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 = []
try:
factor = int(
workflow.cp.get_opt_tags('workflow-optimal-snr',
'parallelization-factor', tags))
except ConfigParser.Error:
factor = 1
if factor == 1:
# parallelization factor not given - default to single optimal snr job
opt_snr_exe = PyCBCOptimalSNRExecutable(workflow.cp,
'optimal_snr',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags)
node = opt_snr_exe.create_node(workflow, inj_file, precalc_psd_files,
'0/1')
workflow += node
return node.output_files[0]
opt_snr_split_files = []
for i in range(factor):
group_str = '%s/%s' % (i, factor)
opt_snr_exe = PyCBCOptimalSNRExecutable(workflow.cp,
'optimal_snr',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags + [str(i)])
opt_snr_exe.update_current_retention_level(
Executable.INTERMEDIATE_PRODUCT)
node = opt_snr_exe.create_node(workflow, inj_file, precalc_psd_files,
group_str)
opt_snr_split_files += [node.output_files[0]]
workflow += node
llwadd_exe = LigolwAddExecutable(workflow.cp,
'optimal_snr_merge',
ifos=workflow.ifos,
out_dir=out_dir,
tags=tags)
llwadd_exe.update_current_retention_level(Executable.MERGED_TRIGGERS)
merge_node = llwadd_exe.create_node(workflow.analysis_time,
opt_snr_split_files,
use_tmp_subdirs=False)
workflow += merge_node
return merge_node.output_files[0]
def add_cumulative_files(workflow, output_file, input_files, out_dir,
execute_now=False, tags=[]):
"""
Function to combine a set of segment files into a single one. This function
will not merge the segment lists but keep each separate.
Parameters
-----------
workflow: pycbc.workflow.core.Workflow
An instance of the Workflow class that manages the workflow.
output_file: pycbc.workflow.core.File
The output file object
input_files: pycbc.workflow.core.FileList
This list of input segment files
out_dir : path
The directory to write output to.
execute_now : boolean, optional
If true, jobs are executed immediately. If false, they are added to the
workflow to be run later.
tags : list of strings, optional
A list of strings that is used to identify this job
"""
llwadd_job = LigolwAddExecutable(workflow.cp, 'llwadd',
ifo=output_file.ifo_list, out_dir=out_dir, tags=tags)
add_node = llwadd_job.create_node(output_file.segment, input_files,
output=output_file)
if execute_now:
if file_needs_generating(add_node.output_files[0].cache_entry.path):
workflow.execute_node(add_node)
else:
add_node.executed = True
for fil in add_node._outputs:
fil.node = None
fil.PFN(fil.storage_path, site='local')
else:
workflow.add_node(add_node)
return add_node.output_files[0]
def get_cumulative_segs(workflow, currSegFile, categories,
segFilesList, out_dir, tags=[],
execute_now=False, segment_name=None):
"""
Function to generate one of the cumulative, multi-detector segment files
as part of the workflow.
Parameters
-----------
workflow: pycbc.workflow.core.Workflow
An instance of the Workflow class that manages the workflow.
currSegFile : pycbc.workflow.core.SegFile
The SegFile corresponding to this file that will be created.
categories : int
The veto categories to include in this cumulative veto.
segFilesList : Listionary of SegFiles
The list of segment files to be used as input for combining.
out_dir : path
The directory to write output to.
tags : list of strings, optional
A list of strings that is used to identify this job
execute_now : boolean, optional
If true, jobs are executed immediately. If false, they are added to the
workflow to be run later.
