Commit d64b7dee authored by Médéric Boquien's avatar Médéric Boquien
Browse files

Align comments.

parent aa4175ef
...@@ -314,7 +314,7 @@ def analysis(idx, obs): ...@@ -314,7 +314,7 @@ def analysis(idx, obs):
best_index = chi2_.argmin() best_index = chi2_.argmin()
# We compute once again the best sed to obtain its info # We compute once again the best sed to obtain its info
global gbl_previous_idx global gbl_previous_idx
if gbl_previous_idx > -1: if gbl_previous_idx > -1:
gbl_warehouse.partial_clear_cache( gbl_warehouse.partial_clear_cache(
...@@ -325,24 +325,23 @@ def analysis(idx, obs): ...@@ -325,24 +325,23 @@ def analysis(idx, obs):
sed = gbl_warehouse.get_sed(gbl_params.modules, sed = gbl_warehouse.get_sed(gbl_params.modules,
gbl_params.from_index([wz[0][wvalid[0][best_index]]])) gbl_params.from_index([wz[0][wvalid[0][best_index]]]))
# We correct the mass-dependent parameters # We correct the mass-dependent parameters
for key in sed.mass_proportional_info: for key in sed.mass_proportional_info:
sed.info[key] *= norm_facts[best_index] sed.info[key] *= norm_facts[best_index]
for index, variable in enumerate(gbl_analysed_variables): for index, variable in enumerate(gbl_analysed_variables):
if variable in sed.mass_proportional_info: if variable in sed.mass_proportional_info:
model_variables[:, index] *= norm_facts model_variables[:, index] *= norm_facts
# We compute the weighted average and standard deviation using the # We compute the weighted average and standard deviation using the
# likelihood as weight. # likelihood as weight.
analysed_averages = np.empty(len(gbl_analysed_variables)) analysed_averages = np.empty(len(gbl_analysed_variables))
analysed_std = np.empty_like(analysed_averages) analysed_std = np.empty_like(analysed_averages)
# We check how many unique parameter values are analysed and if less than # We check how many unique parameter values are analysed and if less
# Npdf (= 100), the PDF is initally built assuming a number of bins equal # than Npdf (= 100), the PDF is initally built assuming a number of
# to the number of unique values for a given parameter (e.g., average_sfr, # bins equal to the number of unique values for a given parameter
# age, attenuation.uv_bump_amplitude, dust.luminosity, attenuation.FUV, # (e.g., average_sfr, age, attenuation.uv_bump_amplitude,
# etc.). # dust.luminosity, attenuation.FUV, etc.).
Npdf = 100 Npdf = 100
var = np.empty((Npdf, len(analysed_averages))) var = np.empty((Npdf, len(analysed_averages)))
pdf = np.empty((Npdf, len(analysed_averages))) pdf = np.empty((Npdf, len(analysed_averages)))
......
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