pint.plot_utils.plot_priors

pint.plot_utils.plot_priors(model, chains, maxpost_fitvals=None, fitvals=None, burnin=100, bins=100, scale=False)[source]

Plot of priors and the post-MCMC histogrammed samples

Show binned samples, prior probability distribution and an initial gaussian probability distribution plotted with 2 sigma, maximum posterior and original fit values marked.

Parameters:

  • model (pint.models.timing_model.TimingModel) – The initial timing model for fitting

  • chains (dict) – Post MCMC integration chains that contain the fitter keys and post MCMC samples, which are histogrammed and normalized. Thinning the samples from the chains is not supported. Can be created using pint.sampler.EmceeSampler.chains_to_dict()

  • maxpost_fitvals (list, optional) – The maximum posterior values returned from MCMC integration for each fitter key. Plots a vertical dashed line to denote the maximum posterior value in relation to the histogrammed samples. If the values are not provided, then the lines are not plotted

  • fitvals (list, optional) – The original parameter fit values. Plots vertical dashed lines to denote the original parameter fit values in relation to the histogrammed samples. If the values are not provided, then the lines are not plotted.

  • burnin (int) – The number of steps that are the burnin in the MCMC integration

  • bins (int) – Number of bins used in the histogram

  • scale (bool) – If True, the priors will be scaled to the peak of the histograms. If False, the priors will be plotted independent of the histograms. In certain cases, such as broad priors, the priors or histograms might not be apparent on the same plot due to one being significantly larger than the other. The scaling is for visual purposes to clearly plot the priors with the samples