[doc] docstring syntax

ultranest/hotstart.py

@@ -47,7 +47,7 @@ def get_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_factor, d
  The default is recommended. For truly gaussian posteriors,
  the student-t can be made more gaussian (by df>=30) for accelation.
 
- Returns:
+ Returns
  ---------
  aux_loglike: function
  auxiliary loglikelihood function.
@@ -122,7 +122,7 @@ def get_extended_auxiliary_problem(loglike, transform, ctr, invcov, enlargement_
  The default is recommended. For truly gaussian posteriors,
  the student-t can be made more gaussian (by df>=30) for accelation.
 
- Returns:
+ Returns
  ---------
  aux_loglike: function
  auxiliary loglikelihood function. Takes d + 1 parameters (see below).
@@ -204,7 +204,7 @@ def get_extended_auxiliary_independent_problem(loglike, transform, ctr, err, df=
  The default is recommended. For truly gaussian posteriors,
  the student-t can be made more gaussian (by df>=30) for accelation.
 
- Returns:
+ Returns
  ---------
  aux_loglike: function
  auxiliary loglikelihood function.
@@ -254,7 +254,7 @@ def compute_quantile_intervals(steps, upoints, uweights):
  uweights: array
  sample weights
 
- Returns:
+ Returns
  ---------
  ulo: array
  list of lower quantiles (at q), one entry for each dimension d.
@@ -263,8 +263,8 @@ def compute_quantile_intervals(steps, upoints, uweights):
  """
  ndim = upoints.shape[1]
  nboxes = len(steps)
- ulos = np.empty((nboxes+1,ndim))
- uhis = np.empty((nboxes+1,ndim))
+ ulos = np.empty((nboxes + 1, ndim))
+ uhis = np.empty((nboxes + 1, ndim))
  for j, pthresh in enumerate(steps):
  for i, ui in enumerate(upoints.transpose()):
  order = np.argsort(ui)
@@ -276,19 +276,20 @@ def compute_quantile_intervals(steps, upoints, uweights):
  uhis[-1] = 1
  return ulos, uhis
 
+
 def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20):
  """Compute lower and upper axis quantiles.
- 
+
  Parameters
  ------------
  steps: array
- list of quantiles q to compute, with dimensions 
+ list of quantiles q to compute, with dimensions
  upoints: array
  samples, with dimensions (N, d)
  uweights: array
  sample weights. N entries.
 
- Returns:
+ Returns
  ---------
  ulo: array
  list of lower quantiles (at q), of shape (M, d), one entry per quantile and dimension d.
@@ -297,32 +298,32 @@ def compute_quantile_intervals_refined(steps, upoints, uweights, logsteps_max=20
  """
  nboxes = len(steps)
  ulos_orig, uhis_orig = compute_quantile_intervals(steps, upoints, uweights)
- assert len(ulos_orig) == nboxes+1
- assert len(uhis_orig) == nboxes+1
+ assert len(ulos_orig) == nboxes + 1
+ assert len(uhis_orig) == nboxes + 1
 
  smallest_axis_width = np.min(uhis_orig[-2,:] - ulos_orig[-2,:])
  logsteps = min(logsteps_max, int(np.ceil(-np.log10(max(1e-100, smallest_axis_width)))))
 
- weights = np.logspace(-logsteps, 0, logsteps+1).reshape((-1, 1))
+ weights = np.logspace(-logsteps, 0, logsteps + 1).reshape((-1, 1))
  # print("logspace:", weights, logsteps)
- assert len(weights) == logsteps+1, (weights.shape, logsteps)
+ assert len(weights) == logsteps + 1, (weights.shape, logsteps)
  # print("quantiles:", ulos_orig, uhis_orig)
- ulos_new = ulos_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 0 * weights
- uhis_new = uhis_orig[nboxes-1, :].reshape((1, -1)) * (1 - weights) + 1 * weights
- 
+ ulos_new = ulos_orig[nboxes - 1, :].reshape((1, -1)) * (1 - weights) + 0 * weights
+ uhis_new = uhis_orig[nboxes - 1, :].reshape((1, -1)) * (1 - weights) + 1 * weights
+
  # print("additional quantiles:", ulos_new, uhis_new)
- 
+
  ulos = np.vstack((ulos_orig[:-1,:], ulos_new))
  uhis = np.vstack((uhis_orig[:-1,:], uhis_new))
  # print("combined quantiles:", ulos, uhis)
  assert (ulos[-1,:] == 0).all()
  assert (uhis[-1,:] == 1).all()
- 
- uinterpspace = np.ones(nboxes+logsteps+1)
- uinterpspace[:nboxes+1] = np.linspace(0, 1, nboxes+1)
- assert 0 < uinterpspace[nboxes-1] < 1, uinterpspace[nboxes]
- uinterpspace[nboxes:] = np.linspace(uinterpspace[nboxes-1], 1, logsteps+2)[1:]
- 
+
+ uinterpspace = np.ones(nboxes + logsteps + 1)
+ uinterpspace[:nboxes + 1] = np.linspace(0, 1, nboxes + 1)
+ assert 0 < uinterpspace[nboxes - 1] < 1, uinterpspace[nboxes]
+ uinterpspace[nboxes:] = np.linspace(uinterpspace[nboxes - 1], 1, logsteps + 2)[1:]
+
  return ulos, uhis, uinterpspace
 
