pax_global_header00006660000000000000000000000064147015005430014511gustar00rootroot0000000000000052 comment=53de916e4c9876393bb1a84af5381c9fe457ed8a resample-1.10.1/000077500000000000000000000000001470150054300134015ustar00rootroot00000000000000resample-1.10.1/.coveragerc000066400000000000000000000001411470150054300155160ustar00rootroot00000000000000[run] source = src/resample relative_files = True [report] exclude_lines = pragma: no cover resample-1.10.1/.github/000077500000000000000000000000001470150054300147415ustar00rootroot00000000000000resample-1.10.1/.github/workflows/000077500000000000000000000000001470150054300167765ustar00rootroot00000000000000resample-1.10.1/.github/workflows/docs.yml000066400000000000000000000020441470150054300204510ustar00rootroot00000000000000name: Docs on: pull_request: push: tags: - '**' workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.head_ref }} cancel-in-progress: true jobs: build: runs-on: ubuntu-latest steps: - uses: actions/checkout@v4 with: fetch-depth: 0 # must come after checkout - uses: actions/setup-python@v5 with: python-version: "3.11" - run: sudo apt-get install pandoc - run: pip install .[doc] - run: cd doc; make html - uses: actions/upload-pages-artifact@v3 with: path: 'doc/_build/html' deploy: if: github.event_name == 'workflow_dispatch' || contains(github.event.ref, '/tags/') needs: build # Set permissions to allow deployment to GitHub Pages permissions: contents: read pages: write id-token: write environment: name: github-pages url: ${{ steps.deployment.outputs.page_url }} runs-on: ubuntu-latest steps: - uses: actions/configure-pages@v4 - uses: actions/deploy-pages@v4 resample-1.10.1/.github/workflows/release.yml000066400000000000000000000015351470150054300211450ustar00rootroot00000000000000name: Release on: push: tags: - '**' workflow_dispatch: concurrency: group: ${{ github.workflow }}-${{ github.head_ref }} cancel-in-progress: true env: PIP_ONLY_BINARY: ":all:" jobs: release: runs-on: ubuntu-latest environment: name: pypi url: https://pypi.org/p/resample permissions: id-token: write steps: - uses: actions/checkout@v4 with: fetch-depth: 0 # needed by setuptools_scm - uses: actions/setup-python@v5 with: python-version: '3.11' - run: python -m pip install --upgrade pip build - run: python -m build - run: python -m pip install --prefer-binary $(echo dist/*.whl)'[test]' - run: python -m pytest - uses: pypa/gh-action-pypi-publish@release/v1 if: github.event_name == 'push' && contains(github.event.ref, '/tags/') resample-1.10.1/.github/workflows/test.yml000066400000000000000000000021561470150054300205040ustar00rootroot00000000000000name: Test on: pull_request: concurrency: group: ${{ github.workflow }}-${{ github.head_ref }} cancel-in-progress: true env: PIP_ONLY_BINARY: ":all:" jobs: test: runs-on: ${{ matrix.os }} strategy: matrix: os: [ubuntu-latest] # version number must be string, otherwise 3.10 becomes 3.1 python-version: ["3.8", "3.10", "3.13"] include: - os: windows-latest python-version: "3.12" - os: macos-latest python-version: "3.9" - os: macos-13 python-version: "3.11" fail-fast: false steps: - uses: actions/checkout@v4 - uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} allow-prereleases: true - uses: astral-sh/setup-uv@v3 - run: uv pip install --system -e .[test] - if: matrix.os != 'ubuntu-latest' run: python -m pytest - if: matrix.os == 'ubuntu-latest' env: JUPYTER_PLATFORM_DIRS: 1 run: coverage run -m pytest - if: matrix.os == 'ubuntu-latest' uses: coverallsapp/github-action@v2 resample-1.10.1/.gitignore000066400000000000000000000004321470150054300153700ustar00rootroot00000000000000.vscode .DS_Store *.swp *checkpoints* build dist prof resample.egg-info install_log.txt *__pycache__ .pytest_cache .mypy_cache .benchmarks .coverage coverage.xml html* .doctrees _build .idea benchmarks/.asv/ junit py[0-9]* venv resample/_ext.cpython* src/resample/_ext.cpython-*.so resample-1.10.1/.pre-commit-config.yaml000066400000000000000000000014341470150054300176640ustar00rootroot00000000000000files: 'resample' repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v5.0.0 hooks: - id: check-case-conflict - id: check-docstring-first - id: check-merge-conflict - id: check-symlinks - id: check-yaml - id: debug-statements - id: end-of-file-fixer - id: mixed-line-ending - id: sort-simple-yaml - id: file-contents-sorter - id: trailing-whitespace # Ruff linter, replacement for flake8, isort, pydocstyle - repo: https://github.com/astral-sh/ruff-pre-commit rev: 'v0.6.9' hooks: - id: ruff args: [--fix, --show-fixes, --exit-non-zero-on-fix] - id: ruff-format # Python type checking - repo: https://github.com/pre-commit/mirrors-mypy rev: 'v1.11.2' hooks: - id: mypy args: [--allow-redefinition, --ignore-missing-imports] resample-1.10.1/.readthedocs.yaml000066400000000000000000000004121470150054300166250ustar00rootroot00000000000000# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details version: 2 sphinx: configuration: doc/conf.py python: version: 3.8 install: - method: pip path: . extra_requirements: - doc system_packages: false resample-1.10.1/CITATION.cff000066400000000000000000000016721470150054300153010ustar00rootroot00000000000000# This CITATION.cff file was generated with cffinit. # Visit https://bit.ly/cffinit to generate yours today! cff-version: 1.2.0 title: scikit-hep/resample message: >- If you use this software, please cite it using the metadata from this file. type: software authors: - given-names: Hans family-names: Dembinski email: hans.dembinski@gmail.com affiliation: TU Dortmund orcid: 'https://orcid.org/0000-0003-3337-3850' - given-names: Daniel family-names: Saxton - given-names: Henry family-names: Schreiner - given-names: Joshua family-names: Adelman - given-names: Eduardo family-names: Rodrigues identifiers: - type: doi value: 10.5281/zenodo.7750255 repository-code: 'https://github.com/scikit-hep/resample' url: 'https://resample.readthedocs.io/en/stable/' abstract: 'Randomization-based inference in Python ' keywords: - Python - statistics - data analysis - Scikit-HEP license: BSD-3-Clause resample-1.10.1/LICENSE000066400000000000000000000027551470150054300144170ustar00rootroot00000000000000Copyright (c) 2018, Daniel D. Saxton All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of {{ project }} nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. resample-1.10.1/MANIFEST.in000066400000000000000000000000201470150054300151270ustar00rootroot00000000000000include LICENSE resample-1.10.1/README.rst000066400000000000000000000055311470150054300150740ustar00rootroot00000000000000.. |resample| image:: doc/_static/logo.svg :alt: resample :target: http://resample.readthedocs.io |resample| ========== .. image:: https://img.shields.io/pypi/v/resample.svg :target: https://pypi.org/project/resample .. image:: https://img.shields.io/conda/vn/conda-forge/resample.svg :target: https://github.com/conda-forge/resample-feedstock .. image:: https://github.com/resample-project/resample/actions/workflows/test.yml/badge.svg :target: https://github.com/resample-project/resample/actions/workflows/tests.yml .. image:: https://coveralls.io/repos/github/resample-project/resample/badge.svg :target: https://coveralls.io/github/resample-project/resample .. image:: https://readthedocs.org/projects/resample/badge/?version=stable :target: https://resample.readthedocs.io/en/stable .. image:: https://img.shields.io/pypi/l/resample :target: https://pypi.org/project/resample .. image:: https://zenodo.org/badge/145776396.svg :target: https://zenodo.org/badge/latestdoi/145776396 `Link to full documentation`_ .. _Link to full documentation: http://resample.readthedocs.io .. skip-marker-do-not-remove Resampling-based inference in Python based on data resampling and permutation. This package was created by Daniel Saxton and is now maintained by Hans Dembinski. Features -------- - Bootstrap resampling: ordinary or balanced with optional stratification - Extended bootstrap resampling: also varies sample size - Parametric resampling: Gaussian, Poisson, gamma, etc.) - Jackknife estimates of bias and variance of any estimator - Compute bootstrap confidence intervals (percentile or BCa) for any estimator - Permutation-based variants of traditional statistical tests (**USP test of independence** and others) - Tools for working with empirical distributions (CDF, quantile, etc.) - Depends only on `numpy`_ and `scipy`_ Example ------- We bootstrap the uncertainty of the arithmetic mean, an estimator for the expectation. In this case, we know the formula to compute this uncertainty and can compare it to the bootstrap result. More complex examples can be found `in the documentation `_. .. code-block:: python from resample.bootstrap import variance import numpy as np # data d = [1, 2, 6, 3, 5] # this call is all you need stdev_of_mean = variance(np.mean, d) ** 0.5 print(f"bootstrap {stdev_of_mean:.2f}") print(f"exact {np.std(d) / len(d) ** 0.5:.2f}") # bootstrap 0.82 # exact 0.83 The amazing thing is that the bootstrap works as well for arbitrarily complex estimators. The bootstrap often provides good results even when the sample size is small. .. _numpy: http://www.numpy.org .. _scipy: https://www.scipy.org Installation ------------ You can install with pip. .. code-block:: shell pip install resample resample-1.10.1/benchmarks/000077500000000000000000000000001470150054300155165ustar00rootroot00000000000000resample-1.10.1/benchmarks/test_bootstrap.py000066400000000000000000000032671470150054300211540ustar00rootroot00000000000000import numpy as np import pytest from resample.bootstrap import confidence_interval, resample def run_resample(n, method): x = np.arange(n) r = [] for b in resample(x, method=method): r.append(b) return r @pytest.mark.benchmark(group="bootstrap-100") @pytest.mark.parametrize("method", ("ordinary", "balanced", "normal")) def test_resample_100(benchmark, method): benchmark(run_resample, 100, method) @pytest.mark.benchmark(group="bootstrap-1000") @pytest.mark.parametrize("method", ("ordinary", "balanced", "normal")) def test_bootstrap_resample_1000(benchmark, method): benchmark(run_resample, 1000, method) @pytest.mark.benchmark(group="bootstrap-10000") @pytest.mark.parametrize("method", ("ordinary", "balanced", "normal")) def test_bootstrap_resample_10000(benchmark, method): benchmark(run_resample, 10000, method) def run_confidence_interval(n, ci_method): x = np.arange(n) confidence_interval(np.mean, x, ci_method=ci_method) @pytest.mark.benchmark(group="confidence-interval-100") @pytest.mark.parametrize("ci_method", ("percentile", "bca")) def test_bootstrap_confidence_interval_100(benchmark, ci_method): benchmark(run_confidence_interval, 100, ci_method) @pytest.mark.benchmark(group="confidence-interval-1000") @pytest.mark.parametrize("ci_method", ("percentile", "bca")) def test_bootstrap_confidence_interval_1000(benchmark, ci_method): benchmark(run_confidence_interval, 1000, ci_method) @pytest.mark.benchmark(group="confidence-interval-10000") @pytest.mark.parametrize("ci_method", ("percentile", "bca")) def test_bootstrap_confidence_interval_10000(benchmark, ci_method): benchmark(run_confidence_interval, 10000, ci_method) resample-1.10.1/benchmarks/test_jackknife.py000066400000000000000000000014101470150054300210500ustar00rootroot00000000000000# ruff: noqa: D100 D103 import numpy as np import pytest from numpy.testing import assert_equal from resample.jackknife import resample def run_resample(n, copy): x = np.arange(n) r = [] for b in resample(x, copy=copy): r.append(np.mean(b)) return r @pytest.mark.benchmark(group="jackknife-100") @pytest.mark.parametrize("copy", (True, False)) def test_jackknife_resample_100(benchmark, copy): result = benchmark(run_resample, 100, copy) assert_equal(result, run_resample(100, resample)) @pytest.mark.benchmark(group="jackknife-1000") @pytest.mark.parametrize("copy", (True, False)) def test_jackknife_resample_1000(benchmark, copy): result = benchmark(run_resample, 1000, copy) assert_equal(result, run_resample(1000, resample)) resample-1.10.1/benchmarks/test_rcont.py000066400000000000000000000012051470150054300202520ustar00rootroot00000000000000import numpy as np import pytest from scipy.stats import random_table @pytest.mark.parametrize("n", (10, 100, 1000, 10000, 100000)) @pytest.mark.parametrize("k", (2, 4, 10, 20, 40, 100)) @pytest.mark.parametrize("method", (None, "boyett", "patefield")) def test_rcont(k, n, method, benchmark): w = np.zeros((k, k)) rng = np.random.default_rng(1) for _ in range(n): i = rng.integers(k) j = rng.integers(k) w[i, j] += 1 r = np.sum(w, axis=1) c = np.sum(w, axis=0) assert np.sum(r) == n assert np.sum(c) == n benchmark(lambda: random_table(r, c).rvs(100, method=method, random_state=rng)) resample-1.10.1/benchmarks/test_usp.py000066400000000000000000000010201470150054300177270ustar00rootroot00000000000000import numpy as np import pytest from resample.permutation import usp @pytest.mark.parametrize("n", (10, 100, 1000, 10000)) @pytest.mark.parametrize("k", (2, 10, 100)) @pytest.mark.parametrize("method", ("patefield", "shuffle")) def test_usp(k, n, method, benchmark): w = np.zeros((k, k)) rng = np.random.default_rng(1) for _ in range(n): i = rng.integers(k) j = rng.integers(k) w[i, j] += 1 assert np.sum(w) == n benchmark(lambda: usp(w, method=method, size=100, random_state=1)) resample-1.10.1/doc/000077500000000000000000000000001470150054300141465ustar00rootroot00000000000000resample-1.10.1/doc/Makefile000066400000000000000000000011031470150054300156010ustar00rootroot00000000000000# Minimal makefile for Sphinx documentation # # You can set these variables from the command line. 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(March 1, 2022) --------------------- This is an API breaking release. The backward-incompatible changes are limited to the ``resample.permutation`` submodule. Other modules are not affected. Warning: It was discovered that the tests implemented in ``resample.permutation`` had various issues and gave wrong results, so any results obtained with these tests should be revised. Since the old implementations were broken anyway, the API of ``resample.permutation`` was altered to fix some design issues as well. Installing resample now requires compiling a C extension. This is needed for the computation of the new USP test. This makes the installation of this package less convenient, since now a C compiler is required on the target machine (or we have to start building binary wheels). The plan is to move the compiled code to SciPy, which would allows us to drop the C extension again in the future. New features ~~~~~~~~~~~~ - ``resample`` functions in ``resample.bootstrap`` and ``resample.jackknife``, and all convenience functions which depend on them, can now resample from several arrays of equal length, example: ``for ai, bi in resample(a, b): ...`` - USP test of independence for binned data was added to ``resample.permutation`` - ``resample.permutation.same_population`` was added as a generic permutation-based test to compute the p-value that two or more samples were drawn from the same population API changes in submodule ``resample.permutation`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - All functions now only accept keyword arguments for their configuration and return a tuple-like ``TestResult`` object instead of a dictionary - Keyword ``b`` in all tests was replaced by ``size`` - p-values computed by all tests are now upper limits to the true Type I error rate - ``corr_test`` was replaced by two separate functions ``pearsonr`` and ``spearmanr`` - ``kruskal_wallis`` was renamed to ``kruskal`` to follow SciPy naming - ``anova`` and ``kruskal`` now accept multiple samples directly instead of using a list of samples; example: ``anova(x, y)`` instead of ``anova([x, y])`` - ``wilcoxon`` and ``ks_test`` were removed, since the corresponding tests in Scipy (``wilcoxon`` and ``ks_2samp``) compute exact p-values for small samples and use asymptotic formulas only when the same size is large; we cannot do better than that 1.0.1 (August 23, 2020) ----------------------- - Minor fix to allow building from source. 1.0.0 (August 22, 2020) ----------------------- API Changes ~~~~~~~~~~~ - Bootstrap and jackknife generators ``resample.bootstrap.resample`` and ``resample.jackknife.resample`` are now exposed to compute replicates lazily. - Jackknife functions have been split into their own namespace ``resample.jackknife``. - Empirical distribution helper functions moved to a ``resample.empirical`` namespace. - Random number seeding is now done through using ``numpy`` generators rather than a global random state. As a result the minimum ``numpy`` version is now 1.17. - Parametric bootstrap now estimates both parameters of the t distribution. - Default confidence interval method changed from ``"percentile"`` to ``"bca"``. - Empirical quantile function no longer performs interpolation between quantiles. Enhancements ~~~~~~~~~~~~ - Added bootstrap estimate of bias. - Added ``bias_corrected`` function for jackknife and bootstrap, which computes the bias corrected estimates. - Performance of jackknife computation was increased. Bug fixes ~~~~~~~~~ - Removed incorrect implementation of Studentized bootstrap. Deprecations ~~~~~~~~~~~~ - Smoothing of bootstrap samples is no longer supported. - Supremum norm and MISE functionals removed. Other ~~~~~ - Benchmarks were added to track and compare performance of bootstrap and jackknife methods. resample-1.10.1/doc/conf.py000066400000000000000000000067651470150054300154630ustar00rootroot00000000000000# -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. # # This file does only contain a selection of the most common options. For a # full list see the documentation: # http://www.sphinx-doc.org/en/master/config # -- Path setup -------------------------------------------------------------- # 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. # # import os # import sys # sys.path.insert(0, os.path.abspath('.')) # -- Project information ----------------------------------------------------- import resample version = resample.__version__ # noqa project = "resample" copyright = "2018, Daniel Saxton" author = "Daniel Saxton and Hans Dembinski" # -- 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.napoleon", "nbsphinx", ] autoclass_content = "both" autosummary_generate = True autodoc_member_order = "groupwise" autodoc_type_aliases = {"ArrayLike": "ArrayLike"} # Add any paths that contain templates here, relative to this directory. templates_path = ["_templates"] # The suffix(es) of source filenames. # You can specify multiple suffix as a list of string: # # source_suffix = ['.rst', '.md'] source_suffix = ".rst" # The master toctree document. master_doc = "index" # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. # # This is also used if you do content translation via gettext catalogs. # Usually you set "language" from the command line for these cases. language = "en" # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [ "_build", "Thumbs.db", ".DS_Store", "__pycache__", ".ipynb_checkpoints", ] # The name of the Pygments (syntax highlighting) style to use. pygments_style = None # -- Options for HTML output ------------------------------------------------- import sphinx_rtd_theme html_theme = "sphinx_rtd_theme" html_theme_path = [sphinx_rtd_theme.get_html_theme_path()] # 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_logo = "_static/logo.svg" # 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"] # Custom sidebar templates, must be a dictionary that maps document names # to template names. # # The default sidebars (for documents that don't match any pattern) are # defined by theme itself. Builtin themes are using these templates by # default: ``['localtoc.html', 'relations.html', 'sourcelink.html', # 'searchbox.html']``. # # html_sidebars = {} # -- Options for HTMLHelp output --------------------------------------------- # Output file base name for HTML help builder. htmlhelp_basename = "resampledoc" resample-1.10.1/doc/example/000077500000000000000000000000001470150054300156015ustar00rootroot00000000000000resample-1.10.1/doc/example/tag_and_probe.ipynb000066400000000000000000005365421470150054300214470ustar00rootroot00000000000000{ "cells": [ { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "# Tag-and-probe analysis\n", "\n", "This is a complex example taken from particle physics how resampling can be used in practice. The actual call into the resample library is very simple, but the setup of the analysis is complex.\n", "\n", "A tag-and-probe analysis in particle physics can be used to obtain the selection efficiency for a cut applied to the products of a two-body decay, if the products have the same mass. For example, in the decay $\\phi \\to K^+K^-$, we can remove background if we use particle identification information and apply a cut that favors kaons. However, applying the cut will also remove some signal.\n", "\n", "The efficiency for such a cut is obtained in the following way. The tag sample is generated by applying the cut only to one of the products. The probe sample is generated by applying the cut on both products. The background is strongly reduced in the probe sample and to a lesser degree in the tag sample.\n", "\n", "Under the assumption that the cut efficiency for both products is equal, we can estimate the total number of decays from the signal reduction in the probe sample relative to the tag sample. For that, the peak in the mass distribution of the decay candidates has to be fitted in both the tag and probe samples. Since the probe sample is a subset of the tag sample, it is not independent and computing the statistical uncertainty for the efficiency and the final result is challenging, as there are correlations to consider.\n", "\n", "The bootstrap is an elegant solution to this problem." ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "We generate some toy data in bins of transverse momentum, $p_T$. The efficiency of the cut on the kaon is 80 %." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# boost-histogram, matplotlib, iminuit, numba-stats are external packages\n", "import numpy as np\n", "import boost_histogram as bh\n", "import matplotlib.pyplot as plt\n", "from iminuit import Minuit, cost\n", "from numba_stats import truncnorm, truncexpon\n", "\n", "a_pt = bh.axis.Regular(4, 0, 10, metadata=\"pt\")\n", "\n", "\n", "def fit(pt, mass_axis, val, var, showfig):\n", " mrange = mass_axis.edges[0], mass_axis.edges[-1]\n", "\n", " def model(x, ns, mu, sigma, nb, slope):\n", " s = ns * truncnorm.cdf(x, *mrange, mu, sigma)\n", " b = nb * truncexpon.cdf(x, *mrange, 0, slope)\n", " return s + b\n", "\n", " if showfig:\n", " fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharex=True, sharey=True, constrained_layout=True)\n", "\n", " results = []\n", " for kind in range(2):\n", " mass_val = val[:, kind]\n", " mass_var = var[:, kind]\n", " ntot = np.sum(mass_val)\n", " nll = cost.ExtendedBinnedNLL(\n", " np.column_stack((mass_val, mass_var))\n", " if any(mass_val != mass_var)\n", " else mass_val,\n", " mass_axis.edges,\n", " model,\n", " )\n", "\n", " m = Minuit(nll, ns=ntot / 2, mu=0.5, sigma=0.1, nb=ntot / 2, slope=1)\n", " m.strategy = 0 # strategy 0 is sufficient, we don't need to compute the Hessian\n", " m.limits[\"ns\", \"nb\", \"sigma\"] = (0, None)\n", " m.limits[\"mu\"] = (0, 1)\n", " m.limits[\"slope\"] = (0, 2)\n", " m.migrad()\n", " results.append(m.values[\"ns\"])\n", "\n", " if showfig:\n", " plt.sca(ax[kind])\n", " nll.visualize(m.values)\n", " plt.title(f\"{['tag', 'probe'][kind]} $\\\\chi^2$/ndof = {m.fval:.1f}/{m.ndof}\")\n", "\n", " if showfig:\n", " fig.suptitle(\n", " f\"pT = [{pt[0]}, {pt[1]}) GeV/c\"\n", " )\n", " fig.supxlabel(\"mass / GeV\")\n", "\n", " return results\n", "\n", "\n", "def fit_all(histogram, showfig=False):\n", " mass_axis = histogram.axes[1]\n", " val = histogram.values()\n", " var = histogram.variances() # if events are weighted, we also need variances\n", " n_tag = np.empty(val.shape[0])\n", " n_probe = np.empty(val.shape[0])\n", " for i, (vali, vari, pti) in enumerate(zip(val, var, a_pt)):\n", " n_tag[i], n_probe[i] = fit(pti, mass_axis, vali, vari, showfig)\n", " return n_tag, n_probe\n", "\n", "\n", "def trafo(inputs, showfig=False):\n", " h = bh.Histogram(\n", " a_pt,\n", " bh.axis.Regular(50, 0, 1, metadata=\"mass\"),\n", " bh.axis.Integer(0, 2, metadata=\"tag|probe\"),\n", " )\n", "\n", " pt, m, is_probe = inputs.T\n", " h.fill(pt, m, is_probe != 0)\n", "\n", " n_tag, n_probe = fit_all(h, showfig)\n", "\n", " with np.errstate(invalid=\"ignore\", divide=\"ignore\"):\n", " eps_k = 2 * n_probe / (n_probe + n_tag)\n", " n_rec = (n_tag + n_probe) ** 2 / (4 * n_probe)\n", "\n", " return eps_k, n_rec\n", "\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "# generate toy data\n", "pt = rng.exponential(2, size=10000)\n", "s = rng.normal(0.5, 0.1, size=len(pt) // 2)\n", "b = rng.exponential(2, size=len(pt) - len(s))\n", "mass = np.append(s, b)\n", "# true if sample is in tag AND probe sets, false if only in tag set\n", "eps_probe = 0.4\n", "is_probe = rng.uniform(0, 1, size=len(pt)) < eps_probe\n", "\n", "expected_eff_k = 2 * eps_probe / (eps_probe + (1 - eps_probe))" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "The actual bootstrapping is happening in the function `variance`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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+iSZNmsDf3x8PPPAADh486OhiERERkQmsv4nIkRjMEzmB27dvIzIyEj/88ANu3LiBxMREDBo0CEVFRY4uGhERERnB+puIHInBPJET8PHxwcsvv4wmTZrAw8MDo0aNgqenJ06dOmXzayclJUGSpGod+9///he9evWCj48PJEnCsWPHrFs4IiKiSrR11tWrVx1dFACuW38DrMOJagMG81SrHThwAElJSbhx44aji2KR06dPIz8/Hy1atKiyT6PRICQkBG+++aYDSvaXiooKDB8+HPn5+Xj33Xfxr3/9CxEREQ4pyxtvvAFJktC+fXuD+48ePYrBgwcjKCgI3t7eaN++Pf7xj3+YPW9GRgaGDx+OZs2awdvbG8HBwejTpw+++uoro8eY+nxMlfP777+HJEkGXz/99JPZsgJ3unvOnTsXYWFhqFu3Lnr06IFdu3bJOpaIiGrOFepvwHF1uCX16s8//4xHHnkE/v7+8PPzQ2xsrEUPHCw93tznY6wOZ/1NjlTH0QUgsqUDBw7glVdewcSJE1GvXj1HF0eWW7duYdy4cZg/fz4CAgKq7D98+DCuXr2KgQMHOqB0fzl79izOnz+P9957D0899ZTDypGdnY3FixfDx8fH4P5vv/0WgwYNQpcuXbBw4UL4+vri7NmzyM7ONnvu8+fPo7CwEBMmTEBYWBhKSkrwn//8B4MHD8a6deswefLkKscY+3zMlVNr5syZuP/++/XSDP0oNGTixIlISUlBYmIiWrZsiY0bNyIuLg579+5F7969ZZ2DiIiqx1Xqb8BxdbjcevXo0aPo3bs3wsPDsWjRImg0GqxevRoPPfQQDh8+jFatWpm8TnWON/X5yKnDWX+TQwiiWmz58uUCgMjMzHR0UWQpLy8XAwcOFGPGjBEajcZgnoULF4qIiAirXXPRokWiOl8F+/btEwDEF198YbWyVMfIkSPFww8/LB566CHRrl07vX03b94UDRs2FI899phQq9VWud7t27dFp06dRKtWrQzuN/b5mCqnEELs3bu3Rvfz0KFDAoBYvny5Lu3WrVuiefPmomfPntU6JxGRrRUVFVmUX1tn5eXl2ahE1eNK9bcQzlOHC2G4Xo2LixOBgYHi6tWrurSLFy8KX19fER8fb/ac1Tne1Odjqg5n/U2OxG72VGslJSXh+eefBwA0bdpU1+Xp3LlzOH/+PKZOnYpWrVqhbt26qF+/PoYPH45z585VOc/333+Pbt26wcvLC82bN8e6detkj1N7//334eXlhQcffBDnz5/XpQsh0LdvXwQHB+PKlSsA7nTvGj9+PCRJwkcffWT0/Dt27NA9NdaW48yZM7reBwEBAXjiiSdQUlJS5dgffvgB999/v957MeSXX37BgAED4O/vD19fX/Tr10+vq9jEiRPx0EMPAQCGDx8OSZIQHR1t9n5Y2/79+5GSkoLk5GSD+zdt2oTLly/jjTfegIeHB4qLi6HRaGp0TYVCgfDwcKNDNyp/PnLLebfCwkLcvn3bonKlpKRAoVDo9Rbw8vLCpEmTcPDgQWRlZVl0PiIiubR10cmTJzFixAj4+/ujfv36mDVrFkpLS6vkO3HiBMaMGYPAwEC9VkdzdU9lV69eNXktrZycHDz55JNo2LAhlEol2rVrhw8++MDse6qt9TfgPHW4lqF6NT09HTExMahfv74uLTQ0FA899BC2b99udoLB6hxvqP4GLKvDWX+TvbGbPdVa8fHx+OOPP/DZZ5/h3XffRXBwMAAgJCQEX3/9NQ4cOIBRo0ZBpVLh3LlzWLNmDaKjo3HixAl4e3sDuFMpPvLIIwgNDcUrr7wCtVqNV199FSEhIbLKcP/99+P555/H4sWL8dZbb2HlypUAgFWrVuH777/Hpk2b0KBBAwDAM888g9zcXHzzzTeoU8fwf81Lly7hl19+wauvvqqXPmLECDRt2hRLlizB0aNH8f7776NBgwZYtmyZLs9vv/2G2NhYhISEICkpCbdv38aiRYvQsGFDvXNlZGQgKioK/v7+eOGFF3DPPfdg3bp1iI6Oxr59+9CjRw8888wzaNy4MRYvXqzrVnb3ee5WUVGBmzdvyrpvQUFB8PAw/axRrVZjxowZeOqpp9ChQweDeb777jv4+/sjJycHQ4cOxR9//AEfHx+MHz8e7777Lry8vGSVp7i4GLdu3cLNmzexbds2fP311xg5cmSVfIY+HznlrOyJJ55AUVERFAoFoqKisHz5cnTr1s3scb/88gvuvfde+Pv766V3794dAHDs2DGEh4ebPQ8RUXWNGDECkZGRWLJkCX766Sf84x//wPXr1/Hxxx/r5Rs+fDhatmyJxYsXQwgBQF7dY+m1Ll++jAceeACSJGH69Om6+n/SpEkoKChAYmKi0fdSW+tvbXktqcOtXX8D5uvVsrIy1K1bt8px3t7eKC8vx/Hjx/HAAw8YPb+lxxv7fCypw1l/k0M4uGcAkU0Z62ZfUlJSJe/BgwcFAPHxxx/r0gYNGiS8vb1FTk6OLu306dOiTp06FnVti42N1XWVOnv2rPDx8RFDhw7V7T937pwAILy8vISPj4/utX//fr3zbNiwQdStW1dXfm0XuyeffFIv32OPPSbq16+vlzZ06FDh5eUlzp8/r0s7ceKEUCgUeu9l6NChwtPTU5w9e1aXdvHiReHn5yf69OmjS7O0W5k2v5yXnGER//znP0VAQIC4cuWKEEIY7PrWsWNH4e3tLby9vcWMGTPEf/7zHzFjxgwBQIwaNUpWuYUQ4plnntGVzcPDQyQkJIj8/Pwq+e7+fOSWUwghfvzxRzFs2DCxYcMGsXXrVrFkyRJRv3594eXlJY4ePWq2jO3atRMPP/xwlfSMjAwBQKxdu1b2+yUisoS2Lho8eLBe+tSpUwUA8euvv+rlGz16dJVzyK175F5LCCEmTZokQkND9bpaCyHEqFGjREBAgMHfAnerjfW3EJbV4dauv4UwX6926NBB3HvvveL27du6tLKyMtGkSRMBQKSkpJg8v6XHG6q/hZBXh7P+Jkdiyzy5pcpPaysqKlBQUIAWLVqgXr16OHr0KMaPHw+1Wo3vvvsOjz32GMLCwnT5W7RogQEDBpic0fxunTt3xpo1a6DRaPDkk09CqVRizZo1uv0RERG61glT0tLS0Ldv3ypPm6dMmaK3HRUVhS+//BIFBQXw9/eHWq3GN998g6FDh6JJkya6fG3atEH//v2RlpYG4M4T6G+//RZDhw5Fs2bNdPlCQ0MxZswYvPfee7pzWqpTp06yZ2Zt1KiRyf3Xrl3Dyy+/jIULF5rsJVFUVISSkhJMmTJFN3t9fHw8ysvLsW7dOrz66qto2bKl2fIkJiYiISEBFy9exOeffw61Wo3y8vIq+e7+fOSWEwB69eqFXr166bYHDx6MhIQEdOzYEfPnz8fOnTtNHn/r1i0olcoq6dreB7du3TL7PomIamLatGl62zNmzMDq1auRlpaGjh076tLvrrOqU/eYu5YQAv/5z38wYsQICCH0lrLr378/Nm/ejKNHj+LBBx80+Z5Yf1u3/tYyV69OnToVzz77LCZNmoQXXngBGo0Gr7/+OnJzcwGYr9MsPd7Q5yO3Dmf9TY7EYJ7c0q1bt7BkyRJ8+OGHyMnJ0auItV3Jrly5glu3bhmciVTu7KRa7du3R2FhIZ5//nns27cP//rXv2RXeFoVFRXYtWsXlixZUmVf5QoeAAIDAwEA169fh7+/P/Ly8nDr1i2DgWurVq10Pwby8vJQUlJicJbXNm3aQKPRICsrC+3atbOo7NoyxcTEWHycIS+99BKCgoIwY8YMk/m0lfLo0aP10seMGYN169bh4MGDsoL51q1bo3Xr1gCAxx9/HLGxsRg0aBAOHTqkGxtp6PORW05jWrRogSFDhiA1NRVqtRoKhcJo3rp166KsrKxKunYMqaHuhkRE1nT392nz5s3h4eFRZT6apk2b6m1Xp+4xd628vDzcuHED69evx/r16w2WVzvm3RTW39atv7XM1atTpkxBVlYWli9fjo8++ggA0K1bN7zwwgt444034Ovra/L8lhxv7POpSR3O+pvshcE8uaUZM2bgww8/RGJiInr27ImAgABIkoRRo0bVeII0Q7Rrkr7zzjt49NFHMW7cOIvP8cMPP6CgoABxcXFV9hmrJOS0FthLeXk58vPzZeUNCQkx+p5Onz6N9evXIzk5GRcvXtSll5aWoqKiAufOnYO/vz+CgoIQFhaGjIyMKmMBteMcr1+/Xq33kpCQgGeeeQZ//PGH7ofT3Z+PJeU0JTw8HOXl5SguLjbZohIaGoqcnJwq6dpWiMq9S4iI7MHYRHC2CE7uvpa2Lh83bhwmTJhg8JjKvQWMYf1tvfrbFEP16htvvIHnnnsOGRkZCAgIQIcOHbBgwQIAwL333mv2nHKPN/T5WKMOZ/1N9sBgnmo1Yz8kUlJSMGHCBLz99tu6tNLSUr2ZVBs0aAAvLy+cOXOmyvGG0kzRVkz16tUzOgOtOTt27EDbtm0RGRlp8bEhISGoW7cuTp8+XWXfqVOn9PJ5e3vrpWmdPHkSHh4e1Z6E5cCBA+jbt6+svJmZmUbfZ05ODjQaDWbOnImZM2dW2d+0aVPMmjULycnJ6Nq1K3bt2oWcnBy91gptxSx3IsO7abu8VZ4Q6O7Px5JymvLnn3/Cy8vLbCtE586dsXfv3irdKA8dOqTbT0RkS6dPn9ZrdT9z5gw0Go3Zeqs6dY+5a4WEhMDPzw9qtbpGrcqsv61Xf5tiqF4FUGXFg++++w4qlUrXqm+OnOMNfT7WqMNZf5M9MJinWs3HxwcAqiwjplAoqjz1XrlyJdRqtV6emJgYbNmyBRcvXtQ9GT1z5gy+/vpri8rx3nvvAbgzjqq6T1jT0tLw6KOPVutYhUKB/v37Y8uWLbhw4YKuW9/vv/+Ob775Ri9fbGwstm7dinPnzukqtsuXL2PTpk3o3bt3tcbbAdYbc9e+fXt8+eWXVdJfeuklFBYWYsWKFWjevDmAO7MEL126FBs2bMDDDz+sy/v++++jTp06ekvxlJSU4MKFCwgODtatfHDlyhVdK75WRUUFPv74Y9StWxdt27bVpd/9+VhSTuBOF8m7Hy78+uuv2LZtGwYMGKA3O7ChsiYkJOCtt97C+vXr8dxzzwG4M5vvhx9+iB49enAmXCKyuVWrViE2Nla3rZ0BfsCAASaPq07dY+5aCoUCw4YNw6ZNm3D8+HFdC7uWoe9cQ1h/W3fMvCX16t3+/e9/47///S/eeusts3WiJccb+nwsqcNZf5NDOW7uPSLbO3z4sAAg4uLixMcffyw+++wzUVRUJB5//HGhUCjErFmzxLp168TEiROFSqUS9evXFxMmTNAdf+TIEeHp6SkiIyPFsmXLxOLFi0VYWJjo3Lmz7Nnsz5w5I7y9vQUA0b1792q9jz///FMAEN9//71eunY23Ly8PL30Dz/8sMqssr/++qvw8vISTZo0EUuXLhWvv/66aNiwoejYsaPeezl+/Ljw8fERjRs3Fm+88YZYtmyZaNasmVAqleKnn37S5bN0NntbMzZL/JNPPikAiBEjRohVq1aJ4cOHCwBi/vz5evm072fRokW6tKFDh4qHH35YJCUliffee0+89tpronXr1gKAePvtt3X5jH0+lpSzb9++Ii4uTrz++uti/fr1IjExUXh7e4uAgABx4sQJs2UVQojhw4eLOnXqiOeff16sW7dO9OrVS9SpU0fs27fPbLmIiKpLWxd16NBBDBo0SKxatUqMGzdOABBjxoypku/uOksI+XWP3GsJIcSlS5dERESE8Pb21tX3S5YsEcOHDxeBgYFm31dtrb+FcFwdLrde3bdvn+jXr59YtmyZeP/998VTTz0lFAqFeOSRR0RFRYXB91K5TpR7vCX1txCG63DW3+RIDOap1nvttddE48aNhYeHh66CvH79unjiiSdEcHCw8PX1Ff379xcnT54UEREResG8EELs3r1bdOnSRXh6eormzZuL999/X/z9738XXl5eZq+t0WjEQw89JAIDA8UTTzwhfH19hUajsfg9aJdGubsCs+THgBB3KreuXbsKT09P0axZM7F27VrdOSo7evSo6N+/v/D19RXe3t6ib9++4sCBA3p5XCWYLy8vF0lJSSIiIkLcc889okWLFuLdd9+tks9QBfvZZ5+JmJgY0bBhQ1GnTh0RGBgoYmJixNatW/WONfb5WFLOFStWiO7du4ugoCBRp04dERoaKsaNGydOnz4tq6xCCHHr1i3x3HPPiUaNGgmlUinuv/9+sXPnTrNlIiKqCW09cuLECZGQkCD8/PxEYGCgmD59urh161aVfIaCeSHk1T1yr6V1+fJlMW3aNBEeHi7uuece0ahRI9GvXz+xfv16k++pNtffQjiuDpdbr545c0bExsaK4OBgoVQqRevWrcWSJUtEWVlZlXMaqhPlHm9J/S2E4Tqc9Tc5kiSEE82wQeQihg4dioyMDINj2CpbtWoVpk+fjo8//hj33HMPRo8ejbNnz+otGyNHXFwcfH198fnnn9ek2GQj/HyIyJ0lJSXhlVdeQV5ensluzq6E9bd74OdDro5j5onMuHXrlt7Mu6dPn0ZaWprR2XG1zp07h3nz5mHQoEEYP348MjIyAABHjx61+MdAdHQ0oqKiLC882QU/HyKi2oP1t/vg50Ouji3zRGaEhoZi4sSJaNasGc6fP481a9agrKwMv/zyi9E1yoUQiImJwS+//IKMjAyEhobi9u3bCAwMRFhYGP7+979j7Nixugn6iIiIXFVtapln/U1ErsTDfBYi9/bII4/gs88+w4wZM7By5Urcf//92L9/v9FAHgDWr1+PPXv2YMWKFQgNDQUA1KlTBytXrkRJSQlmzpwJT09Pe70FIiIikoH1NxG5ErbMExEREREREbkYtswTERERERERuRgG80REREREREQuhsE8ERERERERkYtxyaXpNBoNLl68CD8/P0iS5OjiEBERuTQhBAoLCxEWFgYPD8ue87NOJiIisi659bJLBvMXL15EeHi4o4tBRERUq2RlZUGlUll0DOtkIiIi2zBXL7tkMO/n5wfgzpvz9/d3cGmIiIhcW0FBAcLDw3X1qyVYJxMREVmX3HrZJYN5bTc+f39//nAgIiKykup0k2edTEREZBvm6mVOgEdERERERETkYhjMExEREREREbkYBvNERERERERELobBPBEREREREZGLYTBPRERERERE5GIYzBMRERERERG5GAbzRERERERERC6GwTwRERERERGRi2EwT0RERERERORiGMwTERERERERuRgG80REREREREQuxqJgfsmSJbj//vvh5+eHBg0aYOjQoTh16pRentLSUkybNg3169eHr68vhg0bhsuXL+vluXDhAgYOHAhvb280aNAAzz//PG7fvl3zd0NELq24uBiSJEGSJBQXFzu6OERERERETsuiYH7fvn2YNm0afvrpJ+zatQsVFRWIjY3V+9E9e/ZsfPXVV/jiiy+wb98+XLx4EfHx8br9arUaAwcORHl5OQ4cOICPPvoIGzduxMsvv2y9d0VERERERERUi0lCCFHdg/Py8tCgQQPs27cPffr0wc2bNxESEoJNmzYhISEBAHDy5Em0adMGBw8exAMPPICvv/4ajz76KC5evIiGDRsCANauXYu5c+ciLy8Pnp6eZq9bUFCAgIAA3Lx5E/7+/tUtPhE5meLiYvj6+gIAioqK4OPj4+ASEbmHmtSrrJOJiIisS27dWqMx8zdv3gQABAUFAQB+/vlnVFRUICYmRpendevWaNKkCQ4ePAgAOHjwIDp06KAL5AGgf//+KCgoQEZGhsHrlJWVoaCgQO9FRERE9sc6mYiIyDlUO5jXaDRITEzEgw8+iPbt2wMALl26BE9PT9SrV08vb8OGDXHp0iVdnsqBvHa/dp8hS5YsQUBAgO4VHh5e3WITERFRDbBOJiIicg7VDuanTZuG48ePY/PmzdYsj0Hz58/HzZs3da+srCybX5OIiIiqYp1MRETkHOpU56Dp06dj+/bt2L9/P1QqlS69UaNGKC8vx40bN/Ra5y9fvoxGjRrp8hw+fFjvfNrZ7rV57qZUKqFUKqtTVCIiIrIi1slERETOwaKWeSEEpk+fji+//BJ79uxB06ZN9fZ37doV99xzD3bv3q1LO3XqFC5cuICePXsCAHr27InffvsNV65c0eXZtWsX/P390bZt25q8FyIiIiIiIiK3YFHL/LRp07Bp0yZs3boVfn5+ujHuAQEBqFu3LgICAjBp0iTMmTMHQUFB8Pf3x4wZM9CzZ0888MADAIDY2Fi0bdsW48ePx5tvvolLly7hpZdewrRp0/ikn4iIiIiIiEgGi4L5NWvWAACio6P10j/88ENMnDgRAPDuu+/Cw8MDw4YNQ1lZGfr374/Vq1fr8ioUCmzfvh3PPvssevbsCR8fH0yYMAGvvvpqzd4JERERERERkZuo0TrzjsI1bYlqJ64zT+QYXGeeiIjIedhlnXkiIiIiIiIisj8G80REREREREQuhsE8EdlNcXExJEmCJEkoLi52dHGIiIiIiFwWg3kichpqtVr37/379+ttExERERHRXxjME5FTSE1NRdu2bXXbcXFxiIyMRGpqqgNLRURERETknBjME5HDpaamIiEhATk5OXrpOTk5SEhIYEBPRERERHQXBvNE5FBqtRqzZs2CoVUytWmJiYnsck9EREREVAmDeSIyy5YT16WnpyM7O9vofiEEsrKykJ6ebtXrEhERERG5MgbzRORQubm5Vs1HREREROQOGMwTkUOXjAsNDbVqPiIiIiIid8BgnogcKioqCiqVCpIkGdwvSRLCw8MRFRVl55IRERERETmvOo4uABG5tsh5O0zuP7d0oMn9CoUCK1asQEJCAiRJ0psITxvgJycnQ6FQ1LywRERERES1BFvmicjh4uPjkZKSgrCwML10lUqFlJQUxMfHO6hkRERERETOiS3zRGQVmvJSZL2bAAAIn50CD08vAPot95ryUt2/2yzcqctzbulAxMfHIyYmBgEBAQCAtLQ0xMbGskWeiIiIiMgAtswTkdOoHLj36dOHgTwRERERkREM5onIboRGrft3adZxvW0iIiIiIpKP3eyJyC5KTh1A/nfrdNt5KUlQ+AUjqN9kAKYnySMiIiIiIn1smSeq5Ry5hrxWyakDyNuyGOqia3rp6sKryNuyGKmpqQ4pFxERERGRq2IwT0Q2JTRq5O9ebzJPYmIi1Gp2uSciIiIikovd7InIKO1M9MZmoZejLDsD6sKrJvNkZWUhPT0d999/f/UKSkRERETkZtgyT0Rm1WTiOnXRdVn5cnNzLS4XEREREZG7Yss8EZlkauI671a9zB6v8A2UdZ3Q0NBql5GIiIiIyN2wZZ6I9Mar79+/X7dtbuK6klMHzJ5bqWoHhV+wyTzh4eGIioqqRsmJiIiIiNyTxcH8/v37MWjQIISFhUGSJGzZskVvv3bW7Ltfy5cv1+WJjIyssn/p0qU1fjNEZLnU1FS0bdtWtx0XF4fIyEh88cUXZieuy9+93myXe8lD8b/l54wr7ToezV/ciTYLd+rS2izcich5O3Tj9omIiIiI6C8WB/PFxcXo1KkTVq1aZXB/bm6u3uuDDz6AJEkYNmyYXr5XX31VL9+MGTOq9w6IqNpSU1ORkJCAnJwcvfScnByMGDHC7MR16sKrKMvOMHsd71a9EDJ0ARS+9fXSFX7BCBm6QFZ3fSIiIiIi+ovFY+YHDBiAAQMGGN3fqFEjve2tW7eib9++aNasmV66n59flbxEZD9qtRqzZs2CEKLKPkNpRs8jc4I771a9oIzohOwVIwEAIQlJqNu0CyQPhexrERERERHRHTYdM3/58mXs2LEDkyZNqrJv6dKlqF+/Prp06YLly5fj9u3bRs9TVlaGgoICvRcR1Ux6ejqys7NrfB7tBHdyZryvHLh7hbevEsh7eHohYu52RMzdbtHyd0RkP6yTiYiInINNZ7P/6KOP4Ofnh/j4eL30mTNn4r777kNQUBAOHDiA+fPnIzc3F++8847B8yxZsgSvvPKKLYtK5HassRScwi8YSlW7Gs94T0Sug3UyERGRc7Bpy/wHH3yAsWPHwstLv4Vtzpw5iI6ORseOHTFlyhS8/fbbWLlyJcrKygyeZ/78+bh586bulZWVZctiE7kFaywFF9RvMm6dPlTjGe+JyHWwTiYiInIONgvm09PTcerUKTz11FNm8/bo0QO3b9/GuXPnDO5XKpXw9/fXexFRzURFRUGlUkGSJIP7JUmCwi8YwUPmGZ24rm7LHlaZ8Z6IXAfrZCIiIudgs2B+w4YN6Nq1Kzp16mQ277Fjx+Dh4YEGDRrYqjhEbsvYGvIKhQIrVqwAgCoBvXY7qN9k+LTujdBJq3X7QhKS0HjKBni36oWy7AyrzXhPRERERETyWRzMFxUV4dixYzh27BgAIDMzE8eOHcOFCxd0eQoKCvDFF18YbJU/ePAgkpOT8euvv+LPP//Ep59+itmzZ2PcuHEIDAys/jshoiqMrSGfmpoKAIiPj0dKSgrCwsL0jlOpVEhJSdGNdzc2cZ3cmezl5iMiIiIiInksDuaPHDmCLl26oEuXLgDujH/v0qULXn75ZV2ezZs3QwiB0aNHVzleqVRi8+bNeOihh9CuXTu88cYbmD17NtavN91Vl4gsY2oN+YSEBL2A/sSJE7r9aWlpyMzMrDJxpSHameytlY+IiIiIiOSxeDb76Ohos2tQT548GZMnTza477777sNPP/1k6WWJyALm1pCXJAmJiYkYMmQIFAoFFIq/Wt779Omjt22KUtUOCr9gk13ttTPeExERERGR9dh0aToicgxza8gLIZCVlYXG49+EV5OO0JSX6va1WbhT9hrvkocCQf0mI2/LYqN5gvpNrrKePBERERER1YxNl6YjIseQu4a8Ncaye7fqhZChC4zOeM915omIiIiIrI8t80S1kNw15K01lt27VS8oIzohe8VIAHdmvK/btAtb5ImIiIiIbIQt80S1kJw15MPDw606lt3YjPdERERERGR9DOaJaiE5a8gnJyfbPeD28PRCxNztiJi7Xfa4fCIiIiIiqorBPFEtZW4NeTlLzxERERERkXPimHmiWiw+Ph4xMTEICAgAcGcN+djYWNlLzzmLyHk7zOY5t3SgHUpCREREROQc2DJPVMtVdw15IiIiIiJyXgzmicilaMpLcX7Zozi/7FFoyksdXRwiIiIiIodgN3siMks7cR0RERERETkHBvNExGCdiIiIiMjFsJs9ERERERERkYthME9ERERERETkYhjME1GtUlxcDEmSIEkSiouLHV0cIiIiIiKb4Jh5IrIKjrsnIiIiIrIftswTERERERERuRgG80REREREREQuhsE8ERERERERkYthME/k4sxN+Obj4wMhBIQQ8PHxcUAJiYiIiIjI2hjMExEREREREbkYBvNE5FKERq37d2nWcb1tIiIiIiJ3waXpiGqpyHk7HF0Eqys5dQD5363TbeelJEHhF4ygfpMBDHRcwYiIiIiI7Iwt80TkEkpOHUDelsVQF13TS1cXXkXelsVITU11UMmIiIiIiOzP4mB+//79GDRoEMLCwiBJErZs2aK3f+LEibrJuLSvRx55RC9Pfn4+xo4dC39/f9SrVw+TJk1CUVFRjd4IEdVeQqNG/u71JvMkJiZCrWaXeyIiIiJyDxYH88XFxejUqRNWrVplNM8jjzyC3Nxc3euzzz7T2z927FhkZGRg165d2L59O/bv34/JkydbXnoicgtl2RlQF141mScrKwvp6el6Af3+/fsZ4BMRERFRrWTxmPkBAwZgwIABJvMolUo0atTI4L7ff/8dO3fuxH//+19069YNALBy5UrExcXhrbfeQlhYWJVjysrKUFZWptsuKCiwtNhE5MLURddl5du6dSvGjRun246Li4NKpcKKFSsQHx9vq+IRuRXWyURERM7BJmPmv//+ezRo0ACtWrXCs88+i2vX/hrjevDgQdSrV08XyANATEwMPDw8cOjQIYPnW7JkCQICAnSv8PBwWxSbiJyUwjdQVr7k5GTk5OTopeXk5CAhIYFj6omshHUyERGRc7B6MP/II4/g448/xu7du7Fs2TLs27cPAwYM0HV1vXTpEho0aKB3TJ06dRAUFIRLly4ZPOf8+fNx8+ZN3SsrK8vaxSYiJ6ZUtYPCL9hkHoVCYTBdCAGAY+qJrIV1MhERkXOw+tJ0o0aN0v27Q4cO6NixI5o3b47vv/8e/fr1q9Y5lUollEqltYpIRC5G8lAgqN9k5G1ZbDSPqUBdCKEbUx8dHW2DEhK5D9bJREREzsHmS9M1a9YMwcHBOHPmDACgUaNGuHLlil6e27dvIz8/3+g4eyIi71a9EDJ0ARS+9fXSFX7B8Os2RNY5cnNzbVE0IiIiIiK7s3kwn52djWvXriE0NBQA0LNnT9y4cQM///yzLs+ePXug0WjQo0cPWxeHqNZxp9nbvVv1Quik1brtkIQkNJ6yAd4t5X13aL+HiIiIiIhcncXBfFFREY4dO4Zjx44BADIzM3Hs2DFcuHABRUVFeP755/HTTz/h3Llz2L17N4YMGYIWLVqgf//+AIA2bdrgkUcewdNPP43Dhw/jxx9/xPTp0zFq1CiDM9kTkXGpqalo27atbjsuLg6RkZG1erI3yeOvsfFe4e0heShkjakPDw9HVFQUgDtLbEqSBEmSUFxcbNPyEhERERHZgsXB/JEjR9ClSxd06dIFADBnzhx06dIFL7/8MhQKBf7v//4PgwcPxr333otJkyaha9euSE9P1xtf9+mnn6J169bo168f4uLi0Lt3b6xfv95674rIDaSmpiIhIcHo7O0lpw44qGT2px1Tb0pycrLRSfKIiIiIiFyNxRPgRUdH62aHNuSbb74xe46goCBs2rTJ0ksT0f+o1WrMmjXL4P9FIQQkSUL+7vWo27KHXkt2baYdU5//3Tqoi/5aDlPhF4ygfpO5zjwRERER1SpWn82eiGwvPT0d2dnZRvcLIaAuvIqy7Ax4Nelox5I5lnerXlBGdEL2ipEA7oypr9u0i9s80CAiIpKruLgYvr6+AO4Mo/Xx8XFwiYjIUjafAI+IrE/urOzqous2LonzMTSmnoiIiIiotmEwT+SC5M7KrvANtHFJiIiIiIjIERjME7mgqKgoqFQqSJJkcL8kSVD4BUOpamfnkhERERERkT0wmCdyQQqFAitWrACAKgG9djuo32R2MSciIiIiqqUYzBO5qPj4eKSkpCAsLEwvXaVSISUlBd6tejmoZEREREREZGsM5olcWHx8PE6cOKHbTktLQ2ZmJpdhIyIiIiKq5RjME7k4heKvrvR9+vTR2yYiIiIiotqJ68wTkUvx8PRCxNztNTqHWq3W/Xv//v2IjY3lQxAiIiIicilsmScit5Kamoq2bdvqtuPi4hAZGYnU1FQHloqIiIiIyDIM5onIbaSmpiIhIQE5OTl66Tk5OUhISGBAT0REbuPuXmqVt4nINTCYJyK3oFarMWvWLAghquzTpiUmJvLHDBER1XrspUZUO3DMPJGLipy3AwCgKS/VpbVZuBMenl6OKpJTMDamPj09HdnZ2UaPE0IgKysL6enpiI6OtmEJiYiIHEfbS+3uh9vaXmopKSlcFYfIRbBlnojcQm5urlXzERERuRr2UiOqXRjME5FbCA0NtWo+IiIiV2NJLzUicn4M5onILURFRUGlUkGSJIP7JUlCeHg4oqKi7FwyIiIi+2AvNaLahcE8EbkFhUKBFStWAECVgF67nZyczPXmiYio1mIvNaLahcE8kYvTTvgWMXe7209+Z058fDxSUlIQFhaml65SqTjhDxER1RrFxcWQJAmSJKG4uFiXzl5qRLULg3kicivx8fE4ceKEbjstLQ2ZmZkM5ImIqNZjLzWi2oXBPBG5nco/Uvr06cMfLURE5DbYS42o9mAwT0RERETkQox1o5eLvdSIagcG80REREREboa91IhcXx1HF4CIyB4i5+3Q/VtTXqr7d5uFO3UTB55bOtDu5SIiIiIiqg6LW+b379+PQYMGISwsDJIkYcuWLbp9FRUVmDt3Ljp06AAfHx+EhYXh8ccfx8WLF/XOERkZqesapH0tXbq0xm+GiIiIiIiIyB1YHMwXFxejU6dOWLVqVZV9JSUlOHr0KBYuXIijR48iNTUVp06dwuDBg6vkffXVV5Gbm6t7zZgxo3rvgIiIiIiolqjpeHgich8Wd7MfMGAABgwYYHBfQEAAdu3apZf2z3/+E927d8eFCxfQpEkTXbqfnx8aNWok65plZWUoKyvTbRcUFFhabCIiIrIC1slEzks7pIzDyYjcg83HzN+8eROSJKFevXp66UuXLsVrr72GJk2aYMyYMZg9ezbq1DFcnCVLluCVV16xdVGJiIjIDNbJRK6L88cQ1S42nc2+tLQUc+fOxejRo+Hv769LnzlzJjZv3oy9e/fimWeeweLFi/HCCy8YPc/8+fNx8+ZN3SsrK8uWxSYiIiIjWCcTOT+hUev+XZp1XG9bLnb3J3J+NmuZr6iowIgRIyCEwJo1a/T2zZkzR/fvjh07wtPTE8888wyWLFkCpVJZ5VxKpdJgOhEREdkX62Qi51Zy6gDyv1un285LSYLCLxhB/SbDu1UvB5aMiKzNJi3z2kD+/Pnz2LVrl16rvCE9evTA7du3ce7cOVsUh4hIj4enFyLmbkfE3O26boVERESuQq3+q6V9//79uu2SUweQt2Ux1EXX9PMXXkXelsUoOXXAruUkItuyesu8NpA/ffo09u7di/r165s95tixY/Dw8ECDBg2sXRwiIiIiIpenHe9+d8t7XFwcFH7BCHz4KVzf877Jc+TvXo+6LXtA8lDYtKxEZB8WB/NFRUU4c+aMbjszMxPHjh1DUFAQQkNDkZCQgKNHj2L79u1Qq9W4dOkSACAoKAienp44ePAgDh06hL59+8LPzw8HDx7E7NmzMW7cOAQGBlrvnRERERER2VFxcTF8fX0B3PnN7OPjY9Xza1ve76YuvIqrW5eaPV5deBVl2RnwatJR10uNiFyXxcH8kSNH0LdvX922dvz7hAkTkJSUhG3btgEAOnfurHfc3r17ER0dDaVSic2bNyMpKQllZWVo2rQpZs+erTeOnoiIiIiI/iI0auTvXl/j86iLrluhNETkDCwO5qOjoyGEMLrf1D4AuO+++/DTTz9ZelkiIiIiIrdVlp0BdeHVGp9H4cuesES1hc3XmSciIiIiInnuntwuNjYWCoXCKi3qCr9gKFXtTObRjs03tg49wLXoiZyFTdeZJyIiIiIieVJTU9G2bVvddlxcHCIjI5GammqVFvWgfpM5+R1RLcJgnoiIiIjIwVJTU5GQkICcnBy99JycHCQkJEBdUgCFX7DJcyj8ghE8ZB4UvvWrpIcMXWDROvNC81cPgdKs43rbROQcGMwTEd2luLgYkiRBkiQUFxc7ujhERFTLqdVqzJo1y+DcU9q063veR+DDT5k8T1C/yfBp3Ruhk1br0kISktB4ygaLAvmSUweQu2GqbjsvJQk5aydxnXoiJ8NgnoiIiIjIgdLT05GdnW10vxAC6sKrUHj7I2ToArMt75W70nuFt7eoa712+Tt10TW9dHXhVeRtWawX0PPhN5FjcQI8IieknXyGiIiIar/c3FxZ+dRF1+HT9iEoIzohe8VIAHda3us27WKVsfBylr/L370eavWrUCg49p7I0dgyT0RERERkBXfPRF9525TQ0FBZ+bST4NWk5d0UOcvfqQuvIj093SrXI6KaYTBPRERERFRDpmaiNycqKgoqlQqSJBncL0mSrGXlakru8ndyexKwGz6RbTGYJ3JimvJSnF/2KM4ve1RvvVciIiJyHuZmojcX0CsUCpR1e9zgBHjAnTHz9lhWTu7yd3J7EhCRbTGYJyIiIiKqJjkz0ScmJprtcu/dqpesye1sSalqJ2v5u6ioKJuXhYjMYzBPRERERFRNcmaiz8rKkjXO3LtVrxovK1cTkocCQf0mm8wT1G8yJ78jchIM5omIiIiIqknu+HG5+Ww1uZ1cztBDgIjkYTBPRHSX6s5GTERE7kfu+HFXGmfu6B4CRCQPg3kiB+Isr86nJrMRExGR+5EzE314eLhdx5l7eHohYu52RMzdDg9Pr2qdQ04PAT78JnIsBvNERP9T09mIiYjI/SgUCqxYsQIAqgT02u3k5ORaNc48ct4ONHjsRQQ1bqZLi4uLgzKwERo89qIDS0bkXhjMExEBEBo1Rkx8xuhsxEIIjJj4DFsdiIioivj4eKSkpCAsLEwvXaVSISUlBfHx8Va9njVa3mui5NQB5G1ZDHXRNb10deFV5G1ZzIffRHbCYJ6ICEBZdgbUhVdN5lEXXpU1GzEREbmf+Ph4nDhxQredlpaGzMxMqwfyjiY0auTvXm8yj3YpPnbDJ7ItBvNETkxo/qr0SrOO622TdamLrsvKJ3c2YiIicj+Vu9L36dOnVnWt15Lz8DsrKwtvvPEG56AhsjEG80ROquTUAeRumKrbzktJQs7aSSg5dcCBpaq9FL6BsvK50mzERERE1ib34feiRYs4Bw2RjTGYJ3JC5saiMaC3PqWqHRR+wSbzKPyC7TobMRERuR9Hj4c3R+7Db0O089Jou+ETUc0wmCdyMmq1+bFo+bvXs8u9lUkeCgT1m2wyT1C/ybWyyyQREZFcch5+myKEQFZWFuegIbICBvNEDmRoYpj09HRZE7GVZWfYunhux7tVL4QMXQCFb329dIVfMEKGLoB3q14OKhkREZFzkPPwWw7OQUNUcxYH8/v378egQYMQFhYGSZKwZcsWvf1CCLz88ssIDQ1F3bp1ERMTg9OnT+vlyc/Px9ixY+Hv74969eph0qRJKCoqqtEbIXI1qampBieG2bp1q6zj5Y5ZI8t4t+qF0EmrddshCUloPGUDA3kiInIrprr7m3r4HfDgWFnn5xw0RDVncTBfXFyMTp06YdWqVQb3v/nmm/jHP/6BtWvX4tChQ/Dx8UH//v1RWlqqyzN27FhkZGRg165d2L59O/bv34/Jk2v+hI/IVaSmpiIhIcHgxDDJycmyzlGTMWtkmuTxV1d6r/D2ettERERk/OF3QK8RUKlUkCTJ4HGSJCE8PJxz0BBZQR1LDxgwYAAGDBhgcJ8QAsnJyXjppZcwZMgQAMDHH3+Mhg0bYsuWLRg1ahR+//137Ny5E//973/RrVs3AMDKlSsRFxeHt956C2FhYTV4O0TOT61WY9asWbpJYCrTpUkegNAYPYfCLxhKVTtbFZGIiIisKHLeDkcXwSaMPfxesWIFEhISIEmS3u8dbYCfnJzMOWiIrMCqY+YzMzNx6dIlxMTE6NICAgLQo0cPHDx4EABw8OBB1KtXTxfIA0BMTAw8PDxw6NAhg+ctKytDQUGB3ovIVaWnpyM7O9t0JhOBPHBnIja2FhORI7BOJiJz4uPjkZKSUqWRTqVSISUlBfHx8Q4qGVHtYtVg/tKlSwCAhg0b6qU3bNhQt+/SpUto0KCB3v46deogKChIl+duS5YsQUBAgO4VHh5uzWIT2ZXcCV/8ug3hRGxE5HRYJxMZ5+PjAyEEhBDw8fFxdHEcKj4+HidOnNBtp6WlITMzk4E8kRW5xGz28+fPx82bN3WvrKwsRxeJqNrkTvji3bIHJ2IjIqfDOpmI5Krclb5Pnz7sWk9kZRaPmTelUaNGAIDLly/rBSyXL19G586ddXmuXLmid9zt27eRn5+vO/5uSqUSSqXSmkUlcpioqCioVCrk5OQYHDcvSRI8fOtDqWoHcbtCl86J2IjIGbBOJqoZTXkpst5NAACEz06pMlM8EZFcVm2Zb9q0KRo1aoTdu3fr0goKCnDo0CH07NkTANCzZ0/cuHEDP//8sy7Pnj17oNFo0KNHD2sWh8gpKRQKrFixAgCqzPSq3eaYeCIiIiIiMsXiYL6oqAjHjh3DsWPHANyZ9O7YsWO4cOECJElCYmIiXn/9dWzbtg2//fYbHn/8cYSFhWHo0KEAgDZt2uCRRx7B008/jcOHD+PHH3/E9OnTMWrUKM5kT27D3MQw7EpPRERERESmWNzN/siRI+jbt69ue86cOQCACRMmYOPGjXjhhRdQXFyMyZMn48aNG+jduzd27twJL6+/uhB9+umnmD59Ovr16wcPDw8MGzYM//jHP6zwdohcR3x8PGJiYhAQEADgzsQwsbGxUCgUmHO4di5h4yo8PL0QMXe7yTzFxcXw9fUFcOchp7tPdERERKSlXYpPU16qS2uzcKfekIJzSwfavVxEtY3FwXx0dLTBcb5akiTh1Vdfxauvvmo0T1BQEDZt2mTppYlqhcprzVau5J7ZVQKPfTsdUSQiIiIii8l5+E1EtuMSs9kTERERERER0V8YzBMRERERERG5GKsuTUdERERERASwGz6RrTGYJ3JirASJiIhqF6FR6/5dmnUcdZt24XK0RFQtDOaJiIiIiOyg5NQB5H+3Tredl5IEhV8wgvpN5rK0RGQxjpknIiIiIrKxklMHkLdlMdRF1/TS1YVXkbdlMUpOHXBQyYjIVTGYJyKqBrX6r26S+/fv19smIiKqTGjUyN+93mSe/N3r9brgExGZw2CeyEaKi4shSRIkSUJxcbHBPNox8RFzt8PD08vOJaTqSk1NRdu2bXXbcXFxiIyMRGpqqgNLRUREzqosOwPqwqsm86gLr6IsO8NOJSKi2oDBPBGRBVJTU5GQkICcnBy99JycHCQkJDCgJyKqpeQ8pDdGXXTdqvmIiABOgEdEJJvQqDFi4jMQQlTd97+0xMREDBkyBAoFZyYmInIXkfN2mNyv8A2UdR65+YiIALbMExHJJqebZFZWFtLT0+1UIiIicgVKVTso/IJN5lH4BUOpamenEhFRbcBgnohIJrndH3Nzc21cEiIiciWShwJB/SabzBPUbzLXmyciizCYJyKSSW73x9DQUBuXhIiIXI13q14IGboACt/6eukKv2CEDF3AdeaJyGIcM09EJJO2m6Sprvbh4eGIioqyY6mIiMhVeLfqBWVEJ2SvGAkACElIQt2mXdy6Rb64uBi+vr4AgKKiIvj4+Di4RESugy3zRDbCdchrHzndJJOTkzn5HRERGVU5cPcKb+/WgTwR1QyDeSIb4DrktZe5bpLx8fEOKhkREdkSH9ITkbNhME9kZebWIS85dcBBJSNr8W7VC6GTVuu2QxKS0HjKBo53JCKqpfiQnoicEYN5IitSq9WYNWuWyXXI83evh9Dwab6rYzdJIiL3YO4hPQN6InIUToBHZEXp6enIzs42ul8IAXXhVZRlZ8CrSUc7loyIiIgsZe4hvSRJSExMhDTqn3yoa6HIeTsAAJryUl1am4U74eHpBQA4t3SgQ8pF5ErYMk9kRXLXF5e7XjkRERE5jpyH9FlZWSjLzrBjqWqXyr0VS7OOs/cikQXYMk9kRXLXF5e7Xjm5Li61Q0Tk+viQ3rZKTh1A/nfrdNt5KUlQ+AX/b+UYtswTmcOWeSIrioqKgkqlgiRJBvdLkgSFXzCUqnZ2LhkRERFZyhYP6T08vRAxdzsi5m7XdSl3RyWnDiBvy2Koi67ppasLryJvy2LORUAkA4N5IitSKBRYsWIFAFQJ6LXbQf0mc1wdERGRC5DzkD48PJwP6S0kNGrk715vMk9iYiKX/yMyw+rBfGRkJCRJqvKaNm0aACA6OrrKvilTpli7GEQOM+ewEsFD5sPDJ0gv3cO3PoKHzOfyZURERC5CzkP65ORkPqS3UFl2BtSFV03mycrKQnp6up1KROSarB7M//e//0Vubq7utWvXLgDA8OHDdXmefvppvTxvvvmmtYtB5FBch7z2YzdJIiL3EB8fj5SUFISFhemlq1QqpKSkID4+3kElc11y5xjQzllQXFysawQsLi62ZdGIXIrVJ8ALCQnR2166dCmaN2+Ohx56SJfm7e2NRo0aWfvSRE6F65ATERHVDvHx8YiJiUFAQAAAIC0tDbGxsVAoWLdXh9w5BuTOWUDkrmw6Zr68vByffPIJnnzySb2uSZ9++imCg4PRvn17zJ8/HyUlJSbPU1ZWhoKCAr0XEZEzqzzOb//+/Rz3R7UG62RyV5UD9z59+jCQrwGlqh0UfsEm84SHhyMqKspOJSJyTTYN5rds2YIbN25g4sSJurQxY8bgk08+wd69ezF//nz861//wrhx40yeZ8mSJQgICNC9wsPDbVlsIqIaSU1NRdu2bXXbcXFxiIyM5My8VCuwTiaimpI8FP9bfs645ORkPjAhMkMSQghbnbx///7w9PTEV199ZTTPnj170K9fP5w5cwbNmzc3mKesrAxlZWW67YKCAoSHh+PmzZvw9/e3ermJaiJy3g4AgKa8FFnvJgAAwmencFy1m3inexkSEhJw91ertncSx1eSMyooKEBAQICsepV1Mrmr4uJi+Pr6AgCKiorg4+Oj26et+8ky2nXmKy9Pp11n/sqXb+jSTN17otpIbr1s9THzWufPn8d3331ntiWqR48eAGAymFcqlVAqlVYvIxGRNQmNGrNmzaoSyAOAEAKSJCExMRFDhgxhawO5LNbJ5G4qP6TXarNwJx/SW4F3q15QRnRC9oqRAO5MGFy3aRfOM0Qkk8262X/44Ydo0KABBg4caDLfsWPHAHCCCyJyfWXZGcjOzja6XwjBpXaIiIgqkTNhMOehITLMJsG8RqPBhx9+iAkTJqBOnb8a/8+ePYvXXnsNP//8M86dO4dt27bh8ccfR58+fdCxY0dbFIXIYbh0mfuxdKkdIiIiMo3z0BAZZ5Ng/rvvvsOFCxfw5JNP6qV7enriu+++Q2xsLFq3bo2///3vGDZsmMkx9UREroJL7RAREVlPamoqEhISkJOTo5eek5ODhIQEBvTk9mwyZj42NtbgmNHw8HDs27fPFpckInI47VI76sKrRvMo/IK51A4RkQvS9rgj+1CrOQ8NkTk2XZqOqDYrLi6GJEmQJAnFxcWOLg45ATlL7QT1m8wfHURERGakp6dzHhoiMxjMExFZkXerXggZugAK3/p66Qq/YIQMXQDvVr0cVDIiIiLXIXd+Gc5DQ+7MZkvTERG5Ky61Q0REJI+x4Qty55fhPDTkztgyT0RkA3KW2iEiIiLDoqKioFKpIEmSwf2SJCE8PJzz0JBbYzBPROQgnHeBiIjIsOYv7kRZt8cNToAH3Bkzn5yczHloyK0xmCciIiIiIqdjbh6a+Ph4B5WMyDkwmCeqJrVarfv3/v379baJiIiIqOa8W/VC6KTVuu2QhCQ0nrKBE8oSgcE8UbWkpqaibdu2uu24uDhERkYiNTXVgaUiIiIiqn04Dw2RYQzmiSyUmpqKhIQE5OTk6KXn5OQgISEBJacOOKhkRERERETkLrg0HZEF1Go1Zs2aZXAyFiEEJElC/u71qNuyB58auzljS+0QEREREVkDW+aJLNB4/JvIzs42ul8IAXXhVZRlZ9ixVOSqOO8CEREREVUXg3kiC6iLrls1H7kvzrtARERERDXBYJ7IAgrfQKvmI/dkbt4FBvRERPZXXFwMSZIgSRKKi4sdXRyyAD87clcM5oksoFS1g8Iv2GQehV8wlKp2dioRuRqhMT3vAgAkJiayyz0REdH/aOehiZi7HR6eXo4uDpHT4AR4RBaQPBQI6jcZeVsWG80T1G8yJ78jo8qyM3DZzLwLWVlZSE9PR3R0tP0KRkTkpiLn7QAAaMpLdWltFu5k0EhETo8t80QGmOqu5d2qF0KGLoDCt75eusIvGCFDF8C7VS97FpVcjNz5FHJzc21cEiIiIiJyZWyZJ6oG71a9oIzohOwVIwEAIQlJqNu0C1vkySy58ymEhobauCRERERE5MrYMk9UTZUDd6/w9gzkSRalqh1UKhUkSTK4X5IkhIeHIyoqys4lIyJyb0Lz11wlpVnH9bbJuXGpV3JXDOaJiOxI8lBgxYoVd/59V0Cv3U5OToZCwYdDRET2UnLqAHI3TNVt56UkIWftJJScOuDAUpEcXOqV3BmDeSID+ISXbGnOYSWCh8yHh0+QXrqHb30ED5mP+Ph4B5WMiMj9lJw6gLwti6EuuqaXri68irwtixnQOzEu9UrujsE80V34hJfswbtVL4ROWq3bDklIQuMpGziBIhGRHanVauTvXm8yT/7u9exy74S41CsRg3kiPeae8PLpPFkT510gInKs9PR0qAuvmsyjLryKsuwMO5WI5CrLzkC2zKVeiWorzmZP9D9qteknvJIkIX/3etRt2QOShwIenl6ImLvdASUlIiIia5C7DKjcZUXJfrjUK5ENWuaTkpJ063NrX61bt9btLy0txbRp01C/fn34+vpi2LBhuHz5srWLQWSx9PR0s094+XSeiIio9pC7DKjcZUXJfrjUK5GNutm3a9cOubm5utcPP/yg2zd79mx89dVX+OKLL7Bv3z5cvHiRkz2RU+DTeSIiIvcSFRUFhV+wyTwKv2AoVe3sVCKSS6lqZ/az41KvVNvZpJt9nTp10KhRoyrpN2/exIYNG7Bp0yY8/PDDAIAPP/wQbdq0wU8//YQHHnjAFsUhkoVP58neOFSDiMixFAoFgvpNRt6WxUbzBPWbzDlNnJDkYf6z41KvVNvZpGX+9OnTCAsLQ7NmzTB27FhcuHABAPDzzz+joqICMTExurytW7dGkyZNcPDgQaPnKysrQ0FBgd6LyNqioqKgUqmqrP2tJUkSn84TkdtjnUy1jXerXggZugAK3/p66Qq/YIQMXcBVRpyYuc+OvX+ptrN6MN+jRw9s3LgRO3fuxJo1a5CZmYmoqCgUFhbi0qVL8PT0RL169fSOadiwIS5dumT0nEuWLEFAQIDuFR4ebu1iE0GhUKCs2+MGJ8AD7oyZ59N5InJ3rJOpNuJyoa6Lnx25M6sH8wMGDMDw4cPRsWNH9O/fH2lpabhx4wY+//zzap9z/vz5uHnzpu6VlZVlxRIT/YVP58nZFBcX6yYTLS4udnRxiFgnU63F5UJdFz87clc2X5quXr16uPfee3HmzBn87W9/Q3l5OW7cuKHXOn/58mWDY+y1lEollEqlrYtKBOBOQK+M6ITsFSMB3HnCW7dpF1YMRERgnUxEROQsbB7MFxUV4ezZsxg/fjy6du2Ke+65B7t378awYcMAAKdOncKFCxfQs2dPWxeFSDY+4SUiInJ9kfN2OLoIREQ2Y/Vg/rnnnsOgQYMQERGBixcvYtGiRVAoFBg9ejQCAgIwadIkzJkzB0FBQfD398eMGTPQs2dPzmRPRERERNVSXFwMX19fAHcaknx8fBxcInI2/Buh2sjqwXx2djZGjx6Na9euISQkBL1798ZPP/2EkJAQAMC7774LDw8PDBs2DGVlZejfvz9Wr15t5qxERO5JrVbr/r1//37ExsZymR0iIhvgcqGui58duSurB/ObN282ud/LywurVq3CqlWrrH1pIqJaJTU1FTNnztRtx8XFQaVSYcWKFVxuh4iIiMjN2WSdeSJHssbs39onvBFzt8PD08vKJSQyLzU1FQkJCcjJydFLz8nJQUJCAlJTUx1UMiIi53N3L6bK2wCgKS/F+WWP4vyyR6EpL7V38cgJmPsbIXJFDOaJiJxMxAvbMGLiMxBCVNknhIAQAomJiVCr1Vy6jojcXmpqKtq2bavbjouLQ2RkJB96kg7/Rqi2YjBPRORkyrIzoC68ajJPVlYW0tPT7VQiIiLnZK4XU8mpAw4qGTkL9nSj2ozBPNU67EZFrk5ddF1WvtzcXBuXhIjIeanVasyaNctoLyYAyN+9HkKjhtD89VugNOu43jbVXnL+RrQ93YhcEYN5qlXYjYpqA4VvoKx8oaGhfHhFRG4rPT0d2dnZRvcLIaAuvIqbB/+N3A1Tdel5KUnIWTuJrfZuoPH4N83+jbCnG7kyBvO1AMfM3sFuVFRbKFXtoPALNpknPDwcV69e5cMrInJbcnsn3fxhE9RF1/TS1IVXkbdlMQP6Wo493ai2s/rSdESOYK4blSRJGDHxGTT+qQ4kD67RTc5N8lAgqN9k5G1ZbDTPqFGjMGLEiCp/89qHVykpKVy+johqtdDQ0BqfI3/3etRt2YO/DWopS3q6EbkitsxTrSC3q11ZdoYdS0VUfd6teiFk6AIofOvrpSv8ghE8ZB4+++wzjgEkIrcWFRUFlUoFSZKqfQ7+Nqjd5PZ0i4qKslOJiKyLwXwtwDGz8rtHye1uReQMvFv1Quik1brtkIQkNJ6yAQpvf44BJCK3p1AosGLFCgCoEtBbEuDzt0Htpe3pZkpycjIUCvbMINfEYN7FccK3O+R2j5Lb3YrIWVTu+ukV3h6Sh4JjAImI/ic+Ph4pKSkICwvTS1epVHjllVdknYO/DWo3Uz3dQoYu4JA0cmkcM+/CtBO+cczsX13tcnJyDHY9liQJHr71oVS1c0DpiKyLYwCJiO6InLcDgBJSwjvAipEA7vRikpp2wYaiOwGbuvCq0eMVfsH8beAGvFv1gjKiE7Ir/Y3UbdqFcyWQy2PLvIviupn65HS1C+o3GZKHApryUpxf9ijOL3sUmvJSu5eVyBIenl6ImLsdEXO3w8PTC8CdMYCmxolKksQxgETkVgz1YpLTxVr724BqP0N/I0SujsG8i5Iz4Zu7jZk11dUuJSUF3q16OahkRNYleZh/eMUxgERE5rtY87cBEbkyBvMuSu5YWHcbMxsfH48TJ07ottPS0pCZmek2ww3IfZh7eMW/eSKiO4xNJspAniorLi6GJEmQJAnFxcWOLg6RLBwz76LkjoV1xzGzlVsj+/TpU6V1Umj+GnpQmnWcY6bIJZkaJzrnsAKVY/ni4mL4+voCAIqKiuDj4+OAEhMRVY+c7zDtkCRj2MWazP2NELkitsy7KHNrq3LMrGElpw4gd8NU3XZeShJy1k5CyakDDiwVUfXxByoREVHNcalnckUM5l2UnAnf3G3MbOS8HYictwNtFu7UpbVZuFOXXnLqAPK2LIa66JrecerCq8jbspgBPREREZEb4lLP5KoYzLswjpk1zNDs30KjRv7u9SaPy9+9Xq8LPlFtwdYGInJl1vgOM/TbgAj4a6nnnJwcvXTtUs8M6MmZMZh3cZzwTZ6y7AyT68wCd1roy7Iz7FQiIusw9wOVrQ1E5Mr4HUa2JDRqjJj4jNGlnoUQbrXUM7keBvO1gLkJ31yJrWYSVRddt2o+IlfA1gYicmX8DiNbk9PY425LPZNrYTBvQ1ziQp8j74fCN9Cq+YicnVqtxqxZs4y2NgDQtTbwu4qInI1abb7FdMTEZzg8jmpEbiOOuy31TK6DwXwt4OPjo6vYXH3JKVuN7VWq2kHhF2wyj8IvGEpVO6tcj8jR0tPTkZ2dbXS/EIKtDUTktNLT0zk8jmxObiOOOy71TK6B68zb0N2BaWxsrMVd4O21PrQ9rmPufqSmpmLmzJm67bi4OKhUKqxYsQJzDitrdG3JQ4GgfpORt2Wx0TxB/SZzWS+qNeS2IuTm5lrlu4qIyJrkfodxeBzVhLaxx9SDIy71TM7M6i3zS5Yswf333w8/Pz80aNAAQ4cOxalTp/TyREdH67p0al9TpkyxdlFsyly3VGtN2GKvWahtfR1z98PcuDhrLBvn3aoXQoYugMK3vl66wi8YIUMXwLtVrxpfg8hZzP7qnKx8p0+f5uRSROR05LaEcngc1YS2sccUS5Z65rA1sjerB/P79u3DtGnT8NNPP2HXrl2oqKhAbGxslT/op59+Grm5ubrXm2++ae2iOIy1Jmyx1wyutr6OufvxxRdfmB3ba61l47xb9ULopNW67ZCEJDSesoGBPNU6coaW1K9fH0lJSZxcioicTlRUFIfHkV2Ya+zhClHkzKwezO/cuRMTJ05Eu3bt0KlTJ2zcuBEXLlzAzz//rJfP29sbjRo10r38/f2tXRSbMtaSbcmkU6bYawZXudep7pNGOfdj2rRpZsf2asfFacpLcX7Zozi/7FFoyktll6Oyyl3pvcLbs2s91UpyWhsA1Pi7iohckzO1IBoqi0Jh/juMw+PIWqzV2GOvHrVEWjafAO/mzZsAgKCgIL30Tz/9FMHBwWjfvj3mz5+PkpISo+coKytDQUGB3suRTLVkW2PSKWs9EDDHkutU98tJzv3Iy8uTV96i63qt86VZx6vVWm9uXW6i2sJUa0PAg2Nx7do1o8dygjwyxtnqZKoeZwo6jJWFw+PInmra2CO3p6szPUgj12fTYF6j0SAxMREPPvgg2rdvr0sfM2YMPvnkE+zduxfz58/Hv/71L4wbN87oeZYsWYKAgADdKzw83JbFNslcS/bWrVtlncfUxC72moVa7nXeeOONanfDt+ZSHhXXc5C7YapuOy8lCTlrJ1llPD1RbWWsteGeoDBZx3M5HrqbM9XJVD32GsZnjbJweBy5Akt61DrTgzRyfZIw1CxrJc8++yy+/vpr/PDDD1CpVEbz7dmzB/369cOZM2fQvHnzKvvLyspQVlam2y4oKEB4eDhu3rxp1+75arUakZGRRgNgSZIQHBwsq6V57969iI6ONrjvs88+w5gxY8yeY9OmTRg9erTZfMbIvY4hkiQBAFJSUnRjiQzNiP/999+jb9++Zs/nUdcfmlvGW3c8vPygKS00up9P6ImM05SXIuvdBABA+OwUeHh6ofTC/+HyZwvMHmvqu8qdWWMFEHutViJHQUEBAgICZNWrzlInU/Vog467f/4ZqtetwdjfeeS8HSg5dcDkKjPaut3QdxiRIxj6WxQaNTSfGR8yKkkSVCoVMjMzsXXrVsycOVMv6Neu3MSx+VSZ3HrZZi3z06dPx/bt27F3716TgTwA9OjRAwBw5swZg/uVSiX8/f31Xo4gt8t4SEiIrlK8myRJZpe4kDuDa03XvKzJ8Ya6+xt60hgVFQWVSmX2fgTFTjW4Xy5rTZBHVBsZGlqiVLWT9X9T+13FboH6rNGy4qqtM85SJzsrZ/6/Yq9hfHdfU6vy37nQqJG/e73JY7V1O4fHkbMwNNyzLDtDdk9Xe8yHRe7F6sG8EALTp0/Hl19+iT179qBp06Zmjzl27BiAmgentia3u+nYsWMBoMqPZO22uSUu5AbANV3z0tx1zKnc3d9YN7mtW7eirNvjBn84aM9R2nU8fFr3Njm211SrPADdBHlEJI/koTD7f7Pyd5WrBp62YI0uys7UzZmsy5n/r9hrGJ+Wqb/zsuwMk2t7A6zbybmUnDpgeLjn6UOyjl+xYgUnnSWrs3owP23aNHzyySfYtGkT/Pz8cOnSJVy6dAm3bt0CAJw9exavvfYafv75Z5w7dw7btm3D448/jj59+qBjx47WLo5FIuftMPmSu27zkCFDkJKSgrAw/TGpKpVKVvc1hUKBFStWAKj+AwE55FxHjsHPJ2PYsGFVnjRmZ2dj2LBhACBrApuaju1VF12XXWYikr8cDwPPv1hjpRF7rVZC9met/yu2at2X2yhhjbkyzP2dyw2AWLeTM9AOCVEX6U8cqy68isIj8ubLys/PN7qPk85SdVl9zLyxIPDDDz/ExIkTkZWVhXHjxuH48eMoLi5GeHg4HnvsMbz00kuyu+pZMrbPEpHzdpjcLzRq5KydZPJJssIvGI2nbIDkoYC6tBjZK0YCuBOY1m3axaKZMUtOHUD+d+v0vjgUfsEI6jdZ1vjwc0sHyrpOampqlfE74eHheOqpp7Bo0SKzx5sb7669J5ryUrP3oyZjexuOXgyvJo59IETkiox9V51bOtDu42udhaFxvnLmTdGOizT2sNUa57CFmtSrtqqTXY01/69o7ykApKWlITY21ip/D3LnsdHOlVHdeR3k/J1LXn4mfztosW4nR5P1+1+hgEajMdjyLkkSAgMDTQbzWjWdD4tqD7l1ax1rX9jcs4Hw8HDs27fP2pe1C+26zaYma6m85mlNl7jwbtULyohO1X4gYO7hxF+UkBLeASpdR2raBRuK7gTipr68zAXywF/d5Dwb3atLM3Y/tOPi9Eqname2HAq/YChV7UyWg4gMM/ZdZW58rSRJSExMxJAhQ+waeNrD3V2lY2NjLeqibGzSQGucg5yPNf+vaB+wa8XFxVltgizt8LqcnByjQYdKpdIN4zP0/0DO/3U5f+fiVoGsxgDW7eRosoaE/O//iiRJev+3tA/zZs2aJauBzNmHHJPzsfk687WNJWueWmPClpo+EADutLqdX/Yozi97FCVnjxicKM7QdbQPL0zxaWf+CT9wp5tcde+HnHJUfohCRJYx9n+z8fg37Tq+1l4KCgp0XZi//vrrKmMUTc0BIoepLsr27Obsbsx9rtY4h7H91hqLbs0hGIbKaskwvpoMGZD792vuNwTrdnIGcod6JCYmGh1i++KLL9plPixyPwzmq8Gea57W9IGA0ck67lqb3dh1zD68aNlDVjkUvoEWl70ySx6iEJF1yP0B40qBp7kAxVQwlZycLOsaplpW7LVaibuxx6SEpvZb4yGNNWeaN1XW+Ph4s/P6WPJQwdD4frl/v94te7BuJ6cn9zfsZ5cb3Onp+j8hCUmQRv0Tcw4r0fzFnXaZD4vcj03XmbcVR42ZdzVy12+Vw9iYWkvnEaipms5DQETy1ba16M2Naf73v/+NOXPmmGxhNTcuUu6YeXPdnDlmXj5rjFU3d47nnnsOb731ltH9SUlJsrrQmvq/Yul4dmPk3g9j4/ItndfB0HkAQBnYyG5zDBHZkrV+62rnoTE0T1VycnKtnH+Gqk9u3cpgvpLaFMzbM8i25kMDInIe1vwBI4c1Jv2qSYASHByMvLw8WdcxNi7SksARQLXPYW3OHsxbK/A0xNw5AOiuZewajRs3BoAaPaT57LPPMGbMGKNl0DI1QZY17ofchwoNRy+G5laR0Yl6AfC3AdUa1vitq60LbTXBpavi/TBMbt3KbvYOJGcse3XZc/1WdoEnqp3sOV+FrbtJyxnTLDeQNzUusnIQbmx8tZxuzvSXmn6u5saqmzsHAJNd24UQyM7OxtNPPw2g+l1orTEEwxr3Q+6QgZLTh4wu1aUNevjbgGoLa/7Wrfw90KdPn2oFrtaYI8QZrsOlb2uOLfOV2LNlvqbLzplTfGIfrn613Gy+4EHPw6ftQzW+HsAu8ES1la2/r97pXmbzbtKzZs2SPebdnL179+K+++4z2ZJgqCvl3TOSO1NrhLO2zDd47EWTrWF+3YbIWuPZVF23pFOBrBZxczZt2gSlUlntLrTWGIJhjdZ9uS3z1lyWlshV1OS3rtxeaubIqV9c4TruuvStXOxmXw2Vg3lbBqb26JbOtdmJyJps9Z0otyt/2fVLJsfnWqsLfUhICK5evVqj8eyu+APFGYN5tVptdty1nOVRgTt13T0Nmhv8G5ZbX8q5hleTjkb/r8jpYit3CIaxc1jSRd5Y3S/n/6Ql952/MYjkkRPs26t+sfV1rDEkqLZjMF8N2mDelq1Q9hrLbu+J6YjIvQmN+s7wnqLrUPgGQqlqJ+u7xZIHj8bG5/p27I+bP35q9hxyAvV33nkHI0aMuPOeqjGe3VV/oDhjMG/NFuLAh5/C9d3vGazX67bsYba+hOQBCI3Ja8ipT+X8vjCXx9R+Oe9FTlnNNTpYo0cEEekzF8xbUr8Ad4bd5ObmIjQ0FFFRUbLrHHvUY9aa8LM2k1u31rFjmVyCsQpMOwaspq3mloxlr8nTbO1YV1OVMddvJSJrkPsA1FDAL3f5u5LThwwGD+rCq7ICeQAojegFkWc4ABFCoLTreDx3pC6Ch8yv8n48fOsjqN9kzDmsxJzDxodklV74P1yWOWZZzg8UtVpd7R9krs6StcpNBZY+bfrg6talVdIr1+vm6kv/+4ei4LDxMZxy6lO5vy+8W/VC3ZY9DD4ck3MOa9T92vHBxv5fe9T1lRXM13RZWiL6i9w5Md544w2sX7/ebPd4Y/WLJXNvREVFVauOssZynnQHg/lKhEaN/N3rTebJ370edVv2qHYQLPeHq9x8ppirjDn5DBHVlNwAxVjA79uxv6zrFGfsrXFZvVv2gJeqndnvRFPBlDlyv7uHv5MGn53FJvOYekhy5cs3ZF3HlVmyVrmxzzXw4adwfc/7Jo/P370ejadsMFtfKsNaV7s+tfT3heShqPJAX+455LwXOUz9PxAaNRR+wWZ7AChV7WRdi4jMkxvYGlomMycnBwkJCbreZabGw5eVlcm6ztatWzFu3Lhqjam3xoSfdAeD+Urs0Wou9ym1tZ5me7fqBWVEJ04+Q0RWJze4EEJjtGX05o+fwsPLD5rSQqPnkDs+1xRtYCF5KGR9JxoKpmRdx0rf8eYekqSmdnW6cffWFhUVJTtgNPa5WlKvm6sva1KfWuP3hTXfi1zG/h+w9x+R9ZmbiLv0wrlqn1sIAUmSkJiYCI1GgxEjRlQZcqYN+JOSkmSd09Cksnc/NDAmKioKKpXK7ISfUVFRssoiR23t6cZgvhJ7tJorVe3s/jRb4eWDiLnbrXY+IiJAfnCR/+3qGl3HXDdqOSoHFrb8TrTGd7ychySJiYkYMmRIrfghYoxCYVnAaOhztbReN/e3Ud2/HWv8vrD2e6kp9v4jsg1jc9AoVe1MBsBmz/u/7vFTp041eLw24H/vvffMXkehUBhcpq7yQwNTdZRCocCKFSuQkJAASZIMzlFTeTnPmgbi9loBwBG4znwl9mg1t+e6zUREtiQ3uDDXqq4pLURA7zHG1+9t2UPWdUyew06BhTW+4+U8JDG3VnhtUdO1ne3dG86W5XCW91KZd6teaPzsB2g4ejGCBz2PhqMXo/GUDQzkiaqp5NQB5Kx5Epc/W4CrXy3H5c8WIGftJJScOgDJQ4Gybo9XK5CvzNTKLkIIZGdn4+mnnwbwV2Ctpd02td585TH1psTHxyMlJQVhYWF66SqVSq9lPzU1FREREejbty/GjBmDvn37WrQWvXZm/sqBPPBXLwJXX9OeLfOV2KvVnE+ziag2sGbQcE9gYzR+9oMajc8N6DkSAT1HVmusuzXV9Dte7kMSd5kYqCZzGDiiN5ytyuEs7+Vu1R2SQkT65M5BY6x+kbuyixwtW7ZESkqKwdbsYcOGGexifzc5dVR8fDyGDBlitNXd2BJ5crvzq9VqzJo1y2RPBFfv6cZgvhJ7jgGryY8TIiJnICe4kDveXeEbaLXxuc4QWNTkO17uQxJ3mhiougGjs4zttkY5nOW9EJH1WTJJprH6BQCK/u8bq9TJoaGhiI6ONhhop6enywrm5dZRCoXC4Oou1gjELZmZ31VXmGEwfxfdE6/d6/X+M9ii1ZxPs4nIlckKLmKn4vqe92vcmmjP72Zrqe53vJyHJOHh4VadGKg2c5a/HWuUw1neCxFZl6WTZBqrX6xVJ09IK4C0s/KEfP7Ar8XAzp2ye8tVPYe+c0sHGt0HWBaIG1siz5pL4DnruHsG8waw1ZyISB45XcolycMqrYnu8t0s5yFJ5YmByDxn+duxRjmc5b0QkfVYaxJue9TJ1uolZG72/uIT+0zu1xr8fDJKTg03+IDTo66vrHPM/uoc5v9qvDzvdC+rUXd/W5JETWdRcICCggIEBATg5s2b8Pf3t9p5zf1RERGRYcZm39UytWY6WxMNs+c68zWpV21VJwOsl4nIPZRe+D9c/myB2XwNRy+W1ePLHnWy3HOYK4sxcu+JKcFD5snqidB4ygajZRIaNXLWTjLbWy4zM9OqD9nl1q1smSciohoz16WcrYmW4z0jInIP1p7g0h51spxzlJw6UO1hQXLuCSQPQGiM7r6+530EPvwUrm5dajSPtheBsYcOlqwwI2fcvbUxmCciIrvgPCGW4z0jIqr9HDHBpTXqF1PnkDs7v6lzm7snpgJ57bUU3v5m5xox9dBBqCtMXkPLUSvMMJgnIiIiIiJyoNo0waUls/ObekBh6p54t3oQhUe2mi2Luug6fNo+ZLQXgbmHDgEPjjV7DcBxK8wwmCciIiIiInKw2jK8ytLZ+U0xdk/KsjNkBfPa5V4N9SKQ89Ch8NedUPjW15sb4G6OXGGGwTwREREREZETqA3Dq6w1O7+WoXtijXkG5Dx00BRdQ0DvMbj5wyajeRy5woyHQ64KYNWqVYiMjISXlxd69OiBw4cPO6ooREREREREZAXa1nBr5TNEO6beFHPzDMh9mHBPYGOEDF0AhV+wXrrCLxghQxe43zrz//73vzFnzhysXbsWPXr0QHJyMvr3749Tp06hQYMGjigSERERERER1ZC1Z+c3pqbzDFjy0MGrSUenHALhkGD+nXfewdNPP40nnngCALB27Vrs2LEDH3zwAebNm+eIIhEREREREVEN2XN2/prMM2DpQwdnHAJh92C+vLwcP//8M+bPn69L8/DwQExMDA4ePGjwmLKyMpSVlem2b968CQAoKCiwatk0ZSVWPR8REZEtWLv+055PCGE2r73qZID1MhGRq/KK7Iz6A/+O6/s+hKYoX5fu4RuEwIeegFdkZ6t+x3s2bAE0vPNvUVEG87XZHfX6TMC1HW+b3G/ufLao/2TXy8LOcnJyBABx4MABvfTnn39edO/e3eAxixYtEgD44osvvvjiiy8bvrKysszW46yT+eKLL7744ss+L3P1siSEjMfwVnTx4kU0btwYBw4cQM+ePXXpL7zwAvbt24dDhw5VOebuVgCNRoP8/HzUr18fkiRZpVwFBQUIDw9HVlYW/P39rXJO4n21Fd5X6+M9tQ3eV+uzxT0VQqCwsBBhYWHw8DA9N6496mSAfzu2wHtqG7yv1sd7ahu8r9Znq3sqt162ezf74OBgKBQKXL58WS/98uXLaNSokcFjlEollEqlXlq9evVsUj5/f3/+cdsA76tt8L5aH++pbfC+Wp+172lAQICsfPaskwH+7dgC76lt8L5aH++pbfC+Wp8t7qmcetnuS9N5enqia9eu2L17ty5No9Fg9+7dei31RERERERERGSYQ2aznzNnDiZMmIBu3bqhe/fuSE5ORnFxsW52eyIiIiIiIiIyziHB/MiRI5GXl4eXX34Zly5dQufOnbFz5040bNjQEcUBcKfb4KJFi6p0HaSa4X21Dd5X6+M9tQ3eV+tzl3vqLu/TnnhPbYP31fp4T22D99X6HH1P7T4BHhERERERERHVjN3HzBMRERERERFRzTCYJyIiIiIiInIxDOaJiIiIiIiIXAyDeSIiIiIiIiIX41bB/KpVqxAZGQkvLy/06NEDhw8fNpn/iy++QOvWreHl5YUOHTogLS3NTiV1LZbc1/feew9RUVEIDAxEYGAgYmJizH4O7sjSv1WtzZs3Q5IkDB061LYFdFGW3tcbN25g2rRpCA0NhVKpxL333svvgbtYek+Tk5PRqlUr1K1bF+Hh4Zg9ezZKS0vtVFrXsH//fgwaNAhhYWGQJAlbtmwxe8z333+P++67D0qlEi1atMDGjRttXk5rYL1sfayTbYP1svWxTrYN1svW5fR1snATmzdvFp6enuKDDz4QGRkZ4umnnxb16tUTly9fNpj/xx9/FAqFQrz55pvixIkT4qWXXhL33HOP+O233+xccudm6X0dM2aMWLVqlfjll1/E77//LiZOnCgCAgJEdna2nUvuvCy9p1qZmZmicePGIioqSgwZMsQ+hXUhlt7XsrIy0a1bNxEXFyd++OEHkZmZKb7//ntx7NgxO5fceVl6Tz/99FOhVCrFp59+KjIzM8U333wjQkNDxezZs+1ccueWlpYmXnzxRZGamioAiC+//NJk/j///FN4e3uLOXPmiBMnToiVK1cKhUIhdu7caZ8CVxPrZetjnWwbrJetj3WybbBetj5nr5PdJpjv3r27mDZtmm5brVaLsLAwsWTJEoP5R4wYIQYOHKiX1qNHD/HMM8/YtJyuxtL7erfbt28LPz8/8dFHH9mqiC6nOvf09u3bolevXuL9998XEyZM4I8GAyy9r2vWrBHNmjUT5eXl9iqiy7H0nk6bNk08/PDDemlz5swRDz74oE3L6crk/HB44YUXRLt27fTSRo4cKfr372/DktUc62XrY51sG6yXrY91sm2wXrYtZ6yT3aKbfXl5OX7++WfExMTo0jw8PBATE4ODBw8aPObgwYN6+QGgf//+RvO7o+rc17uVlJSgoqICQUFBtiqmS6nuPX311VfRoEEDTJo0yR7FdDnVua/btm1Dz549MW3aNDRs2BDt27fH4sWLoVar7VVsp1ade9qrVy/8/PPPui5/f/75J9LS0hAXF2eXMtdWrlhfsV62PtbJtsF62fpYJ9sG62XnYO+6qo5Nzupkrl69CrVajYYNG+qlN2zYECdPnjR4zKVLlwzmv3Tpks3K6Wqqc1/vNnfuXISFhVX5o3dX1bmnP/zwAzZs2IBjx47ZoYSuqTr39c8//8SePXswduxYpKWl4cyZM5g6dSoqKiqwaNEiexTbqVXnno4ZMwZXr15F7969IYTA7du3MWXKFCxYsMAeRa61jNVXBQUFuHXrFurWreugkhnHetn6WCfbButl62OdbBusl52Dvetkt2iZJ+e0dOlSbN68GV9++SW8vLwcXRyXVFhYiPHjx+O9995DcHCwo4tTq2g0GjRo0ADr169H165dMXLkSLz44otYu3ato4vmsr7//nssXrwYq1evxtGjR5GamoodO3bgtddec3TRiNwe62TrYL1sG6yTbYP1sutzi5b54OBgKBQKXL58WS/98uXLaNSokcFjGjVqZFF+d1Sd+6r11ltvYenSpfjuu+/QsWNHWxbTpVh6T8+ePYtz585h0KBBujSNRgMAqFOnDk6dOoXmzZvbttAuoDp/q6GhobjnnnugUCh0aW3atMGlS5dQXl4OT09Pm5bZ2VXnni5cuBDjx4/HU089BQDo0KEDiouLMXnyZLz44ovw8ODz5eowVl/5+/s7Zas8wHrZFlgn2wbrZetjnWwbrJedg73rZLf4hDw9PdG1a1fs3r1bl6bRaLB792707NnT4DE9e/bUyw8Au3btMprfHVXnvgLAm2++iddeew07d+5Et27d7FFUl2HpPW3dujV+++03HDt2TPcaPHgw+vbti2PHjiE8PNyexXda1flbffDBB3HmzBndjzAA+OOPPxAaGsofDajePS0pKanyw0D7w+zOvDJUHa5YX7Fetj7WybbBetn6WCfbButl52D3usom0+o5oc2bNwulUik2btwoTpw4ISZPnizq1asnLl26JIQQYvz48WLevHm6/D/++KOoU6eOeOutt8Tvv/8uFi1axCVwDLD0vi5dulR4enqKlJQUkZubq3sVFhY66i04HUvv6d04a65hlt7XCxcuCD8/PzF9+nRx6tQpsX37dtGgQQPx+uuvO+otOB1L7+miRYuEn5+f+Oyzz8Sff/4pvv32W9G8eXMxYsQIR70Fp1RYWCh++eUX8csvvwgA4p133hG//PKLOH/+vBBCiHnz5onx48fr8muXwXn++efF77//LlatWuUyS9OxXrYu1sm2wXrZ+lgn2wbrZetz9jrZbYJ5IYRYuXKlaNKkifD09BTdu3cXP/30k27fQw89JCZMmKCX//PPPxf33nuv8PT0FO3atRM7duywc4ldgyX3NSIiQgCo8lq0aJH9C+7ELP1brYw/Goyz9L4eOHBA9OjRQyiVStGsWTPxxhtviNu3b9u51M7NkntaUVEhkpKSRPPmzYWXl5cIDw8XU6dOFdevX7d/wZ3Y3r17DX5Pau/lhAkTxEMPPVTlmM6dOwtPT0/RrFkz8eGHH9q93NXBetn6WCfbButl62OdbBusl63L2etkSQj2oSAiIiIiIiJyJW4xZp6IiIiIiIioNmEwT0RERERERORiGMwTERERERERuRgG80REREREREQuhsE8ERERERERkYthME9ERERERETkYhjMExEREREREbkYBvNERERERERELobBPBEREREREZGLYTBPRERERERE5GIYzBMRERERERG5GAbzRERERERERC6GwTwREZEb6tq1K6ZOnWo2X2ZmJqZPn457770X3t7e8Pb2Rtu2bTFt2jT83//9n8XXnTlzJiRJwpkzZ4zmefHFFyFJUrXOT0RE5C4kIYRwdCGIiIjIfnJzc9G4cWN89dVXGDhwoNF827dvx8iRI1GnTh2MHTsWnTp1goeHB06ePInU1FScP38emZmZiIiIkH3tQ4cO4YEHHsArr7yCl19+2WCeZs2awdfXl8E8ERGRCQzmiYiI3MwHH3yA6dOn49q1a6hbt67BPGfPnkWnTp3QpEkT7N69G6GhoXr7b9++jdWrV+Oxxx5DeHi4Rddv2bIl6tSpg99//73KvoMHD6JXr15YunQp5s6da9F5iYiI3Am72RMREcmQlJQESZLwxx9/YNy4cQgICEBISAgWLlwIIQSysrIwZMgQ+Pv7o1GjRnj77bf1ji8vL8fLL7+Mrl27IiAgAD4+PoiKisLevXurXGvz5s3o2rUr/Pz84O/vjw4dOmDFihW6/RUVFXjllVfQsmVLeHl5oX79+ujduzd27dol673s2LEDffv2NRrIA8Cbb76J4uJifPjhh1UCeQCoU6cOZs6cWSWQP3nyJBISEhAUFAQvLy9069YN27Zt08szduxYnDx5EkePHq1y3k2bNkGSJIwePVrWeyEiInJXDOaJiIgsMHLkSGg0GixduhQ9evTA66+/juTkZPztb39D48aNsWzZMrRo0QLPPfcc9u/frzuuoKAA77//PqKjo7Fs2TIkJSUhLy8P/fv3x7Fjx3T5du3ahdGjRyMwMBDLli3D0qVLER0djR9//FGXJykpCa+88gr69u2Lf/7zn3jxxRfRpEkTg8Hx3SoqKvDdd98hLi7OZL7t27ejRYsW6NGjh+x7k5GRgQceeAC///475s2bh7fffhs+Pj4YOnQovvzyS12+sWPHArgTuFemVqvx+eefIyoqCk2aNJF9XSIiInfEbvZEREQyaAPoyZMnY926dQDuBJ+RkZHIycnBkiVLdN3Cb9y4gbCwMIwYMQIbN27U5VWr1fD09NSd88aNG2jdujUGDhyIDRs2AAASExPx4YcfIj8/HwqFwmBZOnfuDJVKhe3bt1v8Pvbs2YN+/fohMzMTkZGRBvMUFBQgICCgShCuLfPt27d12z4+ProW/piYGFy5cgX//e9/oVQqAQBCCPTu3Rt5eXn4448/dMd1794dubm5OH/+PDw87rQtfPPNN3jkkUewbt06TJ482eL3RkRE5E7YMk9ERGSBp556SvdvhUKBbt26QQiBSZMm6dLr1auHVq1a4c8//9TLqw3kNRoN8vPzcfv2bXTr1k2vRb1evXooLi422WW+Xr16yMjIwOnTpy0uf1paGtq2bWs0kAfuBPMA4OvrW2VfdHQ0QkJCdK9Vq1YBAPLz87Fnzx6MGDEChYWFuHr1Kq5evYpr166hf//+OH36NHJycnTnGTduHLKzs/V6L2zatAmenp4YPny4xe+LiIjI3TCYJyIissDd3b8DAgLg5eWF4ODgKunXr1/XS/voo4/QsWNH3Tj3kJAQ7NixAzdv3tTlmTp1Ku69914MGDAAKpUKTz75JHbu3Kl3nldffRU3btzAvffeiw4dOuD555+XPfP7jh07TM5gDwB+fn4AgKKioir71q1bh127duGTTz7RSz9z5gyEEFi4cKFesB8SEoJFixYBAK5cuaLLP2rUKCgUCl1X+9LSUnz55ZcYMGAAAgMDZb0XIiIid8ZgnoiIyAKGur4b6w5feSTbJ598gokTJ6J58+bYsGEDdu7ciV27duHhhx+GRqPR5WvQoAGOHTuGbdu2YfDgwdi7dy8GDBiACRMm6PL06dMHZ8+exQcffID27dvj/fffx3333Yf333/fZNkzMzNx8uRJs+PlAwICEBoaiuPHj1fZ16NHD8TExODBBx/US9e+h+eeew67du0y+GrRooXe+/zb3/6G//znP6ioqMBXX32FwsJC3Xh6IiIiMq2OowtARETkDlJSUtCsWTOkpqZCkiRdurbVujJPT08MGjQIgwYNgkajwdSpU7Fu3TosXLhQFxAHBQXhiSeewBNPPIGioiL06dMHSUlJesMA7rZjxw4EBASgd+/eZss7cOBAvP/++zh8+DC6d+9uNn+zZs0AAPfccw9iYmLM5gfuTIS3c+dOfP3119i0aRP8/f0xaNAgWccSERG5O7bMExER2YG29b5ya/2hQ4dw8OBBvXzXrl3T2/bw8EDHjh0BAGVlZQbz+Pr6okWLFrr9xqSlpSE2NhZ16ph/lv/CCy/A29sbTz75JC5fvlxl/93z5zZo0ADR0dFYt24dcnNzq+TPy8urkjZ06FB4e3tj9erV+PrrrxEfHw8vLy+zZSMiIiK2zBMREdnFo48+itTUVDz22GMYOHAgMjMzsXbtWrRt21ZvbPpTTz2F/Px8PPzww1CpVDh//jxWrlyJzp07o02bNgCAtm3bIjo6Gl27dkVQUBCOHDmClJQUTJ8+3ej1b926hb1792Lt2rWyytuyZUts2rQJo0ePRqtWrTB27Fh06tQJQghkZmZi06ZN8PDwgEql0h2zatUq9O7dGx06dMDTTz+NZs2a4fLlyzh48CCys7Px66+/6l3D19cXQ4cO1Y2bZxd7IiIi+RjMExER2cHEiRNx6dIlrFu3Dt988w3atm2LTz75BF988QW+//57Xb5x48Zh/fr1WL16NW7cuIFGjRph5MiRSEpK0i3hNnPmTGzbtg3ffvstysrKEBERgddffx3PP/+80evv2bMHZWVlGDBggOwyDxkyBL/99hvefvttfPvtt/jggw8gSRIiIiIwcOBATJkyBZ06ddLlb9u2LY4cOYJXXnkFGzduxLVr19CgQQN06dIFL7/8ssFrjB07Fps2bUJoaCgefvhh2WUjIiJyd1xnnoiIyA1MnToVR44cweHDhx1dFCIiIrICtswTERG5gc6dO3NyOSIiolqELfNERERERERELoaz2RMRERERERG5GAbzRERERERERC6GwTwRERERERGRi2EwT0RERERERORiGMwTERERERERuRgG80REREREREQuhsE8ERERERERkYthME9ERERERETkYhjMExEREREREbkYBvNERERERERELub/AWvaluFiIcK6AAAAAElFTkSuQmCC", 