fUL_timeseries = [f[1] for f in f_timeseries]
f_timeseries = [f[0] for f in f_timeseries]
### write combined data to disk
if opts.verbose:
print "writing combined fap frames to disk"
for t, ts, tS in zip(f_times, f_timeseries, fUL_timeseries):
truth = (opts.plotting_gps_start <= t) * (t <= opts.plotting_gps_end)
t = t[truth]
ts = ts[truth]
tS = tS[truth]
start = int(t[0])
dt = t[1] - t[0]
dur = int(len(t) * dt)
fapfr = idq.gdb_timeseriesgwf(gdbdir, opts.classifier, ifo,
"_fap%s" % filetag, start, dur)
if opts.verbose:
print " %s" % fapfr
idq.timeseries2frame(fapfr, {
fap_channame: ts,
fapUL_channame: tS
}, t[0], dt)
if not opts.skip_gracedb_upload:
message = "iDQ fap timeseries for %s at %s within [%d, %d] :" % (
opts.classifier, ifo, start, start + dur)
if opts.verbose:
print " %s" % message
gracedb.writeLog(opts.gracedb_id,
message=message,
filename=fapfr,
tagname=idq.tagnames) #+['data_quality'] )
# opts.classifier,
# opts.tag,
# int(_start),
# int(_dur))
merged_rank_filename = idq.gdb_timeseries(opts.output_dir, opts.classifier,
opts.ifo, "_rank%s" % opts.tag,
int(_start), int(_dur))
if opts.verbose:
print "\twriting " + merged_rank_filename
np.save(event.gzopen(merged_rank_filename, 'w'), ts)
merged_rank_filenames.append(merged_rank_filename)
rankfr = idq.gdb_timeseriesgwf(opts.output_dir, opts.classifier,
opts.ifo, "_rank%s" % opts.tag, int(_start),
int(_dur))
if opts.verbose:
print "\twriting " + rankfr
idq.timeseries2frame(rankfr, {rank_channame: ts}, _start,
_dur / (len(t) - 1))
merged_rank_frames.append(rankfr)
# generate and write summary statistics
(r_min, r_max, r_mean, r_stdv) = idq.stats_ts(ts)
if r_max > max_rank:
max_rank = r_max
max_rank_segNo = segNo
rank_summaries.append([
_start,
_end,
fUL_timeseries = [f[1] for f in f_timeseries]
f_timeseries = [f[0] for f in f_timeseries]
### write combined data to disk
if opts.verbose:
print "writing combined fap frames to disk"
for t, ts, tS in zip(f_times, f_timeseries, fUL_timeseries):
truth = (opts.plotting_gps_start <= t)*(t <= opts.plotting_gps_end)
t = t[truth]
ts = ts[truth]
tS = tS[truth]
start = int(t[0])
dt = t[1]-t[0]
dur = int(len(t)*dt)
fapfr = idq.gdb_timeseriesgwf( gdbdir , opts.classifier, ifo, "_fap%s"%filetag, start, dur)
if opts.verbose:
print " %s"%fapfr
idq.timeseries2frame( fapfr, {fap_channame:ts, fapUL_channame:tS}, t[0], dt )
if not opts.skip_gracedb_upload:
message = "iDQ fap timeseries for %s at %s within [%d, %d] :"%(opts.classifier, ifo, start, start+dur)
if opts.verbose:
print " %s"%message
gracedb.writeLog( opts.gracedb_id, message=message, filename=fapfr ) #, tagname=['data_quality'] )
### post min-fap value
if opts.verbose:
print "finding minimum FAP observed within [%.3f, %.3f]"%(opts.start, opts.end)
min_fap = 1.0
for (t, ts) in zip(f_times, f_timeseries):
# ensure time series only fall within desired range
# merged_rank_filename = '%s/%s_idq_%s_rank_%s%d-%d.npy.gz' % (
# opts.output_dir,
# opts.ifo,
# opts.classifier,
# opts.tag,
# int(_start),
# int(_dur))
merged_rank_filename = idq.gdb_timeseries(opts.output_dir, opts.classifier, opts.ifo, "_rank%s"%opts.tag, int(_start), int(_dur))
if opts.verbose:
print "\twriting " + merged_rank_filename
np.save(event.gzopen(merged_rank_filename, 'w'), ts)
merged_rank_filenames.append(merged_rank_filename)
rankfr = idq.gdb_timeseriesgwf(opts.output_dir, opts.classifier, opts.ifo, "_rank%s"%opts.tag, int(_start), int(_dur))
if opts.verbose:
print "\twriting " + rankfr
idq.timeseries2frame( rankfr, {rank_channame:ts}, _start, _dur/(len(t)-1) )
merged_rank_frames.append( rankfr )
# generate and write summary statistics
(r_min, r_max, r_mean, r_stdv) = idq.stats_ts(ts)
if r_max > max_rank:
max_rank = r_max
max_rank_segNo = segNo
rank_summaries.append([
_start,
_end,
_dur / (len(t) - 1),
r_min,
