BISIP

This is the development repository for BISIP2, the successor of BISIP: https://github.com/clberube/BISIP. BISIP is being re-written with a powerful ensemble MCMC sampler, better code practice and improved documentation.

Requirements

• Python 3 (BISIP is developed on Python 3.7)
• emcee (https://emcee.readthedocs.io/en/stable/)
• numpy (https://numpy.org/)
• matplotlib (https://matplotlib.org/)

Documentation

Visit https://bisip.readthedocs.io/en/latest/ to consult the full documentation including API docs, tutorials and examples.

Installation

Clone this repository to your computer. Then navigate to the bisip directory. Finally run the setup.py script with Python. The -f option forces a reinstall if the package is already present.

git clone https://github.com/clberube/bisip2
cd bisip2
python setup.py install -f

Quickstart

Using BISIP is as simple as importing a SIP model, initializing it and fitting it to a data set. BISIP also offers many utility functions for plotting and analyzing inversion results. See the tutorials for more examples.

from bisip import PolynomialDecomposition

# Define a data file to invert
filepath = '/path/to/DataFile_1.csv'
# Initialize the inversion model
model = PolynomialDecomposition(filepath=filepath,
                                nwalkers=32,  # number of walkers
                                nsteps=1000,  # number of MCMC steps
                                )
# Fit the model to this data file
model.fit()

Out:
100%|██████████| 1000/1000 [00:01<00:00, 558.64it/s]