From f160b610fd4a2f61ebbe4234699ad1b8e3f50398 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 00:23:20 +0300 Subject: [PATCH 01/19] fix: remove requirements.txt and setup-files project uses poetry, so this file actually not needed ooetry needs only poetry.lock and pyproject.toml --- requirements.txt | 4 ---- setup.cfg | 33 --------------------------------- setup.py | 27 --------------------------- 3 files changed, 64 deletions(-) delete mode 100644 requirements.txt delete mode 100644 setup.cfg delete mode 100644 setup.py diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index be387b2..0000000 --- a/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -numpy~=2.0.2 -matplotlib~=3.9.3 -scipy~=1.15.0 -pytest~=8.3.4 diff --git a/setup.cfg b/setup.cfg deleted file mode 100644 index 69b2c34..0000000 --- a/setup.cfg +++ /dev/null @@ -1,33 +0,0 @@ -[metadata] -name = CharFuncInverter -version = 0.0.1 -author = lily -author_email = lilyreber404@gmail.com -description = A package for characteristic functions inversion. -long_description = file: README.md -long_description_content_type = text/markdown -url = https://github.com/lilyreber/Numerical-inversion-of-characteristic-functions -classifiers = - Programming Language :: Python :: 3 - License :: OSI Approved :: MIT License - Operating System :: OS Independent - -[options] -package_dir = - = src -packages = find: -python_requires = >=3.6 - -[options.packages.find] -where = src - -[mypy] -python_version = 3.9 -disallow_untyped_defs = True -ignore_missing_imports = True -exclude = ^tests/|^setup\.py$|^setup\.cfg$|^CFInvert/CharFuncInverter/Bohman/BohmansInvertersNaive\.py$ - -[flake8] -exclude = .git, __pycache__ -max-line-length = 120 -max-complexity = 8 diff --git a/setup.py b/setup.py deleted file mode 100644 index d700f54..0000000 --- a/setup.py +++ /dev/null @@ -1,27 +0,0 @@ -from setuptools import setup, find_packages - -import json -import os - - -def read_pipenv_dependencies(fname): - """Получаем из Pipfile.lock зависимости по умолчанию.""" - filepath = os.path.join(os.path.dirname(__file__), fname) - with open(filepath) as lockfile: - lockjson = json.load(lockfile) - return [dependency for dependency in lockjson.get('default')] - - -if __name__ == '__main__': - setup( - name='CFInvert', - version=os.getenv('PACKAGE_VERSION', '0.0.dev0'), - package_dir={'': 'CFInvert'}, - packages=find_packages('CFInvert', include=[ - 'CFInvert*' - ]), - description='A package for characteristic functions inversion.', - install_requires=[ - *read_pipenv_dependencies('Pipfile.lock'), - ] - ) From f96ee3de5629c4da7990b53d7a065c943acdb6d6 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 00:33:50 +0300 Subject: [PATCH 02/19] fix: remove pipenv files we do not need pipenv, as we should use poetry for packaging purposes (see poetry docs on this) --- Pipfile.lock | 3 --- 1 file changed, 3 deletions(-) delete mode 100644 Pipfile.lock diff --git a/Pipfile.lock b/Pipfile.lock deleted file mode 100644 index b4157a6..0000000 --- a/Pipfile.lock +++ /dev/null @@ -1,3 +0,0 @@ -numpy==2.2.1 -scipy==1.15.0 -matplotlib==3.10.0 \ No newline at end of file From 8866ec109a7deb4cb0b969ceeebb267ee8f8ac01 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 00:49:38 +0300 Subject: [PATCH 03/19] fix: rename CFInvert to cfinversion for properly working poetry install It seems to be a bug, that poetry does not allow for uppercase and dash symbol in package name, (see related issue), so package was renamed to `cfinversion`. Related: https://github.com/python-poetry/poetry/issues/8662 --- README.md | 6 +++--- {CFInvert => cfinversion}/Analyzer.py | 0 .../CharFuncInverter/Bohman/BohmanMethod.py | 2 +- .../Bohman/BohmansInverters.py | 2 +- .../Bohman/BohmansInvertersNaive.py | 2 +- .../CharFuncInverter/Bohman/__init__.py | 0 .../CharFuncInverter/CharFuncInverter.py | 0 .../FourierTransform/FTInverter.py | 2 +- .../FourierTransform/__init__.py | 0 .../CharFuncInverter/__init__.py | 0 .../Distributions/AbstractDistribution.py | 0 .../Distributions/__init__.py | 0 .../Distributions/cauchy.py | 0 .../Distributions/laplace.py | 0 .../Distributions/levy.py | 2 +- .../Distributions/lognormal.py | 0 .../Distributions/normal.py | 2 +- .../Distributions/pareto.py | 0 .../Distributions/poisson.py | 2 +- .../Distributions/uniform.py | 0 {CFInvert => cfinversion}/Standardizer.py | 0 {CFInvert => cfinversion}/__init__.py | 0 docs/views.rst | 2 +- poetry.lock | 20 +++++++++---------- pyproject.toml | 4 ++-- tests/test_bohman.py | 2 +- 26 files changed, 24 insertions(+), 24 deletions(-) rename {CFInvert => cfinversion}/Analyzer.py (100%) rename {CFInvert => cfinversion}/CharFuncInverter/Bohman/BohmanMethod.py (91%) rename {CFInvert => cfinversion}/CharFuncInverter/Bohman/BohmansInverters.py (98%) rename {CFInvert => cfinversion}/CharFuncInverter/Bohman/BohmansInvertersNaive.py (98%) rename {CFInvert => cfinversion}/CharFuncInverter/Bohman/__init__.py (100%) rename {CFInvert => cfinversion}/CharFuncInverter/CharFuncInverter.py (100%) rename {CFInvert => cfinversion}/CharFuncInverter/FourierTransform/FTInverter.py (95%) rename {CFInvert => cfinversion}/CharFuncInverter/FourierTransform/__init__.py (100%) rename {CFInvert => cfinversion}/CharFuncInverter/__init__.py (100%) rename {CFInvert => cfinversion}/Distributions/AbstractDistribution.py (100%) rename {CFInvert => cfinversion}/Distributions/__init__.py (100%) rename {CFInvert => cfinversion}/Distributions/cauchy.py (100%) rename {CFInvert => cfinversion}/Distributions/laplace.py (100%) rename {CFInvert => cfinversion}/Distributions/levy.py (87%) rename {CFInvert => cfinversion}/Distributions/lognormal.py (100%) rename {CFInvert => cfinversion}/Distributions/normal.py (95%) rename {CFInvert => cfinversion}/Distributions/pareto.py (100%) rename {CFInvert => cfinversion}/Distributions/poisson.py (90%) rename {CFInvert => cfinversion}/Distributions/uniform.py (100%) rename {CFInvert => cfinversion}/Standardizer.py (100%) rename {CFInvert => cfinversion}/__init__.py (100%) diff --git a/README.md b/README.md index cff2792..e6a5370 100644 --- a/README.md +++ b/README.md @@ -26,9 +26,9 @@ poetry install import numpy as np import matplotlib.pyplot as plt -import CFInvert.CharFuncInverter.Bohman.BohmansInverters as bi -from CFInvert.Distributions.uniform import Unif -from CFInvert.Standardizer import Standardizer +import cfinversion.CharFuncInverter.Bohman.BohmansInverters as bi +from cfinversion.Distributions.uniform import Unif +from cfinversion.Standardizer import Standardizer # Uniform distribution parameters a = -100 diff --git a/CFInvert/Analyzer.py b/cfinversion/Analyzer.py similarity index 100% rename from CFInvert/Analyzer.py rename to cfinversion/Analyzer.py diff --git a/CFInvert/CharFuncInverter/Bohman/BohmanMethod.py b/cfinversion/CharFuncInverter/Bohman/BohmanMethod.py similarity index 91% rename from CFInvert/CharFuncInverter/Bohman/BohmanMethod.py rename to cfinversion/CharFuncInverter/Bohman/BohmanMethod.py index 683a237..efd7faa 100644 --- a/CFInvert/CharFuncInverter/Bohman/BohmanMethod.py +++ b/cfinversion/CharFuncInverter/Bohman/BohmanMethod.py @@ -3,7 +3,7 @@ import numpy as np -from CFInvert.CharFuncInverter.CharFuncInverter import CharFuncInverter +from cfinversion.CharFuncInverter.CharFuncInverter import CharFuncInverter class BohmanMethod(CharFuncInverter, ABC): diff --git a/CFInvert/CharFuncInverter/Bohman/BohmansInverters.py b/cfinversion/CharFuncInverter/Bohman/BohmansInverters.py similarity index 98% rename from CFInvert/CharFuncInverter/Bohman/BohmansInverters.py rename to cfinversion/CharFuncInverter/Bohman/BohmansInverters.py index 8815f27..4861c86 100644 --- a/CFInvert/CharFuncInverter/Bohman/BohmansInverters.py +++ b/cfinversion/CharFuncInverter/Bohman/BohmansInverters.py @@ -2,7 +2,7 @@ import numpy as np from numpy import pi, exp from scipy.stats import norm -from CFInvert.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod +from cfinversion.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod class BohmanA(BohmanMethod): diff --git a/CFInvert/CharFuncInverter/Bohman/BohmansInvertersNaive.py b/cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py similarity index 98% rename from CFInvert/CharFuncInverter/Bohman/BohmansInvertersNaive.py rename to cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py index 231251b..74fbdaa 100644 --- a/CFInvert/CharFuncInverter/Bohman/BohmansInvertersNaive.py +++ b/cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py @@ -4,7 +4,7 @@ from numpy import exp, pi, cos, sin from scipy.stats import norm -from CFInvert.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod +from cfinversion.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod # Straight on diff --git a/CFInvert/CharFuncInverter/Bohman/__init__.py b/cfinversion/CharFuncInverter/Bohman/__init__.py similarity index 100% rename from CFInvert/CharFuncInverter/Bohman/__init__.py rename to cfinversion/CharFuncInverter/Bohman/__init__.py diff --git a/CFInvert/CharFuncInverter/CharFuncInverter.py b/cfinversion/CharFuncInverter/CharFuncInverter.py similarity index 100% rename from CFInvert/CharFuncInverter/CharFuncInverter.py rename to cfinversion/CharFuncInverter/CharFuncInverter.py diff --git a/CFInvert/CharFuncInverter/FourierTransform/FTInverter.py b/cfinversion/CharFuncInverter/FourierTransform/FTInverter.py similarity index 95% rename from CFInvert/CharFuncInverter/FourierTransform/FTInverter.py rename to cfinversion/CharFuncInverter/FourierTransform/FTInverter.py index ff619d3..95b1e8d 100644 --- a/CFInvert/CharFuncInverter/FourierTransform/FTInverter.py +++ b/cfinversion/CharFuncInverter/FourierTransform/FTInverter.py @@ -1,6 +1,6 @@ from typing import Callable, Optional, NoReturn, Union import numpy as np -from CFInvert.CharFuncInverter.CharFuncInverter import CharFuncInverter +from cfinversion.CharFuncInverter.CharFuncInverter import CharFuncInverter class FTInverterNaive(CharFuncInverter): diff --git a/CFInvert/CharFuncInverter/FourierTransform/__init__.py b/cfinversion/CharFuncInverter/FourierTransform/__init__.py similarity index 100% rename from CFInvert/CharFuncInverter/FourierTransform/__init__.py rename to cfinversion/CharFuncInverter/FourierTransform/__init__.py diff --git a/CFInvert/CharFuncInverter/__init__.py b/cfinversion/CharFuncInverter/__init__.py similarity index 100% rename from CFInvert/CharFuncInverter/__init__.py rename to cfinversion/CharFuncInverter/__init__.py diff --git a/CFInvert/Distributions/AbstractDistribution.py b/cfinversion/Distributions/AbstractDistribution.py similarity index 100% rename from CFInvert/Distributions/AbstractDistribution.py rename to cfinversion/Distributions/AbstractDistribution.py diff --git a/CFInvert/Distributions/__init__.py b/cfinversion/Distributions/__init__.py similarity index 100% rename from CFInvert/Distributions/__init__.py rename to cfinversion/Distributions/__init__.py diff --git a/CFInvert/Distributions/cauchy.py b/cfinversion/Distributions/cauchy.py similarity index 100% rename from CFInvert/Distributions/cauchy.py rename to cfinversion/Distributions/cauchy.py diff --git a/CFInvert/Distributions/laplace.py b/cfinversion/Distributions/laplace.py similarity index 100% rename from CFInvert/Distributions/laplace.py rename to cfinversion/Distributions/laplace.py diff --git a/CFInvert/Distributions/levy.py b/cfinversion/Distributions/levy.py similarity index 87% rename from CFInvert/Distributions/levy.py rename to cfinversion/Distributions/levy.py index 1dd3551..c076ea7 100644 --- a/CFInvert/Distributions/levy.py +++ b/cfinversion/Distributions/levy.py @@ -1,7 +1,7 @@ import numpy as np from scipy.special import erfc -from CFInvert.Distributions.AbstractDistribution import AbstractDistribution +from cfinversion.Distributions.AbstractDistribution import AbstractDistribution class Levy(AbstractDistribution): diff --git a/CFInvert/Distributions/lognormal.py b/cfinversion/Distributions/lognormal.py similarity index 100% rename from CFInvert/Distributions/lognormal.py rename to cfinversion/Distributions/lognormal.py diff --git a/CFInvert/Distributions/normal.py b/cfinversion/Distributions/normal.py similarity index 95% rename from CFInvert/Distributions/normal.py rename to cfinversion/Distributions/normal.py index 68d6739..cd590f4 100644 --- a/CFInvert/Distributions/normal.py +++ b/cfinversion/Distributions/normal.py @@ -2,7 +2,7 @@ from numpy import exp from scipy.stats import norm -from CFInvert.Distributions.AbstractDistribution import AbstractDistribution +from cfinversion.Distributions.AbstractDistribution import AbstractDistribution class Norm(AbstractDistribution): def __init__(self, m: float, var: float) -> None: diff --git a/CFInvert/Distributions/pareto.py b/cfinversion/Distributions/pareto.py similarity index 100% rename from CFInvert/Distributions/pareto.py rename to cfinversion/Distributions/pareto.py diff --git a/CFInvert/Distributions/poisson.py b/cfinversion/Distributions/poisson.py similarity index 90% rename from CFInvert/Distributions/poisson.py rename to cfinversion/Distributions/poisson.py index ab3b251..50204f2 100644 --- a/CFInvert/Distributions/poisson.py +++ b/cfinversion/Distributions/poisson.py @@ -4,7 +4,7 @@ from typing import Optional, Union from numpy import exp -from CFInvert.Distributions.AbstractDistribution import AbstractDistribution +from cfinversion.Distributions.AbstractDistribution import AbstractDistribution class Poisson(): diff --git a/CFInvert/Distributions/uniform.py b/cfinversion/Distributions/uniform.py similarity index 100% rename from CFInvert/Distributions/uniform.py rename to cfinversion/Distributions/uniform.py diff --git a/CFInvert/Standardizer.py b/cfinversion/Standardizer.py similarity index 100% rename from CFInvert/Standardizer.py rename to cfinversion/Standardizer.py diff --git a/CFInvert/__init__.py b/cfinversion/__init__.py similarity index 100% rename from CFInvert/__init__.py rename to cfinversion/__init__.py diff --git a/docs/views.rst b/docs/views.rst index 08cfc3b..3490c77 100644 --- a/docs/views.rst +++ b/docs/views.rst @@ -1,7 +1,7 @@ Views ===== -.. automodule:: CFInvert.CharFuncInverter +.. automodule:: cfinversion.CharFuncInverter :members: :undoc-members: diff --git a/poetry.lock b/poetry.lock index ea58d61..1205037 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 2.0.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. [[package]] name = "colorama" @@ -110,7 +110,7 @@ description = "Backport of PEP 654 (exception groups)" optional = false python-versions = ">=3.7" groups = ["main", "dev"] -markers = "python_version < \"3.11\"" +markers = "python_version == \"3.10\"" files = [ {file = "exceptiongroup-1.2.2-py3-none-any.whl", hash = "sha256:3111b9d131c238bec2f8f516e123e14ba243563fb135d3fe885990585aa7795b"}, {file = "exceptiongroup-1.2.2.tar.gz", hash = "sha256:47c2edf7c6738fafb49fd34290706d1a1a2f4d1c6df275526b62cbb4aa5393cc"}, @@ -180,18 +180,18 @@ files = [ ] [package.extras] -all = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres", "pycairo", "scipy", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0)", "xattr", "zopfli (>=0.1.4)"] +all = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "fs (>=2.2.0,<3)", "lxml (>=4.0)", "lz4 (>=1.7.4.2)", "matplotlib", "munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\"", "skia-pathops (>=0.5.0)", "sympy", "uharfbuzz (>=0.23.0)", "unicodedata2 (>=15.1.0) ; python_version <= \"3.12\"", "xattr ; sys_platform == \"darwin\"", "zopfli (>=0.1.4)"] graphite = ["lz4 (>=1.7.4.2)"] -interpolatable = ["munkres", "pycairo", "scipy"] +interpolatable = ["munkres ; platform_python_implementation == \"PyPy\"", "pycairo", "scipy ; platform_python_implementation != \"PyPy\""] lxml = ["lxml (>=4.0)"] pathops = ["skia-pathops (>=0.5.0)"] plot = ["matplotlib"] repacker = ["uharfbuzz (>=0.23.0)"] symfont = ["sympy"] -type1 = ["xattr"] +type1 = ["xattr ; sys_platform == \"darwin\""] ufo = ["fs (>=2.2.0,<3)"] -unicode = ["unicodedata2 (>=15.1.0)"] -woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] +unicode = ["unicodedata2 (>=15.1.0) ; python_version <= \"3.12\""] +woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "brotlicffi (>=0.8.0) ; platform_python_implementation != \"CPython\"", "zopfli (>=0.1.4)"] [[package]] name = "iniconfig" @@ -589,7 +589,7 @@ docs = ["furo", "olefile", "sphinx (>=8.1)", "sphinx-copybutton", "sphinx-inline fpx = ["olefile"] mic = ["olefile"] tests = ["check-manifest", "coverage (>=7.4.2)", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout", "trove-classifiers (>=2024.10.12)"] -typing = ["typing-extensions"] +typing = ["typing-extensions ; python_version < \"3.10\""] xmp = ["defusedxml"] [[package]] @@ -745,7 +745,7 @@ numpy = ">=1.23.5,<2.5" [package.extras] dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"] doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"] -test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] [[package]] name = "six" @@ -766,7 +766,7 @@ description = "A lil' TOML parser" optional = false python-versions = ">=3.8" groups = ["main", "dev"] -markers = "python_version < \"3.11\"" +markers = "python_version == \"3.10\"" files = [ {file = "tomli-2.2.1-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:678e4fa69e4575eb77d103de3df8a895e1591b48e740211bd1067378c69e8249"}, {file = "tomli-2.2.1-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:023aa114dd824ade0100497eb2318602af309e5a55595f76b626d6d9f3b7b0a6"}, diff --git a/pyproject.toml b/pyproject.toml index 21ecd20..a9e7462 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,5 +1,5 @@ [tool.poetry] -name = "CFInvert" +name = "cfinversion" version = "0.1.0" description = "A package for characteristic functions inversion." authors = [ @@ -7,7 +7,7 @@ authors = [ ] license = "MIT" readme = "README.md" -repository = "https://github.com/lilyreber/Numerical-inversion-of-characteristic-functions" +repository = "https://github.com/PySATL/pysatl-cfinversion" [tool.poetry.dependencies] python = ">=3.10" diff --git a/tests/test_bohman.py b/tests/test_bohman.py index 208cb6d..4c34632 100644 --- a/tests/test_bohman.py +++ b/tests/test_bohman.py @@ -1,7 +1,7 @@ import numpy as np import pytest -import CFInvert.CharFuncInverter.Bohman.BohmansInverters as BI +import cfinversion.CharFuncInverter.Bohman.BohmansInverters as BI From cf6f0df0362616b6fd97b5a4c37195af743d4b56 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 01:11:23 +0300 Subject: [PATCH 04/19] fix: clean-up pyproject.toml a few small changes: * removed pytest from requirements (leaving it only in dev group) * add jupyter-notebook dependency to dev group --- poetry.lock | 2315 ++++++++++++++++++++++++++++++++++++++++++++++-- pyproject.toml | 3 +- 2 files changed, 2220 insertions(+), 98 deletions(-) diff --git a/poetry.lock b/poetry.lock index 1205037..8428f13 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,18 +1,453 @@ # This file is automatically @generated by Poetry 2.1.2 and should not be changed by hand. +[[package]] +name = "anyio" +version = "4.9.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.9" +groups = ["dev"] +files = [ + {file = "anyio-4.9.0-py3-none-any.whl", hash 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These implementations do not satisfy interface (in type-hints), and do not use NumPy, so they are quite slow, but have same precision. --- .../Bohman/BohmansInvertersNaive.py | 158 ------------------ 1 file changed, 158 deletions(-) delete mode 100644 cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py diff --git a/cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py b/cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py deleted file mode 100644 index 74fbdaa..0000000 --- a/cfinversion/CharFuncInverter/Bohman/BohmansInvertersNaive.py +++ /dev/null @@ -1,158 +0,0 @@ -from typing import Callable - -import numpy as np -from numpy import exp, pi, cos, sin -from scipy.stats import norm - -from cfinversion.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod - - -# Straight on -class A(BohmanMethod): - def __init__(self, N: int, delta: float) -> None: - super().__init__() - self.phi = None - self.N = int(N) - self.delta = delta - - def fit(self, phi: Callable[[float], complex]) -> None: - self.phi = phi - - def cdf(self, x: float) -> np.ndarray: - if self.phi is None: - raise ValueError("Characteristic function (phi) is not set. Call fit() first.") - F = 0.5 + (self.delta * x) / (2 * pi) - for v in range(1 - self.N, self.N): - if v == 0: - continue - F -= (self.phi(self.delta * v) / (2 * pi * 1j * v)) * exp(-1j * self.delta * v * x) - return F - - -# Battling the truncation error by deforming F -class B(BohmanMethod): - - def __init__(self, N: int, delta: float) -> None: - super().__init__() - self.phi = None - self.N = int(N) - self.delta = delta - - def fit(self, phi: Callable[[float], complex]) -> None: - self.phi = phi - - def __C(self, t): - if t > 1: - return 0 - if t < 0: - return self.__C(-t) - return (1 - t) * cos(pi * t) + sin(pi * t) / pi - - def cdf(self, x: float) -> complex: - if self.phi is None: - raise ValueError("Characteristic function (phi) is not set. Call fit() first.") - F = 0.5 + (self.delta * x) / (2 * pi) - for v in range(1 - self.N, self.N): - if v == 0: - continue - F -= self.__C(v / self.N) * (self.phi(self.delta * v) / (2 * pi * 1j * v)) * exp(-1j * self.delta * v * x) - return F - - -# Reducing importance of trigonometric series by considering difference between F and -class C(BohmanMethod): - def __init__(self, N: int, delta: float): - super().__init__() - self.phi = None - self.N = int(N) - self.delta = delta - - def fit(self, phi: Callable[[float], complex]) -> None: - self.phi = phi - - def cdf(self, x: float) -> complex: - if self.phi is None: - raise ValueError("Characteristic function (phi) is not set. Call fit() first.") - F = norm.cdf(x, loc=0, scale=1) - for v in range(1 - self.N, self.N): - if v == 0: - continue - p = self.delta * v - F += ((exp(- (p ** 2) / 2) - self.phi(p)) / (2 * pi * 1j * v)) * exp(-1j * p * x) - return F - - -# Reducing the aliasing error and reducing importance of trigonometric series -class D(BohmanMethod): - def __init__(self, N: int, delta: float, K: float): - super().__init__() - self.phi = None - self.N = int(N) - self.delta = delta - self.K = K - - def fit(self, phi: Callable[[float], complex]) -> None: - self.phi = phi - - def __H(self, x: float, delta: float) -> complex: - H = 0 - for v in range(1 - self.N, self.N): - if v == 0: - continue - p = delta * v - H += ((exp(- (p ** 2) / 2) - self.phi(p)) / (2 * pi * 1j * v)) * exp(-1j * p * x) - return H - - def cdf(self, x: float) -> complex: - if self.phi is None: - raise ValueError("Characteristic function (phi) is not set. Call fit() first.") - F = norm.cdf(x, loc=0, scale=1) + self.__H(x, self.delta) - d = (2 * pi) / (self.N * self.delta) - for v in range(1, self.K): - L = self.N // self.K - delta_1 = self.delta / self.K - d_1 = self.K * d - F -= self.__H(x + v * L * d_1, delta_1) - return F - - -# Reducing the aliasing error and Reducing importance of trigonometric -# series and Battling the truncation error by deforming F -class E(BohmanMethod): - def __init__(self, N: int, delta: float, K: float): - super().__init__() - self.phi = None - self.N = int(N) - self.delta = delta - self.K = K - - def fit(self, phi: Callable[[float], complex]) -> None: - self.phi = phi - - def __C(self, t: float) -> float: - if t > 1: - return 0 - if t < 0: - return self.__C(-t) - return (1 - t) * cos(pi * t) + sin(pi * t) / pi - - def __G(self, x: float, delta: float) -> np.ndarray: - G = 0 - for v in range(1 - self.N, self.N): - if v == 0: - continue - p = delta * v - G += self.__C(v / self.N) * ((exp(- (p ** 2) / 2) - self.phi(p)) / (2 * pi * 1j * v)) * exp(-1j * p * x) - return G - - def cdf(self, x: np.ndarray) -> np.ndarray: - if self.phi is None: - raise ValueError("Characteristic function (phi) is not set. Call fit() first.") - F = norm.cdf(x, loc=0, scale=1) + self.__G(x, self.delta) - d = (2 * pi) / (self.N * self.delta) - L = self.N // self.K - delta_1 = self.delta / self.K - d_1 = self.K * d - for v in range(1, self.K): - F -= self.__G(x + v * L * d_1, delta_1) - return F From 9e0e084b19fc583185b4efdbbe7f1416890bb463 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 05:27:35 +0300 Subject: [PATCH 06/19] fix: add scipy-stubs to dev deps --- poetry.lock | 38 +++++++++++++++++++++++++++++++++++++- pyproject.toml | 1 + 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/poetry.lock b/poetry.lock index 8428f13..2bba58e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1753,6 +1753,24 @@ files = [ {file = "numpy-2.2.3.tar.gz", hash = "sha256:dbdc15f0c81611925f382dfa97b3bd0bc2c1ce19d4fe50482cb0ddc12ba30020"}, ] +[[package]] +name = "optype" +version = "0.9.3" +description = "Building blocks for precise & flexible type hints" +optional = false +python-versions = ">=3.10" +groups = ["dev"] +files = [ + {file = "optype-0.9.3-py3-none-any.whl", hash = "sha256:2935c033265938d66cc4198b0aca865572e635094e60e6e79522852f029d9e8d"}, + {file = "optype-0.9.3.tar.gz", hash = "sha256:5f09d74127d316053b26971ce441a4df01f3a01943601d3712dd6f34cdfbaf48"}, +] + +[package.dependencies] +typing-extensions = {version = ">=4.10", markers = "python_version < \"3.13\""} + +[package.extras] +numpy = ["numpy (>=1.23.5)"] + [[package]] name = "overrides" version = "7.7.0" @@ -2610,6 +2628,24 @@ dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodest doc = ["intersphinx_registry", "jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.16.5)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0,<8.0.0)", "sphinx-copybutton", "sphinx-design (>=0.4.0)"] test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja ; sys_platform != \"emscripten\"", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"] +[[package]] +name = "scipy-stubs" +version = "1.15.2.2" +description = "Type annotations for SciPy" +optional = false +python-versions = ">=3.10" +groups = ["dev"] +files = [ + {file = "scipy_stubs-1.15.2.2-py3-none-any.whl", hash = "sha256:f02fe66124b58bce5f0897ecd48d0e79226a999cc4e6984a9472520c20b8e4b6"}, + {file = "scipy_stubs-1.15.2.2.tar.gz", hash = "sha256:0137d907d75381d2eda4f6af5b1d3211759cb193a0eadf5195716fb0b01ca3cb"}, +] + +[package.dependencies] +optype = ">=0.9.3" + +[package.extras] +scipy = ["scipy (>=1.15.2,<1.16)"] + [[package]] name = "send2trash" version = "1.8.3" @@ -2938,4 +2974,4 @@ test = ["websockets"] [metadata] lock-version = "2.1" python-versions = ">=3.10" -content-hash = "12898b87ac06c43887d157023bdaf5b05f89876a984bfcc0f96802b6cb539b92" +content-hash = "d68f2fcee6e349e43539f174a42d79f6a79cc6908e92342c825a2d4da42fcafa" diff --git a/pyproject.toml b/pyproject.toml index 5a4c8b8..476b895 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -25,6 +25,7 @@ ruff = "^0.9.6" pytest = "^8.3.4" mypy = "^1.15.0" notebook = "^7.4.1" +scipy-stubs = "^1.15.2.2" [tool.ruff] From 9bebc234fbb4f863e0f9d6ed32ca77bd6c3ac3fc Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 05:29:37 +0300 Subject: [PATCH 07/19] fix: removed pareto distribution It is not possible to implement Pareto distribution using only scipy.special functions lib, because gammainc do not support complex-valued argument. Removed incorrect implementation. --- cfinversion/Distributions/pareto.py | 20 -------------------- 1 file changed, 20 deletions(-) delete mode 100644 cfinversion/Distributions/pareto.py diff --git a/cfinversion/Distributions/pareto.py b/cfinversion/Distributions/pareto.py deleted file mode 100644 index 3a1093e..0000000 --- a/cfinversion/Distributions/pareto.py +++ /dev/null @@ -1,20 +0,0 @@ -import numpy as np -import scipy.special as sc -from typing import Union - - -class Pareto: - def __init__(self, shape: float, scale: float) -> None: - self.shape: float = shape - self.scale: float = scale - - def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: - return 1 - (self.scale / x) ** self.shape - - def chr(self, x: Union[float, np.ndarray]) -> Union[complex, np.ndarray]: - return self.shape * ((-self.shape * self.scale * x) ** self.shape) * sc.gammainc( - -self.shape, -1j * self.scale * x - ) - - def pdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: - return self.shape * (self.scale ** self.shape) / (x ** (self.shape + 1)) \ No newline at end of file From bd3169fe2e0e5c97cbded4f7fc29fddfd0319890 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 05:31:13 +0300 Subject: [PATCH 08/19] fix: clean-up empty files removed files for cauchy and lognormal distributions, as they were empty --- cfinversion/Distributions/cauchy.py | 0 cfinversion/Distributions/lognormal.py | 0 2 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 cfinversion/Distributions/cauchy.py delete mode 100644 cfinversion/Distributions/lognormal.py diff --git a/cfinversion/Distributions/cauchy.py b/cfinversion/Distributions/cauchy.py deleted file mode 100644 index e69de29..0000000 diff --git a/cfinversion/Distributions/lognormal.py b/cfinversion/Distributions/lognormal.py deleted file mode 100644 index e69de29..0000000 From d4de60a0391cd359bf061503134f517d6425eb59 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 05:36:05 +0300 Subject: [PATCH 09/19] fix: add myself to the authors --- pyproject.toml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 476b895..0e87429 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,7 +3,8 @@ name = "cfinversion" version = "0.1.0" description = "A package for characteristic functions inversion." authors = [ - "Liliia Imamutdinova" + "Liliia Imamutdinova", + "Mikhail Mikhailov" ] license = "MIT" readme = "README.md" From 4296a13758ab1444e949ea0e8d47d3a6943df680 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 13:17:03 +0300 Subject: [PATCH 10/19] BREAKING CHANGE: rename methods and files Renamed files related to inversion of charteristic functions to follow PySATL-CPD style. Add imports to __init__.py files --- cfinversion/CharFuncInverter/Bohman/__init__.py | 0 .../CharFuncInverter/FourierTransform/__init__.py | 0 cfinversion/CharFuncInverter/__init__.py | 0 cfinversion/continuous/__init__.py | 2 ++ cfinversion/continuous/bohman/__init__.py | 2 ++ .../bohman/abstract_bohman_inverter.py} | 4 ++-- .../bohman/bohman_inverters.py} | 14 +++++++------- .../continuous_inverter.py} | 2 +- cfinversion/continuous/fft_based/__init__.py | 1 + .../fft_based/naive_gp_inverter.py} | 7 +++---- 10 files changed, 18 insertions(+), 14 deletions(-) delete mode 100644 cfinversion/CharFuncInverter/Bohman/__init__.py delete mode 100644 cfinversion/CharFuncInverter/FourierTransform/__init__.py delete mode 100644 cfinversion/CharFuncInverter/__init__.py create mode 100644 cfinversion/continuous/__init__.py create mode 100644 cfinversion/continuous/bohman/__init__.py rename cfinversion/{CharFuncInverter/Bohman/BohmanMethod.py => continuous/bohman/abstract_bohman_inverter.py} (86%) rename cfinversion/{CharFuncInverter/Bohman/BohmansInverters.py => continuous/bohman/bohman_inverters.py} (95%) rename cfinversion/{CharFuncInverter/CharFuncInverter.py => continuous/continuous_inverter.py} (98%) create mode 100644 cfinversion/continuous/fft_based/__init__.py rename cfinversion/{CharFuncInverter/FourierTransform/FTInverter.py => continuous/fft_based/naive_gp_inverter.py} (89%) diff --git a/cfinversion/CharFuncInverter/Bohman/__init__.py b/cfinversion/CharFuncInverter/Bohman/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cfinversion/CharFuncInverter/FourierTransform/__init__.py b/cfinversion/CharFuncInverter/FourierTransform/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cfinversion/CharFuncInverter/__init__.py b/cfinversion/CharFuncInverter/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cfinversion/continuous/__init__.py