• To find the version of your bxa install:
    import bxa, pkg_resources

  • To use BXA with chi-squared statistics (if you're sure you want to):

  • Renaming BXA parameters: sometimes models can have multiple parameters with the same name (e.g., two or more additive model components with a norm parameter). When the BXA fit is run, the posterior and corner plot will show the same parameter name, making it difficult to interpret which parameters are which. An alternative is to change the parameter name when inside the corresponding prior:
    prior1 = bxa.create_loguniform_prior_for(model, model.zpowerlw.norm)
    prior1["name"] = "log(norm_zpo)"

  • You can use BXA with Sherpa to fit 2D models to images:
    import bxa.sherpa as bxa
    model = gauss2d.g1 + gauss2d.g2 + const2d.bg
    set_par(g1.ampl, val = 20., min = 1., max = 1.e2)
    parameters = [g1.ampl, ...]
    priors = [bxa.create_loguniform_prior_for(g1.ampl), ...]
    priorfunction = bxa.create_prior_function(priors)
    solver = bxa.BXASolver(prior=priorfunction, parameters=parameters, outputfiles_basename = "fit2d_sherpa")
    results = solver.run(resume=True)

    Here is an example fit to the image used in this Sherpa tutorial for 2D fitting (left, middle and right panels show the image, best-fit model and residuals respectively):

  • Using other sampling algorithms: Once the solver object has been created, you can acquire the log-Likelihood, parameter names and priors as follows:
    • PyXspec:
      loglike = solver.log_likelihood
      paramnames = solver.paramnames
      prior = solver.prior_function
    • Sherpa:
      loglike = solver.log_likelihood
      paramnames = solver.paramnames
      prior = solver.prior_transform

    These can then be passed to other sampling algorithms (including nested sampling) easily:
    • MCMC algorithms (e.g., emcee or zeus):
      from autoemcee import ReactiveAffineInvariantSampler
      sampler = ReactiveAffineInvariantSampler(paramnames, loglike, prior)
      result = sampler.run()
    • Laplace approximation, Importance Sampling:
      from snowline import ReactiveImportanceSampler
      sampler = ReactiveImportanceSampler(paramnames, loglike, transform)
      result = sampler.run()
    • Nested sampling:
      from ultranest import ReactiveNestedSampler
      sampler = ReactiveNestedSampler(paramnames, loglike, prior)
      result = sampler.run()

  • Plotting contours:
    There are a number of packages available to produce contour plots in Python. Here are a few examples, with some short code snippets to plot BXA posterior corner plots. Note the code snippets assume the posteriors are a pandas dataframe. To do this, you can type:
    import pandas as pd
    outputfiles_basename = "example_basename"
    posterior = pd.read_csv("./%s/chains/equal_weighted_post.txt" %(example_basename), delim_whitespace=True)

  • Note the confidence regions in 2D histograms are not the same as in 1D histograms. See this webpage from corner for more information.