def save_parameter_names_file(self, model):
"""Create the param_names file listing every parameter's label and Latex tag, which is used for *corner.py*
visualization.
The parameter labels are determined using the label.ini and label_format.ini config files."""
parameter_names = model.model_component_and_parameter_names
parameter_labels = model.parameter_labels
subscripts = model.subscripts
parameter_labels_with_subscript = [
f"{label}_{subscript}"
for label, subscript in zip(parameter_labels, subscripts)
]
parameter_name_and_label = []
for i in range(model.prior_count):
line = formatter.add_whitespace(
str0=parameter_names[i],
str1=parameter_labels_with_subscript[i],
whitespace=70)
parameter_name_and_label += [f"{line}\n"]
formatter.output_list_of_strings_to_file(
file=self.paths.file_param_names,
list_of_strings=parameter_name_and_label)
def results_to_file(samples, filename):
"""Output the full model.results file, which include the most-likely model, most-probable model at 1 and 3
sigma confidence and information on the maximum log likelihood.
"""
results = []
if hasattr(samples, "log_evidence"):
if samples.log_evidence is not None:
results += [
frm.add_whitespace(str0="Bayesian Evidence ",
str1="{:.8f}".format(samples.log_evidence),
whitespace=90)
]
results += ["\n"]
results += [
frm.add_whitespace(str0="Maximum Log Likelihood ",
str1="{:.8f}".format(
max(samples.log_likelihood_list)),
whitespace=90)
]
results += ["\n"]
results += [
frm.add_whitespace(str0="Maximum Log Posterior ",
str1="{:.8f}".format(max(
samples.log_posterior_list)),
whitespace=90)
]
results += ["\n"]
results += ["\n", samples.model.parameterization, "\n\n"]
results += ["Maximum Log Likelihood Model:\n\n"]
formatter = frm.TextFormatter()
for i, prior_path in enumerate(samples.model.unique_prior_paths):
formatter.add(
prior_path,
format_str().format(samples.max_log_likelihood_vector[i]))
results += [formatter.text + "\n"]
if hasattr(samples, "pdf_converged"):
if samples.pdf_converged:
results += samples_text.summary(samples=samples,
sigma=3.0,
indent=4,
line_length=90)
results += ["\n"]
results += samples_text.summary(samples=samples,
sigma=1.0,
indent=4,
line_length=90)
else:
results += [
"\n WARNING: The samples have not converged enough to compute a PDF and model errors. \n "
"The model below over estimates errors. \n\n"
]
results += samples_text.summary(samples=samples,
sigma=1.0,
indent=4,
line_length=90)
results += ["\n\ninstances\n"]
formatter = frm.TextFormatter()
for t in samples.model.path_float_tuples:
formatter.add(*t)
results += ["\n" + formatter.text]
frm.output_list_of_strings_to_file(file=filename, list_of_strings=results)