event.andsegments([[idq.extract_start_stop(fap, suffix=".gwf")],
idqsegs]))
]
### 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]
cache = idq.cache(output_dir, classifier, "_rankcache%s"%usertag)
logger.info('writing list of rank files to %s'%cache)
f = open(cache, 'w')
for rank in ranksD[classifier]:
print >>f, rank
f.close()
logger.info(' analyzing rank timeseries to obtain mapping from rank->fap')
### load in timeseries
_times, timeseries = idq.combine_ts(ranksD[classifier], n=1)
times = []
ranks = []
for t, ts in zip(_times, timeseries):
_t, _ts = idq.timeseries_in_segments(t, ts, idqsegs)
if len(_ts):
times.append( _t )
ranks.append( _ts )
### need to compute deadsecs for every rank in r -> function call (probably within calibration module)!
crank = []
for _r in r:
dsec = 0
for t, ts in zip(times, ranks):
dt = t[1]-t[0] ### get time spacing.
dsec += calibration.timeseries_to_livetime(dt, ts, _r)[0] # we don't care about segments, so just get livetime and smallest stated value
crank.append( dsec )
### dump uroc file