├── package ├── xentropy │ ├── .gitignore │ ├── kde_kernel │ │ ├── .gitignore │ │ └── kde_kernel.pyx │ ├── internal │ │ ├── constants.py │ │ ├── __init__.py │ │ ├── pre_post_processing.py │ │ └── resolution.py │ ├── __init__.py │ ├── reweighting.py │ └── kde.py ├── build.sh ├── requirements.txt ├── bld.bat ├── tests_cpp │ ├── pch.h │ ├── CMakeLists.txt │ ├── Integrator_Tester.cpp │ └── KDE_Tester.cpp ├── src │ ├── CMakeLists.txt │ ├── OMPExceptionHandler.h │ ├── Exceptions.h │ ├── Integrators.cpp │ └── Integrators.h ├── meta.yaml ├── CMakeLists.txt └── setup.py ├── conda ├── linux-64 │ ├── .cache │ │ ├── run_exports │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ │ ├── post_install │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ │ ├── stat.json │ │ ├── index │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ │ ├── recipe │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ │ ├── about │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ │ └── paths │ │ │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json │ ├── repodata.json.bz2 │ ├── current_repodata.json.bz2 │ ├── repodata_from_packages.json.bz2 │ ├── xentropy-1.8-py35h6bb024c_0.tar.bz2 │ ├── xentropy-1.8-py36h2bc3f7f_0.tar.bz2 │ ├── xentropy-1.8-py37h2bc3f7f_0.tar.bz2 │ ├── xentropy-1.8-py38h2bc3f7f_0.tar.bz2 │ ├── repodata.json │ ├── current_repodata.json │ ├── repodata_from_packages.json │ └── index.html ├── osx-64 │ ├── .cache │ │ ├── run_exports │ │ │ ├── xentropy-1.8-py35h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h41f2d22_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h41f2d22_0.tar.bz2.json │ │ ├── post_install │ │ │ ├── xentropy-1.8-py35h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h41f2d22_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h41f2d22_0.tar.bz2.json │ │ ├── stat.json │ │ ├── index │ │ │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py38h41f2d22_0.tar.bz2.json │ │ │ └── xentropy-1.8-py35h41f2d22_0.tar.bz2.json │ │ ├── recipe │ │ │ ├── xentropy-1.8-py35h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h41f2d22_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h41f2d22_0.tar.bz2.json │ │ └── about │ │ │ ├── xentropy-1.8-py38h41f2d22_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2.json │ │ │ └── xentropy-1.8-py37h41f2d22_0.tar.bz2.json │ ├── repodata.json.bz2 │ ├── current_repodata.json.bz2 │ ├── repodata_from_packages.json.bz2 │ ├── xentropy-1.8-py35h41f2d22_0.tar.bz2 │ ├── xentropy-1.8-py36h41f2d22_0.tar.bz2 │ ├── xentropy-1.8-py37h41f2d22_0.tar.bz2 │ ├── xentropy-1.8-py38h41f2d22_0.tar.bz2 │ ├── repodata.json │ ├── current_repodata.json │ ├── repodata_from_packages.json │ └── index.html ├── win-64 │ ├── .cache │ │ ├── run_exports │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ │ ├── post_install │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ │ ├── stat.json │ │ ├── index │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ │ ├── recipe │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ │ ├── about │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ └── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ └── paths │ │ │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2.json │ │ │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2.json │ │ │ └── xentropy-1.8-py38h2e52968_0.tar.bz2.json │ ├── repodata.json.bz2 │ ├── current_repodata.json.bz2 │ ├── repodata_from_packages.json.bz2 │ ├── xentropy-1.8-py35he980bc4_0.tar.bz2 │ ├── xentropy-1.8-py36h2e52968_0.tar.bz2 │ ├── xentropy-1.8-py37h2e52968_0.tar.bz2 │ ├── xentropy-1.8-py38h2e52968_0.tar.bz2 │ ├── repodata.json │ ├── current_repodata.json │ ├── repodata_from_packages.json │ └── index.html ├── noarch │ ├── repodata.json.bz2 │ ├── current_repodata.json.bz2 │ ├── repodata_from_packages.json.bz2 │ ├── repodata.json │ ├── current_repodata.json │ ├── repodata_from_packages.json │ └── index.html ├── channeldata.json └── index.html ├── .gitignore ├── .github └── workflows │ ├── python.yml │ └── c-cpp.yml ├── LICENSE.md └── README.md /package/xentropy/.gitignore: -------------------------------------------------------------------------------- 1 | *.so 2 | -------------------------------------------------------------------------------- /package/build.sh: -------------------------------------------------------------------------------- 1 | $PYTHON setup.py install -------------------------------------------------------------------------------- /package/requirements.txt: -------------------------------------------------------------------------------- 1 | Cython 2 | wheel 3 | -------------------------------------------------------------------------------- /package/xentropy/kde_kernel/.gitignore: -------------------------------------------------------------------------------- 1 | *.cpp 2 | -------------------------------------------------------------------------------- /package/bld.bat: -------------------------------------------------------------------------------- 1 | "%PYTHON%" setup.py install 2 | if errorlevel 1 exit 1 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-------------------------------------------------------------------------------- 1 | from numpy import pi as PI 2 | 3 | TWOPI = 2*PI 4 | 5 | id_gas_kcal = 0.001987204118 6 | id_gas_SI = 8.314 7 | -------------------------------------------------------------------------------- /conda/linux-64/xentropy-1.8-py35h6bb024c_0.tar.bz2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liedllab/X-Entropy/HEAD/conda/linux-64/xentropy-1.8-py35h6bb024c_0.tar.bz2 -------------------------------------------------------------------------------- /conda/linux-64/xentropy-1.8-py36h2bc3f7f_0.tar.bz2: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/liedllab/X-Entropy/HEAD/conda/linux-64/xentropy-1.8-py36h2bc3f7f_0.tar.bz2 -------------------------------------------------------------------------------- /conda/linux-64/xentropy-1.8-py37h2bc3f7f_0.tar.bz2: 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-------------------------------------------------------------------------------- 1 | { 2 | "info": { 3 | "subdir": "noarch" 4 | }, 5 | "packages": {}, 6 | "packages.conda": {}, 7 | "removed": [], 8 | "repodata_version": 1 9 | } 10 | -------------------------------------------------------------------------------- /conda/noarch/current_repodata.json: -------------------------------------------------------------------------------- 1 | { 2 | "info": { 3 | "subdir": "noarch" 4 | }, 5 | "packages": {}, 6 | "packages.conda": {}, 7 | "removed": [], 8 | "repodata_version": 1 9 | } 10 | -------------------------------------------------------------------------------- /conda/noarch/repodata_from_packages.json: -------------------------------------------------------------------------------- 1 | { 2 | "info": { 3 | "subdir": "noarch" 4 | }, 5 | "packages": {}, 6 | "packages.conda": {}, 7 | "removed": [], 8 | "repodata_version": 1 9 | } 10 | -------------------------------------------------------------------------------- /conda/osx-64/.cache/post_install/xentropy-1.8-py35h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": true, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/osx-64/.cache/post_install/xentropy-1.8-py36h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": true, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/osx-64/.cache/post_install/xentropy-1.8-py37h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": true, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/osx-64/.cache/post_install/xentropy-1.8-py38h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": true, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/win-64/.cache/post_install/xentropy-1.8-py35he980bc4_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/win-64/.cache/post_install/xentropy-1.8-py36h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/win-64/.cache/post_install/xentropy-1.8-py37h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/win-64/.cache/post_install/xentropy-1.8-py38h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/linux-64/.cache/post_install/xentropy-1.8-py35h6bb024c_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/linux-64/.cache/post_install/xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/linux-64/.cache/post_install/xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /conda/linux-64/.cache/post_install/xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"binary_prefix": false, "text_prefix": false, "activate.d": false, "deactivate.d": false, "pre_link": false, "post_link": false, "pre_unlink": false} -------------------------------------------------------------------------------- /package/xentropy/__init__.py: -------------------------------------------------------------------------------- 1 | from pkg_resources import get_distribution 2 | 3 | __version__ = get_distribution('xentropy').version 4 | 5 | import xentropy.dihedrals 6 | import xentropy.kde 7 | import xentropy.reweighting 8 | -------------------------------------------------------------------------------- /package/src/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | set(CMAKE_REQUIRED_LIBRARIES "fftw3" "omp") 2 | 3 | add_library(entropy_tests_lib Exceptions.h Integrators.cpp Integrators.h kde.cpp kde.h) 4 | 5 | target_link_libraries(entropy_tests_lib PRIVATE fftw3 gomp) 6 | -------------------------------------------------------------------------------- /conda/osx-64/.cache/stat.json: -------------------------------------------------------------------------------- 1 | {"xentropy-1.8-py38h41f2d22_0.tar.bz2": {"mtime": 1614872857, "size": 64968}, "xentropy-1.8-py35h41f2d22_0.tar.bz2": {"mtime": 1614872857, "size": 65875}, "xentropy-1.8-py36h41f2d22_0.tar.bz2": {"mtime": 1614872857, "size": 65180}, "xentropy-1.8-py37h41f2d22_0.tar.bz2": {"mtime": 1614872857, "size": 64929}} -------------------------------------------------------------------------------- /conda/win-64/.cache/stat.json: -------------------------------------------------------------------------------- 1 | {"xentropy-1.8-py36h2e52968_0.tar.bz2": {"mtime": 1614872857, "size": 65507}, "xentropy-1.8-py35he980bc4_0.tar.bz2": {"mtime": 1614872857, "size": 63910}, "xentropy-1.8-py37h2e52968_0.tar.bz2": {"mtime": 1614872857, "size": 65465}, "xentropy-1.8-py38h2e52968_0.tar.bz2": {"mtime": 1614872857, "size": 65681}} -------------------------------------------------------------------------------- /conda/linux-64/.cache/stat.json: -------------------------------------------------------------------------------- 1 | {"xentropy-1.8-py36h2bc3f7f_0.tar.bz2": {"mtime": 1614874352, "size": 69761}, "xentropy-1.8-py37h2bc3f7f_0.tar.bz2": {"mtime": 1614874352, "size": 69756}, "xentropy-1.8-py35h6bb024c_0.tar.bz2": {"mtime": 1614874352, "size": 69622}, "xentropy-1.8-py38h2bc3f7f_0.tar.bz2": {"mtime": 1614874352, "size": 70398}} -------------------------------------------------------------------------------- /package/tests_cpp/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | include(GoogleTest) 2 | 3 | add_executable(entropy_tests KDE_Tester.cpp Integrator_Tester.cpp pch.h) 4 | add_dependencies(entropy_tests googletest) 5 | 6 | target_link_libraries(entropy_tests PRIVATE gtest_main gtest pthread fftw3 gomp entropy_tests_lib) 7 | 8 | gtest_discover_tests( 9 | entropy_tests 10 | ) 11 | -------------------------------------------------------------------------------- /conda/osx-64/.cache/index/xentropy-1.8-py36h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py36h41f2d22_0", "build_number": 0, "depends": ["fftw", "libcxx >=10.0.0", "numpy", "python >=3.6,<3.7.0a0"], "name": "xentropy", "subdir": "osx-64", "timestamp": 1614869529777, "version": "1.8", "size": 65180, "md5": "7786093b587c29ff0704b6845ca0582b", "sha256": "296a7da91482c96da4485db105addee4c46405a9587459291fa0708e9f31fec8"} -------------------------------------------------------------------------------- /conda/osx-64/.cache/index/xentropy-1.8-py37h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py37h41f2d22_0", "build_number": 0, "depends": ["fftw", "libcxx >=10.0.0", "numpy", "python >=3.7,<3.8.0a0"], "name": "xentropy", "subdir": "osx-64", "timestamp": 1614869850214, "version": "1.8", "size": 64929, "md5": "3d1fbbcc05bb998ca5262732f91500ab", "sha256": "bc9df08ac86dc57ef8fa1f42b97dcf68d13ca4b02cdc330d85b5da9ed40fbb21"} -------------------------------------------------------------------------------- /conda/osx-64/.cache/index/xentropy-1.8-py38h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py38h41f2d22_0", "build_number": 0, "depends": ["fftw", "libcxx >=10.0.0", "numpy", "python >=3.8,<3.9.0a0"], "name": "xentropy", "subdir": "osx-64", "timestamp": 1614871968240, "version": "1.8", "size": 64968, "md5": "00dafeebf14b55e9e282600c97a1d34a", "sha256": "23867bf954e3e91579a3a046037458c76cc55c62ffc3ceea1ba415c6a571c0d1"} -------------------------------------------------------------------------------- /conda/win-64/.cache/index/xentropy-1.8-py35he980bc4_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py35he980bc4_0", "build_number": 0, "depends": ["fftw", "numpy", "python >=3.5,<3.6.0a0", "vc >=14,<15.0a0"], "name": "xentropy", "subdir": "win-64", "timestamp": 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{"build": "py35h6bb024c_0", "build_number": 0, "depends": ["fftw", "libgcc-ng >=7.3.0", "libstdcxx-ng >=7.3.0", "numpy", "python >=3.5,<3.6.0a0"], "name": "xentropy", "subdir": "linux-64", "timestamp": 1614873204420, "version": "1.8", "size": 69622, "md5": "d40717ae8ba20b782548090c5ec530b2", "sha256": "eaaffa6f1d4e3935eafdd0bb4aace26b70e8c7c48bd5f30b32896a6f9319a5b7"} -------------------------------------------------------------------------------- /conda/linux-64/.cache/index/xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py36h2bc3f7f_0", "build_number": 0, "depends": ["fftw", "libgcc-ng >=7.3.0", "libstdcxx-ng >=7.3.0", "numpy", "python >=3.6,<3.7.0a0"], "name": "xentropy", "subdir": "linux-64", "timestamp": 1614873381197, "version": "1.8", "size": 69761, "md5": "30f0663f7fccc84e969b4feec8168faf", "sha256": "59f0f0167744151f67d985e339a6fb790f6dafcfec42abd8500338f5c9ea3258"} 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-------------------------------------------------------------------------------- 1 | {"build": "py37h2e52968_0", "build_number": 0, "depends": ["fftw", "numpy", "python >=3.7,<3.8.0a0", "vc >=14.1,<15.0a0", "vs2015_runtime >=14.16.27012,<15.0a0"], "name": "xentropy", "subdir": "win-64", "timestamp": 1614860080125, "version": "1.8", "size": 65465, "md5": "7c918f330a8fe41323bf2d160c0765e5", "sha256": "1c12be32b95c301432407ba1391464bb27c5198a76e684be34d2586dfb481f61"} -------------------------------------------------------------------------------- /conda/win-64/.cache/index/xentropy-1.8-py38h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"build": "py38h2e52968_0", "build_number": 0, "depends": ["fftw", "numpy", "python >=3.8,<3.9.0a0", "vc >=14.1,<15.0a0", "vs2015_runtime >=14.16.27012,<15.0a0"], "name": "xentropy", "subdir": "win-64", "timestamp": 1614862239976, "version": "1.8", "size": 65681, "md5": "955493f3f6e8a542bf70587bbb671a4a", "sha256": "716c43d4b29c6d0551482f98c5fea41e9eaa5936bdde6143caae1193f22938c7"} -------------------------------------------------------------------------------- /package/meta.yaml: -------------------------------------------------------------------------------- 1 | package: 2 | name: xentropy 3 | version: "1.8" 4 | 5 | source: 6 | path: . 7 | 8 | requirements: 9 | build: 10 | - fftw 11 | - python 12 | - intel-openmp 13 | - {{ compiler('cxx') }} 14 | 15 | host: 16 | - python 17 | - fftw 18 | - setuptools 19 | - Cython 20 | - intel-openmp 21 | 22 | run: 23 | - fftw 24 | - numpy 25 | - python 26 | 27 | test: 28 | imports: 29 | - xentropy 30 | -------------------------------------------------------------------------------- /package/CMakeLists.txt: -------------------------------------------------------------------------------- 1 | cmake_minimum_required(VERSION 3.10) 2 | project(entropy_tests) 3 | 4 | set(CMAKE_CXX_STANDARD 17) 5 | 6 | include(ExternalProject) 7 | 8 | set(EXTERNAL_INSTALL_LOCATION ${CMAKE_BINARY_DIR}) 9 | 10 | ExternalProject_Add(googletest 11 | GIT_REPOSITORY https://github.com/google/googletest.git 12 | GIT_TAG origin/main 13 | CMAKE_ARGS -DCMAKE_INSTALL_PREFIX=${EXTERNAL_INSTALL_LOCATION} 14 | ) 15 | 16 | include_directories(src ${EXTERNAL_INSTALL_LOCATION}/include) 17 | link_directories(${EXTERNAL_INSTALL_LOCATION}/lib) 18 | 19 | add_subdirectory(src) 20 | 21 | ENABLE_TESTING() 22 | add_subdirectory(tests_cpp) 23 | -------------------------------------------------------------------------------- /.github/workflows/python.yml: -------------------------------------------------------------------------------- 1 | name: Python:Pip 2 | 3 | on: 4 | push: 5 | branches: [main] 6 | pull_request: 7 | branches: [main] 8 | 9 | 10 | jobs: 11 | build: 12 | 13 | runs-on: ubuntu-latest 14 | env: 15 | working-directory: ./package 16 | 17 | steps: 18 | - uses: actions/checkout@v1 19 | 20 | - name: Set up Python 21 | uses: actions/setup-python@v2 22 | with: 23 | python-version: 3.8 24 | 25 | 26 | - name: Install dependencies 27 | run: sudo apt-get install -y libfftw3-dev && pip install Cython 28 | 29 | - name: Build 30 | run: pip install -r requirements.txt; pip install . 31 | working-directory: ${{ env.working-directory }} 32 | 33 | #TODO: Run Tests 34 | -------------------------------------------------------------------------------- /package/src/OMPExceptionHandler.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | 6 | class OMPExceptionHandler 7 | { 8 | private: 9 | std::vector m_exceptions; 10 | std::mutex m_lock; 11 | 12 | public: 13 | OMPExceptionHandler() {} 14 | 15 | void Rethrow() 16 | { 17 | if (m_exceptions.size() > 0) 18 | { 19 | std::rethrow_exception(m_exceptions.at(0)); 20 | } 21 | } 22 | 23 | template 24 | void Run(Function f, Parameters... params) 25 | { 26 | try 27 | { 28 | f(params...); 29 | } 30 | catch (...) 31 | { 32 | CaptureException(); 33 | } 34 | } 35 | 36 | void CaptureException() 37 | { 38 | std::unique_lock guard{m_lock}; 39 | m_exceptions.push_back(std::current_exception()); 40 | } 41 | }; -------------------------------------------------------------------------------- /package/src/Exceptions.h: -------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | #include 6 | 7 | class UnknownIntegrator : std::runtime_error 8 | { 9 | 10 | public: 11 | UnknownIntegrator(const std::string &err_msg) 12 | : std::runtime_error{"UnknownIntegrator: " + err_msg} 13 | { 14 | } 15 | }; 16 | 17 | class IntegrationError : std::runtime_error 18 | { 19 | public: 20 | IntegrationError(const std::string &err_msg) 21 | : std::runtime_error{"IntegrationError: " + err_msg} 22 | { 23 | } 24 | }; 25 | 26 | class EmptyListError : std::runtime_error 27 | { 28 | public: 29 | EmptyListError(const std::string &err_msg) 30 | : std::runtime_error{"EmptyListError: " + err_msg} 31 | { 32 | } 33 | }; 34 | 35 | class ValueError : std::runtime_error 36 | { 37 | public: 38 | ValueError(const std::string &err_msg) 39 | : std::runtime_error{"ValueError: " + err_msg} 40 | { 41 | } 42 | }; 43 | -------------------------------------------------------------------------------- /conda/win-64/.cache/recipe/xentropy-1.8-py35he980bc4_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "C:\\Users\\Hannes\\Documents\\GitHub\\X-Entropy\\package"}, "build": {"string": "py35he980bc4_0"}, "requirements": {"build": ["certifi 2020.6.20 pyhd3eb1b0_3", "fftw 3.3.9 h2bbff1b_1", "intel-openmp 2020.2 254", "pip 10.0.1 py35_0", "python 3.5.6 he025d50_0", "setuptools 40.2.0 py35_0", "vc 14.2 h21ff451_1", "vs2015_runtime 14.27.29016 h5e58377_2", "vs2015_win-64 14.0.25420 h55c1224_11", "wheel 0.36.2 pyhd3eb1b0_0", "wincertstore 0.2 py35hfebbdb8_0"], "host": ["certifi 2020.6.20 pyhd3eb1b0_3", "cython 0.28.5 