# mrpy¶

A Python package for calculations with the MRP parameterisation of the Halo Mass Function.

See Murray, Robotham, Power (2018) for more details on what the MRP is.

## Installation¶

>> pip install mrpy.

This should install the required dependencies automatically.

Note, to use the MCMC fitting features, emcee is needed. This is not installed automatically.

To get the bleeding edge, use pip install git+git://github.com/steven-murray/mrpy.git.

## 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.

### 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 SimFit 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.
• 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.

### Acknowledging¶

If you use this code in your work, please cite Murray, Robotham, Power (2018) and/or http://ascl.net/1802.015 (whichever is more appropriate). Also consider starring/following the repo on github so we know how much it is being used. We would also love any input to the code!