b/cfinversion/continuous/__init__.py new file mode 100644 index 0000000..92e6615 --- /dev/null +++ b/cfinversion/continuous/__init__.py @@ -0,0 +1,2 @@ +from .bohman import * +from .fft_based import * diff --git a/cfinversion/continuous/bohman/__init__.py b/cfinversion/continuous/bohman/__init__.py new file mode 100644 index 0000000..51959d7 --- /dev/null +++ b/cfinversion/continuous/bohman/__init__.py @@ -0,0 +1,2 @@ +from .abstract_bohman_inverter import * +from .bohman_inverters import * diff --git a/cfinversion/CharFuncInverter/Bohman/BohmanMethod.py b/cfinversion/continuous/bohman/abstract_bohman_inverter.py similarity index 86% rename from cfinversion/CharFuncInverter/Bohman/BohmanMethod.py rename to cfinversion/continuous/bohman/abstract_bohman_inverter.py index efd7faa..5fc7942 100644 --- a/cfinversion/CharFuncInverter/Bohman/BohmanMethod.py +++ b/cfinversion/continuous/bohman/abstract_bohman_inverter.py @@ -3,10 +3,10 @@ import numpy as np -from cfinversion.CharFuncInverter.CharFuncInverter import CharFuncInverter +from ..continuous_inverter import ContinuousInverter -class BohmanMethod(CharFuncInverter, ABC): +class AbstractBohmanInverter(ContinuousInverter, ABC): """Abstract class for characteristic function inverter, which are implemented using the methods described by Harald Bohman in 1975 diff --git a/cfinversion/CharFuncInverter/Bohman/BohmansInverters.py b/cfinversion/continuous/bohman/bohman_inverters.py similarity index 95% rename from cfinversion/CharFuncInverter/Bohman/BohmansInverters.py rename to cfinversion/continuous/bohman/bohman_inverters.py index 4861c86..d753a41 100644 --- a/cfinversion/CharFuncInverter/Bohman/BohmansInverters.py +++ b/cfinversion/continuous/bohman/bohman_inverters.py @@ -2,10 +2,10 @@ import numpy as np from numpy import pi, exp from scipy.stats import norm -from cfinversion.CharFuncInverter.Bohman.BohmanMethod import BohmanMethod +from .abstract_bohman_inverter import AbstractBohmanInverter -class BohmanA(BohmanMethod): +class BohmanA(AbstractBohmanInverter): """Straight on""" """ @@ -42,7 +42,7 @@ def cdf(self, X: np.ndarray) -> np.ndarray: return F_x.real -class BohmanB(BohmanMethod): +class BohmanB(AbstractBohmanInverter): """Battling the truncation error by deforming F""" def __init__(self, N: int = int(1e3), delta: float = 1e-1) -> None: @@ -72,7 +72,7 @@ def cdf(self, X: np.ndarray) -> np.ndarray: return F_x.real -class BohmanC(BohmanMethod): +class BohmanC(AbstractBohmanInverter): """Reducing importance of trigonometric series by considering difference between F and """ def __init__(self, N: float = 1e3, delta: float = 1e-1) -> None: @@ -96,7 +96,7 @@ def cdf(self, X: np.ndarray) -> np.ndarray: return F_x.real -class BohmanD(BohmanMethod): +class BohmanD(AbstractBohmanInverter): """Reducing the aliasing error and reducing importance of trigonometric series""" def __init__(self, N: int = int(1e3), delta: float = 1e-1, K: int = 2) -> None: @@ -137,7 +137,7 @@ def cdf(self, X: np.ndarray) -> np.ndarray: return F_x.real -class BohmanE(BohmanMethod): +class BohmanE(AbstractBohmanInverter): """Reducing the aliasing error and Reducing importance of trigonometric series and Battling the truncation error by deforming F""" @@ -179,4 +179,4 @@ def cdf(self, X: np.ndarray) -> np.ndarray: F_x = norm.cdf(X, loc=0, scale=1) + (exp(-1j * x_vect * self.delta) @ self.coeff_1) + ( exp(-1j * x_vect * self.delta_1) @ self.coeff_2) - return F_x.real \ No newline at end of file + return F_x.real diff --git a/cfinversion/CharFuncInverter/CharFuncInverter.py b/cfinversion/continuous/continuous_inverter.py similarity index 98% rename from cfinversion/CharFuncInverter/CharFuncInverter.py rename to cfinversion/continuous/continuous_inverter.py index 2b7dfae..65b1ea5 100644 --- a/cfinversion/CharFuncInverter/CharFuncInverter.py +++ b/cfinversion/continuous/continuous_inverter.py @@ -4,7 +4,7 @@ import numpy as np -class CharFuncInverter: +class ContinuousInverter: """Abstract class for characteristic function inverter""" @abstractmethod diff --git a/cfinversion/continuous/fft_based/__init__.py b/cfinversion/continuous/fft_based/__init__.py new file mode 100644 index 0000000..620b1bc --- /dev/null +++ b/cfinversion/continuous/fft_based/__init__.py @@ -0,0 +1 @@ +from .naive_gp_inverter import * diff --git a/cfinversion/CharFuncInverter/FourierTransform/FTInverter.py b/cfinversion/continuous/fft_based/naive_gp_inverter.py similarity index 89% rename from cfinversion/CharFuncInverter/FourierTransform/FTInverter.py rename to cfinversion/continuous/fft_based/naive_gp_inverter.py index 95b1e8d..18811e6 100644 --- a/cfinversion/CharFuncInverter/FourierTransform/FTInverter.py +++ b/cfinversion/continuous/fft_based/naive_gp_inverter.py @@ -1,10 +1,9 @@ from typing import Callable, Optional, NoReturn, Union import numpy as np -from cfinversion.CharFuncInverter.CharFuncInverter import CharFuncInverter +from ..continuous_inverter import ContinuousInverter -class FTInverterNaive(CharFuncInverter): - +class NaiveGPInverter(ContinuousInverter): def __init__(self, N: float = 1e3, delta: float = 1e-1, num_points: Optional[int] = None) -> None: super().__init__() self.N: int = int(N) @@ -39,4 +38,4 @@ def pdf(self, x: np.ndarray) -> Union[float, np.ndarray]: integral = np.trapezoid( phi_t * np.exp(-1j * t * x[:, np.newaxis]), t, axis=1 ) - return (1 / (2 * np.pi)) * integral \ No newline at end of file + return (1 / (2 * np.pi)) * integral From b05a98f3d073712944f69e6f38f2f11186577e2e Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 13:19:29 +0300 Subject: [PATCH 11/19] BREAKING CHANGE: renamed distributions * Renamed distribution related files to follow PySATL-CPD style. * Rename Unif to Uniform * Add squared uniform distribution --- cfinversion/Distributions/__init__.py | 0 .../AbstractDistribution.py | 0 cfinversion/distributions/__init__.py | 2 + .../laplace.py | 0 .../{Distributions => distributions}/levy.py | 0 .../normal.py | 4 +- .../poisson.py | 0 cfinversion/distributions/sqr_uniform.py | 38 +++++++++++++++++++ .../uniform.py | 4 +- 9 files changed, 44 insertions(+), 4 deletions(-) delete mode 100644 cfinversion/Distributions/__init__.py rename cfinversion/{Distributions => distributions}/AbstractDistribution.py (100%) create mode 100644 cfinversion/distributions/__init__.py rename cfinversion/{Distributions => distributions}/laplace.py (100%) rename cfinversion/{Distributions => distributions}/levy.py (100%) rename cfinversion/{Distributions => distributions}/normal.py (94%) rename cfinversion/{Distributions => distributions}/poisson.py (100%) create mode 100644 cfinversion/distributions/sqr_uniform.py rename cfinversion/{Distributions => distributions}/uniform.py (96%) diff --git a/cfinversion/Distributions/__init__.py b/cfinversion/Distributions/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/cfinversion/Distributions/AbstractDistribution.py b/cfinversion/distributions/AbstractDistribution.py similarity index 100% rename from cfinversion/Distributions/AbstractDistribution.py rename to cfinversion/distributions/AbstractDistribution.py diff --git a/cfinversion/distributions/__init__.py b/cfinversion/distributions/__init__.py new file mode 100644 index 0000000..034ebb8 --- /dev/null +++ b/cfinversion/distributions/__init__.py @@ -0,0 +1,2 @@ +from .uniform import * +from .sqr_uniform import * diff --git a/cfinversion/Distributions/laplace.py b/cfinversion/distributions/laplace.py similarity index 100% rename from cfinversion/Distributions/laplace.py rename to cfinversion/distributions/laplace.py diff --git a/cfinversion/Distributions/levy.py b/cfinversion/distributions/levy.py similarity index 100% rename from cfinversion/Distributions/levy.py rename to cfinversion/distributions/levy.py diff --git a/cfinversion/Distributions/normal.py b/cfinversion/distributions/normal.py similarity index 94% rename from cfinversion/Distributions/normal.py rename to cfinversion/distributions/normal.py index cd590f4..bf558f2 100644 --- a/cfinversion/Distributions/normal.py +++ b/cfinversion/distributions/normal.py @@ -4,7 +4,7 @@ from cfinversion.Distributions.AbstractDistribution import AbstractDistribution -class Norm(AbstractDistribution): +class Normal(AbstractDistribution): def __init__(self, m: float, var: float) -> None: """ Конструктор класса Norm. @@ -31,7 +31,7 @@ def cdf(self, x: np.ndarray) -> np.ndarray: :param x: аргумент функции распределения :return: значение функции распределения в точке x """ - return norm.cdf(x, loc=self.m, scale=self.var) + return norm.cdf(x, loc=self.m, scale=np.sqrt(self.var)) def pdf(self, x: np.ndarray) -> np.ndarray: """ diff --git a/cfinversion/Distributions/poisson.py b/cfinversion/distributions/poisson.py similarity index 100% rename from cfinversion/Distributions/poisson.py rename to cfinversion/distributions/poisson.py diff --git a/cfinversion/distributions/sqr_uniform.py b/cfinversion/distributions/sqr_uniform.py new file mode 100644 index 0000000..bc514ee --- /dev/null +++ b/cfinversion/distributions/sqr_uniform.py @@ -0,0 +1,38 @@ +import numpy as np +from numpy import exp +from scipy.special import erf +from typing import Union + + +class UniformSquared: + def __init__(self, a: float, b: float) -> None: + self.a: float = a + self.b: float = b + + def chr(self, x: Union[float, np.ndarray]) -> Union[complex, np.ndarray]: + if isinstance(x, np.ndarray): + result = np.ones_like(x, np.complex128) + result[x != 0] = np.exp(1.0j * x[x != 0] * self.a) * np.sqrt(np.pi) * erf(np.sqrt(-1.0j*(self.b - self.a) * x[x != 0] )) / (2 * np.sqrt(-1.0j * (self.b-self.a) * x[x != 0])) + return result + if x == 0.0: + return 1.0 + 0.0j + return np.exp(1.0j * x * self.a) * np.sqrt(np.pi) * erf(np.sqrt(-1.0j*(self.b - self.a) * x )) / (2 * np.sqrt(-1.0j * (self.b-self.a) * x)) + + def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: + x_arr = np.asarray(x) + + result = np.zeros_like(x_arr) + + result[x_arr >= self.b] = 1 + result[(x_arr >= self.a) & (x_arr < self.b)] = np.sqrt( + (x_arr[(x_arr >= self.a) & (x_arr < self.b)] - self.a) / (self.b - self.a) + ) + + if isinstance(x, float): + return float(result) + return result + + def pdf(self, x: Union[float, np.ndarray]) -> np.ndarray: + result = np.zeros_like(x) + result[(x > self.a) & (x < self.b)] = 1 / (2 * np.sqrt(self.b - self.a) * np.sqrt(x[(x > self.a) & (x < self.b)] - self.a)) + return result diff --git a/cfinversion/Distributions/uniform.py b/cfinversion/distributions/uniform.py similarity index 96% rename from cfinversion/Distributions/uniform.py rename to cfinversion/distributions/uniform.py index 53b6b61..6b39c17 100644 --- a/cfinversion/Distributions/uniform.py +++ b/cfinversion/distributions/uniform.py @@ -3,7 +3,7 @@ from typing import Union -class Unif: +class Uniform: def __init__(self, a: float, b: float) -> None: self.a: float = a self.b: float = b @@ -28,4 +28,4 @@ def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: def pdf(self, x: Union[float, np.ndarray]) -> np.ndarray: result = np.zeros_like(x) result[(x >= self.a) & (x < self.b)] = 1 / (self.b - self.a) - return result \ No newline at end of file + return result From a34767f14f67b363823c703438cfff7a354a3999 Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 17:02:57 +0300 Subject: [PATCH 12/19] fix: division by zero in naive evaluation of GP-formula Value of S(t, x) = Imag[exp(itx) * phi(t) / (it)] is undefined at t = 0; Given that phi'(0) = 0 (which is done via Standardizer), one can assume that at t = 0 S(0, x) = -x, see https://arxiv.org/pdf/1701.08299 eq. 24 --- cfinversion/continuous/fft_based/naive_gp_inverter.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/cfinversion/continuous/fft_based/naive_gp_inverter.py b/cfinversion/continuous/fft_based/naive_gp_inverter.py index 18811e6..3d37e39 100644 --- a/cfinversion/continuous/fft_based/naive_gp_inverter.py +++ b/cfinversion/continuous/fft_based/naive_gp_inverter.py @@ -20,12 +20,11 @@ def cdf(self, x: np.ndarray) -> Union[float, np.ndarray]: raise ValueError("Characteristic function (phi) is not set. Call fit() first.") t: np.ndarray = np.linspace(-self.N, self.N, self.num_points) - phi_t = self.phi(t) - - integral = np.trapezoid( - (phi_t * np.exp(-1j * t * x[:, np.newaxis])) / (1j * t), t, axis=1 - ) - + phi_t = self.phi(t) + tq = (phi_t * np.exp(-1j * t * x[:, np.newaxis])) + tq[:, t != 0] /= (1j * t[t!=0]) + tq[:, t == 0] = -x.reshape(100,1) + integral = np.trapezoid(tq, t, axis=1) return 1 / 2 - (1 / (2 * np.pi)) * integral def pdf(self, x: np.ndarray) -> Union[float, np.ndarray]: From f7e667d146ba555a30f236811dc870fa49af829c Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 17:09:51 +0300 Subject: [PATCH 13/19] fix: division by zero in uniform.chr At poin t = 0 chr(t) should be 1, but previous implementation encountered division by zero trying evaluate cf at this point. --- cfinversion/distributions/uniform.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/cfinversion/distributions/uniform.py b/cfinversion/distributions/uniform.py index 6b39c17..a8a0890 100644 --- a/cfinversion/distributions/uniform.py +++ b/cfinversion/distributions/uniform.py @@ -9,6 +9,12 @@ def __init__(self, a: float, b: float) -> None: self.b: float = b def chr(self, x: Union[float, np.ndarray]) -> Union[complex, np.ndarray]: + if isinstance(x, np.ndarray): + result = np.ones_like(x, np.complex128) + result[x != 0] = (exp(1j * x[x != 0] * self.b) - exp(1j * x[ x!= 0] * self.a)) / (1j * x[x != 0] * (self.b - self.a)) + return result + if x == 0.0: + return 1.0 + 0.0j return (exp(1j * x * self.b) - exp(1j * x * self.a)) / (1j * x * (self.b - self.a)) def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: From ae314327608f31cbb60a7513f550c49e8164f00e Mon Sep 17 00:00:00 2001 From: desiment Date: Sun, 27 Apr 2025 19:59:45 +0300 Subject: [PATCH 14/19] fix: add tools submodule --- cfinversion/Standardizer.py | 61 ------------------ cfinversion/tools/__init__.py | 2 + cfinversion/tools/standardizer.py | 64 +++++++++++++++++++ .../validation_tools.py} | 6 +- 4 files changed, 70 insertions(+), 63 deletions(-) delete mode 100644 cfinversion/Standardizer.py create mode 100644 cfinversion/tools/__init__.py create mode 100644 cfinversion/tools/standardizer.py rename cfinversion/{Analyzer.py => tools/validation_tools.py} (70%) diff --git a/cfinversion/Standardizer.py b/cfinversion/Standardizer.py deleted file mode 100644 index 7cdb39a..0000000 --- a/cfinversion/Standardizer.py +++ /dev/null @@ -1,61 +0,0 @@ -from typing import Callable - -import numpy as np - - -class Standardizer: - - def __init__(self, m: float, sd: float) -> None: - """ - the class makes transitions between random variable and standardized random variable - - :param m: mean - :param sd: standard deviation - """ - self.m = m - self.sd = sd - - def standardize_chf(self, phi: Callable) -> Callable: - """ - :param phi: characteristic function - :return: characteristic function of standardized - random variable - """ - z_phi = lambda args: np.exp(-1j * args * self.m / self.sd) * phi(args / self.sd) - return z_phi - - def unstandardize_chf(self, z_phi: Callable) -> Callable: - """ - :param z_phi: characteristic function of standardized - :return: characteristic function -random variable - """ - phi = lambda args: np.exp(1j * args * self.m) * z_phi(args * self.sd) - return phi - - def standardize_cdf(self, F: Callable) -> Callable: - """ - Returns the distribution function of the standard random variable Z. - - :param F: distribution of the original random variable X. - :return: distribution function of the standardized random variable Z. - """ - z_F = lambda args : F(self.m + self.sd * args) - return z_F - - def unstandardize_cdf(self, z_F: Callable) -> Callable: - """ - Returns the distribution function of the original random variable Z. - - :param z_F: distribution function of the standardized random variable Z. - :return: distribution of the original random variable X. - """ - F = lambda args : z_F((args - self.m) / self.sd) - return F - - - - - - - diff --git a/cfinversion/tools/__init__.py b/cfinversion/tools/__init__.py new file mode 100644 index 0000000..635c43e --- /dev/null +++ b/cfinversion/tools/__init__.py @@ -0,0 +1,2 @@ +from .standardizer import * +from .validation_tools import lre, l0_err diff --git a/cfinversion/tools/standardizer.py b/cfinversion/tools/standardizer.py new file mode 100644 index 0000000..9f177d6 --- /dev/null +++ b/cfinversion/tools/standardizer.py @@ -0,0 +1,64 @@ +from typing import Callable + +import numpy as np + + +class Standardizer: + + def __init__(self, loc: float | None, scale: float | None) -> None: + """ + the class makes transitions between random variable and standardized random variable + + :param loc: location + :param scale: scale + """ + self.loc = loc + self.scale = scale + + def fit(self, cf: Callable, tol_diff : float = 1e-4) -> None: + self.cf_true = cf + cft = [cf(i * tol_diff) for i in range(1,5)] + mean = (-3 * np.imag(cft[3]) + 32 * np.imag(cft[2]) - 168 * np.imag(cft[1]) + 672 * np.imag(cft[0])) / (420 * tol_diff) # estimated mean + var = (9 * np.real(cft[3]) - 128 * np.real(cft[2]) + 1008 * np.real(cft[1]) - 8064 * np.real(cft[0]) + 7175) / (2520 * tol_diff**2) # estimated variance + self.loc = self.loc or mean + self.scale = self.scale or np.sqrt(var - mean**2) + + def cf(self, x): + """ + :return: characteristic function of standardized + random variable at point x + """ + return np.exp(-1j * x * self.loc / self.scale) * self.cf_true(x / self.scale) + + def standardize_cdf(self, F: Callable) -> Callable: + """ + Returns the distribution function of the standard random variable Z. + + :param F: distribution of the original random variable X. + :return: distribution function of the standardized random variable Z. + """ + z_F = lambda x : F(x * self.scale + self.loc) + return z_F + + def unstandardize_cdf(self, z_F: Callable) -> Callable: + """ + Returns the distribution function of the original random variable Z. + + :param z_F: distribution function of the standardized random variable Z. + :return: distribution of the original random variable X. + """ + F = lambda x : z_F((x - self.loc) / self.scale) + return F + + def standardize_pdf(self, f: Callable) -> Callable: + z_f = lambda x: self.scale * f(self.loc + x * self.scale) + return z_f + + def unstandardize_pdf(self, z_f: Callable) -> Callable: + f = lambda x : 1 / self.scale * z_f((x - self.loc) / self.scale) + return f + + + + + diff --git a/cfinversion/Analyzer.py b/cfinversion/tools/validation_tools.py similarity index 70% rename from cfinversion/Analyzer.py rename to cfinversion/tools/validation_tools.py index 1c6d703..6b1dcf1 100644 --- a/cfinversion/Analyzer.py +++ b/cfinversion/tools/validation_tools.py @@ -1,6 +1,6 @@ import numpy as np - - +import scipy as sp +from typing import Callable def lre(v_true: np.ndarray, v: np.ndarray) -> np.ndarray: """ Log Relative Error gives an approximation @@ -13,3 +13,5 @@ def lre(v_true: np.ndarray, v: np.ndarray) -> np.ndarray: """ return -np.log10(np.abs((v_true - v) / v_true)) +def l0_err(f: Callable, tol_diff:float = 1e-3) -> float: + return 1 - sp.integrate.quad(f, -np.inf, np.inf, epsabs = tol_diff) From b79caa49fccd9571998d20e1cd833a6fc807cfc4 Mon Sep 17 00:00:00 2001 From: desiment Date: Mon, 28 Apr 2025 16:39:33 +0300 Subject: [PATCH 15/19] feat: add standardization to Bohman methods Now, fit may take unstandardized cf, and cdf should work fine. Method named ``cdf_`` reffers to implementation for standaridized variant --- .../bohman/abstract_bohman_inverter.py | 39 +++++++++++- .../continuous/bohman/bohman_inverters.py | 59 +++++++++++++------ 2 files changed, 78 insertions(+), 20 deletions(-) diff --git a/cfinversion/continuous/bohman/abstract_bohman_inverter.py b/cfinversion/continuous/bohman/abstract_bohman_inverter.py index 5fc7942..ce2e1b5 100644 --- a/cfinversion/continuous/bohman/abstract_bohman_inverter.py +++ b/cfinversion/continuous/bohman/abstract_bohman_inverter.py @@ -1,4 +1,4 @@ -from abc import ABC +from abc import ABC, abstractmethod from typing import Type import numpy as np @@ -25,4 +25,41 @@ def _C(t: np.ndarray) -> np.ndarray: result[(0 <= t) & (t <= 1)] = (1 - t_positive) * np.cos(np.pi * t_positive) + np.sin(np.pi * t_positive) / np.pi return result + + def cdf(self, x: np.ndarray) -> np.ndarray: + """Function return cumulative distribution function + Attributes + ---------- + x : np.ndarray + Data for which we want to calculate + the value of the cumulative distribution function + + Return + ------ + np.ndarray + The value of the cumulative distribution function for each element x + """ + return self.standardizer.unstandardize_cdf(self.cdf_)(x) + + def pdf(self, x: np.ndarray) -> np.ndarray: + """Function return probability density function + + Attributes + ---------- + x : np.ndarray + Data for which we want to calculate + the value of the probability density function + + Return + ------ + np.ndarray + The value of the probability density function for each element x + """ + raise NotImplementedError + + + @abstractmethod + def cdf_(self, x: np.ndarray) -> np.ndarray: + """Internal variant for standardized cf""" + pass diff --git a/cfinversion/continuous/bohman/bohman_inverters.py b/cfinversion/continuous/bohman/bohman_inverters.py index d753a41..eb1cb5e 100644 --- a/cfinversion/continuous/bohman/bohman_inverters.py +++ b/cfinversion/continuous/bohman/bohman_inverters.py @@ -3,7 +3,7 @@ from numpy import pi, exp from scipy.stats import norm from .abstract_bohman_inverter import AbstractBohmanInverter - +from ...tools import Standardizer class BohmanA(AbstractBohmanInverter): """Straight on""" @@ -22,16 +22,22 @@ def __init__(self, N: int = int(1e3), delta: float = 1e-1) -> None: self.coeff_1: float = 0.0 self.coeff: np.ndarray = np.array([]) - def fit(self, phi: Callable) -> None: + def fit(self, cf: Callable, loc: float | None = None, scale : float | None = None) -> None: + + self.standardizer = Standardizer(loc = loc, scale = scale) + self.standardizer.fit(cf) + self.cf_ = self.standardizer.cf + self.coeff_0 = 0.5 self.coeff_1 = self.delta / (2 * pi) v_values = np.arange(1 - self.N, self.N) v_values = v_values[v_values != 0] - self.coeff = phi(self.delta * v_values) / (2 * pi * 1j * v_values) + self.coeff = self.cf_(self.delta * v_values) / (2 * pi * 1j * v_values) + - def cdf(self, X: np.ndarray) -> np.ndarray: + def cdf_(self, X: np.ndarray) -> np.ndarray: v = np.arange(1 - self.N, self.N) v_non_zero = v[v != 0] @@ -53,15 +59,19 @@ def __init__(self, N: int = int(1e3), delta: float = 1e-1) -> None: self.coeff_1: float = 0.0 self.coeff: np.ndarray = np.array([]) - def fit(self, phi: Callable) -> None: + def fit(self, cf: Callable, loc : float | None = None, scale : float | None = None) -> None: + self.standardizer = Standardizer(loc = loc, scale = scale) + self.standardizer.fit(cf) + self.cf_ = self.standardizer.cf + self.coeff_0 = 0.5 self.coeff_1 = self.delta / (2 * pi) v_values = np.arange(1 - self.N, self.N) v_values = v_values[v_values != 0] - self.coeff = super()._C(v_values / self.N) * phi(self.delta * v_values) / (2 * pi * 1j * v_values) + self.coeff = super()._C(v_values / self.N) * self.cf_(self.delta * v_values) / (2 * pi * 1j * v_values) - def cdf(self, X: np.ndarray) -> np.ndarray: + def cdf_(self, X: np.ndarray) -> np.ndarray: v = np.arange(1 - self.N, self.N) v_non_zero = v[v != 0] @@ -81,16 +91,19 @@ def __init__(self, N: float = 1e3, delta: float = 1e-1) -> None: self.delta: float = delta self.coeff: np.ndarray = np.array([]) - def fit(self, phi: Callable) -> None: - self.coeff = np.array([((exp(- ((self.delta * v) ** 2) / 2) - phi(self.delta * v)) / (2 * pi * 1j * v)) for v in + def fit(self, cf: Callable, loc : float | None = None, scale : float | None = None) -> None: + self.standardizer = Standardizer(loc = loc, scale = scale) + self.standardizer.fit(cf) + self.cf_ = self.standardizer.cf + + self.coeff = np.array([((exp(- ((self.delta * v) ** 2) / 2) - self.cf_(self.delta * v)) / (2 * pi * 1j * v)) for v in range(1 - self.N, self.N) if v != 0]) - def cdf(self, X: np.ndarray) -> np.ndarray: + def cdf_(self, X: np.ndarray) -> np.ndarray: v = np.arange(1 - self.N, self.N) v_non_zero = v[v != 0] x_vect = np.outer(X, v_non_zero) - F_x = norm.cdf(X, loc=0, scale=1) + (exp(-1j * self.delta * x_vect) @ self.coeff) return F_x.real @@ -108,8 +121,12 @@ def __init__(self, N: int = int(1e3), delta: float = 1e-1, K: int = 2) -> None: self.coeff_1: np.ndarray = np.array([]) self.coeff_2: np.ndarray = np.array([]) - def fit(self, phi: Callable) -> None: - self.coeff_1 = np.array([(exp(-((self.delta * v) ** 2) / 2) - phi(self.delta * v)) / (2 * pi * 1j * v) for v in + def fit(self, cf: Callable, loc : float | None = None, scale : float | None = None) -> None: + self.standardizer = Standardizer(loc = loc, scale = scale) + self.standardizer.fit(cf) + self.cf_ = self.standardizer.cf + + self.coeff_1 = np.array([(exp(-((self.delta * v) ** 2) / 2) - self.cf_(self.delta * v)) / (2 * pi * 1j * v) for v in range(1 - self.N, self.N) if v != 0]) L = self.N // self.K d = (2 * pi) / (self.N * self.delta) @@ -122,10 +139,10 @@ def fit(self, phi: Callable) -> None: exp_matrix = np.exp(-1j * self.delta_1 * v_values[:, np.newaxis] * i_values * L * d_1) exp_coeff = np.sum(exp_matrix, axis=1) - self.coeff_2 = - (exp(-((self.delta_1 * v_values) ** 2) / 2) - phi(self.delta_1 * v_values)) / ( + self.coeff_2 = - (exp(-((self.delta_1 * v_values) ** 2) / 2) - self.cf_(self.delta_1 * v_values)) / ( 2 * pi * 1j * v_values) * exp_coeff - def cdf(self, X: np.ndarray) -> np.ndarray: + def cdf_(self, X: np.ndarray) -> np.ndarray: v = np.arange(1 - self.N, self.N) v_non_zero = v[v != 0] @@ -150,13 +167,17 @@ def __init__(self, N: int = int(1e3), delta: float = 1e-1, K: int = 4) -> None: self.coeff_1: np.ndarray = np.array([]) self.coeff_2: np.ndarray = np.array([]) - def fit(self, phi: Callable) -> None: + def fit(self, cf: Callable, loc : float | None = None, scale : float | None = None) -> None: + self.standardizer = Standardizer(loc = loc, scale = scale) + self.standardizer.fit(cf) + self.cf_ = self.standardizer.cf + v_values = np.arange(1 - self.N, self.N) v_values = v_values[v_values != 0] С_values = super()._C(v_values / self.N) - self.coeff_1 = С_values * (exp(-((self.delta * v_values) ** 2) / 2) - phi(self.delta * v_values)) / ( + self.coeff_1 = С_values * (exp(-((self.delta * v_values) ** 2) / 2) - self.cf_(self.delta * v_values)) / ( 2 * pi * 1j * v_values) L = self.N // self.K @@ -168,10 +189,10 @@ def fit(self, phi: Callable) -> None: exp_matrix = np.exp(-1j * self.delta_1 * v_values[:, np.newaxis] * i_values * L * d_1) exp_coeff = np.sum(exp_matrix, axis=1) - self.coeff_2 = -С_values * ((exp(-((self.delta_1 * v_values) ** 2) / 2) - phi(self.delta_1 * v_values)) / ( + self.coeff_2 = -С_values * ((exp(-((self.delta_1 * v_values) ** 2) / 2) - self.cf_(self.delta_1 * v_values)) / ( 2 * pi * 1j * v_values)) * exp_coeff - def cdf(self, X: np.ndarray) -> np.ndarray: + def cdf_(self, X: np.ndarray) -> np.ndarray: v = np.arange(1 - self.N, self.N) v_non_zero = v[v != 0] From 3b7dd6c83147538e94d67af01f0ef0bbd82a9ff0 Mon Sep 17 00:00:00 2001 From: desiment Date: Mon, 28 Apr 2025 16:42:22 +0300 Subject: [PATCH 16/19] fix: removed AbstractDistribution base class This was kind of useless, should add protocol instead --- .../distributions/AbstractDistribution.py | 38 ------------------- cfinversion/distributions/levy.py | 4 +- cfinversion/distributions/normal.py | 4 +- cfinversion/distributions/poisson.py | 2 - 4 files changed, 2 insertions(+), 46 deletions(-) delete mode 100644 cfinversion/distributions/AbstractDistribution.py diff --git a/cfinversion/distributions/AbstractDistribution.py b/cfinversion/distributions/AbstractDistribution.py deleted file mode 100644 index 090a961..0000000 --- a/cfinversion/distributions/AbstractDistribution.py +++ /dev/null @@ -1,38 +0,0 @@ -from abc import abstractmethod - - -import numpy as np - - -class AbstractDistribution: - """Abstract class for distributions""" - - @abstractmethod - def chr(self, x: np.ndarray) -> np.ndarray: - """ - Function return characteristic function of distribution - - :param x: An input value or an array of values. - :return: The value of the characteristic function. - """ - raise NotImplementedError - - @abstractmethod - def cdf(self, x: np.ndarray) -> np.ndarray: - """ - Function return cumulative distribution function - - :param x: An input value or an array of values. - :return: The value of the distribution function. - """ - raise NotImplementedError - - @abstractmethod - def pdf(self, x: np.ndarray) -> np.ndarray: - """ - Function return probability density function - - :param x: An input value or an array of values. - :return: The value of the probability density function. - """ - raise NotImplementedError \ No newline at end of file diff --git a/cfinversion/distributions/levy.py b/cfinversion/distributions/levy.py index c076ea7..bf7f03e 100644 --- a/cfinversion/distributions/levy.py +++ b/cfinversion/distributions/levy.py @@ -1,10 +1,8 @@ import numpy as np from scipy.special import erfc -from cfinversion.Distributions.AbstractDistribution import AbstractDistribution - -class Levy(AbstractDistribution): +class Levy(): def __init__(self, c: float, mu: float) -> None: self.c = c self.mu = mu diff --git a/cfinversion/distributions/normal.py b/cfinversion/distributions/normal.py index bf558f2..474e809 100644 --- a/cfinversion/distributions/normal.py +++ b/cfinversion/distributions/normal.py @@ -2,9 +2,7 @@ from numpy import exp from scipy.stats import norm -from cfinversion.Distributions.AbstractDistribution import AbstractDistribution - -class Normal(AbstractDistribution): +class Normal(): def __init__(self, m: float, var: float) -> None: """ Конструктор класса Norm. diff --git a/cfinversion/distributions/poisson.py b/cfinversion/distributions/poisson.py index 50204f2..87b42c3 100644 --- a/cfinversion/distributions/poisson.py +++ b/cfinversion/distributions/poisson.py @@ -4,8 +4,6 @@ from typing import Optional, Union from numpy import exp -from cfinversion.Distributions.AbstractDistribution import AbstractDistribution - class Poisson(): def __init__(self, mean: float) -> None: From c04c3a82f061e91d5160a0aac00ece18e5c27c6f Mon Sep 17 00:00:00 2001 From: desiment Date: Mon, 28 Apr 2025 16:48:54 +0300 Subject: [PATCH 17/19] fix: notebooks cleanup --- examples/Bohmans_testcase.ipynb | 516 ---- examples/Examples_BohmanMethods.ipynb | 282 ++ examples/Fourier_inversion.ipynb | 334 --- examples/Fourier_inversion_1.ipynb | 2632 ----------------- ...nversion_of_characteristic_functions.ipynb | 1059 ------- ...ersion_of_characteristic_functions_1.ipynb | 1116 ------- examples/Untitled.ipynb | 244 -- examples/Untitled1.ipynb | 496 ---- examples/__init__.py | 0 examples/levi.ipynb | 371 --- examples/loss_test.ipynb | 223 -- examples/pareto.ipynb | 403 --- examples/test.ipynb | 870 ------ 13 files changed, 282 insertions(+), 8264 deletions(-) delete mode 100644 examples/Bohmans_testcase.ipynb create mode 100644 examples/Examples_BohmanMethods.ipynb delete mode 100644 examples/Fourier_inversion.ipynb delete mode 100644 examples/Fourier_inversion_1.ipynb delete mode 100644 examples/Numerical_inversion_of_characteristic_functions.ipynb delete mode 100644 examples/Numerical_inversion_of_characteristic_functions_1.ipynb delete mode 100644 examples/Untitled.ipynb delete mode 100644 examples/Untitled1.ipynb delete mode 100644 examples/__init__.py delete mode 100644 examples/levi.ipynb delete mode 100644 examples/loss_test.ipynb delete mode 100644 examples/pareto.ipynb delete mode 100644 examples/test.ipynb diff --git a/examples/Bohmans_testcase.ipynb b/examples/Bohmans_testcase.ipynb deleted file mode 100644 index 4497047..0000000 --- a/examples/Bohmans_testcase.ipynb +++ /dev/null @@ -1,516 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 10, - "id": "53492b49-45e5-4858-a637-9efe2060c172", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../src')" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "4929960b-164e-4860-abf3-ad92836ccac8", - "metadata": {}, - "outputs": [], - "source": [ - "from numpy import exp, pi, sqrt\n", - "from CharFuncInverter.bohman1975 import A, B, C, D, E\n", - "import pandas as pd\n", - "import numpy as np" - ] - }, - { - "cell_type": "markdown", - "id": "e8d4d24c-348c-4bcf-b11d-ed19cfd40dfc", - "metadata": {}, - "source": [ - "# Провалидируем классы, опираясь на рассчёты описанные в статье Бохмана." - ] - }, - { - "cell_type": "markdown", - "id": "38f833db-e3cb-4437-90e2-8e7dcd9ac056", - "metadata": {}, - "source": [ - "В качестве характеристической функции было следущее выражение:\n", - "$$\\phi(t) = (1 -it \\sqrt{2})^{0.5}\\cdot exp(\\frac{-it}{\\sqrt{2}}).