def create_posterior_files(workflow, samples_files, output_dir,
parameters=None, name="extract_posterior",
analysis_seg=None, tags=None):
"""Sets up job to create posterior files from some given samples files.
Parameters
----------
workflow: pycbc.workflow.Workflow
The workflow instance we are populating
samples_files : str or list of str
One or more files to extract the posterior samples from.
output_dir: str
The directory to store result plots and files.
name: str, optional
The name in the [executables] section of the configuration file
to use, and the section to read for additional arguments to pass to
the executable. Default is ``extract_posterior``.
analysis_segs: ligo.segments.Segment, optional
The segment this job encompasses. If None then use the total analysis
time from the workflow.
tags: list, optional
Tags to add to the inference executables.
Returns
-------
pycbc.workflow.FileList
A list of output files.
"""
if analysis_seg is None:
analysis_seg = workflow.analysis_time
if tags is None:
tags = []
# Catch if a parameters option was specified:
# we need to do this because Executable will automatically add any
# option in the section to the node. However, we need to add the
# appropriate escapes to the parameters option so pegasus will render it
# properly (see _params_for_pegasus for details).
parameters = None
if workflow.cp.has_option(name, 'parameters'):
parameters = workflow.cp.get(name, 'parameters')
workflow.cp.remove_option(name, 'parameters')
extract_posterior_exe = Executable(workflow.cp, name,
ifos=workflow.ifos,
out_dir=output_dir)
node = extract_posterior_exe.create_node()
# add back the parameters option if it was specified
if parameters is not None:
node.add_opt("--parameters", _params_for_pegasus(parameters))
# and put the opt back in the config file in memory
workflow.cp.set(name, 'parameters', parameters)
if not isinstance(samples_files, list):
samples_files = [samples_files]
node.add_input_list_opt("--input-file", samples_files)
node.new_output_file_opt(analysis_seg, ".hdf", "--output-file", tags=tags)
# add node to workflow
workflow += node
return node.output_files
def cut_distant_injections(workflow, inj_file, out_dir, tags=None):
"Set up a job for removing injections that are too distant to be seen"
if tags is None:
tags = []
node = Executable(workflow.cp, 'inj_cut', ifos=workflow.ifos,
out_dir=out_dir, tags=tags).create_node()
node.add_input_opt('--input', inj_file)
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def create_fits_file(workflow,
inference_file,
output_dir,
name="create_fits_file",
analysis_seg=None,
tags=None):
"""Sets up job to create fits files from some given samples files.
Parameters
----------
workflow: pycbc.workflow.Workflow
The workflow instance we are populating
inference_file: pycbc.workflow.File
The file with posterior samples.
output_dir: str
The directory to store result plots and files.
name: str, optional
The name in the [executables] section of the configuration file
to use, and the section to read for additional arguments to pass to
the executable. Default is ``create_fits_file``.
analysis_segs: ligo.segments.Segment, optional
The segment this job encompasses. If None then use the total analysis
time from the workflow.
tags: list, optional
Tags to add to the inference executables.
Returns
-------
pycbc.workflow.FileList
A list of output files.
"""
if analysis_seg is None:
analysis_seg = workflow.analysis_time
if tags is None:
tags = []
create_fits_exe = Executable(workflow.cp,
name,
ifos=workflow.ifos,
out_dir=output_dir)
node = create_fits_exe.create_node()
node.add_input_opt("--input-file", inference_file)
node.new_output_file_opt(analysis_seg, ".fits", "--output-file", tags=tags)
# add node to workflow
workflow += node
return node.output_files
def compute_inj_optimal_snr(workflow, inj_file, precalc_psd_files, out_dir,
tags=None):
"Set up a job for computing optimal SNRs of a sim_inspiral file."
if tags is None:
tags = []
node = Executable(workflow.cp, 'optimal_snr', ifos=workflow.ifos,
out_dir=out_dir, tags=tags).create_node()
node.add_input_opt('--input-file', inj_file)
node.add_input_list_opt('--time-varying-psds', precalc_psd_files)
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def make_skipped_html(workflow, skipped_data, out_dir, tags):
"""
Make a html snippet from the list of skipped background coincidences
"""
exe = Executable(workflow.cp,
'html_snippet',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags)
node = exe.create_node()
parsed_data = {}
for ifo, time in skipped_data:
if ifo not in parsed_data:
parsed_data[ifo] = {}
if time not in parsed_data[ifo]:
parsed_data[ifo][time] = 1
else:
parsed_data[ifo][time] = parsed_data[ifo][time] + 1
n_events = len(skipped_data)
html_string = '"{} background events have been skipped '.format(n_events)
html_string += 'because one of their single triggers already appears '
html_string += 'in the events followed up above. '
html_string += 'Specifically, the following single detector triggers '
html_string += 'were found in these coincidences. '
html_template = '{} event at time {} appeared {} times. '
for ifo in parsed_data:
for time in parsed_data[ifo]:
n_occurances = parsed_data[ifo][time]
html_string += html_template.format(ifo, time, n_occurances)
html_string += '"'
node.add_opt('--html-text', html_string)
node.add_opt('--title', '"Events were skipped"')
node.new_output_file_opt(workflow.analysis_time, '.html', '--output-file')
workflow += node
files = node.output_files
return files
def compute_inj_optimal_snr(workflow, inj_file, precalc_psd_files, out_dir,
tags=None):
"Set up a job for computing optimal SNRs of a sim_inspiral file."