 
@@ -354,7 +355,7 @@ def get_auxiliary_contbox_parameterization(
  and segments it into quantile segments. Within each segment,
  the parameter edges in u-space are linearly interpolated.
  To see the interpolation quantiles for each axis, use::
- 
+
  steps = 10**-(1.0 * np.arange(1, 8, 2))
  ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
 
@@ -367,7 +368,7 @@ def get_auxiliary_contbox_parameterization(
  auxiliary_usamples: array
  Posterior samples (in u-space).
 
- Returns:
+ Returns
  ---------
  aux_loglike: function
  auxiliary loglikelihood function.
@@ -381,7 +382,7 @@ def get_auxiliary_contbox_parameterization(
  nsamples, ndim = upoints.shape
  assert nsamples > 10
  ulos, uhis, uinterpspace = compute_quantile_intervals_refined(steps, upoints, uweights)
- 
+
  aux_param_names = param_names + ['aux_logweight']
 
  def aux_transform(u):
@@ -462,7 +463,7 @@ def reuse_samples(
  log_weight_threshold: float
  Lowest log-weight to consider
 
- Returns:
+ Returns
  ---------
  results: dict
  All information of the run. Important keys:

ultranest/integrator.py

@@ -40,7 +40,7 @@ def _get_cumsum_range(pi, dp):
  dp: float
  Quantile (between 0 and 0.5).
 
- Returns:
+ Returns
  ---------
  index_lo: int
  Index of the item corresponding to quantile ``dp``.
@@ -65,7 +65,7 @@ def _sequentialize_width_sequence(minimal_widths, min_width):
  min_width: int
  Minimum width everywhere.
 
- Returns:
+ Returns
  ---------
  Lsequence: list of (L, width)
  A sequence of L points and the expected tree width at and above it.
@@ -925,6 +925,18 @@ def warmstart_from_similar_file(
 ):
  """Warmstart from a previous run.
 
+ Usage::
+
+ aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
+ 'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
+
+ aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
+ aux_sampler.run()
+ posterior_samples = aux_results['samples'][:,-1]
+
+ See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
+ for more information.
+
  Parameters
  ------------
  usample_filename: str
@@ -934,10 +946,10 @@ def warmstart_from_similar_file(
  min_num_samples: int
  minimum number of samples in the usample_filename file required.
  Too few samples will give a poor approximation.
+ otherparameters: ...
+ The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
 
- The remaining parameters have the same meaning as in :class:ReactiveNestedSampler.
-
- Returns:
+ Returns
  ---------
  aux_param_names: list
  new parameter list
@@ -947,19 +959,6 @@ def warmstart_from_similar_file(
  new prior transform function
  vectorized: bool
  whether the new functions are vectorized
-
- Usage::
-
- aux_paramnames, aux_log_likelihood, aux_prior_transform, vectorized = warmstart_from_similar_file(
- 'model1/chains/weighted_post_untransformed.txt', parameters, log_likelihood_with_background, prior_transform)
-
- aux_sampler = ReactiveNestedSampler(aux_paramnames, aux_log_likelihood, transform=aux_prior_transform,vectorized=vectorized)
- aux_sampler.run()
- posterior_samples = aux_results['samples'][:,-1]
-
- See :py:func:`ultranest.hotstart.get_auxiliary_contbox_parameterization`
- for more information.
-
  """
  # load samples
  try: