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Commit 84e05e0b authored by Aaron Viets's avatar Aaron Viets
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gstlal-calibration: Some plotting scripts now use a standard deviation... 

gstlal-calibration:  Some plotting scripts now use a standard deviation function that is less sensitive to extreme outliers
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tests/check_calibration/act2darm_timeseries.py
+ 15
− 4
View file @ 84e05e0b
......@@ -30,6 +30,7 @@ import os
import numpy
import time
from math import pi
from math import erf
import resource
import datetime
import time
......@@ -88,6 +89,16 @@ options, filenames = parser.parse_args()
ifo = options.ifo
# Function to compute standard deviation that is not strongly affected by outliers
def stdmed(x):
x = numpy.asarray(x)
N = len(x)
xmed = numpy.median(x)
diff = x - xmed
square_diff = diff * diff
diff_med = numpy.percentile(square_diff, 100 * erf(1 / numpy.sqrt(2)))
return numpy.sqrt(diff_med * N / (N - 1))
# Read the config file
def ConfigSectionMap(section):
dict1 = {}
......@@ -448,7 +459,7 @@ for i in range(0, len(frequencies)):
plt.figure(figsize = (25, 15))
plt.subplot(2, len(frequencies), i + 1)
if options.show_stats:
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f, \sigma = %0.3f]$' % (plot_labels[0], numpy.median(magnitudes[0]), numpy.std(magnitudes[0])))
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.4f, \sigma = %0.4f]$' % (plot_labels[0], numpy.median(magnitudes[0]), stdmed(magnitudes[0])))
else:
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[0]))
plt.title(r'${\rm %s} \ \widetilde{\Delta L}_{\rm free} / \tilde{x}_{\rm %s} \ {\rm at \ %0.1f \ Hz}$' % (ifo, stages[i], frequencies[i]), fontsize = 32)
......@@ -460,7 +471,7 @@ for i in range(0, len(frequencies)):
leg.get_frame().set_alpha(0.8)
plt.subplot(2, len(frequencies), len(frequencies) + i + 1)
if options.show_stats:
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.2f^{\circ}, \sigma = %0.2f^{\circ}]$' % (plot_labels[0], numpy.median(phases[0]), numpy.std(phases[0])))
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f^{\circ}, \sigma = %0.3f^{\circ}]$' % (plot_labels[0], numpy.median(phases[0]), stdmed(phases[0])))
else:
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[0]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
......@@ -483,14 +494,14 @@ for i in range(0, len(frequencies)):
phases[j].append(data[k][2])
plt.subplot(2, len(frequencies), i + 1)
if options.show_stats:
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f, \sigma = %0.3f]$' % (plot_labels[j], numpy.median(magnitudes[j]), numpy.std(magnitudes[j])))
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.4f, \sigma = %0.4f]$' % (plot_labels[j], numpy.median(magnitudes[j]), stdmed(magnitudes[j])))
else:
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[j]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
leg.get_frame().set_alpha(0.8)
plt.subplot(2, len(frequencies), len(frequencies) + i + 1)
if options.show_stats:
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.2f^{\circ}, \sigma = %0.2f^{\circ}]$' % (plot_labels[j], numpy.median(phases[j]), numpy.std(phases[j])))
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f^{\circ}, \sigma = %0.3f^{\circ}]$' % (plot_labels[j], numpy.median(phases[j]), stdmed(phases[j])))
else:
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[j]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
......
tests/check_calibration/pcal2darm_timeseries.py
+ 15
− 4
View file @ 84e05e0b
......@@ -30,6 +30,7 @@ import os
import numpy
import time
from math import pi
from math import erf
import resource
import datetime
import time
......@@ -87,6 +88,16 @@ options, filenames = parser.parse_args()
ifo = options.ifo
# Function to compute standard deviation that is not strongly affected by outliers
def stdmed(x):
x = numpy.asarray(x)
N = len(x)
xmed = numpy.median(x)
diff = x - xmed
square_diff = diff * diff
diff_med = numpy.percentile(square_diff, 100 * erf(1 / numpy.sqrt(2)))
return numpy.sqrt(diff_med * N / (N - 1))
# Read the config file
def ConfigSectionMap(section):
dict1 = {}
......@@ -355,7 +366,7 @@ for i in range(0, len(frequencies)):
plt.figure(figsize = (25, 15))
plt.subplot(2, len(frequencies), i + 1)
if options.show_stats:
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f, \sigma = %0.3f]$' % (plot_labels[0], numpy.median(magnitudes[0]), numpy.std(magnitudes[0])))
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.4f, \sigma = %0.4f]$' % (plot_labels[0], numpy.median(magnitudes[0]), stdmed(magnitudes[0])))
else:
plt.plot(times[0], magnitudes[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[0]))
plt.title(r'${\rm %s} \ \widetilde{\Delta L}_{\rm free} / \tilde{x}_{\rm pc} \ {\rm at \ %0.1f \ Hz}$' % ( ifo, frequencies[i]), fontsize = 32)
......@@ -367,7 +378,7 @@ for i in range(0, len(frequencies)):
leg.get_frame().set_alpha(0.8)
plt.subplot(2, len(frequencies), len(frequencies) + i + 1)
if options.show_stats:
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.2f^{\circ}, \sigma = %0.2f^{\circ}]$' % (plot_labels[0], numpy.median(phases[0]), numpy.std(phases[0])))
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f^{\circ}, \sigma = %0.3f^{\circ}]$' % (plot_labels[0], numpy.median(phases[0]), stdmed(phases[0])))
else:
plt.plot(times[0], phases[0], colors[0], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[0]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
......@@ -390,14 +401,14 @@ for i in range(0, len(frequencies)):
phases[j].append(data[k][2])
plt.subplot(2, len(frequencies), i + 1)
if options.show_stats:
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f, \sigma = %0.3f]$' % (plot_labels[j], numpy.median(magnitudes[j]), numpy.std(magnitudes[j])))
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.4f, \sigma = %0.4f]$' % (plot_labels[j], numpy.median(magnitudes[j]), stdmed(magnitudes[j])))
else:
plt.plot(times[j], magnitudes[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[j]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
leg.get_frame().set_alpha(0.8)
plt.subplot(2, len(frequencies), len(frequencies) + i + 1)
if options.show_stats:
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.2f^{\circ}, \sigma = %0.2f^{\circ}]$' % (plot_labels[j], numpy.median(phases[j]), numpy.std(phases[j])))
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s \ [\mu_{1/2} = %0.3f^{\circ}, \sigma = %0.3f^{\circ}]$' % (plot_labels[j], numpy.median(phases[j]), stdmed(phases[j])))
else:
plt.plot(times[j], phases[j], colors[j % 6], linestyle = 'None', marker = '.', markersize = markersize, label = r'$%s$' % (plot_labels[j]))
leg = plt.legend(fancybox = True, markerscale = 16.0 / markersize, numpoints = 1, loc = 'upper right')
......
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