if tags is None:
tags = []
node = Executable(workflow.cp, 'optimal_snr', ifos=workflow.ifos,
out_dir=out_dir, tags=tags).create_node()
node.add_input_opt('--input-file', inj_file)
node.add_input_list_opt('--time-varying-psds', precalc_psd_files)
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def cut_distant_injections(workflow, inj_file, out_dir, tags=None):
"Set up a job for removing injections that are too distant to be seen"
if tags is None:
tags = []
node = Executable(workflow.cp, 'inj_cut', ifos=workflow.ifos,
out_dir=out_dir, tags=tags).create_node()
node.add_input_opt('--input', inj_file)
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def inj_to_hdf(workflow, inj_file, out_dir, tags=None):
""" Convert injection file to hdf format if not already one
"""
_, ext = os.path.splitext(inj_file.name)
if ext == '.hdf':
return inj_file
if tags is None:
tags = []
node = Executable(workflow.cp,
'inj2hdf',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags).create_node()
node.add_input_opt('--injection-file', inj_file)
node.new_output_file_opt(workflow.analysis_time, '.hdf', '--output-file')
workflow += node
return node.output_file
def setup_injection_minifollowups(workflow,
injection_file,
inj_xml_file,
single_triggers,
tmpltbank_file,
insp_segs,
insp_seg_name,
dax_output,
out_dir,
tags=None):
""" Create plots that followup the closest missed injections
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file:
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: dict
A dictionary, keyed by ifo name, of the data read by each inspiral job.
insp_segs_name: str
The name of the segmentlist to read from the inspiral segment file
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering injection minifollowups module')
if not workflow.cp.has_section('workflow-injection_minifollowups'):
logging.info(
'There is no [workflow-injection_minifollowups] section in configuration file'
)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + '/' + '_'.join(tags) +
'injection_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, 'local')
exe = Executable(workflow.cp,
'injection_minifollowup',
ifos=workflow.ifos,
out_dir=dax_output)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--injection-file', injection_file)
node.add_input_opt('--injection-xml-file', inj_xml_file)
node.add_multiifo_input_list_opt('--single-detector-triggers',
single_triggers)
node.add_multiifo_input_list_opt('--inspiral-segments', insp_segs.values())
node.add_opt('--inspiral-segment-name', insp_seg_name)
node.new_output_file_opt(workflow.analysis_time,
'.dax',
'--output-file',
tags=tags)
node.new_output_file_opt(workflow.analysis_time,
'.dax.map',
'--output-map',
tags=tags)
name = node.output_files[0].name
map_loc = node.output_files[1].name
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this is a sub-workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments('--basename %s' %
os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_loc)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving injection minifollowups module')
def setup_single_det_minifollowups(workflow,
single_trig_file,
tmpltbank_file,
insp_segs,
insp_seg_name,
dax_output,
out_dir,
veto_file=None,
veto_segment_name=None,
tags=None):
""" Create plots that followup the Nth loudest clustered single detector
triggers from a merged single detector trigger HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
single_trig_file: pycbc.workflow.File
The File class holding the single detector triggers.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: dict
A dictionary, keyed by ifo name, of the data read by each inspiral job.
insp_segs_name: str
The name of the segmentlist to read from the inspiral segment file
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-minifollowups'):
msg = 'There is no [workflow-minifollowups] section in '
msg += 'configuration file'
logging.info(msg)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
curr_ifo = single_trig_file.ifo
config_path = os.path.abspath(dax_output + '/' + curr_ifo + \
'_'.join(tags) + 'singles_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, 'local')
exe = Executable(workflow.cp,
'singles_minifollowup',
ifos=curr_ifo,
out_dir=dax_output)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--single-detector-file', single_trig_file)
node.add_input_opt('--inspiral-segments', insp_segs[curr_ifo])
node.add_opt('--inspiral-segment-name', insp_seg_name)
node.add_opt('--instrument', curr_ifo)
if veto_file is not None:
assert (veto_segment_name is not None)
node.add_input_opt('--veto-file', veto_file)
node.add_opt('--veto-segment-name', veto_segment_name)
node.new_output_file_opt(workflow.analysis_time,
'.dax',
'--output-file',
tags=tags)
node.new_output_file_opt(workflow.analysis_time,
'.dax.map',
'--output-map',
tags=tags)
name = node.output_files[0].name
map_loc = node.output_files[1].name
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this is a sub-workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments('--basename %s' \
% os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_loc)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving minifollowups module')
def setup_foreground_inference(workflow,
coinc_file,
single_triggers,
tmpltbank_file,
insp_segs,
insp_data_name,
insp_anal_name,
dax_output,
out_dir,
tags=None):
""" Creates workflow node that will run the inference workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file: pycbc.workflow.File
The file associated with coincident triggers.
