg_mean[Ph_sel(Aex='Aem')][i] - plot(t, 1e-3 * bg_aa[i], label=label, color=purple, **plot_style_) + ax.plot(t, 1e-3 * bg_aa[i], label=label, color=purple, **plot_style_) if show_da: bg_da = d.bg_from(Ph_sel(Aex='Dem')) label = "DA: %d cps" % d.bg_mean[Ph_sel(Aex='Dem')][i] - plot(t, 1e-3 * bg_da[i], label=label, + ax.plot(t, 1e-3 * bg_da[i], label=label, color=_ph_sel_color_dict[Ph_sel(Aex='Dem')], **plot_style_) if not nolegend: - legend(loc='best', frameon=False, ncol=ncol) - plt.xlabel("Time (s)") - plt.ylabel("BG rate (kcps)") - plt.grid(True) - plt.ylim(bottom=0) + ax.legend(loc='best', frameon=False, ncol=ncol) + ax.set_xlabel("Time (s)") + ax.set_ylabel("BG rate (kcps)") + ax.grid(True) + ax.set_ylim(bottom=0) # plot per channel always -def timetrace_b_rate(d, i=0): +@_ax_intercept +def timetrace_b_rate(d, i=0, ax=None): """Timetrace of bursts-per-second in each period.""" t = arange(d.bg[i].size)*d.bg_time_s b_rate = r_[[(d.bp[i] == p).sum() for p in range(d.bp[i].max()+1)]] @@ -746,13 +780,16 @@ def timetrace_b_rate(d, i=0): t = t[:-1] # assuming last period without bursts else: assert t.size == b_rate.size - plot(t, b_rate, lw=2, label="CH%d" % (i+1)) - legend(loc='best', fancybox=True, frameon=False, ncol=3) - xlabel("Time (s)"); ylabel("Burst per second"); grid(True) - plt.ylim(bottom=0) + ax.plot(t, b_rate, lw=2, label="CH%d" % (i+1)) + ax.legend(loc='best', fancybox=True, frameon=False, ncol=3) + ax.set_xlabel("Time (s)") + ax.set_ylabel("Burst per second") + ax.grid(True) + ax.set_ylim(bottom=0) # plot per channel always -def time_ph(d, i=0, num_ph=1e4, ph_istart=0): +@_ax_intercept +def time_ph(d, i=0, num_ph=1e4, ph_istart=0, ax=None): """Plot 'num_ph' ph starting at 'ph_istart' marking burst start/end. TODO: Update to use the new matplotlib eventplot. """ @@ -766,11 +803,11 @@ def time_ph(d, i=0, num_ph=1e4, ph_istart=0): start, end = b[BSLICE].start, b[BSLICE].stop u = d.clk_p # time scale - plt.vlines(ph_d*u, 0, 1, color='k', alpha=0.02) - plt.vlines(ph_a*u, 0, 1, color='k', alpha=0.02) - plt.vlines(start*u, -0.5, 1.5, lw=3, color=green, alpha=0.5) - plt.vlines(end*u, -0.5, 1.5, lw=3, color=red, alpha=0.5) - xlabel("Time (s)") + ax.vlines(ph_d*u, 0, 1, color='k', alpha=0.02) + ax.vlines(ph_a*u, 0, 1, color='k', alpha=0.02) + ax.vlines(start*u, -0.5, 1.5, lw=3, color=green, alpha=0.5) + ax.vlines(end*u, -0.5, 1.5, lw=3, color=red, alpha=0.5) + ax.set_xlabel("Time (s)") ## @@ -785,7 +822,7 @@ def _bins_array(bins): return bins # not channel specific hidden function -def _hist_burst_taildist(data, bins, pdf, weights=None, yscale='log', +def _hist_burst_taildist(data, bins, pdf, ax, weights=None, yscale='log', color=None, label=None, plot_style=None, vline=None): hist = HistData(*np.histogram(data[~np.isnan(data)], bins=_bins_array(bins), weights=weights)) @@ -799,18 +836,19 @@ def _hist_burst_taildist(data, bins, pdf, weights=None, yscale='log', if label is not None: plot_style['label'] = label default_plot_style.update(_normalize_kwargs(plot_style, kind='line2d')) - plt.plot(hist.bincenters, ydata, **default_plot_style) + ax.plot(hist.bincenters, ydata, **default_plot_style) if vline is not None: - plt.axvline(vline, ls='--') - plt.yscale(yscale) + ax.axvline(vline, ls='--') + ax.set_yscale(yscale) if pdf: - plt.ylabel('PDF') + ax.set_ylabel('PDF') else: - plt.ylabel('# Bursts') + ax.set_ylabel('# Bursts') +@_ax_intercept def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None, - yscale='log', color=None, plot_style=None, vline=None): + yscale='log', color=None, plot_style=None, vline=None, ax=None): """Plot histogram of burst durations. Parameters: @@ -824,6 +862,7 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None, plot_style (dict): dict of matplotlib line style passed to `plot`. vline (float): If not None, plot vertical line at the specified x position. + ax (mpl.axes): axis where plot will be generated """ if i is None: burst_widths = np.concatenate([mb.width for mb in d.mburst]) * d.clk_p * 1e3 @@ -831,16 +870,17 @@ def hist_width(d, i=0, bins=(0, 10, 0.025), pdf=True, weights=None, weights = weights[i] if weights is not None else None burst_widths = d.mburst[i].width * d.clk_p * 1e3 - _hist_burst_taildist(burst_widths, bins, pdf, weights=weights, vline=vline, + _hist_burst_taildist(burst_widths, bins, pdf, ax, weights=weights, vline=vline, yscale=yscale, color=color, plot_style=plot_style) - plt.xlabel('Burst width (ms)') - plt.xlim(xmin=0) + ax.set_xlabel('Burst width (ms)') + ax.set_xlim(xmin=0) +@_ax_intercept def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None, yscale='log', gamma=1, add_naa=False, ph_sel=Ph_sel('all'), beta=1., donor_ref=True, naa_aexonly=False, naa_comp=False, na_comp=False, - label_prefix=None, color=None, plot_style=None, vline=None): + label_prefix=None, color=None, plot_style=None, vline=None, ax=None): """Plot histogram of burst brightness, i.e. burst size / duration. Parameters: @@ -871,6 +911,7 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None, plot_style (dict): dict of matplotlib line style passed to `plot`. vline (float): If not None, plot vertical line at the specified x position. + ax (mpl.axes): axis where plot will be generated """ weights = weights[i] if weights is not None else None if plot_style is None: @@ -894,10 +935,10 @@ def hist_brightness(d, i=0, bins=(0, 60, 1), pdf=True, weights=None, if 'label' not in plot_style: plot_style['label'] = label - _hist_burst_taildist(brightness, bins, pdf, weights=weights, vline=vline, + _hist_burst_taildist(brightness, bins, pdf, ax, weights=weights, vline=vline, yscale=yscale, color=color, plot_style=plot_style) - plt.xlabel('Burst brightness (kHz)') - plt.legend(loc='best') + ax.set_xlabel('Burst brightness (kHz)') + ax.legend(loc='best') def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa, @@ -927,11 +968,12 @@ def _get_sizes_and_formula(d, ich, gamma, beta, donor_ref, add_naa, # dependent on _hist_burst_taildist +@_ax_intercept def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None, yscale='log', gamma=1, beta=1, donor_ref=True, add_naa=False, ph_sel=None, naa_aexonly=False, naa_comp=False, na_comp=False, vline=None, label_prefix=None, legend=True, color=None, - plot_style=None): + plot_style=None, ax=None): """Plot histogram of "burst sizes", according to different definitions. Arguments: @@ -969,6 +1011,7 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None, plot_style (dict): dict of matplotlib line style passed to `plot`. vline (float): If not None, plot vertical line at the specified x position. + ax (mpl.axes): axis where plot will be generated See also: - :meth:`fretbursts.burstlib.Data.burst_sizes_ich`. @@ -1002,14 +1045,15 @@ def hist_size(d, i=0, which='all', bins=(0, 600, 4), pdf=False, weights=None, elif color is not None: plot_style['color'] = color - _hist_burst_taildist(sizes, bins, pdf, weights=weights, yscale=yscale, + _hist_burst_taildist(sizes, bins, pdf, ax, weights=weights, yscale=yscale, plot_style=plot_style, vline=vline) - plt.xlabel('Burst size') + ax.set_xlabel('Burst size') if legend: - plt.legend(loc='upper right') + ax.legend(loc='upper right') # depends on _hist_burst_taildist -def hist_size_all(d, i=0, **kwargs): +@_ax_intercept +def hist_size_all(d, i=0, ax=None, **kwargs): """Plot burst sizes for all the combinations of photons. Calls :func:`hist_size` multiple times with different `which` parameters. @@ -1020,7 +1064,7 @@ def hist_size_all(d, i=0, **kwargs): elif 'PAX' in d.meas_type: fields += ['nda', 'naa'] for which in fields: - hist_size(d, i, which=which, **kwargs) + hist_size(d, i, which=which, ax=ax, **kwargs) def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True, @@ -1028,7 +1072,7 @@ def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True, alpha=0.5, fillcolor=None): """Plot a fitted model overlay on a FRET histogram.""" if ax is None: - ax2 = gca() + ax2 = plt.gca() else: ax2 = plt.twinx(ax=ax) ax2.grid(False) @@ -1060,9 +1104,11 @@ def _fitted_E_plot(d, i=0, F=1, no_E=False, ax=None, show_model=True, xtext = 0.6 if d.E_fit[i] < 0.6 else 0.2 if d.nch > 1 and not no_E: ax2.text(xtext, 0.81, "CH%d: $E_{fit} = %.3f$" % (i+1, d.E_fit[i]), - transform=gca().transAxes, fontsize=16, + transform=ax2.transAxes, fontsize=16, bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5)) + +@_ax_intercept def hist_burst_data( d, i=0, data_name='E', ax=None, binwidth=0.03, bins=None, vertical=False, pdf=False, hist_style='bar', @@ -1150,8 +1196,6 @@ def hist_burst_data( assert data_name in d fitter_name = data_name + '_fitter' - if ax is None: - ax = gca() ax.set_axisbelow(True) pline = ax.axhline if vertical else ax.axvline bar = ax.barh if vertical else ax.bar @@ -1277,7 +1321,6 @@ def hist_fret( For detailed documentation see :func:`hist_burst_data`. """ - hist_burst_data( d, i, data_name='E', ax=ax, binwidth=binwidth, bins=bins, pdf=pdf, weights=weights, gamma=gamma, add_naa=add_naa, @@ -1300,12 +1343,13 @@ def hist_S( show_model=False, show_model_peaks=True, hist_bar_style=None, hist_plot_style=None, model_plot_style=None, kde_plot_style=None, verbose=False): - """Plot S histogram and KDE. + """ + Plot S histogram and KDE. The most used argument is `binwidth` that sets the histogram bin width. - For detailed documentation see :func:`hist_burst_data`. """ - + For detailed documentation see :func:`hist_burst_data`. + """ hist_burst_data( d, i, data_name='S', ax=ax, binwidth=binwidth, bins=bins, pdf=pdf, weights=weights, gamma=gamma, add_naa=add_naa, @@ -1328,12 +1372,12 @@ def _get_fit_text_stats(fit_arr, pylab=True): return fit_text +@_ax_intercept def _plot_fit_text_ch( fit_arr, ich, fmt_str="CH%d: $E_{fit} = %.3f$", ax=None, bbox=dict(boxstyle='round', facecolor='#dedede', alpha=0.5), xtext_low=0.2, xtext_high=0.6, fontsize=16): """Plot a text box with ch and fit value.""" - if ax is None: ax = gca() if ich is None: xtext = xtext_high if fit_arr[0] < xtext_high else xtext_low else: @@ -1342,14 +1386,14 @@ def _plot_fit_text_ch( transform=ax.transAxes, fontsize=fontsize, bbox=bbox) +@_ax_intercept def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8, interp='bicubic', cmap='hot', under_color='white', over_color='white', scatter=True, scatter_ms=3, scatter_color='orange', scatter_alpha=0.2, gui_sel=False, - cbar_ax=None, grid_color='#D0D0D0'): + cbar_ax=None, grid_color='#D0D0D0', ax=None): """Plot 2-D E-S ALEX histogram with a scatterplot overlay. """ - ax = plt.gca() d._calc_alex_hist(binwidth) ES_hist = np.sum(d.ES_hist, axis=0) if i is None else d.ES_hist[i] E_bins, S_bins, S_ax = d.E_bins, d.S_bins, d.S_ax @@ -1372,7 +1416,7 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8, im.cmap.set_under(under_color) im.cmap.set_over(over_color) if cbar_ax is None: - gcf().colorbar(im) + ax.figure.colorbar(im) else: cbar_ax.colorbar(im) ax.set_xlim(-0.2, 1.2) @@ -1382,11 +1426,12 @@ def hist2d_alex(d, i=0, vmin=2, vmax=0, binwidth=0.05, S_max_norm=0.8, ax.grid(color=grid_color) if gui_sel: # the selection object must be saved (otherwise will be destroyed) - hist2d_alex.gui_sel = gs.rectSelection(gcf(), gca()) + hist2d_alex.gui_sel = gs.rectSelection(ax.figure, ax) +@_ax_intercept def hexbin_alex(d, i=0, vmin=1, vmax=None, gridsize=80, cmap='Spectral_r', - E_name='E', S_name='S', **hexbin_kwargs): + E_name='E', S_name='S', ax=None, **hexbin_kwargs): """Plot an hexbin 2D histogram for E-S. """ if i is None: @@ -1399,10 +1444,10 @@ def hexbin_alex(d, i=0, vmin=1, vmax=None, gridsize=80, cmap='Spectral_r', cmap=cmap, extent=(-0.2, 1.2, -0.2, 1.2), mincnt=1) if hexbin_kwargs is not None: hexbin_kwargs_.update(_normalize_kwargs(hexbin_kwargs)) - poly = plt.hexbin(E, S, **hexbin_kwargs_) + poly = ax.hexbin(E, S, **hexbin_kwargs_) poly.set_clim(vmin, vmax) - plt.xlabel('E') - plt.ylabel('S') + ax.set_xlabel('E') + ax.set_ylabel('S') # channel independent def plot_ES_selection(ax, E1, E2, S1, S2, rect=True, **kwargs): @@ -1456,10 +1501,13 @@ def get_ES_range(): print('E1={E1:.3}, E2={E2:.3}, S1={S1:.3}, S2={S2:.3}'.format(**sel)) return sel + + +@_ax_intercept def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None, ph_sel=Ph_sel('all'), period=None, yscale='log', xscale='linear', xunit='ms', - plot_style=None): + plot_style=None, ax=None): """Plot histogram of interphoton delays for a single photon streams. Arguments: @@ -1489,6 +1537,7 @@ def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None, 'us', 'ns'. Default 'ms'. plot_style (dict): keyword arguments to be passed to matplotlib's `plot` function. Used to customize the plot style. + ax (mpl.axes): axis where plot will be generated """ unit_dict = {'s': 1, 'ms': 1e3, 'us': 1e6, 'ns': 1e9} assert xunit in unit_dict @@ -1529,26 +1578,27 @@ def hist_interphoton_single(d, i=0, binwidth=1e-4, tmax=None, bins=None, plot_style_['color'] = _ph_sel_color_dict[ph_sel] plot_style_['label'] = _ph_sel_label_dict[ph_sel] plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) - plot(t_ax[:n_trim] * scalex, counts[:n_trim], **plot_style_) + ax.plot(t_ax[:n_trim] * scalex, counts[:n_trim], **plot_style_) if yscale == 'log': - gca().set_yscale(yscale) - plt.ylim(1) + ax.set_yscale(yscale) + ax.set_ylim(1) _plot_status['hist_interphoton_single'] = {'autoscale': False} if xscale == 'log': - gca().set_xscale(yscale) - plt.xlim(0.5 * binwidth) + ax.set_xscale(yscale) + ax.set_xlim(0.5 * binwidth) _plot_status['hist_interphoton_single'] = {'autoscale': False} - plt.xlabel('Inter-photon delays (%s)' % xunit.replace('us', 'μs')) - plt.ylabel('# Delays') + ax.set_xlabel('Inter-photon delays (%s)' % xunit.replace('us', 'μs')) + ax.set_ylabel('# Delays') # Return internal variables so that other functions can extend the plot return dict(counts=counts, n_trim=n_trim, plot_style_=plot_style_, t_ax=t_ax, scalex=scalex) +@_ax_intercept def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None, yscale='log', xscale='linear', xunit='ms', plot_style=None, - show_da=False, legend=True): + show_da=False, legend=True, ax=None): """Plot histogram of photon interval for different photon streams. Arguments: @@ -1580,6 +1630,7 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None, show_da (bool): If False (default) do not plot the AexDem photon stream. Ignored when the measurement is not ALEX. legend (bool): If True (default) plot a legend. + ax (mpl.axes): axis where plot will be generated """ # Plot multiple timetraces ph_sel_list = [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')] @@ -1594,18 +1645,20 @@ def hist_interphoton(d, i=0, binwidth=1e-4, tmax=None, bins=None, period=None, hist_interphoton_single(d, i=i, binwidth=binwidth, tmax=tmax, bins=bins, period=period, ph_sel=ph_sel, yscale=yscale, xscale=xscale, xunit=xunit, - plot_style=plot_style) + plot_style=plot_style, ax=ax) if legend: - plt.legend(loc='best', fancybox=True) + ax.legend(loc='best', fancybox=True) if yscale == 'log' or xscale == 'log': _plot_status['hist_interphoton'] = {'autoscale': False} + # TODO: condsider better method for displaying all channel, total bg histogram +@_ax_intercept def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, ph_sel=Ph_sel('all'), period=0, yscale='log', xscale='linear', xunit='ms', plot_style=None, - show_fit=True, fit_style=None, manual_rate=None): + show_fit=True, fit_style=None, manual_rate=None, ax=None): """Plot histogram of photon interval for a single photon streams. Optionally plots the fitted background as an exponential curve. @@ -1619,6 +1672,7 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, rate (ignoring the value saved in Data). fit_style (dict): arguments passed to matplotlib's `plot` for for plotting the exponential curve. + ax (mpl.axes): axis where plot will be generated For a description of all the other arguments see :func:`hist_interphoton_single`. @@ -1626,7 +1680,7 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, hist = hist_interphoton_single(d, i=i, binwidth=binwidth, tmax=tmax, bins=bins, ph_sel=ph_sel, period=period, yscale=yscale, xscale=xscale, xunit=xunit, - plot_style=None) + plot_style=None, ax=ax) if show_fit or manual_rate is not None: # Compute the fit function @@ -1650,13 +1704,14 @@ def hist_bg_single(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, label = str(ph_sel) if plt_label is None else plt_label fit_style_['label'] = '%s, %.2f kcps' % (label, bg_rate * 1e-3) n_trim = hist['n_trim'] - plot(hist['t_ax'][:n_trim] * hist['scalex'], y_fit[:n_trim], + ax.plot(hist['t_ax'][:n_trim] * hist['scalex'], y_fit[:n_trim], **fit_style_) +@_ax_intercept def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0, yscale='log', xscale='linear', xunit='ms', plot_style=None, - show_da=False, legend=True, show_fit=True, fit_style=None): + show_da=False, legend=True, show_fit=True, fit_style=None, ax=None): """Plot histogram of photon interval for different photon streams. Optionally plots the fitted background. @@ -1668,6 +1723,7 @@ def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0, exponential distribution. fit_style (dict): arguments passed to matplotlib's `plot` for for plotting the exponential curve. + ax (mpl.axes): axis where plot will be generated For a description of all the other arguments see :func:`hist_interphoton`. """ @@ -1683,17 +1739,18 @@ def hist_bg(d, i=0, binwidth=1e-4, tmax=0.01, bins=None, period=0, hist_bg_single(d, i=i, period=period, binwidth=binwidth, bins=bins, tmax=tmax, ph_sel=ph_sel, xunit=xunit, show_fit=show_fit, yscale=yscale, xscale=xscale, - plot_style=plot_style, fit_style=fit_style) + plot_style=plot_style, fit_style=fit_style, ax=ax) if legend: - plt.legend(loc='best', fancybox=True) + ax.legend(loc='best', fancybox=True) if yscale == 'log' or xscale == 'log': _plot_status['hist_bg'] = {'autoscale': False} +@_ax_intercept def hist_ph_delays( d, i=0, time_min_s=0, time_max_s=30, bin_width_us=10, mask=None, - yscale='log', hfit_bin_ms=1, efit_tail_min_us=1000, **kwargs): + yscale='log', hfit_bin_ms=1, efit_tail_min_us=1000, ax=None, **kwargs): """Histogram of ph delays and comparison with 3 BG fitting functions. """ if i is None: @@ -1715,9 +1772,9 @@ def hist_ph_delays( ph = ph[mask[i]] ph = ph[(ph < time_max_s/d.clk_p)*(ph > time_min_s/d.clk_p)] dph = np.diff(ph)*d.clk_p - H = hist(dph*1e6, bins=r_[0:1200:bin_width_us], histtype='step', **kwargs) - gca().set_yscale('log') - xlabel(u'Ph delay time (μs)'); ylabel("# Ph") + H = ax.hist(dph*1e6, bins=r_[0:1200:bin_width_us], histtype='step', **kwargs) + ax.set_yscale('log') + ax.set_xlabel(u'Ph delay time (μs)'); ax.set_ylabel("# Ph") F = 1 if 'normed' in kwargs else H[0].sum()*(bin_width_us) efun = lambda t, r: np.exp(-r*t)*r @@ -1739,39 +1796,31 @@ def hist_ph_delays( t = r_[0:1200]*1e-6 if rc_do: - plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple, + ax.plot(t*1e6, 0.65*F*efun(t, rc)*1e-6, lw=3, alpha=0.5, color=purple, label="%d cps - Exp CDF (tail_min_p=%.2f)" % (rc, efit_tail_min_us)) if re_do: - plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red, - label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us)) + ax.plot(t*1e6, 0.65*F*efun(t, re)*1e-6, lw=3, alpha=0.5, color=red, + label="%d cps - Exp ML (tail_min_p=%.2f)" % (re, efit_tail_min_us)) if re_do and rg_do: - plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green, - label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg, + ax.plot(t*1e6, 0.68*F*efun(t, rg)*1e-6, lw=3, alpha=0.5, color=green, + label=u"%d cps - Hist (bin_ms=%d) [Δ=%d%%]" % (hfit_bin_ms, rg, 100*(rg-re)/re)) - plt.legend(loc='best', fancybox=True) + ax.legend(loc='best', fancybox=True) # TODO: update for concatenated data, probably fix bext.calc_mdelays_hist +@_ax_intercept def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0, hold=False, bg_ppf=0.01, ph_sel=Ph_sel('all'), spline=True, - s=1., bg_fit=True, bg_F=0.8): + s=1., bg_fit=True, bg_F=0.8, ax=None): """Histogram of m-photons delays (all-ph vs in-burst ph). """ - ax = gca() if not hold: #ax.clear() for _ind in range(len(ax.lines)): ax.lines.pop() - if i is None: - results = np.concatenate([bext.calc_mdelays_hist(d, ich=j, m=m, period=period, - bins_s=bins_s,ph_sel=ph_sel, - bursts=True, bg_fit=bg_fit, - bg_F=bg_F) - for j in range(d.nch)]) - else: - results = bext.calc_mdelays_hist(d, ich=i, m=m, period=period, bins_s=bins_s, + results = bext.calc_mdelays_hist(d, ich=i, m=m, period=period, bins_s=bins_s, ph_sel=ph_sel, bursts=True, bg_fit=bg_fit, bg_F=bg_F) - bin_x, histog_y = results[:2] - bg_dist = results[2] + bin_x, histog_y, bg_dist = results[:3] rate_ch_kcps = 1./bg_dist.kwds['scale'] # extract the rate if bg_fit: a, rate_kcps = results[3:5] @@ -1805,35 +1854,36 @@ def hist_mdelays(d, i=0, m=10, bins_s=(0, 10, 0.02), period=0, burst_integral = np.trapz(x=bin_x[burst_domain], y=mdelays_hist_y[burst_domain]) - title("I = %.1f %%" % (burst_integral*100), fontsize='small') + ax.set_title("I = %.1f %%" % (burst_integral*100), fontsize='small') #text(0.8,0.8,"I = %.1f %%" % (integr*100), transform = gca().transAxes) ## MDelays plot - plot(bin_x, mdelays_pdf_y, lw=2, color=blue, alpha=0.5, - label="Delays dist.") - plot(bin_x, mdelays_b_pdf_y, lw=2, color=red, alpha=0.5, - label="Delays dist. (in burst)") - plt.axvline(max_delay_th_P, color='k', - label="BG ML dist. @ %.1f%%" % (bg_ppf*100)) - plt.axvline(max_delay_th_F, color=purple, - label="BS threshold (F=%d)" % d.F) + ax.plot(bin_x, mdelays_pdf_y, lw=2, color=blue, alpha=0.5, + label="Delays dist.") + ax.plot(bin_x, mdelays_b_pdf_y, lw=2, color=red, alpha=0.5, + label="Delays dist. (in burst)") + ax.axvline(max_delay_th_P, color='k', + label="BG ML dist. @ %.1f%%" % (bg_ppf*100)) + ax.axvline(max_delay_th_F, color=purple, + label="BS threshold (F=%d)" % d.F) ## Bg distribution plots bg_dist_y = bg_dist.pdf(bin_x) ibin_x_bg_mean = np.abs(bin_x - bg_dist.mean()).argmin() bg_dist_y *= mdelays_pdf_y[ibin_x_bg_mean]/bg_dist_y[ibin_x_bg_mean] - plot(bin_x, bg_dist_y, '--k', alpha=1., - label='BG ML dist.') - plt.axvline(bg_dist.mean(), color='k', ls='--', label="BG mean") + ax.plot(bin_x, bg_dist_y, '--k', alpha=1., + label='BG ML dist.') + ax.axvline(bg_dist.mean(), color='k', ls='--', label="BG mean") if bg_fit: bg_y = a*erlang.pdf(bin_x, a=m, scale=1./rate_kcps) - plot(bin_x, bg_y, '--k', alpha=1.) - plt.legend(ncol=2, frameon=False) - xlabel("Time (ms)") + ax.plot(bin_x, bg_y, '--k', alpha=1.) + ax.legend(ncol=2, frameon=False) + ax.set_xlabel("Time (ms)") +@_ax_intercept def hist_mrates(d, i=0, m=10, bins=(0, 4000, 100), yscale='log', pdf=False, - dense=True, plot_style=None): + dense=True, plot_style=None, ax=None): """Histogram of m-photons rates. See also :func:`hist_mdelays`. """ if i is None: @@ -1853,13 +1903,15 @@ def hist_mrates(d, i=0, m=10, bins=(0, 4000, 100), yscale='log', pdf=False, ydata = hist.pdf if pdf else hist.counts plot_style_ = dict(marker='o') plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) - plot(hist.bincenters, ydata, **plot_style_) - gca().set_yscale(yscale) - xlabel("Rates (kcps)") + ax.plot(hist.bincenters, ydata, **plot_style_) + ax.set_yscale(yscale) + ax.set_xlabel("Rates (kcps)") + ## Bursts stats +@_ax_intercept def hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None, - plot_style=None): + plot_style=None, ax=None): """Histogram of per-burst Signal-to-Background Ratio (SBR). """ if i is None: @@ -1869,13 +1921,14 @@ def hist_sbr(d, i=0, bins=(0, 30, 1), pdf=True, weights=None, color=None, if 'sbr' not in d: d.calc_sbr() sbr = np.concatenate(d.sbr) if i is None else d.sbr[i] - _hist_burst_taildist(sbr, bins, pdf, weights=weights, color=color, + _hist_burst_taildist(sbr, bins, pdf, ax, weights=weights, color=color, plot_style=plot_style) - plt.xlabel('SBR') + ax.set_xlabel('SBR') +@_ax_intercept def hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None, - color=None, plot_style=None, vline=None): + color=None, plot_style=None, vline=None, ax=None): """Histogram of max photon rate in each burst. """ weights = weights[i] if weights is not None else None @@ -1886,13 +1939,14 @@ def hist_burst_phrate(d, i=0, bins=(0, 1000, 20), pdf=True, weights=None, d.calc_max_rate(m=10) max_rate = np.concatenate(d.max_rate) if i is None else d.max_rate[i] - _hist_burst_taildist(max_rate * 1e-3, bins, pdf, weights=weights, + _hist_burst_taildist(max_rate * 1e-3, bins, pdf, ax, weights=weights, color=color, plot_style=plot_style, vline=vline) - plt.xlabel('Peak rate (kcps)') + ax.set_xlabel('Peak rate (kcps)') +@_ax_intercept def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None, - color=None, plot_style=None): + color=None, plot_style=None, ax=None): """Histogram of waiting times between bursts. """ if i is None: @@ -1902,12 +1956,14 @@ def hist_burst_delays(d, i=0, bins=(0, 10, 0.2), pdf=False, weights=None, weights = weights[i] if weights is not None else None bdelays = np.diff(d.mburst[i].start*d.clk_p) - _hist_burst_taildist(bdelays, bins, pdf, weights=weights, color=color, + _hist_burst_taildist(bdelays, bins, pdf, ax, weights=weights, color=color, plot_style=plot_style) - plt.xlabel('Delays between bursts (s)') + ax.set_xlabel('Delays between bursts (s)') + ## Burst internal "symmetry" -def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median): +@_ax_intercept +def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median, ax=None): if i is None: burst_asym = np.concatenate([bext.asymmetry(d, ich=j, func=stat_func) for j in range(d.nch)]) else: @@ -1922,13 +1978,13 @@ def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median): asym_counts_pos = counts[izero:] - counts[:izero][::-1] asym_counts = np.hstack([asym_counts_neg, asym_counts_pos]) - plt.bar(bins[:-1], width=binwidth, height=counts, fc=blue, alpha=0.5) - plt.bar(bins[:-1], width=binwidth, height=asym_counts, fc=red, + ax.bar(bins[:-1], width=binwidth, height=counts, fc=blue, alpha=0.5) + ax.bar(bins[:-1], width=binwidth, height=asym_counts, fc=red, alpha=0.5) - plt.grid(True) - plt.xlabel('Time (ms)') - plt.ylabel('# Bursts') - plt.legend(['{func}$(t_D)$ - {func}$(t_A)$'.format(func=stat_func.__name__), + ax.grid(True) + ax.set_xlabel('Time (ms)') + ax.set_ylabel('# Bursts') + ax.legend(['{func}$(t_D)$ - {func}$(t_A)$'.format(func=stat_func.__name__), 'positive half - negative half'], frameon=False, loc='best') skew_abs = asym_counts_neg.sum() @@ -1938,8 +1994,46 @@ def hist_asymmetry(d, i=0, bin_max=2, binwidth=0.1, stat_func=np.median): ## # Scatter plots # +def linear_scale(arr): + """ + Returns same array, without rescalling values + """ + return arr + + +def log_scale(arr): + """Scale by log of arr""" + return np.log(arr) + + +def kde_density(x, y, bw_method=None, rescalex=linear_scale, rescaley=linear_scale): + xa = rescalex(x) + ya = rescaley(y) + mask = ~np.isnan(xa) * ~np.isnan(ya) * (np.inf != x) * (np.inf != y) * (-np.inf != x) * (-np.inf != y) + if mask.sum() == 0: + raise ValueError("No valid bursts") + xy = np.vstack([xa[mask],ya[mask]]) + colors = gaussian_kde(xy, bw_method=bw_method).evaluate(xy) + return x[mask], y[mask], colors + + +@_ax_intercept +def scatter_burst_data(d, xparam, yparam, i=0, ax=None, color_style='flat', + color_style_kwargs=None, xscale='linear', yscale='linear', + **kwargs): + x = np.concatenate(getattr(d, xparam)) if i is None else getattr(d, xparam)[i] + y = np.concatenate(getattr(d, yparam)) if i is None else getattr(d, yparam)[i] + if callable(color_style) or color_style in ('kde', ): + color_style = kde_density if color_style == 'kde' else color_style + color_style_kwargs = dict() if color_style_kwargs is None else color_style_kwargs + x, y, c = color_style(x, y, **color_style_kwargs) + kwargs['c'] = c + ax.scatter(x, y, **kwargs) + pass -def scatter_width_size(d, i=0): + +@_ax_intercept +def scatter_width_size(d, i=0, ax=None): """Scatterplot of burst width versus size.""" t_ms = arange(0, 50) if i is None: @@ -1953,32 +2047,38 @@ def scatter_width_size(d, i=0): nt = d.nt[i] T = d.T[i] bg_mean = d.bg_mean[Ph_sel('all')][i]*t_ms*1e-3 - plot(b, nt, 'o', mew=0, ms=3, alpha=0.7, + ax.plot(b, nt, 'o', mew=0, ms=3, alpha=0.7, color='blue') - plot(t_ms, ((d.m)/(T))*t_ms*1e-3, '--', lw=2, color='k', - label='Slope = m/T = min. rate = %1.0f cps' % (d.m/T)) - plot(t_ms, bg_mean, '--', lw=2, color=red, - label='Noise rate: BG*t') - xlabel('Burst width (ms)'); ylabel('Burst size (# ph.)') - plt.xlim(0, 10); plt.ylim(0, 300) - legend(frameon=False) - - -def scatter_rate_da(d, i=0): + ax.plot(t_ms, ((d.m)/(T))*t_ms*1e-3, '--', lw=2, color='k', + label='Slope = m/T = min. rate = %1.0f cps' % (d.m/T)) + ax.plot(t_ms, bg_mean, '--', lw=2, color=red, + label='Noise rate: BG*t') + ax.set_label('Burst width (ms)') + ax.set_ylabel('Burst size (# ph.)') + ax.set_xlim(0, 10) + ax.set_ylim(0, 300) + ax.legend(frameon=False) + + +@_ax_intercept +def scatter_rate_da(d, i=0, ax=None): """Scatter of nd rate vs na rate (rates for each burst).""" bw = np.concatenate([burst.width for burst in d.mburst]) if i is None else d.mburst[i].width nd = np.concatenate(d.nd) if i is None else d.nd[i] na = np.concatenate(d.na) if i is None else d.na[i] Rate = lambda nX: nX/bw/d.clk_p*1e-3 - plot(Rate(nd), Rate(na), 'o', mew=0, ms=3, alpha=0.1, color='blue') - xlabel('D burst rate (kcps)'); ylabel('A burst rate (kcps)') - plt.xlim(-20, 100); plt.ylim(-20, 100) - legend(frameon=False) + ax.plot(Rate(nd), Rate(na), 'o', mew=0, ms=3, alpha=0.1, color='blue') + ax.set_xlabel('D burst rate (kcps)') + ax.set_ylabel('A burst rate (kcps)') + ax.set_xlim(-20, 100) + ax.set_ylim(-20, 100) + ax.legend(frameon=False) +@_ax_intercept def scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False, - plot_style=None): + plot_style=None, ax=None): """Scatterplot of FRET efficiency versus burst size. """ if which == 'all': @@ -1995,24 +2095,26 @@ def scatter_fret_size(d, i=0, which='all', gamma=1, add_naa=False, marker='o', markeredgewidth=0, markersize=3) plot_style_.update(_normalize_kwargs(plot_style, kind='line2d')) E = np.concatenate(d.E) if i is None else d.E[i] - plot(E, size, **plot_style_) - xlabel("FRET Efficiency (E)") - ylabel("Corrected Burst size (#ph)") + ax.plot(E, size, **plot_style_) + ax.set_xlabel("FRET Efficiency (E)") + ax.set_ylabel("Corrected Burst size (#ph)") -def scatter_fret_nd_na(d, i=0, gamma=1., **kwargs): +@_ax_intercept +def scatter_fret_nd_na(d, i=0, gamma=1., ax=None, **kwargs): """Scatterplot of FRET versus gamma-corrected burst size.""" default_kwargs = dict(mew=0, ms=3, alpha=0.3, color=blue) default_kwargs.update(**kwargs) E = np.concatenate(d.E) if i is None else d.E[i] nd = np.concatenate(d.nd) if i is None else d.nd[i] na = np.concatenate(d.na) if i is None else d.na[i] - plot(E, gamma*nd+na, 'o', **default_kwargs) - xlabel("FRET Efficiency (E)") - ylabel("Burst size (#ph)") + ax.plot(E, gamma*nd+na, 'o', **default_kwargs) + ax.set_xlabel("FRET Efficiency (E)") + ax.set_ylabel("Burst size (#ph)") -def scatter_fret_width(d, i=0): +@_ax_intercept +def scatter_fret_width(d, i=0, ax=None): """Scatterplot of FRET versus burst width.""" if i is None: b = np.concatenate([mburst.width for mburst in d.mburst])*d.clk_p*1e3 @@ -2020,41 +2122,57 @@ def scatter_fret_width(d, i=0): else: b = d.mburst[i].width*d.clk_p*1e3 E = d.E[i] - plot(E, b, 'o', mew=0, ms=3, alpha=0.1, + ax.plot(E, b, 'o', mew=0, ms=3, alpha=0.1, color="blue") - xlabel("FRET Efficiency (E)") - ylabel("Burst width (ms)") + ax.set_xlabel("FRET Efficiency (E)") + ax.set_ylabel("Burst width (ms)") -def scatter_da(d, i=0, alpha=0.3): +@_ax_intercept +def scatter_da(d, i=0, alpha=0.3, ax=None): """Scatterplot of donor vs acceptor photons (nd, vs na) in each burst.""" nd = np.concatenate(d.nd) if i is None else d.nd[i] na = np.concatenate(d.na) if i is None else d.na[i] - plot(nd, na, 'o', mew=0, ms=3, alpha=alpha, color='blue') - xlabel('# donor ph.'); ylabel('# acceptor ph.') - plt.xlim(-5, 200); plt.ylim(-5, 120) + ax.plot(nd, na, 'o', mew=0, ms=3, alpha=alpha, color='blue') + ax.set_xlabel('# donor ph.'); ax.set_ylabel('# acceptor ph.') + ax.set_xlim(-5, 200) + ax.set_ylim(-5, 120) -def scatter_naa_nt(d, i=0, alpha=0.5): +@_ax_intercept +def scatter_naa_nt(d, i=0, alpha=0.5, color_style='flat', ax=None, **kwargs): """Scatterplot of nt versus naa.""" - nt = np.concatenate(d.nt) if i is None else d.nt[i] - naa = np.concatenate(d.naa) if i is None else d.naa[i] - plot(nt, naa, 'o', mew=0, ms=3, alpha=alpha, color='blue') - plot(arange(200), color='k', lw=2) - xlabel('Total burst size (nd+na+naa)'); ylabel('Accept em-ex BS (naa)') - plt.xlim(-5, 200); plt.ylim(-5, 120) - -def scatter_alex(d, i=0, **kwargs): - """Scatterplot of E vs S. Keyword arguments passed to `plot`.""" - plot_style = dict(mew=1, ms=4, mec='black', color='purple', - alpha=0.1) - plot_style = _normalize_kwargs(plot_style, 'line2d') - plot_style.update(_normalize_kwargs(kwargs, 'line2d')) - E = np.concatenate(d.E) if i is None else d.E[i] - S = np.concatenate(d.S) if i is None else d.S[i] - plot(E, S, 'o', **plot_style) - xlabel("E"); ylabel('S') - plt.xlim(-0.2, 1.2); plt.ylim(-0.2, 1.2) + plot_style = _normalize_kwargs(dict(lw=0, s=17, alpha=alpha), 'scatter') + if color_style == 'flat': + plot_style['c'] = 'blue' + plot_style.update(_normalize_kwargs(kwargs, 'scatter')) + scatter_burst_data(d, 'nt', 'naa', i=i, **plot_style) + ax.plot(arange(200), color='k', lw=2) + ax.set_xlabel('Total burst size (nd+na+naa)') + ax.set_ylabel('Accept em-ex BS (naa)') + ax.set_xlim(-5, 200) + ax.set_ylim(-5, 120) + + +@_ax_intercept +def scatter_alex(d, i=0, color_style='flat', ax=None, **kwargs): + """ + Scatterplot of E vs S. Keyword arguments passed to `plot`. + If `color_style` is 'flat' (default) will use uniform color that can be set + with the 'c' keyword argument. + If `color_style` is 'kde', then will color based on gaussian_kde density. + Control color map with cmap keyword argument + """ + plot_style = dict(s=10, alpha=0.1) + if color_style == 'flat': + plot_style.update(c= 'purple', ec='black', lw=1) + plot_style = _normalize_kwargs(plot_style, 'scatter') + plot_style.update(_normalize_kwargs(kwargs, 'scatter')) + scatter_burst_data(d, 'E', 'S', i=i, color_style=color_style, **plot_style) + ax.set_xlabel("E") + ax.set_ylabel('S') + ax.set_xlim(-0.2, 1.2) + ax.set_ylim(-0.2, 1.2) # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - @@ -2108,9 +2226,8 @@ def _iter_plot(d, func, kwargs, iter_ch, nrows, ncols, figsize, AX, titlex, ha = 1 - titlex, 'left' ax.text(titlex, titley, s, transform=ax.transAxes, ha=ha, va=va, **title_kws) - plt.sca(ax) gui_status['first_plot_in_figure'] = (i == 0) - func(d, ich, **kwargs) + func(d, ich, ax=ax, **kwargs) if ax.legend_ is not None: ax.legend_.remove() [a.set_xlabel('') for a in AX[:-1, :].ravel()] @@ -2175,8 +2292,8 @@ def dplot_48ch(d, func, sharex=True, sharey=True, layout='horiz', def dplot_16ch(d, func, sharex=True, sharey=True, ncols=8, - pgrid=True, figsize=None, AX=None, suptitle=True, - scale=True, skip_ch=None, top=0.93, bottom=None, + grid=True, figsize=None, AX=None, suptitle=True, + tile='out', scale=True, skip_ch=None, top=0.93, bottom=None, hspace=0.15, wspace=None, left=0.08, right=0.96, **kwargs): """Plot wrapper for 16-spot measurements. Use `dplot` instead.""" assert (ncols <= 16), '`ncols` needs to be <= 16.' @@ -2189,11 +2306,11 @@ def dplot_16ch(d, func, sharex=True, sharey=True, ncols=8, return _iter_plot(d, func, kwargs, iter_ch, nrows, ncols, figsize, AX, sharex, sharey, suptitle, grid, scale, skip_ch=skip_ch, top=top, bottom=bottom, hspace=hspace, wspace=wspace, - left=left, right=right) + left=left, right=right, title=tile) def dplot_8ch(d, func, sharex=True, sharey=True, - pgrid=True, figsize=(12, 9), nosuptitle=False, AX=None, + grid=True, figsize=(12, 9), nosuptitle=False, AX=None, scale=True, **kwargs): """Plot wrapper for 8-spot measurements. Use `dplot` instead.""" global gui_status @@ -2222,10 +2339,9 @@ def dplot_8ch(d, func, sharex=True, sharey=True, s += (u', T=%dμs' % (d.T[i]*1e6)) if b is not None: s += (', #bu=%d' % b.num_bursts) ax.set_title(s, fontsize=12) - ax.grid(pgrid) - plt.sca(ax) + ax.grid(grid) gui_status['first_plot_in_figure'] = (i == 0) - func(d, i, **kwargs) + func(d, i, ax=ax, **kwargs) if i % 2 == 1: ax.yaxis.tick_right() [a.set_xlabel('') for a in AX[:-1, :].ravel()] [a.set_ylabel('') for a in AX[:, 1:].ravel()] @@ -2246,7 +2362,7 @@ def dplot_8ch(d, func, sharex=True, sharey=True, return AX -def dplot_1ch(d, func, pgrid=True, ax=None, +def dplot_1ch(d, func, grid=True, ax=None, figsize=(9, 4.5), fignum=None, nosuptitle=False, **kwargs): """Plot wrapper for single-spot measurements. Use `dplot` instead.""" global gui_status @@ -2264,10 +2380,9 @@ def dplot_1ch(d, func, pgrid=True, ax=None, s += (', #bu=%d' % d.num_bursts[0]) if not nosuptitle: ax.set_title(s, fontsize=12) - ax.grid(pgrid) - plt.sca(ax) + ax.grid(grid) gui_status['first_plot_in_figure'] = True - func(d, **kwargs) + func(d, ax=ax, **kwargs) return ax @@ -2471,26 +2586,52 @@ def alex_jointplot(d, i=0, gridsize=50, cmap='Spectral_r', kind='hex', bbox=dict(edgecolor='r', facecolor='none', lw=1.3, alpha=0.5)) return g + def _register_colormaps(): + from sys import version_info + if version_info.minor < 10: + from importlib_metadata import version + else: + from importlib.metadata import version + new = tuple(int(v) for v in version('matplotlib').split('.'))[:2] > (3, 5) import matplotlib as mpl import seaborn as sns - c = sns.color_palette('nipy_spectral', 64)[2:43] - cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c) - cmap.set_under(alpha=0) - mpl.cm.register_cmap(name='alex_lv', cmap=cmap) - - c = sns.color_palette('YlGnBu', 64)[16:] - cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c) - cmap.set_under(alpha=0) - mpl.cm.register_cmap(name='alex_light', cmap=cmap) - mpl.cm.register_cmap(name='YlGnBu_crop', cmap=cmap) - mpl.cm.register_cmap(name='alex_dark', cmap=mpl.cm.GnBu_r) - - # Temporary hack to workaround issue - # https://github.com/mwaskom/seaborn/issues/855 - mpl.cm.alex_light = mpl.cm.get_cmap('alex_light') - mpl.cm.alex_dark = mpl.cm.get_cmap('alex_dark') + if new: + c = sns.color_palette('nipy_spectral', 64)[2:43] + cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c) + cmap.set_under(alpha=0) + mpl.colormaps.register(name='alex_lv', cmap=cmap) + + c = sns.color_palette('YlGnBu', 64)[16:] + cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c) + cmap.set_under(alpha=0) + mpl.colormaps.register(name='alex_light', cmap=cmap) + mpl.colormaps.register(name='YlGnBu_crop', cmap=cmap) + mpl.colormaps.register(name='alex_dark', cmap=mpl.cm.GnBu_r) + + # Temporary hack to workaround issue + # https://github.com/mwaskom/seaborn/issues/855 + mpl.cm.alex_light = mpl.colormaps.get_cmap('alex_light') + mpl.cm.alex_dark = mpl.colormaps.get_cmap('alex_dark') + else: + c = sns.color_palette('nipy_spectral', 64)[2:43] + cmap = mpl.colors.LinearSegmentedColormap.from_list('alex_lv', c) + cmap.set_under(alpha=0) + mpl.cm.register_cmap(name='alex_lv', cmap=cmap) + + c = sns.color_palette('YlGnBu', 64)[16:] + cmap = mpl.colors.LinearSegmentedColormap.from_list('alex', c) + cmap.set_under(alpha=0) + mpl.cm.register_cmap(name='alex_light', cmap=cmap) + mpl.cm.register_cmap(name='YlGnBu_crop', cmap=cmap) + mpl.cm.register_cmap(name='alex_dark', cmap=mpl.cm.GnBu_r) + + # Temporary hack to workaround issue + # https://github.com/mwaskom/seaborn/issues/855 + mpl.cm.alex_light = mpl.cm.get_cmap('alex_light') + mpl.cm.alex_dark = mpl.cm.get_cmap('alex_dark') + # Register colormaps on import if not mocking diff --git a/fretbursts/burstlib.py b/fretbursts/burstlib.py index 4ad0a3bd..095ee052 100644 --- a/fretbursts/burstlib.py +++ b/fretbursts/burstlib.py @@ -789,7 +789,8 @@ def get_ph_mask(self, ich=0, ph_sel=Ph_sel('all')): ph_sel (Ph_sel object): object defining the photon selection. See :mod:`fretbursts.ph_sel` for details. """ - isinstance(ich, numbers.Integral) + if not isinstance(ich, numbers.Integral): + raise TypeError(f"channel must be integer value, got {type(ich)}") if self._is_allph(ph_sel): # Note that slice(None) is equivalent to [:]. @@ -1273,7 +1274,7 @@ def burst_sizes(self, gamma=1., add_naa=False, beta=1., donor_ref=True): donor_ref=donor_ref) bsize_list = [self.burst_sizes_ich(ich, **kwargs) for ich in range(self.nch)] - return np.array(bsize_list) + return bsize_list def iter_bursts_ph(self, ich=0): """Iterate over (start, stop) indexes to slice photons for each burst. diff --git a/fretbursts/burstlib_ext.py b/fretbursts/burstlib_ext.py index bb3c1795..68384380 100644 --- a/fretbursts/burstlib_ext.py +++ b/fretbursts/burstlib_ext.py @@ -434,9 +434,9 @@ def burst_photons(dx, skip_ch=None): else: stream = dx.A_em[ich].view('int8') times_arr = np.hstack( - burstlib.iter_bursts_ph(dx.ph_times_m[ich], dx.mburst[ich])) + list(burstlib.iter_bursts_ph(dx.ph_times_m[ich], dx.mburst[ich]))) stream_arr = np.hstack( - burstlib.iter_bursts_ph(stream, dx.mburst[ich])) + list(burstlib.iter_bursts_ph(stream, dx.mburst[ich]))) burst_id, ph_id = [], [] for i, arr in enumerate(burstlib.iter_bursts_ph(stream, dx.mburst[ich])): @@ -449,7 +449,7 @@ def burst_photons(dx, skip_ch=None): columns = ['timestamp', 'stream'] if dx.lifetime: nanot_arr = np.hstack( - burstlib.iter_bursts_ph(dx.nanotimes[ich], dx.mburst[ich])) + list(burstlib.iter_bursts_ph(dx.nanotimes[ich], dx.mburst[ich]))) bph['nanotime'] = nanot_arr columns = ['timestamp', 'nanotime', 'stream'] burstph = pd.DataFrame(bph, index=[burst_id, ph_id], columns=columns) @@ -554,22 +554,17 @@ def bursts_fitter(dx, burst_data='E', save_fitter=True, return fitter -def _get_bg_distrib_erlang(d, ich=0, m=10, ph_sel=Ph_sel('all'), +def _get_bg_distrib_erlang(d, ich=None, m=10, ph_sel=Ph_sel('all'), period=(0, -1)): """Return a frozen (scipy) erlang distrib. with rate equal to the bg rate. """ + if ich is None: + ich = tuple(range(d.nch)) assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')] # fix negative periods so wrapping occurs coorectly - parr = np.array(period) - for i, p in enumerate(parr): - if p < 0: - parr[i] = len(d.Lim[ich]) - p + 1 - period = tuple(parr) # Compute the BG distribution - bg_ph = d.bg[ph_sel][ich] - - rate_ch_kcps = bg_ph[period[0]:period[1]+1].mean()/1e3 # bg rate in kcps - bg_dist = erlang(a=m, scale=1./rate_ch_kcps) + rate_ch_kcps = np.concatenate([d.bg[ph_sel][i][p[0]:p[1]] for i, p in zip(ich, period)]).mean() / 1e3 + bg_dist = erlang(a=m, scale=1.0/rate_ch_kcps) return bg_dist @@ -611,6 +606,25 @@ def histogram_mdelays(d, ich=0, m=10, ph_sel=Ph_sel('all'), return hist +def _get_mdelay_channel(d, ph_sel, i, period, bursts): + if ph_sel == Ph_sel('all'): + ph = d.ph_times_m[i][period] + if bursts: + phb = ph[d.ph_in_bursts_mask_ich(ich=i)[period]] + elif ph_sel == Ph_sel(Dex='Dem'): + donor_ph_period = ~d.A_em[i][period] + ph = d.ph_times_m[i][period][donor_ph_period] + if bursts: + phb = ph[d.ph_in_bursts_mask(ich=i)[period][donor_ph_period]] + elif ph_sel == Ph_sel(Dex='Aem'): + accept_ph_period = d.A_em[i][period] + ph = d.ph_times_m[i][period][accept_ph_period] + if bursts: + phb = ph[d.ph_in_bursts_mask(ich=i)[period][accept_ph_period]] + if not bursts: + phb = None + return ph, phb + # TODO: add tests beyond simple smoke tests def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02), ph_sel=Ph_sel('all'), bursts=False, bg_fit=True, @@ -620,6 +634,7 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02), Arguments: dx (Data object): contains the burst data to process. ich (int): the channel number. Default 0. + period (tuple): tuple of the range of periods for calculating m (int): number of photons used to compute each delay. period (int or 2-element tuple): index of the period to use. If tuple, the period range between period[0] and period[1] @@ -639,29 +654,24 @@ def calc_mdelays_hist(d, ich=0, m=10, period=(0, -1), bins_s=(0, 10, 0.02), bin_x > bg_mean*bg_F. Returned only if `bg_fit` is True. """ assert ph_sel in [Ph_sel('all'), Ph_sel(Dex='Dem'), Ph_sel(Dex='Aem')] - if np.size(period) == 1: - period = (period, period) - periods = slice(d.Lim[ich][period[0]][0], d.Lim[ich][period[1]][1] + 1) + if ich is None: + ich = tuple(range(d.nch)) + elif np.issubdtype(type(ich), np.integer): + ich = (ich, ) + elif np.size(ich) == 2: + ich = tuple(range(ich[0], ich[1])) + if np.issubdtype(type(ich), np.integer): + period = (ich, ich+1) + if np.issubdtype(type(period), np.integer): + period = (period, period+1) + if np.issubdtype(type(period[0]), np.integer): + period = tuple(period for _ in ich) + periods = tuple(slice(d.Lim[i][p[0]][0], d.Lim[i][p[1]][1] + 1) for i, p in zip(ich, period)) bins = np.arange(*bins_s) - - if ph_sel == Ph_sel('all'): - ph = d.ph_times_m[ich][periods] - if bursts: - phb = ph[d.ph_in_bursts_mask_ich(ich=ich)[periods]] - elif ph_sel == Ph_sel(Dex='Dem'): - donor_ph_period = ~d.A_em[ich][periods] - ph = d.ph_times_m[ich][periods][donor_ph_period] - if bursts: - phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][donor_ph_period]] - elif ph_sel == Ph_sel(Dex='Aem'): - accept_ph_period = d.A_em[ich][periods] - ph = d.ph_times_m[ich][periods][accept_ph_period] - if bursts: - phb = ph[d.ph_in_bursts_mask(ich=ich)[periods][accept_ph_period]] - - ph_mdelays = np.diff(ph[::m])*d.clk_p*1e3 # millisec + ph, phb = zip(*(_get_mdelay_channel(d, ph_sel, i, prds, bursts) for i, prds, in zip(ich, periods))) + ph_mdelays = np.concatenate([np.diff(ph_[::m])*d.clk_p*1e3 for ph_ in ph]) # millisec if bursts: - phb_mdelays = np.diff(phb[::m])*d.clk_p*1e3 # millisec + phb_mdelays = np.concatenate([np.diff(phb_[::m])*d.clk_p*1e3 for phb_ in phb]) # millisec phb_mdelays = phb_mdelays[phb_mdelays < 5] # Compute the PDF through histograming @@ -697,7 +707,7 @@ def err_func(p, x, y): p, flag = leastsq(err_func, x0=[0.9, 3.], args=(_x, _y)) a, rate_kcps = p - results.extend([a, rate_kcps]) + results += [a, rate_kcps] return results diff --git a/fretbursts/fit/weighted_kde.py b/fretbursts/fit/weighted_kde.py index 01f1b0b9..982ad359 100644 --- a/fretbursts/fit/weighted_kde.py +++ b/fretbursts/fit/weighted_kde.py @@ -72,7 +72,7 @@ def evaluate(self, points): (d, self.d) raise ValueError(msg) - result = zeros((m,), dtype=np.float) + result = zeros((m,), dtype=np.float64) if m >= self.n: # there are more points than data, so loop over data diff --git a/fretbursts/loader.py b/fretbursts/loader.py index e1488e97..bfacfc7e 100644 --- a/fretbursts/loader.py +++ b/fretbursts/loader.py @@ -352,33 +352,37 @@ def photon_hdf5(filename, ondisk=False, require_setup=True, validate=False, fix_ return loader_legacy.hdf5(filename) h5file = tables.open_file(filename) - # make sure the file is valid - if validate and version.startswith(u'0.4'): - phc.v04.hdf5.assert_valid_photon_hdf5(h5file, - require_setup=require_setup, + try: + # make sure the file is valid + if validate and version.startswith(u'0.4'): + phc.v04.hdf5.assert_valid_photon_hdf5(h5file, + require_setup=require_setup, + strict_description=False) + elif validate: + phc.hdf5.assert_valid_photon_hdf5(h5file, require_setup=require_setup, strict_description=False) - elif validate: - phc.hdf5.assert_valid_photon_hdf5(h5file, require_setup=require_setup, - strict_description=False) - # Create the data container - h5data = h5file.root - d = Data(fname=filename, data_file=h5data._v_file) - - for grp_name in ['setup', 'sample', 'provenance', 'identity']: - if grp_name in h5data: - d.add(**{grp_name: - phc.hdf5.dict_from_group(h5data._f_get_child(grp_name))}) - - for field_name in ['description', 'acquisition_duration']: - if field_name in h5data: - d.add(**{field_name: h5data._f_get_child(field_name).read()}) - - if _is_multich(h5data): - _photon_hdf5_multich(h5data, d, ondisk=ondisk) - else: - _photon_hdf5_1ch(h5data, d, ondisk=ondisk) - if fix_order: - sort_photon_times(d) + # Create the data container + h5data = h5file.root + d = Data(fname=filename, data_file=h5data._v_file) + + for grp_name in ['setup', 'sample', 'provenance', 'identity']: + if grp_name in h5data: + d.add(**{grp_name: + phc.hdf5.dict_from_group(h5data._f_get_child(grp_name))}) + + for field_name in ['description', 'acquisition_duration']: + if field_name in h5data: + d.add(**{field_name: h5data._f_get_child(field_name).read()}) + + if _is_multich(h5data): + _photon_hdf5_multich(h5data, d, ondisk=ondisk) + else: + _photon_hdf5_1ch(h5data, d, ondisk=ondisk) + if fix_order: + sort_photon_times(d) + finally: + if not ondisk: + h5file.close() return d diff --git a/fretbursts/mfit.py b/fretbursts/mfit.py index 9f330e99..5a6ba13d 100644 --- a/fretbursts/mfit.py +++ b/fretbursts/mfit.py @@ -322,7 +322,7 @@ def _set_hist_data(self, hist_counts, bins): self.hist_binwidth = (bins[1] - bins[0]) self.hist_axis = bins[:-1] + 0.5*self.hist_binwidth self.hist_counts = np.array(hist_counts) - self.hist_pdf = np.array(hist_counts, dtype=np.float) + self.hist_pdf = np.array(hist_counts, dtype=np.float64) self.hist_pdf /= self.hist_counts.sum(axis=1)[:, np.newaxis] self.hist_pdf /= self.hist_binwidth self._hist_computed = True diff --git a/fretbursts/phtools/burstsearch.py b/fretbursts/phtools/burstsearch.py index d1e30a51..8ac3c5cf 100644 --- a/fretbursts/phtools/burstsearch.py +++ b/fretbursts/phtools/burstsearch.py @@ -191,20 +191,15 @@ def mch_count_ph_in_bursts_py(Mburst, Mask): # try: - from burstsearch_c import bsearch_c + from fretbursts.burstsearch_c import bsearch_c, mch_count_ph_in_bursts_c bsearch = bsearch_c + mch_count_ph_in_bursts = mch_count_ph_in_bursts_c print(" - Optimized (cython) burst search loaded.") except ImportError: bsearch = bsearch_py + mch_count_ph_in_bursts = mch_count_ph_in_bursts_py print(" - Fallback to pure python burst search.") -try: - from burstsearch_c import mch_count_ph_in_bursts_c - mch_count_ph_in_bursts = mch_count_ph_in_bursts_c - print(" - Optimized (cython) photon counting loaded.") -except ImportError: - mch_count_ph_in_bursts = mch_count_ph_in_bursts_py - print(" - Fallback to pure python photon counting.") class Burst(namedtuple('Burst', ['istart', 'istop', 'start', 'stop'])): diff --git a/fretbursts/phtools/phrates.py b/fretbursts/phtools/phrates.py index e0e27795..abb943c2 100644 --- a/fretbursts/phtools/phrates.py +++ b/fretbursts/phtools/phrates.py @@ -31,7 +31,7 @@ import numpy as np try: - import phrates_c as cy + import fretbursts.phrates_c as cy except ImportError: has_cython = False else: diff --git a/fretbursts/phtools/setup.py b/fretbursts/phtools/setup.py deleted file mode 100644 index 136488a0..00000000 --- a/fretbursts/phtools/setup.py +++ /dev/null @@ -1,13 +0,0 @@ -from distutils.core import setup -from distutils.extension import Extension -from Cython.Distutils import build_ext -import numpy as NP - -ext_modules = [Extension("burstsearch_c", ["burstsearch_c.pyx"])] - -setup( - name = 'Burst search', - cmdclass = {'build_ext': build_ext}, - include_dirs = [NP.get_include()], - ext_modules = ext_modules -) diff --git a/fretbursts/tests/__init__.py b/fretbursts/tests/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/fretbursts/utils/misc.py b/fretbursts/utils/misc.py index 9fbd51af..3ad001f1 100644 --- a/fretbursts/utils/misc.py +++ b/fretbursts/utils/misc.py @@ -64,7 +64,7 @@ def bincenters(self): @property def pdf(self): if not hasattr(self, '_pdf'): - self._pdf = np.array(self.counts, dtype=np.float) + self._pdf = np.array(self.counts, dtype=np.float64) self._pdf /= (self.counts.sum() * self.binwidth) return self._pdf diff --git a/notebooks/Example - 2CDE Method.ipynb b/notebooks/Example - 2CDE Method.ipynb index ad797ff3..0ef5ed91 100644 --- a/notebooks/Example - 2CDE Method.ipynb +++ b/notebooks/Example - 2CDE Method.ipynb @@ -16,9 +16,36 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " - Optimized (cython) burst search loaded.\n", + "--------------------------------------------------------------\n", + " You are running FRETBursts (version 0.7.post130+g3f024bb.d20240529).\n", + "\n", + " If you use this software please cite the following paper:\n", + "\n", + " FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET\n", + " Ingargiola et al. (2016). http://dx.doi.org/10.1371/journal.pone.0160716 \n", + "\n", + "--------------------------------------------------------------\n" + ] + }, + { + "data": { + "text/plain": [ + "'0.13.2'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from fretbursts import *\n", "from fretbursts.phtools import phrates\n", @@ -28,7 +55,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -48,9 +75,50 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "URL: http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n", + "File: 0023uLRpitc_NTP_20dT_0.5GndCl.hdf5\n", + " \n", + "File already on disk: /home/paul/Python/FRETBursts/notebooks/data/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5 \n", + "Delete it to re-download.\n", + "# Total photons (after ALEX selection): 2,259,522\n", + "# D photons in D+A excitation periods: 721,537\n", + "# A photons in D+A excitation periods: 1,537,985\n", + "# D+A photons in D excitation period: 1,434,842\n", + "# D+A photons in A excitation period: 824,680\n", + "\n", + " - Calculating BG rates ... Channel 0\n", + "[DONE]\n", + " - Performing burst search (verbose=False) ...[DONE]\n", + " - Calculating burst periods ...[DONE]\n", + " - Counting D and A ph and calculating FRET ... \n", + " - Applying background correction.\n", + " [DONE Counting D/A]\n", + "\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 745 + } + }, + "output_type": "display_data" + } + ], "source": [ "url = 'http://files.figshare.com/2182601/0023uLRpitc_NTP_20dT_0.5GndCl.hdf5'\n", "download_file(url, save_dir='./data')\n", @@ -65,12 +133,12 @@ "ds1 = d.select_bursts(select_bursts.size, th1=30)\n", "ds = ds1.select_bursts(select_bursts.naa, th1=30)\n", "\n", - "alex_jointplot(ds)" + "alex_jointplot(ds);" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -79,9 +147,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8000.000000000001" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tau = 100e-6/d.clk_p\n", "tau" @@ -104,9 +183,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 469, + "width": 858 + } + }, + "output_type": "display_data" + } + ], "source": [ "tau = 1\n", "tau2 = 2 * (tau**2)\n", @@ -205,7 +300,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -348,9 +443,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4000" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "tau_s = 50e-6 # in seconds\n", "tau = int(tau_s/d.clk_p) # in raw timestamp units\n", @@ -367,7 +473,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -387,16 +493,27 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_65165/287442945.py:59: RuntimeWarning: invalid value encountered in divide\n", + " ED = np.mean(kde_adi / (kde_adi + nbkde_ddi)) # (E)_D\n", + "/tmp/ipykernel_65165/287442945.py:60: RuntimeWarning: invalid value encountered in divide\n", + " EA = np.mean(kde_dai / (kde_dai + nbkde_aai)) # (1 - E)_A\n" + ] + } + ], "source": [ "fret_2cde = calc_fret_2cde(tau, ph, mask_d, mask_a, bursts)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -405,9 +522,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1488, 1488, 1488, array([1488]))" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "len(fret_2cde), len(fret_2cde_gauss), bursts.num_bursts, ds.num_bursts" ] @@ -421,9 +549,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zzzwTy5cvT7hdly5dcM4552Dw4MHo1asXOnfujObNmyMtLQ379+/H3r178csvvyA3NxdffvklvvjiC5SUlMTcV0lJidn9JxVHHXUUjj766Li3V1RUoLy8HEVFRdi+fTu2bNkSty2Rdu3ahUmTJuGVV17B66+/jvbt26fcxnh69OiBk046Sfh+O3fuLHyfROS9FStWoKysDED9rpQA8M033wAABg4cmLS7mRvGjBmDp556CgCwfft2FBUVsUulhtavX48DBw6Y1y+55JKE21966aX4v//7PwBAdXU1fv31VxxzzDGO2nDllVfipZdeMtsTnkwlluuvvx6GYQAA7r//fhx//PGOju07wuIgkQM6VuIGDhxozJw50zhw4ICt/VRWVhq33npr3OpLhw4djP3796fczp07dxq9e/dOWOE5++yzjYULF9re9/79+43XXnvNGDhwYMz9rlixIuk+4lXipkyZYqstNTU1xsaNG4033njD+NOf/mS0bds2aWWrbdu2xqpVq2w/7rB4lbjx48envE8iCp7HH3/cfP/44Ycfom7bvn27edsdd9wh/NipVOJ+/vnnqPts3bo15nbjx483t5k+fXrS/Ua+p8Z7H421TVVVlfHaa68ZI0eONLp162ZWNd99992o+1ZUVBgvv/yycf755xvdunUzmjVrZqSlpRnt27c3jjzySOOss84yHnnkESM3NzfuMa38xPLdd98Z1157rdG/f38jOzvbaNSokdGyZUvjsMMOM0488UTjxhtvND799FPb5xBOfPPNN1HtjtUtMVJZWVnU9t99953jNqxduzZqn9u2bYu53eLFi6N61CRrq8pYiSNSxKmnnoq7774bp556akr3b9SoER588EG0bNkSd9xxR73bt27dimeeeQY33XST7X2XlZVh1KhRWLt2bczbO3bsiOeffx5nnHGG7X0DQNOmTTFmzBiMGTMGn332Gf76179izZo1Ke3LqVAohK5du6Jr16646KKL8OSTT+LDDz/E3//+d3z//fcx7/Pbb79h+PDhmD9/Pvr27etyi4koaD777DN89tln9X7/xRdfAKh9H3vxxRejJmHasmWLeXnlypVmJSTs9NNPx+mnny6pxbFt377dvNyoUSO0atXK1eNH2rZtG8aMGYOvvvoq4XY///wzRo0aFfMzavv27di+fTtyc3Px8ccf4//+7/+wfv16HHrooY7bV1VVhWuvvRb//e9/6922d+9e7N27F+vXr8c333yDxx57DHfeeSfuu+8+x8e1om7vjh9//BH9+/ePu/0PP/xgXm7cuDH69OnjuA2Rf4+mTZuidevWMbd75ZVXzMsXXXQR0tLSHB/bbxjiiCxq3bo1Pv3005QDUF233347Fi1ahA8++KDebS+88EJKIe7mm2/GokWLYt52/PHH47333kPbtm1t7zeW008/HatXr8a9996Lv//976iurhay31Q1bNgQ5513Hs477zxMnz4dN954I4qKiuptt3v3bpx33nlYvnw5uwMRkVSLFi3CP//5z7i3G4aBRx99NO7tn3zyCT755JOo32VmZroe4l5//XXz8vHHH4/09HRXjx9WXl6OkSNHYtmyZWjUqBFOOOEEHHrooSgrK4saPlBcXIwRI0YgLy8PQO3sxf3790efPn2QmZmJAwcOYOvWrVi1ahV2795d7zh9+vTBtddei+LiYrPrX/PmzTFu3Likbfy///u/qADXoUMHDBw4EIcccggMw8CePXvw008/Yd26dUn3FRnup0+fjgkTJiS9TyIdO3ZE//79sWLFCgDAXXfdhffffz/mOnBVVVVRXzSPGzcubrdHq/bt24cpU6aY10eOHBm3u3C4SzEAnHzyyQCAr7/+Gs8++yy+/vprbN++HZmZmejatStGjBiBP//5z+jSpYuj9mlHWE2PyAEdulPK8NNPP8XtnrF582Zb+/riiy+iZhCL/BkwYIDUddQWLFhgtGrVytXulMmsWbMm4Vp4Y8aMsb1PdqckIjtkTOTk9L3S7uyUDz30UNRny3vvvRd3e9ndKcPrsg4ZMiRmu8vKygzDMIzHHnvMvM/hhx9urF27Nua+a2pqjO+++87485//bGzZsqXe7XYnNtm1a5fZxoYNGxozZsyIO1vjtm3bjH/961/G888/H3d/kX8nK8+nFfPnzzcaN25s7vfoo4823nrrLWPjxo1GaWmp8euvvxqzZ882jjzySHObE088MeVziLKyMmP9+vXGM888Y3Tv3t3cZ5s2bYxNmzbFvE9paWnUGrzLly83/vSnPyX8v0hLSzP+8Y9/OHlqpGF3SiIf6tOnD4444gj8+OOP9W5bvny55YktampqcMMNN5gDgCPl5OTg3XfflVp1GjJkCJYsWYImTZpIO4ZdvXv3xhdffIETTjgBv/32W73bX3/9dfzpT3/CkCFDPGgdEQXB1KlTMXXq1KjfPf3007jmmmsA1K4VFzlZw969e9GqVSsYhoGbb74ZjzzyiPQ2Tpkypd46cZWVldi+fTsWLVpkrhPXuHFjPPjggxg5cqT0NsVTVVWFI488Ep988knMz5twhfDrr782f/fEE0+gV69eMfcXCoVw3HHH4bjjjhPSviVLlqCqqgpA7WQw48ePj7tt+/bt8Ze//EXIce0YOnQoPv30U/zhD3/A3r17sXLlSvzhD3+IuW27du0wceJE3HPPPZa7M+bn56NTp04JtznuuOPw1ltvxT3H2b59u/k8AsB9992Hd955BwCQlZWFU089Fa1bt8bWrVsxf/58lJaWoqKiArfffjv27duHv//975baqjuGOCKPDRw4MGaIs7Mo9yuvvBJzHwDw6KOPujLLoYixBKJ1794dL7/8Ms4444yYAXfy5MlRXTaIiGRbsGABgNpukXVn+vvqq6/M9yq3vmAKdxdM5KSTTsJzzz2H3r17u9CixB588MGkXxhGzpQcb8yVDF4d165hw4Zh06ZNePTRR/H3v/89KjCFNWzYEOeeey4uueQSYePRMjIycP/99+PGG29MuF1hYWHU9XCAmzBhAv71r39Ffemwc+dOTJgwwex2/MADD2DEiBEpz1ugkwZeN4Ao6OKNUSsuLra8j8cffzzm74855hhLffj97LTTTsMf//jHmLctWrQIS5cudblFRBRkX375JYDaYFR3PFD4tgYNGkhZtiRVX3/9NU4++eSYk3W4qWXLlpbGpUd+cRleGsENkcd9++23sWPHDkf7M2rXc4ZhGI7Hw0X69ddfMX78ePztb39DVVUVunbtitGjR2PSpEm48MIL0a5dO1RXV+O5555Dv3798Le//c3yvjMzM3HttdeaP+PGjcPJJ5+MjIwMlJWV4aabbkL//v3jTkAGAPv376/3u7PPPhsvvvhivapxmzZtMGfOHBx11FEAap+zadOmWW6vzhjiiDxWWVkZ8/dWv/launSpOUi5rttuuy1qYHRQTZkyJebAbaB2EhkiIjesWbPG7N49dOjQereHQ9yRRx6Jli1butKmjRs3RoUFwzBQXV2N3bt3Y968eRg/fjxCoRB2796Nq6++GpMnT3alXbEcffTRaNAg+anr6NGjzcv//e9/MWDAADzxxBNxZ24W5fjjjzcn18jLy8MRRxyBm266yezyp4IlS5agf//+mDNnDrKysvDmm2/i119/xeuvv45nn30Wb731FvLy8vDkk08iPT0dNTU1mDJlCh566CFL+8/OzsaTTz5p/sycORMLFy7E1q1bceuttyIUCmHlypU4+eST8fnnn8fcR0ZGRr3fPfTQQ3HPZ9LS0qK6UH755ZeOA7QOGOKIPLZ+/fqYv7c6i+Tbb78d8/c5OTkYNWpUqs3yle7du8edzS3W7KBERDKEu1IC9UNccXExVq5cCcC9rpTxNGjQAK1atcKpp56KGTNm4MUXXzRve+CBB6Ieh5usdlE87bTTorrsrVixAn/961/Rp08ftG7dGqNGjcLjjz9uzl4pSuPGjfHKK6+gRYsWAICCggI89thjGDZsGLKzszF48GDcdtttWLhwYcwu/rLt3bsXF1xwAfbt24dQKIQ5c+bgD3/4Q71w1KhRI1x77bV4+umnzd/dfffdyM/PT/nYOTk5ePDBB83ZWsvLy3HppZdGdUENqzsLZs+ePXH44Ycn3P8ZZ5wR1c023kzdfsIQR+ShqqqqqAHYkayux/LRRx/F/P15552Hxo0bp9w2vxk7dmzM32/dujVqLRwiIqe+/fZbXHfddfV+Irv2TZ8+Peq2K664wlyqJTc3t959P/74Y68eDiZMmIATTjjBvO7GhCux2Jk869FHH8X7778f1W6gdpmZ9957DzfeeCO6dOmCCy+80NYY9GROOukkrFq1CldccQWaNWtm/r6iogJLlizBQw89hCFDhqB379549913hR3Xiv/+97/mmn+nn3560i8LJkyYYE4KU1FRgVdffdVxG2644QYcdthhAIBdu3bFHJNZdx3CZAEOqA2ePXv2NK9v3brVYUvVx4lNiDz04YcfoqCgoN7vMzMzMWDAgKT3LyoqihtAhg8f7rh9fnLaaafFve3777/n4t9EJMyaNWvwn//8J+E2zz77bNzb5s+fj/nz50f97pBDDsFZZ50lpH2pOOOMM8zqxoIFC1BdXR23m7pVNTU1IpoW17nnnotzzz0XW7duxYIFC/DVV1/hq6++wk8//QSgdvzUO++8gy+//BKLFi2KCgFOdO3aFS+88AKefPJJLFq0CAsXLsRXX32FJUuWmN0qf/75Z1xwwQX45z//mdK6sKn49NNPzctWJv4IhUIYOnSouaadiDHkDRo0wPDhw81eSN988w2uu+66qG1at26NVq1amTOjWl2fLnI7O/MK6IqVOCIPPfjggzF/P2rUqLgLYEZavnx53C4ZgwcPdtQ2v2nbtm3chUAjF4klIqL62rdvb17ev3+/eYIdKbL3R6wZD+sqKioS07gkOnTogEsvvRTPPPMMfvzxR+Tl5eHee+81K2V79uyREqSaNGmC4cOHY9q0aZg3bx727NmDt99+25yEAwDuuOMO16pGkcepW+2KJ3I7UX+vyPGesV5HAHDEEUeYl60GssjtZC6rpAqGOCKPvPrqq1iyZEnM2+p+KxVPvGUFmjZtim7duqXcNr+K/OCM9Ouvv7rcEiLyswkTJtSbLOSVV14xbw8vJRD+OXDggDmZ1dVXX13vvoZh1Ftvzm0HDhyIuh5rgpHImQPjnZxHys3Ndd6wFHTs2BF33XUXnnvuOfN3n332GcrLy6O2Ez0xWJMmTXDBBRdg/vz5aNeuHYDabor/+9//hB4n0fHDYvUCiiXy75idnS2kHeEunUDtWLlYInsThSuniVRVVUXNMZBsrTo/YIgj8sCOHTtwww03xLzt7LPPjlr8NZF4g7K7du3KWSljiFeJczq4febMmQiFQkJ/unbt6qhNRKSWhQsXAqhdkLru4tLffvstKioqAAAnn3yy622zIrLHQkZGRsxKTuSXh+FJWuIpKyvzfGKpc845x7xcWVlZL9hEzpIYbybpVLRs2TJqrF54xlLZIpdAmDdvXtLtDcOI6tYrYj3YiooKfPbZZ+b1eOP/L7jgAvPy+vXr435pHfbpp5+aXVUbNGig7P+RSAxxRC6rrq7G2LFjY35LmZGRgccee8zyvuJ1wQh/w0fRIrsDRdq5c6fLLSGioAmHuIEDByI9PT3qtq+++sq8fMopp7jaLivy8vLw1ltvmddPPfXUmF8UDhw40Lz84YcfYvfu3XH3ec899yS83Qmr+92yZYt5uUGDBvWqQtnZ2WbFcefOnUmDnJXqY6xju7Uw+IgRI8zLn332mfmajGf69On4+eefzeux1ugrKioyJ+Sx4u6778a2bdvM65FhLVLfvn0xbNgw8/ptt90Wd/hIRUUF7rrrLvP6yJEjXVuiw0sMcUQumzx5cr0B62H333+/OWuTFfH6iQfhzSsVdRcJDavbTYiISKTdu3eba5TFqhCEQ1zXrl2V6wa2bNkynHHGGVHvk/HGjw0cOBA9evQAAJSUlOCSSy7B3r17o7Y5cOAAbr31Vjz88MP1wqwogwcPxiWXXIKPP/7YrHDWtWbNGowbN868Pnz48HrtSU9PNyc7qaqqSjqb5L///W8cffTReOqpp6K6DEYqLi7GbbfdZk4S0rBhw7gLmEf2zpgxY0bCY1sxYcIEM6gahoFRo0ZFhfOwqqoq/Oc//8E111xj/m7gwIExZ7OcP38+jjjiCDz99NPYtWtX3GP/+uuv+OMf/xi13txll12GI488Mu59HnroIXPynI8++ggTJ05ESUlJ1DY7d+7EqFGjsGrVKgC1a8Z53fXYLZydkshFM2fOjLtg5plnnom//vWvtvYXb/HQWAtlUvzpqVVZhJWI/Cmy4lE3xFVXV2Px4sUxb3PDlClT6n3BVVNTg6KiIqxatapeN7a//vWvURWdSKFQCA888AAuvvhiAMAXX3yBbt26Yfjw4TjkkEOwY8cOLFy4EIWFhfjd736Ha6+9Fnfeeafwx1RZWYnXX38dr7/+Opo0aYJ+/fqhe/fuaNGiBfbu3YsNGzZg2bJl5vZNmjSJu2zChRdeaC4kfdlll2HmzJk49NBDoyZxibzvqlWrcO211+K6665Djx490LdvXxxyyCGorKzEtm3bsGjRIuzfv9/c/vbbb3ctuGdlZeHFF1/EhRdeiOrqauzduxcXXXQRunbtikGDBiErKwu7d+/GN998E7VYdk5OTsylAMLWrVuHa665Btdddx0OPfRQHH744cjJyUHjxo2xd+9e/Pjjj/VeRyeeeGLUkhuxHHPMMfj3v/9thsnp06fj3XffxamnnorWrVtj69atmD9/vvkFQygUwpNPPhl3/LvfMMQRueSzzz7DVVddFfO2rl274tVXX7U9ji1eF4ZYA84p/nTWTscP9ujRAyeddJKjfdR1yCGHCN0fEXknHOIaNGhQb92yFStWmNUFL7pSJjo5j9S0aVPce++9UYtox3LRRRdh2rRpmDJlCoDa7nbvvPNO1Da9evXC22+/je+//z61RicRGUpLS0vx7bff4ttvv425bbdu3fDKK6+gX79+MW+/9dZb8e677+Knn35CZWVlzPX6wiEu8riGYeCXX37BL7/8EnO/aWlpuPPOO3HPPfdYflwinHfeefjggw8wceJEs1q4adMmbNq0Keb2Rx99NF599VVzvbi6IquXNTU1+Pnnn6O6YNaVlpaGm2++GXfffbeldf/+/Oc/Iy0tDTfeeCOKi4tRWFgYsyKanZ2NZ555BqNHj066T79giCNyweLFi3HBBRfE7E9/yCGH4JNPPok7Q1Mi8d4A686wRbXKyspi/t7OArKxnHTSSUK6uhCRP4W7Sx511FFo0aJFzNsAtcbDZWZm4pBDDkG/fv0wbNgwXHbZZZanpb/nnntw2mmn4d///je++uor7Ny5Ey1atMChhx6KMWPGYOLEicjMzJQW4lauXIklS5Zg/vz5+O6777Bu3Tps27YNBw4cQNOmTdGuXTscffTRGDlyJC6++OKE3TpbtGiB7777Dk8//TQ++OADrFmzBoWFhTE/z2+++WZceOGF+Pzzz7Fo0SLk5uZi06ZN2LdvHxo0aIDs7Gz06dMHw4YNw7hx4+JOtiXb73//e/z666+YPXs2PvroI6xYsQK//fYbDhw4gObNm+N3v/sdBg4ciAsvvBBnnXVWwi+Gf//73yMvLw+fffYZlixZgtzcXGzcuBGFhYWorq5G8+bN0aZNGxx11FEYMmQIRo8ebfl1FDZx4kSceeaZmDFjBt5//31s2rQJe/fuRcuWLdG7d2+cddZZmDRpUuCGkoSMeKMEiVyUn59vdifIy8tDx44dPW6ROKtWrcLQoUNRWFhY77YWLVpg7ty5OPbYY1Pa90UXXRSzP/vZZ5+NDz/8MKV9yjZ16lRMmzat3u+nTJkivR/73//+96jBz2Ht2rWLO34h0owZM3D55ZfX+/348eMZ4oiIiKgeWee47HNFJNGaNWtw2mmnxQxwTZs2xYcffphygANqF7COxa3pinUTL6jFex6JiIiIVMQQRyTJhg0bMGLEiJizNaWnp2POnDmOB7HH+zYnPz/f0X79Kl6ff9VmgyMiIiJKhCGOSILNmzdj2LBhUWuhhDVu3BhvvvkmTjvtNMfHibfw5o4dO+pN60zA6tWrY/6+e/fuLreEiIiIKHUMcUSCbd26FcOGDYtayDOsYcOGePXVV3HuuecKOdaAAQPi3rZixQohx/CLbdu2IS8vL+ZtxxxzjMutISIiIkodQxyRQDt27MDw4cPx66+/1rutQYMGmD59Oi666CJhx+vevXvcWZ7iLSgeVJ999lnc24477jgXW0JERETkDJcYIBJk165dGDFiBNatW1fvtlAohGeffRZ//OMfhR/3jDPOwKxZs+r9/oMPPsC9994r/Hi6ivUcAUCXLl3Qp08fl1tDRFRr7OjY7011zZo9VnJLiEgnDHFEAhQUFGDEiBH48ccfY97+73//G1deeaWUY48cOTJmQFm1ahV++OEH9O3bV8pxdfLLL79g7ty5MW8bOXKky60hoqCyGtis3JehjijYGOKIHCoqKsLpp58ed9KMRx55BNdee62045933nnIyclBQUFBvdsee+wxvPDCC9KOrYspU6agpqYm5m0TJ050uTVEFCROgpvV/TLQEQUPx8QROVBcXIwzzzwTy5Yti3n73//+d9x8881S25CRkRFzAWoAePnll7F27Vqpx1fdp59+Grcr5ZAhQ3DUUUe53CIiCoKxo2dJC3BeHouI1MBKHFGKDhw4gLPPPhtLliyJefuUKVMwefJkV9pyyy234JlnnsH+/fujfl9ZWYk//elPmDdvHho0kPudTU1NDcrLy9GkSROpx7Hjl19+wbhx42LeFgqFcP/997vcIiLyOy/DFKtzRMHBShxRCsrKyjBy5Eh89dVXMW+//fbbMXXqVNfa07ZtW9xyyy0xb/vyyy9x1113ST1+aWkpLrroopiTunhlzZo1cRdbB4Bx48bhhBNOcLlVRORXqlXDVGoLEYnHEEdkU0VFBS688MK4E2XceOONeOCBB1xuFXDHHXegX79+MW974IEH8Mgjj0g57po1azB48GC88847UvafihdffBGDBg3C5s2bY97es2dP/Pvf/3a5VUTkV6oGJtWCJRGJwxBHZENVVRXGjBmDjz/+OObt1113HR599FGXW1UrLS0Nr776Kpo3bx7z9ltuuQV//vOfUVZWJuR4FRUVePDBB3Hsscdi1apVQvbpRHV1Nd577z0MHDgQEydOxL59+2Ju16ZNG7z33ntxnyciIjt0CEkMc0T+wxBHZFFNTQ3GjRuHd999N+btkyZNwr/+9S+XWxWtb9++mD17Nho2bBjz9meeeQZ9+/bFO++8A8MwUjpGeXk5nn/+eRx++OG4/fbbceDAASdNdmTLli1466238Kc//QkdOnTAqFGj8P3338fdvkOHDpg/fz569+7tYiuJyI90DEa6tZeI4uPEJkQWPfnkk3jttddi3paRkYGysrK4s0Smonfv3rj99ttt3+/3v/893nzzTYwZMwYVFRX1bt+wYQMuvPBCHHbYYbj00ktxzjnn4KijjkKjRvHfDgoLC/H111/jgw8+wFtvvRVzOQOn5syZg02bNsW9vaqqCuXl5SgsLMSOHTuwefNmFBcXW97/8OHDMWvWLLRp00ZAa6N9/fXXmDBhgvD9AsD111+PAQMGSNk3EaVG5zA0dvQsTnpC5AMMcUQWJQouZWVleOmll4Qeb8iQISmFOAA4//zz8emnn2LMmDHYuXNnzG3Wr1+PqVOnYurUqWjSpAkOPfRQdO7cGZmZmUhLS8OBAwdQUFCADRs2IC8vz1LlLhQKpdReoHZxchndMtu2bYsHHngAEyZMcNS+RDZs2IANGzZI2feoUaMY4ogUonOACws/BoY5In0xxBH51KmnnoqVK1di4sSJ+OSTTxJuW1paitzcXOTm5qZ0rG7duuHee++NO7GKFw477DBcf/31uPzyy9GsWTOvm0NEPuCHABeJVTkifXFMHJGPtW/fHh9//DHee+89HHnkkcL337FjRzz++ONYu3YtLr30UmmVLisaNGiAo446Crfeeiu+/fZb/Pzzz7juuusY4IhICL8FuDC/Pi4iv2MljigARo4ciZEjR+KLL77Aiy++iA8//NDWeLJImZmZuOCCC/DHP/4Rw4YNk76IOAA0bNgQ6enpSEtLQ1ZWFlq3bo22bduiS5cu6NWrFw4//HAcd9xxyMrKkt4WIgoevwcdVuSI9BMyUp2ijkig/Px8dOrUCQCQl5eHjh07etwifysvL8eSJUuwZMkSrFy5Eps2bUJeXh727duH0tJSNGzYEC1btkR2djZycnLQq1cvHHfccTjuuOPQr18/pKWlef0QiIhc4fcAF4lBjkg8Wee4rMQRBVB6ejqGDBmCIUOGeN0UIiJlBSnAAZzwhEgnHBNHREREVEfQAlykID92Il0wxBERERFFYIjhc0CkOoY4IiIiov+P4eUgPhdE6mKIIyIiIgJDSyx8TojUxBBHREREgcewEh+fGyL1MMQRERFRoDGkEJFuGOKIiIgosBjgrOHzRKQWhjgiIiIKJAYTe/h8EamDIY6IiIgCh4EkNXzeiNTAEEdEREREljHIEXmPIY6IiIgChSHEOT6HRN5iiCMiIqLAYPgQh88lkXcY4oiIiCgQGDrE43NK5A2GOCIiIvI9hg0i8hOGOCIiIvI1Bji5+PwSuY8hjoiIiHyLAcMdfJ6J3MUQR0RERL7EYOEuPt9E7mGIIyIiIt9hoPAGn3cidzDEERERERERaYQhjoiIiHyF1SBv8fknko8hjoiIiHyDAUIN/DsQycUQ5xM33ngjQqGQ+dO1a1db9587dy7GjRuHnj17olmzZsjJyUG/fv1wyy23YO3atXIaTUREJMjY0bMYHBTDvweRPCHDMAyvG0HOfPfddxg8eDBqamrM33Xp0gWbNm1Ket99+/Zh0qRJmD17dtxtGjdujGnTpuGOO+4Q0dyY8vPz0alTJwBAXl4eOnbsKO1YRER+FOuEedbssR60xH0MC2oLyuuQKBZZ57iNhOyFPFNZWYkrr7wyKsDZue8FF1yAuXPnmr/r27cvjjnmGJSWlmLhwoXYsWMHKisrMXnyZFRWVuKee+4R2XwiIrLJTmBJtK1fTqwZ4IgoiBjiNPfggw8iNzcXADB27FjMmmX9w+zee+81A1xGRgamT5+OMWPGmLdXVFTgrrvuwsMPPwwAmDJlCoYMGYIhQ4YIfARERJSMjKBSd586hjoGOD2MHT1Ly9cXkcrYnVJja9euxdFHH43y8nJceumlGDFiBC6//HIAybtT7ty5E927d8f+/fsBAM888wyuvvrqmNuOGTPG7G45ePBgLFq0SOwDAbtTEhHF4lVI0eGEmwFOPzq8rohEY3dKimIYBq688kqUl5ejZcuWePTRR/Hxxx9bvv/MmTPNANezZ09MmjQp7rYPPfQQ3nzzTdTU1GDx4sVYsWIF+vfv7/gxEBFRfSqEk8g2qHbircLzQ6lhRY5IHM5Oqamnn34a33zzDQDg4YcfRps2bWzdf86cOeblCRMmIBQKxd22c+fOGD58uHn93XfftddYIiKyRMWAEp710eu2qdAGco5/QyIxWInTUH5+Pm6//XYAwMknn4wrrrjC1v3LysqwZMkS8/rQoUOT3mfo0KH4/PPPAQDz5s3D3/72N1vHJCKi+HQ5sfWiQqfLc0NE5CaGOA39+c9/RnFxMdLS0vDss88mrKLFsm7dOnM2y1AoZKlr5IABA8zLa9assddgIiKKSeeAIjvQ6fzcUGLsVknkHEOcZl5//XV8+OGHAIDbbrsNffr0sb2PdevWmZfbtGmDjIyMpPfp3LmzebmgoAC7du1C69atLR8zPz8/4e3bt2+3vC8iIj/wU0gRsUadn54PSo5BjsgZhjiN7NmzBzfccAMA4LDDDsOdd96Z8n7C2rZta+k+7dq1i7peUFBgK8SFZ+UhIgq6oISVoDxOSh2DHFHqOLGJRm688Ubs3LkTAPDss88iPT09pf2UlJSYl5s0aWLpPnW3i9wHERFZw2BDREQisBKnic8++wwvv/wyAGD8+PE49dRTU95XWVmZeTktLc3SfeoGxtLSUlvHzMvLS3j79u3bMXDgQFv7JCLSCQMcUX2sxhGlhiFOA/v37zcX4m7VqhUeeeQRR/uLHANXUVFh6T7l5eVR161W8MK4eDcRBRXDG1FiDHJE9rE7pQbuvPNObNq0CQDwz3/+E4cccoij/WVmZpqXrVbU6m4XuQ8iIoqNAY7IGv6vENnDEKe45cuX49///jcA4NRTT8X48eMd77NVq1bm5d9++83SfXbs2BF1PScnx3E7iIj8igtTE9nH/xki69idUnGrV68213TbsmULBg0aFHfbXbt2mZe3b98ete3dd9+Ns88+GwDQq1cv8/c7d+5EWVlZ0mUGtmzZYl7OycmxNTMlEVGQ8ESUiIhkY4jTyIYNG7BhwwZL21ZUVODbb781r0cGvF69eqFBgwaoqamBYRhYuXJlwnAI1FYEw1JZm46IKAgY4Iic4fg4ImvYnTKAMjIyokLbggULkt7nyy+/NC8PGzZMRrOIiLTF7pNE4vB/iSi5kGEYhteNIDFmzJiByy+/HADQpUsXczKUWB5++GHceuutAGorc2vXro27bX5+Prp27Yrq6moAwLJlyzBgwABxDf//xwgvCJ6Xl8fZLIkSsHKCw2+y3cMTTiI5+D5GfiDrHJeVuIAaP348mjVrBgBYt24dnn/++bjb3nrrrWaAGzx4sPAAR0TWWQ0M4coQA4ZcfH6J5OH/F1F8DHEB1aZNG9x0003m9euvvx5vvPFG1DYVFRW4/fbb8dprr5m/e+CBB1xrIxFFS/WEhidC4jEgE7mD/2dEsTHEBdjdd99tjm8rLS3F6NGj0a9fP0yYMAFjxoxB165d8eCDD5rbT5s2DUOGDPGquUSB5vREhqFDHD6PRO7i/xxRfZydMsAaN26Md955B5MmTTKrcLm5ucjNza233dSpUzF58mQvmkkUeCJPYDjzW+p4IklERKpgJS7gsrKyMHv2bHz++ee47LLL0KNHDzRt2hRZWVno27cvbr75ZqxatYoBjshHWJWzj88Xkbf4P0gUjbNTkhI4OyVRbLJPXFiVS4wnjkRq4XsW6YazUxIRBYwbAYJVufj4vBCph/+XRLUY4oiIiCdGERhsidTG/08ihjgiImW53W0o6OEl6I+fSCf8X6WgY4gjIqIoQTs5Yngj0hP/bynIGOKIiKieIASbIDxGIr/j/zAFFdeJIyKiuMInSH6ZEY4nfET+w/UvKYgY4oiIKCndwxzDGxER+QlDHBERWRYZhlQPdAxuRMHBahwFDUMcERGlpG5I8voEiqGNKNgY5ChIQoZhGF43gkjWavZEfqB7OBF9UqX780FEcjHIkUpkneOyEkdERFIxdBGRm1iRoyDgEgNERIrjyQgRkT388oj8jiGOiIiIiHyHQY78jCGOiEgDrMYREdnHIEd+xRBHRERERL7FIEd+xBBHRKQJVuOIiFLDIEd+wxBHRERERESkEYY4IiKNsBpHRJQaVuPITxjiiIg0wyBHRJQaBjnyC4Y4IiIiIgoMBjnyA4Y4IiIiIgoUBjnSHUMcEZGG2KWSiMgZBjnSGUMcERERERGRRhjiiIg0xWocEZEzrMaRrhjiiIiIiCiwGORIRwxxRERERBRoDHKkG4Y4IiKNsUslERFR8DDEEREREVHgsRpHOmnkdQOIiIiISL6SrAxb22cWlUlqibrGjp7FHg6kBYY4IiIihdg90Q4L4gk3WZPqa6ru/fgaI1IHQxwREZEHUj2xtrM/nnSTyNdZ5L78/NpiNY50wDFxRERELijJyoj6cfuYFDwy/+5+f21xfBypjpU4IiIiSVQ6wQ1KFYVqufnaCx+Lrysi97ASR0SkOXb7UYcX1bZUqN4+0pPfXlesxpHKWIkjItIcTzS8pfNJKyso/uT1a5KvKyL5WIkjIiKySYdqmx1+ezykBj+8pvglGamKlTgiIiILdD8ZtYpVFBKNryki8ViJIyIiikGX8W2yBPVx607lv5murylW40hFrMQRERH9fzqeYMrGKgqJVpKVwdcTkUOsxBERUaAFudpmB58nEkm31xGrcaQaVuKIiChQdDt5VBGrc2rS7bXN1xFR6liJIyIi32MVSQ4+pySCLq8hVuNIJazEERGR7+hyUugXkc83qyqUCo6TI7KHIY6IiHyBwU0N7CJHqWKQI7KO3SmJiEhb7CapLv5d3OWX51r1x8EulaQKVuKIiDQWxBMK1U/yKBorc2QXK3JEyTHEERGR8hjc9Mdxc2QHgxxRYuxOSURESmJXSf/i35WsUPU1EsQeEKQehjgiIlIKT/CDg39rSoavD6LYGOKIiDTlt2+DeUIfXPy7UyJ8fRDVxxBHRESeYngjgK8DJ4LwvAXhMRLZwRBHRESe4Ek7xcLXBenAbz0hSD8McURE5CqepJMVfI1QXXxNEB3EJQaIiDSk47fAPAEju7jGHBFRbKzEERGRdAxw5ARfPxTG1wJRLYY4IiKShl0nSRS+jmIL4vMSxMdMVBdDHBGRZnTpSskTLRKNrylSiS7vxeRPDHFERCQUq28kE19bBPB1QMQQR0REwvDEitzA1xkRBR1DHBGRRlTuvsMTa3ITX29EFGQMcURE5BhPqMkLQX/d8fEH+/FTsDHEERFpQsUqHMe/kdf4+iOiIGKIIyKilPDkmVTB1yIRBQ1DHBER2caTZlINX5PBxL87BVUjrxtARETJqdSVMqgnTSXZ6XFvyywsd7ElFE9JVgYyi8q8bgYRkXQMcUREZJnfAlyiYCZ6Pwx67ghKkPPb/yIR2cMQR0SkOFWqcLqfNIoKbCKPz2AnR1CCHBEFF0McERElpWOA8zq0WVG3jQx14jDIEZGfMcQRESlMhSqcLgFOh9CWTORjYKAjsoaBnYKIIY6IiLTlh+AWT/ixMcylzq8n97p8sUJE8jDEEREpilW42Pwc3GJhdc4ZvwY5Igo2hjgiIopJtQAXtPAWCwNdahjkiMhvGOKIiBTkdRVOlQDH4BYfu1sGkyr/m0TkLYY4IiKKosJJIsObdazOWcNqHBH5CUMcEZFivKzCeR3gGN6cYaBLjEGOiPyigdcNICIiNTDA+UtJdrr5Qwd5/Tp3Que2E5FYrMQRESnE67FwXmDIkI8VOiIif2GIIyIiT77hZ3jzBgMdu1WSGLNmj/W6CRRgDHFERIrwqgrHABdcdf8OQQp1ugU5dqUkokgMcURECghKgGN4U1vQQp1uQY6IKIwhjogooBjgKJmghTpVsQpHRHUxxBEReczvk5kwvPmHH0Mdq3FEpCMuMUBE5CG/d6NkgPM3vyxjoHKlS+W2EZF3GOKIiAKGAY5k0D3QMSwRkU4Y4oiIPOJFFY4Bjtyga6BTLcip1h46iMsLkNcY4oiIPODXAKfjiTvJpdtrgsGJiHTAiU2IiEgInU7UyX1cZNwehkkiSoSVOCIil/mxCscAR3aoXp1jgNILZxelIGKIIyJyEQMc0UEqv3a8DHIMkUSUDLtTaqKgoABLly7F999/j6VLl2Lz5s3YvXs3du3ahVAohJYtW6Jv374YOnQoxo0bhw4dOtja/9y5czFz5kwsWbIEW7duRXp6Ojp27IgzzjgDEydORO/evSU9MiKSiQGOVBd+DanYxdKLNeQY4NTHSU1IBSHDMAyvG0HJnXPOOfjoo48sbZuWloY77rgD99xzDxo0SFxs3bdvHyZNmoTZs2fH3aZx48aYNm0a7rjjDltttiM/Px+dOnUCAOTl5aFjx47SjkXkFb9V4RjgSAYVw5xbQY4BLjVuB22GOLJD1jkuK3Eaatu2LXr37o3OnTujWbNmOHDgANavX4/vv/8eVVVVqKiowLRp07Bp0ybMmDEj7n4qKytxwQUXYO7cuebv+vbti2OOOQalpaVYuHAhduzYgcrKSkyePBmVlZW45557XHiERP7DAKceO49BxWDhVyXZ6co9315U5IiIEmGI08TQoUNx3nnnYcSIEejWrVvMbXbs2IEbbrgBb7zxBgBg5syZOPfcc3HhhRfG3P7ee+81A1xGRgamT5+OMWPGmLdXVFTgrrvuwsMPPwwAmDJlCoYMGYIhQ4aIfGhEJAEDnNh2JtuXaqFDdyp2sQz/T8kKc6zCEZEd7E7pM4ZhYNiwYViwYAEA4LTTTsNnn31Wb7udO3eie/fu2L9/PwDgmWeewdVXXx1zn2PGjDG7Ww4ePBiLFi0S3m52pyQ/c7sKF9QAp1rbVAogOlPxeRQZ5BjenGN3SlKZrHNczk7pM6FQCFdccYV5ffny5TG3mzlzphngevbsiUmTJsXd50MPPWSOrVu8eDFWrFghsMVE/sYAJ094mnpVp6tXvX26UPG5E/V/xgCnHwY4UgVDnA+1adPGvFxcXBxzmzlz5piXJ0yYgFAoFHd/nTt3xvDhw83r7777rvNGEgWAF+PgZFHpRFrXUMRQlzoVn6+SrAxHIYwBTgyOVaSgYojzoTVr1piXu3TpUu/2srIyLFmyxLw+dOjQpPuM3GbevHmO2kdEcsg6KVThBNqP4cePj0kmVZ8ru2HOafgjIgI4sYnvbNu2DY888oh5PdakJuvWrUNNTQ2A2u6X/fv3T7rfAQMGmJcjQyIRxeaXbpRenzR7fXy31H2cKo4DU4WKs1cCrKwRkbsY4nygtLQUGzduxCeffIKHHnoIO3fuBFA71u3222+vt/26devMy23atEFGRvIPns6dO5uXCwoKsGvXLrRu3dpyG/Pz8xPevn37dsv7IlIdA5yex1VJ5HOgYmDxmqpBjojILQxxGvr6669x8sknJ9zmzDPPxKuvvoqsrKx6t+3Zs8e83LZtW0vHbNeuXdT1goICWyEuPCsPkd/5ZRycF0GK4S02VuliY5Ajt3FSE1IJx8T5THZ2Nl599VV88sknyMnJiblNSUmJeblJkyaW9lt3u8h9EJF3ZFThGODUxrF0B/E5IKKgYiVOQ7/73e9w7bXXAqhdF664uBjr1q3D8uXLUVhYiEsvvRTPP/88nnnmGfTs2bPe/cvKDs7klJaWZumY6enRH5SlpaW22pyXl5fw9u3bt2PgwIG29kmkGj90o3T7pJgn4c6wSseKXJBxZkoKMl+GuPXr12Px4sXm9XHjxnnYGvG6d++OJ598st7vt23bhjvvvBMzZszA/PnzMWjQIMyfPx9HHXVU1HaRY+AqKiosHbO8PPoD0moFL4yLd5PfMcCpe6wgCWqoY5AjoqCRHuIaNmxoXn766acTLiod6f333zcvH3300VETayQzb948XHPNNeZ1v4W4eH73u99h+vTpaNGiBf71r39h7969uOSSS5Cbmxv1d8jMzDQvW62o1d0uch9EQccAp+6xgi5IoY5BjoiCRPqYOMMwYBiG7fuNGjUK559/Ps4//3x8+umnrh3XDx544AG0aNECQO1yAJ988knU7a1atTIv//bbb5b2uWPHjqjr8cbbEZF+3ApVHMflvboLjvvt7+G3x0NEFI8rE5uEQqGU7hfUEOZU06ZNccIJJ5jXv/nmm6jbe/XqZV7euXNn1Bi5eLZs2WJezsnJsTUzJZGf6V6FczPAkZr8Fux0bz+piTNTkmqUnp0y1fBHQMuWLc3LkUsKALUhrkGD2j+9YRhYuXJl0v0tX77cvNynTx8xjSTSHAOctWPwpFo/uoc6HdtMRGSH0iGOUhe5eHbdro8ZGRkYNGiQeX3BggVJ9/fll1+al4cNG+a8gUSa0z3AuYEn0v6he6gjIvIbhjgf2rNnT9TsnLEqZ6NGjTIvz5gxI+H+8vPzMXfu3Jj3JSI9yTwR54m+/+kQ6FRuGznH5QUo6BjiNFBQUGB5W8MwcN1115lLAqSnp+Occ86pt9348ePRrFkzAMC6devw/PPPx93nrbfeiurqagDA4MGDMWDAADvNJ/Id3atwsgMcBYvKgU7FNhERicAQp4GXXnoJxx13HF566SXs27cv7narV6/G73//e7z++uvm72655Zao2SjD2rRpg5tuusm8fv311+ONN96I2qaiogK33347XnvtNfN3DzzwgJOHQqQ9Bjhv9k16UDHQqdQWIiJRfLnYtx8tXboU48ePR6NGjdC7d2/06tULLVu2RCgUwp49e7B69Wr88ssvUfe58MILMWXKlLj7vPvuu/HNN99g3rx5KC0txejRo3HfffdhwIABKCsrw8KFC6PG1k2bNg1DhgyR9hiJVMcA582+SU/h14QKa7dxDTki8huGOA2kpx88OaqqqsIPP/yAH374Ie72zZs3x9SpU3HDDTdELfJdV+PGjfHOO+9g0qRJZhUuNzcXubm59babOnUqJk+e7PCREJFXZIUs3cNb05yquLcdKOBHpAiqhDkGOSLyE35CaeDPf/4zhg8fji+++ALffvstfvzxR2zZsgWFhYUAgBYtWqB9+/Y4+uijMWLECFx44YXIzMy0tO+srCzMnj0bV111FWbOnInFixdj+/btaNy4MTp16oQzzjgDEydO5LICFHg6V+GCGuASBTSR92fYs0aFMMcgR0R+wU8eTfTs2RM9e/bENddcI2X/I0aMwIgRI6Tsm0h3Ogc4WVQLcE4Dm+hjM9jF53WYY5AjIj/gpwwRUQK6BzgZYUuFAOdlaLOCwS45L8MUgxwR6Y6fKEREPuW3AKd6cEumbvsZ6rytyjHIEZHO+AlCRBSHzlU4vwQ43YNbIqzWHcRARURkTzA/LYiIkmCAk7c/K/wc3hKJfNxBC3ReVOUYHolIVyHDMAyZB2jQoAFCoRAAICcnB82bN7d0v02bNqV0PwAoLi7Gnj17AAChUAjV1dU2W01uy8/PR6dOnQAAeXl56Nixo8ctoiBzO8AB4kKc7gEuqOHNiiCFOreDFYOcfjKLylw93qzZY109HvmHrHNc1z4RDMPAnj17zHBl9T4AbN8PqA1vkvMpEZEQqs5G6WaAY3hLLkhj6tyukAW9Ipfofz3IzwuRylz7BAhX1YiIVMZulOL3lQjDW+r8HuoY5OSy+j/u9ZIQRBSbK+/4rIgRkQ4Y4MTvKx6GN/H8GOoY5MRL9f87CM8NkU6kv8NPmTJF9iGIiAJNpwDH8OYev4Q6BjkxRPxv+/W5IdIRQxwREfStwukS4BjevKdzqHO7S5+fwoqMyY788twQ6Uyfd3AiIkl0DXAiMcAFj46hzs0A4YexYF4sD0JE7mjgdQOIiCg1ok7QZJ3oNc2pYoDTSPjvxb9bNB2DUEl2utR26/icOOXF0jNEiTDEEVGg6VqF0yHAkd5UDXReBAhdQovs8EZE6lC/7wQRkSRBD3AyqHbCn5UtrytcUaG6fwfRIv+uKnS79GJclspjwbwKtqo+H0RB4P07MREReUL0iZ/XAU5mYLNzPL+Hu/Df2esw51WQA9QZJ+f1FzpeBrmSrAxkFpV5cmwiFSgV4srLy/HDDz9g8+bN2LFjB/bv34+Kigqkp6ejadOmaN++Pbp06YK+ffsiLS3N6+YSkcaCXoXTPcC5HdjsiNU2PwY7FapzXoUIr6tQXoe3oBo7ehZmzR7rdTOIACgQ4n755Re8/PLL+Oijj7B69WpUV1cnvU+jRo3Qv39/nH322fjjH/+Irl27ym8oEfkGA5y4E0A3w5vKwS0Zvwc7L6tzXgY5wN2qnIrhzetASxRUnoW4tWvX4tZbb8XHH38MwzBgGIbl+1ZWVuL777/H999/j7/97W84//zz8cADD6BHjx4SW0xE5J2gBjidg1sydR+bH0KdV2HO0259LoQ5FcMbEXnLk9kpH330URx99NH46KOPUFNTA8MwEAqFbP2Eg191dTXefvttHHnkkXjmmWe8eDhEpBFdq3Ai6BTgsrLLfR3gYgk/Zj88bi9mtfQ66IRnhhT5hYsus0161UYv3l+51ACpwvVK3F/+8hc89dRTZuUtFAqZt9mpxtW9X1lZGa699lps3LgRDz74oLgGE5Fv6BrgRJwg6RDg/BBeRPFLla5pTpWrVTlVuvZF/r9ZbY8OYY2I1OFqiLv//vvxn//8B8DBEBYObtnZ2Rg0aBAGDRqEfv36oWXLlsjOzkZmZiaKi4tRVFSEgoICrFq1Ct9++y2WLFmCoqIiszIX3tcjjzyC3/3ud7jhhhvcfGhERFKoFOAY3ryjc6hzu4ulKkEuLCjhTLXnncjvQoad8pcDy5Ytw/HHHx9VbTMMA3379sVf//pXXHrppUhPt/5GV1ZWhpdffhn/+te/8OOPP0YFucaNG2P58uU44ogjhD8OkiM/Px+dOnUCAOTl5aFjx44et4j8JqhVOJUDHMObGDoFOjercgwU7vPiOfdqmQHOUklWyTrHdW1M3F/+8hfU1NQAOFh9u+WWW7BixQpcccUVtgIcAGRkZOCqq67CypUrcfPNN0fdVllZieuvv15Mw4mIPKLKN/iiA5xfxn2pInIsnerPqxdj5cg9niw6rtC4YyI3uRLiFi5ciCVLlpgTkoRCITz33HN48MEH0bBhQ0f7btiwIR5++GE899xz5r4BYMGCBViyZImI5hOR5nSswqnSjVLkCbcOIcMPdAh0bgQ5Vb4EIX/iBCfkNVf6NTz//PMAYIasSZMm4YorrhB6jCuuuALff/89nn32WTPIPffccxg0aJDQ4xARJaLKt8IqBjivtciucHT/fYVpglrinsjnXbVul25MesJxWu7z4jkvycrwrFslkVekj4mrrKzEIYccgpKSEhiGgZycHOTl5aFJkybCj1VaWoqOHTuisLAQhmEgOzsbu3fvRoMGnqykQDZwTBzJEsQqnEoBzu3w5jSopUrHgKdSqJMd5hjk3BWUsXEcF0dWaDsmbtmyZSguLgZQOyPlVVddJSXAAUCTJk0wadIkc8xdUVERli5dKuVYRER1McBFcyPAtciuiPrxSt12eNkWq1Tqdim7eyW7VrorKM83u1SSl6SHuEWLFgE4OJnJxIkTpR4v3E0z3KUyfHwiCh5+wNon4mRaZjDQKSjpFOxUCHMMcv7i9vOtSld2IrdID3E//fSTeblVq1Y49NBDpR7vsMMOwyGHHGJe//HHH6Uej4gI8EcVTlSAE02HEGSV6qHO6+ocgxzphl8Wklekh7j169cDqK2MuTXJyKBBg8zKX/j4RBQsbn6wMsCJr+SoHHREUjnUeRXmuASBfwSlGscgR16QPjvlb7/9Zl7u3r277MNFHccwDOzYscOVYxIReUWFACeKakHGbZGPX5XJUryY4VLmzJWcsZKI/EB6JW7Xrl3m+LTs7GzZh6t3nN27d7tyTCJSR9CqcE6oEuBUrER5TcUKnZvVOZkVOXardA+rcURySK/ElZUdnPLVixBXWlrqyjGJiLzg5ATJyUmyyPDmtuaZlZa3LS5pLLEl1qlWoQv//WVX5liR8wc+10TiSQ9x5eUH/2mbNm0q+3AAELWEQeTxicj/glSF0znAyQxvdkKaiH25HfRUCnRZ2eUMcqQcrxb/Hjt6FteOI9dID3E1NTVmd0ovSF7LnIgoZV516fIywIkObyIDm8g2uBXs6j6fXoQ6N6pyDHL6c/t59irIEblF+pg4IiK36FaFc3T8FAOgVwFO1Niu5pmVUT+q8qqdXo6hkz1ejmPk9BeE55lj48gtDHFERB7wohullwHOCR1CWzJuhzovJ0VhkCNVcJIT8jPp3SmJiNwQlCqcFyeaqZ6UiwhvflX3scnsfunFGDqZXSzZtVJv7FZJJIarIW7mzJlYsmSJ9OOsW7dO+jGIKJi8nswkValUMLyovvk5uCUS+bjdCHRuhjm31pYThUFOviA8x5zkhGRzLcQZhoElS5a4EuIAIBQKcVITooDQqeuKLt0o3a6+BTW8xeJGoHOzOicjyMmsxgHBCBlBwmoc+ZFrIS48Q6UbwcrL2TCJyL907EapeoBzI7x1aCZmP1v3i9mPHW4GOplhTkb3SgY5vQWhWyWrcSSTq5U4Px6LiMgqt7tRuhXgVAlvosJaKvt3I+DJDnRuhTkGOQoLwvPLIEeySA9xp5xyCitjRCSNW10pdazC2eVGgBMZ3mSHNjtitUVmsAs/jzqGOQY5iuTm88tuleQn0kPcggULZB+CiEh5qlfhdAhwKoU2KyLbKyvQyazOyQxzDHLkFXarJL/gEgNEpC1W4WJTLcAFLbzFUvcxyAh1sgKdrDCn28yV4f9FhjnxGJKJ7ONi30REkqVahXMjwNllZwFpJwtbd2h28MePZD8+GYuKy1g8XOTC4LJf+2FcFFwON59XL76Y02kWZdIDQxwRUQJeVuHcYOck2o3qm5+DWzwyA52sMCcSgxyFMcgRWcfulESkJV0+DFWuwskIcKkEhqCFtkRkjaMT3dVSdBdLkV0rZY+PC2P3Sjk40QmRNazEERHFoVMVTscAF8Sqmx2yKnQiq3Miu1jqWJEDakMHK3Ni+bkip8sXkKQ+1ypxr776KjZs2GBez8rKwg033CBk34Zh4IknnsC+ffvM3/Xp0wcXXXSRkP0TkVp0+RB0swpnhyoBjqwLP18yqnOiKnMiqnI6VuTCWJkjIje58u72888/4/LLL0d1dTUAoGHDhvjkk0+E7T8UCuHII4/EmWeeiZqaGgBAeno6Bg0ahE6dOgk7DhGRbLK7UYoOcAxv7lI5zKkY5LzAMCeGn7tVcskBEsGV7pR33HEHqqqqYBgGAOCuu+7C8OHDhR5j+PDhmDJlCgzDgGEYKC8vxz333CP0GEQUHE672LjVHUiXAMeuk2LJ6GopopulqO6VorpWutmtsi52s3TOz90qiZwKGeFkJcnWrVvRpUsXM1wdccQRWLlyJRo2bCj8WNXV1RgwYAB++OEHGIaBxo0bY+vWrTjkkEOEH4vEys/PN6umeXl56Nixo8ctIlXpsDacW90oZY2DkxHgVNGjubWPvA3FIcktEU9kdU5EF0sRVTlRFTk3u1Umwupcatx83tysyLEaFwyyznGlV+JeffVV1NTUwDAMhEIh/P3vf5cS4IDabpr333+/eayqqirMmqXH2BkiUocfv5H1IsB5UX3r0dxI+OP2ftwk8vkWVZVzyg8VuUjh6hyrdPb49bnSZXw3qUn6V1Nvv/22eblHjx4YOXKk1OOdffbZ6NmzJ9avXw8AeP3113H99ddLPSYRucPPH3gyq3BeBTg3eBWoYh1XleqdyHFzTsfLiRgnJ2qMnNsTnViRajgJYkXPrTFyXHaAdCG1EldRUYGVK1ciFAohFAphzJgxMg9nuuSSS8zumytWrEBVlRrfwBGR+nQZC2eV2wFOdvVN5YqYam0TXZlLlUoVOb+oW9Gz+qM7tx6Dm70x/PzlJMklNcStXLkSlZWV5oQmI0aMkHk407Bhw8zL4SBJRKQq2WPhkhEZ4GRQKRjZoUq7RYU5J10sRa0l55Qq3Sq94odQ58cgR5QKqSHu119/jbp+1FFHyTycqV+/fgBqlx4AELU+HRHpyY1vK72ownndjVLlAKdCABJFhUAnMsylwunMlX4bH6cCXUOdTm21gtU4SoXUEFdYWGhebtq0KbKysmQezpSVlYVmzQ5+Uu3du9eV4xIRqULUCW+yE3YZ3Se9DjuyeR3oRPzNvKrKMcjJpVOYc6OdrMaRyqSGuMjw1KRJE5mHqqdJkyZmN87IMElE+mEVrpbVE08R4+CsnKSLDG9eBxuv+CHMpYJBTm26hDk/BTlW48guqSGuQYODuy8qKpJ5qHqKiorM7pRERLrzYhxcIqIDXNB5GWK9CnJOMMi5Q4cw56cgR2SH1BCXmZlpXq6qqnKtIlZYWIjKyoMfKs2bN3fluESkJx2qcFaJGAfnVgUuiJU3K7x4XpxW5VIJcpzsRB+qhzmV22YHq3Fkh9QQV3dF8nXr1sk8nGnt2rUAYHan7NChgyvHJSLx+KEmthulCgGO4c0a3cJcKuPkVOhWCTDIWaVymJPdLlbjSDVSQ1yvXr0AHJwl8uOPP5Z5ONMnn3wSsx1ERKKpVIVLxusAx/CWGq/CXKp0DXJknaphzg9Bjl9cklVSQ1zv3r2RnZ0NoLYq9u6778o8nOmdd94xg2NWVhb69OnjynGJSCwdJjSRTfRkJvG4EeDIGbfDnNOqnB0qBDlW4+wLYpAjUkUj2Qf4/e9/j9deew0A8OOPP+KVV17BZZddJu14L7/8Mn788UeEQiGEQiGcccYZ0o5FRMGmShXOaTdKmQHOq/DWI0vseKsNRWlC9+dE+DndUOzO5F0dmgFb99u/X/PMShSXNLa8fYvsCuwrTO15zsouR1Gh8/+tpjlVOFAg/dTIV8LvaZmFwaiKlmRlILOoTOoxxo6ehVmzx0o9BulPaiUOAC699FIAtV0qDcPA5MmTsWvXLinH+u233zB58mTzWADwxz/+UcqxiEh/fqnCJeP3ANcjq6Lej47HsN0mFytzOlTkRGFFLjUqdbH0Q7dKomSkh7izzjrL7M4YCoWQn5+P0047TfiSA0VFRTj99NOxdetW81i9evXCWWedJfQ4ROQO1ccFyK7CiepG6dcAp0KYUiXUuRXmUu1e6VaQ40QnaghKkJNN9c9A8p70EAcA//znP83KGACsXr0agwYNwpIlS4Tsf/HixTj++OORm5sbVYX75z//KWT/ROQ/fvgmVeakDqkGOJmBQoXAlIjX7XMzzNnFIBcsqlTlZLbBD58hpDdXQtyZZ56JiRMnwjAMc6zaunXrcPLJJ2PSpElYvnx5SvtdsWIFrrrqKpxyyilYv369GeBCoRAmTJiA3//+94IfCRG5QfVvIFWpwiWTahXOSYATzetglCov2+2XIJcqBjl1MMg5o/pnIXnLtdG7Tz75JH7++Wd89dVXZpCrrq7GCy+8gBdeeAH9+vXDkCFDcOyxx6Jfv37IyclBdnY2mjVrhv3796OoqAgFBQVYvXo1li5digULFmD16tUAYAa3sBNPPBFPPfWUWw+NiDTjh29QZXWjVCXA6RbaEol8LG5NkOLG5CepTHhiZ7ITJxOdiMTJTpwpyU73fNITFdpAJJpr70rp6en46KOPMGrUKMybN88McuGuj6tWrTJDmRWR3TPDAc4wDAwZMgTvv/8+0tO9//aHiPxHlypcPKoHOD+Ft1jCj8/NMBfEICdqtsowBjlnVJjBUlaQc2O2SqJYXOlOGZaZmYn//e9/uPnmm6O6PkYGOqs/de8XCoXw17/+FZ9//jmaN2/u5sMiIoFkdx8JchVO5QCnY5dJJ9x8vLLHysnuWqnC+DiAXStF8Lp7pazjy/xcYZdKisfVEAcADRs2xMMPP4zFixdj6NChZigDEBXMkv0AMO87ZMgQfPPNN3j00UfRqBG/KSMiOVSowjnpRimSqGAQtPBWl9thThani8Eno1KQY5hzxutJT7wOkkSiuB7iwo477jjMmzcPy5cvx1/+8hd07drVViWuc+fOuPbaa7F06VLMnz8fxx9/vFcPhYgE4TeO8oiswjG8iefW8yGzKmf3daTjRCdhDHLO+S1MsRpHbvO8bHX00UfjiSeewBNPPIG8vDysWLECGzZswLZt21BSUoKKigqkpaUhMzMT7du3R48ePdC/f3907tzZ66YTkWbc7kqpShVOxQBHsbk1Zk7WWDm7Y+R0nOgkjOPknPNqwhFOdEJ+oNS7T6dOndCpUyevm0FEVI/q3xqn0t3M7QDndnjr1txZZ5ONxTWCWmKfG2EuSEFO9EQnYQxyznk16YmMICdzkpOxo2dh1uyxUvZNeuI7DxEpwW8TmrhdhYsnXhXObwHOaWCzs083w53sMCdrKYKgBTkADHMOeVEdY0WOdObZmDgiIl3oWoUTNebISYCTOdarW/MG5o+bvDiu7BAsY5ycimPkZIyPC+M4Oee8eK8VfUw/zIBMemCIIyLP+a0KZ4fMKlw8dk6unQY40bwKbvFEtkd2m2RPfqJCkLPKrVlY7eLslc4xyMXHCU4okhqfgkREipK9rIBTdqtwsqeCDxMZNlQLbom4Feak7dvjIKfj+nGxMMw54/UyBEQ6UP8TkYjIAT9X4WQHuFRP6EWFDF2CWyyyg6fMqhyDnDgMc864GeRYjSPdeDoKd9u2bdixYwdCoRDatWuH9u3bC9nvsmXL8NFHH5nX77nnHiH7JSLxVP5A8roK59aJZiypnMiLDG9+En48MiZE6ZFVIWXSExkzV9qZ7MTORCepkjXRSSyc/CR1bk4+wolOSCeuv5usW7cOjzzyCD744APs2rUr6rZ27drh7LPPxqRJk3DsscemfIylS5di6tSpCIVqP4AY4oiCSfcqXCIyq3BeBTi/hbe6ZIU5WTNYylqCQDQn68e5GeSA6P97BjrrdA1XMpccIHL1E/Puu+9Gv3798OKLL2Lnzp0wDCPqZ/v27XjhhRdw/PHHY/To0dixY4ej4xmG+C4hRCQOq3DxpVKF0zXA6dxtMhWyHq+M7pWiu1aq1q3SS+GuluxuaY1bXSt1GIun8mcnuce1T82JEyfi/vvvR2VlJQzDQCgUivkTDnRvvfUWjjjiCMyZM8etJhKRjwSxCueU2wEuaOGtLga55HRfdsCqyEDHYBefjkFO5c8i0psrn55PPvkkpk+fDgD1wlrdn8jb9+7diwsvvBB33nmnG80kIhep/E