diff --git a/.github/workflows/conda-deployment.yml b/.github/workflows/conda-deployment.yml index 86dc5cd..2a1d182 100644 --- a/.github/workflows/conda-deployment.yml +++ b/.github/workflows/conda-deployment.yml @@ -34,7 +34,7 @@ jobs: - name: Install dev-dependencies run: | - python -m pip install -r requirements-dev.txt + pip install .[dev] --no-deps - name: Run tests shell: bash -el {0} diff --git a/.github/workflows/pull-request-naming-validation.yml b/.github/workflows/pull-request-naming-validation.yml deleted file mode 100644 index 2ebb5f7..0000000 --- a/.github/workflows/pull-request-naming-validation.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Branch Name Validation - -on: - pull_request: - types: - - opened - - synchronize - -jobs: - branch-name-validation: - runs-on: ubuntu-latest - - steps: - - name: Check Branch Name - run: | - # Define your branch name pattern using regex - modules="distributions|tools|sensitivity|plot|parameters|metrics|eva|confidence_interval" - - pattern=f"^({modules})/(feature|bugfix|hotfix|release|docs)/[a-zA-Z0-9_-]+$" - - branch_name=$(echo "${{ github.ref }}" | awk -F/ '{print $3}') - - if [[ ! "${branch_name}" =~ ${pattern} ]]; then - echo "Branch name does not match the naming convention." - echo "Expected format: 'type/branch-name'" - exit 1 - fi - - shell: bash diff --git a/.github/workflows/pypi-deployment.yml b/.github/workflows/pypi-deployment.yml index 0c05caf..3aa6973 100644 --- a/.github/workflows/pypi-deployment.yml +++ b/.github/workflows/pypi-deployment.yml @@ -12,8 +12,8 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-latest, windows-latest, macos-latest] - python-version: ["3.9", "3.10", "3.11"] + os: [ubuntu-latest, windows-latest] # , macos-latest + python-version: ["3.10", "3.11", "3.12"] steps: - uses: actions/checkout@v3 @@ -25,8 +25,7 @@ jobs: - name: Install dependencies run: | - pip install -r requirements.txt -r requirements-dev.txt - python setup.py install + pip install .[dev] - name: Generate coverage report run: | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 7b9f30c..1b81af4 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,7 +1,7 @@ fail_fast: true repos: - repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.5.0 + rev: v4.6.0 hooks: - id: end-of-file-fixer name: "[py - check] validate yaml" @@ -58,7 +58,7 @@ repos: files: ^Hapi/ - repo: https://github.com/pycqa/flake8 - rev: 6.1.0 + rev: 7.1.0 hooks: - id: flake8 name: "[py - check] flake8" @@ -70,7 +70,7 @@ repos: # hooks: # - id: black - repo: https://github.com/ambv/black - rev: 22.8.0 + rev: 24.8.0 hooks: - id: black name: "[py - format] black" @@ -112,3 +112,19 @@ repos: language: system pass_filenames: false always_run: true + + - repo: local + hooks: + - id: examples-notebook-check + name: nbval + entry: pytest --nbval + language: system + files: \.ipynb$ + + - repo: local + hooks: + - id: doctest + name: doctest + entry: pytest --doctest-modules + language: system + files: statista\.py$ diff --git a/.readthedocs.yml b/.readthedocs.yml index 7019f51..a5205f9 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -5,15 +5,20 @@ # Required version: 2 -conda: - environment: docs/environment.yml -# Build documentation in the docs/ directory with Sphinx sphinx: - configuration: docs/conf.py -#Build documentation with MkDocs -#mkdocs: -# configuration: mkdocs.yml + configuration: docs/source/conf.py +build: + os: "ubuntu-22.04" + tools: + python: "3.12" + +python: + install: + - method: pip + path: . + extra_requirements: + - docs # Optionally build your docs in additional formats such as PDF and ePub formats: all diff --git a/HISTORY.rst b/HISTORY.rst index 25614eb..eb2c6cc 100644 --- a/HISTORY.rst +++ b/HISTORY.rst @@ -50,3 +50,27 @@ History * Use factory design pattern to create the distributions. * add tests for the eva module. * use snake_case for the methods and variables. + +0.6.0 (2024-08-18) +------------------ + +dev +""" +* Add documentations for the `distributions`, and `eva` modules. +* Add autodoc for all modules. +* Test docstrings as part of CI and pre-commit hooks. +* Test notebooks as part of CI. +* Simplify test for the distributions module + +distributions +""""""""""""" +* move the `cdf` and `parameters` for all the methods to be optional parameters. +* rename `theoretical_estimate` method to `inverse_cdf`. +* All distributions can be instantiated with the parameters and/or data. +* rename the `probability_plot` method to `plot`. +* move the `confidence_interval` plot from the `probability_plot/plot` to the method `confidence_interval` and can be + called by activating the `plot_figure=True`. + +descriptors +""""""""""" +* rename the `metrics` module to `descriptors`. diff --git a/README.md b/README.md index c3dabe7..ae696af 100644 --- a/README.md +++ b/README.md @@ -28,7 +28,7 @@ Main Features ------------- - Statistical Distributions - GEV - - GUMBL + - GUMBEL - Normal - Exponential - Parameter estimation methods @@ -40,6 +40,7 @@ Main Features - Statistical descriptors - Extreme value analysis +For the full documentation, please visit [statista documentation](https://statista.readthedocs.io/en/latest/?badge=latest) Installing statista =============== @@ -50,22 +51,22 @@ Installing `statista` from the `conda-forge` channel can be achieved by: conda install -c conda-forge statista ``` -It is possible to list all of the versions of `statista` available on your platform with: +It is possible to list all the versions of `statista` available on your platform with: ``` conda search statista --channel conda-forge ``` -## Install from Github -to install the last development to time you can install the library from github +## Install from GitHub +to install the last development to time, you can install the library from GitHub ``` pip install git+https://github.com/MAfarrag/statista ``` ## pip -to install the last release you can easly use pip +to install the last release, you can use pip ``` -pip install statista==0.5.0 +pip install statista==0.6.0 ``` Quick start diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..d0c3cbf --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,20 @@ +# Minimal makefile for Sphinx documentation +# + +# You can set these variables from the command line, and also +# from the environment for the first two. +SPHINXOPTS ?= +SPHINXBUILD ?= sphinx-build +SOURCEDIR = source +BUILDDIR = build + +# Put it first so that "make" without argument is like "make help". +help: + @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) + +.PHONY: help Makefile + +# Catch-all target: route all unknown targets to Sphinx using the new +# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). +%: Makefile + @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index 2afda7d..0000000 --- a/docs/conf.py +++ /dev/null @@ -1,298 +0,0 @@ -# -# sphinx-quickstart on Wed April 23 2021. -# -# This file is execfile()d with the current directory set to its containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import os -import sys - -# import sphinx_rtd_theme - -# General information about the project. -project = "statista" -author = "Mostafa Farrag" - -# copyright = u"2013-2019, " - -html_theme = "sphinxdoc" -# html_theme = "agogo" -html_theme_path = ["."] - - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -sys.path.insert(0, os.path.abspath("../statista")) -sys.path.insert(0, os.path.abspath("..")) -sys.path.insert(0, os.path.abspath("../examples")) - -# If your extensions are in another directory, add it here. If the -# directory is relative to the documentation root, use -# os.path.abspath to make it absolute, like shown here. -sys.path.append(os.path.abspath("sphinxext")) - -# import statista - - -# -- General configuration ----------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be extensions -# coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = [ - "sphinx.ext.autodoc", - "sphinx.ext.coverage", - "sphinx.ext.viewcode", - "sphinx.ext.imgmath", - "easydev.copybutton", - # "matplotlib.sphinxext.plot_directive", - # "sphinx.ext.todo", - # "sphinx.ext.mathjax", - # "sphinx.ext.graphviz", - # "sphinx.ext.doctest", - # "sphinx.ext.autosectionlabel", - "numpydoc", - "nbsphinx", -] - -autosectionlabel_prefix_document = True - -todo_include_todos = True -# Add any paths that contain templates here, relative to this directory. -templates_path = ["templates"] - -# The suffix of source filenames. -source_suffix = ".rst" - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = "index" - - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ["build"] - -# The reST default role (used for this markup: `text`) to use for all documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = "sphinx" - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - - -# -- Options for HTML output --------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# html_theme = "sphinx_rtd_theme" -html_theme = "pydata_sphinx_theme" - - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -""" -html_logo = "images/statista.png" -""" -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ["static"] -# -# html_context = { -# 'css_files': [ -# '_static/theme_overrides.css', # override wide tables in RTD theme -# ], -# } - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -html_sidebars = { - "**": [ - "globaltoc.html", - "relations.html", # needs 'show_related': True theme option to display - "searchbox.html", - ] -} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -html_file_suffix = ".html" - -# Output file base name for HTML help builder. -htmlhelp_basename = "statistadoc" - - -# -- Options for LaTeX output -------------------------------------------------- -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass [howto/manual]). -latex_documents = [ - ( - "index", - "statista.tex", - "statista Documentation", - "Mostafa Farrag", - "report", - ) -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = "_static/logo.png" - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -latex_use_parts = False - -# If true, show page references after internal links. -latex_show_pagerefs = False - -# If true, show URL addresses after external links. -latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -latex_domain_indices = False - - -# -- Options for manual page output -------------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [("index", "statista", "statista Documentation", [author], 1)] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------------ - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ( - "index", - "statista", - "statista Documentation", - "Mostafa Farrag", - "One line description of project.", - "Miscellaneous", - ) -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' - -autodoc_mock_imports = [ - # "osgeo.gdal", - # "osgeo.gdalconst", - # "osgeo", - # "osgeo.ogr", - # "cftime", - # "xarray", - # "netCDF4", - # "netCDF4_utils", - # "netcdftime", - # "pyproj", - # "statista.version", -] diff --git a/docs/environment.yml b/docs/environment.yml deleted file mode 100644 index 0098310..0000000 --- a/docs/environment.yml +++ /dev/null @@ -1,13 +0,0 @@ -channels: - - conda-forge -dependencies: - - python >=3.9,<3.11 - - pip >=22.3.1 - - pandas - - numpy==1.20.* - - numpydoc==1.1.0 - - typing-extensions==3.10.* - - pip: - - pydata-sphinx-theme - - nbsphinx - - easydev diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..dc1312a --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,35 @@ +@ECHO OFF + +pushd %~dp0 + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set SOURCEDIR=source +set BUILDDIR=build + +%SPHINXBUILD% >NUL 2>NUL +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.https://www.sphinx-doc.org/ + exit /b 1 +) + +if "%1" == "" goto help + +%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% +goto end + +:help +%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% + +:end +popd diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..ee820ab --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,11 @@ +easydev +graphviz +matplotlib >=3.8.4 +nbsphinx +numpy >=2.0.0 +numpydoc==1.1.0 +pandas +pip >=21.3.1 +pydata_sphinx_theme>=0.15.2 +sphinxcontrib-napoleon +typing-extensions==3.10.* diff --git a/docs/source/_images/Frankfurt.png b/docs/source/_images/Frankfurt.png new file mode 100644 index 0000000..86dba8e Binary files /dev/null and b/docs/source/_images/Frankfurt.png differ diff --git a/docs/source/_images/expo-random-cdf.png b/docs/source/_images/expo-random-cdf.png 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b/docs/source/_static/theme_overrides.css similarity index 100% rename from docs/static/theme_overrides.css rename to docs/source/_static/theme_overrides.css diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..9f94180 --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,96 @@ +# Configuration file for the Sphinx documentation builder. +# +# For the full list of built-in configuration values, see the documentation: +# https://www.sphinx-doc.org/en/master/usage/configuration.html + +# -- Project information ----------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information +# import pydata_sphinx_theme + +import os +import sys +from importlib.metadata import version, PackageNotFoundError + +# for the auto documentation to work +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +sys.path.insert(0, os.path.abspath("../../statista")) + +# General information about the project. +project = "statista" +copyright = "2024, Mostafa Farrag" +author = "Mostafa Farrag" + +# Read the version from the package +try: + release = version("statista") +except PackageNotFoundError: + release = "unknown" + +version = release + + +# -- General configuration --------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration + +extensions = [ + "sphinx.ext.autodoc", # Enables autodoc + "sphinx.ext.viewcode", # Adds links to the source code + "sphinx.ext.graphviz", # Allows rendering of graphviz diagrams + "sphinx.ext.napoleon", # Allows for Google-style and Numpy docstrings + "sphinx.ext.mathjax", # For rendering LaTeX math equations +] + +templates_path = ["_templates"] +exclude_patterns = [] + +root_doc = "index" + +# -- Options for HTML output ------------------------------------------------- +# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output + +html_theme = "pydata_sphinx_theme" + +# Set the theme name +# Optionally, you can customize the theme's configuration +html_theme_options = { + "logo_link": "index", + "icon_links": [ + { + "name": "GitHub", + "url": "https://github.com/Serapieum-of-alex/statista", + "icon": "fab fa-github-square", + }, + ], + "navbar_end": ["search-field.html", "navbar-icon-links"], + "search_bar_text": "Search this site...", + "navbar_align": "content", + "navigation_depth": 4, + "show_prev_next": False, + "show_toc_level": 2, + # Toc options + "collapse_navigation": True, + # "external_links": [ + # {"name": "External Link", "url": "https://example.com"}, + # ], + "header_links_before_dropdown": 4, +} + +html_static_path = ["_static"] + +# Custom sidebar templates, maps document names to template names. +html_sidebars = { + "**": [ + "globaltoc.html", + "relations.html", # needs 'show_related': True theme option to display + "searchbox.html", + ] +} + +# -- Options for autodoc ----------------------------------------------------- +napoleon_google_docstring = True +napoleon_numpy_docstring = True + +# Ensure that the path to the Graphviz `dot` command is correct +graphviz_dot = "dot" diff --git a/docs/source/descriptors-module.rst b/docs/source/descriptors-module.rst new file mode 100644 index 0000000..1df9004 --- /dev/null +++ b/docs/source/descriptors-module.rst @@ -0,0 +1,9 @@ +################## +descriptors module +################## + +.. automodule:: descriptors + :members: + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/docs/source/distributions-class.rst b/docs/source/distributions-class.rst new file mode 100644 index 0000000..fb4b07e --- /dev/null +++ b/docs/source/distributions-class.rst @@ -0,0 +1,44 @@ +#################### +Distributions module +#################### + +.. automodule:: distributions + :members: Distributions + :undoc-members: + :show-inheritance: + :special-members: __init__ + + +.. automodule:: distributions + :members: PlottingPosition + :undoc-members: + :show-inheritance: + :special-members: __init__ + + +.. automodule:: distributions + :members: Gumbel + :undoc-members: + :show-inheritance: + :special-members: __init__ + + +.. automodule:: distributions + :members: GEV + :undoc-members: + :show-inheritance: + :special-members: __init__ + + +.. automodule:: distributions + :members: Exponential + :undoc-members: + :show-inheritance: + :special-members: __init__ + + +.. automodule:: distributions + :members: Normal + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/docs/source/distributions-tree.rst b/docs/source/distributions-tree.rst new file mode 100644 index 0000000..909e551 --- /dev/null +++ b/docs/source/distributions-tree.rst @@ -0,0 +1,15 @@ +############# +Distributions +############# + +:doc:`Distributions Class ` + +:doc:`Distributions Examples ` + + +.. toctree:: + :hidden: + :maxdepth: 2 + + Distributions Class + Distributions Examples diff --git a/docs/distributions.rst b/docs/source/distributions.rst similarity index 100% rename from docs/distributions.rst rename to docs/source/distributions.rst diff --git a/docs/source/eva-class.rst b/docs/source/eva-class.rst new file mode 100644 index 0000000..44cdfac --- /dev/null +++ b/docs/source/eva-class.rst @@ -0,0 +1,9 @@ +######### +EVA Class +######### + +.. automodule:: eva + :members: + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/docs/index.rst b/docs/source/index.rst similarity index 78% rename from docs/index.rst rename to docs/source/index.rst index b9d3960..a7e8987 100644 --- a/docs/index.rst +++ b/docs/source/index.rst @@ -65,13 +65,25 @@ statista - statistics package Main Features ------------- -- -- +- Statistical Distributions + - GEV + - Gumbel + - Normal + - Exponential +- Parameter estimation methods + - Lmoments + - ML + - MOM +- One-at-time (O-A-T) Sensitivity analysis. +- Sobol visualization +- Statistical descriptors +- Extreme value analysis + .. digraph:: Linking statista -> distributions; - statista -> metrics; + statista -> descriptors; statista -> parameters; statista -> sensitivity; statista -> tools; @@ -82,5 +94,17 @@ Main Features :maxdepth: 1 Installation - Distributions - Sensitivity analysis + Distributions + Sensitivity analysis + Extreme Value Analysis + Metrics + Tools + Plot + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/docs/installation.rst b/docs/source/installation.rst similarity index 62% rename from docs/installation.rst rename to docs/source/installation.rst index 99efcf0..657e07d 100644 --- a/docs/installation.rst +++ b/docs/source/installation.rst @@ -6,9 +6,21 @@ Installation -Stable release --------------- +dependencies +************ + +Required dependencies +===================== +- Python (3.11 or later) +- `numpy `__ (2 or later) +- `pandas `__ (2 or later) +- `SciPy `__ (1.14 or later) +- `scikit-learn `__ (1.5 or later) +- `matplotlib `__ (1.5 or later) + +Stable release +************** Please install ``statista`` in a Virtual environment so that its requirements don't tamper with your system's python. conda @@ -19,14 +31,14 @@ you can use the following command: + ``conda install -c conda-forge statista`` -If this works it will install Hapi with all dependencies including Python and gdal, +If this works it will install `statista` with all dependencies including Python and numpy, scipy and scikit-learn and you skip the rest of the installation instructions. Installing Python and gdal dependencies --------------------------------------- -The main dependencies for statista are an installation of Python 3.9+, and gdal +The main dependencies for statista are an installation of Python 3.9+, and scipy Installing Python ----------------- @@ -43,13 +55,13 @@ makes installation of required dependencies easier using the conda package manag Install as a conda environment ------------------------------ -The easiest and most robust way to install Hapi is by installing it in a separate +The easiest and most robust way to install statista is by installing it in a separate conda environment. In the root repository directory there is an ``environment.yml`` file. -This file lists all dependencies. Either use the ``environment.yml`` file from the master branch -(please note that the master branch can change rapidly and break functionality without warning), +This file lists all dependencies. Either use the ``environment.yml`` file from the main branch +(please note that the main branch can change rapidly and break functionality without warning), or from one of the releases {release}. -Run this command to start installing all Hapi dependencies: +Run this command to start installing all statista dependencies: + ``conda env create -f environment.yml`` @@ -58,8 +70,8 @@ a session, run: + ``conda activate statista`` -For the installation of Hapi there are two options (from the Python Package Index (PyPI) -or from Github). To install a release of Hapi from the PyPI (available from release 2018.1): +For the installation of statista there are two options (from the Python Package Index (PyPI) +or from Github). To install a release of statista from the PyPI (available from release 2018.1): + ``pip install statista=={release}`` @@ -68,38 +80,38 @@ From sources ------------ -The sources for HapiSM can be downloaded from the `Github repo`_. +The sources for statista can be downloaded from the `Github repo`_. You can either clone the public repository: .. code-block:: console - $ git clone git://github.com/MAfarrag/statista + $ git clone git://github.com/Serapieum-of-alex/statista Or download the `tarball`_: .. code-block:: console - $ curl -OJL https://github.com/MAfarrag/statista/tarball/main + $ curl -OJL https://github.com/Serapieum-of-alex/statista/tarball/main Once you have a copy of the source, you can install it with: .. code-block:: console - $ python setup.py install + $ python -m pip install . -.. _Github repo: https://github.com/MAfarrag/statista -.. _tarball: https://github.com/MAfarrag/statista/tarball/master +.. _Github repo: https://github.com/Serapieum-of-alex/statista +.. _tarball: https://github.com/Serapieum-of-alex/statista/tarball/main -To install directly from GitHub (from the HEAD of the master branch): +To install directly from GitHub (from the HEAD of the main branch): -+ ``pip install git+https://github.com/MAfarrag/statista.git`` ++ ``pip install git+https://github.com/Serapieum-of-alex/statista.git`` or from Github from a specific release: -+ ``pip install git+https://github.com/MAfarrag/statista.git@{release}`` ++ ``pip install git+https://github.com/Serapieum-of-alex/statista.git@{release}`` Now you should be able to start this environment's Python with ``python``, try ``import statista`` to see if the package is installed. @@ -109,33 +121,33 @@ More details on how to work with conda environments can be found here: https://conda.io/docs/user-guide/tasks/manage-environments.html -If you are planning to make changes and contribute to the development of Hapi, it is +If you are planning to make changes and contribute to the development of statista, it is best to make a git clone of the repository, and do a editable install in the location of you clone. This will not move a copy to your Python installation directory, but instead create a link in your Python installation pointing to the folder you installed it from, such that any changes you make there are directly reflected in your install. -+ ``git clone https://github.com/MAfarrag/statista.git`` ++ ``git clone https://github.com/Serapieum-of-alex/statista.git`` + ``cd statista`` + ``activate statista`` + ``pip install -e .`` Alternatively, if you want to avoid using ``git`` and simply want to test the latest -version from the ``master`` branch, you can replace the first line with downloading -a zip archive from GitHub: https://github.com/MAfarrag/statista/archive/master.zip -`libraries.io `_. +version from the ``main`` branch, you can replace the first line with downloading +a zip archive from GitHub: https://github.com/Serapieum-of-alex/statista/archive/main.zip +`libraries.io `_. Install using pip ----------------- Besides the recommended conda environment setup described above, you can also install -Hapi with ``pip``. For the more difficult to install Python dependencies, it is best to +statista with ``pip``. For the more difficult to install Python dependencies, it is best to use the conda package manager: -+ ``conda install numpy scipy gdal netcdf4 pyproj`` ++ ``conda install numpy scipy scikit-learn matplotlib pandas loguru`` -you can check `libraries.io `_. to check versions of the libraries +you can check `libraries.io `_. to check versions of the libraries Then install a release {release} of statista (available from release 2018.1) with pip: diff --git a/docs/source/plot-class.rst b/docs/source/plot-class.rst new file mode 100644 index 0000000..ae70f01 --- /dev/null +++ b/docs/source/plot-class.rst @@ -0,0 +1,9 @@ +########## +Plot Class +########## + +.. automodule:: plot + :members: Plot + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/docs/source/sensitivity-class.rst b/docs/source/sensitivity-class.rst new file mode 100644 index 0000000..b9fa90f --- /dev/null +++ b/docs/source/sensitivity-class.rst @@ -0,0 +1,9 @@ +################# +Sensitivity Class +################# + +.. automodule:: sensitivity + :members: Sensitivity + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/docs/source/sensitivity-tree.rst b/docs/source/sensitivity-tree.rst new file mode 100644 index 0000000..efd270f --- /dev/null +++ b/docs/source/sensitivity-tree.rst @@ -0,0 +1,15 @@ +########### +Sensitivity +########### + +:doc:`Sensitivity Class ` + +:doc:`Sensitivity Examples ` + + +.. toctree:: + :hidden: + :maxdepth: 2 + + Sensitivity Class + Sensitivity Examples diff --git a/docs/sensitivity_analysis.rst b/docs/source/sensitivity.rst similarity index 90% rename from docs/sensitivity_analysis.rst rename to docs/source/sensitivity.rst index 1e90326..ee5355e 100644 --- a/docs/sensitivity_analysis.rst +++ b/docs/source/sensitivity.rst @@ -1,6 +1,6 @@ -****************************** -Sensetivity Analysis (OAT) -****************************** +************************** +Sensitivity Analysis (OAT) +************************** OAT sensitivity analysis is a tool that is based One of the simplest and most common approaches of sensitivity analysis is that of changing one-factor-at-a-time (OAT), to see what effect this produces on the output. @@ -31,8 +31,7 @@ Steps: Run the model -------------- -.. code-block:: py - :linenos: +.. code:: py import pandas as pd @@ -132,8 +131,7 @@ to define the argument of the "wrapper" function There are two types of wrappers - The first one returns one value (performance metric) -.. code-block:: py - :linenos: +.. code:: py # For Type 1 def WrapperType1(Randpar,Route, RoutingFn, Qobs): @@ -149,11 +147,9 @@ There are two types of wrappers Instantiate the SensitivityAnalysis object ------------------------------------------- -.. code-block:: py - :linenos: +.. code:: py fn = WrapperType2 - Positions = [10] Sen = SA(parameters,Coello.LB, Coello.UB, fn, Positions, 5, Type=Type) @@ -163,8 +159,8 @@ Instantiate the SensitivityAnalysis object Run the OAT method ------------------- -.. code-block:: py - :linenos: +.. code:: py + Sen.OAT(Route, RoutingFn, Qobs) .. _5: @@ -172,25 +168,25 @@ Run the OAT method Display the result with the SOBOL plot --------------------------------------- -.. code-block:: py - :linenos: +.. code:: py From = '' To = '' - fig, ax1 = Sen.Sobol(RealValues=False, Title="Sensitivity Analysis of the RMSE to models parameters", - xlabel = "Maxbas Values", ylabel="RMSE", From=From, To=To,xlabel2='Time', - ylabel2='Discharge m3/s', spaces=[None,None,None,None,None,None]) + fig, ax1 = Sen.Sobol(RealValues=False, Title="Sensitivity Analysis of the RMSE to models parameters", + xlabel = "Maxbas Values", ylabel="RMSE", From=From, To=To,xlabel2='Time', + ylabel2='Discharge m3/s', spaces=[None,None,None,None,None,None]) - Type 1 with one parameter -.. image:: images/sensitivityAnalysis1.png +.. image:: _images/sensitivityAnalysis1.png :width: 400pt :align: center - Type 1 with all parameters -.. image:: images/sensitivityAnalysis3.png + +.. image:: _images/sensitivityAnalysis3.png :width: 400pt :align: center @@ -199,8 +195,7 @@ The second type - The second wrapper returns two values (the performance metric and the calculated output from the model) -.. code-block:: py - :linenos: +.. code:: py # For Type 2 def WrapperType2(Randpar,Route, RoutingFn, Qobs): @@ -220,6 +215,6 @@ The second type - Type 2 -.. image:: images/sensitivityAnalysis2.png +.. image:: _images/sensitivityAnalysis2.png :width: 400pt :align: center diff --git a/docs/source/tools-module.rst b/docs/source/tools-module.rst new file mode 100644 index 0000000..4239073 --- /dev/null +++ b/docs/source/tools-module.rst @@ -0,0 +1,9 @@ +############ +Tools module +############ + +.. automodule:: tools + :members: + :undoc-members: + :show-inheritance: + :special-members: __init__ diff --git a/environment.yml b/environment.yml index 9192155..3d13e41 100644 --- a/environment.yml +++ b/environment.yml @@ -1,13 +1,12 @@ channels: - conda-forge dependencies: - - python >=3.11 - - numpy >=1.25.2 + - numpy >=2.0.1 - pip >=23.2.1 - - matplotlib >=3.8.0 + - matplotlib >=3.9.0 - pandas >=2.1.0 - - scipy >=1.11.4 - - scikit-learn >=1.3.2 + - scipy >=1.14.0 + - scikit-learn >=1.5.1 - loguru >=0.7.2 - pytest >=7.4.2 - pytest-cov >=4.1.0 diff --git a/examples/Extreme value statistics.py b/examples/Extreme value statistics.py deleted file mode 100644 index d91752a..0000000 --- a/examples/Extreme value statistics.py +++ /dev/null @@ -1,111 +0,0 @@ -"""Extreme value statistics""" -import matplotlib - -matplotlib.use("TkAgg") -import pandas as pd - -from statista.distributions import GEV, Gumbel, PlottingPosition, Distributions -from statista.confidence_interval import ConfidenceInterval - -time_series1 = pd.read_csv("examples/data/time_series1.txt", header=None)[0].tolist() -time_series2 = pd.read_csv("examples/data/time_series2.txt", header=None)[0].tolist() -# %% -gumbel_dist = Distributions("Gumbel", time_series1) -# defult parameter estimation method is maximum liklihood method -param_mle = gumbel_dist.fit_model(method="mle") -gumbel_dist.ks() -gumbel_dist.chisquare() -print(param_mle) -# calculate and plot the pdf -pdf = gumbel_dist.pdf(param_mle, plot_figure=True) -cdf, _, _ = gumbel_dist.cdf(param_mle, plot_figure=True) -# %% lmoments -param_lmoments = gumbel_dist.fit_model(method="lmoments") -gumbel_dist.ks() -gumbel_dist.chisquare() -print(param_lmoments) -# calculate and plot the pdf -pdf = gumbel_dist.pdf(param_lmoments, plot_figure=True) -cdf, _, _ = gumbel_dist.cdf(param_lmoments, plot_figure=True) -# %% -# calculate the CDF(Non Exceedance probability) using weibul plotting position -time_series1.sort() -# calculate the F (Non Exceedence probability based on weibul) -cdf_weibul = PlottingPosition.weibul(time_series1) -# TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution -Qth = gumbel_dist.theoretical_estimate(param_lmoments, cdf_weibul) -# test = stats.chisquare(st.Standardize(Qth), st.Standardize(time_series1),ddof=5) -# calculate the confidence interval -upper, lower = gumbel_dist.confidence_interval(param_lmoments, cdf_weibul, alpha=0.1) -# ProbapilityPlot can estimate the Qth and the lower and upper confidence interval in the process of plotting -fig, ax = gumbel_dist.probability_plot(param_lmoments, cdf_weibul, alpha=0.1) -# %% -""" -if you want to focus only on high values, you can use a threshold to make the code focus on what is higher -this threshold. -""" -threshold = 17 -param_dist = gumbel_dist.fit_model( - method="optimization", obj_func=Gumbel.objective_fn, threshold=threshold -) -print(param_dist) -gumbel_dist.probability_plot(param_dist, cdf_weibul, alpha=0.1) -# %% -threshold = 18 -param_dist = gumbel_dist.fit_model( - method="optimization", obj_func=Gumbel.objective_fn, threshold=threshold -) -print(param_dist) -gumbel_dist.probability_plot(param_dist, cdf_weibul, alpha=0.1) -# %% Generalized Extreme Value (GEV) -gev_dist = Distributions("GEV", time_series2) -# default parameter estimation method is maximum liklihood method -gev_mle_param = gev_dist.fit_model(method="mle") -gev_dist.ks() -gev_dist.chisquare() - -print(gev_mle_param) -# calculate and plot the pdf -pdf, fig, ax = gev_dist.pdf(gev_mle_param, plot_figure=True) -cdf, _, _ = gev_dist.cdf(gev_mle_param, plot_figure=True) -# %% lmoment method -gev_lmom_param = gev_dist.fit_model(method="lmoments") -print(gev_lmom_param) -# calculate and plot the pdf -pdf, fig, ax = gev_dist.pdf(gev_lmom_param, plot_figure=True) -cdf, _, _ = gev_dist.cdf(gev_lmom_param, plot_figure=True) -#%% -time_series1.sort() -# calculate the F (Non Exceedence probability based on weibul) -cdf_weibul = PlottingPosition.weibul(time_series1) -T = PlottingPosition.weibul(time_series1, return_period=True) -# TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution -Qth = gev_dist.theoretical_estimate(gev_lmom_param, cdf_weibul) - -func = GEV.ci_func -upper, lower = gev_dist.confidence_interval( - gev_lmom_param, - prob_non_exceed=cdf_weibul, - alpha=0.1, - statfunction=func, - n_samples=len(time_series1), - method="lmoments", -) -# %% -""" -calculate the confidence interval using the boot strap method directly -""" -CI = ConfidenceInterval.boot_strap( - time_series1, - statfunction=func, - gevfit=gev_lmom_param, - n_samples=len(time_series1), - F=cdf_weibul, - method="lmoments", -) -LB = CI["lb"] -UB = CI["ub"] -# %% -fig, ax = gev_dist.probability_plot( - gev_lmom_param, cdf_weibul, func=func, n_samples=len(time_series1) -) diff --git a/examples/Note books/Extreme value analysis.ipynb b/examples/Note books/Extreme value analysis.ipynb deleted file mode 100644 index e971206..0000000 --- a/examples/Note books/Extreme value analysis.ipynb +++ /dev/null @@ -1,608 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "# Extreme Value Analysis" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "import matplotlib\n", - "%matplotlib inline\n", - "import pandas as pd\n", - "from statista.distributions import GEV, ConfidenceInterval, Gumbel, PlottingPosition" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [], - "source": [ - "import os\n", - "os.chdir(r\"C:\\gdrive\\01Algorithms\\Statistics\\statista\")\n", - "time_series1 = pd.read_csv(\"examples/data/time_series1.txt\", header=None)[0].tolist()\n", - "time_series2 = pd.read_csv(\"examples/data/time_series2.txt\", header=None)[0].tolist()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "# Gumbel Distribution" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----KS Test--------\n", - "Statistic = 0.18518518518518517\n", - "Accept Hypothesis\n", - "P value = 0.7536974563793281\n", - "-----chisquare Test-----\n", - "Statistic = -1.7297426599910237\n", - "P value = 1.0\n", - "-----KS Test--------\n", - "Statistic = 0.18518518518518517\n", - "Accept Hypothesis\n", - "P value = 0.7536974563793281\n", - "-----chisquare Test-----\n", - "Statistic = -1.7297426599910237\n", - "P value = 1.0\n", - "[16.470245610977667, 0.724486313118949]\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Gdist = Gumbel(time_series1)\n", - "# defult parameter estimation method is maximum liklihood method\n", - "Param_mle = Gdist.estimateParameter(method=\"mle\")\n", - "Gdist.ks()\n", - "Gdist.chisquare()\n", - "print(Param_mle)\n", - "loc = Param_mle[0]\n", - "scale = Param_mle[1]\n", - "# calculate and plot the pdf\n", - "pdf = Gdist.pdf(loc, scale, plot_figure=True)\n", - "cdf, _, _ = Gdist.cdf(loc, scale, plot_figure=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Fit distribution using lmoments" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----KS Test--------\n", - "Statistic = 0.14814814814814814\n", - "Accept Hypothesis\n", - "P value = 0.9356622290518453\n", - "-----chisquare Test-----\n", - "Statistic = -1.7297426599910917\n", - "P value = 1.0\n", - "-----KS Test--------\n", - "Statistic = 0.14814814814814814\n", - "Accept Hypothesis\n", - "P value = 0.9356622290518453\n", - "-----chisquare Test-----\n", - "Statistic = -1.7297426599910917\n", - "P value = 1.0\n", - "[16.44841695242862, 0.8328854157603985]\n" - ] - }, - { - "data": { - "text/plain": "
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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Param_lmoments = Gdist.estimateParameter(method=\"lmoments\")\n", - "Gdist.ks()\n", - "Gdist.chisquare()\n", - "print(Param_lmoments)\n", - "loc = Param_lmoments[0]\n", - "scale = Param_lmoments[1]\n", - "# calculate and plot the pdf\n", - "pdf = Gdist.pdf(loc, scale, plot_figure=True)\n", - "cdf, _, _ = Gdist.cdf(loc, scale, plot_figure=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# calculate the CDF(Non Exceedance probability) using weibul plotting position\n", - "time_series1.sort()\n", - "# calculate the F (Non Exceedence probability based on weibul)\n", - "cdf_Weibul = PlottingPosition.weibul(time_series1)\n", - "# TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution\n", - "Qth = Gdist.theporeticalEstimate(loc, scale, cdf_Weibul)\n", - "# test = stats.chisquare(st.Standardize(Qth), st.Standardize(time_series1),ddof=5)\n", - "# calculate the confidence interval\n", - "upper, lower = Gdist.confidenceInterval(loc, scale, cdf_Weibul, alpha=0.1)\n", - "# ProbapilityPlot can estimate the Qth and the lower and upper confidence interval in the process of plotting\n", - "fig, ax = Gdist.probapilityPlot(loc, scale, cdf_Weibul, alpha=0.1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Fit distribution by focuing on part of the data" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "if you want to focus only on high values, you can use a threshold to make the code focus on what is higher\n", - "this threshold." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 8, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.000000\n", - " Iterations: 25\n", - " Function evaluations: 94\n", - "-----KS Test--------\n", - "Statistic = 0.25925925925925924\n", - "reject Hypothesis\n", - "P value = 0.3290078898658627\n", - "-----chisquare Test-----\n", - "Statistic = -1.7297426599910737\n", - "P value = 1.0\n", - "[16.653248339988547, 0.7969349444308436]\n" - ] - }, - { - "data": { - "text/plain": "([
,
],\n [,\n ])" - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "threshold = 17\n", - "Param_dist = Gdist.estimateParameter(\n", - " method=\"optimization\", ObjFunc=Gumbel.ObjectiveFn, threshold=threshold\n", - ")\n", - "print(Param_dist)\n", - "loc = Param_dist[0]\n", - "scale = Param_dist[1]\n", - "Gdist.probapilityPlot(loc, scale, cdf_Weibul, alpha=0.1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 9, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimization terminated successfully.\n", - " Current function value: 0.000000\n", - " Iterations: 26\n", - " Function evaluations: 96\n", - "-----KS Test--------\n", - "Statistic = 0.2222222222222222\n", - "Accept Hypothesis\n", - "P value = 0.5256377612776422\n", - "For each axis slice, the sum of the observed frequencies must agree with the sum of the expected frequencies to a relative tolerance of 1e-08, but the percent differences are:\n", - "56.0\n", - "[16.607497657735827, 0.8351717220676762]\n" - ] - }, - { - "data": { - "text/plain": "([
,
],\n [,\n ])" - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "threshold = 18\n", - "Param_dist = Gdist.estimateParameter(\n", - " method=\"optimization\", ObjFunc=Gumbel.ObjectiveFn, threshold=threshold\n", - ")\n", - "print(Param_dist)\n", - "loc = Param_dist[0]\n", - "scale = Param_dist[1]\n", - "Gdist.probapilityPlot(loc, scale, cdf_Weibul, alpha=0.1)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "# Generalized Extreme Value (GEV)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 10, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----KS Test--------\n", - "Statistic = 0.07407407407407407\n", - "Accept Hypothesis\n", - "P value = 0.9987375782247235\n", - "-----chisquare Test-----\n", - "Statistic = -0.3032646471545644\n", - "P value = 1.0\n", - "-----KS Test--------\n", - "Statistic = 0.07407407407407407\n", - "Accept Hypothesis\n", - "P value = 0.9987375782247235\n", - "-----chisquare Test-----\n", - "Statistic = -0.3032646471545644\n", - "P value = 1.0\n", - "[0.005714016754089981, 466.7783159128223, 214.7439840776729]\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Gevdist = GEV(time_series2)\n", - "# default parameter estimation method is maximum liklihood method\n", - "Param_dist = Gevdist.estimateParameter()\n", - "Gevdist.ks()\n", - "Gevdist.chisquare()\n", - "\n", - "print(Param_dist)\n", - "shape = Param_dist[0]\n", - "loc = Param_dist[1]\n", - "scale = Param_dist[2]\n", - "# calculate and plot the pdf\n", - "pdf, fig, ax = Gevdist.pdf(shape, loc, scale, plot_figure=True)\n", - "cdf, _, _ = Gevdist.cdf(shape, loc, scale, plot_figure=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "## Fitting distribution using L moments method" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 12, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----KS Test--------\n", - "Statistic = 0.07407407407407407\n", - "Accept Hypothesis\n", - "P value = 0.9987375782247235\n", - "-----chisquare Test-----\n", - "Statistic = -0.3202644847766967\n", - "P value = 1.0\n", - "[0.010122582419885787, 464.8250207300632, 222.12098731051674]\n" - ] - }, - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "Param_dist = Gevdist.estimateParameter(method=\"lmoments\")\n", - "print(Param_dist)\n", - "shape = Param_dist[0]\n", - "loc = Param_dist[1]\n", - "scale = Param_dist[2]\n", - "# calculate and plot the pdf\n", - "pdf, fig, ax = Gevdist.pdf(shape, loc, scale, plot_figure=True)\n", - "cdf, _, _ = Gevdist.cdf(shape, loc, scale, plot_figure=True)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 13, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-19 19:49:45.311 | DEBUG | statista.confidence_interval:BootStrap:97 - Some values used top 10 low/high samples; results may be unstable.\n" - ] - } - ], - "source": [ - "time_series1.sort()\n", - "# calculate the F (Non Exceedence probability based on weibul)\n", - "cdf_Weibul = PlottingPosition.weibul(time_series1)\n", - "T = PlottingPosition.weibul(time_series1, option=2)\n", - "# TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution\n", - "Qth = Gevdist.theporeticalEstimate(shape, loc, scale, cdf_Weibul)\n", - "\n", - "func = GEV.ci_func\n", - "upper, lower = Gevdist.confidenceInterval(\n", - " shape,\n", - " loc,\n", - " scale,\n", - " F=cdf_Weibul,\n", - " alpha=0.1,\n", - " statfunction=func,\n", - " n_samples=len(time_series1),\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 14, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-19 19:50:02.989 | DEBUG | statista.confidence_interval:BootStrap:97 - Some values used top 10 low/high samples; results may be unstable.\n" - ] - } - ], - "source": [ - "CI = ConfidenceInterval.BootStrap(\n", - " time_series1,\n", - " statfunction=func,\n", - " gevfit=Param_dist,\n", - " n_samples=len(time_series1),\n", - " F=cdf_Weibul,\n", - ")\n", - "LB = CI[\"LB\"]\n", - "UB = CI[\"UB\"]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 15, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-02-19 19:50:13.909 | DEBUG | statista.confidence_interval:BootStrap:97 - Some values used top 10 low/high samples; results may be unstable.\n" - ] - }, - { - "ename": "ValueError", - "evalue": "x and y must be the same size", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mValueError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn[15], line 1\u001B[0m\n\u001B[1;32m----> 1\u001B[0m fig, ax \u001B[38;5;241m=\u001B[39m \u001B[43mGevdist\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mprobapilityPlot\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 2\u001B[0m \u001B[43m \u001B[49m\u001B[43mshape\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mloc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mscale\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcdf_Weibul\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfunc\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfunc\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mn_samples\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;28;43mlen\u001B[39;49m\u001B[43m(\u001B[49m\u001B[43mtime_series1\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 3\u001B[0m \u001B[43m)\u001B[49m\n", - "File \u001B[1;32mC:\\gdrive\\01Algorithms\\Statistics\\statista\\statista\\distributions.py:1102\u001B[0m, in \u001B[0;36mGEV.probapilityPlot\u001B[1;34m(self, shape, loc, scale, F, alpha, func, n_samples, fig1size, fig2size, xlabel, ylabel, fontsize)\u001B[0m\n\u001B[0;32m 1099\u001B[0m pdf_fitted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mpdf(shape, loc, scale, actualdata\u001B[38;5;241m=\u001B[39mQx)\n\u001B[0;32m 1100\u001B[0m cdf_fitted \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcdf(shape, loc, scale, actualdata\u001B[38;5;241m=\u001B[39mQx)\n\u001B[1;32m-> 1102\u001B[0m fig, ax \u001B[38;5;241m=\u001B[39m \u001B[43mPlot\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdetails\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m 1103\u001B[0m \u001B[43m \u001B[49m\u001B[43mQx\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1104\u001B[0m \u001B[43m \u001B[49m\u001B[43mQth\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1105\u001B[0m \u001B[43m \u001B[49m\u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdata\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1106\u001B[0m \u001B[43m \u001B[49m\u001B[43mpdf_fitted\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1107\u001B[0m \u001B[43m \u001B[49m\u001B[43mcdf_fitted\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1108\u001B[0m \u001B[43m \u001B[49m\u001B[43mF\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1109\u001B[0m \u001B[43m \u001B[49m\u001B[43mQlower\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1110\u001B[0m \u001B[43m \u001B[49m\u001B[43mQupper\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1111\u001B[0m \u001B[43m \u001B[49m\u001B[43malpha\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1112\u001B[0m \u001B[43m \u001B[49m\u001B[43mfig1size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfig1size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1113\u001B[0m \u001B[43m \u001B[49m\u001B[43mfig2size\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfig2size\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1114\u001B[0m \u001B[43m \u001B[49m\u001B[43mxlabel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mxlabel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1115\u001B[0m \u001B[43m \u001B[49m\u001B[43mylabel\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mylabel\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1116\u001B[0m \u001B[43m \u001B[49m\u001B[43mfontsize\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mfontsize\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m 1117\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 1119\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m fig, ax\n", - "File \u001B[1;32mC:\\gdrive\\01Algorithms\\Statistics\\statista\\statista\\plot.py:155\u001B[0m, in \u001B[0;36mPlot.details\u001B[1;34m(Qx, Qth, Qact, pdf, cdf_fitted, F, Qlower, Qupper, alpha, fig1size, fig2size, xlabel, ylabel, fontsize)\u001B[0m\n\u001B[0;32m 152\u001B[0m ax2\u001B[38;5;241m.\u001B[39mplot(Qx, cdf_fitted, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m-\u001B[39m\u001B[38;5;124m\"\u001B[39m, color\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m#27408B\u001B[39m\u001B[38;5;124m\"\u001B[39m, linewidth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m2\u001B[39m)\n\u001B[0;32m 154\u001B[0m Qact\u001B[38;5;241m.\u001B[39msort()\n\u001B[1;32m--> 155\u001B[0m \u001B[43max2\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mscatter\u001B[49m\u001B[43m(\u001B[49m\u001B[43mQact\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mF\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mcolor\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43m#DC143C\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mfacecolors\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[38;5;124;43mnone\u001B[39;49m\u001B[38;5;124;43m\"\u001B[39;49m\u001B[43m)\u001B[49m\n\u001B[0;32m 156\u001B[0m ax2\u001B[38;5;241m.\u001B[39mset_xlabel(xlabel, fontsize\u001B[38;5;241m=\u001B[39mfontsize)\n\u001B[0;32m 157\u001B[0m ax2\u001B[38;5;241m.\u001B[39mset_ylabel(ylabel, fontsize\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m15\u001B[39m)\n", - "File \u001B[1;32mC:\\Miniconda3\\envs\\algorithms\\lib\\site-packages\\matplotlib\\__init__.py:1423\u001B[0m, in \u001B[0;36m_preprocess_data..inner\u001B[1;34m(ax, data, *args, **kwargs)\u001B[0m\n\u001B[0;32m 1420\u001B[0m \u001B[38;5;129m@functools\u001B[39m\u001B[38;5;241m.\u001B[39mwraps(func)\n\u001B[0;32m 1421\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21minner\u001B[39m(ax, \u001B[38;5;241m*\u001B[39margs, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m 1422\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m data \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1423\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m func(ax, \u001B[38;5;241m*\u001B[39m\u001B[38;5;28mmap\u001B[39m(sanitize_sequence, args), \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1425\u001B[0m bound \u001B[38;5;241m=\u001B[39m new_sig\u001B[38;5;241m.\u001B[39mbind(ax, \u001B[38;5;241m*\u001B[39margs, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m 1426\u001B[0m auto_label \u001B[38;5;241m=\u001B[39m (bound\u001B[38;5;241m.\u001B[39marguments\u001B[38;5;241m.\u001B[39mget(label_namer)\n\u001B[0;32m 1427\u001B[0m \u001B[38;5;129;01mor\u001B[39;00m bound\u001B[38;5;241m.\u001B[39mkwargs\u001B[38;5;241m.\u001B[39mget(label_namer))\n", - "File \u001B[1;32mC:\\Miniconda3\\envs\\algorithms\\lib\\site-packages\\matplotlib\\axes\\_axes.py:4520\u001B[0m, in \u001B[0;36mAxes.scatter\u001B[1;34m(self, x, y, s, c, marker, cmap, norm, vmin, vmax, alpha, linewidths, edgecolors, plotnonfinite, **kwargs)\u001B[0m\n\u001B[0;32m 4518\u001B[0m y \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39mma\u001B[38;5;241m.\u001B[39mravel(y)\n\u001B[0;32m 4519\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39msize \u001B[38;5;241m!=\u001B[39m y\u001B[38;5;241m.\u001B[39msize:\n\u001B[1;32m-> 4520\u001B[0m \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mx and y must be the same size\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m 4522\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m s \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[0;32m 4523\u001B[0m s \u001B[38;5;241m=\u001B[39m (\u001B[38;5;241m20\u001B[39m \u001B[38;5;28;01mif\u001B[39;00m mpl\u001B[38;5;241m.\u001B[39mrcParams[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m_internal.classic_mode\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;28;01melse\u001B[39;00m\n\u001B[0;32m 4524\u001B[0m mpl\u001B[38;5;241m.\u001B[39mrcParams[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mlines.markersize\u001B[39m\u001B[38;5;124m'\u001B[39m] \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39m \u001B[38;5;241m2.0\u001B[39m)\n", - "\u001B[1;31mValueError\u001B[0m: x and y must be the same size" - ] - }, - { - "data": { - "text/plain": "