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read and analyzed by each inspiral
job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
dax_output : str
The name of the output DAX file.
out_dir: path
The directory to store inference result plots and files
tags: {None, optional}
Tags to add to the inference executables
"""
logging.info("Entering inference module")
# check if configuration file has inference section
if not workflow.cp.has_section("workflow-inference"):
logging.info(
"There is no [workflow-inference] section in configuration file")
logging.info("Leaving inference module")
return
# default tags is a list
tags = [] if tags is None else tags
# make the directory that will contain the dax file
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + "/" + "_".join(tags) \
+ "foreground_inference.ini")
workflow.cp.write(open(config_path, "w"))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, "local")
# create an Executable for the inference workflow generator
exe = Executable(workflow.cp,
"foreground_inference",
ifos=workflow.ifos,
out_dir=dax_output)
# create the node that will run in the workflow
node = exe.create_node()
node.add_input_opt("--config-files", config_file)
node.add_input_opt("--bank-file", tmpltbank_file)
node.add_input_opt("--statmap-file", coinc_file)
node.add_multiifo_input_list_opt("--single-detector-triggers",
single_triggers)
node.new_output_file_opt(workflow.analysis_time,
".dax",
"--output-file",
tags=tags)
node.new_output_file_opt(workflow.analysis_time,
".dax.map",
"--output-map",
tags=tags)
node.new_output_file_opt(workflow.analysis_time,
".tc.txt",
"--transformation-catalog",
tags=tags)
# get dax name and use it for the workflow name
name = node.output_files[0].name
node.add_opt("--workflow-name", name)
# get output map name and use it for the output dir name
map_file = node.output_files[1]
node.add_opt("--output-dir", out_dir)
# get the transformation catalog name
tc_file = node.output_files[2]
# add this node to the workflow
workflow += node
# create job for dax that will run a sub-workflow
# and add it to the workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments("--basename %s" %
os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_file, tc_file)
workflow._adag.addJob(job)
# make dax a child of the inference workflow generator node
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info("Leaving inference module")
def veto_injections(workflow,
inj_file,
veto_file,
veto_name,
out_dir,
tags=None):
tags = [] if tags is None else tags
make_analysis_dir(out_dir)
node = Executable(workflow.cp,
'strip_injections',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags).create_node()
node.add_opt('--segment-name', veto_name)
node.add_input_opt('--veto-file', veto_file)
node.add_input_opt('--injection-file', inj_file)
node.add_opt('--ifos', ' '.join(workflow.ifos))
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def setup_foreground_inference(workflow, coinc_file, single_triggers,
tmpltbank_file, insp_segs, insp_data_name,
insp_anal_name, dax_output, out_dir, tags=None):
""" Creates workflow node that will run the inference workflow.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file: pycbc.workflow.File
The file associated with coincident triggers.
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read and analyzed by each inspiral
job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
dax_output : str
The name of the output DAX file.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
"""
logging.info("Entering inference module")
# check if configuration file has inference section
if not workflow.cp.has_section("workflow-inference"):
logging.info("There is no [workflow-inference] section in configuration file")
logging.info("Leaving inference module")
return
# default tags is a list
tags = [] if tags is None else tags
# make the directory that will contain the dax file
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + "/" + "_".join(tags) \
+ "foreground_inference.ini")
workflow.cp.write(open(config_path, "w"))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, "local")
# create an Executable for the inference workflow generator
exe = Executable(workflow.cp, "foreground_inference", ifos=workflow.ifos,
out_dir=dax_output)
# create the node that will run in the workflow
node = exe.create_node()
node.add_input_opt("--config-files", config_file)
node.add_input_opt("--bank-file", tmpltbank_file)
node.add_input_opt("--statmap-file", coinc_file)
node.add_multiifo_input_list_opt("--single-detector-triggers",
single_triggers)
node.new_output_file_opt(workflow.analysis_time, ".dax", "--output-file",
tags=tags)
node.new_output_file_opt(workflow.analysis_time, ".dax.map",
"--output-map", tags=tags)
# get dax name and use it for the workflow name
name = node.output_files[0].name
node.add_opt("--workflow-name", name)
# get output map name and use it for the output dir name
map_loc = node.output_files[1].name
node.add_opt("--output-dir", out_dir)
# add this node to the workflow
workflow += node
# create job for dax that will run a sub-workflow
# and add it to the workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments("--basename %s" % os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_loc)
workflow._adag.addJob(job)
# make dax a child of the inference workflow generator node
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info("Leaving inference module")
def rerank_coinc_followup(workflow,
statmap_file,
bank_file,
out_dir,
tags=None,
injection_file=None,
ranking_file=None):
if tags is None:
tags = []
make_analysis_dir(out_dir)
if not workflow.cp.has_section("workflow-rerank"):
logging.info("No reranking done in this workflow")
return statmap_file
else:
logging.info("Setting up reranking of candidates")