$$\n", - "Параметры в примере: $N = 512$, $K = 4$, $d = \\frac{0.35}{16}$" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "01c68a43-b2a1-4b78-981c-45ac9d030327", - "metadata": {}, - "outputs": [], - "source": [ - "phi = lambda t : ((1 - 1j * t * sqrt(2)) ** (-0.5)) * exp((-1j * t) / sqrt(2))\n", - "\n", - "N = 512\n", - "K = 4\n", - "d = 0.35 / 16\n", - "delta = (2 * pi) / (N * d)" - ] - }, - { - "cell_type": "markdown", - "id": "0b6bb98c-31a9-4556-9d69-62ead3ee4c05", - "metadata": {}, - "source": [ - "Также были заданы значения функции распределения в 18 точках" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "819bffe1-992a-4a83-92ab-e7821e486785", - "metadata": {}, - "outputs": [], - "source": [ - "F_exact = {\n", - " -5.60 : 0,\n", - " -4.55 : 0,\n", - " -3.50 : 0,\n", - " -2.45 : 0,\n", - " -1.40 : 0,\n", - " -1.05 : 0,\n", - " -0.70 : 79586,\n", - " -0.35 : 522700,\n", - " 0 : 682690,\n", - " 0.35 : 778553,\n", - " 0.70 : 841654,\n", - " 1.40 : 915695,\n", - " 1.75 : 937693,\n", - " 2.10 : 953678,\n", - " 3.15 : 980485,\n", - " 4.20 : 991570,\n", - " 5.25 : 996298\n", - "}\n", - "xs = F_exact.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "f4ebed3f-b036-4b87-94f6-8153167098a2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-0.35232467-0.03378854j, -0.31605063+0.23168238j,\n", - " -0.09639783+0.43443868j, 0.24470505+0.46627912j,\n", - " 0.60794495+0.28502237j, 0.72351528+0.18736706j,\n", - " 0.83838663+0.08818682j, 0.94654181+0.01673443j,\n", - " 1. +0.j , 0.94654181-0.01673443j,\n", - " 0.83838663-0.08818682j, 0.60794495-0.28502237j,\n", - " 0.4894611 -0.36750803j, 0.3677386 -0.42881365j,\n", - " 0.00861041-0.46792474j, -0.26157177-0.31241396j,\n", - " -0.3613715 -0.05378073j])" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "phi(np.array(list(xs)))" - ] - }, - { - "cell_type": "markdown", - "id": "bd6823a0-1584-4cbb-aa01-d07d58e5ca2a", - "metadata": {}, - "source": [ - "Создадим экземпляры классов, которые будут аппроксимировать характеристическую функцию" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "49eaa7b2-0d1f-4077-a812-9e64f8a509e3", - "metadata": {}, - "outputs": [], - "source": [ - "approxs = {\n", - " 'A' : A(N, delta, phi),\n", - " 'B' : B(N, delta, phi),\n", - " 'C' : C(N, delta, phi),\n", - " 'D' : D(N, delta, phi, K),\n", - " 'E' : E(N, delta, phi, K)\n", - "}\n", - "values = []\n", - "\n", - "for name in approxs:\n", - " approx_func = approxs[name]\n", - " values.append([(approx_func.cdf(x).real * 1e6 - F_exact[x]) for x in xs])\n", - "\n", - "values = np.array(values)" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "08f552b0-f24d-4e15-84b8-0ff707407614", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Usage of Bohman's Methods" + ] + }, + { + "cell_type": "markdown", + "id": "54ec4edc-acab-43ec-acca-2ad045f0c024", + "metadata": {}, + "source": [ + "Let's look, how Bohman's method produce " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "0be53343-e3b8-451e-8f4f-ed30b4b9eaf6", + "metadata": {}, + "outputs": [], + "source": [ + "import cfinversion" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4d52be53-6b32-4084-890e-53b899a47e1a", + "metadata": {}, + "outputs": [], + "source": [ + "from cfinversion.continuous import *\n", + "def BohmanMethodTest(x, cf, ax = None, methods = ['A', 'B', 'C', 'D', 'E'], N = 1000, delta=1e-1):\n", + " bohman = {\n", + " 'A' : BohmanA(N=N,delta=delta),\n", + " 'B' : BohmanB(N=N,delta=delta),\n", + " 'C' : BohmanC(N=N,delta=delta),\n", + " 'D' : BohmanD(N=N,delta=delta), \n", + " 'E' : BohmanE(N=N,delta=delta)\n", + " }\n", + " \n", + " for method in methods:\n", + " bohman[method].fit(cf)\n", + " ax.plot(x, bohman[method].cdf(x), label = f\"Method {method}\")\n", + " return ax, bohman" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0ed9b65b-6b40-4f00-a49d-ca7542136286", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "54eff5ec-9723-4bb8-84b1-106641975263", + "metadata": {}, + "outputs": [], + "source": [ + "from cfinversion.distributions import Uniform, UniformSquared" + ] + }, + { + "cell_type": "markdown", + "id": "b08b7013-7266-43a3-94d1-f2c8ecabef8e", + "metadata": {}, + "source": [ + "## Smooth densities\n", + "1. Normal distribution\n", + "2. Laplace distribution\n", + "3. Cauchy distribution" + ] + }, + { + "cell_type": "markdown", + "id": "7810e373-b825-4d0a-b9bb-b312f9088a59", + "metadata": {}, + "source": [ + "## Distributions with discontinuous and singular densities\n", + "1. Uniform distribution\n", + "2. Uniform squared-distribution" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "c6a449d0-14b3-4ad3-a5d7-07660abed7a5", + "metadata": {}, + "outputs": [], + "source": [ + "a = np.sqrt(15 / 7)\n", + "dist = UniformSquared(-a, a)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "849e3d59-f87b-4af1-9f03-9504b596ae06", + "metadata": {}, + "outputs": [], + "source": [ + "a = 2\n", + "dist = Uniform(-a/2 + 1, a/2 + 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4c308dc0-ec92-4d94-a4ad-332bf9bfb686", + "metadata": {}, + "outputs": [], + "source": [ + "from cfinversion.tools import lre" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "28a9a25c-e89a-47c9-ae14-70dd742e94c8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "x = np.linspace(-a-1, a+1, 100)\n", + "ax, res = BohmanMethodTest(x, dist.chr, ax)\n", + "ax.plot(x, dist.cdf(x), label = \"True CDF\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "markdown", + "id": "a0a78348-3306-4f09-9b8c-085eedd8ea06", + "metadata": {}, + "source": [ + "# Bonus: discrete case" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2f8127c4-8100-4047-8aa1-db0519ce21a4", + "metadata": {}, + "outputs": [], + "source": [ + "import scipy as sc\n", + "cf_ = lambda t: np.exp(10*(np.exp(1j*t) - 1))\n", + "cf = np.vectorize(cf_)\n", + "cdf_ = lambda x: 0.5 -\\\n", + " sc.integrate.quad(lambda t: np.real(cf(t)*np.exp(-1j*t*x) / (2 * np.pi * (1 - np.exp(-1j*t)))), -np.pi, np.pi, points = [0.0], epsabs = 0.0001)[0]\n", + "cdf = np.vectorize(cdf_)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "9d48a238-fbb4-4eff-adef-e597ae5435c1", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(-5, 20, 1001)\n", + "y = cdf(x)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "46cd265f-df0f-4597-8f97-da7dc2332926", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(x, y)\n", + "plt.plot(x, sc.stats.poisson.cdf(x, mu = 10))" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "05bd11b2-a3d9-426c-bb63-c58132155fe2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "BohmanMethodTest(x, cf, ax, N = 10000)\n", + "ax.plot(x, sc.stats.poisson.cdf(x, mu = 10))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/Fourier_inversion.ipynb b/examples/Fourier_inversion.ipynb deleted file mode 100644 index 4536e68..0000000 --- a/examples/Fourier_inversion.ipynb +++ /dev/null @@ -1,334 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 45, - "id": "4585cb36-b192-4bdd-9ba3-8d75efe64c09", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "17ef1a89-a5a7-4cf4-bc15-60d612cdb7ad", - "metadata": {}, - "outputs": [], - "source": [ - "from tests.uniform import Unif\n", - "from tests.normal import Norm" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c8eda83f-3c97-4a99-b188-576ba572f68c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "0b3ca594-a091-4c11-a1e1-30b0e2b27248", - "metadata": {}, - "outputs": [], - "source": [ - "unif = Unif(0, 1)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "b25ef220-8ea5-4888-b350-10ffd5e77483", - "metadata": {}, - "source": [ - "Характеристическая функция имеет следующий вид:\n", - "$$\\phi_X(t) = \\mathbb{E} e^{itX} = \\int\\limits_{\\mathbb{R}}e^{itx}f_X(x)dx.$$\n", - "\n", - "То есть характеристическая функция -- это преобразование Фурье плотности распределения случайной величины.\n", - "\n", - "Таким образом, при помощи обратного преобразования Фурье мы можем получить функцию плотности:\n", - "$$f_X(x) = \\frac{1}{2\\pi}\\int\\limits_{\\mathbb{R}}e^{-itx}\\phi_X(t)dt.$$\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "17c9e28a-4cab-44a3-821b-83b23efe228b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "23a596e1-4272-4818-aa81-fb21dae4ec61", - "metadata": {}, - "source": [ - "Зная функцию плотности, можно получить функцию распределения:\n", - "$$\n", - "\\begin{align*}\n", - "F_X(x) &= \\mathbb{P}(X < x) = \\int\\limits_{-\\infty}^{x}f_X(u)du\n", - "= \\frac{1}{2\\pi}\\int\\limits_{-\\infty}^{x} \\int\\limits_{-\\infty}^{\\infty}e^{-itu}\\phi_X(t)dt du =\\\\\n", - "&= \\frac{1}{2\\pi} \\int\\limits_{-\\infty}^{\\infty} \\int\\limits_{-\\infty}^{x} e^{-itu}\\phi_X(t)dudt =\n", - "\\frac{1}{2\\pi} \\int\\limits_{-\\infty}^{\\infty} \\int\\limits_{-\\infty}^{x} e^{-itu}\\phi_X(t)dudt =\\\\\n", - "&=\\frac{1}{2} - \\frac{1}{2\\pi}\\int\\limits_{\\mathbb{R}}\\frac{e^{-itx}\\phi_X(t)}{it}dt\n", - "\\end{align*}\n", - "$$" - ] - }, - { - "cell_type": "markdown", - "id": "8b216c59-0bc9-4d4b-88ee-b97f0d0d11a7", - "metadata": {}, - "source": [ - "$$\\frac{1}{2\\pi} \\int\\limits_{-\\infty}^{\\infty} -\\frac{e^{-itx}\\phi_X(t)}{it} \n", - "dt$$" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fa7079e-8281-4ddc-beef-bfe2fcd76224", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 65, - "id": "9669ef4c-dfe4-423d-91e2-c9ec1e6cfc8c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "def characteristic_function(t):\n", - " return np.exp(-0.5 * t**2)\n", - "\n", - "def pdf(x, num_points=1000):\n", - " t = np.linspace(-10, 10, num_points)\n", - " \n", - " phi_t = characteristic_function(t)\n", - " \n", - " integral = np.trapezoid((phi_t * np.exp(-1j * t * x))/(1j*t), t)\n", - "\n", - " return 1/2 - (1 / (2 * np.pi)) * integral\n", - "\n", - "def pdf(x, num_points=1000):\n", - " t = np.linspace(-10, 10, num_points)\n", - " \n", - " phi_t = characteristic_function(t)\n", - " integral = np.trapezoid(phi_t * np.exp(1j * t * x), t)\n", - " return (1 / (2 * np.pi)) * integral\n", - "\n", - "# Пример использования\n", - "x_values = np.linspace(-3, 3, 1000)\n", - "distribution_values = [pdf(x) for x in x_values]\n", - "\n", - "# Для визуализации результата можно использовать matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "df_true = norm.pdf(x_values, 0, 1)\n", - "plt.plot(x_values, df_true, label='Функция распределения')\n", - "plt.plot(x_values, distribution_values, label='Функция распределения аппрокс')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2b442c2f-e427-46eb-b59a-e9205a06531b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "700cbcf8-2904-4d8e-b3a5-8cd1c0fef51b", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must have same first dimension, but have shapes (100,) and (1024,)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[46], line 37\u001b[0m\n\u001b[1;32m 34\u001b[0m x \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(a, b, \u001b[38;5;241m100\u001b[39m)\n\u001b[1;32m 35\u001b[0m cumulative_distribution \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mcumsum(distribution) \u001b[38;5;241m*\u001b[39m (x[\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m-\u001b[39m x[\u001b[38;5;241m0\u001b[39m]) \u001b[38;5;66;03m# Нормируем\u001b[39;00m\n\u001b[0;32m---> 37\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcumulative_distribution\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 38\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mФункция распределения для равномерного распределения\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 39\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/pyplot.py:3829\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3821\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3822\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3823\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3827\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3828\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3830\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3831\u001b[0m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3832\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3833\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3834\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3835\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1777\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:494\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 491\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 494\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (100,) and (1024,)" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def characteristic_function_uniform(t, a, b):\n", - " \"\"\"Характеристическая функция равномерного распределения.\"\"\"\n", - " return (np.sin((b - a) * t / 2) / ((b - a) * t / 2)) * np.exp(-1j * (a + b) * t / 2)\n", - "\n", - "def inverse_fourier_transform(cf, num_points=1024):\n", - " \"\"\"Обратное преобразование Фурье для получения функции распределения.\"\"\"\n", - " # Создаем ось частот\n", - " t = np.linspace(-np.pi, np.pi, num_points)\n", - " # Вычисляем обратное преобразование Фурье\n", - " f_x = np.fft.ifft(cf).real\n", - " return f_x\n", - "\n", - "def compute_distribution(a, b, num_points=1024):\n", - " \"\"\"Вычисление функции распределения по характеристической функции.\"\"\"\n", - " # Определяем точки для характеристической функции\n", - " t = np.linspace(-np.pi, np.pi, num_points)\n", - " # Вычисляем характеристическую функцию\n", - " cf = characteristic_function_uniform(t, a, b)\n", - " # Получаем функцию распределения через обратное преобразование Фурье\n", - " distribution = inverse_fourier_transform(cf, num_points)\n", - " return distribution\n", - "\n", - "# Параметры равномерного распределения\n", - "a = 0\n", - "b = 1\n", - "\n", - "# Вычисление функции распределения\n", - "distribution = compute_distribution(a, b)\n", - "\n", - "# Визуализация функции распределения\n", - "x = np.linspace(a, b, 100)\n", - "cumulative_distribution = np.cumsum(distribution) * (x[1] - x[0]) # Нормируем\n", - "\n", - "plt.plot(x, cumulative_distribution)\n", - "plt.title('Функция распределения для равномерного распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.xlim(a - 0.1, b + 0.1)\n", - "plt.ylim(-0.1, 1.1)\n", - "plt.axhline(1, color='r', linestyle='--', label='F(x)=1')\n", - "plt.axvline(a, color='g', linestyle='--', label='a')\n", - "plt.axvline(b, color='g', linestyle='--', label='b')\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "id": "3e7b90f3-fb87-4d78-84eb-fe01ce870e05", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Package Version\n", - "----------------- -----------\n", - "appnope 0.1.4\n", - "asttokens 3.0.0\n", - "comm 0.2.2\n", - "contourpy 1.3.1\n", - "cycler 0.12.1\n", - "debugpy 1.8.11\n", - "decorator 5.1.1\n", - "executing 2.1.0\n", - "fonttools 4.55.3\n", - "ipykernel 6.29.5\n", - "ipython 8.31.0\n", - "jedi 0.19.2\n", - "jupyter_client 8.6.3\n", - "jupyter_core 5.7.2\n", - "kiwisolver 1.4.8\n", - "matplotlib 3.10.0\n", - "matplotlib-inline 0.1.7\n", - "mpmath 1.3.0\n", - "nest-asyncio 1.6.0\n", - "numpy 2.2.1\n", - "packaging 24.2\n", - "pandas 2.2.3\n", - "parso 0.8.4\n", - "pexpect 4.9.0\n", - "pillow 11.1.0\n", - "pip 24.3.1\n", - "platformdirs 4.3.6\n", - "prompt_toolkit 3.0.48\n", - "psutil 6.1.1\n", - "ptyprocess 0.7.0\n", - "pure_eval 0.2.3\n", - "Pygments 2.19.1\n", - "pyparsing 3.2.1\n", - "python-dateutil 2.9.0.post0\n", - "pytz 2024.2\n", - "pyzmq 26.2.0\n", - "scipy 1.15.0\n", - "six 1.17.0\n", - "stack-data 0.6.3\n", - "tornado 6.4.2\n", - "traitlets 5.14.3\n", - "tzdata 2024.2\n", - "wcwidth 0.2.13\n" - ] - } - ], - "source": [ - "!pip3 list" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Fourier_inversion_1.ipynb b/examples/Fourier_inversion_1.ipynb deleted file mode 100644 index 7b55d08..0000000 --- a/examples/Fourier_inversion_1.ipynb +++ /dev/null @@ -1,2632 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 120, - "id": "540b61fb-afef-4f81-99e5-d61945acdbb3", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../')" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "id": "4585cb36-b192-4bdd-9ba3-8d75efe64c09", - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.stats import norm" - ] - }, - { - "cell_type": "code", - "execution_count": 122, - "id": "17ef1a89-a5a7-4cf4-bc15-60d612cdb7ad", - "metadata": {}, - "outputs": [], - "source": [ - "from tests.uniform import Unif\n", - "from tests.normal import Norm" - ] - }, - { - "cell_type": "code", - "execution_count": 123, - "id": "5004d12e-d101-4c8d-a3e0-4c9d15999e66", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.CharFuncInverter import CharFuncInverter" - ] - }, - { - "cell_type": "markdown", - "id": "b25ef220-8ea5-4888-b350-10ffd5e77483", - "metadata": {}, - "source": [ - "Характеристическая функция имеет следующий вид:\n", - "$$\\phi_X(t) = \\mathbb{E} e^{itX} = \\int\\limits_{\\mathbb{R}}e^{itx}f_X(x)dx.$$\n", - "\n", - "То есть характеристическая функция -- это преобразование Фурье плотности распределения случайной величины.\n", - "\n", - "Таким образом, при помощи обратного преобразования Фурье мы можем получить функцию плотности:\n", - "$$f_X(x) = \\frac{1}{2\\pi}\\int\\limits_{\\mathbb{R}}e^{-itx}\\phi_X(t)dt.$$\n" - ] - }, - { - "cell_type": "markdown", - "id": "23a596e1-4272-4818-aa81-fb21dae4ec61", - "metadata": {}, - "source": [ - "Зная функцию плотности, можно получить функцию распределения:\n", - "$$\n", - "\\begin{align*}\n", - "F_X(x) &= \\mathbb{P}(X < x) = \\int\\limits_{-\\infty}^{x}f_X(u)du\n", - "= \\frac{1}{2\\pi}\\int\\limits_{-\\infty}^{x} \\int\\limits_{-\\infty}^{\\infty}e^{-itu}\\phi_X(t)dt du =\\\\\n", - "&=\\frac{1}{2} - \\frac{1}{2\\pi}\\int\\limits_{\\mathbb{R}}\\frac{e^{-itx}\\phi_X(t)}{it}dt\n", - "\\end{align*}\n", - "$$" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "id": "fe8e1d72-f6ca-4d24-b2a2-240bbdc3657a", - "metadata": {}, - "outputs": [], - "source": [ - "class FTInverterNaive(CharFuncInverter):\n", - "\n", - " def __init__(self, N=1e3, delta=1e-1, num_points = None):\n", - " super().__init__()\n", - " self.N = int(N)\n", - " self.delta = delta\n", - " if num_points is None:\n", - " self.num_points = int(N // delta)\n", - " else:\n", - " self.num_points = num_points\n", - "\n", - " def fit(self, phi):\n", - " \"\"\"phi = characteristic function\"\"\"\n", - " self.phi = phi\n", - "\n", - " def cdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - " \n", - " phi_t = self.phi(t)\n", - "\n", - "\n", - " integral = np.trapezoid((phi_t * np.exp(-1j * t * x[:, np.newaxis]))/(1j*t), t, axis=1)\n", - " return 1/2 - (1 / (2 * np.pi)) * integral\n", - "\n", - " def pdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - "\n", - " phi_t = self.phi(t)\n", - "\n", - " integral = np.trapezoid(phi_t * np.exp(-1j * t * x[:, np.newaxis]), t, axis=1)\n", - "\n", - " return (1 / (2 * np.pi)) * integral" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "id": "d32e72b6-39ec-4b46-80d2-dc2c905a0adf", - "metadata": {}, - "outputs": [], - "source": [ - "inv = FTInverterNaive(num_points=10000)" - ] - }, - { - "cell_type": "markdown", - "id": "b493cd7b-c58f-436a-b9e2-9e9b632a2d8b", - "metadata": {}, - "source": [ - "# Нормальное распределение" - ] - }, - { - "cell_type": "code", - "execution_count": 137, - "id": "25bb2a6b-fdd5-4326-aa86-be22185da7fd", - "metadata": {}, - "outputs": [], - "source": [ - "normal = Norm(0, 1)\n", - "inv.fit(normal.chr)" - ] - }, - { - "cell_type": "code", - "execution_count": 138, - "id": "2b442c2f-e427-46eb-b59a-e9205a06531b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.pdf(x)\n", - "df_true = normal.pdf(x)\n", - "plt.plot(x, df_true, label='Плотность')\n", - "plt.plot(x, distribution_values, label='Плотность аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "id": "7ea8bd73-3d8b-40ca-b2fa-e72d7b48fa08", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.cdf(x)\n", - "df_true = normal.cdf(x)\n", - "plt.plot(x, df_true, label='Функция распределения')\n", - "plt.plot(x, distribution_values, label='Функция распределения аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "e92fc5a1-4d78-41dc-846c-e9b7f9053b9f", - "metadata": {}, - "source": [ - "Если брать N = 1e8 то там что-то всё как-то неприятно получается..." - ] - }, - { - "cell_type": "markdown", - "id": "63f450dc-a599-4374-aa24-84883b9867e9", - "metadata": {}, - "source": [ - "# Равномерное распределение" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "id": "700cbcf8-2904-4d8e-b3a5-8cd1c0fef51b", - "metadata": {}, - "outputs": [], - "source": [ - "inv = FTInverterNaive(N=10, num_points=10000)\n", - "uniform = Unif(-1, 0)\n", - "inv.fit(uniform.chr)" - ] - }, - { - "cell_type": "markdown", - "id": "f3f91d25-f121-485b-8577-67337f6f5b94", - "metadata": {}, - "source": [ - "### на [0,1]" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "id": "3e7b90f3-fb87-4d78-84eb-fe01ce870e05", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.cdf(x)\n", - "df_true = uniform.cdf(x)\n", - "plt.plot(x, df_true, label='Функция распределения')\n", - "plt.plot(x, distribution_values, label='Функция распределения аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "id": "c8eda83f-3c97-4a99-b188-576ba572f68c", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.pdf(x)\n", - "df_true = uniform.pdf(x)\n", - "plt.plot(x, df_true, label='Плотность распределения')\n", - "plt.plot(x, distribution_values, label='Плотность распределения аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "5cf48bf8-dde4-4e3f-8aba-8346c5b15f4d", - "metadata": {}, - "source": [ - "### на [-1, 1]" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "id": "c2b111cb-3d3a-47f1-ac0b-ce150279211c", - "metadata": {}, - "outputs": [], - "source": [ - "uniform = Unif(-1, 1)\n", - "inv.fit(uniform.chr)" - ] - }, - { - "cell_type": "code", - "execution_count": 144, - "id": "89c1ac7d-88b0-41e1-bf91-7fd5a6e931a5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-2, 2, 10000)\n", - "distribution_values = inv.cdf(x)\n", - "df_true = uniform.cdf(x)\n", - "plt.plot(x, df_true, label='Функция распределения')\n", - "plt.plot(x, distribution_values, label='Функция распределения аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "id": "b3619354-e4f8-4749-a6cb-2d5fa7c98f3c", - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.pdf(x)\n", - "df_true = uniform.pdf(x)\n", - "plt.plot(x, df_true, label='Плотность')\n", - "plt.plot(x, distribution_values, label='Плотност аппроксимированная')\n", - "plt.title('Плотность из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "id": "ad8df6ec-65cc-482b-8b88-f7b25019df5b", - "metadata": {}, - "source": [ - "# EXPERIMENT \n" - ] - }, - { - "cell_type": "code", - "execution_count": 186, - "id": "eeaf2c47-0496-44b3-bf39-bc187fa30798", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must have same first dimension, but have shapes (10000,) and (9999,)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[186], line 42\u001b[0m\n\u001b[1;32m 40\u001b[0m df_true \u001b[38;5;241m=\u001b[39m normal\u001b[38;5;241m.\u001b[39mpdf(x)\n\u001b[1;32m 41\u001b[0m \u001b[38;5;66;03m#plt.plot(x, df_true, label='Плотность')\u001b[39;00m\n\u001b[0;32m---> 42\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdistribution_values\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mПлотность аппроксимированная\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 43\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mФункция распределения из характеристической функции\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 44\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/pyplot.py:3829\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3821\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3822\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3823\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3827\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3828\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3829\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3830\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3831\u001b[0m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3832\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3833\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3834\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3835\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1777\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1534\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1535\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1536\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1774\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1775\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1776\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1777\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1778\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1779\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:297\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, return_kwargs, *args, **kwargs)\u001b[0m\n\u001b[1;32m 295\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 296\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 297\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 298\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 299\u001b[0m \u001b[43m \u001b[49m\u001b[43mreturn_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreturn_kwargs\u001b[49m\n\u001b[1;32m 300\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:494\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 491\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 493\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 494\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 495\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 496\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 497\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 498\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (10000,) and (9999,)" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "class FFTInverter(CharFuncInverter):\n", - "\n", - " def __init__(self, N=1e3, delta=1e-1, num_points=None):\n", - " super().__init__()\n", - " self.N = int(N)\n", - " self.delta = delta\n", - " if num_points is None:\n", - " self.num_points = int(N // delta)\n", - " else:\n", - " self.num_points = num_points\n", - "\n", - " def fit(self, phi):\n", - " \"\"\"phi = characteristic function\"\"\"\n", - " self.phi = phi\n", - "\n", - " def cdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - " phi_t = self.phi(t)\n", - "\n", - " # Используем IFFT для вычисления интеграла\n", - " integral = np.fft.ifft(phi_t * np.exp(-1j * t[:, np.newaxis] * x)).real\n", - "\n", - " return 1/2 - (1 / (2 * np.pi)) * integral\n", - "\n", - " def pdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - " phi_t = self.phi(t)\n", - "\n", - " # Используем IFFT для вычисления интеграла\n", - " integral = np.fft.ifft(phi_t).real\n", - " return (1 / (2 * np.pi)) * integral\n", - "\n", - "inv = FFTInverter()\n", - "normal = Norm(0, 1)\n", - "inv.fit(normal.chr)\n", - "x = np.linspace(-3, 3, 10000)\n", - "distribution_values = inv.pdf(x)\n", - "df_true = normal.pdf(x)\n", - "#plt.plot(x, df_true, label='Плотность')\n", - "plt.plot(x, distribution_values, label='Плотность аппроксимированная')\n", - "plt.title('Функция распределения из характеристической функции')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.legend()\n", - "plt.grid()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 177, - "id": "9a6fe1ad-78af-4bb5-84d6-6d07646593c2", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([-6.89276124e+08+0.00000000e+00j, -6.58007616e+08+1.42386148e+08j,\n", - " -5.79369541e+08+2.50236243e+08j, ...,\n", - " -4.83563493e+08-3.12240616e+08j, -5.79369541e+08-2.50236243e+08j,\n", - " -6.58007616e+08-1.42386148e+08j], shape=(1024,))" - ] - }, - "execution_count": 177, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import numpy as np\n", - "\n", - "phi = lambda t:(np.sin((b - a) * t / 2) / ((b - a) * t / 2)) * np.exp(-1j * (a + b) * t / 2)\n", - "\n", - "p = (np.sqrt(41) - 5) / 8\n", - "a = (2*p) / (1-p)\n", - "phi = lambda x : (x + 2) + (1-p)*(1-np.exp(-x/a))\n", - "\n", - "N = 1024\n", - "d = 0.02\n", - "x = np.linspace(-10.16, 2, N)\n", - "f = np.fft.fft(phi(x)) / N\n", - "f" - ] - }, - { - "cell_type": "code", - "execution_count": 179, - "id": "d8ae3228-71ab-401d-9b1d-44a14699b33e", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[np.float64(-14444296.148689806),\n", - " np.float64(-24073829.619743526),\n", - " np.float64(-33703368.085319534),\n", - " np.float64(-43332914.04314892),\n", - " np.float64(-52962469.99152693),\n", - " np.float64(-62592038.42950103),\n", - " 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"nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Numerical_inversion_of_characteristic_functions.ipynb b/examples/Numerical_inversion_of_characteristic_functions.ipynb deleted file mode 100644 index 4d17437..0000000 --- a/examples/Numerical_inversion_of_characteristic_functions.ipynb +++ /dev/null @@ -1,1059 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ncqXgW4EFHO" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from tests.uniform import Unif\n", - "from tests.normal import Norm\n", - "\n", - "from src.BohmansInverters import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def make_plot(cdf, start, stop, num):\n", - " x = np.linspace(start, stop, num)\n", - " F_x = cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - " plt.title('График функции распределения')\n", - " plt.xlabel('x')\n", - " plt.ylabel('F(x)')\n", - " plt.grid()\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "POyQFy0itKu-" - }, - "source": [ - "# Проверим на равномерном распределении:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "kCJKN7xmtOyy" - }, - "outputs": [], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 764 - }, - "id": "B-xQ5ISztn3a", - "outputId": "c603a25c-bb3f-4135-da50-ffb950ea2166", - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - " \n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-0.5, 1.5, 10000)\n", - " F_x = inv.cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "deltas = np.linspace(0, 1, 30)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('delta')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for delta in deltas:\n", - " inv.delta = delta\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(deltas, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'n' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mchr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mn\u001b[49m\u001b[38;5;241m.\u001b[39mchr\n\u001b[1;32m 3\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e3\u001b[39m\n\u001b[1;32m 5\u001b[0m bochmans_inversers \u001b[38;5;241m=\u001b[39m [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\u001b[0;31mNameError\u001b[0m: name 'n' is not defined" - ] - } - ], - "source": [ - "chr = n.chr\n", - "\n", - "N = 1e3\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "deltas = np.linspace(0, 1, 30)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('delta')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for delta in deltas:\n", - " inv.delta = delta\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(deltas, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = Norm(0, 1)\n", - "chr = n.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - " \n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 10000)\n", - " F_x = inv.cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "OgClRtjIx8JP", - "outputId": "4915428d-728c-4978-9f20-fe9f872a5cfb" - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'D' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[121], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e3\u001b[39m\n\u001b[1;32m 5\u001b[0m delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e-1\u001b[39m\n\u001b[0;32m----> 7\u001b[0m chr_d \u001b[38;5;241m=\u001b[39m \u001b[43mD\u001b[49m(N, delta, \u001b[38;5;28mchr\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 8\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchr_d.make_plot(-5, 5, 50)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'D' is not defined" - ] - } - ], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "\n", - "chr_d = D(N, delta, chr, 10)\n", - "%time chr_d.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 515 - }, - "id": "Eimj257uyROO", - "outputId": "99553834-6a36-43b9-8e80-bc2acdf54190" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = BohmanD(N, delta, 10)\n", - "inv.fit(chr)\n", - "x = np.linspace(-5, 5, 50)\n", - "F_x = inv.cdf(x)\n", - "plt.plot(x, F_x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MP9mAlcjySR4" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 730 - }, - "id": "_fgGrGZRxJ6f", - "outputId": "2ebc2291-ac12-436f-f02c-9df11d1e2bdf" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - " plt.legend()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.00796194-5.82441030e-17j 0.00796288+4.48012666e-17j\n", - " 0.00796382+1.81349754e-18j ... 1.00795167-5.11989065e-18j\n", - " 1.00795261+1.70355266e-17j 1.00795355+2.50310941e-17j]\n", - "[0.00796147+1.11226414e-17j 0.00796147+2.48549015e-17j\n", - " 0.00796148+1.17767169e-16j ... 1.00795402+4.14393362e-17j\n", - " 1.00795402-9.83956806e-18j 1.00795402-7.90829793e-17j]\n", - "[0.00796194-8.59996787e-17j 0.00796288-1.76487785e-17j\n", - " 0.00796382+1.56912853e-17j ... 1.00795167-2.59365724e-17j\n", - " 1.00795261-1.07200490e-17j 1.00795355-2.72448147e-18j]\n", - "[0.00715038-4.66438719e-07j 0.00715137-4.67517772e-07j\n", - " 0.00715236-4.68594642e-07j ... 1.00717158+4.68594646e-07j\n", - " 1.00717257+4.67517776e-07j 1.00717357+4.66438723e-07j]\n" - ] - }, - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD()]\n", - "\n", - "\n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-0.5, 1.5, 10000)\n", - " F_x = inv.cdf(x)\n", - " print(F_x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-SkolrlDw9_1" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 577 - }, - "id": "8MBJqTOHyve7", - "outputId": "4c5af66e-160e-4194-a754-31e2a23ce0d1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.-0.j -0.-0.j -0.-0.j ... 0.+0.j 0.+0.j 0.+0.j]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1699: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%time\n", - "norm = Norm(0, 1)\n", - "chr = norm.chr\n", - "chrbhm = BohmanA()\n", - "chrbhm.fit(chr)\n", - "make_plot(chrbhm.cdf, -5, 5, 10000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "L-40Nx7yJmBU", - "outputId": "d652c7bd-e746-4e22-ceca-62cfa32e69c4" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.21 s, sys: 275 ms, total: 1.49 s\n", - "Wall time: 1.32 s\n" - ] - } - ], - "source": [ - "%%time\n", - "chrbhm = BohmanA()\n", - "chrbhm.fit(chr)\n", - "make_plot(chrbhm.cdf, -5, 5, 10000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "mpOJ7oE1ji5D", - "outputId": "3d386158-e6e3-40d0-80c3-9479820b99c7" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "make_plot(unif.cdf, -5, 5, 30)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "dLBJ7JnlxVTK", - "outputId": "e36821ca-128b-446a-a472-8fcd21f76889" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 743 ms, sys: 110 ms, total: 853 ms\n", - "Wall time: 933 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "chr_a = A(N, delta, chr)\n", - "chr_a.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "JbEWLSIWxWa_", - "outputId": "6d0e7b71-23f0-4131-a226-552f354d9f51" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.45 s, sys: 101 ms, total: 1.55 s\n", - "Wall time: 1.47 s\n" - ] - } - ], - "source": [ - "chr_b = B(N, delta, chr)\n", - "%time chr_b.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "MwvNWzFIxW7Q", - "outputId": "ee231453-261b-4b39-cf9f-fc4e058040b8" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 812 ms, sys: 88.2 ms, total: 900 ms\n", - "Wall time: 808 ms\n" - ] - } - ], - "source": [ - "chr_c = C(N, delta, chr)\n", - "%time chr_c.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "SqB52ggtxXVl", - "outputId": "bd2095c0-47b7-4424-e3e2-986ed0069fb1" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.16 s, sys: 122 ms, total: 7.28 s\n", - "Wall time: 7.27 s\n" - ] - } - ], - "source": [ - "chr_d = D(N, delta, chr, 10)\n", - "%time chr_d.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "Cfj45thPxX5W", - "outputId": "a769ecad-992a-44af-ce0e-cffaf5dfc1e7" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 8.71 s, sys: 130 ms, total: 8.84 s\n", - "Wall time: 8.82 s\n" - ] - } - ], - "source": [ - "chr_e = E(N, delta, chr, 10)\n", - "%time chr_e.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "h35chYOh2UE3", - "outputId": "ddfc000a-0e0e-43ee-94f1-86f9130546c9" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":7: RuntimeWarning: invalid value encountered in scalar divide\n", - " return (exp(1j * x * self.b) - exp(1j * x * self.a)) / (1j * x * (self.b - self.a))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(nan+nanj)\n", - "(0.0002499414186070202-6.762535332315243e-19j)\n", - "(0.0008875862232762507+3.301580286344398e-18j)\n", - "(0.002045592176573915+1.5799367952258596e-18j)\n", - "(0.0036096187981053624+4.8732232299723125e-18j)\n", - "(0.0056470537986876715-2.247224259600549e-18j)\n", - "(0.008132009531823681-2.0423971073549893e-19j)\n", - "(0.011072482920973868+1.5981356437379623e-18j)\n", - "(0.014462144250016339+5.853883239436446e-18j)\n", - "(0.018295287147039287-5.950420506588283e-18j)\n", - "(0.022589376575070232+4.397179300751025e-18j)\n", - "(0.027333153440452427+6.77206605317268e-18j)\n", - "(0.03253076969486711+1.0800314423920805e-18j)\n", - "(0.03817253603715261-2.8068380760150523e-18j)\n", - "(0.044278629684486315-1.0513017206034133e-18j)\n", - "(0.0508265115623728-3.0356061384471985e-18j)\n", - "(0.057828922025119626-1.3877075729651294e-19j)\n", - "(0.0652848576942221+5.023517977735488e-18j)\n", - "(0.07318729232133578-5.710778763879993e-18j)\n", - "(0.08155025749563313-3.991889035453754e-18j)\n", - "(0.09035662826636429-3.975107939379284e-18j)\n", - "(0.0996194635838266-2.670058102515745e-18j)\n", - "(0.10933251149166684+1.1404053754164611e-17j)\n", - "(0.11949622606412622+7.245026423672217e-18j)\n", - "(0.13011706689967903+1.3789989017968018e-18j)\n", - "(0.14118315265240716-9.106113275494094e-18j)\n", - "(0.15270466540475547+3.50969379366653e-18j)\n", - "(0.1646770321626129-4.022343525202836e-19j)\n", - "(0.17710210716313363-3.650310834588583e-18j)\n", - "(0.18997563533380002-1.4907122866195015e-18j)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "u-gtBNzc3eUf", - "outputId": "8cbc25eb-f1e7-4fd7-fbba-35287119408b" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":7: RuntimeWarning: invalid value encountered in scalar divide\n", - " return (exp(1j * x * self.b) - exp(1j * x * self.a)) / (1j * x * (self.b - self.a))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(nan + nanj)\n", - "(0.00038634499457157 - 5.44442825367956e-19j)\n", - "(0.000897286919903565 - 1.56348752486184e-18j)\n", - "(0.00203211516533013 + 1.2226292200259e-18j)\n", - "(0.00361346176327356 + 6.48526574191118e-18j)\n", - "(0.0056469093168418 - 2.00347135730946e-18j)\n", - "(0.00813189933090962 - 6.24544308602384e-18j)\n", - "(0.0110685839576376 + 2.62149907154718e-18j)\n", - "(0.0144570820834944 + 2.21555265712953e-18j)\n", - "(0.0182973093513688 - 4.27481291416407e-18j)\n", - "(0.0225893344034734 - 8.96640028170143e-19j)\n", - "(0.0273331378854852 - 5.84466533268314e-20j)\n", - "(0.0325287215780115 - 5.77946829928842e-18j)\n", - "(0.0381760963159961 - 8.97324140337815e-18j)\n", - "(0.0442752535107747 + 2.55257569937041e-18j)\n", - "(0.0508261995120664 + 5.85958420067211e-18j)\n", - "(0.0578289333212659 + 4.16133434109437e-18j)\n", - "(0.0652834539923396 - 6.71860957433856e-18j)\n", - "(0.0731897634568402 - 8.85161820953318e-19j)\n", - "(0.0815478610498611 + 1.05003120282329e-17j)\n", - "(0.0903577466708462 - 1.06547039486765e-17j)\n", - "(0.0996194212303731 - 6.68866913798652e-18j)\n", - "(0.109332883992012 + 1.12955132592334e-18j)\n", - "(0.119498135337263 + 4.52142270360577e-18j)\n", - "(0.130115175628487 + 1.7906010578092e-17j)\n", - "(0.141184004177178 - 1.06966308419915e-18j)\n", - "(0.152704621739461 - 2.52204744006529e-18j)\n", - "(0.164677027913269 + 1.11305652466901e-17j)\n", - "(0.177101222753054 + 6.9543041291525e-18j)\n", - "(0.189977206557867 - 2.87049823145739e-18j)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: RuntimeWarning: invalid value encountered in cast\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_b = B(N, delta, chr)\n", - " err = chr_b.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kOq_Y3KW3n6B" - }, - "outputs": [], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LiYF0lQ63kVn" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 418 - }, - "id": "4PG628UY2FYy", - "outputId": "967eabed-d0fc-4f94-b8a4-0879d9b06b95" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(178.817628466268 + 1.10213157603017e-17j)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mN\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mchr_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchr_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_error__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munif\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "Ns = [1e3, 1e4, 1e5, 1e6, 1e7]\n", - "delta = 10\n", - "errors = []\n", - "for N in Ns:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err)\n", - "\n", - "plt.plot(Ns, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 435 - }, - "id": "o94PCGL7xur-", - "outputId": "47d0120d-fa54-4370-e449-67c15128f698" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.00191212599817633 - 5.00863283981901e-18j)\n", - "(0.00189961626801374 + 2.52298778735686e-18j)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - 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This function is\n\u001b[1;32m 799\u001b[0m simply a wrapper of mpf_add that changes the sign of t.\"\"\"\n\u001b[0;32m--> 800\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mmpf_add\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 801\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 802\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmpf_sum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrnd\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mround_fast\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0msman\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msexp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "Ns = [1e3, 1e4, 1e5, 1e6, 1e7]\n", - "delta = 1e-1\n", - "errors = []\n", - "for N in Ns:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err)\n", - "\n", - "plt.plot(Ns, errors)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Numerical_inversion_of_characteristic_functions_1.ipynb b/examples/Numerical_inversion_of_characteristic_functions_1.ipynb deleted file mode 100644 index b4ad5e9..0000000 --- a/examples/Numerical_inversion_of_characteristic_functions_1.ipynb +++ /dev/null @@ -1,1116 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ncqXgW4EFHO" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from tests.uniform import Unif\n", - "from tests.normal import Norm\n", - "\n", - "from src.CharFuncInverter.Bohman.BohmansInverters import *" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "def make_plot(cdf, start, stop, num):\n", - " x = np.linspace(start, stop, num)\n", - " F_x = cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - " plt.title('График функции распределения')\n", - " plt.xlabel('x')\n", - " plt.ylabel('F(x)')\n", - " plt.grid()\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "POyQFy0itKu-" - }, - "source": [ - "# Проверим на равномерном распределении:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "kCJKN7xmtOyy" - }, - "outputs": [], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 764 - }, - "id": "B-xQ5ISztn3a", - "outputId": "c603a25c-bb3f-4135-da50-ffb950ea2166", - "scrolled": true - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/lily/myenv/lib/python3.13/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/Users/lily/myenv/lib/python3.13/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - " \n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-0.5, 1.5, 10000)\n", - " F_x = inv.cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = BohmanA()\n", - "inv.fit(chr)\n", - "x = np.linspace(-0.5, 1.5, 10000)\n", - "dx = (2 / 10000)\n", - "grad = np.gradient(inv.cdf(x), dx)\n", - "\n", - "plt.plot(x, grad, label='Функция распределения')\n" - ] - }, - { - "cell_type": "code", - "execution_count": 47, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "deltas = np.linspace(0, 1, 30)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('delta')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for delta in deltas:\n", - " inv.delta = delta\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(deltas, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 50, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'n' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28mchr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mn\u001b[49m\u001b[38;5;241m.\u001b[39mchr\n\u001b[1;32m 3\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e3\u001b[39m\n\u001b[1;32m 5\u001b[0m bochmans_inversers \u001b[38;5;241m=\u001b[39m [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\u001b[0;31mNameError\u001b[0m: name 'n' is not defined" - ] - } - ], - "source": [ - "chr = n.chr\n", - "\n", - "N = 1e3\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "deltas = np.linspace(0, 1, 30)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('delta')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for delta in deltas:\n", - " inv.delta = delta\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(deltas, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "n = Norm(0, 1)\n", - "chr = n.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - " \n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 10000)\n", - " F_x = inv.cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 121, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "OgClRtjIx8JP", - "outputId": "4915428d-728c-4978-9f20-fe9f872a5cfb" - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'D' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[121], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m N \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e3\u001b[39m\n\u001b[1;32m 5\u001b[0m delta \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1e-1\u001b[39m\n\u001b[0;32m----> 7\u001b[0m chr_d \u001b[38;5;241m=\u001b[39m \u001b[43mD\u001b[49m(N, delta, \u001b[38;5;28mchr\u001b[39m, \u001b[38;5;241m10\u001b[39m)\n\u001b[1;32m 8\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mchr_d.make_plot(-5, 5, 50)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", - "\u001b[0;31mNameError\u001b[0m: name 'D' is not defined" - ] - } - ], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "\n", - "chr_d = D(N, delta, chr, 10)\n", - "%time chr_d.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 515 - }, - "id": "Eimj257uyROO", - "outputId": "99553834-6a36-43b9-8e80-bc2acdf54190" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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l5eXKyclRVVWV9u3bN+r5Z8+e1Zo1azR79mx5vV792Z/9mXbt2jWuggFgPE53h3QuFJZlSXOms/oqkEoyE71gx44dqq+vV1NTk6qqqrRp0ybV1tbq8OHDmjVr1iXnh0Ih/cVf/IVmzZqll19+WXPmzNGxY8c0bdq0iagfAMYkOnl1dkGOvJkZhqsBMFjCYWTjxo1avXq16urqJElNTU3auXOntm7dqnXr1l1y/tatW9XZ2ak33nhDWVlZkqTy8vIrqxoAEtTK5FUgZSU0TBMKhbR//37V1NQMvIDHo5qaGrW0tMS95t/+7d9UXV2tNWvWqLi4WIsXL9YzzzyjcDg8Yju9vb0KBoNDHgBwJQYveAYgtSQURjo6OhQOh1VcXDzkeHFxsfx+f9xr3n//fb388ssKh8PatWuXnnrqKT3//PP6xje+MWI7jY2NKiwsjD3KysoSKRMALhEdpiGMAKln0u+miUQimjVrln70ox9p6dKlWrFihb761a+qqalpxGsaGhoUCARij9bW1skuE4DLDQzTsFsvkGoSmjNSVFSkjIwMtbW1DTne1tamkpKSuNfMnj1bWVlZysgYmDB27bXXyu/3KxQKKTv70s2qvF6vvF5vIqUBwKgYpgFSV0I9I9nZ2Vq6dKmam5tjxyKRiJqbm1VdXR33mltuuUVHjhxRJBKJHfvTn/6k2bNnxw0iADDRei6E5Q/2SCKMAKko4WGa+vp6vfDCC/rpT3+qd999V1/60pfU3d0du7tm5cqVamhoiJ3/pS99SZ2dnVq7dq3+9Kc/aefOnXrmmWe0Zs2aiXsXADCKD8/09YrkezM1PS/LcDUAhkv41t4VK1aovb1d69evl9/vV0VFhXbv3h2b1Orz+eTxDGScsrIy/fu//7see+wxXX/99ZozZ47Wrl2rxx9/fOLeBQCMYvAy8JZlGa4GwHCWbdu26SIuJxgMqrCwUIFAQAUFBabLAeAwP/ndB3r6F3/UXZ8oUdODS02XA6SNsf78Zm8aAK4X25OGBc+AlEQYAeB6vtPs1gukMsIIANfjtl4gtRFGALiabdsM0wApjjACwNVOdfWq92JEHksqnZZruhwAcRBGALhatFekdFqusjL4lgekIr4yAbhadPIqQzRA6iKMAHC1Y0xeBVIeYQSAq7V2clsvkOoIIwBcLXYnzYwphisBMBLCCABXO3aaYRog1RFGALjWudBFdXzUK4kwAqQywggA14oO0RTmZqkwL8twNQBGQhgB4Fo+hmgARyCMAHAt9qQBnIEwAsC1YmGEBc+AlEYYAeBa9IwAzkAYAeBahBHAGQgjAFwpHLH1Yed5SYQRINURRgC4UluwR6FwRJkeS7MLc0yXA2AUhBEArhQdopk7PVeZGXyrA1IZX6EAXCm6xggb5AGpjzACwJWYvAo4B2EEgCsdi+7WyxojQMojjABwJXpGAOcgjABwpdZO5owATkEYAeA6XT0X1NkdkkTPCOAEhBEArhMdopkxJVv5OVmGqwFwOYQRAK4Tva2XXhHAGQgjAFyHyauAsxBGALgOYQRwFsIIANeJhRHWGAEcgTACwHXoGQGchTACwFUuhiM6fua8JFZfBZyCMALAVU4GenQxYis7w6Pi/BzT5QAYA8IIAFeJDtHMnZErj8cyXA2AsSCMAHCVY/1rjMxnvgjgGIQRAK7C5FXAeQgjAFzF19ktSZo3c4rhSgCMFWEEgKvQMwI4D2EEgKuwLw3gPIQRAK5x9lxIwZ6LkggjgJMQRgC4RnSI5qp8r3KzMwxXA2CsCCMAXIPbegFnIowAcA0mrwLORBgB4Bqt/WGkjDACOAphBIBrxIZp2CAPcBTCCADXYJgGcCbCCABXCF2M6GTgvCRpHj0jgKMQRgC4wvGz5xWxpZwsj66a6jVdDoAEEEYAuMLgIRrLsgxXAyARhBEAruA73b9B3gw2yAOchjACwBWYvAo4F2EEgCtwWy/gXIQRAK5AzwjgXIQRAI5n2zarrwIONq4wsmXLFpWXlysnJ0dVVVXat2/fmK7bvn27LMvS8uXLx9MsAMR1ujuk7lBYliXNnZ5ruhwACUo4jOzYsUP19fXasGGDDhw4oCVLlqi2tlanTp0a9bqjR4/qH/7hH3TrrbeOu1gAiCc6RFNSkKOcrAzD1QBIVMJhZOPGjVq9erXq6up03XXXqampSXl5edq6deuI14TDYT3wwAN6+umndfXVV19RwQAwnO8080UAJ0sojIRCIe3fv181NTUDL+DxqKamRi0tLSNe94//+I+aNWuWvvCFL4ypnd7eXgWDwSEPABgJk1cBZ0sojHR0dCgcDqu4uHjI8eLiYvn9/rjXvP766/rxj3+sF154YcztNDY2qrCwMPYoKytLpEwAaYYwAjjbpN5N09XVpQcffFAvvPCCioqKxnxdQ0ODAoFA7NHa2jqJVQJwutgwDWuMAI6UmcjJRUVFysjIUFtb25DjbW1tKikpueT89957T0ePHtW9994bOxaJRPoazszU4cOHdc0111xyndfrldfLRlcAxoaeEcDZEuoZyc7O1tKlS9Xc3Bw7FolE1NzcrOrq6kvOX7Rokd5++20dPHgw9vjc5z6nO+64QwcPHmT4BcAV67kQlj/YI0maP5N9aQAnSqhnRJLq6+u1atUqLVu2TJWVldq0aZO6u7tVV1cnSVq5cqXmzJmjxsZG5eTkaPHixUOunzZtmiRdchwAxuPDM329IlO9mZqel2W4GgDjkXAYWbFihdrb27V+/Xr5/X5VVFRo9+7dsUmtPp9PHg8LuwJIDt+glVctyzJcDYDxsGzbtk0XcTnBYFCFhYUKBAIqKCgwXQ6AFPKT332gp3/xR931iRI1PbjUdDkABhnrz2+6MAA4WmzyKnfSAI5FGAHgaKy+CjgfYQSAo3FbL+B8hBEAjmXbNmEEcAHCCADHOtXVq96LEXksac70XNPlABgnwggAx4r2ipROy1VWBt/OAKfiqxeAYx3rn7w6nztpAEcjjABwLOaLAO5AGAHgWK2DVl8F4FyEEQCOdex0tyRp/gw2yAOcjDACwLF8neclMUwDOB1hBIAjdfdeVMdHvZJYCh5wOsIIAEdqPdM3X6QwN0uFuVmGqwFwJQgjAByJPWkA9yCMAHAkdusF3IMwAsCRWGMEcA/CCABHiq2+ShgBHI8wAsCRWukZAVyDMALAccIRWx+e6VtjhNVXAecjjABwHH+wR6FwRJkeS6XTck2XA+AKEUYAOE70tt6503OV4bEMVwPgShFGADiOr7NvT5p5M9mTBnADwggAx4ne1ls2nSEawA0IIwAcJ7pB3nwWPANcgTACwHF8p/uHaWYwTAO4AWEEgOOw+irgLoQRAI4S7LmgM+cuSGJfGsAtCCMAHCV6W+/MKdma6s00XA2AiUAYAeAo0WXgWXkVcA/CCABHic4X4U4awD0IIwAc5RiTVwHXIYwAcBSGaQD3IYwAcJRj/RNY5xNGANcgjABwjIvhiI6f7Vt9ldt6AfcgjABwjJOBHoUjtrIzPSrOzzFdDoAJQhgB4BjRIZqy6bnyeCzD1QCYKIQRAI7BMvCAOxFGADjGwBojbJAHuAlhBIBj+Dr7duvltl7AXQgjAByDYRrAnQgjABwjukkeS8ED7kIYAeAIZ8+FFOy5KEkqm04YAdyEMALAEaJDNLPyvcrNzjBcDYCJRBgB4AjRNUaYLwK4D2EEgCMweRVwL8IIAEeI7tbLnjSA+xBGADgCwzSAexFGADgCwzSAexFGAKS80MWITgbOS2KYBnAjwgiAlHf87HlFbCkny6OrpnpNlwNgghFGAKS8wUM0lmUZrgbARCOMAEh5A2GE3XoBNyKMAEh5vtN9u/UyeRVwJ8IIgJQX7RlhgzzAnQgjAFIea4wA7kYYAZDSbNuOrb5aRhgBXGlcYWTLli0qLy9XTk6OqqqqtG/fvhHPfeGFF3Trrbdq+vTpmj59umpqakY9HwAG6+wOqTsUlmVJc6fnmi4HwCRIOIzs2LFD9fX12rBhgw4cOKAlS5aotrZWp06dinv+3r17dd999+k//uM/1NLSorKyMt155506fvz4FRcPwP2O9feKlBTkKCcrw3A1ACZDwmFk48aNWr16terq6nTdddepqalJeXl52rp1a9zzX3rpJf393/+9KioqtGjRIr344ouKRCJqbm6+4uIBuB9DNID7JRRGQqGQ9u/fr5qamoEX8HhUU1OjlpaWMb3GuXPndOHCBc2YMSOxSgGkJV//5NX5hBHAtTITObmjo0PhcFjFxcVDjhcXF+vQoUNjeo3HH39cpaWlQwLNcL29vert7Y09DwaDiZQJwEWOsUEe4HpJvZvm2Wef1fbt2/XKK68oJydnxPMaGxtVWFgYe5SVlSWxSgCpJLb6KmuMAK6VUBgpKipSRkaG2trahhxva2tTSUnJqNc+99xzevbZZ/WrX/1K119//ajnNjQ0KBAIxB6tra2JlAnARVrpGQFcL6Ewkp2draVLlw6ZfBqdjFpdXT3idd/+9rf19a9/Xbt379ayZcsu247X61VBQcGQB4D003MhLH+wRxJhBHCzhOaMSFJ9fb1WrVqlZcuWqbKyUps2bVJ3d7fq6uokSStXrtScOXPU2NgoSfrWt76l9evXa9u2bSovL5ff75ckTZ06VVOnTp3AtwLAbT48c162LU31ZmrGlGzT5QCYJAmHkRUrVqi9vV3r16+X3+9XRUWFdu/eHZvU6vP55PEMdLj84Ac/UCgU0l/91V8NeZ0NGzboa1/72pVVD8DVfJ19G+SVzciTZVmGqwEwWRIOI5L0yCOP6JFHHon7d3v37h3y/OjRo+NpAgBit/XOm8HKq4CbsTcNgJTl6zwvSZo/c4rhSgBMJsIIgJQ1eJgGgHsRRgCkrOgaI6y+CrgbYQRASrJte2DBM8II4GqEEQApqb2rVz0XIvJYUuk0JrACbkYYAZCSor0ipdNylZ3JtyrAzfgKB5CSGKIB0gdhBEBKOnaaMAKkC8IIgJTUym69QNogjABISccYpgHSBmEEQEoaWGOE1VcBtyOMAEg550NhtXf1SqJnBEgHhBEAKSfaK1KQk6nCvCzD1QCYbIQRACknNkTDBnlAWiCMAEg5rDECpBfCCICU4zvNbr1AOiGMAEg5A8M0hBEgHRBGAKQchmmA9EIYAZBSIhFbrWfOSyKMAOmCMAIgpbR19Sh0MaJMj6XZhTmmywGQBIQRACklukHenOm5yszgWxSQDvhKB5BSmC8CpB/CCICU0koYAdIOYQRASokO0xBGgPRBGAGQUv7rw7OSpIXFU80WAiBpCCMAUkZr5zkdO31OmR5LlQtmmi4HQJIQRgCkjN/+b4ck6YZ50zTVm2m4GgDJQhgBkDJeP9IuSfrUx64yXAmAZCKMAEgJ4YitN947LUn61MIiw9UASCbCCICU8M6JgM6eu6B8b6aWzC00XQ6AJCKMAEgJ0fkiN10zk5VXgTTDVzyAlPC7I31h5FaGaIC0QxgBYNz5UFhvHT0jSbrlY4QRIN0QRgAYt+9op0LhiEoLc3R10RTT5QBIMsIIAONe/9/+W3oXFsmyLMPVAEg2wggA414/0ndLL0M0QHoijAAwqr2rV++eDEoijADpijACwKg33uu7i+a62QUqmuo1XA0AEwgjAIx6vX99EVZdBdIXYQSAMbZt6/X+9UU+xRANkLYIIwCMea+9WycDPcrO9KhywQzT5QAwhDACwJjoLb03lk9XTlaG4WoAmEIYAWAMt/QCkAgjAAy5EI7o9+/3hZFbP3aV4WoAmEQYAWDEf7We1Ue9FzUtL0ufKC0wXQ4AgwgjAIyI3kVzyzVF8nhYAh5IZ4QRAEawvgiAKMIIgKTr6rmgP7SelcT6IgAIIwAM+P37nQpHbM2fmaeyGXmmywFgGGEEQNL9jlVXAQxCGAGQdL/tX+zsVuaLABBhBECSnQyc13vt3fJYUvXVhBEAhBEASRa9i+aTc6epMC/LcDUAUgFhBEBSRdcXuZX5IgD6EUYAJE0kYscmr7IfDYAowgiApDnk71LHRyHlZmXoz+dPM10OgBRBGAGQNNFekaqrZ8ibmWG4GgCpInM8F23ZskXf+c535Pf7tWTJEm3evFmVlZUjnv8v//Iveuqpp3T06FEtXLhQ3/rWt/SZz3xm3EVPlBd/+75aO8/J7n9u9/+H3X8k+nysrEHba1iy4h6PutK2hrcbbS/a1uAmB7/0WNodrRRrSBsDbQ5/i/HaHNzupcdHbi/aRrTNeJ/n8Ncb3tZI51zSXpz3Npa2xtPeSG2N9G83/PWG/xuO9JkPf+14n6U15NyR94mxbVsRu6/tiB1tx1Yk0nfMtjXk6yn2mfT/8eaxTkmsLwJgqITDyI4dO1RfX6+mpiZVVVVp06ZNqq2t1eHDhzVr1qxLzn/jjTd03333qbGxUZ/97Ge1bds2LV++XAcOHNDixYsn5E2M1863T+oPvrNGawDS0e0fv8p0CQBSiGXbif1OXlVVpRtvvFHf+973JEmRSERlZWX68pe/rHXr1l1y/ooVK9Td3a1f/vKXsWM33XSTKioq1NTUNKY2g8GgCgsLFQgEVFAwcVuNb/tPn/yB831PBv02OPy38UTE/W2//6CtYT0I/Q1MRHt2nIND2hvl/VmX9GuM0NZIv4lf5v1Jw3/zHvq6I7UfrxdlSJtj+MASaXe0noZ47cVrPV5J8d7faJ/lJZ/jKC8+Uk/V4PrsIe0M7b2ItndJfXF6maI9KZZlxdr1DPpvy7JG7XWJ9uBdM2uK/u+i4njvEIDLjPXnd0I9I6FQSPv371dDQ0PsmMfjUU1NjVpaWuJe09LSovr6+iHHamtr9eqrr47YTm9vr3p7e2PPg8FgImWO2f1V8ybldQEAwNglNIG1o6ND4XBYxcVDf6spLi6W3++Pe43f70/ofElqbGxUYWFh7FFWVpZImQAAwEFS8m6ahoYGBQKB2KO1tdV0SQAAYJIkNExTVFSkjIwMtbW1DTne1tamkpKSuNeUlJQkdL4keb1eeb3eREoDAAAOlVDPSHZ2tpYuXarm5ubYsUgkoubmZlVXV8e9prq6esj5krRnz54RzwcAAOkl4Vt76+vrtWrVKi1btkyVlZXatGmTuru7VVdXJ0lauXKl5syZo8bGRknS2rVrddttt+n555/XPffco+3bt+utt97Sj370o4l9JwAAwJESDiMrVqxQe3u71q9fL7/fr4qKCu3evTs2SdXn88njGehwufnmm7Vt2zY9+eSTeuKJJ7Rw4UK9+uqrxtcYAQAAqSHhdUZMmKx1RgAAwOQZ68/vlLybBgAApA/CCAAAMIowAgAAjCKMAAAAowgjAADAKMIIAAAwKuF1RkyI3n08Wbv3AgCAiRf9uX25VUQcEUa6urokid17AQBwoK6uLhUWFo74945Y9CwSiejEiRPKz8+XZVmmyzEqGAyqrKxMra2tLAA3yfisk4PPOTn4nJODz3ko27bV1dWl0tLSIauzD+eInhGPx6O5c+eaLiOlFBQU8D96kvBZJwefc3LwOScHn/OA0XpEopjACgAAjCKMAAAAowgjDuP1erVhwwZ5vV7Tpbgen3Vy8DknB59zcvA5j48jJrACAAD3omcEAAAYRRgBAABGEUYAAIBRhBEAAGAUYcQlent7VVFRIcuydPDgQdPluMrRo0f1hS98QQsWLFBubq6uueYabdiwQaFQyHRpjrdlyxaVl5crJydHVVVV2rdvn+mSXKexsVE33nij8vPzNWvWLC1fvlyHDx82XZarPfvss7IsS48++qjpUhyDMOISX/nKV1RaWmq6DFc6dOiQIpGIfvjDH+qdd97Rd7/7XTU1NemJJ54wXZqj7dixQ/X19dqwYYMOHDigJUuWqLa2VqdOnTJdmqv8+te/1po1a/T73/9ee/bs0YULF3TnnXequ7vbdGmu9Oabb+qHP/yhrr/+etOlOIsNx9u1a5e9aNEi+5133rEl2X/4wx9Ml+R63/72t+0FCxaYLsPRKisr7TVr1sSeh8Nhu7S01G5sbDRYlfudOnXKlmT/+te/Nl2K63R1ddkLFy609+zZY99222322rVrTZfkGPSMOFxbW5tWr16tn/3sZ8rLyzNdTtoIBAKaMWOG6TIcKxQKaf/+/aqpqYkd83g8qqmpUUtLi8HK3C8QCEgS//9OgjVr1uiee+4Z8v81xsYRG+UhPtu29dBDD+nhhx/WsmXLdPToUdMlpYUjR45o8+bNeu6550yX4lgdHR0Kh8MqLi4ecry4uFiHDh0yVJX7RSIRPfroo7rlllu0ePFi0+W4yvbt23XgwAG9+eabpktxJHpGUtC6detkWdaoj0OHDmnz5s3q6upSQ0OD6ZIdaayf82DHjx/XXXfdpb/+67/W6tWrDVUOjM+aNWv0P//zP9q+fbvpUlyltbVVa9eu1UsvvaScnBzT5TgSy8GnoPb2dp0+fXrUc66++mr9zd/8jX7xi1/IsqzY8XA4rIyMDD3wwAP66U9/OtmlOtpYP+fs7GxJ0okTJ3T77bfrpptu0j/90z/J4yHLj1coFFJeXp5efvllLV++PHZ81apVOnv2rF577TVzxbnUI488otdee02/+c1vtGDBAtPluMqrr76qz3/+88rIyIgdC4fDsixLHo9Hvb29Q/4OlyKMOJjP51MwGIw9P3HihGpra/Xyyy+rqqpKc+fONViduxw/flx33HGHli5dqp///Od8Y5kAVVVVqqys1ObNmyX1DSHMmzdPjzzyiNatW2e4OvewbVtf/vKX9corr2jv3r1auHCh6ZJcp6urS8eOHRtyrK6uTosWLdLjjz/OkNgYMGfEwebNmzfk+dSpUyVJ11xzDUFkAh0/fly333675s+fr+eee07t7e2xvyspKTFYmbPV19dr1apVWrZsmSorK7Vp0yZ1d3errq7OdGmusmbNGm3btk2vvfaa8vPz5ff7JUmFhYXKzc01XJ075OfnXxI4pkyZopkzZxJExogwAlzGnj17dOTIER05cuSSkEfH4vitWLFC7e3tWr9+vfx+vyoqKrR79+5LJrXiyvzgBz+QJN1+++1Djv/kJz/RQw89lPyCgDgYpgEAAEYxAw8AABhFGAEAAEYRRgAAgFGEEQAAYBRhBAAAGEUYAQAARhFGAACAUYQRAABgFGEEAAAYRRgBAABGEUYAAIBRhBEAAGDU/wetCegHLQYC9AAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = BohmanD(N, delta, 10)\n", - "inv.fit(chr)\n", - "x = np.linspace(-5, 5, 50)\n", - "F_x = inv.cdf(x)\n", - "plt.plot(x, F_x)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MP9mAlcjySR4" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 730 - }, - "id": "_fgGrGZRxJ6f", - "outputId": "2ebc2291-ac12-436f-f02c-9df11d1e2bdf" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":13: UserWarning: No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n", - " plt.legend()\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.00796194-5.82441030e-17j 0.00796288+4.48012666e-17j\n", - " 0.00796382+1.81349754e-18j ... 1.00795167-5.11989065e-18j\n", - " 1.00795261+1.70355266e-17j 1.00795355+2.50310941e-17j]\n", - "[0.00796147+1.11226414e-17j 0.00796147+2.48549015e-17j\n", - " 0.00796148+1.17767169e-16j ... 1.00795402+4.14393362e-17j\n", - " 1.00795402-9.83956806e-18j 1.00795402-7.90829793e-17j]\n", - "[0.00796194-8.59996787e-17j 0.00796288-1.76487785e-17j\n", - " 0.00796382+1.56912853e-17j ... 1.00795167-2.59365724e-17j\n", - " 1.00795261-1.07200490e-17j 1.00795355-2.72448147e-18j]\n", - "[0.00715038-4.66438719e-07j 0.00715137-4.67517772e-07j\n", - " 0.00715236-4.68594642e-07j ... 1.00717158+4.68594646e-07j\n", - " 1.00717257+4.67517776e-07j 1.00717357+4.66438723e-07j]\n" - ] - }, - { - "data": { - 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD()]\n", - "\n", - "\n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-0.5, 1.5, 10000)\n", - " F_x = inv.cdf(x)\n", - " print(F_x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "-SkolrlDw9_1" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 577 - }, - "id": "8MBJqTOHyve7", - "outputId": "4c5af66e-160e-4194-a754-31e2a23ce0d1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.-0.j -0.-0.j -0.-0.j ... 0.+0.j 0.+0.j 0.