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HPz/cwNKVe6zHTCYYf2cPpj7WHy+PRgZGJ3LjlNyJiIiISL33864EXnj9B84kpVuPdenYkpenDqNn1wADIxOpPkruRERERKTeupydx1v/2sTnK3ZZj7m7OTNlYn8euLsXTo4OBkYnUr2U3ImIiIhIvbT/yDmm/e07Tp6+aD12a4/WvPaXkQQGeBkXmEgNUXInIiIiIvWK2Wzm8xW7eP2DDeTnFwLg5urEtMfC+d09vXFwUBVMqZ+U3ImIiIhIvZGWkcMLr//Ams3HrMe63+zH68+PJiiwuYGRidQ8JXciIiIiUi/sP3KOJ1/6hrPnrhZNeeQ3IUybNEDLG0iDoOROREREROq879cd4oXXfyAntwCwrFs3+9lRDAnrYHBkIrVHyZ2IiIiI1FmFhUXMXbiZf/3nf9ZjPbr6M/flMfi39DAwMpHap+ROREREROqkzKxcnvnbd2zYFm89du+oYF6eMgwXF33NlYZH/+pFREREpM45l5LJ488t51DceQAcHUzM+NNgHhzXC5NJ1TClYVJyJyIiIiJ1yrETKfz+ueWcPZcBWObXvfPyXdzeu43BkYkYS8mdiIiIiNQZO345zRMvrCA9MxeAVn4e/GvOfbRvo2UORJTciYiIiEid8FNMHE+/vJK8KwuTd+3Ukg//cS8tmjc2ODIR++BgdAAiIiIiItfyw4bDPDnzG2ti1//Wdnw6934ldiIlqOdOREREROzat9EHePYfqygqMgMwesjNzJkxCmcnLUwuUpKSOxERERGxW19+v5eZb/6I2ZLXcffIbrw6fQSOjhqAJvJrSu5ERERExC6t+GEfL/7zR+v+b+/qwcynh+HgoKUORMqi5E5ERERE7M6qnw7xwhtXE7uH7wvhuScGaQ07kQrYbX/2vHnzCAoKws3NjZCQEDZt2lRh+w0bNhASEoKbmxvt27fngw8+KNVm+fLldO3aFVdXV7p27cqKFStsnt+4cSNjxowhICAAk8nE119/XeoaJpOpzMcbb7xhbTNo0KBSz99///3X90KIiIiINDA/xcTx579HWefYTbintxI7kUqwy+Ru2bJlTJkyhRdeeIFdu3YRHh7OqFGjSEhIKLN9fHw8o0ePJjw8nF27dvH888/z1FNPsXz5cmubmJgYIiMjmTBhAnv27GHChAmMHz+ebdu2WdtkZWXRo0cP3nvvvXJjS0xMtHksXLgQk8nEvffea9Nu0qRJNu0+/PDDG3xVREREROq/mNiTPP3ySgoKiwC4b3R3ZvxxsBI7kUowmc3F01PtR58+fejduzfz58+3HuvSpQvjxo1j9uzZpdo/++yzrFy5koMHD1qPTZ48mT179hATEwNAZGQk6enprFq1ytpm5MiRNGvWjCVLlpS6pslkYsWKFYwbN67CWMeNG0dGRgZr1661Hhs0aBA9e/Zk7ty5lf2RS0lPT8fT05O0tDQ8PDyu+zoNTVyL8Gq/ZofzFfcai0j9ovff8um1kZq2/8g5Jjy9lMs5+QDcMeRmXn9+tIqnSINX2fdfu/ufkpeXR2xsLBERETbHIyIi2Lp1a5nnxMTElGo/YsQIduzYQX5+foVtyrtmZZw7d47vv/+eiRMnlnpu8eLF+Pj40K1bN6ZPn05GRkaF18rNzSU9Pd3mISIiItJQnE5K4/HnllsTu6H9OvKPGaOU2IlUgd0VVElJSaGwsBBfX1+b476+viQlJZV5TlJSUpntCwoKSElJwd/fv9w25V2zMv7973/TtGlT7rnnHpvjDz74IEFBQfj5+bFv3z5mzJjBnj17iI6OLvdas2fP5pVXXrnuWERERETqqkvp2Uz6y3JSLl4GoHdwK9566U6tYydSRXaX3BX79bhqs9lc4Vjrstr/+nhVr3ktCxcu5MEHH8TNzc3m+KRJk6zbwcHBdOrUidDQUHbu3Env3r3LvNaMGTOYNm2adT89PZ3AwMDrjk1ERESkLsjNK+CJF74m/tQFAIICm/P+q+NwdbHbr6kidsvu/tf4+Pjg6OhYqkctOTm5VM9bMT8/vzLbOzk54e3tXWGb8q55LZs2beLw4cMsW7bsmm179+6Ns7MzR48eLTe5c3V1xdXV9bpiEREREamLzGYzM+b8wM59ZwDwaebOv+bcQzPPRgZHJlI32d0gZhcXF0JCQkoNYYyOjiYsLKzMc/r27Vuq/erVqwkNDcXZ2bnCNuVd81oWLFhASEgIPXr0uGbb/fv3k5+fj7+//3XdS0RERKQ++nDxNqLWHQLA3c2Z+bPvobW/l7FBidRhdtdzBzBt2jQmTJhAaGgoffv25aOPPiIhIYHJkycDliGMZ86c4dNPPwUslTHfe+89pk2bxqRJk4iJiWHBggU2VTCffvppBgwYwJw5cxg7dizffPMNa9asYfPmzdY2mZmZHDt2zLofHx/P7t27ad68OW3atLEeT09P54svvuDNN98sFXtcXByLFy9m9OjR+Pj4cODAAZ555hl69epFv379qv21EhEREamL1m2N452FV7+HvfHCHXS/yc/AiETqPrtM7iIjI0lNTWXWrFkkJiYSHBxMVFQUbdu2BSxrzZVc8y4oKIioqCimTp3K+++/T0BAAO+++67N2nNhYWEsXbqUF198kZkzZ9KhQweWLVtGnz59rG127NjB4MGDrfvFc+AefvhhFi1aZD2+dOlSzGYzv/3tb0vF7uLiwtq1a3nnnXfIzMwkMDCQO+64g5dffhlHR00KFhERETl2IoU///17ihfkevrRfgzt39HYoETqAbtc5060ltD10jp3InKj9P5bPr02Uh3SMnIY/4fPOXnmEgAjB3bm7ZfHaJFykQrU2XXuRERERKR+Kioy8+zsKGti16VjS157dqQSO5FqouRORERERGrFwmXbWR9zHAAvj0a897exuDdyMTgqkfpDyZ2IiIiI1LjYvad5+2PLVAeTCd54YTSt/DwNjkqkflFyJyIiIiI16sKly0yd9R2FRZZSD5N/dzvhtwUZHJVI/aPkTkRERERqTFGRmT//PYrklEwAbusZyJ8evr51hkWkYkruRERERKTGfPLf7WzZcQIAn2bu/PPFO3B01FdQkZqg/1kiIiIiUiMOHUtm7gLLQuUmE/zzxTtp6d3E4KhE6i8ldyIiIiJS7XLzCvjz378nv6AIgEfH38rtvdsYHJVI/abkTkRERESq3Vv/2sTRE6kA3NyhBU8/2s/giETqPyejAxC5XkfjU/gyai87fjlNcmomjVydCQy8lf5pZ+ibkYiz2Wx0iCIiIg3S1tiT/PvLWABcnB1544U7cHHR106Rmqb/ZVLnZGblMmf+Br74/pdSzyV4BrDFMwD/3Ez+kPgLIZnnDYhQRESk4UrPzGHGP1ZZ96f/fgCdgnwMjEik4dCwTKlTTielcf8f/2OT2JlM4OvTBHc3Z+uxRNcmvNQujM9a3kyREYGKiIg0UG98sIFzV5Y9CAtpy+/u6W1wRCINh5I7qTPOnc/goSlLOXbSMn6/sbsLf358AD9/8yc2fDGZ7d89yWvxW+iWlWI9Z2nLm3inVS8leCJSL82bN4+goCDc3NwICQlh06ZNFbZfvHgxPXr0wN3dHX9/f/7v//6P1NTUWopWGoKY2JN88f1eANwbOfPqn0fg4GAyOCqRhkPJndQJWdl5PPbscs6eywCgXWAzvvpoAhPvvw3Ppm4AODo60CMrhTnxW3gscR8OV+bcrWnWhvcDeqAZeCJSnyxbtowpU6bwwgsvsGvXLsLDwxk1ahQJCQlltt+8eTMPPfQQEydOZP/+/XzxxRds376dxx57rJYjl/rqcnYeM99cbd3/8+MDCfD1MDAikYZHyZ3UCa++u5aj8ZYeudb+nnz6diRtWzUrs60JuDs1judObcfBbOmz+6F5O1Z6t6+tcEVEatxbb73FxIkTeeyxx+jSpQtz584lMDCQ+fPnl9n+559/pl27djz11FMEBQXRv39/Hn/8cXbs2FHLkUt9NXfBZk4npgEQektrIsf0MDgikYZHyZ3Yvah1h1jxw37AMsTjo3/cW6kFUPulJ/LM6Z3W/Y/9urGnsSZ0i0jdl5eXR2xsLBERETbHIyIi2Lp1a5nnhIWFcfr0aaKiojCbzZw7d44vv/ySO+64o9z75Obmkp6ebvMQKcvOvWf47CvLZ66ri5OGY4oYRMmd2LWMzFxee2+ddf/lKcNo36Z5pc8flHaG8eePAFBkcuCt1r3JdFCRWBGp21JSUigsLMTX19fmuK+vL0lJSWWeExYWxuLFi4mMjMTFxQU/Pz+8vLz4f//v/5V7n9mzZ+Pp6Wl9BAYGVuvPIfVDXn4hL725muIViJ56tB/tWpc9ukZEapaSO7Fr7/17KykXLwMwtF9HxkZ0q/I1fnfuID2uLImQ4tyIDwJuqdYYRUSMYjLZ9oyYzeZSx4odOHCAp556ipdeeonY2Fh++OEH4uPjmTx5crnXnzFjBmlpadbHqVOnqjV+qR8+/TLWWuws+CZfHr4vxOCIRBoudWGI3Tpx+iKflxjiMeNPg6/rOo7AlDO7+GPHwVx2dOYnr0AGXTpNaGZyNUYrIlJ7fHx8cHR0LNVLl5ycXKo3r9js2bPp168ff/7znwG45ZZbaNy4MeHh4bz66qv4+/uXOsfV1RVXV9fq/wGk3khMTuf9Ty1DgR0cTPx16nCcHNV3IGIU/e8TuzX/0xgKiyxjPCY9cBut/Tyv+1ot87N5PHGvdf9D/+7km/TPX0TqJhcXF0JCQoiOjrY5Hh0dTVhYWJnnXL58GQcH2/c9R0dHwNLjJ3I9XnvvJ7JzCgC4/64eBN/kZ3BEIg2bvt2KXYo/dYFv1x4EwNPDjUeqYYjH0EunrGvgnXVtwnKfjjd8TRERo0ybNo2PP/6YhQsXcvDgQaZOnUpCQoJ1mOWMGTN46KGHrO3HjBnDV199xfz58zl+/Dhbtmzhqaee4rbbbiMgIMCoH0PqsI3bjhO96SgA3s3cmTKxv8ERiYiGZYpd+nDxNoqu9Nr9329CadL4xocFmYAnzv7Ckx0HUWRy4L8tOhFx8STNC3Jv+NoiIrUtMjKS1NRUZs2aRWJiIsHBwURFRdG2bVsAEhMTbda8e+SRR8jIyOC9997jmWeewcvLiyFDhjBnzhyjfgSpw3Jy8/nbu2ut+8/+YRAeTdwMjEhEAExmjcWwS+np6Xh6epKWloaHR8NaAPT8hSyGRH5IfkERnk3dWLv09zRxd6nUuXEtwq/Z5kO/YFb6dADgjtR4nkj8pcL2Hc5vqtS9RaR+aMjvv9ei10aKvf/vrfy/RZa5drf2aM2nb0eWW8xHRG5cZd9/NSxT7M6yb/eQX2BZfHz8nbdUOrGrrPvPH6FRoWV+wA/N25Lo4l6t1xcREanPzp3P4OOl/wPAydGBl54epsROxE4ouRO7kpdfyNJvdgOWqlu/Hduz2u/hWZjH3anHACg0OfB5y5ur/R4iIiL11Vsfb7IWUfnt2J50CvIxOCIRKabkTuzKjxuOWNe1G9a/IwG+NTPs5+6UODyuzLXb4Nma0y5NauQ+IiIi9cnew0l8s/oAAJ5N3fjjw30NjkhESlJyJ3ZlxQ/7rNsP3t2rxu7jXlTAvSmW3juzycSXLVQ5U0REpCJms5l/vP+Tdf+PD/XFy6ORgRGJyK8puRO7kZicTszOkwAEBnhyW4/AGr3f6AsnaFyYB8BPnoGkOKnKl4iISHl+3HiE2L1nAGgX2IzfjutpbEAiUoqSO7EbK6MPUly7dWxEtxqfnO1eVMCdqfEAFDg48JXWvRMRESlTbl4B//xwo3X/L5MH4ezkaGBEIlIWJXdiF8xmMyt+vDokc1xE11q579jU47gWXa2cme7oXCv3FRERqUsWf72L04lpAPTt3YbBfdsbHJGIlEXJndiFvYeSOHHqImBZL6e1v1et3NezMI/hFy2L/OY6OPFDs3a1cl8REZG6IiMzlw8XbwPAZLIsWK6lD0Tsk5I7sQs/bDhs3R47vFut3nts6nFMV8aDRjVvRyH6wBIRESn2yX+3k5aeA8CYYV25uWNLgyMSkfIouRPDmc1mVm88CoCjg4mh/Wt37ltAXha3ZpwD4LyLOz97+NXq/UVEROxVyoUsFn0RC4CzkwNPPhJmcEQiUhEld2K4g8eSreP4+/RqQzPP2i+rPObCcev2Sm/NIxAREQH4cPE2LufkAzB+TA8CA7yMDUhEKqTkTgxX3GsHEDGgkyEx9Mo8T+ucDAD2NfbhuFvNLJ4uIiJSV5xOSmPpyt0ANHJz4g+/u93YgETkmuw2uZs3bx5BQUG4ubkREhLCpk2bKmy/YcMGQkJCcHNzo3379nzwwQel2ixfvpyuXbvi6upK165dWbFihc3zGzduZMyYMQQEBGAymfj6669LXeORRx7BZDLZPG6/3fbNLjc3lyeffBIfHx8aN27MXXfdxenTp6v+IjQAZrOZHzccASyTtIf2Nya5M2Hbe/dd8yBD4hAREbEX/++TLeQXFAHw8H2h+DRvbHBEInItdpncLVu2jClTpvDCCy+wa9cuwsPDGTVqFAkJCWW2j4+PZ/To0YSHh7Nr1y6ef/55nnrqKZYvX25tExMTQ2RkJBMmTGDPnj1MmDCB8ePHs23bNmubrKwsevTowXvvvVdhfCNHjiQxMdH6iIqKsnl+ypQprFixgqVLl7J582YyMzO58847KSwsvIFXpX6KO5lK/KkLAIR0b00LAz84hl46RaNCy9CTDZ6tuezgZFgsIiIiRjp2IoWV0QcA8PRwY2LkrQZHJCKVYZffXt966y0mTpzIY489BsDcuXP58ccfmT9/PrNnzy7V/oMPPqBNmzbMnTsXgC5durBjxw7++c9/cu+991qvMXz4cGbMmAHAjBkz2LBhA3PnzmXJkiUAjBo1ilGjRl0zPldXV/z8yi66kZaWxoIFC/jss88YNmwYAJ9//jmBgYGsWbOGESNGVO3FqOc2/Hy1t2x4uDG9dsUaFRUyMO0MPzRvR46jE5s8AxhxsexfKIiIiNRn8z77mSuFpPn9b2+jaRNXYwMSkUqxu+QuLy+P2NhYnnvuOZvjERERbN26tcxzYmJiiIiIsDk2YsQIFixYQH5+Ps7OzsTExDB16tRSbYoTwqpYv349LVu2xMvLi4EDB/L3v/+dli0tZYFjY2PJz8+3iScgIIDg4GC2bt1abnKXm5tLbm6udT89Pb3KcdVFP765HJq0ACDo6ReI+0OWofGMvHCSH5q3A+DHZm2V3ImISIMTdzKVVT8dAqC5VyN+O7ansQGJSKXZ3bDMlJQUCgsL8fX1tTnu6+tLUlJSmeckJSWV2b6goICUlJQK25R3zfKMGjWKxYsXs27dOt588022b9/OkCFDrIlZUlISLi4uNGvWrEr3mj17Np6entZHYGBgleKqizKzctnf2BsA/9xMAvKMTewAOuZcIijbUrnzsHtzTrg2NTgiERGR2jW/RK/dxMhbcW/kYmxAIlJpdpfcFTOZbBeSNpvNpY5dq/2vj1f1mmWJjIzkjjvuIDg4mDFjxrBq1SqOHDnC999/X+F517rXjBkzSEtLsz5OnTpVpbjqoq2xJyk0Wf4JhmYm28XS4SZgxMWT1v3VzdoaF4yIiEgtO55wgagrvXbNPNVrJ1LX2F1y5+Pjg6OjY6leruTk5FI9b8X8/PzKbO/k5IS3t3eFbcq7ZmX5+/vTtm1bjh49ar1PXl4eFy9erNK9XF1d8fDwsHnUd5v+F2/dDr2yiLg9GHTpNC5FluI367wCyc0rMDgiERGR2vHB5z9TVGT5BfmjkaHqtROpY+wuuXNxcSEkJITo6Gib49HR0YSFhZV5Tt++fUu1X716NaGhoTg7O1fYprxrVlZqaiqnTp3C398fgJCQEJydnW3ulZiYyL59+274XvWJ2Wxm4zZLcudSVEj3rBSDI7qqaVE+/dLPApDh5MKazccMjkhERKTmxZ+6wHdrDwLg5dGIB8b1MjgiEakqu0vuAKZNm8bHH3/MwoULOXjwIFOnTiUhIYHJkycDliGMDz30kLX95MmTOXnyJNOmTePgwYMsXLiQBQsWMH36dGubp59+mtWrVzNnzhwOHTrEnDlzWLNmDVOmTLG2yczMZPfu3ezevRuwLLGwe/du6xIMmZmZTJ8+nZiYGE6cOMH69esZM2YMPj4+3H333QB4enoyceJEnnnmGdauXcuuXbv43e9+R/fu3a3VMwWOHE/hXEomALdkncfVXGRwRLYiSgzNXLl6v4GRiIiI1I4PPrvaa/d/40NprF47kTrH7qplgmVeW2pqKrNmzSIxMZHg4GCioqJo29Yy/ykxMdFmzbugoCCioqKYOnUq77//PgEBAbz77rvWZRAAwsLCWLp0KS+++CIzZ86kQ4cOLFu2jD59+ljb7Nixg8GDB1v3p02bBsDDDz/MokWLcHR0ZO/evXz66adcunQJf39/Bg8ezLJly2ja9GrhjbfffhsnJyfGjx9PdnY2Q4cOtZ4vFltiT1i3QzKSjQukHMFZqbTIu8x5F3c2bz9ByoUsLd4qIiL1VsKZS3x7pdfO08ONB+9Wr51IXWQyF1ceEbuSnp6Op6cnaWlp9XL+3eMzvrKucTf/6Dra5GYYHFFp//btwn9bdAZgxh8H8/B9IQZHJCK1ob6//94IvTb111/fjmbpyj0APP1oP/4woa/BEYlISZV9/7XLYZlSv+UXFLJ9j6UaqFd+DoF2mNgBDL50tWLpN6sPGBiJiIhIzUm5kMVXq/YB4N7IWXPtROowJXdS6/YfOcfl7HwAemSl2MUSCGVpk5tJx+xLABw4eo5jJ+yn6IuIiEh1+eyrneTlW6pER47pgWdTN4MjEpHrpeROat22XVd7xG6xoyqZZRlSsvcuWr13IiJSv2RezmPJN7sBcHZy4BFNQRCp05TcSa3btutqMZweWecNjOTaBl46jaODpW/xuzUHrVXERERE6oP/freH9MxcAMYM64pvi6bXOENE7JmSO6lVeXkFxO49A0CAb1P88i4bHFHFvArz6H9rOwASkzP4355TFZ8gIiJSR+TlFbDoi1jr/sT7bzUwGhGpDkrupFbtPpBIbl4BALf1bGO38+1Kuiuim3X7+ytlokVEROq6b9ccJPnKmrND+3WkQ1tvgyMSkRul5E5q1bbdV4dk3t6rjYGRVN7gvu1xd3MGYPWmo+QXFBockYiIyI0pKjKzYNl26/6kB24zMBoRqS5K7qRW7bwyJBPg1h6tDYyk8twbuTAorAMAaek5xMSeNDgiERGRG7Nx23GOJ1wAIPSW1vTsGmBwRCJSHZTcSa0pKCxiz4FEAPxaNKWVn6fBEVXe6ME3WbejfjpsYCQiIiI37tPlO63bj44PNTASEalOSu6k1hw6lszlHMv6dr2D69ZvCMNvC6JJYxcA1mw+ap03KCIiUtcciT/P1iujUNoEeDGobweDIxKR6qLkTmrNzn1nrdu9glsZGEnVubo4MTSsIwCZWXls/t8JYwMSERG5Tp+V6LWbcG9vHBzqQnkzEakMJXdSa3btuzrfLqR73UruAEYPudm6HfXTIQMjERERuT4XLl3mm9UHAGjS2IW7RwYbHJGIVCcld1IrzGYzsVeSO/dGznRu38LgiKqub0hbPJu6AfDT1jiyrwwxFRERqSv++90v5OVbqj7fN7o7TdxdDI5IRKqTkjupFWfOpVvX0unZNQAnx7r3T8/F2ZHh4Z0AuJyTz4Ztxw2OSEREpPLy8gtZ/PUuABwcTPzu7l4GRyQi1a3ufcOWOmlXiSUQetWxYioljSpRNXOVqmaKiEgd8sP6w5xPzQIsi5a39vcyNiARqXZK7qRW7LSZb1c31rcrS59ebWju1QiA9THHybycZ3BEIiIi12Y2m1n0Rax1/+H7QgyMRkRqipI7qRXFlTIdHEzc0sXf4Giun5OjAyMGdAYgN6+AjT9raKaIiNi/XfvPcuDoOQC6dvKtk4XNROTalNxJjcu8nMeR+PMA3NyhRZ2fvB1xJbkDiN501MBIREREKmfJN7ut2xPu6YXJpOUPROojJXdS4/YfScJstmz3qMO9dsVu7RmIl4dlaOaGn4+Tk6uqmSIiYr8uXLrMDxuOAODp4WaztI+I1C9K7qTG7T2YZN0OvtnPwEiqh5OjA0P7dQAsVTO3bD9pcEQiIiLlW75qL/lXlj+4d1Qwri5OBkckIjVFyZ3UuF8OJVq3b7m57vfcge3QzNUbjxgYiYiISPkKC4tY9u0v1v37x/Q0LhgRqXFK7qTG7Tts6blzb+RM+zbNDY6mevTt3YYmjS1zB3+KibMuCCsiImJPNm8/wenENAD639qONq28jA1IRGqUkjupUecvZHH2XAYAwTf54VgHFy8vi4uLE4NutwzNTM/MZduuBIMjEhERKW3Jyt3W7QfG9jQsDhGpHfXjm7bYrb2Hrs63u6UezLcrKWJAJ+v26o2qmikiIvbldFIaG64s2ePfsikDb29vcEQiUtOU3EmN2ltivl19KKZSUvhtQTRys0xKX7vlKIWFRQZHJCIictV/v/3FWq06ckyPejN6RkTKp//lUqN+OViy565+FFMp1sjNmfDbggC4cCmb2L1nDI5IRETEIi+vgC+jLIVUnBwduHd0d4MjEpHaoOROaozZbLb23Pk0c8e/ZVODI6p+w8NVNVNEROzP6k1HuXApG7BMI2jRvLHBEYlIbVByJzXm5JlLpGfmAtC9iz8mk8ngiKrf4L7tcXZ2BCB601GKiswGRyQiIgJffr/Xun3/XT2NC0REapWSO6kx9bmYSrEmjV0JC2kLwLmUTJufWURExAgJZy7x85Uqzm1bN+PWHq0NjkhEaouSO6kxNsVUbqqfyR38umqmhmaKiIixlq+62mt33+jgejlyRkTKpuROasz+I+es2/U5uRsS1hFHB8sHZ/Smo5jNGpopIiLGKCgsYsWP+wFwdDAxbkSwwRGJSG1Scic1oqjIzMFjyQAE+HrQzLORwRHVnGaejbi1RyAACWcvcexEqsERiYhIQ7Xpf/Ekp2QCMDisgwqpiDQwSu6kRpw8c5HL2fkAdO3U0uBoat7Q/h2t22u3HDMwEhERachKFlLR8gciDY+SO6kRB48mW7e7dvI1MJLaMbTf1eRuzeajBkYiIiIN1fkLWayPiQOgpU8T61qsItJw2G1yN2/ePIKCgnBzcyMkJIRNmzZV2H7Dhg2EhITg5uZG+/bt+eCDD0q1Wb58OV27dsXV1ZWuXbuyYsUKm+c3btzImDFjCAgIwGQy8fXXX9s8n5+fz7PPPkv37t1p3LgxAQEBPPTQQ5w9e9am3aBBgzCZTDaP+++///peiDrqwNGr8+26NICeuwBfD2sP5b7D50g6n2FwRCIi0tB8/eM+Cq8syXPPyGCcHO32a56I1BC7/F+/bNkypkyZwgsvvMCuXbsIDw9n1KhRJCQklNk+Pj6e0aNHEx4ezq5du3j++ed56qmnWL58ubVNTEwMkZGRTJgwgT179jBhwgTGjx/Ptm3brG2ysrLo0aMH7733Xpn3uXz5Mjt37mTmzJns3LmTr776iiNHjnDXXXeVajtp0iQSExOtjw8//PAGX5W65cCxEj13Het/cge2vXfrNDRTRERqkdls5suofdb9e0epkIpIQ2Qy22Fpvz59+tC7d2/mz59vPdalSxfGjRvH7NmzS7V/9tlnWblyJQcPHrQemzx5Mnv27CEmJgaAyMhI0tPTWbVqlbXNyJEjadasGUuWLCl1TZPJxIoVKxg3blyFsW7fvp3bbruNkydP0qZNG8DSc9ezZ0/mzp1blR/bRnp6Op6enqSlpeHh4XHd1zGC2Wym77h5XErPxruZO5uX/6HcMsxxLcJrObqq63C+4l7jYofjzjP2sX8DEBbaloVv/KYmwxKRGlKX339rml4b+7V9zykmTFkGwO292rDorfEGRyQi1amy779213OXl5dHbGwsERERNscjIiLYunVrmefExMSUaj9ixAh27NhBfn5+hW3Ku2ZlpaWlYTKZ8PLysjm+ePFifHx86NatG9OnTycjo+EM00s6n8Gl9GwAunRs2WDW1+nc3ofW/p4A/G/XKdIzcwyOSETqu6pOYcjNzeWFF16gbdu2uLq60qFDBxYuXFhL0UpNKllI5b47VEhFpKFyMjqAX0tJSaGwsBBfX9siHL6+viQlJZV5TlJSUpntCwoKSElJwd/fv9w25V2zMnJycnjuued44IEHbDLoBx98kKCgIPz8/Ni3bx8zZsxgz549REdHl3ut3NxccnNzrfvp6enXHZfRGloxlWImk4mh/Try7y9jKSgsYsPP8YwZ1sXosESkniqewjBv3jz69evHhx9+yKhRozhw4IB1JMmvjR8/nnPnzrFgwQI6duxIcnIyBQUFtRy5VLfMy3n8uPEIAJ5N3Rge3sngiETEKHaX3BX7dW+P2WyusAeorPa/Pl7Va1YkPz+f+++/n6KiIubNm2fz3KRJk6zbwcHBdOrUidDQUHbu3Env3r3LvN7s2bN55ZVXrisWe7O/RDGVhrAMQknFyR1Y5t0puRORmvLWW28xceJEHnvsMQDmzp3Ljz/+yPz588ucwvDDDz+wYcMGjh8/TvPmzQFo165dbYYsNeTHDYfJybUk6XcMuRlXF7v9eiciNczuhmX6+Pjg6OhYqkctOTm5VM9bMT8/vzLbOzk54e3tXWGb8q5Zkfz8fMaPH098fDzR0dHXnHfQu3dvnJ2dOXq0/BL5M2bMIC0tzfo4depUleOyFw215w6gd/dWeHlYFmzfsO04eXn6jbiIVL/rmcKwcuVKQkNDef3112nVqhWdO3dm+vTpZGdnl3uf3Nxc0tPTbR5if75ZfcC6PTaim4GRiIjR7C65c3FxISQkpNQQxujoaMLCwso8p2/fvqXar169mtDQUJydnStsU941y1Oc2B09epQ1a9ZYk8eK7N+/n/z8fPz9/ctt4+rqioeHh82jrjpwJblr0tjFOgetoXBydGBwWHsALmfn8/Ousiu8iojciOuZwnD8+HE2b97Mvn37WLFiBXPnzuXLL7/kj3/8Y7n3mT17Np6entZHYGBgtf4ccuPOJKXxv92WXwi3C2zGLV38DI5IRIxkd8kdwLRp0/j4449ZuHAhBw8eZOrUqSQkJDB58mTA0sv10EMPWdtPnjyZkydPMm3aNA4ePMjChQtZsGAB06dPt7Z5+umnWb16NXPmzOHQoUPMmTOHNWvWMGXKFGubzMxMdu/eze7duwHLEgu7d++2LsFQUFDAfffdx44dO1i8eDGFhYUkJSWRlJREXl4eAHFxccyaNYsdO3Zw4sQJoqKi+M1vfkOvXr3o169fDb9yxruYdtm6xtvNHVri4NAwiqmUNKzf1bkOazZrSQQRqTlVmW5QVFSEyWRi8eLF3HbbbYwePZq33nqLRYsWldt7V59GldRXK6Ov9tqNi+jWYIqYiUjZ7HJQdmRkJKmpqcyaNYvExESCg4OJioqibdu2ACQmJtqseRcUFERUVBRTp07l/fffJyAggHfffZd7773X2iYsLIylS5fy4osvMnPmTDp06MCyZcvo06ePtc2OHTsYPHiwdX/atGkAPPzwwyxatIjTp0+zcuVKAHr27GkT808//cSgQYNwcXFh7dq1vPPOO2RmZhIYGMgdd9zByy+/jKOjY7W/VvbmQIkhmd06N6whmcXCQtvi5upETm4BP22No2iquUEmuSJSc65nCoO/vz+tWrXC0/PqiIouXbpgNps5ffo0nTqVLsLh6uqKq6tr9QYv1cZsNvN1iSGZdw3vamA0ImIP7DK5A3jiiSd44oknynxu0aJFpY4NHDiQnTt3VnjN++67j/vuu6/c5wcNGkRFy/61a9euwucBAgMD2bBhQ4Vt6rMDJYqpdGkgi5f/WiM3Z/qFtmPtlmOcv5DFL4cS6dk1wOiwRKQeKTmF4e6777Yej46OZuzYsWWe069fP7744gsyMzNp0qQJAEeOHMHBwYHWrVvXStxSvfYcTOTk6YsA9OkVSIBv3Z3SISLVwy6HZUrdVbKYSpcGVimzpKH9O1q312popojUgKpOYXjggQfw9vbm//7v/zhw4AAbN27kz3/+M48++iiNGjUy6seQG/D1j/ut2+NUSEVEUHIn1ezw8fMAODs70r5Nc4OjMc7gvu2tQzE1705EakJkZCRz585l1qxZ9OzZk40bN1Y4haFJkyZER0dz6dIlQkNDefDBBxkzZgzvvvuuUT+C3IC8vAKi1h0CoJGbE8MHdDY4IhGxB3Y7LFPqnty8Ak6csgwP6djWG2en+j/HsDzNPN0J6d6K7XtOE3/qAscTUmnf5tqVVUVEqqKqUxhuvvnmUpWjpW76KeY46Zm5AAwP70wTdxeDIxIRe6CeO6k2cSdTKSyyzEns3N7H4GiMN7Tf1aGZ6r0TEZHq9M3qEkMyR2hIpohYKLmTanM47rx1+6YOLQyMxD6UTO7WblFyJyIi1ePCpcts3BYPgK9PE/r01PqDImKh5E6qTfF8O4DOQUruAgO8uKm95XXYcyCR5NRMgyMSEZH64Lu1hygoLAIsyx84OurrnIhY6N1Aqs2R4ynWbfXcWZSsmrluS5yBkYiISH3x/dqD1m2tbSciJSm5k2pz5ErPXXOvRvg0czc4GvtgOzTzqIGRiIhIfZBw5hJ7DiYCcHOHFnQK0hx3EblK1TKl0uJahJf73EVHV1K6jAQg8HQCx1sOqK2walxFP/e1uAItOg/nvIs7MT/Hsdd3MN3P/VR9wYmISIMS9dMh6/adQ7sYGImI2CP13Em1OOHmYd1ul5NuYCT2xQTcnpEEQIGDIzuaNNyF3UVE5MaYzWa+XXPAuj96yM0GRiMi9kjJnVSLksldkJI7G33TE63bMR7+BkYiIiJ12eG488SdvABASPdWBPh6XOMMEWlolNxJtYi36blLMzAS+9MtK5UmBXkAbG/qS15egcERiYhIXfRdiUIqGpIpImVRcifVorjnzsFspk1uhsHR2BcnzPS5MjQz29GZbbtPGRyRiIjUNUVFZr5fZ5lv5+TowMhBnQ2OSETskZI7uWGFmEhwbQpAQG4mruYigyOyP7enJ1m3taC5iIhU1a79Z0hMtvzyNCy0Lc08VZVaREqrUnKXkJDAhQsXaioWqaPOuDYm38ERgHa5mm9Xlt6ZybgUFQKW5K6oyGxwRCIiUpd8t0ZDMkXk2qqU3AUFBfHnP//Zuv/oo4+ycOHCag9K6pYTriqmci1u5kJ6ZSYDcD41i18OJV7jDBEREYv8gkJWrT8CgJurE0NKrKEqIlJSlZI7s9lMUdHVIXeLFi1i8+bN1R6U1C3xbp7WbS2DUL6+JYdmbtbQTBERqZytO05yKT0bgCFhHWji7mJwRCJir6qU3Hl6enLqlIpBiC3bZRBUKbM8t2Uk4WC2DMdco+ROREQqqbiQCsAdQzQkU0TK51SVxrfeeivr1q3j//7v/wgKCgJg9+7dzJo165rnmkwmZs6ceX1Ril0rTu4aFebTMj/b4Gjsl2dhHl0vp7KvsQ/xpy5wPCGV9m28jQ5LRETsWHZOPms2HQXAo4kr4be1MzYgEbFrVUru/vGPfzBmzBj+/e9/W4/t3r2b3bt3X/NcJXf1U5aDE8kulopd7XLSMRkcj73rm57IvsY+AERvOsbjDyq5ExGR8v20NY7LOfkAjBjYGReXKn11E5EGpkrvEL179+bw4cNs376dU6dO8cgjj9C/f38mTpxYU/GJnSs5JFOVMq/t9vQk/uXfHYC1m4/y+IN9DI5IRETs2ar1h63bGpIpItdS5V//NGnShMGDBwPwyCOP0LFjRx5++OFqD0zqhuL17QDa5Wjx8mvxy7/MzR1acCjuPL8cSuLc+Qx8WzS99okiItLgZF7OY8PPxwHwaebOrT1aGxyRiNi7G+rbj4+Pp0mTJtUVi9RBJZO7Nuq5q5Sh/TtyKO48AOu2xvHbsT2NDUhEROzS+pg48vIta6RGDOiMo2OV6uCJSAN0Q+8Sbdu2xdtbc4YaspMlhmW2Uc9dpQzr38m6raqZIiJSnh9KDMkcOegmAyMRkbqiSj13lamKWR4VVKmfinvuPApy8SrMMziauuHmDi0I8PXg7Ll0tu1KID0zB48mbkaHJSIidiQzK5eN2+IBy5DMkO6tDI5IROqCKiV3f/3rXzGZTJivrNVVzGS6WiOx+LlfH1NyV/9kODpz0dmSlLTJVa9dZZlMJob178iny3dSUFjEhp/jGTNMk+RFROSqn2KOXx2SOVBDMkWkcqqU3H3yySeljm3dupV//etftGnThvvuu482bdoAkJCQwPLlyzl58iSTJk0iLCyseiIWu2Ez305DMqtkWP9OfLp8J2CpmqnkTkRESrIZkjlQQzJFpHKqlNz9uirmzz//zOOPP87MmTN56aWXcHR0tHl+zpw5/O1vf+Mf//gHjzzyyA0HK/alZHLXVj13VdK7eyu8PBpxKT2bjf+LJzevAFetXSQiIliGZG76n2VIZovmjTUkU0Qq7Yb6+F966SVuuukmXnnllVKJHYCjoyN//etfuemmm3j55Zdv5FZih0omd4FK7qrEydGBwWHtAbicnc/POxMMjkhEROzFT1vjNCRTRK7LDb1b/O9//yM4OPia7YKDg/nf//53I7cSO1SyUmbbHC2DUFXD+pWsmnnUwEhERMSerLIZktnZwEhEpK654V8FHTp0qFraSN1zSpUyb0hYaFvcXC1DMddtjaOwsMjgiERExGiZWbls2n4CgBbejekdrCGZIlJ5N5Tc9e/fn927d/PGG2+U2+af//wnu3bton///jdyK7EzGQ7OXFClzBvSyM2Z/re2AyD14mX2HEg0NiARETHcuq1x5F8ZkjlCC5eLSBXdUAWH1157jfXr1/Pcc8/xySefWKtlmkwmTp48yfLlyzl06BDu7u689tpr1RWz2IEEN1XKrA5D+3W0LmS+ZstRemvSvIhIg6aFy0XkRtxQcnfLLbewdu1aHnnkEQ4dOsSrr75qXd+ueL27zp07s2jRIm655ZYbj1bshs0yCOq5u26D+nbA0cFEYZGZNZuO8efHB9qsESkiIg1HRqaGZIrIjbnh2ut9+vThwIED/PTTT2zZsoWzZ89iNpsJCAigf//+DB48WF9W6yEld9WjmWcjQnu0ZtuuUyScvcTREyl0DmphdFgiImKAdVuPWYdkjhx4Ew4O+v4kIlVTLQO5TSYTQ4YMYebMmbz++uu88cYbvPTSSwwZMuS6E7t58+YRFBSEm5sbISEhbNq0qcL2GzZsICQkBDc3N9q3b88HH3xQqs3y5cvp2rUrrq6udO3alRUrVtg8v3HjRsaMGUNAQAAmk4mvv/661DXMZjN//etfCQgIoFGjRgwaNIj9+/fbtMnNzeXJJ5/Ex8eHxo0bc9ddd3H69Omqvwh2rOSwTFXKvDFDS1TNXHtliKaIiDQ8P2w4Yt0eOUhVMkWk6qolufvuu+8YNWoUnp6eeHl54eXlhYeHB6NGjeLbb7+t8vWWLVvGlClTeOGFF9i1axfh4eGMGjWKhISy1wKLj49n9OjRhIeHs2vXLp5//nmeeuopli9fbm0TExNDZGQkEyZMYM+ePUyYMIHx48ezbds2a5usrCx69OjBe++9V25sr7/+Om+99Rbvvfce27dvx8/Pj+HDh5ORcbX3asqUKaxYsYKlS5eyefNmMjMzufPOOyksLKzya2GvElwtyyB4FOTiqUqZN2Rovw7W7TVK7kREGqSMzFw2XxmS2dKnCb26aUimiFSdyVw8Oe46mM1mHnvsMRYtWmSdY+fl5YXZbCYtLc1yA5OJCRMm8Mknn1S6F69Pnz707t2b+fPnW4916dKFcePGMXv27FLtn332WVauXMnBgwetxyZPnsyePXuIiYkBIDIykvT0dFatWmVtM3LkSJo1a8aSJUtKXdNkMrFixQrGjRtn8/MGBAQwZcoUnn32WcDSS+fr68ucOXN4/PHHSUtLo0WLFnz22WdERkYCcPbsWQIDA4mKimLEiBGVeg3S09Px9PQkLS0NDw+Pa59QC+JahAOWSpn3dx0NQHBWCnPitxgZVp3T4XzpXuh7fv8pB44mA/DTst/j39I+/s5FGiJ7fP+1F3ptas7K6AP85bUoACbc05sXnhxicEQiYk8q+/57Qz1377zzDp988gn+/v7Mnz+ftLQ0Lly4wMWLF0lLS2P+/Pn4+/vz2Wef8c4771Tqmnl5ecTGxhIREWFzPCIigq1bt5Z5TkxMTKn2I0aMYMeOHeTn51fYprxrliU+Pp6kpCSb67i6ujJw4EDrdWJjY8nPz7dpExAQQHBwcIX3ys3NJT093eZhr06pUma1G9qvo3V77Rb13omINDTRm45atyMGdKqgpYhI+W4oufvoo49wd3dn06ZNPP744zRtevVLf9OmTXn88cfZtGkTjRo14qOPPqrUNVNSUigsLMTX19fmuK+vL0lJSWWek5SUVGb7goICUlJSKmxT3jXLu0/xeeVdJykpCRcXF5o1a1ale82ePRtPT0/rIzAwsNJx1baTKqZS7Yb1v/pBXvIDXkRE6r+c3Hw2b48HoLlXI1XJFJHrdkPJXXx8PEOHDiUoKKjcNkFBQQwdOpT4+PgqXfvXQzjNZnOFwzrLav/r41W9ZnXFVpk2M2bMIC0tzfo4depUleOqLaqUWf06t/chMMATgO17TnPh0mWDIxIRkdqyZftJsnMKAMtIDi1cLiLX64bePVq0aIGLi8s127m4uODj41Opa/r4+ODo6Fiqlys5OblUj1kxPz+/Mts7OTnh7e1dYZvyrlnefYAKr+Pn50deXh4XL16s0r1cXV3x8PCwedgrLWBe/UwmEyMGWCqjFRWZNTRTRKQBWb3papXMkiM5RESq6oaSu7vvvpt169aVSmRKunDhAuvWrbMpTFIRFxcXQkJCiI6OtjkeHR1NWFhYmef07du3VPvVq1cTGhqKs7NzhW3Ku2ZZgoKC8PPzs7lOXl4eGzZssF4nJCQEZ2dnmzaJiYns27evSveyZyUrZXoV5hocTf0xYuDVstc/liiHLSIi9Vd+QSHrY44D0Njdhb692xgckYjUZTeU3L366qu0b9+eIUOGsG7dulLPr1u3juHDh9O+fXtee+21Sl932rRpfPzxxyxcuJCDBw8ydepUEhISmDx5MmAZwvjQQw9Z20+ePJmTJ08ybdo0Dh48yMKFC1mwYAHTp0+3tnn66adZvXo1c+bM4dChQ8yZM4c1a9YwZcoUa5vMzEx2797N7t27Acuw0927d1uXYDCZTEyZMoXXXnuNFStWsG/fPh555BHc3d154IEHAPD09GTixIk888wzrF27ll27dvG73/2O7t27M2zYsEq/BvYqw8GZC85ugGVIppZXrT7BN/kR4GvpFf15ZwJpGTkGRyQiIjVtx57T1vf7gbe3x8XFyeCIRKQuu6F3kLFjx+Li4kJsbCzDhw+nefPmtG3bFoCEhARSU1MBuP322xk7dqzNuSaTibVr15Z53cjISFJTU5k1axaJiYkEBwcTFRVlvXZiYqLNmndBQUFERUUxdepU3n//fQICAnj33Xe59957rW3CwsJYunQpL774IjNnzqRDhw4sW7aMPn36WNvs2LGDwYMHW/enTZsGwMMPP8yiRYsA+Mtf/kJ2djZPPPEEFy9epE+fPqxevdqmmMzbb7+Nk5MT48ePJzs7m6FDh7Jo0SIcHR2r/BrbG1XKrDkmk4nh4Z3595exFBQWsW7LMe4eGWx0WCIiUoNKFtEa3r9jBS1FRK7thta5c3C4/o4/k8lUrxb1rm72uJZQXItwVjVry3utegIw+ewvjLlQtUI5UvY6d8V27jvDA09a1l0c1Lc9H7x2T22FJSJX2OP7r73Qa1O9iorMDIr8kOSUTFycHdn69R9p4n7tWgYi0vBU9v33hnruqloBU+q+U6qUWaN6dg2gpU8TklMy2bLjJJlZuTRp7Gp0WCIiUgP2HkoiOSUTgL4hbZXYicgNu6HkrniYpDQcJzUss0Y5OJiICO/E5yt2kZ9fyE8xxxkzrIvRYYmISA1Ys7nEwuXhqpIpIjdOC6lIlahSZs2LGHC1aubqjaqaKSJSH5nNZlZfmW/n4GBicFgHgyMSkfpAyZ1UWslKmYGqlFljQrq3wruZOwAbt8WTlZ1ncEQiIlLdjp1I5eRpy1JSId1b0dzL3eCIRKQ+UHInlVayUmZbDcmsMY6ODgy/MjwnN6+AjT8fNzgiERGpbiWHZA4P71xBSxGRylNyJ5WWUKKYSqCKqdSoETZDM49W0FJEROqikksgDNMSCCJSTZTcSaWVTO7aKrmrUbf2DMTLoxEAG34+TnZOvsERiYhIdTmdlMaBo8kAdOvsS4CvlpUQkeqh5E4qTZUya4+To4P1N7mXc/LZvP2EsQGJiEi1Wbv5mHV7uKpkikg1UnInlVa8xl3TgjxVyqwFIwaqaqaISH1UckimkjsRqU5K7qRS0jNzSHW2DBNsk5uuSpm1oE+vNng0sSxgvm5rHHl5BQZHJCIiNyr1Yhaxe08DEBTYnA5tvQ2OSETqEyV3UinH4lOt26qUWTtcnB2t6x5lXc5j846TBkckIiI3at3WOMxmy/awcBVSEZHqpeROKuXYyavJnSpl1p6Rg26ybv+w/rCBkYiISHVYU3JIZn8NyRSR6qXkTirl2IkU67YqZdaefqHtrEMz12w+Sk6uqmaKiNRVmVm5bN2ZAIBfi6YE3+RncEQiUt8ouZNKUc+dMVycHa2T7S9n57Ph53iDIxIRkeu1YVs8+fmFAAzt3xEHB81gF5HqpeROKuXYCUty17Qgj2YFqpRZm0YPudm6HfXTIQMjERGRG6EhmSJS05TcyTWlZ+aQnJIJqFKmEfr0akNzL0ul0vUxx8m8nGdwRCIiUlW5eQVs2HYcAE8PN0J7tDY4IhGpj5TcyTUV99oBtNGQzFrn5OjAyIGWwiq5eQX8tOXYNc4QERF7szX2JJezLfOmh4R1wMlRX8FEpPrpnUWuySa50zIIhig5NPN7Dc0UkSvmzZtHUFAQbm5uhISEsGnTpkqdt2XLFpycnOjZs2fNBihWGpIpIrVByZ1cU8lKmeq5M0bv4Fb4+jQBYMv2E6Rl5BgckYgYbdmyZUyZMoUXXniBXbt2ER4ezqhRo0hISKjwvLS0NB566CGGDh1aS5FKQWER67bGAeDu5kxYaFuDIxKR+krJnVxTyUqZSu6M4eBgsq55l19QZPMbYBFpmN566y0mTpzIY489RpcuXZg7dy6BgYHMnz+/wvMef/xxHnjgAfr27VtLkcrOvWe4mJYNQHifINxcnQ2OSETqKyV3ck2qlGkf7tDQTBG5Ii8vj9jYWCIiImyOR0REsHXr1nLP++STT4iLi+Pll1+u1H1yc3NJT0+3eUjVRZf4hdwwDckUkRqk5E4qZFspM0OVMg3U/WY/Wvt7AvDzzgRSL2YZHJGIGCUlJYXCwkJ8fX1tjvv6+pKUlFTmOUePHuW5555j8eLFODk5Veo+s2fPxtPT0/oIDAy84dgbGrPZzNotluTO2cmBQbe3NzgiEanPlNxJhWwrZeo3tkYymUyMHmzpvSsqMvPjhiMGRyQiRjOZbH/lZjabSx0DKCws5IEHHuCVV16hc+fOlb7+jBkzSEtLsz5OnTp1wzE3NPuPnOPsOcuUhtt7t6FpE1eDIxKR+qxyv7qTBkuVMqtfXIvw6z73FlcP6DQYgOWvLaPPpKcA6HC+chXyRKR+8PHxwdHRsVQvXXJycqnePICMjAx27NjBrl27+NOf/gRAUVERZrMZJycnVq9ezZAhQ0qd5+rqiqurkpEboSGZIlKb1HMnFYpTMRW70i43ncArSfb+xj6cd3YzOCIRMYKLiwshISFER0fbHI+OjiYsLKxUew8PD/bu3cvu3butj8mTJ3PTTTexe/du+vTpU1uhNzhrNluSO5MJhvbraHA0IlLfqedOKnRUyyDYFRMwIO00i926ALDRszX3pmhRc5GGaNq0aUyYMIHQ0FD69u3LRx99REJCApMnTwYsQyrPnDnDp59+ioODA8HBwTbnt2zZEjc3t1LHpfocT7hA3MkLAPTq1gqf5o0NjkhE6jsld1Kh4mGZnk3dVCnTTgxKO8NiX0tyt85LyZ1IQxUZGUlqaiqzZs0iMTGR4OBgoqKiaNvWsoZaYmLiNde8k5pV3GsHMDxcQzJFpOZpWKaUq2SlzI7tvFUp004E5GVx82XLb4JPuHkS7+phcEQiYpQnnniCEydOkJubS2xsLAMGDLA+t2jRItavX1/uuX/961/ZvXt3zQfZgJWcbzc8XEMyRaTmKbmTcpUsptKxnY+BkcivDb50tWLdOq/WBkYiIiJlSTqfwd5DloI3N3doQWt/L2MDEpEGQcmdlKtkctehrbeBkcivDUg7g6O5CIANXq0pLCwyOCIRESmp5JDMYRqSKSK1RMmdlKtkpcxO7ZTc2ROPwnxCM84BkOrciG27tfaUiIg9sR2SqeRORGqHkjspV8lKmR2DNCzT3gwpMTRzZfQBAyMREZGSLqZls2PPaQDaBHjRWZ+hIlJLlNxJuayVMj3c8GnmbnA08mu3ZZyjcWE+AKs3HiE7J9/giEREBOCnmDgKi8wADAvviMmkkmQiUjuU3EmZbCpltvXWB5MdcjEX0T/tDACXs/NZu0VLIoiI2IM1NkMyOxsYiYg0NErupEyqlFk3DLl02rqtoZkiIsbLys5j8/YTALTwbkyPLv7GBiQiDYrdJnfz5s0jKCgINzc3QkJC2LRpU4XtN2zYQEhICG5ubrRv354PPvigVJvly5fTtWtXXF1d6dq1KytWrKjyfU0mU5mPN954w9pm0KBBpZ6///77r/OVMIZtcqdiKvaq6+VUWuRdBmDL9hOkXMgyOCIRkYZt0//iycsvBGBov444OGjki4jUHrtM7pYtW8aUKVN44YUX2LVrF+Hh4YwaNYqEhIQy28fHxzN69GjCw8PZtWsXzz//PE899RTLly+3tomJiSEyMpIJEyawZ88eJkyYwPjx49m2bVuV7puYmGjzWLhwISaTiXvvvdcmpkmTJtm0+/DDD6v5VapZx0oUU+mknju75QAMvtJ7V1hkJmrdIWMDEhFp4NZsujpEflh/VckUkdplMpvNZqOD+LU+ffrQu3dv5s+fbz3WpUsXxo0bx+zZs0u1f/bZZ1m5ciUHDx60Hps8eTJ79uwhJiYGgMjISNLT01m1apW1zciRI2nWrBlLliy5rvsCjBs3joyMDNauXWs9NmjQIHr27MncuXOv7wUA0tPT8fT0JC0tDQ8Pj+u+zvV6dPoXbI09CcDm5X/Ap3lj4lqE13occm0Jrk34Q6ehAHTt1JKvPnrI4IhE6jaj33/tmV6biuXlFxJ29/tkZuXh0cSVzV89gYuzo9FhiUg9UNn3X7vrucvLyyM2NpaIiAib4xEREWzdurXMc2JiYkq1HzFiBDt27CA/P7/CNsXXvJ77njt3ju+//56JEyeWem7x4sX4+PjQrVs3pk+fTkZGRgU/NeTm5pKenm7zMNKxk1crZXqrUqZda5ObSfeb/QA4cDSZg8eSDY5IRKRh2rYrgcysPAAG9e2gxE5Eap3dJXcpKSkUFhbi6+trc9zX15ekpKQyz0lKSiqzfUFBASkpKRW2Kb7m9dz33//+N02bNuWee+6xOf7ggw+yZMkS1q9fz8yZM1m+fHmpNr82e/ZsPD09rY/AwMAK29ekkpUyO7XzUaXMOuDeUcHW7a9W7TMwEhGRhqvkwuXD+nc0MBIRaajsLrkr9uuEwmw2V5hklNX+18crc82q3HfhwoU8+OCDuLm52RyfNGkSw4YNIzg4mPvvv58vv/ySNWvWsHPnznLjnzFjBmlpadbHqVOnym1b047Fq5hKXTN6yM24ujgBsHLNAfLyCgyOSESkYSksLGLdlSVpXF2c6H9rO2MDEpEGye6SOx8fHxwdHUv1liUnJ5fqVSvm5+dXZnsnJye8vb0rbFN8zared9OmTRw+fJjHHnvsmj9T7969cXZ25ujRo+W2cXV1xcPDw+ZhlKMliqkouasbPJq4ETHAMnE/LT2HdVvjDI5IRKRh2X3gLCkXLdWLw29rh3sjF4MjEpGGyO6SOxcXF0JCQoiOjrY5Hh0dTVhYWJnn9O3bt1T71atXExoairOzc4Vtiq9Z1fsuWLCAkJAQevTocc2faf/+/eTn5+PvXzfWuok7WaLnrq0qZdYV947qbt1evmqvgZGIiDQ8tkMyVSVTRIzhZHQAZZk2bRoTJkwgNDSUvn378tFHH5GQkMDkyZMByxDGM2fO8OmnnwKWypjvvfce06ZNY9KkScTExLBgwQJrFUyAp59+mgEDBjBnzhzGjh3LN998w5o1a9i8eXOl71ssPT2dL774gjfffLNU7HFxcSxevJjRo0fj4+PDgQMHeOaZZ+jVqxf9+vWriZer2mmNu7rptp6BtPLz4ExSOpu3nyAxOR3/lqpmJyJS08xmM2s2W4ZkOjqYGNS3vcERiUhDZZfJXWRkJKmpqcyaNYvExESCg4OJioqibdu2gGWtuZJrzwUFBREVFcXUqVN5//33CQgI4N1337VZey4sLIylS5fy4osvMnPmTDp06MCyZcvo06dPpe9bbOnSpZjNZn7729+Wit3FxYW1a9fyzjvvkJmZSWBgIHfccQcvv/wyjo51o2pW8bBML49GqpRZhzg4mLhnZDD/b9FWzGb4ZvUBJv/udqPDEhGp9w7Fned0Yhpg+UWbl0cjgyMSkYbKLte5E+PWEkrLyKHPXe8BEHpLaz5/537rc1rnzn51OL8JgDNJaQx74F+YzRAY4MmPnz2Gg4OqnYpUhdZyK59em7K9u3Az8z77GYCXpwzjt2N7GhuQiNQ7dXadOzFWnIZk1mmt/DzpG2LpaT51No0dv5w2OCIRkfov+sqQTIAh/bQEgogYR8md2FClzLpPhVVERGpP/KkLHI23fHb27BaAr08TgyMSkYZMyZ3YsC2mokqZddGw/h3xaOIKwI8bjpCemWNwRCIi9deazVerZA4PV5VMETGWkjuxcaxEz10n9dzVSa4uTowZ3hWAnNwCvll9wOCIRETqr+iNJZI7LYEgIgZTcic2jl1Z487LoxHNvVQps66KHHOLdfu/3+5BdZNERKpf0vkMfjmUBMDNHVrQppWXsQGJSIOn5E6s0jJyOJ+aBVjm25lMqrJYV3UOakFI91YAHD2Rys59ZwyOSESk/ik5JHOYhmSKiB1QcidWGpJZv9w/pod1e+nKPQZGIiJSP0Vv0nw7EbEvdrmIuRjjaLyKqdQnEQM78/f3fuJSejY/bDjC838aTDPP+jPUtibWXSxeL1BE5Foupl1m+x7LcjNtW3nROUifmyJiPCV39VhVv/zu9AsGnw4ANJo6i7jfp1zjDLFnri5O3D2yG5/8dwf5+YV89cN+JkbeanRYIiL1wrqtcRQVWeYzDw/vpKkMImIXNCxTrE66XV3tvm1OuoGRSHUZf6dtYZXiLyIiInJjSg7J1Hw7EbEXSu7E6qRrUwA8CnLxKswzOBqpDkGBzenbuw0AJ89cYtuuBIMjEhGp+zIv57Flx0kAWvo04Zab/Q2OSETEQsmdAJDm6MIlZzcA2qnXrl6JvKtEYZVvVVhFRORGbfz5OPn5hQAM698RBwcNyRQR+6DkTgA4UWJIppK7+mVov460aN4YgLWbj3EuJdPgiERE6raSSyBEhHc2MBIREVtK7gT4VXKXq+SuPnF2cuTe0d0BKCgsYunK3cYGJCJSh+XmFbD+5+MAeHq4EdqjtcERiYhcpeROADjhWrKYSoaBkUhN+O1dPXBytPx3X/btHnLzCgyOSESkbtoae5LL2fkADAnrYH1vFRGxB3pHEsC2566teu7qHd8WTYkYaBk6dOFSNt+vO2RwRCIidVP0xpILl2tIpojYFyV3QhGQcKVSpl9eFo2KCo0NSGrEhHt6W7c//2onZrOWRRARqYqCwiLWbY0DwN3NmX6hbQ2OSETElpI74ZyLOzmOlvXsVUyl/urZ1Z/uN/sBcOBoMrF7zxgckYhI3bLjl9NcSs8GYECfIFxdnAyOSETElpI7sZlvp+Su/jKZTDa9d58ujzUwGhGRumf1hiPWbS1cLiL2SMmdaBmEBmTkoJusyyKs2XyMs+f09y0iUhmFhUWs3mhJ7lycHRnUt4PBEYmIlKbkTlRMpQFxcXa0LmpeVGTmP1/vMjgiEZG6IXbvGVIuXgYsQzKbuLsYHJGISGlK7sQ6LNO5qJBWuVkGRyM17f4xPXB2svzX/+L7vVzOzjM4IhER+/fDhsPW7REDbzIwEhGR8im5a+DyTA6cdbUM0wvMzcARVVCs73yaN2b04JsBSMvIYfmqfQZHJCJi3woLi6xLILg4OzI4TEMyRcQ+Kblr4E65NqXIZPln0E6LlzcYj0beat1e9MUOCgqLDIxGRMS+7dx3hvMXLCNbwm/TkEwRsV9K7ho4m2Iqmm/XYNzUoQXht7UD4ExSOj+WqAAnIiK2Sr5HjhyohctFxH4puWvgTlxZvBygrSplNigTI2+zbi9Yul2LmouIlKGoyMzqTZYhmc6qkikidk7JXQOnZRAarj69AunW2ReAA0fP8fPOBIMjEhGxP7v2nyE5JROA/re2o2kTV4MjEhEpn5K7Bu7kleSuSUEe3gU5BkcjtclkMvHY/Vfn3n28dLuB0YiI2Kcf1mtIpojUHUruGrAMR2dSnRsBlvl2JoPjkdo3fEBnAgM8Adiy4wSHjiUbHJGIiP0oKjLz45WFy52dHRkS1tHgiEREKqbkrgErXt8ONCSzoXJydOCR+0Kt++q9ExG5avf+s9Yhmf1C22pIpojYPSV3DdjxRp7W7SAldw3WPaOC8fKw9OCu+ukQCWcuGRuQiIidKLlw+UgtXC4idYCSuwbsuNvV5K59TpqBkYiRGrk58/B9vQEoLDLz0X+2GRyRiIjxiorMrL6ycLmzkwND+qlKpojYPyV3DdjxK8VUHMxmLYPQwP3u7t40bWwZbvT1j/s5naRkX0Qatj0HE0k6nwFAWGg7PJq4GRyRiMi1KblroPJNJhKuzLlrnZuBq7nI4IjESE2buDLhXkvvXUFhEf/6z/8MjkhExFjfrz1o3VaVTBGpK+w2uZs3bx5BQUG4ubkREhLCpk2bKmy/YcMGQkJCcHNzo3379nzwwQel2ixfvpyuXbvi6upK165dWbFiRZXv+8gjj2AymWwet99+u02b3NxcnnzySXx8fGjcuDF33XUXp0+fvo5Xoeaccm1KgYPlr19DMgXgoXt709jdBYCvVu0lMVm9uSLSMBUUFvHDest8OxdnR4b172RwRCIilWOXyd2yZcuYMmUKL7zwArt27SI8PJxRo0aRkFD2Isvx8fGMHj2a8PBwdu3axfPPP89TTz3F8uXLrW1iYmKIjIxkwoQJ7NmzhwkTJjB+/Hi2bbs6v6iy9x05ciSJiYnWR1RUlM3zU6ZMYcWKFSxdupTNmzeTmZnJnXfeSWFhYTW+SjfGdr6dvsQLeHk04nd39wIgv6BIlTNFpMH63+5TpFy8DMDA29urSqaI1Bl2mdy99dZbTJw4kccee4wuXbowd+5cAgMDmT9/fpntP/jgA9q0acPcuXPp0qULjz32GI8++ij//Oc/rW3mzp3L8OHDmTFjBjfffDMzZsxg6NChzJ07t8r3dXV1xc/Pz/po3ry59bm0tDQWLFjAm2++ybBhw+jVqxeff/45e/fuZc2aNdX7Qt0Am+QuWz13YvHIb0Jwd3MG4IvvfiE5NdPgiEREal/JIZl3DL3ZwEhERKrG7pK7vLw8YmNjiYiIsDkeERHB1q1byzwnJiamVPsRI0awY8cO8vPzK2xTfM2q3Hf9+vW0bNmSzp07M2nSJJKTry78HBsbS35+vs11AgICCA4OLjd+sAzlTE9Pt3nUpOJiKqBhmXJVM093fju2JwB5+YUsUO+diDQweXkF1iqZjd1dGHR7e4MjEhGpPCejA/i1lJQUCgsL8fX1tTnu6+tLUlJSmeckJSWV2b6goICUlBT8/f3LbVN8zcred9SoUfzmN7+hbdu2xMfHM3PmTIYMGUJsbCyurq4kJSXh4uJCs2bNKh0/wOzZs3nllVfKfb46mbm6xp13fjaehXm1cl+pOXEtwqvtWoMdXVnc805ycgtY8s1uHvlNCP4tPa59oohIPbDpfyfIyMoFYFi/jri5OhsckYhI5dldz10xk8lks282m0sdu1b7Xx+vzDWv1SYyMpI77riD4OBgxowZw6pVqzhy5Ajff/99hT/PteKfMWMGaWlp1sepU6cqvN6NOO/ciCxHS+EMDcmUX2tWmGude5eXX8i8T382OCIRkdrz/ToNyRSRusvukjsfHx8cHR1L9XIlJyeX6lUr5ufnV2Z7JycnvL29K2xTfM3ruS+Av78/bdu25ejRo9b75OXlcfHixSpdx9XVFQ8PD5tHTYnT4uVyDRPvv40mja9Wzow/dcHgiEREal5Wdh7rtsYBliJTfUPaGhyRiEjV2F1y5+LiQkhICNHR0TbHo6OjCQsLK/Ocvn37lmq/evVqQkNDcXZ2rrBN8TWv574AqampnDp1Cn9/fwBCQkJwdna2uU5iYiL79u2r8Dq1KV7JnVxDM89GPDr+VgAKi8z8v0XlzxcVEakv1m0+Rk5uAQAjB3XG2cnR4IhERKrG7pI7gGnTpvHxxx+zcOFCDh48yNSpU0lISGDy5MmAZQjjQw89ZG0/efJkTp48ybRp0zh48CALFy5kwYIFTJ8+3drm6aefZvXq1cyZM4dDhw4xZ84c1qxZw5QpUyp938zMTKZPn05MTAwnTpxg/fr1jBkzBh8fH+6++24APD09mThxIs888wxr165l165d/O53v6N79+4MGzasFl69ayuebwdaBkHK99B9ITT3agRA1LpDHDyWfI0zRKS2VWVN2K+++orhw4fTokULPDw86Nu3Lz/++GMtRmv/vlt3yLp9xxANyRSRuscuk7vIyEjmzp3LrFmz6NmzJxs3biQqKoq2bS3DIxITE23WngsKCiIqKor169fTs2dP/va3v/Huu+9y7733WtuEhYWxdOlSPvnkE2655RYWLVrEsmXL6NOnT6Xv6+joyN69exk7diydO3fm4YcfpnPnzsTExNC0aVPrdd5++23GjRvH+PHj6devH+7u7nz77bc4OtrHbwCLK2U2KizALy/L4GjEXjVxd+HxB2+37s9dsNnAaETk16q6JuzGjRsZPnw4UVFRxMbGMnjwYMaMGcOuXbtqOXL7dDEtmy3bTwDg16IpId1bGxuQiMh1MJmLK4+IXUlPT8fT05O0tLTrnn9XVgXFTAcnIrveAUDXrFTeiNcXdimtw3nLb/9z8woYOWEBickZACx+9367+cJTnRVCixX/3NKwVcf7b23o06cPvXv3tlmLtUuXLowbN47Zs2dX6hrdunUjMjKSl156qVLt68prcz2WfbuHl9+yTKl4NDKUv0weZGxAIiIlVPb91y577qTmxDXysm5rvp1ci6uLE3986Opc0dc/2IB+HyRivOtZE/bXioqKyMjIoHnz5uW2qe01WI30zeoD1u07hnQxMBIRkeun5K6BOVaimErH7EvGBSJ1xriR3ejY1lJ1ds+BRFb9dNjgiETketaE/bU333yTrKwsxo8fX26b2bNn4+npaX0EBgbeUNz2KuHMJXbuOwNAp3bedO3U0uCIRESuj5K7BuZYiZ67TkrupBKcHB34yx8GWvf/+dFGcvMKDIxIRIpVdU3YYkuWLOGvf/0ry5Yto2XL8hOZ2lyD1UjfRO+3bo+N6Fap11BExB4puWtgjl5J7lyLCgjMzTQ2GKkzwm8Lol9oOwDOnkvn0+WxxgYk0sBd79qsYCnEMnHiRP773/9es4pzba7BapSiIrN1SKaDg4kxw7saHJGIyPVTcteAZDg4k+jaBID22Wk4orlTUjkmk4m//GEgDg6W32Z/8Pk2Ui+q0qqIUa53bdYlS5bwyCOP8J///Ic77rijpsOsE3buO8PpRMsc9L692+Lr08TgiERErp+SuwZEQzLlRtzUvgX3je4OQNblPC1sLmKwqq4Ju2TJEh566CHefPNNbr/9dpKSkkhKSiItrWEX1/r6x6tDMsdFqNdOROo2JXcNyNGSyV3OJcPikLrrqf/rh3sjZwD++90vHI47b3BEIg1XVdeE/fDDDykoKOCPf/wj/v7+1sfTTz9t1I9guJzcfFattxSJcm/kzLDwTgZHJCJyY5yMDkBqT8meu47ZDfs3tXJ9fJo35vEHb+ftjzdRVGRm1jtr+Pyd+1V8QMQgTzzxBE888USZzy1atMhmf/369TUfUB2zdkscWZfzABg58CYauTkbHJGIyI1Rz10DUtxz51ZYQKvcDGODkTrrkd+E0LaVFwCxe8/YrA0lIlKXfP3jPuv22BEakikidZ+SuwYizdGFZBd3ADrkXMLR4Hik7nJ1ceLFp4Za99/4cAPpmTkGRiQiUnXJqZls2XESgABfD269pX6u4SciDYuSuwbiqIqpSDUKvy2I4VfmpqRevMw7C7cYHJGISNWs+GE/RUWWqtFjI7paqwGLiNRlSu4aCFXKlOr2/J8G08jNMm13yTe72X/knMERiYhUTlGRmeVRe63794wMNjAaEZHqo+SugVAxFalu/i09eOKhvoDli9KsuWsoLCwyOCoRkWv73+5TJJy9BEBYSFsCA7wMjUdEpLoouWsAzMCRK8ldo8J8AvIyDY1H6o+H7wulfZvmAOw5mMjir3cbG5CISCV88f0v1u3i9TtFROoDJXcNwHnnRqQ6NwLgpuyL+kuXauPi7Mgr04Zb99/+eCOnk9QzLCL262JaNqs3HQXAy6MRw/p3NDgiEZHqo+/5DcDhRs2s2zdfvmhgJFIf3dojkN/e1QOA7JwCXnpzNWaz2eCoRETKtjL6APn5hQCMG9EVFxct+Ssi9YeSuwbgoHtz6/bNly8YGInUV8/8fgB+LZoCsHXHSVb8uN/giERESjObzXxZYkjmb+64xcBoRESqn5K7BuCw+9Weu5uy1XMn1a9JY1f+OnWYdf8f7//E+QtZBkYkIlLa7gOJHD2RCkDv4FZ0aOttcEQiItVLyV09l29y4JibJwCtcjPxKMw3OCKprwb17cCYYV0ASM/M5WUNzxQRO/Pf7/ZYt++7Q4VURKT+UXJXz8W5eVLg4AhoSKbUvOf/NJjmXpbiPeu2xvFliXWkRESMdDHtMt+vPQSARxNXRg7sbHBEIiLVT8ldPXdIQzKlFjXzdOfV6SOs+7Pf+4mTZ/TvTkSM92XUXvKuFFK5Z1Qw7o1cDI5IRKT6Kbmr5w6pmIrUsiH9OvKbK8OdLufk8+zsVRRocXMRMVBhYRFLvtlt3f/t2J6GxSIiUpOU3NVzh64sg+BWWEC7nAyDo5GG4rk/DqZNgBcAu/ef5V//2WZsQCLSoK2POc7Zc5bPwAF9gmjbqtk1zhARqZuU3NVjqU5unHdxB6BT9kUcUXELqR2NG7kw5/nRODiYAHj/3zHsOZBocFQi0lAt/nqXdfvBcb0MjEREpGZp5c567EDJIZmabydVENci/Iav4QGMb3kzS1veREFhEVNnreSrjx7Cy6PRjQcoIlJJcSdT2Rp7EoDAAE/CbwsyOCIRkZqjnrt6bF/jq+v3BGelGhiJNFS/TT5M1yv/9s6ey2DGnB+0PIKI1Kr/lJhr98DYXtYRBSIi9ZGSu3psv7sluXMwm+miYipiACfMPHtqBx4FuQD8tDWOT77YYXBUItJQXErP5qtVliVZ3FyduGdUsMERiYjULCV39VRaRg4n3DwACMpJo3FRgcERSUPlU5DDM6d3Wvff+mgTu/afNTAiEWkolq7cQ3aO5fPvnlHBeDZ1MzgiEZGapeSuntq57wxmk2XoiYZkitFCM5P5/QN9ACzz7175lpQLWQZHJSL1WW5eAZ9/ZfnFkoODiUfuCzU4IhGRmqfkrp7asee0dVvJndiDpx7tR+gtrQFIOp/BUy+vtC4oLCJS3VZGHyDl4mUAhod3ok0rL2MDEhGpBUru6qkdv1xN7rpdVnInxnNydOCtl+7E16cJYOld/ts7a1VgRUSqXVGRmYXLrs7vfXT8rQZGIyJSe5Tc1UNZ2XnsP3IOgDY56XgW5hkckYhFS+8mvPe3sbg4OwLwxfe/sHTlHoOjEpH6Zv3Px4k/ZSkkFnpLa3p09Tc4IhGR2qHkrh7acyCRgsIiQEMyxf50v9mfV/88wrr/9/+3jm27EwyMSETqE7PZzEf/2WbdfzRSvXYi0nAouauHduw5Zd3WkEyxR3cN78qjkZbiBgWFRTz10kriTurfqojcuJidCey+UpG3Y1tvBt3e3uCIRERqj5K7eqixuwttArwA9dyJ/Xpm0gAG9AkCLEt3THp2OedVQVNEboDZbOb9f2+17v/hob5atFxEGhS7Te7mzZtHUFAQbm5uhISEsGnTpgrbb9iwgZCQENzc3Gjfvj0ffPBBqTbLly+na9euuLq60rVrV1asWFGl++bn5/Pss8/SvXt3GjduTEBAAA899BBnz9qu2TVo0CBMJpPN4/7777/OV6LqJt5/G6sXP8Znh37ApyCn1u4rUhWOjg689dIYunZqCcDZc+k8/txXZGVrjqiIXJ9tu04Ru/cMAO3bNGfkwM4GRyQiUrvsMrlbtmwZU6ZM4YUXXmDXrl2Eh4czatQoEhLKnpcTHx/P6NGjCQ8PZ9euXTz//PM89dRTLF++3NomJiaGyMhIJkyYwJ49e5gwYQLjx49n27ar4/Kvdd/Lly+zc+dOZs6cyc6dO/nqq684cuQId911V6mYJk2aRGJiovXx4YcfVvOrdG3NC3Jr/Z4iVdHE3YUPZt9DgG9TAA4cPceUv64kv0BLJIhI1b3/aYleuwl9cXS0y685IiI1xmS2wzrkffr0oXfv3syfP996rEuXLowbN47Zs2eXav/ss8+ycuVKDh48aD02efJk9uzZQ0xMDACRkZGkp6ezatUqa5uRI0fSrFkzlixZcl33Bdi+fTu33XYbJ0+epE2bNoCl565nz57MnTv3ul+D9PR0PD09SUtLw8PD47quEdci/LrvL1LdOpwvv/c97mQqv/3Tf0jPtPxCYsywLsyZMbrC4VQ18e+7ohil4aiO99/6yp5fm//tPsVDU5cB0C6wGd9/8n9K7kSk3qjs+6/dvevl5eURGxtLRESEzfGIiAi2bt1a5jkxMTGl2o8YMYIdO3aQn59fYZvia17PfQHS0tIwmUx4eXnZHF+8eDE+Pj5069aN6dOnk5GRUf4PDeTm5pKenm7zEGkoOrT15v1Xx+F8ZYmEb9cc5K9vR2sNPBGpFLPZzJv/2mjd/8PvbldiJyINkt2986WkpFBYWIivr6/NcV9fX5KSkso8Jykpqcz2BQUFpKSkVNim+JrXc9+cnByee+45HnjgAZsM+sEHH2TJkiWsX7+emTNnsnz5cu65554Kf+7Zs2fj6elpfQQGBlbYXqS+ubVHIO+8PAbHK711//3uF/4xb70SPBG5pjWbj7HnQCJgqZB5x9AuBkckImIMu0vuiplMtsOxzGZzqWPXav/r45W5ZmXvm5+fz/33309RURHz5s2zeW7SpEkMGzaM4OBg7r//fr788kvWrFnDzp07y41/xowZpKWlWR+nTp0qt61IfTWkX0def+EOiv/L/fvLWP7fJ1uMDUpE7FpBYRFv/evqkOppk8JxUq+diDRQdvfu5+Pjg6OjY6nesuTk5FK9asX8/PzKbO/k5IS3t3eFbYqvWZX75ufnM378eOLj44mOjr7mvIPevXvj7OzM0aNHy23j6uqKh4eHzUOkIbpjyM28Ov3qIufzPvuZdxZuVg+eiJRpedRe4k9dACCkeysGh3UwOCIREePYXXLn4uJCSEgI0dHRNsejo6MJCwsr85y+ffuWar969WpCQ0NxdnausE3xNSt73+LE7ujRo6xZs8aaPFZk//795Ofn4+/vf822IgL3ju7OC08Ose7P/+xnXv9ggxI8EbGRlZ3HeyXWtXvm9wMqHOUjIlLfORkdQFmmTZvGhAkTCA0NpW/fvnz00UckJCQwefJkwDKE8cyZM3z66aeApTLme++9x7Rp05g0aRIxMTEsWLDAWgUT4Omnn2bAgAHMmTOHsWPH8s0337BmzRo2b95c6fsWFBRw3333sXPnTr777jsKCwutPX3NmzfHxcWFuLg4Fi9ezOjRo/Hx8eHAgQM888wz9OrVi379+tXWSyhS5024pzcmE7z67joAPvnvDnJzC3jxqaFalFhEAPjg8585n5oFwLD+Hekd3MrgiEREjGWXyV1kZCSpqanMmjWLxMREgoODiYqKom3btgAkJibarHkXFBREVFQUU6dO5f333ycgIIB3332Xe++919omLCyMpUuX8uKLLzJz5kw6dOjAsmXL6NOnT6Xve/r0aVauXAlAz549bWL+6aefGDRoEC4uLqxdu5Z33nmHzMxMAgMDueOOO3j55ZdxdHSsqZdMpF763d29cXVx4qU3V2M2w3++2U1ObgGzpkdc+2QRqdfiT11g0X93AODs7MifJw80OCIREePZ5Tp3onXupP65kTXkVkYf4Ll/rKKoyPJ2NfD29jy14F3czNW72LnWuROw77XcjGYvr43ZbGbSs8vZvP0EAJN/dztTJvY3LB4RkZpWZ9e5ExH5tbuGd+Wtl+60roO34efjPBfUj0uOLgZHJiJGWLvlmDWx82/ZlN8/cJuxAYmI2AkldyJSJ4wceBMfv34vTRu7AnDUvRnPdBjAGZfGBkcmIrUpIzOXv72z1rr/3BODcW+kX/SIiICSOxGpQ/r0bMPi/3c/fi2aApDk0phn2g9gd2MfgyMTkdry+gfrOZeSCUD/W9sRMaCTwRGJiNgPJXciUqd0DmrB0vcfoF1OGgAZTi7MbNeXr73bownEIvXblh0n+OL7vQC4N3Jm1jMRWvpARKQEJXciUuf4tWjK68c3c2u6ZSmSIpMD//LvztutepFn0tuaSH2UkZnLzH+utu7/ZfJAAnxV8EZEpCR9CxKROqlxUQEzE7YxPvmI9djaZm34c/twEl3cDYxMRKqb2Wzm5bejOXsuHYDbegYy/s4eBkclImJ/lNyJSJ3lCDycfJDnErbjWlQAwLFGXjzZYRCbPAKMDU5Eqs1Xq/YRte4QAE0au/DaX0bg4KDhmCIiv6bkTkTqvPD0s/zz+CYCci1FFrIdnflHm1t5L+AWcjVMU6ROizuZyqv/72p1zFenj6C1v5dxAYmI2DEnowMQkYYhrkV4jV6/fU4678Rt4P2AW1jvFQjAquZBHHD3ZurpnXS6UoBFROqOtIwc/jjza7JzLD3zv7njFkYOusngqERE7Jd+pS0i9YZ7UQHTT+/k6dO7rMM0T7p5MK3DAD5veRP5qqonUmcUFBbxzN++48SpiwB0bu/D838abHBUIiL2TcmdiNQrJiDiUgJvx22kQ/YlwFJNc0nLm5nafiDH3VRdT8Temc1m3vhgA5u3nwCgmWcj5v39bhq5ORsbmIiInVNyJyL1UtvcDN6K28gDyYdwNBcBEN/IkykdBrLQtyvZDo4GRygi5fnXkv/x7y9jAXBydOCdv95Faz9Pg6MSEbF/Su5EpN5ywsyDyYd5K24jbXMsJdQLTQ4sb9GJP3Qcwtam/lr4XMTOLF25h7f+tcm6//LUYdzWM9DAiERE6g4ldyJS73XMSeOduA08eO4QzkWFAJx3cefvbW/jlTZ9OOPS2OAIRQRg2bd7eGVutHX/md+H85s7bjEwIhGRukXJnYg0CM7mIh44f5j3j/1E74xk6/HtHn78odMQ5vt358KlywZGKNKwLfzvdl5+Kxrzle70iZG3Mum3fYwNSkSkjlFyJyINSqu8LGadjOG5hO1452cDlqGa33m3Z/iDH/Ph4m3k5OYbHKVIw1FYWMSc+et5ff4G67FHI0OZ/vgAA6MSEamblNyJSINjwrLw+UdH1vLguYO4FVqWTci6nMfbH29i+AMf8+8vY5XkidSw9MwcJj//FZ/8d4f12NOP9uPPjw/EpKVLRESqTMmdiDRYbuZCHjh/hH8dXcPICydwcLB8mTx/IYvZ7//EsN/+i0/+u4PsHCV5ItVt594z3PP7z9j0vxOApSrmX6cO4w8T+iqxExG5TkruRKTBa16Qy5Nn9/DNxw8TMaCT9XjKxcvMmb+eYQ/8i/mfxXAxTXPyRG5UTm4+b3+8id9NWcrpxDQAvDwaseCf93H/XT2NDU5EpI4zmc1mVQK3Q+np6Xh6epKWloaHx/UtuhzXIryaoxKp3zqct5RfPxx3nnmfxfDjhiM2z7u6ODFuRDcevq837dt4GxGi1ILqeP+tr27ktTGbzazdcozZ7//EmaR06/Hewa14/YXRWsdORKQClX3/darFmERE6oSbOrTgnb/exeHj5/ngs5/5ceMRiorM5OYVsOzbPSz7dg8D+gQReWcPBvZtj5OjBkGIVORSejbTX/2ezdtPWI85OTrwp0fCmPTb23DU/yERkWqh5E5EpBw3tW/B2y+P4XTiJT77ahdffP8Ll7Mt8+82botn47Z4Wvo04d5Rwdw7urt6HkTK0bSxKxcuZVv3+/ZuwwtPDqFjOx8DoxIRqX/0qzIRkWto7e/FjD8OZsN/J/OXPwwkwLep9bnklEzmf/Yzwx/4F4/++QtW/LCPzKxcA6OVhmTevHkEBQXh5uZGSEgImzZtqrD9hg0bCAkJwc3Njfbt2/PBBx/USpyOjg7MfHooAb5NmfvXMSz852+U2ImI1ADNubNTmnMnUvuK59xdS2FhEZu3n+CL73/hp61xFBbZvo26ODsyOKwDdwy5mYG3t8fVRYMk6pK6Mudu2bJlTJgwgXnz5tGvXz8+/PBDPv74Yw4cOECbNm1KtY+Pjyc4OJhJkybx+OOPs2XLFp544gmWLFnCvffeW6l73uhrk19QiLOTY5XPExFp6Cr7/qvkzk4puROpfZVN7kpKTs3kq1X7WL5qL6fOppV6vkljFwbcFsSQfh0Z0CcIjyZu1RGq1KC6ktz16dOH3r17M3/+fOuxLl26MG7cOGbPnl2q/bPPPsvKlSs5ePCg9djkyZPZs2cPMTExlbpnXXltRETqGxVUERGpBS29mzD5d7fz+IN92H0gke/WHuSH9YdJvWhZNiEzK4+onw4T9dNhnBwduK1nIEP6dWDQ7e1p7e9lbPBSZ+Xl5REbG8tzzz1nczwiIoKtW7eWeU5MTAwRERE2x0aMGMGCBQvIz8/H2dm5xuIVEZHaoeRORKQamEwmenULoFe3AGb8cTA/70zgu7UH+WlrHGkZOQAUFBaxNfYkW2NP8uq762gT4EVYaFv69m7L7b3b4NlUvXpSOSkpKRQWFuLr62tz3NfXl6SkpDLPSUpKKrN9QUEBKSkp+Pv7lzonNzeX3Nyrc0jT09NLtREREfuh5E5EpJo5OTrQ/9Z29L+1HfkFhcTuPcO6LcdYu+WYzfpeCWcvkbDyEktX7sHBwUS3zr7c1iOQ3sGt6N09gGae7gb+FFIXmEwmm32z2Vzq2LXal3W82OzZs3nllVduMEoREaktSu5ERGqQs5Mjt/dqw+292jDjj4M5Ep/CT1vj2LLjBLv3nyW/oAiAoiIzew8lsfdQEguWbQcgKLA5vYMD6B3cip7dAmjXupnWAxMAfHx8cHR0LNVLl5ycXKp3rpifn1+Z7Z2cnPD29i7znBkzZjBt2jTrfnp6OoGBgTcYvYiI1BQldyIitcRkMnFT+xbc1L4Fk393O5ez89jxy2m27DhJzM6THDmeYtM+/tQF4k9dYPmqfQC4uznTpVNLunbypVtnX7p2bkn7Nt5aRL0BcnFxISQkhOjoaO6++27r8ejoaMaOHVvmOX379uXbb7+1ObZ69WpCQ0PLnW/n6uqKq6tr9QUuIiI1SsmdiIhB3Bu5MKBPewb0aQ9A6sUsdu47y859Z9i57wwHjpyz9uwBXM7JJ3bvGWL3nrEec3N1omM7bzq09aZTOx86tPOmUztvAnw9cXAof3ie1H3Tpk1jwoQJhIaG0rdvXz766CMSEhKYPHkyYOl1O3PmDJ9++ilgqYz53nvvMW3aNCZNmkRMTAwLFixgyZIlRv4YIiJSjZTciYjYCe9mjRke3onh4Z0AyMnNZ++hJHbuO8u+w0nsP3KOs+dsC1rk5Baw7/A59h0+Z3O8kZsT7dt4075NcwIDvGjbyovAAC/aBHjh3cy9wnlZUjdERkaSmprKrFmzSExMJDg4mKioKNq2bQtAYmIiCQkJ1vZBQUFERUUxdepU3n//fQICAnj33XcrvcadiIjYP61zZ6e0zp1I7buede5q28W0bA4cPWd5HEnmwNFzJJy9RFXeyd0bORPo70WbVl608vPAv6UHfi2aWh4tm+LTzL1Bz+3TWm7l02sjImIMrXMnIlIPNfNsRL/QdvQLbWc9lpObz/GEC8SdSOXoiVTiTqZy7GQqp85eoqiodNZ3OTufw8fPc/j4+TLv4eToQEufxvj6NMW/ZVNaeDfBu5k73s3c8WnWmObN3PFp5o63lzsuLvoYERERsRf6VBYRqePcXJ3p2smXrp1sqyTm5OZz6mwap85esiy7cPaSZfvMJc4kpVNQWFTm9QoKizh7LoOz5zLYtb/iezdt7Ip3c0vS5+XhhkcTNzw93PBs6oZHE1c8mlq2PZu6Wbc9mrg26J5BERGRmmK3yd28efN44403SExMpFu3bsydO5fw8PKHGW7YsIFp06axf/9+AgIC+Mtf/mKdVF5s+fLlzJw5k7i4ODp06MDf//53mypjlbmv2WzmlVde4aOPPuLixYv06dOH999/n27dulnb5ObmMn36dJYsWUJ2djZDhw5l3rx5tG7duppeHRGRa3NzdaZTkA+dgnxKPVdQWERScjpnzqWTlJxB0vlMkpLTSUrJvLKfwcW07GveIyMrl4ysXE6culil2NwbOdPY3cXyaORi3XZ3K3H8V881cnPGzdUJNxcn3NycLNuulmOuV44raRQRkYbMLpO7ZcuWMWXKFObNm0e/fv348MMPGTVqFAcOHKBNmzal2sfHxzN69GgmTZrE559/zpYtW3jiiSdo0aKFdaJ4TEwMkZGR/O1vf+Puu+9mxYoVjB8/ns2bN9OnT59K3/f111/nrbfeYtGiRXTu3JlXX32V4cOHc/jwYZo2bQrAlClT+Pbbb1m6dCne3t4888wz3HnnncTGxuLo6FhLr6KISPmcHB1o7e9Fa3+vctvk5OaTdD6T1ItZpFy4zIVLl0m5mMWFi5dJuVi8f5nUi1lkZuVV6f6Xs/O5nJ3P+dSsG/xJbDk7O1qTv9+M7s5Tj/av1uuLiIjYM7ssqNKnTx969+7N/Pnzrce6dOnCuHHjmD17dqn2zz77LCtXruTgwYPWY5MnT2bPnj3ExMQAlqpi6enprFq1ytpm5MiRNGvWzFoG+lr3NZvNBAQEMGXKFJ599lnA0kvn6+vLnDlzePzxx0lLS6NFixZ89tlnREZGAnD27FkCAwOJiopixIgRlXoNVFBFpPbVhYIq9io3r4C0jBzS0nNIz7T8mZaRQ3pmLmnp2aRl5to8l3k5l6zL+WRdziMrO6/MuYE36pHfhPDcE4OrfJ6KhpRPr42IiDHqbEGVvLw8YmNjee6552yOR0REsHXr1jLPiYmJISIiwubYiBEjWLBgAfn5+Tg7OxMTE8PUqVNLtZk7d26l7xsfH09SUpLNvVxdXRk4cCBbt27l8ccfJzY2lvz8fJs2AQEBBAcHs3Xr1nKTu9zcXHJzc637aWlpgOUv8nplFBVc97kiDdGN/H8TcHMGN28XfL1dgMp/8TebzeTmFXD5ch5ZV3r0srLzuJydR3Z2/pVjeWTnFpCbW0BObj65eQXk5BZe3c4pIDe/gNwSx9yczdf1d1p8jh3+7tNwxa+J/q+IiNSuyn422V1yl5KSQmFhIb6+toUBfH19SUpKKvOcpKSkMtsXFBSQkpKCv79/uW2Kr1mZ+xb/WVabkydPWtu4uLjQrFmzSscPMHv2bF555ZVSxwMDA8s9R0Sqmaen0RFINdq8Aqb+/vrPz8jIwFP/JmxkZGQA+mwSETHKtT6b7C65K/brBXbNZnOFi+6W1f7Xxytzzepq82vXajNjxgymTZtm3S8qKuLChQt4e3vbxWLD6enpBAYGcurUqTo1FEdx1666GjfU3dgVd/Uzm81kZGQQEBBgdCh2JyAggFOnTtG0aVPDP5vs+d/QtdTV2BV37aqrcUPdjd2e467sZ5PdJXc+Pj44OjqW6uVKTk4u1WNWzM/Pr8z2Tk5OeHt7V9im+JqVua+fnx9g6Z3z9/cvt01eXh4XL1606b1LTk4mLCys3J/b1dUVV1dXm2NeXl7ltjeKh4eH3f1jrwzFXbvqatxQd2NX3NVLPXZlc3BwsLvKz/b6b6gy6mrsirt21dW4oe7Gbq9xV+azye5qRru4uBASEkJ0dLTN8ejo6HKTo759+5Zqv3r1akJDQ3F2dq6wTfE1K3PfoKAg/Pz8bNrk5eWxYcMGa5uQkBCcnZ1t2iQmJrJv374KkzsREREREZEbYXc9dwDTpk1jwoQJhIaG0rdvXz766CMSEhKs69bNmDGDM2fO8OmnnwKWypjvvfce06ZNY9KkScTExLBgwQJrFUyAp59+mgEDBjBnzhzGjh3LN998w5o1a9i8eXOl72symZgyZQqvvfYanTp1olOnTrz22mu4u7vzwAMPAJaMeuLEiTzzzDN4e3vTvHlzpk+fTvfu3Rk2bFhtvYQiIiIiItLA2GVyFxkZSWpqKrNmzSIxMZHg4GCioqJo27YtYOkJS0hIsLYPCgoiKiqKqVOn8v777xMQEMC7775rXeMOICwsjKVLl/Liiy8yc+ZMOnTowLJly6xr3FXmvgB/+cv/b+/eg6I6zz+AfxflYpWsMQLLigJeSCawoTHEqM2AQ1OCDcGEiuEihVRtbNFoaxrNtFYzScdMOtFpJhNNZtBcTNRUJCYiKAQwMqLQ4Bo0UTFccil4IYCoARb2+f3R2fPjuIt4WdiL388MM/i+7zn7nIcjzzyc3XOex08//YQ//vGPykPM9+/frzzjDgA2bNiA4cOHY968ecpDzN955x2Xfsadt7c31qxZY/XWUWfHuIeWq8YNuG7sjJtuV658Drlq7Ix7aLlq3IDrxu6qcffllM+5IyIiIiIiohvjdJ+5IyIiIiIiohvH5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3ACbu9vUunXr8OCDD8LX1xf+/v544okncOrUKdWarKwsaDQa1df06dNVa7q6urB06VKMHTsWI0eORGJiIr7//vtBjX3t2rVWcVkeMA8AIoK1a9dCr9djxIgRmDVrFk6cOOHwuENCQqzi1mg0yM7OBuA8+f7888/x+OOPQ6/XQ6PR4OOPP1bN2yu/ra2tyMjIgFarhVarRUZGBtra2gYlbpPJhJUrV8JgMGDkyJHQ6/X47W9/i//+97+qfcyaNcvqZ5CSkjKocQ8UO2C/c2Mocw7A5vmu0Wjwz3/+U1njqJyTc3LV2uSqdQlgbWJtuvG4AeetS9cTu7vXJjZ3t6kDBw4gOzsbhw8fRlFREXp6ehAXF4fLly+r1sXHx6OpqUn52rt3r2p++fLlyMvLw/bt21FeXo5Lly4hISEBvb29gxp/eHi4Kq6amhpl7tVXX8X69evxxhtvoKqqCjqdDr/61a/Q0dHh0LirqqpUMVsedJ+cnKyscYZ8X758GZGRkXjjjTdsztsrv2lpaTAajSgsLERhYSGMRiMyMjIGJe4rV66guroaq1evRnV1NXbt2oXTp08jMTHRau2iRYtUP4O33npLNW/vuAeK3cIe58ZQ5hyAKt6mpiZs3rwZGo1G9ZgawDE5J+fkyrXJFesSwNrE2nTjcVs4Y126ntjdvjYJkYicO3dOAMiBAweUsczMTJkzZ06/27S1tYmnp6ds375dGfvhhx/Ew8NDCgsLBy3WNWvWSGRkpM05s9ksOp1OXnnlFWWss7NTtFqtbNq0yaFxX23ZsmUyadIkMZvNIuKc+QYgeXl5yr/tld+vvvpKAMjhw4eVNRUVFQJATp48afe4bamsrBQA0tjYqIzFxMTIsmXL+t1msOMWsR27Pc4NZ8j5nDlzJDY2VjXmDDkn5+Uqtcld6pIIa5Ojf086Y21y1brUX+xXc7faxCt3BABob28HAIwZM0Y1XlZWBn9/f4SFhWHRokU4d+6cMvfFF1/AZDIhLi5OGdPr9YiIiMChQ4cGNd7a2lro9XqEhoYiJSUFdXV1AID6+no0NzerYvL29kZMTIwSkyPjtuju7sbWrVvxu9/9DhqNRhl31nxb2Cu/FRUV0Gq1eOihh5Q106dPh1arHbJjaW9vh0ajwejRo1XjH3zwAcaOHYvw8HA899xzqr/6OjLuWz03HJ3zs2fPIj8/HwsWLLCac9ack+O5Um1y9boEsDY5w+8bV6pNrl6XAPesTcMd+urkFEQEf/7zn/Hwww8jIiJCGZ89ezaSk5MRHByM+vp6rF69GrGxsfjiiy/g7e2N5uZmeHl54c4771TtLyAgAM3NzYMW70MPPYT33nsPYWFhOHv2LF5++WXMnDkTJ06cUF43ICDAKqbGxkYAcFjcfX388cdoa2tDVlaWMuas+e7LXvltbm6Gv7+/1f79/f2H5Fg6OzuxatUqpKWl4Y477lDG09PTERoaCp1Oh+PHj+OFF17AsWPHlLcpOSpue5wbjs75u+++C19fXyQlJanGnTXn5HiuVJvcoS4BrE2O/n3jSrXJHeoS4J61ic0dYcmSJfjyyy9RXl6uGn/qqaeU7yMiIhAVFYXg4GDk5+db/SfoS0RUf/Gzt9mzZyvfGwwGzJgxA5MmTcK7776rfJj36te/npgGO+6+cnJyMHv2bOj1emXMWfNtiz3ya2v9UByLyWRCSkoKzGYz3nzzTdXcokWLlO8jIiIwZcoUREVFobq6GlOnTnVY3PY6NxyVcwDYvHkz0tPT4ePjoxp31pyT47lSbXKHugSwNtnax/Xu51a5Wm1yh7oEuGdt4tsyb3NLly7FJ598gtLSUgQFBV1zbWBgIIKDg1FbWwsA0Ol06O7uRmtrq2rduXPnrP56NphGjhwJg8GA2tpa5e5kV//VpG9Mjo67sbERxcXFWLhw4TXXOWO+7ZVfnU6Hs2fPWu3//Pnzg3osJpMJ8+bNQ319PYqKilR/GbVl6tSp8PT0VP0MHBH31W7m3HBk7AcPHsSpU6cGPOcB5805DS1Xr02uVpcA1ibLGtamm+NqdQlw39rE5u42JSJYsmQJdu3ahZKSEoSGhg64TUtLC7777jsEBgYCAB544AF4enoql6iB/92B6Pjx45g5c+agxX61rq4ufP311wgMDFQuofeNqbu7GwcOHFBicnTcW7Zsgb+/Px577LFrrnPGfNsrvzNmzEB7ezsqKyuVNUeOHEF7e/ugHYuleNbW1qK4uBh33XXXgNucOHECJpNJ+Rk4Im5bbubccGTsOTk5eOCBBxAZGTngWmfNOQ0Nd6lNrlaXANYmgLXpVrhaXQLcuDYN1Z1byLn84Q9/EK1WK2VlZdLU1KR8XblyRUREOjo6ZMWKFXLo0CGpr6+X0tJSmTFjhowbN04uXryo7Gfx4sUSFBQkxcXFUl1dLbGxsRIZGSk9PT2DFvuKFSukrKxM6urq5PDhw5KQkCC+vr7S0NAgIiKvvPKKaLVa2bVrl9TU1EhqaqoEBgY6PG4Rkd7eXpkwYYKsXLlSNe5M+e7o6JCjR4/K0aNHBYCsX79ejh49qty5y175jY+Pl/vuu08qKiqkoqJCDAaDJCQkDErcJpNJEhMTJSgoSIxGo+qc7+rqEhGRM2fOyIsvvihVVVVSX18v+fn5cs8998j9998/qHEPFLs9z42hzLlFe3u7/OxnP5ONGzdabe/InJNzctXa5Mp1SYS1ibXpxuJ25ro0UOwW7lyb2NzdpgDY/NqyZYuIiFy5ckXi4uLEz89PPD09ZcKECZKZmSnffvutaj8//fSTLFmyRMaMGSMjRoyQhIQEqzX29tRTT0lgYKB4enqKXq+XpKQkOXHihDJvNptlzZo1otPpxNvbW6Kjo6WmpsbhcYuI7Nu3TwDIqVOnVOPOlO/S0lKb50ZmZqaI2C+/LS0tkp6eLr6+vuLr6yvp6enS2to6KHHX19f3e86XlpaKiMi3334r0dHRMmbMGPHy8pJJkybJs88+Ky0tLYMa90Cx2/PcGMqcW7z11lsyYsQIaWtrs9rekTkn5+SqtcmV65IIa1NfrE0Dx+3MdWmg2C3cuTZpRERu9qofEREREREROQd+5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3ACbOyIiIiIiIjfA5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3ACbOyIHO3LkCDQaDTQaDdatW+focOxu1qxZ0Gg0aGhosMv+QkJCoNFo7LIvIiIiInfC5o7Iwd5//32b398qNkHXtnbtWmg0GrzzzjuODoWIiIjILtjcETmQyWTCjh07oNFooNPp8PXXX6O6utrRYRERERGRC2JzR+RABQUFuHDhAqKjo/H73/8egH2v3hERERHR7YPNHZEDWRq5+fPnY/78+QCAbdu2obe3t99tvvrqKzz99NMIDg6Gt7c3AgICEB0djX/9618AgLKyMmg0GjQ2NgKA8nk+jUaDkJAQZT/XetumZR9ZWVmq8aamJrz66quIiYnBuHHj4OXlBZ1Oh6SkJFRVVd1sGqz09PRg3bp1mDJlCnx8fDBx4kSsXr0a3d3dNteLCLZt24aUlBSEhYVh5MiR8PX1xbRp0/Dmm2/CbDar1oeEhODFF18EADz99NOqHJWVlQEAOjs7kZOTgzlz5mDixIkYMWIERo8ejejoaGzfvt1ux0pERERkL8MdHQDR7aq9vR179uyBt7c35s6di9GjR2PatGmorKxEUVER4uPjrbb597//jYyMDHR1dSE8PBwzZ87Ejz/+iOPHj2P58uVYtmwZdDodMjMzsXPnTly+fBmZmZnK9mPHjr2lmHfv3o2VK1di8uTJMBgMuOOOO3DmzBnk5eVhz5492LNnD+Li4m7pNQAgNTUVO3fuxKhRoxAfHw8Rwfr163H06FGIiNX6rq4upKWl4c4778S9996LqVOn4sKFC6ioqEB2djYqKytVn62bO3cuiouLcezYMfziF7/A5MmTlTmdTgcAaGhowMKFCxEQEIB77rkH06ZNQ3NzMw4dOoSDBw/i5MmTWLt27S0fKxEREZHdCBE5xNtvvy0A5De/+Y0y9vrrrwsASU9Pt1p/+vRp8fHxEU9PT9mxY4dqrre3Vz799FPVWHBwsFzrv/i15ktLSwWAZGZmqsa//PJLOXbsmNX6wsJC8fLykkmTJonZbFbNxcTECACpr6/vN5a+PvzwQwEgEydOlO+//14Zr6urk6CgIAFgFbfJZJLc3Fzp6upSjZ87d06ioqIEgBw4cEA1t2bNGgEgW7ZssRnHhQsXZN++fdLb26sar6urk5CQEPHw8LjuYyIiIiIaCnxbJpGD9H1LpkVKSgqGDx+OvLw8XLp0SbV+w4YN6OzsxDPPPIN58+ap5jw8PJCQkDDoMRsMBtx3331W448++iiSk5PxzTff4Pjx47f0Ghs3bgQAvPTSSxg3bpwyHhoaitWrV9vcZvjw4UhKSoKXl5dq3M/PT3m8xO7du28ojrvuugtxcXHw8FD/mgwNDcVf//pXmM1mfPrppze0TyIiIqLBxLdlEjlAQ0MDysvLMWbMGPz6179Wxv38/PDoo48iPz8feXl5yMjIUOaKi4sBAM8888yQx9tXV1cXCgsLUVlZifPnzyufg6upqQEA1NbWwmAw3NS+TSYTjhw5Ag8PD8ydO9dqPjU19ZrHbzQasX//fjQ2NuLKlSsQEXR0dChx3Yzy8nKUlZXhhx9+QGdnJ0QETU1Nt7RPIiIiosHA5o7IAbZu3QoRwbx586yuNs2fPx/5+fl4//33Vc3dd999BwCYOHHikMbaV01NDRITE6/5QHJLM3UzWlpa0N3djcDAQKu8AICvry9Gjx6NtrY21Xh3dzeysrKwbds2u8XV3t6OpKQklJSU2G2fRERERIOJb8skcoCtW7cCAD777DM8/PDDqq/XXntNmbNcIbKw3NFxsF19d0kASjPa0NCAxYsXw2g04uLFizCbzRARvPDCC8q6m2XZ9kaPcf369di2bRsiIiJQUFCAs2fPoru7GyKCU6dO3VRcK1euRElJCaKjo1FWVoYLFy6gp6cHIoJ9+/bd1D6JiIiIBhOv3BENscrKSqXhqK2t7fetfWazGR9++CFWrFgBABg/fjxqa2vxzTffICIi4pbjsFwZu3TpEkaNGqWas1wl7OvkyZM4efIkoqKilM/F9VVXV3fLMY0dOxZeXl5obm5Gd3e31dW7jo4Oq6t2AJCXlwcASoNnj7jy8vIwbNgwfPLJJ9BqtXbZJxEREdFg4pU7oiFmuZHKX/7yF4iIza/9+/cD+P8rfADwyCOPAADefvvt63odS2PU09Njcz4wMBAAcPr0aas5y+v31draCgAICgqyOVdUVHRdcV2Lp6cnpk2bBrPZjNzcXKv5/p4vZ4lt/PjxVnMfffSRzW0Gyk9rayt8fX2tGrtr7ZOIiIjIkdjcEQ2hnp4e7NixA8D/bg7Sn9jYWPj7+8NoNCp3n1y+fDl8fHywadMmq8bHbDZj7969qjG9Xg8AylXCq8XExAAA1q1bp3po+tatW202UZMnT4aHhwdKSkpUVxs7OzuxePFi/Pjjj/0ez42w3DDl73//u+ptqY2NjXjppZdsbhMWFgYA2LRpk2p8586deO+992xuM1B+wsLC0NbWpvy8LDZs2IDS0tLrOBIiIiKiocXmjmgIFRQU4Pz587j77rtx//3397tu2LBhyt0iLVfvwsLCsHnzZgD/ewi3wWBAamoq4uPjMX78eDz22GOqfSQmJgIAfvnLXyI1NRULFy7EqlWrlPns7Gz4+flh586duPfee5GcnIyf//znyMrKwrJly6xi8vf3x4IFC3Dx4kVERkYiISEBycnJCAkJQUlJCbKysm4pNxbp6el48skncebMGdx999148skn8cQTTyA8PBwGgwETJkyw2ub555/HsGHDsGrVKkRFRSEtLQ0PPvggkpOT8ac//cnm68TFxcHHxwcbNmzA7NmzsWDBAixcuFBp9iyfIUxJSUF0dDTS0tIQHh6O5557rt99EhERETkSmzuiIWR5S2ZKSsqAay1X9j744APlBiepqamoqqpCWloaWlpakJubC6PRiClTpuD1119Xbf/ss8/ib3/7G0aNGoXc3Fzk5OSorsgFBATg888/R0JCApqamlBQUACtVouioiKlMbzaxo0b8dprryE0NBSfffYZDh48iEceeQT/+c9/EBwcfFM5uZpGo8GOHTvwj3/8A35+fti7dy+MRiOWLl2KXbt22bzZSnR0NMrLyxEbG4u6ujrs2bMHXl5eyM3NRXZ2ts3X0ev12L17N6ZPn47y8nJs3rwZOTk5ytXC9PR05OfnY/r06TAajSgoKIBer0dJSUm/+SEiIiJyJI3wdm9EREREREQuj1fuiIiIiIiI3ACbOyIiIiIiIjfA5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3ACbOyIiIiIiIjfA5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3ACbOyIiIiIiIjfA5o6IiIiIiMgNsLkjIiIiIiJyA2zuiIiIiIiI3MD/AYnLdtlgiqlfAAAAAElFTkSuQmCC\n" - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = Gevdist.probapilityPlot(\n", - " shape, loc, scale, cdf_Weibul, func=func, n_samples=len(time_series1)\n", - ")\n" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/Note books/SensitivityAnalysis.ipynb b/examples/Note books/SensitivityAnalysis.ipynb deleted file mode 100644 index 3e88c34..0000000 --- a/examples/Note books/SensitivityAnalysis.ipynb +++ /dev/null @@ -1,503 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "0dad8c28-e8b5-4436-9ed5-bf4565e58939", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "# Modules" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "id": "1d24b952-51c1-42ff-93de-3d880dfe1fdb", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import Hapi.rrm.hbv_bergestrom92 as HBVLumped\n", - "from Hapi.run import Run\n", - "from Hapi.catchment import Catchment\n", - "from Hapi.rrm.routing import Routing\n", - "import Hapi.statistics.performancecriteria as PC\n", - "from Hapi.statistics.sensitivityanalysis import SensitivityAnalysis as SA" - ] - }, - { - "cell_type": "markdown", - "id": "5b7a2c89-1961-48de-850d-54f167abbf2d", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "#### Root path to the examples folder" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "56475416-0ac3-4d30-a7cb-9989193f1881", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "Comp = \"F:/01Algorithms/Hydrology/HAPI/Examples\"" - ] - }, - { - "cell_type": "markdown", - "id": "8fb0151a-4ca0-44bc-9ce9-5ddadc888361", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Paths" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "1d1ea596-42e6-4030-a189-1f1f7af43173", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "Parameterpath = Comp + \"/data/lumped/Coello_Lumped2021-03-08_muskingum.txt\"\n", - "MeteoDataPath = Comp + \"/data/lumped/meteo_data-MSWEP.csv\"\n", - "Path = Comp + \"/data/lumped/\"" - ] - }, - { - "cell_type": "markdown", - "id": "9064ca54-bff2-4f5c-b719-598ab02b1004", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Model data" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "1bc3575a-de9c-4d84-b544-1b631b7db8b8", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Lumped Model inputs are read successfully\n", - "Lumped model is read successfully\n", - "Parameters are read successfully\n" - ] - } - ], - "source": [ - "### meteorological data\n", - "start = \"2009-01-01\"\n", - "end = \"2011-12-31\"\n", - "name = \"Coello\"\n", - "Coello = Catchment(name, start, end)\n", - "Coello.ReadLumpedInputs(MeteoDataPath)\n", - "\n", - "### Basic_inputs\n", - "# catchment area\n", - "CatArea = 1530\n", - "# temporal resolution\n", - "# [Snow pack, Soil moisture, Upper zone, Lower Zone, Water content]\n", - "InitialCond = [0,10,10,10,0]\n", - "\n", - "Coello.ReadLumpedModel(HBVLumped, CatArea, InitialCond)\n", - "\n", - "### parameters\n", - "Snow = 0 # no snow subroutine\n", - "Coello.ReadParameters(Parameterpath, Snow)\n", - "\n", - "parameters = pd.read_csv(Parameterpath, index_col = 0, header = None)\n", - "parameters.rename(columns={1:'value'}, inplace=True)" - ] - }, - { - "cell_type": "markdown", - "id": "8ae40d85-8e27-474d-ae4e-35dde4dfafa8", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Parameters Boundaries" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "f23edbc1-59b6-428a-af54-854fa5ebb96d", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Parameters bounds are read successfully\n" - ] - } - ], - "source": [ - "UB = pd.read_csv(Path + \"/UB-3.txt\", index_col = 0, header = None)\n", - "parnames = UB.index\n", - "UB = UB[1].tolist()\n", - "LB = pd.read_csv(Path + \"/LB-3.txt\", index_col = 0, header = None)\n", - "LB = LB[1].tolist()\n", - "Coello.ReadParametersBounds(UB, LB, Snow)" - ] - }, - { - "cell_type": "markdown", - "id": "e25b5a51-3bb3-479b-8615-a535fc063736", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Observed flow" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "5c1b95e3-13f0-44fd-b009-e293cda30982", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gauges data are read successfully\n" - ] - } - ], - "source": [ - "Coello.ReadDischargeGauges(Path + \"Qout_c.csv\", fmt=\"%Y-%m-%d\")\n", - "### Routing\n", - "Route=1\n", - "# RoutingFn=Routing.TriangularRouting2\n", - "RoutingFn = Routing.Muskingum" - ] - }, - { - "cell_type": "markdown", - "id": "c4a7f6ab-18f1-4c83-84fe-8ff33e443392", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Run the model" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "a8845a3a-1052-44c2-9d64-b3026dcac682", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Run has finished\n" - ] - } - ], - "source": [ - "Run.RunLumped(Coello, Route, RoutingFn)" - ] - }, - { - "cell_type": "markdown", - "id": "c8ab81d9-1671-421f-9d97-45a84a837f82", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "### Performace" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "6b43e8c2-ee0d-4525-9855-b9a0d0a4c866", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "RMSE= 26.03\n", - "NSE= 0.01\n", - "NSEhf= 0.17\n", - "KGE= 0.54\n", - "WB= 96.55\n" - ] - } - ], - "source": [ - "Metrics = dict()\n", - "\n", - "Qobs = Coello.QGauges[Coello.QGauges.columns[0]]\n", - "\n", - "Metrics['RMSE'] = PC.RMSE(Qobs, Coello.Qsim['q'])\n", - "Metrics['NSE'] = PC.NSE(Qobs, Coello.Qsim['q'])\n", - "Metrics['NSEhf'] = PC.NSEHF(Qobs, Coello.Qsim['q'])\n", - "Metrics['KGE'] = PC.KGE(Qobs, Coello.Qsim['q'])\n", - "Metrics['WB'] = PC.WB(Qobs, Coello.Qsim['q'])\n", - "\n", - "print(\"RMSE= \" + str(round(Metrics['RMSE'],2)))\n", - "print(\"NSE= \" + str(round(Metrics['NSE'],2)))\n", - "print(\"NSEhf= \" + str(round(Metrics['NSEhf'],2)))\n", - "print(\"KGE= \" + str(round(Metrics['KGE'],2)))\n", - "print(\"WB= \" + str(round(Metrics['WB'],2)))" - ] - }, - { - "cell_type": "markdown", - "id": "1e4ca197-7ab3-4e6f-9153-7f3a96b05019", - "metadata": { - "pycharm": { - "name": "#%% md\n" - } - }, - "source": [ - "First the SensitivityAnalysis method takes 4 arguments :\n", - " 1-parameters:previous obtained parameters\n", - " 2-LB: upper bound\n", - " 3-UB: lower bound\n", - " 4-wrapper: defined function contains the function you want to run with different\n", - " parameters and the metric function you want to assess the first function\n", - " based on it.\n", - "\n", - "- Wrapper function definition\n", - " define the function to the OAT sesitivity wrapper and put the parameters argument\n", - " at the first position, and then list all the other arguments required for your function\n", - "\n", - " the following defined function contains two inner function that calculates discharge\n", - " for lumped HBV model and calculates the RMSE of the calculated discharge.