py35h6538335_0", "fftw 3.3.9 h2bbff1b_1", "intel-openmp 2020.2 254", "pip 10.0.1 py35_0", "python 3.5.6 he025d50_0", "setuptools 40.2.0 py35_0", "vc 14.2 h21ff451_1", "vs2015_runtime 14.27.29016 h5e58377_2", "wheel 0.36.2 pyhd3eb1b0_0", "wincertstore 0.2 py35hfebbdb8_0"], "run": ["fftw", "numpy", "python >=3.5,<3.6.0a0", "vc >=14,<15.0a0"]}, "test": {"imports": ["xentropy"]}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /conda/channeldata.json: -------------------------------------------------------------------------------- 1 | { 2 | "channeldata_version": 1, 3 | "packages": { 4 | "xentropy": { 5 | "activate.d": false, 6 | "binary_prefix": true, 7 | "deactivate.d": false, 8 | "description": null, 9 | "dev_url": null, 10 | "doc_source_url": null, 11 | "doc_url": null, 12 | "home": null, 13 | "icon_hash": null, 14 | "icon_url": null, 15 | "identifiers": null, 16 | "keywords": null, 17 | "license": null, 18 | "post_link": false, 19 | "pre_link": false, 20 | "pre_unlink": false, 21 | "recipe_origin": null, 22 | "run_exports": {}, 23 | "source_git_url": null, 24 | "source_url": null, 25 | "subdirs": [ 26 | "linux-64", 27 | "osx-64", 28 | "win-64" 29 | ], 30 | "summary": null, 31 | "tags": null, 32 | "text_prefix": false, 33 | "timestamp": 1614863366, 34 | "version": "1.8" 35 | } 36 | }, 37 | "subdirs": [ 38 | "linux-64", 39 | "noarch", 40 | "osx-64", 41 | "win-64" 42 | ] 43 | } 44 | -------------------------------------------------------------------------------- /LICENSE.md: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Liedl Lab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /conda/win-64/.cache/recipe/xentropy-1.8-py36h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "C:\\Users\\Hannes\\Documents\\GitHub\\X-Entropy\\package"}, "build": {"string": "py36h2e52968_0"}, "requirements": {"build": ["certifi 2020.12.5 py36haa95532_0", "fftw 3.3.9 h2bbff1b_1", "intel-openmp 2020.2 254", "pip 21.0.1 py36haa95532_0", "python 3.6.12 h5500b2f_2", "setuptools 52.0.0 py36haa95532_0", "sqlite 3.33.0 h2a8f88b_0", "vc 14.2 h21ff451_1", "vs2015_runtime 14.27.29016 h5e58377_2", 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py37haa95532_0", "python 3.7.10 h6244533_0", "setuptools 52.0.0 py37haa95532_0", "sqlite 3.33.0 h2a8f88b_0", "vc 14.2 h21ff451_1", "vs2015_runtime 14.27.29016 h5e58377_2", "wheel 0.36.2 pyhd3eb1b0_0", "wincertstore 0.2 py37_0", "zlib 1.2.11 h62dcd97_4"], "run": ["fftw", "numpy", "python >=3.7,<3.8.0a0", "vc >=14.1,<15.0a0", "vs2015_runtime >=14.16.27012,<15.0a0"]}, "test": {"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /conda/win-64/.cache/recipe/xentropy-1.8-py38h2e52968_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "C:\\Users\\Hannes\\Documents\\GitHub\\X-Entropy\\package"}, "build": {"string": "py38h2e52968_0"}, "requirements": {"build": ["ca-certificates 2021.1.19 haa95532_0", "certifi 2020.12.5 py38haa95532_0", "fftw 3.3.9 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{"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /package/setup.py: -------------------------------------------------------------------------------- 1 | #from distutils.core import setup, Extension 2 | #import sys 3 | #from glob import glob 4 | from setuptools import Extension, setup 5 | from Cython.Build import cythonize 6 | import platform 7 | import os 8 | #from datetime import datetime 9 | 10 | if platform.system() == 'Windows': 11 | flags = ['/O2', '/openmp'] 12 | libs = ['fftw3'] 13 | elif platform.system() == 'Linux': 14 | flags = ['-fopenmp'] 15 | libs = ['fftw3', 'm', 'gomp'] 16 | elif platform.system() == 'Darwin': 17 | flags = ['-fopenmp'] 18 | libs = ['fftw3'] 19 | 20 | setup( 21 | name = 'XEntropy', 22 | version = '1.8.1', 23 | description = "Calculate the entropy of a given set of data, using the fftw3 library.", 24 | author = "Johannes Kraml", 25 | author_email="johannes.kraml@uibk.ac.at", 26 | install_requires=["Cython"], 27 | py_modules=['xentropy.dihedrals', 'xentropy.kde', 'xentropy.internal.constants', 'xentropy.xentropy', 28 | 'xentropy.reweighting','xentropy.internal.resolution', 29 | 'xentropy.internal.pre_post_processing'], 30 | 31 | ext_modules = cythonize([ 32 | Extension( 33 | 'xentropy.kde_kernel', 34 | sources = [ 35 | 'xentropy/kde_kernel/kde_kernel.pyx', 36 | 'src/Integrators.cpp' 37 | ], 38 | libraries=libs, 39 | extra_compile_args=flags, 40 | language = "c++", 41 | ), 42 | ]) 43 | ) 44 | #print("Installed at:\n{}".format(datetime.now())) 45 | 46 | -------------------------------------------------------------------------------- /conda/osx-64/.cache/recipe/xentropy-1.8-py35h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/Users/jhk/Documents/GitHub/X-Entropy/package"}, "build": 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py35h0a44026_0", "fftw 3.3.8 h1de35cc_3", "intel-openmp 2020.2 258", "libcxx 10.0.0 1", "libedit 3.1.20191231 h1de35cc_1", "libffi 3.2.1 h0a44026_1007", "ncurses 6.2 h0a44026_1", "openssl 1.0.2u h1de35cc_0", "pip 10.0.1 py35_0", "python 3.5.6 hc167b69_0", "readline 7.0 h1de35cc_5", "setuptools 40.2.0 py35_0", "sqlite 3.33.0 hffcf06c_0", "tk 8.6.10 hb0a8c7a_0", "wheel 0.35.1 pyhd3eb1b0_0", "xz 5.2.5 h1de35cc_0", "zlib 1.2.11 h1de35cc_3"], "run": ["fftw >=3.3.8,<3.4.0a0", "libcxx >=10.0.0", "numpy", "python >=3.5,<3.6.0a0"]}, "test": {"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /conda/osx-64/.cache/recipe/xentropy-1.8-py36h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/Users/jhk/Documents/GitHub/X-Entropy/package"}, 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-------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/home/jhk/Documents/Uni/Code/X-Entropy/package"}, "build": {"string": "py35h6bb024c_0"}, "requirements": {"build": ["_libgcc_mutex 0.1 main", "binutils_impl_linux-64 2.33.1 he6710b0_7", "binutils_linux-64 2.33.1 h9595d00_15", "ca-certificates 2021.1.19 h06a4308_0", "certifi 2020.6.20 pyhd3eb1b0_3", "fftw 3.3.9 h27cfd23_1", "gcc_impl_linux-64 7.3.0 habb00fd_1", "gcc_linux-64 7.3.0 h553295d_15", "gxx_impl_linux-64 7.3.0 hdf63c60_1", "gxx_linux-64 7.3.0 h553295d_15", "intel-openmp 2020.2 254", "ld_impl_linux-64 2.33.1 h53a641e_7", "libedit 3.1.20191231 h14c3975_1", "libffi 3.2.1 hf484d3e_1007", "libgcc-ng 9.1.0 hdf63c60_0", "libstdcxx-ng 9.1.0 hdf63c60_0", "ncurses 6.2 he6710b0_1", "openssl 1.0.2u h7b6447c_0", "pip 10.0.1 py35_0", "python 3.5.6 hc3d631a_0", "readline 7.0 h7b6447c_5", "setuptools 40.2.0 py35_0", "sqlite 3.33.0 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/.github/workflows/c-cpp.yml: -------------------------------------------------------------------------------- 1 | name: C++:CMake 2 | 3 | on: 4 | push: 5 | branches: [main] 6 | pull_request: 7 | branches: [main] 8 | 9 | env: 10 | # Customize the CMake build type here (Release, Debug, RelWithDebInfo, etc.) 11 | BUILD_TYPE: Release 12 | 13 | jobs: 14 | build: 15 | 16 | runs-on: ubuntu-latest 17 | 18 | steps: 19 | - uses: actions/checkout@v1 20 | 21 | - name: Install fftw3 22 | run: sudo apt-get install -y libfftw3-dev 23 | 24 | - name: Create Build Environment 25 | # Some projects don't allow in-source building, so create a separate build directory 26 | # We'll use this as our working directory for all subsequent commands 27 | run: cmake -E make_directory ${{runner.workspace}}/build 28 | 29 | - name: Configure CMake 30 | # Use a bash shell so we can use the same syntax for environment variable 31 | # access regardless of the host operating system 32 | shell: bash 33 | working-directory: ${{runner.workspace}}/build 34 | # Note the current convention is to use the -S and -B options here to specify source 35 | # and build directories, but this is only available with CMake 3.13 and higher. 36 | # The CMake binaries on the Github Actions machines are (as of this writing) 3.12 37 | run: cmake $GITHUB_WORKSPACE/package 38 | 39 | - name: Build 40 | working-directory: ${{runner.workspace}}/build 41 | shell: bash 42 | # Execute the build. You can specify a specific target with "--target " 43 | run: cmake --build . 44 | 45 | - name: Test 46 | working-directory: ${{runner.workspace}}/build 47 | shell: bash 48 | # Execute tests defined by the CMake configuration. 49 | # See https://cmake.org/cmake/help/latest/manual/ctest.1.html for more detail 50 | run: ctest 51 | -------------------------------------------------------------------------------- /package/src/Integrators.cpp: -------------------------------------------------------------------------------- 1 | #include "Integrators.h" 2 | 3 | std::unique_ptr getFunction(const std::string &type) 4 | { 5 | std::string newstr{type}; 6 | transform(newstr.begin(), newstr.end(), newstr.begin(), ::tolower); 7 | if (newstr == "simpson" || newstr == "kepler") 8 | { 9 | return std::make_unique(); 10 | } 11 | else if (newstr == "riemann") 12 | { 13 | return std::make_unique(); 14 | } 15 | 16 | throw UnknownIntegrator("The function you searched for is not yet implemented."); 17 | } 18 | 19 | double Simpson::operator()(const std::vector &function, double range) const noexcept 20 | { 21 | // TODO: Should actually throw an error 22 | int steps{static_cast(function.size())}; 23 | if (steps % 2) 24 | { 25 | steps -= 1; 26 | } 27 | double ret = function.at(0); 28 | double h = range / (3 * steps); 29 | ret += function.at(function.size() - 1); 30 | double calc = 0.0; 31 | 32 | // C 33 | #pragma omp parallel for reduction(+ \ 34 | : calc) 35 | for (int j = 1; j <= (steps / 2); ++j) 36 | { 37 | calc += function.at(2 * j - 1); 38 | } 39 | 40 | ret += 4.0 * calc; 41 | calc = 0.0; 42 | 43 | // Check this! 44 | #pragma omp parallel for reduction(+ \ 45 | : calc) 46 | for (int j = 1; j <= ((steps / 2) - 1); ++j) 47 | { 48 | calc += function.at(2 * j); 49 | } 50 | 51 | ret += 2.0 * calc; 52 | return ret * h; 53 | } 54 | 55 | double Riemann::operator()(const std::vector &function, double range) const noexcept 56 | { 57 | double dx{range / static_cast(function.size())}; 58 | double