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Compatibility to "to_change" and "to_reorder" #336

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Compatibility to "to_change" and "to_reorder" #336

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61 changes: 50 additions & 11 deletions montepython/analyze.py
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Original file line number Diff line number Diff line change
Expand Up @@ -565,7 +565,7 @@ def compute_posterior(information_instances):
# 1D posterior normalised to P_max=1 (first step)
#
# simply the histogram from the chains, with few bins
#
#
info.hist, info.bin_edges = np.histogram(
# TB: without posterior_smoothing it can be nice to increase the
# number of bins here.
Expand Down Expand Up @@ -730,13 +730,13 @@ def compute_posterior(information_instances):
# 1D mean likelihood normalised to P_max=1 (first step)
#
# simply the histogram from the chains, weighted by mutiplicity*likelihood
#
#
lkl_mean, _ = np.histogram(
info.chain[:, info.native_index+2],
bins=info.bin_edges,
normed=False,
weights=np.exp(
conf.min_minus_lkl-info.chain[:, 1])*info.chain[:, 0])
conf.min_minus_lkl-info.chain[:, 1])*info.chain[:, 0])
lkl_mean /= lkl_mean.max()

# 1D mean likelihood normalised to P_max=1 (second step)
Expand Down Expand Up @@ -871,13 +871,13 @@ def compute_posterior(information_instances):
# 2D likelihood (first step)
#
# simply the histogram from the chains, with few bins only
#
#
info.n, info.xedges, info.yedges = np.histogram2d(
info.chain[:, info.native_index+2],
info.chain[:, info.native_second_index+2],
weights=info.chain[:, 0],
bins=(info.bins, info.bins),
normed=False)
normed=False)
# Correct for temperature:
info.n = info.n**conf.temperature

Expand Down Expand Up @@ -2245,9 +2245,25 @@ def remap_parameters(self, spam):
self.number_parameters +=1
self.plotted_parameters.append(key)
self.centers = np.append(self.centers,0)
if hasattr(self, 'to_reorder'):
if hasattr(self, 'to_reorder'):
if(len(self.to_reorder)>0):
indices = [self.backup_names.index(name) for name in self.to_reorder]
# Analyze modification #

# Add protection for requested reorder names that only exist for the
# model considered. This is useful when comparing multiple models with
# non-matching parameters.
reorder_names = []
for name in self.to_reorder:
if name in self.ref_names:
reorder_names.append(name)

# Replace backup names on following line with ref names instead.
# This makes it so that derived and changed parameters can be reordered without issue.
# Before: indices = [self.backup_names.index(name) for name in self.to_reorder]
indices = [self.ref_names.index(name) for name in reorder_names]

# End analyze modification #

missing_indices = [x for x in np.arange(len(self.backup_names)) if x not in indices]
indices = np.concatenate([indices,missing_indices],dtype=int)
self.ref_names = [self.ref_names[i] for i in indices]
Expand All @@ -2262,11 +2278,34 @@ def remap_parameters(self, spam):
spam[i][:,2:] = spam[i][:,indices+2]
# Play the same game independently for plotted_parameters
# since these might be a lot fewer
indices = [self.plotted_parameters.index(name) for name in self.to_reorder]

# Analyze modification #

# Add protection for requested reorder names that only exist for the
# model considered. This is useful when comparing multiple models with
# non-matching parameters.
# Before: indices = [self.plotted_parameters.index(name) for name in self.to_reorder]
reorder_names = []
for name in self.to_reorder:
if name in self.plotted_parameters:
reorder_names.append(name)
indices = [self.plotted_parameters.index(name) for name in reorder_names]

# End analyze modification #

if(len(indices)>0):
missing_indices = [x for x in np.arange(len(self.plotted_parameters)) if x not in indices]
indices = np.concatenate([indices,missing_indices])
self.plotted_parameters = [self.plotted_parameters[i] for i in indices]

# Analyze modification #

# Added to the following line the dtype="int" argument to prevent the subsequent lines
# from throwing an error when a float data is used as an index.
# Before: missing_indices = np.array([x for x in np.arange(len(self.plotted_parameters)) if x not in indices])
missing_indices = np.array([x for x in np.arange(len(self.plotted_parameters)) if x not in indices], dtype="int")

# End analyze modification #

indices = np.concatenate([indices,missing_indices])
self.plotted_parameters = [self.plotted_parameters[i] for i in indices]

def define_ticks(self):
"""
Expand Down