\n", - "\n", - " the first function \"RUN.RunLumped\" takes some arguments we need to pass it through\n", - " the SensitivityAnalysis method [ConceptualModel,data,p2,init_st,snow,Routing, RoutingFn]\n", - " with the same order in the defined function \"wrapper\"\n", - "\n", - " the second function is RMSE takes the calculated discharge from the first function\n", - " and measured discharge array\n", - "\n", - " to define the argument of the \"wrapper\" function\n", - " 1- the random parameters valiable i=of the first function should be the first argument\n", - " \"wrapper(Randpar)\"\n", - " 2- the first function arguments with the same order (except that the parameter\n", - " argument is taken out and placed at the first potition step-1)\n", - " 3- list the argument of the second function with the same order that the second\n", - " function takes them\n", - "\n", - "SensitivityAnalysis method returns a dictionary with the name of the parameters\n", - "as keys,\n", - "Each parameter has a disctionary with two keys 0: list of parameters woth relative values\n", - "1: list of parameter values\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c67ce8ad-ec1a-439e-a3b6-443b3c73bc73", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "# For Type 1\n", - "def WrapperType1(Randpar,Route, RoutingFn, Qobs):\n", - " Coello.Parameters = Randpar\n", - "\n", - " Run.RunLumped(Coello, Route, RoutingFn)\n", - " rmse = PC.RMSE(Qobs, Coello.Qsim['q'])\n", - " return rmse\n", - "\n", - "# For Type 2\n", - "def WrapperType2(Randpar,Route, RoutingFn, Qobs):\n", - " Coello.Parameters = Randpar\n", - "\n", - " Run.RunLumped(Coello, Route, RoutingFn)\n", - " rmse = PC.RMSE(Qobs, Coello.Qsim['q'])\n", - " return rmse, Coello.Qsim['q']" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "e92a0378-09b5-40f0-887e-c79e841c08b1", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [], - "source": [ - "Type = 2\n", - "if Type ==1:\n", - " fn = WrapperType1\n", - "elif Type == 2:\n", - " fn = WrapperType2\n", - "\n", - "Positions = [10]" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "16f30bd3-dfa6-465a-9588-71f8d82ddc1b", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Model Run has finished\n", - "10-k -0\n", - "26.026\n", - "Model Run has finished\n", - "10-k -1\n", - "26.011\n", - "Model Run has finished\n", - "10-k -2\n", - "25.997\n", - "Model Run has finished\n", - "10-k -3\n", - "25.983\n", - "Model Run has finished\n", - "10-k -4\n", - "25.97\n", - "Model Run has finished\n", - "10-k -5\n", - "25.957\n" - ] - } - ], - "source": [ - "Sen = SA(parameters, Coello.LB, Coello.UB, fn, Positions, 5, Type=Type)\n", - "Sen.OAT(Route, RoutingFn, Qobs)" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "5ee3134d-ef9c-47bb-9d0f-5154d239917d", - "metadata": { - "pycharm": { - "name": "#%%\n" - } - }, - "outputs": [ - { - "data": { - "image/png": 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7z5tAj/QkUpOsw5malECP9CQeOH8i79xwAtdNG8EHvzmJp684kpNGBk49snF3Obf+dw3H3P8hf3pnI4Wl1fHYFUVRlA5BLJKuDhkyhA8++KDV21VaFx1FdYiICLMm9+f0sTk88vEWnv16B5ccO5jrp40gzeHZSUgQTh7Vm5NH9WbL3grmfZXLayvy6vPnlFS5eeyTrTzx2TbOGt+H354xiqEOr4+iKIoSHk26qgSjHpxWIj3Zxc1njmb1nDO5+czRAeImmOG9u3DPueP5+rZT+f05YxjQI62+zOszvLWmkAS9DhVFUSJib3kNZ//tc2777xpKqtxUu63gyGq3j5IqN7f9dw1n/+1z9pbXxNlSJZaowIkj3dKS+OkJh/HpzdN44pIjOPawngCcOro3g3sGem+KK+v04lQURQmBP+mqM6O8E2fS1dZm48aNDB06lJdeeqnV21YODe2iagMkJghnjOvDGeP6sKGwjIQQbtT/fLmdxz7dyvcn9uPak4Yxqk9mHCxVFEWJDw+9v4m/fbj5oNevdnu57oUVXPeC9RL55OVHHrJNK1as4Nxzz+XRRx/l+9///iG3p7QuMfHgiEiKiDwpIjtEpFxEvhWR6Y7ydBF5VESKRKRURD5roq0sEfmfiFTa7f3YUXaMiLwvIgdEZJ+IvCIi7WqOhDF9uzYSLzVuL89/sxO31/C/b/PZsrciTtYpiqIoAJ9//jkzZ85k3rx5Km7aKLHqonIBu4CTgG7AH4CXRWSIXf4EkAWMsf/f2ERbjwB1QA5wMfCYiIyzy3rYbQ0BBgPlwH9acT/iwr7yWobYAcf9uqVy5ricgHKvz1BcWRcP0xRFUToljz/+OMcddxzTpk2LtylKGCReieZEZDVwF7AWWAoMMMaUNbNOBlAMjDfGbLKXPQvkG2NuDVF/CvCpMabJ/pypU6eaZcuWHdyOxJDVeSUUVdRyyuhAgfPeut388sVvOXdyf6743hBG99F5TxRFad9s2LCBMWPGRFR30epCfvvKqrATHwOkJSXy59mTWmXi4yFDhvDQQw/xpz/9iWOPPZaHHnoobN1w+yEiy40xUw/ZGCUscQkyFpEcYCSwDjga2AHcZXdRrRGR88OsOhLw+sWNzSpgXJj6J9rbCGXD1SKyTESW7du376D2I9ZMHNC9kbgBePqrXGo9PuYv28VZcz/nR08s5r11u/H6NEuyoigdn0iSrg7tlcGRQ3u02jYzMzN55513+Oyzz7j11kbv10obIOZBxiKSBDwPzDPGbBSR84DxwGtAP+BYYJGIrDfGbAhavQtQGrSsFGjkoRGRicAdwKxQdhhjnsDqzmLq1KntVglU1XkorwlMZPX1tv18vW0/A7PSuOzYIcyeOpBuae17ThdFUZRw+JOuLlxVwJyF66h2e6lx+0hNSiAtKZG7Zo1nxsS+rZ4Hp3v37rz//vtMmzaNpKQk7rnnnlZtXzk0YipwRCQBeBYrhuZ6e3E14AbuNcZ4gE9F5GPgDCBY4FQAwf0vXbFibZzbGQ68DdxgjPm8VXeijZGe7GLh9cezfEcx//kyl3ccnptdB6q5d9EG/vr+Ji44YgCXHTeEYdld4myxoihK6xNp0tXWIDc3t/5zVlYWq1atatX2ldYhZgJHLOn8JFZw8NnGGP8Uq6tb0MwmwCUiI4wx/vGCk3B0Q4nIYOAD4B5jzLOHbnnbR0SYOiSLqUOyyC+p5tmvd/DS0p2UVFmHuKrOyzNf7+CZr3dw0shsrjh+CCeOyCZBswkqitLB8CddvfnM0fE2RYkzsYzBeQxrlNQMY4xzwqXPgJ3AbSLiEpHjgZOBd4MbMMZUAv8F7haRDLvuLCyvECLSH/gIeMQY83g0d6at0r97GrdOH83Xt57K/edNYGROoMfm0037uPw/SzntoU959utcKmtbf54WRVEURYk3scqDMxi4BpgM7BaRCvvvYtuTMws4Gyue5l/ApcaYjfa6t4vI247mfgGkAXuBF4GfG2P8HpyfAocBdzq20SmTxqQlJ/Kjowbx7q9P5PmfHs1pY3rj7H7etq+SPyxYxzH3f8i9b65n14Gq+BmrKIqiKK1M3IaJtyXayzDxQ2XH/krmfbWDV5btojzIc5MgcPes8fzkmMFxsk5RFKWBlgwTb8voMPH4oXNRdSIG98zgjhlj+fr2U5kzY2zAbOU+A8cclhVH6xRFUQJp7y/gPp8v3iZ0anQuqk5IlxQXlx8/lEuPHcKnm/bx1JfbERGG9w4cbb+/opanvtzOT44ZTN9uaWFaUxRFaX1SU1PZv38/PXv2bPXh3dHGGIPb7WbPnj1kZGQ0v4ISFbSLis7TRdUUtR4vKa7AoZT/+Ggzf35vE64E4bppw7nx9JFxsk5RlM6G2+0mLy+PmpqaeJtyULhcLrp160avXr1ISGjcWaJdVNFHPTgKQCNx4/b6eHbxDgA8PhPQnaUoihJtkpKSGDp0aLzNUNoxGoOjhCRBhLtmjufooVlkZ6Zw9oTA+Vt8PsNTX2xnX3ltnCxUFEVRlPBoFxXaRdUcxZV19MhIDlj28Xd7ueI/S0lOTGDGpH5ccfwQxvfvFicLFUVR2hfaRRV91IOjNEuwuAF4+stcAOq8Pl5bkcf3H/6C2Y9/xVtrCvF4G48cqKrz8OA7G5k4510efGcjVXWaYFBRFEWJHhqDoxwU5x8xgNJqNyt3ldQvW5pbzNLcYvp3T+OSYwdz0ZHWJJ8LVxVw58J11Lq9VLt9/OfL7bywZCd3zRzHzEn92t0ICUVRFKXto11UaBfVofDtzmKe/iqXRasL8fgCz6UUl5CW5KLGY83sG0x6ciKDs9KZd9VR9M5MjZXJiqIocUe7qKKPdlEph8Thg3rwt4sO58tbT+GXpwynp6M7q9ZjKKl2hxQ3YE0Cmru/iqXbi2NlrqIoitJJUIGjtAo5XVO56YxRfHnrKTx4wUTG9O0a0XqJegYqiqIoUUAfL0qrkpqUyIVTB/LWr77Hr08bQWJC0/E1Hq92kSqKoiitjwocJSqICCN6Z5KW1PQp5vWpwFEURVFaHxU4SlQJMWI8gIQQHp631xSyu7R9pmdXFEVR2gY6TFyJGkcO7cGQnunsOFBFVZ23UXlyojAsuwtHDu1Rv2x/RS2/fPFbfMZw4shsHv/JEaQmJTZaV1EURVGaQgWOEjV6Z6by1g0nsHBVAXMWrqPabQ0XT01KIC0pkbtmjWfGxL4BeXBeX1lQP9y8rNqt4kZRFEU5KFTgKFFFRJg1uT+nj83hkY+38OzXO7jk2MFcP20EacmNxcth2RkcN6wnX23dz+ypAxuVv7JsF6XVbn5weH96dkmJxS4oiqIo7RBN9Icm+muL7DpQRVZGMhkpDRrcGMO0P39C7v4qXAnCKaN7M3vqQE4elU2SjjdXFKUdoYn+oo96cJQ2ycCs9EbLlu0oJnd/FQAen+G99Xt4b/0eenVJ5geH92f21IGMzMmMtamKoihKG0Q9OKgHp71QUevhzVUFvLI8j+U7Qmc/njSgGxdMHcjMif3olp4UYwsVRVEiQz040UcFDipw2iNb91Xw6vI8/rsijz1ltY3Kk10JnDmuD7OPGMDxw3s1m3BQURQllqjAiT4qcFCB057x+gyfbd7Hq8vyeH/9HupCJN7p2y2V86cM4IIjBjCkV0YcrFQURQlEBU70UYGDCpyOQnFlHQtXFfDK8l2szS8LWeeoIVlcMHUA50zoGxDArCiKEktU4EQfvcMrHYYeGclcdtwQLjtuCBsKy3hlWR6vr8znQGVdfZ0luQdYknuAWo+PS44ZHEdrFUVRlGiiY2uVDsmYvl25Y8ZYFt92Ko//5AhOG9O7Pg4n2ZXAzIn9AuobYygsrY6HqYqiKEoUUA+O0qFJdiVw1vg+nDW+D3vLa3j923xKqtyNRlh9u6uE8x/7iuOH9eInxwzirPF942SxoiiK0hqowFE6Db0zU7n6xGEhy15Zlocx8MWWIvp1T1WBoyiK0s7RLipFwZr3yj8lVqgpIj7euJd95Y2HoyuKoihtE/XgKArwyMVTKCip5t11u5k6uEdAWVmNm58/vxy31zBtVDYXHDGQU0b3Jtml7weKoihtFRU4imLTr3saVxw/tNHyRasLqXFb+XU+2LCXDzbsJSsjmXMn92f21AGM6ds11qYqiqIozaCvoIrSDH26pnLUkKyAZQcq63jqy+1M/9vnfP/hz5n3VS4lVXVhWlAURVFiTUwEjoikiMiTIrJDRMpF5FsRme4oTxeRR0WkSERKReSzJtrKEpH/iUil3d6Pg8pPFZGNIlIlIh+LiCY7UQ6JaaN78/K1x/LJb0/m+mnD6dstNaB8bX4Zdy5cx1H3fch1z6/g4+/24vVpAk1FUZR4EpNMxiKSAdwMPA3sBM4GXgQmGGNyReQ5rO6yXwIHgMnGmOVh2noRS5hdBUwGFgHHGWPWiUgvYCvwU+AN4B7gBGPMMU3Zp5mMlZbg9Rm+3FLEK8vzeHfdbuo8jaeHyOmawnn29BDDsrvEwUpFUdoymsk4+sRtqgYRWQ3cBawFlgIDjDGh8+s3rJMBFAPjjTGb7GXPAvnGmFtF5GrgcmPMcY76RcDhxpiN4dpVgaMcLKVVbhauLuDVZbtYlVcass6UQd2ZPXUg35/Yl8xUneFcURQVOLEgLjE4IpIDjATWAUcDO4C77C6qNSJyfphVRwJev7ixWQWMsz+Ps78DYIypxPLojCMIEblaRJaJyLJ9+/Yd8j4pnZNu6UlccsxgFlz/Pd678UR+dsJQenVJDqizYmcJt/13DUfe9wFb9lbEyVJFUZTORcwFjogkAc8D82yvygBgPFAK9AOuB+aJyJgQq3ex6zkpBTIjLK/HGPOEMWaqMWZqdnb2we6OotQzMieT/ztnLF/fdir/unQqZ4zNwWVPDwFWosFh2ZHNZl5V5+HBdzYycc67PPjORqrqPNEyW1EUpUMS02HiIpIAPAvUYQkZgGrADdxrjPEAn4rIx8AZwIagJiqA4DG5XYHyCMsVJeokJSZw+tgcTh+bQ1FFLa9/m8+ry/M4e0JfRCSg7ivLdvHfFflceOQAzhrXl9SkBBauKuDOheuodXupdvv4z5fbeWHJTu6aOY6Zk/o1akNRFEVpTMwEjlh35SeBHOBsY4zbLlrdgmY2AS4RGWGM2Wwvm4TV1YX9/zLHNjOAYY5yRYkpvbqk8NMTDuOq7w3FE2Jk1cvLdrE0t5ivt+0n99Qq3lu3mx0Hqqiq89bXqXb7qHb7uO2/a3j8k63Mu+ooememNmpLURRFaSCWXVSPAWOAGcYY57TNn2GNrLpNRFwicjxwMvBucAN2TM1/gbtFJMOuOwvLKwTwP2C8iJwvIqnAHcDqpgKMFSUWiAhJiYGX256yGpbvKAYgMUHI6ZZC7v5AceOkqs5L7v4qlm4vjrq9iqIo7Z1mBY6I/D3o+1VB31+LoI3BwDVYw7p3i0iF/Xex7cmZhTV0vBT4F3CpX5SIyO0i8rajuV8AacBerKHmPzfGrAMwxuwDzgfuwxptdTRwUXP2KUo8yOmayle3nsrNZ47ikmMG0y01mcRmrsjmyhVFURSLZoeJi0iZMaar4/sBY0xWuPL2iA4TV9oCi1YX8rvXVlFRG9qDA+BKEH57xiiuOekwjcVRlHaMDhOPPpG8DwbfRfWuqihxwuMzPPDORqb/7XOe/GI7+yt0hnNFUZRQRCJwgl08moNeUaKEt3FS5JBs3F3OPW+u55j7P+SaZ5fxwfo9eCJdWVEUpRMQySgql4hMo8FzE/w9MSqWKUon48ihPRjSM73RKCo/Ka4EUpMSqK7zUue13jPcXsO76/bw7ro99OqSwnlT+jP7iAGMyGmU+klRFKVTEUkMTi7NeG2MMUNb0aaYozE4SlvBGMPCVQXMWbiOareXGreP1KQE0pISuWvWeGZM7Et5rYc3VxXyyvJdfLuzJGQ7kwZ2Z/YRA5gxqR/d0nR6CEVpa2gMTvSJ21xUbQkVOEpbo6rOwyMfb+HZr3dwybGDuX7aCNKSGztLt+wt55Xlefx3RT77yhvH46S4EjhnQl/+PHsSCQkaPqcobQUVONHnoASOiIwCxgIrjDE7Wt2qGKMCR2nveLw+Ptu8j1eW5fHBhj24vQ3X9bRR2fzniqPiaJ2iKMGowIk+zcbgiMhfgG+NMc/Z3y8FnsLKM9NFRM4zxrzdVBuKokQXV2ICp4zO4ZTRORyorGPBynxeWZbH+sIyZk8d2Kj+i0t2kpSYwNkT+pCeHNMZWxRFUWJCJDE4W4Fpxpid9vc84I/GmEdF5DKsRHvHRN/U6KEeHKWjsq6glOG9u5Diaujecnt9HHv/RxRV1JKRnMhrvziO0X3adSorRWl3qAcn+kQyTDzbIW7GAz2x5pQCeA4YGSXbFEU5RMb16xYgbgA+/W4fRXb+nIwUF8Ozu8TDNEVRlKgSicApFZEc+/MJwDJjjD+aMQlN/Kco7YpJA7tz6/TRDMvO4LwpA3AFzf/w1ppCLnnyGxauKqDGHT6rsqIoSlsmks73l4GXROR/wE3AA46yo4Gt0TBMUZTokJ2ZwrUnDeOaEw+jLkRywJeW7uLzzUV8vrmIrqkuZk7ux+wjBjJxQDedHkJRlHZDJALnVuB24HTgCeCfjrLJ9jJFUdoZItKo++pAZR1fbSmq/15W4+G5xTt5bvFORuZ0YfYRAzn38P5kZ6bE2lxFUZQWoXlw0CBjRXGSV1zFf1fk8+ryPHYeqGpU7koQTh7Vm9lTB3DK6N4k6RTnitJiNMg4+kQyiurS5hoxxjzTahbFARU4itIYn8+wJPcAryzL4601hVSHiMfpmZHMuYf3Z/bUAToSS1FagAqc6BOJwPEBW4DdhA4oNsaYE6NgW8xQgaMoTVNR6+Gt1db0EEtzi0PWmdC/G7OnDmDmpH50T0+OsYWK0r5QgRN9IhE4c4ELgFXAM8DrjlFUHQIVOIoSOduLKnl1+S5eW57P7rKaRuXJiQmcPi6Hnxw9mGOH9YyDhYrS9lGBE32a7Tw3xvwaGAw8CpwH5IrIv0Tke1G2TVGUNsjQXhncfOZovrz1FOZdeRTfn9iXZFfDraTO62PR6kI+2rgnjlYqitLZiShHuzHGCywCFolIV+D3wCcicrox5uNoGqgoStskMUE4aWQ2J43MpqSqjjdWFfDK8jxW55UChJwi4pPv9jJ1SBZdUnR6CEVRokvEdxkR6QZcBFwGZAP3ACujY5aiKO2J7unJXHLsEC45dggbd5fxxeYiRuZkBtTZU1bDlU8vJcWVyNkT+vKn8yc0SjKoKIrSWkQy2eb3sUTN8cBC4GZjzJfRNkxRlPbJ6D5dQ46o+u+KfHwGqt1e8oqrVNwoihJVIvHgLAS+A54HqoEzReRMZwVjzB1RsE1RlA5En24pjMzpwqY9FSG7r17/Nh8ROHNcH1KTEkO0oCiKEjmRCJxnAAP0ClOumQIVRWmWHxw+gHMn92d1XikjcgIn+PT5DP/v3e/IL6kmM9XFjEn9uOCIARw+sLtOD6EoykHRrMAxxlwerkxEJmEFHCuKojSLiDBpYPdGyxdv209+STUA5TUeXvhmJy98s5PhvbtwwREDOO/w/vTumhpjaxVFac802wkuIukico+IvCEifxWRriJymD355hfA3uibqShKR2Z4Thd+e8ZIhvRMD1i+ZW8FD7y9kWMf+Igrn17KO2sLqfM0niC0qs7Dg+9sZOKcd3nwnY1U1XliZbqiKG2USBL9/Qc4HHgXmA7sAUYD84C5xpiiJlZvF2iiP0VpGxhjWJpbzCvLdrFoTSFVdY2nh8jKSGbWZKsLa2zfrixcVcCdC9dR6/ZS7faRlpRASlIid80cx8xJ/bSLS2mTaKK/6BOJwCkAJhtj9orIAGAncJIx5vNYGBgLVOAoStujstbDW2sKeWV5Hku2HwhZJ8WVgM8Y3N7G97H05EQGZ6Uz76qj6J2p3VtK20IFTvSJJMi4izFmL4AxJk9EKjqSuFEUpW2SkeJi9tSBzJ46kB37K3l1eR6vLc+joLRheojaEN1VfqrqvOTur2Lp9mLOmdg3FiYritKGiETguERkGo6JNoO/G2M+ioJtiqIoAAzumcFNZ4zi16eN5KutRfUznHt8TXugNdWOonReIhE4e4GnHN/3B303wGGtaZSiKEooEhOEE0Zk23+9+P3ra5v04gD4jGF9QRlj+mZqPI6idCIiGSY+JAZ2KIqitIj0ZBdJiUJtMwOmNu8p55cvfsuI3l245NjBXHrskJjYpyhKfFEHrqIo7RZv084bvD5YmmsFKG/eW8G2fZUxsEpRlLZATASOiKSIyJMiskNEykXkWxGZbpcNEREjIhWOvz800dYYEflIREpFZIuI/CCo/EIR2WBvZ72InBvl3VMUJQ4cObQHQ3qmk54celqH9OREhvbKoF/3NNLsqR9mTe7XqN5D72/i1eV5lNe4o2qvoiixpdlh4q2yEZEM4Gbgaaxh5mcDLwIT7CrbgSRjTJPOZhFxAeuBx4G/AScBbwCHG2M2iUh/u61ZwDv2dl4BhvhHgoVCh4krSvvEGMPCVQXMWbiOareXGreP1KQE0pISuWvWeGZM7IuIUFXn4dPv9nHW+D4BcTjFlXUced8HeHyGFFcCp47pzcxJ/Zk2OpsUl86HpUQPHSYefWIicEJuWGQ1cBewnMgFznhgMZBpbMNF5D3gG2PMH0TkaOANY0xvxzr7gJnGmK/DtasCR1HaN1V1Hh75eAvPfr2DS44dzPXTRpAWxrPj5PlvdvB//1vbaHlmqouzx/dl1uR+HH1YTxITNDhZaV1U4ESfuAgcEckBdgCTgRosgVOANSLrfeDmUBmSRWQC8DWBAud9oMIY8wMRSQQ+Av4CLAJmAP8ARhljKoPauhq4GmDQoEFH7NixIwp7qihKW6awtJoFKwtYsLKADYVlIev0zkxhxqR+zJrcjwn9u+lILKVVUIETfWIucEQkCXgb2GqMuUZEumBN/bAS6Ak8giVgzgyz7ndYXVQPAdOAN4GP/fVF5Cqs7qtUoA6YbYxZ1JRN6sFRFGXTnnIWrixgwap8dh2oDllnaK8MZtpi57DsLiHrKEokqMCJPjEVOCKSALwAdAVmGWMaRfWJSB+gEOhmjGn0SiUiE4GHgfHAMmAfUGuMuUpETgPmA2cCK4AjgIXAdGPMynB2qcBRFMWPMYZvd5WwcGUBb64uoKiiLmS9iQO6MXNSP2ZM6keOznSutBAVONEnZgJHLL/uU8AQ4GxjTMhXJLv7ajfQ3RhTGkG7XwHzjDH/FJHfAscbY37gKH8d+MIY8+dwbajAURQlFB6vj6+27mfBygLeXbebihBJd0Tg2MN6cvOZozh8UI84WKm0R1TgRJ9IMhm3Fo8BY4DTnOLGDgwuATYDPYC/A5+EEze2B2cT1hD3XwB9sUZnASwFbhWRycaYlSJyOHAC8Gg0dkhRlI6NKzGBE0dmc+LIbO5zj+ejjXtZsDKfjzfuo85OwmMMfLV1P0k6L4SitCliInBEZDBwDVAL7HYE6V0D+IA/Ar2BMqwg4x851r0dOMEYM91edAnwUyAJ+Bw43RhTC2CM+VRE5gCv2p6gfcAfjTHvRXUHFUXp8KQmJXL2hL6cPaEvpdVu3llbyIKVBXy9bT9De2Uwrl/XgPpFFbX8cdEGZk7ux/eG98KlAkhRYkrchom3JbSLSlGUg2VPWQ35JdVMCeqemvdVLncuXAfAqaN78+TlR8bDPKWNol1U0SeWXVSKoigdjpyuqSGDjBeszK//fPLo3o3KS6vcdEtPiqptitKZUYGjKIoSBe6aOZ4FK/N5e+1uzpnQN6DMGMOsR74gNSmRWZP7M2NSXwb0SI+TpYrSMdEuKrSLSlGU6GGMaZQccNWuEmY98mXAsiOH9GDm5P6cM6EvWRnJsTRRiQPaRRV91IOjKIoSRUJlPt60p5wUVwK1nobp0JfmFrM0t5i7Fq7jhBG9OPfw/pw2JoeMFL1NK8rBoB4c1IOjKErsKa9x8966PSxYVcAXm/fhC3ErTktK5PSxOcya3I8TRmST7NKRWB0F9eBEHxU4qMBRFCW+7Cuv5a01hSxYmc+KnSUh63RPT+LsCX2ZNakfRw7JIkEnAG3XqMCJPipwUIGjKErbYef+Kt5YXcDr3+azeW9FyDp9u6Uyc1I/Zk7ux9i+XXUC0HaICpzoowIHFTiKorQ9jDFs3F3O6yvzeWNlAQWlNSHr/f1HhzNzUr8YW6ccKipwoo9GrymKorRBRIQxfbsypm9XfnfmaJbtKGbBynwWrSmkpMqapzg5MYGTRmQHrOfzGfZX1pGdmRIPsxWlzaACR1EUpY2TkCAcNTSLo4ZmceeMcXyxZR8LVhYg0ChZ4IqdxVz4z685fngvLjpyEOdM7Nuovao6D//4aAvPLd7BT44ZzPWnDCc9WR8HSsdCz2hFUZR2RLIrgVNG53DK6JyQ5QtWFuAz8PnmIgb0SAsQOMYYFq4q4M6F66h1e6l2+/jPl9t5YclO7po5jpmT+mk8j9JhUIGjKIrSgSiqqK3/PHNS//rPe8truOzJJWzZV4Hb2xB7We32Ue32cdt/1/D4J1uZd9VR9M5sPPWEorQ3VOAoiqJ0IB77yRHkl1Tz7trdHDU0q3750u3F5O6vDBA3TqrqvOTur2Lp9uKQ3VqK0t5QgaMoitLB6N89jSu/N7TR8lDJBJ1o75TSkdC0mIqiKJ2ExGYUTFWdlz+9s5F/f76N3WGGpStKe0E9OIqiKJ2EhAheaXceqOLeRRu4760NHDO0J7Mm92P6+L6NRmspSltHBY6iKEonwetrvo4fY+Drbfv5ett+/rBgLSeP6s2syf04dXQOacmJ0TNSUVoJFTiKoiidgCOH9mBIz3R2HKiiqs7bqDw9OZGBPdK5cOoAPvpuL19t3Y8/0b3ba3h//R7eX7+HjOREzhzXh2tOGsaoPpkx3gtFiRydqgGdqkFRlM6BPw/OnIXrqHZ7qXH7SE1KIC0pkbtmjWfGxL71eXD2lNXw5upCFq7MZ1VeaaO2Xr32WKYOyWq0XIkMnaoh+qjAQQWOoiidi6o6D498vIVnv97BJccO5vppI5rsdtq2r4KFqwpYuLKAbUWVDOiRxue3TAtIClha5eaJz7cya3J/RuaoZ6c5VOBEHxU4qMBRFEWJBGMMa/PL2FdR0yiT8vylO/nda2sAOHdyP+ZedHg8TGw3qMCJPhqDoyiKokSEiDBhQDegW6OyBSsL6j+P79+43OP14UrUzCRK7FCBoyiKohwyVxw/lB4ZyXy8cS8zJ/VrVD7rkS/J6ZrKrMn9OG1MDhkp+vhRooueYYqiKMohc/rYHE4fm0ON20tqUmA8z8bdZawrsP4+2riXtKRETh+bw6zJ/ThhRDbJLvXsKK2PChxFURSl1QgWNwBLth8I+F7t9lpBy6sK6J6exNkT+jJrUj+OHJJFQoLOF6G0DhpkjAYZK4qiRJtdB6pYuKqABSvz2bSnImSdvt1SmTmpHzMn92Ns364Bo7Q6GhpkHH1U4KACR1EUJVYYY9i4u5wFKwt4Y1UB+SXVIesN792FWbbYGdwzI8ZWRh8VONFHBQ4qcBRFUeKBz2dYvrOYBSvzWbS6kOIqd8h6kwd2Z9bkfnx/Yj+yM1NibGV0UIETfVTgoAJHURQl3ri9Pr7YXMSClfm8t35PyOkkEgQe+8kRnDmuTxwsbF1U4EQfDTJWFEVR4k5SYgLTRvdm2ujeVNV5eH/9HhauLODTTfvw+KwXcRHhiME9AtYzxlDn9ZHi0glAlUBU4CiKoihtivRkF7Mm92fW5P4UV9bx1tpCFqwsID05kV5dAruo1hWU8aN/LWb6+D6cP2UARx/WM05WK22NmCQfEJEUEXlSRHaISLmIfCsi0+2yISJiRKTC8feHJtoaIyIfiUipiGwRkR8ElaeLyKMiUmTX+Sza+6coiqJEhx4ZyVx89GBevuZY/nVp4x6dBSvzKa/x8PKyPF5auisOFiptlVh5cFzALuAkYCdwNvCyiExw1OlujPE01YiIuIAFwOPA6XZ7b4jI4caYTXa1J+ztjQEOAJNbcT8URVGUOJEUYqqHVbsaZjqfNblxBuV31u5mRE4XhmV3iaptStsjbkHGIrIauAtYDmwHkiIQOOOBxUCmsQ0XkfeAb4wxfxCRUcBSYIAxpixSWzTIWFEUpX1ijGHlrhLeWbub3545KkAE1bi9HHHP+1TWeZnQv1v9SKw+3VLjaLGFBhlHn7jE4IhIDjASWOdYvENEDPA+cLMxpijUqmGWjbc/Hw3sAO4SkUuAQmCOMea1EDZcDVwNMGjQoIPdFUVRFCWOiAiHD+rB4YN6NCr7cMNeKu3RWGvyS1mTX8p9b23gmKE9mTW5H9PH96VbelKsTVZiRMwnABGRJOB5YJ4xZiNQBBwJDAaOADLt8lBsBPYCN4tIkoicgdVNlW6XD8ASO6VAP+B6YJ6IjAluyBjzhDFmqjFmanZ2dqvtn6IoitI26NMthTPH5ZDs8OoYA19v28+t/13D1Pve52fPLOPN1QVUhxiWrrRvYtpFJSIJwAtAV2CWMaZRVicR6YPleekWqptJRCYCD2MJmWXAPqDWGHOViNwI/AlI93d3icgbwAfGmL+Fs0u7qBRFUToupdVu3l27mwWr8vlq635CPfYykhM5c3wfZk3uz/HDeuJyiKKqOg//+GgLzy3ewU+OGcz1pwwnPfnQOkC0iyr6xEzgiDWpyFPAEOBsY0zI/Nx299VurKDj0lB1gup/heUN+qeInAq8jQocRVEUJQR7y2p4Y3UhC1fmsyov9COmZ0Yy35/YlxmT+pFXXMWcN9ZT6/ZS7faRlpRASlIid80cx8xJ/Q56viwVONEnlgLncawRTacZYyocy48GSoDNQA/gUaC3MWZamHYmApuwutd+AVwHjDbG1NrdXxuAecD9WDE57wBH2t1hIVGBoyiK0vnYXlTJwpXWBKDbiipD1hEg1FMyPTmRwVnpzLvqKHpntjxoWQVO9IlVHpzBwDVYAme3I9/NxcBhWCKkHFgL1AI/cqx7u4i87WjOHzy8FzgVON0YUwtgd3nNwhqGXgr8C7i0KXGjKIqidE6G9srghtNG8OFNJ/HmL7/Hz04YSk7XwESC4VwAVXVecvdXsXR7cfQNVQ4KnYsK9eAoiqIoFl6fYcn2A/zj4818uWV/k3W7pCTyp/Mncc7Evi3ejnpwoo9O1aAoiqIoNokJwrHDenKgso6Vu0qorNXRVe2VmA8TVxRFUZT2wMGFDyttBRU4iqIoihICr+/QypX4ogJHURRFUYI4cmgPhvRMJz05MWR5enIiQ3tlcOTQxhmUlbaBxuAoiqIoShC9M1N564YTWLiqgDkL11Ht9lLj9pGalEBaUiJ3zRrPjIl9DzoPjhJ9VOAoiqIoSghEhFmT+3P62Bwe+XgLz369g0uOHcz100aQFsazo7QddJg4OkxcURRFiS06TDz6qMABRGQf1izkrUEvrAlElebRY9Uy9HhFjh6ryNFjFTmteawGG2N0pucoogKnlRGRZarKI0OPVcvQ4xU5eqwiR49V5Oixal/oKCpFURRFUTocKnAURVEURelwqMBpfZ6ItwHtCD1WLUOPV+TosYocPVaRo8eqHaExOIqiKIqidDjUg6MoiqIoSodDBY6iKIqiKB0OFTgtRESuF5FlIlIrIk83U/dGEdktIqUi8pSIpMTIzDZBpMdKRC4TkeUiUiYieSLyoIh0qizbLTmvHOt8JCKmsx0raPF1eJiIvCki5SJSJCIPxsjMNkELrkMRkXtFJN++Z30iIuNiaGpcEZEUEXlSRHbY58q3IjK9ifqd+v7eHlCB03IKgHuBp5qqJCJnArcCpwJDgMOAu6JtXBsjomMFpAO/xkqidTTWMfttVC1re0R6rAAQkYvp3FOtRHodJgPvAx8BfYABwHNRt65tEem5NRu4EjgByAK+Bp6NrmltChewCzgJ6Ab8AXhZRIYEV9T7e/tABU4LMcb81xjzOrC/maqXAU8aY9YZY4qBe4DLo2xemyLSY2WMecwY87kxps4Ykw88DxwfCxvbCi04rxCRbsCdwC3Rtqut0oLjdTlQYIz5qzGm0hhTY4xZHXUD2xAtOFZDgS+MMduMMV4sITg22va1FezzY44xJtcY4zPGvAlsB44IUb3T39/bAypwosc4YJXj+yogR0R6xsme9sSJwLp4G9GG+SPwGLA73oa0A44BckXkbbt76hMRmRBvo9ooLwHDRWSkiCRhPcTfibNNcUNEcoCRhL4X6f29HaACJ3p0AUod3/2fM+NgS7tBRK4ApgJ/jrctbRERmYrl3Xo43ra0EwYAFwF/B/oBi4AFdteVEkgh8DnwHVCN1WV1Y1wtihO2wHsemGeM2Riiit7f2wEqcKJHBdDV8d3/uTwOtrQLRORc4AFgujFGJ/8LQkQSgEeBG4wxnnjb006oxup2edsYU4clnHsCY+JrVpvkTuBIYCCQihVT8pGIpMfVqhhjX2fPAnXA9WGq6f29HaACJ3qsAyY5vk8C9hhjmo2x6IyIyFnAv4AZxpg18banjdIVy7s1X0R2A0vt5XkickL8zGrTrAY0m2lkTALmG2PyjDEeY8zTQA86URyOiAjwJJADnG+McYepqvf3doAKnBYiIi4RSQUSgUQRSQ0zTPcZ4CoRGSsiPYDfA0/H0NS4E+mxEpFTsNzB5xtjlsTazrZAhMeqFKubZbL9d7a9/AjgmxiZ2iZowXX4HHCMiJwmIolYo/WKgA2xsza+tOBYLQVmi0iOiCSIyCVAErAllvbGmcewvHszjDHVTdTr9Pf3doExRv9a8AfMwXojdP7NAQZhuS0HOer+BtgDlAH/AVLibX9bPFbAx4DHXub/ezve9rfFYxW0zhC7nive9rfl4wWch/WQLgM+AcbF2/62eKywuqUewYrFKQNWAGfF2/4YHqfB9rGpCboXXaz39/b5p3NRKYqiKIrS4dAuKkVRFEVROhwqcBRFURRF6XCowFEURVEUpcOhAkdRFEVRlA6HChxFURRFUTocKnAURVEURelwqMBRlE6GiBgRGR5vO8JhT4j503jboShK+0YFjqK0IUQkV0TqRKRX0PKVtjAZEifTIkZEbhORz0Is72Xv2/h42KUoSudCBY6itD22Az/yfxGRCUBa/MxpMc8Cx4nI0KDlFwFrjDFr42CToiidDBU4itL2eBa41PH9Mqy5b+oRkXNE5FsRKRORXSIyx1H2QxHZJiJd7e/TRWS3iGQ7mjjbrlMkIv/PnkEZERkmIh+JyH677HkR6e5o+3ciki8i5SLynYicGmy8MSYP+Ai4JKjoUmCeiPQQkTdFZJ+IFNufB4Q6ECIyR0Sec3wfYnuyXPb3biLypIgU2nbda885hYgMF5FPRaTU3pf54Q64oigdDxU4itL2WAx0FZEx9sP6h1iTRjqpxBIM3YFzgJ+LyLkAxpj5wNfA30WkJ9bsyD81xuxzrP8DrJnJpwCzgCvt5QLcjzWp5xhgINa8RYjIKOB64EhjTCZwJpAbZh/m4RA49rqTgRex7jv/wZr7ZxBQDfyj+cMSdjseYDhwOHAG4I/fuQd4D2tG7AHAwwe5DUVR2iEqcBSlbeL34pwObATynYXGmE+MMWuMMT5jzGos4XCSo8p1wClYk0u+YYx5M6j9PxljDhhjdgJzsbvEjDFbjDHvG2NqbUH0V0e7XiAFGCsiScaYXGPM1jD2/w/IEZHj7O+XYk2gus8Ys98Y85oxpsoYUw7cF2R7RIhIDjAd+LUxptIYsxd4CKsrDMCNJaL6GWNqjDFftHQbiqK0X1TgKErb5Fngx8DlBHVPAYjI0SLysd3NUwpcC9QHJhtjSoBXgPHAX0K0v8vxeQeWxwYR6S0iL9ndPWVYnqNedptbgF9jeXT22vX6hTLeGFNlb/9SERGsGZnn2dtIF5F/isgOexufAd39XUstYDCQBBSKSImIlAD/BHrb5bdgeaSWiMg6EbkydDOKonREVOAoShvEGLMDK9j4bOC/Iaq8ACwEBhpjugGPYz3MARCRyVjdTi8Cfw+x/kDH50FAgf35fsAAE40xXYGfONs1xrxgjPkelrgwwJ+a2I15wIVYXqhMwO9FugkYBRxtb+NEv9kh2qgE0h3f+zg+7wJqgV7GmO72X1djzDjb1t3GmJ8ZY/oB1wCPtuXh8YqitC4qcBSl7XIVcIoxpjJEWSZwwBhTIyJHYXl7ABCRVCzPy+3AFUB/EflF0Po328G+A4EbgPmOdiuAEhHpD9zsaHeUiJwiIilADVbsjLcJ+z8HSoAngJeMMXWObVTb28gC7myijZXAiSIySES6Abf5C4wxhVgxNn8Rka4ikmAHSZ9k2zvbEbxcjCXImrJXUZQOhAocRWmjGGO2GmOWhSn+BXC3iJQDdwAvO8ruB/KMMY8ZY2qxvDD3isgIR50FwHIsAbEIKxAZ4C6swONSe7nTe5QCPAAUAbuxuoJub8J+g9W9NpjAbra5WMPei7ACqt9poo33scTXatve4FiiS4FkYD2WiHkV6GuXHQl8IyIVWN6uG4wx28NtS1GUjoVY9yBFURRFUZSOg3pwFEVRFEXpcKjAURRFURSlw6ECR1EURVGUDocKHEVRFEVROhwqcBRFURRF6XCowFEURVEUpcOhAkdRFEVRlA6HChxFURRFUTocKnAURVEURelwqMBRFEVRFKXD0eYFjoikisgSEVklIutE5C57eZaIvC8im+3/PRzr3CYiW0TkOxE5M37WK4qiKIoSD9r8XFQiIkCGMaZCRJKAL7BmPz4PazblB0TkVqCHMeZ3IjIWeBE4CugHfACMNMboLMKKoiiK0klo8x4cY1Fhf02y/wwwC5hnL58HnGt/ngW8ZIyptWcO3oIldhRFURRF6SS44m1AJIhIIrAcGA48Yoz5RkRyjDGFAMaYQhHpbVfvDyx2rJ5nLwtu82rgaoCMjIwjRo8eHc1dUBRFUZR6li9fXmSMyY63HR2ZdiFw7O6lySLSHfifiIxvorqEaiJEm08ATwBMnTrVLFu2rDVMVRRFUZRmEZEd8baho9Pmu6icGGNKgE+As4A9ItIXwP6/166WBwx0rDYAKIidlYqiKIqixJs2L3BEJNv23CAiacBpwEZgIXCZXe0yYIH9eSFwkYikiMhQYASwJKZGK4qiKIoSV9pDF1VfYJ4dh5MAvGyMeVNEvgZeFpGrgJ3AbABjzDoReRlYD3iA63QElaIoiqJ0Ltr8MPFYoDE4iqIoHZyqKkhPj7cV9YjIcmPM1Hjb0ZFp811UiqIoinJIfPIJZGTARx/F2xIlhqjAURRFUTo2n34a+F/pFKjAURRFUTo2GorRKVGBoyiKonQOJFSaNKWjogJHURRFUZQOhwocRVEUpXOgXVWdChU4iqIoiqJ0OFTgKIqiKJ0DjcHpVKjAURRFURSlw6ECR1EURenYaOxNp0QFjqIoitI50C6qToUKHEVRFEVROhwqcBRFURRF6XCowFEURVE6NhqD0ylRgaMoiqJ0DjQGp1OhAkdRFEVRlA5Hmxc4IjJQRD4WkQ0isk5EbrCXzxGRfBFZaf+d7VjnNhHZIiLficiZ8bNeURSlDfHKK1BWFm8r4od2VXUqXPE2IAI8wE3GmBUikgksF5H37bKHjDF/dlYWkbHARcA4oB/wgYiMNMZ4Y2q1oihKW2LDBrjwQjjvPHjttXhboyhRp817cIwxhcaYFfbncmAD0L+JVWYBLxljao0x24EtwFHRt1RRFKUNU1lp/d+xI752xBONwelUtHmB40REhgCHA9/Yi64XkdUi8pSI9LCX9Qd2OVbLI4QgEpGrRWSZiCzbt29fNM1WFEVRFCXGtBuBIyJdgNeAXxtjyoDHgGHAZKAQ+Iu/aojVG3W8GmOeMMZMNcZMzc7Ojo7RiqIoiqLEhXYhcEQkCUvcPG+M+S+AMWaPMcZrjPEB/6KhGyoPGOhYfQBQEEt7FUVR2iydMdC2M+6z0vYFjogI8CSwwRjzV8fyvo5qPwDW2p8XAheJSIqIDAVGAEtiZa+iKEqbRONP9Bh0MtrDKKrjgUuANSKy0l52O/AjEZmM1f2UC1wDYIxZJyIvA+uxRmBdpyOoFEVRbNSboXQS2rzAMcZ8Qei4mreaWOc+4L6oGaUoitLeUO+F0slo8wJHURRFaUU6owenne/z8uXLe7tcrn8D42kHoSUxwges9Xg8Pz3iiCP2hqoQc4EjItMArzHms1hvW1EUpdOiHpx2ewxcLte/+/TpMyY7O7s4ISGhfau1VsLn88m+ffvG7t69+9/AzFB1oq4EReRTETne/vw74CXgRRG5PdrbVhRFUYJo596MTsr47OzsMhU3DSQkJJjs7OxSLK9W6DoxsGM8sNj+/DPgZOAY4NoYbFtRFKXzsm9fQwbjduq9aFXar7hLUHHTGPuYhNUxsRA4CYARkWGAGGM2GGN2AT2aWU9RFEU5FHr3hsMPj7cVihIXYiFwvgD+AfwZ+B+ALXaKYrBtRVGUzs3mzfG2oO2gXqyDZvbs2UOysrImjRgxYlyocp/Px+WXXz5w0KBB40eOHDn2iy++SI+1jcHEQuBcDpQAq4E77WWjgb/FYNuKoiiKk/bbTaPEkSuvvLJo4cKFYdXyK6+80m3btm2pubm5ax977LEdv/jFLwbF0r5QRG0UlYjci5Wr5mtjTEBAsTFmUbS2qyiKooRAvRfKITB9+vSK7777Ljlc+YIFC7pffPHF+xMSEjj11FMry8rKXDt27EgaPHiwO5Z2OonmMPFK4E/ACBH5EEvsvGOM2R/FbSqKoihN0Rk9OB1on29+ddXATbvLW7X7Z2SfzKr/d8GkXYfSRmFhYdKQIUPq/N/79u1bF2+BE7UuKmPM/caYE7C6oxYCZwJrReQrEfm9iEyJ1rYVRVFam399to1Jd70XbzMOHvXg6DGIIiaEiJQ4H++oJ/ozxpQA84H59sSZRwLnAP8UkX7Ab4wx86Nth6IoyqHwxttvcnHCGuCMeJtycPgfQB3Im9EZOVRPS7To16+fOzc3t74Lq7CwMHnQoEFx895AjFM+G4slxpg7jTFHAocDK2Jpg6IoysEwL+1Opme8Hm8zDp7OLGw6877HiJkzZ5Y8//zzPX0+Hx9++GFGZmamN57dUxBlD46IHA4Mw4q/qQV+bn//0BjzpjFmLxByDglFUZS2xBV9csh1ufjWmLi73g8K9eAoh8CMGTOGLl68OLO4uNiVk5Mz8dZbby1wu90CcMstt+y78MILSxctWtRt8ODB49PS0nz//ve/c+NsclRHUV0F3AsY4Fbgv8BAe5svisgNxpinorV9RVGU1uS0DxM4dZUX76U+XK7EeJvTclTYaAzOIfDGG29sb6o8ISGBZ599dmes7ImEaHpwbgZOAgTYAPzKGPMVgIi8CjwIqMBRFKVdML7gcL6deBxDvW4VOIrSDoimwOlrjNkEICLVwNeOsveBwVHctqIoSquyZvS5+Dz51NXVkpqSGm9zWo7PF28LFCWmRDPIuFJEkuzPT5vAMWRpQERXm4gMFJGPRWSDiKwTkRvs5Vki8r6IbLb/93Csc5uIbBGR70TkzFbbI0VROi11Zc/iqfqA2pqKeJtycGgMjtLJiKbA+RAYDmCMuS6o7PtYUzdEgge4yRgzBmsW8utEZCxWXM+HxpgR9rZuBbDLLgLGAWcBj4pIO/QnK4rStrDeyWqrauJsx0GiwkbpZEQz0d8lxpgNYYo/Ai6IsJ1CY8wK+3M5VjxPf2AWMM+uNg841/48C3jJGFNrjNkObAGOOqidUBRFCaK2ojzeJhwc6sFROhkxzYPjxxizzxjT4tnERWQIVu6cb4AcY0yh3V4h0Nuu1h9wJkLKs5cFt3W1iCwTkWX79u1rqSmKonRSaior423CwdGZhU1n3vdOTNQFjohMEpGPROSAiNTZf24RqWt+7YB2ugCvAb82xpQ1VTXEskZntzHmCWPMVGPM1Ozs7JaYoihKp8S6tdRVV8XZjoNEPTg6TPwQmD179pCsrKxJI0aMGBeq/Ntvv02dPHny6OTk5Cl33HFHjrPsnnvu6T1ixIhxw4cPH3f33Xf3DrV+NIiFB+dF4EvgRGCM/Tfa/h8RdrDya8Dzxpj/2ov3iEhfu7wvDQkD87Dy7fgZABQcyg4oiqL4b5d11dVxtuMg6czCRjlkrrzyyqKFCxduDlfeu3dvz9/+9red11xzzR7n8qVLl6Y+88wz2StWrNiwYcOGde+88073NWvWpETf4tgInD7AHcaYtcaYrc6/SFa25696EthgjPmro2ghcJn9+TJggWP5RSKSIiJDgRHAklbZE0VROjHW27+7vQscFTrKQTB9+vSK7OxsT7jy/v37e0466aSqpKSkgBNszZo1aVOmTKnIzMz0JSUlcfzxx5fPnz+/e9QNJgaTbWIFAP8YeP4g1z8euARYIyIr7WW3Aw8AL9sZk3cCswGMMetE5GVgPdYIrOuMMd6DN19RFAX874Pumto423GQdGZh05H2/fXrBrJ3fXqrttl7bBXnPhKVSTwnT55cfffdd/ffvXt3YkZGhnn//fe7TZo0KSaBbLEQOA8AX4vI7UCA68oYc0pzKxtjviB0XA3AqWHWuQ+4r4V2KoqiNIEtcGp1mHi7RWNwYs6UKVNqbrjhht2nnHLKyPT0dN/YsWOrXK5YSI/YCJxXge3A/4B26ttVFKWzIyIYA1sKijkj3sYcDJrJuGMQJU9LNLnxxhuLbrzxxiKA66+/vv+AAQNaNMjoYImFwJkM9DTGxGSHlCiwaROMGgUbNsDo0fG2RlHihOXB+W5nizNctA00Bqdz73scyc/Pd/Xv39+zefPm5EWLFnVfsmTJxlhsNxYC53NgLLAyBttSosGLLzb8v+uu+NqiKHHD6t6YkbA4znYcJPpwb5JnPlrJcWMHM7xPj+Yrd0JmzJgxdPHixZnFxcWunJycibfeemuB2+0WgFtuuWXfzp07XUceeeTYysrKRBEx//znP3M2bNiwNisryzdz5sxhJSUlLpfLZebOnbszOzs7JnGxsRA424H3ROR/NI7BuSMG21daC71BKp0YQTBAXmJyvE05ONSDEzYGx+f14f5sFh98NpLhc96PsVHtgzfeeGN7U+WDBg3y7NmzJ+QUTMuXL/8uOlY1TSyGiacDi4BkrPw0/r8BMdi20hpoYJ7SQfB4fby9phBzUA9563a5KrF1B7A0x7VPfsLR/ze/5SsG72NnFjbNUOuuoXrbz0le2yjpvdKOiboHxxhzRbS3oSiKEgnPvreYQV/dzgfuf3H6lBEtXNsS+qetjK3g/2X+5SSnVwA/bNmK4QSOCp1G1FRUUFv7BbWutHiborQicZmLSmmn6I1Raeckb/8ztw0oonrTvOYrN8ISNnn9v9e6RjXD71K7MKcup/mKTl5/HW65JXBZZ75+m9n3Gv/0G0YH+nYkYjMYXWnf+LuoOvMNUukQ7N1WyCMf92bFRZtavK5IEhiodsU20d+t/xtBTWpWy1b6wQ8aL1MPTtju9trqihgbosQC9eAoitJpyNp9OF9PvpjUnd1bvG6CdAXAePc2U7N1WT7qeNYO6HXoDXVmYdMM7ir13HRE1IOjKEqnoRJwV/yP2oTBLV43ua4MdyIkeMpb37Am8FR/0joNqcAJS11NO81OrTRJTDw4InK6iDwpIm/Y36eKSLPTNChtBO2iUjoIXmN1L3m97havW+eyzn8f7XQuqs6cybiZe1dtjXpwmmP27NlDsrKyJo0YMWJcqPLnnnuu+8iRI8eOHj167Pjx48e8++67Xfxl99xzT+8RI0aMGz58+Li77767t3/5NddcM2Do0KHjRo4cOfb0008fVlRUlAjw3XffJaempk4ZPXr02NGjR4/98Y9/POhgbI66wBGRXwKPAZuBE+3F1cC90d620kroMHGlg2BlsgFouVg39t3S116TsmsMTth7maeunYrWGHLllVcWLVy4cHO48hkzZpRt3Lhx/caNG9c/+eSTuddee+1ggKVLl6Y+88wz2StWrNiwYcOGde+88073NWvWpACceeaZZZs2bVq3adOm9cOHD6/5wx/+0Mff3sCBA2v97b3wwgs7D8bmWHhwfg2cZox5APC/QmwERsVg20oIPvniTZ5/4YF4m6EoccDvjWy5aE+s1wVtU+Ds/m4bT177G2rDzXbemYVNM7hrVeA0x/Tp0yuys7M94cq7devmS0iwJEV5eXmC2GJyzZo1aVOmTKnIzMz0JSUlcfzxx5fPnz+/O8B5551XlpSUBMCxxx5bmZ+f36pZNGMRg5MJ+CcH819hSbTVu0QnYOvf36XW1QN+3MIV9QapdBDMQXhwfH5xhMEYg8/rpWRPIT37D2xd4w6SV+65jzr3Ht6b+zAzQlVQD07YffeEE4VtkD98+YeBW4q3tGq2yeE9hlfdc/w9hzyJ5zPPPNP9zjvv7H/gwIGk1157bTPA5MmTq+++++7+u3fvTszIyDDvv/9+t0mTJlUGr/v000/3uuCCCw74v+fl5SWPGTNmbJcuXbz33HNP/llnndXioW6x8OB8BtwatOxXwMcx2LYSghLZTrV3ReQraBdVu6e02s11z6+guFLfK4CD6aEKwOfzMv/39/D0b37O3q07WsemQ8Tntab3KS7KD12hMwubZvC49bpoDS699NKS7du3r3vppZe23HHHHf0BpkyZUnPDDTfsPuWUU0ZOmzZtxNixY6tcrkDfyu9+97s+iYmJ5tprrz0AMGjQIPf27dtXb9iwYf1f//rXXZdffvlhBw4caLFeiYUH55fAGyLyMyBTRL4DyiD0S4aiKK3Pog8+YO7mi3jh3We57LyZ8TYnfrSSVjdew57crQBs/Pwjeg9rOwnbfeG639SDE/ZlzduOBE5reFqizfTp0yt++tOfphQWFrr69u3rufHGG4tuvPHGIoDrr7++/4ABA+oP+MMPP9zz3Xff7f75559v8ndxpaWlmbS0NC/ACSecUDVo0KDatWvXpp544olVLbEj6h4cY0whcCRWnvEfA5cBRxtjdkeyvog8JSJ7RWStY9kcEckXkZX239mOsttEZIuIfCciZ7by7nRuOvONsZ3jO7CAadkD8e1/Od6mxBWxFc6hnspeT0Mowv6SokNrLEI8nrDhDxb+fUpsRuAojfDUtXxUnRLI2rVrU3z2SL0vvvgi3e12S05OjgcgPz/fBbB58+bkRYsWdb/qqqsOALz66qtd586d2+ett97akpmZWT/Mr6CgwOU/39evX5+cm5ubMmrUqBb3I8YkD46xZrb7xv5rKU8D/wCeCVr+kDHmz84FIjIWuAgYB/QDPhCRkcaYmEzN3mHRLqp2T1nuLuYu6MVXZ7b5l78oc/CjqJz4nGIjRrqhtqoSV9duzdZLSEgMXdCZPTjN7HOz4lFhxowZQxcvXpxZXFzsysnJmXjrrbcWuN1uAbjlllv2vfjiiz3mz5/f0+VymdTUVN+zzz67ze+RmTlz5rCSkhKXy+Uyc+fO3Zmdne0F+M1vfjOorq4u4ZRTThkJMGXKlIoXXnhh53vvvdfl3nvv7Z+YmGgSExPN3Llzd+Tk5LT4OR51gSMiuwh9C6gF8oD/Ao8ZY0KeYcaYz0RkSISbmwW8ZIypBbaLyBbgKODrFhuuKB2IjIIBfD3lBLoWdPbQN/8oqkNrxSlwJEYKp7qygowmBY5lh4R7IemMwgYo3LqV5Px8ejZRx9eOuqjixRtvvLG9qfL77rtv93333ReyZ2b58uXfhVq+c+fOtaGWX3755SWXX355SYuNDCIWQcZ/B4qBu4CfAncD+4H/APOxAo7/eBDtXi8iq+0urB72sv40jNgCS0D1D7WyiFwtIstEZNm+ffsOYvNtmzXvfMLWxd+2TmOa6K/dU+NJoq78Jap9nXe25AOVda2lb/B4Yt+lUVVaFlG9Zj04nYwXbr+BbSvszoOwMTjq5O+IxELgXA5MN8Y8aYx5zxjzb+D7wMXGmMftzz9qYZuPAcOAyUAh8Bd7eaizN+RVbYx5whgz1RgzNTs7u4Wbb/u8958/8/pDf4i3GUobwWtnZfB6O2dKeo/Xx+X3Po7vkLL5NtxKArqoWityuRlqq0LEVxoD998P+fmOFD9hhExnzmTcDF7touqQxELg9AWCx69XYsXIAGwCurekQWPMHmOM1xjjA/6F1Q0FlsfGmZRiAFDQUoPbG5VV1ewvKY3+hjrpG2BHICHBDq6NVcBIG8PjruUXWfeTKPaD7BAPg9uZ+TZGwqGmslHqEFi3Dm6/HS68sCHExhdm5zpxDI40s8/Gqx6cjkgsBM4bwAIROU1ERovIacBr9nKAY4HcljQoIn0dX38A+PvxFgIXiUiKiAwFRgBLDsX49sC8q3/B/Gtub/F67roI+501yLjdI4nWpW464cMNoM5dy/INPfB5WudcdjvmsvL6YvNwrK0O4cHxP5grGt4hfd4w3ohO+tsHEOYYqAenYxKLUVTXAHOAf2J5bQqAV7BicQC2AeeEW1lEXgROBnqJSB5wJ3CyiEzGeg/LtbeBMWadiLwMrAc8wHWdYQRVtfvgYohqqipISs5qZWuUtkiCy/8u0zkfcp6aanqZOXgTVoF3P4c8iqrOXd8x5XXH5uFYF2pCyIAHtvXZhPModWIPTj1h9t3n6fCPiU5JVAWOiCRiCZurjTHB2YwBaC4fjjEmVHzOk03Uvw+4ryV2dlZqqirJ7N4CgdOZb4ztHL/AMXTOOIy6uloqqhyZJg7xVPZ4GryfsXr7b1LgOLysPk8zAqczE+YYGG/nvC46OlHtorK9J2dAJ72rtnHqaiJMCqldVO2eaHZRLXl5ATtXr2/1dlsTb1B37KEeBa/Hhz+q18RI4HhqIutStkITQxYE/lfqObTg887B7Nmzh2RlZU0aMWLEuFDlb775ZmZmZubk0aNHjx09evTY3/72t/WhJPfcc0/vESNGjBs+fPi4u+++u7d/+W9+85t+vXv3nuhfZ/78+c0nemoBseiiegi4S0TmGGM02UAboqYqxBuh0jEx/iDj1nfFf/7av4Bkbpr/31Zvu7Vo7bmGAjw4MQpQDTnjtS1WrIlAbeES7lntFzad+WEe1oOjXVTNceWVVxbdcMMNe6+44oqh4epMnTq14uOPP97iXLZ06dLUZ555JnvFihUbUlNTfSeddNLIH/zgB6UTJkyoBbj22mv33H333XuiYXMsgox/CdwMlInILhHZ6f+LwbaVJnBXt1Dg6Jtf+yXqv13bfnfxBMfJHMThcK7i7JaKVfyGt4npBPJ2N/T0+8I9rP3nQGcOqA0Xg9OZRV+ETJ8+vSI7O7vFJ8+aNWvSpkyZUpGZmelLSkri+OOPL58/f373KJjYiFh4cH4Sg20oDsIGGQZRG6pPPxTaRdXu6ayjp/z43MHi4BCDjH0OgROj+A1vE8kFk5IONOxRuN/av7wzeyvCHZt2dEgKbv+/gbWbN6e3ZpspI0ZU9fvjfYc8j8u3337bZdSoUWNzcnLcf/3rX3dNnTq1ZvLkydV33313/927dydmZGSY999/v9ukSZPqcx48+eSTvV966aWekyZNqnr00Ud3+adxaA1iMdnmp+H+or3tzkpVVXDaodC467SLqiOxu7SG/RUtno+uU+DxBoqDg5M3DWt5axvai1WAqi+U58V+YHc9kFo/qitcCI4KnPD4YjTUvyNz3HHHVe7YsWP1d999t/66667be/755w8HmDJlSs0NN9yw+5RTThk5bdq0EWPHjq1yuSzfyo033rh3x44dazZs2LC+T58+7l/84hcDm9xIC4nJZJv2kO4TgF440n4aY+6IxfY7G+UlByKq56mNMN28TtXQLvjdn/5KhUnltft/06iss3twPHWB4uBQfZLObqBIPaaHSsiuJ3vbFakZeE1Z0/b4l3dmgRPuOgiXHLEN0hqelmiQlZVVf+L98Ic/LP3Nb34zqLCw0NW3b1/PjTfeWHTjjTcWAVx//fX9BwwYUAcwcODA+gvz+uuv3/f9739/RGvaFHUPjohcDXwJnAL8DpgA3AQMj/a22zJvri7g253FUWm7sjiydj2hghaVdsvU3o9zbvZfQxfW39hb90Yed+F04omQGGbuJQdeT2SjqLYUHuDPz7yCpxmvjDcOAscbKtbHb4epxN/PUhcugad6cMIHGcf7PO4A7Ny50+WPZfr444/TfT4fOTk5HoD8/HwXwObNm5MXLVrU/aqrrjoAsGPHjiT/+i+99FL3UaNGtWq3Qiw8OLcAZxljPheRYmPMD0RkOnBRDLbdZlnw0r8oNFm8ef8vW61Nj8eDy+WisrwkovohR2Uo7ZYJ8/tzoIsXGjtwouZ8q4k0jitafP55RNUinUzx3acvYV/xVtZtGMqk8VPDt+foLjLe2DwcQ2YoDiFWvJ4w17UGGYcXOO3IgxMvZsyYMXTx4sWZxcXFrpycnIm33nprgdvtFoBbbrll33PPPdfjqaee6p2YmGhSU1N9zzzzzLaEBMuHMnPmzGElJSUul8tl5s6du9MfZ3PDDTcMWL9+fRrAgAED6v7zn//saE2bYyFwehtj/Hchn4gkGGPeFpHnY7DtNsuVXR8h2+vDGmTWOny+fhcnTxhCdUWIOWtC4G4UeBkG7aJqF6we+1NEksOURue3q6mIMJdSnAk7fUEQaSv70DflcApWftFY4DgOobO9WD0cfaGCa2yBYxydboZeoRtQD05YYuWFa8+88cYb25sqv/322/fdfvvtIdPqL1++/LtQy19//fUm2zxUYjFMPE9EhtifNwGzROQE2vq40ijzcE02v0zp06pt7n/tfN5ZvJraivKI6gcnP1PaN+6K/1FXPj90YZTu39VVZdFpuJXxBgUZh9N7ZVKHt3YllflNX0NWPIydWyhGAseE6KLanl/UaJnPvTVMAypwwo8wi60ZSmyIhQfnQWAM1pxRdwOvAsnAr2Kw7TbLte+PI7W2BH7bem2apePJy36QId1Piai+z6MCp7NgIonB2bYN+vaFtLSI2nzozz9F9n3HoD1uSjKsUasPzfkRKd368osbw8QCxYlQ80W9vHQX503pjyvR+Z5nJ84L9SB05NLzOmJ0YhW/EcrLsOW/NxI261qjBvQprl1UnYuoCxxjzNOOz2+LSA8g2RgT2VjmDsq3I49DErpxVCu2uTPzAF2/HYD7uMjiIiKNS9Auqg5Ac7+dMTBsGJx9NixaFFGT2V92pSz9TGZ/+oeGZjal422DSUUaCxxD/7dP5ZUDT/GjM7/nWO73yoRweTkOoXF6hGLVRRUi8Llq/3FABDlTjenU16/U63sNMu5MxGSYOICI9Aa6OL8bY7bFavttDU/VB/anK1q1XbfXi7cusngDb6QxOEq7p9nbt7/b4q23Im5zT/ImCE4Q7N1DHVHJun5IBM/PZIxh+YZsXFlzIUDg2OXNiBavp214cLxuKyu+aW7cu9fbqQVOs+ih6ZBEXeCIyFlYs3/3ITD9hAGaH9+ptAyJXLi0eBZkvUG2Wf7+5NyQy5dsP0BhaXXzv10Hj8sIjl8xpOBLmETmsuCAfL+3MmQr9Z+cieFidVmESiiY4Nh2gs9H/6ID7OodIsjY4+mU129w12z5/iIyQ1YMXEc0e3uHIBZBxo8A9wBdjDEJjj8VN1FARCJOHR8yM2roRg/BIiUWuN4PnRh82zPnMWDR0TS8W4R5yHXwocPBYt7ry8fn2UmlJ/Q7XtgZuf3lTkEYKw9OiO1I/TLh2HWbuPCTb+hXFCLRZycVOFaslNSf/bnffhu6ouPYhIrXUtonsRA4PYB/GmN0XoAYIAImwrTjsZokUIk+1Sa01+6TygO8kdur/gYe9hF3sB4c50NzzhwmbtmBqw2eV41GUdmE0zHNDRsOmJwxRrqh0YSQVVXM+mp5/descssb1aU6RB4cjydwFvFOInasKToMCaQCDS918xZ9wO13/JQ6u6vRKR5bPAlxJ2H27NlDsrKyJo0YMWJcqPI//OEPOaNHjx47evTosSNGjBiXmJh4xJ49exIB7rnnnt4jRowYN3z48HF33313b/8655xzzmH+dfr37z9h9OjRYwG+++675NTU1Cn+sh//+MeDDsbmWAicJzmEQBMReUpE9orIWseyLBF5X0Q22/97OMpuE5EtIvKdiJx5iLa3OywPTmQPGBPpQ81/8c+dCztaNQ+TEmWm5l9Flvf65p9nBylwEp0Pzbvu4vQVazlx9YaDaiua+MIm44t8dmlnzYCpGmIlFoLjgtavDywW63aeEEqcBcfgdJK8L3W1Vp4mvwfH790u+ug2xqwuo3h/XqN13HXtI7dTrLnyyiuLFi5cuDlc+T333LNn48aN6zdu3Lj+rrvuyjvyyCPLc3JyvEuXLk195plnslesWLFhw4YN6955553ua9asSQFYtGjRNv86Z599dvH3v//9+jT8AwcOrPWXvfDCCxFE0jcmKgJHRD4Xkc9E5DPgGOBxEdnkX+Yoi4SngbOClt0KfGiMGQF8aH9HRMZiZUgeZ6/zqIh0qq4wQ+SzG3sjnSTQeTP8a9sa/qs0TannfdyVr0ctBichRDBul+qag2ormoRL9Bei0wcIN8LQEYPjjb0Hp1G3WVFDDpwe5RV4EyzbB+9pnBunURdVJxlg4J8Utb6D1v7dUgu+x96UKvbnF1jLHYe2rqaayloP7hhNotpemD59ekV2dnZE/Xcvvvhi1uzZsw8ArFmzJm3KlCkVmZmZvqSkJI4//vjy+fPnd3fW9/l8vPHGG1mXXXZZZBMpRki0goz/3cz3iDHGfOZIFOhnFnCy/Xke8AnWPFezgJeMMbXAdhHZAhwFfH2w228v3PTyIlYPHciXJ/eL2DMTcReV88ao8ThtHo/bjSspqfmKTg5W4IQQTqFET7wJ2+UUZL+IARM6qV7gal4alE2cYnB2767/mOr20M1+mI/PbeyVaCRwOnjMlZ+6uiCxbR+DGmPNV1ld1jhRZW1VNfOvvAq6VnDdY69F3caW8uEzGwYeyK9Ib802s/p3qTr10jGtMolneXl5wmeffdbt3//+906AyZMnV9999939d+/enZiRkWHef//9bpMmTQqI7n/33Xe79OrVyz1hwoT6/tW8vLzkMWPGjO3SpYv3nnvuyT/rrLNanFomKgLHGDMvGu06yDHGFNrbKrSHoAP0BxY76uXZyxphTwJ6NcCgQQfVvXfQRMulPXH7Lr44yQTczN21tSSlpIS2I8JYnYAbY0IsejWVQ6GmooouPboFLGv4CVs3yDhUd0go0RNvwntwgmy1x1s395LgHNFkDnlu8sg48YMP4NVX4YILrAV79waUZ1Y14TnrpB4cty1w6n8hW3wLggGqy0vr6yZ4ffgSE6irraHGsx9a1ZfQeXjppZe6HXHEERU5OTlegClTptTccMMNu0855ZSR6enpvrFjx1a5XIHS47nnnss6//zz64/4oEGD3Nu3b1/dp08f7+eff54+e/bs4evXr1/rnLE8EmIxTPzvWF6VrxzLjgMuNMb8urU3F2JZ6AGfxjwBPAEwderU1r8jX389JCeH7NLxtPLbkzjfmH2BAqe2qjq8wIl0kkD14LQraisrAwTODa++zbeTD+ez4X0wppJNS75g5FFBuV8OOgYnlAcnDq59n69J8d0oBscYhhXsYceAHmHqN70Pez7f0TBGuxUE3dcbd/HZt+v53Y/Chw2O2rgRZs9u2F7QZLnJTYmWjuLBKSyEqiorKWUEuP1dVPZty+fx8dhV1+B/VFSXWx6ckVu3cPpXn/Lvs6dRG+8JZJuhtTwt0eLll1/OuvDCCwPk4Y033lh04403FgFcf/31/QcMGFCfRt/tdvPOO+/0WLJkSX1QWVpamklLS/MCnHDCCVWDBg2qXbt2beqJJ57YogCpWLyO/whYFrRsOfDjQ2hzj4j0BbD/+19l8oCBjnoDgIJD2M7B88gj8NBDIYtqKlsviXNtbQ2JQTk5nDfnmurwE282NxS2HucDSwVOm6euKnAeJZfPx5ErGkbbvPf3pxuvdNAxOI3PoVCiJ+o088AO9siM3lnAuV8uZ+LmwDkAu1RVcuzaTRhP09dGmWxt8P60wu5umX8BXXf8LExp4DX36CM3U1xe2Wifk5uaW87rpbbGIYjaqwenXz8YPjxg0YbCMivXUwg8NYEeHJ+poqoiv35JrT0x8Yhca/6unmXllOwpbH27Own79+9PXLJkSeaPf/zjEufy/Px8F8DmzZuTFy1a1P2qq66qF0ALFizoethhh9UMGzas/qQsKChw+R0B69evT87NzU0ZNWpUiOGBTROLTMaGxkIqMcSylrAQuAx4wP6/wLH8BRH5K9APGAEsOYTtRIWa6tYTONWVVbiC5sVxZmFtSkyFH1kShHZRtSs8TYhaILTH4WA9OCFEckKkwrk1aa5LKcimdPth3708MAbj9CVfMWhPIbvHTArVSui2I7cyLDVrujGg/Cg8v/XicjUeF+H00lZ/toH5G67j2q79AuokNnEMjNvNzrXfMMK/oL16cELw7X9OJt89mJvv/F+jMq/XL/pCe9vqKu1RVvZyn0h94LESyIwZM4YuXrw4s7i42JWTkzPx1ltvLXC73QJwyy237AN4/vnnu59wwgllXbt2DbjgZs6cOaykpMTlcrnM3Llzd2ZnZ9efrM6AZD/vvfdel3vvvbd/YmKiSUxMNHPnzt3h7/JqCbEQOJ8D94rILcYYn4gkAHPs5c0iIi9iBRT3EpE84E4sYfOyiFyFNRHLbABjzDoReRlYj5VE/jpjRQO2KWqqmnkANceaNTBxItx/PzUXnocrIOlYYEBlbVMPu0i7ErSLql3x1l8f5Zp5jeP6u5dXctXbn/DymTMar9SKo6hS4pEoLYT9O/Jy+ebL17nwh79u5MHx2CLCFbQ8xfaCJEa0D63nqapIHcemxM2cVFVG167B3WYSOBwf8BQBj9wfcfue6hrqihyevfbqwQlB5kfD6doj9Kz2nlrrd/T3JgbfversEX9+UW5EqNyvwTeheOONN7Y3V+dXv/rV/l/96lf7g5cvX778u1D1AV577bXc4GWXX355yeWXX17SUhuDiYXAuQF4EygUkR3AIKAQCHGXbYwx5kdhik4NU/8+4L6DsDNmPP/Gh7RwjEsgv/mN9f+22+h9992knjC1vsgYax6dBJ8PX0ICtVXh+5N9kXYlaBdVu6KipmF0jVOcHlZgzRE10nbHB9CKQcY9yyqsB2hLR3JFis8HJSWQldWwLITAWfS7u6n27GW7qw9d8kK/lbuCgo+NfX6nRBSH0XoxOD63lV6kqrQ0hMBpLMQGFea3qH1PeUWgSOpAHpxNPauQxP58vGY70yYEzq3u9liCVRr9Rtax8NTaXVhOr3fZIb6AKm2GqPc3GGPygClYQ7j/H3AucIS9vFOStv/x+s8HNaJq8OD6jwnV1fTdX9JQZqDbvn3c+OrbDMvfTV0TN+rmsrU6jGz4rF1U7Qrnjb1emoYSqa04TBywRvscLJ99BgeaeIu+917o2RP2OCb1DGF/jdfqnh16wY84Y+4jAWXJtoemW0Xgw8w/aWVKEy8G9XWjMDy8sjj0fgd7cFoqqmrKygK9bR3IgwPWJK9bPryl0XJfndUVKUG/lf+389S62bKrsP46SfT5cEfw2yvtg5g8rYwxPmPMYmPMK0Aa0Hj63k7E0MWH13+OWGQ4CRpBMGWz03No6JNrJX0cWriPupomgs4j9eCE6KLakreXJ158JXIvkBIXIh6y3UKBs7XAiuv3P3g3DGqIB6lJcsHrr7eovXrcbjjpJDj++PB1XrNzkxQ6gkFDeiTCexuT7Tw3fQ4UBYkpa53UsOn6g+cLbt1ZDyrKSkIuTwwa1dXs7OFB1JSXUltW3LCgA3lw6lmZ3GiRP1DVL2DqBb8/5qbOzRv//AliH9BEnw9PTQc8Np2UqAscEflURI63P/8OeAl4UURuj/a22ypbshuCCCPNOhxA0MOoV1lgIHFqpfVWWpWagrs2/MiKiL1HIQTOon/PZuuXD5G7a0tkbShxQSJ9+LZQ4Lz3tzOAhhicdUMG1JcV9uyB2bSpRe3VU2w/hDduDF/Hn0PDaXNI+0OrgG4VVRy73uoSSjDG8gZ9/DEAqfYbf1rIbMyGUAKndWJx7FE9ZeUhy1xBOatausUNX36Ct7ikYYHbTWlJEc/96RcRT+3S1nHTpdEyj+2patxFZX33erykbh2D+KzfO9Hr0zn6OhCx8OCMpyH53s+wAoaPAa6NwbbbHmedxfhNDUN23U0N7QxHE+tkFR8AxwXqqQscWRcgaiK9Szq9THYXVZf1k8nZP4LCNSsibESJBwFdVP4JN0M9950PueJi2LatyXbd+dPpv28/P/zEurQ9iYnsHHYYAKUZ6Qc/Z9l+R3xiOC9DYmLj8hAPaQkjcI6xxc2KEUMaFp5yCrzxBpn2qMPU2sYCx58iLnhJ62DnZako56//vIe5TwQGEAd7cHwt9OBUFRYFjm7zeHjxxlvYs2InL95720FZ3NaQpMYviz5b4CQ0+q3soGKPlzpTXn9tuLw+fB71SncUYiFwEgAjIsMAMcZsMMbswpplvFNR6/HCu+9yxvI19ct8ByNwHP3nVX1yAopO/+YLTIXVZorbzfatgXOj1TkEjzmELqrShD0Y337K9umIg7ZMQogYnJCZd51i4Ygjmk2k5nav4aKPG5KG1yW5ePfU03lk1umUZaQhxcXgSIN/oKSMRx/5NTV1zbj/nQInnMiKUOCE8uD0LC1nfG4epelpfHx40KTIM2fissV8Wgu6qFoH61ZcV1VF5gd7yHjfEV+E1Nvlx0uD1/atoyexcWDgkPG6oKHmiT5fYAyOx4Pb1nCVhaG8Ru2QEMrda7/sNfZkWsuDPeiJPp/jvqgDKto7sRA4XwD/AP4M/A/AFjshZoTr2Nwz5xeNltWFeFNsFofAqe7RPbC9RAM+64aV7Pawd+O6gPKAvDiH0EUl9qnjbYMTKwbzy7vu555/vRBvM6KGp4khzRLKY9dckPH2ZkeDNqLO5cJd56EmJZmy9DRrocOL8/Lvr6P683xe/n+/a7ohZzyMM4jYiV/gvPFGw7IQcwqFekBN2mrZ1K2JQNLS9DQyqsLFrjW06feGtk6wsdVuXVUNZQm5lCVsoc6RqbhRDI7j84bBA1g2KnD00NZ+gS8+VSW7AwKVN2zehk/8MUTN2//a2y/x5Et/C1mWX1LNvvIW52A7NELFLoZ4YfN6AruoGoSOve9eHyD1o+cSvV7H8TAs+tvc1rO5nbNly5ako48+euRhhx02bvjw4ePuueee3sF1fD4fl19++cBBgwaNHzly5NgvvviiVefMaimxEDiXAyXAaqz8NwCjgdBXSwcmrWBVo2V1tc1E7N99txUj4MQhcCp79ar/vLVff5Ld7vqbYYrbTWLQm2hlWcPcKxHfmEOOorL+e5qI8WkrVNW8QG3enHibETVqQwSS13qsG3XgKKoQv7fHYwX1fvRR4zL/uk8+CenpDR6ToiJ6lga+9dclufC5rfqlGY0FjvtAEphqqnObSXJZ4Sj3x+MEUWW/lZv/9/8aFs6IKOtE/bWx4Lgp1ubS0gLKvxk3ic0D+tC9rDSEVyhcDE5rYF1P7pqGF4Ydq76t/xw8TDxYulUFTcfS50BJYOvGBAzp37LwUTzefdYXY6WM+OjrJXjDeHWL//066c+tDln20QNTeP2PMR43EmIUWKj8kl5/kHG4JI0+AyL1zh+Xz4fPcc1s/OqDQ7e1g5CUlMRf/vKXvG3btq1bunTphieffLL38uXLU511XnnllW7btm1Lzc3NXfvYY4/t+MUvfhHbiR6DiMUw8f3GmNuNMXcaYyrsZYuMMXOjve22Rlb5lEbLnDe0kNx5p/VW63xjcXRrlfZuEDiVaWl0qamtf1NLqWt8E6iudDyYIu3ID5EHR+pvyG1f4EzafQ1DN10ZbzOiRl2I7pR/X/YznrjrmhDBlQ25XgDIy7OGZd91V+OG/e3++tfWZ//37Gwuf/ezQBtcifWu/XoPzptvWv99Pi5b8DLjt0UwhY6zyzaMwCkqtAKQxXlehoz5aXx7S3W7KerahS0D+gLw8qlnwsCB9dtbPGEy+7t2sYJ6d4WyN7pBxh7HdAoHDuypL0t2Br4GCVeAqtTAEURdbQ9VdbKViyjJ4w2cQqO4u6M5w2sv/ZUtDz3Fs4/cHNK6UlcFBRmhPWr7C3+EZ8fZYfcsKgQIHOt3DuWJ8rqt45ZgQv9WxmcdeX+3baLXh9e0fa90PBg8eLD7e9/7XhVAjx49fMOGDaveuXNnwIm3YMGC7hdffPH+hIQETj311MqysjLXjh07opQQq3mikuhPRP7PTriHiNwdrp4x5o5obD9euOvqqKmsILNHUAIy26X+81cfq188bcU6Pp4yLsAN3SRlZdC9u70h++K+6SbyM5KY8Jo1U0VlWjrptXUk228toTLK1jrekA9mFNXyj97iiD/8Af8N2d3GJ6YDcFe9Q0m8jYgitbWNE5PVePaSuLEXac6fONTP3VTixspKy3Pjp4mUBt7ExPqHblWq7U345z/h1lvB7aZrVSWnLV/Dvw8/tok9IbzAyc+HAQPgrbegtgtE8Is22jNjcHm9eBIb4lPKMrpYsT7FxfXXV60/QWF549gUEWl+ZvYIuPsvd3L4kacz48TvBbTrdbyU1FY2XFvJjms5wWcaJVj0Jiby7n1zOPP/5ljfExJ4dOapJHs8XPPmR6TV1lnJP0VIMAYxHrDTjRoDxR9vplzySF3cD37Zsn3x1iyhRTMgtgYBAseeAT7Ez2H8o8/8XVRB5XU11SRKar0ASvJ6MaZtxyS9+9jcgUW7drRq10+vgYOrzvz5ryOexPO7775LXr9+ffpJJ50U4JItLCxMGjJkSP1F3Ldv37odO3YkDR48OC6Jl6LlwRng+Dywib8OxRNX/Zwnrr08cKF9IQbnu5myJdcqrnMIBI8HlgXNS+q/GTtv9m43DBkCf/5z/RsaQGWq9eacWWW9gQQInLfegnnzqA6YiLHlAqfGDir2j1DxVLd9D05TlFS1b/shfByX11SE7qJyipqmEjdWBgmnZud78jRu/7PPYIU10q46pXGeknp8PvjVr+Dbhm6ZgHPeboNHH8WbEEZohXzCNSxLMAaX14cnMWifXS7Izq6v7/YH6IaIwxmdu4sBe/2B0PZInBYmwjHG0P+rjWx59SbHUvt6cgi8WkcSwiRHQLXL6w2Z38iT1TBuQwzUJSdRmZqKAdJr66wkdvb9JMCbYyAh1XrX9fjayfUQIHDsfQnRvebz+KdqMAFV68tNGT7jrReMSR0xP1ArU1pamnDeeecNe+CBB3ZlZWUFXIyhrgWJY/b7qHhwjDE/d3y+IhrbaJOIAD5qKyup7xGvq4PUVOqqq0gJrm9M4DDxO+6A+++H1athwgRrWdeu1o3+wAEYagcSOtLg19nnU3m3blSmWaK+mz2BXIrzJnDOOQD4nmmYoyji+7JDnNXfKETAgKfObTWUkAC33AJ/+lOEjcaP/eXVvPPVMrqmJ7Hlk5s445JXGDekX/MrtlHc1Q1ewKvf+AC3y8V/pp8M+EJ3UTm/NOfBgYYTZf16yAkMXnUnJvLt8MEA+Gg433LPncWQ1xdYyfjskVF1Sa7w28vLg4cfDrTLKXCSbXFUV4c3ofFklIDl5ezWrf7rERvXcYIjjiXR68Pl8Vp22ISKQ6v38DQSOIazv7Hm7v3LhedwsB4ct9tNiasSV9GRjqXWcfHVNYhIZ5LOZKfA8flCChxxeKaSbDGalNyHmuQk0mrrSPQZPK5EUjyewGHTxtSv6zPtJMOxfW+zAuxtoRmimj/HT3OP2HoPTogcOO7aWpJSGt2940ZLPC2tTW1trZxzzjnDZs+efeCyyy4rCS7v16+fOzc3t/5NprCwMHnQoEFxO6likehvrIhcIyK32f/HRnub8cJ/7y4/sK9hoR23UFvSeDi1y+vFU1NnXazHH2+JG7Dc8X783VLPPdewzCFwvB4fr554FO9cejEVtgcnzXZzp7g9DQ8n27i0tesb2on0/hwil4rU35A9DQ9CZ9BnW+Xdd3n1b5eTu/A2dr44B9fG7/Htaw+0uJkty5bw14t+yKfPPN36NrYQZxxXZnUtWeX+N38TOEw81EtsU16ZYA/OCSfAyJEBi17/3lQ+nzTGatc0CK0N08+yPCOlpfCdNc9eek1teFWdmNj4+8MPN3QTRSJwgkZdjd8amIRy9M4C+h0oCeii8pkSnrz+Z/XfDU17cPxc8Mli6kfi0LLEcFUV1ogvjzfXsdS6nroW7qa7/fs5f1dnDE5WWQWjdjaeW8uV7ILrr6//fsVDT5LaM42q1BQyampIMD7ctvfKmRPH+Ay++jm52kkOGFvg1DoHaYQw3efx0q/oAOn2BMfOYOOhBXtwebx4zf56D05yCA/OgofawX0tBvh8Pi666KLBI0eOrJkzZ07IgKyZM2eWPP/88z19Ph8ffvhhRmZmpjde3VMQRYEjFk8Ba4DbgZnA/wGrReQ/Ek+/VbSwd6lsn0PgbLFusp7tuY2qJ3l9uN111gPgq68aCnbtql+Pw+1pHZw5Qerq6gWOz+thR59sart1oyotsFs2wY45AGD4cABStjadwC0kIQSOH6/H1/AQCn5ItTW2boWzzuLUfy0ntag73gMDqfOso3JTy6+/z559HmMqWfn2J61vZwsJH8cVzoPjuPRaInCC2NOjKztzejmWNBzHuupqS5yXlNQP/U51e0ioCWNrsJ3+B80L9vB+f1xMXR2JoYbLQCOBUxs02ac//5Qn6Dwt2VdYn/HW0BCjs2/HFh77zQ8oqyhtZOPg+m6qQGEXCZWljYOn/S8Ms154jqvsc8pTH8BvAmJwfvjJYkbl7SaYhEQXPPwwX48dzvzTppPVLwfj9VGSkU63iiq6V1TVB4CL43c3PoPP4z+mTQscn69lYi5q2L9XnUOEhuoe8Xm9/Oijr+lhzxA+dOKRQCK9D5Ry3hfLOHH1BqBhtvFQHpzda1ueNqEj8v7773d5/fXXe37xxReZo0ePHjt69Oix8+fP7/bggw9mP/jgg9kAF154YengwYNrBw8ePP7nP//54EceeeQgM362DtGcTfxq7KzFxpil/oUiciTwInAN8HjoVdsnkmhnIz3gSPGzYQPs20fWD37QqL7L68VbW9fYbX/11dZ/5wW723FDc7vr32iNfUEmuhKpTgkYsQdAalBitZR9+6BP98btN4UxmK6ZeCorG7nGjc/XMLS3rWtW+404q3gfMBov1gPcHERq9qR06/j7WvhwiwYed2MbfvzBl3w5fjTlaQ1xL6HETpMCxxZOHp8v5I3C18TvvXPpakvglJYGdDV1CRG4a23EcZ6KNJybqakNywDq6ugdbiLOIIFTlxw65qdRDA6wbdN6Ro6bhMFb78FZ/ez/qBrUhVfm3MZVf3608YSXACRiqGHLxtUMHz0xtF1BVEWYs8ftSMqZHMHkmMZrd7P87TFOHWt51RINlHZJZ1ihNXdYQa8sBu07gPGU0r08iZLMDIzPYDxhRGMQ1WVlZHRvAzla6z04DSL85KVfWaMB77yzfpkJmi2+e5/eUJRHpu1Z72F7yxLrY3DsLi3JpEvXLCrLSqjzVOKuqyMpzPnUWTjzzDMrjDHLm6qTkJDAs88+uzNWNjVHNLuoLgF+5RQ3APb3X9vlHQr/PbimtKRh4YED8EDoLhCX14vH6w4vDDyehht/sMDxd1HZD6gElwsTwnVff2O038aTDxSTVVZuLY/UG/3dd0hZOUak/iF58rKvOWPpKiuwz//QauszjdtzGDU8qOzRFwcxHZh/jq9ozCjdUlybtnD4psC3zL4HSjhj2cqAx+Zx9fMvOXa4qclebYFj3KEDTwMCVQk89+rc+ywv5AsvwLp17O/aHYDMsjB5cJwPcKfnxT+nlV+I7dxJ16rANvL62vnG3nsPnn66fnmNo52qjIz6z76Extfb+3/6E//41XkY46kPxDV1ReQcKOG8f78A27eTHOI4iFhe02/eeyf0foXA30UV1FLAt2sXfgB2LF23igrGhuiSAnjrqEn16/qnZZl62mn06tcfgPT0HpR0afDsFmdax+HoDVu56u1PyCott4b3+/zdbU2fzyV79za9c7HCL3AcozgnbvkO5swJqGaCheG33zJ146b6gRhe+55VH2Rsn2eDTxjD1U88Qnq3rhhTwWt/ui8ae6FEmWg+kcYCn4Yp+9Qu71C4jI9TVqzFu8MhYEtLYU3D1Azb+mTXfx6Wvwevuy78QyYvL1Dg+N9qa2vrPTj+VOOuJBcI/OeskwKa6FpVjafOjafEeotOK9rPFe98xo8+/CryR/NbbwEECJyJWzYyYXue9eDxC5y27sGxb3bd7GzOYgTx+Vo8Cgbs2COrlday7qAZcde9nLJyPVM3bg1YbpDQwajO8y0CD07IqR2wsr46vjVp474eVrLKbo5EkwE4H0TJyfDnP1uf//hH67//OgjxgH3t2CmYhAT417/giivq96nG8cbt9OaE6oaoqS2jdk8dUFPfRZXk8XLCmu/oUVoKzzwT0ouSIFbwacni9Y3KwlFXHqrrTwJ+l4yaWtKKrG6wmV8tDVHfYlfvXohYXi5PiLxXx/zoIkq6NIg7v9jx58nJKq+kzrsXT611fI2p4IlbGuJ4jDF4HFmUy/Y7ut/jif1bOAPsQyG1Qcfk6685afVGBpZaAsd/P/NfJ12Tkhk68RjOv+4OeOABzp00Fkhm74Y245RQWkA0BU6iCZNQwF7eKtsWkVwRWSMiK0Vkmb0sS0TeF5HN9v+Y+FQH5+Vx+JYdHPbUvIaFO3YEBCuuGj6YBSeeCsBJqzfirvOEnzxz796GG3tdXUOitYIC6NMHAOP34CQl4fEd4EDXLiwbOZSVw6yRLed/vpSqpcsQe0qFbrstN36vsgp6HdhvtV8a5qEThE/E8n44ughSq6obUvu3dQ+O4zj3KKvg+19+xvWvvxf5nFwOGjKkxh+f7ak4aXXgDNxOQeokQPQ0JXD+9z9LMISRwk4Pjoi/E6uhM+vJC35U/3lXzzSqk5PoecAx15QTp3ioqIAbb2z4XlMTfuJNwONKpDq94SHuD9JPMI4RSQ6Bs7tHw0irkKa4EqlOTmLAvgN08U9FMmcOPYPEWe/iUs758jMSvV5qvEWsa2aC0vrdqWrsxRKsGCUnKfaLQ2oT89UlJ+aQKlYcVEJq4+tv6BETmPGvZ+u/F2d2ocqR28jfRVPnaRBd5Tty6z/Pf+pPPHXlRfXfK4rD/H6xxj+KqolEqS/PvZvSTaGFycht1stAFzsmLNE+x1Nr6zjv/35vVbrtNnKu+xXJiVm4vQfY9O2S1rL+YPD5fC2dYrXjYx+TsG7oaD6RkkRkmoicEuqP1o3/mWaMmWyMmWp/vxX40BgzAvjQ/h51ku15T1L2O2IEvv46oI43ISHgzdfrqQsUOJMmNXyurg68sZeUWN6eXbtgsD00187UmZKRgf93/nTyWFYf1pBmyHvlFSSGeNBd9PYCzNy5VqyEP89IExgRJm/dGTB1REZlpf0QJLBrwcmyZc3mUIkJjuOcU1LK8PxdJHu8pDcTTBuKSGMWoo7bTcZOa9RoSUZgkLkvjMARX4QC56WX4OmnQ0xUaOHv6jswtKejTsP5WpJQxoaln/HIrNNZPXwwu3r3pP+ewtCNBQsYp1guKGhU/vrx1qW+aYAl9ItTHbeTtWuBhukN3j32xPqA41WHDWLFqMNC2+BHhIKePRhWuJeeZRXs79oFgMP93WU2536xjBG7ttdPi/DhHb9n74EmXhZqauC00zjh/B9xqmPCXUpLGb5rJyd/GzhvXJo9HYbfg7ajd9CULcAPH/srOecMp2uml2Mv+EnIzSaNGgVz57LliCOoTcki4YEHWDt0OLUuF1nlltgyJvAa8AddF32wnXJ7uHqvkjJqd4eZHwxrlE2r8qMfBcTTBGBfy7VNJErd9fUS6qoaB2M76V1SxoyjppKSaN+7ysos8eS4LvoePgzw8uHD/2yR+a3M2n379nVTkdOAz+eTffv2dQPWhqsTzSDjvcBTzZRHi1lYAc4A84BPgGZm+Tt0utpdH5nOm0DQW50nMRG3NAgBnzfIg+OYW4rq6sA325ISa6RSXV19enn/6IeMoEk393XvWv+5xyZrmK5PGkYL+Nn58N8YDGy59SaGv/dxk/tnQnRBnbz4y4YvlZWWdyfLkcl582Y48ki44QaYO7fJ9g+JdeusLpUpjukw1q6FCRPoP+1Y8rOzAo7ztG8buhR6lJbCJZfAP/4RkEelKfwBnXHn1VfrP3avDBrWLITuojIRdlHZhA6u9S9P4vd3P8WTN1xJ7YEqhBQMDQ+djx55jBo7wV9Rt0xG5O2mLC+frgP6W2L9gQfg2mtDzi3Ef/5jief8/EZ27u2eaeeisSjNSKd/kR3MfNFFUFZGgtdLUdcurB2YydBdlkCqTA2dzyTpsAKkbBS+/Sl4TBHvTZ1giXkMS0YP58q3P2FQUPdYqt0ldOR3ueT3ysLtLuO1B37Lzx/8V+gDOX8+fPghAJO37uTTSXYv/bRpfN+Z4NDffkUlxuslo6aWrQMHs2jqaHqWVdCluppZX1kvJOnpqZz/k5vgJzc1Wj+AG25g+A034PeLVRw7mappZzM+N4+87CzWDQ3Mu7ruyy+YdPI0vP7f0hgue+9zSrbkwZVX19fzOX4Xd0UVKbYYPCR+9zt48MGG76GmEfF3UdVUMSY3jy39+4RsKjjjcyhG/vYu6+UsLc26595+e8B95Pybfsfci6+gpqqCwu1b6Tt0WMv2pxXweDw/3b1797937949ntjMIdke8AFrPR7PT8NViJrAMcYMiVbbwZsC3hMRA/zTGPMEkGOMKbTtKBSRRrOeisjVWCO9GDSodeYD2zF6FJOWLGkQERMnWkn7HHgSE9jTuy97u3ela2WVNVeKU+A4giEpKgp8cy0ubvCS2A9iv8Dp2qMhtgcAEZ454wQufe/z+kUlXTIcOVIsBuRZ7nzX2s2hd8pxAwv17pDtdFl7PPDoo/D73zcs83ervfJKZAInN9eKwejXwsR748db/50P9PfeA2BEXmEjgZPumCR0yrrV8GG+NZQ+3BtjEA3dWnF+oQrnNSO8BychTAyOOzGxPsgyoH6YGCVL4PhIS07k+sfmsXr9Jna9tYiNSz+sr1NT1RBQW5KRjgDv/OZmLnz5BSvO5v/+zxKiP/954w0caSfCy8+HoERrNUEjWqqcWZLLy8HnI9HnxesfMWX86yWRmtyHmrqGN/s0GcIv7n+TqpoaNmzeyCf3/p6qtFSWTDwJryknOSWdFWMmctKKJfhE2N4nm2GFe+uP1bCC3YzeWcDGwf2p3dlEJtyCwEDhXv4JS0OIG4CE4kqKNm8g2+ejMKcP7iQXu3t2B7o3VDrI1AzHTD0B711z4KbfctbS1Y0EzoeP/5NJJ09D7Ngq/3nUfVtgMHtNZUN3W01JWesIHKe4CYctcIb88UGmLlnF5v5BgzA2WMO/XaEEjj3YgD//2Zpnzb+O/wXOH/9lI1VVZPbqSune/bz2+z9y/fNPtmRvWoUjjjhiL1aqFaUFdAQleLwxZgowHbhORE6MZCVjzBPGmKnGmKnZ2dnNrxAB5f368uX4UdYXkYCunF0zzqYmycW+bl1BLNd6qtsDNbWwYIHTsIbPl1xi3fz87RQXN+Qm6WLdSIwdANi9X+M3mH3du7J4zPD67/4cGJWpKbx71PFAQxxFWrg5pRxv1qE8OPXk5sJZZ8Hf/x54I/e7kAtCjwIJoKrKytY8bRq8+KJ1DMMNCY6EYHttgbN+yPCAxfVHvLmJT53rtJEeqgBBHESdyxW6e8lXzbqP7MkyHYLmQNfwbYXCeug1rD9x7EimXR32ZYpSO8D1hI/ehy+/tN7Uwcp0HOTBKc6z556CwGB7m/pkfDY1yYFCz/PNN6S66xpGydjXVU1KEgjM+vU99XWv/Lf1OT01lSMmTK5ffv0zj3DDM/O4ft5T9Hn4frb0y+GjKYfz+vemUhe0/QmFu0iU3njNPp5+8PbQB6CkBFJSWPITKzapW2VVQObvOlciH08eS36vHtS5EkmuPcCeL6wXlKouYX4b18G/oyb+5qb6e0uyN4MMb3cmn22lszCmgpryckSs45cYJk6toqKhS64yRDLTsJSVNUzGejC43fDBB/T6xDqPR+Q7vOa//S1MmkTXiioSQtl9xx3WtX7DDfDRRw3Lf/Ur6//hh1sZ5P28+y4/ffhhkhNzMAY8Op1Du6HdCxxjTIH9fy/wP+AoYI+I9AWw/8dkbGNiShLl6al+w+BEW2tdeimbLrqAR849A19iAsZAlf1GmlhWanWNhKOmBnrYMdKVlQ05Z+wHm/9B27tv46m9El1DKevbIHwKs7oDsDurG6Z3YLxGWpj5jPwPnuWnntwocVqpf9boxETo39+aWHHfPjjuuAahFmKm67DYGW/ZtAkeesj6vDmMZ6kF+GWO197HA926B5Qbf4UIBI4xBp/P1I+8ivow8aKigG6oRjQRgJoQYtZpd2ICCT4fHzxpv4U6jm9JE2IpFC+ffAwJCYHnUXrXTG6a/ya9x40iPS2VFPrXl5XaMUJ99xfhdf6uznQINp//72XrIZORYQXqBz9U6sWrJTSCz03XccfRv6gYjy1w/MehzuVCEoThxx7Oj+94iO/N+hmpXQLHIFz+1ye49MFHSExKJjHZuk5HHXcK3Rd/g+fHPwMRDmRaLxhlAwbA/fczaOd+BiSVAS7Kvy0MPTLvwAHo2ZO8EVYMUPeKygCB405MZMXIobx0ynEke7xM3roT998eAaDG0eUcuKOH6IS3s4//8rWXufa15zn1kitwJVgvfPNu+m19tXBdPdUVlhdq5K5C+hw+qcnszwFccw3MmNFwzbcUtxtOPz1k0b6X5gP2/FveEHbn5DR4vqZObVienQ0/+5klqKdMsV62ROC22+DEE/nZH3/PL194EtehHnMlZrRrgSMiGSKS6f8MnIEVcLQQuMyudhmwIHQLrYsrOYmKNEeyvZ//3AoGvuACvB5nvhtDdarlUnc5MqJa1l5GI/xvE+++2yBwbA+Olb8igYwePRqtNuaS4zj5XusGlt+zR/1brpDGuF/dGFA3rbaGv18UYtowW+B4fabRW/Pr35tKdXKKJW5cLjjpJOutaMcOyytlTKBoaK4/3JmozZ/3p6WBi8ZYQvDuuxu8AvazxlNhia2qlMCHcv0w6AgEziNzLuOfN5zhcPtEOXj6vPNg9uyQw6OBJm12eb0M3hM4rLcmOZlkjwePZz/lRfsCzrdQCfDCsWzkUPb27Mv/b++8w6Oo9j/8nt1NL5BCQgm9E6p0VEBsoNcuV0RARRSxUK4/u169ouK9isJVUJAioqKAXBXBgghWpEgRAQEBRVoILQFStp3fH2dmd2azAQIJJTnv8/Bkd3Zm9uxhd+ZzvvXWl/8b9vX+/xzNkLdm03fcM0SJqoDLFv/y01hLjU+v12bB+aJdC/Jzc9TvpWtXmDv3GFlUZkp4eFedz2l3sUghEEYdnGqZDenY95oix6TUqE4VI4jfSmrN2lw6dBBRrlQOpCorYEJ+Ptx7L8TFcf2GP4lyJOH272f2mDA92fbvh+Rk3MLBobgYqhzK5bDLMu4wFtImRmCzOzXZtn1nl07qwalWD+/Sxf7c6eSG5qrn3dHcw2BkolmTFJbPC15OCwyX9/m/blQi6E+jcO2CBerzmBXZQzH3y84u+tqJFNTzeAKB6PM7tsJnqW3k8SmrsZAyfPxYtWrBxwkJwce1akH9+mpMixertiSNGqlFwHffEf2vZ44/Ls1ZxTktcIB04HshxBpgGTBPSvk58AJwqRBiM3Cp8bzMiYiKIseSgkl6unLdXHVVkRoVZsyAc8m3wY01a8INNwRFjIm5sp46VZl2IeiiklBcKNX5nXoR2aE9hXfexcIO5wcu9jgkNS/tBd+oMkW/16pDhM8P7t0snjndfhIzHVMGu5Qzcyafnt+dfZUT2Vinnn0lZbodrrsOvvrKbsGZNy/sOANYBc5fRj+5EriNACVunn9exdJMm2Z7yW2k+eZF2OOQhL/ghN/LuymSvL1RAQOVLGtflXkjKM4SdhyB02lDSG2cahmB4NipQ+1x974SpPlLIXA54kmuFj640yQ5NZ2+k8Yx7L05XD3saX5s1hCAmCOW/2ufzyZwcuNiKTxiWAK6d1dzcBxXZXECxxRVh43sxENxsYgSCLlQnC4XvUf/i5/va8+2QXcgxoxRN8mxY3H8vIJONRIAB7uWbSp6cE4OVKqEz+1hT3ISGdkHcOQeCrycXSl4s/2m10WA6ivnEwJ/jWq2U9X49nsVa3SqtacaN4YPP7RtynjiKaJcVZEyF7dfLcCsFpxvp0/ns+njASjIUxYcs3bQgT8My9wUI79k6dLw72su0MJlMBbX2NIqVjweuPFGADbUqsHEK3uQlWS3cjmk3y5wunRRcYCXXGI/rxkCUK+e+mfyxRdw8cXB5wsWlKBDseZs4JwWOFLKrVLKVsa/TCnlc8b2/VLKi6WUDY2/pxDIceJExMSQGx/LwaQkuOce22t+W40LGXBR9bBk8ywY+YQybcfE2E9svfGYIsB0UUmJEOFXcVFR0eB0EjVxAr1nvx9YoQdiabp2BSmp8aAyRTff9hdr5iy0n8QUOAg+69iKVQ1q47vuWrZkqBXuwtb12DBwQHD/atWCwmLpUvuNubiLnYm1WrNJ2LL2x+DAgWBdn5BjC42bSWiAqtlgb+82ex2ZcPj8xhhPlwXHdL0UV6r/mAKnqPhKbJFJ5Qj1/fL47FYhv+V7ll+MYDA/tl8I/P4Ti0VIjo/C5XTQ8Pz2pIxUq+CUwxYRH+Ki8jkceM0CbmZzTyNolCpV2FKjqDs2uxgXjsMZQ1RkOo1mz2HaVb05UCmBmNiYsPueKOlVa9N/6OvUfXMS9DNSs2+9FerXp93oV0n2ROL172Xa0/+0H5ifD7Gx+L0+NtasSlyhm7hCN5trpLOyYR3mdlGZOy6RRrf5X/NndRWDlBcdRVrb5vZzOZ1BkXCqXH+9unFburlffcdNKOO4UWfLKhRkAes/nc+GNUtxH1W/b9MVuGj0xOBnhaLXMhNz0Ra6mINge45QrFY8jwcKC8nNqAFCkBcTzcaaKjHBdEtFeH12gdOggbIwh45p8WKVkt6qlWooa/K//ykX4htvqDCCnTthzZrwY9OclZzTAudsI9Kw3sz6+00wbpztNV+IiT0vTLpq1qzvWPLd/GMXzDMFjnlxk1BcFVmnJbMkJjaO1FaqcWd8ZmPbfjH11aqlx+r1eOU+5rxsMXiZLiqpqqZ+fV5zNv2wDMsdnrXff42NAQOgaVOYMycoNpzOYNn94li3DtLS7Cbq4noXFcfWrcFVlnHzNzsIew8rwZMfZb95Rxni88jvx66ZYSUYe3OaBE5xQibM9ryYWFbXrx1stGolNZUEJHEJNYq85HU6+LRTG5Y2qV+sRcTcLgX4LengJ0rja3ojXU6qWFo25O7dYxNwPocDn2nxbKgsPqw3FgK//MKnF/YI7Gt4qDkaE83KBnWY37EVW6qnMatbRwD+bFCf+6ZPJiY+gbiWrQFocVHPEo/7uLhc8NBDAHRvm4kQsRz8bRtHD1tEdn4+REfj83rZWi09sHl+xzYsapOJ2/i/drjUAqT6C6qKc6TfT/ergsX2EpynJtCKpUdwXmvtP0TVzGAwvjXIuNfS1QAsGDWRQqNGjpmt5ijMYvP6lcH+Y6EutIEDVayeKXDCFRlNtrvjbBXcTQyB47ec3wz8Nr/3Eb4QgVOcO695c9VSJCpKFVCdPBneeQeuvVZdZwcPVm7iyMhgZW3NOYEWOKVIVHzQqhKK32vJRkLidhX1M+e4stg016hFk5+vMonA/sM2XTeJiRQU5OOVBThQYuni/kOp2+xCYqLVzcsRIpTavTAannqK9Hdn2N/YtmKKZPuy9WT9YaSDGjcevyUdev74UXjlocDznav+KvJZeOwxlQH27LPqeatWxxc4a9eq4D6rmTjECiP9fpa/+gI+TzHWg19/DZqyjVWkaclwG2IpP8SCk2LcbB3eI3w46QRSVAG/NN/fj7u4DtmlgRnQWFzwpiFwdqVUDmyKHv0S7oiosBYcUlJg3z7ufGMcDmGv+SMdsWysVZ21F/YksWHjoscSdPm4fH5iYpLD7nNMnE5Ek6bE5geFWeHBXLyWz+eQ/kABS+rXV24Y04LjdNrcBK2v7MFtY98gXtRgUdv2bKidwUcXtGdP1RaM7n0Fed2DK/Lr/vUQ1z38POdde3nJx30i3Kyyo+o6IolJSsYnDzLt/pCKzDExHN2di9/pYEaPznzWoRVeS2xbekYb+j6n6r5EGAGwUU67C/r6V8eWzfibNVPXmuhoWLKEvk+OZMB/J9D3xXH0eTzYi6nZn6q0hEdm8dtileVluqiiPB6+HjMWvjVc76Hf26lTlVXEFDjhXI/WWmDWc4QKnIICjuYFt7kj1DyZ6fsRXp+9LceJumAHDoRbbrFvS0tT4+7RQ7upziG0wClFYuLUajJclVufrf+NBItbaWrPbky6ojsFUZHs3/4bhXlqpYcZz2O1/mzbpoKOo6OZPPBe/DKXaCO9t/XfLuP6px5myNTXGf7uR0UHmJKimtGFrpAsmQRxCan45AHefeQR1i/9PmjBKVLvxVIfx5/F5/M+sL/crx9cfbXKSAAlcDZvPnbQ8O7dKg7JXLVD8HiDhSPuoP3QR/nq5r/ZjzUviosWBatHG/59s9y+31hNe51OfsxsyNqQ2h8RXh87F/5+Qr2ppKVi74KJbxxjz1PkBAXOnAs7sOrF58HjwXH33TS+6FJcfj9Zyan4rMHhGRngduM8eJBbx9oDYWvXUW7HnsPuxxn6HTHYXUXFgpzXqj13T3n95D5TaGCr9PL92/bYryO5O3m1zyA2b1qnvhP79gEw/bEnbXlrPfrfSUrVDAa/P4GEC9Kg0UEa3fskvce/TO9X3+GGR4Ip206ni3rnnVjH75MiISFguRz03PO4HOnk52fz6ZsqXoX8fP7YspkCj7IU7kpNZn2djMDh1dq2pt/okaTUNraZQv//lAs5ObkJDkclUlNKWCOqJERGqkXGK69AYSFV0mtQrVZt4kKqZMemJgGCnN1qIWS6v6MLPYhdwd/ssi//F+iXV+R9ILzACbU8miUmwlhwPA5LOw7jt2KKGiVwQrrUnwrDh6uilGd7zz1NAC1wSpE4o/heuPuj3+ai8gEOFrVuxsqGdTiQEEeOpc7Fa7f3ZtLD/2DBiuXQuDEFL70YPPS776ByZWb9ZyQFRgzFhXf2tb2XcDhwliSVMSEhUC307nvuJjo6HSmP8tMzz/HL8ypmwuM79o963dvTWfC5PViRl18OPu7cWd2kv/gi/An69VPut7Q0u8BZZy9fH7dEuSoSN++zH2+mS8+bV8RPXvmIEjrOrGzcLieeCBdLMhuxqLW932uUx4PHv4txd/bHX0zdD5fXR3ShG78MXmx/+2FB+M9UGpguqmMEGfscDgojI3B16qwEkRBUqqOKV6ZXScHZs5cqtDh0qMoUAdi+neT0DNup0g7n8MAHn5KR2Vi17whhV0plVrVqC4Djou6Ik73Qn3++7anTL/AeOQTAzw3rsitFZQS65R7mj3oNv6Xo48H9f+CT+3E6KtNz0EO289x132s8MPIHrurakarJCdRKPy0t6OzceiusWEFESgpdLm3HwPlfc2j2PLK2boP8fA7nhP9/7D58KH0feta+MSpK3dSNDtm3v/4SI2a8W8YfgECfO5KTg2LD/H21bg3AkBcnEBUVFMEe43oTX1Co6vsYHFmzjgn3BCsfBzBdq+EETmGhuga88456bvQWCxU42Zs22QLjTYFjWi6jPB67Bcd0m2kqDFrglCJxldUFVYaxUlhLmvvlUYQQrGxUl0VtMgMrgkhRAyGUfz3nj038snEVY1t3ZNz4MfaTbd/O9p9VwG61zNZktr/g1AffwPC3t23LPZ/NJUlUou+ChbR8byYAbo7fkPPXaXOZ+99X+Gm+kUZqplyuWqXicho1UkLGeqHJyVF+7XeNC3daWjCwFFRp++3Bhnnp+1RaaVJOyMXK7VbvF6Y3TaWjeUQXuonYl21L4/c77Dfo1NwjpOQcpvDwIV676+awlpy/L17CvR8vAOz1Z9751/8V2fekOXIk2OLjBCw4pnsgxRp8awZSHjyorIE33ghjx0KdOmr7ryHtW5o1s9VkCW1ZsbJpEz649GL+/vZkNZa77z6JD2Zw2WXqb0IC3HkncU5BnFvN9bctm4AQRIh0Il1peP3ZbN0dzLhSAfIRdLz5OjIvPaGanqeXm24KPGy/4FuSjhyl068/M/PxkbgPHsQt1PczwhEsrl45oyZtO18W/nyRkaffYjBqlPqbl6eEMQQFTl2VQk5ODoMnT6JKk0bUbdUpENhbKd8RbFCKsqLkHdrHF29NtL+HaZndty8YX2VSWAgtWgTbJZgWHEvNp/xDB3F4Cm2lDUJdVJWcMpBAAIRPYtCUa7TAKUUSk1W6YbjMYemzu6jCWXkSu6Xwj/dnEVulJkLEIUQMXrkf8DLZSBsF+CFTCYAIRxX6/vPZoic6GVq0CDwU2dkMdOcTHRL4aRLhTCU2tkj3C/z+fWz6YSE/THuT3buMuJzUVLXqi4qC0aPVim3yZOV2a91aWQoefzx4kvR0u8A5elSt5oyWF2lG3YzkkM7OuN32dHVLrQuHhPYbt+LYmcVhi8AJlxZ92xcqdsBzOJ/X77mjyOvVzGaKUmJt05C1/jfyC06wyNnxuPxyJdbghASOmf6fWt1ikTEFjlE9N0CLFqpu0W23wfffB7fPmmW3rJjvb3Be23aMmDGb6LhYde5TuelWrapSv5cuhYQEojxeOnfpRH5UNH6ng+jo6gx9fzJ3ThlPhDONQ9Z4fEcifV94ic5X33Dy71+W1KkTLJ74888AOEUkbv9enD4vXqcDp0jGFaFcNFXSm3LH6JN09ZUVjRqpRUWVKjBypMqyMrOLTFdwbi4REREM+NfLtLvi+kAwb720SlTyBGPc6lbNAJys//xbdmy1FHfcYpQv+PBDyMy0WV1zdu7gjy0b1fcUwlpw1s79HKfPj8/pxOVMAUTAgmPSsmErGja2uCRLK+tMc86gBU4pEl/ZqKcQ1kUVqnrsFoDI5MP0H6wC+Ya89jqDpk5n0ISJxMRUI7lGA44k1uL9izrx8o29+CmzIZHONO55O2RVdCq0aBHsywIqRdI6fuOGllK1Gf1f+jdDpk7B4YjHIWKJi4km1pGO9Yb/wT8eJzc0Q6JHD3WBfPBB5XoJTblMTYUrrgj2lerXT8UNud3w3HOwZw/xR47gdjlJyj0UjOfx+dRja/8qI0gwt14dfqlbkw6/bSFt985AuwBA3aTj41WtC3PVClTp0BaHSCD/wH6m/fPRsNOlMjXsP5+37j0Fq4aVH39Uf6UMxioUl01WUIDX4cQhKhFpFTJmATO32y5wHI5gxpKZ4gxFM0wGD1Z/mzVT/x9mfaPSolYtFa+SmKhE7AcfEN2kMdUateSyYardQ3RUNNc88yAHKgf/X7vde+cZaXZYIho0UHFthtCpu/MvLly3A6dfktq8Fdf+8wEu6KeyoroPGngmR1o8NWuqtPH16+3XAlPgfPKJapQK1GrdjIx6yq0cseJn2rfMVJa2Jk3IWLCQ+hGR+OUhPng0+B3yhxYA3BbsceVwuzmSk6O+G/HxYQVO1aztVD6ah98Zy1UPDyfCVbloZurOnaRXTVeV4BcvhkmTTm1ONOccWuCUIuoG4wgfg1Mk0C74PL1zfe4a8yUOi8skMSaSxEpJ3PPWm9z+8hjumvAy2VWb4nCmEBNTlfvfm4LrGI0WT4oXXlCBwUYBLSvCWZk2va7ntrH/Iam6SnEdMeN9Rrw/k7vfms2QGZNJ69KOGMOy45MHmDZkBB5rK4HYWFXZ9Npr1fPGjdVK8amn1I03K0vdmKtUUTEnb7+tTNiDB6vYGiMzY1NGNSJ8PmTv3uqcRmd1IiOVdeixx1TMDxC7O4vFllgbs8R+gKwsVcDLYs0Z0PICzuvangh/LPs2rmfyg8OLzEf31Ru4f848W8BV3pEDzBh3CmmkUkKTJsHnBQVBV9G+feGPKSjA6xS4HCEXd2smSmhdEbO4mllEEIoKnJQU5cb6+muV3WaKztKmRw/13jk5iK5d6TvyeRqe1yHwcu0GTWk9SZVc2JNUmaYduhR3prOL++6zPe2wTon5+h07U6dZK1pe1p0HPviUWi2bnonRnRg33miv+guqdlZcnEqJf/TRQEB/tBDq9yclUW9PQyQlBXrsXeuKIja+OsIfFCihDVw3L1PnycvNIcrjoSAygi2rV6lFy59/MuPpJ/ntB5VhWhjhola2it1pfsO11GvVhlvHjKHb/U8qC7DJsmUqczM+XlVZz7DHnGnKP7qpRqnjDBtlLP3F10u56e5/E1Fc9U6DmErJ3P/elFMe3TGJigo2/ty9WwmeLVtg3jx6T59y3A7f/YepTtxrVy3n63+Px+3byxu3D6HOBS1o0KEjTdt2VjfsOXPUHJmi4umnA4GUAaw35euvhwkT4Lnn8AvBb7Wq0/yPHYg5c9TrZgBuSopK8YRAjIkrPx9PRPBrnpWkBMMltw+nZstmwUw16//ZzTfTbf9+ugFZScmsavAnU/rdxm1vTwmsCFptVXFB8fmF5MXXwG9UfN313SqmH3qU/o8HLUKAarWQVtStZ2PzZntvnvz8wLj2LFtCuJrB/rw8fI5g+4EA1gayod+tcG0PwqnyzMxjj7c0uPBCtUJfty7Yuy2EtBat8BQWUvDHZqKslcLPZgYMUNavtDSYOTNQIycQ63Yu4HTCNdeoQncmaWkqWN20eHbpol5fulT1lvrsM+VOzctTrq4RI2DMGIZs2MCvO7bC7Plh32rzou/Y9+nHxMXG0NLroyAygo9GPcml3nyaffIJOb7DrM09QhPgiw6d6d79QhIL3fDEEwBUqpJCix4XqMyzrCw1xgkT1OLFCIzWVDy0BafUcYRtvyjDpUoCkc50Ioqr3HkmqVZNBaVOn66sLscRN1ZatGnPgPEvEulMw+3NYtPir/jsxTF8NtmINRDixGtSgOounpYGv/zCkdg4/kpL4UB8HNurJLPo8QdUQO7rr6vu6yY1a9pO8b8L2rE9LYVdqUm4RAqtel5CsvUzmSXZr7xSWX8aqzow6QcP0HP5GvrMnsGU3vZsNYDB+44yYtqb1O99NdGiJsh89q7dzIThlq7aP/ygVpYhbr8imHEJJgUF7PhVxR7lrFnDBz26887woXgtsVGFhw7gdToCxeECHEvghKuKHK4o4OkiPV1Zco6R+RcRGUmdRqdBcJUWQkD79qoX3YMPqu/ojz8qwXAu8cADcNddKhYHVPzUAw/Y97n7buUKHTFCWfwGD4bxRmr8I48oy84rr9C8Tcdi3+aKn5awavIU/hylmuyazVNXNK6Hy++n0V9ZuPzqOtlx2D0kPvucahRq7foNMHGisp69+GJQVIbuo6kwaAtOKSOEo8hqeMm8OezbHr7IXaL1RnQ2kpQU7GZeksOSUxn81gQm3XU3BQWHkbKA9V8uYOeG3xn00uhij9uzaydzR44ioVpl9m7YQVx6ZVpc1oMOM2dC9+7sqpKO3+Fg2uVdkUIgN//G3g/f5qb/e8p2nqy8XNIJth/YWj2drYZrLToizNe+XTtl2bC6avLzYfVqZM+exObmcuXiMCnuP/0Ekydz7ZAh5PTM572hd5Gf5+XI7ixe638bA14bS+KyZWrfr75SPbqKIXvjOqzfBpmXh++wsk4lHTnKTYu+IXvlSl7ds4+YhESuf+pRIg8cwOdw4owIcTFZXVRmrx2T996DPn3s286kwKkI1K0bzEA6l2jQQFlCQMVhmW7x339X1wXT8ta0qRJ0kZHQ0SJk0tKUNWvCBGUVtnLxxSpL0uCejz4JPHZHuEit0YQeQ26Hz5rTY3Uw6y+9YROKpXnzYMuJW25Rwux0WCI1ZyXiRIqalXfatWsnV6xYUSrnernPTUQ44mzupFf73oHbl2XbL61JY9Krt+DCW3oTY6mBU175bPJ4Ni5Ygk8eJNKVzpWPDKVei1ZF9hs3YBAFhaHpnE6io9K46JLOLProYwojo1CZaMGaIkLE4RTRVKqTSouLL+WbSW9Re9c2cuJjOGiJu4mlFte88iDVq5fsZiOvuQbxySf2jcnJKiusRg0VS2SIqSUL5rJ8yhw8/mycohIdf99C55+Xsq5JE+r89CNxlcILxnUD+5M59Z3A87cu70qPVeuoFdJxfsxNt+AzKknfvPAH3C4XC3vfxB2vvmY/YefOSoB9/rnKzLKyfbuyLphs3Xpu3oA1Zx63Wy0MimuFsGWL3TU3cqRaBPbsCR2C8VbS4UAYiQPeWbNwmbGAhpsrwPr1SlCdCOvWqYB2a9fwswQhxM9SynbH31NzsmiBQ+kKnFf63IxDRCKlHz9eHETik/uJcqUjhIMCj1rFPPDBp6XyfucSu7f9zodPjKLQuxeIJDommfMH3kyrCy8i72gecfFxjOnTH588iEukqCBqJ3gK3Pgs/VKTqtVn4JixHDqQzYwRD5JXmAcyH9WTKxhf4hTJSLz4ZbDdw1UPPUGjtp1KPniPR7maKldWPv6ePeGjj1Sg7rBhqlaHJSAzv7CAKUOHUZhzkG4rl9N28x+sr1WdLzv3IC4lkea9LqNltx7EWS68yy7qRofFwe7yG2pVp97ubKJCXEqeQ4d46/HHyN+fR5/PP+ZIbDz7p46n/WUh1Z137lSp+aNGhe/QbKZ6d++uKkBrNGXFwYPK1bxmjYpJ6t1bBdEnJUGbNur7N3eucm+NGaMy/KylCPx+JdJXrgxaj85xtMApe7TAobQtOH2Q0uhtJBLxy3yEiKTroFvZ/v2vbNvwLU3a9uLKh+4tlfc7F/lg1FPsWfMnXmlmBjkAP0LEIeVRoqOqcu/b9pTOj/79HNtX/45EcufEV4m1CIP8ggJ+W/kjsbGV+OrVN/DmeYhNiePO18bhcbuZPHgoR/NUYbHh732Ms7iVZknIylKxI3PnqsyzZcuUiT6E3Jxc/Bd1pfKqNeyuVo0ZXS9ESiOLiWgiHAm4YiK4bPhd7Os7kE7r1p7Y+z/5pKo+3aSJaoMxc2bJP8NHH8H998Ps2Xa3gkZTFkip0rW7dAkK7oMHVZaT6fqS8tg1lvz+ksXvncVogVP2aIFD6Qqccf0HUeDeQ1RUVe4zbtIet5uIyEi8Hjc/zvqYC266Hkdp3GTPceaNf4Wt36/FL8EhwO/3I4Sgyx1/p90lvUrtfVZ/soCF744lPaM1/UaXUmFEk1WrVMXVtm1Vz6D581WAZXy8ulh7PCo9NTsbMjLI+uFbPvnPS7gP5dNmzSq2V4lnZ2oiCEG31etpt2lb0ffIzCzSsoLkZNXccdw4tdqdPr3ocRqN5qxFC5yyRwscSlfg+LxeDuzcTZXaNY+/s+a0sWbBIpp1O5+IkE7ip4zXqzKAvvsuuK1OHVVDZONGZeEBtc/XX8Mzzygh9PHHge7T6y6+iO2F0Ov7RXhTUnDtt8TcNGgA06YV6d9Es2bBEve9e5+cBUej0ZwxtMApe8qtwBFC9ATGogIzJkkpXyhu39IUOJoKSH6+qrexerUqzx8ajNy5s8qguu22YG8fk1DrzJtvqkJ8LVqoFhXR0aqwWqdOKvPpootUgbVvvgkes2YNtCzDLtkajabU0QKn7CmXAkcI4QQ2AZcCO4DlwM1SyvXh9tcCR1Pq7NkDy5erQnaJicG4gcWL4aWXVCzBww+r2Jfnn1dBysOG2XqCFYvfD716wZdfKteUteWCRqM5J9ACp+wprwKnM/C0lPJy4/mjAFLKUeH21wJHc85RUKAEzt/+Vm6CLjWaioQWOGVPeb0y1gD+sjzfYWwLIIS4SwixQgixItvoUK3RnDNER6vsLS1uNBqNJizl9eoYLs/QZqqSUk6UUraTUrarcrZXE9ZoNBqNRlMiyqvA2QFY05gygF1naCwajUaj0WhOM+VV4CwHGgoh6gohIoE+wCfHOUaj0Wg0Gk05oVw225RSeoUQ9wFfoNLEp0gp1x3nMI1Go9FoNOWEcilwAKSU84H5Z3ocGo1Go9FoTj/lMk28pAghsoE/S+l0qcC+4+5VsdBzUhQ9J3b0fBRFz0lRytOc1JZS6gyXMkQLnFJGCLFC1zawo+ekKHpO7Oj5KIqek6LoOdGUhPIaZKzRaDQajaYCowWORqPRaDSacocWOKXPxDM9gLMQPSdF0XNiR89HUfScFEXPieaE0TE4Go1Go9Foyh3agqPRaDQajabcoQWORqPRaDSacocWOKWIEKKnEGKjEOJ3IcQjZ3o8pwMhRE0hxCIhxAYhxDohxDBje7IQYoEQYrPxN8lyzKPGHG0UQlx+5kZfdgghnEKIVUKIT43nFXo+AIQQlYUQs4UQvxnfl84VeV6EECOM38yvQogZQojoijYfQogpQoi9QohfLdtKPAdCiLZCiLXGa/8VQoRruKypYGiBU0oIIZzAOKAX0Ay4WQjR7MyO6rTgBR6QUjYFOgH3Gp/7EWChlLIhsNB4jvFaHyAT6AmMN+auvDEM2GB5XtHnA2As8LmUsgnQCjU/FXJehBA1gKFAOyllc1RLmT5UvPl4C/V5rJzMHLwO3AU0NP6FnlNTAdECp/ToAPwupdwqpXQD7wPXnOExlTlSyt1SypXG48Oom1YN1GefZuw2DbjWeHwN8L6UslBKuQ34HTV35QYhRAZwJTDJsrnCzgeAECIR6ApMBpBSuqWUh6jY8+ICYoQQLiAW2EUFmw8p5bfAgZDNJZoDIUQ1IFFKuUSqrJm3LcdoKjBa4JQeNYC/LM93GNsqDEKIOkAbYCmQLqXcDUoEAWnGbhVhnsYADwF+y7aKPB8A9YBsYKrhupskhIijgs6LlHIn8BKwHdgN5Egpv6SCzkcIJZ2DGsbj0O2aCo4WOKVHOJ9vhcnBF0LEAx8Cw6WUucfaNcy2cjNPQoi/AXullD+f6CFhtpWb+bDgAs4DXpdStgGOYrgeiqFcz4sRV3INUBeoDsQJIfod65Aw28rNfJwgxc2BnhtNWLTAKT12ADUtzzNQJudyjxAiAiVu3pVSzjE2ZxmmY4y/e43t5X2ezgeuFkL8gXJT9hBCvEPFnQ+THcAOKeVS4/lslOCpqPNyCbBNSpktpfQAc4AuVNz5sFLSOdhhPA7drqngaIFTeiwHGgoh6gohIlHBcJ+c4TGVOUa2wmRgg5TyZctLnwC3Go9vBT62bO8jhIgSQtRFBQQuO13jLWuklI9KKTOklHVQ34GvpZT9qKDzYSKl3AP8JYRobGy6GFhPxZ2X7UAnIUSs8Ru6GBW/VlHnw0qJ5sBwYx0WQnQy5nKA5RhNBcZ1pgdQXpBSeoUQ9wFfoDIipkgp153hYZ0Ozgf6A2uFEKuNbY8BLwAzhRB3oC7mvQGklOuEEDNRNzcvcK+U0nfaR3360fMB9wPvGguArcDtqEVWhZsXKeVSIcRsYCXq861CtSGIpwLNhxBiBtAdSBVC7ACe4uR+K0NQGVkxwGfGP00FR7dq0Gg0Go1GU+7QLiqNRqPRaDTlDi1wNBqNRqPRlDu0wNFoNBqNRlPu0AJHo9FoNBpNuUMLHI1Go9FoNOUOLXA0Gs0xMTpedz/T49BoNJqSoOvgaDQVHCHEEcvTWKAQMOuLDJZSZp7+UWk0Gs2poevgaDSaAEaLiUFSyq/O9Fg0Go3mVNAuKo1Gc0yEEH8IIS4xHj8thJglhHhHCHFYCLFWCNFICPGoEGKvEOIvIcRllmMrCSEmCyF2CyF2CiGeFUI4z9yn0Wg0FQUtcDQaTUm5CpgOJKFaDHyBupbUAJ4BJlj2nYYqq98AaANcBgw6nYPVaDQVEy1wNBpNSflOSvmFlNILzAKqAC8YXbHfB+oIISoLIdKBXsBwKeVRKeVe4BVUE1KNRqMpU3SQsUajKSlZlsf5wD5L08N84288UB2IAHarJs+AWlT9dToGqdFoKjZa4Gg0mrLiL1RGVqph7dFoNJrThnZRaTSaMkFKuRv4EhgthEgUQjiEEPWFEN3O9Ng0Gk35RwscjUZTlgwAIoH1wEFgNlDtjI5Io9FUCHQdHI1Go9FoNOUObcHRaDQajUZT7tACR6PRaDQaTblDCxyNRqPRaDTlDi1wNBqNRqPRlDu0wNFoNBqNRlPu0AJHo9FoNBpNuUMLHI1Go9FoNOUOLXA0Go1Go9GUO/4fknsqrKf75+kAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "From = ''\n", - "To = ''\n", - "if Type ==1:\n", - " fig, ax1 = Sen.Sobol(RealValues=False, Title=\"Sensitivity Analysis of the RMSE to models parameters\",\n", - " xlabel = \"Maxbas Values\", ylabel=\"RMSE\", From=From, To=To,xlabel2='Time',\n", - " ylabel2='Discharge m3/s', spaces=[None,None,None,None,None,None])\n", - "elif Type ==2:\n", - " fig, (ax1,ax2) = Sen.Sobol(RealValues=False, Title=\"Sensitivity Analysis of the RMSE to models parameters\",\n", - " xlabel = \"Maxbas Values\", ylabel=\"RMSE\", From=From, To=To,xlabel2='Time',\n", - " ylabel2='Discharge m3/s', spaces=[None,None,None,None,None,None])\n", - " From = 0\n", - " To = len(Qobs.values)\n", - " ax2.plot(Qobs.values[From:To], label='Observed', color='red')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/data/ams-gauges.csv b/examples/data/ams-gauges.csv new file mode 100644 index 0000000..d3add2e --- /dev/null +++ b/examples/data/ams-gauges.csv @@ -0,0 +1,55 @@ +date,Frankfurt,Mainz,Kaub,Andernach,Cologne,Rees +1951,-9,4250,4480,6080,6490,6830 +1952,-9,4490,4610,6970,7110,7340 +1953,-9,4270,4380,7300,7610,7970 +1954,-9,2850,2910,3440,3620,3840 +1955,-9,5940,6050,9460,9460,9500 +1956,-9,5000,5150,7140,7270,7540 +1957,-9,4500,4520,6650,6750,6950 +1958,-9,5020,5260,8480,8600,9080 +1959,-9,3050,3180,4950,5100,5420 +1960,-9,2560,2660,3310,3400,3550 +1961,-9,2940,3220,5000,5250,5840 +1962,-9,3460,3620,5270,5460,5880 +1963,-9,2360,2550,3300,3540,3820 +1964,432,2780,3090,5160,5480,5510 +1965,913,4430,4460,5890,6130,6350 +1966,967,4430,4560,6960,7410,7720 +1967,962,3950,4240,6920,7290,7570 +1968,1100,4130,4280,6970,7160,7520 +1969,797,3370,3540,5130,5270,5340 +1970,1760,6630,6670,9990,9690,9900 +1971,440,2410,2510,3220,3520,3810 +1972,292,2100,2040,2220,2270,2330 +1973,347,3680,3690,5220,5380,5290 +1974,470,2940,2910,3880,4050,4260 +1975,834,3740,3740,5440,5750,6190 +1976,447,2150,2180,3100,3270,3560 +1977,619,4120,4320,6460,6680,6560 +1978,540,5110,5310,6200,6340,6330 +1979,882,4490,4600,6580,6730,6900 +1980,1240,5470,5630,8450,8800,8760 +1981,1160,4490,4553,6271,6130,6500 +1982,1490,5480,5600,7793,7967,7787 +1983,890,5770,6090,9570,9801,9868 +1984,1230,4520,4880,7980,8433,8502 +1985,548,3040,3230,4420,4880,4780 +1986,795,3850,3980,5860,5890,6210 +1987,1280,4670,4870,6960,7130,7590 +1988,1760,6920,7160,9250,9550,10200 +1989,723,3480,3680,5420,5300,5480 +1990,830,4820,5130,7450,7250,6970 +1991,673,3400,3710,6190,6190,6590 +1992,489,3750,3930,5230,5210,5440 +1993,500,3640,3780,5400,5480,5810 +1994,1220,5490,6310,10400,10600,10600 +1995,1990,5900,6520,10100,10700,11300 +1996,669,3760,3860,4480,4280,4250 +1997,914,4120,4210,6960,7080,6970 +1998,1060,4720,4790,6910,6700,6150 +1999,1420,5480,5730,8160,8530,9240 +2000,625,3750,3900,6390,6370,6550 +2001,1140,5420,5710,8320,8410,8410 +2002,1170,4950,5140,7260,7240,7940 +2003,1800,5090,5350,8620,8840,9470 +2004,197,1150,1190,1470,1580,1810 diff --git a/examples/data/distribution_properties.csv b/examples/data/distribution_properties.csv new file mode 100644 index 0000000..1a9d777 --- /dev/null +++ b/examples/data/distribution_properties.csv @@ -0,0 +1,7 @@ +id,c,loc,scale,D-static,P-Value +Frankfurt,0.0519,718.7208,376.1886,0.0732,0.9999 +Mainz,0.3073,3743.8060,1214.6170,0.0556,1.0000 +Kaub,0.2826,3881.5735,1262.4261,0.0556,1.0000 +Andernach,0.3215,5649.0760,2084.3831,0.0741,0.9987 +Cologne,0.3061,5783.0175,2090.2240,0.0741,0.9987 +Rees,0.2842,5960.0225,2107.1972,0.0741,0.9987 