+0.j]\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1699: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%%time\n", - "norm = Norm(0, 1)\n", - "chr = norm.chr\n", - "chrbhm = BohmanA()\n", - "chrbhm.fit(chr)\n", - "make_plot(chrbhm.cdf, -5, 5, 10000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "L-40Nx7yJmBU", - "outputId": "d652c7bd-e746-4e22-ceca-62cfa32e69c4" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.21 s, sys: 275 ms, total: 1.49 s\n", - "Wall time: 1.32 s\n" - ] - } - ], - "source": [ - "%%time\n", - "chrbhm = BohmanA()\n", - "chrbhm.fit(chr)\n", - "make_plot(chrbhm.cdf, -5, 5, 10000)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "mpOJ7oE1ji5D", - "outputId": "3d386158-e6e3-40d0-80c3-9479820b99c7" - }, - "outputs": [ - { - "data": { - "image/png": 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nKzxuhw4d0Lx5c8yePRt5eXl33e+GDRvg6uqKrl27lrvP8PBweHt7Y9asWRY/s+Xtsypyc3Px/vvv44033qjw7FFxcTGGDRuGhx9+GHPnzkXv3r3LdE2SY+OZG5IFvV6PXr16YdCgQTh9+jQWL16Mrl274plnngEAPP7446hbty4iIyPx+uuvQ6FQYM2aNWW+cDw8PDB+/Hh88MEHcHFxQfv27bF06VIolUr8+++/iIiIwDPPPIPPPvsMOp0Ob731lsX2V69ehaurq8VA5spo3bo1Jk2ahA8++AD/+c9/ynQhlDZ58mSMHTsWzz77LMLDw7F06VIoFAro9Xr06dMHUVFR2LhxIy5evFgmvAElXVMvvPACAGDmzJlVqvNu/P398fHHH+OVV17BSy+9JJ45A0rOppW+ZhAAnD59GkDJVGe1Wm0xlmj37t1Yt26dOIi7PNVtczOtVoudO3ciMjISoaGh+OGHH7B9+3ZMnjxZPCsSERGBOXPmoG/fvhg6dCiuX7+ORYsWoUWLFjhx4oS4rxYtWmDKlCmYOXMmunXrhoEDB0Kj0eDQoUMICAhAfHx8tWps3bo1wsPDLaaCAyUB0OyDDz7Anj17EBoaitGjR+Ohhx5CVlYWjhw5gh9//FEc17Vx40a89dZb0Gg0KCwsxNq1a8V9ZGdnw2g0YuvWrWJQ++yzz9CvXz88/PDDiIqKQuPGjZGSkoI9e/bA29sb3333Hc6ePYu4uDh8+eWXeOedd8qMmynN29sbS5YswfDhw9G+fXv85z//QcOGDZGcnIzt27ejS5cuVrtKK+PIkSNo0KAB3n777QrXmz59Ov78808cPXrUIvyTjEg3UYuofFWdCv7zzz8LY8aMEerWrSt4enoKw4YNEzIzMy3W/eWXX4THHntMcHNzEwICAoS3335b2LVrV5kppQaDQZgwYYLg5eUlBAUFCTt37hQ8PDyEyMhIYdKkSYKnp6dw3333Cd9++63F/s3TiktPDy9dY3lTwc2KioqEVq1aCY8++qhQXFxc4eueNWuWUL9+fcHX11dYvXq1OIV3/vz5Qt26dYVGjRoJy5cvt7qtTqcT6tatK/j4+AiFhYVlnr+X6bdmTz75pBAUFCTk5uaK6+HWlOby/pmneJvbKyQkxGL68cWLF61OBa9sm1tjfq3nz58XwsLCBHd3d8HPz0+Ii4srM5X7888/F1q2bCloNBqhVatWwsqVK8Xpw3dasWKF0K5dO0Gj0Qh169YVevToISQkJIjPV3Uq+Lhx44S1a9eKx2/Xrp3F+2CWlpYmjBs3TggMDBTUarXg7+8v9OrVS1i2bJnFse/2XpSeXi8IgnD06FFh4MCBQv369QWNRiM0bdpUGDRokJCYmCgIgiB8+eWXQuvWrYX58+dbvGeCUHYqeOnl4eHhgo+Pj6DVaoXmzZsLI0eOtJjyXtXPIgBh7ty5Fuve+R7t27dPUKlUwqeffmp1PU4FlweGG3JolQ1B98ocbuzVndcnqYjBYBAaNmwovPzyy7VbVBU0bdrUIrTYSnlfnvbEHG5qyt3aes+ePWXCDZGj4ZgbIiezdetWpKenY8SIEVKXQkRUKzjmhshJ/P777zhx4gRmzpyJdu3aoUePHlKXJOrRo0elrt1D9+65554TZyxZ4+fnV+YWHUSOhuGGyEksWbIEa9euRUhISI1fB+Zelb7JJ9WuuXPnVvj8gw8+eNd1iOydQhDuYX4iERERkZ3hmBsiIiKSFYYbIiIikhWnG3NjMplw7do1eHl51egN/oiIiKj2CIKA3NxcBAQEQKms+NyM04Wba9euITAwUOoyiIiIqBquXLmCJk2aVLiO04UbLy8vACWNU9Elwp2FwWDA7t27ERYWxsuQ1yK2s22wnW2D7Ww7bOvbcnJyEBgYKH6PV8Tpwo25K8rb25vhBiU/OO7u7vD29nb6H5zaxHa2DbazbbCdbYdtXVZlhpRwQDERERHJCsMNERERyQrDDREREcmK0425qSyj0QiDwSB1GbXOYDDAxcUFRUVFMBqNUpcjW2znmuHq6nrXKaBERAw3dxAEAampqbh586bUpdiEIAjw9/fHlStXeN2fWsR2rhlKpRLNmjWDq6ur1KUQkR1juLmDOdj4+vrC3d1d9l9EJpMJeXl58PT05F/EtYjtfO/MF+D8999/ERQUJPufTSKqPoabUoxGoxhs6tevL3U5NmEymaDX66HVavmlW4vYzjWjYcOGuHbtGoqLizktlojKxd+ypZjH2Li7u0tcCRFZY+6O4rglIqoIw40VPN1NZJ/4s0lElcFwQ0RERLIiabjZu3cv+vfvj4CAACgUCmzduvWu2yQlJaF9+/bQaDRo0aIFVq1aVet1EhERkeOQNNzk5+ejbdu2WLRoUaXWv3jxIiIiIvDEE0/g2LFjmDBhAl555RXs2rWrlit1PCNGjED//v2lLoOIiMjmJJ0t1a9fP/Tr16/S6y9duhTNmjXDJ598AgB48MEHsX//fsydOxfh4eG1VabD+PvvvzFjxgz88ssvSElJAVByF/SuXbsiJiYGffr0kbhCIqqu7EIDcovs48KixcXFyNIBKTcL4eJiHzXJlaO2tauLEr5eWsmO71BTwQ8cOIDevXtbLAsPD8eECRPK3Uan00Gn04mPc3JyAJTMjLrzCsQGgwGCIMBkMsFkMtVc4TawZcsWDB48GBEREfjiiy+wdOlS5OTkYPLkyVixYgXCwsKwYMECvPbaa9i7dy/69OmDy5cvw8/PD0DJRebGjx+PI0eO4Oeff8aqVasQExODrKws8RiXLl1C8+bNcfjwYYSEhCApKQm9evVCZmYm6tSpgxs3buCJJ55A27ZtsWrVKigUCjz55JNo27Yt5s6dK+5n+vTp2LZtG44cOQIAVo/Vs2dP7Nu3TzwWAHz//fd47733cObMGRQVFQEAnnnmGWzZssVqm5iP8+qrr2LWrFnIzMxEREQEli1bBh8fHwDAoUOHMGXKFBw7dgwGgwEhISH45JNP0L59e3E/N2/exDvvvINt27YhOzsbLVq0wKxZs/D0009j1apVGDVqlNXj39lOGzZswPvvv48zZ84gJCQEy5YtQ+vWrcX19+/fjylTpuCPP/5AgwYNMGDAAMyaNQseHh7iOtaO17ZtW7EtAeCvv/7C22+/jf3798PDwwN9+vTBnDlz0KBBg0rvx2Qy4aOPPsLy5cuRmpqK+++/H1OmTMELL7wAAGXeezOVSoXNmzdjwIABZT4vADB16lS8//77mDNnDsaPHw8AOHXqFF5//XUcOnRI/Pn08fGx+DyYmUwmCIIAg8EAlUpV5nnzz7Tcri5+4mo2Bi8/iGKTIHUppbhg+pF9UhfhJByvrdsF+uCrMaE1us+q/Fw7VLhJTU0Vv4zN/Pz8kJOTg8LCQri5uZXZJj4+HtOnTy+zfPfu3WWmfLu4uMDf3x95eXnQ6/UASr70iwzSBB2tWlnp2SETJkxA165dsXr1agAlr0WlUqFNmzaYN28eDAYDJk2ahOeeew4hISEIDg7GZ599htdffx0AkJWVhXXr1mH69OnIyclBUVERBEEQv2wAIC8vD0BJd2JOTg4KCgoAALm5uSgoKMDAgQMRGBiIuXPnIjc3F0DJXx16vd5iPzqdDkajUVx257G+++47HD161OJY2dnZGDJkCIYPH44vvvgCWq0WsbGx0Ol0FvsuTafT4dy5c9iwYQPWr1+PnJwcvP766xgzZgyWL18OAEhLS8OLL76IWbNmQRAELFq0CBEREfjjjz/g5eUFk8mEvn37Ijc3VzxzeOrUKfG4RUVF8PLywqFDh8TjpqSkoFevXmXaKS4uDvHx8fD19cXMmTPRv39//PHHH1Cr1bh48SKeeuopTJkyBfPmzUNGRgbefvttjB071qLb9s7jLVy4EElJSWIbZGdno1evXhg+fDhmzJiBoqIiTJs2DS+88AK+/fZbcT+FhYUV7mf27Nn4+uuvMXv2bDRv3hy//vorRowYAQ8PD3Tp0sXivb/zuj2FhYXIyckp83lJSUnB/Pnz4ebmhqKiIvFYI0eOhKurK3744QfUr18fW7ZsQXx8vNX3Va/Xo7CwEHv37kVxcbHV9x0AEhISyn3OEW08r0SxSQmlQkDZSEdkf3Kzb2LHjh01uk/z753KcKhwUx2xsbGIiYkRH+fk5CAwMBBhYWHw9va2WLeoqAhXrlyBp6cntNqS02kF+mK0+1CaX5R/TesDd9e7v0VpaWm4evUqYmJixNekVqvh4uIiPh44cCDWr1+P5ORkhIaG4pVXXsHq1asxZcoU5Obm4ueff4ZOpxO/wLRaLRQKhUUbeXp6AgA8PDzg7e0thkNXV1cMHz4cXl5e2LRpEzQajbiNi4sLXF1dLfaj0WigUqnEZaWPZTAYMGPGDLz99tuYOnWqeKxTp06hoKAA7777LgICAgCUdLkZjcYy72Pp4xQVFWHt2rVo3LgxAGDBggXo378/5s+fD39/fzz99NMW26xYsQL16tXD0aNH8fTTT2P37t04fPgw/v77b9x///0AgDZt2ojrmy/K17JlS3GZ+eJyd7bT22+/jWeeeQYKhQJr165FUFAQEhMTMWjQICxcuBBDhw7FpEmTxP0sWLAATzzxBJYvXy5+HhUKBTQajXi8evXqWbTlggUL0K5dO8yePVvcz6pVq9C0aVPxDAxQcoalvP3odDrMnTsXu3fvRufOncXXfPjwYaxduxb9+vUTX5OXl1eZ9ndzc4O3t3eZz8vrr7+OQYMGITExEVqtVtzur7/+wrJly/DYY48BAHx9fct89syKiorg5uaG7t27i21SmsFgQEJCAvr06SObi/wVG02Y9tHPAAxYEdkRXZpLf4FRObazvWJb31beH7LWOFS48ff3R1pamsWytLQ0eHt7Wz1rA5R8wZX+sjVTq9VlPihGoxEKhQJKpVL8a1TKq8mWrqMi5l/yhYWF4voKhUJ8LQDEbhx3d3colUpERUXhvffew++//46HHnoIq1evxqBBg+Dl5SUeOzs72+ILRhAEi7rM+x4+fDgSExMxffp0q+9D6TrMj837ufO/S5YsgY+PD1566SVMnTpVPE7Tpk3h4uKCjRs34o033oBSqSzzGq0dNygoCIGBgeKyLl26wGQy4ezZswgICEBaWhreffddJCUl4fr16zAajSgoKMDVq1ehVCpx4sQJNGnSBK1atbJ6DGufk9LLSrfTo48+KtbboEEDPPDAAzh9+rR4nBMnTmD9+vUW7W0ymXD58mU8+OCDAIAbN27A29vb4n0ufcwTJ04gKSnJajC4ePGi+Dry8vLg4eFhdT8XLlxAQUFBmXFser0e7dq1s3hNQUFBVtvkzp+hY8eOYevWrTh9+jQSExMt3rdmzZph27ZtGDhwoPj5vLNNS+9boVBY/fkt7W7PO5LfLqXjRoEB9Txc0bWlL1xU9nMFDzm1s71jW6NKr9+hwk3nzp3LnOZKSEgQ/7qsDW5qFU7OkGawspu6cieg69ati9DQUHzxxRcYP368xRgNoKRr6NNPP0WTJk3EMR6+vr7o378/Vq1ahbfeegs7d+5EUlKSxXZeXl4WYzlSUlLQs2fPMsdPTU3F5s2bMXToUDz33HN45JFHqvZCb7lx4wZmzpyJLVu2lOmOa9SoEZYsWYJJkyYhNjYWrq6u0Ol0iIiIqNaxzCIjI5GZmYn58+ejadOm0Gg06Ny5s9gtWV5orml5eXl49dVXxW7C0koHiAsXLqBZs2YV7qd///748MMPyzzXqFEj8f+vXbsmngGztg8A2L59u3jGy+zOPxT27dsnBmIAFmewSnvzzTfx1ltvWdRg9vnnnyMyMhJeXl5wc3NDcXGx1bMyzmrHn/8CAPq29rerYENkzyQNN3l5eTh37pz4+OLFizh27Bjq1auHoKAgxMbGIiUlBV988QUAYOzYsVi4cCHefvttvPzyy/jpp5/w1VdfYfv27bVWo0KhqFTXkNQ+++wzPP3003jwwQcxatQoXLx4EQUFBZg1axa++OILXL9+HVu3brUYhPnKK69gyJAhaNiwIZo3b44uXbpY7FOpVKJFixbiYxcX6+3w7bff4r777sPo0aMRFRWF3377rdx1KzJz5kx069YN3bt3x6VLl8o8HxkZiZUrV6Jdu3aYMGECJk2adNfL8CcnJ1t8kf/2229QKpV44IEHAAC//PILFi9ejKeeegoAcOXKFWRkZIjbt2nTBlevXsWZM2fELp3q+uOPP/Dwww8DKAlyZ86cEc/ItG/fHidPnrRob2v27t2LYcOGlft8+/btsXnzZgQHB1f4Hhw6dAjt2rWz+txDDz0EjUaD5ORk9OjRo8J6mjVrZjGg2Jpvv/0WZ86cKffn9LHHHsMzzzyDvXv3Yu3atdiyZQtmzZpV4T6dhcFows6/UgEATz9SNhgSkXWS/hnwxx9/oF27duIv2ZiYGLRr1w5Tp04FAPz7779ITk4W12/WrBm2b9+OhIQEtG3bFp988gk+++wzTgMH0Lp1a5w+fRqTJ0/G2bNn8c8//+DcuXM4cOAAXn75ZZw+fRrdu3e32CY8PBze3t6YPXs2Ro4cWe1j16tXDwDwwQcf4MaNG/jggw8snjcajSgqKhL/FRcXQxAE8ewIUDJQbNmyZfjoo4/KPc6bb74JhUKBuXPnokWLFhZnDMqj1WoRGRmJ48ePY9++feK4D39/fwAlZxrWrFmDf/75B7///juGDRtmcbamR48e6N69O55//nkkJCTg4sWL+OGHH7Bz584qtREAfPTRR0hMTMRff/2FkSNHijOiAGDSpEn49ddfER0djWPHjuHs2bPYtm0boqOjAZR0OS5YsADnz59Hv379kJqaitTUVOTl5ZVMFb01s2jcuHHIysrCkCFDcOjQIZw/fx67du1CVFQUjEYjMjIyMGXKFPzyyy+IjIy0WqeXlxfeeustvPHGG1i9ejXOnz+PI0eOYMGCBeKA9aq+7v/973/l3rNt8+bNWLVqFb7++mu0bNkSvr6+VT6GXB04n4kbBQY08HRFp2b1pC6HyGFIekqiZ8+e4jgOa6xdfbhnz57iTBqypNFoMHbsWIwdOxYjR47EzZs3K7zqs1KpRGRkJOLj4zF8+PB7Pr6HhwdWrFiBvn37YsCAAWIX2MKFC7Fw4cIy64eFhYldYQaDAa+++mq5Z0e+/PJLfPXVVzhy5EiV+l1btGiBgQMH4qmnnkJWVhaefvppLF68WHz+888/x5gxY9C+fXsEBgZi1qxZeOuttyz2sXnzZrz11lsYMmQI8vPz0aJFizIBrjLi4uLwxhtv4OzZswgJCcF3330n3giyTZs2+PnnnzFlyhR069YNgiCgefPmGDx4MABg48aNYpdVaGjZ6ZUDBw5EUlISAgIC8Msvv2DSpEkICwuDTqdD06ZN0bdvXyiVSqxbtw67du3Cli1b0KlTp3JrnTlzJho2bIj4+HhcuHABderUQfv27TF58uQqv+4WLVqUG6TOnDmDV155BV999ZXV8TvObvsJdkkRVYvgZLKzswUAQnZ2dpnnCgsLhZMnTwqFhYUSVCaNqKgooW/fvoLRaLTpcY8ePSr06NGjVo8RFxcntG3btlaPURl79uwRAAiXLl2qdjuvXLlSiIyMtPqcLdrSXtztZ1Sv1wtbt24V9Hq9jSurefpio9Bm2i6h6aTvhV/PZUhdjgU5tbO9Y1vfVtH3953sfzAJ1Yrs7Gz8+eef+PLLLy1m6NiKUqkUz1rQ3bm5uYkXHryTWq0WuwZJPn45l4HsQgMaeGrYJUVURQw3TurZZ5/FwYMH8eqrr+KJJ56w+fHbtGmD3bt32/y4jmrw4MFiF9WdHn74YXzzzTc2rohqm7lLql9rf6iUlbuYJxGVYLhxUuaxLiaTqUoXRnIk06ZNw7Rp06QuAz179rS4IjPR3eiLTdj1d8ksqYg2nCVFVFUcoUZEZGd+OZeBnKJiNPTS4NFgdkkRVRXDjRVCBTO4iEg6zvKzuf3WhfueYpcUUbUw3JRinmJclZtzEZHtmK+NZO2O4HJh2SVl/SrSRFQxjrkpRaVSoU6dOrh+/TqAkvswVfau3I7KZDJBr9ejqKhI0vtoyR3b+d6ZTCakp6fD3d29WlfAdhT7z6Ujt6gYvl4adGxaV+pyiBySfH9DVJP5yrXmgCN3giCgsLAQbm5usg9yUmI71wylUomgoCBZt+H3t2ZJPfVIIyjZJUVULQw3d1AoFGjUqBF8fX1hMBikLqfWGQwG7N27F927d3f6O87WJrZzzXB1dZX1mS9dsREJf6cBAJ7mLCmiamO4KYdKpZJ1v76ZSqUS78LML93aw3amyth3JgO5umL4e2vRPohdUkTVJd8/gYiIHIx5llS/R/zZJUV0DxhuiIjsQJHBiIST7JIiqgkMN0REdmDvmXTk6YrRyEeLdoHskiK6Fww3RER2QLxwH2dJEd0zhhsiIokVGYz48VaXFO8lRXTvGG6IiCT285l05OuNaFzHDe0C60hdDpHDY7ghIpLYdvHCff6yvkAhka0w3BARSajIYMSP/5i7pHgvKaKawHBDRCShpNPXUXCrS6ptEx+pyyGSBYYbIiIJme8lFdGmEbukiGoIww0RkUQK9UYk/lNyk96IRzhLiqimMNwQEUlkz+nrKDQY0aSuG9qwS4qoxjDcEBFJZDu7pIhqBcMNEZEECvTF+OlUSZfU049wlhRRTWK4ISKSwJ5T6Sg0GBFUzx2tG3tLXQ6RrDDcEBFJYPuf1wCwS4qoNjDcEBHZWL7udpcUZ0kR1TyGGyIiG/vp1HUUGUxoWt8dDwewS4qopjHcEBHZmDhL6hF2SRHVBoYbIiIbytMVY8/pW11SbdglRVQbGG6IiGwo8Z806IpNaNbAAw81YpcUUW1guCEisiF2SRHVPoYbIiIbyS0yIOlMOgB2SRHVJoYbIiIb+enUdeiLTbivoQda+XtJXQ6RbDHcEBHZyPe3uqSeZpcUUa1iuCEisoHcIgN+Pl3SJfUUu6SIahXDDRGRDfz4Txr0RhOaN/TAA37skiKqTQw3REQ2IM6SahPALimiWsZwQ0RkAwcvZgEAwh7yk7gSIvljuCEiqmU3C/TIKSoGADRv6ClxNUTyx3BDRFTLkrMKAAC+Xhq4uaokroZI/hhuiIhq2eXMknATVM9d4kqInAPDDRFRLTOfuQmqz3BDZAsMN0REtexKFs/cENkSww0RUS1jtxSRbTHcEBHVMnO3VFN2SxHZBMMNEVEt0heb8G92IQAgkGduiGyC4YaIqBal3CyESQDc1Co09NRIXQ6RU2C4ISKqRcmlBhPztgtEtsFwQ0RUi8zhhl1SRLbDcENEVIuSM/MBcKYUkS0x3BAR1SLOlCKyPYYbIqJalJxVMlOKZ26IbIfhhoiolgiCIHZLccwNke0w3BAR1ZKsfD3y9UYoFECTum5Sl0PkNCQPN4sWLUJwcDC0Wi1CQ0Nx8ODBCtefN28eHnjgAbi5uSEwMBBvvPEGioqKbFQtEVHlXb413sbfWwutWiVxNUTOQ9Jws3HjRsTExCAuLg5HjhxB27ZtER4ejuvXr1tdf/369XjnnXcQFxeHf/75B59//jk2btyIyZMn27hyIqK74w0ziaQhabiZM2cORo8ejaioKDz00ENYunQp3N3dsWLFCqvr//rrr+jSpQuGDh2K4OBghIWFYciQIXc920NEJIVk3jCTSBIuUh1Yr9fj8OHDiI2NFZcplUr07t0bBw4csLrN448/jrVr1+LgwYPo1KkTLly4gB07dmD48OHlHken00Gn04mPc3JyAAAGgwEGg6GGXo3jMrcB26J2sZ1tw97a+WJGHgCgcR2t3dRUE+ytneWMbX1bVdpAsnCTkZEBo9EIPz8/i+V+fn44deqU1W2GDh2KjIwMdO3aFYIgoLi4GGPHjq2wWyo+Ph7Tp08vs3z37t1wd+dfU2YJCQlSl+AU2M62YS/tfPycCoACWcmnsWOH9d9rjsxe2tkZsK2BgoKCSq8rWbipjqSkJMyaNQuLFy9GaGgozp07h/Hjx2PmzJl47733rG4TGxuLmJgY8XFOTg4CAwMRFhYGb29vW5VutwwGAxISEtCnTx+o1Wqpy5EttrNt2Fs7x//9MwAd+j/RGSGBdaQup8bYWzvLGdv6NnPPS2VIFm4aNGgAlUqFtLQ0i+VpaWnw9/e3us17772H4cOH45VXXgEAPPLII8jPz8eYMWMwZcoUKJVlhxBpNBpoNGXvxKtWq53+g1Ia28M22M62YQ/tXGQwIi23pEv8Pl9vyeupDfbQzs6CbY0qvX7JBhS7urqiQ4cOSExMFJeZTCYkJiaic+fOVrcpKCgoE2BUqpLplYIg1F6xRERVdPVGIQQB8NS4oJ6Hq9TlEDkVSbulYmJiEBkZiY4dO6JTp06YN28e8vPzERUVBQAYMWIEGjdujPj4eABA//79MWfOHLRr107slnrvvffQv39/MeQQEdmDK6XuBq5QKCSuhsi5SBpuBg8ejPT0dEydOhWpqakICQnBzp07xUHGycnJFmdq3n33XSgUCrz77rtISUlBw4YN0b9/f7z//vtSvQQiIqsui3cD55WJiWxN8gHF0dHRiI6OtvpcUlKSxWMXFxfExcUhLi7OBpUREVWf+YaZTet7SFwJkfOR/PYLRERylFyqW4qIbIvhhoioFiRnmbulGG6IbI3hhoiohgmCIJ65acpwQ2RzDDdERDUsPVeHIoMJSgUQUIcDiolsjeGGiKiGmc/aBNRxg6sLf80S2Rp/6oiIapg53HC8DZE0GG6IiGrY5UyGGyIpMdwQEdUw89WJg+oz3BBJgeGGiKiGsVuKSFoMN0RENewyww2RpBhuiIhqUKHeiPRcHQCgaT3eeoFICgw3REQ16MqNkrM23loX+LirJa6GyDkx3BAR1SBxphQHExNJhuGGiKgG3b7tArukiKTCcENEVIOSM0tumMm7gRNJh+GGiKgGcRo4kfQYboiIapDYLcUxN0SSYbghIqohJpOAKzcKAfDMDZGUGG6IiGpIWm4R9MUmuCgVaOSjlbocIqfFcENEVEOSb00Db1zXDS4q/nolkgp/+oiIaghvu0BkHxhuiIhqyBWGGyK7wHBDRFRDOA2cyD4w3BAR1RDx1gsMN0SSYrghIqohYrcUr3FDJCmGGyKiGpCnK0Zmvh4Ab71AJDWGGyKiGmCeBl7XXQ1vrVriaoicG8MNEVENEAcT1+fdwImkxnBDRFQDkrNK7gbOwcRE0mO4ISKqAbengbtJXAkRMdwQEdWA5KySG2Y2rcduKSKpMdwQEdWA5MySbinOlCKSHsMNEdE9MpoEXL1RcuaG17ghkh7DDRHRPfo3uxDFJgGuKiX8vbVSl0Pk9BhuiIjukfkaN03qukGlVEhcDREx3BAR3SPzTCmOtyGyDww3RET3yBxumnK8DZFdYLghIrpHl7N4N3Aie8JwQ0R0j64w3BDZFYYbIqJ7dDnTfF8phhsie8BwQ0R0D7ILDMguNAAAAusy3BDZA4YbIqJ7cOVGyVmbBp4aeGhcJK6GiACGGyKieyJ2SfGGmUR2g+GGiOgeJHMwMZHdYbghIroHYripz7uBE9kLhhsionuQnFVyN3CeuSGyHww3RET3gN1SRPaH4YaIqJoMRhOu3SwCwFsvENkThhsiomq6drMQRpMAjYsSDT01UpdDRLcw3BARVVPpu4ErlQqJqyEiM4YbIqJqEu8GzvE2RHaF4YaIqJqSM2+fuSEi+8FwQ0RUTeKZGw4mJrIrDDdERNV0+9YLDDdE9oThhoioGgRBwBVe44bILjHcEBFVw80CA3J1xQA45obI3kgebhYtWoTg4GBotVqEhobi4MGDFa5/8+ZNjBs3Do0aNYJGo8H999+PHTt22KhaIqISl2+dtfHz1kCrVklcDRGV5iLlwTdu3IiYmBgsXboUoaGhmDdvHsLDw3H69Gn4+vqWWV+v16NPnz7w9fXFpk2b0LhxY1y+fBl16tSxffFE5NR42wUi+yVpuJkzZw5Gjx6NqKgoAMDSpUuxfft2rFixAu+8806Z9VesWIGsrCz8+uuvUKvVAIDg4GBblkxEBAClxtvwbuBE9kaycKPX63H48GHExsaKy5RKJXr37o0DBw5Y3ebbb79F586dMW7cOGzbtg0NGzbE0KFDMWnSJKhU1k8L63Q66HQ68XFOTg4AwGAwwGAw1OArckzmNmBb1C62s23Ysp0vpucBABrX0Tjd+8rPs+2wrW+rShtIFm4yMjJgNBrh5+dnsdzPzw+nTp2yus2FCxfw008/YdiwYdixYwfOnTuH1157DQaDAXFxcVa3iY+Px/Tp08ss3717N9zdeTrZLCEhQeoSnALb2TZs0c7HzikBKJGVfAY7dpyu9ePZI36ebYdtDRQUFFR6XUm7parKZDLB19cXy5Ytg0qlQocOHZCSkoKPP/643HATGxuLmJgY8XFOTg4CAwMRFhYGb29vW5VutwwGAxISEtCnTx+xq49qHtvZNmzZzh+e3AugCM880RntgurU6rHsDT/PtsO2vs3c81IZkoWbBg0aQKVSIS0tzWJ5Wloa/P39rW7TqFEjqNVqiy6oBx98EKmpqdDr9XB1dS2zjUajgUZT9m69arXa6T8opbE9bIPtbBu13c66YiP+zSkCADTz9Xba95SfZ9thW6NKr1+yqeCurq7o0KEDEhMTxWUmkwmJiYno3Lmz1W26dOmCc+fOwWQyicvOnDmDRo0aWQ02RES1IeVGIQQBcHdVoYEnf/cQ2RtJr3MTExOD5cuXY/Xq1fjnn3/w3//+F/n5+eLsqREjRlgMOP7vf/+LrKwsjB8/HmfOnMH27dsxa9YsjBs3TqqXQERO6HKpaeAKhULiaojoTpKOuRk8eDDS09MxdepUpKamIiQkBDt37hQHGScnJ0OpvJ2/AgMDsWvXLrzxxhto06YNGjdujPHjx2PSpElSvQQickLmaeC8MjGRfZJ8QHF0dDSio6OtPpeUlFRmWefOnfHbb7/VclVEROVL5g0zieya5LdfICJyNOZuqab1GW6I7BHDDRFRFbFbisi+MdwQEVWBIAjifaWaMtwQ2SWGGyKiKsjI06NAb4RCATSu6yZ1OURkBcMNEVEVmM/aNPLWQuNi/Z52RCQthhsioioQ7wbOwcREdovhhoioCi5zGjiR3WO4ISKqguQshhsie8dwQ0RUBbe7pTwkroSIysNwQ0RUBZez8gHwzA2RPWO4ISKqpCKDEWk5OgAMN0T2jOGGiKiSzF1SXhoX1HVXS1wNEZWH4YaIqJKSS912QaFQSFwNEZWH4YaIqJI4U4rIMTDcEBFVkvkaN7wbOJF9Y7ghIqok3g2cyDEw3BARVRK7pYgcA8MNEVElmEyCGG7YLUVk31yqs9E///yDDRs2YN++fbh8+TIKCgrQsGFDtGvXDuHh4Xj++eeh0WhqulYiIsmk5+mgKzZBpVQgoI6b1OUQUQWqdObmyJEj6N27N9q1a4f9+/cjNDQUEyZMwMyZM/HSSy9BEARMmTIFAQEB+PDDD6HT6WqrbiIimzKftQmoo4VaxZPeRPasSmdunn/+eUycOBGbNm1CnTp1yl3vwIEDmD9/Pj755BNMnjz5XmskIpIc7wZO5DiqFG7OnDkDtfruV+Xs3LkzOnfuDIPBUO3CiIjsCQcTEzmOKp1brUywAYCCgoIqrU9EZO/Eu4HX493AiexdtTuOe/XqhZSUlDLLDx48iJCQkHupiYjI7lzO5N3AiRxFtcONVqtFmzZtsHHjRgCAyWTCtGnT0LVrVzz11FM1ViARkT1IzioEwHBD5AiqNRUcALZv345Fixbh5ZdfxrZt23Dp0iVcvnwZ33//PcLCwmqyRiIiSeXripGRVzL7M4jXuCGye9UONwAwbtw4XL16FR9++CFcXFyQlJSExx9/vKZqIyKyC1dulIy38XFTw8eNYwmJ7F21u6Vu3LiB559/HkuWLMGnn36KQYMGISwsDIsXL67J+oiIJJfMaeBEDqXaZ25at26NZs2a4ejRo2jWrBlGjx6NjRs34rXXXsP27duxffv2mqyTiEgy4jRwdkkROYRqn7kZO3Ys9u7di2bNmonLBg8ejOPHj0Ov19dIcURE9oDXuCFyLNU+c/Pee+9ZXd6kSRMkJCRUuyAiInvDcEPkWKp05iY5OblKO7d2HRwiIkdjHnPTlOGGyCFUKdw8+uijePXVV3Ho0KFy18nOzsby5cvRunVrbN68+Z4LJCKSktEk4OqNkmvcBDLcEDmEKnVLnTx5Eu+//z769OkDrVaLDh06ICAgAFqtFjdu3MDJkyfx999/o3379vjoo494MT8icnhpOUXQG01wUSrQyEcrdTlEVAlVOnNTv359zJkzB//++y8WLlyIli1bIiMjA2fPngUADBs2DIcPH8aBAwcYbIhIFsx3A29S1w0uqmrPwSAiG6rygOILFy6gWbNmeOGFF/DCCy/URk1ERHbDfMNMdkkROY4q/xnSsmVLpKeni48HDx6MtLS0Gi2KiMhemGdKNeU1bogcRpXDjSAIFo937NiB/Pz8GiuIiMieXOY0cCKHww5kIqIK8Bo3RI6nyuFGoVBAoVCUWUZEJEfJmSVnpoPqeUhcCRFVVpUHFAuCgJEjR0Kj0QAAioqKMHbsWHh4WP7gf/PNNzVTIRGRRHKKDLhRYAAABNZzk7gaIqqsKoebyMhIi8cvvfRSjRVDRGRPzDOl6nm4wkurlrgaIqqsKoeblStX1kYdRER2x3zbBY63IXIsHFBMRFQODiYmckwMN0RE5WC4IXJMDDdEROUQww0v4EfkUBhuiIjKwTM3RI6J4YaIyIpiowkpNwoBMNwQORqGGyIiK/7NLkKxSYCrSgl/b63U5RBRFTDcEBFZYe6SalLPDUolr8JO5EgYboiIrOB4GyLHxXBDRGTF5VsX8GvKcEPkcBhuiIisMN96IZDhhsjhMNwQEVlh7pZqWp93AydyNAw3RERWXM7MB8AxN0SOiOGGiOgO2QUG5BQVAwAC67lJXA0RVZVdhJtFixYhODgYWq0WoaGhOHjwYKW227BhAxQKBQYMGFC7BRKRU7mcVXLWpqGXBu6uLhJXQ0RVJXm42bhxI2JiYhAXF4cjR46gbdu2CA8Px/Xr1yvc7tKlS3jrrbfQrVs3G1VKRM6C08CJHJvk4WbOnDkYPXo0oqKi8NBDD2Hp0qVwd3fHihUryt3GaDRi2LBhmD59Ou677z4bVktEzoDhhsixSRpu9Ho9Dh8+jN69e4vLlEolevfujQMHDpS73YwZM+Dr64tRo0bZokwicjLJmQw3RI5M0s7kjIwMGI1G+Pn5WSz38/PDqVOnrG6zf/9+fP755zh27FiljqHT6aDT6cTHOTk5AACDwQCDwVC9wmXE3AZsi9rFdraNmmpn80ypxj4avmdW8PNsO2zr26rSBg41Ui43NxfDhw/H8uXL0aBBg0ptEx8fj+nTp5dZvnv3bri7868ys4SEBKlLcApsZ9u413Y+naICoMDV08ew499jNVKTHPHzbDtsa6CgoKDS60oabho0aACVSoW0tDSL5WlpafD39y+z/vnz53Hp0iX0799fXGYymQAALi4uOH36NJo3b26xTWxsLGJiYsTHOTk5CAwMRFhYGLy9vWvy5Tgkg8GAhIQE9OnTB2q1WupyZIvtbBs10c76YhPe+O1HAMDgiF7w9dLUZImywM+z7bCtbzP3vFSGpOHG1dUVHTp0QGJiojid22QyITExEdHR0WXWb9WqFf7880+LZe+++y5yc3Mxf/58BAYGltlGo9FAoyn7y0mtVjv9B6U0todtsJ1t417aOSU7HyYB0LgoEVDXAwoF7wheHn6ebYdtjSq9fsm7pWJiYhAZGYmOHTuiU6dOmDdvHvLz8xEVFQUAGDFiBBo3boz4+HhotVq0bt3aYvs6deoAQJnlRETVUXqmFIMNkWOSPNwMHjwY6enpmDp1KlJTUxESEoKdO3eKg4yTk5OhVEo+Y52InMRl8Z5SHJNH5KgkDzcAEB0dbbUbCgCSkpIq3HbVqlU1XxAROS3eDZzI8fGUCBFRKbzGDZHjY7ghIiqF3VJEjo/hhojoFkEQxG4pnrkhclwMN0REt9woMCBPVwwAaFKX4YbIUTHcEBHdYr7tgr+3Flq1SuJqiKi6GG6IiG7h3cCJ5IHhhojoFnGmFAcTEzk0hhsiolt45oZIHhhuiIhuYbghkgeGGyKiW8Rww24pIofGcENEBKDIYERqThEAnrkhcnQMN0REAFJuFkIQAHdXFep7uEpdDhHdA4YbIiJY3lNKoVBIXA0R3QuGGyIicDAxkZww3BARgeGGSE4YboiIAFzO5N3AieSC4YaICBDvBh7IMzdEDo/hhoicniAI7JYikhGGGyJyeul5OhQajFAogCZ1GW6IHB3DDRE5PXOXVICPG1xd+GuRyNHxp5iInJ55MHFgPTeJKyGimsBwQ0ROzzzepmk9D4krIaKawHBDRE6PN8wkkheGGyJyeqVvvUBEjo/hhoicHqeBE8kLww0RObVCvRHXc3UAGG6I5ILhhoic2pUbJWdtvLQuqOOulrgaIqoJDDdE5NRKj7dRKBQSV0NENYHhhoicGsfbEMkPww0ROTVOAyeSH4YbInJqPHNDJD8MN0Tk1BhuiOSH4YaInJbJJPDWC0QyxHBDRE7req4O+mITVEoFGtXRSl0OEdUQhhsiclrmszYBdbRQq/jrkEgu+NNMRE7rcmY+AHZJEckNww0ROa0rt87cBHIwMZGsMNwQkdO6zJlSRLLEcENETkucKcUL+BHJCsMNETmtKzxzQyRLDDdE5JTydMXIyNMD4K0XiOSG4YaInJL5rE0ddzW8tWqJqyGimsRwQ0ROibddIJIvhhsickrJmQw3RHLFcENETolnbojki+GGiJwSww2RfDHcEJFTEsMNZ0oRyQ7DDRE5HaNJwNUbPHNDJFcMN0TkdFJzimAwClCrFGjk4yZ1OURUwxhuiMjpmO8G3qSuO1RKhcTVEFFNY7ghIqfDu4ETyRvDDRE5ncviNW7YJUUkRww3ROR0xLuB1/OQuBIiqg0MN0TkdNgtRSRvDDdE5HQu8wJ+RLLGcENETiW70ICbBQYAvIAfkVzZRbhZtGgRgoODodVqERoaioMHD5a77vLly9GtWzfUrVsXdevWRe/evStcn4ioNHOXVH0PV3hqXCSuhohqg+ThZuPGjYiJiUFcXByOHDmCtm3bIjw8HNevX7e6flJSEoYMGYI9e/bgwIEDCAwMRFhYGFJSUmxcORE5omSOtyGSPcnDzZw5czB69GhERUXhoYcewtKlS+Hu7o4VK1ZYXX/dunV47bXXEBISglatWuGzzz6DyWRCYmKijSsnIkckzpRilxSRbEkabvR6PQ4fPozevXuLy5RKJXr37o0DBw5Uah8FBQUwGAyoV69ebZVJRDLCu4ETyZ+kHc4ZGRkwGo3w8/OzWO7n54dTp05Vah+TJk1CQECARUAqTafTQafTiY9zcnIAAAaDAQaDoZqVy4e5DdgWtYvtbBuVaedLGXkAgMY+Gr4f1cTPs+2wrW+rShs49Gi6Dz74ABs2bEBSUhK0Wq3VdeLj4zF9+vQyy3fv3g13d/7lZpaQkCB1CU6B7WwbFbXz6asqAAqknDmOHanHbVeUDPHzbDts65KemsqSNNw0aNAAKpUKaWlpFsvT0tLg7+9f4bazZ8/GBx98gB9//BFt2rQpd73Y2FjExMSIj3NycsRByN7e3vf2AmTAYDAgISEBffr0gVqtlroc2WI728bd2rnYaELM74kABLzY70k08rH+RxFVjJ9n22Fb32bueakMScONq6srOnTogMTERAwYMAAAxMHB0dHR5W730Ucf4f3338euXbvQsWPHCo+h0Wig0WjKLFer1U7/QSmN7WEbbGfbKK+d/80pgNEkwNVFiSb1PKHkHcHvCT/PtsO2RpVev+TdUjExMYiMjETHjh3RqVMnzJs3D/n5+YiKigIAjBgxAo0bN0Z8fDwA4MMPP8TUqVOxfv16BAcHIzU1FQDg6ekJT09PyV4HEdk/cRp4XTcGGyIZkzzcDB48GOnp6Zg6dSpSU1MREhKCnTt3ioOMk5OToVTentS1ZMkS6PV6vPDCCxb7iYuLw7Rp02xZOhE5GM6UInIOkocbAIiOji63GyopKcni8aVLl2q/ICKSpctZ+QCApvV5N3AiOZP8In5ERLbCu4ETOQeGGyJyGpcz2S1F5AwYbojIKQiCwFsvEDkJhhsicgpnr+cht6gYGhclz9wQyRzDDRE5hX1nMwAAnZrVg1atkrgaIqpNDDdE5BR+OVcSbrq2aCBxJURU2xhuiEj29MUm/HYhEwDQtSXDDZHcMdwQkewdTb6BAr0R9T1c8aA/7ylHJHcMN0Qke/tvdUk93qIBb7tA5AQYbohI9szhphvH2xA5BYYbIpK17EIDjl+5CQDowvE2RE6B4YaIZO3A+UyYBOC+Bh5oXMdN6nKIyAYYbohI1sQp4DxrQ+Q0GG6ISNb28/o2RE6H4YaIZOvqjQJczMiHSqnAY83rS10OEdkIww0Rydb+W7dcaNvEB95atcTVEJGtMNwQkWyJXVItG0pcCRHZEsMNEcmSySTg1/Mlt1zoxsHERE6F4YaIZOnkvznIytfDw1WFkMA6UpdDRDbEcENEsmTuknrsvvpQq/irjsiZ8CeeiGTJPJiY17chcj4MN0QkO0UGIw5eygLA69sQOSOGGyKSncPJN6EvNsHPW4MWvp5Sl0NENsZwQ0Sy88u5kllSXVs0hEKhkLgaIrI1hhsikp1fOAWcyKkx3BCRrOQZgJP/5gIAHm/BWy4QOSOGGyKSlTPZJd1Qrfy94OullbgaIpICww0RycrpW+GGs6SInBfDDRHJhiAIOH3zVrjheBsip8VwQ0SycTmrADf0CqhVCnRqVk/qcohIIgw3RCQb5ing7YPqwN3VReJqiEgqDDdEJBu/nC+5KnGX5pwlReTMGG6ISBaKjSYcuFASbh5nuCFyagw3RCQLJ1KykacrhrtKQOsAb6nLISIJMdwQkSyY7wLe0keASslbLhA5M4YbIpIFc7h5oI4gcSVEJDWGGyJyePm6YhxJvgEAeMCH4YbI2THcEJHD+/1iJopNAprUdUMD3nGByOkx3BCRw9t3q0uKU8CJCGC4ISIZ2C+GG16VmIgYbojIwaXlFOHs9TwoFMBj9zHcEBHDDRE5OPNZm0ca+6Cuu6vE1RCRPWC4ISKHtv9cSbjp2oJ3ASeiEgw3ROSwBEFguCGiMhhuiMhhnUnLQ3quDlq1Eh2C60pdDhHZCYYbInJY+86mAwA6NasPjYtK4mqIyF4w3BCRwzJ3SXVjlxQRlcJwQ0QOSV9swu8XsgAAXRhuiKgUhhsickhHkm+g0GBEA09XtPL3krocIrIjDDdE5JDEqxK3aAClUiFxNURkTxhuiMghmcfbsEuKiO7EcENEDie7wIATV28CALq1ZLghIksMN0TkcA5cyIBJAJo39EAjHzepyyEiO8NwQ0QOZ9+t8TbdWjaUuBIiskcMN0TkcH7heBsiqgDDDRE5lCtZBbiUWQCVUoHH7qsndTlEZIcYbojIoZhnSbULrAMvrVriaojIHtlFuFm0aBGCg4Oh1WoRGhqKgwcPVrj+119/jVatWkGr1eKRRx7Bjh07bFQpEUmNU8CJ6G4kDzcbN25ETEwM4uLicOTIEbRt2xbh4eG4fv261fV//fVXDBkyBKNGjcLRo0cxYMAADBgwAH/99ZeNKyciWzOZBPxqvp8Up4ATUTlcpC5gzpw5GD16NKKiogAAS5cuxfbt27FixQq88847ZdafP38++vbti4kTJwIAZs6ciYSEBCxcuBBLly61ae2l6YqNSM/VSXb86iouLkaWDki5WQgXF4PU5cgW27lmnLuehxsFBnhqXNA2sI7U5RCRnZI03Oj1ehw+fBixsbHiMqVSid69e+PAgQNWtzlw4ABiYmIsloWHh2Pr1q1W19fpdNDpboeOnJwcAIDBYIDBUHNfMsev3MSgZRV3p9kvF0w/sk/qIpwA27mmhDarC5iMMJiMFsvNP9M1+bNNZbGdbYdtfVtV2kDScJORkQGj0Qg/Pz+L5X5+fjh16pTVbVJTU62un5qaanX9+Ph4TJ8+vczy3bt3w93dvZqVl3UpF1ArVDW2PyKyTq0CWiC1wrF2CQkJNqzIebGdbYdtDRQUFFR6Xcm7pWpbbGysxZmenJwcBAYGIiwsDN7e3jV6rNdqdG+2YTAYkJCQgD59+kCt5syT2sJ2tg22s22wnW2HbX2bueelMiQNNw0aNIBKpUJaWprF8rS0NPj7+1vdxt/fv0rrazQaaDSaMsvVarXTf1BKY3vYBtvZNtjOtsF2th22Nar0+iWdLeXq6ooOHTogMTFRXGYymZCYmIjOnTtb3aZz584W6wMlp+vKW5+IiIici+TdUjExMYiMjETHjh3RqVMnzJs3D/n5+eLsqREjRqBx48aIj48HAIwfPx49evTAJ598goiICGzYsAF//PEHli1bJuXLICIiIjshebgZPHgw0tPTMXXqVKSmpiIkJAQ7d+4UBw0nJydDqbx9gunxxx/H+vXr8e6772Ly5Mlo2bIltm7ditatW0v1EoiIiMiOSB5uACA6OhrR0dFWn0tKSiqz7MUXX8SLL75Yy1URERGRI5L8CsVERERENYnhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkheGGiIiIZIXhhoiIiGSF4YaIiIhkxS6uUGxLgiAAqNqt0+XMYDCgoKAAOTk5Tn/H2drEdrYNtrNtsJ1th219m/l72/w9XhGnCze5ubkAgMDAQIkrISIioqrKzc2Fj49PhesohMpEIBkxmUy4du0avLy8oFAopC5Hcjk5OQgMDMSVK1fg7e0tdTmyxXa2DbazbbCdbYdtfZsgCMjNzUVAQIDFDbWtcbozN0qlEk2aNJG6DLvj7e3t9D84tsB2tg22s22wnW2HbV3ibmdszDigmIiIiGSF4YaIiIhkheHGyWk0GsTFxUGj0UhdiqyxnW2D7WwbbGfbYVtXj9MNKCYiIiJ545kbIiIikhWGGyIiIpIVhhsiIiKSFYYbIiIikhWGGypDp9MhJCQECoUCx44dk7ocWbl06RJGjRqFZs2awc3NDc2bN0dcXBz0er3UpcnCokWLEBwcDK1Wi9DQUBw8eFDqkmQlPj4ejz76KLy8vODr64sBAwbg9OnTUpclex988AEUCgUmTJggdSkOg+GGynj77bcREBAgdRmydOrUKZhMJnz66af4+++/MXfuXCxduhSTJ0+WujSHt3HjRsTExCAuLg5HjhxB27ZtER4ejuvXr0tdmmz8/PPPGDduHH777TckJCTAYDAgLCwM+fn5UpcmW4cOHcKnn36KNm3aSF2KQ+FUcLLwww8/ICYmBps3b8bDDz+Mo0ePIiQkROqyZO3jjz/GkiVLcOHCBalLcWihoaF49NFHsXDhQgAl95ELDAzE//3f/+Gdd96RuDp5Sk9Ph6+vL37++Wd0795d6nJkJy8vD+3bt8fixYvxv//9DyEhIZg3b57UZTkEnrkhUVpaGkaPHo01a9bA3d1d6nKcRnZ2NurVqyd1GQ5Nr9fj8OHD6N27t7hMqVSid+/eOHDggISVyVt2djYA8PNbS8aNG4eIiAiLzzVVjtPdOJOsEwQBI0eOxNixY9GxY0dcunRJ6pKcwrlz57BgwQLMnj1b6lIcWkZGBoxGI/z8/CyW+/n54dSpUxJVJW8mkwkTJkxAly5d0Lp1a6nLkZ0NGzbgyJEjOHTokNSlOCSeuZG5d955BwqFosJ/p06dwoIFC5Cbm4vY2FipS3ZIlW3n0lJSUtC3b1+8+OKLGD16tESVE1XPuHHj8Ndff2HDhg1SlyI7V65cwfjx47Fu3TpotVqpy3FIHHMjc+np6cjMzKxwnfvuuw+DBg3Cd999B4VCIS43Go1QqVQYNmwYVq9eXdulOrTKtrOrqysA4Nq1a+jZsycee+wxrFq1Ckol/864F3q9Hu7u7ti0aRMGDBggLo+MjMTNmzexbds26YqToejoaGzbtg179+5Fs2bNpC5HdrZu3YrnnnsOKpVKXGY0GqFQKKBUKqHT6Syeo7IYbggAkJycjJycHPHxtWvXEB4ejk2bNiE0NBRNmjSRsDp5SUlJwRNPPIEOHTpg7dq1/CVVQ0JDQ9GpUycsWLAAQEm3SVBQEKKjozmguIYIgoD/+7//w5YtW5CUlISWLVtKXZIs5ebm4vLlyxbLoqKi0KpVK0yaNIndgJXAMTcEAAgKCrJ47OnpCQBo3rw5g00NSklJQc+ePdG0aVPMnj0b6enp4nP+/v4SVub4YmJiEBkZiY4dO6JTp06YN28e8vPzERUVJXVpsjFu3DisX78e27Ztg5eXF1JTUwEAPj4+cHNzk7g6+fDy8ioTYDw8PFC/fn0Gm0piuCGyoYSEBJw7dw7nzp0rExp5EvXeDB48GOnp6Zg6dSpSU1MREhKCnTt3lhlkTNW3ZMkSAEDPnj0tlq9cuRIjR460fUFE5WC3FBEREckKRzESERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEBERkaww3BAREZGsMNwQERGRrDDcEJHDS09Ph7+/P2bNmiUu+/XXX+Hq6orExEQJKyMiKfDeUkQkCzt27MCAAQPw66+/4oEHHkBISAieffZZzJkzR+rSiMjGGG6ISDbGjRuHH3/8ER07dsSff/6JQ4cOQaPRSF0WEdkYww0RyUZhYSFat26NK1eu4PDhw3jkkUekLomIJMAxN0QkG+fPn8e1a9dgMplw6dIlqcshIonwzA0RyYJer0enTp0QEhKCBx54APPmzcOff/4JX19fqUsjIhtjuCEiWZg4cSI2bdqE48ePw9PTEz169ICPjw++//57qUsjIhtjtxQRObykpCTMmzcPa9asgbe3N5RKJdasWYN9+/ZhyZIlUpdHRDbGMzdEREQkKzxzQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREssJwQ0RERLLCcENERESywnBDREREsvL/9M5s1VXOigIAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "make_plot(unif.cdf, -5, 5, 30)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "dLBJ7JnlxVTK", - "outputId": "e36821ca-128b-446a-a472-8fcd21f76889" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 743 ms, sys: 110 ms, total: 853 ms\n", - "Wall time: 933 ms\n" - ] - } - ], - "source": [ - "%%time\n", - "chr_a = A(N, delta, chr)\n", - "chr_a.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "JbEWLSIWxWa_", - "outputId": "6d0e7b71-23f0-4131-a226-552f354d9f51" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 1.45 s, sys: 101 ms, total: 1.55 s\n", - "Wall time: 1.47 s\n" - ] - } - ], - "source": [ - "chr_b = B(N, delta, chr)\n", - "%time chr_b.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "MwvNWzFIxW7Q", - "outputId": "ee231453-261b-4b39-cf9f-fc4e058040b8" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 812 ms, sys: 88.2 ms, total: 900 ms\n", - "Wall time: 808 ms\n" - ] - } - ], - "source": [ - "chr_c = C(N, delta, chr)\n", - "%time chr_c.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "SqB52ggtxXVl", - "outputId": "bd2095c0-47b7-4424-e3e2-986ed0069fb1" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 7.16 s, sys: 122 ms, total: 7.28 s\n", - "Wall time: 7.27 s\n" - ] - } - ], - "source": [ - "chr_d = D(N, delta, chr, 10)\n", - "%time chr_d.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 506 - }, - "id": "Cfj45thPxX5W", - "outputId": "a769ecad-992a-44af-ce0e-cffaf5dfc1e7" - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "CPU times: user 8.71 s, sys: 130 ms, total: 8.84 s\n", - "Wall time: 8.82 s\n" - ] - } - ], - "source": [ - "chr_e = E(N, delta, chr, 10)\n", - "%time chr_e.make_plot(-5, 5, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "h35chYOh2UE3", - "outputId": "ddfc000a-0e0e-43ee-94f1-86f9130546c9" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":7: RuntimeWarning: invalid value encountered in scalar divide\n", - " return (exp(1j * x * self.b) - exp(1j * x * self.a)) / (1j * x * (self.b - self.a))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(nan+nanj)\n", - "(0.0002499414186070202-6.762535332315243e-19j)\n", - "(0.0008875862232762507+3.301580286344398e-18j)\n", - "(0.002045592176573915+1.5799367952258596e-18j)\n", - "(0.0036096187981053624+4.8732232299723125e-18j)\n", - "(0.0056470537986876715-2.247224259600549e-18j)\n", - "(0.008132009531823681-2.0423971073549893e-19j)\n", - "(0.011072482920973868+1.5981356437379623e-18j)\n", - "(0.014462144250016339+5.853883239436446e-18j)\n", - "(0.018295287147039287-5.950420506588283e-18j)\n", - "(0.022589376575070232+4.397179300751025e-18j)\n", - "(0.027333153440452427+6.77206605317268e-18j)\n", - "(0.03253076969486711+1.0800314423920805e-18j)\n", - "(0.03817253603715261-2.8068380760150523e-18j)\n", - "(0.044278629684486315-1.0513017206034133e-18j)\n", - "(0.0508265115623728-3.0356061384471985e-18j)\n", - "(0.057828922025119626-1.3877075729651294e-19j)\n", - "(0.0652848576942221+5.023517977735488e-18j)\n", - "(0.07318729232133578-5.710778763879993e-18j)\n", - "(0.08155025749563313-3.991889035453754e-18j)\n", - "(0.09035662826636429-3.975107939379284e-18j)\n", - "(0.0996194635838266-2.670058102515745e-18j)\n", - "(0.10933251149166684+1.1404053754164611e-17j)\n", - "(0.11949622606412622+7.245026423672217e-18j)\n", - "(0.13011706689967903+1.3789989017968018e-18j)\n", - "(0.14118315265240716-9.106113275494094e-18j)\n", - "(0.15270466540475547+3.50969379366653e-18j)\n", - "(0.1646770321626129-4.022343525202836e-19j)\n", - "(0.17710210716313363-3.650310834588583e-18j)\n", - "(0.18997563533380002-1.4907122866195015e-18j)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - }, - "id": "u-gtBNzc3eUf", - "outputId": "8cbc25eb-f1e7-4fd7-fbba-35287119408b" - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":7: RuntimeWarning: invalid value encountered in scalar divide\n", - " return (exp(1j * x * self.b) - exp(1j * x * self.a)) / (1j * x * (self.b - self.a))\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(nan + nanj)\n", - "(0.00038634499457157 - 5.44442825367956e-19j)\n", - "(0.000897286919903565 - 1.56348752486184e-18j)\n", - "(0.00203211516533013 + 1.2226292200259e-18j)\n", - "(0.00361346176327356 + 6.48526574191118e-18j)\n", - "(0.0056469093168418 - 2.00347135730946e-18j)\n", - "(0.00813189933090962 - 6.24544308602384e-18j)\n", - "(0.0110685839576376 + 2.62149907154718e-18j)\n", - "(0.0144570820834944 + 2.21555265712953e-18j)\n", - "(0.0182973093513688 - 4.27481291416407e-18j)\n", - "(0.0225893344034734 - 8.96640028170143e-19j)\n", - "(0.0273331378854852 - 5.84466533268314e-20j)\n", - "(0.0325287215780115 - 5.77946829928842e-18j)\n", - "(0.0381760963159961 - 8.97324140337815e-18j)\n", - "(0.0442752535107747 + 2.55257569937041e-18j)\n", - "(0.0508261995120664 + 5.85958420067211e-18j)\n", - "(0.0578289333212659 + 4.16133434109437e-18j)\n", - "(0.0652834539923396 - 6.71860957433856e-18j)\n", - "(0.0731897634568402 - 8.85161820953318e-19j)\n", - "(0.0815478610498611 + 1.05003120282329e-17j)\n", - "(0.0903577466708462 - 1.06547039486765e-17j)\n", - "(0.0996194212303731 - 6.68866913798652e-18j)\n", - "(0.109332883992012 + 1.12955132592334e-18j)\n", - "(0.119498135337263 + 4.52142270360577e-18j)\n", - "(0.130115175628487 + 1.7906010578092e-17j)\n", - "(0.141184004177178 - 1.06966308419915e-18j)\n", - "(0.152704621739461 - 2.52204744006529e-18j)\n", - "(0.164677027913269 + 1.11305652466901e-17j)\n", - "(0.177101222753054 + 6.9543041291525e-18j)\n", - "(0.189977206557867 - 2.87049823145739e-18j)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/matplotlib/cbook.py:1345: RuntimeWarning: invalid value encountered in cast\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 87, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_b = B(N, delta, chr)\n", - " err = chr_b.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kOq_Y3KW3n6B" - }, - "outputs": [], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "deltas = np.linspace(0, 1, 30)\n", - "errors = []\n", - "for delta in deltas:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err.real)\n", - "\n", - "plt.plot(deltas, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "LiYF0lQ63kVn" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 418 - }, - "id": "4PG628UY2FYy", - "outputId": "967eabed-d0fc-4f94-b8a4-0879d9b06b95" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(178.817628466268 + 1.10213157603017e-17j)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mN\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mNs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0mchr_a\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdelta\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchr_a\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_error__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munif\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlinspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m30\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 9\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "Ns = [1e3, 1e4, 1e5, 1e6, 1e7]\n", - "delta = 10\n", - "errors = []\n", - "for N in Ns:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err)\n", - "\n", - "plt.plot(Ns, errors)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 435 - }, - "id": "o94PCGL7xur-", - "outputId": "47d0120d-fa54-4370-e449-67c15128f698" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(0.00191212599817633 - 5.00863283981901e-18j)\n", - "(0.00189961626801374 + 2.52298778735686e-18j)\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - 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\u001b[0msman\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0msexp\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "Ns = [1e3, 1e4, 1e5, 1e6, 1e7]\n", - "delta = 1e-1\n", - "errors = []\n", - "for N in Ns:\n", - " chr_a = A(N, delta, chr)\n", - " err = chr_a.__get_error__(unif.cdf, np.linspace(-3, 3, 30))\n", - " print(err)\n", - " errors.append(err)\n", - "\n", - "plt.plot(Ns, errors)" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/Untitled.ipynb b/examples/Untitled.ipynb deleted file mode 100644 index ab64705..0000000 --- a/examples/Untitled.ipynb +++ /dev/null @@ -1,244 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "e6900b52-a69a-4b15-9ce8-3e774ef7f4aa", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "42ba9a82-f013-48f0-aef0-59fd7b005bc2", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.Bohman.BohmansInverters import BohmanE" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "e7957504-b3ad-4289-9cde-5fa821e442f0", - "metadata": {}, - "outputs": [], - "source": [ - "from tests.levy import Levy\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "0fe712cc-a8fb-4420-9e58-c743bc0a0a85", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "cbc0e92f-0168-43d1-8772-84dea364b653", - "metadata": {}, - "outputs": [], - "source": [ - "l = Levy(2, -5)\n", - "x = np.linspace(-5, 5, 100)\n", - "x = x[x!=0]" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "c15e419d-f533-402e-920a-5e60770a739d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "F_x = l.cdf(x)\n", - "\n", - "plt.plot(x, F_x, label='Функция распределения')\n", - "plt.title('График функции распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "b62a5e68-4ad5-4654-ba95-e2cecbb7734f", - "metadata": {}, - "outputs": [], - "source": [ - "inv = BohmanE()\n", - "inv.fit(l.chr)" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "f606ea92-00d2-47fc-829c-27e4eab58b44", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/lily/myenv/lib/python3.13/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return math.isfinite(val)\n", - "/Users/lily/myenv/lib/python3.13/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part\n", - " return np.asarray(x, float)\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "x = np.linspace(-5, 5, 100)\n", - "\n", - "F_x = inv.cdf(x)\n", - "F_true = inv.cdf(x)\n", - "\n", - "plt.plot(x, F_x, label='Функция распределения апркс')\n", - "plt.plot(x, F_true, label='Функция распределения')\n", - "plt.title('График функции распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "fb09fc09-5bfa-4a76-9b52-d4000404db25", - "metadata": {}, - "outputs": [], - "source": [ - "class FTInverterNaive():\n", - "\n", - " def __init__(self, N=1e3, delta=1e-1, num_points = None):\n", - " super().__init__()\n", - " self.N = int(N)\n", - " self.delta = delta\n", - " if num_points is None:\n", - " self.num_points = int(N // delta)\n", - " else:\n", - " self.num_points = num_points\n", - "\n", - " def fit(self, phi):\n", - " \"\"\"phi = characteristic function\"\"\"\n", - " self.phi = phi\n", - "\n", - " def cdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - " t = t[t!=0]\n", - " \n", - " \n", - " phi_t = self.phi(t)\n", - "\n", - " \n", - " #print(list((phi_t * np.exp(-1j * t * x[:, np.newaxis]))/(1j*t)))\n", - " integral = np.trapezoid((phi_t * np.exp(-1j * t * x[:, np.newaxis]))/(1j*t), t, axis=1)\n", - " #print(integral)\n", - " return 1/2 - (1 / (2 * np.pi)) * integral\n", - "\n", - " def pdf(self, x):\n", - " t = np.linspace(-self.N, self.N, self.num_points)\n", - "\n", - " phi_t = self.phi(t)\n", - "\n", - " integral = np.trapezoid(phi_t * np.exp(-1j * t * x[:, np.newaxis]), t, axis=1)\n", - "\n", - " return (1 / (2 * np.pi)) * integral" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "aac4decb-6bed-478d-9137-25fb01929ec2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = FTInverterNaive(num_points=10000)\n", - "inv.fit(l.chr)\n", - "\n", - "x = np.linspace(-5, 5, 100)\n", - "\n", - "F_x = inv.cdf(x)\n", - "\n", - "F_true = l.cdf(x)\n", - "plt.plot(x, F_x, label='Функция распределения апрокс')\n", - "plt.plot(x, F_true, label='Функция распределения')\n", - "plt.title('График функции распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/Untitled1.ipynb b/examples/Untitled1.ipynb deleted file mode 100644 index f1fd105..0000000 --- a/examples/Untitled1.ipynb +++ /dev/null @@ -1,496 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 11, - "id": "8bdaf0de-4f2c-4223-9d5f-c6d078f5ced2", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def bohman_inversion(f_hat, t, omega_max, N):\n", - " \"\"\"\n", - " Реализация метода численного обращения преобразования Фурье по Боману.