ret{0}; 59 | for (auto val : function) 60 | { 61 | ret += val * dx; 62 | } 63 | return ret; 64 | } -------------------------------------------------------------------------------- /conda/linux-64/.cache/recipe/xentropy-1.8-py36h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/home/jhk/Documents/Uni/Code/X-Entropy/package"}, "build": {"string": "py36h2bc3f7f_0"}, "requirements": {"build": ["_libgcc_mutex 0.1 main", "binutils_impl_linux-64 2.33.1 he6710b0_7", "binutils_linux-64 2.33.1 h9595d00_15", "ca-certificates 2021.1.19 h06a4308_0", "certifi 2020.12.5 py36h06a4308_0", "fftw 3.3.9 h27cfd23_1", "gcc_impl_linux-64 7.3.0 habb00fd_1", "gcc_linux-64 7.3.0 h553295d_15", "gxx_impl_linux-64 7.3.0 hdf63c60_1", "gxx_linux-64 7.3.0 h553295d_15", "intel-openmp 2020.2 254", "ld_impl_linux-64 2.33.1 h53a641e_7", "libedit 3.1.20191231 h14c3975_1", "libffi 3.3 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5.2.5 h7b6447c_0", "zlib 1.2.11 h7b6447c_3"], "run": ["fftw", "libgcc-ng >=7.3.0", "libstdcxx-ng >=7.3.0", "numpy", "python >=3.6,<3.7.0a0"]}, "test": {"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /conda/linux-64/.cache/recipe/xentropy-1.8-py37h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/home/jhk/Documents/Uni/Code/X-Entropy/package"}, "build": {"string": "py37h2bc3f7f_0"}, "requirements": {"build": ["_libgcc_mutex 0.1 main", "binutils_impl_linux-64 2.33.1 he6710b0_7", "binutils_linux-64 2.33.1 h9595d00_15", "ca-certificates 2021.1.19 h06a4308_0", "certifi 2020.12.5 py37h06a4308_0", "fftw 3.3.9 h27cfd23_1", "gcc_impl_linux-64 7.3.0 habb00fd_1", "gcc_linux-64 7.3.0 h553295d_15", "gxx_impl_linux-64 7.3.0 hdf63c60_1", 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"readline 8.1 h27cfd23_0", "setuptools 52.0.0 py37h06a4308_0", "sqlite 3.33.0 h62c20be_0", "tk 8.6.10 hbc83047_0", "wheel 0.36.2 pyhd3eb1b0_0", "xz 5.2.5 h7b6447c_0", "zlib 1.2.11 h7b6447c_3"], "run": ["fftw", "libgcc-ng >=7.3.0", "libstdcxx-ng >=7.3.0", "numpy", "python >=3.7,<3.8.0a0"]}, "test": {"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /conda/linux-64/.cache/recipe/xentropy-1.8-py38h2bc3f7f_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | {"package": {"name": "xentropy", "version": "1.8"}, "source": {"path": "/home/jhk/Documents/Uni/Code/X-Entropy/package"}, "build": {"string": "py38h2bc3f7f_0"}, "requirements": {"build": ["_libgcc_mutex 0.1 main", "binutils_impl_linux-64 2.33.1 he6710b0_7", "binutils_linux-64 2.33.1 h9595d00_15", "ca-certificates 2021.1.19 h06a4308_0", "certifi 2020.12.5 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hdf63c60_0", "libstdcxx-ng 9.1.0 hdf63c60_0", "ncurses 6.2 he6710b0_1", "openssl 1.1.1j h27cfd23_0", "pip 21.0.1 py38h06a4308_0", "python 3.8.8 hdb3f193_4", "readline 8.1 h27cfd23_0", "setuptools 52.0.0 py38h06a4308_0", "sqlite 3.33.0 h62c20be_0", "tk 8.6.10 hbc83047_0", "wheel 0.36.2 pyhd3eb1b0_0", "xz 5.2.5 h7b6447c_0", "zlib 1.2.11 h7b6447c_3"], "run": ["fftw", "libgcc-ng >=7.3.0", "libstdcxx-ng >=7.3.0", "numpy", "python >=3.8,<3.9.0a0"]}, "test": {"imports": ["xentropy"]}, "about": {}, "extra": {"copy_test_source_files": true, "final": true}} -------------------------------------------------------------------------------- /package/xentropy/kde_kernel/kde_kernel.pyx: -------------------------------------------------------------------------------- 1 | import cython 2 | from libcpp.vector cimport vector 3 | from libcpp.string cimport string 4 | 5 | import numpy as np 6 | 7 | cdef extern from "../../src/kde.cpp": 8 | pass 9 | 10 | cdef extern from "../../src/kde.h": 11 | cdef cppclass KDE: 12 | KDE(vector[double]&, int) except + 13 | KDE(vector[double]&, vector[double] &, int) except + 14 | 15 | void calculate() except + 16 | double integrate(string &, double, double) except + 17 | double entropy(string &, double, double) except + 18 | 19 | vector[double] getDensityEstimation() 20 | vector[double] getCenters() 21 | double getBandwidth() 22 | 23 | @cython.embedsignature(True) 24 | cdef class _kde_kernel: 25 | """ 26 | This is a Docstring. 27 | """ 28 | def __init__(self, data, resolution, weights=None): 29 | pass 30 | 31 | cdef KDE* m_kde_kernel 32 | 33 | def __cinit__(self, data, resolution, weights=None): 34 | if not (weights is None): 35 | self.m_kde_kernel = new KDE(data, weights, resolution) 36 | else: 37 | self.m_kde_kernel = new KDE(data, resolution) 38 | 39 | def __dealloc__(self): 40 | del self.m_kde_kernel 41 | 42 | def calculate(self): 43 | self.m_kde_kernel.calculate() 44 | 45 | def integrate(self, start, end, method="Simpson"): 46 | return self.m_kde_kernel.integrate(method.encode("utf-8"), start, end) 47 | 48 | def calculate_entropy(self, start, end, method="Simpson"): 49 | return self.m_kde_kernel.entropy(method.encode("utf-8"), start, end) 50 | 51 | def get_grid(self): 52 | return np.array(self.m_kde_kernel.getCenters()) 53 | 54 | def get_pdf(self): 55 | return np.array(self.m_kde_kernel.getDensityEstimation()) 56 | 57 | def get_bandwidth(self): 58 | return self.m_kde_kernel.getBandwidth() -------------------------------------------------------------------------------- /package/tests_cpp/Integrator_Tester.cpp: -------------------------------------------------------------------------------- 1 | #include "pch.h" 2 | #include "../src/Integrators.h" 3 | 4 | TEST(Integrator_Test, Riemann_Integral) 5 | { 6 | std::vector f1{0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 7 | std::vector f2{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 8 | std::vector f3{1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; 9 | std::vector f4{0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 10 | EXPECT_DOUBLE_EQ(Riemann()(f1, 1.0), 0.1); 11 | EXPECT_DOUBLE_EQ(Riemann()(f2, 1.0), 0.0); 12 | EXPECT_DOUBLE_EQ(Riemann()(f3, 1.0), 1.0); 13 | EXPECT_DOUBLE_EQ(Riemann()(f4, 1.0), 0.2); 14 | } 15 | 16 | TEST(Integrator_Test, Simpson_Integral) 17 | { 18 | std::vector f1{0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 19 | std::vector f2{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 20 | std::vector f3{1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0}; 21 | std::vector f4{0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0}; 22 | // First test case is 4.0 / 30.0, this happens due to the term 4 * x_{2j - 1} 23 | // You can check the calculation, the index is not even, so you get 4 * 1.0 24 | // This is multiplied by the range (1.0) and divided by three times the number 25 | // of sub-intervals (i.e., 30) 26 | EXPECT_DOUBLE_EQ(Simpson()(f1, 1.0), 0.13333333333333333); 27 | EXPECT_DOUBLE_EQ(Simpson()(f2, 1.0), 0.0); 28 | EXPECT_DOUBLE_EQ(Simpson()(f3, 1.0), 1.0); 29 | EXPECT_DOUBLE_EQ(Simpson()(f4, 1.0), 0.2); 30 | } 31 | 32 | TEST(Integrator_Test, Find_Integrator) 33 | { 34 | EXPECT_THROW(getFunction("Random"), UnknownIntegrator); 35 | // Weird people might do that! 36 | auto test = getFunction("SiMpSoN"); 37 | EXPECT_NO_THROW(dynamic_cast(*test)); 38 | test = getFunction("Riemann"); 39 | EXPECT_NO_THROW(dynamic_cast(*test)); 40 | test = getFunction("Kepler"); 41 | EXPECT_NO_THROW(dynamic_cast(*test)); 42 | } -------------------------------------------------------------------------------- /package/tests_cpp/KDE_Tester.cpp: -------------------------------------------------------------------------------- 1 | #include "pch.h" 2 | 3 | #include "../src/kde.h" 4 | 5 | TEST(GceTest, ConstructorTest) 6 | { 7 | KDE gce{{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, 10}; 8 | ASSERT_EQ(gce.getHistogram().size(), 10); 9 | ASSERT_EQ(gce.getGridLength(), 11); 10 | ASSERT_EQ(gce.getGrid().size(), 11); 11 | EXPECT_DOUBLE_EQ(gce.getHistogram()[0], 1.0 / 11.0); 12 | EXPECT_DOUBLE_EQ(gce.getHistogram()[4], 2.0 / 11.0); // Middle is double 13 | EXPECT_DOUBLE_EQ(gce.getGrid()[0], -0.1); 14 | EXPECT_DOUBLE_EQ(gce.getGrid()[10], 1.1); 15 | EXPECT_THROW(KDE(std::vector(), 10), EmptyListError); 16 | } 17 | 18 | TEST(GceTest, CalculateTest) 19 | { 20 | KDE gce{{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, 10}; 21 | ASSERT_EQ(gce.getHistogram().size(), 10); 22 | ASSERT_EQ(gce.getGridLength(), 11); 23 | ASSERT_EQ(gce.getGrid().size(), 11); 24 | ASSERT_EQ(gce.getCenters().size(), 10); 25 | ASSERT_DOUBLE_EQ(gce.getCenters().at(0), -0.04); 26 | ASSERT_DOUBLE_EQ(gce.getCenters().at(9), 1.04); 27 | gce.calculate(); 28 | ASSERT_EQ(gce.getDensityEstimation().size(), 10); 29 | EXPECT_DOUBLE_EQ(gce.getDensityEstimation()[0] / gce.getDensityEstimation()[4], 0.5); 30 | EXPECT_DOUBLE_EQ(gce.integrate("Simpson", -0.1, 1.1), 1.0); 31 | } 32 | 33 | TEST(GceTest, WeightsTest) 34 | { 35 | KDE gce{{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, {1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1}, 10}; 36 | KDE gce2{{0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, 10}; 37 | gce.calculate(); 38 | gce2.calculate(); 39 | EXPECT_DOUBLE_EQ(gce.integrate("Simpson", 0.0, 1.0), gce2.integrate("Simpson", 0.0, 1.0)); 40 | 41 | EXPECT_THROW(KDE({0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0}, {1, 1, 1, 1, 1, 2, 1, 1, 1, 1}, 10), ValueError); 42 | } 43 | 44 | TEST(calcHistogramNormalizer, Tests) 45 | { 46 | EXPECT_DOUBLE_EQ(calcHistogramNormalizer({1., 2., 3., 4., 5.}), 1. / 15.); 47 | EXPECT_DOUBLE_EQ(calcHistogramNormalizer({0.5, 2.5, 3.15, 3.85, 5.