"""
add_inputs = FileList([])
valid_segment = currSegFile.segment
if segment_name is None:
segment_name = 'VETO_CAT%d_CUMULATIVE' % (categories[-1])
cp = workflow.cp
# calculate the cumulative veto files for a given ifo
for ifo in workflow.ifos:
cum_job = LigoLWCombineSegsExecutable(cp, 'ligolw_combine_segments',
out_dir=out_dir, tags=tags + [segment_name], ifos=ifo)
inputs = []
files = segFilesList.find_output_with_ifo(ifo)
for category in categories:
fileList = files.find_output_with_tag('VETO_CAT%d' %(category))
inputs+=fileList
cum_node = cum_job.create_node(valid_segment, inputs, segment_name)
if execute_now:
if file_needs_generating(cum_node.output_files[0].cache_entry.path):
workflow.execute_node(cum_node)
else:
cum_node.executed = True
for fil in cum_node._outputs:
fil.node = None
fil.PFN(fil.storage_path, site='local')
else:
workflow.add_node(cum_node)
add_inputs += cum_node.output_files
# add cumulative files for each ifo together
add_job = LigolwAddExecutable(cp, 'llwadd', ifo=ifo, out_dir=out_dir, tags=tags)
add_node = add_job.create_node(valid_segment, add_inputs,
output=currSegFile)
if execute_now:
if file_needs_generating(add_node.output_files[0].cache_entry.path):
workflow.execute_node(add_node)
else:
add_node.executed = True
for fil in add_node._outputs:
fil.node = None
fil.PFN(fil.storage_path, site='local')
else:
workflow.add_node(add_node)
return add_node.output_files[0]
def setup_coincidence_workflow_ligolw_thinca(
workflow, segsList, timeSlideFiles, inspiral_outs, output_dir,
veto_cats=[2,3,4], tags=[], timeSlideTags=None,
parallelize_split_input=False):
"""
This function is used to setup a single-stage ihope style coincidence stage
of the workflow using ligolw_sstinca (or compatible code!).
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The workflow instance that the coincidence jobs will be added to.
segsList : pycbc.workflow.core.FileList
The list of files returned by workflow's segment module that contains
pointers to all the segment files generated in the workflow. If the
coincidence code will be applying the data quality vetoes, then this
will be used to ensure that the codes get the necessary input to do
this.
timeSlideFiles : pycbc.workflow.core.FileList
An FileList of the timeSlide input files that are needed to
determine what time sliding needs to be done. One of the timeSlideFiles
will normally be "zero-lag only", the others containing time slides
used to facilitate background computations later in the workflow.
inspiral_outs : pycbc.workflow.core.FileList
An FileList of the matched-filter module output that is used as
input to the coincidence codes running at this stage.
output_dir : path
The directory in which coincidence output will be stored.
veto_cats : list of ints (optional, default = [2,3,4])
Veto categories that will be applied in the coincidence jobs. If this
takes the default value the code will run data quality at cumulative
categories 2, 3 and 4. Note that if we change the flag definitions to
be non-cumulative then this option will need to be revisited.
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 ['BNSINJECTIONS'] or
['NOINJECTIONANALYSIS']. This will be used in output names.
timeSlideTags : list of strings (optional, default = [])
A list of the tags corresponding to the timeSlideFiles that are to be
used in this call to the module. This can be used to ensure that the
injection runs do no time sliding, but the no-injection runs do perform
time slides (or vice-versa if you prefer!)
Returns
--------
ligolwThincaOuts : pycbc.workflow.core.FileList
A list of the output files generated from ligolw_sstinca.
ligolwAddOuts : pycbc.workflow.core.FileList
A list of the output files generated from ligolw_add.
"""
from pylal import ligolw_cafe
logging.debug("Entering coincidence module.")
cp = workflow.cp
ifoString = workflow.ifo_string
# setup code for each veto_category
coinc_outs = FileList([])
other_outs = {}
if not timeSlideTags:
# Get all sections by looking in ini file, use all time slide files.
timeSlideTags = [(sec.split('-')[-1]).upper()
for sec in workflow.cp.sections() if sec.startswith('tisi-')]
if parallelize_split_input:
# Want to split all input jobs according to their JOB%d tag.
# This matches any string that is the letters JOB followed by some
# numbers and nothing else.
inspiral_outs_dict = {}
regex_match = re.compile('JOB([0-9]+)\Z')
for file in inspiral_outs:
matches = [regex_match.match(tag) for tag in file.tags]
# Remove non matching entries
matches = [i for i in matches if i is not None]
# Must have one entry
if len(matches) == 0:
warn_msg = "I was asked to parallelize over split inspiral "
warn_msg += "files at the coincidence stage, but at least one "
warn_msg += "input file does not have a JOB\%d tag indicating "
warn_msg += "that it was split. Assuming that I do not have "
warn_msg += "split input files and turning "
warn_msg += "parallelize_split_input off."
logging.warn(warn_msg)
parallelize_split_input = False
break
if len(matches) > 1:
err_msg = "One of my input files has two tags fitting JOB\%d "
err_msg += "this means I cannot tell which split job this "
err_msg += "file is from."