2iylU4Wd0ovQhwJCfIBjXIOflyQ4UgV1esYMeg597MlTpU5CjYpH+Kbt26Fbfddps5Ps0wDDRo0ADjxo3De++9hx9++AHLly/Hm2++iUsuuQQZGRn1wtw//vEPTJw4kd0jicgSlb/5lHHi5cVyAqkGhaBX3+IR/bz4LchZ5bcgl4yVoOfXwKdTyJLxmaTyF6HkDumjap966imUlpaagaxdu3Z4//33601ccvTRR+PCCy/Eb7/9httuuw0vvfRSVJCbMWMGCgsL8frrr6NxY7kzVhGRXCp/+OhYhXPK7sm5kwDnlpz0Do73UVC+VUBL7OnWvIGwiU9kTHji1WQndmar1GmiEzfFC3I6T7Aie8ITXSdUoWCQ/ok6Y8YMM4ilp6fjgw8+SDjzZNu2bTFjxgzMmTMHLVu2BADz/nPmzMHZZ5+NAwcOyG42EWmKVbhasrpRqhjgctI71PtReb/JqF6VE1mRU218HKBnRc4J3St2sityovav8mcT6UlqiNu4cSO2b98OoDaITZw4Ecccc4yl+44cORLfffcd+vTpY3avNAwDc+fOxemnn46ioiKZTVfOpk2b8Nxzz+Gyyy7DUUcdhZYtW6Jx48bIyclBv379cPXVV+PLL79Mad9z587FuHHj0LNnTzRr1szc5y233IK1a9cKfiRE/qJiFU6lACer+6TbwcqL4zLIpc5p1TpoQS6SjoFOp66VIqncq4Xkkxrili9fDuDgVP8TJkywdf/u3btj8eLFOPXUU6OCXPh3ddeZ86MVK1bg+OOPR7du3TBp0iS8+uqrWL16NQoLC1FVVYW9e/ciNzcX//3vfzF06FCceuqp2LJli6V979u3D2PGjMGIESPw8ssvY/369Thw4IC5z0ceeQT9+vXDAw88IPlRUpDI/NBx+k2nzBMBN6twMqQa4ETyKrjF40Z7RIZgPwQ5N5cdCHKQC9Mp0Ml8/w5qSCS1SQ1xkSGrQYMGlqtwkZo3b45PP/0UF154YVSQW7lyJU455RRs27ZNZJOVs27dOnz33XdRv+vZsycuuOACTJo0CRdffDE6duxo3rZgwQIMHjwYv/76a8L9VlZW4oILLsDs2bPN3/Xt2xfjx4/HxRdfjHbt2pnbTZ48GX/7298EPioifwhKFc7rAKdScIvHjTAngspBzioGOW/oEOhUD1vsUkkiSQ1xe/fuNS9nZ2ebM1Ta1bhxY7zxxhu44oorzCAH1Aack046CRs3bhTSXpUdeuih+Mc//oH8/HysW7cOb7/9Np599lnMnj0bmzdvxvPPP4+mTZsCALZt24ZLL7004Wye9957L+bOnQsAyMjIwGuvvYbc3FzMmDHD3Octt9xibj9lypSUu2sShbEKJ06sE1m/BTgdwltdMqtzqgY5UWTOpuoEg1x9Koc5We/lqgZEdqkMLqkhLi3t4OxQxcXFjvYVCoXw/PPP48Ybb4xagmDTpk04+eSTsWbNGqfNVVL79u0xffp0rF27Frfddhs6dKh/YtCgQQNMnDgRr7zyivm7JUuW4LPPPou5z507d+LRRx81rz/++OMYM2ZM1DZpaWl46KGHMHr0aPN3d9xxh9OHQ+QbXlbhnHSjlF05ERE0dAxvsch4HCoGOb93qwQY5OJRtTqncpBjNY5EkRriwrNLArXd8iIrc6n65z//iXvuucesMoVCIWzbtg2nnHIKVqxY4Xj/qhkyZAgmTJiAhg0bJt32/PPPx8CBA83rH330UcztZs6cif379wOo7Zo5adKkuPt86KGH0KBB7ctk8eLFvnyOyR2swsklo4ph90TfacDwS3iri0FODgY5tagW5lStnBGJIjXEHXrooVHXly5dKmS/U6dOxSOPPBIV5Pbs2YNhw4Zh0aJFQo6hqxNPPNG8vGnTppjbzJkzx7w8YcKEhN1cO3fujOHDh5vX3333XcdtJNJdEKpwbgY4v4a3SKIfo6gJT1QMcrKWHWCQc4dqYU40Fatx7FIZTFJD3BFHHAEAZkhYsGCBsH3fdNNNePbZZ819h0IhFBUV4YwzzsD8+fOFHUc3kYGsurq63u1lZWVYsmSJeX3o0KFJ9xm5zbx58xy1j4KJVTj7RE9monKACxIZYc4p3YOc2xjkrFEhyLEaR34mNcS1atUKPXr0AFC7zIDoKs5VV12Fl19+2exqGAqFsH//frz55pspT6Kiu9zcXPNyp06d6t2+bt061NTUAKh9vvr37590nwMGDDAv+3XsIQVTKh/wblXhYnEymYlVbgW4IFTfElEtyOnM7WocwCBnlQpVORlBTsVqHAVPI9kHGD58ODZs2ACgNkB8/fXXOOmkk4Tt/5JLLkFmZiZGjx6N8vJycwmCIIa4vLy8qErZiBEj6m2zbt0683KbNm2QkZH8TaRz587m5YKCAuzatQutW7e21bb8/PyEt4cXhSeyQ+UPQbeqcFbIGq/kJMDJFCpI/H5jhZHTMflGDoWfh4LyrY731a15A2wsrkn5/j2yKrChKC35hlb21dzAhmLnn8EdmgFb91vbtnlmJYpLGlvatkV2BfYVOn+s4SBXVMhqTzLh98MDBdJPO2MqyU5HZqG/g/fY0bMwa/ZYr5tBLpL+9d0ZZ5wB4GA3v6eeekr4Mc4991x8+OGHaNasWdSxgubGG280u1B27twZ5557br1t9uzZY15u27atpf2G14wLKygosN22Tp06JfyJnJCF/EXVvvpBqcLJ6kapUoALFeRH/cjYp6j9xiLqOXFakQtSt0qRX5SwKmedl1U50RU5dtUkr0kPcb///e+RmZkJoLZL5RtvvIEffvhB+HGGDRuGzz//PGpGzCCZOXMm3n77bfP6Aw88gPT0+m8wJSUl5uUmTZpY2nfd7SL3QeSVIFXhnExmYpXsACe6+6Qb4crNYzLIOefG/0k8WdnlDHMWednFUrUgp/LnGKlPeojLyMjAmDFj0KZNG7Rt2xatW7fG448/LuVYxx9/PBYsWIC2bdsmXOjab5YuXYo//elP5vXRo0dj7NjYJfWysjLzcuQ6fonUDYOlpaW225iXl5fw57vvvrO9T1Ifq3CxiTjZE1mFcyPAieBFcEvWFpH8GOREUHm2yroY5KzzKsz5uYKm6mcuyeFK5+T//ve/bhwGANC3b1+sXbtWyJp0Oti4cSPOPfdcM5wdeeSRePbZZ+NuHzkGrqLC2gdYeXn0h5LVCl6kjh3ljy+h4FD520tVqnB+CnAqhLZ4wm0TNYZO1Dg5VcbIeTE+zg5R4+MicaycPU1zqjwbKyeC0/F2JVkZyCwqS74hUR36/tck0KJFC7Ro0cLrZki3fft2nHbaadixYwcAoHv37vjf//6HrKysuPcJd20FrFfU6m4XuQ+ieFT9RjAoVTjRvAhwKoe3umSEORETnqhAVJCzys4kJ4CcIAcwzNnhdpALwkQn5H/BnpdYY3v27MFpp51mzvzZvn17fPHFF2jfvn3C+7Vq1cq8/Ntvv1k6VjgkhuXk5NhsLZE4OlfhRCzs7UU3SrcDnCpdJlMhsu1OQzC7VVono2tlGMfLWeN290qRX875uYsmqYshTkP79u3DmWeeiR9//BFAbTD7/PPP0a1bt6T37dWrl3l5586dUWPk4tmyZYt5OScnx/byAhQ8sqpwXizs7dcPZ1kBzskEJjqHt7pEPRa/BDkvZqtUKcgBB8McA11iugY5J0R+OalqLxgSjyFOM/v378dZZ52FpUuXAqjtOvq///0PRxxxhKX79+rVCw0a1P7ZDcPAypUrk95n+fLl5uU+ffrYbzRRAOhWhZPBafXNjxjkIvbj4WtTNQx0iXm9QHgqVAmEFBxajYlbtmwZPvroI/P6Pffc42Fr3FdWVoaRI0fim2++AQA0bdoUH3/8MY455hjL+8jIyMCgQYOwaNEiAMCCBQswaNCghPf58ssvzcvDhg1LoeUUJKzCecfLbpROqm9+J2K8nNMxck4nOlGJrEXAAXnj4xKpG+Q4hq6WWwuEc3wc6UqrStzSpUsxdepUTJs2DdOmTfO6Oa6qrKzEhRdeiHnz5gGonfb/vffew4knnmh7X6NGjTIvz5gxI+G2+fn5mDt3bsz7EulCdhhTpQpnBQOcd5w+Xi8rcqpV43TuVplMZJWOFTt3qnKiPiOc7IddKskurSpxYYZhIBRyb6Yrr1VXV2Ps2LH4+OOPAQCNGjXCG2+8gREjRqS0v/Hjx2PatGnYv38/1q1bh+effx5XXnllzG1vvfVWVFdXAwAGDx6MAQMGpPYgKBBUrcKldEyfVuGs0D7AbfrF2f27HiqmHTE4rcp5OWulassOyORFRS4ZK0HOr1U83ZchIJJBq0pcEBmGgSuvvBJvvfUWAKBBgwZ4+eWXMXLkyJT32aZNG9x0003m9euvvx5vvPFG1DYVFRW4/fbb8dprr5m/e+CBB1I+JpFXZHej9FsVzun4qWSkTF6y6ZfoH9X2F4OT58BJRU7239dNMqtxgPcVuVTEq+L5oZInuyKnQjWOyA5+raG4p59+OqrLY48ePfD111/j66+/TnrfVq1axe12evfdd+Obb77BvHnzUFpaitGjR+O+++7DgAEDUFZWhoULF2L79u3m9tOmTcOQIUMcPx7yLz9V4dzg9mQmMqaLtxsmhIY3SeEq6bEEVulCBfmeVOScjI9TrRonc3wcoGZFLlXxgpxO1TvZFTmvx8dx4W+ygyFOcTt37oy6vn79eqxfv97Sfbt06RI3xDVu3BjvvPMOJk2aZFbhcnNzkZubW2+7qVOnYvLkySm0nshbKlfhRNGlG6WQAOdmcEvWBkFhjkHO3W6VQQ9yseg2sQq7VlozdvQszJo91utmkET+6VdBtmVlZWH27Nn4/PPPcdlll6FHjx5o2rQpsrKy0LdvX9x8881YtWoVAxx5RuUqnJOuPbp3o3Q9wEns1pgygW0K2gQvoon6P0mkRXaFlt0rU6FDF0yZXStFdIdkl0pyA7/KUNzUqVMxdepUqccYMWJEypOkEAFyulLqvqSAKlU4rQOcasEtFkGVuVQrcqzG1ZLdrTLM71W5uiLfx1Sr0MmsyHnZrZJdKskqVuKIyHe8XlIgEbercCLZCXCOJjBRsfKWjIA2p/p8eTXRiYxxlm5JZaKTsKBU5OpSsTqn8qLgrMaRbAxxROSIilW4lI7JKlxCdgNcynQLb3VpGOS85sXacQCDXKpU624pK8j5IYRxvTh/kx7iunfvbv5ETldPRCSD15OZJCKqCufLAKdj9S0eh4/F7SCnQjVO1yAX5DAHqFOdU7Uil2oQVHk8OKlDeojbtGkTNm3ahM2bN6O4uFj24YjIRX6pwomkwgmNVxwFOD/yIMilSoUgp6ugBzlAjTAnI8j5oRpH/uXKxCahkJgphEeOHInevXsL2RcR+Q+rcP9/Gw+qcCmFDr+Gt0ibfkl50pNUJjtxMtGJ17yY5ARwNtFJWPj/O0iTnsQSDnJeTYIiY7ITr9eOI4pHqzFx7du3x5AhQ8wfIvKOalU4Fb4x9fqbaKsY4FzmoHtlKs8tu1W6260yEqtytbx8L1StayW7VJIsXGKAiALLrYW9VavCWSF1ogzJAa56zeaU7tewTxfBLakjxaqcmxU5FZYdEMWLihzAqlyYl1U50RU5natxXPTbvxjiiEgJKlfhVPtmNx7Ra8JZYbtSJCHApRraku1HSqhzMcjpSlS3ylSICnIAw1yYV2FO5jpydukcAkldWnWnJCI1iO5K6UWAc2tJATercMl42o1S8OyT1Ws2mz+ySDuGS11Jde5WKUoq6y2K6loZxlksa3nRxVLkF3BedNlnl0pKhCGOiALHrclMrBKxsLfb3ShtBzhBZAc3146bwnOiy/g4EUSNjQPUCHLAwTAX5ECny7hhIh0wxBGRLX6owokkogqXjFtVOFUDnBtVN7ttEcKlIOc21SY5SZWMIBcW5EDn9nIEqlTjVPi8In9hiCOiQAliFU5kVcWLAKciYWHOhSDHbpWp/5/JDHJhQQ10ugY5HcmYTZq8xxBHRJ5xuwon+ptQXapwVlg50XczwKlSeUvGqyBnl9TZRiXyulsl4E6QC4sMdEEIdW5W5UQFObcrahwXR/G4Om3P4sWLkZHh/otx3Lhxrh+TyI9U+TbPjQ9RvyzsLbIbpWUOQokOwa2ucJsdzWZpc9ZKt2ar9NOSA06InLXSjljvJX6c7TIru9yzBcLdxFkqSSTXQpxhGHjppZfw0ksvuXVIE0MckXrc/nZRdDdKFQbou9llzXIVLmABLlL1ms1KBzkv1o4TQeSSA3bXjovkVZCry6/Bzo0gJ2rZAYYxUoGr3SkNw3D9h4jECFIVzgndlhQQ2o0yxQCnS9dJK/zyOERRcZITJ+NQ3exaaUfdbpi6dsV048uxoI+PI/9wNcSFQiFXf4hITazCOSfi5FiVAOc3jh6TzedRh0lOVOTHIFeXrqFOhfdXmVIazy3gM1OVL2JJHF9X4ojIX/wymYkqVTiv+THAhTmqLioa5FKlYjXOKV2CXCSdAp3sCU9EVONU7xVC/ufamLhQKISnn34akyZNcuuQRCSIyG/wVJ5pS5duNm5NZiKzCiczwJUv22F52/Rj2klrB+BgnJzN8XFu8NskJ07GxwEHg5wK4+TsigxyKo+nkzlOTtT4OCKv8NVLRFpgFc5dsgKcjPBmJ7Qlu6+MUOd4whML3JrkxGsiJzkBnAc5QJ0JT1KleqBTeebKVCc44cQoJIL6/W2IyFMqVOFUX1LATUotKeBhgCtftsP8EUnWflN67JK7VaZChQXARX/R4WR8XJiO3StjUbW7payulbq87xPFwhBHRL7k5mQmOlXhhHWj9CjAyQhYbh3LjSBnByc5Eat5ZiXDnESqBjm3xsapPBSBvMF3YiJSmgrdKFUhogqXjIxqjogA52Z4U+nYdrlRjXPCz9W4ML8EOUC9MOf3mStl4wyV/sIQR0RxqdCVUjZW4VJgozrkNMCpFKBEtIXVOHWJDnJ+C3OqkBHkvOhW6dcvG8k9/nsXJiLfYBXuIGWqcC4FOJXCW12qBzlW41InMsgB/gpzKlXlVKvI+fVzh9TGEEdE0rEKl/gYKlThVAtwqnMaMlVaI4/VOPkY5tTHSU5IN3wHJqKYvO47L7sKp9MHthtVOJFSDSgqV9/icTXISazGcQHwg0RX4yL5JcgB3nexVK0a5wZVvxAlb6j1yU9EvqPzhw6rcBEkjssC9Ki+xeNq2xXrVqnaFwiiyA5yfglzXlflRAc5J1/usfs/uc2Vd1/DUGPxWiLSgwpVOFW+5XVahRNSYZHcjVLnABeW6mPwQ7fKVKlcjQPkBjngYJjzQ6DzU5Aj0kUj2QeYMmWKefnYY4+VfTgiIul0qsIlI7JaE9QAF1a+bAfSj2ln+37VazajYZ8u1u+w6Reg66GWNg0V5MPI6Wi7TXZ0a94AG4trUrpvj6wKbChKc9yGHs0NbCgOOd5PXR2aAVv3C99tPeH3j+KSxvIPJkmL7ArsK3T+t0xFVnY5igrFVLWa5lThQIH002Mix1wNcUSkB1Hj4VLpSskq3EGyq3Aiu1HaDXB+Cm+RUg1yqshJ74CC8q1eNyOwdA9z4S+4vApzXivJTkdmoRqfH/GMHT0Ls2aP9boZJAC/aiAiskFUFU4LGga4HWutf3HQrneZlDakEuRYjWM1LlLke4qOgc6Lqpyu1Ti7wa8kKwOZRXLeu0gv0l+hL730knl58ODBOOyww2QfEh988AEee+wxAEAoFMLcuXOlH5OIorEKl1iyrpRKVOEkEB3g7IS2ZPcVGepcCXKSsBpXnxdBLkzXQKd7kCNSnfQQN2HCBIRCtd+MPf3005ZD3AUXXGBe/vOf/4zTTjvN8jG3bduGBQsWAIB5bCKyxuulBVTGKlx9dqpwIgOck/CWbJ+iwpz0rpU+qsaJIqsaB3gb5MJ0627JIEckjyu1YsMwbIepOXPmmPc588wzZTSLiDTn1ypcMk6ndRdVhXM7wMkIbomOIyLM2Q1yqlTj3CaqS2VQ6BTmvJzwxIlUu1TqMC6O/MGVJQZYDSMKFre6UrrJahVOBKdTrzueKl7ymnCpcCvAeX1M2yStG5fqa0iFdeNkzvqqWpVdlyUK3F6CQJXJqYhk8v7dlogoBapW4WQvKxC0KtyOtRmehikRx7f7+FVaO85NotaNk021IAfoEeZ0DHJOFv+2Q/UvMUlNDHFEZPJqPJzqH2BunnzoUIVzI8B5Hd7qUjrISaqc6lyNk03FIAeoH+ZaZFd4ujC4W1T/TCN/8P87LRG5KpWulLaPoUAVTscJTZJW4QSHAScBTkWqBctUeDUrqVWiqnEyu1TqQIcw5wa/dqt043OW1McQR0SUgMiTDdldKR1X4SywWiHyW4CLlGobg1SNCwLVv7QB1A5zulTk3OpS6SbOQu0PDHFE5Cm73U78XIXzdEyQApOZ6BDgwtwKcjK4UY1z0qVSp2qcDkEOkDcJk1NuBDm/VuOIGOKICAC/mYtFpypcMiJO3GVW4XQKcGHKtZnVOEpA1SBHRKlhiCMiYVTqpy+rC4xvlxVIggFOHDvPkayZKlWvxulEl2ocoGaQ06Eal8rnicxeJkQAQxwRecjrD7lEH+x2TixUP4lze0ITO3QPcLq3X1U6dakE1H8PiKTiODldxscRqYQhjohIMt0nNJFVhfNLAErlcUirxinWpTIo1ThAryAHqFeVkx3kODaO/CY4765EFJeI8XCyu1JyQhNJBJ30BzXAhen4eFRfbkCUoC83kEjQgpwTfpylkvTGEEdEnlC5/79KE5o45fREXcaYLDcCz9pVleaPqoJSjUuVp19upEi3ahwQrCDHahz5SSOvG0BEJJKbVTg3SO1K6UEVTkaASxbUYt3e+6jGwtuxY20G2vUuE75f3XVr3gAbi2u8bgYl0DyzEsUl4v8nUtUiuwL7CtO8boZjJdnpyCyUExxLsjKQWcT3myBzNcT9/PPPWLhwofT7/fzzz7aPQUTqUrVqp3pXSr93l3NSZYu8r8hAZzfIlS/bgfRj2gk7vl2hgnwYOR09O74fdWgGbN3vdSsonqzschQVqvmZQmSHayHOMAw89thjeOyxx2zdB4Dt+wFAKBQy709EZJWfulI6ZaULnxdVONFdJMP7k1GdE6l6zWY07NPF2sabfgG6Hiq8DTnpHVBQvlX4fuPpkVWBDUXOKzI9mhvYUBwS0CL/YjUuuaY5VThQwE5spAZXx8QZhmH5J9X7xbo/EcXnxaQmsipr7Eppg8vLCogIcLLHuInat93HmsraeroJ0iyVgJ5j44BgjY9Tkaq9TkhNrr2rhkIhV3+IyB/c/FBze204lbtSiq7COeHmBCWijiVr8hYZE834vdst2ROEIMcJTsgPXAlxqVTSRPwQEZH3nAQar2aXVHlWS8s4S2X0fjTv3uwm1YIciSeiFw55S3rH3o0bN8o+BBGRcl0plT5hFHByb7UKp2OAizy+W+PkvJ7ghEhlKo6Ps0rmDJUUbNJDXJcuFgdhE1Eg+KHPvxvjXaSOh0tCRpc9u7wOcCLIWnLA1gQnFrkxS2XQlhrQfZZKv090kuoslZzchFQRrJHGRCSc3UlNbO1b0fFwIqg8Hk6UVKtwKgU4ldqSEpcnsCF/YbdKInUxxBERCaZ0V0oBgjCbYiQnQc5OkNXpeXV7XBwRELzZKokSYYgjCjC/DGxWbTyc0pJUZkR1pfRDFS6Squ0SSZUKbiwyK9eUmGrvgyKDHGepJJ35PsSVl/MflIgSc3tpgWS8HA/nJb8GJRnLDagwbjEVXq8X5/cqOQWLzOEMpD7fhriysjI89thj6N69u9dNIaL/zw+Tmoig83g4WV3+dAhwbrRRyvPLcXHkkJ+rcamw0vsjVfycJKt8F+IOHDiARx55BN26dcP//d//YccOfcYYENFB/CDTl6yFrlWgQ9h0i18rwhSbakFOFDe6VPLzjGTwzRyp+/fvx7///W88+uij2LNnj7nYdygU8rhlROQ1rg/3/7k0Hs4uBiMiskvnteOIRNC+EldcXIz77rsPXbp0wZ133ondu3fDMAyGNyIXqNAfX2a3lrrcGA+nMp1mT5QpldCpy7g4lSc3Ie/5tRpHpCNPKnHffvst/ve//+G7777Dzp07UVRUhGbNmqFbt2448cQTcckll6B9+/YJ91FcXIzHHnsMjz/+OIqKiupV3sLXTz75ZLkPhoi05vXYirp0n9TEblhhFa6+8mU7kH5MO6+bQaQ8VuMoyFwNcatXr8b111+Pr776yvxdOGwBwKpVqzBnzhzceeeduOmmm3DvvfeiQYP6JzTPP/98VNUNqB/ehg8fjrvvvhunnHKKzIdERGQLp0onT2z6Beh6qNetAFD7RcXG4hqvm0Epap5ZieKSxl43Q6is7HIUFXLcGunFte6Un332GU466SR89dVXMAzD/AmFQuZP+Hfl5eX4xz/+gZEjR6Km5uAb/a5du3D66afj6quvxq5du6LuH77vWWedhcWLF+Pzzz9ngCOiQNGtK5zOVThVulQSeUGlbpVe9aZwsys/USyuhLi8vDxcdNFFKCkpqRfcAERV0yJD2SeffIJp06YBADZv3oxBgwZh7ty59cIbAIwaNQrLli3Dhx9+iOOPP96Nh0VEPqXSCQqRylTv3uulrfu9bgER+ZkrIe76669HcXFxveCWkZGBvn374sQTT8QRRxyBtLS0qEBnGAYefvhh5OXlYeTIkdi4cWPUbQBw8cUXY/Xq1XjnnXfQv39/Nx4OkS+MHT3L6ya4wo3po8P8PDOllUlNWGkimdgVWS38skt/QTkP8CvpIW7r1q344IMPoro8HnbYYXjnnXdQWFiIVatW4auvvsLq1atRVFSE2bNno0ePHmZIKy8vx/nnn4/c3NyoAHjqqadixYoVeP3113HEEUfIfhhE5CKuqeN/OnelDJP5GKzOBMoZKslLqgQ51SaoInKD9BD35ptvmuPaQqEQBgwYgO+//x6jRo1C48bRA2PT0tJw0UUX4bvvvkO/fv3M0LZixQozBDZs2BD/+te/MHfuXPTr109284lIEBnBzOmYBDsf/EFfXoCIrGNXSv242WuDSATpIW7p0qUADo57mzlzJpo3b57wPtnZ2XjppZeiZq4Mj4N79dVXcd1118lrMBEREfnShmKuISsaq3HWsIcJiSY9xK1atQpAbRXuhBNOsNz1sV+/fjjhhBOixsiNGjUKF110kbS2EhERycRxg+RHqgQ5oiCRHuL27NljXj7hhBNs3ffEE08EcLCKd/nll4trGBGRZvwyE6AfxsNpKcnkNiQGu1KSU6zakRXSQ1xRUZE5tq1du3a27lt3+2OPPVZYu4iIVNOtuWtLdwoX1AoTA6l7NhSled0ESkCFapzbXSpVWCuuJCuY773kQogrLS01LzdrZm9mgKZNm0Zdb926tZA2EREREYkW9CqcCkGOKCi0+tq3YcOGXjeBiIiIiOLQOchxhkrSiVYhjoiIiEhFQa/CRfIyyKk+SyWRKAxxRERE5HtcXsBdOlfkiHTAEEdERETkAKtwsTHIEcnTyM2DLV68GBkZ1mfRWbx4cdT1l156KaXjjhs3LqX7EZE4mYXlnDaZiChgmmdWoriksdfNIPId10KcYRh46aWXUg5ihmGkvE4cQxwRERHJwCpccm4HuRbZFdhXyCUpyN9c7U5pGIbtH5H3JyJS2cbimoS3F5RvdaklRP7C8XDe06VrJWeoJF24FuJCoZDrP0Skp8xCfogS+VWyLytiUXWhb1bh7NElyAXJ2NGzvG4CpciVEJdKBU3EDxH524ECV4f1UgLtepd53QQiVzHApYZBjkgM6WdANTX2v3EjIiIiwboe6nULiAC4M0bOrXFxTXOqLH+hWJKdzp4mJAyXGCCiwOLAdyL/kzEejlU4IvIaQxwRkQXan7QpVIXpfZR/phu3+1h06HZq5HS0vC0n26FUsVslkTMMcUSUsswi9U9Iiwq5Np0VDft0SXh7+jHtXGoJUTTVJjXR/gsdhTDIxcd1VSkZhjiigJo1e6zXTfAdr6cxt1NBIbVZDc3JwnfQif6fZIATT2aQa5FdIW3fKinJyvC6CeQBhjiNVFdXY/Xq1XjhhRfw5z//GcceeyzS0tLMJRWGDh2a8r7nzp2LcePGoWfPnmjWrBlycnLQr18/3HLLLVi7dq24B0GBpsuAbjcXpSUKklSWFyD/Y0WOyD7ps1M2bNjQvPz0009j0qRJsg/pS3PmzMGll16KAwcOCN3vvn37MGnSJMyePTvq9wcOHMDevXuRm5uLJ554AtOmTcMdd9wh9NhEiWQWllvqTnKgoBGa5lS50CIxNhSloUeWP78dbte7DDvW8hth8idW4YIjK7ucXfFJedJDXHi9NruLb19xxRXm5fHjx2PIkCFC26WbwsJC4QGusrISF1xwAebOnWv+rm/fvjjmmGNQWlqKhQsXYseOHaisrMTkyZNRWVmJe+65R2gbiCjaxuIadGsev5NEQflW5KR3cLFFcvQ+qjHWrtL723c/TdCiKifj4bzu3kz2uLHsAJGfuNKd0m6AA4AZM2Zg5syZmDlzJtatWyehVXpq27YtzjnnHEybNg0ff/wxbrjhhpT3de+995oBLiMjA6+99hpyc3MxY8YMzJ49G5s3b8Ytt9xibj9lyhR8+eWXjh8DkUoCtcyAwxkqObmJM57OTClhdtIgzUzJKpw72K3SG2NHz/K6CZQC6ZU4JwzDSCkA+tGZZ56JzZs3o3PnzlG///bbb1Pa386dO/Hoo4+a1x9//HGMGTMmapu0tDQ89NBD2LJli9nd8o477sCiRYtSOiaRV4oK05GV7Xw83tb9QIdmAhqkqIZ9uqB6zWZH+7DTpdIP1Ti/kTk5jq7j4bwKcHaqUgw/RMGj9MQmDHAHtWvXrl6Ac2LmzJnYv7/2k6lnz54Jxyo+9NBDaNCg9qWyePFirFixQlg7SH86LDOQilS79XjdhYszVLpDZldKzkxZK2hdKYtLGkf9pHpf3YkOpEGZoZKCR+kQR/LMmTPHvDxhwoSEgblz584YPny4ef3dd9+V2TTyOTszVFrd9kCB0p0K6lFt3Ssiik12FU5G+PJLmCOixBjiAqisrAxLliwxr1tZmiBym3nz5kloFZF3VBsXl6zbmZdjkUSPiwvK5CCejoeTQOXxcKKqcDIDnBtBS+cwx+6hRMnp9fU1CbFu3TrU1NSeJIZCIfTv3z/pfQYMGGBeXrNmjbS2AcDIkSORni5mat8xY8YknfzliSeewOuvvy7keGGLFy9OePvOnTtx3nnnCT3m9ddfj0suuSThNnfeeWe9EL5+/e6Uj5mR3gKnnHRzwm32FWzG6q+eifpddUPrJ1nVjep/19TxtAlo0WNAjK0PWvfKE9i/bVPU7xo1MuJu37Bh/eDUsOHB7XO6dcTQ265KeMwN3/6AL556ExkNE24GAEhvGL8tAMx9/PM/E3B4304Jtz3vvLuxe1fRwV9UJek+VJ44UAzt1hL3XnR0wm3eWLsDz6zMi3t7Ran1v3Hpgdrn4u6W/ZHdMPH//s27lyS83a5TmrTHec0Sd0t8b/9mLCzdbl5v0jQEJP4XrydtZfTfe96Y4xJuv7OoDBc8viDhNqEmFt8n02vHKP7lqhG45MJBcTczcjrirjtfxPz5K5PusqrGWhe1Vq2b45W3/prwi4lffsrH/TfPrPf78urUwlhZNTDimovQ4/i+Cbd7997nsPOX/Li3V9gcwmflPeLnr9Zi1UtiP2+Ov/5qtOwWf7hFcUljfHXfP1BWuE/YMTsMOALHT7o44Tbrv1iE3Lf+l/IxqmP8/YdOuRUZ2VkJ7/fpjXfG2Jf9mkVVVe3x2xw7BB2HjUq4bcHKt7BjyUJL+21YZe2Fdcxl/0x4e3lpEb7/7B/m9QbVzsacDh7875TPI5xo3bo13n///YTb5ObmCl+i7L777ovqZRbLVVddhR9++MHxscrL5ayRyxAXQJGzfbZp0wYZGcknIYgcj1dQUIBdu3ahdevWlo+Znx//gxIAtm8/eIIkcszd4MGDk26zefPmqMqkGyoqKoQf86KLLkq6zc8//yz0uE2atEy6TVVlGfbu/FnYMQGg6kBx0m32b9uE4o3iF6pPNLlJ6b79yMv9Rejx9pckf/NfsfwXbN2aehivq4OFFPrbgXJ8t6Mo6XZ2VCFxsAWAdZVij9k7LTvpNjurS6OPm0oTLN4nXOmsqK7Gt7+I+5sCwB9GJg6OAPDz+nwsWSLui7r2v0v+HnFgfxl+WLZB2DGB2v/FZHb+ki/8/zWecEWsoqQEu9euF7rvqrLkVd5dazfhwJ4CYcds1jon6TYHdhfitx/FPr/VlcnXBBX9/Lbo3ifpNmUFO7E/T+4X3HXVVFcJ/Wzds+cXT84jOnRIvmROSUmJ8HOmgoLk/w8//PCD6+eHdjDEBdCePXvMy23btrV0n3btortQFRQU2ApxnTolriSQ3jKLylCS5e0iz7ot+q2z9GPaAcudzWJJFBS6dmkkIrVxTFwAlZSUmJebNGli6T51t4vcBxG5T+aYpFCLptL2TcGj69ICTuk8Jo2I1MdKXACVRXS9SEuzNqFD3TFqpaWlto6Zlxd/7AxQ251y4MCBtvZJ5BW/rxfnlR6HN0LRuuTb+ZWXi6lzaQqxGN6ISDaGuACKHANXUWFtcHrdQZlWK3hhHTtaP0Ho37+/sIlNunRJvo5Sly5dMGhQ/MH+MqSlpQk/Zvv27ZNu07Nnz3rHdTqxSTKNGmegZZue9X5vZ3ITIHqCk0ZNm8fcJrJLZbPfdY3dHpuTm9T+3kBOt+Sv4SYtmqHTkYcCgLDJTZplJv9f6D/gUHTqFKN7c6IJThJMbnJY5+TjXTr2bYOBP+9Mup2dCU7SQg2SLgDeq3HiSQ3satMw+XtZm4ZNcHRWdkr7T2uSfJxfzPs1bIjjDz0k5m2WJzQBzElNAKB92+TPXc/DOmLQoMRjgKxOagLUTmySTNNmGeh7TA/zupMJTcKatEj+LUubQ+v/T9udzCRS+D0iUYBLy8zEIb0PS/0gMTSyMK695aHd0LR1K/N6wyTvPclkd0r+edP0kGy0PeJQR8cBoic4adg4+WlrvOc31clNMnLaJN0uI6cNmnVKPnYuzOrkJok0aNio3mer08lNUj2PcMLK0JzMzEzh50w5Ock/5/r2TTw5klXl5eVS1lgOGYbh7D85iQYNGphrkD399NOWZ5dJ9X5BM3XqVEybNg0AMGTIECxYsCDpfZ5++mlcc801AIB+/fph1apVSe9TUFCAVq0OfgCsXbsWvXr1Sq3RMeTn55vj5vLy8myFPnJu7OhZQvZjd1xcSbb1k1Gr2yYbF5eVnXiikFgLw8aa7tpKJa5H8+Rvrz2y4p8Qd2ue/KQjJz3+oPBQQeIJhbAp8aQD1WsSj3srX7Yj8f4B7Fhrf6xkohDnlVSXQrCztICURb67WjuJtlqJs9uN125XylTXUBSxrIDTJQV0qr7pNoW/iOc2laVkigqtf0bZWa9UxnqpQO34dCdmzR7r6P4Um6xzXFcrcY8++mhKU7mnej+gdgr9uXPnpnRfv4oMY7/99pul++zYEX2yZuUbDCIVFRWmJw1yqthYXGMpyHkl/Zh2SYNcu95ltoNcsmqc21QKcLYIDnB+FqQAB9S2V7cgR0TRXAtxhmFg/fr1WL/e+tSv4SKh3ftF3j9czaODIitoO3fuRFlZWdJlBrZs2WJezsnJsTUzJZEImYXllqpxMmapjHXCo8O4OCOnY/JqXAIN+3RJWo2zQucgp9pi5LaqcJrxsgrnhG4Bjoj8wbWveO2GqVAoZP6QWL169UKDBrV/esMwsHLlyqT3Wb58uXm5Tx/r/b6JErHTTcQtqXS5iceNk0tHs1RarNR4xesA5eT4ulTh7JDdldIrTqpwOgc4ndrOqiFRfa6EOMMwPPmh2DIyMqIGiFoZR/fll1+al4cNGyajWeQDTvvju8XOOAfZklUfVD8RthpA7ISaSF4FObcCnB0yqnDsSpk6nUIQEfmP9O6U48ePl30ISsGoUaOwaNEiAMCMGTNw++23x902Pz8/alzhqFGjZDePXDZr9lhhk5vIxC6V9qnSpdIJt7tWuhkcvVxWwA6/TmiSahWOAU4/LbIrhPa0IPKa9BA3ffp02YegFIwfPx7Tpk3D/v37sW7dOjz//PO48sorY2576623orq6du7mwYMHY8CAAW42lXzOajATLdEEJ/sK02LOUpmKDcUhS7NUOlFQvjXhLJUJdT006SyVyViZ4ARIbWxcWDhYyQxzIsKbrCqcLZzQxBIGOL0mOGmeWemr557IKXWnPSOp2rRpg5tuusm8fv311+ONN96I2qaiogK33347XnvtNfN3DzzwgGttJD3p0qVSJbK7VDo9WRfZjc9pyJFVJfOi26adKhwnNIlxPw8mNGGIID/ToUcOHcTFvjVy1llnYdu2bVG/i5z6f+nSpTj66KPr3e/jjz/G7373u3q/v/vuu/HNN99g3rx5KC0txejRo3HfffdhwIABKCsrw8KFC7F9+3Zz+2nTpmHIkCHiHhBRCtilUgIXq3EiiKzKiQxvOlXh7PDjhCapVOEY4IhIJQxxGvnpp5+weXP8sSn79++PuXB3RUXsbmGNGzfGO++8g0mTJplVuNzcXOTm5tbbburUqZg8ebKD1pPqvBwXxy6VtdWIRAt/J1szzlGXSgusjI1zo1tlpMgAZifQyai62Q1wXlfhVOlK6UUVjgEumk5dKimxkqwM9oYJEIa4gMvKysLs2bNx1VVXYebMmVi8eDG2b9+Oxo0bo1OnTjjjjDMwceJELitAtmQWlaEky/lJetz9e1iNi0WHalzSCU4EVOPsEBXkwrxcjkCJCpwkfqzC2eXnAEdE+mKI08imTZuk7XvEiBEYMWKEtP0TJaNLNS7Vb62Vn+DEApHVOEB8kNOFtCqcZhOa6FCFY4BTCyc3ITqIE5sQkWnW7LFeN0G4AwX6fFfl+QQnHiz+rXsVS/f2J8IqHJF1bvT6IIrEEEdEUsjul59ZGLt65oZY3wSnOl25bqxUhnRZ+8ypVAIcq3DOsAonR5Aeqwrs9DrxoocK6YEhjoiU4VUwKyqM/yEpcnFYKyegTqtxyaonblXj7IQVHatZsgOcbtxY3JsBjtwWr6s9kQoY4ogoisgulapU42R0qWQ1Tqx2vcu0CXNutNPrKpzdrpR2pDoWzi0McESkA4Y4IlIKq3HOuVGNk9WtUvUgl2r7vF5SQCY3xsK5VYVjgCMiXTDEEZFUfq7GxSKiGid7ghM3+SnIuRHgbGMVLiGVquP7CtNi/qiCAZYAeLZeLNnHEEdE9Xg9S6Uu1bhUT3qCVI0D9A9yTrp62n3sMrpR2qHijJRu/L8A4kOM1bCmYqAjIvUxxBGR9lSrxongp2ockHqQ8zrMOTm+1ABngyozUqpchRMZ4JwEMoY5IrKKIY6IpEulS6XO1ThVunCpVI1zwosgp0KATIhVOGFEBTiRAcyrMMculUT6YIgjopi87lJpl27VODeWG3CLzG6VYW6FKlHHYRUumqpVOBGhRWbgYlWO7CrJyvC6CeQShjgicgWrcd5wa904N4IccDBkiQ50IvcpPcCxCpeQm/97boQsdrEkolgY4ogoLlbj5NKmGic4NIiardFpoJMRCFUKcDpX4VTvRulFsHLreOxSSaQHNc5kiCgQMovKbHf1yCwsR0l2/MqYLEWF6cjKtl4JLC5pjOaZlVG/27of6NBMdMvsKyjfipz0DnFvN3I6IlSQ7/g4Dft0QfWazZa2TT+mHcqX7XB8zDAVxq9JXUrAJplLCqjSjTcWN7pRelkV21eYhhbZFZ4dn4jUwUocESXEalx9bi/+7UY1zu1ulYBaocepVB6LCt0o7bL7WnOzCuf3AKdSG4jIewxxROQqv4yNi0XnsXGWMMjVk35MO+UCXFAX9pZNpfAkuy3sUkmkPoY4IvIdVuNS49UYqlSDkNdSbbPMZRl070apYhWOE4tQ0IwdPcvrJpAFDHFElJToLpWsxnlD1WpcmE5BztUAp0A3Srv80o1S5fDGahxRsDHEEZEvsRoXm+MlB4BABzknVUPZAU73KpxMfgtwYTq0kYjkYIgjIktYjbNO5WqcFSK7VaYa5FQMc07aFKQAp2IVzi6dwpFObXUqqNVBL2ZoJvUxxBGRb7EaF5ub3SqB1MeAqRLmnLYjKF0oAXcnM5HVjTJIoSiZoIYmIh0wxBGRZazGWReIapwLQQ7wLsyJOK7MSUzCVKrCpULmwt5BCXC6tpuIUscQR0S+xmpcbFZO5lUKcoB7YU7UcVJ+vIp0o7RLtW6UQQlwYbLaz2ockZoY4ojIFh2rcaKqd4mqcbFOoLysxrnWrRJwNcgBB0OWyEAnep+qBbhUqDqZiYz/Id0DXJifg5wXbbDbA4PITal/9UxEJEhmURlKsjLs3aewXPhg7wMFjdA0p0roPlOxoTiEHs2NxNsUpaFHVoX0thg5HREqyBe6z4Z9uqB6zWYh+4oVusqX7bC1vWhuBDi7/DSZiRV2Tvj9EuCIKFhYiSMi20RX49wgqltlEKtxortVArVBR9Z4sciqWt0f2dwKcCqNg9O9G6Xf+Lka5wTDOvkNQxwRpUTHbpU6ETE2ziqvghzgzsQfbnAUShUKcCpjN0rr/BbkdA+QRDIwxBFRYPixGidikhOhAhjkHLVfYoBLhcpVOCvYjZKIgoIhjohSxmqcXKJOdl3tVgmkHOR0C3OO2yw5wLEbZXxBCHB+qcaxCkcUG0McEQWKbtU4LbtVAilP0qFLkHPcTsUCnMoY4FLnlyBHRPUxxBGRI6zG6UFkt0o3gpyqYU5I2xQMcCpX4UQKUoCTzY0gJ+oY/LuTHzHEEVHgBLUaJ3LtONlBDlArzAlri2Jj4AC1A5zIKpybJ/JFhelRP16S+bhlBjlW+4gSY4gjIsdYjXPGrUlOrBLeFc/h+mdehbnwcYWFN4nrwIWpOA4uVToFuGShzetQp1uQY4AjSo4hjoiUJDvIqVSNs8LNSU4AwePjACEBRmiocvMYKT52v4yDk9WN0usTfSehTJUqnSjFJY2F/T28/rsS6YIhjoiE0HEBcFHsnoh5ueQA4NH4OEBoNSoybDkJXKL2E5fCAU73bpRWia5CiQ5fbgU5N7qTOg1gMgJcqo/b7t8l2Rd+RKLxFUdEwsyaPRZjR88Str/MojKUZGXYu09hOUqyrX34Wt32QEEjNM2pstWOsH2FaWiRXZHSfevaUBxCj+aGkH1ZUVC+FTnpHZJuZ+R0RKgg3/qOux4KbPrFQcvqU2XsnMml8AaoG+BSpWo3SplhK7zvrGy53cJFvh/FE/m3aZ5ZaWt7IrKOlTgiCjRRY+ncqsa5OckJIGGikzAXxod5hgGu9n4pVOGCGODqHkf2sdyc4CXczTLRDxGlhiGOiITy6yQnTrrKeDG9tTZBzk9hzsHjUTXApcrrcXCi/ue8GrfmpyDnpaA8TgomhjgiCjw/VuNEkxbkAH8EOQePQeUAp+M4OJEBzkteH5+I1MYQR0TCsRpXn50TS10nOQlLOcjpGOYcttutAJcKHcfBiQhwKs0aKbMdrFLFp8rfP0znJXRIHoY4IpJChSBna/8uVOO8WHLAi26VgINFqnUJcgJCp5sBLijj4JxS7eQdYJBLlZ8fGxHAEEdEPqZaNc4Ot7tVKhfkVK3MCQpvDHDWuTUOTsUAF8YgR0R1McQRkTR+rcapsgC4VaK7xdkNcimHOUCNMCcwVKb6XLgV4FIlaxwcA9xBMrt5+i3I+e3xEMXCEEdEvqbLWALZk5yIHh9nN1Q4CnKAN9U5wcfTIcAFcSITHQJcJAY5uVJ5fu320NDlc4nUxhBHRFKxGlef6JMlL7pVAh4EuTBZgS5yv4LDm18DXKpUmMhEpQlM7GKQi88Pj4HICncGdxAReSizsBwl2eqfrBWXNEbzzMqo323dD3RoJmb/G4rS0COrIul2G4tr0K25te/4Csq3Iie9g+U2hMNMqCDf8n0S8rqrZRJOgqsOAc7LiUycBjjdFRWmIytbfEVnX2EaWmQnf58gIm+xEkdE0qlQjbPTfUW1apzb3SoBuRU5QGBVTlFOq29+DXBWyB4j6ocAF8aKXDRd260a0Z/ZJAdDHBG5gh8K1jg5gdUxyPktzDl9TE7WgNMhwHk9kYmfAlwYg1wtUWsEEumCIY6ItMRqnHpSDSB+CHIiAmnQA5xVDHD1BT3IedlOt5adIaqLIY6IXBPUapyoJQdU71YJOAtyOoY5Ue1mgJM7Ds7PAS4s6EGOasme/IvUwRBHRNrSpRqXCl27VQLOxnTpEuZEhjddAlyqGODcE8QgJ6ptbr1O7C4vwOUIKB6GOCJyFatx9XnZrVJWkAOcVZdUDHPhNolql5PnB3A/wOk4E2WQAlyYzCCnUphTrT1EbmOIIyKtsRoXm50TblWDHCA+ODlpgygiqm86BDgrGODkkPnYVQhOotuQ6vPF8XDkJYY4InIdq3H1xTspiXWSa7UaJ+PEO9Ug5zTMAdGBTmaok3kcL6pvgDcBTsRMlAxwqZMd5LwKcyqESCIVMMQRkSdEBjm/VONkdKsUPT4OSD1IiAhykeqGLbuBK9b9ZYVDEUE2aAEuVQxwB8l+LtwOcwxwRAexDkxE5KKiwnRkZdsbqF5c0hjNMyslteigDUVp6JFVYWnbjcU16Nbc/veA4SCTk97B9n2tUG0cnajg6qcAZ1UqJ+xuBzg7X9w0zamS2JL4UnnPsSv8t2qRbe39I9X9y+Dma0aHSUqC2lNGR6zEEZFnvK7G2dq/wGqc25OcyBgfB6QeLADxVTnViOpCmur4N0DdACerG6UbJ+MHChpF/aR6X7e5FVTClTkRoUvkvuJx8ry48XfUIfSRdxjiiCiwdPqAdLJ2HCA3yDnpXunHMOd19Q1ggBNNdPjyIsy5XaWMDGFW/qZ2t6f6uEZcsDDEEZGnWI2rz84kJ4D3QQ5wXpXTPcyFHwMDXHw6BjjZYcvtMOfleMG6Ic3r0Maxk6Q7hjgiCjRVq3GyTmpUDXKAnmFOdJudVDYBBjhR3A5Xbh6P4cU5Li1AKmCIIyLPsRpnndNqnF1uBzlA/TAnuuoW5jS8McA559WYNbePH/Qg58XjV/ULQ9IXQxwRBZ6sD1dZSw642a0SSC3IiQxzqgQ6WW3xsvoGMMCFqVRdYZCTR5fH7UXo48yUemGIIyIl+LEaZ0WyEwq7Qc4q2UEOEFOVC/Mi0EUeU9ZxnT5HDHBiqBTgwhjk1KTiawXgpCZBpOYrkYjIZZmF5SjJlnNymGx9KJHrOG3dD3RoZm3bDcUh9GhuWN63nXXkwlJdTy6RWIHK6bpzblf7RARcBjjnVD0hDwu3T+Yac+HnVPZacirwKrSyKyXJoPa7FxEFyqzZYzF29Cwh+8osKkNJVoaQfcXcv6TQF8u+wrSYi+jGWwRcxSAHQHiYi6RKl8tkVAhvAAMcoH6Ai2TlyyCn3FgU3EsiXj86vWbI/9idkojo//NybJzobpV2Jjpxo2slIG6snI5EPXYR1begBzivJy9JFbtXpk63x8XKHVnBEEdEStFpYLXqH7Syg5yTMBcUIsObV90nAX8FOJ25EUB1CzzJiHo8qT7vqn9OhOn02Uu1GOKIyLdSGejtp2ocIDfIAazKxSPy8XnZfRJggFORG0HOD2HOD4/BCk5qEkwMcUREDogOfV7NVhnmZpAD/BfmRD8eBjgx/BTgwti9MjG+fsjvGOKISDk6LTdgh+wTAVELgbsd5ICD4UfXQCcjvDHAOafr+Der3ApyuoU5Vdqbypd8unS/JO8xxBER1WH3Q9TO9jK7VQLeBzkRwUOXMCcjeIoMbwxw/g1vkdwKqrqEuSDPYpoqjofTE0McESkp6B8qToJcPG4EOUBMF0BAzeqczDaJet6chDeAAU5Xbj1mVcOcqu2STaXeJuQuhjgi8j03JjgRXY2zQvZEJ4CzICcqlADR4cnNUOfGcUU+VwxwwQxwYW4+dpVCk6x2OHk+2ZWSZAvuOx0RKU/k4t86crL4bryFwAF7i4ED9hcEj7pvCouDWxEvUKW6oLgX1T6RIddpeAMY4PzCjYXBI4X/dl4sFC4zRPK1RKrjK5SIAiGzqAwlWRn27lNYjpJs6ycJdra3eqKVLMjtK0xDi+zYIUmlIAdASpirS6Wul/GIDG8AA1wYT7oPcjvIAdF/R9mBTpUKYDxuVdREdKUM+tAFnfEdj4jII6JOtNwMcgC0CHMqEh3eAHe6TwJqBziGt9jCz4vbYQ6QE+jcDG5evKbYlZLs4pg4IlKa18sNyBwbZ5WVk5dUZqwE7I+RA5wHB9Hj5VQn4/E6nX0SYIALCq+fo/DYubo/drbVKcAxjJFbGOKIiDxk9YTBb0EO8H+Yk/X43Ow+yQDnDyo+V14GNSI/YIgjIuXp1mdf1jexfgxygL/CXPixyApvKo1/AxjgdMLnLDmvnqOUZrLkeLjA4380EQWKGxOc2OXmJAQix8gBzsfJRe0rIvjoNm5OZggVFZZFdp8EGOB05OU4OdWJeF2xKyW5ie+EREQS2A1+omarBBJPdAKID3KAs9krY+5Pg0DnRvWQAS6aKgHO7sm6zC+BUuHF7JUqU+V1RWQHu1MSkRZ0m+Ak1ftY4bRbJSC+ayUgrrtfvf1K7KKYajtkt0Xkc8kA51xmYXnUj9v3l4HBpZao5yHVvyu7UlKq+B9MRKQIO9+Oq1qRA8RX5aL2HSM8yajUeRUYRYZghjfnZAWu8H69rtAFvXul1wGOyAmGOCIii1IZGyerWyXgTpADUu9eCYgZK5f0WH6ZFIUBrh6dJppwchwVwlxQg5yXvKrCkT+wOyURacPrLpVusXPiKrtrJZB690pAXhdLvwg/PyK7TjLApc6r7o4qdLM8UNAoMF0sRT5Wr/9uFFwMcURENqg0Ns4OL4McwDBXl4znw87fiAEumgohSpV2+D3IqfL4vPw7czycPzDEEZFWWI2rz+pJs4ggxzDnjKzwZqf6xgB3kAqhKRav2+XXqpzox+T230jlzyxyH0McEZFNblXjVAxygPMgBwQvzMl6vLKqb34PcF6HJKu8bqOfwpxfHgdRGEMcEZFLGOTq83uYkxneVKm+AfoFOJ2oEDh1D0Ay2u/kb8KulCQCQxwRaUeFLpVen1TFIzLIudG9Mkz0BB9ekv1YZFTfgNS7T+oS4FQIQ0543XYdq3Ky2uzJ5DfsSkl16PXfSESkuVSWKbA7/beVpQeAgyftqS5BEOZkTblYIsOPG0sUiOBG+LQbmFl9O8jrACSKCksS6LK2nKqBk1U4EoWVOEJFRQVefvllnHXWWejSpQsyMjLQvn17nHDCCXjkkUewe/dur5tIJJXb1TjZ3SoBeyfYorpXiqrKRVK1QhfZLtlts/vcsvtkNL8EuEgqPKZwlUu1sCS7TazCkSpChmHo8TUnSbF27VqMHTsWK1asiLtNmzZtMH36dJx11lnS2pGfn49OnToBAPLy8tCxY0dpxyL/GDt6lrB9lWRlpHa/FL8RT/V+dr/9tlKRC0tUkQtLVpULE1mZi8fNKp0XIVJW5S3M7wFOhaAjm9eLhNflZXVOh9dUyl/8CQpxrMR5Q9Y5rlpfn5Cr8vPzMXz4cGzbtg0AEAqFcMopp+DQQw/Fzp078cUXX6C0tBQ7d+7EqFGj8Mknn2D48OEet5pILal0j3RyP1ldK4Hak/pkQc5K90pAfBfLWBIFq1QCnirVvlQqmrLHvoUxwKlFhe6VkSL/tm4EOreXo9AZA5z/MMQF2KWXXmoGuC5duuD9999Hv379zNt3796NMWPGYO7cuaisrMTFF1+MDRs2IDs726MWE8mTWVSWcjUu5WNqHOSA5FW5cBhxoypXlyqBzA7Z4Q3wtvoGyD/p1v1EO1WpvpfIVPdvLSrUqdZ90yqvq3DkPxwTF1Aff/wxFi5cCABIS0vDBx98EBXgAOCQQw7Be++9h+7duwMACgoK8NBDD7neVqJ4VPlmUYcTR7tj5Kyc7FsNELLGy/lFqs8PA1w0Hf4PZVJ99s3IMXRWxtPZ3V4mlZ9XCi6GuID6z3/+Y14eP348jjzyyJjbNWvWDH/729/M688++yyqqtSekYooVV5845nqyUEqJzN2T8atBjmGudQ4CW92u096Pf6NAc49uj0XKoW1WEQ8n15X4VT5wpPEYogLoJKSEsydO9e8fvnllyfc/g9/+AOaN28OoLYaF67gEdFBXiz8mmqQEzlzZZidUBH0MOdWeAOcVd84/k1ffE7E8DLAESXDEBdAixYtQnl57ZtKs2bNcNxxxyXcPj09HYMGDTKvz5s3T2r7iOwQ/Q2jV+MP3AxygLzulamEuSAEOiePNdXwFoTukzxBjo/PjzNeP3eswlEyDHEBtGbNGvPykUceiUaNkn/QDhgwIOb9ieggrz703QhygL2qnN3Q4dcw5+RxuR3eAL0CHFnD58o7fO5JJnU6HZNr1q1bZ17u0qWLpft07tzZvLx27Vrbx8zPz094+/bt223vkyhs1uyxQteMc8LJLHFO7mt3xsowOzNXAgeDnJU15azOYhkpMvB4MaulCCLCqN3wBni/dADA7pOqUm0pAtV5/RrjjJRkBUNcAO3Zs8e83LZtW0v3adeunXm5oKDA9jHDixwS6cCL5QbMY2sQ5ABrSxGEWV1bri6dAp2oKqLb4Q3QJ8B5fWLtByouRaAaUa8zFV6v7ErpbwxxAVRSUmJebtKkiaX7RG4XeX8iqs/piZJXQQ6AUlW5SHVDktehTnTXz1TCG8AAR/axKhefCgGOVTiyiiEugMrKDr5BpKVZOwFITz/4Zl9aWmr7mHl5eQlv3759OwYOHGh7v0RhortUelmNA5wHOSC1xXXdqMqFpRrogNghSlawkzVWL9XgBqgT3gAGOF2xKhfNb68zVuH8jyEugDIyDp6YVlRYO/EKz2YJWK/eRerYsaPt+xDpTIUTJLe7VwLWqnJhTqtzdekyMYqX4Q3Qp/oG+O/EWjWsytUS+TpjFY7cwhAXQJmZmeZlq1W1yO0i70/kZ06rcV52qwxzs3sloEaYU5GT4AaoFd4AVt/8RoUvnbzA1xnpjEsMBFCrVq3My7/99pul++zYscO8nJOTI7xNRCL4sfuIiJMMJyfcqZ74pxI6wlPqOw08KnH6eJwuGRDGAEfJBG1dORmPVZUqnB8/C6k+VuICqFevXublzZs3W7rPli1bzMu9e/cW3iYiVXldjRO1j1QrckBq3SuB1KpyYaLGznlBRAgVEdwAvcIbwACngiB0sVQtwBGlgpW4AOrTp495OTc3F1VVyU/sli9fHvP+RKpR8RtIER/uKlTknFTlnIQS1St0ke0T0W2SAY5U4Ne/h4oBjlU4SgVDXACdcMIJ5myT+/fvx9KlSxNuX15ejiVLlpjXhw0bJrV9RKoR8QHrhyAHOAsGIgKKyMCkUhtEhzedAlzQuvHpxE9/Gz89FiKAIS6QMjMzMXz4cPP6jBkzEm7/zjvvoLi4GADQsmVLnHLKKTKbR0QJiApyXlXlALGBpW6gEhmuZO03kurhjePfCNA/AMlsO6tw5BWGuIC65pprzMvTp0/Hjz/+GHO7AwcO4J577jGvX3311WjUiEMpSW0yPshUqcaJ3I+IqpyIMCcqxNSVKOBZ+ZFJ9OMWGd4Adp+k2HQLc7Lbq9NzQf7DEBdQZ599Nk4++WQAtWvFnXPOOcjNzY3aZs+ePRg1ahR++eUXALWzUt52222ut5XIT/wW5AAxAUJmmFOFjNAquvoGsPskJaf631D19oWxCkdOsKQSYLNmzcLAgQOxfft2bNq0CUcffTSGDBmC7t27Y9euXfjiiy9w4MABAECjRo3wxhtvIDs729tGE1k0a/ZYjB09S+g+nc5UKZqotZ3CJ+2pzl4JpL6uXF2RASeVWS1VIyuYig5uAKtvZF/k31OF2SzdfH2p1I2SgokhLsA6duyIefPm4ZJLLsHKlStRU1OD+fPnY/78+VHbtW7dGtOnT48aR0cUVCKCnMiFdUXuy8kyBGGpLkcQi66BTnZFkQFODak8HhWCjixeBjq3X1uqvZZZhQsmhriA6927N7799lu8/vrreO211/Djjz/it99+Q3Z2Nrp3747zzz8fV1xxBQ455BCvm0rkK34PcoDzqlwk1QOdG11BdQ1vgHonvamQMcOsX0OdG4HOq9eUkNcBq3AkQMgwDMPrRhDl5+ejU6dOAIC8vDx07NjR4xaRX4juUhkmqlulyBMc0SdLTsNcmMgwF4sXoc7N8XsywhvA6psVbrbfr4EuFruPVZXXkbAxzYJDHCtxapN1jstKHBGRh0RX5ABxJ4MiqnKAnMpcpHiBymm483qiFZ3DG6DOibddKlR4/B7odHxtMMCRahjiiMjXZExwAoid5ERkkBO9P1FBDpAf5uryOoSlSlZ4AxjgElGpzaK/kCE1sBslicQQR0SUItVmq4wkOsgB4rpXuh3mdMHw5g2V2xyk6pzKVH2NsAoXbAxxREQKEF2Nk7FPkVU5gGEOkBvcwhjgYtO1vQxz7lK1GyURF/smIt+T+W2lyA9mGSeVovd5oKCR8FAgY8Fq1bnxmGX8rWLRZWHlMN3aW5fu7deJys8zq3DEShwRkUJkVeQAsd/gi67KAdFVKb9W5/xUeQPUPsmtS6e2WsHKnFwiXy+swpEMrMQRUSDoUo0D5J1s6lCVC/NTdS78WBjgvOH3ypXfH58XVA9wrMIRwEocEZEQoic5kVGRk7Vf0ROfRNK1Oud2AGV4q0+XdorCypwYQXvdkL5YiSOiwNDt20tdKnJhsoNEZEVLtSqdV21za9xbmC4nuLq0U4YgP3anRD93rMKRTKzEEREJImPJAZkVOUD8t/Yyq3J1eV2l8zpIMrzVp0s7ZWNVzj4GONINQxwRBYqsxb/DdApyMvctY+KTROIFKhHhzuuwVpeb4Q3QIxjp0EYvMMxZw9cP6YghjohIMAa5Wm5W5eJRLYA5wfAWmy7t9BLDXGzSuqyzCkcu4Jg4IgocXT8MZZ6sypzhzu1xW37jxfOnQzDirIz28Tk7SKcARxQLQxwRkQSyPshln4DJ3D/DnD1ehTfVT/J1aKPqgv4c6hbgdP3ikeRiiCOiQHLjQ1FmkJNdlZOJQS4xr8KuDif1OrRRJ0ELc0F7vORv/CQlIpJIxvg4c9+Sx8kB8sbQqDBeTjVehVsdTmp1aKPOgjBmTnovBlbhyGWsxBFRYLn14ShzjITO3SsBdrEEvH0OVA9HrJy4y6/Pt64BjiiRYH9yEhH5gMyKnBv7B4JZmfMyvOpwoq5DG/3KL5U5N15DMgMcq3CUCCtxRBRofqjGAe5U5Nw4IQpXpfxcnfP68