fitpdf 0.4.2

Distribution fitting tools.

FitPDF: Distribution fitting tools

This repository contains software to fit complex distribution models to observational data. This is useful for modelling pulse-energy distributions of radio pulsars or repeating fast radio bursts (FRBs). However, the software can fit any distribution data.

Author

The software is primarily developed and maintained by Fabian Jankowski. For more information, feel free to contact me via: fabian.jankowski at cnrs-orleans.fr.

Paper

The corresponding paper is currently in preparation.

Citation

If you make use of the software, please add a link to this repository and cite our corresponding paper. See above and the CITATION and CITATION.bib files.

Installation

The easiest and recommended way to install the software is via the Python command pip directly from the fitpdf GitHub software repository. For instance, to install the master branch of the code, use the following command:
pip install git+https://github.com/fjankowsk/fitpdf.git@master

This will automatically install all dependencies. Depending on your Python installation, you might want to replace pip with pip3 in the above command.

The latest stable version of the code should also be available on the Python package index PyPI.

Usage

$ fitpdf-compare -h
usage: fitpdf-compare [-h] [-o] files [files ...]

Compare fits.

positional arguments:
  files         Names of files to process. The input files must be InferenceData files produced by fitpdf-fit.

options:
  -h, --help    show this help message and exit

Output formatting:
  -o, --output  Output plots to file rather than to screen. (default: False)

$ fitpdf-fit -h
usage: fitpdf-fit [-h] [--fast] [--labels name [name ...]] [--mean value] [--meanthresh value] [--model {NL,NN,NNL}] [--ccdf] [--log] [--nbin value] [-o] [--title text] filename

Fit distribution data.

positional arguments:
  filename              Name of file to process. The input file must be produced by the fluence time series option of plot-profilestack.

options:
  -h, --help            show this help message and exit
  --fast                Enable fast processing. This reduces the number of MCMC steps drastically. (default: False)
  --labels name [name ...]
                        The labels to use for each input file. (default: None)
  --mean value          The global mean fluence to divide the histograms by. (default: 1.0)
  --meanthresh value    Ignore fluence data below this mean fluence threshold, i.e. select only data where fluence / mean > meanthresh. (default: -3.0)
  --model {NL,NN,NNL}   Use the specified distribution model, where N denotes a Normal and L a Lognormal component. For instance, the default NNL model consists of two Normal and one Lognormal distributions.
                        (default: NNL)
  --title text          Set a custom figure title. (default: None)

Output formatting:
  --ccdf                Show the CCDF (cumulative counts) instead of the PDF (differential counts). (default: False)
  --log                 Show histograms in double logarithmic scale. (default: False)
  --nbin value          The number of histogram bins to use. (default: 50)
  -o, --output          Output plots to file rather than to screen. (default: False)

$ fitpdf-simulate -h
usage: fitpdf-simulate [-h] [--nsamp value] [-o]

Simulate distributions.

options:
  -h, --help     show this help message and exit
  --nsamp value  Number of random samples to draw from the simulated distribution. (default: 10000)

Output formatting:
  -o, --output   Output plots to file rather than to screen. (default: False)

Example output

The images below show some example output from the program obtained when fitting simulated test data.