Exemple #1
0
            if abs(seg) > 2000:
                longLen += abs(seg)
                longNum += 1
        print "For ifo %s there is %d seconds of data in %d segments, %d seconds (%d unique segments) in segments longer than 500s and %d seconds (%d unique segments) longer than 2000s." % (
            ifo, fullLen, fullNum, shortLen, shortNum, longLen, longNum)


scienceSegs, segsList = _workflow.setup_segment_generation(workflow, segDir)

segment_report(scienceSegs)

print
print

print "RUNNING DATAFIND"
datafinds, scienceSegs = _workflow.setup_datafind_workflow(
    workflow, scienceSegs, dfDir, segsList)

# This is needed to know what times will be analysed by daily ahope
# Template bank stuff
banks = _workflow.setup_tmpltbank_workflow(workflow, scienceSegs, datafinds,
                                           dfDir)
# Do matched-filtering
insps = _workflow.setup_matchedfltr_workflow(workflow, scienceSegs, datafinds,
                                             banks, dfDir)

# Now construct the summary XML file

outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# FIXME: PROGRAM NAME and dictionary of opts should be variables defined up above
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'dayhopetest',
Exemple #2
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                shortNum+=1
            if abs(seg) > 2000:
                longLen+=abs(seg)
                longNum+=1
        print "For ifo %s there is %d seconds of data in %d segments, %d seconds (%d unique segments) in segments longer than 500s and %d seconds (%d unique segments) longer than 2000s." %(ifo, fullLen, fullNum, shortLen, shortNum, longLen, longNum)


scienceSegs, segsList = _workflow.setup_segment_generation(workflow, segDir)

segment_report(scienceSegs)

print
print

print "RUNNING DATAFIND"
datafinds, scienceSegs = _workflow.setup_datafind_workflow(workflow, scienceSegs,
                     dfDir, segsList)

# This is needed to know what times will be analysed by daily ahope
# Template bank stuff
banks = _workflow.setup_tmpltbank_workflow(workflow, scienceSegs, datafinds,
                                       dfDir)
# Do matched-filtering
insps = _workflow.setup_matchedfltr_workflow(workflow, scienceSegs, datafinds,
                                         banks, dfDir)

# Now construct the summary XML file

outdoc = ligolw.Document()
outdoc.appendChild(ligolw.LIGO_LW())
# FIXME: PROGRAM NAME and dictionary of opts should be variables defined up above
proc_id = ligolw_process.register_to_xmldoc(outdoc, 'dayhopetest',
Exemple #3
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segment_report(scienceSegs)

print "STARTING DF"
print

# Start with SYR comparison
# FIXME: Used to use deecopy here, but now that seems to fail so repeating
#        segment query calls with logging off. This may be slow!
logger.disabled = True
scienceSegsS, _ = _workflow.setup_segment_generation(workflow, segDir)
logger.disabled = False
print "RUNNING DATAFIND FOR SYR"
datafinds, scienceSegsS = _workflow.setup_datafind_workflow(workflow,
                                                            scienceSegsS,
                                                            dfDirSYR,
                                                            segsList,
                                                            tag="SYR")

segment_report(scienceSegsS)

print
print
print "RUNNING DATAFIND FOR CIT"
logger.disabled = True
scienceSegsC, _ = _workflow.setup_segment_generation(workflow, segDir)
logger.disabled = False
datafinds, scienceSegsC = _workflow.setup_datafind_workflow(workflow,
                                                            scienceSegsC,
                                                            dfDirCIT,
                                                            segsList,
Exemple #4
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            longLen,
            longNum,
        )


scienceSegs, segsList = _workflow.setup_segment_generation(workflow, segDir)

segment_report(scienceSegs)

print
print

# Start with SYR comparison
scienceSegsS = copy.deepcopy(scienceSegs)
print "RUNNING DATAFIND FOR SYR"
datafinds, scienceSegsS = _workflow.setup_datafind_workflow(workflow, scienceSegsS, dfDirSYR, segsList, tag="SYR")

segment_report(scienceSegsS)

print
print
print "RUNNING DATAFIND FOR CIT"
scienceSegsC = copy.deepcopy(scienceSegs)
datafinds, scienceSegsC = _workflow.setup_datafind_workflow(workflow, scienceSegsC, dfDirCIT, segsList, tag="CIT")

segment_report(scienceSegsC)

print "Frames present a SYR and not at CIT:"
for ifo in scienceSegsS.keys():
    print "For ifo", ifo
    if ifo in scienceSegsC.keys():
Exemple #5
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# Retrieve segments ahope-style
currDir = os.getcwd()
segDir = os.path.join(currDir, "segments")
sciSegs, segsFileList = _workflow.setup_segment_generation(wflow, segDir)

# Make coherent network segments
onSrc, sciSegs = _workflow.get_triggered_coherent_segment(wflow, segDir,
                                                          sciSegs)
# FIXME: The following two lines are/were crude hacks.
ifo = sciSegs.keys()[0]
wflow.analysis_time = sciSegs[ifo][0]

# Datafind
dfDir = os.path.join(currDir, "datafind")
datafind_files, sciSegs = _workflow.setup_datafind_workflow(wflow, sciSegs,
                                                            dfDir,
                                                            segsFileList)
all_files.extend(datafind_files)

# Template bank and splitting the bank
# TODO: Move from pregenerated to generated coherent network bank
bank_files = _workflow.setup_tmpltbank_workflow(wflow, sciSegs,
                                                datafind_files, dfDir)
splitbank_files = _workflow.setup_splittable_workflow(wflow, bank_files, dfDir)
all_files.extend(bank_files)
all_files.extend(splitbank_files)

# Injections
"""
injDir = os.path.join(currDir, "inj_files")
inj_files, inj_tags = ahope.setup_injection_workflow(wflow,