def makeScatteredPlot(chan,opts,distribution,list11=None,list12=None,list21=None,list22=None,list31=None,list32=None):
p1 = None
p2 = None
p3 = None
parameter = distribution.split('-')[0]
pylab.figure()
ax = pylab.subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
if list11 and list12:
p1 = pylab.plot(list11,list12,linestyle='None', marker='o',\
markerfacecolor='k', markeredgecolor='k',\
markersize=4, markeredgewidth=0)
if list21 and list22:
p2 = pylab.plot(list21,list22,linestyle='None', marker='v',\
markerfacecolor='r', markeredgecolor='r',\
markersize=4, markeredgewidth=0)
if list31 and list32:
p3 = pylab.plot(list31,list32,linestyle='None', marker='s',\
markerfacecolor='r', markeredgecolor='r',\
markersize=8, markeredgewidth=0)
pylab.grid()
pylab.xlabel('Z in ' + chan,size='x-large')
pylab.ylabel('Z in ' + opts.ref_channel,size='x-large')
pylab.title('Scattered plot of significance for channel: ' + chan)
if p1 and p2 and p3:
pylab.legend((p1,p2,p3),('background','foreground','candidate'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list21),max(list22),max(list31),max(list32))
if p1 and p2 and not p3:
pylab.legend((p1,p2),('background','foreground'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list21),max(list22))
if p1 and not p2 and p3:
pylab.legend((p1,p3),('background','candidate'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list31),max(list32))
if p1 and not p2 and not p3:
# pylab.legend((p1),('background'),loc='upper right')
lim = max(max(list11),max(list12))
ax.set_xlim(1e0, lim*2)
ax.set_ylim(1e0, lim*2)
figText = chan.split(':')[0] + '_' + chan.split(':')[1] + '_' + parameter + '_scat'
figName = InspiralUtils.set_figure_name(opts,figText)
InspiralUtils.savefig_pylal(figName)
pylab.close()
return figName
def makeScatteredPlot(chan,opts,distribution,list11=None,list12=None,list21=None,list22=None,list31=None,list32=None):
p1 = None
p2 = None
p3 = None
parameter = distribution.split('-')[0]
pylab.figure()
ax = pylab.subplot(111)
ax.set_xscale('log')
ax.set_yscale('log')
if list11 and list12:
p1 = pylab.plot(list11,list12,linestyle='None', marker='o',\
markerfacecolor='k', markeredgecolor='k',\
markersize=4, markeredgewidth=0)
if list21 and list22:
p2 = pylab.plot(list21,list22,linestyle='None', marker='v',\
markerfacecolor='r', markeredgecolor='r',\
markersize=4, markeredgewidth=0)
if list31 and list32:
p3 = pylab.plot(list31,list32,linestyle='None', marker='s',\
markerfacecolor='r', markeredgecolor='r',\
markersize=8, markeredgewidth=0)
pylab.grid()
pylab.xlabel('Z in ' + chan,size='x-large')
pylab.ylabel('Z in ' + opts.ref_channel,size='x-large')
pylab.title('Scattered plot of significance for channel: ' + chan)
if p1 and p2 and p3:
pylab.legend((p1,p2,p3),('background','foreground','candidate'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list21),max(list22),max(list31),max(list32))
if p1 and p2 and not p3:
pylab.legend((p1,p2),('background','foreground'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list21),max(list22))
if p1 and not p2 and p3:
pylab.legend((p1,p3),('background','candidate'),loc = 'upper right')
lim = max(max(list11),max(list12),max(list31),max(list32))
if p1 and not p2 and not p3:
# pylab.legend((p1),('background'),loc='upper right')
lim = max(max(list11),max(list12))
ax.set_xlim(1e0, lim*2)
ax.set_ylim(1e0, lim*2)
figText = chan.split(':')[0] + '_' + chan.split(':')[1] + '_' + parameter + '_scat'
figName = InspiralUtils.set_figure_name(opts,figText)