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", 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", 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AAACL4XfmAQAAAACwGIp5AAAAAAAshmIeAAAAAACLoZgHAAAAAMBiKOYBAAAAALAYinkAAAAAACyGYh4AAAAAAIuhmAcAAAAAwGIo5gEvKygo0C233KLmzZsrOjpaF198sdauXRvosAAAQBWQ3wEEG4p5wMvOnDmjpKQkffbZZzp8+LAmTZqkQYMG6dixY4EODQAAVBL5HUCwoZgHvCwiIkIPP/ywmjdvrpCQEA0dOlShoaH64YcffL7uadOmyWazVWrer776Sj179lRERIRsNps2bdrk3eAAAPBAUU47ePBgoEORZN38LpHjgeqKYh6WsWbNGk2bNk2HDx8OdCge2bZtmw4dOqQ2bdqUeK6wsFCxsbF66qmnAhDZ/5w+fVo33HCDDh06pH/84x/697//rRYtWgQklieeeEI2m00dOnRwaR81apRsNluZU05OjlfWU1x5r095869cubLMOL/44gu34isoKNBf/vIXxcfHq06dOurRo4eWL1/u/gYCAHzKCvldClyOdzcXVjVnHjt2TFOnTtUf/vAHNWjQQDabTYsWLSp3nopen7JyPPkdwaZWoAMA3LVmzRo98sgjGjVqlOrVqxfocNxy4sQJjRgxQlOmTFFMTEyJ59etW6eDBw9q4MCBAYjuf3bs2KHdu3fr+eef12233RawOPbs2aPp06crIiKixHN33HGH+vTp49JmjNGdd96ppKQkNWvWzCvrKa6s18fd+SdMmKBu3bq5tJW201eaUaNGKSMjQ5MmTdI555yjRYsWacCAAVqxYoUuu+wyt5YBAPANq+R3KfA53t1cWNmcefDgQT366KNq3ry5OnfurJUrV1Y4T3mvjzs5nvyOYEExD/hI0Tfhbdq00cMPP1xqn6ysLLVo0ULt27f3c3Su9u/fL0kB/5Lkvvvu08UXXyyHw1HitMpLLrlEl1xyiUvbZ599puPHj2v48OFeW09xZb0+7s6fnJys1NRUj2KTft/JWLx4sZ5++mndd999kqQ//elP6tChg+6//36tWbPG42UCQKDl5+dX+CWoFVgpv0uBz/Hu5sLK5sy4uDjl5uaqadOmWr9+fYkiuzTlvT7u5HjyO4IFp9nDEqZNm6bJkydLklq2bOk8pWnXrl3avXu3xo4dq7Zt26pOnTpq2LChbrjhBu3atavEclauXKmuXbsqPDxcrVu31vz5892+Du2FF15QeHi4Lr30Uu3evdvZboxR79691ahRI2fCLCws1M033yybzaaXX365zOUvXbrU+a1wURzbt293nn0QExOj0aNH6/jx4yXm/eyzz9StWzeXbSnNxo0b1b9/f0VHRysyMlJXXXWVy6lgo0aN0hVXXCFJuuGGG2Sz2dSrV68Kx8PbVq1apYyMDM2cOdPtedLT02Wz2XTTTTf5ZD3FX5/Kxnn06FGdOXPG7fgkKSMjQ3a7XWPGjHG2hYeH69Zbb9XatWuVnZ3t0fIAwFuKctXWrVs1ZMgQRUdHq2HDhpo4caJOnjxZot+WLVt00003qX79+i5HHSvKTcUdPHiw3HUVycnJ0S233KImTZooLCxM7du310svvVThNlXX/C4FT453NxdWJmeGhYWpadOmHs1TWn6XPMvx5HcEA47MwxJSUlL0448/6vXXX9c//vEPNWrUSJIUGxurDz74QGvWrNHQoUOVkJCgXbt26bnnnlOvXr20ZcsW1a1bV9LvSe8Pf/iD4uLi9Mgjj8jhcOjRRx9VbGysWzF069ZNkydP1vTp0/X3v/9ds2fPliTNmTNHK1euVHp6uho3bizp91PCc3Nz9eGHH6pWrdLfZnv37tXGjRv16KOPurQPGTJELVu2VFpamjZs2KAXXnhBjRs31pNPPuns8+2336pv376KjY3VtGnTdObMGU2dOlVNmjRxWdbmzZuVnJys6Oho3X///apdu7bmz5+vXr166dNPP1WPHj10xx13qFmzZpo+fbrztLGzl3O206dP68iRI26NW4MGDRQSUv73hg6HQ3fffbduu+02dezY0a3lnj59Wm+88YZ69uyppKQkt+bxZD2lvT6exjl69GgdO3ZMdrtdycnJevrpp9W1a9cK59u4caPOPfdcRUdHu7R3795dkrRp0yYlJiZWuBwA8JUhQ4YoKSlJaWlp+uKLL/TPf/5Tv/32m1555RWXfjfccIPOOeccTZ8+XcYYSe7lJk/XtW/fPl188cWy2WwaP368c//g1ltvVV5eniZNmlTmtlTX/F4Uryc53tv5XXI/F1Y2Z3qqrNfHkxxPfkfQMIBFPP3000aS2blzp0v78ePHS/Rdu3atkWReeeUVZ9ugQYNM3bp1TU5OjrNt27ZtplatWsaTt0Lfvn3NJZdcYowxZseOHSYiIsIMHjzY+fyuXbuMJBMeHm4iIiKc06pVq1yW8+KLL5o6deo44586daqRZG655RaXftdff71p2LChS9vgwYNNeHi42b17t7Nty5Ytxm63u2zL4MGDTWhoqNmxY4ez7ZdffjFRUVHm8ssvd7atWLHCSDJvvvmmW2NQ1N+d6ezXqzT/+te/TExMjNm/f78xxpgrrrjCtG/fvtx53nvvPSPJzJ07162YPV3P2a+PJ/N//vnn5o9//KN58cUXzTvvvGPS0tJMw4YNTXh4uNmwYUOFcbZv395ceeWVJdo3b95sJJl58+a5tb0A4G1Fueraa691aR87dqyRZL755huXfsOGDSuxDHdzk7vrMsaYW2+91cTFxZmDBw+69B06dKiJiYkpdV/hbNUxvxvjWY73Zn53NxdWNWcW99VXXxlJZuHChWX2KS2/G+Nejie/I9hQzMMyyirmizt16pQ5ePCgOXDggKlXr56ZNGmSMcaYM2fOmDp16pibbrqpxDyDBg3yqJi///77TVRUlHE4HOaKK64wDRo0MLm5uR5vzx//+EczYMAA5+OiZL9u3TqXfs8++6yRZI4cOeKyLUOHDi2xzAEDBji35cyZM6Zu3bpmyJAhJfrdcccdJiQkxLlMT4v5Q4cOmeXLl7s1nThxotxlHTx40DRo0MD8/e9/d7a5U8wPGzbM1K5du8SOm7fWc/brU9k4i2zbts3UqVPH9OvXr8K+rVq1Mv379y/RvmPHDiPJ/OMf/3BrnQDgbUW56sMPP3Rp//77740kk5aW5tLv008/dennSW5yd12FhYWmXr16ZsyYMebAgQMu08KFC40k89lnn1W4bdUxvxvjWY73Zn4vjbu50JOcWZw7xfzZr48xVcvx5HcEEqfZw/JOnDihtLQ0LVy4UDk5Oc7T+CQ5TxXbv3+/Tpw4UeqdRt29+2iRDh066OjRo5o8ebI+/fRT/fvf//b4Wq3Tp09r+fLlSktLK/Fc8+bNXR7Xr19fkvTbb78pOjpaBw4c0IkTJ3TOOeeUmLdt27bKysqSJB04cEDHjx9X27ZtS/Q7//zzVVhYqOzs7ErdnKd+/fol7ixfWX/729/UoEED3X333W7Pc+zYMb3zzjvq16+fGjZs6PX1lPb6VCbO4tq0aaPrrrtOmZmZcjgcstvtZfatU6eOCgoKSrQXXSNap06dSsUAAN5ydg5q3bq1QkJCStyvpmXLli6PK5ObKlrXgQMHdPjwYS1YsEALFiwoNd6ia97LQ373bn4vjbu50JOc6YmyXp+q5HjyOwKJYh6Wd/fdd2vhwoWaNGmSLrnkEsXExMhms2no0KEqLCz0+vqKfnP02Wef1TXXXKMRI0Z4vIzPPvtMeXl5GjBgQInnykoCxb+kCLRTp07p0KFDbvWNjY0tc5u2bdumBQsWaObMmfrll1+c7SdPntTp06e1a9cuRUdHq0GDBi7zvf322x7dxd7T9Zz9+lQ2zrMlJibq1KlTys/PL3G9XHFxcXHKyckp0Z6bmytJio+Pd2u7AcBfyroRnC+Kk7PXVZTrR4wYoZEjR5Y6T6dOnSpcLvnde/m9PO7mQnf7eaK018cbOZ78jkChmIdllLWjkJGRoZEjR+qZZ55xtp08eVKHDx92Pm7cuLHCw8O1ffv2EvOX1laeom/C69WrV+YdZiuydOlStWvXzu0btxUXGxurOnXqaNu2bSWe++GHH1z61a1b16WtyNatWxUSElLpm6ysWbNGvXv3dqvvzp07y9zOnJwcFRYWasKECZowYUKJ51u2bKmJEyeWuKvsa6+9psjISF177bVuxeDpes5+fSob59l++uknhYeHKzIystx+F1xwgVasWKG8vDyXnYIvv/zS+TwABNK2bdtcjrpv375dhYWFFea1yuSmitYVGxurqKgoORyOKh1VJr97L7+Xx91c6G4/T5T2+ngjx5PfESgU87CMot+mLV6kS79/0332t9qzZ8+Ww+Fw6dOnTx+9/fbb+uWXX5zffG7fvl0ffPCBR3E8//zzkqRrr7220t+gZmVl6ZprrqnUvHa7Xf369dPbb7+tn3/+2Xna3vfff68PP/zQpV/fvn31zjvvaNeuXc7EtW/fPqWnp+uyyy6r9DfdnTt31vLly93qW94pih06dNCSJUtKtP/tb3/T0aNHNWvWLLVu3drluQMHDuijjz7SsGHDnL9UcLbjx4/r559/VqNGjdSoUSOP13P26+Pp/AcOHCjxKwnffPON3n33XfXv39/l7r9nxypJqamp+vvf/64FCxY4f4e2oKBACxcuVI8ePbjTLYCAmzNnjvr27et8XHQH+P79+5c7X2VyU0Xrstvt+uMf/6j09HR99913ziPsRUr7TC4N+d17+V1yPxdWNWd6orTXx5McT35H0AnsJfuA+9atW2ckmQEDBphXXnnFvP766+bYsWPmT3/6k7Hb7WbixIlm/vz5ZtSoUSYhIcE0bNjQjBw50jn/+vXrTWhoqElKSjJPPvmkmT59uomPjzcXXHCB2zfA2759u6lbt66RZLp3716p7fjpp5+MJLNy5UqX9qIb5Bw4cMClvejmPcVv/PfNN9+Y8PBw07x5czNjxgzz+OOPmyZNmphOnTq5bMt3331nIiIiTLNmzcwTTzxhnnzySdOqVSsTFhZmvvjiC2c/T2+A52vl3XRm9uzZRpJZtmxZmfMXbc/UqVM9Xk9Zr48ncfbu3dsMGDDAPP7442bBggVm0qRJpm7duiYmJsZs2bLFrVhvuOEGU6tWLTN58mQzf/5807NnT1OrVq0SN5MCAH8qylUdO3Y0gwYNMnPmzDEjRowwklxuMltWTjPG/dzk7rqMMWbv3r2mRYsWpm7dus79gbS0NHPDDTeY+vXrV7hd1TW/GxO4HO9uLvRGzpw9e7Z57LHHzF133WUkmZSUFPPYY4+Zxx57zBw+fNgY41l+N6b0HE9+R7ChmIelPPbYY6ZZs2YmJCTEmQB/++03M3r0aNOoUSMTGRlp+vXrZ7Zu3WpatGjhUswbY8zHH39sLrzwQhMaGmpat25tXnjhBfPnP//ZhIeHV7juwsJCc8UVV5j69eub0aNHm8jISFNYWOjxNhT99Mnp06dd2j1J9sYY8+mnn5ouXbqY0NBQ06pVKzNv3jznMorbsGGD6devn4mMjDR169Y1vXv3NmvWrHHpY6Vi/uKLLzaNGzc2Z86cKXP+qhTzZb0+nsQ5a9Ys0717d9OgQQNTq1YtExcXZ0aMGGG2bdvmdqwnTpww9913n2natKkJCwsz3bp1K/cLDADwh6I8s2XLFpOammqioqJM/fr1zfjx413ubl5eMW+Me7nJ3XUV2bdvnxk3bpxJTEw0tWvXNk2bNjVXXXWVWbBgQbnbVJ3zuzGBy/Hu5kJv5MwWLVpU+BN6nuR3Y0rP8eR3BBubMUF01w0gAAYPHqzNmzeXeo1acXPmzNH48eP1yiuvqHbt2ho2bJh27NihVq1aebS+AQMGKDIyUm+88UZVwoaP8PoAQNmmTZumRx55RAcOHKjUac7BiPxeM/D6oDrimnnUKCdOnHC5s+62bduUlZVV5t1vi+zatUsPPPCABg0apJtvvlmbN2+WJG3YsMHjZN+rVy8lJyd7Hjz8gtcHAGoO8nvNweuD6ogj86hR4uLiNGrUKLVq1Uq7d+/Wc889p4KCAm3cuLHU33WVfv/JmD59+mjjxo3avHmz4uLidObMGdWvX1/x8fH685//rOHDhztv0AcAQHVVnY7Mk98BWF1IxV2A6uMPf/iDXn/9dd19992aPXu2unXrplWrVpVZyEvSggUL9Mknn2jWrFmKi4uTJNWqVUuzZ8/W8ePHNWHCBIWGhvprEwAAgBeQ3wFYHUfmAQAAAACwGI7MAwAAAABgMRTzAAAAAABYDMU8AAAAAAAW4/efpissLNQvv/yiqKgo2Ww2f68eAIAaxxijo0ePKj4+XiEhnn2PT94GAMC/3M3bfi/mf/nlFyUmJvp7tQAA1HjZ2dlKSEjwaB7yNgAAgVFR3vZ7MR8VFSXp98Cio6P9vXoAAGqcvLw8JSYmOnOwJ8jbAAD4l7t52+/FfNEpetHR0ewUAADgR5U5TZ68DQBAYFSUt7kBHgAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AAAAAAAWQzEPAAAAAIDFUMwDAAAAAGAxFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wB8Ij8/XzabTTabTfn5+YEOBwAAAKhWKOYBAAAAALAYinkAAAAAACyGYh4AAAAAAIuhmAcAAAAAwGIo5gEAAAAAsBiKeQAAAAAALIZiHgAAAAAAi6GYBwAAAADAYijmAQAAAACwGIp5AAAAAAAsxqNi3uFw6KGHHlLLli1Vp04dtW7dWo899piMMb6KDwAAAAAAnKWWJ52ffPJJPffcc3r55ZfVvn17rV+/XqNHj1ZMTIwmTJjgqxgBAAAAAEAxHhXza9as0XXXXaeBAwdKkpKSkvT6669r3bp1Zc5TUFCggoIC5+O8vLxKhgoAAHyNvA0AgDV4dJp9z5499fHHH+vHH3+UJH3zzTf67LPP1L9//zLnSUtLU0xMjHNKTEysWsQAAMBnyNsAAFiDzXhwwXthYaH++te/6qmnnpLdbpfD4dATTzyhKVOmlDlPad/wJyYm6siRI4qOjq5a9ACCVn5+viIjIyVJx44dU0RERIAjAmquvLw8xcTEuJV7ydsAAASWu3nbo9Ps33jjDb322mtKT09X+/bttWnTJk2aNEnx8fEaOXJkqfOEhYUpLCzMs+gBAEBAkLcBALAGj4r5yZMn64EHHtDQoUMlSR07dtTu3buVlpZWZjEPAAAAAAC8y6Nr5o8fP66QENdZ7Ha7CgsLvRoUAAAAAAAom0dH5gcNGqQnnnhCzZs3V/v27bVx40Y9++yzuuWWW3wVHwAAAAAAOItHxfzs2bP10EMPaezYsdq/f7/i4+N1xx136OGHH/ZVfAAAAAAA4CweFfNRUVGaOXOmZs6c6aNwAAAAAABARTy6Zh4AAAAAAAQexTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AAAAAAAWQzEPAAAAAIDFUMwDAAAAAGAxFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPIAS8vPzZbPZZLPZlJ+fX+k+wcJKsQIAqg/yT+Aw9qgJKOYBAAAAALAYinkAAAAAACyGYh4AAAAAAIvxqJhPSkpyXntSfBo3bpyv4gMAAAAAAGep5Unnr776Sg6Hw/n4u+++09VXX60bbrjB64EBAAAAAIDSeVTMx8bGujyeMWOGWrdurSuuuMKrQQEAAAAAgLJ5VMwXd+rUKb366qu69957ZbPZyuxXUFCggoIC5+O8vLzKrhIAAPgYeRsAAGuo9A3w3n77bR0+fFijRo0qt19aWppiYmKcU2JiYmVXCQAAfIy8DQCANVS6mH/xxRfVv39/xcfHl9tvypQpOnLkiHPKzs6u7CoBAICPkbcBALCGSp1mv3v3bn300UfKzMyssG9YWJjCwsIqsxoAAOBn5G0AAKyhUkfmFy5cqMaNG2vgwIHejgcAAAAAAFTA42K+sLBQCxcu1MiRI1WrVqXvnwcAAAAAACrJ42L+o48+0s8//6xbbrnFF/EAAAAAAIAKeHxovW/fvjLG+CIWAAAAAADghkrfzR4AAAAAAAQGxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AAAAAAAWQzEPAAAAAIDFUMwDAAAAAGAxFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AJ9wOBzOv1etWuXyGAAAAEDVeFzM5+TkaMSIEWrYsKHq1Kmjjh07av369b6IDYBFZWZmql27ds7HAwYMUFJSkjIzMwMYFQAAAFB9eFTM//bbb7r00ktVu3ZtffDBB9qyZYueeeYZ1a9f31fxAbCYzMxMpaamKicnx6U9JydHqampFPQAAACAF9TypPOTTz6pxMRELVy40NnWsmVLrwcFwJocDocmTpwoY0yJ54wxstlsmjRpkq677jrZ7fYARAgAAABUDx4dmX/33XfVtWtX3XDDDWrcuLEuvPBCPf/88+XOU1BQoLy8PJcJQPW0evVq7dmzp8znjTHKzs7W6tWr/RgVAE+QtwEAsAaPivmffvpJzz33nM455xx9+OGHuuuuuzRhwgS9/PLLZc6TlpammJgY55SYmFjloAF35Ofny2azyWazKT8/P+hj8Ua8gd7m3Nxcr/YD4H/kbQAArMGjYr6wsFAXXXSRpk+frgsvvFBjxozR7bffrnnz5pU5z5QpU3TkyBHnlJ2dXeWgAQSnuLg4r/YD4H/kbQAArMGja+bj4uJc7lAtSeeff77eeuutMucJCwtTWFhY5aIDYCnJyclKSEhQTk5OqdfN22w2JSQkKDk5OQDRAXAHeRsAAGvw6Mj8pZdeqh9++MGl7ccff1SLFi28GhQAa7Lb7Zo1a5ak3wv34ooez5w5k5vfAQAAAFXkUTF/zz336IsvvtD06dO1fft2paena8GCBRo3bpyv4gNgMSkpKcrIyFB8fLxLe0JCgjIyMpSSkhKgyAAAAIDqw6Nivlu3blqyZIlef/11dejQQY899phmzpyp4cOH+yo+ABaUkpKiLVu2OB9nZWVp586dFPIAAACAl3h0zbwkXXPNNbrmmmt8EQuAaqT4qfSXX345p9YDAAAAXuTRkXkAAAAAABB4FPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AAAAAAAWQzEPAAAAAIDFUMwDAAAAAGAxFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAyjB4XA4/161apXLY0/6BAsrxQoAqD7IP4HD2KMm8KiYnzZtmmw2m8t03nnn+So2AAGQmZmpdu3aOR8PGDBASUlJyszM9KhPsLBSrACA6oP8EziMPWoKj4/Mt2/fXrm5uc7ps88+80VcAAIgMzNTqampysnJcWnPyclRamqqMjMz3eoTLKwUKwCg+iD/BA5jj5rE42K+Vq1aatq0qXNq1KiRL+IC4GcOh0MTJ06UMabEc0VtEydOrLDPpEmTguJUNne2J1hiBQBUH+SfwGHsUdN4XMxv27ZN8fHxatWqlYYPH66ff/653P4FBQXKy8tzmQAEn9WrV2vPnj1lPm+M0Z49eyrsk52drdWrV/siRI+4sz3BEisQTMjbQNWQfwKHsUdN41Ex36NHDy1atEjLli3Tc889p507dyo5OVlHjx4tc560tDTFxMQ4p8TExCoHDcD7cnNzg3JZvo4hGGIFggl5G6ga8k/gMPaoaTwq5vv3768bbrhBnTp1Ur9+/ZSVlaXDhw/rjTfeKHOeKVOm6MiRI84pOzu7ykED8L64uLigXJavYwiGWIFgQt4Gqob8EziMPWqaKv00Xb169XTuuedq+/btZfYJCwtTdHS0ywQg+CQnJyshIUE2m63U5202mxISEirsk5iYqOTkZF+G6hZ3tidYYgWCCXkbqBryT+Aw9qhpqlTMHzt2TDt27ODbLaAasNvtmjVrliSVSIJFj2fNmlVhn5kzZ8put/s63Aq5sz3BEisAoPog/wQOY4+axqNi/r777tOnn36qXbt2ac2aNbr++utlt9s1bNgwX8UHwI9SUlKUkZGh+Ph4l/aEhARlZGQoJSXFrT7BwkqxAgCqD/JP4DD2qElsprTfbijD0KFDtWrVKv3666+KjY3VZZddpieeeEKtW7d2e4V5eXmKiYnRkSNHOHUPPpWfn6/IyEhJv59FEhEREdSxeCNeb21z0ftUkrKystS3b98S32JX1CeYxt+d7QGqs6rkXvI2UHnkn8Bh7GFl7ubeWp4sdPHixVUODEDwK57sLr/88lKTnzt9goWVYgUAVB/kn8Bh7FETVOmaeQAAAAAA4H8U8wAAAAAAWAzFPOAF+fn5stlsstlsys/Pt/x6rIQxAQBUZ97Ic+4sw1v5tKLlkLcB76GYBwAAAADAYijmAQAAAACwGIp5AAAAAAAshmIeAAAAAACLoZgHAAAAAMBiKOYBAAAAALAYinkAAAAAACyGYh4AAAAAAIuhmAcAAAAAwGIo5gEAAAAAsBiKeQAAAAAALIZiHgAAAAAAi6GYBwAAAADAYijmAQAAAACwGIp5AAAAAAAshmIeAAAAAACLoZgHAAAAAMBiKOYBAAAAALCYWoEOAPAVh8Ph/HvVqlXq27ev7HZ70MbijXjLW0bSA0vLnXfXjIEerQsAAABA4FTpyPyMGTNks9k0adIkL4UDeEdmZqbatWvnfDxgwAAlJSUpMzMzKGPxRrzBtM0AAAAAfKvSR+a/+uorzZ8/X506dfJmPECVZWZmKjU1VcYYl/acnBylpqYqIyNDKSkpQROLpCrH6956wspdRvEj94WnTjr/Pv+hZQoJDZfE0XsAAAAgWFTqyPyxY8c0fPhwPf/886pfv763YwIqzeFwaOLEiSWKWknOtkmTJrmcjh7IWCZOnFjleB0Oh4aMuqPMZRhjfn++0PfbDAAAAMA/KlXMjxs3TgMHDlSfPn0q7FtQUKC8vDyXCfCV1atXa8+ePWU+b4xRdna2Vq9eHRSx7Nmzp8rxrl69Wo6jB8uNxXH0oAr2bK44aAA1HnkbAABr8LiYX7x4sTZs2KC0tDS3+qelpSkmJsY5JSYmehwk4K7c3Fyv9qsKb66jvGW5ux7Hsd+8FQ6Aaoy8DQCANXhUzGdnZ2vixIl67bXXFB4e7tY8U6ZM0ZEjR5xTdnZ2pQIF3BEXF+fVflXhzXWUtyx312OP5JIYABUjbwMAYA0e3QDv66+/1v79+3XRRRc52xwOh1atWqV//etfKigoKPFTWmFhYQoLK//GW4C3JCcnKyEhQTk5OaVeQ26z2ZSQkKDk5OSgiKVZs2aSVKV4k5OTZY9qVO6p9vaoRgpLaF+JrQBQ05C3AQCwBo+OzF911VX69ttvtWnTJufUtWtXDR8+XJs2bQrYb3gDRex2u2bNmiXp90K4uKLHM2fO9Mv/qjuxzJo1q8rx2u12NbhqTLmxNLhqjGwhvD8BAACA6sKjI/NRUVHq0KGDS1tERIQaNmxYoh0IlJSUFGVkZGjChAnKyclxtickJGjmzJl++1k6T2Kparx12/ZU7OC/6tBH8+U49quz3R7VSA2uGqO6bXt6ZXv4+ToAAAAgOFT6d+aBYJaSkqI+ffooJiZGkpSVlaW+ffsG5OwRd2Ipr487BbT0e0Ef1qKz9sy6UZIUmzpNdVpeyBF5AAAAoBqqcjG/cuVKL4QBeF/xYvnyyy8P6GUg7sTijXiLF+7hiR0o5AEAAIBqqlK/Mw8AAAAAAAKHYh4AAAAAAIuhmAcAAAAAwGIo5gEAAAAAsBiKeQAAAAAALIZiHgAAAAAAi6GYBwAAAADAYqr8O/MAUFzSA0udf7f4y/uSpPaPrXTps2vGQD9GBAAAAFQ/HJkHAAAAAMBiODIPBFjRkezCUyedbec/tEwhoeGBCgkAAABAkOPIPAAAAAAAFkMxDwAAAACAxVDMAzVM4amT2v3kNdr95DUup/YDAFCa/Px82Ww22Ww25efnBzqcoOGNcbHa2Fot3urGSuNvpVitjGIeAAAAAACLoZgHAAAAAMBiKOYBAAAAALAYinkAAAAAACyGYh4AAAAAAIuhmAcAAAAAwGIo5gEAAAAAsBiKecALHA6H8+9Vq1a5PPYmU/i/5Z7M/s7lcU3lr7EHACAQvJHn3FmGt/JpRcshbwPe41Ex/9xzz6lTp06Kjo5WdHS0LrnkEn3wwQe+ig2whMzMTLVr1875eMCAAUpKSlJmZqZX13P8hzXKfXGs8/GBjGnKmXerjv+wxqvr8YekB5aWO7nLX2MPAEAgeCPPubMMb+XTipZD3ga8y6NiPiEhQTNmzNDXX3+t9evX68orr9R1112nzZs3+yo+IKhlZmYqNTVVOTk5Lu05OTlKTU31WnI6/sMaHXh7uhzHfnVpdxw9qANvT7dkQV9V/hp7AAACwRt5zp1leCufVrSc+++/n7wNeFktTzoPGjTI5fETTzyh5557Tl988YXat2/v1cCAYOdwODRx4kQZY0o8Z4yRzWbTkFF3qNkXtWTOnHY+d/5DyxQSGu72ekyhQ4c+XlBun0MfL1Cdc3q4H7zFuTP2kyZN0nXXXSe73R6ACAEAqDxv5Dl3ljFx4kTn48qux511SdKzzz5L3ga8rNLXzDscDi1evFj5+fm65JJLyuxXUFCgvLw8lwmoDlavXq09e/aU+bwxRo6jB1Wwp2pnrhTs2SzH0YPl9vHGeqzEnbHPzs7W6tWr/RgVUD2Qt4HA80aec2cZe/bs8Uo+rWhdksq9Np68DVSOx8X8t99+q8jISIWFhenOO+/UkiVLXK59OVtaWppiYmKcU2JiYpUCBoJFbm6uW/0cx36r0nrcnb+q67ESd8fe3X4A/oe8DQSeN/KcN3NgRcvy1rrI24BnPC7m27Ztq02bNunLL7/UXXfdpZEjR2rLli1l9p8yZYqOHDninLKzs6sUMBAs4uLi3Opnj6xfpfW4O39V12Ml7o69u/0A/A95Gwg8b+Q5b+bAipblrXWRtwHPeFzMh4aGqk2bNurSpYvS0tLUuXNnzZo1q8z+YWFhzrvfF01AdZCcnKyEhATZbLZSn7fZbLJHNVJYQtXuJxGW0F72qEbl9vHGeqzEnbFPTExUcnKynyMDrI+8DQSeN/KcO8tISEjwSj6taF2SZLfbyduAl1X5d+YLCwtVUFDgjVgAS7Hb7c4vss5OTkWPG1w1RraQqt3IxRZiV4OrxpTbxxvrsRJ3xn7mzJncRAcAYEneyHPuLGPWrFleyacVrctms+nee++t8noAuPKomJ8yZYpWrVqlXbt26dtvv9WUKVO0cuVKDR8+3FfxAUEtJSVFGRkZio+Pd2lPSEhQRkaG6rbt6ZX11G3bU7GD/yp7ZEOXdntUI8UO/qvX1mMlFY19SkpKgCIDAKDqvJHn3FmGt/JpRct56qmnyNuAl3n003T79+/Xn/70J+Xm5iomJkadOnXShx9+qKuvvtpX8QFBLyUlRX369FFMTIwkKSsrS3379pXdbte965Z6bT112/ZUWIvO2jPrRklSbOo01Wl5YY06In+28sYeAACr80aec2cZ3sqnFS2HvA14l0fF/IsvvuirOABLK56ELr/8cp8lpeKFe3hihxpdyBfx19gDABAI3shz7izDW/m0ouWQtwHvqfI18wAAAAAAwL88OjIPoGYICQ1Xi7+8X+U+wSIiIkLGmECHAQCoYcg/gcPYoybgyDwAAAAAABbDkXkAQSfpgYpvHLhrxkA/RAIAAAAEJ47MAwAAAABgMRTzAAAAAABYDKfZA5VU/FTwwlMnnX+f/9AyhYSGByIkAAAAADUER+YBAAAAALAYinkAAAAAACyGYh4AAAAAAIuhmAcAAAAAwGK4AR4AAABQQxXd0Nedm/mW1WfXjIE+jhJAaSjmAQAAgGrIk0IdgPVQzAMAAACoNHd+rpej94D3cc08AAAAAAAWw5F5AAAAwGKKHw0HUDNxZB4AAAAAAIvhyDwAAAAAn3LnZnxcVw94hmIegCWxUwAAAICajNPsAQAAAACwGI7MA6XgpjIAACCQ+I14ABXhyDxQw5hCh/Pvk9nfuTwGAOBsDsf/8sSqVatcHtdk3hgXq40t+xCBZaX/FyvFamUeFfNpaWnq1q2boqKi1LhxYw0ePFg//PCDr2ID4GXHf1ij3BfHOh8fyJimnHm36vgPawIYFQAgWGVmZqpdu3bOxwMGDFBSUpIyMzMDGFXgeWNcrDa2/tiHSHpgablTTWal/xcrxWp1HhXzn376qcaNG6cvvvhCy5cv1+nTp9W3b1/l5+f7Kj4AXnL8hzU68PZ0OY796tLuOHpQB96eTkEPAHCRmZmp1NRU5eTkuLTn5OQoNTW1xu6Ye2NcrDa27EMElpX+X6wUa3XgUTG/bNkyjRo1Su3bt1fnzp21aNEi/fzzz/r66699FR8ALzCFDh36eEG5fQ59vIDT5QAAkn4/RXbixIkyxpR4rqht0qRJNe7UWW+Mi7vLCJaczD5EYFnpvWilWKuLKl0zf+TIEUlSgwYNyuxTUFCgvLw8lwmAfxXs2SzH0YPl9nEcPaiCPZv9FBGAYEXehiStXr1ae/bsKfN5Y4yys7O1evVqP0YVeN4YF3eXESw5mX2IwLLSe9FKsVYXlS7mCwsLNWnSJF166aXq0KFDmf3S0tIUExPjnBITEyu7yqCTn58vm80mm81W5qUG7vQJFsG0Pd6IJSIiQsYYGWMUERFR6fV4Q0houFr85X21+Mv7Zd6F1p0+lV2P49hvbs3vbj9vKTx1UrufvEa7n7zG5W69nvaxEn+9z4Lp/ewvwfT55M/leFt1ztveEKyvW1WUtk25ubluzetuP28J9GebN8bF3WX4OyeXJRD7EO7k/mB6L/oybwfre7EqMQTj54ZVVbqYHzdunL777jstXry43H5TpkzRkSNHnFN2dnZlVwmgkuyR9b3aD0D1Rd6GJMXFxXm1X3XhjXFxdxnBkpPZhwgsK70XrRRrdVGp35kfP3683n//fa1atUoJCQnl9g0LC1NYWFilggPgHWEJ7WWPalTuaXL2qEYKS2jvx6gABCPyNiQpOTlZCQkJysnJKfX6V5vNpoSEBCUnJwcgusDxxrgkJydbKicH0z5E8TvaFz9if/5Dy5xnJO6aMdDncfiTld6LVoq1uvDoyLwxRuPHj9eSJUv0ySefqGXLlr6KC4AX2ULsanDVmHL7NLhqjGwhdj9FBAAIZna7XbNmzZL0+w54cUWPZ86cKbu9ZuUNb4yL3W6tnMw+RGBZ6b1opVirC4+K+XHjxunVV19Venq6oqKitHfvXu3du1cnTpzwVXwAvKRu256KHfxX2SMburTboxopdvBfVbdtzwBFBgAIRikpKcrIyFB8fLxLe0JCgjIyMpSSkhKgyALLG+NitZxstXirGyu9F60Ua3Xg0Wn2zz33nCSpV69eLu0LFy7UqFGjvBUTAB+p27anwlp01p5ZN0qSYlOnqU7LC/k2HQBQqpSUFPXp00cxMTGSpKysLPXt27fGH1nzxrhYLSdbLd7qxkrvRSvFanUeFfOlXfsAwFqKJ93wxA4kYQBAuYrvgF9++eXskP9/ZY1L8eu6K2K1nGy1eKsbK70XrRSrlVXqBngAYAUV7VBVt5vkAAAAoOagmEeNVFTklXUnVAAAAAAIZhTzAAAAAKoFdy5z4Mw8VBce3c0eAAAAAAAEHsU8AAAAAAAWQzEPAAAAAIDFcM08AAAA4CZuogsgWHBkHgAAAAAAi6GYBwAAAADAYjjNHgAAAECNwc/XobrgyDwAAAAAABZDMQ8AAAAAgMVwmj2AGovT7AAAAGBVFPOodtwp0AAAAM7GPgQAK+E0ewAAAAAALIZiHgAAAAAAi+E0ewAAAAAopqJLLrinDoIBxTzgBSGh4Wrxl/erzXqshDEBAFRn3shz7izDW/m0ouWQtwHv4TR7AAAAAAAshiPzAAAAqPa4Uz2A6oYj81XgcDicf69atcrlsSd9gkUwbY+/YjGF/5vnZPZ3Lo/he+6Mf3V7jfz1vx1M72d/CabPJ38uB/5VHV83K21TTfxsq4mstn/gr7yNyqnOY0sxX0mZmZlq166d8/GAAQOUlJSkzMxMj/oEi2DaHn/FcvyHNcp9cazz8YGMacqZd6uO/7CmilsAd7gz/tXtNfLX/3YwvZ/9JZg+n/y5HPhXdXzdrLRNNfGzrSay2v6Bv/I2Kqe6j63NGGM8mWHVqlV6+umn9fXXXys3N1dLlizR4MGD3Z4/Ly9PMTExOnLkiKKjoz2NNyhkZmYqNTVVZw+dzWaTJGVkZEhShX1SUlL8EG3Fgml7vBFLo+umqG7bnuWu5/gPa3Tg7ellPh87+K8VLsOqCk+dVPY/UiVJifdkKCQ03O8xuDP+kizzGrlzR1t/vc+C6f3sL+5ss78+n9xZj7/iLa4qubc65G1vCMTr5mvublN+fr4iIyMlSceOHVNERERQxipVff/AE97Ip8GQkz3h63ittn9QUbxvvfWWV/J20TKC4b3ormCI1cqf2+7mXo+L+Q8++ECff/65unTpopSUlBpXzDscDiUlJWnPnj2lPm+z2dSsWTNJKrdPQkKCdu7cKbvd7rNY3RFM2+NOLLaIBrJJchz7tczl2KMaqdmdL8oWUnosptChnHm3ynH0YKWXYWWB3nFwZ/xDIhtW+XX2p4qKeX+9z4Lp/ewv7myzvz6f3FmPv+I9G8V81QTqdfMlT7bp5MmTAd0p99ZnW0hkQ6/mDYp578Zrtf0Dd+JNTEysct4u/tkSDAWyuwIdq9U/t93NvR6fZt+/f389/vjjuv76693qX1BQoLy8PJfJylavXl3mP4UkGWO0Z8+eCvtkZ2dr9erVvgjRI8G0Pe7EUnjs13I/wCXJcfSgCvZsLvP5gj2by/3gdWcZqDx3xt8br3Mw8df7LJjez/7izjb76/PJnfX4K96qqG552xus8Lp5ykrb5K3PNivljZrIavsH7sTrjbwdLO9Dq6kpY+vza+bT0tIUExPjnBITE329Sp/Kzc0NymUFQwxVXZY3Y3Ec+61Sz1WmHzzjzXG1ymvkr/dZML2f/cXdOP31+VRRP3/FWxXVLW97gxVeN09ZaZv8tX+AwLLa/oG76/BG3g6G96HV1JSx9XkxP2XKFB05csQ5ZWdn+3qVPhUXFxeUywqGGKq6LG/GYo+sX6nnKtMPnvHmuFrlNfLX+yyY3s/+4m6c/vp8qqifv+KtiuqWt73BCq+bp6y0Tf7aP0BgWW3/wN11eCNvB8P70Gpqytj6vJgPCwtTdHS0y2RlycnJSkhIcN444WxF119U1CcxMVHJycm+DNUtwbQ97sQSEtlQ9siG5S7HHtVIYQnty3w+LKG97FGNqrQMVJ474++N1zmY+Ot9FkzvZ39xZ5v99fnkznr8FW9VVLe87Q1WeN08ZaVt8tf+AQLLavsH7sTrjbwdLO9Dq6kpY8tP03nIbrdr1qxZklTin6Po8axZsyrsM3PmzKC42UIwbY87sTTsc4ca9Lmj3OU0uGpMuTc9sYXY1eCqMVVaBirPnfH3xuscTPz1Pgum97O/uLPN/vp8cmc9/ooX3lUdXzcrbZO/9g8QWFbbP3An3pNdblbrB5cp6YGlpU6tH1xmmfeh1VjpM64qKOYrISUlRRkZGYqPj3dpT0hIcP7EgTt9gkUwbU9F66nbtqfqtu2p2MF/LfHNrD2qkds/R+KNZaDy3Bn/6vYa+et9FkzvZ38Jls8nd9dT3ca/pqiOr5uVtslf+wcILKvtH3gjFiu9D62mJoytxz9Nd+zYMW3fvl2SdOGFF+rZZ59V79691aBBAzVv3rzC+avTT9wUbYskZWVlqW/fviW+3XGnT7AIhu1JemCpJMlxMl97Zt0oSYpNnaY6LS8s8S2rO30q4o1lWE0w/QyOv15nX3Pnd+aL+Ot9FgzvZ3/z1/Z4az3+HH9+ms57qtv7Rqp4mwL9E1PFlRVr0f6D5L+8wU/T+S5eq+0fVCWWon0Idz5bgum9WJFgitWKn9vu5t5ani54/fr16t27t/PxvffeK0kaOXKkFi1a5HmkFlb8n+Dyyy8v9Z/CnT7BIpi2p/gHYHhih1I/EN3p4431wHf89Tr7WvGdyLIUJWt/vc+C6f3sL/7aHm+tp7qNf01RHV83K2xT0eds4amTzrY7lh9XyKfLSvS1Qt5A+ay2f+CNWKzwPrSq6jy2HhfzvXr1kocH8wEAAAAAgBdxzTwAAAAAABbj8ZF5AAAAAEDVlXYJyfkPLXPej8CT+/Kg5uHIPAAAAAAAFsORefiNOzcJAwAANQv7BwBQORTzAOAHnEYHAAAAb6KYBwAAAIAgVPzMFQ4I4GwU80ANExIarhZ/eT/QYQAALCIiIoKfJS6FN/Kp1XKy1eKtbqw0/nxu+AfFPLyC690AAMDZ2D8AAN+hmAeAIMBpdAAAAPAExTwq5E6RAQAAAMD/3DkDhgMC1RPFPAAAACqF0+gBIHAo5lHhT2YBCA588w4AAIAi1aaYD8Q3w+4Uv1W59pVvuwEAQKCwHwJUHxW9nzkYYE3Vppi3GhIkAF+o6EwbkjUAAED1QDEPAABQw3BzWwDFcSmfNVHMA0AN4q0deBI6ELw4+w8AagaKeQCAx/xVLPClAVASxTqAQODoffChmAcABC1PihZ/nSrsrfVwX4OahyIcAOBNFPMAAMDvrFrYcn05AJSNo/f+RTEPAAAAAPALb3yZyxcCv6OYBwAAAABYRjBdhhfILxZCKjPTnDlzlJSUpPDwcPXo0UPr1q3zdlwAAAAAAKAMHhfz//nPf3Tvvfdq6tSp2rBhgzp37qx+/fpp//79vogPAAAAAACcxeNi/tlnn9Xtt9+u0aNHq127dpo3b57q1q2rl156yRfxAQAAAACAs3h0zfypU6f09ddfa8qUKc62kJAQ9enTR2vXri11noKCAhUUFDgfHzlyRJKUl5dXmXjLVFhw3KvLc2udxa6/KCw4LpnCSvUJFsG0Pf6KxUqvT3UUTP9z/hJM/9s1cWyDaT1lLcfb+bH4Mo0xFfatznnbG6rb+0ay1jbVxM+2mshqrzP7pMHN12Mb0LxtPJCTk2MkmTVr1ri0T5482XTv3r3UeaZOnWokMTExMTExMQV4ys7OrjDXk7eZmJiYmJiCY6oob9uMceNr+v/vl19+UbNmzbRmzRpdcsklzvb7779fn376qb788ssS85z9DX9hYaEOHTqkhg0bymazubvqcuXl5SkxMVHZ2dmKjo72yjLxO8bWtxhf32FsfYex9R1fja0xRkePHlV8fLxCQsq/wo68bW2MrW8xvr7D2PoOY+s7gc7bHp1m36hRI9ntdu3bt8+lfd++fWratGmp84SFhSksLMylrV69ep6s1m3R0dH8g/oIY+tbjK/vMLa+w9j6ji/GNiYmxq1+5O3qgbH1LcbXdxhb32FsfSdQedujG+CFhoaqS5cu+vjjj51thYWF+vjjj12O1AMAAAAAAN/x6Mi8JN17770aOXKkunbtqu7du2vmzJnKz8/X6NGjfREfAAAAAAA4i8fF/I033qgDBw7o4Ycf1t69e3XBBRdo2bJlatKkiS/ic0tYWJimTp1a4rRAVB1j61uMr+8wtr7D2PpOTRnbmrKdgcDY+hbj6zuMre8wtr4T6LH16AZ4AAAAAAAg8Dy6Zh4AAAAAAAQexTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDGWKebnzJmjpKQkhYeHq0ePHlq3bl25/d98802dd955Cg8PV8eOHZWVleWnSK3Hk7F9/vnnlZycrPr166t+/frq06dPha9FTefp/26RxYsXy2azafDgwb4N0MI8HdvDhw9r3LhxiouLU1hYmM4991w+G8rg6djOnDlTbdu2VZ06dZSYmKh77rlHJ0+e9FO01rBq1SoNGjRI8fHxstlsevvttyucZ+XKlbrooosUFhamNm3aaNGiRT6P01vI275D3vYdcrZvkbd9h7ztfZbI28YCFi9ebEJDQ81LL71kNm/ebG6//XZTr149s2/fvlL7f/7558Zut5unnnrKbNmyxfztb38ztWvXNt9++62fIw9+no7tTTfdZObMmWM2btxovv/+ezNq1CgTExNj9uzZ4+fIrcHT8S2yc+dO06xZM5OcnGyuu+46/wRrMZ6ObUFBgenatasZMGCA+eyzz8zOnTvNypUrzaZNm/wcefDzdGxfe+01ExYWZl577TWzc+dO8+GHH5q4uDhzzz33+Dny4JaVlWUefPBBk5mZaSSZJUuWlNv/p59+MnXr1jX33nuv2bJli5k9e7ax2+1m2bJl/gm4CsjbvkPe9h1ytm+Rt32HvO0bVsjblijmu3fvbsaNG+d87HA4THx8vElLSyu1/5AhQ8zAgQNd2nr06GHuuOMOn8ZpRZ6O7dnOnDljoqKizMsvv+yrEC2tMuN75swZ07NnT/PCCy+YkSNHsmNQBk/H9rnnnjOtWrUyp06d8leIluXp2I4bN85ceeWVLm333nuvufTSS30ap5W5s1Nw//33m/bt27u03XjjjaZfv34+jMw7yNu+Q972HXK2b5G3fYe87XvBmreD/jT7U6dO6euvv1afPn2cbSEhIerTp4/Wrl1b6jxr16516S9J/fr1K7N/TVWZsT3b8ePHdfr0aTVo0MBXYVpWZcf30UcfVePGjXXrrbf6I0xLqszYvvvuu7rkkks0btw4NWnSRB06dND06dPlcDj8FbYlVGZse/bsqa+//tp5St9PP/2krKwsDRgwwC8xV1dWzWXkbd8hb/sOOdu3yNu+Q94OHoHIZbV8tmQvOXjwoBwOh5o0aeLS3qRJE23durXUefbu3Vtq/7179/osTiuqzNie7S9/+Yvi4+NL/OOicuP72Wef6cUXX9SmTZv8EKF1VWZsf/rpJ33yyScaPny4srKytH37do0dO1anT5/W1KlT/RG2JVRmbG+66SYdPHhQl112mYwxOnPmjO6880799a9/9UfI1VZZuSwvL08nTpxQnTp1AhRZ+cjbvkPe9h1ytm+Rt32HvB08ApG3g/7IPILXjBkztHjxYi1ZskTh4eGBDsfyjh49qptvvlnPP/+8GjVqFOhwqp3CwkI1btxYCxYsUJcuXXTjjTfqwQcf1Lx58wIdmuWtXLlS06dP19y5c7VhwwZlZmZq6dKleuyxxwIdGoBiyNveQ872PfK275C3q4+gPzLfqFEj2e127du3z6V93759atq0aanzNG3a1KP+NVVlxrbI3//+d82YMUMfffSROnXq5MswLcvT8d2xY4d27dqlQYMGOdsKCwslSbVq1dIPP/yg1q1b+zZoi6jM/25cXJxq164tu93ubDv//PO1d+9enTp1SqGhoT6N2SoqM7YPPfSQbr75Zt12222SpI4dOyo/P19jxozRgw8+qJAQvjeujLJyWXR0dNAelZfI275E3vYdcrZvkbd9h7wdPAKRt4P+lQoNDVWXLl308ccfO9sKCwv18ccf65JLLil1nksuucSlvyQtX768zP41VWXGVpKeeuopPfbYY1q2bJm6du3qj1AtydPxPe+88/Ttt99q06ZNzunaa69V7969tWnTJiUmJvoz/KBWmf/dSy+9VNu3b3fubEnSjz/+qLi4OHYIiqnM2B4/frxE4i/a+TLG+C7Yas6quYy87Tvkbd8hZ/sWedt3yNvBIyC5zGe31vOixYsXm7CwMLNo0SKzZcsWM2bMGFOvXj2zd+9eY4wxN998s3nggQec/T///HNTq1Yt8/e//918//33ZurUqfzETRk8HdsZM2aY0NBQk5GRYXJzc53T0aNHA7UJQc3T8T0bd8Ytm6dj+/PPP5uoqCgzfvx488MPP5j333/fNG7c2Dz++OOB2oSg5enYTp061URFRZnXX3/d/PTTT+a///2vad26tRkyZEigNiEoHT161GzcuNFs3LjRSDLPPvus2bhxo9m9e7cxxpgHHnjA3Hzzzc7+RT9xM3nyZPP999+bOXPmWOqn6cjbvkHe9h1ytm+Rt32HvO0bVsjblijmjTFm9uzZpnnz5iY0NNR0797dfPHFF87nrrjiCjNy5EiX/m+88YY599xzTWhoqGnfvr1ZunSpnyO2Dk/GtkWLFkZSiWnq1Kn+D9wiPP3fLY4dg/J5OrZr1qwxPXr0MGFhYaZVq1bmiSeeMGfOnPFz1NbgydiePn3aTJs2zbRu3dqEh4ebxMREM3bsWPPbb7/5P/AgtmLFilI/P4vGcuTIkeaKK64oMc8FF1xgQkNDTatWrczChQv9Hndlkbd9h7ztO+Rs3yJv+w552/uskLdtxnAuBQAAAAAAVhL018wDAAAAAABXFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxVDMAwAAAABgMRTzAAAAAABYDMU8AAAAAAAWQzEPAAAAAIDFUMwDAFADdenSRWPHjq2w386dOzV+/Hide+65qlu3rurWrat27dpp3Lhx+r//+z+P1zthwgTZbDZt3769zD4PPvigbDZbpZYPAEBNYTPGmEAHAQAA/Cc3N1fNmjXTe++9p4EDB5bZ7/3339eNN96oWrVqafjw4ercubNCQkK0detWZWZmavfu3dq5c6datGjh9rq//PJLXXzxxXrkkUf08MMPl9qnVatWioyMpJgHAKAcFPMAANQwL730ksaPH69ff/1VderUKbXPjh071LlzZzVv3lwff/yx4uLiXJ4/c+aM5s6dq+uvv16JiYkerf+cc85RrVq19P3335d4bu3aterZs6dmzJihv/zlLx4tFwCAmoTT7AEAcMO0adNks9n0448/asSIEYqJiVFsbKweeughGWOUnZ2t6667TtHR0WratKmeeeYZl/lPnTqlhx9+WF26dFFMTIwiIiKUnJysFStWlFjX4sWL1aVLF0VFRSk6OlodO3bUrFmznM+fPn1ajzzyiM455xyFh4erYcOGuuyyy7R8+XK3tmXp0qXq3bt3mYW8JD311FPKz8/XwoULSxTyklSrVi1NmDChRCG/detWpaamqkGDBgoPD1fXrl317rvvuvQZPny4tm7dqg0bNpRYbnp6umw2m4YNG+bWtgAAUFNRzAMA4IEbb7xRhYWFmjFjhnr06KHHH39cM2fO1NVXX61mzZrpySefVJs2bXTfffdp1apVzvny8vL0wgsvqFevXnryySc1bdo0HThwQP369dOmTZuc/ZYvX65hw4apfv36evLJJzVjxgz16tVLn3/+ubPPtGnT9Mgjj6h3797617/+pQcffFDNmzcvtTg+2+nTp/XRRx9pwIAB5fZ7//331aZNG/Xo0cPtsdm8ebMuvvhiff/993rggQf0zDPPKCIiQoMHD9aSJUuc/YYPHy7p98K9OIfDoTfeeEPJyclq3ry52+sFAKAm4jR7AADcUFRAjxkzRvPnz5f0e/GZlJSknJwcpaWlOU8LP3z4sOLj4zVkyBAtWrTI2dfhcCg0NNS5zMOHD+u8887TwIED9eKLL0qSJk2apIULF+rQoUOy2+2lxnLBBRcoISFB77//vsfb8cknn+iqq67Szp07lZSUVGqfvLw8xcTElCjCi2I+c+aM83FERITzCH+fPn20f/9+ffXVVwoLC5MkGWN02WWX6cCBA/rxxx+d83Xv3l25ubnavXu3QkJ+P7bw4Ycf6g9/+IPmz5+vMWPGeLxtAADUJByZBwDAA7fddpvzb7vdrq5du8oYo1tvvdXZXq9ePbVt21Y//fSTS9+iQr6wsFCHDh3SmTNn1LVrV5cj6vXq1VN+fn65p8zXq1dPmzdv1rZt2zyOPysrS+3atSuzkJd+L+YlKTIyssRzvXr1UmxsrHOaM2eOJOnQoUP65JNPNGTIEB09elQHDx7UwYMH9euvv6pfv37atm2bcnJynMsZMWKE9uzZ43L2Qnp6ukJDQ3XDDTd4vF0AANQ0FPMAAHjg7NO/Y2JiFB4erkaNGpVo/+2331zaXn75ZXXq1Ml5nXtsbKyWLl2qI0eOOPuMHTtW5557rvr376+EhATdcsstWrZsmctyHn30UR0+fFjnnnuuOnbsqMmTJ7t95/elS5eWewd7SYqKipIkHTt2rMRz8+fP1/Lly/Xqq6+6tG/fvl3GGD300EMuxX5sbKymTp0qSdq/f7+z/9ChQ2W3252n2p88eVJLlixR//79Vb9+fbe2BQCAmoxiHgAAD5R26ntZp8MXv5Lt1Vdf1ahRo9S6dWu9+OKLWrZsmZYvX64rr7xShYWFzn6NGzfWpk2b9O677+raa6/VihUr1L9/f40cOdLZ5/LLL9eOHTv00ksvqUOHDnrhhRd00UUX6YUXXig39p07d2rr1q0VXi8fExOjuLg4fffddyWe69Gjh/r06aNLL73Upb1oG+677z4tX7681KlNmzYu23n11Vfrrbfe0unTp/Xee+/p6NGjzuvpAQBA+WoFOgAAAGqCjIwMtWrVSpmZmbLZbM72oqPWxYWGhmrQoEEaNGiQCgsLNXbsWM2fP18PPfSQsyBu0KCBRo8erdGjR+vYsWO6/PLLNW3aNJfLAM62dOlSxcTE6LLLLqsw3oEDB+qFF17QunXr1L179wr7t2rVSpJUu3Zt9enTp8L+0u83wlu2bJk++OADpaenKzo6WoMGDXJrXgAAajqOzAMA4AdFR++LH63/8ssvtXbtWpd+v/76q8vjkJAQderUSZJUUFBQap/IyEi1adPG+XxZsrKy1LdvX9WqVfF3+ffff7/q1q2rW265Rfv27Svx/Nn3z23cuLF69eql+fPnKzc3t0T/AwcOlGgbPHiw6tatq7lz5+qDDz5QSkqKwsPDK4wNAABwZB4AAL+45pprlJmZqeuvv14DBw7Uzp07NW/ePLVr187l2vTbbrtNhw4d0pVXXqmEhATt3r1bs2fP1gUXXKDzzz9fktSuXTv16tVLXbp0UYMGDbR+/XplZGRo/PjxZa7/xIkTWrFihebNm+dWvOecc47S09M1bNgwtW3bVsOHD1fnzp1ljNHOnTuVnp6ukJAQJSQkOOeZM2eOLrvsMnXs2FG33367WrVqpX379mnt2rXas2ePvvnmG5d1REZGavDgwc7r5jnFHgAA91HMAwDgB6NGjdLevXs1f/58ffjhh2rXrp1effVVvfnmm1q5cqWz34gRI7RgwQLNnTtXhw8fVtOmTXXjjTdq2rRpzp9wmzBhgt59913997//VUFBgVq0aKHHH39ckydPLnP9n3zyiQoKCtS/f3+3Y77uuuv07bff6plnntF///tfvfTSS7LZbGrRooUGDhyoO++8U507d3b2b9eundavX69HHnlEixYt0q+//qrGjRvrwgsv1MMPP1zqOoYPH6709HTFxcXpyiuvdDs2AABqOn5nHgCAGmDs2LFav3691q1bF+hQAACAF3BkHgCAGuCCCy7g5nIAAFQjHJkHAAAAAMBiuJs9AAAAAAAWQzEPAAAAAIDFUMwDAAAAAGAxFPMAAAAAAFgMxTwAAAAAABZDMQ8AAAAAgMVQzAMAAAAAYDEU8wAAAAAAWAzFPAAAAAAAFkMxDwAAAACAxfw/tu7StUyEMZwAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from resample.bootstrap import variance\n", "\n", "# inputs must have shape (N, K), where N is the dimension in which the algorithm resamples\n", "inputs = np.column_stack((pt, mass, is_probe))\n", "\n", "# compute results for original data set\n", "eps_k, n_rec = trafo(inputs, showfig=True)\n", "\n", "# compute variance of results with resampled data sets\n", "var_eps_k, var_n_rec = variance(trafo, inputs, size=10)" ] }, { "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "Plot the results." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure()\n", "plt.xlim(a_pt.edges[0], a_pt.edges[-1])\n", "plt.errorbar(a_pt.centers, eps_k, var_eps_k ** 0.5, fmt=\"ok\")\n", "plt.axhline(expected_eff_k, ls=\"--\", color=\"0.5\")\n", "plt.xlabel(\"$p_T$ / GeV/c\")\n", "plt.ylabel(\"kaon efficiency\");" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We recover the correct value within uncertainties." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.8.13 64-bit", "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": "3.11.5" }, "orig_nbformat": 4, "vscode": { "interpreter": { "hash": "1ee38ef4a5a9feb55287fd749643f13d043cb0a7addaab2a9c224cbe137c0062" } } }, "nbformat": 4, "nbformat_minor": 2 } resample-1.10.1/doc/examples.rst000066400000000000000000000003621470150054300165170ustar00rootroot00000000000000Examples ======== These are specific examples which show how to use resample in practice. In contrast to tutorials they are not designed to showcase particular functions in resample. .. toctree:: :maxdepth: 1 example/tag_and_probe resample-1.10.1/doc/index.rst000066400000000000000000000003421470150054300160060ustar00rootroot00000000000000.. |resample| image:: _static/logo.svg |resample| ========== .. include:: ../README.rst :start-after: skip-marker-do-not-remove .. toctree:: :maxdepth: 2 :hidden: reference tutorials examples changelog resample-1.10.1/doc/make.bat000066400000000000000000000014231470150054300155530ustar00rootroot00000000000000@ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=. set BUILDDIR=_build if "%1" == "" goto help %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.http://sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% :end popd resample-1.10.1/doc/make_raw_logo.py000066400000000000000000000022311470150054300173240ustar00rootroot00000000000000""" Generate prototypes of the resample logo. The final logo was further edited in Inkscape. The chosen version uses the Gentium Plus Regular font from https://fontlibrary.org/en/font/gentium-plus#Gentium%20Plus-Regular. """ import numpy as np from matplotlib import pyplot as plt for font, x0 in (("ubuntu", 0.2), ("gentium", 0.17)): plt.figure(figsize=(5, 1.4)) ax = plt.subplot() for k in ax.spines: ax.spines[k].set_visible(False) plt.tick_params( **{k: False for k in ax.spines}, **{f"label{k}": False for k in ax.spines} ) plt.gca().set_facecolor("none") size = 70 w = 0.05 h = 0.15 y0 = 0.1 # original plt.figtext(0, y0, "re", color="r", name=font, size=size, weight="bold") plt.figtext(x0, y0, "sample", color="0.2", name=font, size=size) # copies rng = np.random.default_rng(1) s = np.fromiter("resample", "U1") n = 2 for i, col in enumerate(("0.8", "0.9")): x = (i + 1) * w y = y0 + (i + 1) * h s2 = rng.choice(s, size=len(s)) plt.figtext(x, y, "".join(s2), color=col, name=font, size=size, zorder=-(i + 1)) plt.savefig(f"{font}.svg") resample-1.10.1/doc/plot_bench.py000066400000000000000000000017131470150054300166370ustar00rootroot00000000000000import matplotlib.pyplot as plt import json from pathlib import Path import numpy as np d = Path(__file__).parent if "__file__" in globals() else Path() with open(d / "bench_rcont.json") as f: data = json.load(f) vs = [[[], [], []], [[], [], []]] benchs = data["benchmarks"] for b in benchs: params = b["params"] m = params["method"] n = params["n"] k = params["k"] stats = b["stats"] val = stats["mean"] err = stats["stddev"] / stats["rounds"] ** 0.5 vs[m][0].append(n) vs[m][1].append(k) vs[m][2].append(val) fig, ax = plt.subplots(1, 2, figsize=(10, 4), sharey=True) for i, label in enumerate(("shuffle", "patefield")): ax[0].scatter(vs[i][0], vs[i][2], s=np.add(vs[i][1], 1), label=label) ax[1].scatter(vs[i][1], vs[i][2], s=10 * np.log(vs[i][0]) - 10) ax[0].loglog() ax[1].loglog() ax[0].set_xlabel("N") ax[1].set_xlabel("K") ax[0].set_ylabel("t/sec") plt.figlegend(loc="upper center", ncol=2, frameon=False) resample-1.10.1/doc/reference.rst000066400000000000000000000004661470150054300166440ustar00rootroot00000000000000Reference ========= bootstrap --------- .. automodule:: resample.bootstrap :members: jackknife --------- .. automodule:: resample.jackknife :members: permutation ----------- .. automodule:: resample.permutation :members: empirical --------- .. automodule:: resample.empirical :members: resample-1.10.1/doc/tutorial/000077500000000000000000000000001470150054300160115ustar00rootroot00000000000000resample-1.10.1/doc/tutorial/confidence_intervals.ipynb000066400000000000000000000442401470150054300232440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Confidence intervals\n", "\n", "In this notebook, we look at the confidence interval methods in `resample`. We try them on the median of an exponential distribution." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "jupyter": { "outputs_hidden": false, "source_hidden": false }, "nteract": { "transient": { "deleting": false } } }, "outputs": [], "source": [ "import numpy as np\n", "from resample.bootstrap import confidence_interval as ci, bootstrap\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "jupyter": { "outputs_hidden": false, "source_hidden": false }, "nteract": { "transient": { "deleting": false } } }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.default_rng(1)\n", "\n", "# generate data\n", "data = rng.exponential(size=1000)\n", "\n", "# generate confidence intervals\n", "cis = {\n", " m: ci(np.median, data, cl=0.68, size=100, ci_method=m, random_state=rng)\n", " for m in (\"percentile\", \"bca\")\n", "}\n", "\n", "# compute mean and std. deviation of replicates\n", "rep = bootstrap(np.median, data, size=1000, random_state=rng)\n", "mr = np.mean(rep)\n", "sr = np.std(rep)\n", "\n", "# draw everything\n", "for i, (m, v) in enumerate(cis.items()):\n", " for j in (0, 1):\n", " plt.axvline(v[j], color=f\"C{i}\", label=m if j == 0 else None)\n", "\n", "plt.hist(rep, facecolor=\"0.8\")\n", "plt.axvline(np.log(2), lw=3, color=\"k\")\n", "plt.errorbar(mr, 100, 0, sr, fmt=\"o\") \n", "plt.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean of the replicates and its standard deviation is shown with the dot and the horizontal error bar. The three interval methods are shown as thin vertical lines. The thick black line is the true value of the median for an exponential distribution." ] } ], "metadata": { "kernel_info": { "name": "python3" }, "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": "3.11.9" }, "nteract": { "version": "0.23.3" } }, "nbformat": 4, "nbformat_minor": 4 } resample-1.10.1/doc/tutorial/jackknife_vs_bootstrap.ipynb000066400000000000000000000056641470150054300236210ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bootstrap and Jackknife comparison\n", "\n", "In this notebook we compare the bootstrap to the jackknife. Bootstrap resampling is superior to jackknifing, but the jackknife is deterministic, which may be helpful, and it can exactly remove biases of order 1/N from an estimator. The bootstrap does not have a simple bias estimator.\n", "\n", "We consider as estimators the arithmetic mean and the naive variance $\\hat V = \\langle x^2 \\rangle - \\langle x \\rangle^2$ from a sample of inputs. We use `resample` to compute the variances of these two estimators and their bias. This can be done elegantly by defining a single function `fn` which returns both estimates.\n", "\n", "The exact bias is known for both estimators. It is zero for the mean, because it is a linear function of the sample. For $\\hat V$, the bias-corrected estimate is $\\frac N{N-1} \\hat V$, and thus the bias is $\\frac{- 1}{N - 1} \\hat V$.\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "estimates [0.22 0.636]\n", "std.dev. (jackknife) [0.399 0.539]\n", "std.dev. (bootstrap) [0.345 0.36 ]\n", "bias (jackknife) [ 0. -0.159]\n", "bias (exact) [ 0. -0.159]\n" ] } ], "source": [ "from resample import jackknife as j, bootstrap as b\n", "import numpy as np\n", "from scipy import stats\n", "\n", "rng = np.random.default_rng(1)\n", "data = rng.normal(size=5)\n", "\n", "\n", "def fn(d):\n", " return np.mean(d), np.var(d, ddof=0) # we return the biased variance\n", "\n", "\n", "print(\"estimates \", np.round(fn(data), 3))\n", "print(\"std.dev. (jackknife)\", np.round(j.variance(fn, data) ** 0.5, 3))\n", "print(\"std.dev. (bootstrap)\", np.round(b.variance(fn, data, random_state=1) ** 0.5, 3))\n", "print(\"bias (jackknife) \", np.round(j.bias(fn, data), 3))\n", "print(\"bias (exact) \", np.round((0, -1 / (len(data) - 1) * np.var(data, ddof=0)), 3))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The standard deviations for the estimates computed by bootstrap and jackknife differ by about 10 %. This difference shrinks for larger data sets.\n", "\n", "The Jackknife find the correct bias for both estimators." ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 4 } resample-1.10.1/doc/tutorial/leave-one-out-cross-validation.ipynb000066400000000000000000004127421470150054300250250ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Leave-one-out cross-validation\n", "\n", "Leave-one-out cross-validation is a simple generic tool for selecting the best empirical model. When we model data empirically, for example, with a polynomial, we want to select the model which provides the best compromise between bias and variance. If the empirical model is too simple, it won't be able to describe the data properly and the result is biased. If the model is too flexible, it will start to follow statistical noise, this leads to an increased variance. This is called overfitting and leads to poor generalization of the model.\n", "\n", "The general steps for LOO cross-validation are:\n", "\n", "- Remove i-th datum from input data set\n", "- Fit model to remaining data set\n", "- Use fitted model to predict the i-th datum and store difference to original i-th datum\n", "- Do this for all i and compute variance of the differences\n", "- Select model with the smallest variance\n", "\n", "The variance computed in this way is the mean-squared-error, which consists of a bias term squared and the variance. Minimizing this thus finds the best compromise between the two terms.\n", "\n", "We first use the jackknife resampling to implement this algorithm manually, then we use the function `cross_validation` which simplifies the process.\n", "\n", "I demonstrate below how to select the optimal order for a polynomial model with leave-one-out (LOO) cross validation." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from iminuit import Minuit\n", "from iminuit.cost import LeastSquares\n", "from resample.jackknife import resample, cross_validation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I create some toy data that follows polynomials of increasing degree." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "rng = np.random.default_rng(seed=1)\n", "\n", "x = np.linspace(0, 1, 100)\n", "y_set = np.empty((3, len(x)))\n", "for poly_order in (0, 1, 2):\n", " y_set[poly_order] = np.polyval(np.ones(poly_order + 1), x) + rng.normal(0, 0.1, len(x))\n", " \n", "for poly_order, y in enumerate(y_set):\n", " plt.plot(x, y, \".\", label=f\"order {poly_order}\")\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# apply leave-one-out cross-validation\n", "\n", "def predict(xin, yin, xout, order):\n", " def model(x, par):\n", " return np.polyval(par, x)\n", "\n", " # least-squares cost function to fit a polynomial to the toy data\n", " cost = LeastSquares(xin, yin, 0.1, model)\n", " m = Minuit(cost, np.zeros(order+1))\n", " m.strategy = 0 # faster, do not compute errors automatically\n", " m.migrad()\n", " assert m.valid\n", "\n", " return model(xout, m.values)\n", "\n", "\n", "data = []\n", "for poly_order, y in enumerate(y_set):\n", "\n", " variances = []\n", " poly_orders = np.arange(5)\n", " for poly_order in poly_orders:\n", " deltas = []\n", "\n", " for iloo, (xloo, yloo) in enumerate(resample(x, y)):\n", " yi_loo = predict(xloo, yloo, x[iloo], poly_order)\n", " deltas.append(y[iloo] - yi_loo)\n", "\n", " variances.append(np.var(deltas))\n", " data.append((poly_orders, variances))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, len(data), figsize=(10, 4))\n", "for true_poly_order, (axi, (poly_orders, variances)) in enumerate(zip(ax, data)):\n", " imin = np.argmin(variances)\n", " plt.sca(axi)\n", " plt.title(f\"true polynomial order {true_poly_order}\\nselected {imin}\")\n", " plt.plot(poly_orders, variances)\n", " plt.plot(poly_orders[imin], variances[imin], marker=\"o\")\n", " plt.semilogy()\n", " plt.ylabel(\"LOO variance\")\n", " plt.xlabel(\"model order\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can slightly simplify this by using `cross_validation` from the library." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "data = []\n", "for poly_order, y in enumerate(y_set):\n", " variances = []\n", " poly_orders = np.arange(5)\n", " for poly_order in poly_orders:\n", " variances.append(cross_validation(predict, x, y, poly_order))\n", " data.append((poly_orders, variances))" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots(1, len(data), figsize=(10, 4))\n", "for true_poly_order, (axi, (poly_orders, variances)) in enumerate(zip(ax, data)):\n", " imin = np.argmin(variances)\n", " plt.sca(axi)\n", " plt.title(f\"true polynomial order {true_poly_order}\\nselected {imin}\")\n", " plt.plot(poly_orders, variances)\n", " plt.plot(poly_orders[imin], variances[imin], marker=\"o\")\n", " plt.semilogy()\n", " plt.ylabel(\"LOO variance\")\n", " plt.xlabel(\"model order\")" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 2 } resample-1.10.1/doc/tutorial/permutation_tests.ipynb000066400000000000000000004705451470150054300226640ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "thick-cedar", "metadata": {}, "source": [ "# Permutation tests\n", "\n", "We demonstrate the tests from the permutation module. \n", "\n", "Permutations are generic way to estimate the distribution of the test statistic under the null hypothesis that both samples originate have been drawn from the same population. Once the distribution of the test statistic under the null is known, we can compute the p-value for the actually obtained value of the test statistic.\n", "\n", "The results are compared to the corresponding tests in scipy, which compute the p-value either exactly, if possible, or with asymptotic theory. Our example samples are drawn from the normal distribution, where the mean and the variance is varied." ] }, { "cell_type": "code", "execution_count": 1, "id": "related-costa", "metadata": {}, "outputs": [], "source": [ "from resample import permutation as perm\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats" ] }, { "cell_type": "code", "execution_count": 2, "id": "lesbian-professor", "metadata": {}, "outputs": [], "source": [ "rng = np.random.default_rng(1)\n", "\n", "d = {\n", " \"x\": rng.normal(0, 1, size=100),\n", " \"y\": rng.normal(1, 1, size=100),\n", " \"z\": rng.normal(0, 2, size=100)\n", "}" ] }, { "cell_type": "code", "execution_count": 3, "id": "reverse-boxing", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for test_name in (\"anova\",\n", " \"kruskal\",\n", " \"pearsonr\",\n", " \"spearmanr\",\n", " \"ttest\"):\n", "\n", " test = getattr(perm, test_name)\n", " fig, ax = plt.subplots(1, 4, figsize=(12, 3),\n", " sharex=True, sharey=True)\n", "\n", " correspondence = {\n", " \"ks\": \"ks_2samp\",\n", " \"pearsonr\": \"pearsonr\",\n", " \"spearmanr\": \"spearmanr\",\n", " \"ttest\": \"ttest_ind\",\n", " \"anova\": \"f_oneway\",\n", " }\n", " sc_test = getattr(stats, correspondence.get(test_name, test_name))\n", " \n", " for axi, (a, b) in zip(ax, \"xx xy yx xz\".split()):\n", " r = test(d[a], d[b], random_state=1)\n", " sc = sc_test(d[a], d[b])\n", " plt.sca(axi)\n", " plt.hist(r.samples)\n", " plt.axvline(r.statistic, color=\"k\")\n", " plt.title(f\"{test_name}({a}, {b})\\n\"\n", " f\"t R={r.statistic:.2g} S={sc[0]:.2g}\\n\"\n", " f\"P R={r.pvalue:.2f} S={sc[1]:.2f}\")" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 } resample-1.10.1/doc/tutorial/sklearn.ipynb000066400000000000000000006312641470150054300205270ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "focused-harvest", "metadata": {}, "source": [ "# Bootstrapping and machine learning\n", "\n", "We recently improved the interface of `resample` to make it easy to bootstrap training data sets for machine learning (ML) classifiers. So, this example demonstrates how one can bootstrap the ROC curve of a classifier from the training data set, without a separate validation set. In other words, this allows one to use the full data set for training and one obtains a very smooth ROC curve.\n", "\n", "Sounds too good to be true? Maybe it is! The bootstrap only work well with classifiers that build a smooth representation of the decision boundary, like a neural network. It does not work well with classifiers that use sharp decision boundaries which depend on the locations of individual points, like a boosted decision tree, random forest, or a kNN.\n", "\n", "Below we compute a bootstrapped ROC curve for the MLP and RandomForest classifiers from Scikit-Learn, a standard ROC curve from a train-test split, and finally from a separately generated high-statistics data set. The latter serves as an estimate of the \"true\" ROC curve. In case of the Random Forest, the bootstrapped ROC curve is too optimistic, while in case of the MLP it is ok." ] }, { "cell_type": "code", "execution_count": 8, "id": "approximate-nursing", "metadata": {}, "outputs": [], "source": [ "from resample.bootstrap import resample\n", "from sklearn import datasets\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import roc_curve\n", "from sklearn.model_selection import train_test_split\n", "from matplotlib import pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 9, "id": "laughing-butter", "metadata": {}, "outputs": [], "source": [ "# original data\n", "X, y = datasets.make_moons(1000, noise=0.3, random_state=1)\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)\n", "\n", "# original classifiers\n", "mlp = MLPClassifier(max_iter=1000) # iterations increased to avoid warning\n", "mlp.fit(X_train, y_train)\n", "\n", "rf = RandomForestClassifier(random_state=1)\n", "rf.fit(X_train, y_train);" ] }, { "cell_type": "code", "execution_count": 10, "id": "compatible-patch", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n", "y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n", "h = 0.02\n", "xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n", "\n", "cm = plt.cm.RdBu\n", "\n", "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", "for axi, clf in zip(ax, [mlp, rf]):\n", " plt.sca(axi)\n", "\n", " # plot the training points\n", " plt.plot(X_train[:, 0][y_train == 0], X_train[:, 1][y_train == 0], \"o\",\n", " color=cm(0.0), mec=\"w\", label=\"signal\")\n", " plt.plot(X_train[:, 0][y_train == 1], X_train[:, 1][y_train == 1], \"D\",\n", " color=cm(1.0), mec=\"w\", label=\"background\")\n", "\n", " # plot models\n", " Z = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 1]\n", " Z = Z.reshape(xx.shape)\n", " plt.contourf(xx, yy, Z, cmap=cm, alpha=0.5, zorder=0)\n", " plt.xlabel(\"feature 1\")\n", " plt.ylabel(\"feature 2\")\n", " plt.legend(frameon=False);" ] }, { "cell_type": "code", "execution_count": 11, "id": "reported-finland", "metadata": {}, "outputs": [], "source": [ "# generate ROC curve with validation set (standard method)\n", "fpr1 = {}\n", "tpr1 = {}\n", "for clf in (mlp, rf):\n", " fpr1[clf], tpr1[clf], _ = roc_curve(y_test, clf.predict_proba(X_test)[:, 1])" ] }, { "cell_type": "code", "execution_count": 12, "id": "identical-niagara", "metadata": {}, "outputs": [], "source": [ "# generate ROC curve from separately-generated high-statistics data set\n", "fpr2 = {}\n", "tpr2 = {}\n", "X_hs, y_hs = datasets.make_moons(100000, noise=0.3, random_state=1)\n", "for clf in (mlp, rf):\n", " fpr2[clf], tpr2[clf], _ = roc_curve(y_hs, clf.predict_proba(X_hs)[:, 1])" ] }, { "cell_type": "code", "execution_count": 13, "id": "inside-religious", "metadata": {}, "outputs": [], "source": [ "# generate ROC curve from training data with 20 bootstrap samples\n", "fpr3 = {}\n", "tpr3 = {}\n", "w_s = {}\n", "w_b = {}\n", "for clf in (mlp, rf):\n", " s = 0\n", " b = 0\n", " xrange = (0, 1)\n", " bins = 50\n", " for Xi, yi in resample(X_train, y_train, size=20):\n", " clf.fit(Xi, yi)\n", " pi = clf.predict_proba(X)[:, 1]\n", " s += np.histogram(pi[y == 1], range=xrange, bins=bins)[0]\n", " b += np.histogram(pi[y == 0], range=xrange, bins=bins)[0]\n", "\n", " w_s[clf] = s\n", " w_b[clf] = b\n", " tpr3[clf] = 1 - np.cumsum(s) / np.sum(s)\n", " fpr3[clf] = 1 - np.cumsum(b) / np.sum(b)" ] }, { "cell_type": "code", "execution_count": 14, "id": "hawaiian-minutes", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for clf in (mlp, rf):\n", " # plot score distributions for signal and background\n", " fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", " plt.suptitle({mlp: \"MLP\", rf: \"Random Forest\"}[clf])\n", " \n", " xe = np.linspace(*xrange, bins + 1)\n", " plt.sca(ax[0])\n", " plt.stairs(w_s[clf], xe, fill=True, alpha=0.5, label=\"signal\")\n", " plt.stairs(w_b[clf], xe, fill=True, alpha=0.5, label=\"background\")\n", " plt.legend(frameon=False, loc=\"upper center\")\n", " plt.xlabel(\"classifier score\");\n", " \n", " # plot ROC curves\n", " plt.sca(ax[1])\n", " plt.plot(fpr1[clf], tpr1[clf], ls=\"--\", label=\"train-test split\")\n", " plt.plot(fpr2[clf], tpr2[clf], ls=\":\", label=\"high-statistics\")\n", " plt.plot(fpr3[clf], tpr3[clf], drawstyle=\"steps-post\", label=\"bootstrap\")\n", " plt.legend(frameon=False)\n", " plt.xlim(-0.05, 1.05)\n", " plt.ylim(-0.05, 1.05)\n", " plt.xlabel(\"False positive rate\")\n", " plt.ylabel(\"True positive rate\")" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 } resample-1.10.1/doc/tutorial/usp_continuous_data.ipynb000066400000000000000000002321401470150054300231440ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "sitting-budapest", "metadata": {}, "source": [ "# USP test of on continuous data\n", "\n", "We demonstrate how the [USP test of independence](https://doi.org/10.1098/rspa.2021.0549) can be applied to continuous data.\n", "\n", "A test of independence is stronger than a test for zero correlation. A test of independence can also detect dependencies which give zero correlation. " ] }, { "cell_type": "code", "execution_count": 1, "id": "embedded-warner", "metadata": {}, "outputs": [], "source": [ "from resample import permutation as perm\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "x1 = rng.normal(0, 2, size=100)\n", "y1 = rng.normal(0, 3, size=100)\n", "\n", "cov = np.empty((2, 2))\n", "cov[0, 0] = 2 ** 2\n", "cov[1, 1] = 3 ** 2\n", "rho = 0.5\n", "cov[0, 1] = rho * np.sqrt(cov[0, 0] * cov[1, 1])\n", "cov[1, 0] = cov[0, 1]\n", "\n", "xy2 = rng.multivariate_normal([0, 0], cov, size=500)\n", "\n", "d = {\"x,y are independent\": (x1, y1), \"x,y are correlated\": xy2.T}" ] }, { "cell_type": "code", "execution_count": 2, "id": "prompt-military", "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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1R0SkViUlJfDz84OtrS2WLVuGmJgYZGVlYfLkyTVuY+TIkdi/fz8iIyPRvHlz9O/f/6l1NDU1IZPJlL5FT0tLw/bt22vUp6amJoYMGYKff/5ZWrb5cXfu3Km2/qBBg6ChoYF58+ZVmEEQQgB4dE2Vjo4Oli9fLu0DgHXr1iEvL6/Cinq1YWVlBS8vL3zzzTeVflNfXfzlKwM+HpMQAsuWLatQtlmzZgBQ4Y/8YcOGoaysDPPnz69Qp7S0VCrv7e0NbW1tREVFKfX3+Apsj6vpUt7l1q5di5KSEunx6tWrUVpaKv0OtW/fHt7e3tLm5uYmlfX29oa5uTm2bNmCLVu2oFu3bkoznpU9T9XF/qRWrVopXS9UHu+TMz/Dhg1DfHy8NPv6uNzcXJSWlkqPn+WaozfeeAPm5uZYvXq1UturV6+GgYGB0u/h3bt3ceXKFdy/f1/a9/7776OsrEzp9NOioiJER0fD3d1dWlXOxMQE3t7e+P777/HPP/9IZf/73/+ioKBA6VTZmrZJRFRraloIgohICCFEaGiokMlk4uDBg9K+BQsWCABi9+7d0r7yVdeeXJ1MCCEyMzOFlpaWACDGjx9fo37j4uIEAPH666+L1atXi/DwcGFlZSU6depUYXU1ACIwMLDSfu3t7YWBgYGYOHGi+Oabb0RERIQYOnSoMDMze2oMc+bMEQCEp6en+Prrr0VUVJQYNWqUmDlzplRm7ty5AoDo27evWLFihZgwYYLQ1NQUXbt2VVplrVevXqJDhw4V+ih/3r766qsKx5KTk4WZmZlo3ry5mDlzpli7dq2YP3++GDBggOjUqZNU7snV6oqLi0WrVq2EhYWF+Pzzz0VUVJTw8vISnTt3FgBEdHS0VPfHH38UAMTIkSPF999/L3744Qfp2Lhx4wQA0b9/f7F06VKxYsUKMXHiRGFrayu2bt0qlQsJCREAxIABA8SKFSuEv7+/sLW1FRYWFs+9Wp2zs7N4/fXXRVRUlAgKChIaGhqiR48eSivjVefjjz8WhoaGQiaTicWLF1c43rNnT2FgYCBmzZolVq1aJQYNGiQ9T4+vCFfZanVr1qwRAMR7770nVq9eLT755BPh6OhYYdyFhYXi1VdfFVpaWuLjjz8Wq1evFl9//bUYPXq0aNasmbhz506NxlKdlStXCgDi/fffF99++60YNWqUACA+//xzpXLlv6+Pr2woxKPV5rS0tMS0adPEN998Izw9PYWWlpY4cuSIUrnExEShq6srXF1dxerVq8WsWbOEnp6e6Nu3b4WYatomEVFtMDkiIrVJTEwUWlpaYsKECUr7S0tLRdeuXYWtra24d++eEEKICxcuCABKicPjBgwYIACI48eP17j/devWCScnJ6Grqyvatm0roqOjpT/uHldVciSEEFlZWSIwMFDY2dkJbW1tIZfLRZ8+fcTatWtrFMP69euFq6ur0NXVFWZmZqJXr14iNjZWqcyKFStE27Zthba2trC2thbjx4+Xnpdyz5IcCSHE9evXxahRo4RcLhfa2tripZdeEm+99Zb46aefpDKVLeV96dIl4e3tLQwNDYWFhYUICAgQ586dq5AclZaWigkTJghLS0shk8kqPLdr164Vbm5uQl9fXxgZGQlnZ2cxffp0cfv2balMWVmZCA8PFzY2NkJfX194eXmJixcvVljSWojaJ0dHjhwRY8eOFWZmZsLQ0FD4+vqKv//++6n1y8XGxgoAQiaTiZs3b1Y4/tdff4nBgwcLU1NTYWJiIoYOHSpu375do+SorKxMzJgxQ1hYWAgDAwPh4+Mjrl27Vum4//nnHxESEiJat24tdHR0hIWFhZR0P55EP4+1a9eKNm3aCB0dHdGqVSuxdOnSCklkVcnRgwcPxNSpU4VcLhe6urqia9euYu/evZX28/vvvwtPT0+hp6cnLC0tRWBgoMjPz69QrjZtEhHVlEyIJ+b7iYgaoFWrVmH69Om4fv260s1Nyw0ePBgXLlzAtWvX1BAdNTblN589depUhZuOEhHRi4vXHBFRo3Do0CF89tlnlSZGGRkZ2L17N0aOHKmGyIiIiKip4Gp1RNQobN26tcK+1NRUHDt2DP/5z3+gra2NcePGqSEyIiIiaio4c0REjdaRI0cwcuRIpKamYsOGDZDL5eoOiYiIiBoxXnNEREREREQEzhwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQBAS90B1DWFQoHbt2/DyMgIMplM3eEQEb0whBD4559/YGtrCw0Nfvf2OH42ERGpR20/m5pccnT79m3Y2dmpOwwiohfWzZs38fLLL6s7jAaFn01EROpV08+mJpccGRkZAQB6YAC0oK3maIiIVE9DX0/dIQAASkUJjj78Rfp/mP5P+XNy8+ZNGBsbqzkaIqL6VVhYCFtbWwCPvixq1qxZvfWdn58POzu7Gn82NbnkqPx0BS1oQ0vG5IiImj4NmY66Q1DC08YqKn9OjI2NmRwR0QtHU1NT+tnY2Lhek6NyNf1s4knhREREREREYHJEREREREQEgMkRERERERERgCZ4zRERERER1YxCoUBxcbG6w6AmrqioCPb29tLPj1+D9Ly0tbXrtD0mR0REREQvoOLiYqSmpkKhUKg7FGriFAoF1qxZAwDIyMio83vhmZqaQi6X18mCQEyOiIiIiF4wQghkZGRAU1MTdnZ2vHEzqVRZWRkePHgAAHBwcKizmR4hBO7fv4/s7GwAgI2NzXO3yeSIiIgaraNHj+Krr75CYmIiMjIysG3bNgwaNEipzOXLlzFjxgwcOXIEpaWlaN++PX7++We0aNECAPDw4UNMmTIFmzdvRlFREXx8fLBq1SpYW1tLbaSnp2P8+PE4dOgQDA0NMXr0aEREREBLix+j1DiVlpbi/v37sLW1hYGBgbrDoSaurKxM+llPT69OT4PT19cHAGRnZ8PKyuq52+bXBERE1GgVFhaic+fOWLlyZaXHr1+/jh49eqBt27Y4fPgwzp8/jzlz5kBP7/9unDt58mT8+uuv2Lp1K44cOYLbt2/jvffek46XlZVh4MCBKC4uxvHjx7FhwwbExMQgNDRU5eMjUpXyP1Z1dBrWfdKInkV5gl9SUvLcbT1zcnT06FG8/fbbsLW1hUwmw/bt25WOCyEQGhoKGxsb6Ovrw9vbG1evXn1quytXroSDgwP09PTg7u6OkydPPmuIRETUxPXv3x8LFizA4MGDKz0+a9YsDBgwAIsWLYKrqytatWqFd955B1ZWVgCAvLw8rFu3DkuWLMEbb7wBNzc3REdH4/jx4zhx4gQAYP/+/bh06RK+//57uLi4oH///pg/fz5WrlzJC9mp0eNNm6kpqMvf42dOjp72bd2iRYuwfPlyrFmzBgkJCWjWrBl8fHzw8OHDKtvcsmULgoODMXfuXJw5cwadO3eGj4+PdB4hERFRTSkUCuzevRuvvPIKfHx8YGVlBXd3d6Uv8xITE1FSUgJvb29pX9u2bdGiRQvEx8cDAOLj4+Hs7Kx0mp2Pjw/y8/ORnJxcad9FRUXIz89X2oiIqOF75uSoum/rhBCIjIzE7Nmz8e6776JTp0747rvvcPv27QozTI9bsmQJAgICMGbMGLRv3x5r1qyBgYEB1q9f/6xhEhHRCyo7OxsFBQVYuHAh+vXrh/3792Pw4MF47733cOTIEQBAZmYmdHR0YGpqqlTX2toamZmZUpnHE6Py4+XHKhMREQETExNps7Ozq+PRERGRKqjkmqPU1FRkZmYqfRNnYmICd3d36Zu4JxUXFyMxMVGpjoaGBry9vausA/DbOSIiqlz58sTvvvsuJk+eDBcXF8ycORNvvfWWtKSsqoSEhCAvL0/abt68qdL+iKhpqeySldoICwuDi4tLncVTV27fvg0tLS0kJSWpO5QqqWSZnfJv0ir7pq2qb9nu3r2LsrKySutcuXKlyr4iIiIQHh7+nBETEVFTY2FhAS0tLbRv315pf7t27fDHH38AAORyOYqLi5Gbm6s0e5SVlQW5XC6VefL616ysLOlYZXR1daGrq1tXQ6E64DBz93PVT1s4sI4ioefh5eUFFxcXREZG1lmbfn5+yM3Nfa5k5FnbCAsLw/bt2yskCxkZGTAzM6tRGzKZrMJKnVOnTsWECRNqFQs90uhXq+O3c0REVBkdHR107doVKSkpSvv//PNP6U7tbm5u0NbWRlxcnHQ8JSUF6enp8PDwAAB4eHjgwoULSte/xsbGwtjYuELiRURUF+Ry+XN9wWJoaIjmzZvXYUQNh6oXwlFJclT+TVr5N2vlHv8m7kkWFhbQ1NSsVR3g0bdzxsbGShsREb0YCgoKkJSUJH3rmpqaiqSkJKSnpwMApk2bhi1btuDbb7/FtWvXsGLFCvz666/49NNPATw65dvf3x/BwcE4dOgQEhMTMWbMGHh4eOC1114DAPTt2xft27fHyJEjce7cOezbtw+zZ89GYGAgZ4eI6pGfnx+OHDmCZcuWQSaTQSaTIS0tDQBw8eJF9O/fH4aGhrC2tsbIkSNx9+5dqe5PP/0EZ2dn6Ovro3nz5vD29kZhYSHCwsKwYcMG7NixQ2rz8OHDlfb/LG3MmDEDr7zyCgwMDNCyZUvMmTNHWm46JiYG4eHhOHfunFQvJiYGgPJpdcXFxQgKCoKNjQ309PRgb2+PiIgIAI9uqAoAgwcPhkwmkx5Xdlrd+vXr0aFDB+jq6sLGxgZBQUFVPtdeXl6YNGmS0r5BgwbBz89Peuzg4IAvvvgCH330EYyMjNCiRQusXbtWqc7Jkyfh6uqKZs2aYdSoURW+rAKe/tp5eXkhKCgIkyZNgoWFBXx8fKqMuy6oJDlydHSEXC5X+iYuPz8fCQkJ0jdxT9LR0YGbm5tSHYVCgbi4uCrrEBHRi+306dNwdXWFq6srACA4OBiurq7SPYgGDx6MNWvWYNGiRXB2dsZ//vMf/Pzzz+jRo4fUxtKlS/HWW29hyJAh6NmzJ+RyOX755RfpuKamJnbt2gVNTU14eHjgww8/xKhRozBv3rz6HSzRC27ZsmXw8PBAQEAAMjIykJGRATs7O+Tm5uKNN96Aq6srTp8+jb179yIrKwvDhg0D8OgUtREjRuCjjz7C5cuXcfjwYbz33nsQQmDq1KkYNmwY+vXrJ7Xp6elZoe9nbcPIyAgxMTG4dOkSli1bhm+//RZLly4FAHzwwQeYMmUKOnToINX74IMPKvS9fPly7Ny5Ez/++CNSUlKwceNGKQk6deoUACA6OhoZGRnS4yetXr0agYGBGDt2LC5cuICdO3eidevWz/2aLF68GF26dMHZs2fx6aefYvz48VICVFBQgLfeegvt27fHyZMnERAQgGXLlinVf9prV27Dhg3Q0dHBsWPHVH7N6DNfc1RQUIBr165Jj8u/rTM3N0eLFi0wadIkLFiwAE5OTnB0dMScOXNga2urdD5knz59MHjwYClzDQ4OxujRo9GlSxd069YNkZGRKCwsxJgxY559hERE1GR5eXlBCFFtmY8++ggfffRRlcf19PSwcuXKKm9NAQD29vbYs2fPM8dJRM/PxMQEOjo6MDAwUDqraMWKFXB1dcUXX3wh7Vu/fj3s7Ozw559/oqCgAKWlpXjvvfekU2qdnZ2lsvr6+igqKqr2TKWMjIxnamP27NnSzw4ODpg6dSo2b96M6dOnQ19fH4aGhtDS0qq27/T0dDg5OaFHjx6QyWRS/wBgaWkJADA1Na22jQULFmDKlCmYOHGitK9r165Vlq+pAQMGSDPxM2bMwNKlS3Ho0CG0adMGmzZtgkKhwLp166CtrY3i4mJkZ2dj4cKFUv2nvXavvPIKAMDJyQmLFi167nhr4pmTo9OnT6N3797S4+DgYADA6NGjERMTg+nTp6OwsBBjx45Fbm4uevTogb179yrdlfz69etK02YffPAB7ty5g9DQUGRmZsLFxQV79+6tsEgDEREREREAnDt3DocOHYKhoWGFY9evX0ffvn3Rp08fODs7w8fHB3379sX7779f4wUPAKBz587P1MaWLVuwfPlyXL9+XUrSansJiJ+fH9588020adMG/fr1w1tvvYW+ffvWuH52djZu376NPn361KrfmujUqZP0s0wmg1wul67PvHz5Mjp16gQ9PT2UlZUBUE4ogae/duXJkZubW53HXpVnTo6e9m2dTCbDvHnzqj3toPw80ccFBQVVew4kEREREVG5goICvP322/jyyy8rHLOxsYGmpiZiY2Nx/Phx7N+/H1FRUZg1axYSEhLg6OhYoz6epY34+Hj4+voiPDwcPj4+MDExwebNm7F48eJaje/VV19FamoqfvvtNxw4cADDhg2Dt7c3fvrppxrV19fXr1V/wKPb6Tz5d375tVKP09bWVnosk8mk2yjUxNNeu3LNmjWrcZvPq9GvVkdERERELwYdHR1pFqLcq6++iuTkZDg4OKB169ZKW/kf1TKZDN27d0d4eDjOnj0LHR0dbNu2rco2K1PbNo4fPw57e3vMmjULXbp0gZOTE27cuPHU8VTG2NgYH3zwAb799lts2bIFP//8M3JycgA8SlCqa8PIyAgODg5K1/U/jaWlJTIyMqTHZWVluHjxYo3rA49um3D+/Hk8fPhQ2vdkGzV57eobkyMiIiIiahQcHByQkJCAtLQ03L17FwqFAoGBgcjJycGIESNw6tQpXL9+Hfv27cOYMWNQVlaGhIQEfPHFFzh9+jTS09Pxyy+/4M6dO2jXrp3U5vnz55GSkoK7d+9WOkPyLG04OTkhPT0dmzdvxvXr17F8+XIpmXp8POXX7d+9exdFRUUV+l6yZAl++OEHXLlyBX/++Se2bt0KuVwu3ZutPPHJzMzEvXv3Kn3ewsLCsHjxYixfvhxXr17FmTNnEBUVVeXz/MYbb2D37t3YvXs3rly5gvHjxyM3N7cmL5HkX//6F2QyGQICAnDp0iUcO3YM33//vVKZp7126qCSm8ASERERUePzvDfLra3a3lx36tSpGD16NNq3b48HDx4gNTUVDg4OOHbsGGbMmIG+ffuiqKgI9vb26NevHzQ0NGBsbIyjR48iMjIS+fn5sLe3x+LFi9G/f38AQEBAAA4fPowuXbqgoKAAhw4dgpeXl1K/z9LGO++8g8mTJyMoKAhFRUUYOHAg5syZg7CwMKndIUOG4JdffkHv3r2Rm5uL6OhopeWygUczP4sWLcLVq1ehqamJrl27Ys+ePdDQeDTHsXjxYgQHB+Pbb7/FSy+9VOllK6NHj8bDhw+xdOlSTJ06FRYWFnj//ferfJ4/+ugjnDt3DqNGjYKWlhYmT56stNZATRgaGuLXX3/FJ598gi5dusDBwQFBQUGYMWOGVMbW1rba104dZOJpy/w0Mvn5+TAxMYEX3oWWTPvpFYiIGjmNZzifXBVKRTEOPtiCvLw83nPuCeWfTXxu1Od5/+iv7R/xDd3Dhw+RmpoKR0dHpcWyGnpyRI1TWVkZzp49CwBwdXWFpqZmnbZf1e8zUPv/f3laHREREREREZgcERERERERAWByREREREREBIDJEREREREREQAmR0RERET0Ajl8+DBkMpm0NHVMTIy0LHZl0tLSIJPJkJSUVC/x1daT41EFBwcHREZGqqz9hoRLeRNRtTQ7vKLuECTifzfVHYJEpq/39EL1pCyn8vta1DeFqHhvECIiqjteXl5wcXGp90Tl1KlTarspa31jckRERERERFWytLRUdwj1hskREREREQFo+Pcd8vLyQqdOnaCnp4f//Oc/0NHRwSeffCLdWDUtLQ2Ojo44e/YsXFxcAAC5ubkwMzOr9OautfG///0PkydPRkJCApycnLBmzRp4eHigsLAQNjY2WL9+vdKNVbdv3w5fX19kZmbi77//hqOjI3744QcsX74cZ86cQevWrbFy5Ur06tVLqnPkyBFMmzYN586dg7m5OUaPHo0FCxZAS0sLfn5+OHLkCI4cOYJly5YBAFJTU6W6iYmJmDFjBi5dugQXFxdER0ejTZs20vEdO3YgPDwcly5dgq2tLUaPHo1Zs2ZBS0sLQgiEh4dj/fr1yMrKQvPmzfH+++9j+fLlAB6dVjdp0iRMmjTpqWUbO15zRERERESNxoYNG9CsWTMkJCRg0aJFmDdvHmJjY1Xe76xZszB16lQkJSXhlVdewYgRI1BaWopmzZph+PDhiI6OViofHR2N999/H0ZGRtK+adOmYcqUKTh79iw8PDzw9ttv4++//wYA3Lp1CwMGDEDXrl1x7tw5rF69GuvWrcOCBQsAAMuWLYOHhwcCAgKQkZGBjIwM2NnZKcW3ePFinD59GlpaWvjoo4+kY7///jtGjRqFiRMn4tKlS/jmm28QExODzz//HADw888/Y+nSpfjmm29w9epVbN++Hc7OzpU+D7Up2xhx5oiIiIiIGo1OnTph7ty5AAAnJyesWLECcXFxePPNN1Xa79SpUzFw4KOZtfDwcHTo0AHXrl1D27Zt8fHHH8PT0xMZGRmwsbFBdnY29uzZgwMHDii1ERQUhCFDhgAAVq9ejb1792LdunWYPn06Vq1aBTs7O6xYsQIymQxt27bF7du3MWPGDISGhsLExAQ6OjowMDCAXC6vEN/nn38uzULNnDkTAwcOxMOHD6Gnp4fw8HDMnDkTo0ePBgC0bNkS8+fPx/Tp0zF37lykp6dDLpfD29sb2traaNGiBbp161bp81Cbso0RZ46IiIiIqNHo1KmT0uPyZKQ++7WxsQEAqd9u3bqhQ4cO2LBhAwDg+++/h729PXr27KnUhoeHh/SzlpYWunTpgsuXLwMALl++DA8PD8hkMqlM9+7dUVBQgL/++uu54jt37hzmzZsHQ0NDaSufgbp//z6GDh2KBw8eoGXLlggICMC2bdtQWlpaaT+1KdsYMTkiIiIiokZDW1tb6bFMJoNCoQAAaGg8+tNWCCEdLympm5U0H++3PIEp7xcAPv74Y8TExAB4dErdmDFjlBIdVasuvoKCAoSHhyMpKUnaLly4gKtXr0JPTw92dnZISUnBqlWroK+vj08//RQ9e/as9LmrTdnGiMkRERERETUJ5auqZWRkSPvq6/5EH374IW7cuIHly5fj0qVL0ilsjztx4oT0c2lpKRITE9GuXTsAQLt27RAfH6+U2B07dgxGRkZ4+eWXAQA6OjooKyurdWyvvvoqUlJS0Lp16wpbeUKpr6+Pt99+G8uXL8fhw4cRHx+PCxcuVNpebco2NrzmiIiIiBo8h5m71R0CNQL6+vp47bXXsHDhQjg6OiI7OxuzZ8+ul77NzMzw3nvvYdq0aejbt6+U0Dxu5cqVcHJyQrt27bB06VLcu3dPWjjh008/RWRkJCZMmICgoCCkpKRg7ty5CA4OlhIYBwcHJCQkIC0tDYaGhjA3N69RbKGhoXjrrbfQokULvP/++9DQ0MC5c+dw8eJFLFiwADExMSgrK4O7uzsMDAzw/fffQ19fH/b29hXaqk3ZxogzR0RERETUZKxfvx6lpaVwc3PDpEmTpNXe6oO/vz+Ki4uVVop73MKFC7Fw4UJ07twZf/zxB3bu3AkLCwsAwEsvvYQ9e/bg5MmT6Ny5Mz755BP4+/srJXdTp06FpqYm2rdvD0tLS6Snp9coLh8fH+zatQv79+9H165d8dprr2Hp0qVSQmNqaopvv/0W3bt3R6dOnXDgwAH8+uuvaN68eYW2alO2MZKJx+fumoD8/HyYmJjAC+9CS6b99ApEVC3NDq+oOwSJ+N9NdYcgkenrqTsESVnOPXWHAAAoFSU4jB3Iy8uDsbGxusNpUMo/m/jcPLvGPnPU0O4f9PDhQ6SmpsLR0RF6eg3n/7PG7r///S8mT56M27dvQ0dHR9pf2f2XXiRlZWU4e/YsAMDV1RWampp12n51v8+1/f+Xp9URERERET2H+/fvIyMjAwsXLsS4ceOUEiNqXHhaHRERERHRc1i0aBHatm0LuVyOkJAQdYdDz4EzR0REREREzyEsLAxhYWFVHndwcEATu5KlyeLMEREREREREZgcERFRI3b06FG8/fbbsLW1hUwmw/bt26ss+8knn0AmkyEyMlJpf05ODnx9fWFsbAxTU1P4+/ujoKBAqcz58+fx+uuvSzdLXLRokQpGQ0RE6sbkiIiIGq3CwkJ07twZK1eurLbctm3bcOLECdja2lY45uvri+TkZMTGxmLXrl04evQoxo4dKx3Pz89H3759YW9vj8TERHz11VcICwvD2rVr63w8RESkXrzmiIiIGq3+/fujf//+1Za5desWJkyYgH379mHgQOXllC9fvoy9e/fi1KlT6NKlCwAgKioKAwYMwNdffw1bW1ts3LgRxcXFWL9+PXR0dNChQwckJSVhyZIlSkkUERE1fiqdOXJwcIBMJquwBQYGVlo+JiamQlmuvU9ERM9KoVBg5MiRmDZtGjp06FDheHx8PExNTaXECAC8vb2hoaGBhIQEqUzPnj2Vlub18fFBSkoK7t2r/B5TRUVFyM/PV9qIiKjhU+nM0alTp1BWViY9vnjxIt58800MHTq0yjrGxsZISUmRHstkMlWGSERETdiXX34JLS0tfPbZZ5Uez8zMhJWVldI+LS0tmJubIzMzUyrj6OioVMba2lo6ZmZmVqHdiIgIhIeH18UQiIioHql05sjS0hJyuVzadu3ahVatWqFXr15V1pHJZEp1yj+AiIiIaiMxMRHLli2TzkqoTyEhIcjLy5O2mzdv1mv/RNS4PW2BmacJCwuDi4tLncXzIqm3BRmKi4vx/fff46OPPqr2Q6qgoAD29vaws7PDu+++i+Tk5Grb5akLRERUmd9//x3Z2dlo0aIFtLS0oKWlhRs3bmDKlClwcHAAAMjlcmRnZyvVKy0tRU5ODuRyuVQmKytLqUz54/IyT9LV1YWxsbHSRkTPz8vLC5MmTarTNv38/DBo0CC1tFFVEpORkfHU6ynLVZZITZ06FXFxcbWOh+oxOdq+fTtyc3Ph5+dXZZk2bdpg/fr12LFjB77//nsoFAp4enrir7/+qrJOREQETExMpM3Ozk4F0RMRUWMzcuRInD9/HklJSdJma2uLadOmYd++fQAADw8P5ObmIjExUap38OBBKBQKuLu7S2WOHj2KkpISqUxsbCzatGlT6Sl1RETPSy6XQ1dX95nrGxoaonnz5nUY0Yuj3pKjdevWoX///pUuo1rOw8MDo0aNgouLC3r16oVffvkFlpaW+Oabb6qsw1MXiIheXAUFBVLiAwCpqalISkpCeno6mjdvjo4dOypt2trakMvlaNOmDQCgXbt26NevHwICAnDy5EkcO3YMQUFBGD58uPR59a9//Qs6Ojrw9/dHcnIytmzZgmXLliE4OFhdwyZ6Ifn5+eHIkSNYtmyZtHBXWloagEfXtffv3x+GhoawtrbGyJEjcffuXanuTz/9BGdnZ+jr66N58+bw9vZGYWEhwsLCsGHDBuzYsUNq8/Dhw5X2/yxtzJgxA6+88goMDAzQsmVLzJkzR/qiJSYmBuHh4Th37pxULyYmBoDybFBxcTGCgoJgY2MDPT092NvbIyIiAgCkWfDBgwdDJpNJjyubkVq/fj06dOgAXV1d2NjYICgo6Llej6aqXpbyvnHjBg4cOIBffvmlVvW0tbXh6uqKa9euVVlGV1f3uTJrIiJqvE6fPo3evXtLj8sTltGjR0t/ZDzNxo0bERQUhD59+kBDQwNDhgzB8uXLpeMmJibYv38/AgMD4ebmBgsLC4SGhnIZb6J6tmzZMvz555/o2LEj5s2bB+DR9e25ubl444038PHHH2Pp0qV48OABZsyYgWHDhuHgwYPIyMjAiBEjsGjRIgwePBj//PMPfv/9dwghMHXqVFy+fBn5+fmIjo4GAJibm1fo+1nbMDIyQkxMDGxtbXHhwgUEBATAyMgI06dPxwcffICLFy9i7969OHDgAIBH/988afny5di5cyd+/PFHtGjRAjdv3pQmA06dOgUrKytER0ejX79+0NTUrPS5W716NYKDg7Fw4UL0798feXl5OHbs2HO+Ik1TvSRH0dHRsLKyqnB/iacpKyvDhQsXMGDAABVFRkREjZmXlxeEEDUuX/4t8+PMzc2xadOmaut16tQJv//+e23DI6I6ZGJiAh0dHRgYGChd77dixQq4urriiy++kPatX78ednZ2+PPPP1FQUIDS0lK89957sLe3BwA4OztLZfX19VFUVFTlNYTAo+ToWdqYPXu29LODgwOmTp2KzZs3Y/r06dDX14ehoSG0tLSq7Ts9PR1OTk7o0aMHZDKZ1D/wKDkEAFNT02rbWLBgAaZMmYKJEydK+7p27Vpl+ReZyk+rUygUiI6OxujRo6GlpZyLjRo1CiEhIdLjefPmYf/+/fjf//6HM2fO4MMPP8SNGzfw8ccfqzpMIiIiImqEzp07h0OHDsHQ0FDa2rZtCwC4fv06OnfujD59+sDZ2RlDhw7Ft99+W+U9yqryrG1s2bIF3bt3h1wuh6GhIWbPno309PRa9e3n54ekpCS0adMGn332Gfbv31+r+tnZ2bh9+zb69OlTq3ovKpUnRwcOHEB6ejo++uijCsfS09ORkZEhPb537x4CAgLQrl07DBgwAPn5+Th+/Djat2+v6jCJiIiIqBEqKCjA22+/rbT4SlJSEq5evYqePXtCU1MTsbGx+O2339C+fXtERUWhTZs2SE1NrXEfz9JGfHw8fH19MWDAAOzatQtnz57FrFmzUFxcXKvxvfrqq0hNTcX8+fPx4MEDDBs2DO+//36N6+vr69eqvxedyk+r69u3b5WnPDx5wdvSpUuxdOlSVYdERERERI2Qjo4OysrKlPa9+uqr+Pnnn+Hg4FDhLKVyMpkM3bt3R/fu3REaGgp7e3ts27YNwcHBlbZZF20cP34c9vb2mDVrlrTvxo0bTx1PZYyNjfHBBx/ggw8+wPvvv49+/fohJycH5ubm0NbWrrYNIyMjODg4IC4uTukaTapcva1WR0RERET0PBwcHJCQkIC0tDTcvXsXCoUCgYGByMnJwYgRI3Dq1Clcv34d+/btw5gxY1BWVoaEhAR88cUXOH36NNLT0/HLL7/gzp07aNeundTm+fPnkZKSgrt37yot21/uWdpwcnJCeno6Nm/ejOvXr2P58uXYtm1bhfGUr7J59+5dFBUVVeh7yZIl+OGHH3DlyhX8+eef2Lp1K+RyOUxNTaU24uLikJmZWeWpfmFhYVi8eDGWL1+Oq1ev4syZM4iKinqel6LJYnJERERERI3C1KlToampifbt28PS0hLp6emwtbXFsWPHUFZWhr59+8LZ2RmTJk2CqakpNDQ0YGxsjKNHj2LAgAF45ZVXMHv2bCxevFi6yWpAQADatGmDLl26wNLSstJV3J6ljXfeeQeTJ09GUFAQXFxccPz4ccyZM0ep3SFDhqBfv37o3bs3LC0t8cMPP1To28jICIsWLUKXLl3QtWtXpKWlYc+ePdDQePRn/OLFixEbGws7Ozu4urpW+ryNHj0akZGRWLVqFTp06IC33noLV69efa7XoqmSidos89MI5Ofnw8TEBF54F1oybXWHQ1RrGg3t3OB2LdUdgUShWy8LbNaIVlaeukOQlP4vTd0hAABKRQkOYwfy8vJgbGys7nAalPLPJj43z85h5m51h/Bc0hbWbsVeVXv48CFSU1Ph6OgIPT09dYdDTVxZWRnOnj0LAHB1da1yyfFnVd3vc23//+XMEREREREREerpPkdERERE1HAJIXD//n219G1gYACZTKaWvomexOSIiIiI6AV3//59GBoaqqXvgoICNGvWTC19Ez2Jp9URERERERGBM0dERERELzwDAwMUFBSore+aEkJg3Lhx+Omnn3Dv3j2cPXsWLi4uqguuBhwcHDBp0iRMmjRJrXFQ3WByRERERPSCk8lkjeLUtr179yImJgaHDx9Gy5YtYWFhoe6QcOrUKbU8d35+fsjNzcX27dvrve+mjMkRERERETUK169fh42NDTw9PassU1xcDB0dnXqLydLSst76ItXjNUdERERE1OD5+flhwoQJSE9Ph0wmg4ODAwDAy8sLQUFBmDRpEiwsLODj4wMAOHLkCLp16wZdXV3Y2Nhg5syZKC0tldrz8vLChAkTMGnSJJiZmcHa2hrffvstCgsLMWbMGBgZGaF169b47bffqo3LwcEBkZGR0mOZTIb//Oc/GDx4MAwMDODk5ISdO3dKxw8fPgyZTIbdu3ejU6dO0NPTw2uvvYaLFy9KZcLCwiqcLhgZGSmNOSwsDBs2bMCOHTsgk8kgk8lw+PDh2j+pVAGTIyIiIiJq8JYtW4Z58+bh5ZdfRkZGBk6dOiUd27BhA3R0dHDs2DGsWbMGt27dwoABA9C1a1ecO3cOq1evxrp167BgwQKlNjds2AALCwucPHkSEyZMwPjx4zF06FB4enrizJkz6Nu3L0aOHFnrZc7Dw8MxbNgwnD9/HgMGDICvry9ycnKUykybNg2LFy/GqVOnYGlpibfffhslJSU1an/q1KkYNmwY+vXrh4yMDGRkZFQ7m0Y1x+SIiIiIiBo8ExMTGBkZQVNTE3K5XOl0NicnJyxatAht2rRBmzZtsGrVKtjZ2WHFihVo27YtBg0ahPDwcCxevBgKhUKq17lzZ8yePRtOTk4ICQmBnp4eLCwsEBAQACcnJ4SGhuLvv//G+fPnaxWrn58fRowYgdatW+OLL75AQUEBTp48qVRm7ty5ePPNN+Hs7IwNGzYgKysL27Ztq1H7hoaG0NfXh66uLuRyOeRyeb2eStiUMTkiIiIiokbNzc1N6fHly5fh4eGhdHPZ7t27o6CgAH/99Ze0r1OnTtLPmpqaaN68OZydnaV91tbWAIDs7OxaxfN4u82aNYOxsXGFNjw8PKSfzc3N0aZNG1y+fLlW/VDdY3JERERERI3as64Wp62trfRYJpMp7StPrh6fbXrWdmvThoaGBoQQSvtqesodPR8mR0RERETUpLRr1w7x8fFKCcaxY8dgZGSEl19+WY2R/Z8TJ05IP9+7dw9//vkn2rVrB+DRCniZmZlK8SclJSnV19HRQVlZWb3E+iJhckRERERETcqnn36KmzdvYsKECbhy5Qp27NiBuXPnIjg4GBoaDePP33nz5iEuLg4XL16En58fLCwsMGjQIACPVtK7c+cOFi1ahOvXr2PlypUVVs1zcHDA+fPnkZKSgrt373JmqY40jN8OIiIiIqI68tJLL2HPnj04efIkOnfujE8++QT+/v6YPXu2ukOTLFy4EBMnToSbmxsyMzPx66+/SosqtGvXDqtWrcLKlSvRuXNnnDx5ElOnTlWqHxAQgDZt2qBLly6wtLTEsWPH1DGMJkcmnjyhsZHLz8+HiYkJvPAutGTaT69A1MBo6OurOwRl7VqqOwKJQrfh3LdaKytP3SFISv+Xpu4QAAClogSHsQN5eXkwNjZWdzgNSvlnE5+bZ+cwc7e6Q3guaQsHqjsEJQ8fPkRqaiocHR2hp6en7nBeKIcPH0bv3r1x7949mJqaqjucelFWVoazZ88CAFxdXaGpqVmn7Vf3+1zb/385c0RERERERAQmR0RERERERACAhnOOChERERFRE+fl5VVhmW5qODhzREREREREBCZHRETUiB09ehRvv/02bG1tIZPJsH37dulYSUkJZsyYAWdnZzRr1gy2trYYNWoUbt++rdRGTk4OfH19YWxsDFNTU/j7+6OgoECpzPnz5/H6669DT08PdnZ2WLRoUX0Mj0jlOINBTUFd/h4zOSIiokarsLAQnTt3xsqVKyscu3//Ps6cOYM5c+bgzJkz+OWXX5CSkoJ33nlHqZyvry+Sk5MRGxuLXbt24ejRoxg7dqx0PD8/H3379oW9vT0SExPx1VdfISwsDGvXrlX5+IhUpXy1sOLiYjVHQvT87t+/DwDQ1n7+lap5zRERETVa/fv3R//+/Ss9ZmJigtjYWKV9K1asQLdu3ZCeno4WLVrg8uXL2Lt3L06dOoUuXboAAKKiojBgwAB8/fXXsLW1xcaNG1FcXIz169dDR0cHHTp0QFJSEpYsWaKURD2uqKgIRUVF0uP8/Pw6GjFR3dDS0oKBgQHu3LkDbW3tBnNjVGqaysrKpJ8fPnxYZ0t5CyFw//59ZGdnw9TUtE7aZXJEREQvjLy8PMhkMuneIvHx8TA1NZUSIwDw9vaGhoYGEhISMHjwYMTHx6Nnz57SzRkBwMfHB19++SXu3bsHMzOzCv1EREQgPDxc5eMhelYymQw2NjZITU3FjRs31B0ONXEKhQJ3794FAKSlpdV5Mm5qagq5XF4nbak0OQoLC6vw4dCmTRtcuXKlyjpbt27FnDlzkJaWBicnJ3z55ZcYMGCAKsMkIqIXwMOHDzFjxgyMGDFCuhFgZmYmrKyslMppaWnB3NwcmZmZUhlHR0elMtbW1tKxypKjkJAQBAcHS4/z8/NhZ2dXp+Mhel46OjpwcnLiqXWkcvfv38fAgY9uhHzmzBkYGBjUWdva2tp1elNZlc8cdejQAQcOHPi/DrWq7vL48eMYMWIEIiIi8NZbb2HTpk0YNGgQzpw5g44dO6o6VCIiaqJKSkowbNgwCCGwevVqlfenq6sLXV1dlfdD9Lw0NDSgp6en7jCoiSsrK5NmKHV1dRv075zKTzDV0tKCXC6XNgsLiyrLLlu2DP369cO0adPQrl07zJ8/H6+++ipWrFih6jCJiKiJKk+Mbty4gdjYWGnWCADkcjmys7OVypeWliInJ0c6RUMulyMrK0upTPnjujqNg4iIGgaVzxxdvXoVtra20NPTg4eHByIiItCiRYtKy8bHxyudhgA8Oq/78aVZn8SLXomIqCrlidHVq1dx6NAhNG/eXOm4h4cHcnNzkZiYCDc3NwDAwYMHoVAo4O7uLpWZNWsWSkpKpJWQYmNj0aZNm0pPqSOqjMPM3c9VP23hwDqKhIiqo9KZI3d3d8TExGDv3r1YvXo1UlNT8frrr+Off/6ptHxmZqZ0Hnc5a2tr6bzvykRERMDExETaeE43EdGLo6CgAElJSUhKSgIApKamIikpCenp6SgpKcH777+P06dPY+PGjSgrK0NmZiYyMzOlayzatWuHfv36ISAgACdPnsSxY8cQFBSE4cOHw9bWFgDwr3/9Czo6OvD390dycjK2bNmCZcuWVfgyj4iIGj+Vzhw9vrxqp06d4O7uDnt7e/z444/w9/evkz540SsR0Yvr9OnT6N27t/S4/PNg9OjRCAsLw86dOwEALi4uSvUOHToELy8vAMDGjRsRFBSEPn36QENDA0OGDMHy5culsiYmJti/fz8CAwPh5uYGCwsLhIaGVrmMNxERNV71upS3qakpXnnlFVy7dq3S41Wd113dOd286LXx0jRvQKej2FiqOwLJndeqvi5PHcwvFao7BElO+2bqDkFilZWn7hAkGvr66g4BAKAhNIEH9dunl5dXtXdGr8ld083NzbFp06Zqy3Tq1Am///57reMjIqLGpV7v+FVQUIDr16/Dxsam0uMeHh6Ii4tT2hcbGwsPD4/6CI+IiIiIiF5gKk2Opk6diiNHjiAtLQ3Hjx/H4MGDoampiREjRgAARo0ahZCQEKn8xIkTsXfvXixevBhXrlxBWFgYTp8+jaCgIFWGSUREREREpNrT6v766y+MGDECf//9NywtLdGjRw+cOHEClpaPTmFKT09XukOup6cnNm3ahNmzZ+Pf//43nJycsH37dt7jiIiIiIiIVE6lydHmzZurPX748OEK+4YOHYqhQ4eqKCIiIiJSh+ddypqIqD7U6zVHREREREREDRWTIyIiIiIiIjA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIqJG7OjRo3j77bdha2sLmUyG7du3Kx0XQiA0NBQ2NjbQ19eHt7c3rl69qlQmJycHvr6+MDY2hqmpKfz9/VFQUKBU5vz583j99dehp6cHOzs7LFq0SNVDIyIiNWByREREjVZhYSE6d+6MlStXVnp80aJFWL58OdasWYOEhAQ0a9YMPj4+ePjwoVTG19cXycnJiI2Nxa5du3D06FGMHTtWOp6fn4++ffvC3t4eiYmJ+OqrrxAWFoa1a9eqfHxERFS/tNQdABER0bPq378/+vfvX+kxIQQiIyMxe/ZsvPvuuwCA7777DtbW1ti+fTuGDx+Oy5cvY+/evTh16hS6dOkCAIiKisKAAQPw9ddfw9bWFhs3bkRxcTHWr18PHR0ddOjQAUlJSViyZIlSEvW4oqIiFBUVSY/z8/PreORERKQKTI5eMBr6+uoO4f/YWKo7Asmd1yzUHYIkx+vh0wvVoxwvTXWHIDEwzFN3CJLi6+bqDkGindswnheZQgN4oO4o/k9qaioyMzPh7e0t7TMxMYG7uzvi4+MxfPhwxMfHw9TUVEqMAMDb2xsaGhpISEjA4MGDER8fj549e0JHR0cq4+Pjgy+//BL37t2DmZlZhb4jIiIQHh6u2gESEVGd42l1RETUJGVmZgIArK2tlfZbW1tLxzIzM2FlZaV0XEtLC+bm5kplKmvj8T6eFBISgry8PGm7efPm8w+IiIhUTqXJUUREBLp27QojIyNYWVlh0KBBSElJqbZOTEwMZDKZ0qanp6fKMImIiOqUrq4ujI2NlTYiImr4VJocHTlyBIGBgThx4gRiY2NRUlKCvn37orCwsNp6xsbGyMjIkLYbN26oMkwiImqC5HI5ACArK0tpf1ZWlnRMLpcjOztb6XhpaSlycnKUylTWxuN9EBFR06DS5Gjv3r3w8/NDhw4d0LlzZ8TExCA9PR2JiYnV1pPJZJDL5dL25OkMRERET+Po6Ai5XI64uDhpX35+PhISEuDh4QEA8PDwQG5urtLn0sGDB6FQKODu7i6VOXr0KEpKSqQysbGxaNOmTaXXGxERUeNVr9cc5eU9umjY3Lz6C5kLCgpgb28POzs7vPvuu0hOTq6ybFFREfLz85U2IiJ6MRQUFCApKQlJSUkAHi3CkJSUhPT0dMhkMkyaNAkLFizAzp07ceHCBYwaNQq2trYYNGgQAKBdu3bo168fAgICcPLkSRw7dgxBQUEYPnw4bG1tAQD/+te/oKOjA39/fyQnJ2PLli1YtmwZgoOD1TRqIiJSlXpLjhQKBSZNmoTu3bujY8eOVZZr06YN1q9fjx07duD777+HQqGAp6cn/vrrr0rLR0REwMTERNrs7OxUNQQiImpgTp8+DVdXV7i6ugIAgoOD4erqitDQUADA9OnTMWHCBIwdOxZdu3ZFQUEB9u7dq3Qt68aNG9G2bVv06dMHAwYMQI8ePZTuYWRiYoL9+/cjNTUVbm5umDJlCkJDQ6tcxpuIiBovmRBC1EdH48ePx2+//YY//vgDL7/8co3rlZSUoF27dhgxYgTmz59f4Xhl95Kws7ODF96Flky7TmJvShrSUt6ylg0nkeVS3o2DgWHR0wvVE/kyXXWHINE+n6ruEAAApYpixN3bgLy8PC5A8IT8/HyYmJi80M+Nw8zd6g6hUUtbOFDdIRA9s8LCQhgaGgJ4NOPfrFmzeuu7tv//1st9joKCgqS7jtcmMQIAbW1tuLq64tq1a5Ue19XVha5uw/kjhYiIiIiIGieVnlYnhEBQUBC2bduGgwcPwtHRsdZtlJWV4cKFC7CxsVFBhERERERERI+odOYoMDAQmzZtwo4dO2BkZCTdLM/ExAT6///0rlGjRuGll15CREQEAGDevHl47bXX0Lp1a+Tm5uKrr77CjRs38PHHH6syVCIiIiIiesGpNDlavXo1AMDLy0tpf3R0NPz8/AAA6enp0ND4vwmse/fuISAgAJmZmTAzM4ObmxuOHz+O9u3bqzJUIiIiIiJ6wak0OarJWg+HDx9Werx06VIsXbpURRERERERERFVrl7vc0RERERERNRQMTkiIiIiIiICkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICAGipOwAiIiIiqp7DzN3PVT9t4cA6ioSoaWNypGIa+vrqDkGJTF9P3SFIHtiZqDsEidXINHWHIGmpVazuEJRsaRmn7hAkrX4ap+4QJBpFD9QdgqQs5566QwAAlIkSdYdARET0XHhaHREREREREZgcERERERERAWByRERETVhZWRnmzJkDR0dH6Ovro1WrVpg/fz6EEFIZIQRCQ0NhY2MDfX19eHt74+rVq0rt5OTkwNfXF8bGxjA1NYW/vz8KCgrqezhERKRiTI6IiKjJ+vLLL7F69WqsWLECly9fxpdffolFixYhKipKKrNo0SIsX74ca9asQUJCApo1awYfHx88fPhQKuPr64vk5GTExsZi165dOHr0KMaOHauOIRERkQpxQQYiImqyjh8/jnfffRcDBz5aqcvBwQE//PADTp48CeDRrFFkZCRmz56Nd999FwDw3XffwdraGtu3b8fw4cNx+fJl7N27F6dOnUKXLl0AAFFRURgwYAC+/vpr2NraVui3qKgIRUVF0uP8/HxVD5WIiOoAZ46IiKjJ8vT0RFxcHP78808AwLlz5/DHH3+gf//+AIDU1FRkZmbC29tbqmNiYgJ3d3fEx8cDAOLj42FqaiolRgDg7e0NDQ0NJCQkVNpvREQETExMpM3Ozk5VQyQiojrEmSMiImqyZs6cifz8fLRt2xaampooKyvD559/Dl9fXwBAZmYmAMDa2lqpnrW1tXQsMzMTVlZWSse1tLRgbm4ulXlSSEgIgoODpcf5+flMkIiIGgEmR0RE1GT9+OOP2LhxIzZt2oQOHTogKSkJkyZNgq2tLUaPHq2yfnV1daGrq6uy9omISDXq5bS6lStXwsHBAXp6enB3d5fO9a7K1q1b0bZtW+jp6cHZ2Rl79uypjzCJiKiJmTZtGmbOnInhw4fD2dkZI0eOxOTJkxEREQEAkMvlAICsrCylellZWdIxuVyO7OxspeOlpaXIycmRyhARUdOg8uRoy5YtCA4Oxty5c3HmzBl07twZPj4+FT5oyh0/fhwjRoyAv78/zp49i0GDBmHQoEG4ePGiqkMlIqIm5v79+9DQUP6o09TUhEKhAAA4OjpCLpcjLi5OOp6fn4+EhAR4eHgAADw8PJCbm4vExESpzMGDB6FQKODu7l4PoyAiovqi8uRoyZIlCAgIwJgxY9C+fXusWbMGBgYGWL9+faXlly1bhn79+mHatGlo164d5s+fj1dffRUrVqxQdahERNTEvP322/j888+xe/dupKWlYdu2bViyZAkGDx4MAJDJZJg0aRIWLFiAnTt34sKFCxg1ahRsbW0xaNAgAEC7du3Qr18/BAQE4OTJkzh27BiCgoIwfPjwSleqIyKixkul1xwVFxcjMTERISEh0j4NDQ14e3tLqwA9KT4+XukiVgDw8fHB9u3bKy3P5VKJiKgqUVFRmDNnDj799FNkZ2fD1tYW48aNQ2hoqFRm+vTpKCwsxNixY5Gbm4sePXpg79690NPTk8ps3LgRQUFB6NOnDzQ0NDBkyBAsX75cHUMiIiIVUmlydPfuXZSVlVW6CtCVK1cqrZOZmVntqkFPioiIQHh4eN0ETERETYqRkREiIyMRGRlZZRmZTIZ58+Zh3rx5VZYxNzfHpk2bVBAhERE1JI3+PkchISHIy8uTtps3b6o7JCIiIiIiaoRUOnNkYWEBTU3NalcBepJcLq9VeS6XSkREREREdUGlM0c6Ojpwc3NTWgVIoVAgLi5OWgXoSR4eHkrlASA2NrbK8kRERERERHVB5TeBDQ4OxujRo9GlSxd069YNkZGRKCwsxJgxYwAAo0aNwksvvSTdc2LixIno1asXFi9ejIEDB2Lz5s04ffo01q5dq+pQiYiIiIjoBaby5OiDDz7AnTt3EBoaiszMTLi4uGDv3r3Sogvp6elK96Dw9PTEpk2bMHv2bPz73/+Gk5MTtm/fjo4dO6o6VCIiIiIieoGpPDkCgKCgIAQFBVV67PDhwxX2DR06FEOHDlVxVERERERERP+n0a9WR0REREREVBeYHBEREREREYHJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERAEBL3QE0dYoHD9QdghKNdi3VHYLkoUXD+fW7e89M3SFIvFukqDsEJW3/GKnuECQvxwl1hyDRSPpT3SFIFOoOgIiIqIngzBERERERERGYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREB4FLeREREVAMOM3erOwQiIpXjzBERERERERGYHBERURN369YtfPjhh2jevDn09fXh7OyM06dPS8eFEAgNDYWNjQ309fXh7e2Nq1evKrWRk5MDX19fGBsbw9TUFP7+/igoKKjvoRARkYoxOSIioibr3r176N69O7S1tfHbb7/h0qVLWLx4MczMzKQyixYtwvLly7FmzRokJCSgWbNm8PHxwcOHD6Uyvr6+SE5ORmxsLHbt2oWjR49i7Nix6hgSERGpEK85IiKiJuvLL7+EnZ0doqOjpX2Ojo7Sz0IIREZGYvbs2Xj33XcBAN999x2sra2xfft2DB8+HJcvX8bevXtx6tQpdOnSBQAQFRWFAQMG4Ouvv4atrW2FfouKilBUVCQ9zs/PV9UQiYioDnHmiIiImqydO3eiS5cuGDp0KKysrODq6opvv/1WOp6amorMzEx4e3tL+0xMTODu7o74+HgAQHx8PExNTaXECAC8vb2hoaGBhISESvuNiIiAiYmJtNnZ2alohEREVJeYHBERUZP1v//9D6tXr4aTkxP27duH8ePH47PPPsOGDRsAAJmZmQAAa2trpXrW1tbSsczMTFhZWSkd19LSgrm5uVTmSSEhIcjLy5O2mzdv1vXQiIhIBXhaHRERNVkKhQJdunTBF198AQBwdXXFxYsXsWbNGowePVpl/erq6kJXV1dl7RMRkWpw5oiIiJosGxsbtG/fXmlfu3btkJ6eDgCQy+UAgKysLKUyWVlZ0jG5XI7s7Gyl46WlpcjJyZHKEBFR06CS5CgtLQ3+/v5wdHSEvr4+WrVqhblz56K4uLjael5eXpDJZErbJ598oooQiYjoBdC9e3ekpKQo7fvzzz9hb28P4NHiDHK5HHFxcdLx/Px8JCQkwMPDAwDg4eGB3NxcJCYmSmUOHjwIhUIBd3f3ehgFERHVF5WcVnflyhUoFAp88803aN26NS5evIiAgAAUFhbi66+/rrZuQEAA5s2bJz02MDBQRYhERPQCmDx5Mjw9PfHFF19g2LBhOHnyJNauXYu1a9cCAGQyGSZNmoQFCxbAyckJjo6OmDNnDmxtbTFo0CAAj2aa+vXrh4CAAKxZswYlJSUICgrC8OHDK12pjoiIGi+VJEf9+vVDv379pMctW7ZESkoKVq9e/dTkyMDAgKcpEBFRnejatSu2bduGkJAQzJs3D46OjoiMjISvr69UZvr06SgsLMTYsWORm5uLHj16YO/evdDT05PKbNy4EUFBQejTpw80NDQwZMgQLF++XB1DIiIiFaq3BRny8vJgbm7+1HIbN27E999/D7lcjrfffhtz5sypdvaI95IgIqLqvPXWW3jrrbeqPC6TyTBv3jylsxaeZG5ujk2bNqkiPKJ64TBz93PVT1s4sI4iIWrY6iU5unbtGqKiop46a/Svf/0L9vb2sLW1xfnz5zFjxgykpKTgl19+qbJOREQEwsPD6zpkIiIiIiJ6wdRqQYaZM2dWWDDhye3KlStKdW7duoV+/fph6NChCAgIqLb9sWPHwsfHB87OzvD19cV3332Hbdu24fr161XW4b0kiIiIiIioLtRq5mjKlCnw8/OrtkzLli2ln2/fvo3evXvD09NTuvi1NspXAbp27RpatWpVaRneS4KIiIiIiOpCrZIjS0tLWFpa1qjsrVu30Lt3b7i5uSE6OhoaGrVfNTwpKQnAo/tUEBERERERqZJK7nN069YteHl5oUWLFvj6669x584dZGZmIjMzU6lM27ZtcfLkSQDA9evXMX/+fCQmJiItLQ07d+7EqFGj0LNnT3Tq1EkVYRIREREREUlUsiBDbGwsrl27hmvXruHll19WOiaEAACUlJQgJSUF9+/fBwDo6OjgwIEDiIyMRGFhIezs7DBkyBDMnj1bFSESEREREREpUUly5Ofn99RrkxwcHKRECQDs7Oxw5MgRVYRDRERERET0VCo5rY6IiIiIiKixYXJEREREREQEJkdEREREREQAmBwREREREREBUNGCDNRwaeQWqjuExxirOwCJ5mETdYcg2W/YTd0hKGm5/a66Q/g/GTfVHYGk7MEDdYdAREREdYwzR0RERERERGByREREREREBIDJEREREREREQAmR0RERERERACYHBEREREREQFgckRERERERASAyREREREREREAJkdEREREREQAmBwREREREREBYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWByREREREREBIDJERERvUAWLlwImUyGSZMmSfsePnyIwMBANG/eHIaGhhgyZAiysrKU6qWnp2PgwIEwMDCAlZUVpk2bhtLS0nqOnoiIVI3JERERvRBOnTqFb775Bp06dVLaP3nyZPz666/YunUrjhw5gtu3b+O9996TjpeVlWHgwIEoLi7G8ePHsWHDBsTExCA0NLS+h0BERCrG5IiIiJq8goIC+Pr64ttvv4WZmZm0Py8vD+vWrcOSJUvwxhtvwM3NDdHR0Th+/DhOnDgBANi/fz8uXbqE77//Hi4uLujfvz/mz5+PlStXori4uNL+ioqKkJ+fr7QREVHDx+SIiIiavMDAQAwcOBDe3t5K+xMTE1FSUqK0v23btmjRogXi4+MBAPHx8XB2doa1tbVUxsfHB/n5+UhOTq60v4iICJiYmEibnZ2dCkZFRER1jckRERE1aZs3b8aZM2cQERFR4VhmZiZ0dHRgamqqtN/a2hqZmZlSmccTo/Lj5ccqExISgry8PGm7efNmHYyEiIhUTUvdARAREanKzZs3MXHiRMTGxkJPT6/e+tXV1YWurm699UdERHWDM0dERNRkJSYmIjs7G6+++iq0tLSgpaWFI0eOYPny5dDS0oK1tTWKi4uRm5urVC8rKwtyuRwAIJfLK6xeV/64vAwRETUNTI6IiKjJ6tOnDy5cuICkpCRp69KlC3x9faWftbW1ERcXJ9VJSUlBeno6PDw8AAAeHh64cOECsrOzpTKxsbEwNjZG+/bt631MRESkOipLjhwcHCCTyZS2hQsXVlunJveaICIiqikjIyN07NhRaWvWrBmaN2+Ojh07wsTEBP7+/ggODsahQ4eQmJiIMWPGwMPDA6+99hoAoG/fvmjfvj1GjhyJc+fOYd++fZg9ezYCAwN56hwRUROj0muO5s2bh4CAAOmxkZFRteUnT56M3bt3Y+vWrTAxMUFQUBDee+89HDt2TJVhEhHRC2zp0qXQ0NDAkCFDUFRUBB8fH6xatUo6rqmpiV27dmH8+PHw8PBAs2bNMHr0aMybN0+NURMRkSqoNDkyMjKq8fnY5fea2LRpE9544w0AQHR0NNq1a4cTJ05I3+ARERE9j8OHDys91tPTw8qVK7Fy5coq69jb22PPnj0qjoyIiNRNpdccLVy4EM2bN4erqyu++uorlJaWVlm2JveaqAxvtEdERERERHVBZTNHn332GV599VWYm5vj+PHjCAkJQUZGBpYsWVJp+Zrca6IyERERCA8Pr8vQiYiIiIjoBVSrmaOZM2dWWGThye3KlSsAgODgYHh5eaFTp0745JNPsHjxYkRFRaGoqKhOB8Ab7RERERERUV2o1czRlClT4OfnV22Zli1bVrrf3d0dpaWlSEtLQ5s2bSocl8vl0r0mHp89evxeE5XhjfaIiIiIVMth5u7nqp+2cGAdRUKkWrVKjiwtLWFpaflMHSUlJUFDQwNWVlaVHndzc5PuNTFkyBAAFe81QUREREREpCoqueYoPj4eCQkJ6N27N4yMjBAfH4/Jkyfjww8/hJmZGQDg1q1b6NOnD7777jt069ZN6V4T5ubmMDY2xoQJE5TuNdEYaZqbqTsEJYqMhnPfKPODdXuKJamGePhQ3SFIynLuqTsEIiIiasJUkhzp6upi8+bNCAsLQ1FRERwdHTF58mQEBwdLZUpKSpCSkoL79+9L+552rwkiIiIiIiJVUUly9Oqrr+LEiRPVlnFwcIAQQmlfTe41QUREREREpAoqvc8RERERERFRY8HkiIiIiIiICEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgBMjoiIiIiIiAAwOSIiIiIiIgLA5IiIiIiIiAgAkyMiIiIiIiIATI6IiIiIiIgAAFrqDoCIiIiImjaHmbufq37awoF1FAlR9ThzREREREREBCZHREREREREAJgcERERERERAWByREREREREBIDJEREREREREQAmR0RERERERACYHBERURMWERGBrl27wsjICFZWVhg0aBBSUlKUyjx8+BCBgYFo3rw5DA0NMWTIEGRlZSmVSU9Px8CBA2FgYAArKytMmzYNpaWl9TkUIiKqB0yOiIioyTpy5AgCAwNx4sQJxMbGoqSkBH379kVhYaFUZvLkyfj111+xdetWHDlyBLdv38Z7770nHS8rK8PAgQNRXFyM48ePY8OGDYiJiUFoaKg6hkRERCrEm8ASEVGTtXfvXqXHMTExsLKyQmJiInr27Im8vDysW7cOmzZtwhtvvAEAiI6ORrt27XDixAm89tpr2L9/Py5duoQDBw7A2toaLi4umD9/PmbMmIGwsDDo6OhU6LeoqAhFRUXS4/z8fNUOlIiI6gRnjoiI6IWRl5cHADA3NwcAJCYmoqSkBN7e3lKZtm3bokWLFoiPjwcAxMfHw9nZGdbW1lIZHx8f5OfnIzk5udJ+IiIiYGJiIm12dnaqGhIREdUhJkdERPRCUCgUmDRpErp3746OHTsCADIzM6GjowNTU1OlstbW1sjMzJTKPJ4YlR8vP1aZkJAQ5OXlSdvNmzfreDRERKQKPK2OiIheCIGBgbh48SL++OMPlfelq6sLXV1dlfdDRER1i8kRERE1eUFBQdi1axeOHj2Kl19+Wdovl8tRXFyM3NxcpdmjrKwsyOVyqczJkyeV2itfza68TGPgMHO3ukMgImrweFodERE1WUIIBAUFYdu2bTh48CAcHR2Vjru5uUFbWxtxcXHSvpSUFKSnp8PDwwMA4OHhgQsXLiA7O1sqExsbC2NjY7Rv375+BkJERPWCM0dERNRkBQYGYtOmTdixYweMjIyka4RMTEygr68PExMT+Pv7Izg4GObm5jA2NsaECRPg4eGB1157DQDQt29ftG/fHiNHjsSiRYuQmZmJ2bNnIzAwkKfOERE1MSpJjg4fPozevXtXeuzkyZPo2rVrpce8vLxw5MgRpX3jxo3DmjVrah2Dhr4eNGQVl1etb2U599QdghINfX11hyApvZ2h7hAapIb0GhE1dqtXrwbw6PPlcdHR0fDz8wMALF26FBoaGhgyZAiKiorg4+ODVatWSWU1NTWxa9cujB8/Hh4eHmjWrBlGjx6NefPm1dcwiIionqgkOfL09ERGhvIfvnPmzEFcXBy6dOlSbd2AgAClDxwDAwNVhEhERC8AIcRTy+jp6WHlypVYuXJllWXs7e2xZ8+eugyNiIgaIJUkRzo6OkoXqZaUlGDHjh2YMGECZDJZtXUNDAwa1QWuRERERETUNNTLggw7d+7E33//jTFjxjy17MaNG2FhYYGOHTsiJCQE9+/fr7Z8UVER8vPzlTYiIiIiIqLaqpcFGdatWwcfHx+l5VMr869//Qv29vawtbXF+fPnMWPGDKSkpOCXX36psk5ERATCw8PrOmQiIiIiInrB1GrmaObMmZDJZNVuV65cUarz119/Yd++ffD3939q+2PHjoWPjw+cnZ3h6+uL7777Dtu2bcP169errMO7kBMRERERUV2o1czRlClTpNV9qtKyZUulx9HR0WjevDneeeedWgfn7u4OALh27RpatWpVaRnehZyIiIioaXvemxinLRxYR5FQU1er5MjS0hKWlpY1Li+EQHR0NEaNGgVtbe1aB5eUlAQAsLGxqXVdIiIiIiKi2lDpggwHDx5EamoqPv744wrHbt26hbZt2+LkyZMAgOvXr2P+/PlITExEWloadu7ciVGjRqFnz57o1KmTKsMkIiIiIiJS7YIM69atg6enJ9q2bVvhWElJCVJSUqTV6HR0dHDgwAFERkaisLAQdnZ2GDJkCGbPnq3KEImIiIiIiACoODnatGlTlcccHByUbs5nZ2eHI0eOqDIcIiIiIiKiKtXLfY6IiIiIiIgaOiZHREREREREYHJEREREREQEgMkRERERERERABUvyEBEREREpG68iSzVFGeOiIiIiIiIwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACX8iYiIiIiqhaXAn9xcOaIiIiIiIgITI6IiIiIiIgA8LQ6IiKiRuF5T+shIqKn48wREREREREROHNERERUIytXrsRXX32FzMxMdO7cGVFRUejWrZu6wyKiRoALOjQenDkiIiJ6ii1btiA4OBhz587FmTNn0LlzZ/j4+CA7O1vdoRERUR3izBEREdFTLFmyBAEBARgzZgwAYM2aNdi9ezfWr1+PmTNnqjk6Imrq1H3N4Ys0c9XkkiMhBACgVJSoOZJHFA0kjnIaQlPdIUga2nPTUDSk16ih4e9Mw1aKR69P+f/DTUVxcTESExMREhIi7dPQ0IC3tzfi4+MrrVNUVISioiLpcV5eHgAgPz//meNQFN1/5rpERM/jef7vAoDCwkKltsrKyp43pBorj72mn01NLjn6559/AABHH/6i5kgaqAfqDoCeiq8RNXL//PMPTExM1B1Gnbl79y7KyspgbW2ttN/a2hpXrlyptE5ERATCw8Mr7Lezs1NJjEREqmQSWXdt2dra1l1jtVDTz6YmlxzZ2tri5s2bMDIygkwmU3c4tZKfnw87OzvcvHkTxsbG6g6nzjTFcXFMjUdTHFdDHZMQAv/884/aPvgakpCQEAQHB0uPFQoFcnJy0Lx5c8hksgb7Gj4vjqvxaapj47gaH1WNrbafTU0uOdLQ0MDLL7+s7jCei7GxcZP7hQea5rg4psajKY6rIY6pKc0YlbOwsICmpiaysrKU9mdlZUEul1daR1dXF7q6ukr7TE1NK5RriK9hXeC4Gp+mOjaOq/FRxdhq89nE1eqIiIiqoaOjAzc3N8TFxUn7FAoF4uLi4OHhocbIiIiorjW5mSMiIqK6FhwcjNGjR6NLly7o1q0bIiMjUVhYKK1eR0RETQOTowZEV1cXc+fOrXAqRmPXFMfFMTUeTXFcTXFMDd0HH3yAO3fuIDQ0FJmZmXBxccHevXsrLNJQU031NeS4Gp+mOjaOq/FpKGOTiaa25ioREREREdEz4DVHREREREREYHJEREREREQEgMkRERERERERACZHREREREREAJgcERERERERAWBy1ODt3r0b7u7u0NfXh5mZGQYNGqTukOpEUVERXFxcIJPJkJSUpO5wnllaWhr8/f3h6OgIfX19tGrVCnPnzkVxcbG6Q6u1lStXwsHBAXp6enB3d8fJkyfVHdIzi4iIQNeuXWFkZAQrKysMGjQIKSkp6g6rTi1cuBAymQyTJk1SdygvhNq+P7Zu3Yq2bdtCT08Pzs7O2LNnj9JxIQRCQ0NhY2MDfX19eHt74+rVq0plcnJy4OvrC2NjY5iamsLf3x8FBQVKZc6fP4/XX38denp6sLOzw6JFixr9uNLS0iCTySpsJ06caNDj+vzzz+Hp6QkDAwOYmppW2k96ejoGDhwIAwMDWFlZYdq0aSgtLa3xuBry2Cp7zTZv3txgx1XTz+/G9h6rybjq4j2mjrEBwDvvvIMWLVpAT08PNjY2GDlyJG7fvq1U5nlfMwhqsH766SdhZmYmVq9eLVJSUkRycrLYsmWLusOqE5999pno37+/ACDOnj2r7nCe2W+//Sb8/PzEvn37xPXr18WOHTuElZWVmDJlirpDq5XNmzcLHR0dsX79epGcnCwCAgKEqampyMrKUndoz8THx0dER0eLixcviqSkJDFgwADRokULUVBQoO7Q6sTJkyeFg4OD6NSpk5g4caK6w2nyavv+OHbsmNDU1BSLFi0Sly5dErNnzxba2triwoULUpmFCxcKExMTsX37dnHu3DnxzjvvCEdHR/HgwQOpTL9+/UTnzp3FiRMnxO+//y5at24tRowYIR3Py8sT1tbWwtfXV1y8eFH88MMPQl9fX3zzzTeNelypqakCgDhw4IDIyMiQtuLi4gY9rtDQULFkyRIRHBwsTExMKvRTWloqOnbsKLy9vcXZs2fFnj17hIWFhQgJCanRuBry2IQQAoCIjo5Wes0eb6Ohjasmn9+N8T1Wk3E973tMXWMTQoglS5aI+Ph4kZaWJo4dOyY8PDyEh4eHdPx5XzMhhGBy1ECVlJSIl156SfznP/9Rdyh1bs+ePaJt27YiOTm50SdHlVm0aJFwdHRUdxi10q1bNxEYGCg9LisrE7a2tiIiIkKNUdWd7OxsAUAcOXJE3aE8t3/++Uc4OTmJ2NhY0atXLyZH9aC2749hw4aJgQMHKu1zd3cX48aNE0IIoVAohFwuF1999ZV0PDc3V+jq6ooffvhBCCHEpUuXBABx6tQpqcxvv/0mZDKZuHXrlhBCiFWrVgkzMzNRVFQklZkxY4Zo06ZNox5X+R9uz/rZoI5xPS46OrrSBGLPnj1CQ0NDZGZmSvtWr14tjI2NlV7Dxjg2IR4lR9u2bavROJ6k7nGVe/LzuzG+x2oyrud9jwnRcMa2Y8cOIZPJpMTueV8zIYTgaXUN1JkzZ3Dr1i1oaGjA1dUVNjY26N+/Py5evKju0J5LVlYWAgIC8N///hcGBgbqDkcl8vLyYG5uru4waqy4uBiJiYnw9vaW9mloaMDb2xvx8fFqjKzu5OXlAUCjel2qEhgYiIEDByq9XqQ6z/L+iI+Pr/D6+Pj4SOVTU1ORmZmpVMbExATu7u5Smfj4eJiamqJLly5SGW9vb2hoaCAhIUEq07NnT+jo6Cj1k5KSgnv37jXacZV75513YGVlhR49emDnzp3Vjkfd46qJ+Ph4ODs7w9raWqmf/Px8JCcnN+qxlQsMDISFhQW6deuG9evXQwjRqMb15Od3Y3yP1WRc5Z7lPdaQxpaTk4ONGzfC09MT2traUj/P+ppJY6lRKap3//vf/wAAYWFhmD17Nnbt2gUzMzN4eXkhJydHzdE9GyEE/Pz88Mknnyh9MDYl165dQ1RUFMaNG6fuUGrs7t27KCsrU/rABgBra2tkZmaqKaq6o1AoMGnSJHTv3h0dO3ZUdzjPZfPmzThz5gwiIiLUHcoL41neH5mZmdWWL//3aWWsrKyUjmtpacHc3FypTGVtPN5HYxyXoaEhFi9ejK1bt2L37t3o0aMHBg0aVKM/3tQ1rpp4ntcLaNhjA4B58+bhxx9/RGxsLIYMGYJPP/0UUVFRT63XUMZV2ed3Y3yP1WRcz/MeawhjmzFjBpo1a4bmzZsjPT0dO3bseGo/j/fxNEyO6tnMmTMrvQju8e3KlStQKBQAgFmzZmHIkCFwc3NDdHQ0ZDIZtm7dquZRKKvpmKKiovDPP/8gJCRE3SE/VU3H9Lhbt26hX79+GDp0KAICAtQUOT0pMDAQFy9erNWFwQ3RzZs3MXHiRGzcuBF6enrqDodIZSwsLBAcHAx3d3d07doVCxcuxIcffoivvvpK3aFRNebMmYPu3bvD1dUVM2bMwPTp0xvNa9ZUP7+rGldjf49NmzYNZ8+exf79+6GpqYlRo0bVaJayprTqrCWqkSlTpsDPz6/aMi1btkRGRgYAoH379tJ+XV1dtGzZEunp6aoMsdZqOqaDBw8iPj4eurq6Sse6dOkCX19fbNiwQYVR1k5Nx1Tu9u3b6N27Nzw9PbF27VoVR1e3LCwsoKmpiaysLKX9WVlZkMvlaoqqbgQFBWHXrl04evQoXn75ZXWH81wSExORnZ2NV199VdpXVlaGo0ePYsWKFSgqKoKmpqYaI2yanuX9IZfLqy1f/m9WVhZsbGyUyri4uEhlsrOzldooLS1FTk6OUjuV9fN4H41xXJVxd3dHbGxstWNS57hqQi6XV1jNq6avF9Cwx1YZd3d3zJ8/H0VFRRU+9x+n7nFV9/ndGN9jNRlXZWr6HgPUPzYLCwtYWFjglVdeQbt27WBnZ4cTJ07Aw8PjuV6zcpw5qmeWlpZo27ZttZuOjg7c3Nygq6urtPxwSUkJ0tLSYG9vr8YRVFTTMS1fvhznzp1DUlISkpKSpCUct2zZgs8//1zNo1BW0zEBj76Z8fLykmb3NDQa19uq/PctLi5O2qdQKBAXFwcPDw81RvbshBAICgrCtm3bcPDgQTg6Oqo7pOfWp08fXLhwQXr/JCUlSV8sJCUlMTFSkWd5f3h4eCiVB4DY2FipvKOjI+RyuVKZ/Px8JCQkSGU8PDyQm5uLxMREqczBgwehUCjg7u4ulTl69ChKSkqU+mnTpg3MzMwa7bgqk5SUpPQHU0MbV014eHjgwoULSslhbGwsjI2Nlb4IbYxjq0xSUhLMzMyqTYwA9Y7raZ/fjfE9VpNxVaam7zF1j+1J5WdaFRUVSf0862smqfHSDVTvJk6cKF566SWxb98+ceXKFeHv7y+srKxETk6OukOrE3WxWoq6/fXXX6J169aiT58+4q+//lJaErMx2bx5s9DV1RUxMTHi0qVLYuzYscLU1FRpVaXGZPz48cLExEQcPnxY6TW5f/++ukOrU1ytrn487f0xcuRIMXPmTKn8sWPHhJaWlvj666/F5cuXxdy5cytdstbU1FTs2LFDnD9/Xrz77ruVLnnt6uoqEhISxB9//CGcnJyUlrzOzc0V1tbWYuTIkeLixYti8+bNwsDAoFbLDDfEccXExIhNmzaJy5cvi8uXL4vPP/9caGhoiPXr1zfocd24cUOcPXtWhIeHC0NDQ3H27Flx9uxZ8c8//wgh/m8p7759+4qkpCSxd+9eYWlpWeulvBvi2Hbu3Cm+/fZbceHCBXH16lWxatUqYWBgIEJDQxvsuGry+d0Y32M1GdfzvsfUNbYTJ06IqKgocfbsWZGWlibi4uKEp6enaNWqlXj48GGdvGZCcCnvBq24uFhMmTJFWFlZCSMjI+Ht7S0uXryo7rDqTFNIjqKjowWASrfGJioqSrRo0ULo6OiIbt26iRMnTqg7pGdW1WsSHR2t7tDqFJOj+lPd+6NXr15i9OjRSuV//PFH8corrwgdHR3RoUMHsXv3bqXjCoVCzJkzR1hbWwtdXV3Rp08fkZKSolTm77//FiNGjBCGhobC2NhYjBkzRvpjtNy5c+dEjx49hK6urnjppZfEwoULG/24YmJiRLt27YSBgYEwNjYW3bp1E1u3bm3w4xo9enSl/+8cOnRIKpOWlib69+8v9PX1hYWFhZgyZYooKSlp9GP77bffhIuLizA0NBTNmjUTnTt3FmvWrBFlZWUNdlw1/fxubO+xmoyrLt5j6hjb+fPnRe/evYW5ubnQ1dUVDg4O4pNPPhF//fWXUjvP+5rJhKjDK5iIiIiIiIgaqcZ1cQQREREREZGKMDkiIiIiIiICkyMiIiIiIiIATI6IiIiIiIgAMDkiIiIiIiICwOSIiIiIiIgIAJMjIiIiIiIiAEyOiIiIiIiIADA5IiIiIiIiAsDkiIiIiIiICACTIyIiIiIiIgDA/wOpmeDUauApXgAAAABJRU5ErkJggg==", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for label, (x, y) in d.items():\n", " # input is a histogram\n", " w, xe, ye = np.histogram2d(x, y)\n", "\n", " # apply USP test\n", " r = perm.usp(w, random_state=1)\n", "\n", " fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", " plt.sca(ax[0])\n", " plt.pcolormesh(xe, ye, w.T)\n", " plt.sca(ax[1])\n", " plt.hist(r.samples, bins=20, label=\"test statistic under\\nnull hypothesis\")\n", " plt.axvline(r.statistic, color=\"k\", label=\"test statistic\\nfrom input\")\n", " plt.suptitle(f\"{label}: p-value={r.pvalue:.3f}\")\n", " plt.legend()" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 } resample-1.10.1/doc/tutorial/variance_fit_parameters.ipynb000066400000000000000000001020721470150054300237330ustar00rootroot00000000000000{ "cells": [ { "cell_type": "markdown", "id": "controversial-sally", "metadata": {}, "source": [ "# Variance of fit parameters\n", "\n", "We use the bootstrap and the jackknife to compute the uncertainties of a non-linear least-squares fit. The bootstrap is generally superior to the jackknife, which we will also see here. We use `scipy.optimize.curve_fit` to perform the fit, which also estimates the parameter uncertainties with asymptotic theory. For reference, we also doing a Monte-Carlo simulation of the experiment with a large number of tries, to have a reference for the parameter uncertainties.\n", "\n", "In this case, the asymptotic theory estimate is very accurate, while the bootstrap and the jackknife estimates are similar and off. The accuracy of the non-parametric methods improves with the sample size." ] }, { "cell_type": "code", "execution_count": 1, "id": "major-companion", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "from resample import bootstrap, jackknife\n", "\n", "rng = np.random.default_rng(1)\n", "\n", "# generate some random data, each y value scatters randomly\n", "x = np.linspace(0, 1, 100)\n", "y = 1 + 10 * x ** 2\n", "ye = 0.5 + x\n", "y += rng.normal(0, ye)" ] }, { "cell_type": "code", "execution_count": 2, "id": "intermediate-currency", "metadata": {}, "outputs": [], "source": [ "def model(x, a, b, c):\n", " return a + b * x + c * x ** 2\n", "\n", "def fit(x, y, ye):\n", " return curve_fit(model, x, y, sigma=ye, absolute_sigma=True)\n", "\n", "# fit original data and compute covariance estimate from asymptotic theory\n", "par, cov = fit(x, y, ye)" ] }, { "cell_type": "code", "execution_count": 3, "id": "successful-inquiry", "metadata": {}, "outputs": [ { "data": { "image/png": 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jDgDAu+++i5SUFKxZswajRoVPRT0iIiJXlCo+J1WcNgaHFwy/vKPiD2B5NnpWHYZBNRJl1kSXQywCImfoRdYJpyUlJSgrK0NW1uXqeYmJiejVqxe+/fZbOS9FREQU/SpLgeXZwJkSqJPaIOfOawF4XtYbCWQNPsrKygAAKSkpDvtTUlLsrzmrqalBZWWlww8REVGkq1tMbkfJad+Ly50rrw08Th8EmrQGxq7HkJ5Xu1zWm5qoR96orjK0OjhCvtpl/vz5yM3NDXUziIiIZFNQXI55G/fZt8ct3QlDoh4zh3aSdoLzJ2oDj1O/AIlpwNj1QJM0ALXLevtc2Rxd5nwBoHZZb78OLVBjtng9bb0hnRCRtecjNTUVAFBeXu6wv7y83P6as1mzZqGiosL+c/ToUTmbREREJDtvvRpTVxXVTwRWUY2pq4q8n/zCSeDd24GT+4GEVsDYtUDTNg6HOC/rDedU6q7IGny0a9cOqamp2Lx5s31fZWUlvv/+e/Tu3dvle3Q6HRISEhx+iIiIwlVBcbk9vTlQ26vR9/kvUVB8+Yu334nAqk4D794BHC8G4g3A2HVAUvuA2xxufB52OX/+PH799Vf7dklJCYqKipCUlITWrVtj6tSpePbZZ9GhQwe0a9cOs2fPRsuWLXHnnXfK2W4iIopSoV5t4s3UVUX1AgmpvRoeA5CLZ4AP7gLK9wCNU2oDj2bpAbQ0fPn8N/rDDz9gwIAB9u3p06cDAMaOHYtly5bh73//Oy5cuICJEyfi7Nmz6Nu3L/Lz86HXy5/jnoiIKNjc9WoEMvCRgAvQffAnoOwnoFGL2sCjeYcAzhjefA4++vfvD1F0H7sJgoC5c+di7ty5ATWMiIgokvibYyMeVXhXuwDqsoNAXLPawKNFR1nbFm5YWI6IiMgLX5fJSukF2VFyGpaqs3hPOw9dVQchxiYBD64Fkjv718gIEl4DaURERGEmf08pctbu9fl93jKOjlu6E6nqSsxRNcNp8Thi71uN2NRMv9sZSdjzQURE5EZBcTkmrSist2zWlbq9HXmjutZLBOZKmaUxHjFNxcCal/B9VUvfE5FFKAYfREQUlQLOMApg3sZ9kuZyOA+zDMpIwabpN9u3m8Zp3LxTBUDAGSTYl+zm7yn1uZ2RhsEHERFFnfw9pV5zcUghpccDcJ3evG7irzNVJknnKauoxqQVhVEfgDD4ICKiqJK/p9TlUElZRTX+VicXh7+9Ic4euak9ts+4BYMyUrwf7IWtNbnriqN6CIbBBxERRQ2LVUTuumJJGUblGua4Ib2ZrOnNRQClFdXYUXJatnOGGwYfREQUNXaUnEZpRbXk470Nc6Qk6Lwum+3epqnH15vjDFJVFRBgldwuADh+TvrniDQMPoiIKGr4+sD2NszxxLDanBvOAUjdbU+9Hsk4gw+1z2KO+h0Agk9ZUJPjozczOIMPIiKKGv48sD0NcwzKSMGiMd3qLZtNSfB+HeHcMazSPoN0VSlubVqKRXemSVp+KwAwJOrRs12S1I8QcRh8EBFR1OjZLgmGRL1fdVbc9ZoMyTQ4LJtdNr4HCqbf5PFcQuUf0K24He1VZThqbYGaMesw5IZrHc4DuO9RycnOkHUeSbhh8EFERGGvymhG25kb0HbmBlQZzW6PU6sE5GRnAPC90JunXpO6gUDPdkkeA4M0oRy6926D6kwJfrO2wCjjUxCbtK53nldcJCJLTdRj0ZhuGJJp8LH1kYXBBxERRZUhmQaXQyXuyDnM0V44ho+0z0BV8RusTdtjpPFp/IEWLo91TkS2bHwPbJ9xS9QHHgCDDyIiikLOQyVTBqRDgLLDHMLxYnyonQuDcBrW5h1R/cA6lKKZx/f40qPiTZw2BocXDMfhBcMRpw3v0m0MPoiIyGdSh0FCqe6D/NEBV7rsDZFtmOOPQujfvx0thErstbZB9f1rgcapgZ0zioV3aERERCSTIZkG9LmyObrM+QJA7TBHvw4tAu7xUP2+A/hwJISaSqDV9bh6zCdAbNOwDcrCAXs+iIiowXA1zBFIL05v1V7oPrgHqKkE2vQBHlwDxHpOOkbs+SAiIvJLf1URFmv+CcFkAtoPAEatBLRxoW5WRGDPBxERkY/U+9fjLc0/oBdMMHcYAoxexcDDBww+iIiI6qibZt1V5Vv13k+h/ezP0AoWrLf0gvHuZYAmelOhK4HBBxER0SUFxeXIenmrfdtW+baguBwAcK96C7T/fhiCaMGnln74m2kKoNaEqLWeeQuiQonBBxER0SVTVxWhvLLGYV9ZRTWmrirCzaoivKh5CwJEmK4bh/9nehgWqEPUUs/y95S6DKLcVe8NNgYfREREl7jqGxAv/e8B6xWwiAJMPSfBNOQliGH6CM3fU4pJKwpdBlGTVhSGRQASnneOiIgarHAcLhAhoBTNMcM0AaaBzwBCeBZ9s1hF5K4r9hBEAbnrikN+T7nUloiIwkb+nlLkrN1r3x63dCcMiXrMHNophK267BNrf8wNg8DDlkrd2Y6S0yitcF2dF6gNQEorqrGj5DR6p3tO/a4kBh9ERFGsymhGxtOfAwCK5w4O65oftuEC5+/ktjkX5N3xc+4DD3+OUwqHXYiIKOSkDBcEg7s+jdD3dUiTHC9tya/U45TC4IOIiByEomiclOGCYBGcrhYpgQdQmzLekKj3GEQZEvXo2S4pmM2qh8EHERGFXKiHAQCgBc5iYbOPkYrTDvtTE/XIG9U1NI3ykVolICc7A0D9oMm2nZOdEXAxvUAx+CAiopAL9TDAFcJxfKzNxbALq/HfpOeQhtqkYsvG98D2GbdgUEZKSNvniyGZBiwa0w3JCTqH/amJeiwa0w1DMg0hatllDD6IiCjkpAwXKEU48TM+0eairaoc1iZtYBq7HkeRYm9XqHsJ/DEk04BN02+2b9uCqHAIPAAGH0REFAakDBco4o9d0K/IRqpwBgesrVDzwAaITdspecWgqRs0hVsQxeCDiIjCgqfhAiXmXKhKtgDLb4dw8TSKrOkYYXwaYrx8PQO2XByHFwwPeIlzKCYBKyl8F3wTEVHU8ZZ3ZEimAX2ubI4uc74AUDtc0K9DC9SYLbK24zbVt9B9uBiwmmBp0w/37x+HC4iV/H53Sb5IGvZ8EBFRWFF6uGCs+nO8qnkdgtUEZNyJmpEf+hR4UOAYfBARUcMgitBseRa5muVQCSJM3R8C7nkHiNF5fy/JisEHERFFP6sZWDsFmm/+CQB40TQCplufB1TqEDesYZI9+LBYLJg9ezbatWuH2NhYpKen45lnnoEohr4qIRER+SfUEx4DqXSrRw20n44Fdq+AKKgwwzQBCy13hm1l2oZA9gmnzz//PBYtWoTly5fj6quvxg8//IDx48cjMTERjz32mNyXIyKiKOeu0m1OdobXvBWJOI9/aV9CzC8HgBg9jHcuwYcrfO/t4ARTeckefHzzzTe44447MHx47V9S27Zt8cEHH2DHjh1yX4qIiELEuSeiX4cWfk8MdV4BU1dBcTmmripyWel20opCjxk7hco/8JF2LjqqfoeoT4Qw+kNYDD0AfO5XO+XgHMREw7JZf8g+7HLjjTdi8+bNOHDgAADgxx9/xPbt2zF06FCXx9fU1KCystLhh4iIwldBcTmyXt5q3x63dCf6Pv8l8veUyn6teRv3eax0m7uu2PUQzIn9iH13GDqqfgfiDRDG5wNtesvePvKP7D0fM2fORGVlJTp16gS1Wg2LxYLnnnsO999/v8vj58+fj9zcXLmbQURECvG3J8If5ZU1bl8TAZRWVGNHyWn0Tm92+YWjO4GV9wIXzwDNOgAPfAY0aS1bm+TUUIdzZO/5+Oijj/D+++9j5cqVKCwsxPLly/HSSy9h+fLlLo+fNWsWKioq7D9Hjx6Vu0lERCQjv3oiFORQEffnDcDy7NrAo9X1wJ8/D9vAoyGTvefj8ccfx8yZMzFq1CgAQJcuXXDkyBHMnz8fY8eOrXe8TqeDTsc11kREkc5tT4TC7BVxdywB/vN3QLQCHW4F7l0GaBs5HNtQexrCjew9H1VVVVCpHE+rVqthtVrlvhQREYUhh56IAKUk6DxWujUk6tGzTROg4Glg4/+rDTy6jQVGfVAv8HAnkGW85B/Zez6ys7Px3HPPoXXr1rj66quxe/duvPzyy/jzn/8s96WIiCgM2XsiZPDEsM6YuqoIAhyHe2wBSc6wq6BeMwHY82ntjltmA/3+T3IOD1fLeFMS2BuvNNmDj9deew2zZ8/Go48+iuPHj6Nly5Z4+OGH8fTTT8t9KSIiCgHnQKDu/tREPXq2S5LtWoMyUrBoTDfkrN3rMPk0NVGPnMFtMKTwYeDI14AqBrhjIXDtKMnnzt9TikkrCut9luMeJrmSPGQPPuLj45GXl4e8vDy5T01ERGHCbU9EdobsheBcVrptcRHqlfcCJ/cDugRg5HuouqIvMmZuAOC6Ym5dFquI3HXFHifP2o4j+bG2CxFRFFNiPkPeqK5IdhqaSE3UI29UVzyyolCRFOx1A5pesUehfmdQbeAR3xL4cz7Qvr9P59tRchqlFd7npuw6csbXppIEsvd8EBGR/JyzgHr6Vm8TSFpyTwZlpOCWTsmOPREdWqDGbPH7nFLdrPoR+vcmAKYLQEomcN9HQGIrn88jdVLsiXMcglECez6IiKKQbT6Dc5IuWzKwQLOR1u2J6NkuSfahFldGqL/C25oXIZguAO1uBsZv9CvwAKRPim0Rz8mnSmDwQUQUZaTMZ/CUDCzslp6KIjRb5+MFzRLECFaYu4wE7v8E0Cf6fcqe7ZJgSNS7XcZr071NU7+vQe4x+CAiijLe5jPUTQbmLH9Pqcu6LQXF5Uo01TtzNfDpX6D5+iUAwCvmu2C8bSEQow3otGqVgJzsDACoF4AITseR/Bh8EBFFGanzGZyP8zRUM3VVkVzNkywJldC9fyew5xOIqhg8bpqIf5rvlZzDw5shmQYsGtOt3uTZlAT58pSQaww+iIiijNT5DHWPk7r0NFjShT+wRjsb6j92AvpE1Iz6BB9b+st+nSGZBmyafrN9e9n4HiiYfpPs1yFHDD6IiKKMt/kM9rTkdZKBSRmqCRZVyVas1uagteoErE3bAX/ZDGvbfopdLxSTZxs6Bh9ERFGiymhG25kbkP7ERswc2gmA+/kMzsnA5KzHEgj17neh+3AEEoQq7LReheqxnwPNO4S6WSQzBh9ERFHIlpbcVTKwRWO61cvzIWc9Fn8IsGJmzEro/jMNgtWM1ZY+uN/4JBAXvOq4NrbKt4cXDJeUT4V8x7tKRBSlXKYl79DC5bCCbaimrKLabd0WOYZenJfx9uvQAjBVYZHmFQxR7wQAGPvNwLSCa1C/34aiBYMPIqIoJnU+g23p6aQVhW7rtgTKZcbVeA1mx36EYeqdqBFjgDteh67baBweKNNFKSxx2IWIiAC4X3pqq9sSCLfLeM8ZMfn4nfjYfBPuMz4JS+a9AV2HLgvn4aPwag0REYWUu6GaQOq2eF7GK0CAiJnmCbBA7fc1KLKw54OIiBy4GqoJJOW692W8AgMPL8Iu5X2A2PNBREQeuZqrkZIgveBauCzjjVRKVScOJfZ8EBGRWwXF5S7nahyvlF5qXollvLacJm1nbkCV0Sz7+cOF0tWJQ4XBBxFRA+LrQ3vexn1eU657GwLoqTkEg+oMBFi9Xi8ahhTkEmh14nDG4IOIiNxy/sbtyq4jZ9y/+L+PoF4+HDnqZQAEr8t2bVV0I/UbvZwCqU4c7hh8EBGFmUgbUjhxzkWAIlqBTXOAzyYAlhoM6dwci0ZeXW8ZryuRPqQgF3+rE0cCBh9ERBSQFvGOAUUjXIT2kweA7f+s3dF3OjBqJYZc186hgmzTOI3L80X6kIJc/KlOHCkYfBARkVspCTqvQyXd2zS1/zlNKMen2jmI+SUfUOuAu5cAWTmAqvZxU3cZ75kqk9tzRvKQglz8qU4cKRh8EBGRW08M6wzAfXVc4HJAoTr0FdZpn0In1VFYG6cA4/8DXDMioOtH4pCCXGwp7wHp1YkjBYMPIiJyy1113JSEOl39oghsz4PuwxFoIlxAkTUdNeM3AVd0D/j6kTikICdPKe9dVSeOFAw+iIgiQCgzXA7JNDjM1Vg2vgcKpt8EAIhFNbRr/gJsyoEgWvGhuT9GGmdDjG/p9byehnQieUhBbq7u//YZt0Rs4AEw+CAiCnv5e0qR9fJW+3YolqO6SrmeJpTjM20OYvatAVQxuHjri5hhnoAaaCUFSN6GdHwdUoi2FOR1Sa1OHCkYfBARhTF3GUZDvRw17ret+G9iLjqrjgKNkpHf7zP039IettBBSoDkbkjH1ZCCtwqt4RCgkXQMPoiIgkjuDKPBX44qIubbV4H37wGqzwKtrkf+zf/GpM/P+xUgyTGkEK4BGrnH4IOIKIx5yjAq13JUqQFRLKrxuuY1aL/KrU0idt0DsIzdgNzNpQEFSIEOKYRfgEbeMPggIopwwViOKpw5jM+0ObhN/R1ElQYY/jJw+2vYcfRCyFOAByNAI3kx+CAiinCKL0f9pQD6pQPRWXUUJ8RE1Nz/b6DHQ4AgSA58Ri/5LqSp4htyvpBwxOCDiCiMhXI5qgpWaLYtAN6/F0L1Wey2Xonbap6DNa2X/ZhIycMRKe1sKBh8EBGFMV+Wo8q51LQpKrFM8zw0218EIMLU7c8YaZyNcjgGOlJSgCuN+UIiD4MPIqIwJnU5qrulpgXF5T5fU/XHD1ivexI3qX+CGBML3PUWTENehBH1C8FJSQGutEDzhXhbxkvyY/BBRBTmvC1Hzd9T6nap6dRVRVg8ppvEB6uImF1vQ/febWglnMIhayqqx30BXDvSa/vcBUh5o7pK/pz+8iVfCIUHhnhEFNWqjGZkPP05AKB47mBZv9kqeW5n7pajWqwictcVu11qKqB2qemgjFSP3/5jUY15mreh/fxrAMB/LD3wuOlh7EjOkNS+IZkG9LmyObrM+QJAbYDUr0ML1Jgtkt4fKHfXj/RMoNFKkZ6PP/74A2PGjEGzZs0QGxuLLl264IcfflDiUkREDdqOktMBL3UVTv2CNdqncZf6a4iCGsaBczHJNBXnEedTW0KdAjzU1yfpZA/Tz5w5gz59+mDAgAH4z3/+gxYtWuCXX35B06ZN5b4UEVGDJ3UJqdvjiv8N/ZpH0VF1HsfFJkgYswL6K/vhcD8ZG0nkRPbg4/nnn0daWhqWLl1q39euXTu5L0NERJC+hNT5uBiYodk0G9jxBgQA31s7YYrxMWxt3VuBVhI5kn3YZe3atbj++utx7733Ijk5Gddddx2WLFni9viamhpUVlY6/BARkTRSlro6LzVthRP4SDsXmh1vAABMN/wV9xmfxAk0Uby9RIACwcehQ4ewaNEidOjQAZ9//jkmTZqExx57DMuXL3d5/Pz585GYmGj/SUtLk7tJRERRS8pS17pLTdUHNmKD7gl0U/0KUZcAjFwB0y1zYIE6aG0mkj34sFqt6NatG+bNm4frrrsOEydOxIQJE7B48WKXx8+aNQsVFRX2n6NHj8rdJCKiqOZpqat9qanZCOTPQszHD2KftTVeNd2JbbdugKXjbSFqNTVkss/5MBgMyMhwXJrVuXNnfPrppy6P1+l00Ol0Ll8jIvIkmEtdleT8OZwzlfbr0MLrOTwuNT1zGPh4PPKPqpFrehWlaFb7po+PwvDFCcwc2kn2z0Tkiew9H3369MH+/fsd9h04cABt2rSR+1JERFGnoLjc70ylLpeaFq8FFt+E/KNqTDJNRalTenRbIrJII2cqeQo+2b8mTJs2DTfeeCPmzZuHESNGYMeOHXjrrbfw1ltvyX0pIqKgCVYvy9RVRfUShvkVIJhrgE1PADvehEUUkGv9C0QX01JticgiSf6eUuSs3WvfHrd0JwyJevbgRBDZ/+vp0aMHVq9ejVmzZmHu3Llo164d8vLycP/998t9KSKiqOMpU6lUrYVy6N4dCpT9CADYkfEkSnfH+3TNcGVLJS9LgEYho0joftttt+G22ziJiYhILlIDhOGq7zBfswTqsotAbBJw12Icr8oEdhcp2TzJbEXc/CEllTxFhsicnUVERI5qzkO74XEs1K4EAFiu6AX1vUuBxFZIPngqxI2Th5RU8hQZWNWWiCjSHdsNvHkTYv63EhZRwCvmu1AzZi2Q2AqAtERkkUBqKnkKf+z5ICIKIwJcf4N3ud9qBb59Hdg8F7CaYI1vidEnH8IOsTMmqC7/825LRDZpRWG980gNPAIZLpGL1FTyFP7Y80FEFERSloi6y1Tq4FwZsOJuoGA2YDUBnbNR/Zdt2CF2dnldT4nI8kZ19e1DhEi09OAQgw8ioqDJ31PqNYdH3qiuXgME1a9fAIv6AIe+AmJigexXgBHvAbGeq4cPyTRg0/Sb7dvLxvfA9hm3YFBGSoCfLDikpJKnyMDgg4jCUpXRjLYzN6DtzA2oMppD3ZyA2ZaIllfWOOx3XiI6KCPFbYCggxE5Mcuh/2g0UHUSSOkCPLwV6D4OEKQ9fl0lIoukhF3R0INDnPNBRKQ4X5eIugoQhBM/Y432aXRW/Vb7wg2PAgNzAE1g8yDcJezKyc6orQkTBL7OJ3GXSr7GbFGqiSEXDnNu5MSeDyIihQW0RFS0At8thn7pQHRW/YaTYgKqR6wChswPOPAoKC532xszaUUh8veUBnR+JblMJU8Rg8EHEUU1V0MKwR7S8XeJaCpOQffBPUD+DAjmamy1XIOhNQtgvXKQLO2at3Gf294YAMhdVxzWQzAUuRh8EJGiQjl3Q8oEz2DwZ4nobapv8bluBtSHtwIxsTAOfgFjTTNwAk1ka5dzj0ddIoDSimrsKDkd0DUiaT4JBQ/nfBBRVAq0BohzITlXnB+s9hL2TmxLRMsqqr3n8Lh4FtovZuB17Se11zBcB/WflkDbvAMO9/babNkFktgrHOaTUHhizwcRRR1vEzzl4K5XxdU8CalLRG9U7YH+X/0Qs/cTmEUV8sx3o+bB/wDNO8jUat/5m9grkueTkPIYfBBR1FG6Bog/D1aPS0TvycDsmPewUjsPqnPHYG3aHvcY5yDPfA+g1gTYWvdSEnQeE3YZEvXo2S7Jr3NzPgl5wuCDiKKO0jVA/H2wukzy9WAzZH83Eg/F/AcAYLpuHKof2oIi8Ur5G+7kiWG12VDd9cbkZGf4tIrEthz0gwk3BGU+CUUuBh9EFHWUrgESyIPV9jCPgRk3/vEO1G9nQXVyP06IiRhvfBymof8AtI2UaHY9gzJS3PbGLBrTze95GVKDPxaKa7g44ZSIIpa7CZ8+TfBUiKcHa0fhN7ykWQzttsMAAPNVwzH4f9k4jQSFW1Wfu4RdgeTNkBr8yR0kRlsirmjGng8iikieJnyGQw0Qlw9WixkxX7+Mddon0UV1GKK+CXD3v2D80/KQBB42cifsklIALpD5JBT5GHwQUcTxVCfFNuFTyRogfk3UPP4z8HYWtFufg1awoMDSDRcnfg1cc6/kuiyRQkrw5+t8EoouDD6IKKJIWUZrm/CpVBVXnyZqWszA9n8Cb/YDju2GqE/ENOMkTDD9H9A41f6ZbHxJxOWcwM027HB4wXDEaUM7qu4p+AtkPglFBwYfRCQrpTOaSllGW3fCp1xDCnUDgsRYDRbeV//BmpKghwjgkRWFtZ/9xH5Y3h4EbJoDWIywXHkrqid8jdXWfrCFKq6Gj+puRzJ3wR8DD2LwQURBF0iAEoqVFAXF5fUChGc2FGPmkE72fcvG90DB9JsAACpYEfPda8DiflAfK0SlGIf/Z3oYNfeuhBhvcDivq+Gj4x5W00QaFoAjV7jahYgiSihWUkxdVeQyTfv0j360b9vmeFwlHMXzmiXQfvkrAMDSfiBuLb4TZWiGuU5zO7zlCwHARFwUldjzQUQRJRQrKSSlaTfXQLNtAdZrn8B1ql8h6uKB219HzcgPUYZmLs/rKV+Iza4jZ3xuL1G4Y/BBRBElnFZS2AKQDjgK/TsDoNn+4qWVLN1RPfEboNsDAa9kOXEueoZgiGwYfBBRWPK0AiTcVlJMjvk3VCf3Q4xrgcnGxzDBNB1ifEtZzt0iXuf9IKIIw+CDiMKOlIqx4bSSIkU4A3OXkbg48RtssN4AqanMPOULsenepmnA7SMKNww+iAKg9LLShkhKAjGbYK2kcD+/xIpknMEb5tthzH4DiPNtnom3fCEAuDqEohKDDyIKG74kEAu2+gGCFYCAs2iE/4rX+nVOd4XdUhKULYxHFGpcaktEinJV/M0dXxKI9U53vYJECXm3GTA//xeUmRvb96U2jsHM267B31YVBXRuJQq7ecLiaxQOGHwQkWIKissxb+M++/a4pTthSNRj5tBOLo8PVgIxd9VwnWlgxkT1ety+ZS1uU9fga+FqrDQPxKgHJqJfp1aoMVsCaocNE3FRQ8Pgg4gU4y4511Q3vQXBSCCWv6cUOWv32rdtAVFOdobDZFXVka+xUTsLHVR/AGYA7W5Czv47UCIa8PKVqQwQiALAOR9El3DyqPwkJeeqQ+kEYu7SmTtMZr1wClg9CZoVd+CkmIAV5luw7YYlqBr5KUpE1iQhkgN7Pogo6NwFILYEYpNWFEJwOs62XVpRjRqzxa+qrZ7SmQsAcj/diUG6v6HgQjpyTa+i1JaZdAuQUrjN5+uRcjh3JbKx54OIwoq7BGJyrADxlM5cBFB6UY3Xz92MSaaplwOPS6Kp2BtRqDH4IKKw4yqBmK1irNKWqu6G6GLgh8XeiOTDYRciUozz0Im3/XU5rwAJlrMmtddjdh05g/4dk/06P4cLiNjzQUQK85S9M9g8pTMXADSJ1Ug6D4u9EQVG8eBjwYIFEAQBU6dOVfpSRKQgqauB6g5JTB6Q7rL4W96orko1060EXMDslj8AEC9lJ73M1hNz9qJJ0rlY7I0oMIoOu+zcuRNvvvkmrrnmGiUvQ0QKqDKakfH05wCA4rmDJb3HOYfG618dRHK81r5ty94pV3IuKeJiVDg84jSwaQ5w+CRiND0wBw+jzBRnPyYlQY+yyupLf9bheGWNx2EhFnsjCoxiPR/nz5/H/fffjyVLlqBpU/6HShTt3BWEO3HOaP+zLXunc4ZRxSZw/lEIvJ0FrJ0CVJ0Eml+FIeOeRMGTd9oPcZ7M2hCLvdnmoRxeMNyvJcxEvlIs+Jg8eTKGDx+OrKwsj8fV1NSgsrLS4YcoUgTtIRrmpBSEsx2Xv6cUWS9vte8bt3Qn+j7/pUO12oCdPw6s/Suw5Bbgj12AtjFw67PAI18D6QM8pjMPpNgbfx+IpFEkxF21ahUKCwuxc+dOr8fOnz8fubm5SjSDSFFS03Q3BN4Kwtm8ufUgFn510GXK9UkrCrFoTLeA7l1V1Xm89tz/4dGYfyNeuFi785qRQFYukCD9vK6KvXVv09S+bVN35Yq734e/D+5o3+epjgxRQyJ7z8fRo0fxt7/9De+//z70eu/fFGbNmoWKigr7z9GjR+VuEpHs3A0xOKTpbkCkFnp777vfPPaO5K4r9rO3QIR631ro3+yNGZpViBcuwmLoCozPB+5+y6fAw8aXYm/ufh9KK6ox7aMf7dty9/Kwp4UilezBx65du3D8+HF069YNMTExiImJwdatW/Hqq68iJiYGFovjRDOdToeEhASHH6JwJmWIwf+HaGSSWuitwsNqElvq9B0lp326dqZwCB9qn4Fu9XioKn5DmdgU042PoGZcAdCmt0/n8oen3wdX5ApQgzJ8RaQQ2YddBg4ciJ9++slh3/jx49GpUyfMmDEDarX3BD5E4czbEEPdh2jv9GZuj4smtoJwZRXVkh/C7kjtRUFlKbSbcrFWuwoqQYQYEwvzDX/FgE2dcBF6PCsEJ42R1CEnG3sdmXXFGJThX3VcW0+LUsNXcmJSNXJF9v864+PjkZmZ6fDTqFEjNGvWDJmZmXJfjkg2UruwpT4cJT9Ew0CgFX1tBeGAwJOKJXpJ9KWDETHbXwJe646Y/30AlSACXUZA+OsPMN00AxcReA0YX/jz9+xvLw/AnjeKDsxwSgTfurClDjFIPS5aSCkI5ynDqI3bHBpWC+5WbcOXuv+Ddtt8wHQBuKIn8JfNwJ+WAIlXBPgJ/BPI37M/gYsvPW9E4SoowceWLVuQl5cXjEsR+czXyaO2IQZPaboNifqg1iMJFeceE28F4fzLoSFCdXAT9O8MwMvaxWglnII14QrgT28DD30BXHG9vB/KR95+HzzxJ3CJxp43anjY80ENmj9d2FKGGHKyMxrscko5c2h0EQ5hpeY56D8cCdXxvagU4zDfNBrVD38HdLkHEEJ/jz39PrgTSIDKnjeKBgw+qEHztwvb3RBDaqK+3mS/QOdTBCrU13fmrXcEAHC6BNo1E7BO9xRuVBdDVGth6jUZ/Wry8KYlG9DEBrnVnrn7fXAl0ACVPW8UDZhHlxq0QLqwXSWiYgIpaZx7R2ySUAnNF7OAwqWIsZpgFQWstvbBsMmvQ0xMQ8XWz0PRXADeV224+n2oqrEgd/1ehyG9VBeJ6HxZEWLraZm0otBeEM+GPW8UKRh8UIMWaBe2qyEGfwqyNXjG85isXoNHYtZB80NtZlJLuwHI/nkQisW2GJqYVm81UjgGes6/D3HaGPS7Sv4A1dbTkrPWe2BDFI4YfFCD5i0/hYDaf9DDpQvbObCJ9CJgOhgRs2MxNN/k4XHNCQCANeUaqG6di5q0fii+9FkLissxb+M++/siKZW9L5lSfcGeN4pknPNBDVo4Tx4Nt7kasrKYMFq9GV/ppkO76UkIVSdw2JqCx4yTUf3nzUD6APtQxOIx3TB1VRFT2bugVGBDpDQGH9Tg+TJ5lAJktQA/fgj9W70xX/M2WgqnYY1viZqh/0SW8UWstfYB6mQmZUItoujE4IMIrldgbJ9xCwOPOgIrYiZisGoH9P/qB6yeCNWZEpwQE5BregDVk3bCct2DMLsYBWZCLaLoFNkDxkQyYhe2o7rBxRtf/YqPd/1u37bNuZg5tJPnk4giVIc2Y632KVyjKgFOAtAnwnjDX3FTfntchB6Px7if9MuEWkTRicEHEdWTv6cUOWv32rdf/+pgvWPKKqoxdVWRmzOIGKAqgm75S1AfK8Q1KgDaxsANk4DeU2BWN8bFfO/LZplQiyg6MfggIgcFxeWYuqrIa3VaW3VWx50i1Af+g7Xa2bU9HccAxMQCPR4C+k4DGjWvPU7iBNpgrUZytYyXiJTD4IOIHMzbuM9r4GFz+Tgr1Ps3AF+/BF3Z/3CNCqgSddDcMAGafn8DGif71RalEmrVTerl3MsjeUiJiPzGCadE5MB5SasUM9QfQvfpg0DZ/yBqGmGRORt9a16BaWCu34GHjZKrkTwVFXQ/pEREgWLwQSER1TksGqCuql8hahsD/f4PFyfvxvPm0TiNBNnOL3U1ki8rcqQs4yUiZXDYhYj8zpMhwIoUnMF31k64dvI6xCW2kDyfw1feViO5Gz5xlwVVyjJeAPhgwg0Rn0mWKNyw54OogcvfU4qsl7f6/D7h0pTTCsThFcu9QGxT+RsnkafhE3dZULmMlyh0GHxQ2IjWoZjAknMpy91D2zXHdqcm6JE36jpcRODl7QO5R/5mQeUyXqLQYV8iUQC8lUIPRUE0qVV1PT20azkupk0VKpCEM9gvtsbb429Avw4tHIJEf5eoBnqPfMmC2ju9mX1/pBUVJIom7Pkgklndb9h/C4OCaK56FaqMZqQ/sdHjQ9sWeNyr+grvDjShYPafUCy2gwVq9GyXhILiMofhmnFLd6Lv819i24ETOLxgOA4vGC5prkSgReP8HT4J56KCRNGOwQeRjKTMnwhmQbSC4nKXAUJBcbnkc3xs7Y/rb74N6pjLgURBcbnPcyzcCbRoXCDDJywqSBQaDD6IZOLL/Al/C6L5OjfCXa/C1FVFUEPqvJr63/zdJSKTGjTEaWPwwYQbPF5V6j2yDZ+4658QABg8DJ+wqCBR8DH4oAbJ1eRW2/wNqcMFdXmfP+GapyED50Bj4/9KXfZieOplcB8giGiGSqTiFARYXb7X02CDpwBLatAg12oTOYZPWFSQKLgYfBDJwNukR3fcDRk4D9+MW7oTj66UZ5gDAEQIOI4kDFN9D0Bw+9AOhLegQc7VJhw+IYosDD4o6gVjCa+vuSBsD/fRS76r1yZfh28A/+ePLLUOQd6o61w+tPNGdfX5fHV5CxoCHS5xxuETosjB4INIBr7kgvDUq+DP8I2rYY5EnJf4XhUGZaS4fGgPykhx+76UBF3AQYMSq01CPXwSyNAdUUPC4INCIpwTb/nD27f4ujz1Kvg7fAMAxyurgd++h3bNRHynfRQGH+Zz+PrQfmJYZ5fn8TVoaAjDJQxIiOpj8EFB52o+g6/LP8ONp2/xdXnrVQgklXfylr8D79yKmOJPEasy4/6YTVBqPsegjBTZggYOlxA1PAw+KKgivYS5p/kjbr/FJ1wekvHWq+BPKm8BIgw4iZ5nNwAxepivvR/Da57DS+aRis3nAOQNGkI9XBKp2KtCkYrBBwVNQyhh7uqBXDD9Jsnv92X4BoB9WCWnyX+gvjUXmL4PxuGvYq/YDgD8ms/hCwYNROQPBh8UNFJLmNuOjdR5IIE8kD0P34ioV9xNcxGLBmox5O8rgT5/A+LqT/JkgEBE4YbBBwWNL/MZpCTQilb24Zt4rcN+A07hjZhX8M+Y1zFQ2IVlo67EF0/ehUc2m9D2if9EVSVgIopuHCSkoPF1PoMtgVa0rHqQxGoFDm/DkAMrkSWux05NaxxHEySrzqPn1R1gunYmOi+tgggVXstID3VriYj8wuCDgsZbCXNntoLuueuKMSgjNbqHC04dBIpWAj+uAip/B1D7H2eycBZfWq7DtOk5UDdNRY3RDBGfh7atREQBYvBBQWObzzBpRSEESJtkWjeBVu/0ZvVerzKakfF07cO4eO7giJrxH48qDFd/B927ecDvOy6/oEsEuvwJ1VePwMA3TwEQMK1Rc8nndc6h0r1NU/kaHUK2lR1EFPki519qklWoHtq2+Qw5a/dKSh9uE0j+i7BiMQEHvoS2aBV26tZCL5iA3wEIKiB9IND1PqDjMECjh9VoBnzs5cjfU4qctXvt2+OW7kSK01JbIqJQY/BBQTck04A+VzZHlzlfSH6PP/kvPHHuHejXoYVywzqiFT2FfbhD/Q1iX50CXDyNGAAxArDfegXaZf0F2utGA/GpAV2moLgcU1cV1etROu4iyPOnd4Q9D0QkF9mDj/nz5+Ozzz7Dzz//jNjYWNx44414/vnn0bFjR7kvRRGs7oM+JUGH45U1LodhBNQmxZJaXAyo36vj/KCtqrEgd71j74AhUY+c7AwZJ7aKyBRKoNn8NNTFq/GR7ljt7osAGiXD1PkO3P11G/wktkPxDUOglaHnad7GfV5zqFisouTeEQYbRKQU2YOPrVu3YvLkyejRowfMZjOeeOIJ3HrrrSguLkajRo3kvhxFgSeGdcbUVUX15oH4W1ysroLicszbuM++PW7pTpfHybay5uQv0BR9hM3a95CuKgW+r91dKcYh39IDtz/wGPRX9ofJAvy0Xd6Jo1KGsd7cehALvzooqXeEiEgpsgcf+fn5DtvLli1DcnIydu3ahZtukp7pkSKXr/NJbHVCnOeBpMrQG+FqGMIVv1fWiCJw4meg+N9A8Vrg+F5oAKSrgGpRg5jOw2C5+k/o8b4VNdDitnb9AXUMLCaT/RSKD/vU8d53v0nqHSEiUpLicz4qKioAAElJrrvNa2pqUFNz+YFTWVmpdJMCEurVFaG+vlKc54EsG99Dlgeyv6XpXa2sqXvk1cJhaLY8C+xfD5z65fJLghqW9gPwf/uuQoG1O3befScAoKbOxFFXwx7yD/u4VnHR5PWYXUfOoH/HZEXbQUQNm6JPLqvViqlTp6JPnz7IzMx0ecz8+fORm5urZDMoQoRLGnCXK2usVuCPH6DZswb/1X6ENNUJ4JtLr6m1QPsBQMbtQMdhqIlJwJqnXQ+puJsUKsewj6e5M744cY5DMESkLEWDj8mTJ2PPnj3Yvn2722NmzZqF6dOn27crKyuRlpamZLNCIlp7LKRy/vzhzL6yxnQRKNkGHMgH9ucD545BAyBNBVwUtdB0GoyYzDuBDrcC+oTLJ3BKc26buGmxiuj7/Jduhz0CTajmae6MLwFJi/iGtzSXk2uJgkuxJ+CUKVOwfv16bNu2DVdccYXb43Q6HXS64P9j19CDAVca+j0RAKQmaNDzzHrgg3zg0BbAVHX5AG08aq4cjDG7O6NIvBJLut3o09CQlMJ60oZ9XHM3dyYlQY+yyupLf/a+sqhfhxY+X5uIyBeyP11EUcRf//pXrF69Glu2bEG7du3kvgSFkXAPWKR+6xcuHZVz8QWo119eEXNMTMJmSzfce99fsMWUiZwN+1Eu1j7Y3c3VcPctWmqitEASqrmaO9O9TVP7tpIri8IBezCIIoPsVW0nT56MFStWYOXKlYiPj0dZWRnKyspw8eJFuS8V0aqMZrSduQFtZ26wVyN1tU8pzrkvonmFg+tHqVNpepzCIk0ehqh3Aq26AwOewsWHtuDGmtcw2/xnfG68BpM++F+95ay2uRpSqu9KTZSWGKuRdJw7nubO2HpHkp3yeqQm6htWAT8iCinZv6YuWrQIANC/f3+H/UuXLsW4cePkvlxYCPdv/87crbaYObRTCFuljLxRXTFv4z6HoMGAU5gd8x6aCudqK8bGVKNnh1ZQd3oQ6PA+EJ8CABCNZgC1ycE8JfCSOldDamE9uWuxOPcGyLmySK6eBvZYEDUsigy7UPjK31OKSSsKXa62mLqqyGFfJARVrtKkQxSRJpSjn2oPhhe/j+H4L3ZqWtUGGjiLnqqfIbToiHfK2mGb9RosnvUY1HGNPV7HUwIvqXM1PBXWq7vtKQhwfkj720MWLiuLiKhhCr+nSZhz+bBTiL8Pf3fvs1hF5K4r9vgNPljk+KbrsgdHexGz9R/hv7pLS10P1P5fJ5UVJ62J6Hb7JKivykJVbAqetS2HjZGnboyUuRruCuvVnRRqI+UeBfP3kYhILg0m+JCy1NPbwz4chisCKYgmZbVFpMjfsQeTPjt8qc2XP3+ZUYfJxgfxWsxZJKvOomv/u2FtPwDdFx+HFSoUXzsY0MbUWw4rB6lzOrxNCpUqHH4fiYj80WCCD2e+fmP0ZbhCKYFmxgxWWXrZv41brcDJ/cBv3wFHv4flyPfILZsGEUlw7q8RL82hnmJ+DICAZa16oHtqU1jh24PdmdzF75yHPXwVDr+PRET+apDBh6tiY56+McoxXOFPCXPnNgeaGVPusvSuyPFtXI8aqI5sB0p3Ar99D/y+A6iusL++w9IZpfCWB0OwX99VxVZfhdMS1XAaPiMi8keDDD7cPcTdfWMMdLhCaglzTwJdbVFlNGP0ku8AuM994WsmTGeeAiS338atVuDUr8CxQsT9UYjD7XcCZf8D3ncaFtHE1S6BTeuF46YewBaL5HbJUbFVyeJ3voqm4TMiapgaZPDh6zfGQIYr3HWP132ASRmakGO1RV3eUnD70zvjLUACRLTCSaj3/RsoLwKO7QaOFQHGc/XfFN8SaN0LSLv0k9oFUNfmv0g+eArY8p3kdslVsVWp4ne+CtbwGRGRUhpk8OGOu8eSL8MVdSeBeuoer8s2NPH3wR0dzuPrw1/qQ8lV7ovEOA0gAmcvVT111zvjaeKutwAJEHCX+r8o/GQveqp+hlq4dGdiYgHDtUCrbkDLbrVBR2IaILh+qEvNl+FKoBVbw2GJajCGz4iIlMTgQwJfHnZ1J4Emxmo9do/XVVpRjWkf/ehwHl+HZqQ+lAZlpOCWTsn2b/BTBqRj4VcH6302qcMVCTiPFqjAQbTyeuzrlrvxuuVuGLTVyOluwpCemUCLToBa+q+ip3wZ3kRDxVZvv4+BDp8RESlN9vTq4crX7va6KcdtDztA2mQ+2yTQTcVlvjbTwXGHPBA6t9cWABgCWG3x8a7f3Q6X2FgsVuBcOVSH/4sx6gLMiVkG3cq7oHklE29q/omh6u8lXxsAyox6TPomHvknm/kUeNjY8mU4pwn3Jhoqtnr6feRkUyKKBA0i+MjfU4qsl7f69J5xS3ei7/Nf2mt2+PKwsz20Vxf94WtTXZ4HAO7pVlsZ2N3DJpDVFp6GS2z2vDQU+MdV0K+8E89qlmJczBcoOHgRN516AqNNs/G65W4XrXbPdlTuumK/52EMyTRg0/Sb7dtN4zReH75ypy4PFXe/j6mJeuSN6hqaRhERSRT1wYdtwqeUB6wz56Jhzg87T0QApy+YJD0QpVi45SAS4zT1io7ZHjaPrCi0F6TzVDROBSuEc6VQ/b4Dd6n+iyEqaT0WX1e3hUVUwdq0HTZbrsN04yOYZJrqYsmr9E9bd6Ksv+oGXHNuv9plCwQ3x3sT7sX3nH8fl43vge0zbsGgjJQQtoqIyLuonvMhdcKnO66Wsfrau5B9bUu89+0RWcbhK6pMDuewrbaoMdcuO1XBioLCA5hfcMR+zLilO2HQVGF2s6+wXfc5UnAGmtdqj/+nFvjW0hn51l5er/265W582mg0Zt7cGX+TOYmVXKs33C2H9Sd1eaRkDw2HCbBERL6K6p4Pb/kQpAj02/ktnZL9mpvgri0CRDRCFWaqV6LvT09AveIu6P91E3bqJuH1mFcwdc0hlF1wzIFRZtJjctkw7LG2g0awQBTUsCZcgW8tGSixpiJVZ4IgITQqq6xRJHumnKs3XPUGFEy/yadzuOstY/ZQIiJ5RHXPh5z5EI6fPQ9YEgCLGWpYYIWA5IRYtym3ARExsKCH5hAaaY24KbsaD63ci1NiAg4g7VJPiOBwvJQhCxECLiAO16oOImZPbZZWFYAkCHjG/GC9Wie171FBgIi/mqYgyVSJr+aMAFQxGH1pyewr93Z1mb2z/rV9n9Aod1pyKZx7A3wpYheq7KGB1OxxxvL0RBTuojr4kPMbdfKakcC6fYgDcPDSafMv9sAkTIUA0V5TBAAEWAEIeF3zGhq9uxMAEAfgg0udH/mWHsg1PehivoT0x9uHlgHonjUC2sRUVOuaYcjy3z2mHBchwAQNytEMUDn+tbsbrnB9Ht+EU1pyKUKRPdRduv9gZ04lIgqWqB52seVDcP9oE5GM00gVTl8KGOoTYIUBJ9FT9XO914aod2KRJg+pOOOwP1U4ixdiFuM61S+wJqYBLTrBYrgO31oysNlyHbIyW2FLz+8wVpWP21Tf4L3rD+GNnqeQGiv90bbG2hfm3o8BXe+DNX0gDiNV8ntdTZ70ZTKtjaelvza2wMbVqgwp9WiCLRTZQ6euKnI5xFN3srPcbL0jhxcMr1e9mYhIaVH9r46nZFS2YY/jSMIrI10PO9Q+RFXIGdEX6s6HAdRm+Ow9fzMEiPjmiUEYoo9FHxPQ5ZkvAQh1yqMnAWagePJgxGljUGM024c5iu+qzQy6fEft9gu31x7Tb5jJa1l1OSauuktg5k8PhLsejbrCJS25FKHIHhpIzR4iokgU1T0fgPt8CCkJlx8yXr+dd0sHYpvYfyrQGGcRD+gTAW0c1BotbI/dQFYc1H2fAM9LRt2RemVv2UulnGfygHTJeSYiZVWGt94yuVodp43BBxNu8HiMHEuRiYjCUdQHH4C0FRDuciY4DwtI6a6WIz9E3qiufieQkvKAtH2zNiTqoYtR+3We1786CFG8/NmCkWeiymhG25kb7DlN5BbM7KFSh3hYSI6Iok1UD7vUJWUFhBzfzt1NHvQ1P4Rz/RXnnB511Q1uJg9Ix8e7fpeUVM1TNVxXxedcOXHOaP9zOPdo+MLWW+Y8ATdV5kmgUod4WEiOiKJNg+j5CCZ3kwf9yQ8hJRhyTh3v3Bshhatv1oMyUuqlLndFrnL14UZqT1ggpAzx+Fqzh4goEjD48MLXbn5vBdoCVXfYZ9uBEy6TYdXtjZDC3TfrusHOmSqT1/PsOnLG6zGRROl5KlKGeMJtKTIRkRwYfASJLQD5YMINsixt9JYMy0buariehKJcfaQvGfVUIC4clyITEcmBwUeQyTV5UGrq+Hu7K1cN11k0lKsPhWAM8RARhRMGHzKQsmzSxjbEEeg3dqlBTJtmjWT5Zu2pB8XGVq4+0nsjQiFSliITEcmhwTwZpNS7CKQmhm3yYFlFtd91TJyv72mOidQVEC3idejfMTngJF+e0qTbtsPlgcnaJkRE4Y09HzIJ9uRB76nja9l6I6R8s/bUY+EuEVvdZG0UfOxlIqJIxOBDRsGcPCg1GZacvRFylKsnIiLiVyUvfC11LmcdE2/DB+6SYaUk6FFWqUxWTOceFKk4FEJERDbs+fDAOYHXuKU70ff5L71WGg3m5MFQ90aw25+IiHzFp4Ub+XtKMWlFYb3Jo7ZS5+GUg0FK6ngiIqJwwZ4PF6Qk8MpdVxxV6cRdCcdeDTmK9hERUWgx+HDBWwIvljoPDX+HwYiIKLww+HCBpc7DQ926Ov8u+sNlHRvbMBgDECKiyMHgwwWWOg8/8zbua/DDYERE0SI8BvLDjBzZSsNNpE9Cde7xqKvuMFjv9GayXTPS7xkRUbhiz4cLLHUemTgMRkQUGRQLPhYuXIi2bdtCr9ejV69e2LFjh1KXUkQg2UrDcZVIQ8BhMCKiyKDIk/HDDz/E9OnTsXjxYvTq1Qt5eXkYPHgw9u/fj+TkZCUuqQg5s5VGi1ANRaQk6HC8siZqhsGIiBoyRXo+Xn75ZUyYMAHjx49HRkYGFi9ejLi4OLzzzjtKXE5RkVDqvCH0tDwxrDOA6B0Gawh/h0RENrIHH0ajEbt27UJWVtbli6hUyMrKwrffflvv+JqaGlRWVjr8EDlzV1VXiaJ9RESkLNmDj5MnT8JisSAlJcVhf0pKCsrKyuodP3/+fCQmJtp/0tLS5G4SRQlXdWy2z7iFgQcRUYQJ+WqXWbNmoaKiwv5z9OjRUDeJwlgkDIMREZFnsg8uN2/eHGq1GuXl5Q77y8vLkZqaWu94nU4HnU5Xbz8RERFFJ9l7PrRaLbp3747Nmzfb91mtVmzevBm9e/eW+3JEREQUYRSZVj99+nSMHTsW119/PXr27Im8vDxcuHAB48ePV+JyREREFEEUCT5GjhyJEydO4Omnn0ZZWRm6du2K/Pz8epNQIwFTbIdO3VotO0pOo1+HFiFsDRERyUWxhAJTpkzBlClTlDo9Rbn8PaXIWbvXvj1u6U4YEvWYObRTCFtFRERyYDYjCjv5e0oxaUVhvWymZRXVmLqqKBRNIiIiGYV8qS1RXRariNx1xS7TqLvaR0REkYfBB4WVHSWnUVrhvjotAxAiosjH4IPCyvFz7gMPIiKKDgw+KKwkx+tD3QQiIlIYgw8KKz3bJcGQqK9XvdaGydSJiCIfgw8KK2qVgJzsDAD1Aw3b9uIx3Vh6nogogjH4oLAzJNOARWO6ITnBseZPaqIei8Z0YxVbIqIIx6+OFJaGZBrQ58rm6DLnCwDAsvE90K9DC1axJSKKAuz5oLBVN9Do2S6JgQcRUZRg8EFERERBxeCDiIiIgorBBxEREQUVgw8iIiIKKgYfREREFFQMPoiIiCioGHwQERFRUDH4ICIioqBi8EFERERBxeCDiIiIgoq1XShsxWljcHjB8FA3g4iIZMaeDyIiIgoqBh9EREQUVAw+iIiIKKgYfBAREVFQMfggIiKioGLwQUREREHF4IOIiIiCisEHERERBRWDDyIiIgoqBh9EREQUVAw+iIiIKKgYfBAREVFQMfggIiKioGLwQUREREHF4IOIiIiCKibUDXAmiiIAoLKyMsQtISIiIqlsz23bc9yTsAs+zp07BwBIS0sLcUuIiIjIV+fOnUNiYqLHYwRRSogSRFarFceOHUN8fDwEQZD13JWVlUhLS8PRo0eRkJAg67npMt7n4OB9Dg7e5+DhvQ4Ope6zKIo4d+4cWrZsCZXK86yOsOv5UKlUuOKKKxS9RkJCAn+xg4D3OTh4n4OD9zl4eK+DQ4n77K3Hw4YTTomIiCioGHwQERFRUDWo4EOn0yEnJwc6nS7UTYlqvM/BwfscHLzPwcN7HRzhcJ/DbsIpERERRbcG1fNBREREocfgg4iIiIKKwQcREREFFYMPIiIiCqqoCz4WLlyItm3bQq/Xo1evXtixY4fH4z/++GN06tQJer0eXbp0wcaNG4PU0sjmy31esmQJ+vXrh6ZNm6Jp06bIysry+vdCtXz9fbZZtWoVBEHAnXfeqWwDo4Sv9/ns2bOYPHkyDAYDdDodrrrqKv7bIYGv9zkvLw8dO3ZEbGws0tLSMG3aNFRXVweptZFp27ZtyM7ORsuWLSEIAtasWeP1PVu2bEG3bt2g0+lw5ZVXYtmyZYq3E2IUWbVqlajVasV33nlH3Lt3rzhhwgSxSZMmYnl5ucvjv/76a1GtVosvvPCCWFxcLD711FOiRqMRf/rppyC3PLL4ep/vu+8+ceHCheLu3bvFffv2iePGjRMTExPF33//Pcgtjyy+3mebkpISsVWrVmK/fv3EO+64IziNjWC+3ueamhrx+uuvF4cNGyZu375dLCkpEbds2SIWFRUFueWRxdf7/P7774s6nU58//33xZKSEvHzzz8XDQaDOG3atCC3PLJs3LhRfPLJJ8XPPvtMBCCuXr3a4/GHDh0S4+LixOnTp4vFxcXia6+9JqrVajE/P1/RdkZV8NGzZ09x8uTJ9m2LxSK2bNlSnD9/vsvjR4wYIQ4fPtxhX69evcSHH35Y0XZGOl/vszOz2SzGx8eLy5cvV6qJUcGf+2w2m8Ubb7xR/Ne//iWOHTuWwYcEvt7nRYsWie3btxeNRmOwmhgVfL3PkydPFm+55RaHfdOnTxf79OmjaDujiZTg4+9//7t49dVXO+wbOXKkOHjwYAVbJopRM+xiNBqxa9cuZGVl2fepVCpkZWXh22+/dfmeb7/91uF4ABg8eLDb48m/++ysqqoKJpMJSUlJSjUz4vl7n+fOnYvk5GQ89NBDwWhmxPPnPq9duxa9e/fG5MmTkZKSgszMTMybNw8WiyVYzY44/tznG2+8Ebt27bIPzRw6dAgbN27EsGHDgtLmhiJUz8GwKyznr5MnT8JisSAlJcVhf0pKCn7++WeX7ykrK3N5fFlZmWLtjHT+3GdnM2bMQMuWLev9wtNl/tzn7du34+2330ZRUVEQWhgd/LnPhw4dwpdffon7778fGzduxK+//opHH30UJpMJOTk5wWh2xPHnPt933304efIk+vbtC1EUYTab8cgjj+CJJ54IRpMbDHfPwcrKSly8eBGxsbGKXDdqej4oMixYsACrVq3C6tWrodfrQ92cqHHu3Dk88MADWLJkCZo3bx7q5kQ1q9WK5ORkvPXWW+jevTtGjhyJJ598EosXLw5106LKli1bMG/ePLzxxhsoLCzEZ599hg0bNuCZZ54JddNIBlHT89G8eXOo1WqUl5c77C8vL0dqaqrL96Smpvp0PPl3n21eeuklLFiwAJs2bcI111yjZDMjnq/3+eDBgzh8+DCys7Pt+6xWKwAgJiYG+/fvR3p6urKNjkD+/D4bDAZoNBqo1Wr7vs6dO6OsrAxGoxFarVbRNkcif+7z7Nmz8cADD+Avf/kLAKBLly64cOECJk6ciCeffBIqFb87y8HdczAhIUGxXg8gino+tFotunfvjs2bN9v3Wa1WbN68Gb1793b5nt69ezscDwAFBQVujyf/7jMAvPDCC3jmmWeQn5+P66+/PhhNjWi+3udOnTrhp59+QlFRkf3n9ttvx4ABA1BUVIS0tLRgNj9i+PP73KdPH/z666/24A4ADhw4AIPBwMDDDX/uc1VVVb0AwxbwiSxJJpuQPQcVnc4aZKtWrRJ1Op24bNkysbi4WJw4caLYpEkTsaysTBRFUXzggQfEmTNn2o//+uuvxZiYGPGll14S9+3bJ+bk5HCprQS+3ucFCxaIWq1W/OSTT8TS0lL7z7lz50L1ESKCr/fZGVe7SOPrff7tt9/E+Ph4ccqUKeL+/fvF9evXi8nJyeKzzz4bqo8QEXy9zzk5OWJ8fLz4wQcfiIcOHRK/+OILMT09XRwxYkSoPkJEOHfunLh7925x9+7dIgDx5ZdfFnfv3i0eOXJEFEVRnDlzpvjAAw/Yj7cttX388cfFffv2iQsXLuRSW3+89tprYuvWrUWtViv27NlT/O677+yv3XzzzeLYsWMdjv/oo4/Eq666StRqteLVV18tbtiwIcgtjky+3Oc2bdqIAOr95OTkBL/hEcbX3+e6GHxI5+t9/uabb8RevXqJOp1ObN++vfjcc8+JZrM5yK2OPL7cZ5PJJM6ZM0dMT08X9Xq9mJaWJj766KPimTNngt/wCPLVV1+5/PfWdm/Hjh0r3nzzzfXe07VrV1Gr1Yrt27cXly5dqng7BVFk/xUREREFT9TM+SAiIqLIwOCDiIiIgorBBxEREQUVgw8iIiIKKgYfREREFFQMPoiIiCioGHwQERFRUDH4ICIioqBi8EFERERBxeCDiIiIgorBBxEREQUVgw8iIiIKqv8PC+A1dk48l3QAAAAASUVORK5CYII=", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.errorbar(x, y, ye, fmt=\"o\", label=\"data\")\n", "xm = np.linspace(np.min(x), np.max(x), 1000)\n", "plt.plot(xm, model(xm, *par), label=\"fit\")\n", "plt.legend();" ] }, { "cell_type": "code", "execution_count": 4, "id": "passive-cowboy", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a = 1.10 +/- 0.18 jackknife=0.13 bootstrap=0.13 MC=0.18\n", "b = -0.72 +/- 1.09 jackknife=0.93 bootstrap=0.88 MC=1.04\n", "c = 10.52 +/- 1.22 jackknife=1.11 bootstrap=1.05 MC=1.19\n" ] } ], "source": [ "# now only return fit parameters\n", "def fit2(x, y, ye):\n", " return fit(x, y, ye)[0]\n", "\n", "# jackknife and bootstrap\n", "jvar = jackknife.variance(fit2, x, y, ye)\n", "bvar = bootstrap.variance(fit2, x, y, ye, size=1000, random_state=1)\n", "\n", "# Monte-Carlo simulation for reference\n", "mvar = []\n", "for itry in range(1000):\n", " y2 = 1 + 10 * x ** 2 + rng.normal(0, ye)\n", " mvar.append(fit2(x, y2, ye))\n", "mvar = np.var(mvar, axis=0)\n", "\n", "for n, p, e, ej, eb, em in zip(\"abc\", par,\n", " np.diag(cov) ** 0.5,\n", " jvar ** 0.5,\n", " bvar ** 0.5,\n", " mvar ** 0.5):\n", " print(f\"{n} = {p:5.2f} +/- {e:1.2f} \"\n", " f\"jackknife={ej:1.2f} \"\n", " f\"bootstrap={eb:1.2f} \"\n", " f\"MC={em:1.2f}\")" ] } ], "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": "3.11.9" } }, "nbformat": 4, "nbformat_minor": 5 } resample-1.10.1/doc/tutorials.rst000066400000000000000000000005221470150054300167250ustar00rootroot00000000000000Tutorials ========= The following tutorials show how to use resample. .. toctree:: :maxdepth: 1 tutorial/jackknife_vs_bootstrap tutorial/permutation_tests tutorial/sklearn tutorial/usp_continuous_data tutorial/variance_fit_parameters tutorial/confidence_intervals tutorial/leave-one-out-cross-validationresample-1.10.1/pyproject.toml000066400000000000000000000042241470150054300163170ustar00rootroot00000000000000[build-system] requires = ["setuptools >= 60", "setuptools_scm[toml] >= 8.0"] build-backend = "setuptools.build_meta" [project] name = "resample" requires-python = ">=3.8" dependencies = ["numpy >= 1.21", "scipy >= 1.10"] authors = [ { name = "Daniel Saxton", email = "dsaxton@pm.me" }, { name = "Hans Dembinski", email = "hans.dembinski@gmail.com" }, ] readme = "README.rst" description = "Resampling-based inference in Python" license = { text = "BSD-3-Clause" } classifiers = [ # complete classifier list: http://pypi.python.org/pypi?%3Aaction=list_classifiers 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Science/Research', "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python :: 3", 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3 :: Only', 'Programming Language :: Python :: 3.8', 'Programming Language :: Python :: 3.9', 'Programming Language :: Python :: 3.10', 'Programming Language :: Python :: 3.11', 'Programming Language :: Python :: 3.12', 'Programming Language :: Python :: 3.13', 'Programming Language :: Python :: Implementation :: CPython', 'Programming Language :: Python :: Implementation :: PyPy', ] dynamic = ["version"] [project.urls] repository = "http://github.com/resample-project/resample" documentation = "https://resample.readthedocs.io/en/stable/" [project.optional-dependencies] test = ["pytest", "pytest-cov", "coverage[toml]"] doc = ["ipython", "nbsphinx", "sphinx_rtd_theme"] [tool.setuptools.packages.find] where = ["src"] [tool.setuptools_scm] [tool.ruff.lint] extend-select = ["D", "I"] ignore = ["D212", "D211", "D203"] [tool.ruff.lint.per-file-ignores] "test_*.py" = ["D"] [tool.mypy] strict = true no_implicit_optional = false allow_redefinition = true ignore_missing_imports = true files = "src/resample/*.py" [tool.pytest.ini_options] addopts = "--doctest-modules --strict-config --strict-markers -q -ra --ff" testpaths = ["src/resample", "tests"] log_cli_level = "INFO" xfail_strict = true filterwarnings = ["error::DeprecationWarning", "error::FutureWarning"] resample-1.10.1/src/000077500000000000000000000000001470150054300141705ustar00rootroot00000000000000resample-1.10.1/src/resample/000077500000000000000000000000001470150054300160005ustar00rootroot00000000000000resample-1.10.1/src/resample/__init__.py000066400000000000000000000015711470150054300201150ustar00rootroot00000000000000""" Resampling tools for Python. This library offers randomisation-based inference in Python based on data resampling and permutation. The following functionality is implemented. - Bootstrap samples (ordinary or balanced with optional stratification) from N-D arrays - Apply parametric bootstrap (Gaussian, Poisson, gamma, etc.) on samples - Compute bootstrap confidence intervals (percentile or BCa) for any estimator - Jackknife estimates of bias and variance of any estimator - Permutation-based variants of traditional statistical tests (t-test, K-S test, etc.) - Tools for working with empirical distributions (CDF, quantile, etc.) """ from importlib.metadata import version from resample import bootstrap, empirical, jackknife, permutation __version__ = version("resample") __all__ = [ "jackknife", "bootstrap", "permutation", "empirical", "__version__", ] resample-1.10.1/src/resample/_util.py000066400000000000000000000015421470150054300174700ustar00rootroot00000000000000from typing import Optional, Tuple, Union import numpy as np from numpy.typing import ArrayLike __all__ = ["normalize_rng", "wilson_score_interval"] def normalize_rng( random_state: Optional[Union[int, np.random.Generator]], ) -> np.random.Generator: """Return normalized RNG object.""" if random_state is None: return np.random.default_rng() if isinstance(random_state, np.random.Generator): return random_state return np.random.default_rng(random_state) def wilson_score_interval( n1: "ArrayLike", n: "ArrayLike", z: float ) -> Tuple[np.ndarray, Tuple[np.ndarray, np.ndarray]]: """Return binomial fraction and Wilson score interval.""" p = n1 / n norm = 1 / (1 + z**2 / n) a = p + 0.5 * z**2 / n b = z * np.sqrt(p * (1 - p) / n + 0.25 * (z / n) ** 2) return p, ((a - b) * norm, (a + b) * norm) resample-1.10.1/src/resample/bootstrap.py000066400000000000000000000502571470150054300204000ustar00rootroot00000000000000""" Bootstrap resampling tools. Compute estimator bias, variance, confidence intervals with bootstrap resampling. Several forms of bootstrapping on N-dimensional data are supported: ordinary, balanced, extended, parametric, and stratified sampling, see :func:`resample` for details. Parametric bootstrapping fits a user-specified distribution to the data and samples from the parametric distribution. The distributions are taken from scipy.stats. Confidence intervals can be computed with the ordinary percentile method and with the more efficient BCa method, see :func:`confidence_interval` for details. """ __all__ = [ "resample", "bootstrap", "variance", "covariance", "confidence_interval", ] from typing import ( Any, Callable, Collection, Dict, Generator, List, Optional, Tuple, Union, ) import numpy as np from numpy.typing import ArrayLike from scipy import stats from . import _util from .empirical import quantile_function_gen from .jackknife import jackknife def resample( sample: "ArrayLike", *args: "ArrayLike", size: int = 100, method: str = "balanced", strata: Optional["ArrayLike"] = None, random_state: Optional[Union[np.random.Generator, int]] = None, ) -> Generator[np.ndarray, None, None]: """ Return generator of bootstrap samples. Parameters ---------- sample : array-like Original sample. *args : array-like Optional additional arrays of the same length to resample. size : int, optional Number of bootstrap samples to generate. Default is 100. method : str or None, optional How to generate bootstrap samples. Supported are 'ordinary', 'balanced', 'extended', or a distribution name for a parametric bootstrap. Default is 'balanced'. Supported distribution names: 'normal' (also: 'gaussian', 'norm'), 'student' (also: 't'), 'laplace', 'logistic', 'F' (also: 'f'), 'beta', 'gamma', 'log-normal' (also: 'lognorm', 'log-gaussian'), 'inverse-gaussian' (also: 'invgauss'), 'pareto', 'poisson'. strata : array-like, optional Stratification labels. Must have the same shape as `sample`. Default is None. random_state : numpy.random.Generator or int, optional Random number generator instance. If an integer is passed, seed the numpy default generator with it. Default is to use `numpy.random.default_rng()`. Yields ------ ndarray Bootstrap sample. Examples -------- Compute uncertainty of arithmetic mean. >>> from resample.bootstrap import resample >>> import numpy as np >>> x = np.arange(10) >>> fx = np.mean(x) >>> fb = [] >>> for b in resample(x, size=10000, random_state=1): ... fb.append(np.mean(b)) >>> print(f"f(x) = {fx:.1f} +/- {np.std(fb):.1f}") f(x) = 4.5 +/- 0.9 Compute uncertainty of function applied to multivariate data. >>> from resample.bootstrap import resample >>> import numpy as np >>> x = np.arange(10) >>> y = np.arange(10, 20) >>> fx = np.mean((x, y)) >>> fb = [] >>> for bx, by in resample(x, y, size=10000, random_state=1): ... fb.append(np.mean((bx, by))) >>> print(f"f(x, y) = {fx:.1f} +/- {np.std(fb):.1f}") f(x, y) = 9.5 +/- 0.9 Notes ----- Balanced vs. ordinary bootstrap: The balanced bootstrap produces more accurate results for the same number of bootstrap samples than the ordinary bootstrap, but needs to allocate memory for B integers, where B is the number of bootstrap samples. Since values of B larger than 10000 are rarely needed, this is usually not an issue. Non-parametric vs. parametric bootstrap: If you know that the data follow a particular parametric distribution, it is better to sample from this parametric distribution, but in most cases it is sufficient and more convenient to do a non-parametric bootstrap (using "balanced", "ordinary", "extended"). The parametric bootstrap is essential for estimators sensitive to the tails of a distribution (for example, a quantile close to 0 or 1). In this case, only a parametric bootstrap will give reasonable answers, since the non-parametric bootstrap cannot include rare events in the tail if the original sample did not have them. Extended bootstrap: In particle physics and perhaps also in other fields, estimators are used which are that are a function of both the size and shape of a sample (for example, fit of a peak over smooth background to the mass distribution of decay candidates). In this case, the normal bootstrap (parametric or non-parametric) is not correct, since the sample size is kept constant. For such cases, one needs the "extended" bootstrap. The name alludes to the so-called extended maximum-likelihood (EML) method in particle physics. Estimates obtained with the EML need to be bootstrapped with the "extended" bootstrap. Stratification: If the sample consists of several distinct classes, stratification ensures that the relative proportions of each class are maintained in each replicated sample. This is a stricter constraint than that offered by the balanced bootstrap, which only guarantees that classes have the original proportions over all replicates, but not within each one replicate. """ sample_np = np.atleast_1d(sample) n_sample = len(sample_np) args_np: List[np.ndarray] = [] if args: if not isinstance(args[0], Collection): import warnings warnings.warn( "Calling resample with positional instead of keyword parameters is " "deprecated", FutureWarning, ) kwargs: Dict[str, Any] = { "size": size, "method": method, "strata": strata, "random_state": random_state, } if len(args) > len(kwargs): raise ValueError("too many arguments") for key, val in zip(kwargs, args): kwargs[key] = val size = kwargs["size"] method = kwargs["method"] strata = kwargs["strata"] random_state = kwargs["random_state"] del args else: args_np.append(sample_np) for arg in args: arg = np.atleast_1d(arg) n_arg = len(arg) if n_arg != n_sample: raise ValueError( f"extra argument has wrong length {n_arg} != {n_sample}" ) args_np.append(arg) rng = _util.normalize_rng(random_state) if strata is not None: strata_np = np.atleast_1d(strata) if args_np: raise ValueError("Stratified resampling only works with one sample array") if len(strata_np) != n_sample: raise ValueError("a and strata must have the same shape") return _resample_stratified(sample_np, size, method, strata_np, rng) if method == "balanced": if args_np: return _resample_balanced_n(args_np, size, rng) else: return _resample_balanced_1(sample_np, size, rng) if method == "ordinary": if args_np: return _resample_ordinary_n(args_np, size, rng) else: return _resample_ordinary_1(sample_np, size, rng) if method == "extended": if args_np: return _resample_extended_n(args_np, size, rng) else: return _resample_extended_1(sample_np, size, rng) if args_np: raise ValueError("Parametric resampling only works with one sample array") dist = { # put aliases here "gaussian": stats.norm, "normal": stats.norm, "log-normal": stats.lognorm, "log-gaussian": stats.lognorm, "inverse-gaussian": stats.invgauss, "student": stats.t, }.get(method) # fallback to scipy.stats name if dist is None: try: dist = getattr(stats, method.lower()) except AttributeError: raise ValueError(f"Invalid family: '{method}'") if sample_np.ndim > 1: if dist != stats.norm: raise ValueError(f"family '{method}' only supports 1D samples") dist = stats.multivariate_normal if sample_np.ndim > 2: raise ValueError("multivariate normal only works with 2D samples") return _resample_parametric(sample_np, size, dist, rng) def bootstrap( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike", **kwargs: Any, ) -> np.ndarray: """ Calculate function values from bootstrap samples. This is equivalent to ``numpy.array([fn(b) for b in resample(sample)])`` and implemented for convenience. Parameters ---------- fn : Callable Bootstrap samples are passed to this function. sample : array-like Original sample. *args : array-like Optional additional arrays of the same length to resample. **kwargs Keywords are forwarded to :func:`resample`. Returns ------- np.array Results of `fn` applied to each bootstrap sample. Examples -------- >>> from resample.bootstrap import bootstrap >>> import numpy as np >>> x = np.arange(10) >>> fx = np.mean(x) >>> fb = bootstrap(np.mean, x, size=10000, random_state=1) >>> print(f"f(x) = {fx:.1f} +/- {np.std(fb):.1f}") f(x) = 4.5 +/- 0.9 """ gen = resample(sample, *args, **kwargs) if args: return np.array([fn(*b) for b in gen]) return np.array([fn(x) for x in gen]) def variance( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike", **kwargs: Any, ) -> np.ndarray: """ Calculate bootstrap estimate of variance. If the function returns a vector, the variance is computed elementwise. Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args : array-like Optional additional arrays of the same length to resample. **kwargs Keyword arguments forwarded to :func:`resample`. Returns ------- ndarray Bootstrap estimate of variance. Examples -------- Compute variance of arithmetic mean. >>> from resample.bootstrap import variance >>> import numpy as np >>> x = np.arange(10) >>> v = variance(np.mean, x, size=10000, random_state=1) >>> f"{v:.1f}" '0.8' """ thetas = bootstrap(fn, sample, *args, **kwargs) return np.var(thetas, ddof=1, axis=0) def covariance( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike", **kwargs: Any, ) -> np.ndarray: """ Calculate bootstrap estimate of covariance. Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args : array-like Optional additional arrays of the same length to resample. **kwargs Keyword arguments forwarded to :func:`resample`. Returns ------- ndarray Bootstrap estimate of covariance. In general, this is a matrix, but if the function maps to a scalar, it is scalar as well. Examples -------- Compute covariance of sample mean and sample variance. >>> from resample.bootstrap import variance >>> import numpy as np >>> x = np.arange(10) >>> def fn(x): ... return np.mean(x), np.var(x) >>> np.round(covariance(fn, x, size=10000, random_state=1), 1) array([[0.8, 0. ], [0. , 5.5]]) """ thetas = bootstrap(fn, sample, *args, **kwargs) return np.cov(thetas, rowvar=False, ddof=1) def confidence_interval( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike", cl: float = 0.95, ci_method: str = "bca", **kwargs: Any, ) -> Tuple[float, float]: """ Calculate bootstrap confidence intervals. Parameters ---------- fn : callable Function to be bootstrapped. sample : array-like Original sample. *args : array-like Optional additional arrays of the same length to resample. cl : float, default : 0.95 Confidence level. Asymptotically, this is the probability that the interval contains the true value. ci_method : str, {'bca', 'percentile'}, optional Confidence interval method. Default is 'bca'. See notes for details. **kwargs Keyword arguments forwarded to :func:`resample`. Returns ------- (float, float) Upper and lower confidence limits. Examples -------- Compute confidence interval for arithmetic mean. >>> from resample.bootstrap import confidence_interval >>> import numpy as np >>> x = np.arange(10) >>> a, b = confidence_interval(np.mean, x, size=10000, random_state=1) >>> f"{a:.1f} to {b:.1f}" '2.6 to 6.2' Notes ----- Both the 'percentile' and 'bca' methods produce intervals that are invariant to monotonic transformations of the data values, a desirable and consistent property. The 'percentile' method is straightforward and useful as a fallback. The 'bca' method is 2nd order accurate (to O(1/n) where n is the sample size) and generally preferred. It computes a jackknife estimate in addition to the bootstrap, which increases the number of function evaluations in a direct comparison to 'percentile'. However the increase in accuracy should compensate for this, with the result that less bootstrap replicas are needed overall to achieve the same accuracy. """ if args and not isinstance(args[0], Collection): import warnings warnings.warn( "Calling confidence_interval with positional instead of keyword " "arguments is deprecated", FutureWarning, ) if len(args) == 1: (cl,) = args elif len(args) == 2: cl, ci_method = args else: raise ValueError("too many arguments") args = () if not 0 < cl < 1: raise ValueError("cl must be between zero and one") thetas = bootstrap(fn, sample, *args, **kwargs) alpha = 1 - cl if ci_method == "percentile": return _confidence_interval_percentile(thetas, alpha / 2) if ci_method == "bca": theta = fn(sample, *args) j_thetas = jackknife(fn, sample, *args) return _confidence_interval_bca(theta, thetas, j_thetas, alpha / 2) raise ValueError( f"ci_method must be 'percentile' or 'bca', but '{ci_method}' was supplied" ) def _resample_stratified( sample: np.ndarray, size: int, method: str, strata: np.ndarray, rng: np.random.Generator, ) -> Generator[np.ndarray, None, None]: # call resample on sub-samples and merge the replicates sub_samples = [sample[strata == x] for x in np.unique(strata)] for sub_replicates in zip( *[resample(s, size=size, method=method, random_state=rng) for s in sub_samples] ): yield np.concatenate(sub_replicates, axis=0) def _resample_ordinary_1( sample: np.ndarray, size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: # i.i.d. sampling from empirical cumulative distribution of sample n = len(sample) for _ in range(size): yield rng.choice(sample, size=n, replace=True) def _resample_ordinary_n( samples: List[np.ndarray], size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: n = len(samples[0]) indices = np.arange(n) for _ in range(size): m = rng.choice(indices, size=n, replace=True) yield tuple(s[m] for s in samples) def _resample_balanced_1( sample: np.ndarray, size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: # effectively computes a random permutation of `size` concatenated # copies of `sample` and returns `size` equal chunks of that n = len(sample) indices = rng.permutation(n * size) for i in range(size): m = indices[i * n : (i + 1) * n] % n yield sample[m] def _resample_balanced_n( samples: List[np.ndarray], size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: n = len(samples[0]) indices = rng.permutation(n * size) for i in range(size): m = indices[i * n : (i + 1) * n] % n yield tuple(s[m] for s in samples) def _resample_extended_1( sample: np.ndarray, size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: # randomly generates the sample size from a Poisson distribution n = len(sample) for i in range(size): k = rng.poisson(1, size=n) yield np.repeat(sample, k, axis=0) def _resample_extended_n( samples: List[np.ndarray], size: int, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: n = len(samples[0]) for i in range(size): k = rng.poisson(1, size=n) yield tuple(np.repeat(s, k, axis=0) for s in samples) def _fit_parametric_family( dist: stats.rv_continuous, sample: np.ndarray ) -> Tuple[float, ...]: if dist == stats.multivariate_normal: # has no fit method... return np.mean(sample, axis=0), np.cov(sample.T, ddof=1) if dist in {stats.f, stats.beta}: fit_kwargs = {"floc": 0, "fscale": 1} elif dist in {stats.gamma, stats.lognorm, stats.invgauss, stats.pareto}: fit_kwargs = {"floc": 0} else: fit_kwargs = {} return dist.fit(sample, **fit_kwargs) # type: ignore def _resample_parametric( sample: np.ndarray, size: int, dist: stats.rv_continuous, rng: np.random.Generator ) -> Generator[np.ndarray, None, None]: n = len(sample) # fit parameters by maximum likelihood and sample from that if dist == stats.poisson: # - poisson has no fit method and there is no scale parameter # - random number generation for poisson distribution in scipy seems to be buggy mu = np.mean(sample) for _ in range(size): yield rng.poisson(mu, size=n) else: args = _fit_parametric_family(dist, sample) dist = dist(*args) for _ in range(size): yield dist.rvs(size=n, random_state=rng) def _confidence_interval_percentile( thetas: np.ndarray, alpha_half: float ) -> Tuple[float, float]: quant = quantile_function_gen(thetas) return quant(alpha_half), quant(1 - alpha_half) def _confidence_interval_bca( theta: float, thetas: np.ndarray, j_thetas: np.ndarray, alpha_half: float ) -> Tuple[float, float]: norm = stats.norm # bias correction; implementation notes: # - if prop_less is zero, z_naught would become -inf; # we set z_naught to zero then (no bias) prop_less = np.mean(thetas < theta) # proportion of replicates less than obs z_naught = norm.ppf(prop_less) if prop_less > 0 else 0.0 # acceleration; implementation notes: # - np.mean returns float even if j_thetas are int, # must convert type explicity to make -= operator work # - it is possible that all j_thetas are zero, it then follows # that den and num are zero; we set acc to zero then (no acceleration) j_mean = np.mean(j_thetas) j_thetas = j_thetas.astype(j_mean.dtype, copy=False) j_thetas -= j_mean num = np.sum((-j_thetas) ** 3) den = np.sum(j_thetas**2) acc = num / (6 * den**1.5) if den > 0 else 0.0 z_low = z_naught + norm.ppf(alpha_half) z_high = z_naught + norm.ppf(1 - alpha_half) p_low = norm.cdf(z_naught + z_low / (1 - acc * z_low)) p_high = norm.cdf(z_naught + z_high / (1 - acc * z_high)) quant = quantile_function_gen(thetas) return quant(p_low), quant(p_high) def __getattr__(key: str) -> Any: for match in ("bias", "bias_corrected"): if key == match: msg = ( f"resample.bootstrap.{match} has been removed. The implementation was " "discovered to be faulty, and a generic fix is not in sight. " "Please use resample.jackknife.bias instead." ) raise NotImplementedError(msg) raise AttributeError resample-1.10.1/src/resample/empirical.py000066400000000000000000000046731470150054300203310ustar00rootroot00000000000000""" Empirical functions. Empirical functions based on a data sample instead of a parameteric density function, like the empirical CDF. Implemented here are mostly tools used internally. """ __all__ = ["cdf_gen", "quantile_function_gen", "influence"] from typing import Callable, Union import numpy as np from numpy.typing import ArrayLike from .jackknife import jackknife def cdf_gen(sample: "ArrayLike") -> Callable[[np.ndarray], np.ndarray]: """ Return the empirical distribution function for the given sample. Parameters ---------- sample : array-like Sample. Returns ------- callable Empirical distribution function. """ sample_np = np.sort(sample) n = len(sample_np) return lambda x: np.searchsorted(sample_np, x, side="right", sorter=None) / n def quantile_function_gen( sample: "ArrayLike", ) -> Callable[[Union[float, "ArrayLike"]], Union[float, np.ndarray]]: """ Return the empirical quantile function for the given sample. Parameters ---------- sample : array-like Sample. Returns ------- callable Empirical quantile function. """ class QuantileFn: def __init__(self, sample: "ArrayLike"): self._sorted = np.sort(sample, axis=0) def __call__(self, p: Union[float, "ArrayLike"]) -> Union[float, np.ndarray]: ndim = np.ndim(p) # must come before atleast_1d p = np.atleast_1d(p) result = np.empty(len(p)) valid = (p >= 0) & (p <= 1) n = len(self._sorted) idx = np.maximum(np.ceil(p[valid] * n).astype(int) - 1, 0) result[valid] = self._sorted[idx] result[~valid] = np.nan if ndim == 0: return result[0] return result return QuantileFn(sample) def influence( fn: Callable[["ArrayLike"], np.ndarray], sample: "ArrayLike" ) -> np.ndarray: """ Calculate the empirical influence function for a given sample and estimator. Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Sample. Must be one-dimensional. Returns ------- ndarray Empirical influence values. """ sample = np.atleast_1d(sample) n = len(sample) return (n - 1) * (fn(sample) - jackknife(fn, sample)) resample-1.10.1/src/resample/jackknife.py000066400000000000000000000266441470150054300203130ustar00rootroot00000000000000""" Jackknife resampling tools. Compute estimator bias and variance with jackknife resampling. The implementation supports resampling of N-dimensional data. The interface of this module mimics that of the bootstrap module, so that you can easily switch between bootstrapping and jackknifing bias and variance of an estimator. The jackknife is an approximation to the bootstrap, so in general bootstrapping is preferred, especially when the sample is small. The computational cost of the jackknife increases quadratically with the sample size, but only linearly for the bootstrap. An advantage of the jackknife can be the deterministic outcome, since no random sampling is involved, but this can be overcome by fixing the seed for the bootstrap. The jackknife should be used to estimate the bias, since the bootstrap cannot (easily) estimate bias. The bootstrap should be preferred when computing the variance. """ __all__ = [ "resample", "jackknife", "bias", "bias_corrected", "variance", "cross_validation", ] from typing import Any, Callable, Collection, Generator, List import numpy as np from numpy.typing import ArrayLike def resample( sample: "ArrayLike", *args: "ArrayLike", copy: bool = True ) -> Generator[Any, None, None]: """ Generate jackknifed samples. Parameters ---------- sample : array-like Sample. If the sequence is multi-dimensional, the first dimension must walk over i.i.d. observations. *args: array-like Optional additional arrays of the same length to resample. copy : bool, optional If `True`, return the replicated sample as a copy, otherwise return a view into the internal array buffer of the generator. Setting this to `False` avoids `len(sample)` copies, which is more efficient, but see notes for caveats. Yields ------ ndarray Array with same shape and type as input, but with the size of the first dimension reduced by one. Replicates are missing one value of the original in ascending order, e.g. for a sample (1, 2, 3), one gets (2, 3), (1, 3), (1, 2). See Also -------- resample.bootstrap.resample : Generate bootstrap samples. resample.jackknife.jackknife : Generate jackknife estimates. Notes ----- On performance: The generator interally keeps a single array to the replicates, which is updated on each iteration of the generator. The safe default is to return copies of this internal state. To increase performance, it also possible to return a view into the generator state, by setting the `copy=False`. However, this will only produce correct results if the generator is called strictly sequentially in a single- threaded program and the loop body consumes the view and does not try to store it. The following program shows what happens otherwise: >>> from resample.jackknife import resample >>> r1 = [] >>> for x in resample((1, 2, 3)): # works as expected ... r1.append(x) >>> print(r1) [array([2, 3]), array([1, 3]), array([1, 2])] >>> >>> r2 = [] >>> for x in resample((1, 2, 3), copy=False): ... r2.append(x) # x is now a view into the same array in memory >>> print(r2) [array([1, 2]), array([1, 2]), array([1, 2])] """ sample_np = np.atleast_1d(sample) n_sample = len(sample_np) args_np = [] if args: if not isinstance(args[0], Collection): import warnings warnings.warn( "Calling resample with positional instead of keyword arguments is " "deprecated", FutureWarning, ) if len(args) == 1: (copy,) = args else: raise ValueError("too many arguments") else: args_np.append(sample_np) for arg in args: arg_np = np.atleast_1d(arg) n_arg = len(arg_np) if n_arg != n_sample: raise ValueError( f"extra argument has wrong length {n_arg} != {n_sample}" ) args_np.append(arg_np) if args_np: return _resample_n(args_np, copy) return _resample_1(sample_np, copy) def _resample_1(sample: np.ndarray, copy: bool) -> Generator[np.ndarray, None, None]: # yield x0 x = sample[1:].copy() yield x.copy() if copy else x # update of x needs to change only value at index i # for a = [0, 1, 2, 3] # x0 = [1, 2, 3] (yielded above) # x1 = [0, 2, 3] # override first index # x2 = [0, 1, 3] # override second index # x3 = [0, 1, 2] # ... for i in range(len(sample) - 1): x[i] = sample[i] yield x.copy() if copy else x def _resample_n(samples: List[np.ndarray], copy: bool) -> Generator[Any, None, None]: x = [a[1:].copy() for a in samples] yield (xi.copy() for xi in x) for i in range(len(samples[0]) - 1): for xi, ai in zip(x, samples): xi[i] = ai[i] yield (xi.copy() for xi in x) def jackknife( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike", ) -> np.ndarray: """ Calculate jackknife estimates for a given sample and estimator. The jackknife is a linear approximation to the bootstrap. In contrast to the bootstrap it is deterministic and does not use random numbers. The caveat is the computational cost of the jackknife, which is O(n²) for n observations, compared to O(n x k) for k bootstrap replicates. For large samples, the bootstrap is more efficient. Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args: array-like Optional additional arrays of the same length to resample. Returns ------- ndarray Jackknife samples. Examples -------- >>> from resample.jackknife import jackknife >>> import numpy as np >>> x = np.arange(10) >>> fx = np.mean(x) >>> fb = jackknife(np.mean, x) >>> print(f"f(x) = {fx:.1f} +/- {np.std(fb):.1f}") f(x) = 4.5 +/- 0.3 """ gen = resample(sample, *args, copy=False) if args: return np.array([fn(*b) for b in gen]) return np.asarray([fn(b) for b in gen]) def bias( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike" ) -> np.ndarray: """ Calculate jackknife estimate of bias. The bias estimate is accurate to O(1/n), where n is the number of samples. If the bias is exactly O(1/n), then the estimate is exact. Wikipedia: https://en.wikipedia.org/wiki/Jackknife_resampling Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args: array-like Optional additional arrays of the same length to resample. Returns ------- ndarray Jackknife estimate of bias (= expectation of estimator - true value). Examples -------- Compute bias of numpy.var with and without bias-correction. >>> from resample.jackknife import bias >>> import numpy as np >>> x = np.arange(10) >>> b1 = bias(np.var, x) >>> b2 = bias(lambda x: np.var(x, ddof=1), x) >>> f"bias of naive sample variance {b1:.1f}, bias of corrected variance {b2:.1f}" 'bias of naive sample variance -0.9, bias of corrected variance 0.0' """ sample = np.atleast_1d(sample) n = len(sample) theta = fn(sample) mean_theta = np.mean(jackknife(fn, sample, *args), axis=0) return (n - 1) * (mean_theta - theta) def bias_corrected( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike" ) -> np.ndarray: """ Calculate bias-corrected estimate of the function with the jackknife. Removes a bias of O(1/n), where n is the sample size, leaving bias of order O(1/n²). If the original function has a bias of exactly O(1/n), the corrected result is now unbiased. Wikipedia: https://en.wikipedia.org/wiki/Jackknife_resampling Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args: array-like Optional additional arrays of the same length to resample. Returns ------- ndarray Estimate with O(1/n) bias removed. Examples -------- Compute bias-corrected estimate of numpy.var. >>> from resample.jackknife import bias_corrected >>> import numpy as np >>> x = np.arange(10) >>> v1 = np.var(x) >>> v2 = bias_corrected(np.var, x) >>> f"naive variance {v1:.1f}, bias-corrected variance {v2:.1f}" 'naive variance 8.2, bias-corrected variance 9.2' """ sample = np.atleast_1d(sample) n = len(sample) theta = fn(sample) mean_theta = np.mean(jackknife(fn, sample, *args), axis=0) return n * theta - (n - 1) * mean_theta def variance( fn: Callable[..., np.ndarray], sample: "ArrayLike", *args: "ArrayLike" ) -> np.ndarray: """ Calculate jackknife estimate of variance. Wikipedia: https://en.wikipedia.org/wiki/Jackknife_resampling Parameters ---------- fn : callable Estimator. Can be any mapping ℝⁿ → ℝᵏ, where n is the sample size and k is the length of the output array. sample : array-like Original sample. *args: array-like Optional additional arrays of the same length to resample. Returns ------- ndarray Jackknife estimate of variance. Examples -------- Compute variance of arithmetic mean. >>> from resample.jackknife import variance >>> import numpy as np >>> x = np.arange(10) >>> v = variance(np.mean, x) >>> f"{v:.1f}" '0.9' """ # formula is (n - 1) / n * sum((fj - mean(fj)) ** 2) # = np.var(fj, ddof=0) * (n - 1) sample = np.atleast_1d(sample) thetas = jackknife(fn, sample, *args) n = len(sample) return (n - 1) * np.var(thetas, ddof=0, axis=0) def cross_validation( predict: Callable[..., float], x: "ArrayLike", y: "ArrayLike", *args: "ArrayLike" ) -> float: """ Calculate mean-squared error of model with leave-one-out-cross-validation. Wikipedia: https://en.wikipedia.org/wiki/Cross-validation_(statistics) Parameters ---------- predict : callable Function with the signature (x_in, y_in, x_out, *args). It takes x_in, y_in, which are arrays with the same length. x_out should be one element of the x array. *args are further optional arguments for the function. The function should return the prediction y(x_out). x : array-like Explanatory variable. Must be an array of shape (N, ...), where N is the number of samples. y : array-like Observations. Must be an array of shape (N, ...). *args: Optional arguments which are passed unmodified to predict. Returns ------- float Variance of the difference (y[i] - predict(..., x[i], *args)). """ deltas = [] for i, (x_in, y_in) in enumerate(resample(x, y, copy=False)): yip = predict(x_in, y_in, x[i], *args) deltas.append((y[i] - yip)) return np.var(deltas) # type:ignore resample-1.10.1/src/resample/permutation.py000066400000000000000000000370161470150054300207300ustar00rootroot00000000000000""" Permutation-based tests. A collection of statistical tests that use permutated samples. Permutations are used to compute the distribution of a test statistic under some null hypothesis to obtain p-values without relying on approximate asymptotic formulas. The permutation method is generic, it can be used with any test statistic, therefore we also provide a generic test function that accepts a user-defined function to compute the test statistic and then automatically computes the p-value for that statistic. The other tests internally also call this generic test function. All tests return a TestResult object, which mimics the interface of the result objects returned by tests in scipy.stats, but has a third field to return the estimated distribution of the test statistic under the null hypothesis. Further reading: - https://en.wikipedia.org/wiki/P-value - https://en.wikipedia.org/wiki/Test_statistic - https://en.wikipedia.org/wiki/Paired_difference_test """ __all__ = [ "TestResult", "usp", "same_population", "anova", "kruskal", "pearsonr", "spearmanr", "ttest", ] import sys import warnings from dataclasses import dataclass from typing import Any, Callable, Optional, Tuple, Union import numpy as np from numpy.typing import ArrayLike, NDArray from scipy import stats as _stats from . import _util _dataclass_kwargs = {"frozen": True, "repr": False} if sys.version_info >= (3, 10): _dataclass_kwargs["slots"] = True # pragma: no cover @dataclass(**_dataclass_kwargs) class TestResult: """ Holder of the result of the permutation test. This class acts like a tuple, which means its can be unpacked and the fields can be accessed by name or by index. Attributes ---------- statistic: float Value of the test statistic computed on the original data pvalue: float Estimated chance probability (aka Type I error) for rejecting the null hypothesis. See https://en.wikipedia.org/wiki/P-value for details. samples: array Values of the test statistic from the permutated samples. """ statistic: float pvalue: float samples: NDArray def __repr__(self) -> str: """Return (potentially shortened) representation.""" s = None if len(self.samples) < 7: s = str(self.samples) else: s = "[{}, {}, {}, ..., {}, {}, {}]".format( *self.samples[:3], *self.samples[-3:] ) return ( f"" ) def __len__(self) -> int: """Return length of tuple.""" return 3 def __getitem__(self, idx: int) -> Union[float, NDArray]: """Return fields by index.""" if idx == 0: return self.statistic elif idx == 1: return self.pvalue elif idx == 2: return self.samples raise IndexError def usp( w: "ArrayLike", *, size: int = 9999, method: str = "auto", random_state: Optional[Union[np.random.Generator, int]] = None, ) -> TestResult: """ Test independence of two discrete data sets with the U-statistic. The USP test is described in this paper: https://doi.org/10.1098/rspa.2021.0549. According to the paper, it outperforms the Pearson's χ² and the G-test in both in stability and power. It requires that the input is a contigency table (a 2D histogram of value pairs). Whether the original values were discrete or continuous does not matter for the test. In case of continuous values, using a large number of bins is safe, since the test is not negatively affected by bins with zero entries. Parameters ---------- w : array-like Two-dimensional array which represents the counts in a histogram. The counts can be of floating point type, but must have integral values. size : int, optional Number of permutations. Default 9999. method : str, optional Method used to generate random tables under the null hypothesis. 'auto': Use heuristic to select fastest algorithm for given table. 'boyett': Boyett's algorithm, which requires extra space to store N + 1 integers for N entries in total and has O(N) time complexity. It performs poorly when N is large, but does not depend on the number of K table cells. 'patefield': Patefield's algorithm, which does not require extra space and has O(K log(N)) time complexity. It performs well even if N is huge. For small N and large K, the shuffling algorithm is faster. Default is 'auto'. random_state : numpy.random.Generator or int, optional Random number generator instance. If an integer is passed, seed the numpy default generator with it. Default is to use ``numpy.random.default_rng()``. Returns ------- TestResult """ if size <= 0: raise ValueError("size must be positive") if method == "shuffle": warnings.warn( "method 'shuffle' is deprecated, please use 'boyett'", FutureWarning ) method = "boyett" rng = _util.normalize_rng(random_state) w = np.array(w, dtype=float) if w.ndim != 2: raise ValueError("w must be two-dimensional") r = np.sum(w, axis=1) c = np.sum(w, axis=0) ntot = np.sum(r) m = np.outer(r, c) / ntot f1 = 1.0 / (ntot * (ntot - 3)) f2 = 4.0 / (ntot * (ntot - 2) * (ntot - 3)) t = _usp(f1, f2, w, m) ts = np.empty(size) for b, w in enumerate( _stats.random_table(r, c).rvs( size, method=None if method == "auto" else method, random_state=rng ) ): # m stays the same, since r and c remain unchanged ts[b] = _usp(f1, f2, w, m) # Thomas B. Berrett, Ioannis Kontoyiannis, Richard J. Samworth # Ann. Statist. 