DOCUMENTATION IS UNDER CONSTRUCTION
BISIP is an MIT licensed Python package to perform Bayesian Inversion of Spectral Induced Polarization data with Markov-chain Monte Carlo simulation. See our original paper in Computers & Geosciences for more details. It should be noted that the original version of BISIP was built on top of the PyMC framework when the code was developed in 2015. Now, BISIP uses Goodman & Weare’s Affine Invariant Ensemble sampler as implemented in the emcee library to explore the SIP models’ parameter spaces with multiple walkers. You can read the original paper by Foreman-Mackey et al. explaining the emcee algorithm in detail here.
BISIP is being developed on GitHub.
To perform a Debye decomposition inversion of a SIP data file using all default parameters, you would use the following code:
1 2 3 4 5 6 7 8 9 10 11 12 13 14
from bisip import PolynomialDecomposition # Use one of the example data files provided with BISIP filepath = '/path/to/bisip/data/SIP-K389175.dat' model = PolynomialDecomposition(filepath) # Fit the model to this data file model.fit() # Out: 100%|██████████| 1000/1000 [00:01<00:00, 563.92it/s] # Plot the fit on top of the input data, discarding the burn-in period model.plot_fit(discard=200)
A more detailed example is available in the Quickstart tutorial.