# Generate reduced data files (maybe this could also be used elsewhere?)
stores = FileList([])
for ifo in workflow.ifos:
make_analysis_dir('strain_files')
node = Executable(workflow.cp,
'strain_data_reduce',
ifos=[ifo],
out_dir='strain_files',
tags=tags).create_node()
node.add_opt('--gps-start-time', workflow.analysis_time[0])
node.add_opt('--gps-end-time', workflow.analysis_time[1])
if injection_file:
node.add_input_opt('--injection-file', injection_file)
fil = node.new_output_file_opt(workflow.analysis_time, '.hdf',
'--output-file')
stores.append(fil)
workflow += node
# Generate trigger input file
node = Executable(workflow.cp,
'rerank_trigger_input',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags).create_node()
node.add_input_opt('--statmap-file', statmap_file)
node.add_input_opt('--bank-file', bank_file)
trigfil = node.new_output_file_opt(workflow.analysis_time, '.hdf',
'--output-file')
workflow += node
# Parallelize coinc trigger followup
factor = int(
workflow.cp.get_opt_tags("workflow-rerank", "parallelization-factor",
tags))
exe = Executable(workflow.cp,
'coinc_followup',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags)
stat_files = FileList([])
for i in range(factor):
node = exe.create_node()
node.new_output_file_opt(workflow.analysis_time,
'.hdf',
'--output-file',
tags=[str(i)])
node.add_multiifo_input_list_opt('--hdf-store', stores)
node.add_input_opt('--input-file', trigfil)
node.add_opt('--start-index', str(i))
node.add_opt('--stride', factor)
workflow += node
stat_files += node.output_files
exe = Executable(workflow.cp,
'rerank_coincs',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags)
node = exe.create_node()
node.add_input_list_opt('--stat-files', stat_files)
node.add_input_opt('--statmap-file', statmap_file)
node.add_input_opt('--followup-file', trigfil)
if ranking_file:
node.add_input_opt('--ranking-file', ranking_file)
node.new_output_file_opt(workflow.analysis_time, '.hdf', '--output-file')
workflow += node
return node.output_file
def create_node(self):
node = Executable.create_node(self)
node.set_priority(1000)
return node
def setup_foreground_minifollowups(workflow,
coinc_file,
single_triggers,
tmpltbank_file,
insp_segs,
insp_data_name,
insp_anal_name,
dax_output,
out_dir,
tags=None):
""" Create plots that followup the Nth loudest coincident injection
from a statmap produced HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file:
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read and analyzed by each inspiral
job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-minifollowups'):
logging.info(
'There is no [workflow-minifollowups] section in configuration file'
)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + '/' + '_'.join(tags) +
'foreground_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = resolve_url_to_file(config_path)
exe = Executable(workflow.cp,
'foreground_minifollowup',
ifos=workflow.ifos,
out_dir=dax_output,
tags=tags)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--statmap-file', coinc_file)
node.add_multiifo_input_list_opt('--single-detector-triggers',
single_triggers)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
if tags:
node.add_list_opt('--tags', tags)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--dax-file')
node.new_output_file_opt(workflow.analysis_time, '.dax.map',
'--output-map')
name = node.output_files[0].name
map_file = node.output_files[1]
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
# determine if a staging site has been specified
job = SubWorkflow(fil.name, is_planned=False)
input_files = [tmpltbank_file, coinc_file, insp_segs] + single_triggers
job.add_inputs(*input_files)
job.set_subworkflow_properties(map_file,
staging_site=workflow.staging_site,
cache_file=workflow.cache_file)
job.add_into_workflow(workflow, parents=[node])
logging.info('Leaving minifollowups module')
def setup_single_det_minifollowups(workflow,
single_trig_file,
tmpltbank_file,
insp_segs,
insp_data_name,
insp_anal_name,
dax_output,
out_dir,
veto_file=None,
veto_segment_name=None,
statfiles=None,
tags=None):
""" Create plots that followup the Nth loudest clustered single detector
triggers from a merged single detector trigger HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
single_trig_file: pycbc.workflow.File
The File class holding the single detector triggers.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read by each inspiral job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
statfiles: FileList (optional, default=None)
Supplementary files necessary for computing the single-detector
statistic.
