diff --git a/gstlal-calibration/tests/check_calibration/demod_ratio_timeseries.py b/gstlal-calibration/tests/check_calibration/demod_ratio_timeseries.py
index 4411b95cdd8438073a7564e2d9dd6c484d33413a..91a1bbb96806395cb44203b88ad382df4ad9f2d3 100644
--- a/gstlal-calibration/tests/check_calibration/demod_ratio_timeseries.py
+++ b/gstlal-calibration/tests/check_calibration/demod_ratio_timeseries.py
@@ -84,7 +84,7 @@ options, filenames = parser.parse_args()
ifo = options.ifo
# Set up channel list
-channel_list = [(options.ifo, options.denominator_channel_name), (options.ifo, options.numerator_channel_name)]
+channel_list = [(ifo, options.denominator_channel_name), (ifo, options.numerator_channel_name)]
# Convert the list of frequencies to a list of floats
freq_list = options.frequencies.split(';')
@@ -168,6 +168,7 @@ def demod_ratio(pipeline, name):
test_common.build_and_run(demod_ratio, "demod_ratio", segment = segments.segment((LIGOTimeGPS(0, 1000000000 * options.gps_start_time), LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# Read data from files and plot it
+colors = ['b.', 'y.', 'r.', 'c.', 'm.', 'g.'] # Hopefully the user will not want to plot more than six datasets on one plot.
for i in range(0, len(freq_list)):
data = numpy.loadtxt("%s_%s_over_%s_%0.1fHz.txt" % (ifo, options.numerator_channel_name, options.denominator_channel_name, freq_list[i][0]))
t_start = data[0][0]
@@ -196,20 +197,23 @@ for i in range(0, len(freq_list)):
# Make plots
plt.figure(figsize = (10, 10))
plt.subplot(211)
- plt.plot(times, magnitudes[0], '.', markersize = markersize, label = '%0.1f Hz' % freq_list[i][0])
+ plt.plot(times, magnitudes[0], colors[0], markersize = markersize, label = '%0.1f Hz [avg = %0.5f, std = %0.5f]' % (freq_list[i][0], numpy.mean(magnitudes[0]), numpy.std(magnitudes[0])))
plt.title(options.plot_titles.split(';')[i])
plt.ylabel('Magnitude')
magnitude_range = options.magnitude_ranges.split(';')[i]
plt.ylim(float(magnitude_range.split(',')[0]), float(magnitude_range.split(',')[1]))
plt.grid(True)
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.subplot(212)
- plt.plot(times, phases[0], '.', markersize = markersize)
+ plt.plot(times, phases[0], colors[0], markersize = markersize, label = '%0.1f Hz [avg = %0.5f, std = %0.5f]' % (freq_list[i][0], numpy.mean(phases[0]), numpy.std(phases[0])))
plt.ylabel('Phase [deg]')
plt.xlabel('Time in %s since %s UTC' % (t_unit, time.strftime("%b %d %Y %H:%M:%S", time.gmtime(t_start + 315964782))))
phase_range = options.phase_ranges.split(';')[i]
plt.ylim(float(phase_range.split(',')[0]), float(phase_range.split(',')[1]))
plt.grid(True)
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
for j in range(1, len(freq_list[i])):
data = numpy.loadtxt("%s_%s_over_%s_%0.1fHz.txt" % (ifo, options.numerator_channel_name, options.denominator_channel_name, freq_list[i][j]))
magnitudes.append([])
@@ -218,9 +222,12 @@ for i in range(0, len(freq_list)):
magnitudes[j].append(data[k][1])
phases[j].append(data[k][2])
plt.subplot(211)
- plt.plot(times, magnitudes[j], '.', markersize = markersize, label = '%0.1f Hz' % freq_list[i][j])
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ plt.plot(times, magnitudes[j], colors[j], markersize = markersize, label = '%0.1f Hz [avg = %0.5f, std = %0.5f]' % (freq_list[i][j], numpy.mean(magnitudes[j]), numpy.std(magnitudes[j])))
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.subplot(212)
- plt.plot(times, phases[j], '.', markersize = markersize)
+ plt.plot(times, phases[j], colors[j], markersize = markersize, label = '%0.1f Hz [avg = %0.5f, std = %0.5f]' % (freq_list[i][j], numpy.mean(phases[j]), numpy.std(phases[j])))