}), 1. / 15.); 48 | EXPECT_DOUBLE_EQ(calcHistogramNormalizer(15), 1. / 15.); 49 | } -------------------------------------------------------------------------------- /conda/osx-64/repodata.json: -------------------------------------------------------------------------------- 1 | { 2 | "info": { 3 | 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"python >=3.6,<3.7.0a0", 30 | "vc >=14.1,<15.0a0", 31 | "vs2015_runtime >=14.16.27012,<15.0a0" 32 | ], 33 | "md5": "a2462bfa5594917313854b1c9d723f94", 34 | "name": "xentropy", 35 | "sha256": "f6cfedf6ffc5c7d9e11e3c3f2fb13f3cc69118feee9dd9aaa049c4f0b07831a9", 36 | "size": 65507, 37 | "subdir": "win-64", 38 | "timestamp": 1614863366866, 39 | "version": "1.8" 40 | }, 41 | "xentropy-1.8-py37h2e52968_0.tar.bz2": { 42 | "build": "py37h2e52968_0", 43 | "build_number": 0, 44 | "depends": [ 45 | "fftw", 46 | "numpy", 47 | "python >=3.7,<3.8.0a0", 48 | "vc >=14.1,<15.0a0", 49 | "vs2015_runtime >=14.16.27012,<15.0a0" 50 | ], 51 | "md5": "7c918f330a8fe41323bf2d160c0765e5", 52 | "name": "xentropy", 53 | "sha256": "1c12be32b95c301432407ba1391464bb27c5198a76e684be34d2586dfb481f61", 54 | "size": 65465, 55 | "subdir": "win-64", 56 | "timestamp": 1614860080125, 57 | "version": "1.8" 58 | }, 59 | "xentropy-1.8-py38h2e52968_0.tar.bz2": { 60 | "build": "py38h2e52968_0", 61 | "build_number": 0, 62 | 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>=3.5,<3.6.0a0" 15 | ], 16 | "md5": "d40717ae8ba20b782548090c5ec530b2", 17 | "name": "xentropy", 18 | "sha256": "eaaffa6f1d4e3935eafdd0bb4aace26b70e8c7c48bd5f30b32896a6f9319a5b7", 19 | "size": 69622, 20 | "subdir": "linux-64", 21 | "timestamp": 1614873204420, 22 | "version": "1.8" 23 | }, 24 | "xentropy-1.8-py36h2bc3f7f_0.tar.bz2": { 25 | "build": "py36h2bc3f7f_0", 26 | "build_number": 0, 27 | "depends": [ 28 | "fftw", 29 | "libgcc-ng >=7.3.0", 30 | "libstdcxx-ng >=7.3.0", 31 | "numpy", 32 | "python >=3.6,<3.7.0a0" 33 | ], 34 | "md5": "30f0663f7fccc84e969b4feec8168faf", 35 | "name": "xentropy", 36 | "sha256": "59f0f0167744151f67d985e339a6fb790f6dafcfec42abd8500338f5c9ea3258", 37 | "size": 69761, 38 | "subdir": "linux-64", 39 | "timestamp": 1614873381197, 40 | "version": "1.8" 41 | }, 42 | "xentropy-1.8-py37h2bc3f7f_0.tar.bz2": { 43 | "build": "py37h2bc3f7f_0", 44 | "build_number": 0, 45 | "depends": [ 46 | "fftw", 47 | "libgcc-ng >=7.3.0", 48 | "libstdcxx-ng >=7.3.0", 49 | "numpy", 50 | "python >=3.7,<3.8.0a0" 51 | ], 52 | "md5": "1631f2e54b7eaf8a6611fd329146ba64", 53 | "name": "xentropy", 54 | "sha256": "42bc7a74073484e906d3e66792e2283ab8b144588092675c00ce8075c4358f93", 55 | "size": 69756, 56 | "subdir": "linux-64", 57 | "timestamp": 1614873620606, 58 | "version": "1.8" 59 | }, 60 | "xentropy-1.8-py38h2bc3f7f_0.tar.bz2": { 61 | "build": "py38h2bc3f7f_0", 62 | "build_number": 0, 63 | "depends": [ 64 | "fftw", 65 | "libgcc-ng >=7.3.0", 66 | "libstdcxx-ng >=7.3.0", 67 | "numpy", 68 | "python >=3.8,<3.9.0a0" 69 | ], 70 | "md5": "c29acaacef1add7743f65be12977ed78", 71 | "name": "xentropy", 72 | "sha256": "bce94f47f840f5b40b71f713ab12eef596b6096fe197c68075140a78ed2e8a7b", 73 | "size": 70398, 74 | "subdir": "linux-64", 75 | "timestamp": 1614873762900, 76 | "version": "1.8" 77 | } 78 | }, 79 | "packages.conda": {}, 80 | "removed": [], 81 | "repodata_version": 1 82 | } 83 | -------------------------------------------------------------------------------- /conda/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | conda 4 | 67 | 68 | 69 |

conda

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PackageLatest VersionDocDevLicenselinux-64osx-64win-64noarch Summary
xentropy1.8XXX None
88 |
Updated: 2021-03-04 16:13:07 +0000 - Files: 1
89 | 90 | -------------------------------------------------------------------------------- /conda/win-64/.cache/about/xentropy-1.8-py35he980bc4_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | { 2 | "channels": [ 3 | "defaults" 4 | ], 5 | "conda_build_version": "3.15.1", 6 | "conda_private": false, 7 | "conda_version": "4.5.11", 8 | "env_vars": { 9 | "CIO_TEST": "" 10 | }, 11 | "extra": { 12 | "copy_test_source_files": true, 13 | "final": true 14 | }, 15 | "identifiers": [], 16 | "keywords": [], 17 | "root_pkgs": [ 18 | "conda-verify 3.4.2 py_1", 19 | "click 7.1.2 pyhd3eb1b0_0", 20 | "pip 10.0.1 py35_0", 21 | "conda 4.5.11 py35_0", 22 | "yaml 0.1.7 hc54c509_2", 23 | "idna 2.10 pyhd3eb1b0_0", 24 | "libiconv 1.15 h1df5818_7", 25 | "conda-package-handling 1.0.4 py_0", 26 | "lzo 2.10 he774522_2", 27 | "lz4-c 1.8.1.2 h2fa13f4_0", 28 | "zstd 1.3.3 hfe6a214_0", 29 | "psutil 5.4.7 py35hfa6e2cd_0", 30 | "markupsafe 1.0 py35hfa6e2cd_1", 31 | "vc 14.2 h21ff451_1", 32 | "libxml2 2.9.10 hb89e7f3_3", 33 | "ruamel_yaml 0.15.46 py35hfa6e2cd_0", 34 | "filelock 3.0.12 pyhd3eb1b0_1", 35 | "pywin32 223 py35hfa6e2cd_1", 36 | "menuinst 1.4.14 py35hfa6e2cd_0", 37 | "urllib3 1.23 py35_0", 38 | "python-libarchive-c 2.9 pyhd3eb1b0_0", 39 | "xz 5.2.5 h62dcd97_0", 40 | "backports 1.0 pyhd3eb1b0_2", 41 | "beautifulsoup4 4.6.3 py35_0", 42 | "setuptools 40.2.0 py35_0", 43 | "backports.functools_lru_cache 1.6.1 pyhd3eb1b0_0", 44 | "pyyaml 3.13 py35hfa6e2cd_0", 45 | "conda-build 3.15.1 py35_0", 46 | "pyopenssl 18.0.0 py35_0", 47 | "conda-env 2.6.0 1", 48 | "pytz 2021.1 pyhd3eb1b0_0", 49 | "pysocks 1.6.8 py35_0", 50 | "vs2015_runtime 14.27.29016 h5e58377_2", 51 | "wheel 0.36.2 pyhd3eb1b0_0", 52 | "asn1crypto 1.4.0 py_0", 53 | "wincertstore 0.2 py35hfebbdb8_0", 54 | "jinja2 2.11.3 pyhd3eb1b0_0", 55 | "ca-certificates 2021.1.19 haa95532_0", 56 | "backports.tempfile 1.0 pyhd3eb1b0_1", 57 | "chardet 3.0.4 py35_1", 58 | "future 0.16.0 py35_2", 59 | "cryptography 2.3.1 py35h74b6da3_0", 60 | "glob2 0.7 pyhd3eb1b0_0", 61 | "requests 2.25.1 pyhd3eb1b0_0", 62 | "backports.weakref 1.0.post1 py_1", 63 | "win_inet_pton 1.0.1 py35_1", 64 | "six 1.15.0 pyhd3eb1b0_0", 65 | "zlib 1.2.11 h62dcd97_4", 66 | "certifi 2020.6.20 pyhd3eb1b0_3", 67 | "openssl 1.0.2u he774522_0", 68 | "python 3.5.6 he025d50_0", 69 | "pkginfo 1.4.2 py35_1", 70 | "pycosat 0.6.3 py35hfa6e2cd_0", 71 | "cffi 1.11.5 py35h74b6da3_1", 72 | "libarchive 3.3.3 h798a506_1", 73 | "pycparser 2.20 py_2", 74 | "tqdm 4.56.0 pyhd3eb1b0_0", 75 | "bzip2 1.0.8 he774522_0" 76 | ], 77 | "tags": [] 78 | } -------------------------------------------------------------------------------- /conda/noarch/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | conda/noarch 4 | 44 | 45 | 46 |

conda/noarch

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FilenameSizeLast ModifiedSHA256MD5
repodata.json127 B2020-11-27 07:39:09 +0000b546412dc20b790c5f9c223e394ff0e39a27ba12a99631ca4d1bb6c0ca3bd05c917338f97423c09c911618661fef3056
repodata.json.bz2126 B2020-11-27 07:39:09 +000032d48c11b6d5ee61a54a9076f8c06a62ba795f69b671db3e44b1301fa0efae0e1ecb5b301b9a2ba831f0a68a7ace5f00
repodata_from_packages.json127 B2020-11-27 07:39:09 +0000b546412dc20b790c5f9c223e394ff0e39a27ba12a99631ca4d1bb6c0ca3bd05c917338f97423c09c911618661fef3056
repodata_from_packages.json.bz2126 B2020-11-27 07:39:09 +000032d48c11b6d5ee61a54a9076f8c06a62ba795f69b671db3e44b1301fa0efae0e1ecb5b301b9a2ba831f0a68a7ace5f00
80 |
Updated: 2021-03-04 16:13:07 +0000 - Files: 0
81 | 82 | -------------------------------------------------------------------------------- /conda/linux-64/.cache/about/xentropy-1.8-py35h6bb024c_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | { 2 | "channels": [ 3 | "defaults" 4 | ], 5 | "conda_build_version": "3.15.1", 6 | "conda_private": false, 7 | "conda_version": "4.5.11", 8 | "env_vars": { 9 | "CIO_TEST": "" 10 | }, 11 | "extra": { 12 | "copy_test_source_files": true, 13 | "final": true 14 | }, 15 | "identifiers": [], 16 | "keywords": [], 17 | "root_pkgs": [ 18 | "icu 58.2 he6710b0_3", 19 | "zstd 1.3.3 h84994c4_0", 20 | "libedit 3.1.20191231 h14c3975_1", 21 | "conda-build 3.15.1 py35_0", 22 | "openssl 1.0.2u h7b6447c_0", 23 | "cryptography 2.3.1 py35hc365091_0", 24 | "python 3.5.6 hc3d631a_0", 25 | "xz 5.2.5 h7b6447c_0", 26 | "backports.weakref 1.0.post1 py_1", 27 | "filelock 3.0.12 pyhd3eb1b0_1", 28 | "libarchive 3.3.3 h7d0bbab_1", 29 | "libxml2 2.9.10 hb55368b_3", 30 | "pytz 2021.1 pyhd3eb1b0_0", 31 | "pycparser 2.20 py_2", 32 | "libstdcxx-ng 9.1.0 hdf63c60_0", 33 | "wheel 0.36.2 pyhd3eb1b0_0", 34 | "chardet 3.0.4 py35_1", 35 | "idna 2.10 pyhd3eb1b0_0", 36 | "requests 2.25.1 pyhd3eb1b0_0", 37 | "six 1.15.0 pyhd3eb1b0_0", 38 | "cffi 1.11.5 py35he75722e_1", 39 | "setuptools 40.2.0 py35_0", 40 | "backports 1.0 pyhd3eb1b0_2", 41 | "click 7.1.2 pyhd3eb1b0_0", 42 | "future 0.16.0 py35_2", 43 | "pyyaml 3.13 py35h14c3975_0", 44 | "psutil 5.4.7 py35h14c3975_0", 45 | "lzo 2.10 h7b6447c_2", 46 | "lz4-c 1.8.1.2 h14c3975_0", 47 | "ncurses 6.2 he6710b0_1", 48 | "yaml 0.1.7 had09818_2", 49 | "pyopenssl 18.0.0 py35_0", 50 | "patchelf 0.12 h2531618_1", 51 | "markupsafe 1.0 py35h14c3975_1", 52 | "conda-verify 3.4.2 py_1", 53 | "ca-certificates 2021.1.19 h06a4308_0", 54 | "libffi 3.2.1 hf484d3e_1007", 55 | "beautifulsoup4 4.6.3 py35_0", 56 | "python-libarchive-c 2.9 pyhd3eb1b0_0", 57 | "glob2 0.7 pyhd3eb1b0_0", 58 | "pip 10.0.1 py35_0", 59 | "sqlite 3.33.0 h62c20be_0", 60 | "bzip2 1.0.8 h7b6447c_0", 61 | "ruamel_yaml 0.15.46 py35h14c3975_0", 62 | "pycosat 0.6.3 py35h14c3975_0", 63 | "pkginfo 1.4.2 py35_1", 64 | "jinja2 2.11.3 pyhd3eb1b0_0", 65 | "conda-package-handling 1.0.4 py_0", 66 | "certifi 2020.6.20 pyhd3eb1b0_3", 67 | "readline 7.0 h7b6447c_5", 68 | "urllib3 1.23 py35_0", 69 | "zlib 1.2.11 h7b6447c_3", 70 | "tqdm 4.56.0 pyhd3eb1b0_0", 71 | "tk 8.6.10 hbc83047_0", 72 | "_libgcc_mutex 0.1 main", 73 | "backports.tempfile 1.0 pyhd3eb1b0_1", 74 | "backports.functools_lru_cache 1.6.1 pyhd3eb1b0_0", 75 | "libgcc-ng 9.1.0 hdf63c60_0", 76 | "conda 4.5.11 py35_0", 77 | "asn1crypto 1.4.0 py_0", 78 | "conda-env 2.6.0 1", 79 | "pysocks 1.6.8 py35_0" 80 | ], 81 | "tags": [] 82 | } -------------------------------------------------------------------------------- /package/xentropy/reweighting.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | 4 | # Boltzmann constant in kCal/(mol*K): 5 | from .internal.constants import id_gas_SI, id_gas_kcal 6 | 7 | 8 | def maclaurin_series(xs, mac_order=10): 9 | """Maclaurin series to estimate e^{x}. 10 | 11 | Parameters 12 | ---------- 13 | xs: array of floats 14 | mac_order: int 15 | max order for the Maclaurin series 16 | 17 | Returns 18 | ------- 19 | 20 | """ 21 | # going to the nth order includes [0,n]!! 22 | return np.sum([xs ** i / np.math.factorial(i) for i in np.arange(mac_order+1)], axis=0) 23 | 24 | 25 | def calculate_amd_weight_maclau(boost_ene, mac_order=10, T=300.0, id_gas=id_gas_SI): 26 | """Calculate weights from aMD simulation boost energies for histogram 27 | reweighing using Maclaurin series expansion (see https://doi.org/10.1021/ct500090q). 