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-sngl_minifollowups'):
msg = 'There is no [workflow-sngl_minifollowups] section in '
msg += 'configuration file'
logging.info(msg)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
curr_ifo = single_trig_file.ifo
config_path = os.path.abspath(dax_output + '/' + curr_ifo + \
'_'.join(tags) + 'singles_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = resolve_url_to_file(config_path)
exe = Executable(workflow.cp,
'singles_minifollowup',
ifos=curr_ifo,
out_dir=dax_output,
tags=tags)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--single-detector-file', single_trig_file)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
node.add_opt('--instrument', curr_ifo)
if veto_file is not None:
assert (veto_segment_name is not None)
node.add_input_opt('--veto-file', veto_file)
node.add_opt('--veto-segment-name', veto_segment_name)
if statfiles:
statfiles = statfiles.find_output_with_ifo(curr_ifo)
node.add_input_list_opt('--statistic-files', statfiles)
if tags:
node.add_list_opt('--tags', tags)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--dax-file')
node.new_output_file_opt(workflow.analysis_time, '.dax.map',
'--output-map')
name = node.output_files[0].name
map_file = node.output_files[1]
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
job = SubWorkflow(fil.name, is_planned=False)
input_files = [tmpltbank_file, insp_segs, single_trig_file]
if veto_file is not None:
input_files.append(veto_file)
if statfiles:
input_files += statfiles
job.add_inputs(*input_files)
job.set_subworkflow_properties(map_file,
staging_site=workflow.staging_site,
cache_file=workflow.cache_file)
job.add_into_workflow(workflow, parents=[node])
logging.info('Leaving minifollowups module')
def setup_injection_minifollowups(
workflow,
injection_file,
inj_xml_file,
single_triggers,
tmpltbank_file,
insp_segs,
insp_data_name,
insp_anal_name,
dax_output,
out_dir,
tags=None,
):
""" Create plots that followup the closest missed injections
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file:
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read by each inspiral job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info("Entering injection minifollowups module")
if not workflow.cp.has_section("workflow-injection_minifollowups"):
logging.info("There is no [workflow-injection_minifollowups] section in configuration file")
logging.info("Leaving minifollowups")
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + "/" + "_".join(tags) + "injection_minifollowup.ini")
workflow.cp.write(open(config_path, "w"))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, "local")
exe = Executable(workflow.cp, "injection_minifollowup", ifos=workflow.ifos, out_dir=dax_output)
node = exe.create_node()
node.add_input_opt("--config-files", config_file)
node.add_input_opt("--bank-file", tmpltbank_file)
node.add_input_opt("--injection-file", injection_file)
node.add_input_opt("--injection-xml-file", inj_xml_file)
node.add_multiifo_input_list_opt("--single-detector-triggers", single_triggers)
node.add_input_opt("--inspiral-segments", insp_segs)
node.add_opt("--inspiral-data-read-name", insp_data_name)
node.add_opt("--inspiral-data-analyzed-name", insp_anal_name)
node.new_output_file_opt(workflow.analysis_time, ".dax", "--output-file", tags=tags)
node.new_output_file_opt(workflow.analysis_time, ".dax.map", "--output-map", tags=tags)
name = node.output_files[0].name
map_loc = node.output_files[1].name
node.add_opt("--workflow-name", name)
node.add_opt("--output-dir", out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments("--basename %s" % os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_loc)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info("Leaving injection minifollowups module")
def create_node(self):
node = Executable.create_node(self)
node.set_priority(1000)
return node
def setup_single_det_minifollowups(workflow, single_trig_file, tmpltbank_file,
insp_segs, insp_data_name, insp_anal_name,
dax_output, out_dir, veto_file=None,
veto_segment_name=None, statfiles=None,
tags=None):
""" Create plots that followup the Nth loudest clustered single detector
triggers from a merged single detector trigger HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
single_trig_file: pycbc.workflow.File
The File class holding the single detector triggers.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read by each inspiral job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
statfiles: FileList (optional, default=None)
Supplementary files necessary for computing the single-detector
statistic.
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-sngl_minifollowups'):
msg = 'There is no [workflow-sngl_minifollowups] section in '
msg += 'configuration file'
logging.info(msg)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
curr_ifo = single_trig_file.ifo
config_path = os.path.abspath(dax_output + '/' + curr_ifo + \
'_'.join(tags) + 'singles_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(urljoin('file:', pathname2url(config_path)), site='local')
exe = Executable(workflow.cp, 'singles_minifollowup',
ifos=curr_ifo, out_dir=dax_output, tags=tags)
wikifile = curr_ifo + '_'.join(tags) + 'loudest_table.txt'
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--single-detector-file', single_trig_file)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
node.add_opt('--instrument', curr_ifo)
node.add_opt('--wiki-file', wikifile)
if veto_file is not None:
assert(veto_segment_name is not None)
node.add_input_opt('--veto-file', veto_file)
node.add_opt('--veto-segment-name', veto_segment_name)
if statfiles:
statfiles = statfiles.find_output_with_ifo(curr_ifo)
node.add_input_list_opt('--statistic-files', statfiles)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--output-file')
node.new_output_file_opt(workflow.analysis_time, '.dax.map',
'--output-map')
node.new_output_file_opt(workflow.analysis_time, '.tc.txt',
'--transformation-catalog')
name = node.output_files[0].name
map_file = node.output_files[1]
tc_file = node.output_files[2]
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
# determine if a staging site has been specified
try:
staging_site = workflow.cp.get('workflow-sngl_minifollowups',
'staging-site')
except:
staging_site = None
job = dax.DAX(fil)
job.addArguments('--basename %s' \
% os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_file, tc_file,
staging_site=staging_site)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving minifollowups module')
def create_segs_from_cats_job(cp, out_dir, ifo_string, tag=None):
"""
This function creates the CondorDAGJob that will be used to run
ligolw_segments_from_cats as part of the workflow
Parameters
-----------
cp : pycbc.workflow.configuration.WorkflowConfigParser
The in-memory representation of the configuration (.ini) files
out_dir : path
Directory in which to put output files
ifo_string : string
String containing all active ifos, ie. "H1L1V1"
tag : string, optional (default=None)
Use this to specify a tag. This can be used if this module is being
called more than once to give call specific configuration (by setting
options in [workflow-datafind-${TAG}] rather than [workflow-datafind]). This
is also used to tag the Files returned by the class to uniqueify
the Files and uniqueify the actual filename.
FIXME: Filenames may not be unique with current codes!
