An efficient alternative halo mass function distribution
Project description
A Python package for calculations with the MRP parameterisation of the Halo Mass Function.
See Murray, Robotham, Power 2016 (in prep.) for more details on what the MRP is.
Quick Start
Installation
Required packages are numpy, scipy, mpmath (for incomplete gamma functions), and cached_property. These should be automatically installed when installing mrpy.
To use the MCMC fitting features, emcee and pystan are needed. These are not installed automatically.
The simplest way to install is pip install mrpy. This should install the required dependencies automatically.
To get the bleeding edge, use pip install git+git://github.com/steven-murray/mrpy.git.
If for some reason pip is not an option, manually download the github repo and use python setup.py install.
Getting Started
There’s a lot of things that you can do with mrpy. What you require will depend on the problem at hand. We recommend looking at some of the examples, and the API itself for how to use the code.
Documentation
Features
With mrpy you can:
Calculate basic statistics of the truncated generalised gamma distribution (TGGD) with the TGGD class: mean, mode, variance, skewness, pdf, cdf, generate random variates etc.
Generate MRP quantities with the MRP class: differential number counts, cumulative number counts, various methods for generating normalisations.
Generate the MRP-based halo mass function as a function of physical parameters via the mrp_b13 function.
Fit MRP parameters to data in the form of arbitrary curves with the get_fit_curve function.
Fit MRP parameters to data in the form of a sample of variates with the PerObjFit class: simulation data is supported with extra efficiency, simulation suites fitted simultaneously is also supported, arbitrary priors on parameters, log-normal uncertainties on variates supported through a Stan-based routine.
Calculate analytic hessians, jacobians at any point (including the solution of a fit).
Use alternate parameterisations of the same form via the reparameterise module.
Work with a special entirely analytic model to understand the effects of various parameters in the analytic_model module.
Examples
There are several examples featured in the docs/examples directory of the github repository. These can also be found in the official documentation.
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