\n", - "\n", - " Параметры:\n", - " f_hat: функция, задающая преобразование Фурье (принимает частоту omega и возвращает значение)\n", - " t: массив временных точек, в которых нужно восстановить функцию\n", - " omega_max: максимальная частота для интегрирования\n", - " N: количество точек для численного интегрирования\n", - "\n", - " Возвращает:\n", - " f: восстановленная функция в точках t\n", - " \"\"\"\n", - " d_omega = omega_max / N\n", - " omega = np.linspace(0, omega_max, N)\n", - " f = np.zeros_like(t, dtype=complex)\n", - "\n", - " for i, ti in enumerate(t):\n", - " integrand = f_hat(omega) * np.exp(1j * omega * ti)\n", - " f[i] = np.sum(integrand) * d_omega\n", - "\n", - " return f.real # Возвращаем действительную часть, так как исходная функция предполагается действительной\n", - "\n", - "# Преобразование Фурье нормального распределения\n", - "def f_hat_normal(omega, mu=0, sigma=1):\n", - " return np.exp(1j * mu * omega - 0.5 * sigma**2 * omega**2)\n", - "\n", - "# Параметры\n", - "mu = 0 # Математическое ожидание\n", - "sigma = 1 # Стандартное отклонение\n", - "omega_max = 50 # Максимальная частота для интегрирования\n", - "N = 10000 # Количество точек для численного интегрирования\n", - "t = np.linspace(-5, 5, 1000) # Точки, в которых восстанавливаем функцию\n", - "\n", - "# Восстановление функции\n", - "f_restored = bohman_inversion(lambda omega: f_hat_normal(omega, mu, sigma), t, omega_max, N)\n", - "\n", - "# Точное значение плотности нормального распределения\n", - "def exact_normal(t, mu=0, sigma=1):\n", - " return 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((t - mu) / sigma)**2)\n", - "\n", - "# Построение графика\n", - "plt.plot(t, f_restored/(np.pi), label='Восстановленная функция') # делим на pi\n", - "plt.plot(t, exact_normal(t, mu, sigma), label='Точное значение', linestyle='--')\n", - "plt.xlabel('t')\n", - "plt.ylabel('f(t)')\n", - "plt.title('Восстановление нормального распределения')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "1a3f954d-9069-470e-989b-d80a3229f14d", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "from scipy.special import erf\n", - "\n", - "def bohman_inversion(f_hat, t, omega_max, N):\n", - " \"\"\"\n", - " Реализация метода численного обращения преобразования Фурье по Боману.\n", - "\n", - " Параметры:\n", - " f_hat: функция, задающая преобразование Фурье (принимает частоту omega и возвращает значение)\n", - " t: массив временных точек, в которых нужно восстановить функцию\n", - " omega_max: максимальная частота для интегрирования\n", - " N: количество точек для численного интегрирования\n", - "\n", - " Возвращает:\n", - " f: восстановленная функция в точках t\n", - " \"\"\"\n", - " d_omega = omega_max / N\n", - " omega = np.linspace(0, omega_max, N)\n", - " f = np.zeros_like(t, dtype=complex)\n", - "\n", - " for i, ti in enumerate(t):\n", - " integrand = f_hat(omega) * np.exp(1j * omega * ti)\n", - " f[i] = np.sum(integrand) * d_omega\n", - "\n", - " return f.real # Возвращаем действительную часть\n", - "\n", - "# Преобразование Фурье функции распределения нормального распределения\n", - "def f_hat_normal_distribution(omega, mu=0, sigma=1):\n", - " # Преобразование Фурье плотности нормального распределения\n", - " f_hat_density = np.exp(1j * mu * omega - 0.5 * sigma**2 * omega**2)\n", - " # Преобразование Фурье функции распределения\n", - " return f_hat_density / (1j * omega + 1e-10) # Добавляем 1e-10 для избежания деления на ноль\n", - "\n", - "# Параметры\n", - "mu = 0 # Математическое ожидание\n", - "sigma = 1 # Стандартное отклонение\n", - "omega_max = 100 # Максимальная частота для интегрирования\n", - "N = 10000 # Количество точек для численного интегрирования\n", - "t = np.linspace(-5, 5, 1000) # Точки, в которых восстанавливаем функцию\n", - "\n", - "# Восстановление функции распределения\n", - "F_restored = bohman_inversion(lambda omega: f_hat_normal_distribution(omega, mu, sigma), t, omega_max, N)\n", - "\n", - "# Точное значение функции распределения\n", - "def exact_normal_distribution(t, mu=0, sigma=1):\n", - " return 0.5 * (1 + erf((t - mu) / (sigma * np.sqrt(2))))\n", - "\n", - "# Построение графика\n", - "plt.plot(t, F_restored, label='Восстановленная функция распределения')\n", - "plt.plot(t, exact_normal_distribution(t, mu, sigma), label='Точное значение', linestyle='--')\n", - "plt.xlabel('t')\n", - "plt.ylabel('F(t)')\n", - "plt.title('Восстановление функции распределения нормального распределения')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "e41913c0-5172-4abc-ba4d-5797da9e2a72", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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hYmYNTC7wu8jt8yHUGX1zYM6BMKKXEW95BrKaIeIJyiGEEc9p+dkBjTnK9UcffeT2/K+++kqVm6zyFholdOjQMkBr5A2cR9ywhIC3wYLnVHN0GMaZd9DGYKYZOsbLjdK9gXeKegUNpNHsg84FyytcffXVypyXHSCsIw4QOoxxRplEu6DXj+zOmjNOE0a5ulw5wHd0op4dN5aSwJRmzyUWctpeeGJsz3Av/IZwr/vM5SRPUSYxyEIdv5wJSb8vzOgw8XmCe+qDy6LSduhAawO/PWi6UNcgFJoVam6KAHAG1jUwsMuiMcSoCw6/eoMIdSVMLZiijcYT0/3Q0KExhXOmro2BJgZh0BhA5Tlo0CDlCIdptvDZgUCDe0IogtMy/FPg8AjHus2bNyu/ATSq2QUNOjorxA9TKjEagmkCHbW3Djo7IG1Q7UK1ioYGzsnooBEvCG94D0Yw+oMjNkCnjgYSadQbSAh6aLAxAjWOUL2BKZTQOsF8BidX+C6hQ4W6FyNIT0fSnID8QqOD93U58wc6Qozq8HzEGR0kGmKY3JBHVwJlAPfA9F4IIBBaMM1XV8Hj3aCzwvRbCNLw4UHcMNrLCv39wiwF4QllMDdTW9FRYrotGmsILFg+ABoKjDJRFrMaZaLzgDMzOsCstBYoJyiDKOPQGKFsoqNEXYJ5DGUATrU60Bhgmi7qBjoOdN5417kVzGFuhdYD7x5LIUCDAEEVeYB3nRPef/99JYTAjwJx1KeCX05z5QmckREXOGQjXSgHKBsoVwBmKLQbMIOi3qBcoHP25qcBYRT3gvYCU8ExhR1lAA7gntq57AKnX0z/Rl1HfUM7iPthbR7d3JTdPIX/DMoU6ggEZL28At2RV9fkIq9h2oEgBJ9GmODgoIyyDa0HhECkDRqyotR2bN++XdVxlA/UJdPj6+laJGdTwUNCQtQ00M8++yzTtOz4+Hg1nbdKlSpaYGCgmhqMKX7epm9jSi2mtGJaeenSpdW02vnz57uF+eOPP9QUY0wdjoyM1Nq3b6+mA+d0Kjjug+m6iHtUVJT27rvvqud7Tr/M7lRwkJaWpo0ePVqrVauWSmv16tXVVEfjFGn9nsb3V65cOTXNeOXKla4wmDZ87733qmm5nnibSn769GkVJzwTz65UqZKaov3555+7wuRmOify1ZhXWb1XTCXGdF88F8/HlN7rrrtOmzlzpnY5jNM+P/jgAxV/5D+mTGMasRFMFcdyAaVKlVLTVG+99Vb1fjynN+vTe/UDZaVx48bahAkT3NKS3angxqnfDRs2VOmrWLGi9vDDD2vnz593C5PVPa90HfnUpk0bFVdMX8bU+ueee84t//WyOHfuXFV28Z4QH8/8zMlUcLBhwwa1DEJERIRajqB79+7aihUrsnVPTxYsWKBdddVVrvp5/fXXazt27NCuhF42UZdRZzClHPdAej2ncuN+vXr1UvFF3Rk+fLgqK97K5bZt21xlBnW9QYMG2quvvprrqeCoJyjrqK94VygHuIfnEhbZyVPPcprVgXdjbE/xfurWraumnyP9aA8xjTw1NbVItR3JycmqHF999dVaenp6sZgKribm+1rAIoQUPBhtYkSNUT9G4yRr4NOB2T9wijYbmE0G7SY0G0ZNlT8BbQk0JLpPYV6BtgLpxnG5PMdigDD9kqIPfW4IIYQQYiroc0MIIcTUYAaYt20YjGDBRm/rw5CiCYUbQgghpgYTJ66Ecf89UvShzw0hhBBCTAV9bgghhBBiKijcEEIIIcRUFDufG6zKi5VHsQJvXnesJYQQQkjhAC8arKqNBWex0v7lKHbCDQSb7O7hQgghhBD/4ujRo1KtWrXLhil2wo2+qSReTnb3csnpnk/6EuBmw+zpk4QEkSpV1Ne0w4clsFQpMRtmz0Ozp8/saUxITZAqHzjr4OHHD0upcNbBokhaAaUR+7tBOWHcHDorip1wo5uiINgUhHCDnX9xXzMWWrOnTww77qYhjflcPvwBs+eh2dNn9jTaUm0iIXKpjQ5nHSyKpBVwGrPjUkKHYkIIIYSYCgo3hBBCCDEVxc4sRUiWBASI46675NixY1I5gFWDkMImwBogdzVz1kF8JyS3sPQQohMcLPavvpKNs2dL5eBgX8eGkGJHcECwfHX9VzJ79mz1nZDcQrMUIYQQQkwFhRtCdLDNWkKC2JKTnd8JIYW+SBumgyfbk9V3QnILzVKE6CQmSmDp0nIdpjKePy8SFOTrGBFSrEhMS5TS40qr7+f7npcg1kGSS6i5IYQQQoipoHBDCCGEEFNB4YYQQgghpoLCDSGEEEJMBYUbQgghhJgKCjeEZAGmoial2n0dDUJMDesZKQgo3BCiY7OJY9AgOd65s6SLRQZ/vkpajJkn+87E+zpmhJiW1//YLs1Hz5U/Np8Qm9UmgxoOks4lO6vvhOQWCjeE6ISEiH36dFn33HOyKTpF1hw8J6npDll36LyvY0aIafl65WFJs2vy+I8bJSQgRKYPmi7P1XpOfSckt1C4IcQL0RdTXd9PXEjyaVwIMStchZgUFBRuCPHC+cRLws3xC8k+jQshZuVCYprru81q8WlciLng9guE6CQkSGBEhNwgIp/8vsF1mpobQgqG44a6ZXdoEpMQJ+XGlVS/z/c6L6UCS/kwdqQoQ80NIV64kJTqVYtDCCkYzQ1gXSP5BYUbQrxwISHd9T0++dJ3Qkj+EZfsLtycS6BwQ/IHCjeEeOFi6iWBJi7JvQEmhOQPnnUrIYUDCZI/ULghxAspaZcWFYtPSVf+AISQgtXcJKVzMT+SP1C4IcQLSQbhBlykaYqQfCcuyb1epaQ6fBYXYi4o3BDihaQ0x2VHmISQAtDceAwqCMktnApOiHH7hf795cyZM5Lk0cbGJqVJdV/Fi5Bi4nOTkq5J/zr95czZM9x+geQJCjeEGLdf+P13WT17tsRtR8NqcCqm5oaQfCfOw9zrcATJ74N/l9mzZ3P7BZInaJYixAvJGWapUmGBXn0DCCH5p7kpGeqsZ8k0S5F8gsINIV7Qbf8VSgSrT2puCMl/9Hql17OkVAo3JH+gcEOITkKCBJQqJQMGDxZrYqI6VTEyxKtvwKajF2T5vmifRJOQokZ8cprMXH9MLnqsY6NrRPV6FpscL6XeLyWDtwyWhNQEn8SVmAP63BBiwJKY6FYpKpQIyeQbgFVUh3y+UpmuvhzaVno1ruiDmBJSdHjgm/Wy8kCMbDxyXt66qVlmzU1khuYm3S6Jac6BBSF5gZobQi6D3ugaNTd/bz3p8sn5beNxn8WNkKKyOSYEG/D96iOSbnfWnTS7QxIzzFC65obr3JD8gsINIVkQFGCVUhmOjkbhZseJWNf31QfP+SRuhBQV1h1yryOHzyVmqlMunxs6FJN8gsINIVkQHGCVyAzhBuvc6Ow5fdH1Pfpiipz32Oxv6vKDMnLGJm4CSIoVc7adlEd/2CD7zsS7nd99yv333tPxbqbe8CCbhAXZXNocQvID+twQkgVBNqtriqpxZ/ADZy8JN+BgTIKUDg9S31fsi5bRf+5wXrCIjL+tpVvY1HSHpKTbpUSI876EFCUcDk35yZQKc5Z3ndNxyfL49E2qfO87fVHmPtXVde1QjLtj8P6zCZmmgQfanONs/J8Q02huJk2aJFFRURISEiIdOnSQNWvWZBl22rRpYrFY3A78j5D8Bg1uZIYQojs+Yh2O84nO740qR6rPQ9GXGu9Zmy754MzZdspt3Q7co++HS6TNGwvkt43HMj0P2qFtx2NF07hJJ/EdKKfeyiGE8tu/WCUtx8yXzxbtd7u2bG+0SzDZfTpe9mRoZ8DBaKcZqnFGfYEgpD8HRBqEG2puiGmEmxkzZsjIkSNl1KhRsmHDBmnRooX07dtXLYGfFZGRkXLy5EnXcfjw4UKNMzEpVqs4unaVYw2biMNiUT43kaEBbqPMM3EpLpNVy+olMwk3m49e8seBs+T6w+ddv79fdUQORidIqt0hr/+xQ3UWOqdik6XX+MVy3cfL5I2/dmaK2oy1R+T5mVtku8HfRwfmr5X7Y7Ic9VJYMjdZ5e/Rc4lqdpLndWhfpi0/qMqTsezq5bDnB85yOOavDA1kBr9uOO7yMZswf4+bOXbdYXe/GjxXj9vhDM1Nx9plXc8wTgPHAAJ1DWAs0LVGV2kS3kSsFp93T6QI43Oz1Pjx42X48OEybNgw9Xvy5Mny999/y5QpU+SFF17w+h9oaypVqpSn5yYkJEiJEiXUvUBqaqqkpaVJQECABAcHu4UDoaGhYrVmVMC0NBXeZrO5aY0QNjk5WRyOS51MVmETExNVxcc5XAPp6emSkpKinoPn5SZsUlKSej7SgLQAu92u4pWTsHgvYWFhrrA4hwPP1blcWFwLCgqSwECn5gPPwfNAeHi4KyzSgHsiHMLnNCzeC94PQBw88zMnYfEOrAsWyIwfZ0vKjgCxpidLgD1VNM3h8g84Gh0njtRkqVAyQqLKOuN2MCbRmfdpdtl7Jk7Zo9rXKiOr952R5TuPS5tqESrvFu9xCuyOtGQ5n5osS/eckV6NK6tzE+btkNMxsWKxWmXK8oNye/vqUq9iCRXfOVtPynO/bBeL1SbzdpyS+U9eLaE2TeXnqQSH3PTpcrmQmCbNKobK1/e2lZIR4aqcYE2Rh75ZIyt2n5JBbarJe4PbGcpqony25KAsO3BBbmhdXYZ3qa3eu56fC/ZekAU7TkvPRhWkT4MybuUEAtucLcelbfUI6VK/glve7zgaLQt3nJT2dSpKh7oV3PLzTFyyrDmWIE2qlJSmVUu6lZPEdJGle89K3fLhUj0ywC3vE1PT5b8dJ6R6ySBpWLWMW37GxV+UFfujpV7VclK3QglXnUN+BASFyLqjcVKpZIg0yHiXet6n2TVZvj9aSgZapEHFMLd6DyFxzqZDylzSpVFVV71HObmYlCyL9sRIZHiY9GhYQaxWi6uNCAgKlj82n1Tvr3/j8iKOdFe9xzovmCmUmJAgd3SoIRVKR7rue+ZCgkycv0PSHRZ5om8TqVLKWUcR3xPnE2Xs/P2SkOqQ5/s1lGZVSqjnoS7Al+XR6ZslOj5FXupbR25sWcXVRizec1bum7pKUlNS5aY21WTinR1cefTN0r3y2u9bxRIQqGYxLRjZTWwWTeXHuNnb5Wy8U4CftuKQ3Ny8gtQqF6beDTSR6r077JKcmiyz1h2UYd0aqHPrDp0XLT1VosqEyKHzqbLteJyguJ28kCgXLyZg3CDtokqrsg3NDZ515nysaPZ0NYCACRj1LDXJLr/f+bssXrxYQgND/bKNuFL/cKWwelub0/4hP/qSpIw2P699iWebn1VYpBnvNrf9g7ew2UbzISkpKZrNZtN+++03t/NDhw7VBg4c6PU/U6dOVf+pUaOGVq1aNRVu27ZtWT4jOTlZi42NdR1Hjx7FMEYdx48f11JTU9UxevRode7ee+91ncMRFhamzu/Zs8d1bty4cerckCFD3MKWK1dOnV+7dq3r3GeffabOXX/99W5ha9asqc6vWLHCdW7atGnqXM+ePd3CNmrUSJ2fP3++69zPP/+sznXq1MktbJs2bdT5WbNmuc7Nnj1bnWvevLlb2K5du6rzP/zwg+vcokWL1Lm6deu6he3fv786/9hjj2kJCQnq3Jo1a9S5KlWquIUdNGiQOj9x4kTXue3bt6tzJUuWdAt71113qfNjx451nTt48KA6FxAQ4Bb2oYceUudfeeUV17kzZ8648lOPF46RI0eqc/jUz+G6Hhb/08/jfjiH+yPMO9N+12o+/5dmsdrU+aqPTFO/E5OStaGPv6jO1ezYX/t70zF1/rqPlqh0qXcx/H9ay9FztSlL92llejvji/dxPj5Rq/vS3yp8aClnObn7venq+RcTk7VqNzjjG1G3nQrz5p/b1DXkA85XvPM9dR7HHS+OV+eQf3d/tcp1PrBCLXUe+Y3/vvrbFq38LaPUuaBK9bSvluxT6UPZqNu0tTpf/qaX1X9/XnNIlS+cq1arnuueOJq1v1qdR/lcuvuUVvvFv7VKQ51xKF+5qus9rtl/Rouo31GdL9N3hPbb+iPq/MaNG9U5W1hJdb+oF/7SZqw+pOoPzr/+1jtax7cXqGtVH/pKnUO9w3+PxcRrXd/9V4to3kedv/WBp13PO3TkUl3GPb9dcUCl77rrrlPn6vT+P1caPpy96VK9P31WG/jxUnU+stNtrnKt58Wtny13hX3um8Wu57026nVnHjXvo/77xA/rVRumtxH3fXzpnbUd/ISrjcA99edZQyOdbcT6Dc4ymZSsNRvyrDoXWq+jdtU7C7XoWGc5rlHD2UbgXeO/LV6fq3302RcZdbmFds17/17K+3I1XG0E7nn1OwtV3uJccNVG2n87T6p7JiWnaBHVGjjz7pZR6r8z1x52tRHBFWurc41f/Ud91m7WTp2f8s13rvJ782tfqnORFaure56+cNFZrmu3VefLXvukdsMnS9W1qbMWOMtfZFltw8FoFa79m/NdbQTqCN7jop0nVd3R2wiUUb0++1sbYXweno/ziI9+DvHEOcTbGFZvIzZv3uxKH9pIvY0whkWbivNoY/VzX37pfO9oi41h9TYCbbd+7ocffnC1Ecaw6AOMbQQOxAXn0HcYw6JvwXn0Nfo5vY1An2QMiz5LbyP09/j+++8728qaNd3Coi/EefSN+jm9jUAfagyrtxHoc/E7Ojpa/UZffiV8qrmJjo5WUlnFiu6LoOH3rl27vP6nQYMGSqvTvHlziY2NlXHjxknnzp1l+/btUq1atUzhx44dK6NHj/Z6rwULFkjJkk7Twp49e9Tn0aNH1aZtOogf+O+//1zx3LHDqa49ceKEW1hI1WDlypVy/LjT92Lr1q3q8/Tp025h9dHB8uXLXSa4zZs3u96LMezFi04H1lWrVrmk//Xr16vP8+fPu4XFOwHr1q1zndu0aZP6jIuLcwsbE+Nce2Ljxo0uyXrnTqdJBM8xhjWaCefPn68+Dxw44JKujWFPnXKO8JAn+nm8K330YQx77JjT9wT5rZ/X44XRiDGsbn7cu3ev67z+bsA///zjGjXoccOnHtaodZo3b55ERES47qffH2lL15yjNU9++2uO7D/qTIeWliJHd+AdB8jeU7EqXTrlA1Mk8cg21++TJ0/JpzPnS5rdJmWDNTkvDsEYZdme0/LX37Nl5wWLJGWsm1M51CEoRT+tPiiN0/dJbLwzvzGyvrWWXX4+aJPFu08773s2Rg7viRaLaHJNZU2+z3jeilVr5ERcqny/yX1X5XFzd0romW3i0Jxrj4Aw1TaLvDZrq9wU5CwnZ+OTBXrR0kGanE+1yP4zzne8bsNmmXigrNgdFglRcp8o/6Npv8yWkkEi7222id1gAXlu5mY5s2eDXDx1xJWfgVZN0hwWeem3rVL6iPNdfr98n6Q2a+oW1/R0u/z512yZtMMmh+MtyjkbLNh1Wj6ePltqlxCZuvmSXwcsL6/+vl2O79mqeiaVjoupUkY00cQi4+c78xg8+MV/sjU+TKyukCLLth1S5eTnA1ZZc/qSOeSHtcfEmvyPdKygye9r9qlzGMzjnc/afFIC4o652og5209LQElnG7H/bKKr3I/8ap5sPmaVIOul570zc4XcdfSIzDtmkePnnXkRYNHk2PkkeeDzhTK0nkMuXLx0vlSQJheS0uSbxc76GZsqEhuTKOEBmpQOFjmRcWu0EfMPJMrR8zYJychb8Oava+WxJg7ZHGNxmS9blnEIWr2J/2yRng5ne+LQNFVG+1VPle/32eRsnDMO/6zYImklu0q5EE2qOs6qc0mp6ar87jiPzLEJJjw5Q4tsP35B/vx7tixef9D5zkSTbWuXqfpyJj5ZbCedbQQ4d/q4rF971PVbr0t6O+NvbYTxebrJ799//5WyZZ1mN73vQryNYfV0oc2vUqWKSh/aSL3NNIZFmwqWLVvmajv1/gFtsTGs3ieg37lw4YKrTdffkzEs+gAAv1a9v9L7CvQdxrDoW/S+Rtea6P0Z3qkxLPosPY6lSpUSI+jrjGHRF+r30s8fOeJsIxAnY1g97ehzcV7vN7ODBRKO+AhEvGrVqrJixQrp1KmT6/xzzz2n1JKrV6++4j1QYBo1aiS33367vPHGG5muQ92Gw5i51atXV4UUpq38NEuhYKGQDxgwwHUPs5mllixZIv3791eF3XRmqZQUCW/aVBKTU6Xt8ClSv3pJ+XpYW+n0wQpJStNkwVNXy9fLDsjXyw/IsKtrybP9m0izMQtUx7pwRFv539KDMmPTWbkf1/rUl9Zj5snFpBT59eFO8sf2GPlm1REZ0q6aPH1NDek6brEk2q3y2yOd5dvVR+XXdUdkcOuK8ly/RtLro9WqI5sytLX8ueGQ/LLhhFzXqrqaedVn4jI5HH1RXuxdS7adiJe/dp6Xvo0ryPhbm0uvD/6V4+cS5cUBTWTn6UT5Y8tJ6RRVUj6+tbHc9uVaOXA+XYZ2qCZ7Dx6R5cfTpH75MJn5yNUyZMoG2XUqXm5tVUniLibJPztOS6Nq5WTmgx3k9i/XypbDZ6VbvbJSNjJMZm05I9VKh8pvD7aTR75dK6sPnZemNcpL5ZIhsnDXWakYZpUZ97eV52btlDVH4qVRpRJSrVSwzN1yTMpGBMpfT/aQV37fLv/tjpYqETZpWilC5u6OkRJhIep54+bulnlbj0nVUsHSolYlJTCEB9tkxrBW8tGCvTJ3d7SULxkuXeqVUz4glvQUmTi4hSw9dFFmbjguZcICpXrgRdl4VpOgoAD5+aGr1fnvVh+RCJtdejWsILO2xygH1m/vbatMh+Pn7RarzSbXNKosi/Y4G+mJgxrIzlNx8tnyExIaZJPBbavJtGX7RbPb5X9D28jR2HR5+5/dEmizyLB2FeWLZYfFYQuUl69tJGXCg+TpGRvF4kiX+7vUlimrTyiB8qPBzSU5KVGemblNAoKDZexNTeWV33dIamqavHl9fYkqFyH3fLdV7A5NXuhXXz6ev0Pik9Ll1YHNpF2tsjJo8ipVr94dWFdG/7lTkiwh8tqAhsrcc93E/0RziMx4+Cp55pftcvxCsjzfu450r1tKBkxaKXZrkMwY3l7enbtH1u0/LcOvriHDutST7hOWKxPdrw+2k3f/3i4rD56XJ/o0kmGdo6Tju4skOSlJfn6gnfyw7qT8vvWs3Nu5pjzdq450eHOexKWkyy+PdpV5O87IF8sOyaBm5eSV/vXl6g+WS6LdIrNHdJaZ64/Kl4v3yp0dqsvrN7aUxqMXqPTNf6y9/G/xfvl542kZ0bO+XFO/nNzyv1VSMSxFDtgekjRJk30j9kmp8FKmNEstXLhQevfu7QpvNrNUWlqazJkzR7p06ZKvZin03+XKlVOCGHxvL4dPNTeIJF6wLsnp4Hd2fWqQ4FatWsm+fc5RlSd42cYCpgPpUi/Q+n284SmF6mGNmWUMi0KD5+n3yyqsrjHyvK+xcOUmrLd04Jy3GWU5DYvzKLj4rh/ZvS/wlg/5EdaYj3kKi8YnOlr0qhUWHqHytGRosCSlJQt8is+laGINCpEaFUpLRGiw6uiPnkuSmLRAOXDBIRaLVZpVKy0hwUHSulZ5WbYvWnbGpMny/c6RZrf6FaR82dLSrXF1mbP9lCzYFS0LdpwRiy1Abu1UX8qVLik3tqqq/B2mrjwi6w7Fqufdc3VddU90lq/9vl3eWegc6YBHe9ST8NBgebJvE3n+l63y7vxLM1levr6ZlCtbUl6/qbUMnbJGvlmNUbBVrIHBMnZIeykVGSFjbmgqt/1vpfy80Tmahq/Ke7e0UALHuNtayPUfL1PCg8hFpbWAkFW+VAn5eGhnNftrx8l4ddisFvn4rg4SVaWMfHRnCek3cYnsPBWvDltwiEwa2kGqlomQCYNbyYCPlint0Yl9sSrtH9zWQhpWKSXjb28t151NlCPnEuXk9tPqeR8ObiVNoyrKh0PLyqBPVyhB7LeNJ1QDO3ZwO7mhfQ3p28qunrP9RJyck2CxBom8c2sLaRVVVppUKy27Tl9UvkK/7zin/jfq+sbSqW4F6VinvJxN0pQ/jC7YPNmrntzQvq5c79Bk+9k0Wbo3Wr5eeUQstkB5vHcj6d8ySnUmaw5dkAU7T8vnq06JBATLza2ryvCuddT9Vxw4p4SvL1c5R56DWleVga2qq++LDyXI31tOyvO/OkftvZpUkf/r0lD9b0T3JJm4cK+8Mwc6lQBpWbus3Nu1nnq3/9expnyz8rC88NdBEUuI8k+6q3MtJagNaldHrZZ9+1dODUy5iGAZ1rWehATa5Jb2dWTGuqMy+AvnLNSQkFB5sFdTtbVInyaVVFwmLT4sa44nqnJxW7uaUqZEqFxTv7zM23Fa/j1wUZbsd2oE+jStLOFhoXJ1o2qq/K46eEE2ZDjRd2pQRcqUKS2NqpVR73pvdKIcjU1V5bdRjQoSHBykFus7GZssCY5ASbUEq7wvHR4sYSFBqu7YbcESY3fWFWM74zdtRDb7h8uF1TU4etpy0j/ktS8JzKLNL4i+BH070ux5LS/5mRO/G5+6o6MQtWnTRkmxOpDg8NuoybkckOqg3qpc2emYSUh+oc/gcM2YSk6T0xkzPSplLBdfq5xTbb3/7EXZedKp8m1cxTmiaFOztPr8a/MJtbaH1SLSuU45dQ7OqODTRfslPiVdaT7a1HCGv62tsxNEp4oVWxtWKqFG5+CWNtWkVNilCt6rUQVpXs3ZaA5qXU2F1UGHCsdd0LV+ebmu+aU6ghF4u6gy6jucnx/vWc917dm+DaVFdec9G1aKlLdvaqbeBbQUowc2UeHVOygZIpPuaC3lSwRLieAAeffm5m7Xpt7TTsWnaqlQ+eDWFtK5rjPtWCPl63vbqbB1K0TIxCEtpW+TSq6ZM9/f30F6Nqygptp/OLil9M7YuyssKEC+vre9DGhWWZpXK6nueXv7GuoaOvFv7m0vA5tXlurhmoy9qYl6V3o+fnV3W/U+mlSJVGmAoAAgUOD33Z1qSv2KEcpp94mMdwFn4Ul3tpYbWlaR6mVC5eFr6siTveq7/jd+cAu5tlklKR0WqBzAxw5q5hrpQ2C8pkF5lefIo7duvLSf0ls3NnWVDcTnnZubu/73WI+6rncIAWXC4JZKsAFP92mgyoluphw9sJFrCvXI3vXVYng6T/epr94JeOiaOioeOkPaV3ftmXZ7O+f7W7jrjNJAdqpdVqqXcXag17eooj7/t/iAMj/CwbptRryvrufMy/k7T8vWY07hpm1GedLLIIRQzA501pNwt20WMGPKOBUcDsUAWiRCTDFbCtPA7777bmnbtq20b99ePvzwQ6W+02dPDR06VJmu4DsDxowZIx07dpS6desqM9D777+vTEz333+/j1NCzIbecbjWuklKk1MZa3RUKukcZdQuFy5L9pyVRbvPSEKqXXWkOAfaZggk+vTZltVLSckMwQSd+et/bnftrTOwZRXVmerCETrN2VtPKc3FC/2do3q9gx93Swt59fdtUiEyRHWixvhOG9Ze/rdkv1ok8JFr6rilBx1l74blZfuWjfJMP2cnrYPOEZ04nlK7vFNg07m1bXXp38wpGEUEuzcZ6OTWvNRTdYx6/HVa1Sgtc568tJibEcxs+ulB7wMYdK5f3XNpZpcRdI4QOLxRNiJYPri1mcyefVSubV3V7RoEKs8FFXUCbFYZbXiPRpD3E4e0yvLap3e28XoN7wl5AROMLpwY4zLzoU5y9mKKlAsPdntviMsP93eQLcdjpV6FCLfFHiFc/DHiapm//aTEHdws7TOECf2d/TC8o0xfe0QJu0PaOQVkXbB448amyuSHPHmmr3OGE7iqblnpVr+8ml2FeD7V+1K5wEy5EiEBrgUsb2pVVcUPwCwItmQINhBgo8o6haKGGevZYK0cfRq4Xqb0QQFmTOnLKxingqfDfscZ4MQMws3gwYPl7Nmz8tprrymnqpYtWypbne68C0cj3ZapOzlh6jjCli5dWml+4LPTuHFjH6aCmBGsZQOMWzDo69zoI9Da5Z2CzNztp12jVr0DQEeCRlt34Lw2Q0AAEHIgtIz6Y7vUKR8hD3fLLIj0aHhSdRj6iFgHu5BntRM5NCajrm/i9RqEHwhNclRzCUtGEI+s8BRqjDgX08zycrHGU7AxvjNde+IJyk/rDC2eJ9CS3dqmqsw+7XQuNQJtm65x8+TODjXV4S0e/7urjczaeFwJ1boWUBekodV6duYWqVIqxE1Yrlk2XGqUCVPmQ10Q0ssU/Kx0zaNejypn1BeUT4BBgr68AjSjbisUZ7YKEVL0hBswYsQIdXhj0aJFbr8nTJigDkIKGphhQGSIs5ociklUC/ABvWOCWcUIzAxGgeDBrrXl43/3KXPCbYbRNBjaKUoJPNicUxeIdIIDbC6zCiEFCcxXQzLMe57A1NmzYUUJDrS6zFw6EHZe+HWr0ijdd3Ut1/kGlbB+mHMGmy4069opfVAA8643zY1evwgxhXBDiF+bpTI0N/qGf+UiglyNcavqpd0act2nxmju6dWoolqYTTdvGYFfBSH+jG5K9QQCEbQ90MYYtVAwpTWvWlI2Z5isOtS+pHnUzblOzU3mvaUIyS9Yoggxbr/Qpo0cqlHXtf0C0DfPhIOkcfQJME0YwguAQ2fXeuXdbglVPUwFMCcQYjZgxvJmXruuudMZGfTLcBY31p3DMYmSnLG2k9GhGCvitKrYWuqG1uX2CyRPUHNDiE5oqNhXrpS3Js+RlOOYHeRsXHXBRF/4TneK1MEsmZplwpTTbVajXEKKE8OuilLbkVQtHSodMvaUMtYdvS6p/dtCAtQ6QMAqwTL39qWyYtF81/YLhOQGCjeEeKDvP6lrbrA2h5GKGU6RRtPSK9fRoZ0QHfiQPWZYXkBHdyjWQd2CdhPubXDLgZBDvxuSH1DvR4gH+lIbukMxplwb8dTcEEKyB2ZgYXq5jtHE63IqzmJ3e0JyAoUbQnQSEyWgXj35YMz9EpKW7DJLeWpuPEefhJDsoy9E6Fm3AuHzJsnS5dumMnz7cElMy/4+QoR4QrMUITqaJpbDhwUuwRZNJMAwfdW4Xk2DipdWASaE5Iz6FUvIntPOzSyxVo6OLUNTevyicxNNH257SEwANTeEZIG+9gw0OLXKhrut40EIyR369iTAuOigPpggJD+gcENIFhgb235NndNZB7etnmkxM0JI9rm+eRW1vAJWLtb3N7vcas6E5AaapQjJAuPCYthMEXsv6RsAEkJyB/bBWvp8d0lKtbut/xRg2GaHkLxC4YaQLAjI8AEAWD7ec0NJQkjuwGrdnit2G+sbIXmFojIhWUAfAEIKD5qlSH5CzQ0hOhaLaI0ayZGYi6JZqCYnpDDBVHAQFVlf7KlJXneuJyS7ULghRCcsTNI3b5b7358jyXFWqskJKWTNjVVC5Kt+iyR+zxoJC7w0TZyQnMKhKSEeOKC28XAoJoQULPpgIl1fIpyQPMDWmxAP9LaVPjeEFL7PjV3fRZOQPEDhhhDj9gstWsjUjx5R2y/QLEVI4aFvv/Dggh7y2K7HuP0CyRP0uSHEuP3Czp1Sy7X9AmV/Qgpbc3M0fo/65PYLJC+w9SYkC6i5IaTwYH0j+QmFG0KygA7FhBQe9HEj+Qlbb0KygIuKEVJ42GgGJvkISxMhV1hUjBBS8FBzQ/ITtt6EZAF9AAgpPFjfSH7C2VKEGLdfqFlTTlxIUtsvBLKxJaTQNTdlg6uK1ZHK7RdInqBwQ4hx+4W9e6XH63MlxW7hVHBCCtnnBtsvjG7/r1SL38ntF0ieYOtNiAf6Aql0KCak8DU3XKGY5AcUbgjJYvsFTgUnxAd7S1G4IfkAW29CdJKSxNapk/w2baQEp6XQwZGQQtbcOCRFJmy5VZ7Z/YwkpSX5OkqkCEOfG0J0HA6xrl8vLSD1axqnghNS6OvcaHIsYZv67dAcvo4SKcKw9SYkC2zU3BBSaHB2IslPKNwQkgU2TkUlpNCgAz/JTyjcEJIFbGwJKTy4QjHJTyjcEJIFFG4IKTy4txTJT1iaCMkCyjaEFB6cnUjyE86WIsSAvWxZuZCYprQ2XP6dkMI3S4XaSkmgcKYUyRsUbgjRCQ+X47sOStdxSzhzg5BCBgMKbL9wX+0F0iv8mIQHhfs6SqQIQ7MUIQb0pd/p3EhI4aKvCM7tF0h+QOGGEAN6w2qlcEOITxz4uf0CyQ8o3BCik5QkVQYNkOk/vCBh6am+jg0hxXL7hT9PPCQv732Z2y+QPEGfG0J0HA4JW7lcOqJiUHFDSKESoMxSmpxK2SCnUrj9Askb1NwQ4gVuCE5I4UI/N5KfsAknxAtcwI+QwoV1juQnFG4I8QL3lSKkcKHmhuQnFG4I8QJnSxHiC58bQvIHliZCvMBRJCGFC+scyU84W4oQA/bQUElJd4iVZilCfOJzY5MQCbByrRtiAs3NpEmTJCoqSkJCQqRDhw6yZs2abP1v+vTpav+fG2+8scDjSIoB4eGyfOMBaTzyF0kPCfN1bAgpdhtnYvuF7iX+kRnNZ3D7BVK0hZsZM2bIyJEjZdSoUbJhwwZp0aKF9O3bV86cOXPZ/x06dEieeeYZ6dKlS6HFlZgffXVU+twQUrgEWLn9AjGRcDN+/HgZPny4DBs2TBo3biyTJ0+WsLAwmTJlSpb/sdvtcuedd8ro0aOldu3ahRpfYm4cGQ0rfRsJ8Y1ZKs1O4YYUcZ+b1NRUWb9+vbz44ouuc1arVXr16iUrV67M8n9jxoyRChUqyH333SdLly697DNSUlLUoRMXF6c+09LS1JGf6PfL7/v6C2ZPnyQnS/OH/k+mnIiXTx5525TpNHsemj19Zk6jRbOLJqmyLWWMvHHALl2SukgJKSFmw6z5VxhpzMn9fCrcREdHKy1MxYoV3c7j965du7z+Z9myZfLVV1/Jpk2bsvWMsWPHKg2PJ/PmzVMaooJg/vz5YmbMmj5bcrJct+w/6SEi78XGyuzZs8WsmDUPi0v6zJjGYwnYfMEhF7Q1sj5OZP6C+RJiCxGzYrb8K4w0JiYmmnO2VHx8vNx1113yxRdfSLly5bL1H2iF4NNj1NxUr15d+vTpI5GRkfkuVSIze/fuLYGBgWI2zJ4+SUhwfS1TuqRce+01YjbMnodmT5+Z07jndLy8u+U/1+8ePXpIqfBSYjbMmn+FkUbd8uL3wg0EFJvNJqdPn3Y7j9+VKlXKFH7//v3Kkfj66693nXM4nJurBQQEyO7du6VOnTpu/wkODlaHJ3jhBVWwCvLe/oBp02dIExYUM2UazZ6HxSR9ZkxjcFCQqdPnidnTVxBpzMm9fOo2GRQUJG3atJGFCxe6CSv43alTp0zhGzZsKFu3blUmKf0YOHCgdO/eXX2HRoaQ/IDbLxBSuATaWOdI/uFzsxRMRnfffbe0bdtW2rdvLx9++KEkJCSo2VNg6NChUrVqVeU7g3VwmjZt6vb/UqWcakvP84TkBW7iR0jhwjpHTCXcDB48WM6ePSuvvfaanDp1Slq2bClz5sxxORkfOXJEzaAipDCxsKElxCfr3BBiCuEGjBgxQh3eWLRo0WX/O23atAKKFSnOcJ8bQgp/hWJC8guKyoTohIfL18v2S9Tzf3H7BUJ8MKDA9gs1k/6SX1vM4vYLJE9QuCHEgEPTVyjmKJKQwsRY57hIMckrFG4IMaDva0PhhhDf+dxweyliCp8bQvyC5GTp/tpjUvlUvCxs+L6vY0NIsQKyDbZfiA76QMYf1qR/eg/TrwNDCg4KN4To2O1SZ/EcwTKQSywcOhJS2JobbL+QaFsuq+OgRbX7OkqkCEOzFCFe4CJ+hBQutAST/ITCDSFe4JIbhBQuFouFAg7JN9iEE+IFOhQTUvhwIT+SX7AkEeIFridGSOFD2YbkFyxKhHjBSs0NIYUONTckv2BJIsQL3H6BkMKH1Y7kF5wKTohOWJiMnbFavll1VP6P2y8QUugEWkOleuJMebpZuoQFsg6S3EPhhhAdi0WSg0MlKShEAmxUahJS2NisVrW/VKAlXc2eIiS3sAUnxMv2C/S5IcR35mAuoUnyCoUbQnRSUuSmj16RcX9PkKC0NF/HhpBih8WSJtGBE+SbUxMlJT3F19EhRRiapQjRSU+XNov+lDYiMknj0u+EFDZWq0MSAhbKmniRdEe6r6NDijDU3BDiBS7iR0jhw3pH8gsKN4R4gY0sIYUP6x3JLyjcEOIFNrKEFD5WzpAi+QSFG0K8QNmGkMKHgwqSX1C4IcQLXKGYkMKHwg3JLyjcEOIFrnNDSOFD4YbkF5wKTohOWJg88eEcWbo/Rp4J49LvhBQ2QdZQqZb0vdxRx87tF0ieoHBDiI7FIhciSsm5sHSxcndiQgodbHtik5ISZrNz+wWSJ9iCE2LArjkXfqd6nJDCx5Yh0GTsgkJIrqFwQ4hOSooM/e59GTPvMwlMT/V1bAgpdlisaRIT+Jn8fX4yt18geYJmKUJ00tOlz+Jf1de/HB/4OjaEFEMccjHgb1mfwO0XSN6g5oYQL9AsRUjhwyUYSH5B4YaQy9j+CSGFB5dgIPkFhRtCvMBGlpDCh4MKkl9QuCHECzRLEVL42GysdyR/oHBDiBe4gR8hhQ81NyS/oHBDiBcCWDMIKXToUEzyC04FJ0QnNFRuf/EHOXo+Sd4NC/V1bAgpdgTZQqRq8lfSs4pdQgNZB0nuoXBDiI7VKidKVZJjjkSx2Wy+jg0hxY5Am00CtIoSYbWL1UL1Kck9FG4IMcDtF4oedrtd0tLS1Hd8BgQESHJysjpvRsycxpJBIlVL2KREsMWU6TN7/uVHGoOCgvJlbz8KN4TopKbKw39Plospdgm4t5WvY0OugKZpcurUKblw4YLbuUqVKsnRo0dNu/GimdN4TRVNWpcLkwCLyJGjR0ypvTFz/uVHGiHY1KpVSwk5eYHCDSE6aWly59Kf1detjgm+jg25ArpgU6FCBQkLC1ONqMPhkIsXL0pERIRpd3Y3cxpPxSZKYIpTC1ezbE0JsJmvizJz/uU1jfjfiRMn5OTJk1KjRo08CX/mKzmE5AOcCu7fQNWtCzZly5Z1axxTU1MlJCTE1B2HWdMYmGwXybBiIH1mFW7Mmn/5kcby5csrASc9PV0CAwMlt5jzzRKSR7hCsX+j+9hAY0PMA2sdCcowR+XVH4nCDSFe4HobRQOz+iwQUlyx5FOdpnBDiBdoliKk8KGwSvILCjeEeIHLwBNCSNGFwg0hXuA6N6QguOeee5R2Qj/gDN2vXz/ZsmWLr6PmF7DWiURFRcmHH37o62gUeSjcEKITGip975skve+dJNZwOqqSggHCDKa64li4cKFa7Oy6667zdbT8AqxrE+ioIZHWGqZc44YUHiw9hGSgWSyyu1xN2Vu+ptgCuP0CKRiCg4PVAmc4WrZsKS+88IJa7Ozs2bOuMFu3bpUePXpIaGio0u488MADat0QI1OmTJEmTZqo+1WuXFlGjBjhuoZp8g8++KBUrFhRTcdt2rSp/PXXX7Jo0SI3zZHnAWJiYuT222+XqlWrqtlozZo1kx9//NHt2ddcc408+eSTbudef/11lR7jdOAxY8ZItWrVVBxxbc6cOa7rhw4dcnt2mTJlZNj/DZbY8xfFJkFZartwGJ+NtN5///1qCnFkZKR6b5s3b84yXkB/D/oCkNOmTZNSpUq5hdHjt2nTJtfsnfvuu08tMId8adCggUycONHtPwgzcuRI9e4wBVqP76xZs7IoDSJ79uyRtm3bSnh4uLz88svq3JEjR6R169bq3GOPPabeJbj33nszCcKYOYglEb766qts580999wjN954o+v3P//8o9akwae3tGelVfJ81u7du9X0bcQ9L+/ENMLNpEmT1EtDJezQoYOsWbMmy7C//vqrKggoiMh4ZNi3335bqPEl5sTucG69AOhzU/RWRE1MTVdHUqrd9b0wDjw7t0Bg+e6776Ru3bqu9XoSEhKkb9++Urp0aVm7dq38/PPPsmDBAjfhBR0ZOj0IPRCE/vjjD3UPgI6wf//+snz5cnXvHTt2yDvvvKP2S+vcubNLa/TLL7+o8PpvHABL5rdp00b+/vtv2bZtm3rGXXfdddl22Rvo+D/44AMZN26cMrshTQMHDpS9e/e6hUPa8Gw8b+P69TL1s4lifKN4v0ZtV6dOndz+f+utt8qZM2dUx7x+/XrVsfbs2VPOnTsn+QneKwQ15Afe6WuvvSYvvfSS/PTTT2758vnnn8vkyZPl2LFjrnd6OfBuIeiuW7dOdfr4H57x0UcfqTz64Ycf5Msvv1RhIcRBQDTeF0JrYmKiDB48OFfpWrp0qdx2220q7ig3eeHZZ59V/biR3LyT/MDnKyTNmDFDSXVIOAQbSIWoBJAAIY16Auke0m3Dhg3VfHhk7LBhw1RY/I+Q3GJPSZEnl32vvtvSr8IqKr6OEskmSWl2afzaXJ88e8eYvhIWlP2mFG0WRsm6IAOtC87pi52hM4OA8c0336gBHPjkk0/k+uuvl3fffVdpKCA0oN184oknXPdt166dS1iAILJz506pX7++Ole7dm1XOHSkeltq/K2DEfYzzzzj+g0hau7cuaoTb9++fbbTCaHm+eeflyFDhqjfiPt///2n2ngMaHUg1OlxQMcYHhkoydo50aSESzOB96WHMS7Lv2zZMpVWCDfQDunPhVZg5syZSjDLL6CRGD16tOs3NDgrV65U7wXCAYCmAwIk8io7QDBF/CHwQTh98803lRYJ+Xr11VerMNDAffHFFyotuDc0RhjQP/fcc+r61KlTlYCnl6mcsGHDBhVXlKfcCkc6yNsVK1YoAQzfdXL6TkyjuRk/frwMHz5cCSiNGzdWQg5UoVC5egNqsJtuukkaNWokderUUYWgefPmqpATkhfsKany5PIf1WGzp/s6OsSkdO/eXTX4ONCxYVCGEfPhw4fVdQglLVq0cAk24KqrrlKaAwz60JFj9AvzizdwX2gYdMEmp8CM8MYbbyhzFAQgdJoQbmAqMfLpp5+qa/rx9ttvu67FxcWpVWYRbyP4jfQZQceH/0PIq1qtmtzx4CCncJOhEcO9jO/CCMxP0H5BQDLG5eDBg7J//343IcJ43ZuGIjY21i0MTH6eQCiDVgsCJsJAI2F8LxB4oD3atWtXNt60yL59+5TQhL5MBz5YOHTQLyKcDoQHCDTg9OnTSmMFc1V280YH7whlD4I0+tW8gLx6+umnZdSoUVKyZEm3azl9J6bQ3GB5ZiT6xRdfdJ3D6KVXr15KIs7OC/33339VhceowBspKSnq0EFF0UcD+iqn+YVxZ2IzYvb0JaekuXQ1Dnu6KdNpljxE/FH/0eHjCLZZZNvrvdW5i/EXJaJERKGtmYJn6z4RVwLxw+DNqElBBwkTFD4hVOiduvGe+nd86mp/Pf2e6NevFCfjPY289957yqSEgScEHAgWTz31lGpHjWHvuOMOZZbR+fjjj5WJQ88T/d7G/xjTpp+HPw8Gq9gr7LEnnpCJb0yUZ9981pU+CEmIh+d98Ds+Pl4JRegHPIHrAsIgLLQdRh+P1atXy9ChQ13xwFGiRAllGtI5fvy4EiD169OnT1caLWiGOnbsqMLjOwRUPW4PPfSQMiXqvlC6Ns6YXv0dGPMPn8by6nnN+Pv//u//lJ8WzI7oJyE86MJvdvIG4H4wFUKzBmEZwhH8kIzx1T89y4dnucNvaJughYR2yShI4Vp23okRPb2o4zClGslJu+VT4SY6OlqNEuD0ZgS/LyflQcKG6hSVDYmHlNq7d2+vYceOHeumStSZN29egS3dPn/+fDEzZk1fysVkcSqXRZYsXiwS6m47NhNFPQ8xsoWZAqN2DJKMhAbZxJ6SVGhxiU/Oflg0ztgzRx9k6Y05Gny0azgP/0N0FtDO6BoL5BfCVKlSRf3GpoLwvYD/oSfQAsC3ASYH3Q/HG/DTAMa4gMWLFyvNBvxj9PhhAAkBQQ+LNMCp1ug6gLiiPdfD6EJHq1atXGHQwcInBmF0B2loh3AfHLfcNkQ++Wi8Em4guCQlJilNz+OPP+72bOQ5fiNOEIqgfcA78QRh9H7CGFcIkwDPwHvF/yFcGMPgHECnjfug84dZ7s4773RzBjamGTzyyCMqb2B9gBALTU9SUlKm94xn43koE9BA6QIvfuPd6OGhiUOZ0H9D0zNgwABlqoJgBbOf8d7ZyZu0tDSlMYOQhHKH7xBqEXeg542edh2UBbwXY17g/6+88or6P9KJ961vnYA05uSdAOQtri1ZskTd31uZLRI+N7kBEjMyHBmAqZSwPeOFeVOtQSuE6zp4mdWrV5c+ffooz/r8BAUGjRAErbxs+OWvmD19586cd33v1aO7BGU0gGbCLHmIBhYzjKByNzowYsSHBhVthD+udot3joZfb6TPnz+vTB1oywYNGqTaJMzIgSYaHTrU/JhFhXYMI3YIK0gjOiW0a2jL4GyLNMPfAU7HEEy6du2qTP3QLOA/GCzifSCsjj6482wHoUWBIyuciSEETJgwQcUBI289LIRL+L4Y/4tROYQI/RycSzFLB2YVTPyAwAbzEHyKEEb3EUFe4n1Ag/Dn77OkZt2aLs3JSy++pDQweDd6eOOzIYDBwRhaGDhNwxQHTc/s2bPVbCAIf57xMqYd5QTnUYbwfoxh9OdBMMB5pB8+orq2BM7aGzduVN/1/8GJGe8dg2rEWQfChh7GWEahcYGw9+qrryrhAFosvAcILt26dVPl4uuvv1azzoxxgx8O0o6yBG2J8Vp28iYwMFDKlSunfuOA1hB+QzfffLPUq1fPlXaEM/o44R3h/ngnuB++w7cJwgpm2Bmfpb9flPErvRMjKA+4hjLs6ZzsTRjyS+EGLxcvAXZDI/jt6eRmBJK2PiJBpYFkjxfnTbjBi9YdzYwg0wqqcS/Ie/sDZk2fxaACRYU2YxrNkodo1NHQoi0w7jqsq7n1a/4G4gX/FWie9cYfkyMwO0b3odF9XOBPiEkW6IjR6cBMhDQhjXpHAvMRhAi0pbfccosrzRBOYEKBlgGjb7SX6PyN70T/7vme0NHCHwNCEp6NzhOCAkboxrCe71gXJvVziD86I8QPHTaEHMzqgrbFGA4DTQAhpn2nzvLoqAfV7zffeFPlMxykPTtB47MhyGCSCYRCCGHoO9AxQnOkTz32TKcx7cYydLkwMK9gUI13j3viExoJ+LzgOoQWCFlwBH700Ufd4mt8hmcZhXMw8glaIZj/UDaQl5hiDaH07rvvVs82xg3vDOmDwAX/Km/lzHqZvNGnY+u/oQmCBgj+PNDc6ec9Z6YBODLD5wjTyQEEUzgkG+9tJDvvxPM87uGtjcpRm6X5mPbt22sjRoxw/bbb7VrVqlW1sWPHZvsew4YN07p165atsLGxsTB4qs/8JjU1VZs1a5b6NCNmT9/J42dhDVdH6vnzmhkxSx4mJSVpO3bsUJ9G0H6cP39efZoVM6cx5mKStvb4WnWkpadpxTH/atasqU2YMOGy94iPj9ciIyO1X375RStsnnjiCW3q1KkFVkazqts57b99bpaCahWSKdSHkFwxTRAjDaixdKkPkiw0MwCfCAu7Mmx7kNoh+X722Wc+Tgkx0zo3hBDib0DrA19VaEqg6dL9ogqTwMDATI6+/ojPhRvMrYcqEQsiwTFMX8VSdzLGNDuj6gqCD1SBcJiDXQ4qXdg+8zpHnxB7UIgMHDpegiya/Ohh6yWEFNb2C9Ul0Kpx+wUvoD+Ejw9MUfBhMk4ZLyzef/99KQr4XLgBcIIzrr5pBB7qRrDIEQ5C8pt0i0W2VK4voTZNpAiMTAgxG8oXRIKlONc+bH2QFZg1lZcVsYsTFI0JycCR0Wj44SQbQooFetVj901MobkhxB+wJ6fKA6t/kWAMG1O7w7js6ygRUszQxG65IFi5SN9+gZDcQOGGkAzsqSny0iLnsuZpaeN9HR1CiqlwEy1YAk7TMk9xJiS70CxFSAbZXEGfEFJQ0CRM8gkKN4RkYKd0Q4hPgTsxIfkBhRtCPByKCSGEFG0o3BCSgZ2KG0J8CmcqkvyCwg0hGaTTLEWKCb/++qvan6hs2bJqbRnsmUSImaBwQ0gGlG1IcQErvWMzQ+w+7k9QcUPyC04FJySD1MAgGXL721I+RGQ8t18gBQC2lsEK69u2bVP782DXZezsjb3yCpO77rrriqvh+m77harKPMXtF0heYOkhJAOH1SqrajSXTbWacfuFokpCQtZHcnL2wyYlZS9sjqOXoDYLXrdunSxcuFDtm3fTTTepDRGz4u2335aIiAjXERkZqfYWwqd+DnsOmWf7hVCxSqivo0KKONTcEOLhUGyjbrzIYo2MlFJZXbz2WpG//770u0IFkcRE72G7dcPGdpd+R0WJREdnDpfDGXY333yz2+8pU6ZI+fLlZceOHdK0aVOv/3nooYfktttuc/2GIHTx4kUl1OibClepUkXMAKseyS8o3BCSgSMlRe7a8JeUC8YSxT24/QLJd/bu3SuvvfaarF69WqKjo10aG2heshJuypQpow4d/CcuLk5pbnThJiu+//57efDBB12///nnH+nSpYv4L5o4LLGCt8LtF0heoHBDSAZaSpq8MX+y+p6WOlYkLMzXUSI5xBEXl3XH72lqPHMm6xt5/jeffFOuv/56qVmzpnzxxRdK2wJBBUJNaip2U8raLIXjckDzU6NGjUznBw4cKB06dHD9rlq1qvgzmmiSbjnr/K75d1yJf0PhhpAM0jVOlyryhIeL2O3OzytoNVSYnNw3j8TExMju3buVYKNrT5YtW3bF/+XFLFWiRAl1FCWfG0J8KtykpaXJqVOnJDExUdmMjWpTQooiXKGYFCSlS5dW68p8/vnnUrlyZWWKeuGFF674v7yYpbLi3Llz6vknTpxQvyF0gUqVKqmDkKJOjmpGfHy8fPbZZ9KtWzdVsaKioqRRo0ZKuIGqdfjw4bJ27dqCiy0hBQhXKCYFCQSR6dOny/r165Up6qmnnpL333/fJ3H5448/pFWrVjJgwAD1e8iQIer35MlOs6yvoN6GFLrmZvz48fLWW2+p9RhgN37ppZeUKjQ0NFSNArBuw9KlS9Wql7Dxfvzxx1KvXr18iyghBQ03ziQFTa9evZR/jBHNBxrDe+65Rx3+DBWppFCEG2hklixZIk2aNPF6vX379nLvvfcqzc60adOUoEPhhhQlqLkhxLfQ5YYUunDz448/ZitcSEiIcoAjpKhxuYXUCCEFD5bwM86cIiS35MobDRoa+N94W30T1wgpiqTaAmXYLaPkzWGvigRjsRtCSGHPlgpwVFGHhQvokzyQq9Lz9ddfS5Ln8uSCFcuT5JtvvslLfAjxGem2APmvTjvZ2LidSABXSSCksLFaINKEqYOQvJCjFhzTD+H8hgOaG5igdOx2u8yePVsqYElzQoqwQzHHi4T4fp0bGqVIoQk3pUqVcm5sZrFI/fr1M13H+dGjR+cpQoT4Ci01TW7ZukBqldBE0npy+wVCChmH5hCHxenyoGkRvo4OKS7CzX///ae0Nj169JBffvnFbWGpoKAgtdaNWTZwI8UPLSVFxs3+UH1Pm/w6t18gpJBB/5JuOa2+O7SKvo4OKS7CDRbvAwcPHlT7mHCpbGImOBWcEELMQbbdC7BUtw40NFcSbI4fP563mBFSyNi5ahghfgNrIykU4aZdu3by4IMPXnZ7hdjYWLUpHJYWh9mKkKJm7yekILnmmmvkySef9HU0igQca5BCMUvt3LlT3nzzTendu7eaJdWmTRvlX4Pv58+fV0uKb9++XVq3bi3vvfeeXHvttXmKGCGFDc1SxIycPHlSnn76aVm3bp3s27dPHn/8cfnwQ6dvGSFS3DU3x44dU5u8oaJMmjRJba0QHR0te/fuVdfvvPNOtSHcypUrKdiQIgn3liJmJCUlRW1u/Morr0iLFi2kqEDNDSkUzQ12jD116pSqJM8++6wyT5UtWzZPDyfEn6DmhhQG6enpMmLECPn2228lMDBQHn74YRkzZkyBTdCIioqSiRMnqu9TpkyRogOlG1IIwg3WuDlw4IASbg4dOsR9eIjpSAsIlEdueEFalHHIvdx+oUiSkJogCWkJYku1idXqrpi2WW0SEhDiFjYrrBarhAaGXjFseFB4rlZ4v++++2TNmjXKVPTAAw+o2afDhw/3Gh6bEPfv3/+y9/zf//6ntOdFHbz3EEtlSbdDsOFymqQQhJubb75ZTQWvXLmyGmG0bdtWbDab17AQgggpaqRarDK74dXiqObg9gtFlMh3I7O8dm29a+XvO/52/a4wroIkpiV6DdutZjdZdM8i1++oiVESnRidKZw2KufaherVq8uECRNUO9qgQQPZunWr+p2VcIO2dtOmTa7fGFhevHhRIiIiXAJcxYoVzbO3lKWEOISDZ5I3st2Cf/755zJo0CCXQxoqYokSJfL4eEL8zyxltVAdTgqOjh07upmgOnXqJB988IHawsbbgDE0NFTq1q3rJtxgK5zIyMhM2ikzwNXTSH6Qo+Fpv3791Ccch5944gkKN8RcpKXJtbuWSZtoh0h6b26/UASJez5O4uLjJLJEpFezlJEzz5y5rHnEyKEnDomvKE5mKbVCsXZRaW4cGlcIJ7knV7r3qVOn5uGRhPgpKany6e/vqK9p417EkNnXMSI5BD4w9kC7+rySViMn/jK58a3JitWrV7v9XrVqlZp9mpWZvziZpbDWVJJ2QrnbaFLe19EhRRg6FhCSgYNzT0khgNXeR44cqRZF3bBhg3z88cfKLJUV+WGW0oUjCEVnz55Vv7EfYOPGjcWftTiE5BYKN4RkkO5gY0oKnqFDh0pSUpK0b99eaWtg4seMqYIES3nowK3ghx9+UNvoYOYrIWaEwg0hGTgo3JACZtGiSzOwPvvss0J7blHUghTBKBM/wnyu9oTkEm6cSYj/wNpI8gKFG0IyoFmKEELMAYUbQjKgQzEh/kNRNKUR/4E+N4RkkGIJkGeufVI6VnDIDUFBvo4OIcUOLG4YbqssKelYUZPL+ZHcQ+GGkAzSrDaZ2ayXhNa1yw1cwI8Q3+wtZS0paVq6WCjckDxAsxQhGdgzfG6sbFMJ8Rn61hQ0SpG8QM0NIRloaWnSff9aaZDE7RcI8ZWfTaoD2y/YRdMu7eBOSJHU3EyaNEmioqIkJCREOnToIGvWrMky7BdffCFdunSR0qVLq6NXr16XDU9IdrGmpcrUmaPljo/eEElJ8XV0CCl2YPuFuPSjkm49wZ3BSdEWbmbMmKGWIh81apRairxFixbSt29fOXPmTJaLYN1+++3y33//ycqVK6V69erSp08fOX78eKHHnZgLTgUnxI9gdSRFWbgZP368DB8+XIYNG6b2OZk8ebKEhYXJlClTvIb//vvv5ZFHHpGWLVtKw4YN5csvv1R7rSxcuLDQ407MBVcoJv7Mq6++6rZNQ48ePeTJJ5/0SVxeeOEFeeyxxwr0GayNpMgKN6mpqWqfE5iWXBGyWtVvaGWyQ2JioqSlpUmZMmUKMKakODkUE1JQLFmyRK6//nqpUqWKcpydNWtWlmG7d++uBm/g1KlTMnHiRHn55ZfFH3jmmWfk66+/lgMHDhS4D85rr70mlStXVhuIom/Yu3fvZf/z+uuvq3drPDAQNpKcnCyPPvqolC1bVu2ufvPNN8vp06cLNC2kGDkUR0dHi91ul4oVK7qdx+9du3Zl6x7PP/+8aiiMApKRlJQUdehgN10AgQhHfqLfL7/v6y+YPX12+yUbv0qjCdNpljxE/NHxQWuLw3PhN/2avxEfHy/NmzeXe+65R2655ZZM8dc5d+6cLF++XG1wievwNezUqZMywxvT6Kt0YjAJd4BPP/1U3nvvvQJZuA/f3333Xfnoo49k6tSpUqtWLSXowG1h27Ztykczq3s0adJE5s2b5zoXEBDg9p6g8Zo9e7ZyiyhZsqQ8/vjjMmjQIFm6dKkUNP5eRn2dRoTH/1DHsbGskZy0W0V6ttQ777wj06dPV344WRX0sWPHyujRozOdR8GH+asgmD9/vpgZs6Yv9uKlivPvv/+KPYsyZQaKeh6is6pUqZJcvHhRaYB1EhIS1CcaR31KMa6jUcR/goODM4WFRgAaY4BwCI9G1dimZBU2MIcz6q666ip16GB3cH3AZWTmzJnK/xDPw3UIOffee69bWAwMobl+8MEHVSeNuCDMSy+95Ep7bsB90flDywTfx2rVqsl9990nDz30kFs4DCjfeOMNeeWVVyQ/HYp1kpKT5cMPP5Snn35aabHAxx9/LA0aNJAff/xRaVu8gcEs0u/ZvuvvLjY2Vrk9QGBs27atOgetGCazwL2hXbt2UliCrtmJz0UaUf9QL1D+0tPT3a6hvBcJ4aZcuXKqEfFUB+I3Gq7LMW7cOCXcLFiwQI2EsuLFF19UDsvGAq47IUdGRkp+gsYOnUbv3r1z3OgVBcyevs+2zXfzZwgsVUrMhlnyEGaFo0ePKpOCUQjBDEpw8uRJqVChgvr+9ttvK38VdNCff/65K2zVqlVVY7l//341W1Pv5NBeYNLCd9995wpbr149pWnesmWL0goAdI7wF8wLEF68tUNo12666SZ1DVqc3bt3y9VXX61+Q3BDp4G2E4M7CDSrV6+WdevWKQGkbt26eYoXygi0JPCpgdlmxYoV6r54R7fddpsrXNeuXeXEiRMqfvr78+Thhx9WfpKXwyiwKeEmyfn99MmTqi8YMGCA6x3hE0LI5s2blZ+mNyDAwlwGH06UjY4dO6oyUKNGDXUd7wlphHlQvy+EHFzfunWr9OzZUwoSPf9KlCiRJyHUn9HykEbUbdQLlC9PpYW3gYBfCjdBQUHSpk0bJS3feOON6pzuHDxixIgs/wc16FtvvSVz5851Sd5ZgYJuHK3poGEvqMa9IO/tD5g1fSnWQHm190PSvbJDuoSHmzKNZslDaBfQaEKLomtSjOjX9O+e54wY75GTsFk9Oyd4uwc0D2jboHHGtWPHjqnOAhoU/NbV/IgjBmrQbuB7o0aNZPv27UpAgzYnt6C9HDNmjOt3nTp1lPAEbdKQIUNc5xEfACGzdu3aXu8Fzc6zzz57xXfgQhOJDKwkial2OXF6vzoFfxtjGLgtQOjJ6t1DmJk2bZrS8EDIxXvs1q2bMmWhs4U2Cn2Pp5/mle6bXxjzr6Cf5SvykkaEx/+8tVE5abN8bpbCKOnuu+9WQkr79u1VRYUKWJfKhw4dqkZYMC8B2GBhd4WaFqMFONoBjOBwEJJbUi02+bb1dVK+Wbp0KcIdf3EGIzsc0ArroHOFmQVmKSP6chMYJerAyRRaD09b/6FDhzKFhd9MQQCTKLROuoYIKnrgzfSOjtw4MoZfzgcffKCEP880HDlyRGkzdGC+wpHV2mMw3eA/eD5MBZihakR/F5czFSAdugYtu9svRASWkeSUVPSMkhv69+/v+g6tPjQ9NWvWlJ9++klp70jxwOfCzeDBg+Xs2bNKYIGgggo0Z84cl5MxKpdR8vvss89URYMznhGskwMveULyvP2CryNCck14eLhLq6ODUToOb2Gzq9HKKmxB8Mcff8jAgQNdv3VB7fz581K+fPlc3xcTLzZt2uT6ndUMU5i6MBsKQhKEJWg73n//faW9MQJzFLhcnGDOMpr3vAG/KXeceVe+grMPgDYF2hsd/PYUtC5HqVKlpH79+rJv3z71Gy4P6EMuXLigruXEHYIUHXwu3ACYoLIyQ8FZ2NsIipD8RrOnS8cjW6RagF3E3pfbL5BCB+anP//8000ggFkIviE7duxQnbQRT4Fj1apVyj/IU2sDoLmCP86VwCytzp07q/XEdOCX5AnMPBDwdA2TN2DegqCUo+0X7AmiSZrUqFlTCRtwU9CFGWjlkGb48mQXCE+I/1133aV+wxUC8cZ9dadk+DRhIA1hjpgDvxBuCPEHbKkpMv1Hp5o+7ZkRsAP4OkrEZKCj1TUI4ODBg0qbAi0KHFqx7hfMPHAe9lz7a9myZS7fRB10yDDtw8cGK7xjNhE0LnkBwtE333yj/H7gWPztt9/K2rVr1XcjmDaNrXCMprq8mqXgUBydckipTzUpocyJb775pooTng/HcGigjO8BDsBwvtYHyBCm4CwMUxQcnqHVh7AHJ3GAqd8wT+G94b1DcITzNAQbmPmIOaBwQ0gGJl1ygvgRmKmjT2sG+kxO+B3CCfb333+Xa6+9NpN/0P333698gTzXlIFPInxi4K+IDvyJJ55wW8U4N0BQ2rhxo3IZgHkPQgG0OP/8808m81VBuwI899xzygcTaYIZCUIf3BaM/kfQymAmmw4csBHnmJgYZTLDf6DRMprPJkyYoIRGaG7gwI21c7BmDzEPFG4IyYDbL5CC5pprrnFbqM4TCDfe1o3p16+f0lhgPRsIHbrjse6PCF/E/AKzpbBoHg4j+qQOAEEHz/b0fcxP8JogXMG0ZZy95YmnqwKErisB4QhO0ziIOaHvJCEZcONM4kvg5ApNgnG2jw46eazR47moma+ANgXCj6eGKT9hbSR5gZobQjKwX2ZETUhBgxld8A/JCjjV4vCHJfsLUmPjgtWR5AFqbgjJgGYpQvwH1kaSFyjcEJIBzVKE+BOsjyT30CxFSIbWJt1mk7evGSb9qzmkKde4KRJczjmXFD3gW1Q6uJLEJcO3yJz7LpHCqdMUbgjJ0Nqk2QLl8w43S6126dLUy4q2xH/QVwfGmjCXW2eFFC2w/ULJ4PKSkJQkmkbhprg61gNvC1HmBAo3hKjFwy6NFqxsU/0eNHxYOl/fHyosLEyN+uFsi8YROwubeVNCM6cxLSVVtPRUsac5JDk5bx2cP2L2/MtLGvE/bMeE+pzXmXgUbgjJ2FfK6rBL09P7pew+bL9g5/YLfo6+D5Au4OgqbSxqB22OcX8pM2HmNCJtsclJcjE5XeICgyX1QrCYDTPnX36kEcIQVuvO67uhcENIhlkqOD1N/vjGuWJs2gP3c/sFPweNHzZUxPL+aWlp6hw+lyxZIl27di2wjS19jZnTmJiaKNd+3lp9H1Dha/ngtg5iNsycf/mRRiyJkB8aLQo3hHAaeJE3Uen2eXxioTusQGvWjsPMabRb7XI44bD6fjbJ7rbNglkwc/75UxrNafAjJIdwGjgh/gUHHCQvULghxMOhmBDie+y+jgAp0lC4ISTDoZgQ4j+wTpK8QOGGEDakhPgdrJMkL1C4IYQNKSF+B+skyQucLUVIhkMxtl/4rNud0r1SutQx6SwGQvyZQFugDG08Un7ffFI0h/kW8COFB4UbQjIcitX2Cz2GSpUWSVKH2y8QUugE2YLk3mbPyuL160XT2D2R3EOzFCEGFXgA914gxKfYMuqgnTMYSR6gcENIhnBj0RxS5+xhKXHkCBbZ8HWUCCl2ODSHHI7bLamWw5KOLVAIySXU+xGSIdyEpKXK9I+Gq99pd90lEmy+fW0I8WeS0pLk//7uJhIiEuX4y9fRIUUYam4I4QrFhPgdnC1F8gKFG0K4QjEhfgd9bkheoHBDCEeJhPgdrJMkL1C4IYQNKSF+h50+/SQPULghhMINIX4HTcUkL1C4IYTCDSF+B31uSF7gVHBCMoQbbL/wW88h0rREskRx+wVCfLL9wvAWj8mMdcfFYeHYm+Qelh5CMqaCY/uF7waNkB333CPC7RcI8cn2C69c/ZaUTr9XHA6OvUnuoXBDiMEspS/9Tgjx8fYLNBWTPEDRmBCluXGo7RcqnTslodZobr9AiI+2XzgRf1jSLafFppUXTdPEYuGAg+QcCjeEGLZf+OjlW9TvtNtu4/YLhPhg+4X205qq7ReqJ81U9TLARuGG5ByapQjh9guE+CWcMUVyC4UbQmjfJ8QvYb0kuYXCDSHU3BDil1C4IbmFwg0haES51jshfgf9+kluoXBDCDU3hPjtLEZCcgOFG0Ko/ibEL6FDMcktnApOSIbmxm61yYo+t0llW7xUC2DVIKSwCbAGyEOtH5If1hwRi9g46CC5hi04IRmam9SAQPnjgZekU8AhqcY1bggpdIIDguWjfh/JgpVzJU0sFG5IrqFZihCDz00At18gxG86Jgo3JLdQuCFENaIOEU2TiLjzEhQbq74TQgoXbLdwNuGsOCyxoolG4YbkGpqlCMnQ3ISmpcgL91yvfqcNHMidwQkpZBLTEqXqxKoiQZe2XyAkN1BzQ4ha54aNKCH+BmdLkSIr3EyaNEmioqIkJCREOnToIGvWrMky7Pbt2+Xmm29W4bFT7IcffliocSXmhevcEOJ/pHPQQYqicDNjxgwZOXKkjBo1SjZs2CAtWrSQvn37ypkzZ7yGT0xMlNq1a8s777wjlSpVKvT4EvPi4AiREL+D9ZIUSeFm/PjxMnz4cBk2bJg0btxYJk+eLGFhYTJlyhSv4du1ayfvv/++DBkyRII5VZfkI9TcEOJ/sF6SIifcpKamyvr166VXr16XImO1qt8rV670VbRIMYU+N4T4Hw4KN6SozZaKjo4Wu90uFStWdDuP37t27cq356SkpKhDJy4uTn2mpaWpIz/R75ff9/UXzJy+VLvd7bdKownTaeY8LA7pM3saPdOUnJr/7bSvMXP+FXQac3I/008FHzt2rIwePTrT+Xnz5ikTWEEwf/58MTNmTN+Ro1a1/cKajj2kUqgmmxcvFkdgoJgVM+ZhcUqfWdOY5kiT7qW7y9bzFrX9wspVqyVmpzm1N2bMv4JOI/xu/V64KVeunNhsNjl9+rTbefzOT2fhF198UTktGzU31atXlz59+khkZKTkt1SJzOzdu7cEmrBjNHP65s3YIuujT8mmsZ9Jxdidpkyj2fOwOKSvOKTx2rRrpfe4f+W4WKRNu9bSpW45MRNmz7+CTKNuefFr4SYoKEjatGkjCxculBtvvFGdczgc6veIESPy7TlwPPbmfIwXXlAFqyDv7Q+YMX2OjM+gwADTptEI01f0MXMaLRm7oFisNtOm0cz5V1BpzMm9fGqWgkbl7rvvlrZt20r79u3VujUJCQlq9hQYOnSoVK1aVZmWdCfkHTt2uL4fP35cNm3aJBEREVK3bl1fJoWYYVaGpklwSpLYkpO5/QIhPtp+ISE1Ad42okkIHf1JrvGpcDN48GA5e/asvPbaa3Lq1Clp2bKlzJkzx+VkfOTIETWDSufEiRPSqlUr1+9x48apo1u3brJo0SKfpIGYA3vG9gu392yqfqedP8/tFwjxwfYLpceVVt+ry0yuUExyjc8dimGCysoM5SmwYGViSPaE5DdcT4MQ/4N7S5Eiu/0CIX6zKzghxK+gcENyC4UbQriHDSF+CYUbklso3BDCRpQQv4T1kuQWCjeE0OeGEL+EDsUkt1C4IYQjREL8EtZLUmRnSxHiL5obh9UqZ/pcJ2kXz0kFm83XUSKk2GGz2mRQw0Gycs8psYiVwg3JNdTcEJIxWyolIEh2f/SlrHvuOZGQEF9HiZBiR0hAiEwfNF3ah7wgFgmicENyDYUbQgw+N4Y1IwkhPkKvhhRuSG5hU06IoRENoHRDiM+xZuwtReGG5Bb63BCSsc5NaGqydKhb4dL2C6VK+TpahBQrsK9UxNgI9Z3bL5C8wGEqIRwhEuKXsF6S3ELhhhCup0GIX0LhhuQWCjeEsBElxC/h4pokt1C4IUT53HDjTEL8DQeFG5JLKNwQQs0NIX4JNTckt1C4IYSNKCF+iYO+cCSXcCo4IbrmxmqVpF59JPZ8jJTl9guE+GT7hf51+svWw2fV9gtYooGQ3EDNDSn2aJqmNDfYfiH2p19k9auvcvsFQny0/cLvg3+X/pGvqe0XqLkhuYXCDSn2GC1SNq5QTIjfrFCc7qCjP8kdbMlJscfYgNr0VpUQ4jOsFueIg5MYSW6hzw0p9ugzpbD9QulK5WSA3S7aqVPcfoEQH2y/UGFcBUlNs0tl+V7s1NyQXELhhhR7jDOlLImJqlKk+TRGhBRfEtMSXd+puSG5hWYpUuyxc0YGIX4JNTckt1C4IcUeXXNjobsNIX4Fxx0kt1C4IcUe3ecmgM7EhPgV1NyQ3ELhhhR79NlSnClFiH/BbVFIbqFwQ4o9lzQ3rA6E+BN0KCa5hbOlSLHHNVvKahFH165yLiZGSlLQIaTQsVqs0rVGVzl46pxIkoVmKZJr2IKTYo+uubGHhIp9wQJZ/tZbIqGhvo4WIcWO0MBQWfB/C2Ro+bfEKsF0KCa5hsINKfbom/PR54YQ/0Cviem0S5FcQuGGFHt0h+JACjeE+AU2a+YFNgnJCfS5IcWetAzNTQlHigRUqSL9UlNFDh/m9guE+GD7hagPoyQxJVXKyhRqbkiuoXBDij1pGQ1ogMUiluhoCeb2C4T4jOikaPVZ1jDwICSn0CxFij26z02ArgsnhPjVwIOQnMLWnBR79AY0kMINIX4FhRuSW9iak2KPyyxlo0MxIf4EHYpJbqFwQ4odmubeYOp2fWpuCPEv0tIdl627hGQFW3NSrJi+5og0eHWOzNt+yotZipobQvyJVINDscOhydApa+Tqd/+V2ES6/JPLQ+GGFCte+HWrpKY75IFv12cSbmwBNnG0aSPn69YV4fYLhPhk+4U2ldtIzeC6aik/fQ0qsGTvWVm6N1qOnU+Sf7ad9Gk8if/DFpwUG84lpLr9Tk6zu5mltJAwsa9cKUvGjeP2C4T4aPuFlcNWyqu1xqntF4xmqW3HY13fVx6I8VEMSVGBwg0pNpy4kOT2+8i5RPWpjw6DAmiWIsQf0BcLTzM4FB+OcdZXcCg6wRfRIkUICjek2HAyNtntt95YwkwFAmiKIsQv0McZxqnghzMGI+BgdAKdi8ll4QrFpNhwMjbJ6299umlYeooE1KsnvRMTRfbuFSlZ0ifxJKS4kpiWKI0nNZaExEQJlc/EqoWI3aGpTW3PxF0anMQlp0tsUpqUCgvyaXyJ/8KhKjEdianpsvnoBUlJd/rU6Jy44K65ibno9MHR7fqBVhHL4cMSdvYs5pwWYowJIQDamMOxhyU67azrnK69gTBzufqMwcre0/GFFFPi71C4IaZj2NS1csOk5fLiL1vdzp/K0NQEB1jdHIx1uz7XuSHE/4BwA6EH2hpQLgK7v4mcirukiT12PlG6j1skvScskfk7TvssrsR/YGtOiizebO7bT8TK6oPn1PdfNx6X84YZUicyfG6aVIl0F264QjEhfgtmM15MSVfmKdCwUolMmpuf1x2T5DRnPZ6y7KDXtoI+OsULvxBuJk2aJFFRURISEiIdOnSQNWvWXDb8zz//LA0bNlThmzVrJrNnzy60uBLfg0bqtd+3SfPR8+SntUfdrq3NEGx0Nhw5n8nHpmlVpy9NTEKK+kzPEG6CqLkhxO9mTKF+6lob1NGocmHq+ynDBIHVBy9NDV93+JxrmQeQkJIut3+xSq56519Zf9i9fSDmxeet+YwZM2TkyJEyatQo2bBhg7Ro0UL69u0rZ86c8Rp+xYoVcvvtt8t9990nGzdulBtvvFEd27ZtK/S4k7yD0RhGZd4EmPHz98itk1fIin3RbtdW7I+Rb1YelvjkdHntj21utvgthrUwAHxv9NVNT8c6hZmmVUp6aG70XcGpuSHEX9DNxKl2h2tF4sjQQKlc0rkG1YmMwQrq9tZjl+o96rNe78HXKw/JqgPnlOb25d+2uWlwoLUd8+cOuW3ySrd1dHSc5jCuhlwU8blwM378eBk+fLgMGzZMGjduLJMnT5awsDCZMmWK1/ATJ06Ufv36ybPPPiuNGjWSN954Q1q3bi2ffPJJoce9OAoiHlu9uNh/9qKsOhDjUh0b//Pl0gPy+h/b1fRNIxh59ZmwWJq/PlfGz9vtdm3hzjPy0cK9svbQeXnou/Vuy63/tvG46ztU0XMNWynoDVSXeuXU565TTgfDmIRU1UhaLCL1Kkaoc7pQ5Fqh2OLz6kAIyUAfbKTbNVddjQwNkCqlQtw0N8cvJElCql2CAqzSs2EFdW6rQVD5feMJ13e0B3tOX3T9/nrFIZmy/KCsOXROHvx2vdskBMzOgg9Py9HzMrVPEHp+WndUXpm11U2wcv03PlmW7DmrJjd4I9V9rgMpAHzamqempsr69eulV69elyJktarfK1eu9PofnDeGB9D0ZBW+MPl76ylJ9FKWoZlYsR/Lhl9ap0EHnTb2OfI2aoi+mCI/rzsq/+0+k0lowIJ0Xyw5IDPWHnFTweoLXI39Z6d8uGCPm88J2HkyTp6YvlFGztgk+864zyxYe+icDPp0uVz/8TL1TCOIX7dxS+T5NTb5fKm7Tfu7VYel5weLZcjnq+TBb9epkZTOe3N3yZt/75RpKw7J4P+tlAuJl+Lz7pxdsv9sgiD4R//uk40GE9L3qw+7vkMl/ddWZwOFey/a7ZxJ0apGKfUJoUpXP+8742y4bmpVVX3uO3vRrSEsHxHsckj0FG6CAm2iNWokcdWri5KCCCGFisVikUblGkn1kOoSlLHuFOqnrj0pGRoolSJD3datOpAxaIoqGybNqznbhB0n4tRnzMUU2Z0xg6pFdec1vW2DgPLjmiOuZ0NI+mvzpW0dPvlvn2pP9PZpy7ELbm3eczO3yHerjsiQz1e6Ddwg7PQYt1jtg9V/4lK3Kez4fuvnq+XZNQFy79frJd6gFUI7Pur3bdJj3CIlNBkFI8QVz7xv2lr55N+9buv/AAhSGED+sPpIpmu7T8XLp4v2yT9bT2byO0J8IKTh/8Z2W++3Fuw4rdrlzJsNO2TdoXPqPXtew+/tJ+Lk8CUZsvitcxMdHS12u10qVqzodh6/d+3a5fU/p06d8hoe572RkpKiDp24OGehT0tLU0d+sed0vDz50xYJtNhkh3WHjOxdX8KDbDJ722l54+9dSnMAG/ItravKi/0bSERwgPyz7ZS8/PsOZV4B/ZpUlDEDG0npsCBZtOesPDtzm1zI6IA71S4jE25tJmUjgmXJ3mh5fPpmNVoBU5cdlM/vai2VS4bIusPnZfi3G12mnpnrjsqXQ9tInfLhsv7weRn29XpJynC8W7jrtHwzrK00rhwp247HydCv1riuPfDNOpl2TxtpH1VGNSL3TlsrZ+LxHi3y/ry96lnXN6+s0j36z+2u97Bg5xmZuvyADO1YQzUMXxoEIfx/0r975bm+9dUCer9vcmpg6lUIl71nEuTT//bJp3e0VIIb0g8Gt60mM9Ydk982HJPbWldRIzIIfXi3D3WtJQ9+t1HWHIhRebnl6HnVEFUsESztajpNT0diEiUxOUWOxjhrWqWSwRIWcEnrczEpRVIzhEN7SLAkrVsn/82fL70DA1FIxGzoZT4/y74/Yfb0mT2NgRIo64atk/nz58sbWyHEpEpiSqqcu+gUECKDA6R8RIDLhw4D5P2nnW16zTJh0qBimGswhvezcp+zHalfIUJubFFJmasW7jwt93WuobQ4GFxB43N3xxryxbJD8uOawzKweUW5kJimBpYA7cnp+BT5YN5u+fKu1kog+WDeHlec0Q6/+dd2mXxnK9XpP/PzJlf7i3buqRmbZMrQ1gIR4OHv1sumo86B7NJ9MfLk9I3y6e0t1Vhq5E9bVH+hC2yY1v7F/7WWkECrjPl7l3y32hmfhbvOKMHik9tbqpmfkxcfkA8W7HPFZ+62k/LRkOYSFhQgv208IS/N2u5ay6tL3bIy4bbmSkj8d/dZefrnra644tr7tzSTsuFBsmxfjDw9c4ucS3CWsc61y8g7g5qqdn/j0Quqb9IXVcS10QMbSVTZcCVIoU/bfCxWGpa0yv35XEZzUuYtmg9dyE+cOCFVq1ZVfjSdOnVynX/uuedk8eLFsnr16kz/CQoKkq+//lr53eh8+umnMnr0aDl9OvMUwNdff11d8+SHH35Q5q/8Yl+cyMyDNjmZ6BzxlwzSpFSQyOGLzt9hNk0S7c7vZYM1qRCqyc4LzpFJmWBNLqSIOMQipYI0qV1Ckw0xzmvlQjSJSxVJdVhUuGalNVlyyiKaWKRauCYXUkUupjn/16GCJv+esEiawyLVwzWlRYpJsUhEoCZ9qzpk9lGrJNktUjfSISl2ixxNsEiJQE1ureWQnw5a1X3ql3RIkFVk23mrhAdo8mhju3y7z5muSqGa1InUZPlpqwRbNXmqmV2+32dT92la2iENS2nqHQRZNXm5pV1+3G+VXbFWaVbaIR0ravLFLpsEWjR5pZVdxWX1Was0LuWQG2o6ZOzmAEGqcG3NGavMPW6VepEO+b+6Dnl9g02ld1TrdFl9xiJzjtnUPf+vnkNeXGNT7w3XtpyzyG+HbCou9zdwKC1TisMiL7VMl90XLPLLIZu0KOOQe+o7ZOQq5z3faJMuvx6yysYYq9wcZZeulTmjghB/YPQGm5xLscjIpulyIN4isw7bpE05h9xexyHPrHYKOG+3TZc5x6yy5JRVelZxSNdKDhm1IUCsosm77e3y1xGrLD5llasrOqRHFYeM2ei89lY7uyw8YZUFx63SvIxDbo661M6gvdgcY5G/j9qkapgmw+rb5e1Nznbm2ea4ZpV5x61SMdR57b3NzmsPN7LLkYui/oe28/4Gdvl0p021x2hbEtJFtV3BNk1uiXLIjANWSdcsMqC6XeD2h2s2iyb9qjlkwQmraqPRBlYNF1l00qrax84VNVlz1tnGow0sFyLy30lnX4G2bccF57WoCE31AUtPO6/h9/EEkTTNIuVDnO34qjPOaxVCNDmf4rxWGv1PpCYbop19DPqV+DQRu2aRsABN6kZqsvWc8xr6tBSH81qg1Xltd6xFHBm/m5bW5O56TleA/CIxMVHuuOMOiY2NlchI56xXv9TclCtXTmw2WyahBL8rVark9T84n5PwL774onJYNmpuqlevLn369Lniy8kpD6WmysczF8rfp8Ll6PlkiU2FU5zFqWHoWlu2HIuVZ3/ZKscvJCuhA5qcB7rUksd71FF2YGh+DsUkyoYYZ2m4q2MNeb5vfTl6LlEe/H6jHDmXJItPOa8NalVF3hjYWGlD7vtmg5L05x5zXuveoJx8NLiFJKbalaZmx8l41bGDdlGl5au7Wqv9lO74ap0avUzZY3NNsfzhvnYSYLXIHV+tlW0n4uS9Lc4iUj4iSL69t41sXbNUkoNLy/ojsfLOZue1kqEB8tn9naVCRLAc+GqtbDhyQSbsDFVaJ6R/wt1dpUaZUNnw5VpZf+SC/BlTTjbFYPSiyeu3dZRW1UvJ8sT1smRvjOwPjJJNcRhtpciIfi3l2maV5M+YNbLu8AVJq9hEjp+G2jhObu/WTAa1qSrfH18lW47HSUStluKwwzx1Unq0qicDuteRr46ukq3H46Rao7ZyFg6Ghw5JywZRct21DWXUpn+Vuatt566yKGGvSMxZadm8mfRuWVGNGnv37i2B0N6YDIx8mL6ijdnTqKevZES4nEtJlDYdOkryvnMihw9Iw9o15YbrGsnb2/5TWoWm7bvIPxegRYmRnu2bKs34xN2L1LXara6S00d2iEi83NKtpQxoVkl+PL5caYmDo1rJrn3QdiTJfb2c7czihI1Km3E0pLasvYA+JkWe6t9MbmhZRTY7tsifW07JvHNlZY8y5zvk1RtaSt8mFeVU2C6ZtvKIfHsgOMNFQJPRNzj/V2r1ERn91y5X+wtGX99YQk9vlYaNG8qrf+5WwpDOmIFN5La21ZSW/d5vNsjeOFEHeOnahnJPp5pqQsX9326QrecveZU83auuPNSttmp7h3+7QQ5dTJdDGQPr+6+Okmd715M9Zy4qTTccq88mZ/QxHarLC/0aKA3Twz9sUtqY9dHOa7e2qSqjBjSUU/Ep8uSMLao/wAASXN+8krx+XSOJTU6TV2btkBUHzsnOC85rvRtVkJf61pUtq5fkexnVLS/ZwafCDbQwbdq0kYULF6oZT8DhcKjfI0aM8PofaHhw/cknn3SdQ0Uwan6MBAcHq8MTvPCCaBgaldLkkVuuknm7opUPSL+mlaRipNMBrnO9CjLnya4ya9MJZQse0Kyy1KvoXLOhZc1g+evxLvL9qsPKDIT/daxd1nnPqsHy52Nd5KtlB5U/Tc9GFWRgiyrKPl0rNFh+faSzTFy4V3adjJdejSvKsM5RYrVapESYyI/DO8nrf25XU6K71isvLw9oJCGBzsr0zX3t5emfNsvyfdHqWR8OaSllSjjj+uXd7WTolNVK6KoYGSxf3d1OosqHyQ6LyITbWsigyauVeQjLok8Y3FKql3Wm462bmsmNk5a7zGmPXFNX6lZymoheGtBIbv5spRJUQNf65aV97fLq+/CudZRw88OaY+o3/GL6Na8igQE2ua55FfWfb1cfkaPnnDMkejWppPKvU51ySrhZe/iCqnygVY0y6lrdCiWUcHPwXJKcjnf6+lQrHa6ulQwLVMJNYromqRmzpUpo6RLatq10v3hRArdulcB81Oz5GwVV/v0Fs6fPrGnE9gttp7aVixcvSoOgL9U5u2Z1meBLhwerNGPGFASYswlpcijDPFK3YknVpzSpUlKW7o2WVYcuuPxtOtcrr/7Xs1El2Xtmv3z07345dj5JQgNt0rtpZQkMDJA7OtRUws23q5x+OJUiQ2Rgq+oSGGCVR3vUUz6VMLfo/n4DWlRVbfDIvg1lzvYzcirDtwZOzTe3raGu3XNVbVm275wyJYGhnWrKzW2qy+zZW2VwuxpyPC5dJi/er7QbT/SsJ3d2qqXCdaxbQabc0075R2KQ+mL/RnJHhxrqWreGleTb+zrI+3N3qz7mga61ZVDraupahzrl5ddHrlKTMdAGD+1YU/UJoFn1MvLHY1crX83TcclyY6uqck0DpwN242rB8vcTXeTH1UeU79G1zSpL+1plnO81LETd88/NJ+RwTIJ0a1Be2tR0XisbKfL98I7KDxKTSjrUKivNqpV0ugkUQBnNyb18vrcUtCp33323tG3bVtq3by8ffvihJCQkqNlTYOjQocp0NXbsWPX7iSeekG7duskHH3wgAwYMkOnTp8u6devk888/F38hNMgmt7RxFjZPSoQEyl0da3q9Bj+cB7vV8XoNNlL48XgD+6uMur6J9/+FBSrhwxsVSoSoSgLLJCqikUolQ2T2412UJql6mVAJDrC57J2wu859sosq0CjI9TMENNCocqR8f38H+X71EeXLc9/VzsoKUCGe6VNfJizYq5z/3r6pqeva1XXLSdOqTt8fMKJ7HfVM0K9pZRnz1w6XYNOyeikVd9CxTln535IDalVSXaDS17GBnxFApdPXuKmcMdMC7/OoJElcUrqkZEwBC7ZZxLJzp0Cfl8YFvwgpdNAW7Yzeqb43C3O2SaifuvM/6i2AcKOcVmMS5fh5Z93W17/RhRss5odqXLdChKu96N24ghIm0K45f1dUvingmgblpXa5cJeDMtpb+OOAhpUi5ZUBjdVEDQxWx93awtVmRoYEyvQHOspni/ZLmYggGdG9rusaPiff1Ub5+eA5mMWZnp7uuvZC/4Yy7KooQegKGYNgHQw4V73Y0xXW89ovD3f2+g6R3o9ub+X1GgaNL17bKMv+Z3jX2l6v4T3cnEWfhrh1b1hBHf6Ez4WbwYMHy9mzZ+W1115TTsEtW7aUOXPmuJyGjxw5omZQ6XTu3Fn5y7zyyivy0ksvSb169WTWrFnStOmljpLkDM+KoxNgs6qK4g04NmdV2NtGlVGHN0b0qKcqEBbjMj4X378c2k5Ny6xeJkz+L2OUogtat7apLjMyHPygftVpF1VGaY/OZ0wVR+NUvoRTU6fHHU6D0coZ2imYGRtJNJr6ruB6Q0YI8T36NimpXoWbENfiffCVRceMmZC66X3yYufyD6Bj7UttUesapaVtzdJq4gXM7w8ZBpNo76DNxiQIDJBubu2ccalz79W15K5ONcVmsSjNuJGocuHy7i3Ns1yvBwO0rNA1+zlpm0kREG4ATFBZmaEWLVqU6dytt96qDlI00TUynkCIeSmLUcXoG5pInQrhUr10mFtDgUatU+2ysixjob8ehtFDnfIRrunv+vTImmXDMwk3Ls0NhRtC/AZ9xXCsPWNc58aogV2xz7kMRK1y4S5BoHOdcsrclJQxC7Jnw0uzaxHmi6Ft5dtVh5X2o3HGViw61UqHyesDvWvBAfefKzowp0iRAH5CD3StI/2bZR4BPd2nvhJyoHI1qlVrlA1TWh2M/KCeRhhMc8ws3DgbQQo3hPgPuiYVgw99+wWsUGzU3MRnTGOunWGC1t0Cnupdz2Vq6lbf6denUzo8SB7vWc/lU0LMiV9obgjJC61qlJa1L/cSTTSX/VzXENUoE+ZaYAvfXXZyo3CTsbZPcIajNSHE9+iDjZS0S5obfVBSJWMLBh19M1wdDIRubl1NyoQH0bRTTOFQlZgCjNaMgo2nacq4QqnuBOhplqLKmRA/FG4MPjd6vW3kIczo+8V5+gVSsCm+sDUnpsbog3NVXefUeuMIME45FGeYpbD9Qs2akli+PLdfIMQHQBipWbKmlA8sL0EZvnlYwV13+sfsT13I0WdDlggJkDZRpX0Ya+KP0CxFTA32mFp3+JyaQXWtwRHZm0NxUGSEpO/dK/Nnz5ZrTbzGDSH+SlhgmOx9dK/Mnj1b1mkQXmLUJpQAE5QiDNrZT+5oLePn71FrfmU1SYEUXyjcENObq8bflnmdH++zpdhAEuJvZinnnnZOPznjFGysqYWZT4R4g2YpUizRhRussqwTHMjqQIjfCTdxKW7+NoRkB2puSDEXbpwLfYHgtBSxXX2NdI2NFeneHWt9+zCGhBQ/ktKSpMvULhJ7IVYebDFTnTubMQDR6ywh2YHCDSmWeGsogywilvXrBa6JaQ6nqYoQUng4NIesP7lefdfda85mmKUo3JCcQD08KZZghoWR8CAbp40S4kd4Lu1A4YbkBAo3pFiCfWSwYrFOhIewQwjxLRhwGNG3XiAkO1C4IcUW40gw3CDoEEJ8j2ed1FcVJyQ7ULghxRZjY1mCwg0hfkV4sIfmhrOlSA6gcEOKLSUNam6apQjxL8I9fG70TW8JyQ5s0Umxxc0sldGQauXKSWpqKqV+QnxEuVBnHfTU3GCvKEKyC9twUmwxCjdKcxMeLuknTsicb75R3wkhhUt4ULiceOqEfNPsG6kQ4b4ZZtkIam5I9qFwQ4otbsINfW4I8WuH4nLh1NyQ7EPhhhRbjA6KZdlwEuJ32y/YDHtJUXNDcgKHq6TYUrV0qOt73QoRIklJYuvXT66KieH2C4T4aPuFft/1k5iYGOme1l3sDs11Lcxj3RtCLgeFG1JsaRdVxvW9VrlwEYdDrEuWSDluv0CIz7ZfWHJkiet7mfAgOZfg3P+NK4iTnECzFCm2VCsdKoNaVZWeDStI/YoRvo4OIcSDz+5sLVFlw+TH4R19HRVSxKDmhhRbMBIcP7ilr6NBCMmCDrXLyqJnu/s6GqQIQs0NIYQQQkwFhRtCCCGEmAoKN4QQQggxFfS5IcSAFhYmdrvd19EgpNgSFsg6SPIONTeE6GD7hQsX5O8ZM7j9AiE+2n7hwrMXZEbzGeo7IbmFwg0hhBBCTAWFG0IIIYSYCvrcEKKTnCy2QYOkw5kzIj16cPsFQgqZ5PRkGTRjkJw5e0Z6pPeQQNZBkkso3BCiY7eL9Z9/pBK2X6BDIyGFjt1hl3/2/+P6TkhuoVmKEEIIIaaCwg0hhBBCTAWFG0IIIYSYCgo3hBBCCDEVFG4IIYQQYiqK3WwpTdPUZ1xcXL7fOy0tTRITE9W9zTiF0ezpk4QE19c0pNFqPtnf7Hlo9vSZPY0JqQkiyc7vSJ/VzjpYFEkroDTq/bbej18Oi5adUCbi2LFjUr16dV9HgxBCCCG54OjRo1KtWrXLhil2wo3D4ZATJ05IiRIlxGKx5Ou9IVVCcMKLj4yMFLNh9vQVhzQyfUUfs6eR6Sv6xBVQGiGuxMfHS5UqVcR6Bc16sTNL4YVcSeLLK8hMsxba4pC+4pBGpq/oY/Y0Mn1Fn8gCSGPJkiWzFc58Bk1CCCGEFGso3BBCCCHEVFC4yUeCg4Nl1KhR6tOMmD19xSGNTF/Rx+xpZPqKPsF+kMZi51BMCCGEEHNDzQ0hhBBCTAWFG0IIIYSYCgo3hBBCCDEVFG4IIYQQYioo3OSBQ4cOyX333Se1atWS0NBQqVOnjvIQT01Nvez/kpOT5dFHH5WyZctKRESE3HzzzXL69GnxR9566y3p3LmzhIWFSalSpbL1n3vuuUet/mw8+vXrJ2ZJH3zwX3vtNalcubLK9169esnevXvFXzl37pzceeedajEtpBFl9uLFi5f9zzXXXJMpDx966CHxByZNmiRRUVESEhIiHTp0kDVr1lw2/M8//ywNGzZU4Zs1ayazZ88WfycnaZw2bVqmvML//JUlS5bI9ddfr1aZRVxnzZp1xf8sWrRIWrdurWbf1K1bV6XZLOlD2jzzD8epU6fEHxk7dqy0a9dOrfJfoUIFufHGG2X37t1X/F9h10MKN3lg165dajuH//3vf7J9+3aZMGGCTJ48WV566aXL/u+pp56SP//8U2X24sWL1XYQgwYNEn8Egtqtt94qDz/8cI7+B2Hm5MmTruPHH38Us6Tvvffek48++kjl9erVqyU8PFz69u2rhFZ/BIINyuf8+fPlr7/+Uo3vAw88cMX/DR8+3C0PkW5fM2PGDBk5cqQaRGzYsEFatGih3v2ZM2e8hl+xYoXcfvvtSqDbuHGjaohxbNu2TfyVnKYRQHA15tXhw4fFX0lISFBpggCXHQ4ePCgDBgyQ7t27y6ZNm+TJJ5+U+++/X+bOnStmSJ8OBARjHkJw8EcWL16sBuerVq1SbQo2yezTp49Kd1b4pB5iKjjJP9577z2tVq1aWV6/cOGCFhgYqP3888+uczt37sR0fG3lypWavzJ16lStZMmS2Qp79913azfccINWlMhu+hwOh1apUiXt/fffd8vT4OBg7ccff9T8jR07dqiytXbtWte5f/75R7NYLNrx48ez/F+3bt20J554QvM32rdvrz366KOu33a7XatSpYo2duxYr+Fvu+02bcCAAW7nOnTooD344IOav5LTNOakbvobKJu//fbbZcM899xzWpMmTdzODR48WOvbt69mhvT9999/Ktz58+e1osiZM2dU/BcvXpxlGF/UQ2pu8pnY2FgpU6ZMltfXr1+vJF2YMnSgqqtRo4asXLlSzAJUrRh5NGjQQGlFYmJixAxgFAl1sTH/sNcJTAf+mH+IE0xRbdu2dZ1D3LHHGrROl+P777+XcuXKSdOmTeXFF1+UxMRE8bWWDfXH+O6RDvzO6t3jvDE8gBbEH/Mqt2kEMDPWrFlTbVZ4ww03KE2dWShqeZhbWrZsqUzdvXv3luXLl0tR6vPA5fo9X+Rhsds4syDZt2+ffPzxxzJu3Lgsw6BjDAoKyuTfUbFiRb+1seYUmKRgZoMv0v79+5WZrn///qog22w2KcroeYT8Kgr5hzh5qrcDAgJUQ3S5+N5xxx2qs4TfwJYtW+T5559XavNff/1VfEV0dLTY7Xav7x4mYm8gjUUlr3KbRgwgpkyZIs2bN1cdDdof+JFBwCnoTYILg6zyEDtPJyUlKb+3ogwEGpi4MQBJSUmRL7/8Uvm8YfABPyN/xuFwKDPhVVddpQZBWeGLekjNjRdeeOEFrw5exsOzoTl+/Ljq1OG/AV8Fs6UvJwwZMkQGDhyonMZgV4Wfx9q1a5U2xwzp8wcKOo3wycHICnkIn51vvvlGfvvtNyWsEv+iU6dOMnToUDXy79atmxJAy5cvr3wBif8D4fTBBx+UNm3aKKEUgio+4cPp7zz66KPKb2b69Onib1Bz44Wnn35azfi5HLVr13Z9h0MwnN1QID///PPL/q9SpUpK9XzhwgU37Q1mS+GaP6Yvr+BeMG9As9WzZ08pyunT8wj5hRGXDn6jcykssptGxNfTETU9PV3NoMpJeYPZDSAPMSvQF6AMQfPnObPwcnUH53MS3tfkJo2eBAYGSqtWrVRemYGs8hBO1EVda5MV7du3l2XLlok/M2LECNcEhStpCH1RDynceAGjHhzZARobCDaQuqdOnars45cD4dD4LFy4UE0BB1D3HzlyRI3A/C19+cGxY8eUz41RGCiq6YOpDRUS+acLM1CPQ4Wc0xllhZFGlCkI0vDjQNkD//77r1In6wJLdsAsFVBYeegNmHORBrx7aAQB0oHfaGizSj+uQ3WugxkehVXXCiONnsCstXXrVrn22mvFDCCvPKcN+3Me5geob76sa5cDftKPPfaY0uRCG4828Ur4pB4WmKtyMeDYsWNa3bp1tZ49e6rvJ0+edB3GMA0aNNBWr17tOvfQQw9pNWrU0P79919t3bp1WqdOndThjxw+fFjbuHGjNnr0aC0iIkJ9xxEfH+8Kg/T9+uuv6jvOP/PMM2rm18GDB7UFCxZorVu31urVq6clJydrRT194J133tFKlSql/f7779qWLVvUzDDMkEtKStL8kX79+mmtWrVSZXDZsmUqL26//fYsy+i+ffu0MWPGqLKJPEQ6a9eurXXt2lXzNdOnT1cz06ZNm6Zmgj3wwAMqL06dOqWu33XXXdoLL7zgCr98+XItICBAGzdunJqVOGrUKDVbcevWrZq/ktM0ouzOnTtX279/v7Z+/XptyJAhWkhIiLZ9+3bNH0Hd0usZuqDx48er76iLAGlDGnUOHDighYWFac8++6zKw0mTJmk2m02bM2eOZob0TZgwQZs1a5a2d+9eVS4xS9Fqtaq20x95+OGH1ey8RYsWufV5iYmJrjD+UA8p3OQBTMFE4fV26KBzwG9M99NBJ/jII49opUuXVpX2pptuchOI/AlM6/aWPmN68BvvAqCA9+nTRytfvrwqvDVr1tSGDx/uapiLevr06eCvvvqqVrFiRdUJQbjdvXu35q/ExMQoYQbCW2RkpDZs2DA34c2zjB45ckQJMmXKlFHpgwCPjiU2NlbzBz7++GM1OAgKClLTpletWuU2hR15auSnn37S6tevr8JjSvHff/+t+Ts5SeOTTz7pCosyee2112obNmzQ/BV96rPnoacJn0ij539atmyp0ghB21gfi3r63n33Xa1OnTpKIEWdu+aaa9TA11+RLPo8Y574Qz20ZESWEEIIIcQUcLYUIYQQQkwFhRtCCCGEmAoKN4QQQggxFRRuCCGEEGIqKNwQQgghxFRQuCGEEEKIqaBwQwghhBBTQeGGEEIIIaaCwg0hxDRcc801bvvXEEKKJxRuCCGEEGIquP0CIcQU3HPPPfL111+7nTt48KBERUX5LE6EEN9A4YYQYgpiY2Olf//+0rRpUxkzZow6V758ebHZbL6OGiGkkAko7AcSQkhBULJkSQkKCpKwsDCpVKmSr6NDCPEh9LkhhBBCiKmgcEMIIYQQU0HhhhBiGmCWstvtvo4GIcTHULghhJgGzIxavXq1HDp0SKKjo8XhcPg6SoQQH0DhhhBiGp555hk1O6px48ZqptSRI0d8HSVCiA/gVHBCCCGEmApqbgghhBBiKijcEEIIIcRUULghhBBCiKmgcEMIIYQQU0HhhhBCCCGmgsINIYQQQkwFhRtCCCGEmAoKN4QQQggxFRRuCCGEEGIqKNwQQgghxFRQuCGEEEKIqaBwQwghhBAxE/8PWjbA8tOYNv8AAAAASUVORK5CYII=", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def bohman_inversion(f_hat, t, omega_max, N):\n", - " \"\"\"\n", - " Реализация метода численного обращения преобразования Фурье по Боману.\n", - "\n", - " Параметры:\n", - " f_hat: функция, задающая преобразование Фурье (принимает частоту omega и возвращает значение)\n", - " t: массив временных точек, в которых нужно восстановить функцию\n", - " omega_max: максимальная частота для интегрирования\n", - " N: количество точек для численного интегрирования\n", - "\n", - " Возвращает:\n", - " f: восстановленная функция в точках t\n", - " \"\"\"\n", - " d_omega = omega_max / N\n", - " omega = np.linspace(0, omega_max, N)\n", - " omega = omega[omega!=0]\n", - " f = np.zeros_like(t, dtype=complex)\n", - "\n", - " for i, ti in enumerate(t):\n", - " integrand = f_hat(omega) * np.exp(1j * omega * ti)\n", - " f[i] = np.sum(integrand) * d_omega\n", - "\n", - " return f.real # Возвращаем действительную часть, так как исходная функция предполагается действительной\n", - "\n", - "# Преобразование Фурье равномерного распределения на интервале [a, b]\n", - "def f_hat_uniform(omega, a=-1, b=1):\n", - " return (np.exp(1j * b * omega) - np.exp(1j * a * omega)) / (1j * omega * (b - a))\n", - "\n", - "# Параметры\n", - "a = -1 # Нижняя граница равномерного распределения\n", - "b = 1 # Верхняя граница равномерного распределения\n", - "omega_max = 100 # Максимальная частота для интегрирования\n", - "N = 10000 # Количество точек для численного интегрирования\n", - "t = np.linspace(-2, 2, 1000) # Точки, в которых восстанавливаем функцию\n", - "\n", - "# Восстановление функции\n", - "f_restored = bohman_inversion(lambda omega: f_hat_uniform(omega, a, b), t, omega_max, N)\n", - "\n", - "# Построение графика\n", - "plt.plot(t, f_restored/np.pi, label='Восстановленная функция') #делим на pi\n", - "plt.axvline(a, color='red', linestyle='--', label=f'a = {a}')\n", - "plt.axvline(b, color='green', linestyle='--', label=f'b = {b}')\n", - "plt.axhline(1/(b - a), color='black', linestyle=':', label=f'1/(b - a) = {1/(b - a):.2f}')\n", - "plt.xlabel('t')\n", - "plt.ylabel('f(t)')\n", - "plt.title('Восстановление равномерного распределения')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "id": "2e340dc7-02d3-436b-a783-dac23ae3c4ad", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "x and y must have same first dimension, but have shapes (9999,) and (19996,)", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[55], line 45\u001b[0m\n\u001b[1;32m 42\u001b[0m f_restored, omega \u001b[38;5;241m=\u001b[39m bohman_inversion(\u001b[38;5;28;01mlambda\u001b[39;00m omega: f_hat_uniform(omega, a, b), t, omega_max, N)\n\u001b[1;32m 44\u001b[0m \u001b[38;5;66;03m# Построение графика\u001b[39;00m\n\u001b[0;32m---> 45\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43momega\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mf_restored\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mВосстановленная функция\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m#делим на pi\u001b[39;00m\n\u001b[1;32m 46\u001b[0m \u001b[38;5;28mprint\u001b[39m(f_restored)\n\u001b[1;32m 47\u001b[0m \u001b[38;5;66;03m#plt.axvline(a, color='red', linestyle='--', label=f'a = {a}')\u001b[39;00m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;66;03m#plt.axvline(b, color='green', linestyle='--', label=f'b = {b}')\u001b[39;00m\n\u001b[1;32m 49\u001b[0m \u001b[38;5;66;03m#plt.axhline(1/(b - a), color='black', linestyle=':', label=f'1/(b - a) = {1/(b - a):.2f}')\u001b[39;00m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/pyplot.py:3794\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3786\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3787\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3788\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3792\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3793\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3794\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3795\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3796\u001b[0m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3797\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3798\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3799\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3800\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_axes.py:1779\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1536\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1537\u001b[0m \u001b[38;5;124;03mPlot y versus x as lines and/or markers.\u001b[39;00m\n\u001b[1;32m 1538\u001b[0m \n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 1776\u001b[0m \u001b[38;5;124;03m(``'green'``) or hex strings (``'#008000'``).\u001b[39;00m\n\u001b[1;32m 1777\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 1778\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m cbook\u001b[38;5;241m.\u001b[39mnormalize_kwargs(kwargs, mlines\u001b[38;5;241m.\u001b[39mLine2D)\n\u001b[0;32m-> 1779\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1780\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[1;32m 1781\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_line(line)\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:296\u001b[0m, in \u001b[0;36m_process_plot_var_args.__call__\u001b[0;34m(self, axes, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 294\u001b[0m this \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m0\u001b[39m],\n\u001b[1;32m 295\u001b[0m args \u001b[38;5;241m=\u001b[39m args[\u001b[38;5;241m1\u001b[39m:]\n\u001b[0;32m--> 296\u001b[0m \u001b[38;5;28;01myield from\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_plot_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 297\u001b[0m \u001b[43m \u001b[49m\u001b[43maxes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mthis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mambiguous_fmt_datakey\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/matplotlib/axes/_base.py:486\u001b[0m, in \u001b[0;36m_process_plot_var_args._plot_args\u001b[0;34m(self, axes, tup, kwargs, return_kwargs, ambiguous_fmt_datakey)\u001b[0m\n\u001b[1;32m 483\u001b[0m axes\u001b[38;5;241m.\u001b[39myaxis\u001b[38;5;241m.\u001b[39mupdate_units(y)\n\u001b[1;32m 485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m]:\n\u001b[0;32m--> 486\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y must have same first dimension, but \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 487\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhave shapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 488\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m y\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 489\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx and y can be no greater than 2D, but have \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 490\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshapes \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n", - "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (9999,) and (19996,)" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "\n", - "def bohman_inversion(f_hat, t, omega_max, N):\n", - " \"\"\"\n", - " Реализация метода численного обращения преобразования Фурье по Боману.\n", - "\n", - " Параметры:\n", - " f_hat: функция, задающая преобразование Фурье (принимает частоту omega и возвращает значение)\n", - " t: массив временных точек, в которых нужно восстановить функцию\n", - " omega_max: максимальная частота для интегрирования\n", - " N: количество точек для численного интегрирования\n", - "\n", - " Возвращает:\n", - " f: восстановленная функция в точках t\n", - " \"\"\"\n", - " d_omega = omega_max / N\n", - " omega = np.linspace(0, omega_max, N)\n", - " omega = omega[omega!=0]\n", - " f = np.zeros_like(t, dtype=complex)\n", - "\n", - " for i, ti in enumerate(t):\n", - " integrand = f_hat(omega) * np.exp(1j * omega * ti)\n", - " f[i] = np.sum(integrand) * d_omega\n", - "\n", - " f = np.fft.irfft(f_hat(omega))\n", - " return f.real, omega # Возвращаем действительную часть, так как исходная функция предполагается действительной\n", - "\n", - "# Преобразование Фурье равномерного распределения на интервале [a, b]\n", - "def f_hat_uniform(omega, a=-1, b=1):\n", - " return (np.exp(1j * b * omega) - np.exp(1j * a * omega)) / (1j * omega * (b - a))\n", - "\n", - "# Параметры\n", - "a = -1 # Нижняя граница равномерного распределения\n", - "b = 1 # Верхняя граница равномерного распределения\n", - "omega_max = 100 # Максимальная частота для интегрирования\n", - "N = 10000 # Количество точек для численного интегрирования\n", - "t = np.linspace(-2, 2, 1000) # Точки, в которых восстанавливаем функцию\n", - "\n", - "# Восстановление функции\n", - "f_restored, omega = bohman_inversion(lambda omega: f_hat_uniform(omega, a, b), t, omega_max, N)\n", - "\n", - "# Построение графика\n", - "plt.plot(omega, f_restored, label='Восстановленная функция') #делим на pi\n", - "print(f_restored)\n", - "#plt.axvline(a, color='red', linestyle='--', label=f'a = {a}')\n", - "#plt.axvline(b, color='green', linestyle='--', label=f'b = {b}')\n", - "#plt.axhline(1/(b - a), color='black', linestyle=':', label=f'1/(b - a) = {1/(b - a):.2f}')\n", - "plt.xlabel('t')\n", - "plt.ylabel('f(t)')\n", - "plt.title('Восстановление равномерного распределения')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "id": "1a2b2d1f-5ff0-431f-98f7-bb60d6ed1c09", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def bohman_inversion(f_hat, t, omega_max, N):\n", - " \"\"\"\n", - " Реализация метода численного обращения преобразования Фурье по Боману.\n", - "\n", - " Параметры:\n", - " f_hat: функция, задающая преобразование Фурье (принимает частоту omega и возвращает значение)\n", - " t: массив временных точек, в которых нужно восстановить функцию\n", - " omega_max: максимальная частота для интегрирования\n", - " N: количество точек для численного интегрирования\n", - "\n", - " Возвращает:\n", - " f: восстановленная функция в точках t\n", - " \"\"\"\n", - " d_omega = omega_max / N\n", - " omega = np.linspace(0, omega_max, N)\n", - " omega = omega[omega!=0]\n", - " f = np.zeros_like(t, dtype=complex)\n", - "\n", - " p_n = np.fft.fft(f_hat(omega))\n", - " \n", - " \n", - " for i, ti in enumerate(t):\n", - " integrand = f_hat(omega) * np.exp(1j * omega * ti)\n", - " f[i] = np.sum(integrand) * d_omega\n", - " \n", - " #p_n = np.cumsum(p_n[N//2 : ]) - 0.5 * p_n[N//2 : ]\n", - " return f.real, omega[N//2:], p_n # Возвращаем действительную часть, так как исходная функция предполагается действительной\n", - "\n", - "# Преобразование Фурье равномерного распределения на интервале [a, b]\n", - "def f_hat_uniform(omega, a=-1, b=1):\n", - " #return (np.exp(1j * b * omega) - np.exp(1j * a * omega)) / (1j * omega * (b - a))\n", - " f_hat_density = np.exp(1j * mu * omega - 0.5 * sigma**2 * omega**2)\n", - " # Преобразование Фурье функции распределения\n", - " return f_hat_density / (1j * omega + 1e-10) # Добавляем 1e-10 для избежания деления на ноль\n", - "\n", - "\n", - "# Параметры\n", - "a = -1 # Нижняя граница равномерного распределения\n", - "b = 1 # Верхняя граница равномерного распределения\n", - "omega_max = 100 # Максимальная частота для интегрирования\n", - "N = 10000 # Количество точек для численного интегрирования\n", - "t = np.linspace(-2, 2, 1000) # Точки, в которых восстанавливаем функцию\n", - "\n", - "# Восстановление функции\n", - "f_restored, omega, p_n = bohman_inversion(lambda omega: f_hat_uniform(omega, a, b), t, omega_max, N)\n", - "\n", - "# Построение графика\n", - "#plt.plot(t, f_restored, label='Восстановленная функция') #делим на pi\n", - "plt.plot(omega, p_n, label='Восстановленная функция')\n", - "#plt.axvline(a, color='red', linestyle='--', label=f'a = {a}')\n", - "#plt.axvline(b, color='green', linestyle='--', label=f'b = {b}')\n", - "#plt.axhline(1/(b - a), color='black', linestyle=':', label=f'1/(b - a) = {1/(b - a):.2f}')\n", - "plt.xlabel('t')\n", - "plt.ylabel('f(t)')\n", - "plt.title('Восстановление равномерного распределения')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "id": "14cef731-d6b4-4584-835a-1672d716a5aa", - "metadata": {}, - "outputs": [ - { - "data": { - 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "# Преобразование Фурье плотности нормального распределения\n", - "def f_hat_normal(omega, mu=0, sigma=1):\n", - " return np.exp(1j * mu * omega - 0.5 * sigma**2 * omega**2)\n", - "\n", - "# Параметры\n", - "mu = 0 # Математическое ожидание\n", - "sigma = 1 # Стандартное отклонение\n", - "omega_max = 50 # Максимальная частота\n", - "N = 10000 # Количество точек\n", - "\n", - "# Частотная сетка\n", - "omega = np.linspace(-omega_max, omega_max, N)\n", - "d_omega = omega[1] - omega[0] # Шаг по частоте\n", - "\n", - "# Вычисляем преобразование Фурье\n", - "f_hat = f_hat_normal(omega, mu, sigma)\n", - "\n", - "# Обратное FFT\n", - "f_restored = np.fft.ifft(np.fft.ifftshift(f_hat)) * N * d_omega / (2 * np.pi)\n", - "\n", - "# Временная сетка\n", - "t = np.fft.fftfreq(N, d=d_omega) * 2 * np.pi\n", - "t = np.fft.fftshift(t) # Сдвигаем частоты для правильного отображения\n", - "\n", - "# Точное значение плотности нормального распределения\n", - "def exact_normal(t, mu=0, sigma=1):\n", - " return 1 / (sigma * np.sqrt(2 * np.pi)) * np.exp(-0.5 * ((t - mu) / sigma)**2)\n", - "\n", - "# Построение графика\n", - "plt.plot(t, np.real(f_restored), label='Восстановленная плотность (FFT)')\n", - "plt.plot(t, exact_normal(t, mu, sigma), label='Точное значение', linestyle='--')\n", - "plt.xlabel('t')\n", - "plt.ylabel('f(t)')\n", - "plt.title('Восстановление плотности с использованием FFT')\n", - "plt.legend()\n", - "plt.grid(True)\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "44f29f92-d90f-4828-80fa-ef4608c3c2f8", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "79f43afa-4cf4-4944-a8f9-0368ebff0750", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/__init__.py b/examples/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/examples/levi.ipynb b/examples/levi.ipynb deleted file mode 100644 index 0b584b9..0000000 --- a/examples/levi.ipynb +++ /dev/null @@ -1,371 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "1363601b-79d2-4b12-9e2f-add45a648d9e", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "1ae61afc-ca9e-40c8-a3ef-fa6ce0a4b570", - "metadata": {}, - "outputs": [], - "source": [ - "from src.Distributions.levy import Levy" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "81fde386-0c5c-46e8-b77a-74c3a741e2b1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "e89a57b5-a1e8-4661-bcbd-2b0538b57a7b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cs = [0.5, 1, 2, 4, 8] \n", - "mu = 0 \n", - "x = np.linspace(mu, 5, 100) \n", - "\n", - "for c in cs:\n", - " l = Levy(c, mu)\n", - " dens = l.pdf(x)\n", - " plt.plot(x, dens, label=('Плотность равпределения c c=' + str(c)))\n", - " plt.title('График плотности распределения c различными параметрами c' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('pdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "d9dbfdf1-5ce4-41bd-aea1-60b2bb91e248", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "cs = [0.5, 1, 2, 4, 8] \n", - "mu = 0 \n", - "x = np.linspace(mu, 5, 100) \n", - "x = x[x!