InspiralUtils.savefig_pylal(figName)
pylab.close()
return figName
def plotHistogram(chan,opts,distribution,histoList,binList,percentiles=None,candidate=None,candidateRank=None):
parameter = distribution.split('-')[0]
step = binList[1] - binList[0]
counter = sum(histoList)
xlimInf = min(binList)
if candidate and not parameter == 'dt':
xlimSup = max(max(binList),candidate + 2.0)
else:
xlimSup = max(binList)
pylab.figure()
# semilogy(bins + step/2., z_dist+0.0001, 'r^',markerfacecolor="b",markersize=12)
# plot(bins + step/2., z_dist)
pylab.bar(binList[0:len(binList)-1], histoList, width=step, bottom=0)
if percentiles:
line1 = pylab.axvline(x=percentiles[0], ymin=0, ymax=max(histoList), color='g', label='50th percentile', linewidth=2, linestyle='--')
line2 = pylab.axvline(x=percentiles[1], ymin=0, ymax=max(histoList), color='m', label='95th percentile', linewidth=2, linestyle='--')
line3 = pylab.axvline(x=percentiles[2], ymin=0, ymax=max(histoList), color='r', label='99th percentile', linewidth=2, linestyle='--')
if parameter == 'dt':
pylab.axvline(x=-percentiles[0], ymin=0, ymax=max(histoList), color='g', label='50th percentile', linewidth=2, linestyle='--')
pylab.axvline(x=-percentiles[1], ymin=0, ymax=max(histoList), color='m', label='95th percentile', linewidth=2, linestyle='--')
pylab.axvline(x=-percentiles[2], ymin=0, ymax=max(histoList), color='r', label='99th percentile', linewidth=2, linestyle='--')
if candidate:
line0 = pylab.axvline(x=candidate, ymin=0, ymax=max(histoList), color='k', label='candidate value (%s percentile)' % (candidateRank), linewidth=2, linestyle='-')
if percentiles and candidate:
pylab.legend((line0,line1,line2,line3),('candidate','50%','95%','99%'),loc = 'upper right')
if percentiles and not candidate:
pylab.legend((line1,line2,line3),('50%','95%','99%'),loc = 'upper right')
pylab.xlim(xlimInf,xlimSup)
pylab.xlabel(parameter + ' value',size='large')
# ylabel(r'#',size='x-large')
pylab.grid()
pylab.title("Histogram of the " + parameter + " value for " + chan + ', Statistics = ' + str(counter))
figText = chan.split(':')[0] + '_' + chan.split(':')[1] + '_' + parameter + '_dist'
figFileName = InspiralUtils.set_figure_name(opts,figText)
InspiralUtils.savefig_pylal(figFileName)
pylab.close()
return figFileName
def plotHistogram(chan,opts,distribution,histoList,binList,percentiles=None,candidate=None,candidateRank=None):
parameter = distribution.split('-')[0]
step = binList[1] - binList[0]
counter = sum(histoList)
xlimInf = min(binList)
if candidate and not parameter == 'dt':
xlimSup = max(max(binList),candidate + 2.0)
else:
xlimSup = max(binList)
pylab.figure()
# semilogy(bins + step/2., z_dist+0.0001, 'r^',markerfacecolor="b",markersize=12)
# plot(bins + step/2., z_dist)
pylab.bar(binList[0:len(binList)-1], histoList, width=step, bottom=0)
if percentiles:
line1 = pylab.axvline(x=percentiles[0], ymin=0, ymax=max(histoList), color='g', label='50th percentile', linewidth=2, linestyle='--')
line2 = pylab.axvline(x=percentiles[1], ymin=0, ymax=max(histoList), color='m', label='95th percentile', linewidth=2, linestyle='--')
line3 = pylab.axvline(x=percentiles[2], ymin=0, ymax=max(histoList), color='r', label='99th percentile', linewidth=2, linestyle='--')
if parameter == 'dt':
pylab.axvline(x=-percentiles[0], ymin=0, ymax=max(histoList), color='g', label='50th percentile', linewidth=2, linestyle='--')
pylab.axvline(x=-percentiles[1], ymin=0, ymax=max(histoList), color='m', label='95th percentile', linewidth=2, linestyle='--')