raise ValueError(err_msg)
# Extract the job ID
id = int(matches[0].string[3:])
if not inspiral_outs_dict.has_key(id):
inspiral_outs_dict[id] = FileList([])
inspiral_outs_dict[id].append(file)
else:
# If I got through all the files I want to sort the dictionaries so
# that file with key a and index 3 is the same file as key b and
# index 3 other than the tag is JOBA -> JOBB ... ie. it has used
# a different part of the template bank.
sort_lambda = lambda x: (x.ifo_string, x.segment,
x.tagged_description)
for key in inspiral_outs_dict.keys():
inspiral_outs_dict[id].sort(key = sort_lambda)
# These should be in ascending order, so I can assume the existence
# of a JOB0 tag
inspiral_outs = inspiral_outs_dict[0]
for index, file in enumerate(inspiral_outs):
# Store the index in the file for quicker mapping later
file.thinca_index = index
else:
inspiral_outs_dict = None
for timeSlideTag in timeSlideTags:
# Get the time slide file from the inputs
tisiOutFile = timeSlideFiles.find_output_with_tag(timeSlideTag)
if not len(tisiOutFile) == 1:
errMsg = "If you are seeing this, something batshit is going on!"
if len(tisiOutFile) == 0:
errMsg = "No time slide files found matching %s." \
%(timeSlideTag)
if len(tisiOutFile) > 1:
errMsg = "More than one time slide files match %s." \
%(timeSlideTag)
raise ValueError(errMsg)
tisiOutFile = tisiOutFile[0]
# Next we run ligolw_cafe. This is responsible for
# identifying what times will be used for the ligolw_thinca jobs and
# what files are needed for each. If doing time sliding there
# will be some triggers read into multiple jobs
cacheInspOuts = inspiral_outs.convert_to_lal_cache()
if workflow.cp.has_option_tags("workflow-coincidence",
"maximum-extent", tags):
max_extent = float( workflow.cp.get_opt_tags(
"workflow-coincidence", "maximum-extent", tags) )
else:
# hard-coded default value for extent of time in a single job
max_extent = 3600
logging.debug("Calling into cafe.")
time_slide_table = lsctables.TimeSlideTable.get_table(\
ligolw_utils.load_filename(tisiOutFile.storage_path,
gz=tisiOutFile.storage_path.endswith(".gz"),
contenthandler=ContentHandler,
verbose=False))
time_slide_table.sync_next_id()
time_slide_dict = time_slide_table.as_dict()
cafe_seglists, cafe_caches = ligolw_cafe.ligolw_cafe(cacheInspOuts,
time_slide_dict.values(), extentlimit=max_extent, verbose=False)
logging.debug("Done with cafe.")
# Take the combined seglist file
dqSegFile=segsList.find_output_with_tag('COMBINED_CUMULATIVE_SEGMENTS')
if not len(dqSegFile) == 1:
errMsg = "Did not find exactly 1 data quality file."
print len(dqSegFile), dqSegFile
raise ValueError(errMsg)
dqSegFile=dqSegFile[0]
# Set up llwadd job
llwadd_tags = [timeSlideTag] + tags
ligolwadd_job = LigolwAddExecutable(cp, 'llwadd', ifo=ifoString,
out_dir=output_dir, tags=llwadd_tags)
ligolwAddOuts = FileList([])
# Go global setup at each category
# This flag will add a clustering job after ligolw_thinca
if workflow.cp.has_option_tags("workflow-coincidence",
"coincidence-post-cluster", llwadd_tags):
coinc_post_cluster = True
else:
coinc_post_cluster = False
# Go global setup at each category
ligolwthinca_job = {}
cluster_job = {}
thinca_tags = {}
for category in veto_cats:
logging.debug("Preparing %s %s" %(timeSlideTag,category))
dqVetoName = 'VETO_CAT%d_CUMULATIVE' %(category)