akejvxaDdKRrn8LXdsdiQGOkvHvpyQRkWJkjo8D3KmYuXGMMD9V51QIpaqf1KreviDTpTLn9muI3SjJS6zEEVHgufmNpxsVOb9U5cJ0rc6p0m7VqxKqt48OCv+tVPt7edEmdqMkrzHEERHBX0EOcGksiAcnciqEomRUaqNqJ9t1qd4+is/rQOfl8VmBIxWo8SlDRBQwsrtWAv7rXhmpbkjyssulKoEtkurhSPX2kT2Rf0/Zkyx5TXaAYxWOrFLvk4eIyCOzZo/F2NGzXDuen4Ic4O14GTdDnYqhLZIKJ7rxqNw2EiPW3zjV9wbVXi8McKQStT+JiIh8zi9Bzs3jWBEvaNkJd6qHtbpUO+GtS/X2kTx++NuzCyWpRq9PKCIiydyuxgHuBTlAfrVMhapcIroFMytUP0FWvX1EybgR4FiFI7s4sQkRUR1efJi69S2vWyfUPHF3h8rPs4qzGBLZxQocqYohjohIEX4McjyJl0Pl51blthHZ4dZ7MqtwlAqGOCKiGLz6UPVbkHP7WH6nekBSuW1EdjDAkeoY4oiI4ghCkGNVTh8qP3/8+5KfsAsl6YAhjohIQW6eRLAqpzaVA5LKbSNKhZvvvazCkRMMcURECXj5IevnIMcT/+RUfp5UbhtRqhjgSCcMcURESTDIyTseg0B9Kj8vKreNyAl2oSTdMMQRESnO7SDnRZijWio/Fyq3jcgJtwMcq3AkAkMcEZEFXn/oun2Swaqcu1R+/Cq3jcgpBjjSFUMcEZFFXn/4+j3IhY8ZpMCg8uNVuW1ETmUWlbELJWmNIY6ISCNBCHLh4/o5QKj++FRuG5FTXoU3r78IJH9hiCMiskGFD2G3v0H2MnCoHnbsUv3xqN4+IqcY4MgvGOKIiGxS5cM4KFW58LF1Dheqt1/19hE55WX3SVU+M8hfGOKIiFKgyodykIJc+Phet8GqcFtVb6/q7SNyimPfyI8aed0AIiJdzZo9FmNHz/K6GcgsKkNJVoZ7xyssR0l2umvHi9eGMK/bUpcuoUiXdvqNyEDh5v+9jv5fe3cfHFV1+H/8k0h4kCAGjEAMCZAEYgQVGiJGASUiLS20dCg/RRGitWrtyNSOjp2xCvXrOG21dawOaTtVKmKrRelXRazQAI4GRUUJIEKqIQHzQBKREAgJD/f3R75cdslms8nu5t5z9/2ayXhvOPecezfHs+ez92HdEN7c8oEfvIcQBwAe4ESQk9wRoNwQ6EwKRCbtqxdEM0icXTehro0bwptEgEN0cTklAITBTW/STkxc3BYIevISRlMul/Rl0r6a6vS9V07cg+Vk227hluN203sDvIkzcQAQJrdcVimdmcDE2uWVgQQKLOHsp8kByOR9N4VbwoMv333y+lk6N73+BDj0BEIcAESAm4KcFNuXVwYTi2EmFo+5p7gpOHTGq5deuu1vQIBDT+FySgCIELe9eXN5ZWwz7VJPU3jlckWTj8PkfQcihRAHABFEkCPIuQF/g8jzcmgwJRS5fR/dNv7D27icEgA8rqcvrZTce5+c1xHeIs/NoSEa3HbZpSmvPwEOPY0zcQAQYW58M3fqjByhoufwWkeOKWemekJPP/HSxCdsunHMh/dxJg4AosBtDzqRnHlypcRZuWgjvEWOKaHBScFeo1DHFi+9zgQ4OIUQBwBR4sYgJ3F5pZcQ4CLDS6HCSbH2OhLg4CQupwSAKHLrmzyXV5qN1zJ8pl2yB3dx69iO2EGIA4Aoc+ubvVOTV8JHeHj9wkNwQ7jcOqYjthDiAKAHuPVNnyBnFl637uGsGyLFrWM5Yg8hDgB6iFvf/J0McoSS0PBadR3BDZHm1jEcsYkQBwA9yK2TACcnu4ST4Hh9QmPio+lhDreO3YhdhDgA6GFungxwVs5deE2CI7ShJ7h5zEbsIsQBgAPcPClwckJMaGlDqG3v7DNtBDf0BDeP1YhthDgAcIibJwdOB7lYDjCxfOynEdjgBm4eowFCHAA4yM2TBKcnzrEWZmI1vBLY4EZuHpsBSerl9A4AQKx78aX5mv//XnR6NwJKPHRMTQP7Otf+/4WapvP7OLYPPSFWwhsBDW5HeIMpOBMHAC7g5omDGybeXg45nj42zrDBIG4eh4GzEeIAwCXcPIFwwyTca5cbeu14JEIbzOXm8RcIhBAHAC7i9omEGybmXgg+XjiG0whtMJ3bx10gEO6JAwCXcfM9cpLz98lJ5t4r55XwRmCDFxDeYDLOxAGAC7l9cuGWSbxJlySasp8d4YwbvMTtYyzQGc7EAYBLcUYudG4+M+eF8AZ4BeENXsGZOABwMbdPONw2wXfTmTk37Ut3cNYNXuP28RToCs7EAQDC4qYzcqc5eWbO5OAmuS+YA5FAgIPXEOIAwOXcflml5M4gJ/kHqmgHOtPDm0SAg/cQ3uBVhDgAMABBLnzRCHReCG4S4Q3eQ3iD13FPHAAYwoRJiSn3UZ2+X62r9611dzu3MuXvBXSFCWMlEC7OxAGAQUw4Iye5/6zc2bwQyLqK8AavIbwhlnAmDgAMY8pEhZDgTpx9g9e8+NJ8Y8ZFIFIIcQBgIFMmLIQFd+HvAS8hvCGWEeIAwFCmTF4IDu7A3wFeQXgDCHEAYDRTJjIECOdw+SS8gvAGnEGIAwDDmTKpIUj0PF5zeAHhDWiPp1MCgAfw1EqcjQAHkxHagOAIcQDgEQQ5nEaAg4kIbkDoCHEA4CEEORDgYBKCG9A9hDgA8BiCXOwiwMHtCG1AZBDiAMCDTApykghzEUCAiw3dDUFOjAcENiB6CHEA4FGmBDmJs3LhIsB5T6QDEIEK8BZCHAB4GEHO+whw3kDIAtAVhDgAgGsQ5LqGAGc+whuA7iDEAYDHmXQ2TiLIhYoAZy6CG4BwxTu9AwCA6DNt0khACY7Xx0wvvjTfuP8XAbgTIQ4AYoRpk0eCSmC8LuYhvAGINEKcIVpbW/Xhhx9q2bJluvXWWzVu3Dj16tVLcXFxiouL06JFi7pdt2VZWr16tebOnauMjAz169dPycnJys3N1dKlS1VZWRm5AwHgKNMmkgQWf7weZiG8AYgW7okzwNNPP61f/OIXam1tjXjdVVVVWrBggYqLi/1+f+zYMdXX1+vjjz/W448/rj/+8Y9hBUUA7mHiPXIS3yVHgDMHwQ1AtHEmzgD19fVRCXCNjY2aMWOGX4DLy8tTYWGhfvjDH+r888+XJDU1NamwsFDPP/98xPcBgDNMnGTGcoiJ5WM3jYn/bwEwD2fiDDJ8+HBNnDjR/ikqKtKqVau6Xd/PfvYz7dixQ5I0aNAgrVq1Stdee639701NTbrjjjv04ottn9jffvvtys/PV2ZmZngHAgDdFItPriTAmYHwBqAncSbOAIWFhaqpqVFlZaVeeeUVPfDAAyooKFD//v27XeeOHTu0cuVKe/3FF1/0C3CSlJiYqBUrVig/P19S2315Dz30ULfbBOAupk46YynUxNKxmor73gA4gRBngPT0dA0ZMiSidS5btkynTp2SJE2fPl0zZswIWC4+Pl6//e1v7fWXX35ZdXV1Ed0XAM4xdfIZC+EmFo7RdKb+/wPAfIS4GGRZll577TV7vbCwMGj5q666SllZWZKkkydP6vXXX4/q/gHoWaZORBMPHfNs0PHqcXkFZ98AOI0QF4PKysq0f/9+e/2aa67pdBvfMmc/yRKA+UyekHop8Hg5mHqFyf+vAPAOQlwM2rVrl708dOhQDRs2rNNtJkyYEHB7AN5h8uTUC8HHC8fgZZx9A+AmPJ0yBu3evdteTk9PD2mbtLQ0e/nzzz/vcpu+Z/4C2bdvn71cXV3d5foBRMbRow1O70K3xR9t+++RAeY9vbL/4WM66vROIKCnl82R1Pn7GAAE4juvPXHiRMTqJcTFoIaGM5O0UB+YMnToUHv56NGjamlpUZ8+fUJuc/jw4SGXzcvLC7ksAADR9L9v3OP0LgDwiLq6Oo0YMSIidXE5ZQxqamqyl/v16xfSNmeX860DAAAAQM/hTFwMOnbszH0XvXv3Dmmbs8+6NTc3d6lN38slO9qnzz//XEOGDFFycrJ69XJn16yurrbPFG7ZsiWk+wkRu+gvCBV9BV1Bf0FX0F+cdeLECfvrucaNGxexet05UzZAUVGRnnzyyYjW+dhjj2nOnDkRrTOQvn3P3C/S2toa0jYtLS1+66GewTstNTW10zKZmZldqtNpw4YNC+m4AIn+gtDRV9AV9Bd0Bf3FGZG6hNIXIa6b6uvr/R4QEgmHDh2KaH0dSUxMtJdDPaN2djnfOgAAAAD0HO6Ji0GDBw+2l2tra0Papqamxl4+99xzu/RQEwAAAACRQ4jrpgcffFCWZUX0Z9GiRT2y72PGjLGXKyoqQtqmsrLSXs7Ozo74PgEAAAAIDSEuBl188cX2ck1Njd9Zto5s3bo14PYAAAAAehYhLgZlZWX53dS6cePGTrfZtGmTvTxt2rRo7BYAAACAEBDiYlBcXJxmz55try9fvjxo+c2bN2vPnj2SpPj4eM2aNSuauwcAAAAgCEJcjLrzzjsVH9/25//3v/+tdevWBSx36tQp3X///fb6vHnzlJyc3CP7CAAAAKA9QlyMGjdunG666SZ7/cYbb2x3WeWRI0e0cOFCvfvuu5Lavhj8kUce6cnddJ3U1FT7QTR8zwo6Q39BqOgr6Ar6C7qC/uJNcZZlWU7vBDp3+eWXt/tdZWWlDh48KElKSkpSWlpauzKffvpph3U2NjYqPz9fO3futH83adIk5eTk6NChQyouLrbrl9ouu1y4cGH3DwIAAABA2AhxhoiLi+vWdp39eauqqrRgwQIVFxd3WCYxMVFPPfWUCgsLu7UPAAAAACKHEGeIaIW402VWr16tlStXauvWraqurlZiYqLS0tI0a9Ys3XbbbQHP8gEAAADoeYQ4AAAAADAIDzYBAAAAAIMQ4gAAAADAIIQ4AAAAADAIIQ4AAAAADEKIAwAAAACDEOIAAAAAwCCEOBirtbVVK1as0MyZM5Wenq6+fftq2LBhys/P1+OPP676+vqotd3U1KRly5bp2muvVWpqqvr06aPU1FRNmzZNRUVFampq6nKdp7+vb+7cucrIyFC/fv2UnJys3NxcLV26VJWVlVE4ktjhRH/ZtWuXnnrqKc2bN085OTkaOHCgEhISdMEFFyg3N1eLFy/WJ598EnJ9GzduVFxcXJd+fvzjH0f8uLyup/vKkiVLuvx3feGFF0Kun7ElunqyvyxfvrzLfcX3Z+PGjQHrZWyJvpMnT6q0tFR//etfdddddyk3N1e9e/e2X89rrrkm6vvA3MVjLMBAu3btssaPH29J6vDnwgsvtNasWRPxtktKSqyRI0cGbXvUqFHW+++/H3KdX331lTVt2rSgdSYmJlrPPfdcxI8nFvR0f3n77betSy65JGh7vj8/+tGPrIaGhk7r3bBhQ8h1nv657bbbInJMscKJseXhhx/u8t91xYoVIdXN2BJdPd1fnnvuuS73Fd+fXbt2BayXsSW6Vq9ebZ177rlBX8+pU6dGdR+Yu3hPLwGG2b9/vwoKClRVVSVJiouL05QpU5SZmakDBw5o/fr1am5u1oEDB/SDH/xAa9euVUFBQUTaLi0t1YwZM3T48GFJUkJCgqZNm6bU1FTt27dPxcXFOnHihL788ktdf/31eu+99zR27NigdTY2NmrGjBnasWOH/bu8vDxdcsklOnTokIqLi/XNN9+oqalJhYWFio+P1y233BKR44kFTvSXjz/+WDt37rTX4+LidOmll2r06NFKSkpSXV2d3n33XdXV1UmS/vnPf+qzzz7Tpk2bNHjw4JDaSElJ0Zw5czotl5+f372DiEFOji2nTZw4UXl5eZ2WGzNmTKdlGFuiy4n+cvHFF+vuu+8OufyqVatUW1srSfrWt76l7OzsTrdhbIm8b775RkePHnWsfeYuHuV0igS6asqUKfYnPOnp6da2bdv8/r2urs4qKCiwywwaNMg6ePBg2O22trZaGRkZdr2XXXaZtXfvXr8y5eXl1mWXXWaXGT16tHX8+PGg9S5YsMBvX4uLi/3+/fDhw9b8+fPtMr1797bKysrCPp5Y4UR/eeyxxyxJ1uWXX24988wzVn19fbsyLS0t1mOPPWadc845dtvz5s0LWq/vp+XR/tQ2Fjk1tvieiXv44YfDru80xpbocqq/hKqhocHq3bu33f5TTz3VYVnGlug6fQZ1yJAh1ve+9z1r6dKl1ptvvmktXrw46q87cxfvIsTBKGvWrPEbEEpLSwOWa2pqskaNGmWX/eUvfxl2288884xdX1JSklVVVRWwXFVVlZWUlGSX/dOf/tRhndu3b7fi4+Ptsm+99VbAcidPnrTy8/PtcjfeeGPYxxMLnOovr7zyirV69eqQyv7+978P6XIny2KiFU1Oji3RCHGMLdHlZH8Jle97VkJCglVXV9dhWcaW6KqurrYqKira/d73//1ove7MXbyLEAejzJw50x4Mbr/99qBlX3jhBb9PiTr7VKkzOTk5dn2PPvpo0LL/8z//Y5e99NJLOyz305/+1C43ffr0oHW+++67dtlzzjnHOnDgQLeOI5Y42V9CdeLECSslJcVu+4knnuiwLBOt6HGyr0QjxDG2RJcJY0teXp7d7pw5c4KWZWxxRk+EOOYu3sXTKWGMpqYm/ec//7HXCwsLg5afO3euBgwYIEn6+uuv9c4773S77f/+97/67LPP7PVFixYFLe/776Wlpfriiy/albEsS6+99pq93tnxXHXVVcrKypLU9pSr119/PYQ9j11O9peuOOecc3TFFVfY63v37u2RdnGGKX0lVIwt0WVCf9m9e7e2bNliry9cuDDqbcJ9mLt4GyEOxigpKVFLS4skqX///po4cWLQ8n369NGkSZPs9eLi4m637bvt6NGjlZKSErT8RRddZA9akrRhw4Z2ZcrKyrR//357PZTHC/uWCed4YoGT/aWr4uLi7OWTJ0/2WLtoY1JfCQVjS3SZ0F/+9re/2cvJycmaOXNm1NuE+zB38TaeTglj7Nq1y14eN26cevXqvPtOmDBB69ata7d9OG1PmDAhpG0mTJigsrKyDtv2/d3QoUM1bNiwkOoMtD3ac7K/dNX27dvt5eHDh4e0TXNzs1577TVt27ZNBw8eVGJiooYOHaorr7xSl112meLj+YwuVG7qK7W1tVqxYoX27NmjI0eOKCkpSWlpaZo8ebJGjRoVUh2MLdHlpv4SyKlTp/y+R3D+/PlKSEgIeXvGFu9g7uJthDgYY/fu3fZyenp6SNukpaXZy59//rmr2nbyeGKBKa9vSUmJ/YYpSdddd11I223ZskXf//73A/7byJEjdf/99+uOO+7wO8uHwNzUV4qKilRUVBTw36666iotWbKk0z7ipuPxIre/vsXFxdq3b5+93tVLKRlbvIO5i7fxcQqM0dDQYC8PGTIkpG2GDh1qL3/99deuajvcOo8ePWpf0oP2nOwvoTp16pR+/vOf2+tXXHGFcnNzw663vLxcd911l7773e+qqakp7Pq8zoS+Iknvvfeerr/+ej344INByzG2RJfb+4vvpZTjxo3T+PHjI1Y3Y4tZmLt4GyEOxvB9w+jXr19I2/iWC+cNJxpth1tnR/WijZP9JVSPPPKI/fCB+Ph4Pf74451uM2TIEC1evFhr1qxRRUWFmpub1dzcrLKyMv35z39WTk6OXXbt2rW64YYbdOrUqagdgxe4oa+MHTtWS5cu1TvvvKPa2lq1traqsbFRW7du1a9//WtdcMEFktoeKvDoo4/qN7/5TYd1MbZElxv6S0eampq0evVqe72zB1n4YmzxHuYu3sbllDDGsWPH7OXevXuHtE2fPn3s5ebmZle1HW6dHdWLNk72l1C8/vrrWrp0qb1+33336eqrrw66TW5uriorKwMeT2ZmpjIzM7Vo0SLdfffd+stf/iJJWrNmjVauXKkFCxZE9gA8xOm+cs8992jJkiXtfp+QkKDx48dr/Pjx+slPfqLZs2fbof9Xv/qV5s6dq4yMjHbbMbZEl9P9JZhVq1bpyJEjkqRevXrppptuCmk7xhZvYu7ibZyJgzH69u1rL7e2toa0je8p+1A/MeqptsOts6N60cbJ/tKZDz/8UDfeeKMsy5IkFRQU6JFHHul0u8TExE7fNBMSElRUVKSpU6favwt21gbO95VBgwZ1WmbIkCF644037LLHjx/XH/7wh4BlGVuiy+n+EozvpZQzZswI+XI3xhZvYu7ibYQ4GCMxMdFeDvVTHN9yvtu7oe1w6+yoXrRxsr8E89lnn+k73/mO/Wn5xIkT9a9//atLT4/rTHx8vB566CF7fefOnX4POoA/t/aVsyUnJ+vuu++219euXRuwHGNLdLm1v1RUVGjTpk32elcupQwVY4tZmLt4GyEOxhg8eLC9XFtbG9I2NTU19nIon3b3ZNvh1nnuuee2u0QBZzjZXzpSXl6u6dOn2zeG5+TkaO3atVF5Q5s8ebJfMOSxzh1zY1/pSEFBgb385ZdfBvwknLElutzaX1asWGGf3U9KStKsWbOi0g5jizmYu3gbIQ7GGDNmjL1cUVER0jaVlZX2cnZ2tqvadvJ4YoHbXt+vvvpKBQUFqqqqkiRlZGRo3bp1fm+IkZSQkGA/DEPyf6IY/LmtrwRz9ncyBfq7mnQ8JnLr6/v888/byzfccEPUJsqMLeZg7uJthDgY4+KLL7aXt2/frhMnTnS6zdatWwNuH07bn3zySUjbdNa27+9qamr8Pqnqbp04w8n+crYDBw6ooKBA5eXlkqTU1FStX79eKSkpEWsjkNOXbEpS//79o9qWydzUVzrj+zeVAv9dGVuiy4395ezvm4zGpZS+GFvMwNzF2whxMEZ+fr79yeKRI0f00UcfBS3f0tKi999/316fNm1at9u+9tpr7eXdu3eruro6aPmqqiq/N1Tf7U/LyspSamqqvb5x48ZO98P3fodwjicWONlffDU0NOi6666zvyD1wgsv1Pr16zVixIiI1N+R8vJyNTY22utnn8HBGW7pK6HwnYj1799f5513XrsyjC3R5cb+4nsWLjs7W3l5eRFv4zTGFnMwd/E2QhyMkZiY6Hc/yPLly4OWf/XVV3X48GFJbfcHTJkypdttZ2Vl+X1Hju8TwAI5+8tWAz0GPC4uTrNnz7bXOzuezZs3a8+ePZLabi6P1v0OXuFkfzmtsbFR3/72t7V9+3a73nXr1vldjhItzz77rL183nnnRfQLf73GDX0lVL771lG7jC3R5bb+0tLSopdeesleX7hwYUTrPxtjizmYu3icBRjkjTfesCRZkqzevXtbO3bsCFjuyJEjVmZmpl32gQceCLvtp59+2q5v8ODBVk1NTcBy1dXV1qBBg+yyRUVFHdZZWlpqxcfH22XffvvtgOVOnjxpXX311Xa5G264IezjiQVO9pcjR474/c0GDBhgffDBB2HVd/LkyZDKbt682erbt6/d9u23397tdmOFU33l8OHDIZd98skn7XYlWStXruywLGNLdDk5tpztpZdesuuPj4+39u/f36XtGVuc8/DDD9uv5dSpU6PSBnMX7yLEwTiTJ0+2B4QRI0ZYpaWlfv9eX19vTZ8+3S4zaNAg6+DBgwHrKi8v95sUbdiwocN2W1tbrYyMDLvs+PHjrYqKCr8ye/futS6//HK7zOjRo63jx48HPZ4FCxb4DbBn70NTU5N18803+00YysrKgtaJM5zoL8eOHfOrs1+/ftamTZvCOo4NGzZY2dnZ1rJly6wDBw502O4zzzxj9e/f3277vPPOs/bt2xdW27HCib6yZMkSa/r06darr75qNTc3Byxz4MAB65577vGrLzc3t9OJN2NLdDn1XnS2mTNn2ttdf/31XT4OxhbndDfEMXeBZVlWnGX93/NoAUPs379feXl59rXd8fHxmjp1qkaNGqW6ujqtX79eR48elST16tVLb731lt+lL7727t2rkSNH2usbNmzQNddc02Hb27Zt09VXX62mpiZJbU/pKigoUGpqqvbt26fi4mIdP35ckjRgwACVlJRo7NixQY+nsbFR+fn52rlzp/27SZMmKScnR4cOHVJxcbEOHjxo/9vy5cujfrmMlzjRX+6//3797ne/s9cnTJigK6+8MqT9zcrK0uLFi9v9fuPGjfb9CfHx8Ro9erRycnKUlJQkqe1ehs2bN+ubb76xt+nTp4/WrFnT4fHAnxN9ZcmSJVq6dKmkti/RHTt2rDIyMjRw4EC1tLToiy++0JYtW/y+SiA9PV3vvfeeLrrooqDHw9gSXU6+F51WW1ur1NRU++EqK1eu1Pz587t0HIwtPWPmzJn204lPq6mpsR/T379/f2VmZrbb7s0332z3ECzmLpDE5ZQw065du/w+NQr0k5ycbL3xxhtB6+nOp58lJSXWyJEjg7Y9atQoa/PmzSEfz1dffWVNmzYtaJ2JiYnWs88+G3KdOKOn+8vChQuDthXsp6NPYzds2NClesaPH29t27YtzFcu9vR0X/H9JL6zn7i4OGvu3LlWfX19yMfD2BJdTr4XWZZlPfHEE35nxo4ePdrlY2Bs6Rnp6endek8oLy9vVxdzF1iWZfUSYKDs7Gx98MEH+sc//qG///3v2rlzp2pra3X++edr1KhRmjNnjm699Va/77KJlCuvvFKlpaV6/vnn9fLLL2vPnj1qaGjQ4MGDNXr0aM2bN0+33HJLl77AOSUlRevXr9fq1au1cuVKbd26VdXV1UpMTFRaWppmzZql2267TWlpaRE/nljgZH+JlMmTJ+ujjz5SSUmJSkpKtGfPHtXX16uhoUHHjx/XwIEDlZ6erkmTJmnOnDk8Aayberqv3HfffZoyZYo2b96s999/XxUVFaqvr9fXX3+t+Ph4JSUlacyYMcrPz9fNN9/c5e9YYmyJLqfHFt8HUcybN0/9+vXrch2MLbGBuYv3cDklAAAAABiErxgAAAAAAIMQ4gAAAADAIIQ4AAAAADAIIQ4AAAAADEKIAwAAAACDEOIAAAAAwCCEOAAAAAAwCCEOAAAAAAxCiAMAAAAAgxDiAAAAAMAghDgAAAAAMAghDgAAAAAMQogDAAAAAIMQ4gAAAADAIIQ4AAAAADAIIQ4AAAAADEKIAwAAAACDEOIAAAAAwCCEOAAAAAAwCCEOAACPWbRokeLi4iL2M2LECKcPCQDggxAHAAAAAAYhxAEAAACAQXo5vQMAACC6srKydO+993Z7+wEDBkRwbwAA4SLEAQDgcSkpKbrzzjud3g0AQIRwOSUAAAAAGIQQBwAAAAAGIcQBAAAAgEEIcQAAAABgEEIcAAAAABiEEAcAAAAABiHEAQAAAIBBCHEAAAAAYBBCHAAAAAAYhBAHAIDHbdq0SXFxcd3++fTTT50+BACAD0IcAAAAABiEEAcAAAAABunl9A4AAIDoysrK0r333tvt7YcPHx7BvQEAhIsQBwCAx6WkpOjOO+90ejcAABHC5ZQAAAAAYBBCHAAAAAAYhBAHAAAAAAYhxAEAAACAQQhxAAAAAGAQQhwAAAAAGIQQBwAAAAAGIcQBAAAAgEEIcQAAAABgEEIcAAAAABikl9M7AAAAoquqqkpFRUVh1TF79mylpKREaI8AAOEgxAEA4HFlZWW66667wqojOzubEAcALsHllAAAAABgEEIcAAAAABgkzrIsy+mdAAAAAACEhjNxAAAAAGAQQhwAAAAAGIQQBwAAAAAGIcQBAAAAgEEIcQAAAABgEEIcAAAAABiEEAcAAAAABiHEAQAAAIBBCHEAAAAAYBBCHAAAAAAYhBAHAAAAAAYhxAEAAACAQQhxAAAAAGAQQhwAAAAAGIQQBwAAAAAGIcQBAAAAgEEIcQAAAABgEEIcAAAAABiEEAcAAAAABiHEAQAAAIBBCHEAAAAAYBBCHAAAAAAYhBAHAAAAAAYhxAEAAACAQQhxAAAAAGAQQhwAAAAAGIQQBwAAAAAG+f8cte+wtGt62AAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 426, + "width": 440 + } + }, + "output_type": "display_data" + } + ], "source": [ "plt.figure(figsize=(4.5, 4.5))\n", "hist_kws = dict(edgecolor='k', linewidth=0.2,\n", @@ -431,7 +575,7 @@ "\n", "valid = np.isfinite(fret_2cde)\n", "sns.kdeplot(x=ds.E[0][valid], y=fret_2cde[valid],\n", - " cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n", + " cmap='Spectral_r', fill=True, thresh=0.05, n_levels=20)\n", "plt.xlabel('E', fontsize=16)\n", "plt.ylabel('FRET-2CDE', fontsize=16);\n", "plt.ylim(-10, 50);\n", @@ -444,9 +588,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 612, + "width": 619 + } + }, + "output_type": "display_data" + } + ], "source": [ "valid = np.isfinite(fret_2cde)\n", "x, y = ds.E[0][valid], fret_2cde[valid]\n", @@ -454,7 +614,7 @@ " facecolor=sns.color_palette('Spectral_r', 100)[7])\n", "\n", "g = sns.JointGrid(x=x, y=y, ratio=3)\n", - "g.plot_joint(sns.kdeplot, cmap='Spectral_r', shade=True, shade_lowest=False, n_levels=20)\n", + "g.plot_joint(sns.kdeplot, cmap='Spectral_r', fill=True, thresh=0.05, n_levels=20)\n", "g.ax_marg_x.hist(x, bins=np.arange(-0.2, 1.2, 0.0333), **hist_kws)\n", "g.ax_marg_y.hist(y, bins=70, orientation=\"horizontal\", **hist_kws)\n", "\n", @@ -501,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -543,9 +703,20 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2859, 2656, 2859)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "ALEX_2CDE.size, np.isfinite(ALEX_2CDE).sum(), np.isfinite(ds1.E[0]).sum()" ] @@ -559,9 +730,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 612, + "width": 616 + } + }, + "output_type": "display_data" + } + ], "source": [ "hist_kws = dict(edgecolor='k', linewidth=0.2,\n", " facecolor=sns.color_palette('Spectral_r', 100)[7])\n", @@ -578,9 +765,32 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of bursts (removing NaNs/Infs): 2656\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 612, + "width": 616 + } + }, + "output_type": "display_data" + } + ], "source": [ "valid = np.isfinite(ALEX_2CDE)\n", "print('Number of bursts (removing NaNs/Infs):', valid.sum())\n", @@ -596,7 +806,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -606,9 +816,48 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "image/png": 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uueceo/3LL7+U2c/f39/Uvu6660rdaXG2G264wdjeuXOnsQ7/uXr88ceN5X6Ki4u1ePHi8xoPpxUUFFidAgAAqARrpAMAAKBeW79+vT7//PNS8TMzziVp5syZpj7Hjh0zllKIj4/Xvffeazr2mmuu0TXXXFNLGVds8uTJ+uijj7R27VpJ0uuvv17mjPraVtna6CW9+eabuuyyy/Svf/3LyFuSUlJSNH/+fM2fP18PP/ywbrjhBr355ptVvqW3MoMGDVJcXJxeeuklzZ0711j6pKCgQDExMYqJidG0adPUuXNn/etf/zIVleuaESNG6KWXXpIk7du3TwUFBfL09DT1adq0qandvXv3Ssc9u8/Ro0dLjVMd/v7+6t+/v/EBz/bt2895LPyprtxFAQCoOXa7TXY3m7U5OKw9f0NDIR0AAAD12vbt2/Xf//63wj4ffPBBuftWrFihFStWmGKhoaGWFdIl6corrzQK0itXrlRxcbHc3NzOa0yHw1ETqZXruuuu03XXXacjR45o5cqVWrVqlVatWqWEhARJp9cgnzdvnn777TetXbtWnTt3rpHztm3bVp988oneffddrV27VtHR0Vq1apViYmKM4uSuXbs0ZswYvfHGG3r44Ydr5Lw1LTIy0tROTU0tFTv7AbRnz1Avy9l9MjMzzzHDP5XM63xnuOO0sta/BwAAdQtLuwAAAAB1TMlCZXZ2dpnFSg8PD2O7Kusrnzp1qmaSq0SLFi1066236v3339e2bdt06NAhvfTSS6aHXtZGMdvHx0cjRozQ1KlTtXz5cqWmpuq7775T7969jT5PPvmkjhw5UuPnrglnF1LPfL1K6tKli2kpl6oUxc/uExgYeI4Z/qlkrmXlierLycmxOgUAAFAJCukAAACo1yZPnlzqAZ5z5swx9q9atcq0Lycnx1gy4+9//3upY51Op1544QWL3s1pZxfVynroZ0BAgLFdlVnB8fHx55/YOWjZsqWeeeYZffTRR0Zs8eLFys/PN/Wz2Wr21mMfHx+NGTNGK1asULNmzSSdXvJl0aJFNXqemrJ582Zj28/Pz1j7vSRPT08NHjzYaJ+Z7V+Rkn1sNptatGhxnpnKeL6AVHomPc4NM9IBoOGx2yU3i19VeG48qoEvJwAAABqc6OhoSZKXl5eioqJM+9avX2882K9kUbIu2bRpk7Ht7e1d5prW7dq1M7ZLFjbLkpeXpx9//LHG8jsX1157rbFdWFiotLQ0035vb2/T/poSHBysgQMHGu1jx47V2Ng1adasWcb2kCFDyu03ZswYY/unn36q9G6E77//3tju3bu3goKCzjlHSVq2bJkOHjxotK1+WG9DkZ6eXucfigsAQGNHIR0AAAANzplCer9+/eTl5WXat2rVKmO7ooKlVQ4dOqRvv/3WaF922WVlztbu16+fsf3TTz8pJSWl3DGfe+65Cvefj6qOW7L4arfbFRISYtofFBRkzLw/fvx4pcX06qzNXfLcYWFhVT7ufGRlZVW579tvv236vpwwYUK5fSdMmGB87ZKSkvTWW2+V23ffvn16//33jfbkyZPPK9fU1FT94x//MNpdunTRxRdfXKVjUbGCggKXLb8EAADODYV0AAAANCgpKSnasWOHpLJnnJ8pWLZt21atWrVyaW6V+f3333XllVealnYpbz3xfv36qUOHDpJOF0JvvvlmnTx50tQnJydHjz32mF577bVSHyjUlAEDBujmm2/WwoULjZn+Z9u+fbsmTpxotEeMGFEqHy8vL+MBpEVFRaaZ1GX5z3/+owsvvFAzZsxQUlJSmX0yMzP1+OOPa+PGjZIkNzc3XXnllWX2tdlsxqvk7PBz9cYbb+iKK67Q999/r7y8vDL7nDhxQg888IAefPBBI3bxxRdr/Pjx5Y4bFBSkZ5991mg/+eSTevfdd0vNZo6Li9MVV1xhLBnSrl073XnnnWWO2bZtWz3//PPatWtXueddtGiRoqKitGfPHiM2bdq0Mpcdwrk5fvy41SkAAIAKuFfeBQAAAKg/zsxGl0oX0ouLi7Vu3boy97nC888/b1rbXJIcDodOnTqluLg4bdu2zbTvwQcf1MiRI8scy2az6dVXX9XYsWMlSUuXLlW7du00YsQIhYaGKjk5WdHR0UpPT1fz5s11zz336Omnn67x91RYWKivv/5aX3/9tXx8fNSrVy+1b99eTZo00cmTJ7V37179/vvvRn8fHx+9/vrrZY5144036n//938lnZ55PXv2bHXs2NH0YNWSx8bFxemee+7Rvffeqw4dOqhHjx4KDQ1VYWGhjh49qrVr15rWnn7iiSdc9uGJ0+nUkiVLtGTJEnl7e6tHjx7q0KGDAgMDlZ+fr7179yo2Ntb04UObNm30ww8/VFqcfuCBBxQTE6O5c+equLhY9913n15//XUNGjRI3t7e2rlzp9auXSuHwyHp9Hr63333nXx9fcscLzU1VS+++KJefPFFtWzZUr169VJ4eLi8vLyUkpKi2NhYHTp0yHTMyy+/rOuvv/48v0oo6dixY8aHSQCA+s/uZpPdrWafAVPtHBzWnr+hoZAOAACABuVMId1ut5vWxpZOP9DxzDIWVizr8tlnn1Wpn6+vr1566SU99NBDFfb761//qqlTp+r555+XJJ06dUrz5s0z9enSpYu+++47bdiw4dySrkTJDwZyc3O1fv16rV+/vsy+7dq105w5c9SrV68y9z/22GP6/vvvlZCQoMLCQi1cuLBUnzOF9JLndTqd2rNnj2m2dEmenp56+umn9dxzz1X5fdWkvLw8bdy40ZgZfzabzaYbb7xR77//fpnr4ZfV/7PPPlNERIT+85//yOl0KjExUYmJiaX6du7cWd9++6169uxZpVwPHz6sw4cPl7s/IiJC7777rm666aYqjYeq27dvX519bgMAAKCQDgAAgAbmzNItvXv3VpMmTcrcJ9Wt9dH9/f0VGhqqXr16afjw4ZowYUKVCqrS6fXPL7/8cv3nP//RqlWrdPz4cTVp0kQdO3bU+PHjdccdd8jf37/WCulbtmxRTEyMVqxYodjYWO3cuVNHjx5VTk6OfH191axZM1144YW6/vrrNXbs2AqXmGnSpIliY2P13nvv6ccff9T27duVnp5e5nrp//znP3XjjTdqyZIlWrt2reLj43XgwAFlZGTIbrcrKChI3bp10/DhwzVx4kS1adOmVt5/eR599FENGTJE69atU0xMjBITE5WSkqK0tDTZ7XYFBwerS5cuGjhwoCZMmKCuXbtWa3xPT0+9/fbbmjx5smbNmqVly5bpyJEjys3NVVhYmPr27asbbrhBt956q9zdK/6zb9euXVq7dq3WrVunuLg4nThxQikpKcrOzlZAQIAiIiIUFRWlK6+8Un/961/l6el5Pl8alKOipXUAAID1bE4eDY7zdPjwYeMW2UOHDqlly5YWZwQAAACgoavPf4eUzP2CCy6Qt7e32rZta3rQMGpHbm6uFi9eLEm64oor5OPjY3FGjUd9/drX5581cL2S3y/fdxmucA9rv8+PF+bqhp3LJfH9WxN4MgwAAAAAABY7cOCATpw4YXUaAACgHBTSAQAAAACoA848DBkAANQ9FNIBAAAAAKgDfv31V6tTAADUELvNJrvd4pfNZvWXoUGhkA4AAAAAQB0QGxuro0ePWp0GAAAoA4V0AAAAAAAs4u/vb2rPnj3bokwAAEBFKKQDAAAAAGCRYcOGmdo//PCDDh8+bE0yAIAaY3OT7Ba/bG5WfxUaFgrpAAAAAABYZPTo0fL09DTaxcXFmjZtmpxOp4VZAQCAs1FIBwAAAADAIk2bNtXYsWNNsbVr12revHkWZQQAAMpCIR0AAAAAAAvdfvvtCg0NNcVee+01bd261aKMAADny2631YkXag6FdAAAAAAALNSkSRM99dRTplhRUZEefPBB7dq1y6KsAABASRTSAQAAAACw2JAhQ3TbbbeZYhkZGbr77ru1bds2i7ICAABnUEgHAAAAAKAOuPfeezVw4EBTLD09XVOmTNHKlSutSQoAcE7sbnXjhZpDIR0AAAAAgDrAzc1N06ZNU58+fUzx/Px8PfLII3r77bdVVFRkUXYAADRuFNIBAAAAAKgjvL29NX36dPXr16/Uvs8//1y33367du/ebUFmAAA0bhTSAQAAAACoQ3x9ffX222/r2muvLbUvISFBEyZM0IwZM5Sbm2tBdgCAqnCz2eRmt/hls1n9ZWhQKKQDAAAAAFDHeHh46Pnnn9djjz0mDw8P077i4mJ9+umnGjNmjBYsWCCHw2FRlgAANB4U0gEAAAAAqINsNpvGjh2rTz/9VG3bti21/8SJE3rxxRd18803a/HixRTUAQCoRRTSAQAAAACow7p166Yvv/xSd9xxh9zc3Ert37t3r5566imNHTtWP/30kwoKCizIEgBQks0u2S1+2aj81ii+nAAAAAAA1HGenp6666679OWXX2rAgAFl9jlw4IBeeOEFXXvttfrwww+Vmprq4iwBAGi4KKQDAAAAAFBPdOjQQf/5z3/0n//8Rx07diyzT1pamj788EONGjVKTz75pNatW8eyLwAAnCd3qxMAAAAAAADVM2DAAPXv318rV67Uxx9/rF27dpXqU1RUpCVLlmjJkiWKiIjQddddp2uuuUatW7e2IGMAaFzsbjbZ3WyW54Caw4x0AAAAAADqIbvdruHDh+uLL77Qm2++qb59+5bb99ixY/r44481ZswY3XLLLfr000916NAhF2YLAED9xox0AAAAAADqMZvNpiFDhmjIkCHavXu35s6dq4ULF5b70NFdu3Zp165dmjFjhrp06aLLL79cl19+uVq0aOHizAEAqD8opAMAAAAA0EB06tRJzzzzjO6//379+uuvmj9/vnbu3Flu/507d2rnzp1699131aFDBw0aNEiDBg1Sr1695Obm5sLMAQCo2yikAwAAAADQwDRp0kRjx47V2LFjtXPnTi1YsEBLly5Vampqucfs3btXe/fu1ezZs9WkSRNdcsklGjRokAYOHKigoCDXJQ8ADYDdfvpldQ6oORTSAQAAAABowLp06aJHH31U//znP7V582YtWbJEy5Yt08mTJ8s9JiMjQ4sXL9bixYtls9nUs2dP9e/fX1FRUerRo4c8PT1d+A4AALAehXQAAAAAABoBu92uvn37qm/fvnr00Ue1adMmLVmyRMuXL1d6enq5xzmdTm3dulVbt27VRx99JC8vL1100UWKiopSVFSUunbtKjvTHgEADRyFdAAAAAAAGhk3NzejEP7EE08oPj5eq1ev1urVq7V79+4Kj83Pz1dMTIxiYmIkSQEBAerbt6/69euniy66SB06dKCwDqDRs9mcstmdlueAmkMhHUCdtHL9QZ3KLNC1l7WXm1v1fwnfuy9NMbGHdN2ormoS4FXt49Oy8vX1mgO6vFekOkU2qfbxhUUO/V9MoiICvTWiZ2S1jwcAAABcxW63q3fv3urdu7fuueceHTt2TGvWrNHq1asVGxurvLy8Co/PzMzUypUrtXLlSkmSv7+/evfurQsvvFAXXXSRunfvzlIwAIB6j0I6gDpl/6F0ffBVnBL2nH4I0uJV+zXl5t7q3TW8SsdnZObrq7lxWr5yv5xOp5Yu36txN/XQFSM7VmlWTFGxQ9/GJGrmir3Kzi/S3LUHdP3FrXTnyE5q4uNRpRxidp/Q2z9v16HUHEnSgo2H9MA13dQ+IqBKxwMAAABWioiI0JgxYzRmzBgVFBRo48aNio2N1YYNG7Rr1y45nRXPcMzKytKaNWu0Zs0aSZKnp6e6d+9uFNZ79eqlgAB+NwYA1C8U0gHUCVnZBZozP0G/Ru+Xw/HnL+aHkjL17JurNbBPC93x154Ka+pb5vEOh0OLl+7R3G//UHZ2gRHPzi7Qp7M3admKfbp9Uh91q6Agv3Fvqqb/vF0HTmQZsWKHU9/HHtSKP5J058jOuq5vS9nttjKPP5KWo//8skOrdxw3xX/fl6a/zVirG/u31u3DO8rfu2oFeQAAAMBqnp6eGjhwoAYOHChJOnXqlH7//XejsJ6YmFjpGAUFBdqyZYu2bNmiWbNmSZLatGmjCy64wHh17tyZWesAGhSb/fTL6hxQcyikA7CUw+HU4tUHNOeHbcrIKii339pNR/T7H8m66eouGnNFJ3l4uBn7tu84rk9nb1LiwfRyj088mK7nX1quSwe01m23XqSQYB9jX3J6rt79ZYdWJhwr9/j0nEK9tmCbFmw8pIeu7a4erYKMffmFxfo8ep++XL1fBUWOMo8vdjj1zbpELY1P0j8u76yrL2ohm63sgjwAAABQVwUGBmr48OEaPny4JOn48ePGjPVNmzbp6NGjVRonMTFRiYmJWrhwoSTJ3d1dnTp10gUXXKDu3burR48eatu2LWutAwDqDArpACyzY2+qPvw6TnsS06vUP7+gWF/MT9CyNYm6Y2xPdWoTrM+/2Kw16w5W+Zxr1h3U75uP6sbRF2jk5R30f+sOas6q/corLK7S8TuPZuiuj2J01YXNddcVXRR34KTe/XWHjp2qeN3IM9KyCvTK939o/sZDemhUd3VtEVjl3AEAAIC6Jjw8XNdcc42uueYaSacL62dmn2/ZskW7d++udCkYSSoqKtL27du1fft2I+br66uuXbuaZq43a9aMCSkAAEtQSAdgiZzcQj0+7TdV4XfqUpJTsvW/M2LUKcRHhw6dqvbxeXlF+uLrOK06fFIxRzOqfbzTKf2y+aj2JmdqV1JmtY+XpG2HTmnKB+u0/Pkr5H4OD1MFAAAA6qLw8HBdccUVuuKKKySdXi9969at2rJlizZv3qxt27apoKD8O1FLysnJ0aZNm7Rp0yYjFhwcbJq13r17dwUFBdXGWwGA82KzOWWznUPRo4ZzQM2hkA7AEg6n85yK6KYxHOc3wHkff55vwOHUeX8NAAAAUL9t3bpV6enpVqdRqzw8PBQVFaWoqCgVFhbq0KFD2rdvn/bt26f9+/fr6NGjVZq1LknZ2dk6fPiwFi1aZMTCwsLUvn1749WmTRt5e3uXeXxeXp52794tSQoJCSm3n5V69+7NevEAUAdRSAcAAAAAwCJP/LxdHgHJVqdhgSaS74XSBRfK0Tlf+WnJp1+pSSpIS1JRTtXvHD2Qlq0NOw9IWn46YLPLs0lTeYZEyqtppLxCmskzKEw2+5nnLJ1+XtKnh7bW5BuqEXnJiZr7iBQVFWV1KgCAs1BIBwAAAADAIn7NO8ozKMzqNKzXvrepWZSTodzkROUeO6DcY4nKTT6g4vycKg9XlJuloiO7lXPk9Oxzm5u7fMJayTuijXyatZVPRBt5BobJxsNMAdQSm/30y+ocUHMopAMAAAAAgDrF3beJAtr3VED7npIkp9OpwlMpRlE993ii8o4dlKO4sErjOYuLlJO8XznJ+6W40zE3Lx95h7eWb7O2pwvsEW3l4R9US+8IAFDfUUgHAAAAAAB1ms1mk2dQmDyDwhTY5WJJktNRrPzUpNPF9WOJyk3er/zUqq+3Xpyfq+xDO5V9aKcR8/ALlHdEW/lEtJZPs3byCW8tN2/fWnlPAID6hUI6AAAAAACod2x2N3mHtZR3WEsF97hUkuQoLFDeicPKPbb//xfXE1Vw6kSVxyzMPqXCfXHK3BdnxDyDwkvMWm8j77BWsrt71Pj7AdCw2OxO2e1V+2CvNnNAzaGQDgAAAAAAGgS7h6d8m7eXb/P2Rqw4L7vErPUDyj12QEU5mVUesyD9uArSj0s7YiVJNptd3mEtT89Yb9ZWPs3ayjMoXDabrcbfDwCg7qCQfh6Ki4u1bds2bdiwQRs3btSGDRu0detWFRaeXqNt6NChWrlyZa3mkJWVpc8//1zffPONdu/erRMnTigsLEydO3fW2LFjNWHCBPn7+9dqDgAAAAAA1FVu3n7yb9Nd/m26Szq93npRdvr/L6qfLq7nHT+o4oK8Ko3ndDqUe/ygco8flLb+ZpzDp1lb+TZre7rAHtGWJWEAoIGhkH6OfvjhB916663Kyan6U8Nr2rp163Trrbdq//79pviRI0d05MgRrVixQq+99pq+/PJL9e/f36IsURsKC4uVm1OoJv+PvTuPj6q+9z/+PjOZZLLvCUtIIAQStoBsssiiICpuqCguCKiV/qreetve22o3q+3torbXbre1tkXcRetaNxRQlEXZdwIEQsi+75N15vdHdEwgmUwmEyaB15PHPB5nzvl+z/czZ4Yk5zPf8znhVo/3UVpap+hoz/+wK6mzKSYo0OP+DS1NCvA3q6GxxaP+JpOhoKCe/QgLMnp2iZNVksmQ7B7uJsjfLLXUS36evQ+ttR8bZBiefw6a7TXyM3n+ZVtlQ72CLf7yM3l2K/DG5hbZmlsUbvX3OIaefpbLSmoVFRPscf+eammxq7q6QRERnv9/AgAAgPsMw5AlJFKWlEiFpVwgSXLY7WqsKGo3a72+OEcOu3vnKy31tarJOqCarAPOdQFRAxQYP1SBA4cpaMAwBUQPlGEy98prAtD3GIZkeHaq7NUY4D0k0j1UUVHh0yT63r17ddlll6m6uvVyNIvFoksuuUQJCQk6deqU1q9fr+bmZh0/flwLFizQpk2bNHbsWJ/FC+/ZvS1Hz/9ju6qr6rVoSbouvSpNZrP7P5lzciu16pmd2n+gSJfMHaZbloxXWGiA2/1rGxv14v7Dei/zhFKjo3T3xHQNiwh3u7/d4dCWwmN679Qepd5oVsveSB3YV+52f0kaOyJcK29waFC0TW98EK833y1WU5Pd7f4xkVZd3HBCMU88q5Rrr9Eb0SkqqW1yu7/VYtK8hnIN+MEfNXHKGH1+1eXKqGjs1mu4dIS/vhWzQeZP18qRukhKmNGtS0Edjio5dFhStRyOITKULMNw/0d6i71OFY2fq7b5qKzmREUGTJfF5P772Gy3693jR/TmscOKtAZq2ZjxSo8d4HZ/Sdp8skB//eKgqhubtGzCSF0zKknmbiTkc3K+/CwfLNK8i5N1y03pCu3OZ7mmQa+9sEfr3z+iEaNitfTuqUocGtmt19BT+w8UatUzO1VUXKNrrx6la64cJX9/Tq4AAADONsNkUkDUAAVEDVDEqNaJaPaWZjWU5LZLrjeUFbi9z4ayAjWUFaji0FZJrWVnAuOSFDR4hIIGpyho4DCZLO7//QoA8C3D4e7trNHO008/rTvuuEPx8fGaMmWK8/HBBx/o97//vaTeK+3S1NSkUaNGKTMzU5I0fvx4vfnmm0pKSnK2ycrK0qJFi7RnT+sNUkaOHKkDBw7Iz8/7353k5ORoyJAhkqRTp04pISHB62NAKsir0gv/2K49O3LbrR80JFxLvzFFY8YPdNnfZmvSK6/t13sfHFVLy9dJ5+Bgfy1ZPFYL5qfI5CKJ6XA4tO7EST2776CqGr5OGpsMQwuSh+q2caMU4u96VvGJqmK9lrVdubXtE+dRldHK2SzlFdS67B8TadUdi4I1a0JFu/VFJQFa/YpZ23aUuuxvsZg0J65Fwz94TSbb11+EtYSGaOdNt+o9m1VNLa5/JF4YbtLof7wsU0aWc53DMFS98katS0xRWZ3rhPyIOKvuTz6odGNv+w3hQ2WMWSIjYpjL/g5Hoxw6JinvtC3+MjRC0gCXCXmHw67qpv2qbNwhh9rGalaYZZzC/C+QyXB946TdRfl67uAe5dfWtFs/OX6Qbhs9XnFBrmd351TW6P8+P6jtue1v+jQ0IlT3TButCQNjXPavq2v9LL+/9oha2rxfISH+WrJ4nC6dN9zlZ9lud2jjR8f06nO7VF3V4FxvMhm65PKRuv7W8QoO6d0TmpLSWj3z/G5t/fxUu/XxccFavnSiJk8a3KvjAwBwLujP5yFtYx//oxflHxHr44jgrpbGetV/mVivK8iSLf+4mm3u11tvyzBMssYlKmhwioIHpyho0HDZik9p9dKpmjJlipcj9z2bzaa1a9dKkhYsWKDAwP5xRWZ//lmDs6/t5+Wzi+dpoI8/5/k2my7asE4Sn19vIJHuoYKCAjU2NioxMbHd+p/97Gd6+OGHJfVeIv3//u//dO+990qSIiMjdeDAAQ0ceGYSNT8/X2PGjFF5eWvS8sknn9TKlSu9Hg+/VHpXQ32T3nxlnz5485CamzufdT15eqJuvXOyomPbJzEdDoc2fpal51/ao4qKzmv+JSVG6I5lEzV6VNwZ246UluupXXt0rKyi0/5hAf66bexozU9Okum0RG5Vo01vn9ylHSVZnfY3HIYiTw3Q/s3VqrM1t9tm8TNp0bxo3TivQtaAzo/BnoMhWvVinfLyz0zIjx8SqEk7PlDAyZOd9q8dOUIfzr9aO0rPTIYnhfvrok2bZX1zQ6f9HVHhyr5/uT5utqr5tHovYYF+umtUpa4JWCezOnsNhpQwXUbqdTICQtvv2+GQdEoOHZfU3GHvVuEylCrDCDtjS31zjsoaNqvZUdFpb7MRrAj/CxVsSTljW1FdjZ49sEc7i/I77W8xmXTV8FRdMzxN/ub2M6ttTc16fvdRvX4wS032zt/H2UMHauWUUYoLaf8Hh8Ph0CcbT+j5l/eqstL1Z/muFZOUlnrmCemxjGI999Q2nTjW+ZcuoWEBWrz0As2enyKTybvXwTU1teitfx/WG28fVEND55cJT0gfqBXLLtCggWe+jwAAoFV/Pg8hkX7ucDgcaqouky3/xJez1k/IVnRKjhZXf7N3zi8wRMsum6FFixZpypQpCgs7d/4eJJGO80Hbz8umeZf0iUT6zHXrJfH59QYS6V52NhLpY8aM0cGDByVJ//M//6Mf/vCHnbb9n//5H/34xz+WJKWnpztnqHsTv1R6z9ZPT+jlp3eqrNS9MkL+AWZddf1YXXHdGPn7m3Uiq1z/fHqHMo6WuD3mzOmJuv3WCYqKClJFfYOe23dA609ky90fFMMjI/SNC9KVFhOlFrtdn+Qf1oe5+9Xg5h+SAc1W+R2I1N5d5XI4pMljI/WN65o0KMbmVv/mZund9eH611slstU3a0BMoOZWHlLk5k/dfAVS3mUL9GbiOOVVNSo4wKz5lYWK/sOzMprcew0tU8Zo503Xam9Fk0yGdHWqn74R+ZHCVeFeAH5BMkZeJSXNlWGY5HCUy6EMSTVddv3aYBkaLsPwV7O9RuUNW2RrOdF1ty8FmAcq0n+m/M1Ramhp1lvHDuud40dcJsDbigkM0tJR4zVlYOvM6vWZuXpq+yGV1jV00fPL8f3MuiV9uBaPTZa/2azjJ8r0j6d36KiLBPjpLpqRpKW3TlBUZKAqK2xa88wubdqQKXd/6w1LidbtK6do+EjvnNhu35mr1c/tUmGhe++jn59JV16RqhsWjZbV6voqAQAAzkf9+TyERPq57auSMHX5x2UrOCFb/gk1Vrn3d6y90aahUcEKDg6WYRgaNWqULrzwQk2dOlXjx4+XfxdXAfdlJNJxPiCRfm4jke5lvZ1IP3bsmEaMGOF8npubq0GDBnXaPjc3t91/kmPHjmn48OFejYlfKt7ncDj06x9/qMMHCj3qHxsforEzEvXeh8fkyX9xq9VPC5eN0ttlJ1TnZvK4LUPSFSOSVGrkqshW1e3+khReE6ErooI1dXSFR/3LKyxa93S1Ip97UaYm9+uff8VuDdCxxTfJ759vy5Ttfh3Etpq+db1mXdmoVGV41F8hg6QZ10lmzz4HkkV1zeEqb9ghhzy5qauh+qbx+tOuEpXaPLsnxLiYOBWXWrSvsMyj/oNCgzSrIVxr3zvq8Wf56gUjtP7NQ6rrouxORwxDuuLa0VqyYlK3+7b1+P9+pi+253jUNyoyUA9+f46SEiN6FAMAAOea/nweQiL9/NNcVy1bwQnV5WWqNueo6ouy5XCcOUmlbSL9dAEBAZo8ebJmz56tWbNmKS7uzKuJ+zIS6TgfkEg/t3Gz0X5m/fr1zuWRI0e6TKJL0uDBgzVixAgdPXpUkrRhwwavJ9LhfQ67w+MkuiQVF9bo4OFijxKPklRf36wD+SWqM3l2OaJD0qGyYinIsyS6JFWGVGjKaI+7KzKiSaMKD6nIgyS6JJnqG5T2+Rc67mESXZIGbPxUqVdGeNxfNXmS0b0bsbbXpMaWQg+T6JLkUG5NkUptnZdR6cqBkmIVFlk97p9XXadDxxt69FnOOFDkURJdkhwO6VAP/i9+Zf9Bz/dRVm5TXn4ViXQAAIB+zC8oVKHJ6QpNTpck2ZsaVJd/QnW5x1SXe1R1BSe6LAfT0NCgTZs2adOmTfrVr36l1NRUzZ49WxdffLFGjBjh8l5JAM4+w9T68HUM8B4S6f3MoUOHnMsTJ050q8/EiROdifS2/QEAAAAAwNlnsgQoJDFNIYlpkiR7c5Pqi06pbN9GjYtsUG5urhoaXJdFzMjIUEZGhp566iklJSXpsssu0+WXX37GvdwAAN5BIr2fycj4ukREUlKSW33a/hI9fPhwt8fMyXFdjiA/v/MbDwIAAADA+YZzKHSXyc+ioEHJsrc06j8Wpys9PV379u3Ttm3btH37dh0+fNjlVZonTpzQX//6V/31r39Vamqqrr76al122WUKCgo6i6/Ctfr6+g6X+zqbzb37dQE495FI72dKS7++QUl8fLxbfQYMGOBcLivrfp3ir2o7AQAAAAC61p1zqNq8Y2qsdv+G6ji31Rec1KuvHtHnn3/uXHfBBRcoLS1N2dnZOn78uE6ePOlytnpxcbE+++wz/fjHP9aoUaM0fvx4RUVFnY3w3fbVVfO+NnToUFksFpdtSkpKzlI0APo6Eun9TE1NjXPZ3RtztG3Xtj8AAAAAwLfqS3LVXOf5vYVwjjGktS3JMk6dnq4JlIKipLETZBndopaS3Nb66qeOqLmusuN9NTYrf/s+rd++T8GJoxQ57iJZQvtWQt2X6gtO6h5JI0aM8HUoOEeZTK0PX8cA7yGR3s+0vfzJ39/frT4BAQHOZU8uSTp16pTL7fn5+Zo6dWq39wsAAAAA56LunENFp8+Rf0Ts2QgL55DQpDHSpAVyOByy5Z9QZcY2VR3dqWZbdYftbQVZqi88qYgxMxU34xr5BYac5Yj7