pylab.axvline(x=-percentiles[2], ymin=0, ymax=max(histoList), color='r', label='99th percentile', linewidth=2, linestyle='--')
if candidate:
line0 = pylab.axvline(x=candidate, ymin=0, ymax=max(histoList), color='k', label='candidate value (%s percentile)' % (candidateRank), linewidth=2, linestyle='-')
if percentiles and candidate:
pylab.legend((line0,line1,line2,line3),('candidate','50%','95%','99%'),loc = 'upper right')
if percentiles and not candidate:
pylab.legend((line1,line2,line3),('50%','95%','99%'),loc = 'upper right')
pylab.xlim(xlimInf,xlimSup)
pylab.xlabel(parameter + ' value',size='large')
# ylabel(r'#',size='x-large')
pylab.grid()
pylab.title("Histogram of the " + parameter + " value for " + chan + ', Statistics = ' + str(counter))
figText = chan.split(':')[0] + '_' + chan.split(':')[1] + '_' + parameter + '_dist'
figFileName = InspiralUtils.set_figure_name(opts,figText)
InspiralUtils.savefig_pylal(figFileName)
pylab.close()
return figFileName
pylab.xlabel('False Alarm Probability')
pylab.ylabel('Efficiency')
pylab.xscale('log')
pylab.yscale('log')
pylab.xlim(10**-5, 10**0)
pylab.ylim(10**-3.5, 10**0)
pylab.title('Fig. '+str(fig_num)+': ROC Curves from total\_ranked\_data[]')
l1 = pylab.legend(loc = 'lower right')
pfile.close()
if opts.diagnostic_plots:
# save log-log figure
name = '_ROC_log-log'
fname = InspiralUtils.set_figure_name(opts, name)
fname_thumb = InspiralUtils.savefig_pylal(filename=fname, doThumb=True, dpi_thumb=opts.figure_resolution)
fnameList.append(fname)
tagList.append(name)
# save lin-lin figure
fig_num +=1
pylab.title('Fig. '+str(fig_num)+': ROC Curves from total\_ranked\_data[]')
pylab.legend(loc = 'lower right')
pylab.yscale('linear')
pylab.xscale('linear')
pylab.xlim(0, 1)
pylab.ylim(0, 1)
name = '_ROC_lin-lin'
fname = InspiralUtils.set_figure_name(opts, name)
fname_thumb = InspiralUtils.savefig_pylal(filename=fname, doThumb=True, dpi_thumb=opts.figure_resolution)
fnameList.append(fname)
def plotHistogram(chan, opts, distribution, histoList, binList, percentiles=None, candidate=None, candidateRank=None):
parameter = distribution.split("-")[0]
step = binList[1] - binList[0]
counter = sum(histoList)
xlimInf = min(binList)
if candidate and not parameter == "dt":
xlimSup = max(max(binList), candidate + 2.0)
else:
xlimSup = max(binList)
pylab.figure()
# semilogy(bins + step/2., z_dist+0.0001, 'r^',markerfacecolor="b",markersize=12)
# plot(bins + step/2., z_dist)
pylab.bar(binList[0 : len(binList) - 1], histoList, width=step, bottom=0)
if percentiles:
line1 = pylab.axvline(
x=percentiles[0],
ymin=0,
ymax=max(histoList),
color="g",
label="50th percentile",
linewidth=2,
linestyle="--",
)
line2 = pylab.axvline(
x=percentiles[1],
ymin=0,
ymax=max(histoList),
color="m",
label="95th percentile",
linewidth=2,
linestyle="--",
)
line3 = pylab.axvline(
x=percentiles[2],
ymin=0,
ymax=max(histoList),
color="r",
label="99th percentile",
linewidth=2,
linestyle="--",
)
if parameter == "dt":
pylab.axvline(
x=-percentiles[0],
ymin=0,
ymax=max(histoList),
color="g",
label="50th percentile",
linewidth=2,
linestyle="--",
)
pylab.axvline(
x=-percentiles[1],
ymin=0,
ymax=max(histoList),
color="m",
label="95th percentile",
linewidth=2,
linestyle="--",
)
pylab.axvline(
x=-percentiles[2],
ymin=0,
ymax=max(histoList),
color="r",
label="99th percentile",
linewidth=2,
linestyle="--",
)
if candidate:
line0 = pylab.axvline(
x=candidate,
ymin=0,
ymax=max(histoList),
color="k",
