def extract_ovl_vconfigs(rank_frames,
channame,
traindir,
start,
end,
metric='eff/dt'):
"""
returns a dictionary mapping active vconfigs to segments
does NOT include "none" channel
"""
vconfigs = []
for rnkfr in rank_frames:
trained, calib = idq.extract_timeseries_ranges(rnkfr)
classifier = idq.extract_fap_name(rnkfr)
vetolist = glob.glob("%s/%d_%d/ovl/ovl/*vetolist.eval" %
(traindir, trained[0], trained[1]))
if len(vetolist) != 1:
raise ValueError(
"trouble finding a single vetolist file for : %s" % rnkfr)
vetolist = vetolist[0]
v = event.loadstringtable(vetolist)
rankmap = {0: [(None, None, None, None, 0, 0)]}
for line in v:
metric_exp = float(line[ovl.vD['metric_exp']])
if metric == 'eff/dt':
rnk = ovl.effbydt_to_rank(metric_exp)
elif metric == 'vsig':
rnk = ovl.vsig_to_rank(metric_exp)
elif metric == 'useP':
rnk = ovl.useP_to_rank(metric_exp)
else:
raise ValueError("metric=%s not understood" % metric)
if rankmap.has_key(rnk):
rankmap[rnk].append(
(line[ovl.vD['vchan']], float(line[ovl.vD['vthr']]),
float(line[ovl.vD['vwin']]), metric, metric_exp, rnk))
else:
rankmap[rnk] = [
(line[ovl.vD['vchan']], float(line[ovl.vD['vthr']]),
float(line[ovl.vD['vwin']]), metric, metric_exp, rnk)
]
for key, value in rankmap.items():
rankmap[key] = tuple(value)
t, ts = idq.combine_gwf([rnkfr], [channame])
t = t[0]
truth = (start <= t) * (t <= end)
t = t[truth]
ts = ts[0][truth]
if not len(ts):
continue
configs = rankmap[ts[0]]
segStart = t[0]
for T, TS in zip(t, ts):
if rankmap[TS] != configs:
vconfigs.append((configs, [segStart, T]))
segStart = T
configs = rankmap[TS]
else:
pass
vconfigs.append((configs, [segStart, T + t[1] - t[0]]))
configs = {}
for vconfig, seg in vconfigs:
if configs.has_key(vconfig):
configs[vconfig].append(seg)
else:
configs[vconfig] = [seg]
for key, value in configs.items():
value = event.andsegments([event.fixsegments(value), [[start, end]]])
if event.livetime(value):
configs[key] = event.fixsegments(value)
else:
raise ValueError(
"somehow picked up a config with zero livetime...")
return vconfigs, configs, {
"vchan": 0,
"vthr": 1,
"vwin": 2,
"metric": 3,
"metric_exp": 4,
"rank": 5
}
thisD['%s livetime' % dq_name] = None
thisD['start'] = None
thisD['end'] = None
### extract Ng, Nc
if flavor == "ovl":
vetolist = glob.glob(
"%s/%d_%d/%s/ovl/*vetolist.eval" %
(traindir, train_start, train_end, opts.classifier))
if len(vetolist) != 1:
raise ValueError(
"something odd with %s vetolists : %s/%d_%d/%s/ovl/" %
(opts.classifier, traindir, train_start, train_end))
else:
thisD['Num glitches'] = int(
event.loadstringtable(vetolist[0])[0][idq.ovl.vD['#gwtrg']])
elif flavor in idq.mla_flavors:
pat = glob.glob("%s/%d_%d/%s_mla*.pat" %
(traindir, train_start, train_end, ifo))
if len(pat) != 1:
raise ValueError(
"something odd with %s patfile : %s/%d_%d/" %
(opts.classifier, traindir, train_start, train_end))
else:
i = [
float(i) for i in idq.slim_load_datfiles(pat[0], columns=['i'])
]
Ng = np.sum(i)
Nc = len(i) - Ng
thisD['Num glitches'] = int(Ng)
thisD['Num cleans'] = int(Nc)
def extract_ovl_vconfigs( rank_frames, channame, traindir, start, end, metric='eff/dt' ):
"""
returns a dictionary mapping active vconfigs to segments
does NOT include "none" channel
"""
vconfigs = []
for rnkfr in rank_frames:
trained, calib = idq.extract_timeseries_ranges( rnkfr )
classifier = idq.extract_fap_name( rnkfr )
vetolist = glob.glob( "%s/%d_%d/ovl/ovl/*vetolist.eval"%(traindir, trained[0], trained[1]) )
if len(vetolist) != 1:
raise ValueError( "trouble finding a single vetolist file for : %s"%rnkfr )
vetolist=vetolist[0]
v = event.loadstringtable( vetolist )
rankmap = { 0:[(None, None, None, None, 0, 0)] }
for line in v:
metric_exp = float(line[ovl.vD['metric_exp']])
if metric == 'eff/dt':
rnk = ovl.effbydt_to_rank( metric_exp )
elif metric == 'vsig':
rnk = ovl.vsig_to_rank( metric_exp )
elif metric == 'useP':
rnk = ovl.useP_to_rank( metric_exp )