diff --git 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+Mainz,4153.3333,1181.7078,1150.0000,2286.5000,3415.0000,4190.0000,4987.5000,5914.0000,6920.0000,1951.0000,2004.0000,53.0000,3627.9072,4164.8247,5203.5025,5716.9056,6217.2439,6504.7768,6504.7768,6734.8834,6919.9487,7110.7671 +Kaub,4327.0926,1243.0196,1190.0000,2394.5000,3635.0000,4350.0000,5147.5000,6383.5000,7160.0000,1951.0000,2004.0000,53.0000,3761.2533,4321.1147,5425.0055,5983.7382,6539.6898,6865.8500,6865.8500,7131.4309,7348.7381,7577.2635 +Andernach,6333.4074,2016.2113,1470.0000,3178.0000,5175.0000,6425.0000,7412.5000,9717.0000,10400.0000,1951.0000,2004.0000,53.0000,5450.0504,6369.7349,8129.5379,8987.5813,9813.8563,10283.0847,10283.0847,10654.8745,10950.9409,11252.7701 +Cologne,6489.2778,2037.0057,1580.0000,3354.5000,5277.5000,6585.0000,7560.0000,9728.8500,10700.0000,1951.0000,2004.0000,53.0000,5583.5790,6507.6947,8296.9962,9182.3978,10046.1020,10542.9299,10542.9299,10940.8513,11261.1394,11591.6871 +Rees,6701.4259,2074.9944,1810.0000,3556.5000,5450.0000,6575.0000,7901.7500,10005.0000,11300.0000,1951.0000,2004.0000,53.0000,5759.1727,6693.4716,8533.3085,9463.0691,10386.9206,10928.1713,10928.1713,11368.3842,11728.1679,12106.0276 diff --git a/examples/extreme-value-statistics.py b/examples/extreme-value-statistics.py new file mode 100644 index 0000000..4170b2b --- /dev/null +++ b/examples/extreme-value-statistics.py @@ -0,0 +1,105 @@ +"""Extreme value statistics""" + +import matplotlib + +matplotlib.use("TkAgg") +import pandas as pd + +from statista.distributions import GEV, Gumbel, PlottingPosition, Distributions +from statista.confidence_interval import ConfidenceInterval + +time_series1 = pd.read_csv("examples/data/time_series1.txt", header=None)[0].tolist() +time_series2 = pd.read_csv("examples/data/time_series2.txt", header=None)[0].tolist() +# %% +gumbel_series_1 = Distributions("Gumbel", time_series1) +# defult parameter estimation method is maximum liklihood method +param_mle = gumbel_series_1.fit_model(method="mle") +gumbel_series_1.ks() +gumbel_series_1.chisquare() +print(param_mle) +# calculate and plot the pdf +pdf = gumbel_series_1.pdf(plot_figure=True) +cdf, _, _ = gumbel_series_1.cdf(plot_figure=True) +# %% lmoments +param_lmoments = gumbel_series_1.fit_model(method="lmoments") +gumbel_series_1.ks() +gumbel_series_1.chisquare() +print(param_lmoments) +# calculate and plot the pdf +pdf = gumbel_series_1.pdf(plot_figure=True) +cdf, _, _ = gumbel_series_1.cdf(plot_figure=True) +# %% +# calculate the CDF(Non Exceedance probability) using weibul plotting position +cdf_weibul = PlottingPosition.weibul(time_series1) +# test = stats.chisquare(st.Standardize(Qth), st.Standardize(time_series1),ddof=5) +# calculate the confidence interval +upper, lower = gumbel_series_1.confidence_interval(alpha=0.1) +# probability_plot can estimate the Qth and the lower and upper confidence interval in the process of plotting +fig, ax = gumbel_series_1.plot() +# %% +""" +if you want to focus only on high values, you can use a threshold to make the code focus on what is higher +this threshold. +""" +threshold = 17 +param_dist = gumbel_series_1.fit_model( + method="optimization", obj_func=Gumbel.truncated_distribution, threshold=threshold +) +print(param_dist) +gumbel_series_1.plot(parameters=param_dist) +# %% +threshold = 18 +param_dist = gumbel_series_1.fit_model( + method="optimization", obj_func=Gumbel.truncated_distribution, threshold=threshold +) +print(param_dist) +gumbel_series_1.plot(parameters=param_dist) +# %% Generalized Extreme Value (GEV) +gev_series_2 = Distributions("GEV", time_series2) +# default parameter estimation method is maximum likelihood method +gev_mle_param = gev_series_2.fit_model(method="mle") +gev_series_2.ks() +gev_series_2.chisquare() + +print(gev_mle_param) +# calculate and plot the pdf +pdf, fig, ax = gev_series_2.pdf(plot_figure=True) +cdf, _, _ = gev_series_2.cdf(plot_figure=True) +# %% lmoment method +gev_lmom_param = gev_series_2.fit_model(method="lmoments") +print(gev_lmom_param) +# calculate and plot the pdf +pdf, fig, ax = gev_series_2.pdf(plot_figure=True) +cdf, _, _ = gev_series_2.cdf(plot_figure=True) +# %% + +# calculate the F (Non-Exceedance probability based on weibul) +cdf_weibul = PlottingPosition.weibul(time_series2) +# inverse_cdf method calculates the theoretical values based on the Gumbel distribution +Qth = gev_series_2.inverse_cdf(cdf_weibul) + +func = GEV.ci_func +upper, lower = gev_series_2.confidence_interval( + prob_non_exceed=cdf_weibul, + alpha=0.1, + state_function=func, + n_samples=len(time_series1), + method="lmoments", +) +# %% +""" +calculate the confidence interval using the bootstrap method directly +""" +CI = ConfidenceInterval.boot_strap( + time_series2, + state_function=func, + gevfit=gev_lmom_param, + n_samples=100, + F=cdf_weibul, + method="lmoments", +) +lower_bound = CI["lb"] +upper_bound = CI["ub"] +# %% +fig, ax = gev_series_2.plot() +lower_bound, upper_bound, fig, ax = gev_series_2.confidence_interval(plot_figure=True) diff --git a/examples/heavy-tail-example.py b/examples/heavy-tail-example.py index ad6560e..12e3705 100644 --- a/examples/heavy-tail-example.py +++ b/examples/heavy-tail-example.py @@ -1,4 +1,5 @@ """Heavy tail example.""" + import pandas as pd rdir = rf"examples/data" diff --git a/examples/lmoments.py b/examples/lmoments.py index 1e3c045..0dddf2b 100644 --- a/examples/lmoments.py +++ b/examples/lmoments.py @@ -4,7 +4,7 @@ time_series1 = pd.read_csv("examples/data/time_series1.txt", header=None)[0].tolist() time_series2 = pd.read_csv("examples/data/time_series2.txt", header=None)[0].tolist() -#%% +# %% L = Lmoments(time_series1) l1, l2, l3, l4 = L.Lmom(4) diff --git a/examples/notebooks/extreme-value-analysis.ipynb b/examples/notebooks/extreme-value-analysis.ipynb new file mode 100644 index 0000000..24df8ca --- /dev/null +++ b/examples/notebooks/extreme-value-analysis.ipynb @@ -0,0 +1,775 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "source": [ + "# Extreme Value Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T21:42:10.561111Z", + "start_time": "2024-08-16T21:42:10.557276Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib\n", + "%matplotlib inline\n", + "import pandas as pd\n", + "from statista.distributions import ConfidenceInterval, PlottingPosition, Distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T21:42:13.544335Z", + "start_time": "2024-08-16T21:42:13.534977Z" + } + }, + "outputs": [], + "source": [ + "# import os\n", + "time_series1 = pd.read_csv(\"../data/time_series1.txt\", header=None)[0].tolist()\n", + "time_series2 = pd.read_csv(\"../data/time_series2.txt\", header=None)[0].tolist()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T21:42:15.892490Z", + "start_time": "2024-08-16T21:42:15.888291Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[15.999737471905252, 16.10571623488743, 17.947809230275304, 16.14775206414929, 15.991427126788327, 16.687542227378565, 17.12513922944536, 19.39645340792385, 16.837044960487795, 15.804473320190723, 16.018569387471025, 16.60087672428902, 16.16130698520315, 17.338636901595873, 18.477371969176406, 17.89723672222028, 16.626465201654593, 16.196548622931672, 16.013794215070927, 16.30367884232831, 17.182106070966608, 18.98456693176845, 16.885737663740024, 16.088051117522948, 15.790480003140171, 18.160947973898388, 18.31815885337604]\n" + ] + } + ], + "source": [ + "print(time_series1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gumbel Distribution" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Maximum-Likelihood-method" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fit the data to the gumbel distribution and estimate parameters using Maximum-Likelihood-method" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "ExecuteTime": { + "end_time": "2024-08-16T21:42:25.545059Z", + "start_time": "2024-08-16T21:42:25.357902Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----KS Test--------\n", + "Statistic = 0.18518518518518517\n", + "Accept Hypothesis\n", + "P value = 0.7536974563793281\n", + "-----KS Test--------\n", + "Statistic = 0.18518518518518517\n", + "Accept Hypothesis\n", + "P value = 0.7536974563793281\n", + "-----chisquare Test-----\n", + "Statistic = -28.899809016096718\n", + "P value = 1.0\n", + "{'loc': np.float64(16.470245610977667), 'scale': 0.7244863131189486}\n" + ] + } + ], + "source": [ + "gumbel_series_1 = Distributions(\"Gumbel\", time_series1)\n", + "# defult parameter estimation method is maximum liklihood method\n", + "param_mle_series_1 = gumbel_series_1.fit_model(method=\"mle\")\n", + "gumbel_series_1.ks()\n", + "gumbel_series_1.chisquare()\n", + "print(param_mle_series_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate and plot the probability distribution function (pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf, fig, ax = gumbel_series_1.pdf(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate and plot the cumulative distribution function (cdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cdf, _, _ = gumbel_series_1.cdf(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Lmoments" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fit the data to the gumbel distribution and estimate parameters using the Lmoments method." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----KS Test--------\n", + "Statistic = 0.14814814814814814\n", + "Accept Hypothesis\n", + "P value = 0.9356622290518453\n", + "-----KS Test--------\n", + "Statistic = 0.14814814814814814\n", + "Accept Hypothesis\n", + "P value = 0.9356622290518453\n", + "-----chisquare Test-----\n", + "Statistic = -28.899809016097002\n", + "P value = 1.0\n", + "{'loc': np.float64(16.44841695242862), 'scale': np.float64(0.8328854157603985)}\n" + ] + } + ], + "source": [ + "param_lmoments_series_1 = gumbel_series_1.fit_model(method=\"lmoments\")\n", + "gumbel_series_1.ks()\n", + "gumbel_series_1.chisquare()\n", + "print(param_lmoments_series_1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate and plot the probability distribution function (pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pdf, fig, ax = gumbel_series_1.pdf(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate and plot the cumulative distribution function (cdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "cdf, fig, ax = gumbel_series_1.cdf(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Calculate the confidence interval" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# calculate the confidence interval\n", + "upper, lower, fig, ax = gumbel_series_1.confidence_interval(alpha=0.1, plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### pdf and cdf plot" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = gumbel_series_1.plot()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fit distribution by focuing on part of the data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "if you want to focus only on high values, you can use a threshold to make the code focus on what is higher\n", + "this threshold." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 26\n", + " Function evaluations: 96\n", + "-----KS Test--------\n", + "Statistic = 0.2222222222222222\n", + "Accept Hypothesis\n", + "P value = 0.5256377612776422\n", + "{'loc': np.float64(16.607497657735827), 'scale': np.float64(0.8351717220676762)}\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([15.91178114, 16.08450282, 16.212135 , 16.32058251, 16.41878046,\n", + " 16.51103276, 16.59982545, 16.68678573, 16.77309045, 16.85966884,\n", + " 16.94731667, 17.03676873, 17.12875169, 17.22402881, 17.32344449,\n", + " 17.42797507, 17.53879392, 17.65736161, 17.78555934, 17.92589793,\n", + " 18.08186425, 18.25853443, 18.46374947, 18.71061839, 19.02369257,\n", + " 19.45807984, 20.18969779]),\n", + " array([15.29273185, 15.50955483, 15.6605537 , 15.78241539, 15.88766247,\n", + " 15.98225498, 16.06957847, 16.1517952 , 16.23041438, 16.30657042,\n", + " 16.38117482, 16.45500747, 16.52877752, 16.60316895, 16.67887999,\n", + " 16.75666267, 16.83736884, 16.92201008, 17.01184322, 17.10850167,\n", + " 17.21421083, 17.33216721, 17.46726308, 17.62762637, 17.82841373,\n", + " 18.10353691, 18.5609423 ]),\n", + "
,\n", + " )" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from statista.distributions import Gumbel\n", + "threshold = 18\n", + "gev_param_mle_series_1 = gumbel_series_1.fit_model(\n", + " method=\"optimization\", obj_func=Gumbel.truncated_distribution, threshold=threshold\n", + ")\n", + "print(gev_param_mle_series_1)\n", + "gumbel_series_1.plot()\n", + "gumbel_series_1.confidence_interval(plot_figure=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Optimization terminated successfully.\n", + " Current function value: 0.000000\n", + " Iterations: 19\n", + " Function evaluations: 80\n", + "-----KS Test--------\n", + "Statistic = 0.37037037037037035\n", + "reject Hypothesis\n", + "P value = 0.04843826268679447\n", + "{'loc': np.float64(17.01925379801025), 'scale': np.float64(0.7486358568895808)}\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "(array([16.39562357, 16.55044879, 16.66485643, 16.7620672 , 16.85009043,\n", + " 16.93278406, 17.01237655, 17.09032648, 17.16768878, 17.2452964 ,\n", + " 17.32386265, 17.40404618, 17.48649838, 17.57190341, 17.66101819,\n", + " 17.75471789, 17.85405431, 17.96033667, 18.07525126, 18.20104874,\n", + " 18.3408547 , 18.49921929, 18.68317108, 18.90446082, 19.18509599,\n", + " 19.57447445, 20.23028621]),\n", + " array([15.84071674, 16.03507372, 16.17042692, 16.27966198, 16.37400394,\n", + " 16.4587953 , 16.53707081, 16.61076871, 16.6812418 , 16.74950698,\n", + " 16.81638128, 16.8825638 , 16.9486902 , 17.01537361, 17.08323989,\n", + " 17.15296316, 17.225307 , 17.30117819, 17.38170332, 17.46834656,\n", + " 17.56310271, 17.66883711, 17.78993509, 17.93368243, 18.11366531,\n", + " 18.36028176, 18.77029333]),\n", + "
,\n", + " )" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "threshold = 15\n", + "gev_param_mle_series_1 = gumbel_series_1.fit_model(\n", + " method=\"optimization\", obj_func=Gumbel.truncated_distribution, threshold=threshold\n", + ")\n", + "print(gev_param_mle_series_1)\n", + "gumbel_series_1.plot()\n", + "gumbel_series_1.confidence_interval(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Generalized Extreme Value (GEV)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----KS Test--------\n", + "Statistic = 0.07407407407407407\n", + "Accept Hypothesis\n", + "P value = 0.9987375782247235\n", + "-----KS Test--------\n", + "Statistic = 0.07407407407407407\n", + "Accept Hypothesis\n", + "P value = 0.9987375782247235\n", + "-----chisquare Test-----\n", + "Statistic = -0.30326464715456913\n", + "P value = 1.0\n", + "{'loc': np.float64(466.7783159128223), 'scale': np.float64(214.7439840776729), 'shape': np.float64(0.005714016754089981)}\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gev_series_2 = Distributions(\"GEV\", time_series2)\n", + "# default parameter estimation method is maximum liklihood method\n", + "gev_param_mle_series_2 = gev_series_2.fit_model()\n", + "gev_series_2.ks()\n", + "gev_series_2.chisquare()\n", + "\n", + "print(gev_param_mle_series_2)\n", + "# calculate and plot the pdf\n", + "pdf, fig, ax = gev_series_2.pdf(plot_figure=True)\n", + "cdf, _, _ = gev_series_2.cdf(plot_figure=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Fitting distribution using L moments method" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----KS Test--------\n", + "Statistic = 0.07407407407407407\n", + "Accept Hypothesis\n", + "P value = 0.9987375782247235\n", + "{'loc': np.float64(464.8250207300632), 'scale': np.float64(222.12098731051674), 'shape': np.float64(0.010122582419885787)}\n" + ] + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gev_param_lm_series_2 = gev_series_2.fit_model(method=\"lmoments\")\n", + "print(gev_param_lm_series_2)\n", + "# calculate and plot the pdf\n", + "pdf, fig, ax = gev_series_2.pdf(plot_figure=True)\n", + "cdf, _, _ = gev_series_2.cdf(plot_figure=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-08-18 21:40:08.668\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mstatista.confidence_interval\u001b[0m:\u001b[36mboot_strap\u001b[0m:\u001b[36m110\u001b[0m - \u001b[34m\u001b[1mSome values used top 10 low/high samples; results may be unstable.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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i6+vL3r17OXz4cLbzeXp6cuTIEQ4ePIiNjQ0eHh60bNkSCwsLJk+ezIwZM3j06BFz5syhcuXKha63IAjln1arZdu2bTx58iTXMkqlMltyyuIkBr8KxapVq1b8888/JCQkMG7cOHr27Mn8+fMxMzOjZs2aQGa0vnfvXnr16sUHH3zAoEGDUKvVHDhwgCZNmuR7jSFDhrB3715u3LjBiBEj6Nu3L0uWLKFatWrymBR7e3tWrFjB8ePHadu2Lc2aNcv1fN999x2fffYZO3bsoHfv3rz55pvEx8dTqVIlAJYvX0779u2ZOnUqr7/+Og0aNMg3gMrL1q1b8fX1pX379rzxxhtcunSJzz77jHPnzul0RU2YMIFp06axfPlyBg4cSEhICBs3bsx2vs8++4wqVaowaNAgmjVrxq5du3BycmLLli08fvyYfv36sXTpUn788Uf5/0AQhJfT+fPndbpwlEolKpWKli1bolKpUCgUaLXaQmWhLiyFlNdoF0GHWq3GysqKuLg4LC0tdfZlZcvz9PQs8Sx5glDRic+XIBQPrVbLrl27uHTpEpIkYWdnJw9wffLkCZs3byYqKkoeL1jQWTzPyusZ+izRlSMIgiAIL5FnUxEolUq6detGeHi4nP8payxJ1sDYffv2ER4ejkajeaHARF8iMBEEQRCEl4QkSRw+fJj4+Hj69euHUqnE1NSUCRMmZJv1CJkD5vv164ckSTnuLw5ijIkgCIIgvCROnDjByZMnuXz5Mnfv3pW35xd0lFRQAiIwEQRBEISXwqlTpzh27Bjw37IfZZEITARBEAShgjtz5gwHDx4EMvM1Pb+WVlkiAhNBEARBqMAuXLjAvn37AGjbti1t27Yt5RrlTQQmgiAIglBBJSQksHfvXgBatmxJx44dS7lG+ROzcgRBEAShgrKwsGD48OHcvn2bbt26legg1sISgYkgCIIgVDBarRalMrNTpEaNGtSoUaOUa6Q/0ZVTxoWEhLB69WpCQkJKuyqCIAhCOXDv3j1WrFiR78J8ZZUITMq4wMBAHj16xJkzZ0rsmnPnzsXCwqLErlecOnToQO/eveX3RXlva9euRaFQyB/+2NhY5s6dy/Xr1/U+x4oVK7Kt2xMcHEzPnj0xNzfHxsaGV155Jcc/MDdu3KBr166Ym5vj7OzMhx9+SFpaWr7X/PTTT3FycsLNzY21a9dm2z9u3Djefvttve8B4M8//6Rbt27Y2tpiZGSEh4cHEyZM4NatW3KZatWqMWXKFPn9woUL6dq1a4GuIwhC3kJCQti0aRPR0dGcPHmytKtTKCIwKcOy1gcBuH79OklJSaVco/LvjTfe4OjRo0Vyrl69ehEQEIC1tTWQGZjMmzdP78AkKSmJBQsWMGPGDHmbWq2mU6dOPHnyhI0bN/L999/z999/06tXL7RarVwuJiaGTp06kZaWxvbt2/nss89YtWoV7733Xp7XPHDgAEuWLGH58uW8+eabvPHGG9y8eVPef+bMGfbu3cu8efP0/jnMmDGDfv36YWVlxU8//cShQ4eYPXs2169fZ9iwYbkeN3nyZM6cOVNk/x+C8LILCwtjw4YNpKenU6NGDXr16lXaVSoUMcakDLt48aK8poEkSVy6dAlfX99SrlXZk5ycjKmpqV5lq1SpQpUqVYrkug4ODjg4OBT6+M2bN5Oenk6/fv3kbd9//z1xcXFcvHhRXhm5Vq1aNGvWjD/++IMBAwYA8MMPP6BWq9mxYwe2trYAaDQaJk2axMyZM3VWJX7WwYMHGTVqFEOHDgXg119/5fDhw9SpUwdJkpg6dSoLFiyQg6387N27l88//5xZs2Yxf/58eXu7du0YN24cu3fvzvVYa2trBg0axLJly8rFTAFBKMsiIyNZv349qampuLu7M2zYsBJZ16Y4iBaTMkKtVhMeHq7zCgoK0glMzp49m62MWq0ulfpeuXIFPz8/zM3NsbKyYvDgwTx8+FDe//rrr+vMlX/69ClKpVKn2yIhIQFDQ0O2bNkibwsODpa/fZubm9OrVy/+/fdfnWsrFAoWL17M9OnTcXZ2xtHRUe96P9+Vc+zYMRQKBfv372fo0KFYWFjg5ubGxo0bAfj2229xc3PD1taWN954g9TUVPnYZ7ty7t+/j4eHBwBDhgxBoVCgUCi4f/9+rnVZt24d/fr10/njceHCBby9veWgBKBp06bY2dmxa9cuedu+ffvo0qWLHJQADB06FK1Wy4EDB3K9Zmpqqk4QZ2ZmJt/T2rVrycjI4PXXX8/1+OctWbIEJycnZs2aleP+Z7vRcjJkyBD27NlTbvvCBaEsePr0KevXryclJYXKlSszYsQIeSG+8qh8hlPlTF79/kqlEpVKxbZt23Qe7DmJiYlh1apVOtuqVq3K6NGjs5VVKBTF9osZEhJCu3btqFGjBr/99hspKSl8/PHHtG/fnsuXL1OpUiXatWvHhg0bSElJwcTEhBMnTmBsbMyFCxeIj4+nUqVKnDp1Co1GQ7t27QC4e/curVq1on79+qxduxalUsnChQvp3LkzN2/exNjYWK7DsmXLaNmyJT///DMajeaF7+nNN99k7NixjB8/np9++olXXnmFS5cucfXqVX744Qfu3r3Le++9R/Xq1Zk5c2a2411cXNi+fTsDBw7ks88+k1sAXFxccrxecnIyp06dYsyYMTrbU1JSdO4zi7GxsdytB5njS1577TWdMtbW1ri4uHDjxo1c77NZs2bMmTOHyZMnc/fuXS5evMiyZctQq9XMnDmTbdu2ySP586PRaDh58iSDBg0q9O+ar68vGRkZHDt2jMGDBxfqHILwsjt48CCJiYk4OzszevToHP+GlCciMCkBixYtynVfrVq1GDlyJD4+PoSFhRX4IRsSEpLj+V1dXRk/fnyB66qPb775hvT0dA4cOCB/Y2/cuDH16tVj7dq1TJ06lXbt2pGamkpgYCDt27fnxIkTDBgwgAMHDnDy5Em6d+/OiRMnqF27ttw6MG/ePGxtbTl48CAmJiYAtGrViurVq/Pzzz8zadIkuQ62trZs3769yObkDxkyhNmzZwPQvHlztm/fzqZNm/j333/lh+6xY8fYsmVLjoGJsbExjRs3BjL/T/NL93zx4kXS09Np2LChzvZatWqxZs0ane6phw8fEh4ertPSExMTk2N3i42NDdHR0bled8SIEfzvf/+jevXqQOY4jzZt2jBt2jS6dOlCq1at8qz3s6KiokhNTcXNzU3vY55nbW2Nm5sbgYGBIjARhELq378/+/fvp1u3bvLfzvJMdOWUEd7e3vj7++tVVqFQYGdnV2q/gH///TedOnXS6UaoW7cu3t7e/PPPPwB4eHhQpUoVTpw4AWSuaNmhQwfatm3L8ePH5W1ZrSWQOTCzb9++qFQqNBoNGo0GGxsbGjduzNmzZ3Xq0KNHjyJNFPTs7BArKyscHR1p166dTktA7dq1i2zadnh4OEC2MSrjx49HrVYzYcIEwsLCuHPnDmPHjkWpVBbJ/apUKnbt2sWDBw+IiIjgu+++48aNG6xdu5YvvviCiIgI+vbti62tLc2aNSMoKCjfc75ovezt7eWfhyAI+nn2S6ypqSn9+/fHzMysFGtUdESLSQn46KOPct33bLO5g4MD06ZNY+/evTrN9s/z8vKib9++8viTnBRndr+YmBgaNWqUbbuTk5POt/WslhK1Ws2lS5do164diYmJbN26ldTUVM6cOaPTqvP06VOWLl3K0qVLs53byMgo27WK0vOtD0ZGRjluS0lJKZLrZZ3n+SbXOnXq8PPPP/P222+zfv16AAYOHEjPnj2Jj4+Xy9nY2BAXF5ftvDExMToBY26ebeV45513+PDDD3FxcWHo0KGoVCpCQkL47rvvGDRoELdv38728wfk4Di/Lsj8GBsbk5yc/ELnEISXSWJiIuvWrcPHx6dML8ZXWCIwKQE5/VHPjYWFBR4eHnkGJu7u7qU6sMnW1pbHjx9n2x4ZGUnt2rXl9+3ateO9997j2LFj2NvbU7duXRITE5k+fTpHjx4lNTVVZ4Csra0tvXr10umyyVKpUiWd9+UhrXJesoKH2NhYnJ2ddfaNGTOG4cOHc+vWLWxsbKhcubIcjGapW7dutrEkcXFxhIeHU7duXb3r8ccff3D37l3+/PNPAA4dOsSvv/6Kubk5kydPZsaMGdy6dYv69etnO1alUtG6dWsOHz6MRqMp9AyA2NhYvLy8CnWsILxskpOT+e2333jy5AmnTp2iUaNGFaL75lmiK6cMCg8Pz3UAolKpJCwsrIRrpKtNmzYcPnyYmJgYedvNmze5fPkybdq0kbdltZB8/fXXcpdNo0aNMDU1ZfHixVStWpVq1arJ5bt06cLVq1dp3LgxTZs21XnVqVOnxO6vsLICUH1aVbLu5969e7meq379+lSuXJkjR45w69Ytxo4dK+/v0aMHhw4dIjY2Vt62ZcsWlEol3bp106u+qampvPfee3zzzTc6wXNWvpzExESAPFvm3nvvPSIiIli4cGGO+7MWD8uNVqvl4cOH5eL/VxBKW2pqKhs2bCAiIgJzc3PGjBlT4YISEC0mZVJoaKi8zoFSqaRp06YEBQWRkZGBVqslNDS02OuQkZHB1q1bs21v3rw57777LmvWrKFbt258/PHHpKSk8Mknn+Dm5qbz8Kxbty6Ojo4cP36cb7/9FgADAwNat27Nvn37GDVqlM65582bR7NmzfDz88Pf3x8nJyciIiI4fvw4bdu2ZcSIEcV6zy/K2dkZa2trNm3ahIeHB8bGxjRs2DDHFjMPDw9cXFw4d+4cPXr0kLcnJiYyd+5c2rVrh4mJCadPn2bRokXMnTtX5+E9ceJEli9fTv/+/Zk5cyaPHj3igw8+YOLEibnmMHneV199Rd26dXWSMHXq1InFixdjZWXFr7/+SpUqVfIMGnr27MmHH34oZ7wdPnw49vb23Lt3j19++YW4uDh69uyZ6/E3b94kISGhzC/DLgilLT09nU2bNvHo0SNMTU155ZVXsLe3L+1qFQvRYlLGaDQaOaeDjY0N/v7+8oM6q/n/6dOnRTJFNi8pKSkMGTIk2+vEiRNUrVqV48ePY2Njw6hRo/D398fb25tjx45l63LJail5dpBr+/bts20DqFmzJmfOnMHOzo5Jkybh5+fHjBkzSExMzDZ7pSxSKpWsWbOGe/fu0blzZ5o1a5Zn69bgwYPZt29ftnNcuXKFcePG0adPH7Zt28b333/Pxx9/rFPOxsaGw4cPo1Kp6N+/PzNmzOCNN97g66+/1quuoaGhLFmyJNt4nm+//RZnZ2cGDx7M1atX2bJlS75dkZ9//jk7d+4kOjqa1157jc6dOzNnzhzq1q2rk6MmJ/v27cPd3T1bWn5BEP6j0WjYvHkzDx48wNjYmNGjRxf5OLuyRCHl1U4r6FCr1VhZWREXF4elpaXOvqz08Z6eni80Mjo5OZl169bh4uJCz549dcaSpKWlsW/fPsLDwxk7dmyFbMJ7mVy+fJnGjRtz9+5d3N3dS7s6paJZs2b06dNHnqqdm6L6fAlCeXT16lW2bduGoaEhr7zyClWrVi3tKhVKXs/QZ4nApABKIjCBzD79vAZ35rdfKD8GDBiAh4eH3i0dFcmJEyfo378/d+/ezTcFvghMhJddQEAAzs7Ocobp8kjfwER05ZRB+QUdIiipOL744gu9x4RUNGq1ml9//VXvdXkE4WUiSRLp6enye19f33IdlBSEGPwqCKWoVq1avP/++6VdjVKR3zo6gvCykiSJPXv28PjxY0aNGlXuU8wXlGgxEQRBEIQyQpIk9u/fz7lz5wgJCXnhBIblkQhMBEEQBKGMOHr0KIGBgQD07duXWrVqlXKNSp4ITARBEAShDDhx4gR///03kJkjKGth0JeNCEwEQRAEoZQFBARw9OhRIHNR0Zc5t48ITARBEAShFCUnJ8srs3fo0IFWrVqVco1KV6kHJidOnKBPnz64urqiUCjYuXNnrmUnTpyIQqHIlq0yOjqaUaNGYWlpibW1Na+//joJCQk6ZS5fvkzbtm0xMTGhatWqfPHFF8VwN0UvJCSE1atXExISUtpVEQRBEIqBqakpY8eOpVOnTtkyYr+MSj0wSUxMxNvbmxUrVuRZbseOHZw+fTrHnA+jRo3i2rVrHDx4kN27d3PixAn8/f3l/Wq1mm7duuHu7s65c+f48ssvmTt3LqtWrSry+ylqgYGBPHr0iDNnzpTYNceOHZvjarIA77zzjs7Ce2Xd/fv3USgUOuv+VKtWjSlTphTJ+Tt06KAz7fXYsWN89tlnBTpH8+bNs/3+r1mzhrp162JsbEzNmjVZvnx5jsf+/PPP1K5dGxMTE7y9vdm9e3e+14uMjKRHjx5YWlrSpk0b7ty5o7M/OjoaR0dHzp07p/c9JCQkMG/ePOrXr4+ZmRnm5uY0b96cr7/+Wl7U8NixYygUCoKCgoDMBfzq1KnDhg0b9L6OIFQkzy746eDgQNu2bUWeKgCpDAGkHTt2ZNseGhoqVa5cWbp69ark7u4uffPNN/K+69evS4B09uxZedu+ffskhUIhPXr0SJIkSfr+++8lGxsbKTU1VS4zffp0qU6dOgWqX1xcnARIcXFx2fYlJiZKQUFBUmJiYoHOmZfExERp/vz50ty5c6X58+cX6bnz8uqrr0peXl457nv77bcld3f3EqlHUbh3754ESFu2bJG3nT9/Xrp3716RnP/atWvSjRs35Pdz5syRzM3N9T5++/btkoODg5SUlCRv27x5swRIb7/9tnTgwAFp1qxZkoGBgbR8+XKdYzdt2iQpFArpk08+kY4cOSJNmDBBUqlUUkBAQJ7XHDFihNSlSxdp//79UqdOnaTWrVvr7J80aZL0xhtv6H0PT548kerXry9ZWVlJc+bMkQ4ePCgdPHhQmj9/vuTg4CAtXbpUkiRJOnr0aLbP6i+//CLVqFFDSk9Pz/MaxfH5EoTSdOPGDWnx4sXS3bt3S7sqJSavZ+izynyCNa1WyyuvvMIHH3yAl5dXtv0BAQFYW1vTtGlTeVuXLl1QKpUEBgYyYMAAAgICaNeunc5iZH5+fnz++efExMRgY2OT47VTU1NJTU2V36vV6iK8s/xdvHhRXnJekiQuXbqEr69vidahvEhOTsbU1FSvskU50r1evXovdPzSpUsZMWKETt1nz57NwIED5S7Lrl27EhMTw9y5c5kwYYK8ftKcOXMYPnw4n376KQAdO3bk8uXLzJ8/n7179+Z6zYMHD7J3716aNWuGlZUVLVu2JDExEXNzcy5fvszmzZsJDg7W+x4mTZrE3bt3CQwM1Glp69KlC5MnT+bGjRu5Hjts2DCmTp3K7t276d+/v97XFITy7N9//2XLli1kZGRw5cqVlyajq75KvSsnP59//jkqlYq33norx/0RERE4OjrqbFOpVNja2hIRESGXeX4lxqz3WWVysmjRIqysrORXcS6cpFarCQ8P13kFBQXpBCZnz57NVqakg6XnrV27FoVCwenTp+nUqRNmZmZUq1aNX375RadcVvfQvn37qF+/PiYmJjRp0oTTp0/neM6GDRtiYmJC5cqV+fjjj8nIyMh2zYCAALp27Yq5uTkffPCB3nV+visnq26HDh2iYcOGmJqa0r59e+7fv090dDRDhw7F0tKSGjVqsHnzZp1zPduVM3fuXObNm0diYiIKhQKFQkGHDh1yrce9e/f4+++/GTx4sLwtKSmJW7du0a1bN52yfn5+REVFERAQAMDdu3e5desWQ4cO1Sk3fPhwDh8+rBNQPy81NVUOhLLWnUlLSwPgrbfeYtasWTg4OOR6/LMePHjA1q1bmThxYo7df7a2tnkO5DMzM6NXr16sW7dOr+sJQnl3//59fv/9dzIyMvD09BQZkHNQpltMzp07x7Jlyzh//nyp9Lt99NFHvPfee/J7tVpdqOAk649+TpRKJSqVim3btuWb4S8mJibbuJiqVasyevTobGUVCoXOysTFbfjw4UyYMIHp06fz+++/8/rrr+Pq6kr37t3lMuHh4UyaNIm5c+diY2PD4sWL8fPz4/bt23Jw+fXXX/Phhx/y7rvvsmTJEoKDg+XAZPHixTrXHDlyJP7+/sycOfOFF3aLiIhg2rRpfPzxxxgaGvLWW28xatQozMzMaNeuHePHj+enn35i9OjRtGzZMsfVgN944w1CQ0PZuHEjR44cAchzoarDhw+jUqlo3ry5vC01NRVJkrKloM56HxwcTLt27eRWiLp16+qU8/T0JC0tjXv37mXbl6VZs2Z8//33LFy4kBUrVlCjRg1sbGzYvHkzT58+ZfLkyXr8xDL9/fffSJKk8/9cUK1atWL27NlotVqUyjL/XUkQCi00NJRNmzah0WioVasWgwYNEr/zOSjTgcnff//N48ePcXNzk7dlZGQwbdo0li5dyv3793F2dubx48c6x2k0GqKjo3F2dgbA2dmZyMhInTJZ77PK5MTY2LhI1ihYtGhRrvtq1arFyJEj8fHxISwsDI1GU6Bzh4SE5Hh+V1dXxo8fX+C6FtaYMWP46KOPgMxv93fv3mXevHk6D6zo6Gi2bNlCp06dAGjfvj1Vq1blm2++YdGiRcTHxzNnzhw+/PBDeQBp165dMTIy4r333uODDz7Azs5OPt/EiROZPn16kdQ/Ojqa48ePy92FYWFhTJ06lenTpzNr1iwg84G+fft2du7cydtvv53tHFWqVKFKlSoolUpatmyZ7zXPnj1L7dq1dX7HbGxssLOz48yZM4wdO1bentWyFB0dDWQGqUC2BfCyuiWzyuXkq6++omfPnqxcuRIrKyu2bdtGUlISH3zwAWvWrEGl0v/PwqNHjwB0PqMF5e3tjVqtJjg4OMfuWkGoCMLDw/ntt99IS0vDw8ODoUOHYmBgUNrVKpPKdKj2yiuvcPnyZS5evCi/XF1d+eCDD9i/fz+QueJibGyszgyCI0eOoNVqadGihVzmxIkTOis1Hjx4kDp16uQ6vqSkeXt768wkyotCocDOzg4TE5NirpX+BgwYoPN+0KBBnDt3TqcLxsrKSg5Kst536dJFTr986tQpEhISGDJkCBqNRn516dKF5ORkrl69qnONXr16FVn9XV1ddR6KtWvXBjLHSWSxtrbG0dGxyKZuh4eH59hlMmnSJNasWcPGjRuJiYlh9+7dLFu2DCialaUbN27Mw4cPuXHjBhEREXTu3JlFixbRrFkzOnfuzJ49e/Dy8sLe3p6xY8eSmJiY7zlfpF729vZA5s9DECqqM2fOkJqaipubG8OHDy/QF4CXTan/ZBISEnSmK967d4+LFy9ia2uLm5ubzjdkAENDQ5ydnalTpw6Q2XTdvXt3xo8fzw8//EB6ejpTpkxh+PDh8tTikSNHMm/ePF5//XWmT5/O1atXWbZsGd98802J3GNWS0JOnm3Gc3BwYNq0aezduzfPwYdeXl707dtXHn+Skxd5UKhUKp2A4lkZGRk5dhE9P87HycmJ9PR0nj59Ko/nyekh7OTkJN/r06dPAfDx8cnx2s8HBM+PG3oRz7c8ZA2Uzmn7s1P8XkRKSkqOLXIfffQR//77L6NHj0aSJMzNzfn888+ZMmUKLi4uwH8tI3FxcTqtflktKba2tnle29DQUP4M3bt3jxUrVnD+/HkeP37MsGHD+OWXX+jWrRt+fn4sWLAg11a/ypUrA/Dw4UM5mCuorJ9BcnJyoY4XhPKgd+/eWFpa0qpVK52JGEJ2pd5iEhQUROPGjeWZEu+99x6NGzdm9uzZep9jw4YN1K1bl86dO9OzZ0/atGmjMxbDysqKAwcOcO/ePZo0acK0adOYPXu23i0UL8rIyCjX1/NRs4WFRb4jtN3d3TE0NMzzvC8yvsTBwSHXQcFhYWHZghAgW3daZGQkhoaG8rdhgCdPnmQ7LjIyUn7YZj1Mt2/fztmzZ7O9evTooXNseZ/vb2trS2xsbLbtpqambNiwgcjISC5fvkxkZKQ8DiWriyhr/MjzM15u3LiBkZER1atX17se7733HlOnTqVatWqcPn0aExMThg4dirW1Na+88goHDx7M9dh27dqhUCjkFszCyPoZPP8lRBDKu6SkJPkLpIGBAR07diyS4QEVXam3mHTo0CHPb/7Pu3//frZttra2bNy4Mc/jGjZsKC+OVNaFh4ejVCrRarXZ9imVSsLCwor1+u3bt2fx4sWcOHFCJwuhWq3m6NGjTJgwIdsxO3bs0JmGu23bNpo0aaLThxoXF8eRI0fk7py4uDgOHTokD7b09fXFzMyM0NDQbF1D5YWRkVGeM2KeVadOHXltjJw4ODjIrUzfffcdbdu2lVs5qlevTu3atdmyZQv9+vWTj9m8eTOdO3fW+xvZoUOHOH/+vM7nJy0tjYyMDAwMDEhMTMzz8+nm5sbgwYNZuXIl48aNyzZ9OjY2luDg4DynuWd9pgvb4iIIZZFarWbNmjXUqFGDXr16lfsvUiWp1AMTIbvQ0FB5hoJSqaRp06YEBQWRkZGBVqslNDS0WK/frVs32rZty8CBA5k9ezb169cnLCyML774AgMDgxynbv/666+Ympri4+PD77//zokTJ9izZ49OGVtbW15//XXmzZuHtbU1ixcvRpIk3nnnHSCz22T+/Pl8+OGHhIaG0qFDBwwMDLh79y5//PEH27Zte+HZN8XN09MTjUbDsmXLaNWqFZaWlnIw8bzWrVszf/58QkNDqVKlirx937593LlzBy8vL6Kjo9mwYQNHjx7l5MmTOsfPnTuXUaNGUaNGDTp27MjmzZsJDAzkxIkTetVVo9Hw1ltv8dVXX8nTh1u0aEFGRgYffvghnTp1YsWKFQwfPjzP83z//fd06NCB1q1b8+6779K6dWsgM2vx8uXLmTFjRp6BSVBQEJ6enjqta4JQniUkJPDrr78SGxvL3bt3SU5OLvN/u8oSEZiUMRqNRh5rYWNjw7Bhw3BwcMDHx4fNmzcTFRXF06dP0Wg0xTZ4SqlUsmfPHmbPns2SJUsICwuTB65u27ZN7np51qZNm/joo4+YP38+jo6OrFq1ip49e+qUcXFx4fPPP+eDDz7g33//xcvLi/379+uMFZk2bRqVK1fm66+/Zvny5RgaGlKjRg169+5dLvpl+/Tpw6RJk1i0aBGPHz+mXbt2HDt2LMeyHTp0wM7Ojn379unMoFKpVPz888/cvn0bQ0NDOnToQEBAAJ6enjrHjxgxgqSkJBYvXszixYupU6cOO3bs0DsJ3/Lly3F2dmbIkCHyNicnJzZt2sT777/P6tWr6dWrlzwrKTf29vYEBATw9ddfs3nzZhYtWoRSqcTLy4vp06fn2ML2rH379unkchGE8iIkJIT9+/fj5+cnp5JISkpi/fr1REVFYWlpyZgxY0RQUkAKqSD9KC85tVqNlZUVcXFx2fJTJCUlERwcjKen5wv9EiYnJ7Nu3TpcXFzo2bOnzliRtLQ09u3bR3h4OGPHji0Ts3LWrl3LuHHjePLkSZ7feMeOHUtQUFC2mTUvu2nTpnHhwgU578nL5tq1a3h7e3P79u08x1YV1edLEIrS1q1buXbtGvXr12fQoEGkpKTw66+/Eh4ejoWFBePGjct3IPrLJK9n6LNEi0kZY2pqyoQJE3LsjzQyMqJfv35IkiT6KyuI999/n5o1a3Lp0iW8vb1LuzolbsmSJYwZM0ak5BbKnaxgGeD69et07tyZ7du3Ex4ejpmZGWPGjBFBSSGJwKQMyi/oEEFJxeHi4sLatWtznLFU0Wm1WmrWrMmYMWNKuyqCUGDPr2X2999/ExoaiomJCa+88oreyzoI2YmunAIoia4cQRCyE58voTSp1epsiQa3bNki5w2CzDGBTZs2xdzcXE6pYG5unmeXxctGdOUIgiAIQhHQdy2z53P+uLm5MW7cuOKsWoVU6gnWBEEQBKEs8/HxKfAsSJVKlWsWayFvosVEEARBEPLg7e2Nq6srmzdvJjo6Ot/lQGxtbeVUD0LBiRYTQRAEQciHg4MD/v7+2bIbP8/Ly4sJEyaIoOQFiMBEEARBEPRgaGhIXFxcnmWy1jITCk8EJoIgCIKQD0mS2L17d55LgpTEWmYvAxGYCIIgCEI+UlNTCQkJkd8rlUpUKhUtW7ZEpVKhUChKZC2zl4EITIQ8eXt7o1AoCr0y89y5czl16lQR10qXQqHgq6++yrNMhw4dUCgUKBQKVCoVdnZ2tG7dmk8//ZSoqKhCXXft2rX5rmotCELFYGJiwqhRo+QElzY2Nvj7++Pn54e/v7+c5TVrLTOh8ERgIuTq2rVrXL58GaDQD+B58+YVe2Cir9atWxMQEMCJEydYt24d7du3Z+nSpdSvX1++z4IQgYkgVGySJPHo0SP5vZGREY6OjjRq1EhngGvWwNhGjRrh6OgoApMXJKYLl1HaxGQS955AE/YEAztrzHu3x8C6UonWYcOGDSiVStq3b8+WLVv49ttvy/WgLmtra1q2bCm/7927NxMnTqRFixYMHTqU69evo1SKWF0QhMyg5MCBA5w+fZq+ffvSuHFjsZZZCRF/hcsg9fpdPGg4kMeTFxK7YhNPpn3Jg4YDiPnm1zznzxclSZLYtGkTnTp14r333iMqKoq//vorW7ng4GAGDhyIra0tZmZmeHt7s2nTJuC/NX0++OADuRvl2LFj3L9/H4VCwdatW3XO9c4771CtWjX5fXh4OK+99hrVq1fH1NSUWrVqMXPmTFJTU4vsPt3c3Jg1axY3b97k0KFD8vYZM2bQoEEDLCwsqFy5MiNGjCA8PFze36FDB44fP86ePXvke5s7dy4Ae/bsoWvXrjg6OmJpaUmLFi1y/NkJglA2SZLEoUOHOH36NJC5rlOWir6WWUqqhp0H/y2xZ01ORItJGRO/7SBP3vuCSiN6YjPtVQzdXdFERhG78neiP/sJDFXYTBlZ7PU4deoU9+/fZ/bs2fj5+WFnZ8fGjRvp06ePXOb27dv4+vpStWpVvv32W5ydnbl69aqcujkgIABfX1+mTp3KyJGZda5Xrx7R0dF61eHp06fY2try9ddfY2Njw61bt5g7dy7h4eGsWbOmyO61W7ducn2z/v348WNmzpyJq6srT548YcmSJbRv357r16+jUqn4/vvvGT16NGZmZvL4lipVqgBw7949+vTpw/vvv49SqWTfvn307NmTI0eO0KFDhyKrtyAIRU+SJI4cOSJ3Qffs2ZMmTZqUcq1KRlhkAp8sDeDOgzjS0jMY2rN2qdRDBCZliJSRQfSinzDv1R6HZTPkyFvlZIf93MmQpiH261+xGjcApblpsdZl48aNmJiYMHDgQAwNDRk8eDDr168nISEBCwsLIHNgq5GRESdPnpQXZOrSpYt8jqxuEzc3N50uFH0DkwYNGugMam3dujXm5ua8+uqrrFixosgWc6tatSoAERER8rZffvlF/ndGRga+vr5UqVKFI0eO0K1bN+rVq4elpSUWFhY69wYwZcoU+d9arZaOHTty7do1Vq1aJQITQSjDJEni6NGj/PPPPwD06NGDZs2alXKtSsbpi+F8uuIM8YnpAKzdHkyPdtWoZGFU4nURXTllSOr5YDQPwrGaODTH5kCriUPRxieSdOh0sdZDo9GwZcsWevbsiZWVFQAjR44kKSmJHTt2yOUOHz7M4MGDi231TEmSWLp0KfXq1cPU1BRDQ0NGjRqFRqPh7t27RXod0G2C3bdvH61atcLKygqVSiW3hty6dSvf84WGhvLqq69SuXJlVCoVhoaGHDhwQK9jBUEoPcePH5dnIPr5+dG8efNSrlHx02ol1m2/zvQvT8pBSVUXC1bM7VAqQQmIwKRMyYhRA2Do7pLjflVVZ1AoyIhVF2s9Dhw4wJMnT+jTpw+xsbHExsbSoEEDXFxcdGahREVF4erqWmz1WLp0KdOmTaNfv3788ccfnDlzhhUrVgCQkpJSZNfJyjvg7OwMwNmzZ+nbty+urq6sX7+egIAAua85v+tqtVr69u3LP//8w/z58zl69Chnz56lR48eRVpnQRCKliRJpKdnPpi7deuWrSW0IopPTGPm16f4eet1soaUtGniyo+fdsajilWp1Ut05ZQhWQFJyrnrWPRun21/6oVgkCQM3YsvGID/pgaPGzcu25LdT5484fHjxzg6OmJnZ1eoLIcmJiYApKWl6WyPiYnReb9lyxb69u3LokWL5G3Xr18v8PXys3//fgBatWoFwI4dO7CysuJ///ufPEvnwYMHep3rzp07XLhwgZ07d9KvXz95e3JychHXWhCEoqRQKOjSpQu1a9fG3d29tKtTIs5dfcyp85mD+hUKeGOIF6P61kWpLN0BvKLFpAwxquOBcbP6xHy9Dm2i7oNM0miIXvwzKjcXTNsW31LaSUlJ/PHHH/Tv35+jR4/qvDZt2oRGo2Hz5s1A5niSrVu3Eh8fn+v5DA0Ns7UUODo6YmhoSHBwsLwtLS2N48eP65RLTk7GyEi3KXHDhg0veos6Hj58yKeffkq9evXo1KmTfF1DQ0Odrp2crmtkZJTt3rICkGfr/eDBA06ePFmk9RYEoWhcv35dzjuiUChemqAEoEOLKgzsVgNLCyO+nN6GV/p7lnpQAqLFpMyx/+xtwvq9xaOeE7GePAKjBrVJv/OQuJWbSbkQjPP6xSgMDIrt+n/88QcJCQm89dZbOQ7U/OKLL9i4cSNTp05lzpw57N69mzZt2vDhhx/i4uLC9evXSUpK4sMPPwTA09OTP/74g7Zt22Jubk6dOnWoVKkSAwcO5LvvvqNmzZrY29vz3XffZZv/37VrV5YtW8Z3331H7dq1+e2337hz506h7y02NpbTp08jSRLR0dGcOnWKH374AWNjYzZv3iy3jnTt2pWlS5cydepUBgwYQEBAAOvXr892Pk9PT9atW8euXbtwcXHB1dWVunXrUqVKFWbMmEFGRgYJCQnMmTOHypUrF7regiAUj4CAAA4cOED16tUZOXIkBsX4t7Us0GqlbIHH5NHejOhTBye7oplMUCQkQW9xcXESIMXFxWXbl5iYKAUFBUmJiYkvfJ2UizekR4Peke7Yt5FfIT0mSkl/n3vhc+end+/ekpubm6TVanPcv3TpUgmQ7ty5I0mSJF27dk3q27evZGlpKZmZmUmNGjWSfv/9d7n833//Lfn4+EimpqYSIB09elSSJEl6/Pix1L9/f8nS0lKqXLmytHTpUuntt9+W3N3d5WPj4+OlsWPHSjY2NpKNjY00fvx4adeuXRIgnT17Vi4HSF9++WWe99W+fXsJkABJqVRKNjY2UsuWLaX58+dLT58+zVb+888/l6pUqSKZmZlJXbt2lW7dupXtOqGhoVLPnj0la2trCZDmzJkjSZIknTlzRmrWrJlkYmIi1apVS1q3bp306quvSl5eXnnWUchdUX6+BEGSJCkgIECaO3euNHfuXOnIkSOlXZ1iFxWbLE2df1Q68M+DUqtDXs/QZykkqRSzqJQzarUaKysr4uLiss1ESUpKIjg4GE9PzyKbxpoeGklG2GMM7G0wrF6lSM4pCOVRcXy+hJfXmTNn2LdvHwBt27alY8eO5T4xWl5i4lJ4feYhnsakYGxkwA/zO1LDzbrE65HXM/RZYoxJGWZYxQmT5g1EUCIIglBEgoKC5KCkdevWFT4oAbC2NKa5d+asw0rmhqSmafM5onSJMSaCIAjCS+HChQvs2bMHAF9fXzp37lzhgxLIHNT77tjGGBkaMHagJ7ZWJqVdpTyJwEQQBEF4KTg4OGBsbEzjxo3p2rVrhQ1Kwh4nEhaZQNMGTvI2YyMD3hvXuBRrpT8RmAiCIAgvhSpVqjBx4kSsrKwqbFBy5nIE8787gyZDy6pPO+PmWrKr0hcFMcakiImxxIJQ9MTnSiisK1eu6CSCtLa2rpBBiVYr8evOYD74/B/UCWkkJWv4fuPl0q5WoYgWkyJiaGgIIKc0FgSh6GR9rrI+Z4KgjytXrrBjxw6MjIyYMGECNjY2pV2lYpGQlM5nK8/yz7n/ArBWPi58/GbBFyDUJiSRdOg0GbFqDKu6YNqhabHmzsqJCEyKiEqlQqVSERMTg7W1dWlXRxAqlJiYGPkzJgj6uHbtGjt27ECSJLy8vCrs3+V7oXF88k0AIeEJQGZq+dcGe/FKv4KllpckibjvfydmyTq08YmgVIJWi6qyI/ZfvId5t9bFdQvZiE95EVEoFFSuXJkHDx5gYmKCpaVlhWwuFISSJEkSarWa6Oho3N3dxWdK0Mv169fZtm0bkiTRqFEjevfuXSF/d46cDuHzH4NITs0AMqcCz5rcnJaNcl4INi+x324gesGPWL0xCKs3h6Gq6kzqpZvEfP4zEWM+xmXzV5i1b1rUt5AjkWCtAPJLDiNJEg8fPiQqKkr0iQtCEVEoFNjZ2eHm5lYhHy5C4YWEhLB//378/PyoWrUqAMHBwWzduhWtVou3tzf9+vWrcL83mgwtP/5+hc17bsvbarlb8+k7LXF1sijw+TLUCTxoMBDLV/tiP3+Kzj5JoyFswDtIqWlUObDqheqtb4I10WJShLIWgKpcuXK2lXMFQSgcIyMj0YUj5CgwMJBHjx5x5swZqlatyoMHD+SgpGHDhvTt27fCBSUxcSnMXR7IhetP5G1+bdyY9roPJsaF+5wk7jmBlJyC9aTh2fYpVCqsJw0jYsxM0u48xKimW6Hrri/xaS8Goi9cEASheGUtUwCZXTc9evTA1dWVatWqYWZmRr9+/eSFOSuK63eimLX0NE+iM1cxNzBQMGW0NwO71XihACwjKhalhRkqZ/sc9xv+fzCS8SQGRGAiCIIgCNldvHhR7jKXJIlLly7h6+vLiBEjUCqVFSookSSJXUfusWzdRdI1menk7axNmP92SxrUyTmYKAhVZUe08Ymk33uEoUf2ldBTL9/KLOfq8MLX0qs+JXIVQRAEQSgktVpNYmKizragoCCdwOTs2bNUq1ZNp4y5uXmeYxnKg9S0DL5Ze4G9x+7L2xrWsWPuWy2xtzEtkmuYd2+L0roS0V+txfG7mTqtL9qkFGKXb8SkjQ+G7q5Fcr38iMBEEARBKNO2bdvGw4cP8ywTExPDqlW6gzPd3NwYN25ccVat2K3fGawTlAzyq8nkUQ1RqYquRUhpaozd/Ck8eWsR2vgErCcOw7CaKynng4n55lfS74VSedl3RXa9/IjARBAEQSjTfHx8CAsLQ6PR6H2MSqXCx8enGGtVMkb2rcvfQWGEPU7kgzea0K1N8YzxsBzRE6WJMdGLVhPWb6q83bhZfVx3fouxd51iuW5OxHThAtB3qpMgCIJQtJ48ecLmzZuJjo7OMx2DQqHA1taWYcOG4eBQMmMiiltIeDypaRnUdLcu9mtJWi2pF4LJiFZj6OaMUR2PIju3vs9QEZgUgAhMBEEQSk9aWhobN27kwYMHuZapX78+ffv2LZfLFyQmpbN8/SVeHeiJi4N5aVenyIk8JoIgCEKFkpaWRkhISJ5l3N3dy2VQEhaZwIdfnuRhWDx3HsSyYm5HjI1Kdo2asqLizKcSBEEQKjQLCwuqVKmS636lUqmzknB5UsnCiIyMzA6MsMeJ3H+kLuUalR4RmAiCIAhl2rMjDpKTM5OLKZVKVCoVLVu2RKVSoVAo0Gq1hIaGllY1X0glcyMWvOuLVy1bflrYmToeFXMlZH2IwEQQBEEos27fvs3q1atJTExEo9Hw9OlTAGxsbPD398fPzw9/f39sbW0BePr0aYFm75SWWHUqT2OSdbbVcLPi+7kdqVyI9W4qklIPTE6cOEGfPn1wdXVFoVCwc+dOeV96ejrTp0+nQYMGmJub4+rqypgxY7I11UVHRzNq1CgsLS2xtrbm9ddfJyEhQafM5cuXadu2LSYmJlStWpUvvviiJG5PEARBKKTbt2+zefNmwsLCOHnyJOnp6Tg6OtKoUSMmTJggz7pxcHDA39+fRo0a4ejoWOYDk+B/o3nj40PMWhogZ3LNUtHW9imMUg9MEhMT8fb2ZsWKFdn2JSUlcf78eWbNmsX58+fZvn07N2/epG/fvjrlRo0axbVr1zh48CC7d+/mxIkT+Pv7y/vVajXdunXD3d2dc+fO8eWXXzJ37txsyXgEQRCEsuHWrVts3ryZjIwMPD096dy5M6ampkyYMIF+/fphaGiIlJqGpM18sBsZGdGvXz8mTJiAiYlJidRRStcgpRcsCNp99B5T5h3jcVQy125H88vWa8VUu/KrTE0XVigU7Nixg/79++da5uzZszRv3pwHDx7g5uZGcHAw9erV4+zZszRt2hSAv/76i549exIaGoqrqysrV67k448/JiIiAiMjIwBmzJjBzp07uXHjht71E9OFBUEQit/Nmzf53//+h1arpV69egwcOBADg8wZKlJqGnGrt6Fe9yfp90JBZYB59zZYTx2JiU+9Yq+bJEkk/nGUuFVbSDl7FQCTZvWxmjAUi34dcz0uLT2DZWsvsuvoPXlb/dp2zH+76FLLl3X6PkNLvcWkoOLi4lAoFFhbWwMQEBCAtbW1HJQAdOnSBaVSSWBgoFymXbt2clAC4Ofnx82bN4mJicn1WqmpqajVap2XIAiCUHzyC0rCR3xA1MJVGDfxxGH5TOw+mUDarfs86jWJhD0nir1+0Z/+SOT4OShMjHBY8gH2X70PxkZEvjGbqAU/5nhMZFQSU+Yd0wlKBnarwbJP2r80QUlBlKs8JikpKUyfPp0RI0bI0VZERASOjo465VQqFba2tkRERMhlPDx0s9c5OTnJ+2xsch79vGjRIubNm1fUtyEIgiDkICMjg4MHD+YYlADEfv87yYFXcN3yNaatG8vbrfyHEDlhHo8nL8C07XYMLItn8GjyyQvELt+A3fzJWL85/L/rv9qPmO82Ej1vJWadW2Lq6y3vO3ftMfOWBxKrTgXAyFDJB280wa+te7HUsSIoNy0m6enpDB06FEmSWLlyZYlc86OPPiIuLk5+5ZfYRxAEQSg8AwMDRo8eTYsWLRg0aJBOUCJptajX/UGlId10ghIAhaEK+8/eRkpJI2HLgWKrX9yanRjWcsdq4rBs+6wnDcewRlXUa3dm1leS2LT7JtM+OyEHJS4O5qyc10kEJfkoFy0mWUHJgwcPOHLkiE7flLOzM48fP9Ypr9FoiI6OxtnZWS4TGRmpUybrfVaZnBgbG2NsbFxUtyEIgiDkIDExEXPzzBTs1tbWdO/ePVsZbWw8mkePMevcMsdzqJztMW5Qi9Srt4utnmnX7mDWpWWOM2cUSiVmnVuSdOwMScnpfL7qHEcD/8up0sLbiVmTW2BpYZTtWEFXmW8xyQpKbt++zaFDh7Czs9PZ7+vrS2xsLOfOnZO3HTlyBK1WS4sWLeQyJ06cID09XS5z8OBB6tSpk2s3jiAIglD8goODWbp0KcHBwXmWUxhlppnXxsbnuF+SJDJi41EYF9+DX2FsmOv1ATJi40lXqpgw+4hOUPLqAE8Wf9BGBCV6KvXAJCEhgYsXL3Lx4kUA7t27x8WLF3n48CHp6ekMHjyYoKAgNmzYQEZGBhEREURERJCWlgaAp6cn3bt3Z/z48Zw5c4aTJ08yZcoUhg8fjqurKwAjR47EyMiI119/nWvXrrF582aWLVvGe++9V1q3LQiC8NK7fv06W7duRaPRcOvWrTzLKi3MMGndGPWmvTmuLpxy6iKa+48w92tdXNXF3K8NCbuPkxGXPTjJiI1H/edR9modePAoc7+5qYpF01rx+hAvDJQiP4nepFJ29OhRCcj2evXVV6V79+7luA+Qjh49Kp8jKipKGjFihGRhYSFZWlpK48aNk+Lj43Wuc+nSJalNmzaSsbGxVLlyZWnx4sUFrmtcXJwESHFxcS9624IgCC+1a9euSfPmzZPmzp0rbd++XcrIyMj3mMRDp6U79m2kxx98JWni/vsbn3zminSvwQAppPPrklaP8xRWethj6W717lJon8lSWkiEvD3lfpgU1PxV6ZJzZ6nPgDVS2xFbpDEf7pcehqmLrS7lkb7P0DKVx6SsE3lMBEEQ9CNJEqnnrxNx4Srnbt2g2cQxuFXPnB157do1tm3bhiRJNGzYkH79+qFU6teAH7fuD57O+AaFkREmTTzJeBpLWvBdjOrXwmXTF6ic7Yvztkg5c4XwVz5CGxuPSZPMvCnJQddIUJmwrFEf/rV2obNvVT4c3wRTk3IxjLPE6PsMFYFJAYjARBAEIX/JAZd4Ov1r0oLvytvSLc1wmT2J0KY15aDE29ubvn376h2UZNGEP0G9cQ/pN+6hMDXBvFe7zEGpz8ziKU7ahCTitx0kJeASALG1avHuOQXJSkPeHNmAIT1qidTyORCBSTEQgYkgCELeUoKuEdb/LYy962D21gh++PsQZnGJeF4KoUZwGBEjOnPITlHooKSsOvDPAxxsTWlczzH/wi+pCpv5VRAEQSi7ouavxKiuB67blxJcyYB0IwNi7StxuosXST1b4fLnKfp19Su3QUlaegZb9t1Gk6G7+F63Nu4iKCki5e+3QhAEQShz1Go1j85eIiXgEtLI7kRERxEUFCTPoJEkiYBaNkjJKdhdvkdkZCTh4eHlaqmPJ9HJvP3pcZavv8Tq/4nF94qLGJkjCIIgvLBt27aRFHiZ7sC2i4HEhWR/cIdnpJJqqOLCnv1ci8ycHuzm5sa4ceNKuLaFE/44kRv3MtdX2/rXbQZ0q4GTnVkp16riES0mgiAIwgvz8fEh3TLzIW0dnZBjGbOEFAzT0kk2z8yorVKp8PHxKbE6vqiGde2ZNLIhzvZmrJjTUQQlxUS0mAiCIAgvzNvbG1dXV+4fCabehYeEVHdEa6D73bfeuftkqAwIqemEnZ0dw4YNw8HBoZRqnL+UVA1GhgYon0mONrh7TXq2r4a5mWEp1qxiEy0mgiAIQpFwcHCg7rezsI5OoPMf53EOiUKVpsH6aQItD1+j7pVQLrWoQZ3G3kyYMKFMByUh4fFMmHWE9Tt1U+UrFAoRlBQz0WIiCIIgFJmbRhmc7dOY5sdv0OWPC/L2ZFMjAtvX5XaDKvRyd8fQsOw+3E+eC2PB92dITNbwy7br1KluQ8tGLqVdrZeGCEwEQRCEImNra8uTKrbsHtES+0g1FnHJpJqoiKxii9ZAiVKpJCwsrLSrmaMMrcSardf4decNeZu7qyWujhalWKuXjwhMBEEQhCJTvXp1bGxsiI6OJtrVhtgqdjRt2pQnQUFIGRlotVpCQ0PzP1EJUyek8emKQAIvRcrbOraowvQJTTETqeVLlPhpC4IgCC8kMDCQ6tWr4+DggEajISYmc0qtjY2NPMDVx8eHzZs3ExUVxdOnT9FoNKhUZeMRdPt+LJ98E0D4k0QAlAqYOLIhw3qK1PKloWz8VgiCIAjl0vHjxzl27BgWFha8+eabKBQKHB0dcXFxoWfPnvJYEgcHB/z9/dm3bx/h4eFlJjDZ//cDvlx9jrT0zEyu1pbGzJ3aAh8vkcW1tIi1cgpArJUjCIKQSZIkjh07xokTJwDo1KkTbdu2lffl1dKQ3/6SkK7R8t36S+w4+K+8zbOGDfPf8RX5SYqJvs/Q0g9XBUEQhHJFkiQOHz7MyZMnAejatSutWrWS9+cXdJR2UPI0JpnZy05z9VaUvK1PJw/efrURRoYls0KxkDsRmAiCIAh6kySJgwcPEhAQAICfnx8tW7Ys5Vrp79KNJ8z5NpDo2BQAjAyVvDO2Mb07epRyzYQsIjARBEEQ9HbmzBk5KOnRowfNmzcv5RrpR5Iktu2/w4oNl8nIyBzB4GhnyoJ3falb3baUayc8SwQmgiAIAgAZT2OI+2kbMRv3kPE0GgNne2xH98XyjYEYWFUCMlPPX7lyhcaNG9OkSZNSrrF+JEnisx+C2P/3A3lbEy9H5kxtgbWlcSnWTMiJCEwEQRAE0h+GE9ZvKhmx8TzxqcH9WrZU1xqiWLae+K0HcP1jOSpHW0xMTHjttddQKsvPiiYKhYKqLv8lSRvZpw5vDPVCZVB+7uFlIv5XBEEQBB6/tQgMlDgcWsXBRi7c9HZjf5PK2O79nqQnUdx842O5bHkKSrKM7luXbm3cmP9OSyaOaCCCkjJM/M8IgiC85NJu3CPl5AXsPpnIlchHZGWRkCSJ/506xvnGVTE6fZXHV2/kc6ayQauVdGbcACiVCj6Z1JwOzauUUq0EfYnARBAE4SWlVqsJDw8n8tjpzPeNahAUFKQTmISHhxPq4YBSgtTLtwkPD0etVpdmtfMUn5DGjK9OMnX+MS4GPynt6giFIMaYCIIgvKS2bdvGw4cPqXYzgjbAuh9WkWZqlK2cKj0DgH2HDhB++zxubm6MGzeuhGurn11H73H6YgQA85YH8vvSHhgbidwk5YloMREEQXhJ+fj4oFKpiKhiQ4ZSQY0b4TmWq34jnHRDA564WKNSqfDx8SnhmupvWK/aNG3giJWFER+/2UwEJeWQaDERBEF4SXl7e+Pq6srmzZu5V9cF78B/UVub8aiaPSgUIEm4346k3vn73PR2w8rFSV6Ur6x4Pr29gVLB7MktSEnV4OxgXoo1EwpLrJVTAGKtHEEQKqK0tDR2bd2Gw5LNVH4YRaytOXE25tg8TcAyLokHNR2JeXcYfQcMkBflKwuexiSz8PuzjB/mRb2adqVdHSEfYq0cQRAEQS+SJOHqUY0DfRrhHBJN9ZvhmCSl8djVitOdPHnsak2v6tXLVFBy5eZTZi87TVRsCg+XxrN6YWdsrExKu1pCERCBiSAIwkssJSWFDRs2EB0djdLAgAg3OyLcdFsflEolYWFhpVRDXZIksf3Av3z32yU5tTxAdFyKCEwqCBGYCIIgvKSSk5P57bffCAsLQ6FQIEkSSqUSpVJJ06ZNCQoKIiMjA61WS2hoaGlXl5RUDV/9fJ4D/zyUtzWu58DcqS1EUFKBiMBEEAThJZSYmMj69euJjIzE1NSUlJTM1XZtbGzkAa4+Pj5s3ryZqKgonj59ikajQaUqncdGWGQCnywN4M6DOHnb8F618R9eX2RxrWBEYCIIgvCSSUhI4Ndff+XJkyeYm5szbNgw9uzZg4uLCz179pTHkjg4OODv78++ffsIDw8vtcDk9MVwPl1xhvjEdABMjQ2YMaEZHVuKLK4VkZiVUwBiVo4gCOVFSEgI+/fvx8/Pj6pVq8rb1Wo1v/76K1FRUVSqVIkxY8Zgb2+fbdrt8/LbXxy0Won1O4P5Zdt1sp5UVV0sWPCuLx5VrEq0LsKLE7NyBEEQXmKBgYE8evSIM2fO6AQmaWlppKSkYGVlxZgxY7C1tQXIN+go6aAkPjGNhSvPcur8f0nf2jRxZeabzbAwKzuzg4SiJwITQRCECiYpKYng4GAArl+/To8ePTAzMwPA3t6eMWPGYGRkhLW1dSnWMnd3Q+L4+OsAHkUmAJm53t4Y4sWovnVRKks2QBJKnghMBEEQKpiLFy/qLMR36tQpatSogYeHBwCOjo6lWb08HT4Vwuc/BZGSmrk+j6WFEbOnNKd5Q+dSrplQUkRgIgiCUI6p1WoSExN1tj2/QvCpU6c4ffo0vXr1wtk58wFvbm5epsbKaTRafth0hf/tuy1vq1XNmgXv+uIiUsu/VERgIgiCUI5lrRCcF0mSyMjI4M8//5S3lbUVgiUg+N9o+X2Pdu6895qPWITvJSQmfwuCIJRjWSsEF0RZXCHYUKVk3tstcbY3Y9prjZkxoakISl5SYrpwAYjpwoIglEVPnjxh8+bNREdHk9efdIVCga2tbZlYIViSJNQJaVhVMtbZnpqWIQKSCkrfZ6hoMREEQSjnshKh1atXL89yXl5eTJgwodSDktS0DBb/GMSEWUeIT0jT2SeCEkEEJoIgCBWAkZER7u7ueZZxd3cvEysEf7P2AvtOPCDscSILVp7Js5VHePmIwEQQBKGCCA8PR6nM+c96WVoheOwAT6wsjDAxNqBba/cST94mlG1iVo4gCEI5d+nSJR4+fEhoaCharbZMrxAM4Oxgzqfv+lLJ3IgabiK1fFmT23IGJUUEJoIgCOXYuXPn2L17N/Bf2viytEJwYlI6v2y7xuuDvTAz/a8bqZFn6Y5zEXKX23IGJUV05QiCIJQDISEhrF69mpCQEHnb6dOn5aCkcePGODo60qhRI50BrlkDYxs1aoSjoyMajabE6nw/VI3/rMNs2XeHxT8GibEk5cDzyxkkJSWVeB1Ei4kgCEI58Py32H/++YfDhw8D4OvrS9euXYGcF9szMjKiX79+JbpC8NHToSz+8SzJ/59aPujqY8IfJ+LqZFEi1xcK5/nlDC5duoSvr2+J1kEEJoIgCGXc899iK1WqREBAAADt27enffv2egUcJRGUaDK0rPr9Kr/vuSVvq+luxYJ3fEVQUsbos5zB2bNnqVatmk6Z4l7OoNQDkxMnTvDll19y7tw5wsPD2bFjB/3795f3S5LEnDlz+Omnn4iNjaV169asXLmSWrVqyWWio6OZOnUqu3btQqlUMmjQIJYtW4aFxX8fgsuXLzN58mTOnj2Lg4MDU6dO5cMPPyzJWxUEQSiUZ7/FarVaTp8+DUDnzp1p06ZNaVZNR0xcCnOXB3Lh+hN5m18bN6a97oOJcak/boTn6LOcQUxMDKtWrdLZVtzLGZT6GJPExES8vb1ZsWJFjvu/+OILvv32W3744QcCAwMxNzfHz8+PlJQUucyoUaO4du0aBw8eZPfu3Zw4cQJ/f395v1qtplu3bri7u3Pu3Dm+/PJL5s6dm+2HLQiCUNrUajXh4eE6r2e/xQKYmZnh6+tLjRo15DJqtboUaw3X70TzxseH5aDEwEDB2682YuabzURQUkaV1eUMylRKeoVCodNiIkkSrq6uTJs2jffffx+AuLg4nJycWLt2LcOHDyc4OJh69epx9uxZmjZtCsBff/1Fz549CQ0NxdXVlZUrV/Lxxx8TERGBkZERADNmzGDnzp3cuHFD7/qJlPSCIBS3NWvW5PstNieluSjfriN3Wbr2IukaLQB21ibMf7slDerYl0p9BP2Fhoby+++/Z+vSeV5RLGdQIVLS37t3j4iICLp06SJvs7KyokWLFnL/akBAANbW1nJQAtClSxeUSiWBgYFymXbt2slBCYCfnx83b94kJiYm1+unpqaiVqt1XoIgCMWprH6LzUlqWgafrwriy9Xn5aCkYR07flrYWQQl5cDFixdZu3ZtvkEJlOxyBmW6fS0iIgIAJycnne1OTk7yvoiICBwdHXX2q1QqbG1tdcp4eHhkO0fWPhsbmxyvv2jRIubNm/fiNyIIgqAnb29vXF1dX2hRvoynMST8cZSMJ9EYuDhg0a8TBtaVirSekU+TmLU0gBt3//tyN8ivJpNHNUSlKtPfeV9KGo2GO3fuYGlpiaurKwDOzs5kZGTg6OiItbU1t27dyvX4klzOoEwHJqXto48+4r333pPfq9XqUkk2IwjCy8XBwYE33niDH374gbi4uFzLeXl50bdvX/mBIUkSMV+uIWbZbwAY2NuQ8TiKqFnLsZ3pj/XEoUVSv6ArkcxbHkjc/y/AZ2xkwAdvNKFbG7ciOb9QNDIyMrh37x5Xr17lxo0bpKam0rBhQwYMGABkfkGfPHky9vb2/PnnnyiVSrRabbbzlPRyBmU6MHF2dgYgMjISFxcXeXtkZCSNGjWSyzx+/FjnOI1GQ3R0tHy8s7MzkZGROmWy3meVyYmxsTHGxsa57hcEQSgOWq2Wffv25RmUQPZvsbHLfiPmyzXYvPcqVhOGYGBrhSYyithlvxE1azlKc1MsX+lT6HpJksTGXTf5afNVtP/fkOPqaM6Cd32p6W5d6PMKRUeSJB48eMDVq1cJDg7WSZBmaWmp00OgUCiwt8/scitLyxmU6cDEw8MDZ2dnDh8+LAciarWawMBA3nzzTSAzsVBsbCznzp2jSZMmABw5cgStVkuLFi3kMh9//DHp6enyh/jgwYPUqVMn124cQRCE0qDVavnjjz+4fPkykPnwyKk75/lvsdqEJGK/3YDVhCHYfvSGvF3lZIf9Z2+TEasm+ss1VBrRA0UhU9I/iU7mtz9uyEFJy0bOzJrUnEoWRnkfKJSovXv38uRJ5uwoMzMzvLy8qF+/PlWrVs0xl41Go+Hp06dA2VjOoMAdgX/99RebNm2S34eEhNC1a1eqVKnC2LFj9RpE86yEhAQuXrzIxYsXgcwBrxcvXuThw4coFAreeecdFixYwJ9//smVK1cYM2YMrq6u8swdT09Punfvzvjx4zlz5gwnT55kypQpDB8+XO5HGzlyJEZGRrz++utcu3aNzZs3s2zZMp1uGkEQhJKUU4p5yBx0HxERgVKpxNLSEoUmA7e7T/C6FELXVBPMNFoUCkW2b7FJR8+gjU/EavzgHK9nNX4wGeFPSAm8Uug6O9qZMfPN5igVMHagJ4vfby2CklIiSRKRkZEcPnyYlStXkp6eDmQGsj4+PjRq1IjRo0czbdo0evbsiZubW64J9tLT08vUcgYFDn1mz57N0KH/9VNOmTKF4OBghg8fzm+//cbs2bNZsmSJ3ucLCgqiY8eO8vusYOHVV19l7dq1fPjhhyQmJuLv709sbCxt2rThr7/+wsTERD5mw4YNTJkyhc6dO8sJ1r799lt5v5WVFQcOHGDy5Mk0adIEe3t7Zs+erZPrRBAEoSTltlCaqakpY8aMISQkhIDPltP1+A1Mk9LA3BRO3magoYo7zWtyuqGLzrdYbVwCAKoqTjleT1U5c7s2vmBfHrVaCaXyvwda26aurP/Kj6ouRTuYVtBPVFQUV69e5erVq3IrB8CtW7fw8vICoGXLlgU6p6mpKRMmTCgzyxkUODC5ffs23t7eQGa3yl9//cWGDRsYPHgw9evXZ968eQUKTDp06JDvqPP58+czf/78XMvY2tqycePGPK/TsGFD/v77b73rJQiCUFyeTzHv5+fH06dP5dTf5ubmON1/Qtu/rqBuXJPqX3+EmVctNE9iUK/eRs1vfsWyUiUu+daUAxNDj8oApARewbRVo2zXTDmT2VJiWK2yXnXUZGhZ/b9rxKpTme7fROehJIKSkhcaGsrevXsJDw+XtxkYGFCrVi3q16+vkw29MPILOkoqKIFCBCYajQalMrMH6MSJE0iSRPfu3QGoXr26PEVXEARByNnzC6Vt2rSJsLAw+vTpI+cjSVzyK6a+3tTYsQzF///NVTnYZI4fUSpQLN/IG0s+RvX/rccmvt4Y1qhK9OLVuG75GoXxf10s2oQkYr5ai3Gz+hjV9SA/kiQxe+lp/jmXOYbFs4YN/brUKNKfgZC3hIQEUlNTsbOzAzKD1fDwcBQKBTVq1MDLy4u6devq9B5UFAUOTOrWrcuGDRto2bIlq1atolWrVvKaNOHh4fIPURAEQdBvobSsQazJycmEh4ejfRBO6sUbOK9bKAclz7J6YxAxS9eTuOcEVq/2A0ChVOKw5APCh71PaPeJWE8cimFtd9Ku3iZ25f/QhD+h8h/L9aqzQqGgs29V/jkXhoGBggxtmUkQXqElJycTHBzM1atXuX//PrVr12b48OFA5qDUwYMHU61aNczNzUu5psWrwIHJrFmzGDJkCOvWrcPAwIDdu3fL+/76669SyT4oCIJQVumzUFqWQ4cOAWAfEUd3QJVLt4uBnTVKSwu00brTiU1bN8b1j2+JXrSax1MWZm5UKDDr6ovT6nkY19O/1aNzq6qERsTTqJ4D3nWLP9vnyyo1NZWbN29y9epV/v33X508IikpKTpjO7LGkFR0BQ5M+vbtS3BwMBcuXKBhw4Y6/Vq+vr40bNiwSCsoCIJQnvn4+BAWFlagGQ0p1uZICgWp54NzDCbS74aijY5D5eaSbZ9JEy9ct36DJuIpGU9iMHCyQ+Vom+f10tIz+PtsGJ1b6SaQfHVgPb3r/DIKCQlh//79+Pn5FTr55qZNm3jw4IH83snJifr16+Pl5fXSprMo1ITk6tWrU7169WzbxSwXQRAEXYVNMa+5n0Lsik2Y9+2AgaWFXEaSJKK/+AWljSXmPdvlei6Vsz0q5/zXq4mMSmL20gCC/40hXaOlezv3gt3gSyy3mVU5ycjI4O7du1y7dg0/Pz9MTU2BzOERCQkJcq6RkliLpqwr1OrCT58+5auvvuLs2bOEhISwY8cOvLy8WLZsGS1atCjwVKXyQqwuLAhCYaWlpfHnn39y7dq1XMvUr19fTjGfdvMej3pNwsDRDuupIzFp6kX6g3DiVm0h+egZHL//hEpD/F6oTuevPWbu8kBi1akAWFoY8b9lPTAzLZk1UcqzpKQklixZImdLnTZtGmZmZjpltFqtThbW5ORkAJ1BzlptZl6akpz1Ulr0fYYWuMXk/PnzdO7cGSsrK9q3b8+xY8dITc38pX706BHffPMNmzdvLnzNBUEQKiAjIyPc3d3zDEyeTTFvVMeDyru/5+ms5Tx5a9F/5/GsjvOvn2Heo22h6yJJEr/vucWPm67IWVxdHDJTy4ugRD/Pz6y6dOkSvr6+AMTHx/PPP/9w/fp1EhIS5GPMzc3x8vKicuX/xg4pcxjc/LIrcGDy7rvv4uvryx9//IFCoWD9+vXyvhYtWoigRBAEIQfJycmEhYUVaKE0o7oeuG75mvTQSDQPw1HaWGJU1+OFvl0nJafz+apzHA38L2tsC28nZk1ugaXI4pojfWZWBQYGynlokpKSOHPmDAAmJibUq1eP+vXr4+7uLgIRPRQ4MDl79izbt2/H0NCQjIwMnX0ODg7ZFtQTBEF42Wk0GjZu3EhkZGShFkozrOKEYS4ZXQviYVg8H39zigeP4uVtrw7wZOygehgoK35XQmHpM7MqLi6OVatW6WxzcHBgwoQJGBgYFGf1KpwCBybm5uao1eoc9z18+FDkMREE4aX2/EwNSZL4888/dYKO0lgo7e+zj1j4w1mSkjNnB5mbqvhkUnNaN3Et8mtVNIWZWaVSqWjdurUISgqhwL/9fn5+LFiwgM6dO2NtbQ1kjiRPTk5m2bJl9OzZs6jrKAiCUG48P1Pj77//5sqVKygUCqytrXF3d6dnz57yWJKshdL27dtHeHh4kQcmGVqJn/93ld/+vClv86hqyYJ3fEVqeT1lzaxau3YtSUlJeZZ9dmaVmGFTOAWelfPo0SNat26NWq2mY8eO7Ny5k+7du3P9+nUUCgWnT5/G0dGxuOpbqsSsHEEQ8vL8TI1evXqxa9cuAHr37o2Pj0+e40OKeqG0WHUq81cEEnTlvy72zr5V+XB8E0xNin/5+vIuOTkZpVKJsbExkDmuZM+ePXke8+zMKkGXvs/QAo/CqVy5MhcvXmTq1KmEh4dTo0YNoqKiGDVqFEFBQRU2KBEEQcjPszM1tFqt/BBr0aIFTZo0KdGF0m7ei8H/k8NyUGKgVDBldENmT2kugpI8SJLEvXv32L59O0uWLOH8+fPyvoYNG9KiRYs8j392ZpVQOIX67bS2tmbevHnMmzevqOsjCIJQLuQ3UwMyg5OqVavSoEEDeVVYc3PzYm9x3Xf8Pkt+OU9aeubsHxtLY+a93ZJGnqJrITfx8fFcvHiRCxcuEBMTI29/dmyQkZERaWlpBZpZJRScCJsFQRAKQd81cEJCQli9erX83s3NjXHjxhVbva7cfMqiH4Pk9141bZn3dksc7czyOOrlJUkSW7Zs4caNG3JQaWRkRIMGDfDx8cHFRTftf2hoaKFmVgn6K3Bg4uGR/xz6u3fvFrpCgiAI5UFhZ2oU90KnDerY07ujB7uP3qN/l+pMecUbI8OKOTOksGvVxMXFYWVlBSBnXZUkCTc3Nxo3bky9evUwMsqe00Wj0fD06VOgdGZWvSwK/JPr169ftsAkJiaG48ePI0kSAwcOLLLKCYIglFWFXQOnJGZqvP1qI1o2cqZds5xXJ64oCrJWTXp6OtevX+fChQs8ePCAyZMnY2+fuZZQhw4d6Nixo/w+r3M4Ojri4uJSojOrXjaFWisnJ2lpafTv358ePXowderUojhlmSNm5QiC8LyCroFTlCRJYsu+2zjZm9O+ecUOQp6nz1o1AOHh4Zw/f54rV67Iy6coFAp5llRB5TdzqqhnVlUkxbZWTm6MjIyYMmUKEydOrLCBiSAIwvOMjIzklWJzUxwzNdI1Wj5beZbDASGYmqhwr1yJapVfni9Mea1VAxAdHc2WLVuIiIiQt1lbW9O4cWMaNWpU6C+XJTmz6mVVpG1NT58+JT4+Pv+CgiAI5VBOYxrCw8M5d+5crscU10wNlYGCrGdgcoqGc1cfV9jARJ+1as6ePYu9vT0WFhZA5niQmJgYDAwM8PT0pHHjxnqNkRRKX4EDk+3bt2fblpaWRnBwMN999x2dOnUqkooJgiCUNc+PaYiLi2Pjxo3yA7IkZ2ooFAo+eKMJj6OSGNqzdoUeT6LPDKiYmBg2btyos83JyYkxY8bk2MUjlF0FDkwGDx6c43ZDQ0MGDhzI8uXLX7hSgiAIZU1SUhLBwcEAXL9+nU6dOvH777/rLGtfnDM1MrQSjyIScHP9L428qYmK5bM7VPhWgMLOgPL19RVBSTlU4E/JvXv3sm0zMTHB0dGxwn84BEF4eT0/puH69es4OzuTmJiIqakpVapUKbaZGuqEND5dEci129GsXtgZVycLed/L8Hf32RlQUVFReZYVa9WUf0U2K+dlIGblCMLLIacxDVu2bNHJCGpjY8PgwYNJTEzE3NwchUKRa1bXF5mpcft+LJ98E0D4k8z61PGw4cdPO6FUVvyAJEvWzJu0tDQ2bdrE/fv3cy0r1qopu4p0Vs6zawXoo7gTCAmCIBQnfcc0/PTTTzrbcsvqWtig5K8TD/jq53NyanmrSkZMHNngpQhKtFotN2/e5MyZMzg7O+Pn54eRkRH16tXLMzARa9WUf3oFJk2bNtXrg5X1rSAjI+OFKyYIglBaSjura7pGy3frL7Hj4L/ytrrVbfj0HV+c7MvXmImCZmdNTEzk3LlznDt3DrVaDUBkZCRdunTBwMCA8PBwsVZNBadXYHL06NHirocgCEKpe/Yh6u/vXypZXZ/GJDN72Wmu3vpvLEWfjh689WojjI3KX2p5fbOzhoWFERgYyLVr1+Qvt2ZmZjRp0oQmTZpgYJB572KtmopPr8Ckffv2xV0PQRCEUvfsQ3TQoEH4+/uzfft2bt68mesxXl5eBR7TIKVrSD51EW2MGpW7C8aN6qJQKLh04wlzlp0mOi4zQ6mhSsm74xrTu6PHC99baXh+JlOPHj1ynSVz9epVLl++DEDlypVp3rw59erV0xkwLNaqeTmI/zlBEARyfohGR0fnOZ4BCj6mQb1+F9Gf/0xG5H8tIkb1anBlwBC+PJdCRkZm64yjnSmfvuOLZw3bgt9MGZFbdtbY2FiCgoKoVasW7u7uADRr1oykpCSaNWtG5co552QRa9W8HAo1K2f9+vX8+OOP3Lp1i5SUlGz7s/oFKxoxK0cQKq5Tp05x6NAheaxc586dCQwMzDObtVKpxNvbm759+wIgaTSkBF5Bq07A0KMKRnV1Wzrift7O0xnfYDGkG9YTh6Fyd0EdcIWbM1di/SiExU0GcdfahSZejsyZ2gJrS+NiveeipM9MJgsLC+zs7Hjw4AEA1apVY8CAAQX6eyrWqim/im2tnN9++43x48czduxYTp06xWuvvUZGRga7du3C2tqaMWPGvFDFBUEQips+Kc7PnTtH27ZtOXjwIOnp6SgUCpRKJc2aNctxTIN6/S6iv1xDRvgT+ZzGTb1wWPwuxt510CYkEbXgRyzH9sPhy/cBCI1I4JOjiTys3YuPY7cw5PZJHs6fyfih9VEZKEvop1E09JnJlJCQoJOQ7v79+2zbti3HmUy5EWvVVHwFDkyWLFnCrFmzmDFjBqtWrWLSpEn4+PgQHx9Pt27d5HUKBEEQyip9pwPv3btXfi9JEo6Ojvj5+WUb0xC98ndiZq/AYnBXrPyHoKrsRMqZK8QsWcejflOpvOd70q7eQUpMxvrtVwAIuBDOpyvOkJCUDkoVh2s14/Xze+jma1/ughIo/ZlMQsVR4N/+27dv07p1awwMDDAwMJC7bSpVqsT06dP59ttvi7ySgiAIRSEkJITVq1fj7u5e4DEIBgYGtGjRAvhvTEOjRo1wrWRN7KKfsXpjEE4rZ2PS2BOVoy0WvdtTedd3qCo7Er1wFZrIKJRWFhhWceLI6RCmf3kyMygB3FwrMX5mLwA0kU+L9qZLiLe3N/7+/lhaWurVqmFnZ4e/vz/e3t4lVEOhvChwYGJlZUVqauaI8cqVK3P9+nV5X0ZGRr7pggVBEEpL1qybmJgY/P39sbOz06vp387OjgkTJug8RI2MjOjXrx9D7KohpaVj/c4r2Y5TWphh/eZwkg6dRmFihDYuAU3YY5o3dKaKc2brcttmrvw4vxMOMZldQCpHuyK628LLCuBCQkL0Kq/Varl27Rp//vknarUaFxeXPMt7eXkxYcIEkTJeyFGBu3KaNm3K5cuX8fPzo2/fvsybNw+tVouhoSGLFy+mZcuWxVFPQRCEF5LTrJtXXnmF1atX64x7eF5+Kc4zIp5i4GiLyinngMKofk2QJIzr10JhZkLMtxtwWPwuC971JfBiBMN714Z0DY++/x2TFg0x9Cj9VYL1zT2SmprKhQsXCAwMJDY2FshsWcqvS19kZxXyUuDA5KOPPpJHVM+fP58HDx7wzjvvoNVqadasGT/++GORV1IQBOFFPT919Z9//uHGjRt5BiWQ/0PUwMGGjKcxZETHYWBrlW1/+u3Mv5dxVjbYzniDqFnLkZJTqfLmMKp1difl5AWiv1xD6rU7uG5fWvgbLCL65B6RJImDBw9y/vx5uQXdzMyMpk2b0qxZM44cOSKyswqFpldg4uHhwahRoxgxYgQtW7aUW0Wsra35448/SE1NJTU1VUyhFQShTNBn1k3WQ1WlUpGRkZFjdld9HqIWfTsSNes7Yn/4H3Yzx+vsS09M4dbCdTyyrcq+zf+ybNYgFEaGRH/5C/Eb98jlDGu547r5K0xbNCzsLReZ3HKPPEuhUPD48WNSU1Oxt7enZcuWNGzYUA7gRHZW4UXoFZj4+Pjw9ddfs2jRIho0aMDo0aMZPnw4VapUAcDY2Bhj4/Iz314QhIpNn1k3Wd/0n59F0rJlywI9RA3srLF+ezQxX/yClJiM1fjBqKo6kRJ4hSefrcY8PIztTQZx9040xwJD6fbaACxH9SLpxDm0sWpUbi6YNG9QKtNc9Qngzpw5gyRJXL16lY4dO2JhYYG5uTkdO3akRYsW1KxZU6fuIjur8KL0TrAWHx/Ptm3b+P333zl8+DCSJNGmTRtGjx7N4MGDsba2Luaqlj6RYE0Qyr6QkBB27NiBWq0u8IKinTp1om3btjx58kR+iCoUCmbOnJnnQ1SSJGK/3UDsst/Qxv/3oDesUZWUaRN5+69o3hzZkD6dPMpUno01a9bkG8DlJLdVlAGSk5NZt25dtuysAGlpaXJ21rFjx2JiYlLougvlj77P0EJlfs360P7+++8EBARgaGhI9+7dGTVqFH379q2wrSciMBGEsm/r1q1cu3aNmjVrEhMTk+8ifJA5w+bVV1/F1dVV3vb8Q1QVrSbxr5Nok5Ixqu2OWacWKJ4LVjISklAfPI1BUjKG1VwxadUIhUJBfGIalcyNiuV+X8SlS5fYvXt3gXOP9O7dO89pviI7q5CTYg1MnnX//n02b97Mxo0buXr1KpUqVZJHZ1c0IjARhLItKSmJJUuWyOMbpkyZwpYtWwgPD8/1GC8vL/r165frAFdtSipPZy4jfuNeUCpQmpqgVSegquKE4/KZmLbJTBCWkqphyc/neRydzJKP2pabJGlZXzRLehVl4eWj7zP0hT85VlZW2NraYmNjA2T+YRAEQSgNzw7c1Gq1bN26Nc+gBDLXa8lr1s2Tdz8n4X/7sZs7CY9be/D4dx9VDq3G0KMy4SM+IPXSTcIiE5g09yj7/3nIhetP+PH3K0V6X8UpK1lcvXr18iwnco8IJaVQgUlSUhKbNm2ib9++uLi4MGHCBNLT0/n222/FNDBBEEqEWq0mPDxc5/XswE0g379H+c26Sbtxj4StB7Ff/C7WE4eitMicNmvsXQfnjV+gcnPh3zk/Mv6Tw9x5EAeAqbFBmVgRWJ8kacnJyezbt4+oqCh5ld/ciNwjQknRe0i0RqNh3759bNq0iV27dpGYmEi9evWYPXs2I0eOpFq1asVYTUEQhP+EhITw22+/kZaWVuBjK1WqRHJysl6zbuK3H0Jpa0WloX7ZdxoZccPHF4/fN5PesSmojKjqYsGCd33xqJI9n0lJyytJmlar5dy5cxw9epTk5GQiIiKws7MTuUeEMkGvwMTf35/t27cTHR1N1apVmTRpEqNGjaJhw9Kfcy8IwssnMDCQtLQ0FApFvgNbn2VgYEDnzp1xdXXVa+qqNlaNytURhZFuS0F8YhoLV54l4UYS7yJhqkmlaYtqzHyzGRZmpd+qkFeStHv37rF//34iIyOBzK6c9u3b89dff4ncI0KZoFdgsn37dgYPHsyoUaNo27ZtcddJEAQhV88+dCEzV0ZsbGy+AYqtrS3D+vTD1rwSBraW+Pv7y7NuNBoN2gfhpJy9isJAiWnrxqhcHVG5uZD++z4yYuMxsK4EwL8P4/jkmwAeRSbQXx1BioEhQ0Y0YeSgBiiVZWOmSU5J0urWrcvBgwfln52JiQkdO3akadOmaLVakXtEKDP0+g2LiIgQv4yCIJQJzz50ATw9PTl37pycMC0nLQ2tqH/8LvHzRxFPZhr5SqN60+ft0WQkJPJ07CySDp/+7wADAyz6dcRm+mtEL1xFzNJfsZ87mcOnQvj8pyBSUjOwSYmnU+hV6NGB0UNKr/VYnyRpZ8+eJTExUQ5K6tWrR7t27XBycgIyk805Ojpmyz2SNTD22QBOPAuE4vbC04WLW0ZGBnPnzuW3334jIiICV1dXxo4dyyeffCLPg5ckiTlz5vDTTz8RGxtL69atWblyJbVq1ZLPEx0dzdSpU9m1axdKpZJBgwaxbNmyfBebepaYLiwIJSunh+6WLVuIiYmR3+fXnVPtVgStD13DpGEdLMf0wcDehuS/z6PesAfDGlWQEpPRJqVgN3si5r3aI6Wnk7D9ENELVmFU1wMzv9ZEf/oD4d6NWa/yIM7IjHrRIfQJPY+VpSluB35E5WxfbD+D/BRVkjSRe0Qobvo+Q8t86Pv555+zcuVK1q1bh5eXF0FBQYwbNw4rKyveeustAL744gu+/fZb1q1bh4eHB7NmzcLPz4/r16/LmQVHjRpFeHg4Bw8eJD09nXHjxuHv78/GjRtL8/YEQcjDxo0b5bEQuckrKDFMTaflkevENa9LjZ0rURgYAGDeoy2VhvcgtPsE0GqpevI3jGpkDRA1wWrcAIw8axDWZzIGo/txoMsAvE8e4cPkCwBolUrMe7XHccGUUg1KIHPJkLCwsAInSfPx8dHZll/QIYISoaSU+RaT3r174+TkxM8//yxvGzRoEKampvz2229IkoSrqyvTpk3j/fffByAuLg4nJyfWrl3L8OHDCQ4Opl69epw9e5amTZsC8Ndff9GzZ09CQ0N1sj3mRbSYCELJWr16NY8ePSrwcTVq1ODBgwfUOH+PJv/c4vh7/Xhj+rRs5e7V6Y02MYnq9w9ky+IKcLuzPzeepPFl/d4oJAn3pChe6VaNjkOao3Is/SnBWUSSNKE8KLEEa8WtVatWHD58mFu3bgGZKZT/+ecfevToAWSOMI+IiKBLly7yMVZWVrRo0YKAgAAAAgICsLa2loMSgC5duqBUKgkMDMz12qmpqajVap2XIAglIykpSSc5mj7f2JVKJcOHD2f06NH4+/vjlKgh1tacsNTEnFsUMjIgNZ2MqLhsu/b//YB/og0wSUwAwM7WlA+/HELXyd3LVFACmWNBXnvtNTnRZW5EkjShPCjzXTkzZsxArVZTt25dDAwMyMjIYOHChYwaNQrIHJgLyIO4sjg5Ocn7IiIicHR01NmvUqmwtbWVy+Rk0aJFzJs3ryhvRxAEPT0/yNXR0THPbh1ra2vGjx8vT4t1cHDAq6kP0Xf/wtHeIceBm8pK5mjjElCYZl/fy9XRHIP4J0SbWOBd1555b7fE1qr0Fp0LCQlh//79+Pn5ZctL8uTJE7Zt20Z0dHSe5xBJ0oTyQK/A5Ouvv9b7hAqFgnfffbfQFXre//73PzZs2MDGjRvx8vLi4sWLvPPOO7i6uvLqq68W2XVy8tFHH/Hee+/J79VqdbY/CIIgvLj8ZpZA5sM3L61bt5aDkiyV+nQg/sctjK7RMNtKtpJWC//fCqMJicDAq6bO/hqP/sUsLpLo1/rxzcx2qFSl28CcV8K0w4cPExkZiYGBAVqtNsfuHJEkTSgv9ApMssZu6KOoA5MPPviAGTNmMHz4cAAaNGjAgwcPWLRoEa+++irOzs4AREZG4uLiIh8XGRlJo0aNAHB2dubx48c659VoNERHR8vH58TY2LjCrpQsCGWJPoNcc8pImiW3h65J8waYtG7Mk7cWoVg+E7MuLVEolWgeRxM9fyWakAhUHpUJG/gOaa8OwePVHig0GhK2HSJm6a+YdmzOkM9GoijlBfnySpgG0KtXLwwNDQkPDycqKkokSRPKNb0+bVqtVu9XRkZGkVYwKSkJpVK3mlnfCgA8PDxwdnbm8OHD8n61Wk1gYCC+vr4A+Pr6Ehsby7lz5+QyR44cQavV0qJFiyKtryAIBVeY3BgKhQKlUolCocj1oatQKHBeswAjz+pEjJrOQ58hhHZ5gweNBpHw51EcV3xM5f2riKxZB5b+QkijQTxsOoyYpb9SaXhPnNd9Js/kKU3PJ0w7evQoR44ckfdXqlSJfv36yV05NjY2+Pv74+fnh7+/P7a2mWNispKkCUJZVubHmPTp04eFCxfi5uaGl5cXFy5c4Ouvv+a1114DMv/wvPPOOyxYsIBatWrJ04VdXV3p378/kJmAqXv37owfP54ffviB9PR0pkyZwvDhw/WekSMIQvHIaZBrfpMFrays5HFm+WUmNbCxxHXHMlLOXCFx93G0iclYDPGj0rDuGFhX4tb9GGZY+2LRtiHV1ZG8Odqb2v1ayZleS5o+CdOCgoIAMDMzkxffMzAwEEnShAqh0NOFU1JSuHv3LikpKdn2PT8//kXEx8cza9YsduzYwePHj3F1dWXEiBHMnj0bIyMj4L8Ea6tWrSI2NpY2bdrw/fffU7t2bfk80dHRTJkyRSfB2rfffisSrAlCKTt16hSHDh2SH7y2trZ5DuKsV68e/fv3lx+8aWlp8kN37Nix2caS6OO3P2/w0+arvDaoHq/09yzV1PIvkjBt7NixIkmaUGbp+wwtcGCSlpbGm2++yW+//ZZrk2BRd+eUFSIwEYQXo08mV2Nj4zzTy/fq1Utn6n+WF3noSpLEjbsxeNYo/WnAly5dYvfu3QVOmNa7d2+8vb2LsWaC8GKKLfPrvHnzOHDgAGvXrmXUqFGsWLECc3NzfvvtN/7991+WL1/+QhUXBKHi2rZtW76tAXkFJXnNLNEnKNFkaFn1+1VsrIwZ0buOzrFlISgB8Pb2llc/FgnThJdRgYeab9myhblz5zJ06FAAmjdvzpgxYzhw4ABt2rRh165dRV5JQRAqhqzxEAWhzyDXLKlX7xC7YhMxy34j+Z/zOg/1mLgUpi36m9/33OLHTVc4d+1xrucpCSEhIaxevZqQkJBs+7LGhdSrVy/Pc4iEaUJFVOAWk9DQUGrXro2BgQEmJiY6TbCjR49mxIgRrFy5skgrKQhCxZA1dsTIyIj09PQiG+Sa8TSGyInzST4ehMLMFIWhAdq4BAzreuC8eh53DCyZtfQ0T6KTAVAoFUQ8SczxmiUlt7wkGRkZREVF4ejoiLu7O9euXcv1HCJhmlARFbjFxMXFhdjYWCBzqu6xY8fkfVlp4wVBEJ73bC6O9PR06tSpk2f5evXqMXnyZBwcHOQWhEaNGuHo6Kgz/kJKSyds2PukXf8Xp5/n43FnL9Vu78V157eggLu9pvLJzD1yUGJrbcKyj9vTq4NH8d1sPp7PS5KUlARkBm5r1qxhzZo1qNVqwsPDs6VLyCISpgkVVYFbTDp06MDff/9Nnz59GD9+PO+//z7BwcEYGRmxc+dORo4cWRz1FAShnHs+xfzTp0/zLO/h4aHTGmBkZES/fv2yDXJN2HOctMu3qHxgFSaNPeXtymYN2dZ/HF2+XEC7+xfZXrMVDevYMfetltjbmBbhnRXc83lJLl68iJmZGfv27SMtLQ0TExOioqIIDQ1Fq9WKhGnCS6XAgcnChQvlPyjvvPMOkiSxdetWkpOTeeutt5g9e3aRV1IQhPJFn1wceQUmubUGSBkZJB06TcL2Q2TExmPo7krajXuZGV6fCUoinyYxa2kAN+7GYOFchxYRN1FMHsPkUQ1LPLW8Pj+LY8eOkZ6eDmRmqu7UqRNWVlbyz8jGxkYe4Orj45Nv7hZBKM8KncfkZSSmCwuCfgqbiwP+S7Dm4ODApEmT5O0ZsfFEjJpOypkrGNWvhaG7CykXbpAR9hjDGlWpGrABhULBuauRzF0eSFx8GgB9Qs7T7+E5aj/YXyT3VlCF/VlUqVKF9PT0bAnToGhytwhCSSu26cKCIAj58fHx4dGjRwXKaaRSqWjXrh2XLl3KsTXg8eQFpN1+gOvObzFt3RgASaPhUa9JpJ4PJub7zeyr2oifNl9F+/9ft1wdzRmg0WBsWLnI7zE3z68C7OPjQ1hYWIHzkjRt2pSGDRvmOA06t24tQagIChyYeHh45PtBuHv3bqErJAhC+eft7c2VK1f4999/9SpvZ2cnd1W0aNEiW/r0tJv3SDpwCseVs+SgBEChUmE3bwphfSYTsegXfmrzBtr/HyzaspEzM1qaEzMkEMtF7xTHbebo+dk2xZmXRAQlQkVU4MCkX79+2T4MMTExHD9+HEmSGDhwYJFVThCE8ikpKYl79+7pVbZ+/fr07dtX7qrIqTUg6UggClNjLPp2zHZ8ZBV3blauSZ1Hd5h4ZR8H3Rrh16UWnZKvE/vKVkyaN6DSyJ5Fd3N5yG0VYAcHB/r168emTZtITk7O9XgvLy+dn4UgvIwKHJgsXbo0x+1paWn0798fD4/Sm4InCELZ8PwMnLzklovj2S9AUpoGhZEhGOr+yTp+5hGf/XAWx8pNmfvoDp6xj2j2+A4EQXwlcyxH98Z25niUJsYvdkN6en62zaVLl2jUqBFHjhzh3Llz+f5MRF4SQSjCMSZGRkZMmTKFiRMnMnXq1KI6rSAIZVx+s07yom8uDuPGddHGJZASeAXTlg3l7bfux5CcoqHR03ukqQyxObwGp/QkpIwMjGq5ozQvvmnB+sy2+eeffzh+/LicZr9SpUokJCTk+LMReUkEIVORDn59+vQp8fHxRXlKQRDKOH3Wv8mNvrk4TNv4YFjLnacfL8N16zcY2GSO6H9tsBdRAdfoeeIS1iN64FK3eAe5Pjuw9dChQ/ned1bitCxZfx9FXhJByF2BA5Pt27dn25aWlkZwcDDfffcdnTp1KpKKCYJQPvj4+BASEqJ31w2AgYEBpqamJCQk6JWLQ6FU4rRqLo8Gvs1D35FUGtYDQzcXUs5dY8gfRzGuVwOneZNyPb6oPDuwtTCzbbKIvCSCkLsC//YPHjw4x+2GhoYMHDhQrC4sCC+ZWrVq6V322VknVlZW2Wbf5OXQUwM2NBnGHJtwErbsJyNGjaG7K3Yzx2M5tn+xdttAzgNb/f399Z5tY21tjVKppGrVqjp5SbLS7RfkZyEIFVmBf/tzGmlvYmKCo6OjmLomCC+hixcv6l32+Vkn+eXiyHrYHwt8xOerzoHCjI8UXvwc8DY2ViWbVCynga1NmzalQYMGnDhxIs/AJOu+VSqVyEsiCPkocGDy4MEDfHx8sLCwyLYvMTGRc+fO0a5duyKpnCAIZcuNGzc4cuQIvr6+ODs7A/oPdIWcZ53k9CBOPHSauB//R/LJC6CVqNWsPgNtPNme7kjbppWxMDd68ZvJQdYYkjZt2mBlZaWzL6eBrf/880+2cSQ50Xe2jQhKBKEQgUnHjh0JCAigefPm2fbduHGDjh07FijboyAI5ceuXbtISkrizz//LPCx+s46iVn2G9ELfsS4ST3sZr+JwkBJwq5j9Nn3O20H9qHJuCGFqLl+ssaQZN1nXvQJSEDMthGEgipwYJLXN6PExERMTUt31U5BEIpHUlJSnsnB8qJQKPSadZJ66SbRC37kUd8+OH/yBlU8bAGwGj+Y2B82w6zvSBrdGbO2TQpVj7w8O4YkOTkZAwODAn/JUigUGBgYiNk2gvAC9ApMTp8+zalTp+T3Gzdu5J9//tEpk5KSwh9//IGnp+fzhwuCUAEUZCxJFjs7O7p27crBgwdznXWiTU4l7dodkCSiftlBgqU1s5Kq4bw0kJ8WdsbSIrPbxmrCUOI37kX9845iCUyeTwrXokULbt68me/A1mc9m05ezLYRhMLR61Oyf/9+5s2bB2R+I/j222+zlTE0NMTT05Pvv/++aGsoCEKJ0ydpWtYqwLl5NtW8h4dHtlknUlo60Z//jPrXP9HGZub30KIg2sIOA62W8CeJnL4YTrc27vL1zLr6krDzSKHvqyBjSK5evYqNjQ1RUVF5ntPT05OoqChcXV3FbBtBKAIKqSDJB8jsLw0ICKBFixbFVacyS98lmwWhvFuzZk2hk6YBGKZq6FG/MfUaN8KoTjUUSiWSJJF2+wFSYjKqKk48mfYlSYcDsRo/iDu1G7J6+3UmBuzAMi2JWw5uWK9dRJvmVXXO++TDJSSfOIfb6Y2FqtfWrVu5du0aZmZmeo8RyU+vXr1o0qRJngNXxWwbQdD/GVrg8F2r1b5QxQRBKPsKmzzMKCWdxqfu4HErHJXmGKGAqporpm2bkBJ0lfTg/083YKCEDC1230znfyp3fvvzJhjZcbRqA/rcP4vnkwc4htyAZwITbWIyCX8cpdKw7oW6p6IYQ/K8rIGtTZs2zbOcCEoEQX/Kgh6wefNmvvzyyxz3ffXVV2zZsuWFKyUIQuny9vbmlVdeKdAxjWvVpdvOc7jdfczVph6cmuCHy/alGNhYEb9+F1JKGs4bv6DKkV8wrOkGhiruz/qBPf8Lks+R0bcrRhamKC0tiPv5vyzTmsfRRL4+GyklFavXCreCeU5jSOzs7PQOGhQKBSqVipYtW8r5SMTAVkEoegUOTBYtWoSxcc4rdZqamrJ48eIXrpQgCKVP3weukZER48ePp01oItaJ6Zx5rTNXm3pwz0iLgWd10m7cxbhxXTT3HmFYvQrGDWqRppE4W6UeGWnpDPj3NAZKBZNHNWT6R11x3fgFUlo6qWev8qj/W4QNeocHjQaRHHgZ53WfYVjNNc/6hISE8OOPP3LhwgXCw8Pl1/NjSK5du0br1q2pXr26Xvdpa2uLv78/fn5++Pv7Y2ubOWMoa2CrIAhFo8BdObdv36Z+/fo57qtXrx63bt164UoJglCy1Go1//77LwEBAXLytKCgoPwPBDp16oSrqyv3f9uN5dDujPz4LXnAZ/y2g0iaDBxXz+dR1/HEb9jDmfbdIUaDoVLNITdv+t09i/fPM2ncNHOQq0nzBph2akHapRsYWFsCEnazJlBpRC8MrCvlW5/AwEAiIiLyzbUSFxenVz6WSpUqUaNGDTGwVRBKSIE/SSYmJkRGRua4Lzw8XHw4BaEcenaF4IImT4uMjERKTSMj/AnGTb100qtHzVmBoZsLRm4uGHrV5Nqxayy6YU8Hpzq8EnyUsAaNMLyjoYatgXy+9PthJB08hd2sCVi/ObxAdXl2HElRUCqV1KhRg379+mXbJ9LIC0LxKHBXTvv27Vm8eHG2qYSJiYl88cUXdOjQoajqJghCCfHy8ir4Qf/X3n2HR1WlDxz/3plJJr2SCknoJZRACJDQm0aJFEUQRQHlJ6hgw1Xs2HFlxbaWRV1sNBHpSJGOhF6kd0lI75OeKff3RzYjAyEkIZAE3s/z5Flz75k75143M6/nvOc9qop3cg5uXy4maezLoNVSuHUv6v8S5BVFQevuijk1k5SELJL+/ItTmaVTHtsDWpPn60vMvt9L2zo5oJrN5K/5g8Thz6Br6Ivr6Luq3KVL80icnJyuOYckISHhqq8TQtScKg9vvPfee0RFRdGsWTPuvfdeAgMDSUxM5JdffqGkpIT58+dfj34KIWpQWT2P6OhogoKCqpQjoSgKbi6udFy5l6A/z5Pn6gDd/VCcHcj7eQ2mpHQCfnofjZMDzsP6k/n+N6y+Zxo9czLY2bwP9nYapkyIol3z/sT3LE2wjes6ChQFtaAIhy7t8J31Blq3y/fjurj/q1atomvXrtY9e+DyWiv29va4urpecZT3YlIcTYi6ocp/aa1bt2b37t1MmzaNRYsWkZGRYa3uOG3aNJo3b349+imEqCEGg4FNmzaRkJDA5s2bGTBgQKXzSaB0dKX3yUwMh+O5MC6aY028GPfIIyjxKST0H0/RH/tJGf86AfM+QBvowwX/YHqc3U+SkwcFjUP4/IV+NDFmk/7iR6jFJfh88iIWQz6g4hAZhkPH1lftQ2XzSLKzs696LckhEaJuqXKBtas5d+4cTZo0qclL1hlSYE3cDL755purTk9UJGbAbXg//A6u9w+iwVuTbXIsivYcIemBF7BkGdB4uqEWl6AWFJHh5IZnYS4aOx1adxfMaVlofbzw+Xgqzrd3r9L7FxQU8OGHH9ZITSWNRkOHDh3KzSEpIzkkQtSM61ZgrTzp6eksWLCAuXPnsmPHDtldWIg6zMPDo9qBiUajIXv7Pjyzc3EddSdgm2PhENGWoJ1zOd/yLhy6d8SxW3ucbuuOvbMnvqZ8Cn/bhppXgF3zIJzv6Ilib1flPpSXR1JYWFip/Wy0Wi1dunSx2WBPckiEqFuqHZgUFBSwePFi5s6dy++//47RaKRTp0589NFHNdk/IcQ1KG/Pm8TExCpdw8HBAaPRiMViwcFQQGFKOgAaF6fL2v6xN5E5i4/ynEaDU7+uuI8tHYkord/qin7Cvdfc/0vzSIxGY6WCEi8vL0aNGiU5JELUcVX6SzSbzaxevZq5c+eybNkyCgoK8Pf3x2QyMX/+fEaOHHm9+imEqIaLlwFXR/PmzRk5ciQZW3Zz/qWZNDj3dxJp0sjn8P3yNRw6le4ovnT9WT78dh9haWfBYkEf1uqa+z937tyrJq4ajcarXqdRo0aMGTNGckiEqAcq9Zf4xx9/MHfuXBYuXEh6ejre3t48+OCDPPDAA7Rr1w5vb2+bzHghRN1Q3T1vyrRq1QrTvmMUjHuNgGbBnB3fmQt6ld6x5zAeOkXCXZMI/PVjHLt1oHNbXwI0JYw8tY20wCAahba45v7XRLCgKAo+Pj7WoKSM1CERom6q1F99r169UBSFfv36MWXKFG6//XbrB0ZOTs517aAQovrCwsLw9PRk9uzZVX6tRqMh8Xwcfp8sRd++JQG/fkywgx5VVTGnZ5M4ZDLGs/EkP/giXi89isPZeN7ZvgqTTkfTRe+jt9de/U0qUFBQQFJSkvV3RVGqlUeiqmqF5fUlKBGibqlUgbX27dujqiqbN2/mk08+Ye7cueTm5l7vvgkhqiE+Pp5vvvmG+Ph4oPJ73iiKQteuXXE0qYTtPMOwbzfT9rGPKDl2Fo27K+akdGs7nY8nce9Nw/Ge27Bk55L+0kfk/boezzGDabHtO/TNg6+pz3B5kqujo+NVr+Pl5cXEiRNlPxsh6rFKBSYHDx7k8OHDPP/885w6dYpx48bh7+/PyJEjWbp0qfwXhxB1xKU1Sso2r6uMXr16cXvXKEasO0mbA/FcaNKAk20bAlB08AQXbn+U4j9PYjRZ+Gj2fl7++k9+aNkHAN+vXqfx0aU0eGMSOv8GVe73tm3bbPp86aZ7UDqCUpFGjRrx2GOP4ePjA/ydR9KxY0d8fX0lMBGinqhWHZOynJNffvmFtLQ0FEVh2LBhPP300/Tu3ft69LNOkDomoq67lholnTp1otuaQxSsj8X310/4/dRhSrbsJfyHjQSu+oKMlz7BmFvAjDv+j8OnMgFomJfOO7FzCJj/L5wGdKvW+xYUFDBjxoxqvbaMoih07NiRIUOGlHte8kiEqH2V/Q6t8l45AD169ODzzz8nMTGRFStW8MADD7Bu3Tr69etX6S3EhRA1z8PDo8qv8U7JIer3IwRP/Yq8hWuwD22O3sOVoUOHMvzdV9A08CB37iqyxj+I5Ww8pp0HAbDTafiH4wW0Pp449uxU7T4fOHCgWq9TFAWNRmPNPZE8EiFuDteU8q7Vahk0aBCDBg2isLCQJUuWMG/evJrqmxCiAmU1PpKTk4mNjSUqKqpKNUoURSH8UCJtNh0h182RrAauuKbnUrTnCHE9HiLgp/dx7NEJz+fGkvHSJ2zbmkBvjY7g3DRKGgXysuM5tPM34/n+syh6+0q95/Hjx9mwYQNRUVHWlXxVKYdfxt3dndGjRwNIPRIhbjI1XpL+ZiZTOaIumT179jXVKIlSXGj22RLcnn2I7W18yN97lG7/WU3AwplkfzaH4gMn8N02hw9/OYlu3lKGndmBvcWE0V6PvdmIorfH64VHcH9iVKVHJGbMmHHVXJGrCQ0NZdiwYdblvyUlJdZ6JOPGjcPBweGari+EuD5uaEl6IcSNFx4eTkJCQrW3gAiOPY6+Y2savPQoQxUFS8xdxC3dQ96S9fh++Trnw4bzw/99zu/ubaBxZ8yKwv0nt+I9dggObZrgPKQvWnfXSr9fQUEBhYWF1errxZo0aWJTk0TqkQhxc6lWjokQovaULa318vKia9eu1bqGRqNBe/A0LncPsH6Za+zt8Jh0P7lzVnL4k1845hGIz7nToKp0zf6LUfG7cb1nIL7vPYXbQ4OvGpRcugS4urkkl/b7StNVEpQIcXOQwESIesJgMJCUlGSzHPj48eOVem1ZjRKdToeiKFgsFlTVApd8l7s+ei/xAwbiOutHmmdcoFl2Ev/a+SOP716Kc2QHfGa+UOm+lrdsuaozx1qtlsjISJt+V7YuixCifpKpHCHqiUv3vTlz5kylX9urVy/69etHRESENVk0NcADxyUb8Hh8FAC5eSW8/cUudmja0iLCg6l7FmHycKPRwAg874vGoXvHSo9K/Pzzz9Zly2fOnKlSX8s4OzszduxY2XRPiFuMjJgIUU+Eh4ej0VTvT7asUvPFRceS+3SgZN8xsmb+wJnzWUx4bT07DiRjbzYy+NxuVAc9nbZ+Q+CnL+LYo1OVpkqqs2z5Us2bN5diaULcgupFYJKQkMCDDz6It7c3jo6OtG/f3maJoaqqvP766wQEBODo6MjAgQM5deqUzTUyMzMZPXo0bm5ueHh4MH78ePLy8m70rQhRaWVTN/v37+eLL77AYrHg4uJSpWuUBRMXT3+UJYsOn/kWni88Qub0r0ntO5aeW35j7NH1zNz6X9rlJxP003R03h6V6mdZH/fv309SUlKVli2XCQsLsxkFufQaZf2eOHGirLwR4iZW58dCs7Ky6NGjB/369eO3337Dx8eHU6dO4enpaW3zwQcf8Omnn/L999/TpEkTXnvtNaKjozl69Kj1A2z06NEkJSWxbt06jEYjDz/8MBMmTGDu3Lm1dWtCVOjSqZtly5ZV+rXt2rUjKiqKX3/99YrTH4qi4PX8wzj26kz+jDl03HMUrd4Oj4fuImDSSOyCA6rcz6r0sYy9vT1jx44lMDCQHj16XHXKRpJchbi51fk6Ji+++CJ//PEHW7duLfe8qqoEBgby3HPP8Y9//AMo3fHYz8+P7777jlGjRnHs2DFCQ0PZvXs3ERERAKxevZpBgwZx4cIFAgMDK9UXqWMibqSDBw+yfPnyai0HjomJISIioko1PjbuuED38IAq7wq8a9cufvvttyr3scwdd9xBt25/l7OXuiRC3Jyua0n6G2nZsmVEREQwYsQIfH196dSpE19//bX1/Llz50hOTmbgwIHWY+7u7nTr1o3Y2FgAYmNj8fDwsAYlAAMHDkSj0bBz584rvndxcTEGg8HmR4jr7VqXA1+8pLa86Y9DJ9P55ufDl72uX2SjKgclwDXle2g0GlJSUmyOyZSNELe2Oh+YnD17li+//JIWLVqwZs0aHn/8cZ566im+//57AJKTkwHw8/OzeZ2fn5/1XHJyMr6+vjbndTodXl5e1jblmT59Ou7u7tafoKCgmrw1IWxcy3Jgb29vtFrtFZfUlk1/LF1/lqfe3swPS47z2+a/qtzH48eP2+SSVGX34rJ+XLps+UrLf2XKRohbU53PMbFYLERERPDee+8BpTugHj58mK+++oqxY8de1/d+6aWXmDJlivV3g8EgwYm4bq5lOXBkZCQhISFXzc+w02owm0tnbzfsiOeO3iFVCgCWL19OQUFBtXJJoPxly7L8VwhxsTo/YhIQEEBoaKjNsTZt2lg/wMs2Art0ODglJcV6zt/fn9TUVJvzJpOJzMxMa5vy6PV63NzcbH6EuF6quxxYURQSExMrtaR2UN/GDB3QlPtiWjD9Hz2qFJRca0l5RVHKXbYsy3+FEBer8/+J0qNHD06cOGFz7OTJk4SEhACl+2b4+/uzfv16OnbsCJSObOzcuZPHH38cgKioKLKzs9m7dy+dO3cGYMOGDVgsFpukOyFupPJ2B3ZxcalSLpOiKKiqap0OuXTfmPikXIICbEvHT3mkajVJylSnpLyDgwMlJSWoqmrTz/L6KoQQUA8Ck2effZbu3bvz3nvvMXLkSHbt2sWsWbOYNWsWUPrB/Mwzz/DOO+/QokUL63LhwMBAhg0bBpSOsNxxxx08+uijfPXVVxiNRiZPnsyoUaMqvSJHiJp2PZcDqyr8sOQYs385wuuTu9K3ox+KvQ5TUjqGbxeRt2ILakEh9q0a4zZmKM5D+qJcNFpTXtBU1ZLyzZs3Z+TIkWRnZ1c4bSNBiRDiYnU+MOnSpQuLFy/mpZde4q233qJJkyZ8/PHHjB492trmhRdeID8/nwkTJpCdnU3Pnj1ZvXq1TUb/nDlzmDx5MgMGDECj0TB8+HA+/fTT2rglIYBr2x04JCSEwMBAJkyYYF1aW/aFn5tfwrtf7mbXnnhujzuI06jvOFeQAxoNaBQUBz2uI6LR+npSuGUvKY9Ow+W3gfh+8SqKtnRVzrUETWVatWqFnZ2dddrm0n4KIUR56nwdk7pE6piImhAfH8+aNWuIjo7m2LFj1mXtlaXRaAgLC2PIkCHWY2XTIWfjc3hlZiwpidlM2b+UFtmJZHbrSpv7+pD5+mdgNKMajfh98xYud/UBIG/ZRlIefQPvtybjMXEEcG01VK7Ux4v7KYS49dw0dUyEuFlcj+XAqqpiSkjBFJfE+m1/8cxLvxF8YA+T/1xJi+xE8j94lajlM9A66lFzC2i4bhbOMX1InfQuZkPplgwuQ/rhcs8ADN/+ap2qCQsLq3INlbKS8hUtA5agRAhxNTKeKsQNUqPLgdPSyPp+Kbn/WYjx1HkA/LR2zLCY0akWVEABvD78gnxfPUU7D2HXugn61k1p8O5TnF+1hbyf1+D+f8MB0PTvivGXdXw7Yyadb+uPv79/pYOmqpaUF0KIisiIiRA3SE0sBx7XvT/9chRuX32EzH/8C5oFs3zwaP4IaI2j2YiiqqSENCkNSt6chL5Ta5Iffg1TUhoYS5fk6vwboG/XguIjp63vsXPbHwCkZ2aybNkyZs2aRVZWVqX6179/f2sSuSwDFkJcK/lPGSGuk5pcDpzx5zESvvqNot2Hafi/c6qisONYJhuDmvN+yilWNYmgzai+NJn+LwC0rs74z36HxHueoeTEOUznEig6cBx9WCvM2bkoenvr+zQ7k0ZmAxeKHeyqdI+KolyxpLzkkwghqkMCEyGukyqvbFFVfJJycM0uIKBFU1qPG8HGb3/E7ngcHXafwxTgg//375K/djsZv8Xyi28H7j71B8+lJ6KzWOj36WTaRTYjafc2Cnf+iWHeKlwfvAuPx0aSPOZldMEBpE56B8/nxmH6KwHnO3qims3kzFqI/c4jHBsYClUIJC6toVLeeSGEqCoJTIS4TmyWA6sq/vGZND+aiGtOASV6HdneLjjnFuGcWwSAY34xTgUlpS/ecJTir1fR3WKxXs+UnEH+nmOc3XaM0/Y+rAsKI8nRnef2LwVnR9pFNgPAoUcnCrcfoHj3YdJf+BDXkXcA4Pny/5H52uekTnwT1cmB3e9+RmCyAU1yBmeiWnGuVUCl7utqNVSEEOJaSI6JEDXs0t2BFYtK99+PMHDZftwz88j0ccU9M582B+NpeD6dIgc7vNJzcSwoIc/VgXV3d8bsYI/Gq3Q5neLqhMt90ejG3E3Ov+egpKThUZwPQOiDA9E29IX8QoxnS0cuzEnpaD3d8Jn5PIY5K0kY9hQA2TO+w5yWSZG7M2nuehySMjjnYc9v93YhtnNQpUdLLq6hIrkkQoiaJv+JI0QNKcspuXg5cGZmJqH7/qLxyWS23daWv1r6037PORwKjezt3pzWB+PwSTGQ6+fBH/3a0GfZPnqu/pNiR3ua75pP1mdzyf7oB0oCAph03oduLXox6uQWGhTl8s6QhvQe1ZGk9a0oSMkg81+z8Z7+DLm/rMX1vjtwe2gIDj06kRAzCUWj4BDZAe+3JnPEVcPatWuqdY8ajYbExERAckmEENeHjJgIUUMWLVrErFmzOHv2LFC6HDg7PYNWf8ZzOrQhf7UKQGNRafnnBU61a8ix8MYciGyOfYkJu/5due+15zl+RziOhUbOB3tjcdTj8dhIAJStu4js6M+mRu0pttODhxshH8ykYPMeSo6exSGiHXkL1xLf+T7U4mKc7+xF7q+/k/Lwq6hFxSifvsCydg3IbBOESbVUdBuXcXBwKLeGShkJSoQQNUlGTIS4RmWVXJs2bcqFCxewXJQX4p5VgFNBCX+1LN3F2j0zD8fCEs797/ccbxcAChJT8fHxIeaFJ0laFIubVofJZMLByx3F1ZniAyd4+sMGeLjpcU0OwqFtU4wnz5N077MAqEXFAFjyC8BkJnHIZADsIjugn/0GG+JP24ziVFZl97sRQoiaIp8sQlTTpVM3Dg4O5SwHLq2katGUjioo/9sAQv3f74UuelTAlJwGgN6vAQAN9R4cOZ1DmG8BakEh2gaeZIx4htGPjyLzrwtou4eh7RVO8aFT2DUPxmlgJA4RbXGK7oHx+DnMWQbsggP4adNa4javtfamKkXdQPa7EULcePLJIkQ1VaaSq8HDiWK9HcFnUkkP8CDH05livY5wg0rjVx9l04dfogDOCRmU5ORi2l9abTV37zGmf7CRf/kkotjbEbj032T98xsy3/kKLCo53yxC4+aCx1Oj8Zr6CMpFQUKqpyNrdm0huqkf4eHhl43iVNbF+SQgOSVCiBtDAhMhqqkyX/oWnZZTbRvS5mAciSHeJAd5cya0IW02HsRtzwl6xJ4l39cDh+w8ku6ahJqdS7aXN3aGPKat/RqzuQTP5x9GLSlB4+EGFhXnIf1wGzcUh/BQNM6ONu9nMBhskm8HDBhQpaJuDg4OGI1GLBaL7HcjhKgVsrtwFcjuwre28iq5btq06Ypf+q5Z+TQ/mohbVj7eqQacCkpIauRFhr8brU6lYZeTD/Z2uD00GGNcEoW/74CL/hzNWi3ai3b31Xi74zH5ATwm3X/F4OCbb74hISGhWvdXXj6Joii8/PLLMm0jhLhmlf0OlU8bISqpKpVc2+8+S9jOsxQ52JHh54bJxREKSmiQkoNHZh7FTnrcY3pjTk4nb8VmtJ5ueL34fzhEdsCSbaDQTo9Xn3BMcUkYT/6F4uyEQ7f2aBz0l71XWfJtdHQ0Hh4e1Q5MJJ9ECFEXyKeNEJVkU8m1HI55xTQ/lkDgX+n4pBg418KPHf3aYLbXERMTg1N6AVmPvYXB3ZHf74ng5Vde4bctcXz36zG+eLMfnt5O1ms5/+9/7ZsHY988uNz3u1LdlOqQfBIhRF0hgYkQV3HxiETXrl2JjY29rE2LwxfosuUE5v+ttimx09LkVAruWflsGtqZxMREIoYMwenfr6J/+DWalWj51ze7WbWldHTjtY9i+ez1vujttZXuj8lkstlAr6orbry9vcnOzpZ8EiFEnSIF1oS4AoPBQFJSks2IxPHjxy9rF3A+g26bjnMqtCGr7+2KncnCobsjWTuyGw6FRvos28eFuHgA3O7sheLljnlPoTUoAWjbwhuNpnJBwM6dO0lISECn06HRVP9PODIykokTJ+Ll5QVgrU8ihBC1SUZMhLiCyiwHBmi77y/S/N3Z3acVrjmFALQMD6PhkIGsc/yMbt9vRHfgJCaTiYPHMzAVQU5mPniD3l7L8//Xmdt7lj9dczGDwUBGRgZHjx4FIDExEWdnZ/Ly8qp8b2VTNxEREZJPIoSoU+QTSIgrqMxyYLeMPPwTsjjWIQid0Uy+qwPFDnYUb9iFz/j7Gf7uK5xZtZ8WyXnMW36M377bztsFOZx39SXQ15l3no2ieYhHhf240tSNqqqVDkoURSEiIoL9+/djNpttpm4kn0QIUZdIYCLE/5S3HPhKNUAc84rotuk4Df9KB6DNn/E0O5bIqfZBnG4TQKtNByjadxSH8FBcAvw4X+zG9/MP8/TxTWTbO6G7rTtfP9kdVxf7q/anbCrJ19e32vfWq1cv+vXrR5cuXa5YWl6CEiFEXSCBiRD/U9nlwPrCEm7/dS8ai4Ud/VoTtussxa1C8AlvR5v/LiGuqQ9ZXs7oBk9G6dUF858nKPQO4b3jP+BsLOb4U0/y3tQ+V80pubQ/qamp1bovRVHIzc0FkKXAQog6Tz6RhPifqy0HLtP6YBz6whJW3h9JvpsjjoVGwvacxXfaUxS1a4nmmX+y65GBaIyeOM9dggPQMjuRAw1b0eK1hxk+rGuF169oU8DK8vb2JisrC1VVUVXVZsWNTN0IIeoyCUzELa8yy4Ev1vRYEudaB5DvVloO/nh4Y5pnG0m891lchvRF8XKn5frT5KcbcLeY+azDILIjInjn2Sga+btc8bqV2xSwciIjIwkJCalwR2AJSoQQdZEEJuKWVa0CZaqKc34xxhB/tFotFosFkwa2j4jkfp0PWf9dgpqZg3tmDqd8m/Nt29todmcXpv9fZxwdKv5zq+wqoKuRFTdCiPpMPqXELatagYCiUOhkT3OdE90nTrSOSKRlZ5E+5gFePubKMylfcNyrET+2v50nRnfg3juaVzg6URNTN40bN+bChQuy4kYIUe9JgTVxy4mPj+ebb74hJCSkWgXKzrUOwG7tTjzNChMmTKBjx474+vri5mJHuwsn8CsycKhFGB+90psRd7a4YkBwaQG3xMREXFyuPNVzJYqi4OnpyYQJE65YLE2CEiFEfSEjJuKWURM5HIqicCwsmKanU0kc8iRer0xgcPQdWAz55M5dzgO7V3KmWRtenjUOHy+nCq91LVM3Wq3WmqRbltwqK26EEDcD+dQSt4xrCQTatWtHVFQUv/76KxlqBmuGhXP30RxS/u/1vxvZ2+E+Oobb3n4SRX/l+iQ1MXUTHR1N48aNL0tulakbIUR9J4GJuOnVRCAQEhJCYGCgdUTi7Ll4phR0o2Orvjzd1RWdox7HXuFoG3he8Ro1tepGo9GQlJREly5drjhCIkGJEKK+ksBE3LRqIhCwLyzBNzUXw29bMfo1ojj2AN12nCB3RwIhTnlsbNCUxg6NGXt36FWvdS0jNl5eXuTk5Fy2E7CMkAghbjYSmIib1rUEAj5u7jRbtpMmxxLRmi3AfuI+/RUAu9ZN6GY0EnVwHzkeXjR+6cMKr1UTIzZRUVEV1iWRoEQIcbOQVTniplPtVTeqin9cBuFbTzBw1u80P5nCmd6hrL6nMyX2WnTBASguTmicHGi29TtKvvuIBn7uGMZMxWy4fDO9mlp1U1aXpCy5tWwV0MWrboQQ4mYhIybipnEtUzdOuUX0XXkAr/Q8ihzs0BYZAWh9OAHXQG9URYPXqi+wS0on4fYJ5C1cS5uHBmMMm0FcxH3k/bwG9/8bbnNNmboRQoiqkxETcdNYtGgRs2bN4uzZs0BpIFCZoERjMnP7ioPYl5hZd08EGb5uZAX70PCPn8hwb4DfwdPs9WzKz1tScOjYGqeBkeTOWwWAXSM/nAZ0I2/lFuv1rrVOCpRO3UycOFHqkgghbjkSmIh671oDgZDTKbhk5OL343RM7ZvhlF9MqrsDr/xyjpeCBgLgV5DNheQ8LBYV+7bNMSWlWV+vbeCJWlgkUzdCCFEDZCpH1Fs1tfw25Ewa+S0b0ax7ZyZEtOfPedtxySxk/9EUTDp7Mh1caZKfzm2Tu6IoCsZT59H6lI5kqGYzhdv24dijk0zdCCFEDZARE1FvVXfqBkoDAa1Wi6IoaI0mDHalfwrrdySy2DGUhgnpBGWl4emmx61POBqzmfwlGyg5cY781X/get8dAOT8ZyGmuCQ2eiJTN0IIUQNkxETUW+Hh4TWy/Nbg6UzQ2WRmztrBkk0X0DVoTh+3Azx/cBkegx5F2ZhHiYcrKRPfRLG3Q+fvjeJgT/zIKZRs3M2F/h05rinB+L+pm+oUTJPdgIUQopR86ol6o2zqJjk5mdjYWKKiomokEFht1OH03hws3y2Cxt0waXQcf/YZ2u1bQ+Gbn4Gq/v1irQZTQippz/yT3EBv/rytLedaegMydSOEEDVBAhNRb1yaw7Fs2bJKv/ZKgcDRMzl8e6IhfZp05e5TO2iWk0KDB2MIb6mQf8YFtBrsWzfB/dF7cYzqiK5xIPGHjrJh8yaC27bh/B9/gBRME0KIGiOBiag3wsPDSUhIsO6qWxXlBQLzVxzjP/OPYraoLGkeRZG/HyPTD6PM/DfJgK6hL15Tx+Mx6X4UO13piE1yMpsP7uN8Zjo6mboRQogaJ5+Cok4rK+ceHR1NWFgYKSkpxMbGVukalwYCy1es5NDRc8ya/ydmixaAiPa+PDFpMO6u9pjTssBsRuvrhaLVWq8jq26EEOL6k1U5os4yGAzWpcCbN28mKSmJ48ePV+q1F6+6uTgQSMkoZtlOZzYcDcL0v6DkoaGtmTG1Fx5uehRFQefrhS7AxxqUSME0IYS4cWTERNRZP//8MwkJCUDp6ERVRiiulMORmlnI+QsGQMHdXmVaixIarZ5P6nIT+vYtcHtoMLpAX6Dm6qTI1I0QQlSefCqKOsvDw8MamFSFoihXDAQ6t/Vl4qj27Fq2h8k7F8HKNMzdOqBxdSL7ywVkffwjPh9NxW3UnTJ1I4QQtUACE1EnlLcUODExscrXURQFVVWtgYDRpDBkyBDrOYCRA0OIev0VNO4u+C/+GPtmwQBYcvNJf/Uz0p6ezprD+wnpH1kjdVJk1Y0QQlRevcsxef/991EUhWeeecZ6rKioiEmTJuHt7Y2LiwvDhw8nJSXF5nVxcXHExMTg5OSEr68vzz//vOw9UoeUVXFdtmwZaWlpLFu2jKysrEq/vl27djz66KM2ORzHz6TzyEu/M3f5CZtAIH/pRswXUvCf/Y41KAHIU82Ynn+IAl8PGqzbI3vdCCFELahXIya7d+/mP//5Dx06dLA5/uyzz7Jy5UoWLlyIu7s7kydP5p577uGPP/4AwGw2ExMTg7+/P9u3bycpKYkxY8ZgZ2fHe++9Vxu3Ii5xLUuBobQcfGBgoHXq5sKFRJ59bxP5hfD1gsO0auJJRHs/AAp+34FDl3bYNw+2uUbZ1E1oU2/Cdp5ly+nTUMmRDZm6EUKImlFvRkzy8vIYPXo0X3/9NZ6entbjOTk5fPvtt8ycOZP+/fvTuXNnZs+ezfbt29mxYwcAa9eu5ejRo/z000907NiRO++8k7fffpvPP/+ckpKS2rolcZGwsDC6du1ardeWjVDA34HAE088xog72wDQsoknQQGuf7/AaERxcbL+eumqG6O9Do25atM3supGCCFqRr0JTCZNmkRMTAwDBw60Ob53716MRqPN8datWxMcHGytdxEbG0v79u3x8/OztomOjsZgMHDkyJErvmdxcTEGg8HmR9QMg8HA/v37+eKLL9i/f3+VlgIrikLXrl3R6XSXLQe+uM24e0J5emxHPn2pJz7OWlRVxZKbj+LkQOEf+8nYvo+kpCTrqpuyqZtG59LI9HGt9GiJTN0IIUTNqRdTOfPnz2ffvn3s3r37snPJycnY29vj4eFhc9zPz4/k5GRrm4uDkrLzZeeuZPr06bz55pvX2HtRnotXvFSltDxAr1696NevHxERETbJpSs2nOGu/s2s7Yq27CHqm3kkPLQHVBWNpxtqfiFqiRGA7KFPk9jIk5T+oeDmyJkzZwg5lUzD8xls7x9aYR9k6kYIIa6POj9iEh8fz9NPP82cOXNwcHC4oe/90ksvkZOTY/2Jj4+/oe9/M2vbtm21X5ubmwuAj48Pjz76KB4NmmAo0vPLZ2s48cibnO8yinOtYkgaMQVTYioNpj+Dvmt7LNm5qCVGHPt3o8GM51AVBf/EbGLm76DtnnP0W7afXmsOc7aVP2fbBFTYB5m6EUKI66POj5js3buX1NRUwsPDrcfMZjNbtmzh3//+N2vWrKGkpITs7GybUZOUlBT8/f0B8Pf3Z9euXTbXLVu1U9amPHq9Hr1eX4N3I8pUZ5pDa1EJPJeG9vTv5BbroVVTNrwxh5Cjp2lXXEBwXjpFrq54Du5F7rxVKK7OGE+exxiXRPGuQ7i8/zQljvYUPPMvdruqNPn4WXI/m4vf6WQ67jhDhq8b2weEcrZ1QIXTOFIwTQghrp86/yk6YMAADh06ZHPs4YcfpnXr1kydOpWgoCDs7OxYv349w4cPB+DEiRPExcURFRUFlP7X7bvvvktqaiq+vqVVPdetW4ebmxuhoRUP2YtrZzAYOHPmjLU+ib+/P3v27Kn069u1a0eXHJW8t75Cn1+MSachdfV+VKCVRstJ9wBC8tIAcHHUgUZB4+pM8IFfyPrwO3I+n4+2gSeryCHu1AX6BnsTsP0Yi4Pc4I52dN5qT5PjiaweeeXkW5m6EUKIG6POByaurq60a9fO5pizszPe3t7W4+PHj2fKlCl4eXnh5ubGk08+SVRUFJGRkQDcfvvthIaG8tBDD/HBBx+QnJzMq6++yqRJk2RE5Aa4lnwSgOZJuRjf+i8eMb35s2tTkk7H0fOHDSQ6e9EoPwO9YsHi5krj1V+SMvFNcheuwalXZ7Suzni//jiG2UtAqyE8IoKEpCQSQ7zpvO2k9fpZDVxoU2xCMVtQteXPbkrBNCGEuDHqfI5JZXz00UfcddddDB8+nN69e+Pv78+vv/5qPa/ValmxYgVarZaoqCgefPBBxowZw1tvvVWLvb51XEs+iUZR0H29BMe+XfD7+k2SfCNxWv0XSc6evB41mtg2XWmenYR7dBT2LULwn/0OFBspOVOaD6RoNKhB/hhTM/F096Br167oi42YLwpAPDLzKdbbXTEokVU3Qghx49T5EZPybNq0yeZ3BwcHPv/8cz7//PMrviYkJIRVq1Zd556J8lT1C9zBwQGj0YjFYsE91YBDYgb691/g5Y9iid2fzJcZcaxo0oWeXRsyZPAU0qJGYYovXV1lFxKIXfNgjKfjyNx3mOIAb9KctPioKkc//pZTIV70Op7EhaY+pe+VX0yzo4mleSUXkakbIYSoHfUyMBF117XmkzRv3pyRI0eSnZ3NggULsItLB+C1X89xOL902k1nMdO5SzADnolCURTStBqKDxzHkl+IxtkRXXAApoQUEkZPZWP/VvQ9GU+umyPN5m3Cx90JJ0MhJweEEnwqhY47TmPWaTjaKcSmHzJ1I4QQteOmmMoRdceiRYts9ruZNWtWlfa8adWqFXZ2dtZpE33TVgC4nDsHgLuLPYS2IDThFIqiUHz0DJgtqGYLqZPewZicTmHsAeI6NcVOpyPm59045ZdQYq8Fi4p7Vj5aFe74dS+91xyi0FnP2ns6U+jyd65R2e7EMnUjhBA3noyYiBrVtm1ba6JrVV1cWt5ksvDDJxtIWZFAmoMrd5+MJa9TOK+/0AfnrU6kTnwTw9yVFKyNRevrRYN/PkvKY2+T3+leVLMZU1omGPKxAGkB7hS4OnKudQDxTXzwSstFa7aQ1cCVHG/bTfou3Z1Ypm6EEOLGksBE1KhryScpy+VIi09n370v0+/sMYo1OkyKFmdzMc/NmYG9fxr6uwfg2L8baU+/D4Dmrl7smLMIfwc7HHJKyHNzxC27gNMtfDnZrhG5ns4275nv7nRZP9q1a0dUVBS//vqrTN0IIUQtUlRVVWu7E/WFwWDA3d2dnJwc3Nzcars7te748eNs2LDBmksCsHDhwkpP3VyaT5KRkYECDFoXj/7MOea06sv+hq2Y9EgEfdJPkPHSJ6i5+dbXaxv6onFxovjsBUwKZLUJYn9rX9J9XK/8plcQExNDREQEJSUl1oJp48aNu+HVhoUQ4mZV2e9QGTER1bZ8+XIKCgqqVZsELs8n+e233yjauhfPEydYOnQMpxwC+eiZKEKbewFNcY3pw/lu96Nv3wLv1x6jqJEPmZmZzPvxR+tUi7OzM+TlVakf5e1OLFM3QghROyQwEdVSUFBAYWFhtV9/cTBgzskl7+tf6PjTCkwJqaAojHDKYMyjA2nQ3Ovv17g44T52KFn/nsuP2zdiMputWwsAqKpKXiWDEm9vb7Kzsy9bDlxGghIhhKgdsipHVMuBAweq/BoHBwe0Wi2KoliDgYQTiezuOo7MT+bgNCAS+w4t0QX5Y14fS+7dT1D8598VWg0GA/lujlBYTGL8Ba5lFjIyMrLCTfiEEELUDglMxFUdP36cL774gv3795OUlERSUhJ79uypUmDQvHlzpkyZclkwsOmBt3DINfBR/zHYv/EUTn0isOQV0HDjf7Fr0oiUiW9a32fRokUcmb+UfBc9qlZDampqte5HKrkKIUTdJVM54qquNZcEys8nST37F50TNrKkSTeyvH0x5JXgNzqG7M/mkjdnJd5vTSZxyGR+fekduk4aR4RXAPYnkjgS3rjK7y+VXIUQon6QwERU6FpzScA2nwRAa8ind1weBVvPUmQ20bKpJ5Ne742rhxPgisczD5H53tfoh/TBbKdFv/sYp1/5iOA/jpLj5crxjsFV7oNUchVCiPpBpnJEhaqTSwIQFhaGTqezySc5G5/DudmrON/pXrL++V8suQWlbdeuIPO2R6wb73m9/CgN/vUPsrfuRWs00+bPeALX7eVUkAdrhnTEaF+1eFqmboQQov6QERNhZTAYyM/Ptzl2aS6JnZ0dRqPxitewt7dn7NixBAYG0qNHD+sIRVpaOtOfnsPz2xbgNLgPATOeQ+PmzPnwkeg7tcF46jxJ9z2HMn86azdtJDo6mtT9XQmZs4GNgzqQ2sirSgFJ48aNuXDhAmazWaZuhBCiHpHARBAfH8+aNWswmUw2y2/LU1FQAtC/f38CAwMB8PHx4ZFH/o9PvpxHRnoq/U/tIdXRjUN97uZxL3cAPJ4YRcZrn+E44V4KZ/3CqU+/I8FLy8Evf6T5wi2cb+5LQlPfKt2Poih4enoyaNAgmboRQoh6RgITwc6dO0lISKBhw4bodLpqT3FoNBqbwCYzp4hpn+zg4HFXwIWH09Zwst9tPDKqg7WN+8QRmBJTyflyAWaNQqMl2/HV63DPLiCpkRex/UMr9d5arRaz2Qxg3evm4kTbpKQkm8BECCFE3SSf0re4goICjh07BkBSUhJjx45l2bJlZGZmVmo5cFhYGEeOHLlsyuTIqQxe+ziW9MxCWmUl0DQvFb3FRO+OfujttdbXK4pCg7cmk9S5OZYpH6I1WUgL9GBX39akNPSESo5sREdH07hx48tGSGTqRggh6hcJTG5xBw4csAYgqqqSkJDAgAEDWLRokXUEojxXyiVJT0/n1zUn+fdPh/DPTuXtQ2tolJ+B6uiAAmT/azbG42dxeGcy51KTiI2NJSoqCr92rTAUmTjeIYiDkc2qdA8ajYakpCS6dOlyxRESCUqEEKJ+kE38qqC+b+JXXnLrpZvuubu7YzAYrjpacscdd9CtWzfr7yUlJaxYsZLDx86x6VhD3PPzmbZzHoXuHjT5+B/43d4Vw/dLSX/hQxQHB7ICPVlxR2jpiIiq0nXzcZofSWTJmB4UuFa8cZ6iKERERLB//37MZjOqquLj48MTTzxhbSMjJEIIUbfIJn7iMosWLSIuLq7CNjk5OVe9zqW5JADpaQXEzTtL+ME99C/eiIPJiKNGpdWaz3AMLt152O2hwRT9sZ+8pRvxOJNE+DY78t0caHY8Cc+0XHb2a3PVoASgV69e9OvXjy5dukhyqxBC3GSkjsktID4+nm+++YaQkJBqJX9qNBoiIyMvq0tSZtfOcxwa8DiDNy3Boigc9m+Gh6UIXXEx6WNfxpyRDYCi1eL71es0+GAKqp2O0INxdN52kgInPb8PC+d024ZX7YuiKOTm5gJIXRIhhLgJyYjJLaBs1Y2npycTJkxgwYIFlU5u9fDw4IEHHsDHx4fw8HAWLFhAVmoaztsPk5r2JUfi8kiPPUyb7FTe7TKCwlYteOeZKOgyBI9nx2D4cRmJk98l/YUHrPkk/tHdSJ3lg7G4hE2DO141wdXb25usrCxUVbWuuCkjya1CCHFzkcDkJnfxqpujR49y5513MmHCBH788UebL/hLuShaul3IJ2jVDnJnrqTA1xPXkXfwYMtQkj/6J3aGAhJj4/EqKCTYVEyykweNotrw/JQ+uDrbcz7YH3NyOt6vPkbqs++zIUBDnrsTy5YtQ7FYGJaYyYWmPpVadRMZGVlhOXmQqRshhLhZSGByk7t01c2ePXtIS0urMCjRF5QweN0JdGlZOA7tj3275pScPE/Wp3OguBjXrh1YEHYbS86ZCEs9yzMHl+OtFjNh1xJcHAcA4Db6LrJmfo/buKEoKvgkZZPn7gRAi8MJOOcXczo08Kr9LysnHxERITVJhBDiFiCf7DeRq5WUV1WVTZs2XXUKp9vm41hy8mi0cTb2zf/eMM+cnk3B2u1gp2PUUwNZ/+p69I52AATOmELa5Hcp3LoPpz4RuP/fcPKWbiDpvn8A4JhXRIOkbJodT6LFkQROtG9Elk/5Wdl6vR6TySQ7AQshxC1IApObSGVW3VwpKImMjGTPnj3Y5+TT6Gwqx2IiaH1RUKJaLBRu2Inz4D7kL9lAUE46707pjo8SQfHAFZiT07FrFkTWwtXktCxNYtX+5xVMT36Auv844TvOAmcpcLJnb/cWHOtU/g7BzZs3Z+TIkWRnZ8uKGyGEuAVJYHITCQ8PJzExscqrU/r370+vXr0IDw9n41sz0ahw3NeJQf8LBrJyivh+/gEGF5fg2CeC/CUbKN5/nI733QFA6ojbyfr4R+yaNiLu+EnWzpoFgEd6LgOPnSE92JsjEU2waBUyG7iiaq+8GKxVq1bY2dlJOXkhhLhFySd9PVa2+V50dDRBQUGEhYURGBhY6VU3F1dvhdLlt9GDBpE+fxt+7h6YTCbOxOXy8sztpGUU0N/NHZftB0tfrPu7rHyDt5+k5ORfFO86TAN/bzpvO4lbVj6B5zPIauDC9tvaUuJof9X7Kcsnubh/MnUjhBC3FqljUo+VLQPetWuX9ZiPjw9DhgzB3r7iQKBt27b84x//sAYlZVx6dEJxcuAujRcODg7o9VryCoygKGwJak/e4vWg0+LYM9z6Go2LEw5d2wOgb+hHw7/S0RnN7OzXhjX3dqkwKHFwcECr1ZZbH6WMBCVCCHHrkBGTeqq8ZcB6vZ6tW7eydetWLBZLha9v3LgxdnZ2lx3XurngNmYI2Z/NQR/alJDBfXnpsQh+WXWSe4b0o/DpP0CjJec/C3EaGIklJxfDTysoWLsdr9ce41hEE37//fdK1UipTD6JEEKIW4t8+tdTly4D3r59O2fOnCE5ORko3fMmNze33ADl0imTi6VmFOA29VFM8cmkjH8du5YhtGnbnKkn/qLw8zM49AzHvlVjDN8vJfuzOQAoLYKxn/4URTE92bNwYaWCEpB8EiGEEJeTT/96oDLLgA8fPkxBQQF6vZ4ePXqwf/9+LBYLGo0GjUZDREQEe/bswWw2X3HKZO/hFN74bCe9Ixryj9nvULhtH7nzVmFKSse+VWO8X38cx35dUDQavKc9wc+ffsGF1BTyXR0g4SjMOlrpe5J8EiGEEOWRwKQeqOrmexs2bLD+s6enJ/fdd59NSflLp0xUVWXu8hN8veAwFhWWbzxHWBsfbu/VGadenct9P42jHt8uYZzYurXK9yP5JEIIIa5Ekl/rgfDw8GpNbQQHBzNx4kR8fHyA8je9Kyg08vonO/jP/NKgBCCyoz9RHf2vev3MzEygdLSjMgGFt7c3o0aNwsvLC8AaHAkhhBBlZMSkHqjKMmBFUfDy8mLkyJH4+vpedv7iKZO4xFzenb6egIP7GVpSQJbehRaP3sVDozuj0VQcaFycfGs0GmnatClnzpy5Yvt27doxZMgQ7OzsaNKkieSTCCGEKJd8I9QTXl5etGnThm3btlXYrm3bttYAoCKbd13g4JTPmHJqFwoqeXon3EsK0Lz6B4aCCXhMHFHh6y9Nvr3aVFNISIi1T5JPIoQQ4kokMKkHkpKSWLJkCampqVdte3EAUB6T2cI3Px8h+9OfGHE6luVNunAiqg+vvDQAf4rI+vhHMl79FI2LE26jY4CrJ99C6ajJlVxpFZAEJUIIIS4lgUktu7R6a3kMBgOpqak4OTnh6+tLXFxclZcBA2Qbinnzs50cPniBmX/tYU1wRwxj7mPm/3XG0UEHuODz/rNYsnPJmvFfXO+LRtHpKpV8eyUVJboKIYQQl5Lk11pWXvVWgKKiIus/t2rVijvvvJMnnniC/Px86zJgnU5HZGQkOp3uqgHA8bOZPPrqevYeSaV9xnmcTCU0fPoBXpvU9X9Byd/cJ9yLKSGVot1HgOol3+p0Ovr37y+JrkIIIapERkxqUXnVW3U6HRs2bODPP//kiSeewMXFBYCuXbtiMplIT08HKrcMuMzKTef4aPZ+Soyloyw+dmYAhjzYrdzpFF2j0hU5FkMeUJrf4uXlRVFREbm5uVctoObt7W3tW7du3STRVQghRKXJt0QtujSBdOPGjZw5c4asrCwAjh07RpcuXaztjUYjvr6+BAQEMGjQIGsuyZUqp5YYzXzy/QGWbzhnvUbbFl48HDWQ/IfWULTrMI6RHS7rV9GuQwDYNS7dR2fnzp2kpqYSGhqKxWLh+PHjV7yni1ffgCS6CiGEqBoJTG6QylRv3bNnDwDOzs707t2bRo0aYTAYcHNzA8DR0ZGJEyeW+wV/aQCgqirTXl2Jz6bNvJF2Dp3FjKVFEzpPGodLVAfimzYi8/1vCPz5QxT7v5NlLXkFZH34Pfou7bBv1cRmVOf48eM4OztXeJ9XSr6VoEQIIURlSGByg1QlgTQ/P5/ffvsNKC2S9vDDD1vPlX3BqyYT5vRsFEc9WnfXy84X7z7Mw/M/p6TYyAH/5rQOC8L32FFShz2JccpYfD58nsT7/sGFOx/D47GR2LUIoeTQKbK/WoApIZWGy/4N2I7qWCwWcnNzr9jvqyXfCiGEEFcjgckNEh4eTmJiYpUSQHU6HeHh4TbHLAVFZH/6E4YflmNOK6286hAVhufTD+E0oFtpm7wCksa8hHOHFuy6bxxdezajZWNPVIuF7H/PI/Ptr7Bv34KGSz4lc/rXpD7xTunFFQVNr07o35tMpo8rJCVdtiy4jKIoaDQaQkNDOXr0KBaLRVbfCCGEuGaKWtmtYAUGgwF3d3dycnKs0ytVkZaWVqXqrWUJpGUsBUUk3fssxYdP4Xp/DE79u2LOzCH3pxUU7TpE6uPjiXprHDnfLSF96kcE7/0Zu0Z+l10/YchkUKHh8tJREVNiKua0LBZu/p0zWelVvq+AgABKSkrIyMhAURRefvllSXIVQghho7LfofLtcQOVJakuW7aMI0eOXLHdlaq35ny5gOJDJwlc8ikOndtaj2f17smeka/R8avZbIyMIHT7ARy6tCs3KAFwuXsA6VM/QjWbUbRadIG+6AJ9aU8R51esqPKoTrdu3WjTpo2svhFCCHHNpI7JDWZvb09ISEiFbcpLIFVVFcOPy3AZEW0TlAAcPJ7O7MAumBUNh/81H7PZAhUlmyoKXDRiEx8fzzfffIOXlxcTJkzA29v7qsmqiqLg7e3NhAkTCAsLsybfTpw4EQcHhwpfK4QQQlxJvQhMpk+fTpcuXXB1dcXX15dhw4Zx4sQJmzZFRUVMmjQJb29vXFxcGD58OCkpKTZt4uLiiImJsVZQff7552ul6FdSUhIaTfmP/koJpGp+IaaEVBx7hV92bnD/JvQZ0IoUnwDuCtHg3LMTRbsPY0osv4R93pINOHTrgKLVArZF3nx8fBg/fjz+/hXvLty2bVubnYvLyOobIYQQ16JeBCabN29m0qRJ7Nixg3Xr1mE0Grn99tttlt8+++yzLF++nIULF7J582YSExO55557rOfNZjMxMTGUlJSwfft2vv/+e7777jtef/31G34/Fy5cqHL1VsXeDjQazGnZlBjNtucUhWcf7kQTJxWXBm64johG4+ZMyhPvYP5fkTQoHXXJ/mI+RX/sx33CvcDlRd7y8vJYsWIFSUlJFd7D1fbkEUIIIaqjXiQCrF692ub37777Dl9fX/bu3Uvv3r3Jycnh22+/Ze7cufTv3x+A2bNn06ZNG3bs2EFkZCRr167l6NGj/P777/j5+dGxY0fefvttpk6dyhtvvIG9vf0NuZfqVm9V7O1wiu5O+n+X8NohRyaN60TPzoHW85ZdBzGfu4DzjOfQuDjh/8N0kh94gfNhw3EZ3BeNhysFv+/AeOo8Hk+NxmVIP+DyIm/z5s0jMTHx72XJ5STpyrJgIYQQ10u9GDG5VE5ODoB1H5a9e/diNBoZOHCgtU3r1q0JDg4mNjYWgNjYWNq3b4+f398JodHR0RgMhgoTUWtaWfXWjh072kyFlCXGduzYEV9f33KnmI737I/lbDxDNi7i8w/XE59UWh6+YP1OUie+iT6irXWqxzGyA0HbfsD90Xsp/vMEBWu3o2kRjP6/b1Dyf0NJSkoi6ZLlwKqqWgMOJycna7E2rVZb6T15hBBCiGtRL0ZMLmaxWHjmmWfo0aMH7dq1AyA5ORl7e3s8PDxs2vr5+ZGcnGxtc3FQUna+7Fx5iouLKS4utv5uMBiuuf9Vqd5axmiy8NkPB1iyKYfO7e9k/JF1RPz+DaaRvxOXm4cpIRWHru3x//5dlItyV3SBvni//CjeLz8KlI4ixe3bBvu2XbWfZdNkqqri6+tLdHT0VUd1hBBCiGtV775VJk2axOHDh9m27epfrtdq+vTpvPnmmzV+3cqseCmTllnI6x/HcuR0aTG1vX7NCbmnN2P0yZhPnkNx0ON8R08cosKuet3qFHnTarV061ZauO1Ke/IIIYQQNaVefatMnjyZFStWsGXLFho1amQ97u/vT0lJCdnZ2TajJikpKdbVJf7+/uzatcvmemWrdq60AuWll15iypQp1t8NBgNBQUE1dTtXdeBYGtM+2UGWoXTUxt5Ow5SHwxnUt3G1rhcWFkZgYGCliryB7S7BZWRTPiGEENdTvcgxUVWVyZMns3jxYjZs2ECTJk1sznfu3Bk7OzvWr19vPXbixAni4uKIiooCICoqikOHDpGa+vcS2nXr1uHm5kZoaGi576vX63Fzc7P5uRFUVeXnVSd59t0t1qDEv4ETn0/rV62gpKxOSXx8vHXU40r3XKZdu3blLgcuI0GJEEKI66FejJhMmjSJuXPnsnTpUlxdXa05Ie7u7jg6OuLu7s748eOZMmUKXl5euLm58eSTTxIVFUVkZCQAt99+O6GhoTz00EN88MEHJCcn8+qrrzJp0iT0en1t3p6NwiIT/5y1hw07/k4ujWjvy+uTuuHhVr1+XlynJCgoCFVVycrKqvA1shxYCCFEbagXgcmXX34JQN++fW2Oz549m3HjxgHw0UcfodFoGD58OMXFxURHR/PFF19Y22q1WlasWMHjjz9OVFQUzs7OjB07lrfeeutG3cZVxSfl8urHsZyL/zvJ9sEhrRg/sh1aTfVGKC6tU9K7d2+WLFlS4XJfWQ4shBCitsgmflVwrZv4VeSPvYm888Uu8gtLE1OdHHW8/FgXendpeE3X3b59O7///rs1n8TJyYmCggIURUFVVTQaDRqNhoiICPbs2YPZbEZVVXx8fHjiiSeu+b6EEEIIkE386g2zRWX2L0f4Yclx67GQhq68+2x3ggNdq3Qtg8FgUw0XsKlTAqUjKM7OzhQUFADg6urK0KFDadKkiSwHFkIIUevkW6cWFRWbeO3jWHYe/HtPn75dG/LixAicHKue37Fo0SLi4uKu2u7i4CUnJ4dNmzbRpEkTWQ4shBCi1sm3Ti3S22tx1Jf+K9AoMPH+9oyKaVntFS/VqVOi0+kID/97Y0BZDiyEEKI2SWBSixRF4cWJEWTnFjP2nlA6t/W9putVpU6Joih4eXldVqfk4vNCCCHEjVYv6pjcrCxFxdilZzBzcni1gpKL65OU8fHxYcSIEfj6Vny9tm3bVlinRAghhKgNMmJSC0xpWWTNmE3uz6tR8wsBcOzXFc/nxuLYrUOlr3NpfRIoLSy3ePFimz1+yiN1SoQQQtRFMmJyg5lSM0kY9Dh5Szfg8dhIAhb8C5+ZL2BOzyJx2FPkr/mjUte5tD5JXl4e69evZ/78+RQXF+Ps7IxGU/6/XqlTIoQQoq6SEZMbLPPtr1DzC2i0dhZ2IYHW466j7iT5kddIfXo6IQcWoXGouMrrgQMHrDkkFouF2bNnk5lZutFf165dOXfuHPn5+eXWKbFYLFy4cKGiywshhBC1QkZMbiCzIY+8JetxnzjSJigBUOx0eE97HEtGDvmrttqcMxgMJCUl2fxcWp8kMzMTnU5Hv379aNeuHenp6QB4enoyYcIEoqOjmTBhAl5eXgDWOiVCCCFEXSIjJjeQKT4ZtagExx6dyj1v3zwYbaAvxpN/2RyvbH0Sk8nExo0b2bhxIwDOzs5MnDjRmksidUqEEELUdfKtdANpnJ0AMKdmlHveUliMJTsXxdnR5nh16pNotVpuu+22yxJcpU6JEEKIukymcm4gXUgA9u1akDN7Sbk1RvIWrkEtKMQlpo/N8bCwMCZMmIC3t/dVgwlFUfD29mbixImEhYVV2E4IIYSoayQwuYEURcHzubEUbtpN+vMfYkrLAkAtMWKY/xvpr36Ky/DbsGva6LLX+vj48Mgjj+DqWvH+OVKfRAghRH0mUzk3mMtdfTD/6x9kvPophnmrsG8WhCklA0tmDs5D++Pz0dRyX1dSUsKiRYswGAwVXl/qkwghhKjPJDCpBe5jh+IyuC+5v6zDdO4CGjcXnIf1R9+mabntCwsLmTNnDgkJCdYpmPKmgqQ+iRBCiPpOApNaovVyx2PCvVdtZzAY+Omnn0hLS8PR0RG9Xk92drbUJxFCCHFTkhyTOm7FihWkpaXh6urKgw8+SE5ODiD1SYQQQtycZMSkjrvrrrtYunQpgwcPRq/X4+vrS0BAAIMGDZL6JEIIIW46ilpesoIol8FgwN3dnZycHNzc3GrkmvHx8axZs4bo6GjrRnz5+fk4OzuX2/5q9UekPokQQoi6qLLfoTKVU8su3iEY4NSpU3zyySccPny43PaVqWMihBBC1FcSmNSiS3cI3rNnD/Pnz8doNHL48OFyV94IIYQQNzNJRKhFl+4QvHLlSgDat2/P0KFDZfRDCCHELUcCkxvEYDCQn59vc+zSHYIBQkNDiYyMJDU1FSjdiK+m8lmEEEKIuk4CkxuksjsEHz16lKNHj1p/Dw4O5uGHH76eXRNCCCHqDMkxuUHCw8OrvIRXp9MRHh5+nXokhBBC1D0yYnKDhIWFERgYyIIFC8jMzKwwsVVRFLy8vLjvvvtkMz4hhBC3FBkxuYHKCqGFhoZW2E52CBZCCHGrksDkBrO3tyckJKTCNrJDsBBCiFuVBCa1ICkpCY2m/EcvOwQLIYS4lUlgUgsuXLiAxWJBo9Gg0+mIjIxEp9OhKIrsECyEEOKWJsmvN5jJZCI9PR0o3SG4LME1PDycBQsWkJGRYd0hWDbiE0IIcauRb74bzGg0yg7BQgghxBXI7sJVUFO7C8sOwUIIIW41srtwHSY7BAshhBDlk8BECCGEEHWGBCZCCCGEqDMkMBFCCCFEnSGBiRBCCCHqDAlMhBBCCFFnSGAihBBCiDpDAhMhhBBC1BkSmAghhBCizpDARAghhBB1hgQmQgghhKgzJDARQgghRJ0h29dWQdl+hwaDoZZ7IoQQQtQvZd+dV9s7WAKTKsjNzQUgKCiolnsihBBC1E+5ubm4u7tf8byiXi10EVYWi4XExERcXV1rbQdgg8FAUFAQ8fHxFW4bfSuTZ1Q58pwqR57T1ckzqpxb/Tmpqkpubi6BgYFoNFfOJJERkyrQaDQ0atSotrsBgJub2y35f+yqkGdUOfKcKkee09XJM6qcW/k5VTRSUkaSX4UQQghRZ0hgIoQQQog6QwKTekav1zNt2jT0en1td6XOkmdUOfKcKkee09XJM6oceU6VI8mvQgghhKgzZMRECCGEEHWGBCZCCCGEqDMkMBFCCCFEnSGBiRBCCCHqDAlM6oDp06fTpUsXXF1d8fX1ZdiwYZw4ccKmTVFREZMmTcLb2xsXFxeGDx9OSkqKTZu4uDhiYmJwcnLC19eX559/HpPJdCNv5YZ5//33URSFZ555xnpMnlGphIQEHnzwQby9vXF0dKR9+/bs2bPHel5VVV5//XUCAgJwdHRk4MCBnDp1yuYamZmZjB49Gjc3Nzw8PBg/fjx5eXk3+lauC7PZzGuvvUaTJk1wdHSkWbNmvP322zb7d9yKz2jLli0MHjyYwMBAFEVhyZIlNudr6pn8+eef9OrVCwcHB4KCgvjggw+u963VqIqek9FoZOrUqbRv3x5nZ2cCAwMZM2YMiYmJNte4FZ7TNVFFrYuOjlZnz56tHj58WD1w4IA6aNAgNTg4WM3Ly7O2eeyxx9SgoCB1/fr16p49e9TIyEi1e/fu1vMmk0lt166dOnDgQHX//v3qqlWr1AYNGqgvvfRSbdzSdbVr1y61cePGaocOHdSnn37aelyekapmZmaqISEh6rhx49SdO3eqZ8+eVdesWaOePn3a2ub9999X3d3d1SVLlqgHDx5UhwwZojZp0kQtLCy0trnjjjvUsLAwdceOHerWrVvV5s2bq/fff39t3FKNe/fdd1Vvb291xYoV6rlz59SFCxeqLi4u6ieffGJtcys+o1WrVqmvvPKK+uuvv6qAunjxYpvzNfFMcnJyVD8/P3X06NHq4cOH1Xnz5qmOjo7qf/7znxt1m9esoueUnZ2tDhw4UF2wYIF6/PhxNTY2Vu3atavauXNnm2vcCs/pWkhgUgelpqaqgLp582ZVVUv/z25nZ6cuXLjQ2ubYsWMqoMbGxqqqWvrHotFo1OTkZGubL7/8UnVzc1OLi4tv7A1cR7m5uWqLFi3UdevWqX369LEGJvKMSk2dOlXt2bPnFc9bLBbV399fnTFjhvVYdna2qtfr1Xnz5qmqqqpHjx5VAXX37t3WNr/99puqKIqakJBw/Tp/g8TExKiPPPKIzbF77rlHHT16tKqq8oxUVb3sC7emnskXX3yhenp62vy9TZ06VW3VqtV1vqPro7wA7lK7du1SAfX8+fOqqt6az6mqZCqnDsrJyQHAy8sLgL1792I0Ghk4cKC1TevWrQkODiY2NhaA2NhY2rdvj5+fn7VNdHQ0BoOBI0eO3MDeX1+TJk0iJibG5lmAPKMyy5YtIyIighEjRuDr60unTp34+uuvrefPnTtHcnKyzXNyd3enW7duNs/Jw8ODiIgIa5uBAwei0WjYuXPnjbuZ66R79+6sX7+ekydPAnDw4EG2bdvGnXfeCcgzKk9NPZPY2Fh69+6Nvb29tU10dDQnTpwgKyvrBt3NjZWTk4OiKHh4eADynCpDNvGrYywWC8888ww9evSgXbt2ACQnJ2Nvb2/9P3YZPz8/kpOTrW0u/sItO1927mYwf/589u3bx+7duy87J8+o1NmzZ/nyyy+ZMmUKL7/8Mrt37+app57C3t6esWPHWu+zvOdw8XPy9fW1Oa/T6fDy8ropntOLL76IwWCgdevWaLVazGYz7777LqNHjwaQZ1SOmnomycnJNGnS5LJrlJ3z9PS8Lv2vLUVFRUydOpX777/fummfPKerk8Ckjpk0aRKHDx9m27Zttd2VOiU+Pp6nn36adevW4eDgUNvdqbMsFgsRERG89957AHTq1InDhw/z1VdfMXbs2FruXd3w888/M2fOHObOnUvbtm05cOAAzzzzDIGBgfKMRI0xGo2MHDkSVVX58ssva7s79YpM5dQhkydPZsWKFWzcuJFGjRpZj/v7+1NSUkJ2drZN+5SUFPz9/a1tLl2BUvZ7WZv6bO/evaSmphIeHo5Op0On07F582Y+/fRTdDodfn5+t/wzAggICCA0NNTmWJs2bYiLiwP+vs/ynsPFzyk1NdXmvMlkIjMz86Z4Ts8//zwvvvgio0aNon379jz00EM8++yzTJ8+HZBnVJ6aeia3wt8g/B2UnD9/nnXr1llHS0CeU2VIYFIHqKrK5MmTWbx4MRs2bLhsCK9z587Y2dmxfv1667ETJ04QFxdHVFQUAFFRURw6dMjm//BlfxCXflHVRwMGDODQoUMcOHDA+hMREcHo0aOt/3yrPyOAHj16XLbU/OTJk4SEhADQpEkT/P39bZ6TwWBg586dNs8pOzubvXv3Wtts2LABi8VCt27dbsBdXF8FBQVoNLYffVqtFovFAsgzKk9NPZOoqCi2bNmC0Wi0tlm3bh2tWrW6aaYnyoKSU6dO8fvvv+Pt7W1zXp5TJdR29q1Q1ccff1x1d3dXN23apCYlJVl/CgoKrG0ee+wxNTg4WN2wYYO6Z88eNSoqSo2KirKeL1sKe/vtt6sHDhxQV69erfr4+NxUS2EvdfGqHFWVZ6SqpSsAdDqd+u6776qnTp1S58yZozo5Oak//fSTtc3777+venh4qEuXLlX//PNPdejQoeUu++zUqZO6c+dOddu2bWqLFi3q9VLYi40dO1Zt2LChdbnwr7/+qjZo0EB94YUXrG1uxWeUm5ur7t+/X92/f78KqDNnzlT3799vXU1SE88kOztb9fPzUx966CH18OHD6vz581UnJ6d6tQy2oudUUlKiDhkyRG3UqJF64MABm8/zi1fY3ArP6VpIYFIHAOX+zJ4929qmsLBQfeKJJ1RPT0/VyclJvfvuu9WkpCSb6/z111/qnXfeqTo6OqoNGjRQn3vuOdVoNN7gu7lxLg1M5BmVWr58udquXTtVr9errVu3VmfNmmVz3mKxqK+99prq5+en6vV6dcCAAeqJEyds2mRkZKj333+/6uLiorq5uakPP/ywmpubeyNv47oxGAzq008/rQYHB6sODg5q06ZN1VdeecXmi+NWfEYbN24s93No7NixqqrW3DM5ePCg2rNnT1Wv16sNGzZU33///Rt1izWioud07ty5K36eb9y40XqNW+E5XQtFVS8qdyiEEEIIUYskx0QIIYQQdYYEJkIIIYSoMyQwEUIIIUSdIYGJEEIIIeoMCUyEEEIIUWdIYCKEEEKIOkMCEyGEEELUGRKYCFEPKYpy1Z/vvvuOTZs2oSgKe/bsqe0uV8pff/3FG2+8QWJios3x63Uff/31F4qi8Msvv5R7PjU1FZ1OxzvvvHPFa3Tu3JnevXtX6v2+++47FEUhPT29Wv0V4lYggYkQ9VBsbKzND8CTTz5pcywmJqaWe1l1f/31F2+++eZlgUl4eDixsbG0adPmhvbH19eXAQMGMG/evHLPnzx5kn379jF69Ogb2i8hbma62u6AEKLqIiMjLzsWHBxc7vHaZjabsVgs2NnZVfsabm5utXZvo0ePZuzYsRw8eJCwsDCbc3PnzsXOzo4RI0bUSt+EuBnJiIkQt4CsrCweeOABXF1dCQkJ4YMPPrisTWxsLP3798fZ2Rl3d3ceeOCBy7Znz8zM5JFHHqFBgwY4OjrSvXt3tmzZYtOmb9++3HXXXXz//fe0atUKvV7PwYMHAVi5ciXdunXD0dERHx8fHn/8cfLz84HS6Zp+/foB0KVLF+uUVNm5S6dyLBYLM2fOpE2bNuj1evz9/RkxYgQ5OTkAHD9+nFGjRhEUFISTkxOhoaF8+OGH1l2EK+vuu+/G0dGx3FGTefPmcccdd+Dl5cXKlSu57bbb8PX1xc3NjW7durF69eoKr32lKaphw4bRt29fm2PHjh1j6NChuLu74+zsTExMDGfOnLFp89///pe2bdvi6OiIt7c3PXv2ZPfu3VW6XyFqmwQmQtwCHnvsMVq2bMnixYsZPHgwU6dOtfnSjI2NpW/fvri7u7NgwQJmzZrF7t27GTp0qLWN2WzmzjvvZPny5fzzn/9k4cKFuLi4cNttt9ls4Q6wZ88eZsyYwVtvvcWqVasICgril19+YciQIbRv357FixfzwQcf8OuvvzJ+/HigdLrm888/B2D27Nk201TlefLJJ3nhhRe46667WL58OZ9//jmurq7k5eUBkJCQQKtWrfjiiy9YtWoVEyZM4K233uLtt9+u0rNzdXXlrrvuYv78+Vy8tdjevXs5efKkdRrn3LlzDB48mB9//JFFixbRo0cPBg0axKZNm6r0fuU5e/Ys3bt3JzMzk++++465c+eSlpbGgAEDKC4uBmDLli2MHz+eQYMGsWrVKn744QcGDBhAdnb2Nb+/EDdULW8iKISoAYA6Y8aMy46X7YT6/PPPW49ZLBa1cePG6vjx463HevfurXbv3l21WCzWY0eOHFEVRVFXrlypqqqqLl26VAXU1atXW9uUlJSowcHB6j333GM91qdPH9XOzk6Ni4uzec+QkBCbrd1VVVV/++03VVEU9fDhwzb93b17d7n3UXb8xIkTqqIo6nvvvVep52OxWFSj0ai+++67akBAgPV42W6wCxcurPD1S5YsUQF127Zt1mPPPfec6uLiohYUFFzW3mw2q0ajUb399ttt7nn27NkqoKalpVV4v0OHDlX79Olj/X3MmDFq06ZN1cLCQuux1NRU1cXFRf38889VVVXVGTNmqF5eXpV4GkLUbTJiIsQt4Pbbb7f+s6IotGnThgsXLgBQUFDAH3/8wYgRIzCbzZhMJkwmEy1btiQoKMg6FbB161bc3NyIjo62XsvOzo577rmHbdu22bxfhw4dCAoKsv5+8uRJzp8/z8iRI63XN5lM9OnTB41GU+XVNhs2bEBVVetoS3mKioqYNm0azZs3R6/XY2dnxyuvvEJSUpJ1VKWy7rzzTjw9Pa3TOaqqsmDBAus0D8CFCxcYO3YsDRs2RKfTYWdnx9q1azl58mSV3qs8a9euZciQIeh0Ouuz8/T0pFOnTtZ/P+Hh4WRmZjJu3DjWrVtHQUHBNb+vELVBAhMhbgEeHh42v9vb21NUVASU5p+YzWaeffZZ7OzsbH7i4uKIj4+3tvP19b3s2n5+fmRmZl527GJly2Pvvvtum+s7OTlhNput71FZGRkZ6HS6cvtTZurUqcyYMYNHH32UVatWsXv3bl599VUA671Xlr29PcOHD2fhwoWYTCa2bNnChQsXrNM4FouFIUOGsG3bNt566y02btzI7t27ufPOO6v8XuVJT0/n448/vuzfz9atW63Prn///vz4448cOXKE6OhoGjRowJgxYy77dyNEXSercoS4xXl4eKAoCi+//DLDhg277HyDBg0A8PLyuiwZFiAlJQUvLy+bY2VJq2XKzv/73/+mW7dul10jMDCwSn329vbGZDKRmpp6xeBk4cKFTJw4kalTp1qPrVy5skrvc7HRo0fzzTffsH79ehYvXoyvry8DBw4E4PTp0+zfv58lS5bY5OUUFhZWeE0HBwcASkpKbI5nZWXZPEMvLy9iYmJ44oknLruGq6ur9Z8ffPBBHnzwQdLT01m6dKk12Pz222+rfsNC1BIJTIS4xTk7OxMVFcWxY8cqLCTWs2dPZsyYwdq1a61TQyaTicWLF9OzZ88K36N169Y0atSIs2fPMmnSpCu2s7e3B64+otG/f38URWH27Nk2gcfFCgsLrdeD0uTd+fPnV3jdivTu3ZuGDRvy/fffs3btWh544AG0Wq31vS7uP8D58+f5448/aNmy5RWv2ahRI6B0xU337t2B0tGRffv20blzZ2u7gQMHcvjwYTp16mR9z4o0aNCA8ePHs2rVKo4dO1b1mxWiFklgIoRgxowZ9O/fn/vuu49Ro0bh6enJhQsXWLduHQ8//DB9+/YlJiaGrl278uCDD/L+++/j5+fHZ599RlJSEi+//HKF11cUhZkzZ/LAAw+Qn59PTEwMzs7OnD9/npUrV/Lee+/RsmVLWrZsiVar5b///S86nQ6dTkdERMRl12vZsiWPPfYYr776KpmZmQwYMICCggJWrlzJG2+8QcOGDbntttv4+uuvCQ0NpUGDBnzxxRfWFSzVodFoGDVqFDNnzkRVVZuiamWB14svvojZbCYvL49p06bRsGHDCq/ZqFEjunXrxptvvom7uzs6nY5//vOfuLu727R788036dKlC9HR0UyYMAE/Pz+Sk5PZvHkzvXr14v7772fatGlkZGTQt29ffH19OXToEKtXr2bKlCnVvmchakUtJ98KIWoAV1mVc7VVH6qqqrt371YHDRqkuru7q46OjmqLFi3Uxx57TI2Pj7e2SU9PV8eNG6d6eXmper1ejYqKUjdt2mRznT59+qgxMTHl9nPt2rVqnz59VGdnZ9XZ2Vlt27at+txzz6nZ2dnWNl999ZXatGlTVafTqWUfUeXdh9lsVj/44AO1RYsWqp2dnerv76/ed999ak5OjqqqqpqcnKwOGzZMdXV1Vf38/NSpU6eqX3/9tc2qmMquyimzb98+FVCbNWt22bldu3apXbp0UR0cHNQWLVqo33//vTp27Fi1bdu21jaXrspRVVU9ffq02q9fP9XZ2Vlt1qyZOm/evHL//Zw8eVIdOXKk6u3trer1erVx48bqmDFjrCuali9frg4YMED18fFR9Xq92qxZM3XatGmq0Wis1L0JUVcoqnrRwnwhhBBCiFokq3KEEEIIUWdIYCKEEEKIOkMCEyGEEELUGRKYCCGEEKLOkMBECCGEEHWGBCZCCCGEqDMkMBFCCCFEnSGBiRBCCCHqDAlMhBBCCFFnSGAihBBCiDpDAhMhhBBC1BkSmAghhBCizvh/SYaGw2B1bCwAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_IGNORE_OUTPUT\n", + "from statista.distributions import GEV\n", + "# calculate the F (Non Exceedence probability based on weibul)\n", + "cdf_Weibul = PlottingPosition.weibul(time_series2)\n", + "#T = PlottingPosition.weibul(time_series2)\n", + "# inverse_cdf method calculates the theoretical values based on the Gumbel distribution\n", + "qth = gev_series_2.inverse_cdf(cdf_Weibul)\n", + "\n", + "upper, lower, fig, ax = gev_series_2.confidence_interval(\n", + " prob_non_exceed=cdf_Weibul,\n", + " alpha=0.1,\n", + " n_samples=100,\n", + " plot_figure=True\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-08-18 21:42:39.355\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mstatista.confidence_interval\u001b[0m:\u001b[36mboot_strap\u001b[0m:\u001b[36m110\u001b[0m - \u001b[34m\u001b[1mSome values used top 10 low/high samples; results may be unstable.\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 248.55080779 279.26333781 300.94886892 318.60283258 333.93567222\n", + " 347.75848368 360.52667037 372.52471064 383.944131 396.22426951\n", + " 408.21605493 419.82552089 431.13010658 442.1926571 450.68191758\n", + " 462.13004273 473.4350262 484.63412476 495.99946577 507.74044479\n", + " 519.47195194 531.22234306 543.01892712 554.88844149 566.85748211\n", + " 578.9529084 591.20223973 603.63405917 616.27843962 629.16740878\n", + " 642.3354712 655.82020926 669.6629901 683.9098131 698.61234325\n", + " 713.82919118 729.62752297 746.08511561 763.29302255 781.35908789\n", + " 800.41266272 820.61106033 844.85576736 872.29856827 904.66993395\n", + " 941.80448176 972.26172561 1006.23101288 1044.6869703 1103.20900569\n", + " 1180.20002711 1282.67461257 1420.73221677 1688.41484047]\n", + "[ 66.92146073 117.64238867 148.18532433 172.37671098 194.01345764\n", + " 213.3162537 230.85712961 247.09388698 258.65307347 274.32450482\n", + " 289.33387811 303.81041049 316.85462343 329.43634659 341.74795137\n", + " 353.21120288 362.01208608 370.78051957 379.54176884 388.31937358\n", + " 397.88371331 408.70742982 419.54569044 430.42270195 441.36228646\n", + " 452.38824859 463.52472789 474.79655038 486.22959268 497.85117245\n", + " 509.69048039 521.77907139 534.15143645 545.38045198 555.90751082\n", + " 566.7169649 577.847794 589.34457303 601.25886793 613.65105894\n", + " 626.59276564 640.17013675 658.57014966 678.78609398 698.04865243\n", + " 715.4646515 734.2600668 755.12256428 778.67672597 805.87970024\n", + " 838.31364864 878.89677831 941.32275385 1033.035424 ]\n" + ] + } + ], + "source": [ + "# NBVAL_IGNORE_OUTPUT\n", + "CI = ConfidenceInterval.boot_strap(\n", + " time_series2,\n", + " gevfit=gev_param_lm_series_2,\n", + " n_samples=100,\n", + " F=cdf_Weibul,\n", + " method=\"lmoments\",\n", + " state_function=GEV.ci_func\n", + ")\n", + "lower_bound = CI[\"lb\"]\n", + "upper_bound = CI[\"ub\"]\n", + "print(lower_bound)\n", + "print(upper_bound)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-08-18 21:43:52.365\u001b[0m | \u001b[34m\u001b[1mDEBUG \u001b[0m | \u001b[36mstatista.confidence_interval\u001b[0m:\u001b[36mboot_strap\u001b[0m:\u001b[36m110\u001b[0m - \u001b[34m\u001b[1mSome values used top 10 low/high samples; results may be unstable.\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# NBVAL_IGNORE_OUTPUT\n", + "fig, ax = gev_series_2.plot()\n", + "upper_bound, lower_bound, fig, ax = gev_series_2.confidence_interval(plot_figure=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/rhine_example.py b/examples/rhine_example.py index e79f59f..25994f3 100644 --- a/examples/rhine_example.py +++ b/examples/rhine_example.py @@ -1,4 +1,5 @@ """ Rhine gauges example """ + import matplotlib matplotlib.use("TkAgg") @@ -6,24 +7,19 @@ import pandas as pd from statista.distributions import ( Distributions, - GEV, - Exponential, - Gumbel, - PlottingPosition, ) -from statista.confidence_interval import ConfidenceInterval # %% ams = pd.read_csv("examples/data/rhine.csv") ams.head() ams.replace(0, np.nan, inplace=True) ams.dropna(axis=0, inplace=True) -#%% +# %% rees_gauge = ams.loc[:, "rees"].values cologne_gauge = ams.loc[:, "cologne"].values maxau_gauge = ams.loc[:, "maxau"].values rockenau_gauge = ams.loc[:, "rockenau"].values -#%% Exponential distribution (mle) +# %% Exponential distribution (mle) dist_obj = Distributions("Exponential", cologne_gauge) # default parameter estimation method is maximum liklihood method mle_param = dist_obj.fit_model(method="mle") @@ -32,22 +28,22 @@ print(mle_param) # calculate and plot the pdf -pdf, fig, ax = dist_obj.pdf(mle_param, plot_figure=True) -cdf, _, _ = dist_obj.cdf(mle_param, plot_figure=True) -#%% exponential distribution (lmoments) +pdf, fig, ax = dist_obj.pdf(plot_figure=True) +cdf, _, _ = dist_obj.cdf(plot_figure=True) +# %% exponential distribution (lmoments) dist_obj = Distributions("Exponential", cologne_gauge) -# default parameter estimation method is maximum liklihood method +# default parameter estimation method is maximum likelihood method mle_param = dist_obj.fit_model(method="lmoments") dist_obj.ks() dist_obj.chisquare() print(mle_param) # calculate and plot the pdf -pdf, fig, ax = dist_obj.pdf(mle_param, plot_figure=True) -cdf, _, _ = dist_obj.cdf(mle_param, plot_figure=True) -#%% GEV (mle) +pdf, fig, ax = dist_obj.pdf(plot_figure=True) +cdf, _, _ = dist_obj.cdf(plot_figure=True) +# %% GEV (mle) gev_cologne = Distributions("GEV", cologne_gauge) -# default parameter estimation method is maximum liklihood method +# default parameter estimation method is maximum likelihood method mle_param = gev_cologne.fit_model(method="mle") gev_cologne.ks() gev_cologne.chisquare() @@ -55,11 +51,11 @@ print(mle_param) # shape = -1 * mle_param[0] # calculate and plot the pdf -pdf, fig, ax = gev_cologne.pdf(mle_param, plot_figure=True) -cdf, _, _ = gev_cologne.cdf(mle_param, plot_figure=True) -#%% cologne (lmoment) +pdf, fig, ax = gev_cologne.pdf(plot_figure=True) +cdf, _, _ = gev_cologne.cdf(plot_figure=True) +# %% cologne (lmoment) gev_cologne = Distributions("GEV", cologne_gauge) -# default parameter estimation method is maximum liklihood method +# default parameter estimation method is maximum likelihood method lmom_param = gev_cologne.fit_model(method="lmoments") gev_cologne.ks() gev_cologne.chisquare() @@ -67,5 +63,7 @@ print(lmom_param) # shape = -1 * `lmom_param[0] # calculate and plot the pdf -pdf, fig, ax = gev_cologne.pdf(lmom_param, plot_figure=True) -cdf, _, _ = gev_cologne.cdf(lmom_param, plot_figure=True) +pdf, fig, ax = gev_cologne.pdf(plot_figure=True) +cdf, _, _ = gev_cologne.cdf(plot_figure=True) + +# %% diff --git a/examples/SensitivityAnalysis.py b/examples/sensitivity-analysis.py similarity index 95% rename from examples/SensitivityAnalysis.py rename to examples/sensitivity-analysis.py index eba0249..2b3a64e 100644 --- a/examples/SensitivityAnalysis.py +++ b/examples/sensitivity-analysis.py @@ -1,5 +1,5 @@ # import os -Path = "F:/01Algorithms/Hydrology/HAPI/examples" +Path = "F:/algorithms/Hydrology/HAPI/examples" import matplotlib matplotlib.use("TkAgg") @@ -54,10 +54,10 @@ Route = 1 # RoutingFn=Routing.TriangularRouting2 RoutingFn = Routing.Muskingum -#%% +# %% ### run the model Run.RunLumped(Coello, Route, RoutingFn) -#%% +# %% Metrics = dict() Qobs = Coello.QGauges[Coello.QGauges.columns[0]] @@ -73,7 +73,7 @@ print("NSEhf= " + str(round(Metrics["NSEhf"], 2))) print("KGE= " + str(round(Metrics["KGE"], 2))) print("WB= " + str(round(Metrics["WB"], 2))) -#%% +# %% """ first the Sensitivity method takes 4 arguments : 1-parameters:previous obtained parameters @@ -110,6 +110,8 @@ Each parameter has a disctionary with two keys 0: list of parameters woth relative values 1: list of parameter values """ + + # For Type 1 def WrapperType1(Randpar, Route, RoutingFn, Qobs): Coello.Parameters = Randpar @@ -137,13 +139,15 @@ def WrapperType2(Randpar, Route, RoutingFn, Qobs): Positions = [10] -Sen = SA(parameters, Coello.LB, Coello.UB, fn, Positions, 5, Type=Type) -Sen.OAT(Route, RoutingFn, Qobs) -#%% +Sen = SA( + parameters, Coello.lower_bound, Coello.upper_bound, fn, Positions, 5, Type=Type +) +Sen.one_at_a_time(Route, RoutingFn, Qobs) +# %% From = "" To = "" if Type == 1: - fig, ax1 = Sen.Sobol( + fig, ax1 = Sen.sobol( real_values=False, title="Sensitivity Analysis of the RMSE to models parameters", xlabel="Maxbas Values", @@ -155,7 +159,7 @@ def WrapperType2(Randpar, Route, RoutingFn, Qobs): spaces=[None, None, None, None, None, None], ) elif Type == 2: - fig, (ax1, ax2) = Sen.Sobol( + fig, (ax1, ax2) = Sen.sobol( real_values=False, title="Sensitivity Analysis of the RMSE to models parameters", xlabel="Maxbas Values", diff --git a/examples/truncated-distribution.py b/examples/truncated-distribution.py new file mode 100644 index 0000000..327b9aa --- /dev/null +++ b/examples/truncated-distribution.py @@ -0,0 +1,65 @@ +import matplotlib + +matplotlib.use("TkAgg") +import pandas as pd + +from statista.distributions import Gumbel, PlottingPosition, Distributions + +time_series1 = pd.read_csv("examples/data/time_series1.txt", header=None)[0].tolist() +time_series2 = pd.read_csv("examples/data/time_series2.txt", header=None)[0].tolist() +# %% +gumbel_series_1 = Distributions("Gumbel", time_series1) +param_lmoments = gumbel_series_1.fit_model(method="lmoments") +gumbel_series_1.ks() +gumbel_series_1.chisquare() +print(param_lmoments) +# calculate and plot the pdf +pdf = gumbel_series_1.pdf(plot_figure=True) +cdf, _, _ = gumbel_series_1.cdf(plot_figure=True) +upper, lower, fig, ax = gumbel_series_1.confidence_interval(alpha=0.1, plot_figure=True) +# %% +# calculate the F (Non-Exceedance probability based on weibul) +cdf_weibul = PlottingPosition.weibul(time_series1) +# %% +import numpy as np + + +def truncated_distribution(p, x, threshold): + # threshold = p[0] + loc = p[1] + scale = p[2] + + truncated_data = x[x < threshold] + nx2 = len(x[x >= threshold]) + # pdf with a scaled pdf + # L1 is pdf based + parameters = {"loc": loc, "scale": scale} + pdf = Gumbel._pdf_eq(truncated_data, parameters) + # the CDF at the threshold is used because the data is assumed to be truncated, meaning that observations below + # this threshold are not included in the dataset. When dealing with truncated data, it's essential to adjust + # the likelihood calculation to account for the fact that only values above the threshold are observed. The + # CDF at the threshold effectively normalizes the distribution, ensuring that the probabilities sum to 1 over + # the range of the observed data. + adjusted_cdf = 1 - Gumbel._cdf_eq(threshold, parameters) + # calculates the negative log-likelihood of a Gumbel distribution + # Adjust the likelihood for the truncation + # likelihood = pdf / (1 - adjusted_cdf) + + l1 = (-np.log((pdf / scale))).sum() + # L2 is cdf based + l2 = (-np.log(adjusted_cdf)) * nx2 + # print x1, nx2, L1, L2 + return l1 * l2 # -np.sum(np.log(likelihood)) + + +# %% +threshold = 18 +param_dist = gumbel_series_1.fit_model( + method="optimization", obj_func=truncated_distribution, threshold=threshold +) +print(param_dist) +# gumbel_series_1.plot(parameters=param_dist) +upper, lower, fig, ax = gumbel_series_1.confidence_interval( + parameters=param_dist, alpha=0.1, plot_figure=True +) +# %% diff --git a/requirements-dev.txt b/requirements-dev.txt index b359f2e..c7a8bfd 100644 --- a/requirements-dev.txt +++ b/requirements-dev.txt @@ -1,13 +1,13 @@ -black >=23.11.0 +black >=24.4.2 darglint >=1.8.1 -flake8 >=6.1.0 flake8-bandit >=4.1.1 -flake8-bugbear >=23.9.16 +flake8-bugbear >=24.4.26 flake8-docstrings >=1.7.0 flake8-rst-docstrings >=0.3.0 -pep8-naming >=0.13.3 -pre-commit >=3.5.0 -pre-commit-hooks >=4.5.0 -pytest >=7.4.3 -pytest-cov >= 4.1.0 -reorder-python-imports >=3.12.0 +nbval >=0.11.0 +pep8-naming >=0.14.1 +pre-commit >=3.7.1 +pre-commit-hooks >=4.6.0 +pytest >=8.2.2 +pytest-cov >=5.0.0 +reorder-python-imports >=3.13.0 diff --git a/requirements.txt b/requirements.txt index cb7a8db..e10012e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,7 @@ -loguru >=0.6.0 -matplotlib >=3.6.3 -numpy >=1.25.2 +loguru >=0.7.2 +matplotlib >=3.9.0 +numpy >=2.0.1 pandas >=2.1.0 pip >=23.2.1 -scikit-learn >=1.3.2 -scipy >=1.11.4 +scikit-learn >=1.5.1 +scipy >=1.14.0 diff --git a/setup.py b/setup.py index 0a7cc25..d4e024f 100644 --- a/setup.py +++ b/setup.py @@ -8,15 +8,22 @@ requirements = [line.strip() for line in open("requirements.txt").readlines()] requirements_dev = [line.strip() for line in open("requirements-dev.txt").readlines()] +requirements_docs = [line.strip() for line in open("docs/requirements.txt").readlines()] setup( name="statista", - version="0.5.0", + version="0.6.0", description="statistics package", author="Mostafa Farrag", author_email="moah.farag@gmail.come", - url="https://github.com/MAfarrag/statista", - keywords=["remote sensing", "ecmwf"], + url="https://github.com/Serapieum-of-alex/statista", + keywords=[ + "statistics", + "distributions", + "extreme-value-analysis", + "probability", + "sensitivity-analysis", + ], long_description=readme + "\n\n" + history, long_description_content_type="text/markdown", license="GNU General Public License v3", @@ -25,6 +32,10 @@ test_suite="tests", tests_require=requirements_dev, install_requires=requirements, + extras_require={ + "dev": requirements_dev, + "docs": requirements_docs, + }, classifiers=[ "Development Status :: 5 - Production/Stable", "Environment :: Console", diff --git a/statista/confidence_interval.py b/statista/confidence_interval.py index a61a407..a08a9c6 100644 --- a/statista/confidence_interval.py +++ b/statista/confidence_interval.py @@ -1,4 +1,5 @@ """Confidence interval module.""" + from collections import OrderedDict from loguru import logger from typing import Union @@ -45,29 +46,35 @@ def bs_indexes(data, n_samples=10000) -> np.ndarray: @staticmethod def boot_strap( data: Union[list, np.ndarray], - statfunction, + state_function: callable, alpha: float = 0.05, n_samples: int = 100, - **kargs, + **kwargs, ): # -> Dict[str, OrderedDict[str, Tuple[Any, Any]]] """boot_strap - Calculate confidence intervals using parametric bootstrap and the percentil interval method This is used to + Calculate confidence intervals using parametric bootstrap and the percentile interval method This is used to obtain confidence intervals for the estimators and the return values for several return values. More info about bootstrapping can be found on: - Efron: "An Introduction to the Bootstrap", Chapman & Hall (1993) - https://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29 - parameters: - ----------- - alpha : [numeric] + Parameters + ---------- + data: [list, np.ndarray] + data to be used to calculate the confidence interval + state_function: [callable] + function to be used to calculate the confidence interval + n_samples: int, Default is 100. + number of samples to be generated. . + alpha: numeric, optional, default is 0.05 alpha or SignificanceLevel is a value of the confidence interval. - kwargs : - gevfit : [list] + kwargs: + gevfit: [list] list of the three parameters of the GEV distribution [shape, loc, scale] - F : [list] - non exceedence probability/ cdf + F: [list] + non-exceedance probability/ cdf method: [str] method used to fit the generated samples from the bootstrap method ["lmoments", "mle", "mm"]. Default is "lmoments". @@ -76,22 +83,22 @@ def boot_strap( tdata = (np.array(data),) # We don't need to generate actual samples; that would take more memory. - # Instead, we can generate just the indexes, and then apply the statfun + # Instead, we can generate just the indexes, and then apply the stat-fun # to those indexes. - bootindexes = ConfidenceInterval.bs_indexes(tdata[0], n_samples) + boot_indexes = ConfidenceInterval.bs_indexes(tdata[0], n_samples) stat = np.array( [ - statfunction(*(x[indexes] for x in tdata), **kargs) - for indexes in bootindexes + state_function(*(x[indexes] for x in tdata), **kwargs) + for indexes in boot_indexes ] ) stat.sort(axis=0) # Percentile Interval Method - avals = alphas - nvals = np.round((n_samples - 1) * avals).astype("int") + a_vals = alphas + n_vals = np.round((n_samples - 1) * a_vals).astype("int") - if np.any(nvals == 0) or np.any(nvals == n_samples - 1): + if np.any(n_vals == 0) or np.any(n_vals == n_samples - 1): logger.debug( "Some values used extremal samples; results are probably unstable." ) @@ -99,7 +106,7 @@ def boot_strap( # "Some values used extremal samples; results are probably unstable.", # InstabilityWarning, # ) - elif np.any(nvals < 10) or np.any(nvals >= n_samples - 10): + elif np.any(n_vals < 10) or np.any(n_vals >= n_samples - 10): logger.debug( "Some values used top 10 low/high samples; results may be unstable." ) @@ -108,14 +115,14 @@ def boot_strap( # InstabilityWarning, # ) - if nvals.ndim == 1: - # All nvals are the same. Simple broadcasting - out = stat[nvals] + if n_vals.ndim == 1: + # All n_vals are the same. Simple broadcasting + out = stat[n_vals] else: - # Nvals are different for each data point. Not simple broadcasting. - # Each set of nvals along axis 0 corresponds to the data at the same + # n_vals are different for each data point. Not simple broadcasting. + # Each set of n_vals along axis 0 corresponds to the data at the same # point in other axes. - out = stat[(nvals, np.indices(nvals.shape)[1:].squeeze())] + out = stat[(n_vals, np.indices(n_vals.shape)[1:].squeeze())] ub = out[0, 3:] lb = out[1, 3:] diff --git a/statista/metrics.py b/statista/descriptors.py similarity index 86% rename from statista/metrics.py rename to statista/descriptors.py index df67a8d..30fa6e6 100644 --- a/statista/metrics.py +++ b/statista/descriptors.py @@ -1,4 +1,5 @@ -""" Performance Metrics """ +"""Statistical descriptors. """ + from numbers import Number from typing import Union @@ -7,7 +8,7 @@ def rmse(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]) -> float: - """Root Mean Squared Error. Metric for the estimation of performance of the hydrological model. + """Root Mean Squared Error. Parameters ---------- @@ -200,10 +201,9 @@ def rmse_lf( return error -def kge(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]): - """kge. +def kge(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]) -> float: + """kling–Gupta efficiency. - ling–Gupta efficiency (Gupta et al. 2009) have showed the limitation of using a single error function to measure the efficiency of calculated flow and showed that Nash-Sutcliff efficiency (NSE) or RMSE can be decomposed into three component correlation, variability and bias. @@ -232,34 +232,36 @@ def kge(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]): return kge -def wb(obs, qsim): - """wb. - Water balance error. - The mean cumulative error measures how much the model succeed to reproduce the stream flow volume correctly. - This error allows error compensation from time step to another and it is not an indication on how accurate is the +def wb(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]) -> float: + """Water balance error. + + The mean cumulative error measures how much the model succeeds to reproduce the stream flow volume correctly. + This error allows error compensation from time step to another, and it is not an indication on how accurate is the model in the simulated flow. the naive model of Nash-Sutcliffe (simulated flow is as accurate as average observed flow) will result in WB error equals to 100 %. (Oudin et al. 2006) - inputs: - ---------- + Parameters + ---------- obs: [list/array] - observed flow - sim: [list/array] - simulated flow + observed flow + sim: [list/array] + simulated flow Returns ------- error values """ qobs_sum = np.sum(obs) - qsim_sum = np.sum(qsim) + qsim_sum = np.sum(sim) wb = 100 * (1 - np.abs(1 - (qsim_sum / qobs_sum))) return wb -def nse(obs: np.ndarray, sim: np.ndarray): - """Nash-Sutcliffe efficiency. Metric for the estimation of performance of the hydrological model. +def nse(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]) -> float: + """Nash-Sutcliffe efficiency. + + Metric for the estimation of performance of the hydrological model. Parameters ---------- @@ -284,11 +286,10 @@ def nse(obs: np.ndarray, sim: np.ndarray): return e -def nse_hf(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]): - """NSEHF. +def nse_hf(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]) -> float: + """Modified Nash-Sutcliffe efficiency. - Modified Nash-Sutcliffe efficiency. Metric for the estimation of performance of the - hydrological model + Metric for the estimation of performance of the hydrological model. reference: Hundecha Y. & Bárdossy A. Modeling of the effect of land use @@ -396,7 +397,9 @@ def mae(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]): return np.abs(np.array(obs) - np.array(sim)).mean() -def pearson_corre(x: Union[list, np.ndarray], y: Union[list, np.ndarray]) -> Number: +def pearson_corr_coeff( + x: Union[list, np.ndarray], y: Union[list, np.ndarray] +) -> Number: """Pearson correlation coefficient. - Pearson correlation coefficient is independent of the magnitude of the numbers. @@ -426,17 +429,17 @@ def pearson_corre(x: Union[list, np.ndarray], y: Union[list, np.ndarray]) -> Num def r2(obs: Union[list, np.ndarray], sim: Union[list, np.ndarray]): """R2. - the coefficient of determination measures how well the predicted - values match (and not just follow) the observed values. - It depends on the distance between the points and the 1:1 line - (and not the best-fit line) - Closer the data to the 1:1 line, higher the coefficient of determination. - The coefficient of determination is often denoted by R². However, - it is not the square of anything. It can range from any negative number to +1 - - R² = +1 indicates that the predictions match the observations perfectly - - R² = 0 indicates that the predictions are as good as random guesses around - the mean of the observed values - - Negative R² indicates that the predictions are worse than random + the coefficient of determination measures how well the predicted + values match (and not just follow) the observed values. + It depends on the distance between the points and the 1:1 line + (and not the best-fit line) + Closer the data to the 1:1 line, higher the coefficient of determination. + The coefficient of determination is often denoted by R². However, + it is not the square of anything. It can range from any negative number to +1 + - R² = +1 indicates that the predictions match the observations perfectly + - R² = 0 indicates that the predictions are as good as random guesses around + the mean of the observed values + - Negative R² indicates that the predictions are worse than random Since R² indicates the distance of points from the 1:1 line, it does depend on the magnitude of the numbers (unlike r² peason correlation coefficient). diff --git a/statista/distributions.py b/statista/distributions.py index 62a8678..bf76371 100644 --- a/statista/distributions.py +++ b/statista/distributions.py @@ -1,10 +1,13 @@ """Statistical distributions.""" + from numbers import Number from typing import Any, List, Tuple, Union, Dict, Callable from abc import ABC, abstractmethod import numpy as np +from statistics import mode import scipy.optimize as so from matplotlib.figure import Figure +from matplotlib.axes import Axes from numpy import ndarray from scipy.stats import chisquare, genextreme, gumbel_r, ks_2samp, norm, expon @@ -14,6 +17,7 @@ from statista.plot import Plot from statista.confidence_interval import ConfidenceInterval + ninf = 1e-5 __all__ = [ @@ -39,13 +43,27 @@ def return_period(prob_non_exceed: Union[list, np.ndarray]) -> np.ndarray: Parameters ---------- prob_non_exceed: [list/array] - non exceedence probability. + non-exceedance probability. Returns ------- array: return period. + + Examples + -------- + - First generate some random numbers between 0 and 1 as a non-exceedance probability. then use this non-exceedance + to calculate the return period. + + >>> data = np.random.random(15) + >>> rp = PlottingPosition.return_period(data) + >>> print(rp) # doctest: +SKIP + [ 1.33088992 4.75342173 2.46855419 1.42836548 2.75320582 2.2268505 + 8.06500888 10.56043917 18.28884687 1.10298241 1.2113997 1.40988022 + 1.02795867 1.01326322 1.05572108] """ + if any(prob_non_exceed > 1): + raise ValueError("Non-exceedance probability should be less than 1") prob_non_exceed = np.array(prob_non_exceed) t = 1 / (1 - prob_non_exceed) return t @@ -69,6 +87,14 @@ def weibul(data: Union[list, np.ndarray], return_period: int = False) -> np.ndar ------- cdf/T: [list] list of cumulative distribution function or return period. + + Examples + -------- + >>> data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] + >>> cdf = PlottingPosition.weibul(data) + >>> print(cdf) + [0.09090909 0.18181818 0.27272727 0.36363636 0.45454545 0.54545455 + 0.63636364 0.72727273 0.81818182 0.90909091] """ data = np.array(data) data.sort() @@ -86,13 +112,10 @@ class AbstractDistribution(ABC): AbstractDistribution. """ - parameters: Dict[str, Union[float, Any]] - cdf_Weibul: ndarray - def __init__( self, data: Union[list, np.ndarray] = None, - parameters: Dict[str, str] = None, + parameters: Dict[str, float] = None, ): """Gumbel. @@ -107,20 +130,70 @@ def __init__( - scale: [numeric] scale parameter """ - if isinstance(data, list) or isinstance(data, np.ndarray): - self.data = np.array(data) - self.data_sorted = np.sort(data) - self.cdf_Weibul = PlottingPosition.weibul(data) - self.KStable = 1.22 / np.sqrt(len(self.data)) - - self.parameters = parameters + if data is None and parameters is None: + raise ValueError("Either data or parameters must be provided") - self.Dstatic = None - self.KS_Pvalue = None - self.chistatic = None - self.chi_Pvalue = None + if isinstance(data, list) or isinstance(data, np.ndarray): + self._data = np.array(data) + elif data is None: + self._data = data + else: + raise TypeError("The `data` argument should be list or numpy array") - pass + if isinstance(parameters, dict) or parameters is None: + self._parameters = parameters + else: + raise TypeError("The `parameters` argument should be dictionary") + + def __str__(self) -> str: + message = "" + if self.data is not None: + message += f""" + Dataset of {len(self.data)} value + min: {np.min(self.data)} + max: {np.max(self.data)} + mean: {np.mean(self.data)} + median: {np.median(self.data)} + mode: {mode(self.data)} + std: {np.std(self.data)} + Distribution : {self.