{ "info": { "author": "Robert Feldmann", "author_email": "RobertFeldmann@gmx.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Programming Language :: Python :: 3", "Topic :: Scientific/Engineering :: Mathematics" ], "description": "********\nLEO-Py\n********\n.. inclusion-marker-do-not-remove\n\nData with uncertain, missing, censored, and correlated values are commonplace\nin many research fields including astronomy. Unfortunately, such data are often\ntreated in an ad hoc way potentially resulting in inconsistent parameter\nestimates. Furthermore, in a realistic setting, the variables of interest or\ntheir errors may have non-normal distributions which complicates the modeling.\nLEO-Py uses a novel technique to compute the likelihood function for such data\nsets. This approach employs Gaussian copulas to decouple the correlation\nstructure of variables and their marginal distributions resulting in a flexible\nmethod to compute likelihood functions of data in the presence of measurement\nuncertainty, censoring, and missing data.\n\nIf you use any version of this code, please properly reference the code paper:\n*Feldmann, R. (2019) \"LEO-Py: Estimating likelihoods for correlated, censored,\nand uncertain data with given marginal distributions\", Astronomy & Computing*\n\nCopyright 2019 University of Zurich, Robert Feldmann\n\n----\n\nLEO-Py requires a working python3.5 installation or later to run.\n\nBefore installing LEO-Py, you may want to set up a virtual environment\n``python -mvenv /path/to/new/virtual/environment`` and activate it via\n``source /path/to/new/virtual/environment/bin/activate``.\n\nTo install LEO-Py from a repository:\n\n* Download the source code from .\n* Go to the package directory.\n* Run ``python setup.py install``.\n\nTo install LEO-Py via PyPI:\n\n* Run ``pip install leopy-stat``.\n* Note, example scripts and the documentation are not installed in this case.\n\nTo test the installation:\n\n* Run ``python setup.py test`` from the package directory (if installed from\n source).\n* Go to the 'site-packages' directory and run ``python -m pytest leopy``\n (if installed via PyPI).\n\nTo access the code documentation (if installed from source):\n\n* Run ``python setup.py build_html`` from the package directory.\n* Open ./build/sphinx/html/index.html to read the documentation.\n\nUsing leopy is very simple and consists of 4 steps:\n\n* Load the module (``import leopy``).\n* Create an observational data set (leopy.Observation).\n* Create a likelihood instance (leopy.Likelihood).\n* Call function p() of the likelihood instance.\n\nFor instance, a minimal example is::\n\n import pandas as pd\n from leopy import Observation, Likelihood\n d = {'v0': [1, 2], 'e_v0': [0.1, 0.2],\n 'v1': [3, 4], 'e_v1': [0.1, 0.1]}\n obs = Observation(pd.DataFrame(d), 'testdata')\n # Reading dataset 'testdata' and extracting 2 variables (['v0', 'v1'])\n # Errors of different observables are assumed to be uncorrelated\n l = Likelihood(obs, p_true='lognorm', p_cond='norm')\n l.p([0.5, 0.7], [1, 2], shape_true=[[1.4], [2.]])\n # array([[0.0441545],\n # [0.0108934]])\n\nFurther examples are provided in the 'paper' and 'examples' sub-directories\n(if installed from source).\n\n----\n\nLEO-Py is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\n\nLEO-Py is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\nGNU General Public License for more details.\n\nYou should have received a copy of the GNU General Public License\nalong with LEO-Py. 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