def make_inference_corner_plot(workflow, mcmc_file, output_dir, config_file,
name="mcmc_corner", analysis_seg=None, tags=None):
""" Sets up the corner plot of the posteriors in the workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
mcmc_file: pycbc.workflow.File
The file with MCMC samples.
output_dir: str
The directory to store result plots and files.
config_file: str
The path to the inference configuration file that has a
[variable_args] section.
name: str
The name in the [executables] section of the configuration file
to use.
analysis_segs: {None, glue.segments.Segment}
The segment this job encompasses. If None then use the total analysis
time from the workflow.
tags: {None, optional}
Tags to add to the minifollowups executables.
Returns
-------
pycbc.workflow.FileList
A list of result and output files.
"""
# default values
tags = [] if tags is None else tags
analysis_seg = workflow.analysis_time \
if analysis_seg is None else analysis_seg
# read config file to get variables that vary
cp = WorkflowConfigParser([config_file])
variable_args = cp.options("variable_args")
# add derived mass parameters if mass1 and mass2 in variable_args
if "mass1" in variable_args and "mass2" in variable_args:
variable_args += ["mchirp", "eta"]
# make the directory that will contain the output files
makedir(output_dir)
# make a node for plotting the posterior as a corner plot
node = PlotExecutable(workflow.cp, name, ifos=workflow.ifos,
out_dir=output_dir, universe="local",
tags=tags).create_node()
# add command line options
node.add_input_opt("--input-file", mcmc_file)
node.new_output_file_opt(analysis_seg, ".png", "--output-file")
# add node to workflow
workflow += node
return node.output_files
def make_inference_single_parameter_plots(workflow, mcmc_file, output_dir,
config_file, samples_name="mcmc_samples",
auto_name="mcmc_acf", analysis_seg=None, tags=None):
""" Sets up single-parameter plots from MCMC in the workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
mcmc_file: pycbc.workflow.File
The file with MCMC samples.
output_dir: str
The directory to store result plots and files.
config_file: str
The path to the inference configuration file that has a
[variable_args] section.
samples_name: str
The name in the [executables] section of the configuration file
to use for the plot that shows all samples.
auto_name: str
The name in the [executables] section of the configuration file
to use for the autocorrelation function plot.
analysis_segs: {None, glue.segments.Segment}
The segment this job encompasses. If None then use the total analysis
time from the workflow.
tags: {None, optional}
Tags to add to the minifollowups executables.
Returns
-------
files: pycbc.workflow.FileList
A list of result and output files.
"""
# default values
tags = [] if tags is None else tags
analysis_seg = workflow.analysis_time \
if analysis_seg is None else analysis_seg
# read config file to get variables that vary
cp = WorkflowConfigParser([config_file])
variable_args = cp.options("variable_args")
# make the directory that will contain the output files
makedir(output_dir)
# list of all output files
files = FileList()
# make a set of plots for each parameter
for arg in variable_args:
# make a node for plotting all the samples
samples_node = PlotExecutable(workflow.cp, samples_name,
ifos=workflow.ifos, out_dir=output_dir,
tags=tags + [arg]).create_node()
# add command line options
samples_node.add_input_opt("--input-file", mcmc_file)
samples_node.new_output_file_opt(analysis_seg, ".png", "--output-file")
samples_node.add_opt("--variable-args", arg)
samples_node.add_opt("--labels", arg)
# make node for plotting the autocorrelation function for each walker
auto_node = PlotExecutable(workflow.cp, auto_name, ifos=workflow.ifos,
out_dir=output_dir, tags=tags + [arg]).create_node()
# add command line options
auto_node.add_input_opt("--input-file", mcmc_file)
auto_node.new_output_file_opt(analysis_seg, ".png", "--output-file")
auto_node.add_opt("--variable-args", arg)
# add nodes to workflow
workflow += samples_node
workflow += auto_node
# add files to output files list
files += samples_node.output_files
files += auto_node.output_files
return files