__class__.__name__} + parameters: {self.parameters} + """ + if self.parameters is not None: + message += f""" + Distribution : {self.__class__.__name__} + parameters: {self.parameters} + """ + return message + + @property + def parameters(self) -> Dict[str, float]: + """Distribution parameters""" + return self._parameters + + @parameters.setter + def parameters(self, value: Dict[str, float]): + self._parameters = value + + @property + def data(self) -> ndarray: + """data.""" + return self._data + + @property + def data_sorted(self) -> ndarray: + """data_sorted.""" + return np.sort(self.data) + + @property + def kstable(self) -> float: + """KStable.""" + return 1.22 / np.sqrt(len(self.data)) + + @property + def cdf_weibul(self) -> ndarray: + """cdf_Weibul.""" + return PlottingPosition.weibul(self.data) @staticmethod @abstractmethod @@ -132,58 +205,74 @@ def _pdf_eq( @abstractmethod def pdf( self, - parameters: Dict[str, Union[float, Any]], + parameters: Dict[str, Union[float, Any]] = None, plot_figure: bool = False, - figsize: tuple = (6, 5), + fig_size: tuple = (6, 5), xlabel: str = "Actual data", ylabel: str = "pdf", fontsize: Union[float, int] = 15, - actual_data: Union[bool, np.ndarray] = True, - ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: + data: Union[List[float], np.ndarray] = None, + **kwargs, + ) -> Union[np.ndarray, Tuple[np.ndarray, Figure, Axes]]: """pdf. - Returns the value of Gumbel's pdf with parameters loc and scale at x . + Returns the value of Gumbel's pdf with parameters loc and scale at x. - Parameters: - ----------- + Parameters + ---------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - - kwargs: - figsize: tuple = (6, 5), - xlabel: str = "Actual data", - ylabel: str = "pdf", - fontsize: Union[float, int] = 15, - actual_data: np.ndarray = None, + data : np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. + plot_figure: [bool], Default is False. + True to plot the figure. + fig_size: [tuple] + Default is (6, 5). + xlabel: [str] + Default is "Actual data". + ylabel: [str] + Default is "cdf". + fontsize: [int] + Default is 15. Returns ------- - pdf : [array] + pdf: [array] probability density function pdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. """ - if actual_data is None: + if data is None: ts = self.data + data_sorted = self.data_sorted else: - ts = actual_data + ts = data + data_sorted = np.sort(data) + + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters pdf = self._pdf_eq(ts, parameters) if plot_figure: - qx = np.linspace( - float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 - ) - pdf_fitted = self.pdf(parameters, actual_data=qx) + qx = np.linspace(float(data_sorted[0]), 1.5 * float(data_sorted[-1]), 10000) + pdf_fitted = self.pdf(parameters=parameters, data=qx) fig, ax = Plot.pdf( qx, pdf_fitted, - self.data_sorted, - figsize=figsize, + data_sorted, + fig_size=fig_size, xlabel=xlabel, ylabel=ylabel, fontsize=fontsize, @@ -202,48 +291,64 @@ def _cdf_eq( @abstractmethod def cdf( self, - parameters: Dict[str, Union[float, Any]], + parameters: Dict[str, Union[float, Any]] = None, plot_figure: bool = False, - figsize: tuple = (6, 5), + fig_size: tuple = (6, 5), xlabel: str = "data", ylabel: str = "cdf", fontsize: int = 15, - actual_data: Union[bool, np.ndarray] = True, - ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: - """cdf. + data: Union[List[float], np.ndarray] = None, + ) -> Union[np.ndarray, Tuple[np.ndarray, Figure, Axes]]: + """Cumulative distribution function. - cdf calculates the value of Gumbel's cdf with parameters loc and scale at x. - - parameter: + Parameters ---------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. + data : np.ndarray, default is None. + array if you want to calculate the cdf for different data than the time series given to the constructor + method. + plot_figure: [bool], Default is False. + True to plot the figure. + fig_size: [tuple] + Default is (6, 5). + xlabel: [str] + Default is "Actual data". + ylabel: [str] + Default is "cdf". + fontsize: [int] + Default is 15. """ - if isinstance(actual_data, bool): + if data is None: ts = self.data + data_sorted = self.data_sorted else: - ts = actual_data + ts = data + data_sorted = np.sort(data) + + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters cdf = self._cdf_eq(ts, parameters) if plot_figure: - qx = np.linspace( - float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 - ) - cdf_fitted = self.cdf(parameters, actual_data=qx) + qx = np.linspace(float(data_sorted[0]), 1.5 * float(data_sorted[-1]), 10000) + cdf_fitted = self.cdf(parameters=parameters, data=qx) - cdf_weibul = PlottingPosition.weibul(self.data_sorted) + cdf_weibul = PlottingPosition.weibul(data_sorted) fig, ax = Plot.cdf( qx, cdf_fitted, - self.data_sorted, + data_sorted, cdf_weibul, - figsize=figsize, + fig_size=fig_size, xlabel=xlabel, ylabel=ylabel, fontsize=fontsize, @@ -261,16 +366,16 @@ def fit_model( threshold: Union[None, float, int] = None, test: bool = True, ) -> Union[Dict[str, str], Any]: - """estimateParameter. + """fit_model. - EstimateParameter estimate the distribution parameter based on MLM - (Maximum liklihood method), if an objective function is entered as an input + fit_model estimates the distribution parameter based on MLM + (Maximum likelihood method), if an objective function is entered as an input There are two likelihood functions (L1 and L2), one for values above some - threshold (x>=C) and one for values below (x < C), now the likeliest parameters - are those at the max value of mutiplication between two functions max(L1*L2). + threshold (x>=C) and one for the values below (x < C), now the likeliest parameters + are those at the max value of multiplication between two functions max(L1*L2). - In this case the L1 is still the product of multiplication of probability + In this case, the L1 is still the product of multiplication of probability density function's values at xi, but the L2 is the probability that threshold value C will be exceeded (1-F(C)). @@ -289,9 +394,9 @@ def fit_model( ------- Dict[str, str]: {"loc": val, "scale": val} - - loc: [numeric] + loc: [numeric] location parameter of the gumbel distribution. - - scale: [numeric] + scale: [numeric] scale parameter of the gumbel distribution. """ method = method.lower() @@ -301,29 +406,31 @@ def fit_model( ) return method - @staticmethod @abstractmethod - def theoretical_estimate( - parameters: Dict[str, Union[float, Any]], cdf: np.ndarray + def inverse_cdf( + self, + cdf: Union[np.ndarray, List[float]], + parameters: Dict[str, Union[float, Any]], ) -> np.ndarray: - """theporeticalEstimate. + """theoretical Estimate. - TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution + Theoretical Estimate method calculates the theoretical values based on the Gumbel distribution - Parameters: - ----------- + Parameters + ---------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. cdf: [list] - cummulative distribution function/ Non Exceedence probability. + cumulative distribution function/ Non-Exceedance probability. - Return: + Returns ------- - theoreticalvalue : [numeric] + theoretical value: [numeric] Value based on the theoretical distribution """ pass @@ -332,30 +439,27 @@ def theoretical_estimate( def ks(self) -> tuple: """Kolmogorov-Smirnov (KS) test. - The smaller the D static the more likely that the two samples are drawn from the same distribution - IF Pvalue < signeficance level ------ reject + The smaller the D static, the more likely that the two samples are drawn from the same distribution + IF Pvalue < significance level ------ reject - returns: - -------- + returns + ------- Dstatic: [numeric] The smaller the D static the more likely that the two samples are drawn from the same distribution Pvalue : [numeric] - IF Pvalue < signeficance level ------ reject the null hypotethis + IF Pvalue < significance level ------ reject the null hypothesis. """ if self.parameters is None: raise ValueError( - "Value of parameters is unknown please use " - "'EstimateParameter' to obtain estimate the distribution parameters" + "The Value of parameters is unknown. Please use 'fit_model' to estimate the distribution parameters" ) - qth = self.theoretical_estimate(self.parameters, self.cdf_Weibul) + qth = self.inverse_cdf(self.cdf_weibul, self.parameters) test = ks_2samp(self.data, qth) - self.Dstatic = test.statistic - self.KS_Pvalue = test.pvalue print("-----KS Test--------") print(f"Statistic = {test.statistic}") - if self.Dstatic < self.KStable: + if test.statistic < self.kstable: print("Accept Hypothesis") else: print("reject Hypothesis") @@ -369,122 +473,148 @@ def chisquare(self) -> Union[tuple, None]: """ if self.parameters is None: raise ValueError( - "Value of loc/scale parameter is unknown please use " - "'EstimateParameter' to obtain them" + "The Value of parameters is unknown. Please use 'fit_model' to estimate the distribution parameters" ) - qth = self.theoretical_estimate(self.parameters, self.cdf_Weibul) + qth = self.inverse_cdf(self.cdf_weibul, self.parameters) try: test = chisquare(st.standardize(qth), st.standardize(self.data)) - self.chistatic = test.statistic - self.chi_Pvalue = test.pvalue print("-----chisquare Test-----") print("Statistic = " + str(test.statistic)) print("P value = " + str(test.pvalue)) return test.statistic, test.pvalue except Exception as e: print(e) - return def confidence_interval( self, - parameters: Dict[str, Union[float, Any]], - prob_non_exceed: np.ndarray, alpha: float = 0.1, - ) -> Tuple[np.ndarray, np.ndarray]: + plot_figure: bool = False, + prob_non_exceed: np.ndarray = None, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Union[ + Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray, Figure, Axes] + ]: """confidence_interval. - Parameters: - ----------- - parameters: Dict[str, str] + Parameters + ---------- + alpha: numeric, default is 0.1 + alpha or Significance level is a value of the confidence interval. + plot_figure: bool, optional, default is False. + to plot the confidence interval. + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} - - loc: [numeric] + + - loc: numeric location parameter of the gumbel distribution. - - scale: [numeric] + - scale: numeric scale parameter of the gumbel distribution. - prob_non_exceed : [list] - Non Exceedence probability - alpha : [numeric] - alpha or SignificanceLevel is a value of the confidence interval. + prob_non_exceed: list, default is None. + Non-Exceedance probability, if not given, the plotting position will be calculated using the weibul method. + kwargs: + fig_size: Tuple[float, float], optional, default=(6, 6) + Size of the second figure. + fontsize: int, optional, default=11 + Font size. - Return: + Returns ------- - parameters: Dict[str, str] - {"loc": val, "scale": val, "shape": value} - - loc: [numeric] - location parameter - - scale: [numeric] - scale parameter - q_upper : [list] - upper bound coresponding to the confidence interval. - q_lower : [list] + q_upper: [list] + upper-bound coresponding to the confidence interval. + q_lower: [list] lower bound coresponding to the confidence interval. + fig: matplotlib.figure.Figure + Figure object. + ax: matplotlib.axes.Axes + Axes object. """ pass - def probability_plot( + def plot( self, - parameters: Dict[str, Union[float, Any]], - prob_non_exceed: np.ndarray, - alpha: float = 0.1, - fig1size: tuple = (10, 5), - fig2size: tuple = (6, 6), + fig_size: tuple = (10, 5), xlabel: str = "Actual data", ylabel: str = "cdf", fontsize: int = 15, + cdf: np.ndarray = None, + parameters: Dict[str, Union[float, Any]] = None, ) -> Tuple[List[Figure], list]: - """probapilityPlot. + """Probability Plot. - ProbapilityPlot method calculates the theoretical values based on the Gumbel distribution - parameters, theoretical cdf (or weibul), and calculate the confidence interval. + Probability Plot method calculates the theoretical values based on the Gumbel distribution + parameters, theoretical cdf (or weibul), and calculates the confidence interval. Parameters ---------- + fig_size: tuple, Default is (10, 5). + Size of the figure. + xlabel: [str] + Default is "Actual data" + ylabel: [str] + Default is "cdf" + fontsize: [float] + Default is 15. parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - prob_non_exceed : [np.ndarray] + cdf: [np.ndarray] theoretical cdf calculated using weibul or using the distribution cdf function. - alpha : [float] - value between 0 and 1. - fig1size: [tuple] - Default is (10, 5) - fig2size: [tuple] - Default is (6, 6) - xlabel: [str] - Default is "Actual data" - ylabel: [str] - Default is "cdf" - fontsize: [float] - Default is 15. Returns ------- - Qth : [list] - theoretical generated values based on the theoretical cdf calculated from - weibul or the distribution parameters. - q_upper : [list] - upper bound coresponding to the confidence interval. - q_lower : [list] - lower bound coresponding to the confidence interval. + Figure: + matplotlib figure object + Tuple[Axes, Axes]: + matplotlib plot axes """ pass class Gumbel(AbstractDistribution): - """Gumbel distribution.""" + """Gumbel distribution (Maximum - Right Skewed). + + The Gumbel distribution is used to model the distribution of the maximum (or the minimum) of a number of samples of + various distributions. + + - The probability density function (PDF) of the Gumbel distribution (Type I) is: + + .. math:: + f(x; \\zeta, \\delta) = \\frac{1}{\\delta} \\exp\\left(-\\frac{x - \\zeta}{\\delta} \\right) + \\exp\\left(-\\exp\\left(-\\frac{x - \\zeta}{\\delta} \\right) \\right) + :label: gumbel-pdf + + where :math:`\\zeta` (zeta) is the location parameter, and :math:`\\delta` (delta) is the scale parameter. + + - The location parameter :math:`\\zeta` shifts the distribution along the x-axis. It essentially determines the mode + (peak) of the distribution and its location. Changing the location parameter moves the distribution left or + right without altering its shape. The location parameter ranges from negative infinity to positive infinity. + - The scale parameter :math:`\\delta` controls the spread or dispersion of the distribution. A larger scale parameter + results in a wider distribution, while a smaller scale parameter results in a narrower distribution. It must + always be positive. + + - The probability density function above is defined in the “un-standardized” form. + + The Gumbel distribution is a special case of the Generalized Extreme Value (GEV) distribution for a particular + choice of the shape parameter, :math:`\\xi = 0` (xi). - cdf_Weibul: ndarray - parameters: dict[str, Union[float, Any]] - data: ndarray + - The cumulative distribution functions. + + .. math:: + F(x; \\zeta, \\delta) = \\exp\\left(-\\exp\\left(-\\frac{x - \\zeta}{\\delta} \\right) \\right) + :label: gumbel-cdf + + """ def __init__( self, data: Union[list, np.ndarray] = None, - parameters: Dict[str, str] = None, + parameters: Dict[str, float] = None, ): """Gumbel. @@ -494,10 +624,30 @@ def __init__( data time series. parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. + + Examples + -------- + - First load a sample data. + + >>> data = np.loadtxt("examples/data/gumbel.txt") + + - I nstantiate the Gumbel class only with the data. + + >>> gumbel_dist = Gumbel(data) + >>> print(gumbel_dist) # doctest: +SKIP + + + - You can also instantiate the Gumbel class with the data and the parameters if you already have them. + + >>> parameters = {"loc": 0, "scale": 1} + >>> gumbel_dist = Gumbel(data, parameters) + >>> print(gumbel_dist) # doctest: +SKIP + """ super().__init__(data, parameters) pass @@ -517,31 +667,33 @@ def _pdf_eq( def pdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: np.ndarray = None, + parameters: Dict[str, Union[float, Any]] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, - ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: + ) -> Union[np.ndarray, Tuple[np.ndarray, Figure, Any]]: """pdf. - Returns the value of Gumbel's pdf with parameters loc and scale at x . + Returns the value of Gumbel's pdf with parameters loc and scale at x. - Parameters: - ----------- - parameters: Dict[str, str] + Parameters + ---------- + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data : np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -552,18 +704,88 @@ def pdf( Returns ------- - pdf : [array] + pdf: [np.ndarray] probability density function pdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/gumbel.txt") + >>> parameters = {'loc': 0, 'scale': 1} + >>> gumbel_dist = Gumbel(data, parameters) + >>> gumbel_dist.pdf(plot_figure=True) + + .. image:: /_images/gumbel-random-pdf.png + :align: center """ result = super().pdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, ) return result + def random( + self, + size: int, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: + """Generate Random Variable. + + Parameters + ---------- + size: int + size of the random generated sample. + parameters: Dict[str, str] + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. + + Returns + ------- + data: [np.ndarray] + random generated data. + + Examples + -------- + - To generate a random sample that follow the gumbel distribution with the parameters loc=0 and scale=1. + + >>> parameters = {'loc': 0, 'scale': 1} + >>> gumbel_dist = Gumbel(parameters=parameters) + >>> random_data = gumbel_dist.random(1000) + + - then we can use the `pdf` method to plot the pdf of the random data. + + >>> gumbel_dist.pdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/gumbel-random-pdf.png + :align: center + + >>> gumbel_dist.cdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/gumbel-random-cdf.png + :align: center + """ + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + + loc = parameters.get("loc") + scale = parameters.get("scale") + if scale <= 0: + raise ValueError("Scale parameter is negative") + + random_data = gumbel_r.rvs(loc=loc, scale=scale, size=size) + return random_data + @staticmethod def _cdf_eq( data: Union[list, np.ndarray], parameters: Dict[str, Union[float, Any]] @@ -579,33 +801,33 @@ def _cdf_eq( def cdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: Union[bool, np.ndarray] = True, + parameters: Dict[str, Union[float, Any]] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[ - Tuple[np.ndarray, Figure, Any], np.ndarray + np.ndarray, Tuple[np.ndarray, Figure, Axes] ]: # pylint: disable=arguments-differ - """cdf. - - cdf calculates the value of Gumbel's cdf with parameters loc and scale at x. + """Cumulative distribution function. - parameter: - ---------- - parameters: Dict[str, str] + parameter + --------- + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series - plot_figure: [bool] - Default is False. + data : np.ndarray, default is None. + array if you want to calculate the cdf for different data than the time series given to the constructor + method. + plot_figure: [bool], Default is False. + True to plot the figure. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -613,70 +835,121 @@ def cdf( Default is "cdf". fontsize: [int] Default is 15. + + Returns + ------- + cdf: [array] + cumulative distribution function cdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/gumbel.txt") + >>> parameters = {'loc': 0, 'scale': 1} + >>> gumbel_dist = Gumbel(data, parameters) + >>> gumbel_dist.cdf(plot_figure=True) # doctest: +SKIP + + .. image:: /_images/gumbel-random-cdf.png + :align: center """ result = super().cdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, ) return result - def get_rp(self, loc, scale, data): - """getRP. + def return_period( + self, + data: Union[bool, List[float]] = None, + parameters: Dict[str, Union[float, Any]] = None, + ): + """Calculate return period. - getRP calculates the return period for a list/array of values or a single value. + return_period calculates the return period for a list/array of values or a single value. Parameters ---------- data:[list/array/float] - value you want the coresponding return value for - loc: [float] - location parameter - scale: [float] - scale parameter + value you want the corresponding return value for + parameters: Dict[str, str] + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. Returns ------- float: return period """ - # if isinstance(data, list) or isinstance(data, np.ndarray): - cdf = self.cdf(loc, scale, actual_data=data) - # else: - # cdf = gumbel_r.cdf(data, loc, scale) + if data is None: + ts = self.data + else: + ts = data + + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + + cdf: np.ndarray = self.cdf(parameters, data=ts) rp = 1 / (1 - cdf) return rp @staticmethod - def objective_fn(p, x): - """ObjectiveFn. + def truncated_distribution(opt_parameters: list[float], data: list[float]): + """function to estimate the parameters of a truncated Gumbel distribution. Link : https://stackoverflow.com/questions/23217484/how-to-find-parameters-of-gumbels-distribution-using-scipy-optimize + function calculates the negative log-likelihood of a Gumbel distribution that is truncated(i.e., the data only + includes values above a certain threshold) + + the function calculates the negative log-likelihood, effectively fitting the truncated Gumbel distribution + to the data. + + This approach is useful when the dataset is incomplete or when data is only available above a certain threshold, + a common scenario in environmental sciences, finance, and other fields dealing with extremes. + Parameters ---------- - p: - x: + opt_parameters: + data: list + data """ - threshold = p[0] - loc = p[1] - scale = p[2] + threshold = opt_parameters[0] + loc = opt_parameters[1] + scale = opt_parameters[2] - x1 = x[x < threshold] - nx2 = len(x[x >= threshold]) + non_truncated_data = data[data < threshold] + nx2 = len(data[data >= threshold]) # pdf with a scaled pdf # L1 is pdf based parameters = {"loc": loc, "scale": scale} - pdf = Gumbel._pdf_eq(x1, parameters) - cdf = Gumbel._cdf_eq(threshold, parameters) + pdf = Gumbel._pdf_eq(non_truncated_data, parameters) + # the CDF at the threshold is used because the data is assumed to be truncated, meaning that observations below + # this threshold are not included in the dataset. When dealing with truncated data, it's essential to adjust + # the likelihood calculation to account for the fact that only values above the threshold are observed. The + # CDF at the threshold effectively normalizes the distribution, ensuring that the probabilities sum to 1 over + # the range of the observed data. + cdf_at_threshold = 1 - Gumbel._cdf_eq(threshold, parameters) + # calculates the negative log-likelihood of a Gumbel distribution + # Adjust the likelihood for the truncation + # likelihood = pdf / (1 - adjusted_cdf) + l1 = (-np.log((pdf / scale))).sum() # L2 is cdf based - l2 = (-np.log(1 - cdf)) * nx2 + l2 = (-np.log(cdf_at_threshold)) * nx2 # print x1, nx2, L1, L2 return l1 + l2 @@ -689,14 +962,14 @@ def fit_model( ) -> Dict[str, float]: """fit_model. - EstimateParameter estimate the distribution parameter based on MLM - (Maximum liklihood method), if an objective function is entered as an input + fit_model estimates the distribution parameter based on MLM + (Maximum likelihood method), if an objective function is entered as an input There are two likelihood functions (L1 and L2), one for values above some - threshold (x>=C) and one for values below (x < C), now the likeliest parameters - are those at the max value of mutiplication between two functions max(L1*L2). + threshold (x>=C) and one for the values below (x < C), now the likeliest parameters + are those at the max value of multiplication between two functions max(L1*L2). - In this case the L1 is still the product of multiplication of probability + In this case, the L1 is still the product of multiplication of probability density function's values at xi, but the L2 is the probability that threshold value C will be exceeded (1-F(C)). @@ -715,10 +988,59 @@ def fit_model( ------- Dict[str, str]: {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. + + Examples + -------- + - Instantiate the Gumbel class only with the data. + + >>> data = np.loadtxt("examples/data/gumbel.txt") + >>> gumbel_dist = Gumbel(data) + + - Then use the `fit_model` method to estimate the distribution parameters. the method takes the method as + parameter, the default is 'mle'. the `test` parameter is used to perform the Kolmogorov-Smirnov and chisquare + test. + + >>> parameters = gumbel_dist.fit_model(method="mle", test=True) + -----KS Test-------- + Statistic = 0.019 + Accept Hypothesis + P value = 0.9937026761524456 + >>> print(parameters) + {'loc': 0.010101355750222706, 'scale': 1.0313042643102108} + + - You can also use the `lmoments` method to estimate the distribution parameters. + + >>> parameters = gumbel_dist.fit_model(method="lmoments", test=True) + -----KS Test-------- + Statistic = 0.019 + Accept Hypothesis + P value = 0.9937026761524456 + >>> print(parameters) + {'loc': 0.006700226367219564, 'scale': 1.0531061622114444} + + - You can also use the `fit_model` method to estimate the distribution parameters using the 'optimization' + method. the optimization method requires the `obj_func` and `threshold` parameter. the method + will take the `threshold` number and try to fit the data values that are greater than the threshold. + >>> threshold = np.quantile(data, 0.80) + >>> print(threshold) + 1.5717000000000005 + >>> parameters = gumbel_dist.fit_model(method="optimization", obj_func=Gumbel.truncated_distribution, threshold=threshold) + Optimization terminated successfully. + Current function value: 0.000000 + Iterations: 39 + Function evaluations: 116 + -----KS Test-------- + Statistic = 0.107 + reject Hypothesis + P value = 2.0977827855404345e-05 + + - As you see, the P value is less than the significance level, so we reject the null hypothesis, + but we are trying to fit the distribution to part of the data, not the whole data. """ # obj_func = lambda p, x: (-np.log(Gumbel.pdf(x, p[0], p[1]))).sum() # #first we make a simple Gumbel fit @@ -753,62 +1075,94 @@ def fit_model( if test: self.ks() - self.chisquare() + # self.chisquare() return param - @staticmethod - def theoretical_estimate( - parameters: Dict[str, Union[float, Any]], cdf: np.ndarray + def inverse_cdf( + self, + cdf: Union[np.ndarray, List[float]] = None, + parameters: Dict[str, float] = None, ) -> np.ndarray: - """theporeticalEstimate. + """inverse CDF. - TheporeticalEstimate method calculates the theoretical values based on the Gumbel distribution + inverse_cdf method calculates the theoretical values based on a given cumulative distribution function. - Parameters: - ----------- + Parameters + ---------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. cdf: [list] - cummulative distribution function/ Non Exceedence probability. + cumulative distribution function/ Non Exceedance probability. - Return: + Returns ------- - theoreticalvalue : [numeric] + theoretical value: [numeric] Value based on the theoretical distribution - """ - loc = parameters.get("loc") - scale = parameters.get("scale") - if scale <= 0: - raise ValueError("Scale parameter is negative") + Examples + -------- + - Instantiate the Gumbel class only with the data. + + >>> data = np.loadtxt("examples/data/gumbel.txt") + >>> parameters = {'loc': 0, 'scale': 1} + >>> gumbel_dist = Gumbel(data, parameters) + + - We will generate a random numbers between 0 and 1 and pass it to the inverse_cdf method as a probabilities + to get the data that coresponds to these probabilities based on the distribution. + + >>> cdf = [0.1, 0.2, 0.4, 0.6, 0.8, 0.9] + >>> data_values = gumbel_dist.inverse_cdf(cdf) + >>> print(data_values) + [-0.83403245 -0.475885 0.08742157 0.67172699 1.49993999 2.25036733] + """ + if parameters is None: + parameters = self.parameters if any(cdf) <= 0 or any(cdf) > 1: raise ValueError("cdf Value Invalid") cdf = np.array(cdf) - # Qth = loc - scale * (np.log(-np.log(cdf))) + qth = self._inv_cdf(cdf, parameters) + + return qth - # the main equation form scipy + @staticmethod + def _inv_cdf(cdf: Union[np.ndarray, List[float]], parameters: Dict[str, float]): + # the main equation from scipy + loc = parameters.get("loc") + scale = parameters.get("scale") + if scale <= 0: + raise ValueError("Scale parameter is negative") + # the main equation from scipy + # Qth = loc - scale * (np.log(-np.log(cdf))) qth = gumbel_r.ppf(cdf, loc=loc, scale=scale) + return qth def ks(self) -> tuple: """Kolmogorov-Smirnov (KS) test. - The smaller the D static the more likely that the two samples are drawn from the same distribution - IF Pvalue < signeficance level ------ reject + The smaller the D static, the more likely that the two samples are drawn from the same distribution + IF P value < significance level ------ reject - returns: - -------- + Returns + ------- Dstatic: [numeric] - The smaller the D static the more likely that the two samples are drawn from the same distribution - Pvalue : [numeric] - IF Pvalue < signeficance level ------ reject the null hypotethis + The smaller the D static the more likely that the two samples are drawn from the same distribution. + - The KS test statistic measures the maximum distance between the empirical cumulative distribution function + (ECDF) of the sample (like Weibul plotting position) and the cumulative distribution function (CDF) of + the reference distribution. + - A smaller KS statistic indicates a smaller difference between the sample distribution and the reference + distribution. + P value: [numeric] + A high p-value (close to 1) suggests that there is a high probability that the sample comes from the + specified distribution. IF P value < significance level ------ reject the null hypothesis """ return super().ks() @@ -818,44 +1172,88 @@ def chisquare(self) -> tuple: def confidence_interval( self, - parameters: Dict[str, Union[float, Any]], - prob_non_exceed: np.ndarray, alpha: float = 0.1, - ) -> Tuple[np.ndarray, np.ndarray]: + prob_non_exceed: np.ndarray = None, + parameters: Dict[str, Union[float, Any]] = None, + plot_figure: bool = False, + **kwargs, + ) -> Union[ + Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray, Figure, Axes] + ]: """confidence_interval. - Parameters: - ----------- - parameters: Dict[str, str] + Parameters + ---------- + alpha: numeric, default is 0.1 + alpha or Significance level is a value of the confidence interval. + plot_figure: bool, optional, default is False. + to plot the confidence interval. + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} - - loc: [numeric] + + - loc: numeric location parameter of the gumbel distribution. - - scale: [numeric] + - scale: numeric scale parameter of the gumbel distribution. - prob_non_exceed : [list] - Non Exceedence probability - alpha : [numeric] - alpha or SignificanceLevel is a value of the confidence interval. + prob_non_exceed: list, default is None. + Non-Exceedance probability, if not given, the plotting position will be calculated using the weibul method. + kwargs: + fig_size: Tuple[float, float], optional, default=(6, 6) + Size of the second figure. + fontsize: int, optional, default=11 + Font size. - Return: + Returns ------- - parameters: Dict[str, str] - {"loc": val, "scale": val, "shape": value} - - loc: [numeric] - location parameter - - scale: [numeric] - scale parameter q_upper : [list] - upper bound coresponding to the confidence interval. + upper bound corresponding to the confidence interval. q_lower : [list] - lower bound coresponding to the confidence interval. + lower bound corresponding to the confidence interval. + fig: matplotlib.figure.Figure + Figure object. + ax: matplotlib.axes.Axes + Axes object. + + Examples + -------- + - Instantiate the Gumbel class with the data and the parameters. + + >>> import matplotlib.pyplot as plt + >>> data = np.loadtxt("examples/data/time_series2.txt") + >>> parameters = {"loc": 463.8040, "scale": 220.0724} + >>> gumbel_dist = Gumbel(data, parameters) + + - to calculate the confidence interval, we need to provide the confidence level (`alpha`). + + >>> upper, lower = gumbel_dist.confidence_interval(alpha=0.1) + + - You can also plot confidence intervals + + >>> upper, lower, fig, ax = gumbel_dist.confidence_interval(alpha=0.1, plot_figure=True, marker_size=10) + + .. image:: /_images/gumbel-confidence-interval.png + :align: center """ - scale = parameters.get("scale") + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + scale = parameters.get("scale") if scale <= 0: raise ValueError("Scale parameter is negative") - qth = self.theoretical_estimate(parameters, prob_non_exceed) + if prob_non_exceed is None: + prob_non_exceed = PlottingPosition.weibul(self.data) + else: + # if the prob_non_exceed is given, check if the length is the same as the data + if len(prob_non_exceed) != len(self.data): + raise ValueError( + "Length of prob_non_exceed does not match the length of data, use the `PlottingPosition.weibul(data)` " + "to the get the non-exceedance probability" + ) + + qth = self._inv_cdf(prob_non_exceed, parameters) y = [-np.log(-np.log(j)) for j in prob_non_exceed] std_error = [ (scale / np.sqrt(len(self.data))) @@ -865,83 +1263,110 @@ def confidence_interval( v = norm.ppf(1 - alpha / 2) q_upper = np.array([qth[j] + v * std_error[j] for j in range(len(self.data))]) q_lower = np.array([qth[j] - v * std_error[j] for j in range(len(self.data))]) - return q_upper, q_lower - def probability_plot( + if plot_figure: + fig, ax = Plot.confidence_level( + qth, self.data, q_lower, q_upper, alpha=alpha, **kwargs + ) + return q_upper, q_lower, fig, ax + else: + return q_upper, q_lower + + def plot( self, - parameters: Dict[str, Union[float, Any]], - cdf: Union[np.ndarray, list], - alpha: float = 0.1, - fig1_size: Tuple[float, float] = (10, 5), - fig2_size: Tuple[float, float] = (6, 6), + fig_size: Tuple[float, float] = (10, 5), xlabel: str = "Actual data", ylabel: str = "cdf", fontsize: int = 15, - ) -> tuple[list[Figure], list[Any]]: # pylint: disable=arguments-differ - """probapilityPlot. + cdf: Union[np.ndarray, list] = None, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Tuple[Figure, Tuple[Axes, Axes]]: # pylint: disable=arguments-differ + """Probability plot. - ProbapilityPlot method calculates the theoretical values based on the Gumbel distribution - parameters, theoretical cdf (or weibul), and calculate the confidence interval. + Probability Plot method calculates the theoretical values based on the Gumbel distribution + parameters, theoretical cdf (or weibul), and calculates the confidence interval. Parameters ---------- - parameters: Dict[str, str] - {"loc": val, "scale": val} - - loc: [numeric] - location parameter of the gumbel distribution. - - scale: [numeric] - scale parameter of the gumbel distribution. - cdf : [np.ndarray] + fig_size: tuple, Default is (10, 5). + Size of the figure. + cdf: [np.ndarray] theoretical cdf calculated using weibul or using the distribution cdf function. - alpha : [float] - value between 0 and 1. - fig1_size: [tuple] + fig_size: [tuple] Default is (10, 5) - fig2_size: [tuple] - Default is (6, 6) xlabel: [str] Default is "Actual data" ylabel: [str] Default is "cdf" fontsize: [float] Default is 15. + parameters: Dict[str, str] + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. Returns ------- - Qth : [list] - theoretical generated values based on the theoretical cdf calculated from - weibul or the distribution parameters. - q_upper : [list] - upper bound coresponding to the confidence interval. - q_lower : [list] - lower bound coresponding to the confidence interval. + Figure: + matplotlib figure object + Tuple[Axes, Axes]: + matplotlib plot axes + + Examples + -------- + - Instantiate the Gumbel class with the data and the parameters. + + >>> import matplotlib.pyplot as plt + >>> data = np.loadtxt("examples/data/time_series2.txt") + >>> parameters = {"loc": 463.8040, "scale": 220.0724} + >>> gumbel_dist = Gumbel(data, parameters) + + - to calculate the confidence interval, we need to provide the confidence level (`alpha`). + + >>> fig, ax = gumbel_dist.plot() + >>> print(fig) + Figure(1000x500) + >>> print(ax) + (, ) + + .. image:: /_images/gumbel-plot.png + :align: center """ + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + scale = parameters.get("scale") if scale <= 0: raise ValueError("Scale parameter is negative") - q_th = self.theoretical_estimate(parameters, cdf) - q_upper, q_lower = self.confidence_interval(parameters, cdf, alpha) + if cdf is None: + cdf = PlottingPosition.weibul(self.data) + else: + # if the cdf is given, check if the length is the same as the data + if len(cdf) != len(self.data): + raise ValueError( + "Length of cdf does not match the length of data, use the `PlottingPosition.weibul(data)` " + "to the get the non-exceedance probability" + ) q_x = np.linspace( float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 ) - pdf_fitted = self.pdf(parameters, actual_data=q_x) - cdf_fitted = self.cdf(parameters, actual_data=q_x) + pdf_fitted: np.ndarray = self.pdf(parameters=parameters, data=q_x) + cdf_fitted: np.ndarray = self.cdf(parameters=parameters, data=q_x) fig, ax = Plot.details( q_x, - q_th, self.data, pdf_fitted, cdf_fitted, cdf, - q_lower, - q_upper, - alpha, - fig1_size=fig1_size, - fig2_size=fig2_size, + fig_size=fig_size, xlabel=xlabel, ylabel=ylabel, fontsize=fontsize, @@ -951,31 +1376,97 @@ def probability_plot( class GEV(AbstractDistribution): - """GEV (Genalized Extreme value statistics)""" + """GEV (Generalized Extreme value statistics) + + - The Generalized Extreme Value (GEV) distribution is used to model the largest or smallest value among a large + set of independent, identically distributed random values. + - The GEV distribution encompasses three types of distributions: Gumbel, Fréchet, and Weibull, which are + distinguished by a shape parameter (:math:`\\xi` (xi)). + + - The probability density function (PDF) of the Generalized-extreme-value distribution is: + + .. math:: + f(x; \\zeta, \\delta, \\xi)=\\frac{1}{\\delta}\\mathrm{*}{\\mathrm{Q(x)}}^{\\xi+1}\\mathrm{ + *} e^{\\mathrm{-Q(x)}} + + .. math:: + Q(x; \\zeta, \\delta, \\xi)= + \\begin{cases} + \\left(1+ \\xi \\left(\\frac{x-\\zeta}{\\delta} \\right) \\right)^\\frac{-1}{\\xi} & + \\quad\\land\\xi\\neq 0 \\\\ + e^{- \\left(\\frac{x-\\zeta}{\\delta} \\right)} & \\quad \\land \\xi=0 + \\end{cases} + :label: gev-pdf + + Where the :math:`\\delta` (delta) is the scale parameter, :math:`\\zeta` (zeta) is the location parameter, + and :math:`\\xi` (xi) is the shape parameter. + + - The location parameter :math:`\\zeta` shifts the distribution along the x-axis. It essentially determines the mode + (peak) of the distribution and its location. Changing the location parameter moves the distribution left or + right without altering its shape. The location parameter ranges from negative infinity to positive infinity. + - The scale parameter :math:`\\delta` controls the spread or dispersion of the distribution. A larger scale parameter + results in a wider distribution, while a smaller scale parameter results in a narrower distribution. It must + always be positive. + - The shape parameter :math:`\\xi` (xi) determines the shape of the distribution. The shape parameter can be positive, + negative, or zero. The shape parameter is used to classify the GEV distribution into three types: :math:`\\xi = 0` + Gumbel (Type I), :math:`\\xi > 0` Fréchet (Type II), and :math:`\\xi < 0` Weibull (Type III). The shape + parameter determines the tail behavior of the distribution. + + In hydrology, the distribution is reparametrized with :math:`k=-\\xi` (xi) (El Adlouni et al., 2008) + The cumulative distribution functions. + + - The cumulative distribution functions. + + .. math:: + F(x; \\zeta, \\delta, \\xi)= + \\begin{cases} + \\exp\\left(- \\left(1+ \\xi \\left(\\frac{x-\\zeta}{\\delta} \\right) \\right)^\\frac{-1}{\\xi} \\right) & + \\quad\\land\\xi\\neq 0 and 1 + \\xi \\left( \\frac{x-\\zeta}{\\delta}\\right) \\\\ + \\exp\\left(- \\exp\\left(- \\frac{x-\\zeta}{\\delta} \\right) \\right) & \\quad \\land \\xi=0 + \\end{cases} + :label: gev-cdf - parameters: dict[str, Union[float, Any]] - data: ndarray + """ def __init__( self, data: Union[list, np.ndarray] = None, - parameters: Dict[str, str] = None, + parameters: Dict[str, float] = None, ): """GEV. Parameters ---------- - data : [list] + data: [list] data time series. - parameters: Dict[str, str] {"loc": val, "scale": val, "shape": value} + - loc: [numeric] location parameter of the GEV distribution. - scale: [numeric] scale parameter of the GEV distribution. - shape: [numeric] shape parameter of the GEV distribution. + + Examples + -------- + - First load the sample data. + + >>> data = np.loadtxt("examples/data/gev.txt") + + - I nstantiate the Gumbel class only with the data. + + >>> gev_dist = GEV(data) + >>> print(gev_dist) # doctest: +SKIP + + + - You can also instantiate the Gumbel class with the data and the parameters if you already have them. + + >>> parameters = {"loc": 0, "scale": 1, "shape": 0.1} + >>> gev_dist = GEV(data, parameters) + >>> print(gev_dist) # doctest: +SKIP + """ super().__init__(data, parameters) pass @@ -1021,33 +1512,35 @@ def _pdf_eq( def pdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: np.ndarray = None, + parameters: Dict[str, float] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: """pdf. - Returns the value of GEV's pdf with parameters loc and scale at x . + Returns the value of GEV's pdf with parameters loc and scale at x. Parameters ---------- - parameters: Dict[str, str] + parameters: Dict[str, float], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val, "shape": value} + - loc: [numeric] location parameter of the GEV distribution. - scale: [numeric] scale parameter of the GEV distribution. - shape: [numeric] shape parameter of the GEV distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data : np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -1058,18 +1551,90 @@ def pdf( Returns ------- - TYPE - DESCRIPTION. + pdf: [np.ndarray] + probability density function pdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/gev.txt") + >>> parameters = {"loc": 0, "scale": 1, "shape": 0.1} + >>> gev_dist = GEV(data, parameters) + >>> gev_dist.pdf(plot_figure=True) + + .. image:: /_images/gev-random-pdf.png + :align: center + """ + result = super().pdf( + parameters=parameters, + data=data, + plot_figure=plot_figure, + *args, + **kwargs, + ) + + return result + + def random( + self, + size: int, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: + """Generate Random Variable. + + Parameters + ---------- + size: int + size of the random generated sample. + parameters: Dict[str, str] + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. + + Returns + ------- + data: [np.ndarray] + random generated data. + + Examples + -------- + - To generate a random sample that follow the gumbel distribution with the parameters loc=0 and scale=1. + + >>> parameters = {'loc': 0, 'scale': 1, "shape": 0.1} + >>> gev_dist = GEV(parameters=parameters) + >>> random_data = gev_dist.random(100) + + - then we can use the `pdf` method to plot the pdf of the random data. + + >>> gev_dist.pdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/gev-random-pdf.png + :align: center + + >>> gev_dist.cdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/gev-random-cdf.png + :align: center """ - result = super().pdf( - parameters, - actual_data=actual_data, - plot_figure=plot_figure, - *args, - **kwargs, - ) + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters - return result + loc = parameters.get("loc") + scale = parameters.get("scale") + shape = parameters.get("shape") + + if scale <= 0: + raise ValueError("Scale parameter is negative") + + random_data = genextreme.rvs(loc=loc, scale=scale, c=shape, size=size) + return random_data @staticmethod def _cdf_eq( @@ -1101,33 +1666,35 @@ def _cdf_eq( def cdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: Union[bool, np.ndarray] = True, + parameters: Dict[str, Union[float, Any]] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[ - Tuple[np.ndarray, Figure, Any], np.ndarray + Tuple[np.ndarray, Figure, Axes], np.ndarray ]: # pylint: disable=arguments-differ """cdf. cdf calculates the value of Gumbel's cdf with parameters loc and scale at x. - parameter: + Parameters ---------- - parameters: Dict[str, str] + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data : np.ndarray, default is None. + array if you want to calculate the cdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -1135,20 +1702,39 @@ def cdf( Default is "cdf". fontsize: [int] Default is 15. + + Returns + ------- + cdf: [array] + cumulative distribution function cdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/gev.txt") + >>> parameters = {"loc": 0, "scale": 1, "shape": 0.1} + >>> gev_dist = GEV(data, parameters) + >>> gev_dist.cdf(plot_figure=True) + + .. image:: /_images/gev-random-cdf.png + :align: center """ result = super().cdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, ) return result - def get_rp(self, parameters: Dict[str, Union[float, Any]], data: np.ndarray): - """get_rp. + def return_period(self, parameters: Dict[str, Union[float, Any]], data: np.ndarray): + """return_period. - getRP calculates the return period for a list/array of values or a single value. + calculate return period calculates the return period for a list/array of values or a single value. Parameters ---------- @@ -1156,6 +1742,7 @@ def get_rp(self, parameters: Dict[str, Union[float, Any]], data: np.ndarray): value you want the coresponding return value for parameters: Dict[str, str] {"loc": val, "scale": val, "shape": value} + - shape: [float] shape parameter - loc: [float] @@ -1168,7 +1755,7 @@ def get_rp(self, parameters: Dict[str, Union[float, Any]], data: np.ndarray): float: return period """ - cdf = self.cdf(parameters, actual_data=data) + cdf = self.cdf(parameters, data=data) rp = 1 / (1 - cdf) @@ -1181,16 +1768,16 @@ def fit_model( threshold: Union[int, float, None] = None, test: bool = True, ) -> Dict[str, float]: - """estimateParameter. + """Fit model. - EstimateParameter estimate the distribution parameter based on MLM - (Maximum liklihood method), if an objective function is entered as an input + fit_model estimates the distribution parameter based on MLM + (Maximum likelihood method), if an objective function is entered as an input There are two likelihood functions (L1 and L2), one for values above some - threshold (x>=C) and one for values below (x < C), now the likeliest parameters - are those at the max value of mutiplication between two functions max(L1*L2). + threshold (x>=C) and one for the values below (x < C), now the likeliest parameters + are those at the max value of multiplication between two functions max(L1*L2). - In this case the L1 is still the product of multiplication of probability + In this case, the L1 is still the product of multiplication of probability density function's values at xi, but the L2 is the probability that threshold value C will be exceeded (1-F(C)). @@ -1207,8 +1794,51 @@ def fit_model( Returns ------- - Param : [list] - shape, loc, scale parameter of the gumbel distribution in that order. + Dict[str, str]: + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the GEV distribution. + - scale: [numeric] + scale parameter of the GEV distribution. + - shape: [numeric] + shape parameter of the GEV distribution. + + Examples + -------- + - Instantiate the Gumbel class only with the data. + + >>> data = np.loadtxt("examples/data/gev.txt") + >>> gev_dist = GEV(data) + + - Then use the `fit_model` method to estimate the distribution parameters. the method takes the method as + parameter, the default is 'mle'. the `test` parameter is used to perform the Kolmogorov-Smirnov and chisquare + test. + + >>> parameters = gev_dist.fit_model(method="mle", test=True) + -----KS Test-------- + Statistic = 0.06 + Accept Hypothesis + P value = 0.9942356257694902 + >>> print(parameters) + {'loc': -0.05962776672431072, 'scale': 0.9114319092295455, 'shape': 0.03492066094614391} + + - You can also use the `lmoments` method to estimate the distribution parameters. + + >>> parameters = gev_dist.fit_model(method="lmoments", test=True) + -----KS Test-------- + Statistic = 0.05 + Accept Hypothesis + P value = 0.9996892272702655 + >>> print(parameters) + {'loc': -0.07182150513604696, 'scale': 0.9153288314267931, 'shape': 0.018944589308927475} + + - You can also use the `fit_model` method to estimate the distribution parameters using the 'optimization' + method. the optimization method requires the `obj_func` and `threshold` parameter. the method + will take the `threshold` number and try to fit the data values that are greater than the threshold. + >>> threshold = np.quantile(data, 0.80) + >>> print(threshold) + 1.39252 """ # obj_func = lambda p, x: (-np.log(Gumbel.pdf(x, p[0], p[1]))).sum() # #first we make a simple Gumbel fit @@ -1243,34 +1873,61 @@ def fit_model( if test: self.ks() - try: - self.chisquare() - except ValueError: - print("chisquare test failed") + # try: + # self.chisquare() + # except ValueError: + # print("chisquare test failed") return param - @staticmethod - def theoretical_estimate( - parameters: Dict[str, Union[float, Any]], - cdf: np.ndarray, + def inverse_cdf( + self, + cdf: Union[np.ndarray, List[float]] = None, + parameters: Dict[str, Union[float, Any]] = None, ) -> np.ndarray: - """TheporeticalEstimate. + """Theoretical Estimate. - TheporeticalEstimate method calculates the theoretical values based on a given non exceedence probability + Theoretical Estimate method calculates the theoretical values based on a given non-exceedance probability - Parameters: - ----------- - param : [list] - location ans scale parameters of the gumbel distribution. + Parameters + ---------- + parameters: [list] + location and scale parameters of the gumbel distribution. cdf: [list] - cummulative distribution function/ Non Exceedence probability. + cumulative distribution function/ Non-Exceedance probability. - Return: + Returns ------- - theoreticalvalue : [numeric] + theoretical value: [numeric] Value based on the theoretical distribution + + Examples + -------- + - Instantiate the Gumbel class only with the data. + + >>> data = np.loadtxt("examples/data/gev.txt") + >>> parameters = {'loc': 0, 'scale': 1, "shape": 0.1} + >>> gev_dist = GEV(data, parameters) + + - We will generate a random numbers between 0 and 1 and pass it to the inverse_cdf method as a probabilities + to get the data that coresponds to these probabilities based on the distribution. + + >>> cdf = [0.1, 0.2, 0.4, 0.6, 0.8, 0.9] + >>> data_values = gev_dist.inverse_cdf(cdf) + >>> print(data_values) + [-0.86980039 -0.4873901 0.08704056 0.64966292 1.39286858 2.01513112] """ + if parameters is None: + parameters = self.parameters + + if any(cdf) < 0 or any(cdf) > 1: + raise ValueError("cdf Value Invalid") + + q_th = self._inv_cdf(cdf, parameters) + return q_th + + @staticmethod + def _inv_cdf(cdf: Union[np.ndarray, List[float]], parameters: Dict[str, float]): loc = parameters.get("loc") scale = parameters.get("scale") shape = parameters.get("shape") @@ -1280,10 +1937,6 @@ def theoretical_estimate( if shape is None: raise ValueError("Shape parameter should not be None") - - if any(cdf) < 0 or any(cdf) > 1: - raise ValueError("cdf Value Invalid") - # q_th = list() # for i in range(len(cdf)): # if cdf[i] <= 0 or cdf[i] >= 1: @@ -1307,15 +1960,15 @@ def theoretical_estimate( def ks(self): """Kolmogorov-Smirnov (KS) test. - The smaller the D static the more likely that the two samples are drawn from the same distribution - IF Pvalue < signeficance level ------ reject + The smaller the D static, the more likely that the two samples are drawn from the same distribution + IF Pvalue < significance level ------ reject - returns: - -------- - Dstatic: [numeric] - The smaller the D static the more likely that the two samples are drawn from the same distribution - Pvalue : [numeric] - IF Pvalue < signeficance level ------ reject the null hypotethis + Returns + ------- + Dstatic: [numeric] + The smaller the D static the more likely that the two samples are drawn from the same distribution + Pvalue : [numeric] + IF Pvalue < significance level ------ reject the null hypothesis """ return super().ks() @@ -1325,151 +1978,210 @@ def chisquare(self) -> tuple: def confidence_interval( self, - parameters: Dict[str, Union[float, Any]], - prob_non_exceed: np.ndarray, alpha: float = 0.1, - statfunction=np.average, + plot_figure: bool = False, + prob_non_exceed: np.ndarray = None, + parameters: Dict[str, Union[float, Any]] = None, + state_function: callable = None, n_samples: int = 100, method: str = "lmoments", - **kargs, - ): # pylint: disable=arguments-differ + **kwargs, + ) -> Union[ + Tuple[np.ndarray, np.ndarray], Tuple[np.ndarray, np.ndarray, Figure, Axes] + ]: # pylint: disable=arguments-differ """confidence_interval. - Parameters: - ----------- - loc : [numeric] - location parameter of the gumbel distribution. - scale : [numeric] - scale parameter of the gumbel distribution. + Parameters + ---------- + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. + {"loc": val, "scale": val, "shape": value} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. prob_non_exceed : [list] - Non Exceedence probability + Non-Exceedance probability alpha : [numeric] alpha or SignificanceLevel is a value of the confidence interval. - statfunction: [callable] - Default is np.average. + state_function: callable, Default is GEV.ci_func + function to calculate the confidence interval. n_samples: [int] number of samples generated by the bootstrap method Default is 100. method: [str] method used to fit the generated samples from the bootstrap method ["lmoments", "mle", "mm"]. Default is "lmoments". + plot_figure: bool, optional, default is False. + to plot the confidence interval. - Return: + Returns ------- - q_upper : [list] - upper bound coresponding to the confidence interval. - q_lower : [list] - lower bound coresponding to the confidence interval. + q_upper: [list] + upper-bound coresponding to the confidence interval. + q_lower: [list] + lower-bound coresponding to the confidence interval. + fig: matplotlib.figure.Figure + Figure object. + ax: matplotlib.axes.Axes + Axes object. + + Examples + -------- + - Instantiate the GEV class with the data and the parameters. + + >>> import matplotlib.pyplot as plt + >>> data = np.loadtxt("examples/data/time_series1.txt") + >>> parameters = {"loc": 16.3928, "scale": 0.70054, "shape": -0.1614793,} + >>> gev_dist = GEV(data, parameters) + + - to calculate the confidence interval, we need to provide the confidence level (`alpha`). + + >>> upper, lower = gev_dist.confidence_interval(alpha=0.1) + + - You can also plot confidence intervals + + >>> upper, lower, fig, ax = gev_dist.confidence_interval(alpha=0.1, plot_figure=True, marker_size=10) + + .. image:: /_images/gev-confidence-interval.png + :align: center """ + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + scale = parameters.get("scale") if scale <= 0: raise ValueError("Scale parameter is negative") + if prob_non_exceed is None: + prob_non_exceed = PlottingPosition.weibul(self.data) + else: + # if the prob_non_exceed is given, check if the length is the same as the data + if len(prob_non_exceed) != len(self.data): + raise ValueError( + "Length of prob_non_exceed does not match the length of data, use the `PlottingPosition.weibul(data)` " + "to the get the non-exceedance probability" + ) + if state_function is None: + state_function = GEV.ci_func + ci = ConfidenceInterval.boot_strap( self.data, - statfunction=statfunction, + state_function=state_function, gevfit=parameters, F=prob_non_exceed, alpha=alpha, n_samples=n_samples, method=method, - **kargs, + **kwargs, ) q_lower = ci["lb"] q_upper = ci["ub"] - return q_upper, q_lower + if plot_figure: + qth = self._inv_cdf(prob_non_exceed, parameters) + fig, ax = Plot.confidence_level( + qth, self.data, q_lower, q_upper, alpha=alpha, **kwargs + ) + return q_upper, q_lower, fig, ax + else: + return q_upper, q_lower - def probability_plot( + def plot( self, - parameters: Dict[str, Union[float, Any]], - cdf: Union[np.ndarray, list], - alpha: Number = 0.1, - func: Callable = None, - method: str = "lmoments", - n_samples=100, - fig1_size=(10, 5), - fig2_size=(6, 6), + fig_size=(10, 5), xlabel="Actual data", ylabel="cdf", fontsize=15, - ): - """probapilityPlot. + cdf: Union[np.ndarray, list] = None, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Tuple[Figure, Tuple[Axes, Axes]]: + """Probability Plot. - ProbapilityPlot method calculates the theoretical values based on the Gumbel distribution - parameters, theoretical cdf (or weibul), and calculate the confidence interval. + Probability Plot method calculates the theoretical values based on the Gumbel distribution + parameters, theoretical cdf (or weibul), and calculates the confidence interval. Parameters ---------- parameters: Dict[str, str] {"loc": val, "scale": val, shape: val} - - loc : [numeric] + + - loc: [numeric] Location parameter of the GEV distribution. - - scale : [numeric] + - scale: [numeric] Scale parameter of the GEV distribution. - shape: [float, int] Shape parameter for the GEV distribution. - cdf : [list] + cdf: [list] Theoretical cdf calculated using weibul or using the distribution cdf function. - method: [str] - Method used to fit the generated samples from the bootstrap method ["lmoments", "mle", "mm"]. Default is - "lmoments". - alpha : [float] - Value between 0 and 1. - fontsize : [numeric] + fontsize: [numeric] Font size of the axis labels and legend - ylabel : [string] + ylabel: [string] y label string - xlabel : [string] + xlabel: [string] X label string - fig1_size : [tuple] + fig_size: [tuple] size of the pdf and cdf figure - fig2_size : [tuple] - size of the confidence interval figure - n_samples : [integer] - number of points in the condidence interval calculation - alpha : [numeric] - alpha or SignificanceLevel is a value of the confidence interval. - func : [function] - function to be used in the confidence interval calculation. + + Returns + ------- + Figure: + matplotlib figure object + Tuple[Axes, Axes]: + matplotlib plot axes + + Examples + -------- + - Instantiate the Gumbel class with the data and the parameters. + + >>> import numpy as np + >>> data = np.loadtxt("examples/data/time_series1.txt") + >>> parameters = {"loc": 16.3928, "scale": 0.70054, "shape": -0.1614793,} + >>> gev_dist = GEV(data, parameters) + + - to calculate the confidence interval, we need to provide the confidence level (`alpha`). + + >>> fig, ax = gumbel_dist.plot() + >>> print(fig) + Figure(1000x500) + >>> print(ax) + (, ) + + .. image:: /_images/gev-plot.png + :align: center """ + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters scale = parameters.get("scale") if scale <= 0: raise ValueError("Scale parameter is negative") - q_th = self.theoretical_estimate(parameters, cdf) - if func is None: - func = GEV.ci_func - - ci = ConfidenceInterval.boot_strap( - self.data, - statfunction=func, - gevfit=parameters, - n_samples=n_samples, - F=cdf, - method=method, - ) - q_lower = ci["lb"] - q_upper = ci["ub"] + if cdf is None: + cdf = PlottingPosition.weibul(self.data) + else: + # if the prob_non_exceed is given, check if the length is the same as the data + if len(cdf) != len(self.data): + raise ValueError( + "Length of prob_non_exceed does not match the length of data, use the `PlottingPosition.weibul(data)` " + "to the get the non-exceedance probability" + ) q_x = np.linspace( float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 ) - pdf_fitted = self.pdf(parameters, actual_data=q_x) - cdf_fitted = self.cdf(parameters, actual_data=q_x) + pdf_fitted = self.pdf(parameters=parameters, data=q_x) + cdf_fitted = self.cdf(parameters=parameters, data=q_x) fig, ax = Plot.details( q_x, - q_th, self.data, pdf_fitted, cdf_fitted, cdf, - q_lower, - q_upper, - alpha, - fig1_size=fig1_size, - fig2_size=fig2_size, + fig_size=fig_size, xlabel=xlabel, ylabel=ylabel, fontsize=fontsize, @@ -1488,11 +2200,11 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): data: [list, np.ndarray] time series kwargs: - - gevfit: [list] + gevfit: [list] GEV parameter [shape, location, scale] - - F: [list] - Non Exceedence probability - - method: [str] + F: [list] + Non-Exceedance probability + method: [str] method used to fit the generated samples from the bootstrap method ["lmoments", "mle", "mm"]. Default is "lmoments". """ @@ -1500,7 +2212,7 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): prob_non_exceed = kwargs["F"] method = kwargs["method"] # generate theoretical estimates based on a random cdf, and the dist parameters - sample = GEV.theoretical_estimate(gevfit, np.random.rand(len(data))) + sample = GEV._inv_cdf(np.random.rand(len(data)), gevfit) # get parameters based on the new generated sample dist = GEV(sample) @@ -1512,7 +2224,7 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # T = np.linspace(0.1, 999, len(data)) + 1 # coresponding theoretical estimate to T # prob_non_exceed = 1 - 1 / T - q_th = GEV.theoretical_estimate(new_param, prob_non_exceed) + q_th = GEV._inv_cdf(prob_non_exceed, new_param) res = list(new_param.values()) res.extend(q_th) @@ -1561,17 +2273,17 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # loc: Union[float, int], # scale: Union[float, int], # plot_figure: bool = False, -# figsize: tuple = (6, 5), +# fig_size: tuple = (6, 5), # xlabel: str = "Actual data", # ylabel: str = "pdf", # fontsize: Union[float, int] = 15, -# actual_data: Union[bool, np.ndarray] = True, +# data: Union[bool, np.ndarray] = True, # ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: # """pdf. # # Returns the value of Gumbel's pdf with parameters loc and scale at x . # -# Parameters: +# Parameters # ----------- # loc : [numeric] # location parameter of the gumbel distribution. @@ -1586,10 +2298,10 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # if scale <= 0: # raise ValueError("Scale parameter is negative") # -# if isinstance(actual_data, bool): +# if isinstance(data, bool): # ts = self.data # else: -# ts = actual_data +# ts = data # # # pdf = [] # # @@ -1608,13 +2320,13 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # q_x = np.linspace( # float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 # ) -# pdf_fitted = self.pdf(loc, scale, actual_data=q_x) +# pdf_fitted = self.pdf(loc, scale, data=q_x) # # fig, ax = Plot.pdf( # q_x, # pdf_fitted, # self.data_sorted, -# figsize=figsize, +# fig_size=fig_size, # xlabel=xlabel, # ylabel=ylabel, # fontsize=fontsize, @@ -1628,11 +2340,11 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # loc: Union[float, int], # scale: Union[float, int], # plot_figure: bool = False, -# figsize: tuple = (6, 5), +# fig_size: tuple = (6, 5), # xlabel: str = "data", # ylabel: str = "cdf", # fontsize: int = 15, -# actual_data: Union[bool, np.ndarray] = True, +# data: Union[bool, np.ndarray] = True, # ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: # """cdf. # @@ -1650,10 +2362,10 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # if loc <= 0: # raise ValueError("Threshold parameter should be greater than zero") # -# if isinstance(actual_data, bool): +# if isinstance(data, bool): # ts = self.data # else: -# ts = actual_data +# ts = data # # # Y = (ts - loc) / scale # # cdf = 1 - np.exp(-Y) @@ -1667,7 +2379,7 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # q_x = np.linspace( # float(self.data_sorted[0]), 1.5 * float(self.data_sorted[-1]), 10000 # ) -# cdf_fitted = self.cdf(loc, scale, actual_data=q_x) +# cdf_fitted = self.cdf(loc, scale, data=q_x) # # cdf_Weibul = PlottingPosition.weibul(self.data_sorted) # @@ -1676,7 +2388,7 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # cdf_fitted, # self.data_sorted, # cdf_Weibul, -# figsize=figsize, +# fig_size=fig_size, # xlabel=xlabel, # ylabel=ylabel, # fontsize=fontsize, @@ -1686,21 +2398,21 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # else: # return cdf # -# def estimateParameter( +# def fit_model( # self, # method: str = "mle", # obj_func=None, # threshold: Union[int, float, None] = None, # test: bool = True, # ) -> tuple: -# """estimateParameter. +# """fit_model. # -# EstimateParameter estimate the distribution parameter based on MLM -# (Maximum liklihood method), if an objective function is entered as an input +# fit_model estimates the distribution parameter based on MLM +# (Maximum likelihood method), if an objective function is entered as an input # # There are two likelihood functions (L1 and L2), one for values above some # threshold (x>=C) and one for values below (x < C), now the likeliest parameters -# are those at the max value of mutiplication between two functions max(L1*L2). +# are those at the max value of multiplication between two functions max(L1*L2). # # In this case the L1 is still the product of multiplication of probability # density function's values at xi, but the L2 is the probability that threshold @@ -1765,23 +2477,23 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): # return Param # # @staticmethod -# def theporeticalEstimate( +# def inverse_cdf( # loc: Union[float, int], # scale: Union[float, int], # prob_non_exceed: np.ndarray, # ) -> np.ndarray: -# """TheporeticalEstimate. +# """inverse_cdf. # -# TheporeticalEstimate method calculates the theoretical values based on a given non exceedence probability +# inverse_cdf method calculates the theoretical values based on a given non-exceedance probability # -# Parameters: +# Parameters # ----------- # param : [list] # location ans scale parameters of the gumbel distribution. # prob_non_exceed : [list] # cummulative distribution function/ Non Exceedence probability. # -# Return: +# Returns # ------- # theoreticalvalue : [numeric] # Value based on the theoretical distribution @@ -1798,23 +2510,55 @@ def ci_func(data: Union[list, np.ndarray], **kwargs): class Exponential(AbstractDistribution): - """ - f(x: threshold, scale) = (1/scale) e **(- (x-threshold)/scale) + """Exponential distribution. + + - The exponential distribution assumes that small values occur more frequently than large values. + + - The probability density function (PDF) of the Exponential distribution is: + + .. math:: + f(x; \\delta, \\beta) = + \\begin{cases} + f(x; \\delta, \\beta) = \\frac{1}{\\beta} e^{-\\frac{x - \\delta}{\\beta}} & \\quad x \\geq 0 \\\\ + 0 & \\quad x < 0 + \\end{cases} + :label: exp-equation + + - The probability density function above uses the location parameter :math:`\\delta` and the scale parameter + :math:`\\beta` to define the distribution in a standardized form. + - A common parameterization for the exponential distribution is in terms of the rate parameter :math:`\\lambda`, + such that :math:`\\lambda = 1 / \\beta`. + - The Location Parameter (:math:`\\delta`): This shifts the starting point of the distribution. The distribution is + defined for :math:`x \\geq \\delta`. + - Scale Parameter (:math:`\\beta`): This determines the spread of the distribution. The rate parameter + :math:`\\lambda` is the inverse of the scale parameter, so :math:`\\lambda = \\frac{1}{\\beta}`. + + - The cumulative distribution functions. + + .. math:: + F(x; \\delta, \\beta) = + \\begin{cases} + F(x; \\delta, \\beta) = 1 - e^{-\\frac{x - \\delta}{\\beta}} & \\quad x \\geq 0 \\\\ + 0 & \\quad x < 0 + \\end{cases} + :label: exp-cdf + """ def __init__( self, data: Union[list, np.ndarray] = None, - parameters: Dict[str, str] = None, + parameters: Dict[str, float] = None, ): """Exponential Distribution. Parameters ---------- - data : [list] + data: [list] data time series. parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the exponential distribution. - scale: [numeric] @@ -1849,31 +2593,33 @@ def _pdf_eq( def pdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: np.ndarray = None, + parameters: Dict[str, float] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: """pdf. - Returns the value of Gumbel's pdf with parameters loc and scale at x . + Returns the value of Gumbel's pdf with parameters loc and scale at x. - Parameters: - ----------- - parameters: Dict[str, str] + Parameters + ---------- + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data: np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -1884,12 +2630,26 @@ def pdf( Returns ------- - pdf : [array] + pdf: [array] probability density function pdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/expo.txt") + >>> parameters = {'loc': 0, 'scale': 2} + >>> expo_dist = Exponential(data, parameters) + >>> expo_dist.pdf(plot_figure=True) + + .. image:: /_images/expo-random-pdf.png + :align: center """ result = super().pdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, @@ -1897,6 +2657,62 @@ def pdf( return result + def random( + self, + size: int, + parameters: Dict[str, Union[float, Any]] = None, + ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: + """Generate Random Variable. + + Parameters + ---------- + size: int + size of the random generated sample. + parameters: Dict[str, str] + {"loc": val, "scale": val} + + - loc: [numeric] + location parameter of the gumbel distribution. + - scale: [numeric] + scale parameter of the gumbel distribution. + + Returns + ------- + data: [np.ndarray] + random generated data. + + Examples + -------- + - To generate a random sample that follow the gumbel distribution with the parameters loc=0 and scale=1. + + >>> parameters = {'loc': 0, 'scale': 2} + >>> expon_dist = Exponential(parameters=parameters) + >>> random_data = expon_dist.random(1000) + + - then we can use the `pdf` method to plot the pdf of the random data. + + >>> expon_dist.pdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/expo-random-pdf.png + :align: center + + >>> expon_dist.cdf(data=random_data, plot_figure=True, xlabel="Random data") + + .. image:: /_images/expo-random-cdf.png + :align: center + """ + # if no parameters are provided, take the parameters provided in the class initialization. + if parameters is None: + parameters = self.parameters + + loc = parameters.get("loc") + scale = parameters.get("scale") + if scale <= 0: + raise ValueError("Scale parameter is negative") + + random_data = expon.rvs(loc=loc, scale=scale, size=size) + return random_data + @staticmethod def _cdf_eq( data: Union[list, np.ndarray], parameters: Dict[str, Union[float, Any]] @@ -1905,8 +2721,8 @@ def _cdf_eq( scale = parameters.get("scale") if scale <= 0: raise ValueError("Scale parameter is negative") - if loc <= 0: - raise ValueError("Threshold parameter should be greater than zero") + # if loc <= 0: + # raise ValueError("Threshold parameter should be greater than zero") # Y = (ts - loc) / scale # cdf = 1 - np.exp(-Y) # @@ -1918,9 +2734,9 @@ def _cdf_eq( def cdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: Union[bool, np.ndarray] = True, + parameters: Dict[str, Union[float, Any]] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[ @@ -1932,19 +2748,21 @@ def cdf( parameter: ---------- - parameters: Dict[str, str] + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data: np.ndarray, default is None. + array if you want to calculate the cdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -1952,10 +2770,29 @@ def cdf( Default is "cdf". fontsize: [int] Default is 15. + + Returns + ------- + cdf: [array] + probability density function cdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. + + Examples + -------- + >>> data = np.loadtxt("examples/data/expo.txt") + >>> parameters = {'loc': 0, 'scale': 2} + >>> expo_dist = Exponential(data, parameters) + >>> expo_dist.cdf(plot_figure=True) # doctest: +SKIP + + .. image:: /_images/expo-random-cdf.png + :align: center """ result = super().cdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, @@ -1969,16 +2806,16 @@ def fit_model( threshold: Union[int, float, None] = None, test: bool = True, ) -> Dict[str, float]: - """estimateParameter. + """fit_model. - EstimateParameter estimate the distribution parameter based on MLM - (Maximum liklihood method), if an objective function is entered as an input + fit_model estimates the distribution parameter based on MLM + (Maximum likelihood method), if an objective function is entered as an input There are two likelihood functions (L1 and L2), one for values above some - threshold (x>=C) and one for values below (x < C), now the likeliest parameters - are those at the max value of mutiplication between two functions max(L1*L2). + threshold (x>=C) and one for the values below (x < C), now the likeliest parameters + are those at the max value of multiplication between two functions max(L1*L2). - In this case the L1 is still the product of multiplication of probability + In this case, the L1 is still the product of multiplication of probability density function's values at xi, but the L2 is the probability that threshold value C will be exceeded (1-F(C)). @@ -1997,6 +2834,36 @@ def fit_model( ------- param : [list] shape, loc, scale parameter of the gumbel distribution in that order. + + Examples + -------- + - Instantiate the `Exponential` class only with the data. + + >>> data = np.loadtxt("examples/data/expo.txt") + >>> expo_dist = Exponential(data) + + - Then use the `fit_model` method to estimate the distribution parameters. the method takes the method as + parameter, the default is 'mle'. the `test` parameter is used to perform the Kolmogorov-Smirnov and chisquare + test. + + >>> parameters = expo_dist.fit_model(method="mle", test=True) + -----KS Test-------- + Statistic = 0.019 + Accept Hypothesis + P value = 0.9937026761524456 + Out[14]: {'loc': 0.0009, 'scale': 2.0498075} + >>> print(parameters) + {'loc': 0, 'scale': 2} + + - You can also use the `lmoments` method to estimate the distribution parameters. + + >>> parameters = expo_dist.fit_model(method="lmoments", test=True) + -----KS Test-------- + Statistic = 0.021 + Accept Hypothesis + P value = 0.9802627322900355 + >>> print(parameters) + {'loc': -0.00805012182182141, 'scale': 2.0587576218218215} """ # obj_func = lambda p, x: (-np.log(Gumbel.pdf(x, p[0], p[1]))).sum() # #first we make a simple Gumbel fit @@ -2031,38 +2898,58 @@ def fit_model( if test: self.ks() - try: - self.chisquare() - except ValueError: - print("chisquare test failed") + # try: + # self.chisquare() + # except ValueError: + # print("chisquare test failed") return param - @staticmethod - def theoretical_estimate( - parameters: Dict[str, Union[float, Any]], - cdf: np.ndarray, + def inverse_cdf( + self, + cdf: Union[np.ndarray, List[float]] = None, + parameters: Dict[str, Union[float, Any]] = None, ) -> np.ndarray: - """TheporeticalEstimate. + """Theoretical Estimate. - TheporeticalEstimate method calculates the theoretical values based on a given non exceedence probability + Theoretical Estimate method calculates the theoretical values based on a given non-exceedance probability - Parameters: + Parameters ----------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the gumbel distribution. - scale: [numeric] scale parameter of the gumbel distribution. cdf: [list] - cummulative distribution function/ Non Exceedence probability. + cumulative distribution function/ Non-Exceedance probability. - Return: + Returns ------- - theoreticalvalue : [numeric] + theoretical value: [numeric] Value based on the theoretical distribution + + Examples + -------- + - Instantiate the Exponential class only with the data. + + >>> data = np.loadtxt("examples/data/expo.txt") + >>> parameters = {'loc': 0, 'scale': 2} + >>> expo_dist = Exponential(data, parameters) + + - We will generate a random numbers between 0 and 1 and pass it to the inverse_cdf method as a probabilities + to get the data that coresponds to these probabilities based on the distribution. + + >>> cdf = [0.1, 0.2, 0.4, 0.6, 0.8, 0.9] + >>> data_values = expo_dist.inverse_cdf(cdf) + >>> print(data_values) + [0.21072103 0.4462871 1.02165125 1.83258146 3.21887582 4.60517019] """ + if parameters is None: + parameters = self.parameters + loc = parameters.get("loc") scale = parameters.get("scale") @@ -2079,15 +2966,15 @@ def theoretical_estimate( def ks(self): """Kolmogorov-Smirnov (KS) test. - The smaller the D static the more likely that the two samples are drawn from the same distribution - IF Pvalue < signeficance level ------ reject + The smaller the D static, the more likely that the two samples are drawn from the same distribution + IF Pvalue < significance level ------ reject - returns: - -------- + Returns + ------- Dstatic: [numeric] The smaller the D static the more likely that the two samples are drawn from the same distribution Pvalue : [numeric] - IF Pvalue < signeficance level ------ reject the null hypotethis + IF Pvalue < significance level ------ reject the null hypothesis """ return super().ks() @@ -2097,14 +2984,25 @@ def chisquare(self) -> tuple: class Normal(AbstractDistribution): - """ - f(x: threshold, scale) = (1/scale) e **(- (x-threshold)/scale) + """Normal Distribution. + + - The probability density function (PDF) of the Normal distribution is: + + .. math:: + f(x: threshold, scale) = (1/scale) e **(- (x-threshold)/scale) + :label: normal-equation + + - The cumulative distribution functions. + + .. math:: + F(x: threshold, scale) = 1 - e **(- (x-threshold)/scale) + :label: normal-cdf """ def __init__( self, data: Union[list, np.ndarray] = None, - parameters: Dict[str, str] = None, + parameters: Dict[str, float] = None, ): """Gumbel. @@ -2114,6 +3012,7 @@ def __init__( data time series. parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the exponential distribution. - scale: [numeric] @@ -2135,31 +3034,33 @@ def _pdf_eq( def pdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: np.ndarray = None, + parameters: Dict[str, float] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: """pdf. - Returns the value of Gumbel's pdf with parameters loc and scale at x . + Returns the value of Gumbel's pdf with parameters loc and scale at x. - Parameters: + Parameters ----------- - parameters: Dict[str, str] + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val, "shape": value} + - loc: [numeric] location parameter of the GEV distribution. - scale: [numeric] scale parameter of the GEV distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data : np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -2170,12 +3071,16 @@ def pdf( Returns ------- - pdf : [array] + pdf: [array] probability density function pdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. """ result = super().pdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, @@ -2200,9 +3105,9 @@ def _cdf_eq( def cdf( self, - parameters: Dict[str, Union[float, Any]], plot_figure: bool = False, - actual_data: Union[bool, np.ndarray] = True, + parameters: Dict[str, Union[float, Any]] = None, + data: Union[List[float], np.ndarray] = None, *args, **kwargs, ) -> Union[Tuple[np.ndarray, Figure, Any], np.ndarray]: @@ -2210,21 +3115,23 @@ def cdf( cdf calculates the value of Normal distribution cdf with parameters loc and scale at x. - parameter: + Parameters ---------- - parameters: Dict[str, str] + parameters: Dict[str, str], optional, default is None. + if not provided, the parameters provided in the class initialization will be used. {"loc": val, "scale": val, "shape": value} + - loc: [numeric] location parameter of the Normal distribution. - scale: [numeric] scale parameter of the Normal distribution. - actual_data : [bool/array] - true if you want to calculate the pdf for the actual time series, array - if you want to calculate the pdf for a theoretical time series + data : np.ndarray, default is None. + array if you want to calculate the pdf for different data than the time series given to the constructor + method. plot_figure: [bool] Default is False. kwargs: - figsize: [tuple] + fig_size: [tuple] Default is (6, 5). xlabel: [str] Default is "Actual data". @@ -2232,10 +3139,19 @@ def cdf( Default is "cdf". fontsize: [int] Default is 15. + + Returns + ------- + cdf: [array] + probability density function cdf. + fig: matplotlib.figure.Figure, if `plot_figure` is True. + Figure object. + ax: matplotlib.axes.Axes, if `plot_figure` is True. + Axes object. """ result = super().cdf( - parameters, - actual_data=actual_data, + parameters=parameters, + data=data, plot_figure=plot_figure, *args, **kwargs, @@ -2249,33 +3165,33 @@ def fit_model( threshold: Union[int, float, None] = None, test: bool = True, ) -> Dict[str, float]: - """estimateParameter. + """fit_model. - EstimateParameter estimate the distribution parameter based on MLM - (Maximum liklihood method), if an objective function is entered as an input + fit_model estimates the distribution parameter based on MLM + (Maximum likelihood method), if an objective function is entered as an input There are two likelihood functions (L1 and L2), one for values above some - threshold (x>=C) and one for values below (x < C), now the likeliest parameters - are those at the max value of mutiplication between two functions max(L1*L2). + threshold (x>=C) and one for the values below (x < C), now the likeliest parameters + are those at the max value of multiplication between two functions max(L1*L2). - In this case the L1 is still the product of multiplication of probability + In this case, the L1 is still the product of multiplication of probability density function's values at xi, but the L2 is the probability that threshold value C will be exceeded (1-F(C)). Parameters ---------- - obj_func : [function] + obj_func: [function] function to be used to get the distribution parameters. - threshold : [numeric] + threshold: [numeric] Value you want to consider only the greater values. - method : [string] + method: [string] 'mle', 'mm', 'lmoments', optimization test: bool Default is True Returns ------- - param : [list] + parameters: [list] shape, loc, scale parameter of the gumbel distribution in that order. """ # obj_func = lambda p, x: (-np.log(Gumbel.pdf(x, p[0], p[1]))).sum() @@ -2311,38 +3227,42 @@ def fit_model( if test: self.ks() - try: - self.chisquare() - except ValueError: - print("chisquare test failed") + # try: + # self.chisquare() + # except ValueError: + # print("chisquare test failed") return param - @staticmethod - def theoretical_estimate( - parameters: Dict[str, Union[float, Any]], - cdf: np.ndarray, + def inverse_cdf( + self, + cdf: Union[np.ndarray, List[float]] = None, + parameters: Dict[str, Union[float, Any]] = None, ) -> np.ndarray: - """TheporeticalEstimate. + """Theoretical Estimate. - TheporeticalEstimate method calculates the theoretical values based on a given non exceedence probability + Theoretical Estimate method calculates the theoretical values based on a given non exceedence probability - Parameters: + Parameters ----------- parameters: Dict[str, str] {"loc": val, "scale": val} + - loc: [numeric] location parameter of the Normal distribution. - scale: [numeric] scale parameter of the Normal distribution. cdf: [list] - cummulative distribution function/ Non Exceedence probability. + cumulative distribution function/ Non-Exceedance probability. - Return: + Returns ------- numeric: Value based on the theoretical distribution """ + if parameters is None: + parameters = self.parameters + loc = parameters.get("loc") scale = parameters.get("scale") @@ -2359,15 +3279,15 @@ def theoretical_estimate( def ks(self): """Kolmogorov-Smirnov (KS) test. - The smaller the D static the more likely that the two samples are drawn from the same distribution - IF Pvalue < signeficance level ------ reject + The smaller the D static, the more likely that the two samples are drawn from the same distribution + IF Pvalue < significance level ------ reject - returns: - -------- - Dstatic: [numeric] - The smaller the D static the more likely that the two samples are drawn from the same distribution - Pvalue : [numeric] - IF Pvalue < signeficance level ------ reject the null hypotethis + Returns + ------- + Dstatic: [numeric] + The smaller the D static the more likely that the two samples are drawn from the same distribution + Pvalue: [numeric] + IF Pvalue < significance level ------ reject the null hypothesis """ return super().ks() @@ -2398,10 +3318,10 @@ def __init__( self.distribution = self.available_distributions[distribution](data, parameters) def __getattr__(self, name: str): - """Delegate method calls to the sub-class""" - # Retrieve the attribute or method from the animal object + """Delegate method calls to the subclass""" + # Retrieve the attribute or method from the distribution object try: - # Retrieve the attribute or method from the sub-classes + # Retrieve the attribute or method from the subclasses attribute = getattr(self.distribution, name) # If the attribute is a method, return a callable function diff --git a/statista/eva.py b/statista/eva.py index 8a7ba47..ab0d0a7 100644 --- a/statista/eva.py +++ b/statista/eva.py @@ -1,4 +1,36 @@ -"""Extreme value analysis.""" +"""Extreme value analysis. + +Annual Maximum Series (AMS) Analysis is a statistical method commonly used in fields like hydrology, meteorology, and +environmental engineering to analyze extreme events, such as floods, rainfall, or temperatures. The primary goal of AMS +analysis is to assess the frequency and magnitude of extreme events over time. + +Key Concepts of AMS Analysis + +Definition: + The Annual Maximum Series is a time series composed of the maximum values observed within each year. For example, + in hydrology, the AMS might consist of the highest daily flow recorded in each year for a river. + +Purpose: + The AMS is used to model and predict the probability of extreme events occurring in the future. This is crucial for + risk assessment and the design of infrastructure to withstand such events (e.g., dams, levees, drainage systems). + +Advantages of AMS Analysis + - Simplicity: AMS analysis is straightforward and focuses on extreme events, which are often of primary interest. + - Historical Context: Provides insights based on historical extreme values, which are directly relevant for + planning and design. + +Limitations of AMS Analysis + - Data Limitations: The accuracy of AMS analysis depends on the availability and quality of long-term data. + - Ignores Sub-Annual Events: AMS considers only one value per year, potentially ignoring significant events that + occur more than once in a year. + +Common Applications: + - Flood Frequency Analysis: AMS is often used to estimate the probability of extreme flood events to help design + flood control infrastructure. + - Rainfall Analysis: Used to assess the risk of extreme rainfall events for urban drainage design. + - Temperature Extremes: AMS can be used to evaluate the risk of extremely high or low temperatures. +""" + from typing import Union, Tuple from pathlib import Path import matplotlib.pyplot as plt @@ -7,7 +39,7 @@ from loguru import logger from pandas import DataFrame -from statista.distributions import PlottingPosition, Distributions +from statista.distributions import Distributions def ams_analysis( @@ -21,71 +53,167 @@ def ams_analysis( method: str = "lmoments", obj_func: callable = None, quartile: float = 0, - significance_level: float = 0.1, + alpha: float = 0.1, ) -> Tuple[DataFrame, DataFrame]: - """ams_analysis. + """Annual Maximum Series analysis. - ams analysis method reads resamples all the the time series in the given dataframe to annual maximum, then fits + ams analysis method reads resamples all the time series in the given dataframe to annual maximum, then fits the time series to a given distribution and parameter estimation method. Parameters ---------- - time_series_df : [DataFrame] + time_series_df: DataFrame DataFrame containing multiple time series to do the statistical analysis on. - ams: [bool] - True if the the given time series is annual mean series. Default is False. - ams_start: [str] + ams: bool + True if the given time series is annual mean series. Default is False. + ams_start: str The beginning of the year which is used to resample the time series to get the annual maximum series. - Default is"A-OCT". - save_plots : [Bool] + Default is "A-OCT". + save_plots: bool True if you want to save the plots. - save_to : [str] - The rdir where you want to save the statistical properties. - filter_out: [Bool] - For observed or hydraulic model data it has gaps of times where the - model did not run or gaps in the observed data if these gap days - are filled with a specific value and you want to ignore it here + save_to: str + The rdir where you want to save the statistical properties. + filter_out: bool + For observed or hydraulic model data it has gaps of times where the model did not run or gaps in the observed + data if these gap days are filled with a specific value and you want to ignore it here give filter_out = Value you want - distribution: [str] - Default is "GEV". - method: [str] - available methods are 'mle', 'mm', 'lmoments', 'optimization'. Default is "lmoments" - obj_func: [callable] + distribution: str, Default is "GEV". + distribution name. + method: str, Default is "lmoments". + available methods are 'mle', 'mm', 'lmoments', 'optimization'. + obj_func: callable objective function to be used in the optimization method, default is None. for Gumbel distribution there is the - Gumbel.objective_fn and similarly for the GEV distribution there is the GEV.objective_fn. - quartile: [float] - the quartile is only used when estinating the distribution parameters based on optimization and a threshould - value, the threshould value will be calculated as a the quartile coresponding to the value of this parameter. - significance_level: - Default is [0.1]. + `Gumbel.truncated_distribution` and similarly for the GEV distribution there is the GEV.truncated_distribution. + quartile: float + the quartile is only used when estimating the distribution parameters based on optimization and a threshould + value, the threshold value will be calculated as the quartile coresponding to the value of this parameter. + alpha: float, optional, Default is [0.1]. + alpha or Significance level is a value of the confidence interval. Returns ------- DataFrame: - Statistical properties like mean, std, min, 5%, 25%, - median, 75%, 95%, max, t_beg, t_end, nyr, q1.5, q2, q5, q10, q25, q50, - q100, q200, q500. - - id,mean,std,min,5%,25%,median,75%,95%,max,t_beg,t_end,nyr,q1.5,q2,q5,q10,q25,q50,q100,q200,q500,q1000 - Frankfurt,694.4,552.8,-9.0,-9.0,220.8,671.0,1090.0,1760.0,1990.0,1951.0,2004.0,,683.3,855.3,1261.6,1517.8,1827.5,2047.6,2047.6,2258.3,2460.8,2717.0 - Mainz,4153.3,1192.8,1150.0,2286.5,3415.0,4190.0,4987.5,5914.0,6920.0,1951.0,2004.0,,3627.9,4164.8,5203.5,5716.9,6217.2,6504.8,6504.8,6734.9,6919.9,7110.8 - Kaub,4327.1,1254.7,1190.0,2394.5,3635.0,4350.0,5147.5,6383.5,7160.0,1951.0,2004.0,,3761.3,4321.1,5425.0,5983.7,6539.7,6865.8,6865.8,7131.4,7348.7,7577.3 - Andernach,6333.4,2035.1,1470.0,3178.0,5175.0,6425.0,7412.5,9717.0,10400.0,1951.0,2004.0,,5450.1,6369.7,8129.5,8987.6,9813.9,10283.1,10283.1,10654.9,10950.9,11252.8 - Cologne,6489.3,2056.1,1580.0,3354.5,5277.5,6585.0,7560.0,9728.9,10700.0,1951.0,2004.0,,5583.6,6507.7,8297.0,9182.4,10046.1,10542.9,10542.9,10940.9,11261.1,11591.7 - Rees,6701.4,2094.5,1810.0,3556.5,5450.0,6575.0,7901.8,10005.0,11300.0,1951.0,2004.0,,5759.2,6693.5,8533.3,9463.1,10386.9,10928.2,10928.2,11368.4,11728.2,12106.0 - date,1977.5,15.7,1951.0,1953.7,1964.2,1977.5,1990.8,2001.3,2004.0,1951.0,2004.0,,1970.3,1977.4,1991.6,1998.7,2005.8,2010.0,2010.0,2013.4,2016.1,2019.1 + Statistical properties like mean, std, min, 5%, 25%, median, 75%, 95%, max, start_year, end_year, nyr, q1.5, + q2, q5, q10, q25, q50, q100, q200, q500, q1000. DataFrame: Distribution properties like the shape, location, and scale parameters of the fitted distribution, plus the D-static and P-Value of the KS test. - id,c,loc,scale,D-static,P-Value - Frankfurt,0.1,718.7,376.2,0.1,1.0 - Mainz,0.3,3743.8,1214.6,0.1,1.0 - Kaub,0.3,3881.6,1262.4,0.1,1.0 - Andernach,0.3,5649.1,2084.4,0.1,1.0 - Cologne,0.3,5783.0,2090.2,0.1,1.0 - Rees,0.3,5960.0,2107.2,0.1,1.0 - date,0.3,1971.8,16.2,0.1,1.0 + Examples + -------- + - First read the data as `pandas.DataFrame`. + + >>> import pandas as pd + >>> ams_gauges = pd.read_csv(f"examples/data/ams-gauges.csv", index_col=0) + >>> print(ams_gauges) # doctest: +SKIP + Frankfurt Mainz Kaub Andernach Cologne Rees + date + 1951 -9 4250 4480 6080 6490 6830 + 1952 -9 4490 4610 6970 7110 7340 + 1953 -9 4270 4380 7300 7610 7970 + 1954 -9 2850 2910 3440 3620 3840 + 1955 -9 5940 6050 9460 9460 9500 + 1956 -9 5000 5150 7140 7270 7540 + 1957 -9 4500 4520 6650 6750 6950 + ..... + 1998 1060 4720 4790 6910 6700 6150 + 1999 1420 5480 5730 8160 8530 9240 + 2000 625 3750 3900 6390 6370 6550 + 2001 1140 5420 5710 8320 8410 8410 + 2002 1170 4950 5140 7260 7240 7940 + 2003 1800 5090 5350 8620 8840 9470 + 2004 197 1150 1190 1470 1580 1810 + + - The time series data we have just read are the annual maximum series of the gauges, the first column is an + index of the year (54 years in total) and the rest are dischate values in m3/s for each the station. a value 0f + "-9" is used to fill the missing data. + - The `ams_analysis` function takes the time series `DataFrame` as the first and only positional argument, + all the other arguments are optional. Since the time series is annual maximum series already, so we don't + need the function to do any resampling, we set `ams=True`. The `ams_start` could be used to provide the + beginning of the year to resample the time series to ams (i.e., `ams_start = "A-OCT"`). + - We want to save the plots, so we set `save_plots=True` and provide the directory where we want to save the plots in + `save_to`. + - We also want to filter out the missing data, so we set `filter_out=-9`. + - In order to fit the time series to a distribution we also to provide the parameter estimation method (i.e., + `lmoments`, `mle`, `mm`, `optimization`), the default is the `lmoments`, and you need to provide the name of + the distribution you want to fit the time series to (i.e., `GEV`, `Gumbel`). So for that we + will use `method="lmoments"`, and `distribution="GEV"`. + - The `alpha` is the significance level of the confidence interval, the default is 0.1. The `alpha` parameter is + necessary for the confidence interval calculation. + + >>> method = "lmoments" + >>> save_to = "examples/data/gauges" + >>> statistical_properties, distribution_properties = ams_analysis( + ... time_series_df=ams_gauges, + ... ams=True, + ... save_plots=True, + ... save_to=save_to, + ... filter_out=-9, + ... method=method, + ... alpha=0.05, + ... ) # doctest: +SKIP + -----KS Test-------- + Statistic = 0.07317073170731707 + Accept Hypothesis + P value = 0.9999427584427157 + -----KS Test-------- + Statistic = 0.07317073170731707 + Accept Hypothesis + P value = 0.9999427584427157 + 2024-08-18 12:45:04.779 | DEBUG | statista.confidence_interval:boot_strap:104 - Some values used top 10 low/high samples; results may be unstable. + 2024-08-18 12:45:05.221 | INFO | statista.eva:ams_analysis:300 - Gauge Frankfurt done. + … + - The `ams_analysis` function will iterate over all the gauges in the time series and fit the time series to the + distribution and calculate the statistical properties and the distribution properties of the fitted distribution. + - One of the outputs of the function is the statistical properties of the time series, which includes the mean, std, + min, and some quantile (5%, 25%, ..., 95%, max). + + >>> print(statistical_properties.loc[:, statistical_properties.columns[:9]]) # doctest: +SKIP + mean std min 5% 25% median 75% 95% max + id + Frankfurt 917.439024 433.982918 197.0 347.00 548.00 882.0 1170.00 1760.00 1990.0 + Mainz 4153.333333 1181.707804 1150.0 2286.50 3415.00 4190.0 4987.50 5914.00 6920.0 + Kaub 4327.092593 1243.019565 1190.0 2394.50 3635.00 4350.0 5147.50 6383.50 7160.0 + Andernach 6333.407407 2016.211257 1470.0 3178.00 5175.00 6425.0 7412.50 9717.00 10400.0 + Cologne 6489.277778 2037.005658 1580.0 3354.50 5277.50 6585.0 7560.00 9728.85 10700.0 + Rees 6701.425926 2074.994365 1810.0 3556.50 5450.00 6575.0 7901.75 10005.00 11300.0 + + - The rest of the columns in the `statistical_properties` are start_year, end_year, nyr, q1.5, q2, q5, q10, q25, + q50, q100, q200, q500, q1000, which are the return periods of the fitted distribution. + + >>> print(statistical_properties.loc[:, statistical_properties.columns[9:]]) # doctest: +SKIP + start_year end_year nyr q1.5 q2 ... q200 q500 q1000 + id + Frankfurt 1964.0 2004.0 40.0 683.254634 855.296864 ... 2258.332886 2460.823383 2717.037039 + Mainz 1951.0 2004.0 53.0 3627.907224 4164.824744 ... 6734.883442 6919.948680 7110.767115 + Kaub 1951.0 2004.0 53.0 3761.253314 4321.114689 ... 7131.430892 7348.738113 7577.263513 + Andernach 1951.0 2004.0 53.0 5450.050443 6369.734950 ... 10654.874462 10950.940916 11252.770123 + Cologne 1951.0 2004.0 53.0 5583.579049 6507.694660 ... 10940.851299 11261.139356 11591.687060 + Rees 1951.0 2004.0 53.0 5759.172691 6693.471602 ... 11368.384249 11728.167908 12106.027638 + + - The other output is the distribution properties of the fitted distribution, which includes the shape, location, and + scale parameters of the fitted distribution, plus the D-static and P-Value of the KS test. + + >>> print(distribution_properties) # doctest: +SKIP + c loc scale D-static P-Value + id + Frankfurt 0.051852 718.720761 376.188608 0.073171 0.999943 + Mainz 0.307295 3743.806013 1214.617042 0.055556 0.999998 + Kaub 0.282580 3881.573477 1262.426086 0.055556 0.999998 + Andernach 0.321513 5649.076008 2084.383132 0.074074 0.998738 + Cologne 0.306146 5783.017454 2090.224037 0.074074 0.998738 + Rees 0.284227 5960.022503 2107.197210 0.074074 0.998738 + + - Since we have set `save_plots=True`, the function will save the plots in the directory we have provided in `save_to`. + For example, the plot of Frankfurt's time series data is saved as "Frankfurt.png" for the `pdf` and `cdf` and + "f-Frankfurt.png" for the confidince interval plot in the specified directory.' + + .. image:: /_images/Frankfurt.png + :align: center + + .. image:: /_images/f-Frankfurt.png + :align: center + """ gauges = time_series_df.columns.tolist() # List of the table output, including some general data and the return periods. @@ -99,8 +227,8 @@ def ams_analysis( "75%", "95%", "max", - "t_beg", - "t_end", + "start_year", + "end_year", "nyr", ] rp_name = [ @@ -119,7 +247,7 @@ def ams_analysis( # In a table where duplicates are removed (np.unique), find the number of # gauges contained in the .csv file. - # Declare a dataframe for the output file, with as index the gaugne numbers + # Declare a dataframe for the output file, with as index the gauge numbers # and as columns all the output names. statistical_properties = pd.DataFrame(np.nan, index=gauges, columns=col_csv) statistical_properties.index.name = "id" @@ -140,7 +268,7 @@ def ams_analysis( return_period = np.array(return_period) # these values are the Non Exceedance probability (F) of the chosen # return periods non_exceed_prop = 1 - (1/return_period) - # Non Exceedance propabilities + # Non Exceedance probabilities # non_exceed_prop = [1/3, 0.5, 0.8, 0.9, 0.96, 0.98, 0.99, 0.995, 0.998] non_exceed_prop = 1 - (1 / return_period) save_to = Path(save_to) @@ -151,21 +279,24 @@ def ams_analysis( rpath.mkdir(parents=True, exist_ok=True) for i in gauges: - q_ts = time_series_df.loc[:, i] + q_ts = time_series_df.loc[:, i].to_frame() # The time series is resampled to the annual maxima, and turned into a numpy array. # The hydrological year is 1-Nov/31-Oct (from Petrow and Merz, 2009, JoH). if not ams: - ams_df = q_ts.resample(ams_start).max().values + ams_df = q_ts.resample(ams_start).max() + ams_arr = ams_df.values else: - ams_df = q_ts.values + ams_df = q_ts + ams_arr = q_ts.values if filter_out is not None: - ams_df = ams_df[ams_df != filter_out] + ams_df = ams_df.loc[ams_df[ams_df.columns[0]] != filter_out, :] + ams_arr = ams_arr[ams_arr != filter_out] - dist = Distributions(distribution, data=ams_df) + dist = Distributions(distribution, data=ams_arr) # estimate the parameters through the given method try: - threshold = np.quantile(ams_df, quartile) + threshold = np.quantile(ams_arr, quartile) param_dist = dist.fit_model( method=method, obj_func=obj_func, @@ -192,45 +323,45 @@ def ams_analysis( # Return periods from the fitted distribution are stored. # get the Discharge coresponding to the return periods - q_rp = dist.theoretical_estimate(param_dist, non_exceed_prop) - - # to get the Non Exceedance probability for a specific Value - # sort the ams_df - ams_df.sort() - # calculate the F (Exceedence probability based on weibul) - cdf_weibul = PlottingPosition.weibul(ams_df) - # Gumbel.probapilityPlot method calculates the theoretical values + q_rp = dist.inverse_cdf(non_exceed_prop, param_dist) + + # Gumbel.plot method calculates the theoretical values # based on the Gumbel distribution # parameters, theoretical cdf (or weibul), and calculate the confidence interval if save_plots: - fig, _ = dist.probability_plot( - param_dist, - cdf_weibul, - alpha=significance_level, - method=method, + fig, _ = dist.plot() + _, _, fig2, _ = dist.confidence_interval( + method=method, plot_figure=True, alpha=alpha ) - fig[0].savefig(f"{save_to}/figures/{i}.png", format="png") + fig.savefig(f"{save_to}/figures/{i}.png", format="png") plt.close() - fig[1].savefig(f"{save_to}/figures/f-{i}.png", format="png") + fig2.savefig(f"{save_to}/figures/f-{i}.png", format="png") plt.close() - statistical_properties.loc[i, "mean"] = q_ts.mean() - statistical_properties.loc[i, "std"] = q_ts.std() - statistical_properties.loc[i, "min"] = q_ts.min() - statistical_properties.loc[i, "5%"] = q_ts.quantile(0.05) - statistical_properties.loc[i, "25%"] = q_ts.quantile(0.25) - statistical_properties.loc[i, "median"] = q_ts.quantile(0.50) - statistical_properties.loc[i, "75%"] = q_ts.quantile(0.75) - statistical_properties.loc[i, "95%"] = q_ts.quantile(0.95) - statistical_properties.loc[i, "max"] = q_ts.max() - statistical_properties.loc[i, "t_beg"] = q_ts.index.min() - statistical_properties.loc[i, "t_end"] = q_ts.index.max() - if not ams: + quantiles = np.quantile(ams_arr, [0.05, 0.25, 0.50, 0.75, 0.95]) + statistical_properties.loc[i, "mean"] = ams_arr.mean() + statistical_properties.loc[i, "std"] = ams_arr.std() + statistical_properties.loc[i, "min"] = ams_arr.min() + statistical_properties.loc[i, "5%"] = quantiles[0] + statistical_properties.loc[i, "25%"] = quantiles[1] + statistical_properties.loc[i, "median"] = quantiles[2] + statistical_properties.loc[i, "75%"] = quantiles[3] + statistical_properties.loc[i, "95%"] = quantiles[4] + statistical_properties.loc[i, "max"] = ams_arr.max() + statistical_properties.loc[i, "start_year"] = ams_df.index.min() + statistical_properties.loc[i, "end_year"] = ams_df.index.max() + + if ams: + statistical_properties.loc[i, "nyr"] = ( + statistical_properties.loc[i, "end_year"] + - statistical_properties.loc[i, "start_year"] + ) + else: statistical_properties.loc[i, "nyr"] = ( - statistical_properties.loc[i, "t_end"] - - statistical_properties.loc[i, "t_beg"] + statistical_properties.loc[i, "end_year"] + - statistical_properties.loc[i, "start_year"] ).days / 365.25 for irp, irp_name in zip(q_rp, rp_name): diff --git a/statista/parameters.py b/statista/parameters.py index cac30b1..197b30e 100644 --- a/statista/parameters.py +++ b/statista/parameters.py @@ -1,4 +1,5 @@ -"""L moments.""" +"""Parameters estimation.""" + from __future__ import annotations from typing import Any, List, Union @@ -66,9 +67,9 @@ def _samlmularge(self, nmom: int = 5) -> list[ndarray | float | int | Any]: raise ValueError("Insufficient length of data for specified nmoments") # Calculate first order - coefl1 = 1.0 / self._comb(n, 1) - suml1 = sum(x) - lmoments = [coefl1 * suml1] + coef_l1 = 1.0 / self._comb(n, 1) + sum_l1 = sum(x) + lmoments = [coef_l1 * sum_l1] if nmom == 1: return lmoments[0] @@ -84,23 +85,23 @@ def _samlmularge(self, nmom: int = 5) -> list[ndarray | float | int | Any]: coefl = 1.0 / mom * 1.0 / self._comb(n, mom) xtrans = [] for i in range(0, n): - coeftemp = [] + coef_temp = [] for _ in range(0, mom): - coeftemp.append(1) + coef_temp.append(1) for j in range(0, mom - 1): - coeftemp[j] = coeftemp[j] * comb[mom - j - 2][i] + coef_temp[j] = coef_temp[j] * comb[mom - j - 2][i] for j in range(1, mom): - coeftemp[j] = coeftemp[j] * comb[j - 1][n - i - 1] + coef_temp[j] = coef_temp[j] * comb[j - 1][n - i - 1] for j in range(0, mom): - coeftemp[j] = coeftemp[j] * self._comb(mom - 1, j) + coef_temp[j] = coef_temp[j] * self._comb(mom - 1, j) for j in range(0, int(0.5 * mom)): - coeftemp[j * 2 + 1] = -coeftemp[j * 2 + 1] - coeftemp = sum(coeftemp) - xtrans.append(x[i] * coeftemp) + coef_temp[j * 2 + 1] = -coef_temp[j * 2 + 1] + coef_temp = sum(coef_temp) + xtrans.append(x[i] * coef_temp) if mom > 2: lmoments.append(coefl * sum(xtrans) / lmoments[1]) @@ -134,7 +135,7 @@ def _samlmusmall(self, nmom: int = 5) -> list[ndarray | float | int | Any]: # for i in range(1,n+1): # # comb1.append(comb(i-1,1)) # # comb2.append(comb(n-i,1)) - # Can be simplifed to comb1 = range(0,n) + # Can be simplified to comb1 = range(0,n) comb1 = range(0, n) comb2 = range(n - 1, -1, -1) @@ -142,8 +143,8 @@ def _samlmusmall(self, nmom: int = 5) -> list[ndarray | float | int | Any]: coefl2 = 0.5 * 1.0 / self._comb(n, 2) xtrans = [] for i in range(0, n): - coeftemp = comb1[i] - comb2[i] - xtrans.append(coeftemp * sample[i]) + coef_temp = comb1[i] - comb2[i] + xtrans.append(coef_temp * sample[i]) l_moment_2 = coefl2 * sum(xtrans) @@ -158,15 +159,15 @@ def _samlmusmall(self, nmom: int = 5) -> list[ndarray | float | int | Any]: comb3 = [] comb4 = [] for i in range(0, n): - combtemp = self._comb(i, 2) - comb3.append(combtemp) - comb4.insert(0, combtemp) + comb_temp = self._comb(i, 2) + comb3.append(comb_temp) + comb4.insert(0, comb_temp) coefl3 = 1.0 / 3 * 1.0 / self._comb(n, 3) xtrans = [] for i in range(0, n): - coeftemp = comb3[i] - 2 * comb1[i] * comb2[i] + comb4[i] - xtrans.append(coeftemp * sample[i]) + coef_temp = comb3[i] - 2 * comb1[i] * comb2[i] + comb4[i] + xtrans.append(coef_temp * sample[i]) l_moment_3 = coefl3 * sum(xtrans) / l_moment_2 @@ -179,17 +180,17 @@ def _samlmusmall(self, nmom: int = 5) -> list[ndarray | float | int | Any]: comb5 = [] comb6 = [] for i in range(0, n): - combtemp = self._comb(i, 3) - comb5.append(combtemp) - comb6.insert(0, combtemp) + comb_temp = self._comb(i, 3) + comb5.append(comb_temp) + comb6.insert(0, comb_temp) coefl4 = 1.0 / 4 * 1.0 / self._comb(n, 4) xtrans = [] for i in range(0, n): - coeftemp = ( + coef_temp = ( comb5[i] - 3 * comb3[i] * comb2[i] + 3 * comb1[i] * comb4[i] - comb6[i] ) - xtrans.append(coeftemp * sample[i]) + xtrans.append(coef_temp * sample[i]) l_moment_4 = coefl4 * sum(xtrans) / l_moment_2 @@ -200,21 +201,21 @@ def _samlmusmall(self, nmom: int = 5) -> list[ndarray | float | int | Any]: comb7 = [] comb8 = [] for i in range(0, n): - combtemp = self._comb(i, 4) - comb7.append(combtemp) - comb8.insert(0, combtemp) + comb_temp = self._comb(i, 4) + comb7.append(comb_temp) + comb8.insert(0, comb_temp) coefl5 = 1.0 / 5 * 1.0 / self._comb(n, 5) xtrans = [] for i in range(0, n): - coeftemp = ( + coef_temp = ( comb7[i] - 4 * comb5[i] * comb2[i] + 6 * comb3[i] * comb4[i] - 4 * comb1[i] * comb6[i] + comb8[i] ) - xtrans.append(coeftemp * sample[i]) + xtrans.append(coef_temp * sample[i]) l_moment_5 = coefl5 * sum(xtrans) / l_moment_2 diff --git a/statista/plot.py b/statista/plot.py index 5e2ce70..37fc62c 100644 --- a/statista/plot.py +++ b/statista/plot.py @@ -1,9 +1,11 @@ """Plotting functions for statista package.""" -from typing import Union, Tuple, List, Any + +from typing import Union, Tuple from numbers import Number import matplotlib.pyplot as plt from matplotlib import gridspec from matplotlib.figure import Figure +from matplotlib.axes import Axes import numpy as np @@ -18,11 +20,11 @@ def pdf( qx: np.ndarray, pdf_fitted, data_sorted: np.ndarray, - figsize: Tuple[float, float] = (6, 5), + fig_size: Tuple[float, float] = (6, 5), xlabel: str = "Actual data", ylabel: str = "pdf", fontsize: int = 11, - ) -> Tuple[Figure, Any]: + ) -> Tuple[Figure, Axes]: """pdf. Parameters @@ -30,7 +32,7 @@ def pdf( qx pdf_fitted data_sorted - figsize + fig_size xlabel ylabel fontsize @@ -39,10 +41,10 @@ def pdf( ------- Figure: matplotlib figure object - Axis: + Axes: matplotlib plot axis """ - fig = plt.figure(figsize=figsize) + fig = plt.figure(figsize=fig_size) # gs = gridspec.GridSpec(nrows=1, ncols=2, figure=fig) # Plot the histogram and the fitted distribution, save it for each gauge. ax = fig.add_subplot() @@ -52,6 +54,7 @@ def pdf( ) # , alpha=0.2 ax.set_xlabel(xlabel, fontsize=fontsize) ax.set_ylabel(ylabel, fontsize=fontsize) + plt.show() return fig, ax @staticmethod @@ -60,11 +63,11 @@ def cdf( cdf_fitted, data_sorted, cdf_weibul, - figsize=(6, 5), + fig_size=(6, 5), xlabel="Actual data", ylabel="cdf", fontsize=11, - ) -> Tuple[Figure, Any]: + ) -> Tuple[Figure, Axes]: """cdf. Parameters @@ -73,7 +76,7 @@ def cdf( cdf_fitted data_sorted cdf_weibul - figsize + fig_size xlabel ylabel fontsize @@ -85,7 +88,7 @@ def cdf( Axis: matplotlib plot axis """ - fig = plt.figure(figsize=figsize) + fig = plt.figure(figsize=fig_size) ax = fig.add_subplot() ax.plot( qx, cdf_fitted, "-", label="Estimated