How to use the mc3.stats.log_prior function in mc3

def test_log_prior_uniform():
    post = np.array([[3.0, 2.0], [3.1, 1.0], [3.6, 1.5]])
    prior    = np.array([3.5, 0.0])
    priorlow = np.array([0.0, 0.0])
    priorup  = np.array([0.0, 0.0])
    pstep    = np.array([1.0, 1.0])
    log_prior = ms.log_prior(post, prior, priorlow, priorup, pstep)
    np.testing.assert_equal(log_prior, np.array([0.0, 0.0, 0.0]))

def test_log_prior_single_sample():
    params = np.array([3.0, 2.0])
    prior    = np.array([3.5, 0.0])
    priorlow = np.array([0.1, 0.0])
    priorup  = np.array([0.1, 0.0])
    pstep    = np.array([1.0, 1.0])
    log_prior = ms.log_prior(params, prior, priorlow, priorup, pstep)
    np.testing.assert_allclose(log_prior, -12.5)

def test_log_prior_fixed_params():
    post = np.array([[3.0, 2.0], [3.1, 1.0], [3.6, 1.5]])
    prior    = np.array([3.5, 0.0, 0.0])
    priorlow = np.array([0.1, 0.0, 0.0])
    priorup  = np.array([0.1, 0.0, 0.0])
    pstep    = np.array([1.0, 0.0, 1.0])
    log_prior = ms.log_prior(post, prior, priorlow, priorup, pstep)
    np.testing.assert_allclose(log_prior, np.array([-12.5, -8.0, -0.5]))

def test_log_prior_gaussian():
    post = np.array([[3.0, 2.0], [3.1, 1.0], [3.6, 1.5]])
    prior    = np.array([3.5, 0.0])
    priorlow = np.array([0.1, 0.0])
    priorup  = np.array([0.1, 0.0])
    pstep    = np.array([1.0, 1.0])
    log_prior = ms.log_prior(post, prior, priorlow, priorup, pstep)
    np.testing.assert_allclose(log_prior, np.array([-12.5, -8.0, -0.5]))

elif leastsq == 'trf':
      lsfit = so.least_squares(residuals, fitparams,
          bounds=(pmin[ifree], pmax[ifree]), args=args,
          ftol=3e-16, xtol=3e-16, gtol=3e-16, method='trf')
      params[ifree] = lsfit["x"]
      resid = lsfit["fun"]

  # Update shared parameters:
  for s in ishare:
      params[s] = params[-int(pstep[s])-1]

  # Compute best-fit model:
  best_model = func(params, *indparams)
  # Calculate chi-squared for best-fitting values:
  best_log_post = -0.5*np.sum(resid**2.0)
  log_prior = ms.log_prior(params[ifree], prior, priorlow, priorup, pstep)
  best_chisq = -2*(best_log_post - log_prior)

  return {
      'bestp':params,
      'best_log_post':best_log_post,
      'best_chisq':best_chisq,
      'best_model':best_model,
      'optimizer_res':lsfit,
  }

# Evaluate model for best fitting parameters:
  fitpars = np.asarray(params)
  fitpars[ifree] = np.copy(bestp[ifree])
  for s in ishare:
      fitpars[s] = fitpars[-int(pstep[s])-1]
  best_model = chains[0].eval_model(fitpars)

  # Remove pre-MCMC and post-MCMC alocated samples:
  zvalid = zchain>=0
  Z = Z[zvalid]
  zchain = zchain[zvalid]
  log_post = log_post[zvalid]
  log_prior = ms.log_prior(Z, prior, priorlow, priorup, pstep)
  chisq = -2*(log_post - log_prior)
  best_log_prior = ms.log_prior(bestp[ifree], prior, priorlow, priorup, pstep)
  best_chisq = -2*(best_log_post.value - best_log_prior)
  # And remove burn-in samples:
  posterior, _, zmask = mu.burn(Z=Z, zchain=zchain, burnin=zburn)

  # Number of evaluated and kept samples:
  nsample  = len(Z)*thinning
  nzsample = len(posterior)

  # Print out Summary:
  log.msg('\nMCMC Summary:'
          '\n-------------')
  fmt = len(str(nsample))
  log.msg("Number of evaluated samples:        {:{}d}".
          format(nsample,  fmt), indent=2)
  log.msg("Number of parallel chains:          {:{}d}".
          format(nchains,  fmt), indent=2)

for chain in chains:  # Make sure to terminate the subprocesses
      chain.terminate()

  # Evaluate model for best fitting parameters:
  fitpars = np.asarray(params)
  fitpars[ifree] = np.copy(bestp[ifree])
  for s in ishare:
      fitpars[s] = fitpars[-int(pstep[s])-1]
  best_model = chains[0].eval_model(fitpars)

  # Remove pre-MCMC and post-MCMC alocated samples:
  zvalid = zchain>=0
  Z = Z[zvalid]
  zchain = zchain[zvalid]
  log_post = log_post[zvalid]
  log_prior = ms.log_prior(Z, prior, priorlow, priorup, pstep)
  chisq = -2*(log_post - log_prior)
  best_log_prior = ms.log_prior(bestp[ifree], prior, priorlow, priorup, pstep)
  best_chisq = -2*(best_log_post.value - best_log_prior)
  # And remove burn-in samples:
  posterior, _, zmask = mu.burn(Z=Z, zchain=zchain, burnin=zburn)

  # Number of evaluated and kept samples:
  nsample  = len(Z)*thinning
  nzsample = len(posterior)

  # Print out Summary:
  log.msg('\nMCMC Summary:'
          '\n-------------')
  fmt = len(str(nsample))
  log.msg("Number of evaluated samples:        {:{}d}".
          format(nsample,  fmt), indent=2)

log.msg('Running dynesty dynamic nested-samping run:\n')
  sampler = dynesty.DynamicNestedSampler(loglike, prior_transform, nfree,
      **dyn_args)
  sampler.run_nested(**kwargs)

  weights = np.exp(sampler.results.logwt - sampler.results.logz[-1])
  isample = resample_equal(weights)
  posterior = sampler.results.samples[isample]
  chisq = -2.0*sampler.results.logl[isample]

  # Contribution to chi-square from non-uniform priors:
  if skip_logp:
      # TBD: Compute from prior_transform()
      log_prior = 0.0
  else:
      log_prior = ms.log_prior(posterior, prior, priorlow, priorup, pstep)
  log_post = -0.5*chisq + log_prior

  # Best-fit statistics from sample:
  ibest = np.argmin(log_post)
  bestp = np.copy(params)
  bestp[ifree] = posterior[ibest]
  for s in ishare:
      bestp[s] = bestp[-int(pstep[s])-1]
  best_model = func(bestp, *indparams)
  best_chisq = chisq[ibest]
  best_log_post = log_post[ibest]

  # Print out Summary:
  log.msg("\nNested Sampling Summary:"
          "\n------------------------")