49(5): 2457-2490 (October 2021). DOI: 10.1214/20-AOS2041 # Eq. 5 says we need to add 1 to n_pass and n_total pvalue = (np.sum(t <= ts) + 1) / (size + 1) return TestResult(t, pvalue, ts) def _usp(f1: float, f2: float, w: NDArray, m: NDArray) -> NDArray: # Eq. 2.1 from https://doi.org/10.1098/rspa.2021.0549 return f1 * np.sum((w - m) ** 2) - f2 * np.sum(w * m) def same_population( fn: Callable[..., float], x: "ArrayLike", y: "ArrayLike", *args: "ArrayLike", transform: Optional[Callable[[NDArray], NDArray]] = None, size: int = 9999, random_state: Optional[Union[np.random.Generator, int]] = None, ) -> TestResult: """ Compute p-value for hypothesis that samples originate from same population. The computation is based on a user-defined test statistic. The distribution of the test statistic under the null hypothesis is generated by generating random permutations of the inputs, to simulate that they are actually drawn from the same population. The test statistic is recomputed on these permutations and the p-value is computed as the fraction of these resampled test statistics which are larger than the original value. Some test statistics need to be transformed to fulfill the condition above, for example if they are signed. A transform can be passed to this function for those cases. Parameters ---------- fn : Callable Function with signature f(x, ...), where the number of arguments corresponds to the number of data samples passed to the test. x : array-like First sample. y : array-like Second sample. *args: array-like Further samples, if the test allows to compare more than two. transform : Callable, optional Function with signature f(x) for the test statistic to turn it into a measure of deviation. Must be vectorised. size : int, optional Number of permutations. Default 9999. random_state : numpy.random.Generator or int, optional Random number generator instance. If an integer is passed, seed the numpy default generator with it. Default is to use `numpy.random.default_rng()`. Returns ------- TestResult """ if size <= 0: raise ValueError("max_size must be positive") rng = _util.normalize_rng(random_state) r = [] for arg in (x, y) + args: a = np.array(arg) if a.ndim != 1: raise ValueError("input samples must be 1D arrays") if len(a) < 2: raise ValueError("input arrays must have at least two items") if a.dtype.kind == "f" and np.any(np.isnan(a)): raise ValueError("input contains NaN") r.append(a) args = r del r # compute test statistic for original input t = fn(*args) # compute test statistic for permutated inputs slices = [] start = 0 for a in args: stop = start + len(a) slices.append(slice(start, stop)) start = stop joined_sample = np.concatenate(args) # For algorithm below, see comment in usp function. ts = np.empty(size) for b in range(size): rng.shuffle(joined_sample) ts[b] = fn(*(joined_sample[sl] for sl in slices)) if transform is None: u = t us = ts else: u = transform(t) us = transform(ts) # see usp for why we need to add 1 to both numerator and denominator pvalue = (np.sum(u <= us) + 1) / (size + 1) return TestResult(t, pvalue, ts) def anova( x: "ArrayLike", y: "ArrayLike", *args: "ArrayLike", **kwargs: Any ) -> TestResult: """ Test whether the means of two or more samples are compatible. This test uses one-way analysis of variance (one-way ANOVA) which tests whether the samples have the same mean. This test is typically used when one has three groups or more. For two groups, Welch's ttest is preferred, because ANOVA assumes equal variances for the samples. Parameters ---------- x : array-like First sample. y : array-like Second sample. *args : array-like Further samples. **kwargs : Keyword arguments are forward to :meth:`same_population`. Returns ------- TestResult Notes ----- https://en.wikipedia.org/wiki/One-way_analysis_of_variance https://en.wikipedia.org/wiki/F-test """ kwargs["transform"] = None return same_population(_ANOVA(), x, y, *args, **kwargs) def kruskal( x: "ArrayLike", y: "ArrayLike", *args: "ArrayLike", **kwargs: Any ) -> TestResult: """ Test whether two or more samples have the same mean rank. This performs a permutation-based Kruskal-Wallis test. In a sense, it extends the Mann-Whitney U test, which also uses ranks, to more than two groups. It does so by comparing the means of the rank distributions. Parameters ---------- x : array-like First sample. y : array-like Second sample. *args : array-like Further samples. **kwargs : Keyword arguments are forward to :meth:`same_population`. Returns ------- TestResult Notes ----- https://en.wikipedia.org/wiki/Kruskal%E2%80%93Wallis_one-way_analysis_of_variance """ kwargs["transform"] = None return same_population(_kruskal, x, y, *args, **kwargs) def pearsonr(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult: """ Test whether two samples are drawn from same population using correlation. The test statistic is the Pearson correlation coefficient. The test is very sensitive to linear relationship of x and y. If the relationship is very non-linear but monotonic, :func:`spearmanr` may be more sensitive. https://en.wikipedia.org/wiki/Pearson_correlation_coefficient Parameters ---------- x : array-like First sample. y : array-like Second sample. **kwargs : Keyword arguments are forward to :meth:`same_population`. Returns ------- TestResult """ if len(x) != len(y): raise ValueError("x and y must have have the same length") kwargs["transform"] = np.abs return same_population(_pearson, x, y, **kwargs) def spearmanr(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult: """ Test whether two samples are drawn from same population using rank correlation. The test statistic is Spearman's rank correlation coefficient. The test is very sensitive to monotonic relationships between x and y, even if it is very non-linear. Parameters ---------- x : array-like First sample. y : array-like Second sample. **kwargs : Keyword arguments are forward to :meth:`same_population`. Returns ------- TestResult """ if len(x) != len(y): raise ValueError("x and y must have have the same length") kwargs["transform"] = np.abs return same_population(_spearman, x, y, **kwargs) def ttest(x: "ArrayLike", y: "ArrayLike", **kwargs: Any) -> TestResult: """ Test whether the means of two samples are compatible with Welch's t-test. See https://en.wikipedia.org/wiki/Welch%27s_t-test for details on this test. The p-value computed is for the null hypothesis that the two population means are equal. The test is two-sided, which means that swapping x and y gives the same p-value. Welch's t-test does not require the sample sizes to be equal and it does not require the samples to have the same variance. Parameters ---------- x : array-like First sample. y : array-like Second sample. **kwargs : Keyword arguments are forward to :meth:`same_population`. Returns ------- TestResult """ kwargs["transform"] = np.abs return same_population(_ttest, x, y, **kwargs) def _ttest(x: NDArray, y: NDArray) -> float: n1 = len(x) n2 = len(y) m1 = np.mean(x) m2 = np.mean(y) v1 = np.var(x, ddof=1) v2 = np.var(y, ddof=1) r: float = (m1 - m2) / np.sqrt(v1 / n1 + v2 / n2) return r def _pearson(x: NDArray, y: NDArray) -> float: m1 = np.mean(x) m2 = np.mean(y) s1 = np.mean((x - m1) ** 2) s2 = np.mean((y - m2) ** 2) r: float = np.mean((x - m1) * (y - m2)) / np.sqrt(s1 * s2) return r def _spearman(x: NDArray, y: NDArray) -> float: x = _stats.rankdata(x) y = _stats.rankdata(y) return _pearson(x, y) def _kruskal(*args: NDArray) -> float: # see https://en.wikipedia.org/wiki/ # Kruskal%E2%80%93Wallis_one-way_analysis_of_variance # method 3 and 4 joined = np.concatenate(args) r = _stats.rankdata(joined) n = len(r) start = 0 r_args = [] for i, a in enumerate(args): r_args.append(r[start : start + len(a)]) start += len(a) # method 3 (assuming no ties) h: float = 12.0 / (n * (n + 1)) * sum( len(r) * np.mean(r) ** 2 for r in r_args ) - 3 * (n + 1) # apply tie correction h /= _stats.tiecorrect(r) return h class _ANOVA: # see https://en.wikipedia.org/wiki/F-test km1: int = -2 nmk: int = 0 a_bar: float = 0.0 def __call__(self, *args: NDArray) -> float: if self.km1 == -2: self._init(args) between_group_variability: float = ( sum(len(a) * (np.mean(a) - self.a_bar) ** 2 for a in args) / self.km1 ) within_group_variability: float = sum(len(a) * np.var(a) for a in args) / ( self.nmk ) return between_group_variability / within_group_variability def _init(self, args: Tuple[NDArray, ...]) -> None: n = sum(len(a) for a in args) k = len(args) self.km1 = k - 1 self.nmk = n - k self.a_bar = np.mean(np.concatenate(args)) resample-1.10.1/tests/000077500000000000000000000000001470150054300145435ustar00rootroot00000000000000resample-1.10.1/tests/test_bootstrap.py000066400000000000000000000341251470150054300201760ustar00rootroot00000000000000# ruff: noqa: D100 D103 import numpy as np import pytest from numpy.testing import assert_equal, assert_allclose from scipy import stats from resample.bootstrap import ( _fit_parametric_family, bootstrap, confidence_interval, resample, variance, covariance, ) PARAMETRIC_CONTINUOUS = { # use scipy.stats names here "norm", "t", "laplace", "logistic", "f", "beta", "gamma", "lognorm", "invgauss", "pareto", } PARAMETRIC_DISCRETE = {"poisson"} PARAMETRIC = PARAMETRIC_CONTINUOUS | PARAMETRIC_DISCRETE NON_PARAMETRIC = {"ordinary", "balanced"} ALL_METHODS = NON_PARAMETRIC | PARAMETRIC def chisquare( obs, exp=None ): # we do not use scipy.stats.chisquare, because it is broken n = len(obs) if exp is None: exp = 1.0 / n t = np.sum(obs**2 / exp) - n return stats.chi2(n - 1).cdf(t) @pytest.fixture def rng(): return np.random.Generator(np.random.PCG64(1)) @pytest.mark.parametrize("method", ALL_METHODS) def test_resample_shape_1d(method): if method == "beta": x = (0.1, 0.2, 0.3) else: x = (1.0, 2.0, 3.0) n_rep = 5 count = 0 with np.errstate(invalid="ignore"): for bx in resample(x, size=n_rep, method=method): assert len(bx) == len(x) count += 1 assert count == n_rep @pytest.mark.parametrize("method", NON_PARAMETRIC | {"norm"}) def test_resample_shape_2d(method): x = [(1.0, 2.0), (4.0, 3.0), (6.0, 5.0)] n_rep = 5 count = 0 for bx in resample(x, size=n_rep, method=method): assert bx.shape == np.shape(x) count += 1 assert count == n_rep @pytest.mark.parametrize("method", NON_PARAMETRIC) def test_resample_shape_4d(method): x = np.ones((2, 3, 4, 5)) n_rep = 5 count = 0 for bx in resample(x, size=n_rep, method=method): assert bx.shape == np.shape(x) count += 1 assert count == n_rep @pytest.mark.parametrize("method", NON_PARAMETRIC | PARAMETRIC_CONTINUOUS) def test_resample_1d_statistical_test(method, rng): # distribution parameters for parametric families args = { "t": (2,), "f": (25, 20), "beta": (2, 1), "gamma": (1.5,), "lognorm": (1.0,), "invgauss": (1,), "pareto": (1,), }.get(method, ()) if method in NON_PARAMETRIC: dist = stats.norm else: dist = getattr(stats, method) x = dist.rvs(*args, size=1000, random_state=rng) # make equidistant bins in quantile space for this particular data set with np.errstate(invalid="ignore"): par = _fit_parametric_family(dist, x) prob = np.linspace(0, 1, 11) xe = dist(*par).ppf(prob) # - in case of parametric bootstrap, wref is exactly uniform # - in case of ordinary and balanced, it needs to be computed from original sample if method in NON_PARAMETRIC: wref = np.histogram(x, bins=xe)[0] else: wref = len(x) / (len(xe) - 1) # compute P values for replicates compared to original prob = [] wsum = 0 with np.errstate(invalid="ignore"): for bx in resample(x, size=100, method=method, random_state=rng): w = np.histogram(bx, bins=xe)[0] wsum += w pvalue = chisquare(w, wref) prob.append(pvalue) if method == "balanced": # balanced bootstrap exactly reproduces frequencies in original sample assert_equal(wref * 100, wsum) # check whether P value distribution is flat # - test has chance probability of 1 % to fail randomly # - if it fails due to programming error, value is typically < 1e-20 wp = np.histogram(prob, range=(0, 1))[0] pvalue = chisquare(wp) assert pvalue > 0.01 def test_resample_1d_statistical_test_poisson(rng): # poisson is behaving super weird in scipy x = rng.poisson(1.5, size=1000) mu = np.mean(x) xe = (0, 1, 2, 3, 10) # somehow location 1 is needed here... wref = np.diff(stats.poisson(mu, 1).cdf(xe)) * len(x) # compute P values for replicates compared to original prob = [] for bx in resample(x, size=100, method="poisson", random_state=rng): w = np.histogram(bx, bins=xe)[0] pvalue = chisquare(w, wref) prob.append(pvalue) # check whether P value distribution is flat # - test has chance probability of 1 % to fail randomly # - if it fails due to programming error, value is typically < 1e-20 wp = np.histogram(prob, range=(0, 1))[0] pvalue = chisquare(wp) assert pvalue > 0.01 def test_resample_invalid_family_raises(): msg = "Invalid family" with pytest.raises(ValueError, match=msg): next(resample((1, 2, 3), method="foobar")) @pytest.mark.parametrize("method", PARAMETRIC - {"norm"}) def test_resample_2d_parametric_raises(method): with pytest.raises(ValueError): next(resample(np.ones((2, 2)), method=method)) def test_resample_3d_parametric_normal_raises(): with pytest.raises(ValueError): next(resample(np.ones((2, 2, 2)), method="normal")) def test_resample_equal_along_axis(): data = np.reshape(np.tile([0, 1, 2], 3), (3, 3)) for b in resample(data, size=2): assert_equal(data, b) @pytest.mark.parametrize("method", NON_PARAMETRIC) def test_resample_full_strata(method): data = np.arange(3) for b in resample(data, size=2, strata=data, method=method): assert_equal(data, b) def test_resample_invalid_strata_raises(): msg = "must have the same shape" with pytest.raises(ValueError, match=msg): next(resample((1, 2, 3), strata=np.arange(4))) def test_bootstrap_2d_balanced(rng): data = ((1, 2, 3), (2, 3, 4), (3, 4, 5)) def mean(x): return np.mean(x, axis=0) r = bootstrap(mean, data, method="balanced") # arithmetic mean is linear, therefore mean over all replicates in # balanced bootstrap is equal to mean of original sample assert_allclose(mean(data), mean(r)) @pytest.mark.parametrize("action", [bootstrap, variance, confidence_interval]) def test_bootstrap_several_args(action): x = [1, 2, 3] y = [4, 5, 6] xy = np.transpose([x, y]) if action is confidence_interval: def f1(x, y): return np.sum(x + y) def f2(xy): return np.sum(xy) else: def f1(x, y): return np.sum(x), np.sum(y) def f2(xy): return np.sum(xy, axis=0) r1 = action(f1, x, y, size=10, random_state=1) r2 = action(f2, xy, size=10, random_state=1) assert_equal(r1, r2) @pytest.mark.parametrize("ci_method", ["percentile", "bca"]) def test_confidence_interval(ci_method, rng): data = rng.normal(size=1000) par = stats.norm.fit(data) dist = stats.norm( par[0], par[1] / len(data) ** 0.5 ) # accuracy of mean is sqrt(n) better cl = 0.9 ci_ref = dist.ppf(0.05), dist.ppf(0.95) ci = confidence_interval( np.mean, data, cl=cl, size=1000, ci_method=ci_method, random_state=rng ) assert_allclose(ci_ref, ci, atol=6e-3) def test_confidence_interval_invalid_p_raises(): msg = "must be between zero and one" with pytest.raises(ValueError, match=msg): confidence_interval(np.mean, (1, 2, 3), cl=2) def test_confidence_interval_invalid_ci_method_raises(): msg = "method must be 'percentile' or 'bca'" with pytest.raises(ValueError, match=msg): confidence_interval(np.mean, (1, 2, 3), ci_method="foobar") def test_bca_confidence_interval_estimator_returns_int(rng): def fn(data): return int(np.mean(data)) data = (1, 2, 3) ci = confidence_interval(fn, data, ci_method="bca", size=5, random_state=rng) assert_allclose((1.0, 2.0), ci) @pytest.mark.parametrize("ci_method", ["percentile", "bca"]) def test_bca_confidence_interval_bounded_estimator(ci_method, rng): def fn(data): return max(np.mean(data), 0) data = (-3, -2, -1) ci = confidence_interval(fn, data, ci_method=ci_method, size=5, random_state=rng) assert_allclose((0.0, 0.0), ci) @pytest.mark.parametrize("method", NON_PARAMETRIC) def test_variance(method, rng): data = np.arange(100) v = np.var(data) / len(data) r = variance(np.mean, data, size=1000, method=method, random_state=rng) assert r == pytest.approx(v, rel=0.05) @pytest.mark.parametrize("method", NON_PARAMETRIC) def test_covariance(method, rng): cov = np.array([[1.0, 0.1], [0.1, 2.0]]) data = rng.multivariate_normal([0.1, 0.2], cov, size=1000) r = covariance( lambda x: np.mean(x, axis=0), data, size=1000, method=method, random_state=rng ) assert_allclose(r, cov / len(data), atol=1e-3) def test_resample_deprecation(rng): data = [1, 2, 3] with pytest.warns(FutureWarning): r = list(resample(data, 10)) assert np.shape(r) == (10, 3) with pytest.warns(FutureWarning): resample(data, 10, "balanced") with pytest.warns(FutureWarning): with pytest.raises(ValueError): resample(data, 10, "foo") with pytest.warns(FutureWarning): resample(data, 10, "balanced", [1, 1, 2]) with pytest.warns(FutureWarning): with pytest.raises(ValueError): resample(data, 10, "balanced", [1, 1]) with pytest.warns(FutureWarning): resample(data, 10, "balanced", [1, 1, 2], rng) with pytest.warns(FutureWarning): resample(data, 10, "balanced", [1, 1, 2], 1) with pytest.warns(FutureWarning): with pytest.raises(TypeError): resample(data, 10, "balanced", [1, 1, 2], 1.3) with pytest.warns(FutureWarning): with pytest.raises(ValueError): # too many arguments resample(data, 10, "balanced", [1, 1, 2], 1, 2) def test_confidence_interval_deprecation(rng): d = [1, 2, 3] with pytest.warns(FutureWarning): r = confidence_interval(np.mean, d, 0.6, random_state=1) assert_equal(r, confidence_interval(np.mean, d, cl=0.6, random_state=1)) with pytest.warns(FutureWarning): r = confidence_interval(np.mean, d, 0.6, "percentile", random_state=1) assert_equal( r, confidence_interval(np.mean, d, cl=0.6, ci_method="percentile", random_state=1), ) with pytest.warns(FutureWarning): with pytest.raises(ValueError): confidence_interval(np.mean, d, 0.6, "percentile", 1) def test_random_state(): d = [1, 2, 3] a = list(resample(d, size=5, random_state=np.random.default_rng(1))) b = list(resample(d, size=5, random_state=1)) c = list(resample(d, size=5, random_state=[2, 3])) assert_equal(a, b) assert not np.all([np.all(ai == ci) for (ai, ci) in zip(a, c)]) with pytest.raises(TypeError): resample(d, size=5, random_state=1.5) @pytest.mark.parametrize("method", NON_PARAMETRIC) def test_resample_several_args(method): a = [1, 2, 3] b = [(1, 2), (2, 3), (3, 4)] c = ["12", "3", "4"] r1 = [[], [], []] for ai, bi, ci in resample(a, b, c, size=5, method=method, random_state=1): r1[0].append(ai) r1[1].append(bi) r1[2].append(ci) r2 = [[], [], []] abc = np.empty(3, dtype=[("a", "i"), ("b", "i", 2), ("c", "U4")]) abc[:]["a"] = a abc[:]["b"] = b abc[:]["c"] = c for abci in resample(abc, size=5, method=method, random_state=1): r2[0].append(abci["a"]) r2[1].append(abci["b"]) r2[2].append(abci["c"]) for i in range(3): assert_equal(r1[i], r2[i]) def test_resample_several_args_incompatible_keywords(): a = [1, 2, 3] b = [(1, 2), (2, 3), (3, 4)] with pytest.raises(ValueError): resample(a, b, size=5, method="norm") resample(a, size=5, strata=[1, 1, 2]) with pytest.raises(ValueError): resample(a, b, size=5, strata=[1, 1, 2]) resample(a, b, a, b, size=5) with pytest.raises(ValueError): resample(a, [1, 2]) with pytest.raises(ValueError): resample(a, [1, 2, 3, 4]) with pytest.raises(ValueError): resample(a, b, 5) def test_resample_extended_1(): a = [1, 2, 3] bs = list(resample(a, size=100, method="extended", random_state=1)) # check that lengths of bootstrap samples are poisson distributed w, xe = np.histogram([len(b) for b in bs], bins=10, range=(0, 10)) wm = stats.poisson(len(a)).pmf(xe[:-1]) * np.sum(w) t = np.sum((w - wm) ** 2 / wm) pvalue = 1 - stats.chi2(len(w)).cdf(t) assert pvalue > 0.1 def test_resample_extended_2(): n = 10 a = np.arange(n) ts = [] for b in resample(a, size=1000, method="extended", random_state=1): ts.append(np.mean(b)) t = np.var(ts) expected_not_extended = np.var(a) / n k = np.arange(100) pk = stats.poisson(n).pmf(k) expected = expected_not_extended * np.sum(pk[1:] * n / k[1:]) / (1 - pk[0]) assert expected / expected_not_extended > 1.1 assert t > expected_not_extended assert_allclose(t, expected, atol=0.02) def test_resample_extended_3(): n = 10 a = np.arange(n) b = 5 + a ns = [] for ai, bi in resample(a, b, size=1000, method="extended", random_state=1): assert len(ai) == len(bi) assert_equal(bi - ai, 5) ns.append(len(ai)) assert_allclose(np.var(ns), 10, rtol=0.05) def test_resample_extended_4(): x = np.ones(10) a = np.transpose((x, 3 * x)) ts = [] for b in resample(a, size=1000, method="extended", random_state=1): ts.append(np.sum(b, axis=0)) t = np.var(ts, axis=0) mu = np.sum(x, axis=0) assert_allclose(t, (mu, 3**2 * mu), rtol=0.05) def test_resample_extended_5(): x = np.ones(10) a = np.transpose((x, 3 * x)) ts1 = [] ts2 = [] for b1, b2 in resample(a, 3 * a, size=1000, method="extended", random_state=1): ts1.append(np.sum(b1, axis=0)) ts2.append(np.sum(b2, axis=0)) t1 = np.var(ts1, axis=0) t2 = np.var(ts2, axis=0) mu1 = np.sum(x, axis=0) mu2 = 3**2 * np.sum(x, axis=0) assert_allclose(t1, (mu1, 3**2 * mu1), rtol=0.05) assert_allclose(t2, (mu2, 3**2 * mu2), rtol=0.05) def test_bias_error(): with pytest.raises(NotImplementedError): from resample.bootstrap import bias # noqa with pytest.raises(NotImplementedError): import resample.bootstrap as b b.bias_corrected # noqa resample-1.10.1/tests/test_empirical.py000066400000000000000000000033671470150054300201320ustar00rootroot00000000000000# ruff: noqa: D100 D103 import numpy as np import pytest from numpy.testing import assert_equal from resample.empirical import cdf_gen, influence, quantile_function_gen # high-quality platform-independent reproducible sequence of pseudo-random numbers @pytest.fixture def rng(): return np.random.Generator(np.random.PCG64(1)) def test_cdf_increasing(rng): x = rng.normal(size=100) cdf = cdf_gen(x) result = [cdf(s) for s in np.linspace(x.min(), x.max(), 100)] assert np.all(np.diff(result) >= 0) def test_cdf_at_infinity(): cdf = cdf_gen(np.arange(10)) assert cdf(-np.inf) == 0.0 assert cdf(np.inf) == 1.0 def test_cdf_simple_cases(): cdf = cdf_gen([0, 1, 2, 3]) assert cdf(0) == 0.25 assert cdf(1) == 0.5 assert cdf(2) == 0.75 assert cdf(3) == 1.0 def test_cdf_on_array(): x = np.arange(4) cdf = cdf_gen(x) assert_equal(cdf(x), (x + 1) / len(x)) assert_equal(cdf(x + 1e-10), (x + 1) / len(x)) assert_equal(cdf(x - 1e-10), x / len(x)) def test_quantile_simple_cases(): q = quantile_function_gen([0, 1, 2, 3]) assert q(0.25) == 0 assert q(0.5) == 1 assert q(0.75) == 2 assert q(1.0) == 3 def test_quantile_on_array(): x = np.arange(4) q = quantile_function_gen(x) prob = (x + 1) / len(x) assert_equal(q(prob), x) def test_quantile_is_inverse_of_cdf(rng): x = rng.normal(size=30) y = cdf_gen(x)(x) assert_equal(quantile_function_gen(x)(y), x) @pytest.mark.parametrize("arg", [-1, 1.5]) def test_quantile_out_of_bounds_is_nan(arg): q = quantile_function_gen(np.array([0, 1, 2, 3])) assert np.isnan(q(arg)) def test_influence_shape(): n = 100 data = np.random.random(n) emp = influence(np.mean, data) assert len(emp) == n resample-1.10.1/tests/test_jackknife.py000066400000000000000000000071561470150054300201120ustar00rootroot00000000000000import numpy as np import pytest from numpy.testing import assert_almost_equal, assert_equal from scipy.optimize import curve_fit from resample.jackknife import ( bias, bias_corrected, jackknife, resample, variance, cross_validation, ) def test_resample_1d(): data = (1, 2, 3) r = [] for x in resample(data): r.append(x.copy()) assert_equal(r, [[2, 3], [1, 3], [1, 2]]) def test_resample_2d(): data = ((1, 2), (3, 4), (5, 6)) r = [] for x in resample(data): r.append(x.copy()) assert_equal(r, [[(3, 4), (5, 6)], [(1, 2), (5, 6)], [(1, 2), (3, 4)]]) def test_jackknife(): data = (1, 2, 3) r = jackknife(lambda x: x.copy(), data) assert_equal(r, [[2, 3], [1, 3], [1, 2]]) def test_bias_on_unbiased(): data = (0, 1, 2, 3) # bias is exactly zero for linear functions r = bias(np.mean, data) assert r == 0 def test_bias_on_biased_order_n_minus_one(): # this "mean" has a bias of exactly O(n^{-1}) def bad_mean(x): return (np.sum(x) + 2) / len(x) data = (0, 1, 2) r = bias(bad_mean, data) mean_jk = np.mean([bad_mean([1, 2]), bad_mean([0, 2]), bad_mean([0, 1])]) # (5/2 + 4/2 + 3/2) / 3 = 12 / 6 = 2 assert mean_jk == 2.0 # f = 5/3 # (n-1) * (mean_jk - f) # (3 - 1) * (6/3 - 5/3) = 2/3 # note: 2/3 is exactly the bias of bad_mean for n = 3 assert r == pytest.approx(2.0 / 3.0) def test_bias_on_array_map(): # compute mean and (biased) variance simultanously def fn(x): return np.mean(x), np.var(x, ddof=0) data = (0, 1, 2) r = bias(fn, data) assert_almost_equal(r, (0.0, -1.0 / 3.0)) def test_bias_corrected(): # this "mean" has a bias of exactly O(n^{-1}) def bad_mean(x): return (np.sum(x) + 2) / len(x) # bias correction is exact up to O(n^{-1}) data = (0, 1, 2) r = bias_corrected(bad_mean, data) assert r == 1.0 # which is the correct unbiased mean def test_variance(): data = (0, 1, 2) r = variance(np.mean, data) # formula is (n - 1) / n * sum((jf - mean(jf)) ** 2) # fj = [3/2, 1, 1/2] # mfj = 1 # ((3/2 - 1)^2 + (1 - 1)^2 + (1/2 - 1)^2) * 2 / 3 # (1/4 + 1/4) / 3 * 2 = 1/3 assert r == pytest.approx(1.0 / 3.0) def test_resample_several_args(): a = [1, 2, 3] b = [(1, 2), (2, 3), (3, 4)] c = ["12", "3", "4"] for ai, bi, ci in resample(a, b, c): assert np.shape(ai) == (2,) assert np.shape(bi) == (2, 2) assert np.shape(ci) == (2,) assert set(ai) <= set(a) assert set(ci) <= set(c) bi = list(tuple(x) for x in bi) assert set(bi) <= set(b) def test_resample_several_args_incompatible_keywords(): a = [1, 2, 3] with pytest.raises(ValueError): resample(a, [1, 2]) with pytest.raises(ValueError): resample(a, [1, 2, 3, 4]) def test_resample_deprecation(): data = [1, 2, 3] with pytest.warns(FutureWarning): r = list(resample(data, False)) assert_equal(r, list(resample(data, copy=False))) with pytest.warns(FutureWarning): with pytest.raises(ValueError): # too many arguments resample(data, True, 1) @pytest.mark.filterwarnings("ignore:Covariance") def test_cross_validation(): x = [1, 2, 3] y = [3, 4, 5] def predict(xi, yi, xo, npar): def model(x, *par): return np.polyval(par, x) popt = curve_fit(model, xi, yi, p0=np.zeros(npar))[0] return model(xo, *popt) v = cross_validation(predict, x, y, 2) assert v == pytest.approx(0) v2 = cross_validation(predict, x, y, 1) assert v2 == pytest.approx(1.5) resample-1.10.1/tests/test_permutation.py000066400000000000000000000157451470150054300205370ustar00rootroot00000000000000# ruff: noqa: D100 D101 D103 D105 D107 import numpy as np from numpy.testing import assert_allclose from resample import permutation as perm from scipy import stats import pytest @pytest.fixture() def rng(): return np.random.Generator(np.random.PCG64(1)) def test_TestResult(): p = perm.TestResult(1, 2, [3, 4]) assert p.statistic == 1 assert p.pvalue == 2 assert p.samples == [3, 4] assert repr(p) == "" assert len(p) == 3 first, *rest = p assert first == 1 assert rest == [2, [3, 4]] p2 = perm.TestResult(1, 2, np.arange(10)) assert repr(p2) == ( "" ) class Scipy: def __init__(self, **kwargs): self.d = kwargs def __getitem__(self, key): if key in self.d: return self.d[key] return getattr(stats, key) scipy = Scipy( anova=stats.f_oneway, ttest=lambda x, y: stats.ttest_ind(x, y, equal_var=False), ) @pytest.mark.parametrize( "test_name", ( "anova", "kruskal", "pearsonr", "spearmanr", "ttest", ), ) @pytest.mark.parametrize("size", (10, 100)) def test_two_sample_same_size(test_name, size, rng): x = rng.normal(size=size) y = rng.normal(1, size=size) test = getattr(perm, test_name) scipy_test = scipy[test_name] for a, b in ((x, y), (y, x)): expected = scipy_test(a, b) got = test(a, b, size=999, random_state=1) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1], atol={10: 0.2, 100: 0.02}[size]) @pytest.mark.parametrize( "test_name", ( "anova", "kruskal", "pearsonr", "spearmanr", "ttest", ), ) @pytest.mark.parametrize("size", (10, 100)) def test_two_sample_different_size(test_name, size, rng): x = rng.normal(size=size) y = rng.normal(1, size=2 * size) test = getattr(perm, test_name) scipy_test = scipy[test_name] if test_name in ("pearsonr", "spearmanr"): with pytest.raises(ValueError): test(x, y) return for a, b in ((x, y), (y, x)): expected = scipy_test(a, b) got = test(a, b, size=999, random_state=1) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1], atol=5e-2) @pytest.mark.parametrize( "test_name", ( "anova", "kruskal", ), ) @pytest.mark.parametrize("size", (10, 100)) def test_three_sample_same_size(test_name, size, rng): x = rng.normal(size=size) y = rng.normal(1, size=size) z = rng.normal(0.5, size=size) test = getattr(perm, test_name) scipy_test = scipy[test_name] for a, b, c in ((x, y, z), (z, y, x)): expected = scipy_test(a, b, c) got = test(a, b, c, size=999, random_state=1) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1], atol=5e-2) @pytest.mark.parametrize( "test_name", ( "anova", "kruskal", ), ) @pytest.mark.parametrize("size", (10, 100)) def test_three_sample_different_size(test_name, size, rng): x = rng.normal(size=size) y = rng.normal(1, size=2 * size) z = rng.normal(0.5, size=size * 2) test = getattr(perm, test_name) scipy_test = scipy[test_name] for a, b, c in ((x, y, z), (z, y, x)): expected = scipy_test(a, b, c) got = test(a, b, c, size=500, random_state=1) assert_allclose(expected[0], got[0]) assert_allclose(expected[1], got[1], atol=5e-2) def test_bad_input(): with pytest.raises(ValueError): perm.ttest([1, 2, 3], [1.0, np.nan, 2.0]) @pytest.mark.parametrize("method", ("auto", "patefield", "boyett")) def test_usp_1(method, rng): x = rng.normal(0, 2, size=100) y = rng.normal(1, 3, size=100) w = np.histogram2d(x, y, bins=(5, 10))[0] r = perm.usp(w, method=method, size=100, random_state=1) assert r.pvalue > 0.05 @pytest.mark.parametrize("method", ("auto", "patefield", "boyett")) def test_usp_2(method, rng): x = rng.normal(0, 2, size=100).astype(int) w = np.histogram2d(x, x, range=((-5, 5), (-5, 5)))[0] r = perm.usp(w, method=method, size=99, random_state=1) assert r.pvalue == 0.01 @pytest.mark.parametrize("method", ("auto", "patefield", "boyett")) def test_usp_3(method, rng): cov = np.empty((2, 2)) cov[0, 0] = 2**2 cov[1, 1] = 3**2 rho = 0.5 cov[0, 1] = rho * np.sqrt(cov[0, 0] * cov[1, 1]) cov[1, 0] = cov[0, 1] xy = rng.multivariate_normal([0, 1], cov, size=500).astype(int) w = np.histogram2d(*xy.T)[0] r = perm.usp(w, method=method, random_state=1) assert r.pvalue < 0.0012 @pytest.mark.parametrize("method", ("auto", "patefield", "boyett")) def test_usp_4(method): # table1 from https://doi.org/10.1098/rspa.2021.0549 w = [[18, 36, 21, 9, 6], [12, 36, 45, 36, 21], [6, 9, 9, 3, 3], [3, 9, 9, 6, 3]] r1 = perm.usp(w, method=method, size=9999, random_state=1) r2 = perm.usp(np.transpose(w), method=method, size=1, random_state=1) assert_allclose(r1.statistic, r2.statistic) expected = 0.004106 # checked against USP R package assert_allclose(r1.statistic, expected, atol=1e-6) # according to paper, pvalue is 0.001, but USP R package gives correct value expected = 0.0024 # computed from USP R package with b=99999 assert_allclose(r1.pvalue, expected, atol=0.001) @pytest.mark.parametrize("method", ("auto", "patefield", "boyett")) def test_usp_5(method, rng): w = np.empty((100, 100)) for i in range(100): for j in range(100): w[i, j] = (i + j) % 2 r = perm.usp(w, method=method, size=99, random_state=1) assert r.pvalue > 0.1 def test_usp_bias(rng): # We compute the p-value as an upper limit to the type I error rate. # Therefore, the p-value is not unbiased. For size=1, we expect # an average p-value = (1 + 0.5) / (1 + 1) = 0.75 got = [ perm.usp(rng.poisson(1000, size=(2, 2)), size=1, random_state=i).pvalue for i in range(1000) ] assert_allclose(np.mean(got), 0.75, atol=0.05) def test_usp_bad_input(): with pytest.raises(ValueError): perm.usp([[1, 2], [3, 4]], size=0) with pytest.raises(ValueError): perm.usp([[1, 2], [3, 4]], size=-1) with pytest.raises(ValueError): perm.usp([1, 2]) with pytest.raises(ValueError): perm.usp([[1, 2], [3, 4]], method="foo") def test_usp_deprecrated(): w = [[1, 2, 3], [4, 5, 6]] r1 = perm.usp(w, method="boyett", size=100, random_state=1) with pytest.warns(FutureWarning): r2 = perm.usp(w, method="shuffle", size=100, random_state=1) assert r1.statistic == r2.statistic def test_ttest_bad_input(): with pytest.raises(ValueError): perm.ttest([1, 2], [3, 4], size=0) with pytest.raises(ValueError): perm.ttest([1, 2], [3, 4], size=-1) with pytest.raises(ValueError): perm.ttest(1, 2) with pytest.raises(ValueError): perm.ttest([1], [2]) resample-1.10.1/tests/test_util.py000066400000000000000000000012061470150054300171300ustar00rootroot00000000000000# ruff: noqa: D100 D103 from resample import _util as u import numpy as np from numpy.testing import assert_allclose def test_wilson_score_interval(): n = 100 for n1 in (10, 50, 90): p, lh = u.wilson_score_interval(n1, n, 1) s = np.sqrt(p * (1 - p) / n) assert_allclose(p, n1 / n) assert_allclose(lh, (p - s, p + s), atol=0.01) n = 10 n1 = 0 p, lh = u.wilson_score_interval(n1, n, 1) assert_allclose(p, 0.0) assert_allclose(lh, (0, 0.1), atol=0.01) n1 = 10 p, lh = u.wilson_score_interval(n1, n, 1) assert_allclose(p, 1.0) assert_allclose(lh, (0.9, 1.0), atol=0.01)