% classifier)
elif opts.mode == "npy":
### write list of dats to cache file
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):
else:
logger.warning('WARNING: not enough samples to trust calibration. skipping calibration update for %s'%classifier)
elif opts.mode == "npy":
### write list of dats to cache file
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):
) # this may be fragile if fap=0 for all points in the plot. That's super rare, so maybe we don't have to worry about it?
r_ax.set_title(opts.ifo)
#=================================================
# RANK
#=================================================
if opts.verbose:
print "reading rank timeseries from:"
for filename in rank_filenames:
print '\t' + filename
# merge time-series
if opts.verbose:
print "merging rank timeseries"
(r_times, r_timeseries) = idq.combine_ts(rank_filenames)
# for each bit of continuous data:
# add to plot
# write merged timeseries file
# generate and write summary statistics
if opts.verbose:
print "plotting and summarizing rank timeseries"
merged_rank_filenames = []
merged_rank_frames = []
rank_summaries = []
max_rank = -np.infty
max_rank_segNo = 0
segNo = 0
end = opts.plotting_gps_start
f_ax.set_yscale('log') # this may be fragile if fap=0 for all points in the plot. That's super rare, so maybe we don't have to worry about it?
r_ax.set_title(opts.ifo)
#=================================================
# RANK
#=================================================
if opts.verbose:
print "reading rank timeseries from:"
for filename in rank_filenames:
print '\t' + filename
# merge time-series
if opts.verbose:
print "merging rank timeseries"
(r_times, r_timeseries) = idq.combine_ts(rank_filenames)
# for each bit of continuous data:
# add to plot
# write merged timeseries file
# generate and write summary statistics
if opts.verbose:
print "plotting and summarizing rank timeseries"
merged_rank_filenames = []
merged_rank_frames = []
rank_summaries = []
max_rank = -np.infty
max_rank_segNo = 0
segNo = 0
end = opts.plotting_gps_start
elif (opts.mode == "npy") or (opts.mode == "gwf"):
### write list of dats to cache file
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
if opts.mode == "npy":
_times, timeseries = idq.combine_ts(ranksD[classifier], n=1)
else: ### opts.mode=="gwf"
_times, timeseries = idq.combine_gwf(
ranksD[classifier], [channameD[classifier]['rank']])
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:
logger.warning('WARNING: not enough samples to trust calibration. skipping calibration update for %s'%classifier)
elif (opts.mode == "npy") or (opts.mode == "gwf"):
### write list of dats to cache file
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
if opts.mode == "npy":
_times, timeseries = idq.combine_ts(ranksD[classifier], n=1)
else: ### opts.mode=="gwf"
_times, timeseries = idq.combine_gwf(ranksD[classifier], [channameD[classifier]['rank']])
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:
