def check_calibration(segs, times, timeseries, FAPthrs):
"""
checks the pipeline's calibration at each "FAPthr in FAPThrs"
this may be sped up with a call to timeseries_to_livetime() instead of idq.timeseries_to_segments() -> event.livetime
however, we currently use some segment logic and it is not clear that we can avoid actually generating segments
"""
idq_livetime = event.livetime(segs)
segments = []
deadtimes = []
statedFAPs = []
errs = []
for FAPthr in FAPthrs:
SEGS = []
max_statedFAP = 0.0
for (t, ts) in zip(times, timeseries):
(_segs, _min_ts) = idq.timeseries_to_segments(
t, -ts, -FAPthr) # we want FAP <= FAPthr <--> -FAP >= -FAPthr
SEGS += (
_segs
) ### ensure this is a list in case the behavior of timeseries_to_segments changes...
if _min_ts != None:
statedFAP = -_min_ts
if max_statedFAP < statedFAP:
max_statedFAP = statedFAP
SEGS = event.andsegments([SEGS, segs])
segments.append(SEGS)
SEGS_livetime = event.livetime(SEGS)
if not idq_livetime:
if SEGS_livetime:
raise ValueError(
"something is weird with segments... idq_livetime is zero but SEGS_livetime is not"
)
else:
deadtime = 0.0
else:
deadtime = 1.0 * SEGS_livetime / idq_livetime
if deadtime > 1.0:
raise ValueError(
"deadtime > 1.0, something is weird...\n SEGS_livetime = %f\n idq_livetime = %f"
% (SEGS_livetime, idq_livetime))
deadtimes.append(deadtime)
statedFAPs.append(max_statedFAP)
if max_statedFAP > 0:
err = deadtime / max_statedFAP - 1
elif deadtime:
err = 1
else:
err = 0
errs.append(err)
return segments, deadtimes, statedFAPs, errs
def check_calibration( segs, times, timeseries, FAPthrs):
"""
checks the pipeline's calibration at each "FAPthr in FAPThrs"
this may be sped up with a call to timeseries_to_livetime() instead of idq.timeseries_to_segments() -> event.livetime
however, we currently use some segment logic and it is not clear that we can avoid actually generating segments
"""
idq_livetime = event.livetime(segs)
segments = []
deadtimes = []
statedFAPs = []
errs = []
for FAPthr in FAPthrs:
SEGS = []
max_statedFAP = 0.0
for (t, ts) in zip(times, timeseries):
(_segs, _min_ts) = idq.timeseries_to_segments(t, -ts, -FAPthr) # we want FAP <= FAPthr <--> -FAP >= -FAPthr
SEGS += (_segs) ### ensure this is a list in case the behavior of timeseries_to_segments changes...
if _min_ts != None:
statedFAP = -_min_ts
if max_statedFAP < statedFAP:
max_statedFAP = statedFAP
SEGS = event.andsegments([SEGS, segs])
segments.append(SEGS)
SEGS_livetime = event.livetime(SEGS)
if not idq_livetime:
if SEGS_livetime:
raise ValueError("something is weird with segments... idq_livetime is zero but SEGS_livetime is not")
else:
deadtime = 0.0
else:
deadtime = 1.0 * SEGS_livetime / idq_livetime
if deadtime > 1.0:
raise ValueError("deadtime > 1.0, something is weird...\n SEGS_livetime = %f\n idq_livetime = %f"%(SEGS_livetime, idq_livetime))
deadtimes.append( deadtime )
statedFAPs.append(max_statedFAP)
if max_statedFAP > 0:
err = deadtime/max_statedFAP - 1
elif deadtime:
err = 1
else:
err = 0
errs.append( err )
return segments, deadtimes, statedFAPs, errs
### compute total time covered
#T = event.livetime( [idq.extract_start_stop(fap, suffix='.gwf') for fap in faps] )*1.0
T = event.livetime(idqsegs) * 1.0
### combine timeseries and generate segments
if opts.verbose:
print "generating segments from %d fap files" % (len(faps))
segs = dict((fapThr, [[], 1.0]) for fapThr in opts.FAPthr)
t, ts = idq.combine_gwf(faps, [fap_channame])
for t, ts in zip(t, ts):
t, ts = idq.timeseries_in_segments(t, ts, idqsegs)
for fapThr in opts.FAPthr:
s, minFAP = idq.timeseries_to_segments(
t, -ts, -fapThr) # we want FAP <= FAPthr <--> -FAP >= -FAPthr
s = event.andsegments(
[s, idqsegs]
) ### necessary because of how timeseries_to_segments may interact with timeseries_in_segments