28 | 29 | Paramteres: 30 | ----------------------------- 31 | boostEne: list(list(float)) 32 | Boost energies from the aMD simulation 33 | mac_order: int, optional 34 | The order of the Maclaurin series (default is 10) 35 | T: float, optional 36 | The simulation temperature, used to calculate the Boltzmann factor from the energies (default is 300.0) 37 | id_gas: float, optional 38 | ideal gas constant. Default: 8.314 J/(mol*K) 39 | 40 | Returns 41 | ----------------------------- 42 | Weights for the histogram calculation 43 | """ 44 | 45 | scaled_biasE = np.array(boost_ene) / (T * id_gas) 46 | # MCweight = np.zeroslike(boostEne) 47 | # for x in range(mc_order+1): 48 | # MCweight = np.add(MCweight, (np.divide(np.power(boostEne, x), math.factorial(x)))) 49 | # return MCweight 50 | non_norm_weights = maclaurin_series(scaled_biasE, mac_order=mac_order) 51 | return non_norm_weights/np.sum(non_norm_weights) 52 | 53 | 54 | def calculate_amd_weight_exp(boost_ene, T=300.0, id_gas=id_gas_kcal): 55 | """Calculate weights from aMD simulation boost energies for histogram 56 | reweighing using exponential function. 57 | 58 | Paramteres: 59 | ----------------------------- 60 | boostEne: list(list(float)) 61 | Boost energies from the aMD simulation 62 | 63 | T: float, optional 64 | The simulation temperature, used to calculate the Boltzmann factor from the energies (default is 300.0) 65 | kb: float, optional 66 | Boltzmann constant. Default: 0.001987204118 kCal/(mol*K) 67 | 68 | Returns 69 | ----------------------------- 70 | Weights for the histogram calculation 71 | """ 72 | 73 | scaled_biasE = np.array(boost_ene) / (T * id_gas) 74 | 75 | non_norm_weights = np.exp(scaled_biasE) 76 | return non_norm_weights/np.sum(non_norm_weights) 77 | -------------------------------------------------------------------------------- /conda/osx-64/.cache/about/xentropy-1.8-py38h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | { 2 | "channels": [ 3 | "https://repo.anaconda.com/pkgs/main" 4 | ], 5 | "conda_build_version": "3.21.4", 6 | "conda_private": false, 7 | "conda_version": "4.9.2", 8 | "env_vars": { 9 | "CIO_TEST": "" 10 | }, 11 | "extra": { 12 | "copy_test_source_files": true, 13 | "final": true 14 | }, 15 | "identifiers": [], 16 | "keywords": [], 17 | "root_pkgs": [ 18 | "brotlipy 0.7.0 py38h9ed2024_1003", 19 | "readline 8.1 h9ed2024_0", 20 | "pkginfo 1.7.0 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-------------------------------------------------------------------------------- /conda/osx-64/.cache/about/xentropy-1.8-py37h41f2d22_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | { 2 | "channels": [ 3 | "https://repo.anaconda.com/pkgs/main" 4 | ], 5 | "conda_build_version": "3.21.4", 6 | "conda_private": false, 7 | "conda_version": "4.9.2", 8 | "env_vars": { 9 | "CIO_TEST": "" 10 | }, 11 | "extra": { 12 | "copy_test_source_files": true, 13 | "final": true 14 | }, 15 | "identifiers": [], 16 | "keywords": [], 17 | "root_pkgs": [ 18 | "readline 8.1 h9ed2024_0", 19 | "conda 4.9.2 py37hecd8cb5_0", 20 | "pyopenssl 20.0.1 pyhd3eb1b0_1", 21 | "zlib 1.2.11 h1de35cc_3", 22 | "libcxx 10.0.0 1", 23 | "six 1.15.0 py37hecd8cb5_0", 24 | "pytz 2021.1 pyhd3eb1b0_0", 25 | "pkginfo 1.7.0 py37hecd8cb5_0", 26 | "backports 1.0 pyhd3eb1b0_2", 27 | "setuptools 52.0.0 py37hecd8cb5_0", 28 | "tk 8.6.10 hb0a8c7a_0", 29 | "pysocks 1.7.1 py37hecd8cb5_0", 30 | 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-------------------------------------------------------------------------------- 1 | #pragma once 2 | 3 | #include 4 | #include 5 | #include 6 | 7 | #include 8 | 9 | #include "Exceptions.h" 10 | 11 | class IIntegration 12 | { 13 | public: 14 | virtual ~IIntegration() {} 15 | virtual double operator()(const std::vector &function, double range) const noexcept = 0; 16 | }; 17 | 18 | class Simpson : public IIntegration 19 | { 20 | public: 21 | Simpson() {} 22 | /***** 23 | * @brief Uses the composite Simpson's Rule. 24 | * This function implements the Simpson's rule of numerical integration. 25 | * In the following is the formula, it is split into different lines in 26 | * the source code, which represent the different summands. The comments 27 | * in the code (stated line 1 to 4), are to be understood in the sense 28 | * of these summands, where the first line is the first summand. 29 | * @f{eqnarray} 30 | * \int_{a}^{b}f(x)dx \approx \frac{h}{3}\left[ 31 | * f(x_0) + 32 | * 2 \Sum_{j = 1}^{n/2 - 1} f(x_{2j} + 33 | * 4 \Sum_{j = 1}^{n / 2} f(x_{2j - 1} + 34 | * f(x_n) 35 | * \right] 36 | * @f} 37 | * 38 | * @param function The values of the function for which the area should be 39 | * calcualted. 40 | * @param range The range for the integration, i.e., a - b. 41 | * 42 | * @returns The integral for the function, assuming range as a - b. 43 | */ 44 | virtual double operator()(const std::vector &function, double range) const noexcept override; 45 | }; 46 | 47 | class Riemann : public IIntegration 48 | { 49 | public: 50 | Riemann(){}; 51 | /***** 52 | * @brief Uses the Riemann type integral. 53 | * This function implements the Riemann type integral, which uses rectangles 54 | * to approximate the area under the curve of a dataset. 55 | * 56 | * @param function The values of the function for which the area should be 57 | * calcualted. 58 | * @param range The range for the integration, i.e., a - b. 59 | * 60 | * @returns The integral for the function, assuming range as a - b. 61 | */ 62 | virtual double operator()(const std::vector &function, double range) const noexcept override; 63 | }; 64 | 65 | /**** 66 | * @brief Returns the integration functor. 67 | * Get the function, so the numerical integration scheme associated with the type string. 68 | * If the chosen function does not exist, throws an error, hinting the user towards that 69 | * problem. 70 | * @param type The type of the numerical integration scheme, i.e., Simpson or Riemann. 71 | * @return An object that implements the IIntegration interface. 72 | * @throw An UnknownIntegral exception, if the chosen integral does not exist (or is) 73 | * not implemented. 74 | * 75 | * @see Simpson 76 | * @see Riemann 77 | */ 78 | std::unique_ptr 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-------------------------------------------------------------------------------- 1 | # X-Entropy 2 | 3 | ![C++:CMake](https://github.com/jokr91/dihedral_entropy/workflows/C++:CMake/badge.svg) ![Python:Pip](https://github.com/jokr91/dihedral_entropy/workflows/Python:Pip/badge.svg) 4 | 5 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 6 | 7 | This library is primarily meant to calculate dihedral entropies from MD 8 | simulation data. For this, we use a KDE with automatic bandwidth selection as 9 | suggested by Z. Botev et al. We tried to keep the package as generalized as 10 | possible, therefore, the package can be used to calculate the entropy of any 11 | data, or also to simply calculate the KDE. 12 | 13 | ## Installation 14 | 15 | ### Using Conda 16 | 17 | This is probably the easiest way to install this package. After cloning this 18 | repository (or downloading the conda directory directly), you can easily install 19 | the package using conda. Be aware that in this case you need Python 3.8 installed 20 | for Linux and MacOS and Python 3.7 for Windows. 21 | ```bash 22 | conda install -c ${ABSOLUTE_PATH_TO_XENTROPY_REPOSITORY}/conda xentropy 23 | ``` 24 | This should work for Windows 10, Linux, MacOS. Be aware, that for Windows you 25 | need to specify the full path to the conda directory. If it for some reason does 26 | not work, you might have to build the conda installer yourself. If you want to 27 | do that, you need to install conda-build and conda-verify and then just build the 28 | installer in the xentropy root diretory with 29 | ```bash 30 | conda-build package 31 | ``` 32 | On MacOS you will need to follow the instructions on 33 | https://docs.conda.io/projects/conda-build/en/latest/resources/compiler-tools.html#macos-sdk 34 | before building the package. 35 | 36 | ### Building with pip 37 | 38 | Be aware that you are responsible for the dependencies, if you decide to build 39 | this way. 40 | 41 | Please ensure that you have a python version higher than 3.3 for this package to 42 | work properly. First change to the directory containing the entropy module. The 43 | package can then be installed via 44 | 45 | ```bash 46 | pip install . 47 | ``` 48 | 49 | ### Building with setup.py 50 | 51 | Be aware that you are responsible for the dependencies, if you decide to build 52 | this way. 53 | 54 | ```bash 55 | python setup.py build 56 | python setup.py install 57 | ``` 58 | 59 | ### Import 60 | 61 | After this, the package is callable by using 62 | 63 | ```python 64 | import xentropy 65 | ``` 66 | 67 | ### Dependencies 68 | 69 | - openmp 70 | - fftw3 71 | - numpy 72 | - Cython 73 | 74 | If installed using conda, these are added automatically. 75 | 76 | ## Usage 77 | 78 | For usage examples, please check the examples folder. 79 | 80 | ## Contributors 81 | 82 | - Johannes Kraml 83 | - Patrick Quoika 84 | - Florian Hofer 85 | 86 | ## Bugs 87 | 88 | As of now, there are no known bugs, if you find any, please contact me. 89 | 90 | johannes@kraml.dev 91 | 92 | ## License Info 93 | 94 | This module was parallelized using the openmp toolkit (license: 95 | ). Parts of the 96 | module use the [fftw3 library](). 97 | 98 | When using the code in a publication, please cite: 99 | [Johannes Kraml, Florian Hofer, Patrick K. Quoika, Anna S. Kamenik, and Klaus R. Liedl 100 | Journal of Chemical Information and Modeling 2021 61 (4), 1533-1538 101 | doi: 10.1021/acs.jcim.0c01375 ](https://pubs.acs.org/doi/10.1021/acs.jcim.0c01375) 102 | 103 | The KDE in this package is based on the publication by Z. Botev: 104 | [Kernel density estimation via diffusion 105 | Z. I. Botev, J. F. Grotowski, and D. P. Kroese (2010) 106 | Annals of Statistics, Volume 38, Number 5, pages 2916-2957 107 | doi:10.1214/10-AOS799](https://projecteuclid.org/journals/annals-of-statistics/volume-38/issue-5/Kernel-density-estimation-via-diffusion/10.1214/10-AOS799.full). 108 | -------------------------------------------------------------------------------- /conda/osx-64/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | conda/osx-64 4 | 44 | 45 | 46 |