CDF", color="#27408B", linewidth=2 @@ -100,88 +103,140 @@ def cdf( ax.set_xlabel(xlabel, fontsize=fontsize) ax.set_ylabel(ylabel, fontsize=fontsize) plt.legend(fontsize=fontsize, framealpha=1) + plt.show() return fig, ax @staticmethod def details( qx: Union[np.ndarray, list], - qth: Union[np.ndarray, list], q_act: Union[np.ndarray, list], pdf: Union[np.ndarray, list], cdf_fitted: Union[np.ndarray, list], cdf: Union[np.ndarray, list], - q_lower: Union[np.ndarray, list], - q_upper: Union[np.ndarray, list], - alpha: Number, - fig1_size: Tuple[float, float] = (10, 5), - fig2_size: Tuple[float, float] = (6, 6), + fig_size: Tuple[float, float] = (10, 5), xlabel: str = "Actual data", ylabel: str = "cdf", fontsize: int = 11, - ) -> Tuple[List[Figure], List[Any]]: + ) -> Tuple[Figure, Tuple[Axes, Axes]]: """details. Parameters ---------- - qx - qth - q_act - pdf - cdf_fitted + qx: [np.ndarray, list] + 10,000 values generated between the minimum and maximum values of the actual data. + q_act: [np.ndarray, list] + Actual data. + pdf: [np.ndarray, list] + Probability density function. + cdf_fitted: [np.ndarray, list] + Cumulative distribution function of the fitted distribution. cdf - q_lower - q_upper - alpha - fig1_size - fig2_size - xlabel - ylabel - fontsize + fig_size: Tuple[float, float], optional, default=(10, 5) + Size of the first figure. + xlabel: str, optional, default="Actual data" + Label for x-axis. + ylabel: str, optional, default="cdf" + Label for y-axis. + fontsize: int, optional, default=11 + Font size. Returns ------- + Figure: + matplotlib figure object + Tuple[Axes, Axes]: + matplotlib plot axes """ - fig1 = plt.figure(figsize=fig1_size) - gs = gridspec.GridSpec(nrows=1, ncols=2, figure=fig1) + fig = plt.figure(figsize=fig_size) + gs = gridspec.GridSpec(nrows=1, ncols=2, figure=fig) # Plot the histogram and the fitted distribution, save it for each gauge. - ax1 = fig1.add_subplot(gs[0, 0]) + ax1 = fig.add_subplot(gs[0, 0]) ax1.plot(qx, pdf, "-", color="#27408B", linewidth=2) ax1.hist(q_act, density=True, histtype="stepfilled", color="#DC143C") ax1.set_xlabel(xlabel, fontsize=fontsize) ax1.set_ylabel("pdf", fontsize=fontsize) - ax2 = fig1.add_subplot(gs[0, 1]) + ax2 = fig.add_subplot(gs[0, 1]) ax2.plot(qx, cdf_fitted, "-", color="#27408B", linewidth=2) q_act.sort() ax2.scatter(q_act, cdf, color="#DC143C", facecolors="none") ax2.set_xlabel(xlabel, fontsize=fontsize) ax2.set_ylabel(ylabel, fontsize=15) + plt.show() + return fig, (ax1, ax2) + + @staticmethod + def confidence_level( + qth: Union[np.ndarray, list], + q_act: Union[np.ndarray, list], + q_lower: Union[np.ndarray, list], + q_upper: Union[np.ndarray, list], + fig_size: Tuple[float, float] = (6, 6), + fontsize: int = 11, + alpha: Number = None, + marker_size: int = 10, + ) -> Tuple[Figure, Axes]: + """details. - fig2 = plt.figure(figsize=fig2_size) - plt.plot(qth, qth, "-.", color="#3D59AB", linewidth=2, label="Theoretical Data") + Parameters + ---------- + qth: [np.ndarray, list] + Theoretical quantiles (obtained using the inverse_cdf method). + q_act: [np.ndarray, list] + Actual data, unsorted. + q_lower: [np.ndarray, list] + Lower limit of the confidence interval. + q_upper: [np.ndarray, list] + Upper limit of the confidence interval. + alpha: [float] + Significance level. + fig_size: Tuple[float, float], optional, default=(6, 6) + Size of the second figure. + fontsize: int, optional, default=11 + Font size. + marker_size: int, default is 10. + Size of the markers for the upper and lower bounds. + + Returns + ------- + Figure: + matplotlib figure object + Axes: + matplotlib plot axes + """ + q_act.sort() + + fig = plt.figure(figsize=fig_size) + ax = fig.add_subplot() + ax.plot(qth, qth, "-.", color="#3D59AB", linewidth=2, label="Theoretical Data") # confidence interval - plt.plot( + ax.plot( qth, q_lower, "*--", color="grey", - markersize=10, + markersize=marker_size, label=f"Lower limit ({int((1 - alpha) * 100)} % CI)", ) - plt.plot( + ax.plot( qth, q_upper, "*--", color="grey", - markersize=10, + markersize=marker_size, label=f"Upper limit ({int((1 - alpha) * 100)} % CI)", ) - plt.scatter( - qth, q_act, color="#DC143C", facecolors="none", label="Actual Data" + ax.scatter( + qth, + q_act, + color="#DC143C", + facecolors="none", + label="Actual Data", + zorder=10, ) # "d", markersize=12, - plt.legend(fontsize=fontsize, framealpha=1) - plt.xlabel("Theoretical Values", fontsize=fontsize) - plt.ylabel("Actual Values", fontsize=fontsize) - - return [fig1, fig2], [ax1, ax2] + ax.legend(fontsize=fontsize, framealpha=1) + ax.set_xlabel("Theoretical Values", fontsize=fontsize) + ax.set_ylabel("Actual Values", fontsize=fontsize) + plt.show() + return fig, ax diff --git a/statista/sensitivity.py b/statista/sensitivity.py index f944c8e..929210e 100644 --- a/statista/sensitivity.py +++ b/statista/sensitivity.py @@ -1,20 +1,15 @@ -"""Created on Mon Mar 29 21:32:29 2021. +"""Sensitivity Analysis.""" -@author: mofarrag -""" -from typing import List +from typing import List, Union import matplotlib.pyplot as plt import numpy as np +from pandas import DataFrame class Sensitivity: """Sensitivity. Sensitivity class - - Methods - 1- OAT - 2- Sobol """ MarkerStyleList = [ @@ -32,7 +27,14 @@ class Sensitivity: ] def __init__( - self, parameter, LB, UB, function, positions=None, n_values=5, return_values=1 + self, + parameter: DataFrame, + lower_bound: List[Union[int, float]], + upper_bound: List[Union[int, float]], + function: callable, + positions=None, + n_values=5, + return_values=1, ): """Sensitivity. @@ -41,26 +43,26 @@ def __init__( Parameters ---------- - parameter : [dataframe] + parameter: [dataframe] dataframe with the index as the name of the parameters and one column with the name "value" contains the values of the parameters. - LB : [list] + lower_bound: [list] lower bound of the parameter. - UB : [list] + upper_bound: [list] upper bound of the parameter. - function : TYPE + function: Callable DESCRIPTION. - positions : [list], optional + positions: [list], optional position of the parameter in the list (the beginning of the list starts - with 0), if the Position argument is empty list the sensitivity will + with 0), if the Position argument is an empty list, the sensitivity will be done for all parameters. The default is None. - n_values : [integer], optional + n_values: [integer], optional number of parameter values between the bounds you want to calculate the - metric for, if the values does not include the value if the given parameter + metric for, if the values do not include the value if the given parameter it will be appended to the values. The default is 5. - return_values : [integer], optional - return_values equals 1 if the function resurns one value (the measured metric) - return_values equals 2 if the function resurns two values (the measured metric, + return_values: [integer], optional + return_values equals 1 if the function returns one value (the measured metric) + return_values equals 2 if the function returns two values (the measured metric, and any calculated values you want to check how they change by changing the value of the parameter). The default is 1. @@ -69,29 +71,29 @@ def __init__( None. """ self.parameter = parameter - self.LB = LB - self.UB = UB + self.lower_bound = lower_bound + self.upper_bound = upper_bound assert ( - len(self.parameter) == len(self.LB) == len(self.UB) - ), "Length of the boundary shoulf be of the same length as the length of the parameters" + len(self.parameter) == len(self.lower_bound) == len(self.upper_bound) + ), "The Length of the boundary should be of the same length as the length of the parameters" assert callable( function - ), "function should be of type callable (function that takes arguments)" + ), "function should be of type-callable (function that takes arguments)" self.function = function self.NoValues = n_values self.return_values = return_values - # if the Position argument is empty list the sensitivity will be done for all parameters + # if the Position argument is empty list, the sensitivity will be done for all parameters if positions is None: - self.NoPar = len(parameter) - self.Positions = list(range(len(parameter))) + self.num_parameters = len(parameter) + self.positions = list(range(len(parameter))) else: - self.NoPar = len(positions) - self.Positions = positions + self.num_parameters = len(positions) + self.positions = positions @staticmethod - def markerStyle(style): + def marker_style(style): """MarkerStyle. Marker styles for plotting @@ -107,64 +109,69 @@ def markerStyle(style): DESCRIPTION. """ if style > len(Sensitivity.MarkerStyleList) - 1: - style = style % len(Sensitivity.MarkerStyleList) + style %= len(Sensitivity.MarkerStyleList) return Sensitivity.MarkerStyleList[style] - def OAT(self, *args, **kwargs): + def one_at_a_time(self, *args, **kwargs): """OAT. OAT one-at-a-time sensitivity analysis. Parameters ---------- - *args : [positional argument] + *args: [positional argument] arguments of the function with the same exact names inside the function. - **kwargs : [keyword argument] + **kwargs keyword arguments of the function with the same exact names inside the function. - - parameter : [dataframe] - parameters dataframe including the parameters values in a column with - name 'value' and the parameters name as index. - - LB : [List] + + parameter: [dataframe] + parameters dataframe including the parameter values in a column with + name 'value' and the parameters' name as index. + LB: [List] parameters upper bounds. - - UB : [List] + UB: [List] parameters lower bounds. - - function : [function] + function: [function] the function you want to run it several times. Returns ------- - sen : [Dict] + sen: [Dict] for each parameter as a key, there is a list containing 4 lists, 1-relative parameter values, 2-metric values, 3-Real parameter values - 4- adition calculated values from the function if you choose return_values=2. + 4- addition calculated values from the function if you choose return_values=2. """ self.sen = {} - for i in range(self.NoPar): - k = self.Positions[i] + for i in range(self.num_parameters): + k = self.positions[i] if self.return_values == 1: self.sen[self.parameter.index[k]] = [[], [], []] else: self.sen[self.parameter.index[k]] = [[], [], [], []] # generate 5 random values between the high and low parameter bounds - rand_value = np.linspace(self.LB[k], self.UB[k], self.NoValues) + rand_value = np.linspace( + self.lower_bound[k], self.upper_bound[k], self.NoValues + ) # add the value of the calibrated parameter and sort the values rand_value = np.sort(np.append(rand_value, self.parameter["value"][k])) # store the relative values of the parameters in the first list in the dict self.sen[self.parameter.index[k]][0] = [ - ((h) / self.parameter["value"][k]) for h in rand_value + (h / self.parameter["value"][k]) for h in rand_value ] - Randpar = self.parameter["value"].tolist() + random_param = self.parameter["value"].tolist() for j in range(len(rand_value)): - Randpar[k] = rand_value[j] + random_param[k] = rand_value[j] # args = list(args) - # args.insert(Position,Randpar) + # args.insert(Position, random_param) if self.return_values == 1: - metric = self.function(Randpar, *args, **kwargs) + metric = self.function(random_param, *args, **kwargs) else: - metric, CalculatedValues = self.function(Randpar, *args, **kwargs) - self.sen[self.parameter.index[k]][3].append(CalculatedValues) + metric, calculated_values = self.function( + random_param, *args, **kwargs + ) + self.sen[self.parameter.index[k]][3].append(calculated_values) try: # store the metric value in the second list in the dict self.sen[self.parameter.index[k]][1].append(round(metric, 3)) @@ -179,7 +186,7 @@ def OAT(self, *args, **kwargs): print(str(k) + "-" + self.parameter.index[k] + " -" + str(j)) print(round(metric, 3)) - def Sobol( + def sobol( self, real_values: bool = False, title: str = "", # CalculatedValues=False, @@ -197,35 +204,35 @@ def Sobol( Parameters ---------- - real_values : [bool], optional + real_values: [bool], optional if you want to plot the real values in the x-axis not the relative values, works properly only if you are checking the sensitivity of one parameter as the range of parameters differes. The default is False. - CalculatedValues : [bool], optional + CalculatedValues: [bool], optional if you choose return_values=2 in the OAT method, then the function returns calculated values, and here you can True to plot it . The default is False. - title : [string], optional + title: [string], optional DESCRIPTION. The default is ''. - xlabel : [string], optional + xlabel: [string], optional DESCRIPTION. The default is 'xlabel'. - ylabel : [string], optional + ylabel: [string], optional DESCRIPTION. The default is 'Metric values'. - labelfontsize : [integer], optional + labelfontsize: [integer], optional DESCRIPTION. The default is 12. plotting_from : TYPE, optional the calculated values are in array type and From attribute is from where the plotting will start. The default is ''. - plotting_to : TYPE, optional + plotting_to: TYPE, optional the calculated values are in array type and plotting_to attribute is from where the plotting will end. The default is ''. - title2 : TYPE, optional + title2: TYPE, optional DESCRIPTION. The default is ''. - xlabel2 : TYPE, optional + xlabel2: TYPE, optional DESCRIPTION. The default is 'xlabel2'. - ylabel2 : TYPE, optional + ylabel2: TYPE, optional DESCRIPTION. The default is 'ylabel2'. - spaces : TYPE, optional - DESCRIPTION. The default is [None,None,None,None,None,None]. + spaces: TYPE, optional + DESCRIPTION. The default is [None, None, None, None, None, None]. Returns ------- @@ -233,13 +240,13 @@ def Sobol( if self.return_values == 1: fig, ax = plt.subplots(ncols=1, nrows=1, figsize=(8, 6)) - for i in range(self.NoPar): - k = self.Positions[i] + for i in range(self.num_parameters): + k = self.positions[i] if real_values: ax.plot( self.sen[self.parameter.index[k]][2], self.sen[self.parameter.index[k]][1], - Sensitivity.markerStyle(k), + Sensitivity.marker_style(k), linewidth=3, markersize=10, label=self.parameter.index[k], @@ -248,7 +255,7 @@ def Sobol( ax.plot( self.sen[self.parameter.index[k]][0], self.sen[self.parameter.index[k]][1], - Sensitivity.markerStyle(k), + Sensitivity.marker_style(k), linewidth=3, markersize=10, label=self.parameter.index[k], @@ -267,14 +274,14 @@ def Sobol( try: fig, (ax1, ax2) = plt.subplots(ncols=1, nrows=2, figsize=(8, 6)) - for i in range(self.NoPar): + for i in range(self.num_parameters): # for i in range(len(self.sen[self.parameter.index[0]][0])): - k = self.Positions[i] + k = self.positions[i] if real_values: ax1.plot( self.sen[self.parameter.index[k]][2], self.sen[self.parameter.index[k]][1], - Sensitivity.markerStyle(k), + Sensitivity.marker_style(k), linewidth=3, markersize=10, label=self.parameter.index[k], @@ -283,7 +290,7 @@ def Sobol( ax1.plot( self.sen[self.parameter.index[k]][0], self.sen[self.parameter.index[k]][1], - Sensitivity.markerStyle(k), + Sensitivity.marker_style(k), linewidth=3, markersize=10, label=self.parameter.index[k], @@ -296,8 +303,8 @@ def Sobol( ax1.legend(fontsize=12) - for i in range(self.NoPar): - k = self.Positions[i] + for i in range(self.num_parameters): + k = self.positions[i] # for j in range(self.n_values): for j in range(len(self.sen[self.parameter.index[k]][0])): if plotting_from == "": @@ -334,23 +341,8 @@ def Sobol( except ValueError: assert ValueError( - "to plot Calculated Values you should choose return_values==2 in the sentivivity object" + "To plot calculated values, you should choose return_values==2 in the sensitivity object" ) plt.tight_layout() return fig, (ax1, ax2) - - def ListAttributes(self): - """Print Attributes List.""" - - print("\n") - print( - f"Attributes List of: {repr(self.__dict__['name'])} - {self.__class__.__name__} Instance\n" - ) - self_keys = list(self.__dict__.keys()) - self_keys.sort() - for key in self_keys: - if key != "name": - print(str(key) + " : " + repr(self.__dict__[key])) - - print("\n") diff --git a/statista/tools.py b/statista/tools.py index fdf6e91..8c5b60a 100644 --- a/statista/tools.py +++ b/statista/tools.py @@ -1,8 +1,6 @@ -"""Created on Thu May 17 04:26:42 2018. - -@author: Mostafa -""" +""""Statistical tools""" +from typing import List, Union import numpy as np @@ -16,30 +14,28 @@ def __init__(self): pass @staticmethod - def normalize(x): + def normalize(x: Union[List[float], np.ndarray]) -> np.ndarray: """Normalizer. to normalize values between 0 and 1 Parameters ---------- - x : [List] + x: List[float], np.ndarray list of values Returns ------- - normalized numbers : [List] + normalized numbers: [List] list of normalized values """ x = np.array(x) - DataMax = max(x) - DataMin = min(x) - N = (x - DataMin) / (DataMax - DataMin) - # [i - DataMin / (DataMax - DataMin) for i in x] - return N + data_max = max(x) + data_min = min(x) + return (x - data_min) / (data_max - data_min) @staticmethod - def standardize(x): + def standardize(x: Union[List[float], np.ndarray]) -> np.ndarray: """Standardize. to standardize (make the average equals 1 and the standard deviation @@ -47,12 +43,12 @@ def standardize(x): Parameters ---------- - x: [List] + x: List[float], np.ndarray list of values Returns ------- - Standardized values: [List] + Standardized values: np.ndarray list of normalized values """ x = np.array(x) @@ -60,46 +56,45 @@ def standardize(x): mean = np.mean(x) std = np.std(x) s = (x - mean) / std - # [i - mean / (std) for i in x] return s @staticmethod - def rescale(OldValue, OldMin, OldMax, NewMin, NewMax): + def rescale(old_value, old_min, old_max, new_min, new_max): """Rescale. - Rescale nethod rescales a value between two boundaries to a new value - bewteen two other boundaries + Rescale method rescales a value between two boundaries to a new value bewteen two other boundaries. Parameters ---------- - OldValue: [float] - value need to transformed - OldMin: [float] + old_value: [float] + The old value you want to transform + old_min: [float] min old value - OldMax: [float] + old_max: [float] max old value - NewMin: [float] + new_min: [float] min new value - NewMax: [float] + new_max: [float] max new value Returns ------- - NewValue: [float] + float: transformed new value """ - OldRange = OldMax - OldMin - NewRange = NewMax - NewMin - NewValue = (((OldValue - OldMin) * NewRange) / OldRange) + NewMin + old_range = old_max - old_min + new_range = new_max - new_min + new_value = (((old_value - old_min) * new_range) / old_range) + new_min - return NewValue + return new_value @staticmethod - def logarithmicRescale(x, min_old, max_old, min_new, max_new): - """LogarithmicRescale. + def log_rescale(x, min_old, max_old, min_new, max_new): + """Logarithmic Rescale. + + log_rescale transforms the value between two normal values to a logarithmic scale between logarithmic value + of both boundaries - this function transform the value between two normal values to a logarithmic scale - between logarithmic value of both boundaries np.log(base)(number) = power the inverse of logarithmic is base**power = number @@ -141,16 +136,18 @@ def logarithmicRescale(x, min_old, max_old, min_new, max_new): return y @staticmethod - def invLogarithmicRescale(x, min_old, max_old, min_new, max_new, base=np.e): - """LogarithmicRescale. + def inv_log_rescale(x, min_old, max_old, min_new, max_new, base=np.e): + """Inverse Logarithmic Rescale. + + inv_log_rescale transforms the value between two normal values to a logarithmic scale between logarithmic + value of both boundaries. - this function transform the value between two normal values to a logarithmic scale - between logarithmic value of both boundaries np.log(base)(number) = power the inverse of logarithmic is base**power = number Parameters ---------- + base x: [float] new value needed to be transformed to a logarithmic scale min_old: [float] @@ -181,5 +178,19 @@ def invLogarithmicRescale(x, min_old, max_old, min_new, max_new, base=np.e): return y @staticmethod - def round(number, roundto): - return round(number / roundto) * roundto + def round(number: float, precision: int) -> float: + """round + + Parameters + ---------- + number: float + number to be rounded. + precision: int + precision of the rounding. + + Returns + ------- + float: + rounded number + """ + return round(number / precision) * precision diff --git a/tests/conftest.py b/tests/conftest.py index 3d8403c..bf96573 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -21,6 +21,542 @@ def dist_estimation_parameters() -> List[str]: return ["mle", "lmoments"] +@pytest.fixture(scope="module") +def generated_cdf() -> List[float]: + return [0.1, 0.2, 0.4, 0.6, 0.8, 0.99] + + +@pytest.fixture(scope="module") +def gev_dist_parameters() -> Dict[str, Dict[str, float]]: + return { + "lmoments": { + "loc": 16.392889171307772, + "scale": 0.7005442761744839, + "shape": -0.1614793298009645, + }, + "mle": { + "loc": 16.303264414285966, + "scale": 0.5411914328865949, + "shape": -0.5013795739666272, + }, + } + + +@pytest.fixture(scope="module") +def gev_pdf() -> np.array: + return np.array( + [ + 0.46686268, + 0.50674728, + 0.13568617, + 0.5171857, + 0.46290923, + 0.4572899, + 0.31771916, + 0.03121843, + 0.40982638, + 0.34582871, + 0.47538097, + 0.48229776, + 0.51992017, + 0.25731877, + 0.07774146, + 0.14318118, + 0.47520795, + 0.52563445, + 0.47327913, + 0.53154392, + 0.3007426, + 0.04651425, + 0.39390943, + 0.50145893, + 0.33531555, + 0.10824839, + 0.09175549, + ] + ) + + +@pytest.fixture(scope="module") +def gev_cdf() -> np.array: + return np.array( + [ + 0.16514997, + 0.21691403, + 0.86068789, + 0.23844545, + 0.16128652, + 0.51392107, + 0.68341415, + 0.96226777, + 0.57878182, + 0.08491463, + 0.17402306, + 0.47318641, + 0.24547473, + 0.74463972, + 0.91571458, + 0.85363809, + 0.48543756, + 0.2639041, + 0.17175803, + 0.32067829, + 0.70102732, + 0.94649051, + 0.59835005, + 0.20800819, + 0.08014869, + 0.88657059, + 0.9022544, + ] + ) + + +@pytest.fixture(scope="module") +def gev_inverse_cdf() -> np.array: + return np.array( + [ + 280.25644453, + 359.07484643, + 483.04312657, + 611.63267666, + 793.89957452, + 1476.17034852, + ] + ) + + +@pytest.fixture(scope="module") +def exp_dist_parameters() -> Dict[str, Dict[str, float]]: + return { + "mle": {"loc": 144.0, "scale": 446.83333333333337}, + "lmoments": {"loc": 285.74807826694627, "scale": 305.0852550663871}, + } + + +@pytest.fixture(scope="module") +def gum_dist_parameters() -> Dict[str, Dict[str, float]]: + return { + "mle": {"loc": 466.1208189815563, "scale": 214.3001449633138}, + "lmoments": {"loc": 463.8040433832974, "scale": 220.0724922663106}, + } + + +@pytest.fixture(scope="module") +def gum_pdf() -> np.ndarray: + return np.array( + [ + 0.0002699, + 0.00062362, + 0.00066007, + 0.00080406, + 0.00107551, + 0.00108773, + 0.00113594, + 0.00118869, + 0.0012884, + 0.00136443, + 0.00141997, + 0.00151536, + 0.00151886, + 0.00153245, + 0.00154542, + 0.00154856, + 0.00160752, + 0.00166602, + 0.00166918, + 0.00166958, + 0.00166028, + 0.00164431, + 0.00163473, + 0.00158442, + 0.00158442, + 0.00158017, + 0.00158017, + 0.00156466, + 0.00155064, + 0.00154824, + 0.00152589, + 0.00151815, + 0.00135704, + 0.00132178, + 0.00128594, + 0.00122319, + 0.00116002, + 0.00116002, + 0.00113677, + 0.00109378, + 0.00097405, + 0.00093331, + 0.00079382, + 0.00079099, + 0.00073328, + 0.00064623, + 0.0006293, + 0.00041714, + 0.00039389, + 0.00023869, + 0.00018416, + 0.00016156, + 0.00016156, + 0.00012409, + ] + ) + + +@pytest.fixture(scope="module") +def gum_cdf() -> np.ndarray: + return np.array( + [ + 0.01388876, + 0.0439083, + 0.04775908, + 0.06458624, + 0.10503254, + 0.10719578, + 0.11609119, + 0.12655328, + 0.14885964, + 0.16876461, + 0.18547596, + 0.2207432, + 0.22226031, + 0.22836314, + 0.23451909, + 0.23606609, + 0.2708187, + 0.33815079, + 0.34815705, + 0.34982644, + 0.41156915, + 0.43636163, + 0.44783903, + 0.4929493, + 0.4929493, + 0.4961139, + 0.4961139, + 0.50712133, + 0.51646753, + 0.51801697, + 0.53185153, + 0.53641763, + 0.61562948, + 0.63036359, + 0.64470648, + 0.66854451, + 0.69118511, + 0.69118511, + 0.69922382, + 0.71372206, + 0.75196207, + 0.76435904, + 0.80489541, + 0.80568781, + 0.82168757, + 0.84511655, + 0.8495807, + 0.90337148, + 0.90904737, + 0.94598445, + 0.9586031, + 0.96378174, + 0.96378174, + 0.97230356, + ] + ) + + +@pytest.fixture(scope="module") +def gum_inverse_cdf() -> np.ndarray: + return np.array( + [15.84624901, 16.07199809, 16.45456617, 16.88993364, 17.58184473, 21.17313605] + ) + + +@pytest.fixture(scope="module") +def exp_pdf() -> np.ndarray: + return np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.00326435, + 0.00317986, + 0.00308743, + 0.00291054, + 0.0027709, + 0.00266403, + 0.00246249, + 0.00245443, + 0.00242246, + 0.00239091, + 0.00238308, + 0.00221728, + 0.00193846, + 0.00190071, + 0.00189449, + 0.00167812, + 0.00159761, + 0.00156137, + 0.00142445, + 0.00142445, + 0.00141514, + 0.00141514, + 0.00138304, + 0.00135611, + 0.00135167, + 0.00131238, + 0.00129953, + 0.00108516, + 0.00104673, + 0.00100966, + 0.0009487, + 0.00089142, + 0.00089142, + 0.0008712, + 0.00083486, + 0.00073951, + 0.00070866, + 0.00060748, + 0.00060549, + 0.00056522, + 0.00050561, + 0.00049414, + 0.0003514, + 0.00033564, + 0.00022724, + 0.00018667, + 0.00016918, + 0.00016918, + 0.00013898, + ] + ) + + +@pytest.fixture(scope="module") +def exp_cdf() -> np.ndarray: + return np.array( + [ + 0.0, + 0.0, + 0.0, + 0.0, + 0.0, + 0.00409511, + 0.02987048, + 0.05807125, + 0.1120373, + 0.15463951, + 0.18724486, + 0.24873132, + 0.25118978, + 0.26094342, + 0.27057001, + 0.272957, + 0.32353911, + 0.40860393, + 0.42012107, + 0.42201867, + 0.48803105, + 0.51259402, + 0.52364994, + 0.56542203, + 0.56542203, + 0.56826161, + 0.56826161, + 0.57805481, + 0.58627199, + 0.58762587, + 0.59961321, + 0.60353104, + 0.66893317, + 0.68065731, + 0.69196627, + 0.71056475, + 0.72804029, + 0.72804029, + 0.7342092, + 0.74529692, + 0.77438705, + 0.78379871, + 0.81466693, + 0.81527342, + 0.82756099, + 0.8457462, + 0.84924516, + 0.89279246, + 0.89760091, + 0.9306738, + 0.94305098, + 0.94838443, + 0.94838443, + 0.95759963, + ] + ) + + +@pytest.fixture(scope="module") +def exp_inverse_cdf() -> np.ndarray: + return np.array( + [ + 317.89201806, + 353.82588554, + 441.59344399, + 565.29486992, + 776.7638543, + 1690.71759908, + ] + ) + + +@pytest.fixture(scope="module") +def normal_dist_parameters() -> Dict[str, Dict[str, float]]: + return { + "mle": {"loc": 590.8333333333334, "scale": 269.6701517423475}, + "lmoments": {"loc": 590.8333333333334, "scale": 270.3747675984547}, + } + + +@pytest.fixture(scope="module") +def normal_pdf() -> np.ndarray: + return np.array( + [ + 3.76585954e-04, + 5.55761639e-04, + 5.73125382e-04, + 6.41927252e-04, + 7.78226317e-04, + 7.84743776e-04, + 8.10920185e-04, + 8.40533721e-04, + 9.00047780e-04, + 9.49628806e-04, + 9.89058789e-04, + 1.06657670e-03, + 1.06975234e-03, + 1.08240168e-03, + 1.09496091e-03, + 1.09808586e-03, + 1.16509775e-03, + 1.27830274e-03, + 1.29327066e-03, + 1.29572025e-03, + 1.37639246e-03, + 1.40300604e-03, + 1.41411093e-03, + 1.44966678e-03, + 1.44966678e-03, + 1.45164457e-03, + 1.45164457e-03, + 1.45795977e-03, + 1.46261414e-03, + 1.46332125e-03, + 1.46879622e-03, + 1.47026369e-03, + 1.46511057e-03, + 1.45683004e-03, + 1.44620057e-03, + 1.42246642e-03, + 1.39222958e-03, + 1.39222958e-03, + 1.37953409e-03, + 1.35385335e-03, + 1.26721227e-03, + 1.23261366e-03, + 1.09391791e-03, + 1.09078491e-03, + 1.02378558e-03, + 9.12182430e-04, + 8.89014525e-04, + 5.60559129e-04, + 5.20897748e-04, + 2.50526184e-04, + 1.60940416e-04, + 1.26634169e-04, + 1.26634169e-04, + 7.55582794e-05, + ] + ) + + +@pytest.fixture(scope="module") +def normal_cdf() -> np.ndarray: + return np.array( + [ + 0.04920163, + 0.08114019, + 0.08452673, + 0.0984933, + 0.12899683, + 0.13055979, + 0.13694234, + 0.14437377, + 0.16003869, + 0.1739115, + 0.18554393, + 0.21021605, + 0.21128421, + 0.21558855, + 0.21994331, + 0.22103983, + 0.24594131, + 0.29609117, + 0.30380612, + 0.30510062, + 0.35459687, + 0.37544707, + 0.38530748, + 0.42543415, + 0.42543415, + 0.42833548, + 0.42833548, + 0.43851965, + 0.44728172, + 0.44874469, + 0.46194042, + 0.46634906, + 0.5473507, + 0.56342355, + 0.57939235, + 0.60665528, + 0.63340487, + 0.63340487, + 0.64310652, + 0.66087644, + 0.70942419, + 0.72567516, + 0.78042151, + 0.78151386, + 0.80372058, + 0.83663889, + 0.84294308, + 0.91792954, + 0.92549804, + 0.97016259, + 0.98235882, + 0.9866563, + 0.9866563, + 0.99261508, + ] + ) + + +@pytest.fixture(scope="module") +def normal_inverse_cdf() -> np.ndarray: + return np.array( + [ + 244.33412663, + 363.2801879, + 522.3346692, + 659.33199747, + 818.38647877, + 1219.81909913, + ] + ) + + @pytest.fixture(scope="module") def dist_estimation_parameters_ks() -> str: return "lmoments" @@ -81,6 +617,7 @@ def ams_gauges() -> DataFrame: """AMS gauges""" ams = pd.read_csv(f"tests/data/ams-gauges.csv") ams.index = ams["date"] + ams.drop("date", axis=1, inplace=True) return ams diff --git a/tests/data/distribution_properties.csv b/tests/data/distribution_properties.csv index 2d177b6..3fa1a48 100644 --- a/tests/data/distribution_properties.csv +++ b/tests/data/distribution_properties.csv @@ -1,8 +1,7 @@ id,c,loc,scale,D-static,P-Value -Frankfurt,0.051851826887363194,718.7207607719855,376.1886075385216,0.07317073170731707,0.9999427584427157 -Mainz,0.3072948665432741,3743.8060125160628,1214.6170423632827,0.05555555555555555,0.9999984404687655 -Kaub,0.28257953512255224,3881.573476779092,1262.4260864019852,0.05555555555555555,0.9999984404687655 -Andernach,0.3215125279410673,5649.076007828818,2084.3831316091128,0.07407407407407407,0.9987375782247235 -Cologne,0.3061460861379136,5783.017454258022,2090.224036968223,0.07407407407407407,0.9987375782247235 -Rees,0.2842269596672276,5960.022502574694,2107.1972100234184,0.07407407407407407,0.9987375782247235 -date,0.2837753,1971.8005910014972,16.185304148413852,0.05555555555555555,0.9999984404687655 +Frankfurt,0.051852,718.720761,376.188608,0.073171,0.999943 +Mainz,0.307295,3743.806013,1214.617042,0.055556,0.999998 +Kaub,0.282580,3881.573477,1262.426086,0.055556,0.999998 +Andernach,0.321513,5649.076008,2084.383132,0.074074,0.998738 +Cologne,0.306146,5783.017454,2090.224037,0.074074,0.998738 +Rees,0.284227,5960.022503,2107.197210,0.074074,0.998738 diff --git a/tests/data/statistical_properties.csv b/tests/data/statistical_properties.csv index b8f933b..88c8b93 100644 --- a/tests/data/statistical_properties.csv +++ b/tests/data/statistical_properties.csv @@ -1,8 +1,7 @@ -id,mean,std,min,5%,25%,median,75%,95%,max,t_beg,t_end,nyr,q1.5,q2,q5,q10,q25,q50,q100,q200,q500,q1000 -Frankfurt,694.4074074074074,552.7567453832984,-9.0,-9.0,220.75,671.0,1090.0,1760.0,1990.0,1951.0,2004.0,,683.254633541981,855.2968644540772,1261.59649434511,1517.7587689069342,1827.4881315023772,2047.6221433340565,2047.6221433340565,2258.332885614113,2460.823382586069,2717.037039149113 -Mainz,4153.333333333333,1192.8038950621649,1150.0,2286.5,3415.0,4190.0,4987.5,5914.0,6920.0,1951.0,2004.0,,3627.907223538407,4164.8247438013,5203.502461613553,5716.905569818638,6217.243858574832,6504.77677815569,6504.77677815569,6734.883441589332,6919.948680476143,7110.76711483629 -Kaub,4327.092592592592,1254.6913665229047,1190.0,2394.5,3635.0,4350.0,5147.5,6383.499999999999,7160.0,1951.0,2004.0,,3761.2533136087277,4321.114689217054,5425.005521555241,5983.738153728853,6539.689757719358,6865.849995253274,6865.849995253274,7131.430892159442,7348.738113198606,7577.263513026446 -Andernach,6333.407407407408,2035.1432337383255,1470.0,3178.0,5175.0,6425.0,7412.5,9716.999999999998,10400.0,1951.0,2004.0,,5450.050443190599,6369.73494997472,8129.537878847686,8987.581310753912,9813.85630446624,10283.08467020549,10283.08467020549,10654.874461924472,10950.940916338675,11252.770123424481 -Cologne,6489.277777777777,2056.1328917541928,1580.0,3354.5,5277.5,6585.0,7560.0,9728.849999999999,10700.0,1951.0,2004.0,,5583.579049437877,6507.694660012967,8296.99616881387,9182.397798404068,10046.101957290637,10542.929863583495,10542.929863583495,10940.851299007954,11261.139355948999,11591.687059501717 -Rees,6701.425925925926,2094.47830764655,1810.0,3556.5,5450.0,6575.0,7901.75,10004.999999999998,11300.0,1951.0,2004.0,,5759.172691179417,6693.471602097722,8533.308506102763,9463.069144160174,10386.920556361936,10928.17130944753,10928.17130944753,11368.384249416642,11728.167907979188,12106.027638139985 -date,1977.5,15.732132722552274,1951.0,1953.65,1964.25,1977.5,1990.75,2001.35,2004.0,1951.0,2004.0,,1970.2579039098111,1977.4346447381406,1991.572128206143,1998.7195933109315,2005.824331043915,2009.9883077483444,2009.9883077483444,2013.3760478216004,2016.145701283059,2019.055555233493 +id,mean,std,min,5%,25%,median,75%,95%,max,start_year,end_year,nyr,q1.5,q2,q5,q10,q25,q50,q100,q200,q500,q1000 +Frankfurt,917.4390,433.9829,197.0000,347.0000,548.0000,882.0000,1170.0000,1760.0000,1990.0000,1964.0000,2004.0000,40.0000,683.2546,855.2969,1261.5965,1517.7588,1827.4881,2047.6221,2047.6221,2258.3329,2460.8234,2717.0370 +Mainz,4153.3333,1181.7078,1150.0000,2286.5000,3415.0000,4190.0000,4987.5000,5914.0000,6920.0000,1951.0000,2004.0000,53.0000,3627.9072,4164.8247,5203.5025,5716.9056,6217.2439,6504.7768,6504.7768,6734.8834,6919.9487,7110.7671 +Kaub,4327.0926,1243.0196,1190.0000,2394.5000,3635.0000,4350.0000,5147.5000,6383.5000,7160.0000,1951.0000,2004.0000,53.0000,3761.2533,4321.1147,5425.0055,5983.7382,6539.6898,6865.8500,6865.8500,7131.4309,7348.7381,7577.2635 +Andernach,6333.4074,2016.2113,1470.0000,3178.0000,5175.0000,6425.0000,7412.5000,9717.0000,10400.0000,1951.0000,2004.0000,53.0000,5450.0504,6369.7349,8129.5379,8987.5813,9813.8563,10283.0847,10283.0847,10654.8745,10950.9409,11252.7701 +Cologne,6489.2778,2037.0057,1580.0000,3354.5000,5277.5000,6585.0000,7560.0000,9728.8500,10700.0000,1951.0000,2004.0000,53.0000,5583.5790,6507.6947,8296.9962,9182.3978,10046.1020,10542.9299,10542.9299,10940.8513,11261.1394,11591.6871 +Rees,6701.4259,2074.9944,1810.0000,3556.5000,5450.0000,6575.0000,7901.7500,10005.0000,11300.0000,1951.0000,2004.0000,53.0000,5759.1727,6693.4716,8533.3085,9463.0691,10386.9206,10928.1713,10928.1713,11368.3842,11728.1679,12106.0276 diff --git a/tests/test_confidence_interval.py b/tests/test_confidence_interval.py index d73092d..e2b7b7d 100644 --- a/tests/test_confidence_interval.py +++ b/tests/test_confidence_interval.py @@ -1,4 +1,5 @@ """Confidence Interval Tests""" + from typing import Dict import numpy as np from statista.confidence_interval import ConfidenceInterval @@ -16,7 +17,7 @@ def test_boot_strap( """ ci = ConfidenceInterval.boot_strap( time_series1, - statfunction=GEV.ci_func, + state_function=GEV.ci_func, gevfit=ci_param, n_samples=len(time_series1), F=ci_cdf, diff --git a/tests/test_distributions.py b/tests/test_distributions.py index f3a50c4..8bdb0ba 100644 --- a/tests/test_distributions.py +++ b/tests/test_distributions.py @@ -1,10 +1,14 @@ """Test distributions module.""" -from typing import List + +import matplotlib + +matplotlib.use("Agg") +from typing import List, Dict import numpy as np from matplotlib.figure import Figure +from matplotlib.axes import Axes -from statista.confidence_interval import ConfidenceInterval from statista.distributions import ( GEV, Gumbel, @@ -13,6 +17,7 @@ Normal, Distributions, ) +import pytest class TestPlottingPosition: @@ -34,6 +39,24 @@ def test_plotting_position_rp( assert isinstance(rp, np.ndarray) +class TestAbstractDistribution: + def test_abstract_distribution(self, time_series1: list, gev_dist_parameters): + text_1 = "\n Dataset of 27 value\n min: 15.790480003140171\n max: 19.39645340792385\n mean: 16.929171461473548\n median: 16.626465201654593\n mode: 15.999737471905252\n std: 1.0211514099144634\n Distribution : Gumbel\n parameters: None\n " + parameters = gev_dist_parameters["lmoments"] + dist = Gumbel(time_series1) + assert str(dist) == text_1 + + text_2 = ( + "\n Distribution : Gumbel\n parameters: {'loc': 16.392889171307772, " + "'scale': 0.7005442761744839, 'shape': -0.1614793298009645}\n " + ) + dist = Gumbel(parameters=parameters) + assert str(dist) == text_2 + dist = Gumbel(data=time_series1, parameters=parameters) + text_3 = "\n Dataset of 27 value\n min: 15.790480003140171\n max: 19.39645340792385\n mean: 16.929171461473548\n median: 16.626465201654593\n mode: 15.999737471905252\n std: 1.0211514099144634\n Distribution : Gumbel\n parameters: {'loc': 16.392889171307772, 'scale': 0.7005442761744839, 'shape': -0.1614793298009645}\n \n Distribution : Gumbel\n parameters: {'loc': 16.392889171307772, 'scale': 0.7005442761744839, 'shape': -0.1614793298009645}\n " + assert str(dist) == text_3 + + class TestGumbel: def test_create_instance( self, @@ -42,19 +65,45 @@ def test_create_instance( dist = Gumbel(time_series1) assert isinstance(dist.data, np.ndarray) assert isinstance(dist.data_sorted, np.ndarray) + assert dist.parameters is None + + def test_create_instance_with_wrong_data_type(self): + data = {"key": "value"} + with pytest.raises(TypeError): + dist = Gumbel(data=data) + + def test_create_instance_with_wrong_parameter_type(self): + parameters = [1, 2, 3] + with pytest.raises(TypeError): + dist = Gumbel(parameters=parameters) - def test_estimate_parameter( + def test_random( + self, + dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], + ): + # param = gum_dist_parameters[dist_estimation_parameters_ks] + param = {"loc": 0, "scale": 1} + dist = Gumbel(parameters=param) + rv = dist.random(100) + # new_dist = Gumbel(rv, parameters=param) + assert isinstance(rv, np.ndarray) + assert rv.shape == (100,) + + def test_fit_model( self, time_series2: list, dist_estimation_parameters: List[str], + gum_dist_parameters: Dict[str, float], ): dist = Gumbel(time_series2) - for i in range(len(dist_estimation_parameters)): - param = dist.fit_model(method=dist_estimation_parameters[i], test=False) + for method in dist_estimation_parameters: + param = dist.fit_model(method=method, test=False) assert isinstance(param, dict) assert all(i in param.keys() for i in ["loc", "scale"]) assert dist.parameters.get("loc") is not None assert dist.parameters.get("scale") is not None + assert param == gum_dist_parameters[method] def test_parameter_estimation_optimization( self, @@ -65,7 +114,7 @@ def test_parameter_estimation_optimization( dist = Gumbel(time_series2) param = dist.fit_model( method="optimization", - obj_func=Gumbel.objective_fn, + obj_func=Gumbel.truncated_distribution, threshold=parameter_estimation_optimization_threshold, ) assert isinstance(param, dict) @@ -77,88 +126,123 @@ def test_ks( self, time_series2: list, dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], ): - dist = Gumbel(time_series2) - dist.fit_model(method=dist_estimation_parameters_ks, test=False) - dist.ks() - assert dist.Dstatic - assert dist.KS_Pvalue + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + dstatic, pvalue = dist.ks() + assert dstatic == 0.07407407407407407 + assert pvalue == 0.9987375782247235 def test_chisquare( self, time_series2: list, dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], ): - dist = Gumbel(time_series2) - dist.fit_model(method=dist_estimation_parameters_ks, test=False) - dist.chisquare() - assert dist.chistatic - assert dist.chi_Pvalue + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + dstatic, pvalue = dist.chisquare() + assert dstatic == -0.2813945052127964 + assert pvalue == 1 def test_pdf( self, time_series2: list, dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], + gum_pdf: np.ndarray, ): - dist = Gumbel(time_series2) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - - pdf, fig, ax = dist.pdf(param, plot_figure=True) + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + pdf, fig, ax = dist.pdf(plot_figure=True) assert isinstance(pdf, np.ndarray) + np.testing.assert_almost_equal(gum_pdf, pdf) assert isinstance(fig, Figure) + # test if you provide the pdf method with the data parameter + pdf, fig, ax = dist.pdf(data=time_series2, plot_figure=True) + assert isinstance(pdf, np.ndarray) + np.testing.assert_almost_equal(gum_pdf, pdf) def test_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], + gum_cdf: np.ndarray, ): - dist = Gumbel(time_series2) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - cdf, fig, ax = dist.cdf(param, plot_figure=True) - + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + cdf, fig, ax = dist.cdf(plot_figure=True) assert isinstance(cdf, np.ndarray) + np.testing.assert_almost_equal(gum_cdf, cdf) assert isinstance(fig, Figure) + # test if you provide the cdf method with the data parameter + cdf, fig, ax = dist.cdf(data=time_series2, plot_figure=True) + assert isinstance(cdf, np.ndarray) - def test_theoretical_estimate( + def test_inverse_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], + gev_inverse_cdf: np.ndarray, + generated_cdf: List[float], ): - dist = Gumbel(time_series2) - cdf_weibul = PlottingPosition.weibul(time_series2) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - qth = dist.theoretical_estimate(param, cdf_weibul) + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + qth = dist.inverse_cdf(generated_cdf) assert isinstance(qth, np.ndarray) + np.testing.assert_almost_equal(gev_inverse_cdf, qth) def test_confidence_interval( self, time_series2: list, dist_estimation_parameters_ks: str, confidence_interval_alpha: float, + gum_dist_parameters: Dict[str, Dict[str, float]], ): - dist = Gumbel(time_series2) + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) cdf_weibul = PlottingPosition.weibul(time_series2) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) + # test by providing the cdf function upper, lower = dist.confidence_interval( - param, cdf_weibul, alpha=confidence_interval_alpha + prob_non_exceed=cdf_weibul, alpha=confidence_interval_alpha ) assert isinstance(upper, np.ndarray) assert isinstance(lower, np.ndarray) + # test the default parameters + upper, lower = dist.confidence_interval() + assert isinstance(upper, np.ndarray) + assert isinstance(lower, np.ndarray) + + # test with plot_figure + upper, lower, fig, ax = dist.confidence_interval(plot_figure=True) + assert isinstance(upper, np.ndarray) + assert isinstance(lower, np.ndarray) + assert isinstance(fig, Figure) + assert isinstance(ax, Axes) - def test_probability_plot( + def test_plot( self, time_series2: list, dist_estimation_parameters_ks: str, confidence_interval_alpha: float, + gum_dist_parameters: Dict[str, Dict[str, float]], ): - dist = Gumbel(time_series2) + param = gum_dist_parameters[dist_estimation_parameters_ks] + dist = Gumbel(time_series2, param) + # test default parameters. + fig, ax = dist.plot() + assert isinstance(fig, Figure) + assert isinstance(ax[0], Axes) + assert isinstance(ax[1], Axes) + # test with the cdf parameter cdf_weibul = PlottingPosition.weibul(time_series2) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - (fig1, fig2), (_, _) = dist.probability_plot( - param, cdf_weibul, alpha=confidence_interval_alpha - ) - assert isinstance(fig1, Figure) - assert isinstance(fig2, Figure) + fig, ax = dist.plot(cdf=cdf_weibul) + assert isinstance(fig, Figure) + assert isinstance(ax[0], Axes) + assert isinstance(ax[1], Axes) class TestGEV: @@ -170,126 +254,158 @@ def test_create_gev_instance( assert isinstance(dist.data, np.ndarray) assert isinstance(dist.data_sorted, np.ndarray) - def test_gev_estimate_parameter( + def test_gev_fit_model( self, time_series1: list, dist_estimation_parameters: List[str], + gev_dist_parameters: Dict[str, str], ): dist = GEV(time_series1) - for i in range(len(dist_estimation_parameters)): - param = dist.fit_model(method=dist_estimation_parameters[i], test=False) + for method in dist_estimation_parameters: + param = dist.fit_model(method=method, test=False) assert isinstance(param, dict) assert all(i in param.keys() for i in ["loc", "scale", "shape"]) assert dist.parameters.get("loc") is not None assert dist.parameters.get("scale") is not None assert dist.parameters.get("shape") is not None + assert param == gev_dist_parameters[method] def test_gev_ks( self, time_series1: list, dist_estimation_parameters_ks: str, + gev_dist_parameters: Dict[str, Dict[str, float]], ): - dist = GEV(time_series1) - dist.fit_model(method=dist_estimation_parameters_ks, test=False) - dist.ks() - assert dist.Dstatic - assert dist.KS_Pvalue + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) + dstatic, pvalue = dist.ks() + assert dstatic == 0.14814814814814814 + assert pvalue == 0.9356622290518453 def test_gev_chisquare( self, time_series1: list, dist_estimation_parameters_ks: str, + gev_dist_parameters: Dict[str, Dict[str, float]], ): - dist = GEV(time_series1) - dist.fit_model(method=dist_estimation_parameters_ks, test=False) - dist.chisquare() - assert dist.chistatic - assert dist.chi_Pvalue + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) + dstatic, pvalue = dist.chisquare() + assert dstatic == -22.906818156545253 + assert pvalue == 1 def test_gev_pdf( self, time_series1: list, dist_estimation_parameters_ks: str, + gev_dist_parameters: Dict[str, Dict[str, float]], + gev_pdf: np.ndarray, ): - dist = GEV(time_series1) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) - pdf, fig, ax = dist.pdf(param, plot_figure=True) + pdf, fig, ax = dist.pdf(plot_figure=True) assert isinstance(pdf, np.ndarray) + np.testing.assert_almost_equal(gev_pdf, pdf) assert isinstance(fig, Figure) + # test if you provide the pdf method with the data parameter + pdf, fig, ax = dist.pdf(data=time_series1, plot_figure=True) + assert isinstance(pdf, np.ndarray) def test_gev_cdf( self, time_series1: list, dist_estimation_parameters_ks: str, + gev_dist_parameters: Dict[str, Dict[str, float]], + gev_cdf: np.ndarray, ): - dist = GEV(time_series1) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - cdf, fig, ax = dist.cdf(param, plot_figure=True) + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) + cdf, fig, ax = dist.cdf(plot_figure=True) assert isinstance(cdf, np.ndarray) + np.testing.assert_almost_equal(gev_cdf, cdf) assert isinstance(fig, Figure) + # test if you provide the cdf method with the data parameter + cdf, fig, ax = dist.cdf(data=time_series1, plot_figure=True) + assert isinstance(cdf, np.ndarray) - def test_gev_theoretical_estimate( + def test_random( + self, + dist_estimation_parameters_ks: str, + gum_dist_parameters: Dict[str, Dict[str, float]], + ): + # param = gum_dist_parameters[dist_estimation_parameters_ks] + param = {"loc": 0, "scale": 1, "shape": 0.1} + dist = Gumbel(parameters=param) + rv = dist.random(100) + # new_dist = Gumbel(rv, parameters=param) + assert isinstance(rv, np.ndarray) + assert rv.shape == (100,) + + def test_gev_inverse_cdf( self, time_series1: list, dist_estimation_parameters_ks: str, + gev_dist_parameters: Dict[str, Dict[str, float]], + generated_cdf: List[float], + gum_inverse_cdf: np.ndarray, ): - dist = GEV(time_series1) - cdf_weibul = PlottingPosition.weibul(time_series1) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - qth = dist.theoretical_estimate(param, cdf_weibul) + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) + qth = dist.inverse_cdf(generated_cdf) assert isinstance(qth, np.ndarray) + np.testing.assert_almost_equal(gum_inverse_cdf, qth) def test_gev_confidence_interval( self, time_series1: list, dist_estimation_parameters_ks: str, confidence_interval_alpha: float, + gev_dist_parameters: Dict[str, Dict[str, float]], ): - dist = GEV(time_series1) + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) cdf_weibul = PlottingPosition.weibul(time_series1) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - func = GEV.ci_func upper, lower = dist.confidence_interval( - param, prob_non_exceed=cdf_weibul, alpha=confidence_interval_alpha, - statfunction=func, - n_samples=len(time_series1), + n_samples=100, + ) + assert isinstance(upper, np.ndarray) + assert isinstance(lower, np.ndarray) + # test with plot_figure + upper, lower, fig, ax = dist.confidence_interval( + prob_non_exceed=cdf_weibul, + alpha=confidence_interval_alpha, + plot_figure=True, ) assert isinstance(upper, np.ndarray) assert isinstance(lower, np.ndarray) + assert isinstance(fig, Figure) + assert isinstance(ax, Axes) - def test_confidence_interval_directly( + def test_gev_plot( self, time_series1: list, dist_estimation_parameters_ks: str, confidence_interval_alpha: float, + gev_dist_parameters: Dict[str, Dict[str, float]], ): - dist = GEV(time_series1) + param = gev_dist_parameters[dist_estimation_parameters_ks] + dist = GEV(time_series1, param) + # test default parameters. + fig, ax = dist.plot() + assert isinstance(fig, Figure) + assert isinstance(ax[0], Axes) + assert isinstance(ax[1], Axes) + # test with the cdf parameter cdf_weibul = PlottingPosition.weibul(time_series1) - param = dist.fit_model(method=dist_estimation_parameters_ks, test=False) - - func = GEV.ci_func - - ci = ConfidenceInterval.boot_strap( - time_series1, - statfunction=func, - gevfit=param, - n_samples=len(time_series1), - F=cdf_weibul, - method="lmoments", - ) - lb = ci["lb"] - ub = ci["ub"] - - assert isinstance(lb, np.ndarray) - assert isinstance(ub, np.ndarray) - - -# class TestAbstractDistrition: + fig, ax = dist.plot(cdf=cdf_weibul) + assert isinstance(fig, Figure) + assert isinstance(ax[0], Axes) + assert isinstance(ax[1], Axes) class TestExponential: @@ -301,53 +417,68 @@ def test_create_instance( assert isinstance(expo_dist.data, np.ndarray) assert isinstance(expo_dist.data_sorted, np.ndarray) - def test_estimate_parameter( + def test_fit_model( self, time_series2: list, dist_estimation_parameters: List[str], + exp_dist_parameters: Dict[str, float], ): expo_dist = Exponential(time_series2) - for i in range(len(dist_estimation_parameters)): - param = expo_dist.fit_model( - method=dist_estimation_parameters[i], test=False - ) + for method in dist_estimation_parameters: + param = expo_dist.fit_model(method=method, test=False) assert isinstance(param, dict) assert all(i in param.keys() for i in ["loc", "scale"]) assert expo_dist.parameters.get("loc") is not None assert expo_dist.parameters.get("scale") is not None + assert param == exp_dist_parameters[method] def test_pdf( self, time_series2: list, dist_estimation_parameters_ks: str, + exp_dist_parameters: Dict[str, Dict[str, float]], + exp_pdf: np.ndarray, ): - expo_dist = Exponential(time_series2) - param = expo_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - pdf, fig, ax = expo_dist.pdf(param, plot_figure=True) + param = exp_dist_parameters[dist_estimation_parameters_ks] + expo_dist = Exponential(time_series2, param) + pdf, fig, ax = expo_dist.pdf(plot_figure=True) assert isinstance(pdf, np.ndarray) + np.testing.assert_almost_equal(exp_pdf, pdf) assert isinstance(fig, Figure) + # test if you provide the pdf method with the data parameter + pdf, fig, ax = expo_dist.pdf(data=time_series2, plot_figure=True) + assert isinstance(pdf, np.ndarray) def test_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + exp_dist_parameters: Dict[str, Dict[str, float]], + exp_cdf: np.ndarray, ): - expo_dist = Exponential(time_series2) - param = expo_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - cdf, fig, ax = expo_dist.cdf(param, plot_figure=True) + param = exp_dist_parameters[dist_estimation_parameters_ks] + expo_dist = Exponential(time_series2, param) + cdf, fig, ax = expo_dist.cdf(plot_figure=True) assert isinstance(cdf, np.ndarray) + np.testing.assert_almost_equal(exp_cdf, cdf) assert isinstance(fig, Figure) + # test if you provide the cdf method with the data parameter + cdf, fig, ax = expo_dist.cdf(data=time_series2, plot_figure=True) + assert isinstance(cdf, np.ndarray) - def test_theoretical_estimate( + def test_inverse_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + exp_dist_parameters: Dict[str, Dict[str, float]], + generated_cdf: List[float], + exp_inverse_cdf: np.ndarray, ): - expo_dist = Exponential(time_series2) - cdf_weibul = PlottingPosition.weibul(time_series2) - param = expo_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - qth = expo_dist.theoretical_estimate(param, cdf_weibul) + param = exp_dist_parameters[dist_estimation_parameters_ks] + expo_dist = Exponential(time_series2, param) + qth = expo_dist.inverse_cdf(generated_cdf) assert isinstance(qth, np.ndarray) + np.testing.assert_almost_equal(exp_inverse_cdf, qth) class TestNormal: @@ -359,10 +490,11 @@ def test_create_instance( assert isinstance(norm_dist.data, np.ndarray) assert isinstance(norm_dist.data_sorted, np.ndarray) - def test_estimate_parameter( + def test_fit_model( self, time_series2: list, dist_estimation_parameters: List[str], + normal_dist_parameters: Dict[str, Dict[str, float]], ): norm_dist = Normal(time_series2) for method in dist_estimation_parameters: @@ -371,39 +503,55 @@ def test_estimate_parameter( assert all(i in param.keys() for i in ["loc", "scale"]) assert norm_dist.parameters.get("loc") is not None assert norm_dist.parameters.get("scale") is not None + assert param == normal_dist_parameters[method] def test_pdf( self, time_series2: list, dist_estimation_parameters_ks: str, + normal_dist_parameters: Dict[str, Dict[str, float]], + normal_pdf: np.ndarray, ): - norm_dist = Normal(time_series2) - param = norm_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - pdf, fig, ax = norm_dist.pdf(param, plot_figure=True) + param = normal_dist_parameters[dist_estimation_parameters_ks] + norm_dist = Normal(time_series2, param) + pdf, fig, ax = norm_dist.pdf(plot_figure=True) assert isinstance(pdf, np.ndarray) + np.testing.assert_almost_equal(normal_pdf, pdf) assert isinstance(fig, Figure) + # test if you provide the pdf method with the data parameter + pdf, fig, ax = norm_dist.pdf(data=time_series2, plot_figure=True) + assert isinstance(pdf, np.ndarray) def test_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + normal_dist_parameters: Dict[str, Dict[str, float]], + normal_cdf: np.ndarray, ): - norm_dist = Normal(time_series2) - param = norm_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - cdf, fig, ax = norm_dist.cdf(param, plot_figure=True) + param = normal_dist_parameters[dist_estimation_parameters_ks] + norm_dist = Normal(time_series2, param) + cdf, fig, ax = norm_dist.cdf(plot_figure=True) assert isinstance(cdf, np.ndarray) + np.testing.assert_almost_equal(normal_cdf, cdf) assert isinstance(fig, Figure) + # test if you provide the cdf method with the data parameter + cdf, fig, ax = norm_dist.cdf(data=time_series2, plot_figure=True) + assert isinstance(cdf, np.ndarray) - def test_theoretical_estimate( + def test_inverse_cdf( self, time_series2: list, dist_estimation_parameters_ks: str, + normal_dist_parameters: Dict[str, Dict[str, float]], + generated_cdf: List[float], + normal_inverse_cdf: np.ndarray, ): - norm_dist = Normal(time_series2) - cdf_weibul = PlottingPosition.weibul(time_series2) - param = norm_dist.fit_model(method=dist_estimation_parameters_ks, test=False) - qth = norm_dist.theoretical_estimate(param, cdf_weibul) + param = normal_dist_parameters[dist_estimation_parameters_ks] + norm_dist = Normal(time_series2, param) + qth = norm_dist.inverse_cdf(generated_cdf) assert isinstance(qth, np.ndarray) + np.testing.assert_almost_equal(normal_inverse_cdf, qth) class TestDistribution: diff --git a/tests/test_eva.py b/tests/test_eva.py index 74bfd6e..78de16f 100644 --- a/tests/test_eva.py +++ b/tests/test_eva.py @@ -1,5 +1,9 @@ """ Tests for the eva module. """ -import numpy as np + +import matplotlib +import pandas as pd + +matplotlib.use("Agg") from pandas import DataFrame import shutil from pathlib import Path @@ -29,23 +33,15 @@ def test_eva( save_to=save_to, filter_out=-9, method=method, - significance_level=0.05, + alpha=0.05, ) statistical_properties.drop(columns=["nyr"], inplace=True) gauges_statistical_properties.drop(columns=["nyr"], inplace=True) - assert ( - np.isclose( - statistical_properties.values, - gauges_statistical_properties.values, - atol=0.01, - ) - ).all() - assert ( - np.isclose( - distribution_properties.values, - gauges_distribution_properties.values, - atol=0.01, - ) - ).all() + pd.testing.assert_frame_equal( + statistical_properties, gauges_statistical_properties, rtol=1e-4 + ) + pd.testing.assert_frame_equal( + distribution_properties, gauges_distribution_properties, rtol=1e-4 + ) # try: shutil.rmtree(path)