segs[fapThr][0] += s
if minFAP != None:
segs[fapThr][1] = min(segs[fapThr][1], -minFAP)
if opts.verbose:
print "computing associated deadtimes"
dt = [event.livetime(segs[fapThr][0]) / T for fapThr in opts.FAPthr]
maxFAP = [segs[fapThr][1] for fapThr in opts.FAPthr]
### write json for calibration check
jsonfilename = idq.gdb_calib_json(gdbdir, ifo, opts.classifier, filetag,
opts.start, opts.end - opts.start)
### compute total time covered
#T = event.livetime( [idq.extract_start_stop(fap, suffix='.gwf') for fap in faps] )*1.0
T = event.livetime( idqsegs )*1.0
### combine timeseries and generate segments
if opts.verbose:
print "generating segments from %d fap files"%(len(faps))
segs = dict( (fapThr, [[], 1.0]) for fapThr in opts.FAPthr )
t, ts = idq.combine_gwf(faps, [fap_channame])
for t, ts in zip(t, ts):
t, ts = idq.timeseries_in_segments( t, ts, idqsegs )
for fapThr in opts.FAPthr:
s, minFAP = idq.timeseries_to_segments(t, -ts, -fapThr) # we want FAP <= FAPthr <--> -FAP >= -FAPthr
s = event.andsegments( [s, idqsegs] ) ### necessary because of how timeseries_to_segments may interact with timeseries_in_segments
segs[fapThr][0] += s
if minFAP!=None:
segs[fapThr][1] = min(segs[fapThr][1], -minFAP)
if opts.verbose:
print "computing associated deadtimes"
dt = [event.livetime(segs[fapThr][0])/T for fapThr in opts.FAPthr]
maxFAP = [segs[fapThr][1] for fapThr in opts.FAPthr]
### write json for calibration check
jsonfilename = idq.gdb_calib_json( gdbdir, ifo, opts.classifier, filetag, opts.start, opts.end-opts.start )
if opts.verbose:
print " %s"%jsonfilename
file_obj = open(jsonfilename, "w")
segsum_row = lsctables.SegmentSum()
segsum_row.segment_sum_id = segsum.get_next_id()
segsum_row.comment = ''
segsum_row.process_id = proc_id
segsum_row.segment_def_id = segdef_id
segsum_row.start_time = start_time
segsum_row.start_time_ns = start_time_ns
segsum_row.end_time = end_time
segsum_row.end_time_ns = end_time_ns
segsum.append( segsum_row )
### generate segments for this threshold
segs, min_TS = idq.timeseries_to_segments(T[:], -TS[:], -FAPthr)
if opts.right_padding != 0: ### transform all segments from [start, end] -> [start, end+right_padding]
logger.info(' moving right edge of segments: end->end+%.6f'%(opts.right_padding))
for ind, (s, e) in enumerate(segs):
segs[ind] = [s, e + opts.right_padding]
if opts.left_padding != 0: ### transform all segments from [start, end] -> [start-left_padding, end]
logger.info(' moving left edge of segments: start->start+%.6f'%(opts.right_padding))
for ind, (s, e) in enumerate(segs):
segs[ind] = [s - opts.left_padding, e]
if (opts.right_padding != 0) or (opts.left_padding != 0): ### clean up segments
logger.info(' ensuring segments still make sense after moving the edges')
good_segs = []
for (s, e) in segs:
segsum_row = lsctables.SegmentSum()
segsum_row.segment_sum_id = segsum.get_next_id()
segsum_row.comment = ''
segsum_row.process_id = proc_id
segsum_row.segment_def_id = segdef_id
segsum_row.start_time = start_time
segsum_row.start_time_ns = start_time_ns
segsum_row.end_time = end_time
segsum_row.end_time_ns = end_time_ns
segsum.append(segsum_row)
### generate segments for this threshold
segs, min_TS = idq.timeseries_to_segments(T[:], -TS[:], -FAPthr)
if opts.right_padding != 0: ### transform all segments from [start, end] -> [start, end+right_padding]
logger.info(
' moving right edge of segments: end->end+%.6f' %
(opts.right_padding))
for ind, (s, e) in enumerate(segs):
segs[ind] = [s, e + opts.right_padding]
if opts.left_padding != 0: ### transform all segments from [start, end] -> [start-left_padding, end]
logger.info(
' moving left edge of segments: start->start+%.6f' %
(opts.right_padding))
for ind, (s, e) in enumerate(segs):
segs[ind] = [s - opts.left_padding, e]
