def generate_segments(ifo1_data, ifo2_data, ifo3_data, ifo4_data):
"""
compute the segments arising as the overlap of the four sets of single
ifo segment lists.
ifo1_data = data segments for ifo1
ifo2_data = data segments for ifo2
ifo3_data = data segments for ifo3
ifo4_data = data segments for ifo4
"""
segment_list = pipeline.ScienceData()
segment_list = copy.deepcopy(ifo1_data)
segment_list.intersect_4(ifo2_data, ifo3_data, ifo4_data)
return segment_list
playground_only = 0
print "reading in single ifo science segments and creating master chunks...",
sys.stdout.flush()
segments = {}
data = {}
for ifo in ifo_list:
try:
segments[ifo] = cp.get('input', ifo + '-segments')
except:
segments[ifo] = None
data[ifo] = pipeline.ScienceData()
if segments[ifo]:
data[ifo].read(segments[ifo], length + 2 * pad)
data[ifo].make_chunks(length, overlap, playground_only, 0, overlap / 2,
pad)
data[ifo].make_chunks_from_unused(length, overlap / 2, playground_only,
0, 0, overlap / 2, pad)
print "done"
sys.stdout.flush()
# work out the earliest and latest times that are being analyzed
if not gps_start_time:
gps_start_time = 10000000000
for ifo in ifo_list:
if data[ifo] and (data[ifo][0].start() < gps_start_time):
epoch_cnt = 0;
# loop over the segments defined by the calibration epochs
print("\n")
for epoch in epochs.epoch_segs():
noise_output_files = []
noise_output_files2 = []
print("setting up jobs for calibration epoch " + str(epoch[1])+" - "+str(epoch[2]) + "...")
#output the epochs in their own directories
epoch_dir = 'EPOCH'+'-'+str(epoch[1])+'-'+str(epoch[2])
mkdir_node2 = strain.MkdirNode(mkdir_job,epoch_dir)
if opts.write_dax: dag.add_node(mkdir_node2)
if opts.cat_noise_jobs: catfile = strain.open_noise_cat_file(epoch_dir)
# Make a ScienceData class for the calibration epochs
epoch_data = pipeline.ScienceData()
epoch_data.append_from_tuple(epoch)
# read science segs that are greater or equal to a chunk from the input file
data = pipeline.ScienceData()
data.read(opts.segment_filename,0)
# intersect the science segments with the calibration epoch
data.intersection(epoch_data)
# create the chunks from the science segments
data.make_chunks(length,0,0,0,0)
data.make_short_chunks_from_unused(0,0,0,0,0)
# create all the LSCdataFind jobs to run in sequence
prev_df1 = None
prev_df2 = None
# only do data find jobs if requested
# find all the h(t) data
print("Double check the parameter file's injection section!")
os.abort()
#We assume the input segment list has entries exceeding layerTopBlockSize
#so we will try to loop it. If the pipe builder was invoked with a FLOATING
#top block size then we will issue an error IFF there is more than 1 layer configured
segmentListName = segmentList
dataBlockSize = int(
float(str.strip(cp.get('layerconfig', 'layerTopBlockSize'))))
if not (topBlockFloat):
#Convert the segment list to smaller blocks
reformatSegList = tracksearch.tracksearchConvertSegList(
segmentList, dataBlockSize, cp, topBlockFloat, overrideBurn)
reformatSegList.writeSegList()
segmentListName = reformatSegList.getSegmentName()
allData = pipeline.ScienceData()
allData.read(segmentListName, dataBlockSize)
allData.make_chunks(dataBlockSize)
else:
#Do optimized floating blocks
#Check for layer2
setSize = int(cp.get('layerconfig', 'layer1SetSize'))
timeScale = float(cp.get('layerconfig', 'layer1TimeScale'))
minSize = setSize * timeScale
print("Building pipe with rubber block size option enabled.")
print("Minimum duration block: " + str(minSize))
print("Maximum duration block: " + str(dataBlockSize))
for opt in cp.options('layerconfig'):
if str(opt).lower().__contains__(str('layer2TimeScale').lower()):
print(
"Error found additional layerconfig options for multi-resolution search."
calibrated = False
for opt in cp.options('data'):
if (opt.find('calibrated') > -1):
calibrated = True
# get the pad and chunk lengths from the values in the ini file
pad = int(cp.get('data', 'pad-data'))
n = int(cp.get('data', 'segment-length'))
s = int(cp.get('data', 'number-of-segments'))
r = int(cp.get('data', 'sample-rate'))
o = int(cp.get('inspiral', 'segment-overlap'))
length = (n * s - (s - 1) * o) / r
overlap = o / r
# read science segs that are greater or equal to a chunk from the input file
data = pipeline.ScienceData()
data.read(cp.get('input', 'segments'), length + 2 * pad)
# create the chunks from the science segments
data.make_chunks(length, overlap, playground_only, 0, overlap / 2, pad)
data.make_chunks_from_unused(length, overlap / 2, playground_only, overlap / 2,
0, overlap / 2, pad)
# get the order of the ifos to filter
ifo1 = cp.get('pipeline', 'ifo1')
ifo2 = cp.get('pipeline', 'ifo2')
ifo1_snr = cp.get('pipeline', 'ifo1-snr-threshold')
ifo2_snr = cp.get('pipeline', 'ifo2-snr-threshold')
ifo1_chisq = cp.get('pipeline', 'ifo1-chisq-threshold')
ifo2_chisq = cp.get('pipeline', 'ifo2-chisq-threshold')
# Step 1: read science segs that are greater or equal to a chunk
# from the input file
print "reading in single ifo science segments and creating master chunks...",
sys.stdout.flush()
segments = {}
data = {}
for ifo in ifo_list:
try:
segments[ifo] = cp.get('input', ifo +'-segments')
except:
segments[ifo] = None
data[ifo] = pipeline.ScienceData()
if segments[ifo]:
data[ifo].read(segments[ifo],length + 2 * pad)
data[ifo].make_chunks(length,overlap,playground_only,0,overlap/2,pad)
data[ifo].make_chunks_from_unused(length,overlap/2,playground_only,
0,0,overlap/2,pad)
print "done"
# work out the earliest and latest times that are being analyzed
if not gps_start_time:
gps_start_time = 10000000000
for ifo in ifo_list:
if data[ifo] and (data[ifo][0].start() < gps_start_time):
gps_start_time = data[ifo][0].start()
print "GPS start time not specified, obtained from segment lists as " + \