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xentropy-1.8-py37h2e52968_0.tar.bz264 KB2021-03-04 12:14:40 +00001c12be32b95c301432407ba1391464bb27c5198a76e684be34d2586dfb481f617c918f330a8fe41323bf2d160c0765e5
xentropy-1.8-py38h2e52968_0.tar.bz264 KB2021-03-04 12:50:39 +0000716c43d4b29c6d0551482f98c5fea41e9eaa5936bdde6143caae1193f22938c7955493f3f6e8a542bf70587bbb671a4a
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xentropy-1.8-py36h2bc3f7f_0.tar.bz268 KB2021-03-04 15:56:21 +000059f0f0167744151f67d985e339a6fb790f6dafcfec42abd8500338f5c9ea325830f0663f7fccc84e969b4feec8168faf
xentropy-1.8-py37h2bc3f7f_0.tar.bz268 KB2021-03-04 16:00:20 +000042bc7a74073484e906d3e66792e2283ab8b144588092675c00ce8075c4358f931631f2e54b7eaf8a6611fd329146ba64
xentropy-1.8-py38h2bc3f7f_0.tar.bz269 KB2021-03-04 16:02:42 +0000bce94f47f840f5b40b71f713ab12eef596b6096fe197c68075140a78ed2e8a7bc29acaacef1add7743f65be12977ed78
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Updated: 2021-03-04 16:13:07 +0000 - Files: 4
105 | 106 | -------------------------------------------------------------------------------- /package/xentropy/internal/pre_post_processing.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | import warnings 3 | 4 | from .constants import PI, TWOPI 5 | 6 | 7 | def reshape_arrays_eventually(some_array): 8 | # some_array = np.array(some_array) # this is implicitly done below 9 | out = np.squeeze(some_array) 10 | if out.shape == (): # special case: single number 11 | out = out[()] # weird way of accessing a squeezed single number for np arrays 12 | return out 13 | 14 | 15 | def start_end_from_grid(grid): 16 | # You will need this for non dihedrals... 17 | return np.nanmin(grid), np.nanmax(grid) 18 | 19 | 20 | def process_weights_argument(weights, verbose=False): 21 | """This function will mainly return a switch, whether 22 | or whether not weights will be passed on to the kde""" 23 | weight_switch = True 24 | if weights is None: 25 | if verbose: 26 | print("No weights been given.") 27 | weight_switch = False 28 | else: 29 | if verbose: 30 | print("Weights have been given.") 31 | weights = np.array(weights) 32 | if True in (weights < 0): 33 | err_msg = "You gave negative values in you weights array.\n" 34 | raise ValueError(err_msg) 35 | 36 | return weights, weight_switch 37 | 38 | 39 | def deg_to_rad(dat_deg): 40 | return dat_deg/180*PI 41 | 42 | 43 | def rad_to_deg(dat_rad): 44 | return dat_rad*180/PI 45 | 46 | 47 | def sanity_check_dihedral_units(diheds, input_unit="radian"): 48 | if input_unit == "radian": 49 | return diheds 50 | elif input_unit == "degree": 51 | diheds = deg_to_rad(diheds) 52 | else: 53 | if not input_unit == "auto": 54 | print("Unkown dihedral unit: {}. We expect either 'degree', 'radian' or 'auto." 55 | "Falling back to 'auto'") 56 | if np.max(diheds) > TWOPI or np.min(diheds) < -TWOPI: 57 | warn_msg = "X-Entropy detected dihedral values that are much larger than pi. " \ 58 | "Your values are assumed to be in degree. " \ 59 | "X-Entropy expects dihedrals in radian and will transform the unit of your input. " \ 60 | "If you do not want this transformation, " \ 61 | "explicitly specify your input unit with the 'input_unit'-argument" 62 | warnings.warn(warn_msg, RuntimeWarning) 63 | diheds = deg_to_rad(diheds) 64 | return diheds 65 | 66 | 67 | def preprocess_dihedral_data(diheds, weights, weight_switch, input_unit="auto"): 68 | # mirror data 69 | # # <- for multiple data sets <- # # 70 | # diheds_out = [] 71 | # for dihed in diheds: 72 | # diheds_out.append(np.concatenate([dihed-360, dihed, dihed+360])) 73 | # diheds = np.array(diheds_out) 74 | # if weight_switch: # 75 | # weights_out = [] 76 | # for weight in weights: 77 | # weights_out.append(np.concatenate([weight, weight, weight])) 78 | # weights = np.array(weights_out) 79 | diheds = sanity_check_dihedral_units(diheds, input_unit=input_unit) 80 | diheds = np.concatenate([diheds - TWOPI, diheds, diheds + TWOPI]) 81 | if weight_switch: # 82 | weights = np.concatenate([weights, weights, weights]) 83 | 84 | return diheds, weights 85 | 86 | 87 | def postprocess_dihedral_pdf(pdf, pdf_x, norm_for_mirrored_data=1/3): 88 | # mirror data 89 | lower_idx = np.argmin(np.abs(pdf_x + PI)) 90 | if pdf_x[lower_idx] < -PI: 91 | lower_idx = lower_idx + 1 92 | upper_idx = np.argmin(np.abs(pdf_x - PI)) 93 | if pdf_x[upper_idx] > PI: 94 | upper_idx = upper_idx - 1 95 | 96 | pdf_out = pdf[lower_idx:upper_idx] 97 | pdf_out /= norm_for_mirrored_data # we normalized in the "mirrored data", which is 3 times the actual data 98 | pdf_x_out = pdf_x[lower_idx:upper_idx] 99 | assert len(pdf_out) == len(pdf_x_out) 100 | return pdf_out, pdf_x_out 101 | 102 | 103 | def sanity_check_input_data(data, weights=None, weight_switch=True): 104 | if False in np.isfinite(data): 105 | err_msg = "Non-finite values in data!" 106 | raise ValueError(err_msg) 107 | 108 | if weight_switch: 109 | weights = np.array(weights) 110 | if False in np.isfinite(weights): 111 | err_msg = "Non-finite values in weights!" 112 | raise ValueError(err_msg) 113 | if data.shape[-1] != weights.shape[-1]: 114 | err_msg = "Shapes of data and weights is inconsistent!\n" \ 115 | "data: {}, weights: {}".format(data.shape, weights.shape) 116 | raise ValueError(err_msg) 117 | 118 | 119 | def process_data_shapes(data, weights=None, weight_switch=True): 120 | data = np.array(data) 121 | 122 | if len(data.shape) == 0: 123 | err_msg = "Shape of data is suspicious\n" \ 124 | "{}".format(data.shape) 125 | raise ValueError(err_msg) 126 | elif len(data.shape) == 1: 127 | data = np.array([data]) 128 | if weight_switch: 129 | weights = np.array(weights) 130 | # this is as good as pass... 131 | elif len(data.shape) == 2: 132 | if data.shape[0] > data.shape[1]: 133 | warn_msg = "Your input data has suspicious shape. First dim is expected to be smaller than second dim. " \ 134 | "Data should have shape: (N_features, N_frames)." 135 | warnings.warn(warn_msg, RuntimeWarning) # potentially transposed data 136 | pass # This is all good 137 | else: # This 3 dim or higher... 138 | err_msg = "Shape of data is suspicious\n" \ 139 | "{}".format(data.shape) 140 | raise ValueError(err_msg) 141 | return data, weights 142 | 143 | 144 | def process_method_argument(method, available_methods=("simpson", "riemann")): 145 | """Catches unknown integration methods on a python 146 | level. Deals with upper and lower case writing. 147 | 148 | """ 149 | if not (method.lower() in available_methods): 150 | err_msg = "{} is not a valid integration method\nChoose from {}.".format(method, available_methods) 151 | raise ValueError(err_msg) 152 | return method.lower().capitalize() # first letter caps, rest lower case 153 | -------------------------------------------------------------------------------- /package/xentropy/kde.py: -------------------------------------------------------------------------------- 1 | """ 2 | part of the entroPy module 3 | 4 | @author: paq 5 | """ 6 | import numpy as np 7 | from xentropy.kde_kernel import _kde_kernel 8 | from .internal.resolution import process_resolution_argument 9 | from .internal.pre_post_processing import sanity_check_input_data, process_weights_argument, reshape_arrays_eventually 10 | 11 | def check_dims(data): 12 | data = np.squeeze(data) 13 | if len(np.shape(data))>1: 14 | err_msg = "Kde is designed for single data sets only!\n" \ 15 | "You provided data of the following shape\n" \ 16 | "data: {}".format(data.shape) 17 | raise ValueError(err_msg) 18 | 19 | class Kde(object): 20 | 21 | def __init__(self, data, weights=None, resolution="auto", verbose=False): 22 | # to be set after calculation 23 | self.__is_finished = False 24 | self.__pdf = None 25 | self.__pdf_x = None 26 | self.__bandwidth = None 27 | # input 28 | # TODO preprocess and sanitycheck data here, too (code in dihedrals.py should be applicable) 29 | weights, weight_switch = process_weights_argument(weights, verbose=verbose) 30 | sanity_check_input_data(data, weights, weight_switch) 31 | self.__has_weights = weight_switch 32 | check_dims(data) # kde can only take single data sets currently 33 | self.__data = data 34 | self.__weights = weights 35 | self.__verbose = verbose 36 | self.__resolution = process_resolution_argument(resolution, self.data, verbose=self.verbose) 37 | 38 | def calculate(self, resolution=None, verbose=None): 39 | """Perform the kde 40 | 41 | Parameters 42 | ---------- 43 | verbose: bool 44 | You may overwrite the kde.verbose property for this function specifically 45 | resolution: int 46 | You may specifically reset the resolution for the kde calculation. 47 | 48 | Returns 49 | ------- 50 | 51 | """ 52 | if not (verbose is None): # it is possible to overwrite kde.verbose for this subroutine 53 | verbose = verbose 54 | else: 55 | verbose = self.verbose 56 | if not (resolution is None): 57 | new_res = process_resolution_argument(resolution, self.data, verbose=verbose) 58 | if verbose: 59 | print("Using resolution of {}".format(new_res)) 60 | self.__resolution = new_res 61 | 62 | if verbose: 63 | print("Initializing C++ kernel for kde...") 64 | if self.has_weights: 65 | # print(self.weights) # DEBUG 66 | kernel = _kde_kernel(self.data, self.resolution, self.weights) 67 | else: 68 | kernel = _kde_kernel(self.data, self.resolution) 69 | kernel.calculate() 70 | # print(kernel.get_pdf()) # DEBUG 71 | self.__is_finished = True 72 | if verbose: 73 | print("KDE finished.") 74 | self.__pdf_x = reshape_arrays_eventually(kernel.get_grid()) 75 | self.