=0]\n", - "\n", - "for c in cs:\n", - " l = Levy(c, mu)\n", - " dens = l.cdf(x)\n", - " plt.plot(x, dens, label=('Функция распределения c c=' + str(c)))\n", - " plt.title('График функций распределения c различными параметрами c' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "08de6648-fb77-4352-b505-53d482e4bef9", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.Bohman import BohmansInverters as bi" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "id": "675298aa-960d-457f-8892-6d87a7fe6f6f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = bi.BohmanE()\n", - "\n", - "c = 2\n", - "mu = 0\n", - "\n", - "l = Levy(c, mu)\n", - "\n", - "inv.fit(l.chr)\n", - "\n", - "x = np.linspace(mu, 5, 1000) \n", - "cdf_true = l.cdf(x) \n", - "\n", - "plt.plot(x, cdf_true, label=('Функция распределения с c=' + str(c)))\n", - "\n", - "cdf_calc = inv.cdf(x)\n", - "cdf_calc = cdf_calc - cdf_calc[0] ###получаем сдвинутую ф-ю распределения\n", - "plt.plot(x, cdf_calc, label=('Функция распределения аппроксимированная c c=' + str(c)))\n", - "\n", - "plt.title('График функции распределения' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "55f3c59b-7537-4f14-bda3-18a4a8140245", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.FourierTransform import FTInverter as fi" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "id": "d6193478-5c98-4bdf-9371-c5b830db7d29", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj nan+nanj\n", - " nan+nanj nan+nanj nan+nanj nan+nanj 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = fi.FTInverterNaive()\n", - "\n", - "c = 2\n", - "mu = 1\n", - "\n", - "l = Levy(c, mu)\n", - "\n", - "inv.fit(l.chr)\n", - "\n", - "x = np.linspace(mu, 5, 1000) \n", - "\n", - "cdf_true = l.cdf(x) \n", - "\n", - "#plt.plot(x, cdf_true, label=('Функция распределения с c=' + str(c)))\n", - "\n", - "cdf_calc = inv.cdf(x)\n", - "plt.plot(x, cdf_calc, label=('Функция распределения аппроксимированная c c=' + str(c)))\n", - "\n", - "plt.title('График функции распределения' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/loss_test.ipynb b/examples/loss_test.ipynb deleted file mode 100644 index 0f1c7c8..0000000 --- a/examples/loss_test.ipynb +++ /dev/null @@ -1,223 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "_ncqXgW4EFHO" - }, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from tests.uniform import Unif\n", - "from tests.normal import Norm\n", - "\n", - "from src.BohmansInverters import *" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "def make_plot(cdf, start, stop, num):\n", - " x = np.linspace(start, stop, num)\n", - " F_x = cdf(x)\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - " plt.title('График функции распределения')\n", - " plt.xlabel('x')\n", - " plt.ylabel('F(x)')\n", - " plt.grid()\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "POyQFy0itKu-" - }, - "source": [ - "# Проверим на равномерном распределении:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "id": "kCJKN7xmtOyy" - }, - "outputs": [], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 764 - }, - "id": "B-xQ5ISztn3a", - "outputId": "c603a25c-bb3f-4135-da50-ffb950ea2166", - "scrolled": true - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - " \n", - "plt.title('График функций распределения')\n", - "plt.xlabel('x')\n", - "plt.ylabel('F(x)')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " inv.fit(chr)\n", - " x = np.linspace(-0.5, 1.5, 10000)\n", - " F_x = inv.cdf(x).real\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "\n", - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "deltas = np.linspace(1e100, 1, 30)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('delta')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for delta in deltas:\n", - " inv.delta = delta\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(deltas, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "bochmans_inversers = [BohmanA(), BohmanB(), BohmanC(), BohmanD(), BohmanE()]\n", - "\n", - "Ns = np.arange(1500, 10000, 300)\n", - "plt.title('График ошибок')\n", - "plt.xlabel('N')\n", - "plt.ylabel('loss')\n", - "plt.grid()\n", - "\n", - "for inv in bochmans_inversers:\n", - " errors = []\n", - " for N in Ns:\n", - " inv.N = N\n", - " inv.fit(chr)\n", - " x = np.linspace(-5, 5, 1000)\n", - " F_x = inv.cdf(x).real\n", - " F_x_true = unif.cdf(x)\n", - " errors.append(np.sum((F_x - F_x_true) ** 2))\n", - "\n", - " plt.plot(Ns, errors, label='Функция ошибок')\n", - "\n", - "plt.legend(('A', 'B', 'C', 'D', 'E'))\n", - "plt.show()" - ] - } - ], - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/examples/pareto.ipynb b/examples/pareto.ipynb deleted file mode 100644 index 4c42656..0000000 --- a/examples/pareto.ipynb +++ /dev/null @@ -1,403 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 97, - "id": "1363601b-79d2-4b12-9e2f-add45a648d9e", - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append('../')" - ] - }, - { - "cell_type": "code", - "execution_count": 98, - "id": "1ae61afc-ca9e-40c8-a3ef-fa6ce0a4b570", - "metadata": {}, - "outputs": [], - "source": [ - "from tests.pareto import Pareto" - ] - }, - { - "cell_type": "code", - "execution_count": 99, - "id": "81fde386-0c5c-46e8-b77a-74c3a741e2b1", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "e89a57b5-a1e8-4661-bcbd-2b0538b57a7b", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas = [3, 2, 1] #shape\n", - "x_m = 1 #scale\n", - "x = np.linspace(1, 5, 100) \n", - "\n", - "for alpha in alphas:\n", - " pa = Pareto(alpha, x_m)\n", - " dens = pa.pdf(x)\n", - " plt.plot(x, dens, label=('Плотность равпределения c alpha=' + str(alpha)))\n", - " plt.title('График плотности распределения c различными параметрами alpha' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('pdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "d9dbfdf1-5ce4-41bd-aea1-60b2bb91e248", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas = [3, 2, 1] #shape\n", - "x_m = 1 #scale\n", - "x = np.linspace(1, 5, 100) \n", - "\n", - "for alpha in alphas:\n", - " pa = Pareto(alpha, x_m)\n", - " dens = pa.cdf(x)\n", - " plt.plot(x, dens, label=('Функция распределения c alphas=' + str(alpha)))\n", - " plt.title('График функций распределения c различными параметрами alpha' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "08de6648-fb77-4352-b505-53d482e4bef9", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.Bohman import BohmansInverters as bi" - ] - }, - { - "cell_type": "markdown", - "id": "fd770bde-5cb7-4b39-baec-bed6380e514a", - "metadata": {}, - "source": [ - "Характеристическая функция имеет вид:\n", - "$${\\displaystyle \\alpha (-ix_{\\mathrm {m} }t)^{\\alpha }\\Gamma (-\\alpha ,-ix_{\\mathrm {m} }t)}$$" - ] - }, - { - "cell_type": "code", - "execution_count": 101, - "id": "dc95ad54-f444-45e9-956c-b802d99b8f69", - "metadata": {}, - "outputs": [], - "source": [ - "import mpmath as mp\n", - "\n", - "\n", - "class Pareto:\n", - " def __init__(self, shape, scale):\n", - " self.shape = shape\n", - " self.scale = scale\n", - "\n", - " def cdf(self, x):\n", - " return 1 - (self.scale / x) ** self.shape\n", - "\n", - " def chr(self, x):\n", - " res = self.shape * ((-1j * self.scale * x) ** self.shape) * (np.array([mp.gammainc(-self.shape,\n", - " a=( -1j * self.scale * y)) for y in x], dtype=complex))\n", - " return res\n", - "\n", - " def pdf(self, x):\n", - " return self.shape * (self.scale ** self.shape) / (x ** (self.shape + 1))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 92, - "id": "8ec47565-b322-463e-a73d-71cfadeecee3", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/rr/t8v6v0js2pv84hw_jl41vsmh0000gn/T/ipykernel_52839/3289096441.py:19: IntegrationWarning: The maximum number of subdivisions (1000) has been achieved.\n", - " If increasing the limit yields no improvement it is advised to analyze \n", - " the integrand in order to determine the difficulties. If the position of a \n", - " local difficulty can be determined (singularity, discontinuity) one will \n", - " probably gain from splitting up the interval and calling the integrator \n", - " on the subranges. Perhaps a special-purpose integrator should be used.\n", - " integral, error = quad(integrand, x_m, np.inf, limit=1000)\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.0\n", - "1.0\n" - ] - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from scipy.integrate import quad\n", - "\n", - "def characteristic_function_pareto(t, x_m, alpha):\n", - " \"\"\"\n", - " Вычисляет характеристическую функцию распределения Парето.\n", - " \n", - " :param t: Аргумент для характеристики функции\n", - " :param x_m: Минимальное значение (параметр распределения)\n", - " :param alpha: Параметр формы (альфа)\n", - " :return: Значение характеристической функции\n", - " \"\"\"\n", - " \n", - " # Определяем интеграл\n", - " def integrand(x):\n", - " return np.exp(1j * t * x) * (alpha * x_m**alpha / x**(alpha + 1))\n", - "\n", - " # Вычисляем интеграл от x_m до бесконечности\n", - " integral, error = quad(integrand, x_m, np.inf, limit=1000)\n", - "\n", - " return integral\n", - "\n", - "alphas = [3, 2, 1] #shape\n", - "x_m = 1 #scale\n", - "x = np.linspace(-5, 5, 100) \n", - "\n", - "for alpha in alphas:\n", - " pa = Pareto(alpha, x_m)\n", - " dens = np.array([ characteristic_function_pareto(t, x_m, alpha) for t in x])\n", - " print(characteristic_function_pareto(0, x_m, alpha))\n", - " plt.plot(x, dens, label=('aboba=' + str(alpha)))\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "id": "d8f07f84-3b7d-4936-bb74-49eecbbaa4e5", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "alphas = [3, 2, 1] #shape\n", - "x_m = 1 #scale\n", - "x = np.linspace(-5, 5, 100) \n", - "\n", - "for alpha in alphas:\n", - " pa = Pareto(alpha, x_m)\n", - " dens = pa.chr(x)\n", - " plt.plot(x, dens, label=('характеристическая ф-я c alphas=' + str(alpha)))\n", - " \n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 106, - "id": "675298aa-960d-457f-8892-6d87a7fe6f6f", - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = bi.BohmanE()\n", - "\n", - "x_m = 0.5\n", - "alpha = 2\n", - "pa = Pareto(alpha, x_m)\n", - "\n", - "inv.fit(pa.chr)\n", - "\n", - "x = np.linspace(x_m, 5, 1000) \n", - "cdf_true = pa.cdf(x)\n", - "plt.plot(x, cdf_true, label=('Функция распределения с alpha=' + str(alpha)))\n", - "\n", - "cdf_calc = inv.cdf(x)\n", - "plt.plot(x, cdf_calc, label=('Функция распределения аппроксимированная c alpha=' + str(alpha)))\n", - "\n", - "plt.title('График функции распределения' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": 108, - "id": "55f3c59b-7537-4f14-bda3-18a4a8140245", - "metadata": {}, - "outputs": [], - "source": [ - "from src.CharFuncInverter.FourierTransform import FTInverter as fi" - ] - }, - { - "cell_type": "code", - "execution_count": 111, - "id": "d6193478-5c98-4bdf-9371-c5b830db7d29", - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "gamma function pole", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[111], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m cdf_true \u001b[38;5;241m=\u001b[39m pa\u001b[38;5;241m.\u001b[39mcdf(x)\n\u001b[1;32m 11\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(x, cdf_true, label\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mФункция распределения с alpha=\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(alpha)))\n\u001b[0;32m---> 13\u001b[0m cdf_calc \u001b[38;5;241m=\u001b[39m \u001b[43minv\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m plt\u001b[38;5;241m.\u001b[39mplot(x, cdf_calc, label\u001b[38;5;241m=\u001b[39m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mФункция распределения аппроксимированная c alpha=\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(alpha)))\n\u001b[1;32m 16\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mГрафик функции распределения\u001b[39m\u001b[38;5;124m'\u001b[39m )\n", - "File \u001b[0;32m~/PycharmProjects/Numerical-inversion-of-characteristic-functions/examples/../src/CharFuncInverter/FourierTransform/FTInverter.py:26\u001b[0m, in \u001b[0;36mFTInverterNaive.cdf\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mcdf\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[1;32m 24\u001b[0m t \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m-\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mN, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mN, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_points)\n\u001b[0;32m---> 26\u001b[0m phi_t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mphi\u001b[49m\u001b[43m(\u001b[49m\u001b[43mt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 28\u001b[0m integral \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mtrapezoid((phi_t \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mexp(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m t \u001b[38;5;241m*\u001b[39m x[:, np\u001b[38;5;241m.\u001b[39mnewaxis])) \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m t), t, axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m)\n\u001b[1;32m 29\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m-\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m2\u001b[39m \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mpi)) \u001b[38;5;241m*\u001b[39m integral\n", - "Cell \u001b[0;32mIn[101], line 13\u001b[0m, in \u001b[0;36mPareto.chr\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;21mchr\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 13\u001b[0m res \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m*\u001b[39m ((\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39mj \u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mscale \u001b[38;5;241m*\u001b[39m x) \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mshape) \u001b[38;5;241m*\u001b[39m (np\u001b[38;5;241m.\u001b[39marray([\u001b[43mmp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgammainc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43ma\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mscale\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m y \u001b[38;5;129;01min\u001b[39;00m x], dtype\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mcomplex\u001b[39m))\n\u001b[1;32m 15\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m res\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/mpmath/functions/expintegrals.py:158\u001b[0m, in \u001b[0;36mgammainc\u001b[0;34m(ctx, z, a, b, regularized)\u001b[0m\n\u001b[1;32m 156\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 157\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mnan\n\u001b[0;32m--> 158\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgamma\u001b[49m\u001b[43m(\u001b[49m\u001b[43mz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 159\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m a \u001b[38;5;241m==\u001b[39m b:\n\u001b[1;32m 160\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mzero\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/mpmath/ctx_mp_python.py:1000\u001b[0m, in \u001b[0;36mPythonMPContext._wrap_libmp_function..f\u001b[0;34m(x, **kwargs)\u001b[0m\n\u001b[1;32m 998\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(x, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_mpf_\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m 999\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1000\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mmake_mpf(\u001b[43mmpf_f\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mpf_\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mprec\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrounding\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 1001\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m ComplexResult:\n\u001b[1;32m 1002\u001b[0m \u001b[38;5;66;03m# Handle propagation to complex\u001b[39;00m\n\u001b[1;32m 1003\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ctx\u001b[38;5;241m.\u001b[39mtrap_complex:\n", - "File \u001b[0;32m~/myenv/lib/python3.13/site-packages/mpmath/libmp/gammazeta.py:1733\u001b[0m, in \u001b[0;36mmpf_gamma\u001b[0;34m(x, prec, rnd, type)\u001b[0m\n\u001b[1;32m 1731\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mtype\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m 1732\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m fzero\n\u001b[0;32m-> 1733\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgamma function pole\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;66;03m# n = x\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m n \u001b[38;5;241m=\u001b[39m man \u001b[38;5;241m<<\u001b[39m exp\n", - "\u001b[0;31mValueError\u001b[0m: gamma function pole" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "inv = fi.FTInverterNaive()\n", - "\n", - "x_m = 0.5\n", - "alpha = 2\n", - "pa = Pareto(alpha, x_m)\n", - "\n", - "inv.fit(pa.chr)\n", - "\n", - "x = np.linspace(x_m, 4.5, 1000) \n", - "cdf_true = pa.cdf(x)\n", - "plt.plot(x, cdf_true, label=('Функция распределения с alpha=' + str(alpha)))\n", - "\n", - "cdf_calc = inv.cdf(x)\n", - "plt.plot(x, cdf_calc, label=('Функция распределения аппроксимированная c alpha=' + str(alpha)))\n", - "\n", - "plt.title('График функции распределения' )\n", - "\n", - "plt.xlabel('x')\n", - "plt.ylabel('сdf(x)')\n", - "plt.grid()\n", - "plt.legend()\n", - "plt.show()\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/test.ipynb b/examples/test.ipynb deleted file mode 100644 index 95c934a..0000000 --- a/examples/test.ipynb +++ /dev/null @@ -1,870 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "view-in-github" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "id": "_ncqXgW4EFHO" - }, - "outputs": [], - "source": [ - "from src.bohman1975 import A, B, C, D, E\n", - "from tests.uniform import Unif\n", - "#import tests.normal" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [], - "source": [ - "def make_plot(cdf, start, stop, num):\n", - " x = np.linspace(start, stop, num)\n", - " F_x = np.array([cdf(y).real for y in x])\n", - "\n", - " plt.plot(x, F_x, label='Функция распределения')\n", - " plt.title('График функции распределения')\n", - " plt.xlabel('x')\n", - " plt.ylabel('F(x)')\n", - " plt.grid()\n", - " plt.legend()\n", - " plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "POyQFy0itKu-" - }, - "source": [ - "# Проверим на равномерном распределении:" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "zh7c3qNyu4Xj" - }, - "outputs": [], - "source": [ - "unif = Unif(0, 1)\n", - "chr = unif.chr\n", - "\n", - "N = 1e3\n", - "delta = 1e-1" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 472 - }, - "id": "mpOJ7oE1ji5D", - "outputId": "6571b9a1-fa26-4412-9664-309727ee1167" - }, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr_a = A(N, delta, chr)\n", - "chr_a.make_plot(-2, 2, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 509 - }, - "id": "JbEWLSIWxWa_", - "outputId": "11b4cc62-a52c-4450-9393-49ed51bf202d" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr_b = B(N, delta, chr)\n", - "chr_b.make_plot(-2, 2, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 509 - }, - "id": "MwvNWzFIxW7Q", - "outputId": "aa970635-e02c-435c-f8bc-0a47c1b596c7" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr_c = C(N, delta, chr)\n", - "chr_c.make_plot(-2, 2, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 509 - }, - "id": "SqB52ggtxXVl", - "outputId": "27122082-b71b-47b4-8224-d9d8084d947d" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr_d = D(N, delta, chr, 10)\n", - "chr_d.make_plot(-2, 2, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 509 - }, - "id": "Cfj45thPxX5W", - "outputId": "7d69e3eb-b8c1-48c3-cf9e-deaf27dd6712" - }, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "chr_e = E(N, delta, chr, 10)\n", - "chr_e.make_plot(-2, 2, 50)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PTQjn3UTQJi-" - }, - "outputs": [], - "source": [ - "from mpmath import mp" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "je_u_xMWPrWk" - }, - "outputs": [], - "source": [ - "phi = lambda t : ((1 - 1j * t * sqrt(2)) ** (-0.5)) * exp((-1j * t) / sqrt(2))\n", - "N = 512\n", - "K = 4\n", - "d = 0.35 / 16\n", - "delta = (2 * pi) / (N * d)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "JOqiytlgQLC6" - }, - "outputs": [], - "source": [ - "F_exact = {\n", - " -5.60 : 0,\n", - " -4.55 : 0,\n", - " -3.50 : 0,\n", - " -2.45 : 0,\n", - " -1.40 : 0,\n", - " -1.05 : 0,\n", - " -0.70 : 79586,\n", - " -0.35 : 522700,\n", - " 0 : 682690,\n", - " 0.35 : 778553,\n", - " 0.70 : 841654,\n", - " 1.40 : 915695,\n", - " 1.75 : 937693,\n", - " 2.10 : 953678,\n", - " 3.15 : 980485,\n", - " 4.20 : 991570,\n", - " 5.25 : 996298\n", - "}\n", - "xs = F_exact.keys()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jh3Cwud7SXyd" - }, - "outputs": [], - "source": [ - "approxs = {\n", - " 'A' : A(N, delta, phi),\n", - " 'B' : B(N, delta, phi),\n", - " 'C' : C(N, delta, phi),\n", - " 'D' : D(N, delta, phi, K),\n", - " 'E' : E(N, delta, phi, K)\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "7shlF_j1TISN", - "outputId": "ff8786cc-6ba4-45e8-c09d-09497ded1673" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[mpf('-2835.6557302544452'), mpf('-1269.5157399394418'),\n", - " mpf('-574.60516745452469'), mpf('-260.97346261114222'),\n", - " mpf('-106.93877761549368'), mpf('-56.835251529290289'),\n", - " mpf('-4123.270735977494'), mpf('-117.33553835598286'),\n", - " mpf('-80.103089482174255'), mpf('-61.057529963203706'),\n", - " mpf('-50.572631704271771'), mpf('-35.741209859144874'),\n", - " mpf('-32.067856270703487'), mpf('-27.953954378608614'),\n", - " mpf('-21.113144348491915'), mpf('-17.787240787991323'),\n", - " mpf('-14.791953039821237')],\n", - " [mpf('-2822.0218915324617'), mpf('-1257.417101170957'),\n", - " mpf('-564.68639699687571'), mpf('-255.15063682993318'),\n", - " mpf('-115.76078106156453'), mpf('-88.472087398096747'),\n", - " mpf('-8451.4769495937508'), mpf('-130.9534186712699'),\n", - " mpf('-70.163900483748876'), mpf('-45.994061670964584'),\n", - " mpf('-33.737768919207156'), mpf('-18.076166681130417'),\n", - " mpf('-14.473948836908676'), mpf('-10.563881858834065'),\n", - " mpf('-4.6757434387691319'), mpf('-2.4808512557065114'),\n", - " mpf('-0.71425993146840483')],\n", - " [mpf('-2835.6450126641698'), mpf('-1269.5157252846889'),\n", - " mpf('-574.60516744780182'), mpf('-260.97346261099085'),\n", - " mpf('-106.93877761567757'), mpf('-56.835251528624156'),\n", - 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"execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "column_names = [\"A\", \"B\", \"C\", \"D\", \"E\"]\n", - "res = pd.DataFrame(values.transpose(), index=xs, columns=column_names)\n", - "res" - ] - } - ], - "metadata": { - "colab": { - "authorship_tag": "ABX9TyN/rbfESVW72hUvfBzaRXaD", - "include_colab_link": true, - "provenance": [] - }, - "kernelspec": { - "display_name": "myenv", - "language": "python", - "name": "myenv" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.13.1" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} From 383d865c1567f71cfb51457ad283933b75045aca Mon Sep 17 00:00:00 2001 From: desiment Date: Mon, 28 Apr 2025 16:49:25 +0300 Subject: [PATCH 18/19] fix tests --- tests/test_bohman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_bohman.py b/tests/test_bohman.py index 4c34632..633db2c 100644 --- a/tests/test_bohman.py +++ b/tests/test_bohman.py @@ -1,7 +1,7 @@ import numpy as np import pytest -import cfinversion.CharFuncInverter.Bohman.BohmansInverters as BI +import cfinversion.continuous.bohman as BI From a3141e823553162034c2eceeff2f9925e8c81567 Mon Sep 17 00:00:00 2001 From: desiment Date: Mon, 28 Apr 2025 16:57:19 +0300 Subject: [PATCH 19/19] fix: minor fixes --- .../bohman/abstract_bohman_inverter.py | 4 +++- cfinversion/distributions/sqr_uniform.py | 5 +++-- cfinversion/tools/standardizer.py | 10 ++++----- cfinversion/tools/validation_tools.py | 2 +- poetry.lock | 22 +++++++++++++++++-- pyproject.toml | 1 + 6 files changed, 33 insertions(+), 11 deletions(-) diff --git a/cfinversion/continuous/bohman/abstract_bohman_inverter.py b/cfinversion/continuous/bohman/abstract_bohman_inverter.py index ce2e1b5..948f475 100644 --- a/cfinversion/continuous/bohman/abstract_bohman_inverter.py +++ b/cfinversion/continuous/bohman/abstract_bohman_inverter.py @@ -4,7 +4,7 @@ import numpy as np from ..continuous_inverter import ContinuousInverter - +from ...tools import Standardizer class AbstractBohmanInverter(ContinuousInverter, ABC): """Abstract class for characteristic function inverter, @@ -12,6 +12,8 @@ class AbstractBohmanInverter(ContinuousInverter, ABC): Bohmans methods allows to implement only cumulative distribution function """ + def __init__(self): + self.standardizer = Standardizer() @staticmethod def _C(t: np.ndarray) -> np.ndarray: diff --git a/cfinversion/distributions/sqr_uniform.py b/cfinversion/distributions/sqr_uniform.py index bc514ee..a39cc0c 100644 --- a/cfinversion/distributions/sqr_uniform.py +++ b/cfinversion/distributions/sqr_uniform.py @@ -33,6 +33,7 @@ def cdf(self, x: Union[float, np.ndarray]) -> Union[float, np.ndarray]: return result def pdf(self, x: Union[float, np.ndarray]) -> np.ndarray: - result = np.zeros_like(x) - result[(x > self.a) & (x < self.b)] = 1 / (2 * np.sqrt(self.b - self.a) * np.sqrt(x[(x > self.a) & (x < self.b)] - self.a)) + x_arr = np.asarray(x) + result = np.zeros_like(x_arr) + result[(x_arr > self.a) & (x_arr < self.b)] = 1 / (2 * np.sqrt(self.b - self.a) * np.sqrt(x_arr[(x_arr > self.a) & (x_arr < self.b)] - self.a)) return result diff --git a/cfinversion/tools/standardizer.py b/cfinversion/tools/standardizer.py index 9f177d6..3d61ba9 100644 --- a/cfinversion/tools/standardizer.py +++ b/cfinversion/tools/standardizer.py @@ -5,23 +5,23 @@ class Standardizer: - def __init__(self, loc: float | None, scale: float | None) -> None: + def __init__(self, loc: float | None = None, scale: float | None = None) -> None: """ the class makes transitions between random variable and standardized random variable :param loc: location :param scale: scale """ - self.loc = loc - self.scale = scale + self.loc = loc or 0.0 + self.scale = scale or 1.0 def fit(self, cf: Callable, tol_diff : float = 1e-4) -> None: self.cf_true = cf cft = [cf(i * tol_diff) for i in range(1,5)] mean = (-3 * np.imag(cft[3]) + 32 * np.imag(cft[2]) - 168 * np.imag(cft[1]) + 672 * np.imag(cft[0])) / (420 * tol_diff) # estimated mean var = (9 * np.real(cft[3]) - 128 * np.real(cft[2]) + 1008 * np.real(cft[1]) - 8064 * np.real(cft[0]) + 7175) / (2520 * tol_diff**2) # estimated variance - self.loc = self.loc or mean - self.scale = self.scale or np.sqrt(var - mean**2) + self.loc = mean + self.scale = np.sqrt(var - mean**2) def cf(self, x): """ diff --git a/cfinversion/tools/validation_tools.py b/cfinversion/tools/validation_tools.py index 6b1dcf1..fc02232 100644 --- a/cfinversion/tools/validation_tools.py +++ b/cfinversion/tools/validation_tools.py @@ -14,4 +14,4 @@ def lre(v_true: np.ndarray, v: np.ndarray) -> np.ndarray: return -np.log10(np.abs((v_true - v) / v_true)) def l0_err(f: Callable, tol_diff:float = 1e-3) -> float: - return 1 - sp.integrate.quad(f, -np.inf, np.inf, epsabs = tol_diff) + return 1 - sp.integrate.quad(f, -np.inf, np.inf, epsabs = tol_diff)[0] diff --git a/poetry.lock b/poetry.lock index 2bba58e..d8b21f9 100644 --- a/poetry.lock +++ b/poetry.lock @@ -644,6 +644,24 @@ files = [ [package.extras] devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"] +[[package]] +name = "finufft" +version = "2.3.1" +description = "Python interface to FINUFFT" +optional = false +python-versions = ">=3.8" +groups = ["dev"] +files = [ + {file = "finufft-2.3.1-py3-none-macosx_13_0_arm64.whl", hash = "sha256:0ea7ac30c046625b0a46bc4d8d71b09dd936483cce0eb417c30e075913111f59"}, + {file = "finufft-2.3.1-py3-none-macosx_13_0_x86_64.whl", hash = "sha256:438ccca7e384800bc379510a9d539a77843fbe84c04030b64719807e59236bd6"}, + {file = "finufft-2.3.1-py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd2dc98d5962e82cb2b67d2cfa3bdc29d67088936cb7c0a8babf0172e3f91f3e"}, + {file = "finufft-2.3.1-py3-none-win_amd64.whl", hash = "sha256:083a43fa98cd2cea626d0f003c2e316db8eb4403838e5f262c020a4e2f992f22"}, + {file = "finufft-2.3.1.tar.gz", hash = "sha256:9f205b959f6f24751c8fd0ccf2a7af8515a5d5f69d5d66dadf4952aa52728e10"}, +] + +[package.dependencies] +numpy = ">=1.12.0" + [[package]] name = "fonttools" version = "4.56.0" @@ -1694,7 +1712,7 @@ version = "2.2.3" description = "Fundamental package for array computing in Python" optional = false python-versions = ">=3.10" -groups = ["main"] +groups = ["main", "dev"] files = [ {file = "numpy-2.2.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:cbc6472e01952d3d1b2772b720428f8b90e2deea8344e854df22b0618e9cce71"}, {file = "numpy-2.2.3-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:cdfe0c22692a30cd830c0755746473ae66c4a8f2e7bd508b35fb3b6a0813d787"}, @@ -2974,4 +2992,4 @@ test = ["websockets"] [metadata] lock-version = "2.1" python-versions = ">=3.10" -content-hash = "d68f2fcee6e349e43539f174a42d79f6a79cc6908e92342c825a2d4da42fcafa" +content-hash = "1e33c9b547fb8299b93d6d82c1eae2d17978515d420bb4bb88209767871ab118" diff --git a/pyproject.toml b/pyproject.toml index 0e87429..f3709c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -27,6 +27,7 @@ pytest = "^8.3.4" mypy = "^1.15.0" notebook = "^7.4.1" scipy-stubs = "^1.15.2.2" +finufft = "^2.3.1" [tool.ruff]