Returns
--------
job : Job instance
The Job instance that will run segments_from_cats jobs
"""
segServerUrl = cp.get_opt_tags("workflow-segments",
"segments-database-url", [tag])
vetoDefFile = cp.get_opt_tags("workflow-segments",
"segments-veto-definer-file", [tag])
if tag:
currTags = [tag]
else:
currTags = []
job = Executable(cp, 'segments_from_cats', universe='local',
ifos=ifo_string, out_dir=out_dir, tags=currTags)
job.add_opt('--separate-categories')
job.add_opt('--segment-url', segServerUrl)
job.add_opt('--veto-file', vetoDefFile)
# FIXME: Would like the proxy in the Workflow instance
# FIXME: Explore using the x509 condor commands
# Set up proxy to be accessible in a NFS location
# If the user has logged in with gsissh then X509_USER_PROXY will be set
# However, certain users log in with an ssh key and then ligo-proxy-init
# This route does not set X509_USER_PROXY, so use the default file location
if os.environ.has_key('X509_USER_PROXY'):
proxy = os.getenv('X509_USER_PROXY')
else:
proxy = "/tmp/x509up_u%d" % os.getuid()
proxyfile = os.path.join(out_dir, 'x509up.file')
try:
shutil.copyfile(proxy, proxyfile)
except IOError:
raise RuntimeError('Cannot find certificate in %s. '
'Make sure that ligo-proxy-init '
'has been run.' % proxy)
job.add_profile('condor', 'environment',
'USER=$ENV(USER);X509_USER_PROXY=%s' % proxyfile)
return job
def veto_injections(workflow, inj_file, veto_file, veto_name, out_dir, tags=None):
tags = [] if tags is None else tags
make_analysis_dir(out_dir)
node = Executable(workflow.cp, 'strip_injections', ifos=workflow.ifos,
out_dir=out_dir, tags=tags).create_node()
node.add_opt('--segment-name', veto_name)
node.add_input_opt('--veto-file', veto_file)
node.add_input_opt('--injection-file', inj_file)
node.add_opt('--ifos', ' '.join(workflow.ifos))
node.new_output_file_opt(workflow.analysis_time, '.xml', '--output-file')
workflow += node
return node.output_files[0]
def setup_single_det_minifollowups(workflow, single_trig_file, tmpltbank_file,
insp_segs, insp_data_name, insp_anal_name,
dax_output, out_dir, veto_file=None,
veto_segment_name=None, tags=None):
""" Create plots that followup the Nth loudest clustered single detector
triggers from a merged single detector trigger HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
single_trig_file: pycbc.workflow.File
The File class holding the single detector triggers.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read by each inspiral job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-sngl_minifollowups'):
msg = 'There is no [workflow-sngl_minifollowups] section in '
msg += 'configuration file'
logging.info(msg)
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
curr_ifo = single_trig_file.ifo
config_path = os.path.abspath(dax_output + '/' + curr_ifo + \
'_'.join(tags) + 'singles_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, 'local')
exe = Executable(workflow.cp, 'singles_minifollowup',
ifos=curr_ifo, out_dir=dax_output, tags=tags)
wikifile = curr_ifo + '_'.join(tags) + 'loudest_table.txt'
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--single-detector-file', single_trig_file)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
node.add_opt('--instrument', curr_ifo)
node.add_opt('--wiki-file', wikifile)
if veto_file is not None:
assert(veto_segment_name is not None)
node.add_input_opt('--veto-file', veto_file)
node.add_opt('--veto-segment-name', veto_segment_name)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--output-file', tags=tags)
node.new_output_file_opt(workflow.analysis_time, '.dax.map', '--output-map', tags=tags)
name = node.output_files[0].name
map_file = node.output_files[1]
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments('--basename %s' \
% os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_file)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving minifollowups module')
def setup_plotthinca(workflow,
input_files,
cache_filename,
coinc_cachepattern,
slide_cachepattern,
output_dir,
tags=[],
**kwargs):
"""
This function sets up the nodes that will generate summary from a list of
thinca files.
Parameters
-----------
Workflow : ahope.Workflow
The ahope workflow instance that the coincidence jobs will be added to.
input_files : ahope.FileList
An FileList of files that are used as input at this stage.
cache_filename : str
Filename of the ihope cache.
coinc_cachepattern : str
The pattern that will be used to find zero-lag coincidence filenames in the cache.
slide_cachepattern : str
The pattern that will be used to find time slide filenames in the cache.
output_dir : path
The directory in which output files will be stored.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['full_data'].
This will be used in output names and directories.
Returns
--------
plot_files : ahope.FileList
A list of the output files from this stage.
