def make_inference_inj_plots(workflow,
inference_files,
output_dir,
parameters,
name="inference_recovery",
analysis_seg=None,
tags=None):
""" Sets up the recovered versus injected parameter plot in the workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
inference_files: pycbc.workflow.FileList
The files with posterior samples.
output_dir: str
The directory to store result plots and files.
parameters : list
A ``list`` of parameters. Each parameter gets its own plot.
name: str
The name in the [executables] section of the configuration file
to use.
analysis_segs: {None, ligo.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 inference 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
output_files = FileList([])
# make the directory that will contain the output files
makedir(output_dir)
# add command line options
for (ii, param) in enumerate(parameters):
plot_exe = PlotExecutable(workflow.cp,
name,
ifos=workflow.ifos,
out_dir=output_dir,
tags=tags + ['param{}'.format(ii)])
node = plot_exe.create_node()
node.add_input_list_opt("--input-file", inference_files)
node.new_output_file_opt(analysis_seg, ".png", "--output-file")
node.add_opt("--parameters", param)
workflow += node
output_files += node.output_files
return output_files
def make_inference_inj_plots(workflow, inference_files, output_dir,
parameters, name="inference_recovery",
analysis_seg=None, tags=None):
""" Sets up the recovered versus injected parameter plot in the workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
inference_files: pycbc.workflow.FileList
The files with posterior samples.
output_dir: str
The directory to store result plots and files.
parameters : list
A ``list`` of parameters. Each parameter gets its own plot.
name: str
The name in the [executables] section of the configuration file
to use.
analysis_segs: {None, ligo.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 inference 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
output_files = FileList([])
# make the directory that will contain the output files
makedir(output_dir)
# add command line options
for (ii, param) in enumerate(parameters):
plot_exe = PlotExecutable(workflow.cp, name, ifos=workflow.ifos,
out_dir=output_dir,
tags=tags+['param{}'.format(ii)])
node = plot_exe.create_node()
node.add_input_list_opt("--input-file", inference_files)
node.new_output_file_opt(analysis_seg, ".png", "--output-file")
node.add_opt("--parameters", param)
workflow += node
output_files += node.output_files
return output_files
def make_singles_timefreq(workflow,
single,
bank_file,
trig_time,
out_dir,
veto_file=None,
time_window=10,
data_segments=None,
tags=None):
""" Generate a singles_timefreq node and add it to workflow.
This function generates a single node of the singles_timefreq executable
and adds it to the current workflow. Parent/child relationships are set by
the input/output files automatically.
Parameters
-----------
workflow: pycbc.workflow.core.Workflow
The workflow class that stores the jobs that will be run.
single: pycbc.workflow.core.File instance
The File object storing the single-detector triggers to followup.
bank_file: pycbc.workflow.core.File instance
The File object storing the template bank.
trig_time: int
The time of the trigger being followed up.
out_dir: str
Location of directory to output to
veto_file: File (optional, default=None)
If given use this file to veto triggers to determine the loudest event.
FIXME: Veto files *should* be provided a definer argument and not just
assume that all segments should be read.
time_window: int (optional, default=None)
The amount of data (not including padding) that will be read in by the
singles_timefreq job. The default value of 10s should be fine for most
cases.
data_segments: glue.segments.segmentlist (optional, default=None)
The list of segments for which data exists and can be read in. If given
the start/end times given to singles_timefreq will be adjusted if
[trig_time - time_window, trig_time + time_window] does not completely
lie within a valid data segment. A ValueError will be raised if the
trig_time is not within a valid segment, or if it is not possible to
find 2*time_window (plus the padding) of continuous data around the
trigger. This **must** be coalesced.
tags: list (optional, default=None)
List of tags to add to the created nodes, which determine file naming.
"""
tags = [] if tags is None else tags
makedir(out_dir)
name = 'plot_singles_timefreq'
curr_exe = PlotExecutable(workflow.cp,
name,
ifos=[single.ifo],
out_dir=out_dir,
tags=tags)
node = curr_exe.create_node()
node.add_input_opt('--trig-file', single)
node.add_input_opt('--bank-file', bank_file)
# Determine start/end times, using data segments if needed.
# Begin by choosing "optimal" times
start = trig_time - time_window
end = trig_time + time_window
# Then if data_segments is available, check against that, and move if
# needed
if data_segments is not None:
# Assumes coalesced, so trig_time can only be within one segment
for seg in data_segments:
if trig_time in seg:
data_seg = seg
break
else:
err_msg = "Trig time {} ".format(trig_time)
err_msg += "does not seem to lie within any data segments. "
err_msg += "This shouldn't be possible, please ask for help!"
raise ValueError(err_msg)
# Check for pad-data
if curr_exe.has_opt('pad-data'):
pad_data = int(curr_exe.get_opt('pad-data'))
else:
pad_data = 0
if abs(data_seg) < (2 * time_window + 2 * pad_data):
tl = 2 * time_window + 2 * pad_data
err_msg = "I was asked to use {} seconds of data ".format(tl)
err_msg += "to run a plot_singles_timefreq job. However, I have "
err_msg += "only {} seconds available.".format(abs(data_seg))
raise ValueError(err_msg)
if data_seg[0] > (start - pad_data):
start = data_seg[0] + pad_data
end = start + 2 * time_window
if data_seg[1] < (end + pad_data):
end = data_seg[1] - pad_data
start = end - 2 * time_window
# Sanity check, shouldn't get here!
if data_seg[0] > (start - pad_data):
err_msg = "I shouldn't be here! Go ask Ian what he broke."
raise ValueError(err_msg)
node.add_opt('--gps-start-time', int(start))
node.add_opt('--gps-end-time', int(end))
node.add_opt('--center-time', int(trig_time))
if veto_file:
node.add_input_opt('--veto-file', veto_file)
node.add_opt('--detector', single.ifo)
node.new_output_file_opt(workflow.analysis_time, '.png', '--output-file')
workflow += node
return node.output_files