# FIXME: Should we resolve this now?
# FIXME: Here we set the dqVetoName to be compatible with pipedown
# For pipedown must put the slide identifier first and
# dqVetoName last.
pipedownDQVetoName = 'CAT_%d_VETO' %(category)
curr_thinca_job_tags = [timeSlideTag] + tags + [pipedownDQVetoName]
thinca_tags[category]=curr_thinca_job_tags
# Set up jobs for ligolw_thinca
ligolwthinca_job[category] = LigolwSSthincaExecutable(cp, 'thinca',
ifo=ifoString, out_dir=output_dir,
dqVetoName=dqVetoName,
tags=curr_thinca_job_tags)
if coinc_post_cluster:
cluster_job[category] = SQLInOutExecutable(cp, 'pycbccluster',
ifo=ifoString, out_dir=output_dir,
tags=curr_thinca_job_tags)
for idx, cafe_cache in enumerate(cafe_caches):
ligolwAddOuts = FileList([])
ligolwThincaOuts = FileList([])
ligolwThincaLikelihoodOuts = FileList([])
ligolwClusterOuts = FileList([])
if not len(cafe_cache.objects):
raise ValueError("One of the cache objects contains no files!")
# Determine segments to accept coincidences.
# If cache is not the first or last in the timeseries, check if the
# two closes caches in the timeseries and see if their extent
# match. If they match, they're adjacent and use the time where
# they meet as a bound for accepting coincidences. If they're not
# adjacent, then there is no bound for accepting coincidences.
coincStart, coincEnd = None, None
if idx and (cafe_cache.extent[0] == cafe_caches[idx-1].extent[1]):
coincStart = cafe_cache.extent[0]
if idx + 1 - len(cafe_caches) and \
(cafe_cache.extent[1] == cafe_caches[idx+1].extent[0]):
coincEnd = cafe_cache.extent[1]
coincSegment = (coincStart, coincEnd)
# Need to create a list of the File(s) contained in the cache.
# Assume that if we have partitioned input then if *one* job in the
# partitioned input is an input then *all* jobs will be.
if not parallelize_split_input:
inputTrigFiles = FileList([])
for object in cafe_cache.objects:
inputTrigFiles.append(object.workflow_file)
llw_files = inputTrigFiles + [dqSegFile] + [tisiOutFile]
# Now we can create the nodes
node = ligolwadd_job.create_node(cafe_cache.extent, llw_files)
ligolwAddFile = node.output_files[0]
ligolwAddOuts.append(ligolwAddFile)
workflow.add_node(node)
for category in veto_cats:
node = ligolwthinca_job[category].create_node(\
cafe_cache.extent, coincSegment, ligolwAddFile)
ligolwThincaOuts += \
node.output_files.find_output_without_tag('DIST_STATS')
ligolwThincaLikelihoodOuts += \
node.output_files.find_output_with_tag('DIST_STATS')
workflow.add_node(node)
if coinc_post_cluster:
node = cluster_job[category].create_node(\
cafe_cache.extent, ligolwThincaOuts[-1])
ligolwClusterOuts += node.output_files
workflow.add_node(node)
else:
for key in inspiral_outs_dict.keys():
curr_tags = ["JOB%d" %(key)]
curr_list = inspiral_outs_dict[key]
inputTrigFiles = FileList([])
for object in cafe_cache.objects:
inputTrigFiles.append(
curr_list[object.workflow_file.thinca_index])
llw_files = inputTrigFiles + [dqSegFile] + [tisiOutFile]
# Now we can create the nodes
node = ligolwadd_job.create_node(cafe_cache.extent,
llw_files, tags=curr_tags)
ligolwAddFile = node.output_files[0]
ligolwAddOuts.append(ligolwAddFile)
workflow.add_node(node)
if workflow.cp.has_option_tags("workflow-coincidence",
"coincidence-write-likelihood",curr_thinca_job_tags):
write_likelihood=True
else:
write_likelihood=False
for category in veto_cats:
node = ligolwthinca_job[category].create_node(\
cafe_cache.extent, coincSegment, ligolwAddFile,
tags=curr_tags, write_likelihood=write_likelihood)
ligolwThincaOuts += \
node.output_files.find_output_without_tag(\
'DIST_STATS')
ligolwThincaLikelihoodOuts += \
node.output_files.find_output_with_tag(\
'DIST_STATS')
workflow.add_node(node)
if coinc_post_cluster:
node = cluster_job[category].create_node(\
cafe_cache.extent, ligolwThincaOuts[-1])
ligolwClusterOuts += node.output_files
workflow.add_node(node)
other_returns = {}
other_returns['LIGOLW_ADD'] = ligolwAddOuts
other_returns['DIST_STATS'] = ligolwThincaLikelihoodOuts
if coinc_post_cluster:
main_return = ligolwClusterOuts
other_returns['THINCA'] = ligolwThincaOuts
else:
main_return = ligolwThincaOuts
logging.debug("Done")
coinc_outs.extend(main_return)
for key, file_list in other_returns.items():
if other_outs.has_key(key):
other_outs[key].extend(other_returns[key])
else:
other_outs[key] = other_returns[key]
return coinc_outs, other_outs
def setup_coincidence_workflow_ligolw_thinca(workflow,
segsList,
timeSlideFiles,
inspiral_outs,
output_dir,
veto_cats=[2, 3, 4],
tags=[],
timeSlideTags=None,
parallelize_split_input=False):
"""
This function is used to setup a single-stage ihope style coincidence stage
of the workflow using ligolw_sstinca (or compatible code!).