"""
plot_files = FileList([])
# create executable
plotthinca_job = Executable(workflow.cp, 'plotthinca', 'vanilla',
workflow.ifos, output_dir, tags)
# get all ifo combinations of at least 2 coincident ifos
ifo_combos = []
for n in xrange(len(plotthinca_job.ifo_list) + 1):
for ifo_list in itertools.combinations(plotthinca_job.ifo_list, n + 2):
ifo_combos.append(ifo_list)
for tag in tags:
for ifo_list in ifo_combos:
ifo_string = ''.join(ifo_list)
# create node
node = Node(plotthinca_job)
node.add_opt('--gps-start-time', workflow.analysis_time[0])
node.add_opt('--gps-end-time', workflow.analysis_time[1])
node.add_opt('--cache-file', cache_filename)
node.add_opt('--ifo-times', ifo_string)
node.add_opt('--ifo-tag', 'SECOND_' + ifo_string)
for ifo in ifo_list:
node.add_opt('--%s-triggers' % ifo.lower(), '')
node.add_opt('--user-tag', tag.upper() + '_SUMMARY_PLOTS')
node.add_opt('--output-path', output_dir)
node.add_opt('--coinc-pattern', coinc_cachepattern)
node.add_opt('--slide-pattern', slide_cachepattern)
node.add_opt('--enable-output')
# add node to workflow
workflow.add_node(node)
# make all input_files parents
#for f in input_files:
# dep = dax.Dependency(parent=f.node._dax_node, child=node._dax_node)
# workflow._adag.addDependency(dep)
return plot_files
def setup_foreground_minifollowups(workflow, coinc_file, single_triggers,
tmpltbank_file, insp_segs, insp_data_name,
insp_anal_name, dax_output, out_dir, tags=None):
""" Create plots that followup the Nth loudest coincident injection
from a statmap produced HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file:
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read and analyzed by each inspiral
job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-minifollowups'):
logging.info('There is no [workflow-minifollowups] section in configuration file')
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + '/' + '_'.join(tags) + 'foreground_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(config_path, 'local')
exe = Executable(workflow.cp, 'foreground_minifollowup', ifos=workflow.ifos, out_dir=dax_output)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--statmap-file', coinc_file)
node.add_multiifo_input_list_opt('--single-detector-triggers', single_triggers)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--output-file', tags=tags)
node.new_output_file_opt(workflow.analysis_time, '.dax.map', '--output-map', tags=tags)
name = node.output_files[0].name
map_loc = node.output_files[1].name
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
job = dax.DAX(fil)
job.addArguments('--basename %s' % os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_loc)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving minifollowups module')
def setup_hardware_injection_page(workflow,
input_files,
cache_filename,
inspiral_cachepattern,
output_dir,
tags=[],
**kwargs):
"""
This function sets up the nodes that will create the hardware injection page.
Parameters
-----------
Workflow : ahope.Workflow
The ahope workflow instance that the coincidence jobs will be added to.
input_files : ahope.FileList
An FileList of files that are used as input at this stage.
cache_filename : str
Filename of the ihope cache.
inspiral_cachepattern : str
The pattern that will be used to find inspiral filenames in the cache.
output_dir : path
The directory in which output files will be stored.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['full_data'].
This will be used to search the cache.
Returns
--------
plot_files : ahope.FileList
A list of the output files from this stage.
"""
logging.info("Entering hardware injection page setup.")
out_files = FileList([])
# check if hardware injection section exists
# if not then do not do add hardware injection job to the workflow
if not workflow.cp.has_section('workflow-hardware-injections'):
msg = "There is no workflow-hardware-injections section. "
msg += "The hardware injection page will not be added to the workflow."
logging.info(msg)
logging.info("Leaving hardware injection page setup.")
return out_files
# make the output dir
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# create executable
hwinjpage_job = Executable(workflow.cp, 'hardware_injection_page',
'vanilla', workflow.ifos, output_dir, tags)
# retrieve hardware injection file
hwinjDefUrl = workflow.cp.get_opt_tags('workflow-hardware-injections',
'hwinj-definer-url', tags)
hwinjDefBaseName = os.path.basename(hwinjDefUrl)
hwinjDefNewPath = os.path.join(output_dir, hwinjDefBaseName)
urllib.urlretrieve(hwinjDefUrl, hwinjDefNewPath)
# update hwinj definer file location
workflow.cp.set("workflow-hardware-injections", "hwinj-definer-file",
hwinjDefNewPath)
# query for the hardware injection segments
get_hardware_injection_segment_files(workflow, output_dir, hwinjDefNewPath)
# create node
node = Node(hwinjpage_job)
node.add_opt('--gps-start-time', workflow.analysis_time[0])
node.add_opt('--gps-end-time', workflow.analysis_time[1])
node.add_opt('--source-xml', hwinjDefNewPath)
node.add_opt('--segment-dir', output_dir)
node.add_opt('--cache-file', cache_filename)
node.add_opt('--cache-pattern', inspiral_cachepattern)
node.add_opt('--analyze-injections', '')
for ifo in workflow.ifos:
node.add_opt('--%s-injections' % ifo.lower(), '')
outfile = File(node.executable.ifo_string,
'HWINJ_SUMMARY',
workflow.analysis_time,
extension='html',
directory=output_dir)
node.add_opt('--outfile', outfile.storage_path)
# add node to workflow
workflow.add_node(node)
# make all input_files parents
#for f in input_files:
# dep = dax.Dependency(parent=f.node._dax_node, child=node._dax_node)
# workflow._adag.addDependency(dep)
out_files += node.output_files
logging.info("Leaving hardware injection page setup.")
return out_files
def setup_foreground_minifollowups(workflow, coinc_file, single_triggers,
tmpltbank_file, insp_segs, insp_data_name,
insp_anal_name, dax_output, out_dir, tags=None):
""" Create plots that followup the Nth loudest coincident injection
from a statmap produced HDF file.