__bandwidth = kernel.get_bandwidth() 76 | self.__pdf = reshape_arrays_eventually(kernel.get_pdf()) 77 | 78 | @property 79 | def resolution(self): 80 | """Resolution for kde. Needs to be a power of 2. If no power of two is given, 81 | the next higher power of two is set automatically.""" 82 | return self.__resolution 83 | 84 | @resolution.setter 85 | def resolution(self, value): 86 | if self.is_finished: 87 | print("After changing the resolution you should use .calculate() again, before accessing any results...\n" 88 | "It is probably better to explicitly call calculate with a specific resolution.") 89 | self.__resolution = process_resolution_argument(value, self.data) 90 | 91 | @property 92 | def verbose(self): 93 | """Extend of print messages. """ 94 | return self.__verbose 95 | 96 | @verbose.setter 97 | def verbose(self, value): 98 | print("Verbosity cannot be changed after initialization...") 99 | pass 100 | 101 | @property 102 | def is_finished(self): 103 | """Is the kde finished? """ 104 | return self.__is_finished 105 | 106 | @is_finished.setter 107 | def is_finished(self, value): 108 | print("You really shouldn't change this flag yourself. Use .calculate()") 109 | pass 110 | 111 | @property 112 | def has_weights(self): 113 | """Have weights been given?""" 114 | return self.__has_weights 115 | 116 | @has_weights.setter 117 | def has_weights(self, value): 118 | print("This flag cannot be changed after initialization.") 119 | pass 120 | 121 | @property 122 | def bandwidth(self): 123 | """Bandwidth from the kde. """ 124 | if not self.is_finished: 125 | self.calculate() 126 | return self.__bandwidth 127 | 128 | @bandwidth.setter 129 | def bandwidth(self, value): 130 | print("You really shouldn't change this property yourself. Use .calculate()") 131 | pass 132 | 133 | @property 134 | def pdf(self): 135 | """Probability density function. """ 136 | if not self.is_finished: 137 | self.calculate() 138 | return self.__pdf 139 | 140 | @pdf.setter 141 | def pdf(self, value): 142 | print("You really shouldn't change .pdf yourself. Use .calculate()") 143 | pass 144 | 145 | @property 146 | def data(self): 147 | """"Data to do kde on. """ 148 | return self.__data 149 | 150 | @data.setter 151 | def data(self, value): 152 | print("Data cannot be changed after initialization...") 153 | pass 154 | 155 | @property 156 | def weights(self): 157 | """Weights for the pdf calculation. """ 158 | return self.__weights 159 | 160 | @weights.setter 161 | def weights(self, value): 162 | print("Weights cannot be changed after initialization...") 163 | pass 164 | 165 | @property 166 | def pdf_x(self): 167 | """Grid for probability density function. """ 168 | if not self.is_finished: 169 | self.calculate() 170 | return self.__pdf_x 171 | 172 | @pdf_x.setter 173 | def pdf_x(self, value): 174 | print("You really shouldn't change .pdf_x yourself. Use .calculate()") 175 | pass 176 | -------------------------------------------------------------------------------- /package/xentropy/internal/resolution.py: -------------------------------------------------------------------------------- 1 | import warnings 2 | import numpy as np 3 | from xentropy.internal.pre_post_processing import start_end_from_grid 4 | 5 | 6 | def is_power_of_two(val): 7 | """This function will evaluate whether $val is 8 | a power of two between 1 and 524288 or not. 9 | Higher powers are not tested here. 10 | 11 | Parameters 12 | ---------- 13 | val : numeric 14 | 15 | Returns 16 | ------- 17 | bool 18 | 19 | """ 20 | 21 | pows_of_two = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 22 | 4096, 8192, 16384, 32768, 65536, 131072, 262144, 524288] 23 | # val = int(val) # this will cut off decimals, therefore example: int(16.6) in pows_of_two -> True 24 | return val in pows_of_two 25 | 26 | 27 | def next_power_of_two(val): 28 | """Returns the next higher power of two. 29 | 30 | Parameters 31 | ---------- 32 | val : numeric 33 | 34 | Returns 35 | ------- 36 | pow_of_two : int 37 | 38 | """ 39 | return int(2**(np.log(val) // np.log(2) + 1)) 40 | 41 | 42 | def is_integer(resolution): 43 | return isinstance(resolution, int) or isinstance(resolution, np.int64) or \ 44 | isinstance(resolution, np.int) or isinstance(resolution, np.int32) 45 | 46 | 47 | def is_float(resolution): 48 | return isinstance(resolution, float) or isinstance(resolution, np.float64) or \ 49 | isinstance(resolution, np.float) or isinstance(resolution, np.float32) 50 | 51 | 52 | def auto_dihedral_resolution(data): 53 | data_size = len(data) 54 | step = (np.sqrt(data_size*6)//64+1)*256 55 | if not is_power_of_two(step): 56 | step = next_power_of_two(step) 57 | return np.min([int(step), 4096]) 58 | 59 | 60 | def minim_or_sqrt(data, minim=32): 61 | return np.max([minim, square_root_choice(data)]) 62 | 63 | 64 | def interquartiles(data): 65 | data = np.sort(data) 66 | data_quarts = np.array_split(data, 4) 67 | return data_quarts[1][0], data_quarts[-2][-1] 68 | 69 | 70 | def scott(data): 71 | bin_edges = np.histogram_bin_edges(data, bins="scott") 72 | return len(bin_edges) - 1 73 | 74 | 75 | def freedman_diaconis(data): 76 | bin_edges = np.histogram_bin_edges(data, bins="fd") 77 | return len(bin_edges)-1 78 | 79 | 80 | def square_root_choice(data): 81 | return int(np.ceil(np.sqrt(len(data)))) 82 | 83 | 84 | def sturges(data): 85 | bin_edges = np.histogram_bin_edges(data, bins="sturges") 86 | return len(bin_edges)-1 87 | 88 | 89 | def doane(data): 90 | bin_edges = np.histogram_bin_edges(data, bins="doane") 91 | return len(bin_edges)-1 92 | 93 | 94 | # below we have legacy functions, which we do not use 95 | def silverman(data): 96 | """This is a legacy function""" 97 | n_dat = len(data) 98 | iqr = np.diff(interquartiles(data))[0] 99 | either_or = np.min([np.std(data), iqr / 1.34]) 100 | return 0.9 * either_or * n_dat ** (-1 / 5) 101 | 102 | 103 | def res_from_silverman(data): 104 | """This is a legacy function""" 105 | start, end = start_end_from_grid(data) 106 | data_range = end-start 107 | predicted_bandw = silverman(data) 108 | return data_range / predicted_bandw 109 | 110 | 111 | def rules_of_thumb(): 112 | return {"auto": minim_or_sqrt, 113 | "freedman_diaconis": freedman_diaconis, "fd": freedman_diaconis, 114 | "sturges": sturges, 115 | "doane": doane, 116 | "sqrt": square_root_choice, 117 | "scott": scott} 118 | 119 | 120 | def rules_of_thumb_dihedral(): 121 | return {"auto": auto_dihedral_resolution, 122 | "freedman_diaconis": freedman_diaconis, "fd": freedman_diaconis, 123 | "sturges": sturges, 124 | "doane": doane, 125 | "sqrt": square_root_choice, 126 | "scott": scott} 127 | 128 | 129 | def resolution_from_rule_of_thumb(resolution, data, verbose=False, rules_of_thumb=rules_of_thumb()): 130 | resolution = resolution.lower() 131 | resolution = resolution.replace(" ", "_") 132 | 133 | if not (resolution in list(rules_of_thumb.keys())): 134 | err_msg = "Cannot interpret given argument for resolution. " \ 135 | "Give either an integer, or choose of the following:\n{}".format(rules_of_thumb.keys()) 136 | raise ValueError(err_msg) 137 | data = np.squeeze(data) # you need to do this, because otherwise, you will have issues with single data sets... 138 | squeezed_shape = data.shape 139 | if len(squeezed_shape) == 1: 140 | return rules_of_thumb[resolution](data) 141 | elif len(squeezed_shape) == 2: 142 | if verbose: 143 | print("Found multiple data sets. Applying rule of thumb on all, and take the maximum resolution estimated.") 144 | return np.max([rules_of_thumb[resolution](dat) for dat in data]) 145 | else: # bad paq. you really should handle this properly... 146 | print("Suspicious data shape...") 147 | return 4096 148 | 149 | 150 | def process_resolution_argument(resolution, data, rules_of_thumb=rules_of_thumb(), verbose=False): 151 | """Warns about potentially too high or too low 152 | values and picks the next higher power of two, 153 | if it was no power of two initially. 154 | 155 | Parameters 156 | ---------- 157 | resolution 158 | data 159 | 160 | Returns 161 | ------- 162 | 163 | """ 164 | if isinstance(resolution, str): 165 | resolution = resolution_from_rule_of_thumb(resolution, data, verbose=verbose, rules_of_thumb=rules_of_thumb) 166 | # will be checked for whether it is a power of two or not below 167 | elif is_integer(resolution): 168 | pass 169 | elif is_float(resolution): 170 | print("Resolution is not of type int. Trying to cast it to int...") 171 | resolution = int(resolution) 172 | else: 173 | err_msg = "Cannot interpret given argument for resolution:\n{}\n" \ 174 | "Please give either a single integer or a string.".format(resolution) 175 | raise ValueError(err_msg) 176 | if resolution < 100: 177 | warn_msg = "You are using a rather small resolution. " \ 178 | "This may potentially lead to inaccurate results..." 179 | warnings.warn(warn_msg, RuntimeWarning) 180 | elif resolution > 10000: 181 | warn_msg = "You are using a rather large resolution. " \ 182 | "Amongst other things, this may potentially lead to very long runtimes " \ 183 | "without necessarily improving the accuracy of the result..." 184 | warnings.warn(warn_msg, RuntimeWarning) 185 | 186 | if not is_power_of_two(resolution): 187 | resolution = next_power_of_two(resolution) 188 | return resolution 189 | -------------------------------------------------------------------------------- /conda/win-64/.cache/paths/xentropy-1.8-py35he980bc4_0.tar.bz2.json: -------------------------------------------------------------------------------- 1 | { 2 | "paths": [ 3 | { 4 | "_path": "Lib/site-packages/XEntropy-1.8-py3.5-win-amd64.egg-info/PKG-INFO", 5 | "path_type": "hardlink", 6 | "sha256": "e986271c6842106158fe2f2a70729e18e3d91b85ae672b443266a4b39ba2121d", 7 | "size_in_bytes": 278 8 | }, 9 | { 10 | "_path": "Lib/site-packages/XEntropy-1.8-py3.5-win-amd64.egg-info/SOURCES.txt", 11 | "path_type": "hardlink", 12 | "sha256": 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