Parameters
-----------
workflow : pycbc.workflow.core.Workflow
The workflow instance that the coincidence jobs will be added to.
segsList : pycbc.workflow.core.FileList
The list of files returned by workflow's segment module that contains
pointers to all the segment files generated in the workflow. If the
coincidence code will be applying the data quality vetoes, then this
will be used to ensure that the codes get the necessary input to do
this.
timeSlideFiles : pycbc.workflow.core.FileList
An FileList of the timeSlide input files that are needed to
determine what time sliding needs to be done. One of the timeSlideFiles
will normally be "zero-lag only", the others containing time slides
used to facilitate background computations later in the workflow.
inspiral_outs : pycbc.workflow.core.FileList
An FileList of the matched-filter module output that is used as
input to the coincidence codes running at this stage.
output_dir : path
The directory in which coincidence output will be stored.
veto_cats : list of ints (optional, default = [2,3,4])
Veto categories that will be applied in the coincidence jobs. If this
takes the default value the code will run data quality at cumulative
categories 2, 3 and 4. Note that if we change the flag definitions to
be non-cumulative then this option will need to be revisited.
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 ['BNSINJECTIONS'] or
['NOINJECTIONANALYSIS']. This will be used in output names.
timeSlideTags : list of strings (optional, default = [])
A list of the tags corresponding to the timeSlideFiles that are to be
used in this call to the module. This can be used to ensure that the
injection runs do no time sliding, but the no-injection runs do perform
time slides (or vice-versa if you prefer!)
Returns
--------
ligolwThincaOuts : pycbc.workflow.core.FileList
A list of the output files generated from ligolw_sstinca.
ligolwAddOuts : pycbc.workflow.core.FileList
A list of the output files generated from ligolw_add.
"""
from pylal import ligolw_cafe
logging.debug("Entering coincidence module.")
cp = workflow.cp
ifoString = workflow.ifo_string
# setup code for each veto_category
coinc_outs = FileList([])
other_outs = {}
if not timeSlideTags:
# Get all sections by looking in ini file, use all time slide files.
timeSlideTags = [(sec.split('-')[-1]).upper()
for sec in workflow.cp.sections()
if sec.startswith('tisi-')]
if parallelize_split_input:
# Want to split all input jobs according to their JOB%d tag.
# This matches any string that is the letters JOB followed by some
# numbers and nothing else.
inspiral_outs_dict = {}
regex_match = re.compile('JOB([0-9]+)\Z')
for file in inspiral_outs:
matches = [regex_match.match(tag) for tag in file.tags]
# Remove non matching entries
matches = [i for i in matches if i is not None]
# Must have one entry
if len(matches) == 0:
warn_msg = "I was asked to parallelize over split inspiral "
warn_msg += "files at the coincidence stage, but at least one "
warn_msg += "input file does not have a JOB\%d tag indicating "
warn_msg += "that it was split. Assuming that I do not have "
warn_msg += "split input files and turning "
warn_msg += "parallelize_split_input off."
logging.warn(warn_msg)
parallelize_split_input = False
break
if len(matches) > 1:
err_msg = "One of my input files has two tags fitting JOB\%d "
err_msg += "this means I cannot tell which split job this "
err_msg += "file is from."
raise ValueError(err_msg)
# Extract the job ID
id = int(matches[0].string[3:])
if not inspiral_outs_dict.has_key(id):
inspiral_outs_dict[id] = FileList([])
inspiral_outs_dict[id].append(file)
else:
# If I got through all the files I want to sort the dictionaries so
# that file with key a and index 3 is the same file as key b and
# index 3 other than the tag is JOBA -> JOBB ... ie. it has used
# a different part of the template bank.
sort_lambda = lambda x: (x.ifo_string, x.segment, x.