Parameters
----------
workflow: pycbc.workflow.Workflow
The core workflow instance we are populating
coinc_file:
single_triggers: list of pycbc.workflow.File
A list cointaining the file objects associated with the merged
single detector trigger files for each ifo.
tmpltbank_file: pycbc.workflow.File
The file object pointing to the HDF format template bank
insp_segs: SegFile
The segment file containing the data read and analyzed by each inspiral
job.
insp_data_name: str
The name of the segmentlist storing data read.
insp_anal_name: str
The name of the segmentlist storing data analyzed.
out_dir: path
The directory to store minifollowups result plots and files
tags: {None, optional}
Tags to add to the minifollowups executables
Returns
-------
layout: list
A list of tuples which specify the displayed file layout for the
minifollops plots.
"""
logging.info('Entering minifollowups module')
if not workflow.cp.has_section('workflow-minifollowups'):
logging.info('There is no [workflow-minifollowups] section in configuration file')
logging.info('Leaving minifollowups')
return
tags = [] if tags is None else tags
makedir(dax_output)
# turn the config file into a File class
config_path = os.path.abspath(dax_output + '/' + '_'.join(tags) + 'foreground_minifollowup.ini')
workflow.cp.write(open(config_path, 'w'))
config_file = wdax.File(os.path.basename(config_path))
config_file.PFN(urljoin('file:', pathname2url(config_path)), site='local')
exe = Executable(workflow.cp, 'foreground_minifollowup',
ifos=workflow.ifos, out_dir=dax_output, tags=tags)
node = exe.create_node()
node.add_input_opt('--config-files', config_file)
node.add_input_opt('--bank-file', tmpltbank_file)
node.add_input_opt('--statmap-file', coinc_file)
node.add_multiifo_input_list_opt('--single-detector-triggers',
single_triggers)
node.add_input_opt('--inspiral-segments', insp_segs)
node.add_opt('--inspiral-data-read-name', insp_data_name)
node.add_opt('--inspiral-data-analyzed-name', insp_anal_name)
if tags:
node.add_list_opt('--tags', tags)
node.new_output_file_opt(workflow.analysis_time, '.dax', '--output-file')
node.new_output_file_opt(workflow.analysis_time, '.dax.map', '--output-map')
node.new_output_file_opt(workflow.analysis_time, '.tc.txt',
'--transformation-catalog')
name = node.output_files[0].name
map_file = node.output_files[1]
tc_file = node.output_files[2]
node.add_opt('--workflow-name', name)
node.add_opt('--output-dir', out_dir)
workflow += node
# execute this in a sub-workflow
fil = node.output_files[0]
# determine if a staging site has been specified
try:
staging_site = workflow.cp.get('workflow-foreground_minifollowups',
'staging-site')
except:
staging_site = None
job = dax.DAX(fil)
job.addArguments('--basename %s' % os.path.splitext(os.path.basename(name))[0])
Workflow.set_job_properties(job, map_file, tc_file, staging_site=staging_site)
workflow._adag.addJob(job)
dep = dax.Dependency(parent=node._dax_node, child=job)
workflow._adag.addDependency(dep)
logging.info('Leaving minifollowups module')
def setup_plotnumtemplates(workflow,
input_files,
cache_filename,
tmpltbank_cachepattern,
output_dir,
tags=[],
**kwargs):
"""
This function sets up the nodes that will generate a plot of the number
of templates against time.
Parameters
-----------
Workflow : ahope.Workflow
The ahope workflow instance that the coincidence jobs will be added to.
input_files : ahope.FileList
An FileList of files that are used as input at this stage.
cache_filename : str
Filename of the ihope cache.
tmpltbank_cachepattern : str
The pattern that will be used to find template_bank filenames in the cache.
output_dir : path
The directory in which output files will be stored.
tags : list of strings (optional, default = [])
A list of the tagging strings that will be used for all jobs created
by this call to the workflow. An example might be ['full_data'].
This will be used in output names and directories.
Returns
--------
plot_files : ahope.FileList
A list of the output files from this stage.
"""
plot_files = FileList([])
# create executable
plotnumtemplates_job = Executable(workflow.cp, 'plotnumtemplates',
'vanilla', workflow.ifos, output_dir,
tags)
for tag in tags:
# create node
node = Node(plotnumtemplates_job)
node.add_opt('--gps-start-time', workflow.analysis_time[0])
node.add_opt('--gps-end-time', workflow.analysis_time[1])
node.add_opt('--cache-file', cache_filename)
node.add_opt('--ifo-times', node.executable.ifo_string)
node.add_opt('--user-tag', tag.upper() + '_SUMMARY_PLOTS')
node.add_opt('--output-path', output_dir)
node.add_opt('--bank-pattern', tmpltbank_cachepattern)
node.add_opt('--enable-output')
# add node to workflow
workflow.add_node(node)
# make all input_files parents
#for f in input_files:
# dep = dax.Dependency(parent=f.node._dax_node, child=node._dax_node)
# workflow._adag.addDependency(dep)
return plot_files