{ "info": { "author": "Steven Murray", "author_email": "steven.murray@curtin.edu.au", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Intended Audience :: Science/Research", "License :: OSI Approved :: MIT License", "Natural Language :: English", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Topic :: Scientific/Engineering :: Mathematics", "Topic :: Scientific/Engineering :: Physics" ], "description": "mrpy\n====\n.. image:: https://travis-ci.org/steven-murray/mrpy.png?branch=master\n\t\t:target: https://travis-ci.org/steven-murray/mrpy\n.. image:: https://coveralls.io/repos/steven-murray/mrpy/badge.svg?branch=master&service=github\n :target: https://coveralls.io/github/steven-murray/mrpy?branch=master\n.. image:: https://api.codacy.com/project/badge/Grade/e5d5d9b72d024bd09e24ea833745c6da\n :target: https://www.codacy.com/app/steven-murray/mrpy?utm_source=github.com&utm_medium=referral&utm_content=steven-murray/mrpy&utm_campaign=Badge_Grade\n\nA Python package for calculations with the MRP parameterisation of the Halo Mass Function.\n\nSee Murray, Robotham, Power 2016 (in prep.) for more details on what the MRP is.\n\n\nQuick Start\n-----------\n\nInstallation\n++++++++++++\nRequired packages are `numpy`, `scipy`, `mpmath` (for incomplete gamma functions),\nand `cached_property`.\nThese should be automatically installed when installing `mrpy`.\n\nTo use the MCMC fitting features, `emcee` and `pystan` are needed. These are *not*\ninstalled automatically.\n\nThe simplest way to install is ``pip install mrpy``. This should install the required\ndependencies automatically.\n\nTo get the bleeding edge, use ``pip install git+git://github.com/steven-murray/mrpy.git``.\n\nIf for some reason ``pip`` is not an option, manually download the github\nrepo and use ``python setup.py install``.\n\nGetting Started\n+++++++++++++++\nThere's a lot of things that you can do with `mrpy`. What you require will depend on the problem at hand. We recommend\nlooking at some of the examples, and the API itself for how to use the code.\n\nDocumentation\n+++++++++++++\n`Read the docs `_.\n\n\nFeatures\n--------\nWith `mrpy` you can:\n\n- Calculate basic statistics of the truncated generalised gamma distribution (TGGD) with the `TGGD` class: mean,\n mode, variance, skewness, pdf, cdf, generate random variates etc.\n- Generate MRP quantities with the `MRP` class: differential number counts, cumulative number counts, various methods\n for generating normalisations.\n- Generate the MRP-based halo mass function as a function of physical parameters via the `mrp_b13` function.\n- Fit MRP parameters to data in the form of arbitrary curves with the `get_fit_curve` function.\n- Fit MRP parameters to data in the form of a sample of variates with the `SimFit` class: simulation data is supported\n with extra efficiency, simulation suites fitted simultaneously is also supported, arbitrary priors on parameters,\n log-normal uncertainties on variates supported.\n- Calculate analytic hessians, jacobians at any point (including the solution of a fit).\n- Use alternate parameterisations of the same form via the `reparameterise` module.\n- Work with a special entirely analytic model to understand the effects of various parameters in the `analytic_model` module.\n\nExamples\n--------\nThere are several examples featured in the ``docs/examples`` directory of the github repository. 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