def channel_performance( vchans, ranges, performances, num_gchs, num_clns, figax=None, cmap=None):
if figax == None:
figrank = plt.figure(figsize=chan_perf_figsize)
axrank = figrank.add_axes(chan_perf_axpos)
figeff = plt.figure(figsize=chan_perf_figsize)
axeff = figeff.add_axes(chan_perf_axpos)
figfap = plt.figure(figsize=chan_perf_figsize)
axfap = figfap.add_axes(chan_perf_axpos)
else:
(figrank, axrank), (figeff, axeff), (figfap, axfap) = figax
if cmap==None:
cmap = matplotlib.cm.get_cmap()
S = int(np.max(ranges))
ind = 0
for ind, vchan in enumerate(vchans):
for (s, e), performance, num_gch, num_cln in zip(ranges, performances, num_gchs, num_clns):
if performance.has_key(vchan):
gch = performance[vchan]['gch']
cln = performance[vchan]['cln']
if num_gch:
eff = 1.0*gch/num_gch
else:
eff = 0
if num_cln:
fap = 1.0*cln/num_cln
else:
fap = 0
if fap > 0:
rank = ovl.effbydt_to_rank( eff/fap )
elif eff > 0:
rank = 1
else:
rank = 0
axrank.fill_between( [s-S, e-S], [ind-0.5]*2, [ind+0.5]*2, color=cmap(rank) )
axeff.fill_between( [s-S, e-S], [ind-0.5]*2, [ind+0.5]*2, color=cmap(eff) )
axfap.fill_between( [s-S, e-S], [ind-0.5]*2, [ind+0.5]*2, color=cmap(fap) )
ticks = range(ind+1)
for ax in [axrank, axeff, axfap]:
ax.yaxis.set_ticks( ticks )
ax.yaxis.set_ticklabels( [vchan.replace("_","\_") for vchan in vchans] )
ax.set_xlabel('sec relative to %d'%S)
ax.set_ylim(ymin=-0.5, ymax=ind+0.5)
return (figrank, axrank), (figeff, axeff), (figfap, axfap)
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
}
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}
def chanlist_trending(
gps_start,
gps_stop,
glob_dir,
classifier='ovl',
figure_name=False,
verbose=False,
annotated=True,
yvalue="rank"
):
"""
builds a channel performance trending plot using chanlist files
"""
if yvalue not in ["rank", "eff", "fap"]:
raise ValueError, "yvalue=%s not understood"%yvalue
ax_bounds = [0.025, 0.075, 0.5, 0.775]
ax_cb_bounds = [0.025, 0.875, 0.5, 0.05]
# figure out appropriat time variable:
dur = gps_stop - gps_start
for (tau, tau_name) in [(1., 'seconds'), (60., 'minutes'), (3600.,
'hours'), (86400., 'days'), (7 * 86400.,
'weeks')]:
if dur / tau < 100:
break
# ## find only the channel lists within this range
if verbose:
print 'finding %s*channel_summary.txt files within [%f, %f]' \
% (classifier, gps_start, gps_stop)
chanlists = []
for _dir in sorted(glob.glob(glob_dir + '/*_*')):
try:
(_start, _stop) = [int(l) for l in _dir.split('/'
)[-1].split('_')]
if gps_start <= _start and gps_stop > _start or gps_start \
< _stop and gps_stop >= _stop:
chanlists.append(glob.glob(_dir + '/' + classifier
+ '*_channel_summary.txt')[0]) # expect only one channel_summary.txt per _dir per classifier
except:
pass
### read in data from chanlists and build dictionary
chans = {}
for chanlist in chanlists:
if verbose:
print 'reading data from %s' % chanlist
# get time range for this chan list
(_start, _dur) = [int(l) for l in chanlist.split('/'
)[-1].split('_')[0].split('-')[-2:]] # eg: ovl-1043366400-86400_channel_summary.txt
# read in this chanlist
c = [[line[0]] + [float(l) for l in line[1:]] for line in
[_line.strip().split() for _line in open(chanlist, 'r'
).readlines()[1:]]]
for chan_dat in c:
channel = chan_dat[chanlistD['channel']]
if yvalue == "rank":
yValue = ovl.effbydt_to_rank(chan_dat[chanlistD['eff/fap']])
elif yvalue == "eff":
yValue = chan_dat[chanlistD['c_eff']]
elif yvalue == "fap":
yValue = chan_dat[chanlistD['c_fap']]
else:
raise ValueError, "yvalue=%s not understood"%yvalue
if chans.has_key(channel):
chans[channel].append([_start, _start + _dur, yValue])
else:
chans[channel] = [[_start, _start + _dur, yValue]]
# ## build plot
if verbose:
print 'generating figure'
color_map = matplotlib.cm.get_cmap()
norm = mpl.colors.Normalize(vmin=0, vmax=1)
fig = plt.figure()
ax = fig.add_axes(ax_bounds)
yticklabels = []
time_min = 0
ind = 0
for (ind, channel) in enumerate([chan for chan in
sorted(chans.keys()) if chan
!= 'none']): # we don't care about the "none" channel
yticklabels.append(channel.replace("_","\_"))
for (_start, _stop, yValue) in chans[channel]:
if _start - gps_stop < time_min:
time_min = _start - gps_stop
ax.fill_between([(_start - gps_stop) / tau, (_stop
- gps_stop) / tau], [ind + 0.49, ind
+ 0.49], [ind - 0.49, ind - 0.49],
facecolor=color_map(yValue), edgecolor='none')
if annotated:
ax.text(((_start + _stop) / 2. - gps_stop) / tau, ind,
'%.2f' % rank, ha='center', va='center')
ax.set_xlim(xmin=time_min / tau, xmax=0)
ax.set_ylim(ymin=-0.5, ymax=ind + 0.5)
ax.set_xlabel('%s relative to gps=%.0f' % (tau_name, gps_stop))
ax.set_yticks(range(ind + 1))
ax.set_yticklabels(yticklabels)
ax.yaxis.tick_right()
# plt.setp(plt.getp(ax, 'yticklabels'), fontsize=7.5)
# plt.setp(plt.getp(ax, 'xticklabels'), fontsize=10)
ax_colorbar = fig.add_axes(ax_cb_bounds)
cb = mpl.colorbar.ColorbarBase(ax_colorbar, cmap=color_map,
norm=norm, orientation='horizontal')
if yvalue == "rank":
cb.set_label(classifier + ' rank')
elif yvalue == "eff":
cb.set_label(classifier + ' efficiency')
elif yvalue == "fap":
cb.set_label(classifier + ' false alarm probability')
else:
raise ValueError, "yvalue=%s not understood"%yvalue
ax_colorbar.xaxis.tick_top()
ax_colorbar.xaxis.set_label_position('top')
plt.setp(fig, figheight=max(2.54, 1.0 + 0.15 * (ind + 1)), figwidth=10)
ax.grid(True)
if figure_name:
if verbose:
print 'saving figure into %s' % figure_name
plt.savefig(figure_name)
return figure_name
else:
return fig