else:
raise ValueError("metric=%s not understood"%metric)
if rankmap.has_key(rnk):
rankmap[rnk].append( (line[ovl.vD['vchan']], float(line[ovl.vD['vthr']]), float(line[ovl.vD['vwin']]), metric, metric_exp, rnk ))
else:
rankmap[rnk] = [(line[ovl.vD['vchan']], float(line[ovl.vD['vthr']]), float(line[ovl.vD['vwin']]), metric, metric_exp, rnk )]
for key, value in rankmap.items():
rankmap[key] = tuple(value)
t, ts = idq.combine_gwf( [rnkfr], [channame])
t = t[0]
truth = (start <= t)*(t <= end)
t = t[truth]
ts = ts[0][truth]
if not len(ts):
continue
configs = rankmap[ts[0]]
segStart = t[0]
for T, TS in zip(t, ts):
if rankmap[TS] != configs:
vconfigs.append( (configs, [segStart, T] ) )
segStart = T
configs = rankmap[TS]
else:
pass
vconfigs.append( (configs, [segStart, T+t[1]-t[0]] ) )
configs = {}
for vconfig, seg in vconfigs:
if configs.has_key( vconfig ):
configs[vconfig].append( seg )
else:
configs[vconfig] = [ seg ]
for key, value in configs.items():
value = event.andsegments( [event.fixsegments( value ), [[start,end]] ] )
if event.livetime( value ):
configs[key] = event.fixsegments( value )
else:
raise ValueError("somehow picked up a config with zero livetime...")
return vconfigs, configs, {"vchan":0, "vthr":1, "vwin":2, "metric":3, "metric_exp":4, "rank":5}
thisD['%s livetime'%dq_name] = event.livetime( idq.extract_dq_segments( sciseg_files[0], dq_name)[0] )
segstart, segend = idq.extract_start_stop( sciseg_files[0], suffix='.xml.gz')
thisD['start'] = int(segstart)
thisD['end'] = int(segend)
else:
thisD['%s livetime'%dq_name] = None
thisD['start'] = None
thisD['end'] = None
### extract Ng, Nc
if flavor == "ovl":
vetolist = glob.glob("%s/%d_%d/%s/ovl/*vetolist.eval"%(traindir, train_start, train_end, opts.classifier))
if len(vetolist) != 1:
raise ValueError("something odd with %s vetolists : %s/%d_%d/%s/ovl/"%(opts.classifier, traindir, train_start, train_end))
else:
thisD['Num glitches'] = int( event.loadstringtable( vetolist[0] )[0][idq.ovl.vD['#gwtrg']] )
elif flavor in idq.mla_flavors:
pat = glob.glob("%s/%d_%d/%s_mla*.pat"%(traindir, train_start, train_end, ifo))
if len(pat) != 1:
raise ValueError("something odd with %s patfile : %s/%d_%d/"%(opts.classifier, traindir, train_start, train_end))
else:
i = [float(i) for i in idq.slim_load_datfiles( pat[0], columns=['i'] )]
Ng = np.sum( i )
Nc = len(i) - Ng
thisD['Num glitches'] = int(Ng)
thisD['Num cleans'] = int(Nc)
else:
raise ValueError("do not know how to extract training information for flavor=%s"%(flavor))
### add to dictionary
jsonD["%d_%d"%(train_start, train_end)] = thisD
thisD['%s livetime'%dq_name] = event.livetime( idq.extract_dq_segments( sciseg_files[0], dq_name)[0] )
segstart, segend = idq.extract_start_stop( sciseg_files[0], suffix='.xml.gz')
thisD['start'] = int(segstart)
thisD['end'] = int(segend)
else:
thisD['%s livetime'%dq_name] = None
thisD['start'] = None
thisD['end'] = None
### extract Ng, Nc
if flavor == "ovl":
vetolist = glob.glob("%s/%d_%d/%s/ovl/*vetolist.eval"%(traindir, train_start, train_end, opts.classifier))
if len(vetolist) != 1:
raise ValueError("something odd with %s vetolists : %s/%d_%d/%s/ovl/"%(opts.classifier, traindir, train_start, train_end))
else:
thisD['Num glitches'] = int( event.loadstringtable( vetolist[0] )[0][idq.ovl.vD['#gwtrg']] )
elif flavor in idq.mla_flavors:
pat = glob.glob("%s/%d_%d/%s_mla*.pat"%(traindir, train_start, train_end, ifo))
if len(pat) != 1:
raise ValueError("something odd with %s patfile : %s/%d_%d/"%(opts.classifier, traindir, train_start, train_end))
else:
i = [float(i) for i in idq.slim_load_datfiles( pat[0], columns=['i'] )]
Ng = np.sum( i )
Nc = len(i) - Ng
thisD['Num glitches'] = int(Ng)
thisD['Num cleans'] = int(Nc)
else:
raise ValueError("do not know how to extract training information for flavor=%s"%(flavor))
### add to dictionary
jsonD["%d_%d"%(train_start, train_end)] = thisD