label="candidate value (%s percentile)" % (candidateRank),
linewidth=2,
linestyle="-",
)
if percentiles and candidate:
pylab.legend((line0, line1, line2, line3), ("candidate", "50%", "95%", "99%"), loc="upper right")
if percentiles and not candidate:
pylab.legend((line1, line2, line3), ("50%", "95%", "99%"), loc="upper right")
pylab.xlim(xlimInf, xlimSup)
pylab.xlabel(parameter + " value", size="large")
# ylabel(r'#',size='x-large')
pylab.grid()
pylab.title("Histogram of the " + parameter + " value for " + chan + ", Statistics = " + str(counter))
figText = chan.split(":")[0] + "_" + chan.split(":")[1] + "_" + parameter + "_dist"
figFileName = InspiralUtils.set_figure_name(opts, figText)
InspiralUtils.savefig_pylal(figFileName)
pylab.close()
return figFileName
def makeScatteredPlot(
chan, opts, distribution, list11=None, list12=None, list21=None, list22=None, list31=None, list32=None
):
p1 = None
p2 = None
p3 = None
parameter = distribution.split("-")[0]
pylab.figure()
ax = pylab.subplot(111)
ax.set_xscale("log")
ax.set_yscale("log")
if list11 and list12:
p1 = pylab.plot(
list11,
list12,
linestyle="None",
marker="o",
markerfacecolor="k",
markeredgecolor="k",
markersize=4,
markeredgewidth=0,
)
if list21 and list22:
p2 = pylab.plot(
list21,
list22,
linestyle="None",
marker="v",
markerfacecolor="r",
markeredgecolor="r",
markersize=4,
markeredgewidth=0,
)
if list31 and list32:
p3 = pylab.plot(
list31,
list32,
linestyle="None",
marker="s",
markerfacecolor="r",
markeredgecolor="r",
markersize=8,
markeredgewidth=0,
)
pylab.grid()
pylab.xlabel("Z in " + chan, size="x-large")
pylab.ylabel("Z in " + opts.ref_channel, size="x-large")
pylab.title("Scattered plot of significance for channel: " + chan)
if p1 and p2 and p3:
pylab.legend((p1, p2, p3), ("background", "foreground", "candidate"), loc="upper right")
lim = max(max(list11), max(list12), max(list21), max(list22), max(list31), max(list32))
if p1 and p2 and not p3:
pylab.legend((p1, p2), ("background", "foreground"), loc="upper right")
lim = max(max(list11), max(list12), max(list21), max(list22))
if p1 and not p2 and p3:
pylab.legend((p1, p3), ("background", "candidate"), loc="upper right")
lim = max(max(list11), max(list12), max(list31), max(list32))
if p1 and not p2 and not p3:
# pylab.legend((p1),('background'),loc='upper right')
lim = max(max(list11), max(list12))
ax.set_xlim(1e0, lim * 2)
ax.set_ylim(1e0, lim * 2)
figText = chan.split(":")[0] + "_" + chan.split(":")[1] + "_" + parameter + "_scat"
figName = InspiralUtils.set_figure_name(opts, figText)
InspiralUtils.savefig_pylal(figName)
pylab.close()
return figName
pylab.ylabel('Efficiency')
pylab.xscale('log')
pylab.yscale('log')
pylab.xlim(10**-5, 10**0)
pylab.ylim(10**-3.5, 10**0)
pylab.title('Fig. ' + str(fig_num) +
': ROC Curves from total\_ranked\_data[]')
l1 = pylab.legend(loc='lower right')
pfile.close()
if opts.diagnostic_plots:
# save log-log figure
name = '_ROC_log-log'
fname = InspiralUtils.set_figure_name(opts, name)
fname_thumb = InspiralUtils.savefig_pylal(
filename=fname, doThumb=True, dpi_thumb=opts.figure_resolution)
fnameList.append(fname)
tagList.append(name)
# save lin-lin figure
fig_num += 1
pylab.title('Fig. ' + str(fig_num) +
': ROC Curves from total\_ranked\_data[]')
pylab.legend(loc='lower right')
pylab.yscale('linear')
pylab.xscale('linear')
pylab.xlim(0, 1)
pylab.ylim(0, 1)
name = '_ROC_lin-lin'
fname = InspiralUtils.set_figure_name(opts, name)
fname_thumb = InspiralUtils.savefig_pylal(