pgsvTNfkyZNdtumqVBOA8weJ9H7GarU6lxsbG93q0/aSL3dnsbeVkJDQ7T4AAAAAcL7iHApni2EYChqUrKBByRowZ7Fqsg+rfO9G1WTtP6OmusPhUPn+z1SduUeDFyxXyNDRPoq677BarV3mSTzJowA4N5FI72dCQr7+1tjd2eVt27XtDwAAAAAAzg2GyazQoWMUOnSMGqtKVb7vU5Xv+1QtDe1zB822amW/9WcNmLtEUemzfRQtcO4zDIcMo/ObBJ+tGOA9VMrpZ6Kjo53LhYWFbvUpKChwLve1G4wAAAAAAADv8g+LVvzMRRqx4hHFTF4gk7n9DTUdDofyN7ykyoztPooQAPofEun9TGpqqnP55MmTbvXJzs52LqelpXk9JnifyWzS4tsmyD/A7FH/EaNiFWoYio6wdt24o/4p0br2ghEaGxvjUf9w/wCZGkMU0TJYchjd7m+SSdGOQfr7wVDVNnl24UxZS7BM/5muoFme3Tgm8tLRGvPX2Rr7s0s96h+aEqsZLy1Vy6Qr5DC7vgt8h0xmKf1SGc0myeHZMdhXbNXT+y0qrI3uunEHSqoj9MHeKA3yi/eof4DZrJFR0Zo2PFQBft3/dWM4HLqgOURB/hZFevhZTkwIV4MhDR7u2ZeI4RFWBQT4acMHR2S3d/+b/MbGFr3x8l6NGBimQKtn7+O0C4doVFqcR30BoK262ka98M/tev3FPWpsbPF1OACAs8BsDVb8zEVKWfEzhQ4be8b2/PUvqLmu47rqAID2KO3Sz4waNcq5vGvXLrf67Ny5s8P+6NuuvnGcZsxN1ktP79AXm9z70mRQQrgMk3T0ULEkyd/frPS0WB06Wa6mZnuX/cPDrbptSbrmzB4mwzA0OXWQPsvO0dN7DqjUjVJCZsOk4eExOlRYpbzycqlASowYqCEDGlSpUrdeQ7QpVieL7cqsq5AkfZbjr6WjIjU/oUyG0XVS3mYPUHZjk6qaT0nBkvnXFyguc6pK/+tttRRVddnff1C4RvxpkSwjmiXVKuUHQzT0rvu0bcUHKlx3tMv+htmkmatvVeKNgyRTs+zyk/2Sa2XOPCHT8R1d9pckJY6XkTxchmGT7KVSg58cflGSuVZy43uJ4jo/PX/IoR2FrVej7CyUZg4erkuSchVoqe+it9TQZNF7+1P0xr4GNbbUSJJGxg1SSESNSpu7PoaSNCIyWoW1NTpcViJJShoYqEBHqPbkuPdHerIRqMAjDTqRnS+p9bM8elSsjhwtVbMbn+WwsAANHBCqI0dL9FVpyNTxA1RxqlJVZW58ls2GUtJilXWsTIf3F+rw/kJt+OColq2cqpQ0924GtvPzU3rhn9tVXNh6DEPDAjQsOUqHjpfK4cYbOSQhXHcsm6ixYzz7IgMAvuJwOPTp+ky9+uwuVVa0/h74bEOmbrlzsiZPS/RxdACAs8ESEqkhV39LxVvfUfEX7zrXtzTWq+LAZsVMucyH0QHnJsOQDB9PYXYjjYJuIJHez1x88cXO5YyMDOXn52vgwIGdts/Ly9PRo18n/9r2R98XHRuse/97ti65vEDPPbVNOdkVHbYLDvHX4MQIHT1cLEebWbONjS06urdAsdFBCk0IU0ZWx/3NZkOXLxipG68fq6Cg9rOnL0pM0ORBA/TqoSN6K+OYmuwdJzGHhkWqtLpFu3LL263PrqhVdoU0YXCizKFFalDHidxQU4jqa0K1vbSy3fqqhkb93+4yrT0ZqrvHSiMjajrs3+IwKbc5QEUN+XLo6xgdsqtueINCX79apndKVfarDyRHBzOLzSYl//wqhV0VJRntb+TrF1On6f+eo6ovZmnT4pfVUFzbYQwjvnmRJj42S+bABknNX28wNahlxCDZE5Nl3rNZRnluh/0VFi9j3EwZ/jZJXyd7DTXLaC6SoyVYDr9AydxxIrixxdC/MwP0zvFiNdq/nmnokPRZbrl2FYVrYXK8JsRny9RJnbSdJ4dp9RcWFde0H+NIUY3MxYYmJiWoyq9Q9famDvsPDA6Rn8mko+Xtvzgpb7CpXDZNSo5QUZmhUxUdfw7CDT+NLLHo2O4ilbUJsbGxRQcPFSs6KkgREVZlHi/rsL/JZCh1ZIyyTpYr40hJu20Zx0oVEGDWiAsGKmtfoVo6ScgPHR6lmuoGZRwoarf+5PEy/eLB9zVjbrJuWjZREZEd33SoILdKz/19m/btymu3vrqqQdX7CjU8MUINFpNOFXT8pUJgoEU33jBWVywYIbOZC8cA9MyJY6V69qkvlJnR/mdiSVGt/vjrTzR2wkAtvXuKBg4O91GEAICzxTAMxU2/Sg3lBao6+vWEu7r8TB9GBQD9B4n0fmbEiBEaPXq0Dh48KElavXq1HnjggU7br1692rk8btw4DR8+vNdjhPeNGjdAj/zvlVr3boZef3GP6upak5iGIY0YFaeckxU6crCo0/7lpXUqL61T2vAolTe1qLC0zrlt7Jg43blskhISOj+Btvr5aem40Zo3NFH/2L1PO/K/rs8fZQ1UsClYB/IrO+0vSbtzKxVoCdIFibGqseTK/mWy209+CrXHa29upZrtne/jWHm1HvhUuiQpSktTaxUR0JrsdjgcKrWHKKe+WE2Okk77N6tBujJEMfOXq/4321XzwX7ntphFFyjhh5OlwFpJjZ3swa6wqdIVJ5bp5N9Oadd3/+3cEjE+QXNeuVnBw+ySGjqNwRFQr+apk2UqnyjTzrUymr9sa7ZI6fNkRPjJUOezpQ1HrYymWjlaouSwtEjG18n67QWBeuFQhYpt5Z32r21q0isZTdqaN0TXptRrcNjXn5n8img98/kA7cnr/Bi0OBzallWlMGuYxib6K7cp3zlDPtjPoiFh4cooK5GrAijZNRUyBxianhKrPSfrVNfUmvA3SbqgMUT520t0tK7jJL0klZbVqbSsTsnDIlVd06jiNl9qDBsaobq6Jh06XNxp/4aGFu0/UqLYwaGKCvBT9pGvE/5R0UGKiArU8aOdXz3hcEibNhzXzs9PadGSdF16VZoz2V1va9Kba/Zp7duHXM6az82ukGFI40bHKau4VtW1rcfbMKQ5s4bp1pvHKyLcs1I2APCV6qp6vfLsLm1cl9nuS/bT7d+drx/d/28tuCpN1y5JV2CgB+XIAAD9SkDkgHbP7c2d//0NAPgaifR+6J577tF9990nSXr88cd1xx13KD7+zEv/CwoK9Pjjjzuf33vvvWctRnif2WzSgqtHadqsoVrz7C5lZZaqqdHuMoF+upOZZTL7mZQ+KlaltibddOM4Tb/Q/Uu6B4aG6Mezpmt7XoGe2XtAViNQBwsr1djiOon+FVtTizZnVmlQWJyGDWqRxWTSscJGHa3vPPnblkPSupNl2pLnp5vTojQ3oU45zfWqaT7l9muoD6iRfjpKcXdeINsftyrpwVnyS6yX1PEs8zOYG5T0rTgNue0+7b5/k4bfcoEGXhEtGe7WmrXLHinZL7lK5qw8mZqaZSQlyDDqJbm3D8NeJjWY5fCLVr6tSc8eatK+knw3x5dOVdfoT7ukqQOSddHgMq07NEjvHLSpxe7eMaiqb9LmI00aFj1A0dH1igr1U05NpbOMS1daHA4dqyrSgPgARZoiVJ3dJNNBmzLz3H8Nx0+Uy8/PpFFpsSorsyk0NEDHMt0rHyRJxSV1KpaUMjZOTaU2RUYFKvNIicrafMnkiq2uSS+u2qFPPjympXdPUVVFvV5avUMVbpSNkVoT8scOFCkwyKJxKdGqc9h1x7JJGjnCs/sSAMBX7C12rXv/iF5/cY9qazr7cri9lma73nvjoLZ8ckI3LZ+omXOTezlKAICv1JfmqWz3+nbrTk+sA/ASk0OGqfv32vJ2DPAerhnvI7KysmQYhvPx8ccfd9p25cqVzpnlpaWluuKKK9rdUFRqvRHpFVdcobKy1vIHI0eO1F133dVr8ePsCYsI1Df+Y4ZstU0qyHOvXnVbLc12Hd1XqGsXjOxWEr2tyYMGaHHqaO3OK1djS9f1qk+XV2XT7uMt2nayWuX1nc/g7kxdU7P+ua9UR+vrVNPccYkP1xyqS7Ap8c8Lv0yid58prE4XPn2pBi6M6EYSvQ2jUS3DYmQMjf0yid7N7mqRqblILxxu0r4S976ION0XBRV6aXeC3tpfpxYPbqR5orROxaV+yigvUW1T92exVDc2KLu+UI07qlSQ1/0bHDU323XocLEiIqzdSqK3dexEuYKiApVxoEjNTR58lnMq9afHPtFf//czt5PobdnqmnRsb4FWLp9MEh2AV5w6WaHnntrmdhK9rYpym/72xCZVVXT/5xkAoO+ryT6krFd+q5bG9ucf4WlTfBQRAPQvzEjvgYULFyovr30N3IKCAufy9u3bNWHChDP6vfvuuxo0aJDH41osFv3rX//SRRddpJqaGu3atUspKSmaN2+eEhISdOrUKa1fv15NXya2QkND9a9//Ut+frzdAAAAAACcT1oa61W89d8q271BjtPuFxWRNlVBA7kSCQDcQWa1Bw4ePKiTJ092ur22tlZ79uw5Y31jY/dnCJ1u/PjxWrt2rW677TadOHFCTU1Nev/9989ol5ycrOeff15jx47t8ZgAAAAAAKB/cNhbVJmxTUWb31JTTcUZ2wPjh2rgJbec/cCA84Rhan34OgZ4D4n0fmz69Onau3evnnnmGa1Zs0ZHjhxRaWmpoqOjNXLkSN10001atmyZQkJCfB0qAAAAAAA4CxwtLao4/LlKtr2vxsqO72MUlnKBBi9YJpMl4CxHBwD9F4n0HsjKyvLavoYOHXrGJVbuCAkJ0T333KN77rnHa7EAAAAAAID+pbm2UuX7N6l8/2cdzkCXJMPsp9gLFypm0gIZJqaqAkB3kEgHAAAAAADohxx2u2pzjqji4GZVHd0ph93eaduQxFEaMPcmBUTGn8UIgfOXySSZTN2fNOvtGOA9JNIBAAAAAAD6kfqSXFUe/kKVGds6nX3+lcC4RMVeeKVCho2VYRhnJ0AAOAeRSAcAAAAAwEdq846psbrU12GgH2iuq1btyUOqOXlAjRVFXbYPiBmsiDEzFDhgmAzDUG1OxlmIsv+oLzgpaaqvwwDQj5BIBwAAAADAR+pLctVcV+XrMOCGxtJ8/XLpfI0ePfqsjVlYWKjt27dr+/btyszMVJikMJOkqOAO21ssFk2fPl3z58/X0KFDz1qc7qivr9fnn38uSbrwwgtltVp9HNFUjR8/3scx4FxmmFofvo4B3kMiHeinYuJDVFJc61Ffs59JdbWNPRq/trhO/maTGls6r8HnSqTVKoefQxUNDR71D/LzU3OLWYbZo+6SDDW2mGXpwS+VuhaLwkwOGUaLR/3tjgC1OBzyM+o86u+QWYF+Fo/6fiXQbJbZMNTiwc2OJSnU3192P4tqm5s86h9mCVBImEUV5fUe9ffzM2lAfIgyjpR41F+SrIEW+VlMam7y7LMcGxeqSn+bKspsHvUPDLKootyzvgBwuuAQfwWH+Ku2xrPf8xGRgbIEcIoAnE3R6XPkHxHr6zDghppThzV69GhNmTKl18ZwOBzKzMzU+vXrtWHDBh09etS5LTi44+S5JKWmpurKK6/UlVdeqfDw8F6LrydsNpvKysokSZMnT1ZgYKCPIwKA7uGvZKCf+sHD87Xu/SN6/cU93TpZTkqOUl1tg15evVP7duXp9runatAQ9//QKsyv1vP/2KY923M1LDFEptkxOlTt/gyaQD+zkkIi9cXRGvlbrLpwVLiyakrU7OKmOG0ZklKjo5RbXaNfb6zX4jEJSosvUYvD/URsgClSRTaTMisLlBgcprRIyTC6MwvIqq2FIXrtRKESggO1clSMoqyV3ehv0tHyMP1hR5HsDofuHx+rsRGVMuR+Qr60MUJ/2FejY5VFGh4eqbKGepXXu5+MjQ8KUW15sNYfr1RCeKCsFrOOldS43T8swE9Dg4O1b2+5woKDNHFygE7UF8nddLzZMDSiJVK5zxepuLpZY8fFKzO3Sjab+wn58ekDdMftEzVoUJhmzRyqVc/sVG6e++9jbEyQoqwWHd6eq6iYYIVHWHXimPuXVVsDLVq0JF2XXpWm5qYWvfnyXn3w78NqaXbzs2xII9JilZtTqd//coNmXjxcS5ZdoLAITigAeC4mLkS//vO1evW5Xdq4LlMOu3s/mc1+Ji24Kk3XLklXYGDPvqQFAHRPfX29duzYoU2bNmnz5s3Kyclxq19cXJyuuOIKXXnllUpOTu7lKAEAhsPh4TRE4Es5OTkaMmSIJOnUqVNKSEjwcUTnl+qqer3ybNcny5HRQYqMDtTxI+0ThWY/ky5dmKpFN6crMMi/0/4NDc16+5V9ev/Ng2o6bebuwBnxykvyU0Gd60RuWlSkMnKaVFrd3G79kBh/DUuw60RVucv+CWGhMiSdqqputz7CatKyC6wKseZJLlK5foZVjS0ROlFdqtaUfCtD0vjoWMUHVUpy9aWESbm1Mfrn4XJVN7VPes8eEK3rki2ymFzPLq9qCNf/7a7SkfL27YaHWXV/epii/V0fgyZHkF45btI7J9u3sxgmDY+MUmZFmZpcfCkR5GdRtClaWzLq1HLaoRozIFwFVTaV1nV+DMyGobGx4crMrFKNrf37mJIYqLiURuXWuf5SYWhAuBrW1ar4UPv3MSQsQDHJUTp8rESufjPFxQZr2dILNHVy+581zc12vfvBEf3rtf2y1Td30lsKCDBrRGKkTh4oPGMW+rCUaFVV1qvUxdUehiFNn5OsJcsnKiKyfdI7P7dSzz21Tft353f+AqQvv7xyKO9U+8R/UJBFi24er/lXpsps5ho8AD1z/GiJnv3bFzp+1PWXhGPGD9TSu6doUELfnMEIdKY/n4e0jX38j15kRno/UXPqsFYvndrjGekOh0OnTp1yJs537Nihxkb3JkeFhITo4osv1sKFCzVp0iSZTP3nb0abzaa1a9dKkhYsWNBvZqT35581OPvafl52XTdHg4J9W8Ior7ZeF7z+iSQ+v95AIh09xi+VvuHEsVI9+7cvlHlaiQuLxazkkdE6fqTkjAR4W+GRgbrp9gs08+LkM+7k/vlnWXrp6R0qK+k8SWy2mDTwyiHaZ65TfXP7JHNCWLAaa/11OM91on3S8EDZrdUqsbVvF+pvUUJYqA6XlLmc8Tw2zqprRzfJYZxe5sOQxYjX8aoqNdo7T7AGmS2aHBehQL8SnZ6QtzVH6cWjjTpc6eIYyNDytEG6INomw2g/Tos9UG9lGno703UyY2FShG5KdshyWrkXh/y0ozRUf9pXrCYXX5hEBQQqMtCqzIr2iXZD0rCQOO0+1qTyus5nvlv9TEqLC9OBgsozxhkeFaLG8madKuz8GBiGQxdeEKrakHJVNbYv2xPpb1Vspr+Ov1/YaX9JGpAUoSZ/s3JOm13u72/WoqtH6ZqrRsnfv/OaPuXlNj3/0h59uinrjIR86vAoVeZUqdJFGRY/P0MpqXE6caxUDQ3t38ek5CjdvnKKRqTFuXwNO7Zm64V/bldJUfuEfEhogAYmhOnooWKX/Qcnhuv2u6dq1LgBLtsBQFccDoc+XZ+pV57ZparK9ldvxcQF65Y7J2vytEQfRQf0TH8+DyGR3j/1JJFus9m0fft2bdmyRZs2bVJubq7bfaOiojR37lxdfPHFmjx5siyW/nnlEIl0nA9IpJ/bSKSjx/il0necfrKcPCJa5WV1Ki91v+RHSmqsbl85RUOHRysnu0LPPfWFDu1znfhsK2RgkKyXxGlfTZVC/C0aFBiubUdrzpj93JkAP0MzRluVbStRs92htOgonaysVG1T5wnw012TFqzxg8rU4qhTgCla+XV2lTe4X09+YFCoxkaZZTIq5HAE6ZP8IL2bXeZ2/7jAAH1zVJzigyrlcJh1oDRUf95VqAY368lbTIbuHRerydHVkppVWB+p/91boZxulPAZFh6hmsYmFdtqNSg4TKXFVh0pcL/8TXyoVZGB/jpcVKWoQH8N8rdq/9EKt/sHB5o0dWqgTjYWyTAMjWyI0Ik1BWpykcRvx5CSx8Yru6hG1TWNmjolQctvu0CxsZ3XhTzd4YxirXpmp05klWtgfIiCDUO5me6/jxERVsXEh+hYRomCQ/21+LYLNHfBCJlMRtedJTU2tuid1/br3dcOqLnZrhGjYpV9oly2OvfL10ydmaSbV0xSdDdeNwB0pK62Ua+/uEfr3suQyWzSldeN0ZXXj5E/9dDRj/Xn8xAS6f1TdxLpDodDJ0+e1ObNm7Vp0ybt2rXL7VnnkjRgwABdcskluvjiizV+/Ph+NfO8MyTScT4gkX5uI5GOHuOXSt9TV9uov/1+k3Z94V5tvdMZJkNz5qfo03XH1OJuBvw0g+cm6HOTSZV17ifA2xoQYdHwoc3KrupO7fKvBVtMuntqsPJtBR71l6TEkIF66VixbB7eUHV6XLSOlTTpZJVnN5JMCPHX6MhQrT3lft3utkyShgclaN3+SjnkXvL3dJMGRunAoTLVN3p2DIYNClDM0QqVHXe//npb1mCLbv9/F+qiWcM86m+3O/TKS3v0/r/2y+7hZ3nchIH6f9+bpZDQAI/6lxTV6H9/sV452d2po/81/wCzfvDwpUpJ4wQbQM/lZFcoIMCs2PhQX4cC9Fh/Pg8hkd4/dZVIt9ls2rZtmzZv3qzNmzcrLy/P7X2bTCalp6drxowZmjlzpkaOHHnGlcL9HYl0nA/afl5239A3EukT/kUi3VuYggKcg4KC/VXfjZs2ns5hdyg/t9LjJLok1ebZVBnlWeJRkgoqmhRhc38G9RnjN9lV0eD+zTs7cqyy2eMkuiTtL6tVYZXnMeTUNKqxxfNjYJdUWSOPk+iSVFvX5HESXZJO5TfI5GESXZLqa5sUExnkcX+TyVCgxexxEl2SamobPU6iS603/nNVFqkrjQ0tKiv1vD8AtJWQGOHrEADgnOFwOJSVldVu1nlTk/vnYTExMZoxY4ZmzJihCy+8UKGhfMkJAH0ZiXQAAAAAAAA31NfX65NPPtGmTZu0ZcsW5ee7vsl8WyaTSRMmTHAmz0eMGHHOzToHgHMZiXQAAAAAAIAOOBwONZTlqybrgMoPbNI96/+qgAD3r1aMjY11lmuZOnWqQkJCejFaAEBvIpEOAAAAAADwpZbGetWeOqyarIOqydqvppoKSZK90abmqGCXiXSz2azx48dr5syZmjFjhlJSUph1DpynDJNDhsm3t6b09fjnGhLpAAAAAADgvOVwONRQmqearAOqyTqgurxMORzu3ycoLi6uXa3z4ODgXowWAOArJNIBAAAAAMB5pXXWeYZqsvarJuuAc9a5O/z8/Jy1zmfOnKnk5GRmnQPAeYBEOgAAAAAAOOc1VBSp5sR+1ZzYr9rco3LYW9zuawmJkH9kqu5ftlDLli1TUFBQL0YK4FxgmCSTyfcxwHtIpAMAAAAAgHOOvblJdXmZqjmxX9VZ+9VYUeR2X8NkUtCgFIUMHaOQoWMUEDVQtTkZmjRpEkl0ADhPkUgHAAAAAADnhKaactVkHVT1if2qPXVY9qYGt/taQqMUMnS0QpLGKHhIqsz+1l6MFADQ35BIB85Rt9wxWc/87QsdO1zc7b7TZg3VlTeM1YdvH9Kn6zPl6OZNnpOSo3TrismaXG3T3zedUG2j+5dMSlK0xaSJTZIO+KtshFlFRn23+vtJGlURrk//blP6pVHyH1LWrf6SVFU0QJ/tsmjQkASVh+XKbnTvIIQ4wlV+IkrhASY1BBeo3tHYrf6hFj/NHhois6lJ+wuDdbyitlv95ZAG+w1UbnmzRseH6WBhVff6Sxrtb1XwoQrNig7W59U2Ndq7dwwGWv00qq5FltRYFRVWq6qie++jxd+k4SNitebZnbr1zslKSY3tVv+vzL00RYX51dq0ofuf5WEp0br97ikejdvWN79zkZ7/x3YVFVR3q5/ZbGhEWqzeeW2/gkP8NWb8wG6PvfOLU3rjpb266JJkzb8iVSYz1/YB6L8yjxTrxVU7lDo6XtfcOFYBVouvQwJ6rDbvmBqrS30dRr/lcDjUWFGkutyjqss9psbyQvc7GyZZYxMUNGi4AgcmyxIW7ax1bivMOqN5fcFJSVO9EziAc55hcsgwdfMktBdigPcYDkd30wpAezk5ORoyZIgk6dSpU0pISPBxRPiKw+HQpo+Pa80zu1RZbuuy/ZChkVp69xSljYl3rss8UqJnn/pCJ452/cd9SGiAFi+doDmXjpDJ1PoHaElNg36/4ZjeO1DQZX+zIc2yBqh4X6FstiZJksVi0vApsToSW60G2bvcR0pTqGw76lRU+HXieeyEKI2YXyMjtOtj0FQXqi92RmtX5tcJzyGxgUoc1agy/5Iu+1scfvKvHKKtGXVq/jLxHGb104ThFhUaeVIX9yAy5NDsxEgF+Feowd7w5TpDIeY4fZJVo+qm5i5jiLVEqrg4QCfL6pzrRsaGqqahWXlVXR+DOD8/pZY7lHng6y9homOCZE4M1+6Krvv7mwxdFOCvvN35avryS5QAq5+GDo/SscPFamnp+tfO8NQYlRbXqqKsdTzDkGbOTdZNyycqPCKwy/4dOZZRrOee2qYTx7r+LIeGBWjx0gs0e36K87PcU01NLXrvjYN6+9V9amzo+sulocOjVFPdoJKirz/Lk6cl6pY7JykmLqTL/gW5VXruH9u0b2eec11CUoRuv3uq0sbGu+gJAH1PVYVNLz+zq92XolHRQbr5jkm68KKhPo0NvtOfz0Paxp5w9f+TX1CojyPqmcbSfP1y6XyNHj36rIzX0tKiI0eOaOfOndqxY4dKSrr+O/0rISEhioqK0rBhw3T99dcrMjKyW2OPHz9e/v7+3Q0Zkmw2m9auXStJWrBggQIDPfu7/mzrzz9rcPa1/bzsu2WWBgf79sqW3Np6jXvxU0l8fr2BRDp6jF8qfZ+trlFvvLRXH75zuMMkZlCwv66/dbzmXT6yw9mqDodDn3x0TK8+t0vVlWdeGmkyGbr4shG6/tYJCgkN6DCG3acq9OiHGTpSVNPh9vFBAQo6WanCTmbsRkRaFT01XIetlR1uj3YEKDbTT5kHOk6S+vubNW1epGIml0h+ZyYxHc1+On4kQet21aqxueOE/YSUMJkTi1Vnqutwe2RDgvZkOFRa29Th9qExgRo8uF4l9o5nyKdFhWhEbLOqmjp+jQGmADU0RmhjdrkcHWTkg81WBdTHaPepKnX0g93PZGjsgAgdKa5SXdOZx8AiaZqsytlVqIZOEr1DU6J1MtCs3NqOZ9hPDrVKR0tVXtzxMYqJC1ZIaICyMjs+BnEDQmQNtCj7RHmH2wODLFp0c7ouvTJNZg9mVtvtDm386rNc1cln+fKRuuHW8QoO6fiz3FOlxbV6cdV2bduc3eH2qOggRUQF6ngnX175+5t15Q1jtfC6MfL3N5+xvaG+SW+u2acP3jqk5k4+yxfOGqqbV0xSVDT1PQH0bS0tdn30TobeeGmP6uo6/v2aNjZet989RQlJ3UuGof/rz+chbWMf/6MX5R/h2ZV3fUXNqcNavXSqpkzp+ZV8nbHZbNqyZYs++eQTffrpp6qqcu+KS8MwNGbMGM2cOVMXXXSREhMT9dFHH0nqX8nccwGJdJwPSKSf20iko8f4pdJ/5J2q1HN//0IH9rTODjdMhmbPG67FSy9QWHjXP9xraxr1+ou7te69I7J/Odt6xKhY3X73VCUlR3XZv8Xu0Gu7c/XXjZmqrG+dWT0gwKyxdXYdP+jejX+GpkSqZpSh/C+T2RaZNKo0VJlflKjRjRIyMTGBmnqlVdbkr2etlOUP0tqtDpVUdV1+xWoxa3J6kMojcmQ3WpOUYY5IFWSFK6Og4+Tx6SYPC1VLaJHqHK2lTqKsFs1MDFJVs3vHIMwSrqMlZh0ubZ2pbJKhAaaB2pNtU50bxyAy0F+DwwO1v+DrhP0Ei1X2jMp2s5874+dn0tCx8dpW1yDbl4naIYEWDa9o1Kkj7s0GSh4RrYpym8pKWo9ZYKCfEpPdn7E+ODFcS78xRaPTu1/qRJJqaxr0rxf2aMP7X3+WR46O0+0rpypx6NlJxBzcm69nn9qmvFOt74PFYlLyyFgdP1Kipg6+6DhdbHyIbr1zsiZeOMS5bsvGE3p59U6Vl3b9WQyw+umaG8fp8mtGyc9yZkIeAHzt0L4CPffUNuVkV3TZ1mw2NO+KVF13y3gFBTNT9HzRn89DSKS7ud+aGm3cuFEfffSRtm7dqsZG98olhoaGavr06Zo5c6amT5+uqKivz1X6azL3XNBfj31//lmDs6/t52X/rbM0OMTHifSaeo19gUS6t5BIR4/xS6X/2bb5pD5bn6lrl6QreURMt/tnZ5Xr9Rd2a/KMJM2cm9zt/hW2Jv3542OqzChR/t6CTmc/d8ZsNjRicpyaI6XybVUqLe263Mjp0kZHKnmuoS0Hg3Qgu+NZ8q4MjLRqxBiHqmuD9PnRGnWzfLiC/M2aPMKqscm1MvmVqtHe8Sw7V8L84rQ326LMXLNyK7t/DJKjQxTUIoWdqFVWRvfrckZEBCo0JUohdU3K2ZXf6eznzlgsZg1PjZG9xa783KoOZ4h3ZcqMJC39xmRFRHk2szr7RJlee3GPLpw5VNPnDPNoHz3x1UzL7VuzVVxY41YC/HTjJg7S5deM0tuv7NfhA92oCfql+EGhWrZyqsZOGNTtvgDQGyrKbXr+79v0xaaT3e4bFm7VkuUTddElw3shMvQ1/fk8hER652pra/XJJ5/oo48+0pYtW9TU5N7fyYMGDdLcuXM1Z84cTZgwQWZzxxMF+msy91zQX499f/5Zg7OPRPq5jZuNAuehKTOSNGVGksf9E4dG6v4fXuxx/4hAi/5z9nDd8dwej/q3tDh0+PNCJQwO8yiJLkmHD5arJmKIDnTz5o9fyS+vV+DxMB2s7H4SXpLqGlu08UCtRqcUqdnevQT0V6qai1RVNUy5HsZwvLRGM0yBHiXRJamiwiZrVrmyTnZciqYrTU0tOry/UOGRVo+S6FLrl0LpEwdp9vwUj/onDovSf/bgs9xTZrNJl10zSps+Pu5REl2S9u3MU2NDszIOuHdFw+kK86r173/tJ5EOoM/YvyvPoyS6JFVV1mvNs7tIpAP9TF1dnTZu3KgPP/xQW7ZscXvmeVpamjN5npKS4rxRKAAAvYFEOgAAAAAAOKsaGxu1ceNGvf/++9q8ebNbyXOTyaTJkydrzpw5mjNnjgYMGHAWIgUAzxiGQ4bh20Igvh7/XEMiHQAAAAAA9Dq73a49e/bonXfe0UcffaSamq6vrPTz89P06dM1f/58zZo1S2FhYWchUgAAzkQiHQAAAAAA9Jrs7Gy98847eu+995SXl9dlez8/P02bNk2XXnqpZs+erdDQ0LMQJQAArpFIBwAAAAAAXlVdXa33339f77zzjvbv399le7PZ3C55zsxzAP2dydT68HUM8B4S6QAAAAAAoMccDof27dun119/XWvXrlVDg+sbyhuGoUmTJmnhwoWaO3cuyXMAQJ9GIh0AAAAAAHispcGmDz/8UI8//rgyMzO7bD9s2DBdeeWVuuKKKxQfH38WIgQAoOdIpAMAAAAAgG5xOByy5Z9Q+b5PVb7/Mz0bEaDg4OBO20dFRemyyy7TwoULlZaWJsMwzmK0AHD2GSaHDJPD5zHAe0ikAwAAAAAAtzhaWlR1dIdKd62XrSi7dZ29WVLAGW3NZrPmzJmja665RtOmTZOfHykIAED/xW8xAD7h72/WsKGROpFV7lH/6OgghUcGKie3yqP+1gA/BVnMCvAzqaHZ3u3+hiHFBPgpLsRfRTWNHsUwPCpIIQpXlTw7BlaTVVEOk0yGZPfgS2aL2aQgP7MCAy2y2Zo8iiEqKlD+9S0qLqzxqH/8oFCFhFtVWV7vUf/gYH+VFNWoudkuP7/+exeVkaNidfJ4mUd9rYEWpaTG6sTRUjU2tnS7v2FIKWmxHo0NAL1hwOAwhYQGqKbadW3lzozgZxrQK5ptNSrf/5nK93yiptpKl20HDx6s66+/XldffbWioqLOUoQAAPQuEukAfMJiMetXP1+gdRsy9dKavap2Mxnt72/WsBHROpRTqdwT5UpOiZajtlH5+dVuj52SFqvjDU36/HCxYiKsihgcrgO5rk8G2hoaE6zQmiYd25qjwACzpk0YoJ3ldWp0MyEfEWjRKIufTu7I1wvbpUsWxitqVKXq7e4lk00yyVoeo4+eqVBddZbSR0SqeniIMitq3X4NYyJD5NhbpoO5JQoNDVDqyBgdOVoih5sJ+fi4YIX7mXRiV4FMZkNpY+J0IrNMDfXNbvUPDLJoUEqUDh4vk72yXiNGxcpWUqfSYvdeg2EyNCItVjnZ5XpzzT59sfmkln5jisZOGOTeC+hjlt49VZOmJeq5p7YpJ7vC7X7T5wzTzcsnKiIqSJdcPlIv/HO7dmw95Xb/YSnRun3lFA0fSdIJQN+Rkhqr3/zftXrthd3a8MFR2d38tjg2PkS33jVZE6cO6eUIgfNLQ1m+SndtUOWhz2Vv6XzyhZ+fny699FJdd911mjx5skym/jvJAQC8wTC1PnwdA7yHRDoAnzGZDF06L0XTLxyil17Zp4/WZ7o8WU4ZEaP8mgbtyapwrjueXy2TIY0dE6/cE2Wqrev8j/sBA0PVEmXVtpI657qyinqVVdRrdGKEKux25ZXbOu0fFmhRaqhVx/cXqurLMJsaWpT9ea5GxAbLNDxC+4o6n5ltNhmaHBOi4r2FOtkmzvXvVinkEz9dvmSAGiOKZHd0npAPt0dpx2tNyjpc6lxXdLRcOlauC2cMUoalRRX1Lo5BiFVDypqV+162c111dYMyqhuUkBAmh0PKdTHL32r1U8qQCJ3YX6iqL784sLc4dPhAkcIirBoyNFLHDhd32t8wpJTRcTpRXKv9mV/PwD6aXSGLn6FR6QN0MqNYjQ2dz6xOSIpQS7NdRw4WOdfl51TpsZ+t0+RpibrlzkmKiQvptH9fNWrcAD3yv1dq3XsZev2FPapz8VlOHBap2++eqpGj45zrYuJC9O0H5mr/7jw99/dtys/p/H0MDQvQ4tsv0Ox5KTKZqE8KoO8JCQ3Qsm9eqDmXjtBzT23TkUNFnbb19zfrqhvG6orrxsjf33wWowTObbaibJV88b6qMne7bGcJjVJQwgj97/eXaf78+WcnOAAAfIBEOgCfCwkJ0DfumKz5Fw/XP5/ZocMZJe22x8eHyBIWoP15HScG7Q5pb1a5QoP9NTIxQkczSuRoM7U6KMiigSOitbOwWvY2SfS2TmRXyGw2NDElRoeLq1XXJpFrNhkaPzBMRYdLlJnV8cz18uJaqbhWU9NilGM1K6+q/ezytJhg+efW6NTWnA7719S26NV/Vio5JVzTrjKpyihttz3IFKTibUH66L1OysA4pFOb8hQZbNGIiwZpZ3WNWtp8KWH1M2t8QKDyN+Yot7HjRH3Ol4nXtNQY5eRWqabNVQKGIaUOj1bpyQpl7s7vsH9VRb2qKuo1JClCTU12FZz2fg0aEq6mALP2dlLOp6nZob2ZpYqMDlJymFXHT0vIh4VbFTcwRMcOl3TYX5K2b83W3p25Wnj9GF15/dh+l1Axm01acNUoTbtoqF55dpc+XZ/Z7iqB4FB/3XDLBF182QiZzB1PLRg7YZB+8cTVWvvvQ3rz5X2qb1O2x2QydMnlI3X9reMVHHJmHVMA6GuSkqP0o19dps2fHNfLT+9UxWlfeE+Zkahb7pis6NjOb3AI9HW1ecfUWF3adcOzpL4kVxUHtsiWn+myXUBMgsJTJyto8Ag1FJ1SeHj4WYoQAADfMBwOdy/kBzqWk5OjIUNaL6E9deqUEhISfBwR+rtPN2XpuRd2q76hWYnDo7U/q1wt3fhJlRgXLP8mu3Jzq5QyKlYZtQ2qdLPkiCSFh/grbnCY9uVWanhsiCxl9SrsRi12s59JQyYO1K5Km0L8zRouk7L3Fbr/AiRdNC9MgyZUq8HRIEthjNY+U6bGevdruUcnhalxdIQyymuUHhmqxh1FqirqfLb96YKCLEoaEqGMoyUaGB+igBaH8k+4X8vdMKSRo+J06mSFTCZDcUMjdPB4qRxyf/Zz8pBwtVQ2qLS4VimpMcrqRukYqfUS/1vumKRJ0xLd7tPXZB4p0bNPfaGszDLNvTRFi2+7QCFh7ifAK8rq9PIzu7Tlk+MaOTpOS++eqsShkb0YMQD0HputSW+9vFcf/Puw4geGauk3pmjM+IG+Dgs+1J/PQ9rGnnD1/5NfUKjH+2oszdcvl87X6NGjPd6Hw+HQoUOH9NZbb+ngwYOdtjObzbrwwgu1YMECJScnt9s2fvx4+fv7exzD2WKz2bR27VpJ0oIFCxQYGOjjiM4f/fXY9+efNTj72n5eMu6YqcEhVp/Gk1tTr9RVmyTx+fUGZqQD6HNmzRyqKZMG69sPr9OebiRvv5JdVCtDDo2aOlhfeHAz08qaRlVmlGhmSrQO7i7odv+WZruyvshV2sBQlZVVKttFmZLOfLauStZNhkYMCNfRA53PwO5M6ckq6WSVLp08UAfeO9nt/nV1TTqUUazRI2N0Yk9Bt+9m6nBIGQeLFBIWoFqLSQeOl0ndSKJL0vFTlTKbpFEp0co40Pkl/Z0pLqzRH379iW65Y5Iuv9bzE0tfGj4yRg89eoWKC2sUN6D7J9gRUUH65n/O1KIl6Yof6PkJOgD0BYGBFi1ZMUnzFqYqMjpI5k6uzAH6m+j0OfKP8Px+JTWnDmv06NGaMmWKR/337t2rP/3pT9q5c6ckKTj4zCs8QkNDdeONN+rGG29UbCz3VgEAt5gMyezjUpqU8vQqEukA+iSr1aLabsw+Pp1DhmzdTP6errkH40tSQ22jmjxIon+lvt6hyuKexWCv6Vn/pvrmbifR26qra1JdgOflVVrsUmNDz15DTXVDj/r7mmEYHiXR2yKJDuBc0h/vgwH0RcePH9ef//xnffLJJ52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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 607, + "width": 745 + } + }, + "output_type": "display_data" + } + ], "source": [ "alex_jointplot(ds2, vmax_fret=False)" ] @@ -617,7 +866,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -631,7 +880,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.13" + "version": "3.9.19" }, "toc": { "colors": { @@ -655,5 +904,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebooks/Example - Customize the us-ALEX histogram.ipynb b/notebooks/Example - Customize the us-ALEX histogram.ipynb index 06b61508..bed7d37f 100644 --- a/notebooks/Example - Customize the us-ALEX histogram.ipynb +++ b/notebooks/Example - Customize the us-ALEX histogram.ipynb @@ -47,7 +47,7 @@ "import matplotlib as mpl\n", "mpl.rcParams['font.sans-serif'].insert(0, 'Arial')\n", "mpl.rcParams['font.size'] = 12\n", - "%config InlineBackend.figure_format = 'retina'" + "# %config InlineBackend.figure_format = 'retina'" ] }, { @@ -448,7 +448,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -462,7 +462,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.13" + "version": "3.9.19" }, "toc": { "colors": { @@ -493,5 +493,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb index 0e48a66f..cf30f84d 100644 --- a/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb +++ b/notebooks/FRETBursts - us-ALEX smFRET burst analysis.ipynb @@ -1139,7 +1139,7 @@ "metadata": {}, "outputs": [], "source": [ - "dplot(ds, scatter_alex, figsize=(4,4), mew=1, ms=4, mec='black', color='purple');" + "dplot(ds, scatter_alex, figsize=(4,4), lw=1, s=10, ec='black', color='purple');" ] }, { @@ -1901,7 +1901,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1915,9 +1915,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.13" + "version": "3.12.3" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/fretbursts/tests/nbrun.py b/notebooks/nbrun.py similarity index 96% rename from fretbursts/tests/nbrun.py rename to notebooks/nbrun.py index 47634dc7..84bed757 100644 --- a/fretbursts/tests/nbrun.py +++ b/notebooks/nbrun.py @@ -146,8 +146,9 @@ def check_out_path(notebook_path, out_path, ext, save): kernel_name = 'python%d' % sys.version_info[0] execute_kwargs.update(kernel_name=kernel_name) ep = ExecutePreprocessor(**execute_kwargs) + print("executed preprocessor") ################################ nb = nbformat.read(str(notebook_path), as_version=4) - + print("read notebook") ####################################### if hide_input: nb["metadata"].update({"hide_input": True}) @@ -155,9 +156,11 @@ def check_out_path(notebook_path, out_path, ext, save): nb['cells'].insert(insert_pos, nbformat.v4.new_code_cell(code_cell)) start_time = time.time() + print("starting process") try: # Execute the notebook ep.preprocess(nb, {'metadata': {'path': working_dir}}) + print("finished ep.preprocess") except: # Execution failed, print a message then raise. msg = ('Error executing the notebook "%s".\n' @@ -169,6 +172,7 @@ def check_out_path(notebook_path, out_path, ext, save): raise finally: # Add timestamping cell + print("fiished main notebook") #####################3 duration = time.time() - start_time timestamp_cell = timestamp_cell % (time.ctime(start_time), duration, notebook_path, out_path_ipynb) @@ -176,10 +180,12 @@ def check_out_path(notebook_path, out_path, ext, save): nb['cells'].append(nbformat.v4.new_markdown_cell(timestamp_cell)) # Save the executed notebook to disk if save_ipynb: + print("saving ipynb") nbformat.write(nb, str(out_path_ipynb)) if display_links: display(FileLink(str(out_path_ipynb))) if save_html: + print("saving html") html_exporter = HTMLExporter() body, resources = html_exporter.from_notebook_node(nb) with open(str(out_path_html), 'w') as f: diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 00000000..09978734 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,63 @@ +[build-system] +requires = [ + "setuptools>=64", + "setuptools_scm>=6.0", + "cython>=0.29", + "oldest-supported-numpy", +] +build-backend = "setuptools.build_meta" + +[project] +name = "fretbursts" +dynamic = ["version", ] +authors = [ + {name="Antonio Ingargiola", email="tritemio@gmail.com"}, + {name="Paul David Harris", email="harripd@gmail.com"}] +description = "Burst analysis toolkit for single and multi-spot smFRET data." +readme = "README.md" +license = {file = "LICENSE.txt"} +keywords = ["single-molecule FRET","smFRET", "burst-analysis", "biophysics"] +classifiers = [ + "Intended Audience :: Science/Research", + "Operating System :: OS Independent", + "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", + "Topic :: Scientific/Engineering" + ] +requires-python = ">= 3.7" +dependencies = [ + "importlib_metadata;python_version<='3.9'", + "numpy>=1.19", + "matplotlib>=3.0.1", + "scipy>=1.2", + "pandas >= 0.23", + "seaborn>=0.11.1", + "tables>=3.5", + "lmfit>=1.0.1", + "phconvert>=0.8" + ] + +[project.urls] +Homepage = "http://opensmfs.github.io/FRETBursts/" +Documentation = "https://fretbursts.readthedocs.io/en/latest/" +Issues = "https://github.com/OpenSMFS/FRETBursts/" +Repository = "https://github.com/OpenSMFS/FRETBursts/issues" + +[project.optional-dependencies] +scientific = ["jupyter", "matplotlib>=3.0.1"] +gui = ["matplotlib>=3.0.1", "PyQt5"] + +[tool.setuptools.packages.find] +include = ['fretbursts*'] + +[tool.setuptools.package-data] +fretbursts = ["phtools/*.pyx", ] + +[tool.setuptools_scm] +version_scheme = "post-release" +write_to = "fretbursts/_version.py" + +[tool.pytest.ini_options] diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 9a2af6ec..00000000 --- a/setup.cfg +++ /dev/null @@ -1,7 +0,0 @@ -[versioneer] -VCS = git -style = pep440 -versionfile_source = fretbursts/_version.py -versionfile_build = fretbursts/_version.py -tag_prefix = -parentdir_prefix = fretbursts- diff --git a/setup.py b/setup.py index dcbf6031..1c63469e 100644 --- a/setup.py +++ b/setup.py @@ -1,84 +1,24 @@ from setuptools import setup from setuptools.extension import Extension import numpy as np -import versioneer ## Metadata project_name = 'fretbursts' -long_description = """ -FRETBursts -========== -**FRETBursts** is a software toolkit for burst analysis of confocal -single-molecule FRET (smFRET) measurements. It can analyze both single-spot -and multi-spot smFRET data with or without alternating laser excitation (ALEX). - -For more info please refer to: - -- **FRETBursts: An Open Source Toolkit for Analysis of Freely-Diffusing Single-Molecule FRET** - *Ingargiola et. al.* (2016). PLoS ONE doi: `10.1371/journal.pone.0160716 <10.1371/journal.pone.0160716>`__. - - -Quick links: - -- `FRETBursts Homepage `_ -- `FRETBursts Reference Documentation `_ -- `FRETBursts Tutorials `_ - -See also `Release Notes `__. -""" - -## Configuration to build Cython extensions -try: - from Cython.Distutils import build_ext -except ImportError: - # cython is not installed: do not build extensions - has_cython = False - ext_modules = [] -else: - # cython is installed: register the extensions to be built - has_cython = True - ext_modules = [Extension("burstsearch_c", - [project_name + "/phtools/burstsearch_c.pyx"]), - Extension("phrates_c", - [project_name + "/phtools/phrates_cy.pyx"], - include_dirs = ["."],)] +from Cython.Build import cythonize +ext_modules = [Extension("fretbursts.burstsearch_c", + [project_name + "/phtools/burstsearch_c.pyx"]), + Extension("fretbursts.phrates_c", + [project_name + "/phtools/phrates_cy.pyx"], + include_dirs = ["."],)] ## Configure setup.py commands -cmdclass = versioneer.get_cmdclass() -if has_cython: - cmdclass.update(build_ext=build_ext) -else: - print('WARNING: No cython found. Fast routines will not be installed.') -setup(name = project_name, - version = versioneer.get_version(), - cmdclass = cmdclass, +setup( include_dirs = [np.get_include()], - ext_modules = ext_modules, - author = 'Antonino Ingargiola', - author_email = 'tritemio@gmail.com', - url = 'http://opensmfs.github.io/FRETBursts/', - download_url = 'http://opensmfs.github.io/FRETBursts/', - python_requires='>=3.6', - install_requires = ['numpy', 'scipy', 'matplotlib', 'lmfit', 'seaborn', - 'phconvert'], + ext_modules = cythonize(ext_modules), include_package_data = True, - license = 'GPLv2', - description = ("Burst analysis toolkit for single and multi-spot " - "smFRET data."), - long_description = long_description, - platforms = ['Windows', 'Linux', 'Mac OS X'], - classifiers=['Intended Audience :: Science/Research', - 'Operating System :: OS Independent', - 'Programming Language :: Python', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Topic :: Scientific/Engineering', - ], packages = ['fretbursts', 'fretbursts.utils', 'fretbursts.fit', - 'fretbursts.phtools', 'fretbursts.dataload', - 'fretbursts.tests'], - keywords = 'single-molecule FRET smFRET burst-analysis biophysics', + 'fretbursts.phtools', 'fretbursts.dataload'], ) diff --git a/fretbursts/tests/importtest.py b/tests/importtest.py similarity index 100% rename from fretbursts/tests/importtest.py rename to tests/importtest.py diff --git a/fretbursts/tests/test_Bursts.py b/tests/test_Bursts.py similarity index 97% rename from fretbursts/tests/test_Bursts.py rename to tests/test_Bursts.py index 72141cf5..a601a2e8 100644 --- a/fretbursts/tests/test_Bursts.py +++ b/tests/test_Bursts.py @@ -72,4 +72,4 @@ def test_BurstsGap(): if __name__ == '__main__': - pytest.main("-x -v fretbursts/tests/test_Bursts.py") + pytest.main("-x -v tests/test_Bursts.py") diff --git a/fretbursts/tests/test_burst_plot.py b/tests/test_burst_plot.py similarity index 90% rename from fretbursts/tests/test_burst_plot.py rename to tests/test_burst_plot.py index 1b7653e0..17bd746c 100644 --- a/fretbursts/tests/test_burst_plot.py +++ b/tests/test_burst_plot.py @@ -13,20 +13,15 @@ import numpy as np -try: - import matplotlib -except ImportError: - has_matplotlib = False # OK to run tests without matplotlib -else: - has_matplotlib = True - matplotlib.use('Agg') # but if matplotlib is installed, use Agg - -try: - import numba -except ImportError: - has_numba = False -else: - has_numba = True +import matplotlib +matplotlib.use('Agg') # but if matplotlib is installed, use Agg +import matplotlib.pyplot as plt +# try: +# import numba +# except ImportError: +# has_numba = False +# else: +# has_numba = True import fretbursts.background as bg @@ -37,12 +32,11 @@ from fretbursts import select_bursts from fretbursts.ph_sel import Ph_sel from fretbursts.phtools import phrates -if has_matplotlib: - import fretbursts.burst_plot as bplt +import fretbursts.burst_plot as bplt # data subdir in the notebook folder -DATASETS_DIR = u'data/' +DATASETS_DIR = u'../notebooks/data/' def _alex_process(d): @@ -59,7 +53,7 @@ def load_dataset_1ch(process=True): return d def load_dataset_1ch_nsalex(process=True): - fn = "dsdna_d7_d17_50_50_1.hdf5" + fn = "HP3_TE150_SPC630.hdf5" fname = DATASETS_DIR + fn d = loader.photon_hdf5(fname) if process: @@ -149,14 +143,17 @@ def data_alex(request): def test_mch_plot_bg(data_mch): d = data_mch bplt.mch_plot_bg(d) + plt.close() def test_mch_plot_bg_ratio(data_mch): d = data_mch bplt.mch_plot_bg_ratio(d) + plt.close() def test_mch_plot_bsize(data_mch): d = data_mch bplt.mch_plot_bsize(d) + plt.close() ## # Timetrace plots @@ -175,6 +172,7 @@ def test_trace_single(data, ratetraces): for i in range(d.nch): for ph_sel in ph_sel_list: bplt.dplot(d, ratetraces, i=i, ph_sel=ph_sel) + plt.close() @pytest.fixture(scope='module', params = (bplt.timetrace, bplt.ratetrace, bplt.timetrace_bg, bplt.timetrace_fret, @@ -186,7 +184,8 @@ def test_trace(data, timetraces): """Test general time trace type functions""" d = data for i in range(d.nch): - bplt.dplot(d, timetraces, i=i) + bplt.dplot(d, timetraces, i=i) + plt.close() @pytest.fixture(scope='module', params = (bplt.hist_size, bplt.hist_width, @@ -204,12 +203,14 @@ def test_hist(data, hists): bplt.dplot(d, hists, i=None) for i in range(d.nch): bplt.dplot(d, hists, i=i) + plt.close() def test_hist_S(data_alex): d = data_alex bplt.dplot(d, bplt.hist_S, i=None) for i in range(d.nch): bplt.dplot(d, bplt.hist_S) + plt.close() @pytest.fixture(scope='module', params = (bplt.hist2d_alex, bplt.hexbin_alex, bplt.scatter_alex, bplt.scatter_naa_nt)) @@ -219,8 +220,11 @@ def ES_plots(request): def test_ES_plots(data_alex, ES_plots): d = data_alex bplt.dplot(d, ES_plots, i=None) + if ES_plots in (bplt.scatter_alex, bplt.scatter_naa_nt): + bplt.dplot(d, ES_plots, i=0, color_style='kde') for i in range(d.nch): bplt.dplot(d, ES_plots, i=i) + plt.close() @pytest.fixture(scope='module', params = (bplt.scatter_width_size, bplt.scatter_rate_da, bplt.scatter_fret_size, bplt.scatter_fret_nd_na, @@ -232,4 +236,5 @@ def test_scatterplots(data, scatterplots): d = data bplt.dplot(d, scatterplots, i=None) for i in range(d.nch): - bplt.dplot(d, scatterplots, i=i) \ No newline at end of file + bplt.dplot(d, scatterplots, i=i) + plt.close() diff --git a/fretbursts/tests/test_burstlib.py b/tests/test_burstlib.py similarity index 99% rename from fretbursts/tests/test_burstlib.py rename to tests/test_burstlib.py index b4a06d56..f3e4f0ee 100644 --- a/fretbursts/tests/test_burstlib.py +++ b/tests/test_burstlib.py @@ -44,7 +44,7 @@ # data subdir in the notebook folder -DATASETS_DIR = u'data/' +DATASETS_DIR = u'../notebooks/data/' def _alex_process(d): @@ -790,6 +790,7 @@ def test_phrates_mtuple(data): if has_numba: +# if True: def test_phrates_kde(data): d = data tau = 5000 # 5000 * 12.5ns = 6.25 us @@ -800,12 +801,12 @@ def test_phrates_kde(data): ratesl, nph = phrates.nb.kde_laplace_nph(ph, tau) assert (rates == ratesl).all() assert (nph == nrect).all() - + # Test consistency of kde_laplace and _kde_laplace_self_numba ratesl2, nph2 = phrates.nb.kde_laplace_self_numba(ph, tau) assert (nph2 == nrect).all() assert (ratesl2 == rates).all() - + # Smoke test laplace, gaussian, rect with time_axis ratesl = phrates.kde_laplace(ph, tau, time_axis=ph+1) assert ((ratesl >= 0) * (ratesl < 5e6)).all() @@ -1172,4 +1173,4 @@ def test_norm_pdf(): assert np.allclose(normpdf(x, c, mu), norm.pdf(x, loc=c, scale=mu)) if __name__ == '__main__': - pytest.main("-x -v fretbursts/tests/test_burstlib.py") + pytest.main("-x -v tests/test_burstlib.py") diff --git a/fretbursts/tests/test_burstlib_ext.py b/tests/test_burstlib_ext.py similarity index 97% rename from fretbursts/tests/test_burstlib_ext.py rename to tests/test_burstlib_ext.py index ac50db1e..bfb38446 100644 --- a/fretbursts/tests/test_burstlib_ext.py +++ b/tests/test_burstlib_ext.py @@ -23,12 +23,12 @@ has_matplotlib = True matplotlib.use('Agg') # but if matplotlib is installed, use Agg -try: - import numba -except ImportError: - has_numba = False -else: - has_numba = True +# try: +# import numba +# except ImportError: +# has_numba = False +# else: +# has_numba = True import fretbursts.background as bg @@ -44,7 +44,7 @@ # data subdir in the notebook folder -DATASETS_DIR = u'data/' +DATASETS_DIR = u'../notebooks/data/' def _alex_process(d): @@ -229,4 +229,4 @@ def test_burst_fitter(data): assert hasattr(d, 'E_fitter') if d.alternated: bext.bursts_fitter(d, burst_data='S') - assert hasattr(d, 'S_fitter') \ No newline at end of file + assert hasattr(d, 'S_fitter') diff --git a/fretbursts/fit/test_exp_fitting.py b/tests/test_exp_fitting.py similarity index 97% rename from fretbursts/fit/test_exp_fitting.py rename to tests/test_exp_fitting.py index 0c3d2e3d..2d647a33 100644 --- a/fretbursts/fit/test_exp_fitting.py +++ b/tests/test_exp_fitting.py @@ -60,4 +60,4 @@ def test_expon_fit_histw(sample): assert relative_error < max_relative_error if __name__ == '__main__': - pytest.main("-x -v -s fretbursts/fit/test_exp_fitting.py") + pytest.main("-x -v -s fretbursts/fit/test_exp_fitting.py") \ No newline at end of file diff --git a/fretbursts/tests/test_ph_sel.py b/tests/test_ph_sel.py similarity index 100% rename from fretbursts/tests/test_ph_sel.py rename to tests/test_ph_sel.py diff --git a/versioneer.py b/versioneer.py deleted file mode 100644 index 181766af..00000000 --- a/versioneer.py +++ /dev/null @@ -1,1698 +0,0 @@ - -# Version: 0.15 - -""" -The Versioneer -============== - -* like a rocketeer, but for versions! -* https://github.com/warner/python-versioneer -* Brian Warner -* License: Public Domain -* Compatible With: python2.6, 2.7, 3.2, 3.3, 3.4, and pypy -* [![Latest Version] -(https://pypip.in/version/versioneer/badge.svg?style=flat) -](https://pypi.python.org/pypi/versioneer/) -* [![Build Status] -(https://travis-ci.org/warner/python-versioneer.png?branch=master) -](https://travis-ci.org/warner/python-versioneer) - -This is a tool for managing a recorded version number in distutils-based -python projects. The goal is to remove the tedious and error-prone "update -the embedded version string" step from your release process. Making a new -release should be as easy as recording a new tag in your version-control -system, and maybe making new tarballs. - - -## Quick Install - -* `pip install versioneer` to somewhere to your $PATH -* add a `[versioneer]` section to your setup.cfg (see below) -* run `versioneer install` in your source tree, commit the results - -## Version Identifiers - -Source trees come from a variety of places: - -* a version-control system checkout (mostly used by developers) -* a nightly tarball, produced by build automation -* a snapshot tarball, produced by a web-based VCS browser, like github's - "tarball from tag" feature -* a release tarball, produced by "setup.py sdist", distributed through PyPI - -Within each source tree, the version identifier (either a string or a number, -this tool is format-agnostic) can come from a variety of places: - -* ask the VCS tool itself, e.g. "git describe" (for checkouts), which knows - about recent "tags" and an absolute revision-id -* the name of the directory into which the tarball was unpacked -* an expanded VCS keyword ($Id$, etc) -* a `_version.py` created by some earlier build step - -For released software, the version identifier is closely related to a VCS -tag. Some projects use tag names that include more than just the version -string (e.g. "myproject-1.2" instead of just "1.2"), in which case the tool -needs to strip the tag prefix to extract the version identifier. For -unreleased software (between tags), the version identifier should provide -enough information to help developers recreate the same tree, while also -giving them an idea of roughly how old the tree is (after version 1.2, before -version 1.3). Many VCS systems can report a description that captures this, -for example `git describe --tags --dirty --always` reports things like -"0.7-1-g574ab98-dirty" to indicate that the checkout is one revision past the -0.7 tag, has a unique revision id of "574ab98", and is "dirty" (it has -uncommitted changes. - -The version identifier is used for multiple purposes: - -* to allow the module to self-identify its version: `myproject.__version__` -* to choose a name and prefix for a 'setup.py sdist' tarball - -## Theory of Operation - -Versioneer works by adding a special `_version.py` file into your source -tree, where your `__init__.py` can import it. This `_version.py` knows how to -dynamically ask the VCS tool for version information at import time. - -`_version.py` also contains `$Revision$` markers, and the installation -process marks `_version.py` to have this marker rewritten with a tag name -during the `git archive` command. As a result, generated tarballs will -contain enough information to get the proper version. - -To allow `setup.py` to compute a version too, a `versioneer.py` is added to -the top level of your source tree, next to `setup.py` and the `setup.cfg` -that configures it. This overrides several distutils/setuptools commands to -compute the version when invoked, and changes `setup.py build` and `setup.py -sdist` to replace `_version.py` with a small static file that contains just -the generated version data. - -## Installation - -First, decide on values for the following configuration variables: - -* `VCS`: the version control system you use. Currently accepts "git". - -* `style`: the style of version string to be produced. See "Styles" below for - details. Defaults to "pep440", which looks like - `TAG[+DISTANCE.gSHORTHASH[.dirty]]`. - -* `versionfile_source`: - - A project-relative pathname into which the generated version strings should - be written. This is usually a `_version.py` next to your project's main - `__init__.py` file, so it can be imported at runtime. If your project uses - `src/myproject/__init__.py`, this should be `src/myproject/_version.py`. - This file should be checked in to your VCS as usual: the copy created below - by `setup.py setup_versioneer` will include code that parses expanded VCS - keywords in generated tarballs. The 'build' and 'sdist' commands will - replace it with a copy that has just the calculated version string. - - This must be set even if your project does not have any modules (and will - therefore never import `_version.py`), since "setup.py sdist" -based trees - still need somewhere to record the pre-calculated version strings. Anywhere - in the source tree should do. If there is a `__init__.py` next to your - `_version.py`, the `setup.py setup_versioneer` command (described below) - will append some `__version__`-setting assignments, if they aren't already - present. - -* `versionfile_build`: - - Like `versionfile_source`, but relative to the build directory instead of - the source directory. These will differ when your setup.py uses - 'package_dir='. If you have `package_dir={'myproject': 'src/myproject'}`, - then you will probably have `versionfile_build='myproject/_version.py'` and - `versionfile_source='src/myproject/_version.py'`. - - If this is set to None, then `setup.py build` will not attempt to rewrite - any `_version.py` in the built tree. If your project does not have any - libraries (e.g. if it only builds a script), then you should use - `versionfile_build = None` and override `distutils.command.build_scripts` - to explicitly insert a copy of `versioneer.get_version()` into your - generated script. - -* `tag_prefix`: - - a string, like 'PROJECTNAME-', which appears at the start of all VCS tags. - If your tags look like 'myproject-1.2.0', then you should use - tag_prefix='myproject-'. If you use unprefixed tags like '1.2.0', this - should be an empty string. - -* `parentdir_prefix`: - - a optional string, frequently the same as tag_prefix, which appears at the - start of all unpacked tarball filenames. If your tarball unpacks into - 'myproject-1.2.0', this should be 'myproject-'. To disable this feature, - just omit the field from your `setup.cfg`. - -This tool provides one script, named `versioneer`. That script has one mode, -"install", which writes a copy of `versioneer.py` into the current directory -and runs `versioneer.py setup` to finish the installation. - -To versioneer-enable your project: - -* 1: Modify your `setup.cfg`, adding a section named `[versioneer]` and - populating it with the configuration values you decided earlier (note that - the option names are not case-sensitive): - - ```` - [versioneer] - VCS = git - style = pep440 - versionfile_source = src/myproject/_version.py - versionfile_build = myproject/_version.py - tag_prefix = "" - parentdir_prefix = myproject- - ```` - -* 2: Run `versioneer install`. This will do the following: - - * copy `versioneer.py` into the top of your source tree - * create `_version.py` in the right place (`versionfile_source`) - * modify your `__init__.py` (if one exists next to `_version.py`) to define - `__version__` (by calling a function from `_version.py`) - * modify your `MANIFEST.in` to include both `versioneer.py` and the - generated `_version.py` in sdist tarballs - - `versioneer install` will complain about any problems it finds with your - `setup.py` or `setup.cfg`. Run it multiple times until you have fixed all - the problems. - -* 3: add a `import versioneer` to your setup.py, and add the following - arguments to the setup() call: - - version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), - -* 4: commit these changes to your VCS. To make sure you won't forget, - `versioneer install` will mark everything it touched for addition using - `git add`. Don't forget to add `setup.py` and `setup.cfg` too. - -## Post-Installation Usage - -Once established, all uses of your tree from a VCS checkout should get the -current version string. All generated tarballs should include an embedded -version string (so users who unpack them will not need a VCS tool installed). - -If you distribute your project through PyPI, then the release process should -boil down to two steps: - -* 1: git tag 1.0 -* 2: python setup.py register sdist upload - -If you distribute it through github (i.e. users use github to generate -tarballs with `git archive`), the process is: - -* 1: git tag 1.0 -* 2: git push; git push --tags - -Versioneer will report "0+untagged.NUMCOMMITS.gHASH" until your tree has at -least one tag in its history. - -## Version-String Flavors - -Code which uses Versioneer can learn about its version string at runtime by -importing `_version` from your main `__init__.py` file and running the -`get_versions()` function. From the "outside" (e.g. in `setup.py`), you can -import the top-level `versioneer.py` and run `get_versions()`. - -Both functions return a dictionary with different flavors of version -information: - -* `['version']`: A condensed version string, rendered using the selected - style. This is the most commonly used value for the project's version - string. The default "pep440" style yields strings like `0.11`, - `0.11+2.g1076c97`, or `0.11+2.g1076c97.dirty`. See the "Styles" section - below for alternative styles. - -* `['full-revisionid']`: detailed revision identifier. For Git, this is the - full SHA1 commit id, e.g. "1076c978a8d3cfc70f408fe5974aa6c092c949ac". - -* `['dirty']`: a boolean, True if the tree has uncommitted changes. Note that - this is only accurate if run in a VCS checkout, otherwise it is likely to - be False or None - -* `['error']`: if the version string could not be computed, this will be set - to a string describing the problem, otherwise it will be None. It may be - useful to throw an exception in setup.py if this is set, to avoid e.g. - creating tarballs with a version string of "unknown". - -Some variants are more useful than others. Including `full-revisionid` in a -bug report should allow developers to reconstruct the exact code being tested -(or indicate the presence of local changes that should be shared with the -developers). `version` is suitable for display in an "about" box or a CLI -`--version` output: it can be easily compared against release notes and lists -of bugs fixed in various releases. - -The installer adds the following text to your `__init__.py` to place a basic -version in `YOURPROJECT.__version__`: - - from ._version import get_versions - __version__ = get_versions()['version'] - del get_versions - -## Styles - -The setup.cfg `style=` configuration controls how the VCS information is -rendered into a version string. - -The default style, "pep440", produces a PEP440-compliant string, equal to the -un-prefixed tag name for actual releases, and containing an additional "local -version" section with more detail for in-between builds. For Git, this is -TAG[+DISTANCE.gHEX[.dirty]] , using information from `git describe --tags ---dirty --always`. For example "0.11+2.g1076c97.dirty" indicates that the -tree is like the "1076c97" commit but has uncommitted changes (".dirty"), and -that this commit is two revisions ("+2") beyond the "0.11" tag. For released -software (exactly equal to a known tag), the identifier will only contain the -stripped tag, e.g. "0.11". - -Other styles are available. See details.md in the Versioneer source tree for -descriptions. - -## Debugging - -Versioneer tries to avoid fatal errors: if something goes wrong, it will tend -to return a version of "0+unknown". To investigate the problem, run `setup.py -version`, which will run the version-lookup code in a verbose mode, and will -display the full contents of `get_versions()` (including the `error` string, -which may help identify what went wrong). - -## Updating Versioneer - -To upgrade your project to a new release of Versioneer, do the following: - -* install the new Versioneer (`pip install -U versioneer` or equivalent) -* edit `setup.cfg`, if necessary, to include any new configuration settings - indicated by the release notes -* re-run `versioneer install` in your source tree, to replace - `SRC/_version.py` -* commit any changed files - -### Upgrading to 0.15 - -Starting with this version, Versioneer is configured with a `[versioneer]` -section in your `setup.cfg` file. Earlier versions required the `setup.py` to -set attributes on the `versioneer` module immediately after import. The new -version will refuse to run (raising an exception during import) until you -have provided the necessary `setup.cfg` section. - -In addition, the Versioneer package provides an executable named -`versioneer`, and the installation process is driven by running `versioneer -install`. In 0.14 and earlier, the executable was named -`versioneer-installer` and was run without an argument. - -### Upgrading to 0.14 - -0.14 changes the format of the version string. 0.13 and earlier used -hyphen-separated strings like "0.11-2-g1076c97-dirty". 0.14 and beyond use a -plus-separated "local version" section strings, with dot-separated -components, like "0.11+2.g1076c97". PEP440-strict tools did not like the old -format, but should be ok with the new one. - -### Upgrading from 0.11 to 0.12 - -Nothing special. - -### Upgrading from 0.10 to 0.11 - -You must add a `versioneer.VCS = "git"` to your `setup.py` before re-running -`setup.py setup_versioneer`. This will enable the use of additional -version-control systems (SVN, etc) in the future. - -## Future Directions - -This tool is designed to make it easily extended to other version-control -systems: all VCS-specific components are in separate directories like -src/git/ . The top-level `versioneer.py` script is assembled from these -components by running make-versioneer.py . In the future, make-versioneer.py -will take a VCS name as an argument, and will construct a version of -`versioneer.py` that is specific to the given VCS. It might also take the -configuration arguments that are currently provided manually during -installation by editing setup.py . Alternatively, it might go the other -direction and include code from all supported VCS systems, reducing the -number of intermediate scripts. - - -## License - -To make Versioneer easier to embed, all its code is hereby released into the -public domain. The `_version.py` that it creates is also in the public -domain. - -""" - -try: - import configparser -except ImportError: - import ConfigParser as configparser -import errno -import json -import os -import re -import subprocess -import sys - - -class VersioneerConfig: - pass - - -def get_root(): - # we require that all commands are run from the project root, i.e. the - # directory that contains setup.py, setup.cfg, and versioneer.py . - root = os.path.realpath(os.path.abspath(os.getcwd())) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - # allow 'python path/to/setup.py COMMAND' - root = os.path.dirname(os.path.realpath(os.path.abspath(sys.argv[0]))) - setup_py = os.path.join(root, "setup.py") - versioneer_py = os.path.join(root, "versioneer.py") - if not (os.path.exists(setup_py) or os.path.exists(versioneer_py)): - err = ("Versioneer was unable to run the project root directory. " - "Versioneer requires setup.py to be executed from " - "its immediate directory (like 'python setup.py COMMAND'), " - "or in a way that lets it use sys.argv[0] to find the root " - "(like 'python path/to/setup.py COMMAND').") - raise VersioneerBadRootError(err) - try: - # Certain runtime workflows (setup.py install/develop in a setuptools - # tree) execute all dependencies in a single python process, so - # "versioneer" may be imported multiple times, and python's shared - # module-import table will cache the first one. So we can't use - # os.path.dirname(__file__), as that will find whichever - # versioneer.py was first imported, even in later projects. - me = os.path.realpath(os.path.abspath(__file__)) - if os.path.splitext(me)[0] != os.path.splitext(versioneer_py)[0]: - print("Warning: build in %s is using versioneer.py from %s" - % (os.path.dirname(me), versioneer_py)) - except NameError: - pass - return root - - -def get_config_from_root(root): - # This might raise EnvironmentError (if setup.cfg is missing), or - # configparser.NoSectionError (if it lacks a [versioneer] section), or - # configparser.NoOptionError (if it lacks "VCS="). See the docstring at - # the top of versioneer.py for instructions on writing your setup.cfg . - setup_cfg = os.path.join(root, "setup.cfg") - parser = configparser.SafeConfigParser() - with open(setup_cfg, "r") as f: - parser.readfp(f) - VCS = parser.get("versioneer", "VCS") # mandatory - - def get(parser, name): - if parser.has_option("versioneer", name): - return parser.get("versioneer", name) - return None - cfg = VersioneerConfig() - cfg.VCS = VCS - cfg.style = get(parser, "style") or "" - cfg.versionfile_source = get(parser, "versionfile_source") - cfg.versionfile_build = get(parser, "versionfile_build") - cfg.tag_prefix = get(parser, "tag_prefix") - cfg.parentdir_prefix = get(parser, "parentdir_prefix") - cfg.verbose = get(parser, "verbose") - return cfg - - -class NotThisMethod(Exception): - pass - -# these dictionaries contain VCS-specific tools -LONG_VERSION_PY = {} -HANDLERS = {} - - -def register_vcs_handler(vcs, method): # decorator - def decorate(f): - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): - assert isinstance(commands, list) - p = None - for c in commands: - try: - dispcmd = str([c] + args) - # remember shell=False, so use git.cmd on windows, not just git - p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) - break - except EnvironmentError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %s" % dispcmd) - print(e) - return None - else: - if verbose: - print("unable to find command, tried %s" % (commands,)) - return None - stdout = p.communicate()[0].strip() - if sys.version_info[0] >= 3: - stdout = stdout.decode() - if p.returncode != 0: - if verbose: - print("unable to run %s (error)" % dispcmd) - return None - return stdout -LONG_VERSION_PY['git'] = ''' -# This file helps to compute a version number in source trees obtained from -# git-archive tarball (such as those provided by githubs download-from-tag -# feature). Distribution tarballs (built by setup.py sdist) and build -# directories (produced by setup.py build) will contain a much shorter file -# that just contains the computed version number. - -# This file is released into the public domain. Generated by -# versioneer-0.15 (https://github.com/warner/python-versioneer) - -import errno -import os -import re -import subprocess -import sys - - -def get_keywords(): - # these strings will be replaced by git during git-archive. - # setup.py/versioneer.py will grep for the variable names, so they must - # each be defined on a line of their own. _version.py will just call - # get_keywords(). - git_refnames = "%(DOLLAR)sFormat:%%d%(DOLLAR)s" - git_full = "%(DOLLAR)sFormat:%%H%(DOLLAR)s" - keywords = {"refnames": git_refnames, "full": git_full} - return keywords - - -class VersioneerConfig: - pass - - -def get_config(): - # these strings are filled in when 'setup.py versioneer' creates - # _version.py - cfg = VersioneerConfig() - cfg.VCS = "git" - cfg.style = "%(STYLE)s" - cfg.tag_prefix = "%(TAG_PREFIX)s" - cfg.parentdir_prefix = "%(PARENTDIR_PREFIX)s" - cfg.versionfile_source = "%(VERSIONFILE_SOURCE)s" - cfg.verbose = False - return cfg - - -class NotThisMethod(Exception): - pass - - -LONG_VERSION_PY = {} -HANDLERS = {} - - -def register_vcs_handler(vcs, method): # decorator - def decorate(f): - if vcs not in HANDLERS: - HANDLERS[vcs] = {} - HANDLERS[vcs][method] = f - return f - return decorate - - -def run_command(commands, args, cwd=None, verbose=False, hide_stderr=False): - assert isinstance(commands, list) - p = None - for c in commands: - try: - dispcmd = str([c] + args) - # remember shell=False, so use git.cmd on windows, not just git - p = subprocess.Popen([c] + args, cwd=cwd, stdout=subprocess.PIPE, - stderr=(subprocess.PIPE if hide_stderr - else None)) - break - except EnvironmentError: - e = sys.exc_info()[1] - if e.errno == errno.ENOENT: - continue - if verbose: - print("unable to run %%s" %% dispcmd) - print(e) - return None - else: - if verbose: - print("unable to find command, tried %%s" %% (commands,)) - return None - stdout = p.communicate()[0].strip() - if sys.version_info[0] >= 3: - stdout = stdout.decode() - if p.returncode != 0: - if verbose: - print("unable to run %%s (error)" %% dispcmd) - return None - return stdout - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - # Source tarballs conventionally unpack into a directory that includes - # both the project name and a version string. - dirname = os.path.basename(root) - if not dirname.startswith(parentdir_prefix): - if verbose: - print("guessing rootdir is '%%s', but '%%s' doesn't start with " - "prefix '%%s'" %% (root, dirname, parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None} - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - f = open(versionfile_abs, "r") - for line in f.readlines(): - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - f.close() - except EnvironmentError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - if not keywords: - raise NotThisMethod("no keywords at all, weird") - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = set([r.strip() for r in refnames.strip("()").split(",")]) - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %%d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = set([r for r in refs if re.search(r'\d', r)]) - if verbose: - print("discarding '%%s', no digits" %% ",".join(refs-tags)) - if verbose: - print("likely tags: %%s" %% ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - if verbose: - print("picking %%s" %% r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None - } - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags"} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): - # this runs 'git' from the root of the source tree. This only gets called - # if the git-archive 'subst' keywords were *not* expanded, and - # _version.py hasn't already been rewritten with a short version string, - # meaning we're inside a checked out source tree. - - if not os.path.exists(os.path.join(root, ".git")): - if verbose: - print("no .git in %%s" %% root) - raise NotThisMethod("no .git directory") - - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - # if there is a tag, this yields TAG-NUM-gHEX[-dirty] - # if there are no tags, this yields HEX[-dirty] (no NUM) - describe_out = run_command(GITS, ["describe", "--tags", "--dirty", - "--always", "--long"], - cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparseable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%%s'" - %% describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%%s' doesn't start with prefix '%%s'" - print(fmt %% (full_tag, tag_prefix)) - pieces["error"] = ("tag '%%s' doesn't start with prefix '%%s'" - %% (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], - cwd=root) - pieces["distance"] = int(count_out) # total number of commits - - return pieces - - -def plus_or_dot(pieces): - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - # now build up version string, with post-release "local version - # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - # exceptions: - # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%%d.g%%s" %% (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%%d.g%%s" %% (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_pre(pieces): - # TAG[.post.devDISTANCE] . No -dirty - - # exceptions: - # 1: no tags. 0.post.devDISTANCE - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += ".post.dev%%d" %% pieces["distance"] - else: - # exception #1 - rendered = "0.post.dev%%d" %% pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that - # .dev0 sorts backwards (a dirty tree will appear "older" than the - # corresponding clean one), but you shouldn't be releasing software with - # -dirty anyways. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%%s" %% pieces["short"] - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%%s" %% pieces["short"] - return rendered - - -def render_pep440_old(pieces): - # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%%d" %% pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty - # --always' - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty - # --always -long'. The distance/hash is unconditional. - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%%d-g%%s" %% (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"]} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%%s'" %% style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None} - - -def get_versions(): - # I am in _version.py, which lives at ROOT/VERSIONFILE_SOURCE. If we have - # __file__, we can work backwards from there to the root. Some - # py2exe/bbfreeze/non-CPython implementations don't do __file__, in which - # case we can only use expanded keywords. - - cfg = get_config() - verbose = cfg.verbose - - try: - return git_versions_from_keywords(get_keywords(), cfg.tag_prefix, - verbose) - except NotThisMethod: - pass - - try: - root = os.path.realpath(__file__) - # versionfile_source is the relative path from the top of the source - # tree (where the .git directory might live) to this file. Invert - # this to find the root from __file__. - for i in cfg.versionfile_source.split('/'): - root = os.path.dirname(root) - except NameError: - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to find root of source tree"} - - try: - pieces = git_pieces_from_vcs(cfg.tag_prefix, root, verbose) - return render(pieces, cfg.style) - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - return versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - except NotThisMethod: - pass - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, - "error": "unable to compute version"} -''' - - -@register_vcs_handler("git", "get_keywords") -def git_get_keywords(versionfile_abs): - # the code embedded in _version.py can just fetch the value of these - # keywords. When used from setup.py, we don't want to import _version.py, - # so we do it with a regexp instead. This function is not used from - # _version.py. - keywords = {} - try: - f = open(versionfile_abs, "r") - for line in f.readlines(): - if line.strip().startswith("git_refnames ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["refnames"] = mo.group(1) - if line.strip().startswith("git_full ="): - mo = re.search(r'=\s*"(.*)"', line) - if mo: - keywords["full"] = mo.group(1) - f.close() - except EnvironmentError: - pass - return keywords - - -@register_vcs_handler("git", "keywords") -def git_versions_from_keywords(keywords, tag_prefix, verbose): - if not keywords: - raise NotThisMethod("no keywords at all, weird") - refnames = keywords["refnames"].strip() - if refnames.startswith("$Format"): - if verbose: - print("keywords are unexpanded, not using") - raise NotThisMethod("unexpanded keywords, not a git-archive tarball") - refs = set([r.strip() for r in refnames.strip("()").split(",")]) - # starting in git-1.8.3, tags are listed as "tag: foo-1.0" instead of - # just "foo-1.0". If we see a "tag: " prefix, prefer those. - TAG = "tag: " - tags = set([r[len(TAG):] for r in refs if r.startswith(TAG)]) - if not tags: - # Either we're using git < 1.8.3, or there really are no tags. We use - # a heuristic: assume all version tags have a digit. The old git %d - # expansion behaves like git log --decorate=short and strips out the - # refs/heads/ and refs/tags/ prefixes that would let us distinguish - # between branches and tags. By ignoring refnames without digits, we - # filter out many common branch names like "release" and - # "stabilization", as well as "HEAD" and "master". - tags = set([r for r in refs if re.search(r'\d', r)]) - if verbose: - print("discarding '%s', no digits" % ",".join(refs-tags)) - if verbose: - print("likely tags: %s" % ",".join(sorted(tags))) - for ref in sorted(tags): - # sorting will prefer e.g. "2.0" over "2.0rc1" - if ref.startswith(tag_prefix): - r = ref[len(tag_prefix):] - if verbose: - print("picking %s" % r) - return {"version": r, - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": None - } - # no suitable tags, so version is "0+unknown", but full hex is still there - if verbose: - print("no suitable tags, using unknown + full revision id") - return {"version": "0+unknown", - "full-revisionid": keywords["full"].strip(), - "dirty": False, "error": "no suitable tags"} - - -@register_vcs_handler("git", "pieces_from_vcs") -def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command): - # this runs 'git' from the root of the source tree. This only gets called - # if the git-archive 'subst' keywords were *not* expanded, and - # _version.py hasn't already been rewritten with a short version string, - # meaning we're inside a checked out source tree. - - if not os.path.exists(os.path.join(root, ".git")): - if verbose: - print("no .git in %s" % root) - raise NotThisMethod("no .git directory") - - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - # if there is a tag, this yields TAG-NUM-gHEX[-dirty] - # if there are no tags, this yields HEX[-dirty] (no NUM) - describe_out = run_command(GITS, ["describe", "--tags", "--dirty", - "--always", "--long"], - cwd=root) - # --long was added in git-1.5.5 - if describe_out is None: - raise NotThisMethod("'git describe' failed") - describe_out = describe_out.strip() - full_out = run_command(GITS, ["rev-parse", "HEAD"], cwd=root) - if full_out is None: - raise NotThisMethod("'git rev-parse' failed") - full_out = full_out.strip() - - pieces = {} - pieces["long"] = full_out - pieces["short"] = full_out[:7] # maybe improved later - pieces["error"] = None - - # parse describe_out. It will be like TAG-NUM-gHEX[-dirty] or HEX[-dirty] - # TAG might have hyphens. - git_describe = describe_out - - # look for -dirty suffix - dirty = git_describe.endswith("-dirty") - pieces["dirty"] = dirty - if dirty: - git_describe = git_describe[:git_describe.rindex("-dirty")] - - # now we have TAG-NUM-gHEX or HEX - - if "-" in git_describe: - # TAG-NUM-gHEX - mo = re.search(r'^(.+)-(\d+)-g([0-9a-f]+)$', git_describe) - if not mo: - # unparseable. Maybe git-describe is misbehaving? - pieces["error"] = ("unable to parse git-describe output: '%s'" - % describe_out) - return pieces - - # tag - full_tag = mo.group(1) - if not full_tag.startswith(tag_prefix): - if verbose: - fmt = "tag '%s' doesn't start with prefix '%s'" - print(fmt % (full_tag, tag_prefix)) - pieces["error"] = ("tag '%s' doesn't start with prefix '%s'" - % (full_tag, tag_prefix)) - return pieces - pieces["closest-tag"] = full_tag[len(tag_prefix):] - - # distance: number of commits since tag - pieces["distance"] = int(mo.group(2)) - - # commit: short hex revision ID - pieces["short"] = mo.group(3) - - else: - # HEX: no tags - pieces["closest-tag"] = None - count_out = run_command(GITS, ["rev-list", "HEAD", "--count"], - cwd=root) - pieces["distance"] = int(count_out) # total number of commits - - return pieces - - -def do_vcs_install(manifest_in, versionfile_source, ipy): - GITS = ["git"] - if sys.platform == "win32": - GITS = ["git.cmd", "git.exe"] - files = [manifest_in, versionfile_source] - if ipy: - files.append(ipy) - try: - me = __file__ - if me.endswith(".pyc") or me.endswith(".pyo"): - me = os.path.splitext(me)[0] + ".py" - versioneer_file = os.path.relpath(me) - except NameError: - versioneer_file = "versioneer.py" - files.append(versioneer_file) - present = False - try: - f = open(".gitattributes", "r") - for line in f.readlines(): - if line.strip().startswith(versionfile_source): - if "export-subst" in line.strip().split()[1:]: - present = True - f.close() - except EnvironmentError: - pass - if not present: - f = open(".gitattributes", "a+") - f.write("%s export-subst\n" % versionfile_source) - f.close() - files.append(".gitattributes") - run_command(GITS, ["add", "--"] + files) - - -def versions_from_parentdir(parentdir_prefix, root, verbose): - # Source tarballs conventionally unpack into a directory that includes - # both the project name and a version string. - dirname = os.path.basename(root) - if not dirname.startswith(parentdir_prefix): - if verbose: - print("guessing rootdir is '%s', but '%s' doesn't start with " - "prefix '%s'" % (root, dirname, parentdir_prefix)) - raise NotThisMethod("rootdir doesn't start with parentdir_prefix") - return {"version": dirname[len(parentdir_prefix):], - "full-revisionid": None, - "dirty": False, "error": None} - -SHORT_VERSION_PY = """ -# This file was generated by 'versioneer.py' (0.15) from -# revision-control system data, or from the parent directory name of an -# unpacked source archive. Distribution tarballs contain a pre-generated copy -# of this file. - -import json -import sys - -version_json = ''' -%s -''' # END VERSION_JSON - - -def get_versions(): - return json.loads(version_json) -""" - - -def versions_from_file(filename): - try: - with open(filename) as f: - contents = f.read() - except EnvironmentError: - raise NotThisMethod("unable to read _version.py") - mo = re.search(r"version_json = '''\n(.*)''' # END VERSION_JSON", - contents, re.M | re.S) - if not mo: - raise NotThisMethod("no version_json in _version.py") - return json.loads(mo.group(1)) - - -def write_to_version_file(filename, versions): - os.unlink(filename) - contents = json.dumps(versions, sort_keys=True, - indent=1, separators=(",", ": ")) - with open(filename, "w") as f: - f.write(SHORT_VERSION_PY % contents) - - print("set %s to '%s'" % (filename, versions["version"])) - - -def plus_or_dot(pieces): - if "+" in pieces.get("closest-tag", ""): - return "." - return "+" - - -def render_pep440(pieces): - # now build up version string, with post-release "local version - # identifier". Our goal: TAG[+DISTANCE.gHEX[.dirty]] . Note that if you - # get a tagged build and then dirty it, you'll get TAG+0.gHEX.dirty - - # exceptions: - # 1: no tags. git_describe was just HEX. 0+untagged.DISTANCE.gHEX[.dirty] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += plus_or_dot(pieces) - rendered += "%d.g%s" % (pieces["distance"], pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - else: - # exception #1 - rendered = "0+untagged.%d.g%s" % (pieces["distance"], - pieces["short"]) - if pieces["dirty"]: - rendered += ".dirty" - return rendered - - -def render_pep440_pre(pieces): - # TAG[.post.devDISTANCE] . No -dirty - - # exceptions: - # 1: no tags. 0.post.devDISTANCE - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += ".post.dev%d" % pieces["distance"] - else: - # exception #1 - rendered = "0.post.dev%d" % pieces["distance"] - return rendered - - -def render_pep440_post(pieces): - # TAG[.postDISTANCE[.dev0]+gHEX] . The ".dev0" means dirty. Note that - # .dev0 sorts backwards (a dirty tree will appear "older" than the - # corresponding clean one), but you shouldn't be releasing software with - # -dirty anyways. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += plus_or_dot(pieces) - rendered += "g%s" % pieces["short"] - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - rendered += "+g%s" % pieces["short"] - return rendered - - -def render_pep440_old(pieces): - # TAG[.postDISTANCE[.dev0]] . The ".dev0" means dirty. - - # exceptions: - # 1: no tags. 0.postDISTANCE[.dev0] - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"] or pieces["dirty"]: - rendered += ".post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - else: - # exception #1 - rendered = "0.post%d" % pieces["distance"] - if pieces["dirty"]: - rendered += ".dev0" - return rendered - - -def render_git_describe(pieces): - # TAG[-DISTANCE-gHEX][-dirty], like 'git describe --tags --dirty - # --always' - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - if pieces["distance"]: - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render_git_describe_long(pieces): - # TAG-DISTANCE-gHEX[-dirty], like 'git describe --tags --dirty - # --always -long'. The distance/hash is unconditional. - - # exceptions: - # 1: no tags. HEX[-dirty] (note: no 'g' prefix) - - if pieces["closest-tag"]: - rendered = pieces["closest-tag"] - rendered += "-%d-g%s" % (pieces["distance"], pieces["short"]) - else: - # exception #1 - rendered = pieces["short"] - if pieces["dirty"]: - rendered += "-dirty" - return rendered - - -def render(pieces, style): - if pieces["error"]: - return {"version": "unknown", - "full-revisionid": pieces.get("long"), - "dirty": None, - "error": pieces["error"]} - - if not style or style == "default": - style = "pep440" # the default - - if style == "pep440": - rendered = render_pep440(pieces) - elif style == "pep440-pre": - rendered = render_pep440_pre(pieces) - elif style == "pep440-post": - rendered = render_pep440_post(pieces) - elif style == "pep440-old": - rendered = render_pep440_old(pieces) - elif style == "git-describe": - rendered = render_git_describe(pieces) - elif style == "git-describe-long": - rendered = render_git_describe_long(pieces) - else: - raise ValueError("unknown style '%s'" % style) - - return {"version": rendered, "full-revisionid": pieces["long"], - "dirty": pieces["dirty"], "error": None} - - -class VersioneerBadRootError(Exception): - pass - - -def get_versions(verbose=False): - # returns dict with two keys: 'version' and 'full' - - if "versioneer" in sys.modules: - # see the discussion in cmdclass.py:get_cmdclass() - del sys.modules["versioneer"] - - root = get_root() - cfg = get_config_from_root(root) - - assert cfg.VCS is not None, "please set [versioneer]VCS= in setup.cfg" - handlers = HANDLERS.get(cfg.VCS) - assert handlers, "unrecognized VCS '%s'" % cfg.VCS - verbose = verbose or cfg.verbose - assert cfg.versionfile_source is not None, \ - "please set versioneer.versionfile_source" - assert cfg.tag_prefix is not None, "please set versioneer.tag_prefix" - - versionfile_abs = os.path.join(root, cfg.versionfile_source) - - # extract version from first of: _version.py, VCS command (e.g. 'git - # describe'), parentdir. This is meant to work for developers using a - # source checkout, for users of a tarball created by 'setup.py sdist', - # and for users of a tarball/zipball created by 'git archive' or github's - # download-from-tag feature or the equivalent in other VCSes. - - get_keywords_f = handlers.get("get_keywords") - from_keywords_f = handlers.get("keywords") - if get_keywords_f and from_keywords_f: - try: - keywords = get_keywords_f(versionfile_abs) - ver = from_keywords_f(keywords, cfg.tag_prefix, verbose) - if verbose: - print("got version from expanded keyword %s" % ver) - return ver - except NotThisMethod: - pass - - try: - ver = versions_from_file(versionfile_abs) - if verbose: - print("got version from file %s %s" % (versionfile_abs, ver)) - return ver - except NotThisMethod: - pass - - from_vcs_f = handlers.get("pieces_from_vcs") - if from_vcs_f: - try: - pieces = from_vcs_f(cfg.tag_prefix, root, verbose) - ver = render(pieces, cfg.style) - if verbose: - print("got version from VCS %s" % ver) - return ver - except NotThisMethod: - pass - - try: - if cfg.parentdir_prefix: - ver = versions_from_parentdir(cfg.parentdir_prefix, root, verbose) - if verbose: - print("got version from parentdir %s" % ver) - return ver - except NotThisMethod: - pass - - if verbose: - print("unable to compute version") - - return {"version": "0+unknown", "full-revisionid": None, - "dirty": None, "error": "unable to compute version"} - - -def get_version(): - return get_versions()["version"] - - -def get_cmdclass(): - if "versioneer" in sys.modules: - del sys.modules["versioneer"] - # this fixes the "python setup.py develop" case (also 'install' and - # 'easy_install .'), in which subdependencies of the main project are - # built (using setup.py bdist_egg) in the same python process. Assume - # a main project A and a dependency B, which use different versions - # of Versioneer. A's setup.py imports A's Versioneer, leaving it in - # sys.modules by the time B's setup.py is executed, causing B to run - # with the wrong versioneer. Setuptools wraps the sub-dep builds in a - # sandbox that restores sys.modules to it's pre-build state, so the - # parent is protected against the child's "import versioneer". By - # removing ourselves from sys.modules here, before the child build - # happens, we protect the child from the parent's versioneer too. - # Also see https://github.com/warner/python-versioneer/issues/52 - - cmds = {} - - # we add "version" to both distutils and setuptools - from distutils.core import Command - - class cmd_version(Command): - description = "report generated version string" - user_options = [] - boolean_options = [] - - def initialize_options(self): - pass - - def finalize_options(self): - pass - - def run(self): - vers = get_versions(verbose=True) - print("Version: %s" % vers["version"]) - print(" full-revisionid: %s" % vers.get("full-revisionid")) - print(" dirty: %s" % vers.get("dirty")) - if vers["error"]: - print(" error: %s" % vers["error"]) - cmds["version"] = cmd_version - - # we override "build_py" in both distutils and setuptools - # - # most invocation pathways end up running build_py: - # distutils/build -> build_py - # distutils/install -> distutils/build ->.. - # setuptools/bdist_wheel -> distutils/install ->.. - # setuptools/bdist_egg -> distutils/install_lib -> build_py - # setuptools/install -> bdist_egg ->.. - # setuptools/develop -> ? - - from distutils.command.build_py import build_py as _build_py - - class cmd_build_py(_build_py): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - _build_py.run(self) - # now locate _version.py in the new build/ directory and replace - # it with an updated value - if cfg.versionfile_build: - target_versionfile = os.path.join(self.build_lib, - cfg.versionfile_build) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - cmds["build_py"] = cmd_build_py - - if "cx_Freeze" in sys.modules: # cx_freeze enabled? - from cx_Freeze.dist import build_exe as _build_exe - - class cmd_build_exe(_build_exe): - def run(self): - root = get_root() - cfg = get_config_from_root(root) - versions = get_versions() - target_versionfile = cfg.versionfile_source - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, versions) - - _build_exe.run(self) - os.unlink(target_versionfile) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % - {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - cmds["build_exe"] = cmd_build_exe - del cmds["build_py"] - - # we override different "sdist" commands for both environments - if "setuptools" in sys.modules: - from setuptools.command.sdist import sdist as _sdist - else: - from distutils.command.sdist import sdist as _sdist - - class cmd_sdist(_sdist): - def run(self): - versions = get_versions() - self._versioneer_generated_versions = versions - # unless we update this, the command will keep using the old - # version - self.distribution.metadata.version = versions["version"] - return _sdist.run(self) - - def make_release_tree(self, base_dir, files): - root = get_root() - cfg = get_config_from_root(root) - _sdist.make_release_tree(self, base_dir, files) - # now locate _version.py in the new base_dir directory - # (remembering that it may be a hardlink) and replace it with an - # updated value - target_versionfile = os.path.join(base_dir, cfg.versionfile_source) - print("UPDATING %s" % target_versionfile) - write_to_version_file(target_versionfile, - self._versioneer_generated_versions) - cmds["sdist"] = cmd_sdist - - return cmds - - -CONFIG_ERROR = """ -setup.cfg is missing the necessary Versioneer configuration. You need -a section like: - - [versioneer] - VCS = git - style = pep440 - versionfile_source = src/myproject/_version.py - versionfile_build = myproject/_version.py - tag_prefix = "" - parentdir_prefix = myproject- - -You will also need to edit your setup.py to use the results: - - import versioneer - setup(version=versioneer.get_version(), - cmdclass=versioneer.get_cmdclass(), ...) - -Please read the docstring in ./versioneer.py for configuration instructions, -edit setup.cfg, and re-run the installer or 'python versioneer.py setup'. -""" - -SAMPLE_CONFIG = """ -# See the docstring in versioneer.py for instructions. Note that you must -# re-run 'versioneer.py setup' after changing this section, and commit the -# resulting files. - -[versioneer] -#VCS = git -#style = pep440 -#versionfile_source = -#versionfile_build = -#tag_prefix = -#parentdir_prefix = - -""" - -INIT_PY_SNIPPET = """ -from ._version import get_versions -__version__ = get_versions()['version'] -del get_versions -""" - - -def do_setup(): - root = get_root() - try: - cfg = get_config_from_root(root) - except (EnvironmentError, configparser.NoSectionError, - configparser.NoOptionError) as e: - if isinstance(e, (EnvironmentError, configparser.NoSectionError)): - print("Adding sample versioneer config to setup.cfg", - file=sys.stderr) - with open(os.path.join(root, "setup.cfg"), "a") as f: - f.write(SAMPLE_CONFIG) - print(CONFIG_ERROR, file=sys.stderr) - return 1 - - print(" creating %s" % cfg.versionfile_source) - with open(cfg.versionfile_source, "w") as f: - LONG = LONG_VERSION_PY[cfg.VCS] - f.write(LONG % {"DOLLAR": "$", - "STYLE": cfg.style, - "TAG_PREFIX": cfg.tag_prefix, - "PARENTDIR_PREFIX": cfg.parentdir_prefix, - "VERSIONFILE_SOURCE": cfg.versionfile_source, - }) - - ipy = os.path.join(os.path.dirname(cfg.versionfile_source), - "__init__.py") - if os.path.exists(ipy): - try: - with open(ipy, "r") as f: - old = f.read() - except EnvironmentError: - old = "" - if INIT_PY_SNIPPET not in old: - print(" appending to %s" % ipy) - with open(ipy, "a") as f: - f.write(INIT_PY_SNIPPET) - else: - print(" %s unmodified" % ipy) - else: - print(" %s doesn't exist, ok" % ipy) - ipy = None - - # Make sure both the top-level "versioneer.py" and versionfile_source - # (PKG/_version.py, used by runtime code) are in MANIFEST.in, so - # they'll be copied into source distributions. Pip won't be able to - # install the package without this. - manifest_in = os.path.join(root, "MANIFEST.in") - simple_includes = set() - try: - with open(manifest_in, "r") as f: - for line in f: - if line.startswith("include "): - for include in line.split()[1:]: - simple_includes.add(include) - except EnvironmentError: - pass - # That doesn't cover everything MANIFEST.in can do - # (http://docs.python.org/2/distutils/sourcedist.html#commands), so - # it might give some false negatives. Appending redundant 'include' - # lines is safe, though. - if "versioneer.py" not in simple_includes: - print(" appending 'versioneer.py' to MANIFEST.in") - with open(manifest_in, "a") as f: - f.write("include versioneer.py\n") - else: - print(" 'versioneer.py' already in MANIFEST.in") - if cfg.versionfile_source not in simple_includes: - print(" appending versionfile_source ('%s') to MANIFEST.in" % - cfg.versionfile_source) - with open(manifest_in, "a") as f: - f.write("include %s\n" % cfg.versionfile_source) - else: - print(" versionfile_source already in MANIFEST.in") - - # Make VCS-specific changes. For git, this means creating/changing - # .gitattributes to mark _version.py for export-time keyword - # substitution. - do_vcs_install(manifest_in, cfg.versionfile_source, ipy) - return 0 - - -def scan_setup_py(): - found = set() - setters = False - errors = 0 - with open("setup.py", "r") as f: - for line in f.readlines(): - if "import versioneer" in line: - found.add("import") - if "versioneer.get_cmdclass()" in line: - found.add("cmdclass") - if "versioneer.get_version()" in line: - found.add("get_version") - if "versioneer.VCS" in line: - setters = True - if "versioneer.versionfile_source" in line: - setters = True - if len(found) != 3: - print("") - print("Your setup.py appears to be missing some important items") - print("(but I might be wrong). Please make sure it has something") - print("roughly like the following:") - print("") - print(" import versioneer") - print(" setup( version=versioneer.get_version(),") - print(" cmdclass=versioneer.get_cmdclass(), ...)") - print("") - errors += 1 - if setters: - print("You should remove lines like 'versioneer.VCS = ' and") - print("'versioneer.versionfile_source = ' . This configuration") - print("now lives in setup.cfg, and should be removed from setup.py") - print("") - errors += 1 - return errors - -if __name__ == "__main__": - cmd = sys.argv[1] - if cmd == "setup": - errors = do_setup() - errors += scan_setup_py() - if errors: - sys.exit(1)