tagged_description)
for key in inspiral_outs_dict.keys():
inspiral_outs_dict[id].sort(key=sort_lambda)
# These should be in ascending order, so I can assume the existence
# of a JOB0 tag
inspiral_outs = inspiral_outs_dict[0]
for index, file in enumerate(inspiral_outs):
# Store the index in the file for quicker mapping later
file.thinca_index = index
else:
inspiral_outs_dict = None
for timeSlideTag in timeSlideTags:
# Get the time slide file from the inputs
tisiOutFile = timeSlideFiles.find_output_with_tag(timeSlideTag)
if not len(tisiOutFile) == 1:
errMsg = "If you are seeing this, something batshit is going on!"
if len(tisiOutFile) == 0:
errMsg = "No time slide files found matching %s." \
%(timeSlideTag)
if len(tisiOutFile) > 1:
errMsg = "More than one time slide files match %s." \
%(timeSlideTag)
raise ValueError(errMsg)
tisiOutFile = tisiOutFile[0]
# Next we run ligolw_cafe. This is responsible for
# identifying what times will be used for the ligolw_thinca jobs and
# what files are needed for each. If doing time sliding there
# will be some triggers read into multiple jobs
cacheInspOuts = inspiral_outs.convert_to_lal_cache()
if workflow.cp.has_option_tags("workflow-coincidence",
"maximum-extent", tags):
max_extent = float(
workflow.cp.get_opt_tags("workflow-coincidence",
"maximum-extent", tags))
else:
# hard-coded default value for extent of time in a single job
max_extent = 3600
logging.debug("Calling into cafe.")
time_slide_table = lsctables.TimeSlideTable.get_table(\
ligolw_utils.load_filename(tisiOutFile.storage_path,
gz=tisiOutFile.storage_path.endswith(".gz"),
contenthandler=ContentHandler,
verbose=False))
time_slide_table.sync_next_id()
time_slide_dict = time_slide_table.as_dict()
cafe_seglists, cafe_caches = ligolw_cafe.ligolw_cafe(
cacheInspOuts,
time_slide_dict.values(),
extentlimit=max_extent,
verbose=False)
logging.debug("Done with cafe.")
# Take the combined seglist file
dqSegFile = segsList.find_output_with_tag(
'COMBINED_CUMULATIVE_SEGMENTS')
if not len(dqSegFile) == 1:
errMsg = "Did not find exactly 1 data quality file."
print len(dqSegFile), dqSegFile
raise ValueError(errMsg)
dqSegFile = dqSegFile[0]
# Set up llwadd job
llwadd_tags = [timeSlideTag] + tags
ligolwadd_job = LigolwAddExecutable(cp,
'llwadd',
ifo=ifoString,
out_dir=output_dir,
tags=llwadd_tags)
ligolwAddOuts = FileList([])
# Go global setup at each category
# This flag will add a clustering job after ligolw_thinca
if workflow.cp.has_option_tags("workflow-coincidence",
"coincidence-post-cluster",
llwadd_tags):
coinc_post_cluster = True
else:
coinc_post_cluster = False
# Go global setup at each category
ligolwthinca_job = {}
cluster_job = {}
thinca_tags = {}
for category in veto_cats:
logging.debug("Preparing %s %s" % (timeSlideTag, category))
dqVetoName = 'VETO_CAT%d_CUMULATIVE' % (category)
# FIXME: Should we resolve this now?
# FIXME: Here we set the dqVetoName to be compatible with pipedown
# For pipedown must put the slide identifier first and
# dqVetoName last.
pipedownDQVetoName = 'CAT_%d_VETO' % (category)
curr_thinca_job_tags = [timeSlideTag] + tags + [pipedownDQVetoName]
thinca_tags[category] = curr_thinca_job_tags
# Set up jobs for ligolw_thinca
ligolwthinca_job[category] = LigolwSSthincaExecutable(
cp,
'thinca',
ifo=ifoString,
out_dir=output_dir,
dqVetoName=dqVetoName,
tags=curr_thinca_job_tags)
if coinc_post_cluster:
cluster_job[category] = SQLInOutExecutable(
cp,
'pycbccluster',
ifo=ifoString,
out_dir=output_dir,
tags=curr_thinca_job_tags)
for idx, cafe_cache in enumerate(cafe_caches):
ligolwAddOuts = FileList([])
ligolwThincaOuts = FileList([])
ligolwThincaLikelihoodOuts = FileList([])
ligolwClusterOuts = FileList([])
if not len(cafe_cache.objects):
raise ValueError("One of the cache objects contains no files!")
