gstlal-calibration: improved latency plotting script

gstlal-calibration/tests/check_calibration/latency_plot.py

@@ -7,7 +7,7 @@ import time
 import matplotlib
 matplotlib.rcParams['font.family'] = 'Times New Roman'
 matplotlib.rcParams['font.size'] = 16
-matplotlib.rcParams['legend.fontsize'] = 14
+matplotlib.rcParams['legend.fontsize'] = 12
 matplotlib.rcParams['mathtext.default'] = 'regular'
 matplotlib.use('Agg')
 import matplotlib.pyplot as plt
@@ -51,10 +51,18 @@ common_dt = long_dt
 while common_dt % short_dt:
 	common_dt = common_dt + long_dt
 
-# Number of sample of input and output corresponding to least common multiple sample period
+# Number of samples of input and output corresponding to least common multiple sample period
 in_step = round(common_dt / in_dt)
 out_step = round(common_dt / out_dt)
 
+# Sometimes the incoming data has dropouts... we need to deal with this :(
+for i in range(0, len(intimes)):
+	j = 1
+	while j < len(intimes[i]):
+		if round((intimes[i][j][0] - intimes[i][j - 1][0]) / in_dt) > 1:
+			intimes[i] = numpy.insert(intimes[i], j, [intimes[i][j - 1][0] + in_dt, intimes[i][j - 1][1] + in_dt], axis = 0)
+		j = j + 1
+
 # Find a start time that is not before any of the data sets start and an end time that is not after any of the data sets end
 t_start = intimes[0][0][0]
 t_end = intimes[0][-1][0]
@@ -99,33 +107,31 @@ for i in range(0, len(intimes)):
 		latency[i].append(outtimes[i][outtimes_start_index + j * out_step][1] - intimes[i][intimes_start_index + j * in_step][1])
 
 # Make the plot
-colors = ['purple', 'b', 'c', 'g', 'y', 'r']
-markersize = 200.0 * numpy.sqrt(len(intimes[0]))
-markersize = min(markersize, 10.0)
-markersize = max(markersize, 0.2)
-plt.figure(figsize = (10, 5))
+colors = ['b', 'c', 'g', 'm', 'y', 'r']
+markersize = 150.0 / numpy.sqrt(len(intimes[0]))
+markersize = min(markersize, 8.0)
+markersize = max(markersize, 1.0)
+plt.figure(figsize = (10, 6))
 if len(labels[0]):
 	plt.plot(gps_time, latency[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = labels[0])
+	leg = plt.legend(fancybox = True, loc = 'upper right', markerscale = 4.0 / markersize, numpoints = 3)
+	leg.get_frame().set_alpha(0.8)
 else:
 	plt.plot(gps_time, latency[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize)
 if len(options.plot_title):
 	plt.title(options.plot_title)
 plt.ylabel('Latency [s]')
 plt.xlabel('Time in %s since %s UTC' % (t_unit, time.strftime("%b %d %Y %H:%M:%S", time.gmtime(t_start + 315964782))))
-plt.ylim(0, 15)
+plt.ylim(0, 8)
 plt.grid(True, which = "both", linestyle = ':', linewidth = 0.3, color = 'black')
-if len(labels[0]):
-	leg = plt.legend(fancybox = True, loc = 'upper right')
-	leg.get_frame().set_alpha(0.8)
 
 for i in range(1, len(intimes)):
 	if len(labels) > 1:
 		plt.plot(gps_time, latency[i], colors[i % len(colors)], linestyle = 'None', marker = '.', markersize = markersize, label = labels[i])
+		leg = plt.legend(fancybox = True, loc = 'upper right', markerscale = 4.0 / markersize, numpoints = 3)
+		leg.get_frame().set_alpha(0.8)
 	else:
 		plt.plot(gps_time, latency[i], colors[i % len(colors)], linestyle = 'None', marker = '.', markersize = markersize)
-	if len(options.labels):
-		leg = plt.legend(fancybox = True, loc = 'upper right')
-		leg.get_frame().set_alpha(0.8)
 
 # Save the plot to a file
 plt.savefig('%s_%d-%d.png' % (options.plot_filename_prefix, int(t_start), int(dur)))