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.savefig('%s_%d-%d.png' % (options.plot_titles.split(';')[i].replace(' ', '_'), int(t_start), int(dur)))
diff --git a/gstlal-calibration/tests/check_calibration/pcal2darm_timeseries.py b/gstlal-calibration/tests/check_calibration/pcal2darm_timeseries.py
index 189d4aaa7269ba3da456aae6bb334dc11a8a6a2b..c0e4464c0fe15c1e5052cc392577b4abfe0dc268 100644
--- a/gstlal-calibration/tests/check_calibration/pcal2darm_timeseries.py
+++ b/gstlal-calibration/tests/check_calibration/pcal2darm_timeseries.py
@@ -238,10 +238,10 @@ def pcal2darm(pipeline, name):
# =============================================================================
#
-
test_common.build_and_run(pcal2darm, "pcal2darm", segment = segments.segment((LIGOTimeGPS(0, 1000000000 * options.gps_start_time), LIGOTimeGPS(0, 1000000000 * options.gps_end_time))))
# Read data from files and plot it
+colors = ['r.', 'g.', 'y.', 'c.', 'm.', 'b.'] # Hopefully the user will not want to plot more than six datasets on one plot.
channels = calcs_channels
channels.extend(calibrated_channels)
for i in range(0, len(frequencies)):
@@ -272,16 +272,18 @@ for i in range(0, len(frequencies)):
# Make plots
plt.figure(figsize = (10, 10))
plt.subplot(211)
- plt.plot(times, magnitudes[0], '.', markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[0], numpy.mean(magnitudes[0]), numpy.std(magnitudes[0])))
+ plt.plot(times, magnitudes[0], colors[0], markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[0], numpy.mean(magnitudes[0]), numpy.std(magnitudes[0])))
plt.title(r'%s Delta $L_{\rm free}$ / Pcal at %0.1f Hz' % ( ifo, frequencies[i]))
plt.ylabel('Magnitude')
magnitude_range = options.magnitude_ranges.split(';')[i]
plt.ylim(float(magnitude_range.split(',')[0]), float(magnitude_range.split(',')[1]))
plt.grid(True)
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.subplot(212)
- plt.plot(times, phases[0], '.', markersize = markersize, label = 'avg = %0.5f, std = %0.5f' % (numpy.mean(phases[0]), numpy.std(phases[0],)))
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ plt.plot(times, phases[0], colors[0], markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[0], numpy.mean(phases[0]), numpy.std(phases[0])))
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.ylabel('Phase [deg]')
plt.xlabel('Time in %s since %s UTC' % (t_unit, time.strftime("%b %d %Y %H:%M:%S", time.gmtime(t_start + 315964782))))
phase_range = options.phase_ranges.split(';')[i]
@@ -295,11 +297,13 @@ for i in range(0, len(frequencies)):
magnitudes[j].append(data[k][1])
phases[j].append(data[k][2])
plt.subplot(211)
- plt.plot(times, magnitudes[j], '.', markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[j], numpy.mean(magnitudes[j]), numpy.std(magnitudes[j])))
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ plt.plot(times, magnitudes[j], colors[j], markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[j], numpy.mean(magnitudes[j]), numpy.std(magnitudes[j])))
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.subplot(212)
- plt.plot(times, phases[j], '.', markersize = markersize, label = 'avg = %0.5f, std = %0.5f' % (numpy.mean(phases[j]), numpy.std(phases[j],)))
- plt.legend(markerscale = 4.0 / markersize, numpoints = 3)
+ plt.plot(times, phases[j], colors[j], markersize = markersize, label = '%s [avg = %0.5f, std = %0.5f]' % (channels[j], numpy.mean(phases[j]), numpy.std(phases[j])))
+ leg = plt.legend(fancybox = True, markerscale = 4.0 / markersize, numpoints = 3)
+ leg.get_frame().set_alpha(0.5)
plt.savefig("deltal_over_pcal_at_%0.1fHz.png" % frequencies[i])