# Determine segments to accept coincidences.
# If cache is not the first or last in the timeseries, check if the
# two closes caches in the timeseries and see if their extent
# match. If they match, they're adjacent and use the time where
# they meet as a bound for accepting coincidences. If they're not
# adjacent, then there is no bound for accepting coincidences.
coincStart, coincEnd = None, None
if idx and (cafe_cache.extent[0]
== cafe_caches[idx - 1].extent[1]):
coincStart = cafe_cache.extent[0]
if idx + 1 - len(cafe_caches) and \
(cafe_cache.extent[1] == cafe_caches[idx+1].extent[0]):
coincEnd = cafe_cache.extent[1]
coincSegment = (coincStart, coincEnd)
# Need to create a list of the File(s) contained in the cache.
# Assume that if we have partitioned input then if *one* job in the
# partitioned input is an input then *all* jobs will be.
if not parallelize_split_input:
inputTrigFiles = FileList([])
for object in cafe_cache.objects:
inputTrigFiles.append(object.workflow_file)
llw_files = inputTrigFiles + [dqSegFile] + [tisiOutFile]
# Now we can create the nodes
node = ligolwadd_job.create_node(cafe_cache.extent, llw_files)
ligolwAddFile = node.output_files[0]
ligolwAddOuts.append(ligolwAddFile)
workflow.add_node(node)
for category in veto_cats:
node = ligolwthinca_job[category].create_node(\
cafe_cache.extent, coincSegment, ligolwAddFile)
ligolwThincaOuts += \
node.output_files.find_output_without_tag('DIST_STATS')
ligolwThincaLikelihoodOuts += \
node.output_files.find_output_with_tag('DIST_STATS')
workflow.add_node(node)
if coinc_post_cluster:
node = cluster_job[category].create_node(\
cafe_cache.extent, ligolwThincaOuts[-1])
ligolwClusterOuts += node.output_files
workflow.add_node(node)
else:
for key in inspiral_outs_dict.keys():
curr_tags = ["JOB%d" % (key)]
curr_list = inspiral_outs_dict[key]
inputTrigFiles = FileList([])
for object in cafe_cache.objects:
inputTrigFiles.append(
curr_list[object.workflow_file.thinca_index])
llw_files = inputTrigFiles + [dqSegFile] + [tisiOutFile]
# Now we can create the nodes
node = ligolwadd_job.create_node(cafe_cache.extent,
llw_files,
tags=curr_tags)
ligolwAddFile = node.output_files[0]
ligolwAddOuts.append(ligolwAddFile)
workflow.add_node(node)
if workflow.cp.has_option_tags(
"workflow-coincidence",
"coincidence-write-likelihood",
curr_thinca_job_tags):
write_likelihood = True
else:
write_likelihood = False
for category in veto_cats:
node = ligolwthinca_job[category].create_node(\
cafe_cache.extent, coincSegment, ligolwAddFile,
tags=curr_tags, write_likelihood=write_likelihood)
ligolwThincaOuts += \
node.output_files.find_output_without_tag(\
'DIST_STATS')
ligolwThincaLikelihoodOuts += \
node.output_files.find_output_with_tag(\
'DIST_STATS')
workflow.add_node(node)
if coinc_post_cluster:
node = cluster_job[category].create_node(\
cafe_cache.extent, ligolwThincaOuts[-1])
ligolwClusterOuts += node.output_files
workflow.add_node(node)
other_returns = {}
other_returns['LIGOLW_ADD'] = ligolwAddOuts
other_returns['DIST_STATS'] = ligolwThincaLikelihoodOuts
if coinc_post_cluster:
main_return = ligolwClusterOuts
other_returns['THINCA'] = ligolwThincaOuts
else:
main_return = ligolwThincaOuts
logging.debug("Done")
coinc_outs.extend(main_return)
for key, file_list in other_returns.items():
if other_outs.has_key(key):
other_outs[key].extend(other_returns[key])
else:
other_outs[key] = other_returns[key]
return coinc_outs, other_outs
