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Commit e50d1139 authored by Aaron Viets's avatar Aaron Viets
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gstlal-calibration: plotting script updates

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Pipeline #191736 passed
Stage: level0
Stage: level1
Stage: level2
Stage: test-gstlal
Stage: test-inspiral
Stage: test-burst
Stage: test-offline
Stage: docker
Stage: docker-latest
Stage: nightly-pages
Stage: deploy
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gstlal-calibration/tests/check_calibration/plot_kappas_from_txt.py
+ 28
− 27
View file @ e50d1139
......@@ -69,12 +69,11 @@ txt_list = options.txt_list.split(':')
for i in range(len(txt_list)):
data.append([])
labels.append([])
ylabels.append([])
ylabels.append(None)
txt_list[i] = txt_list[i].split(';')
for j in range(len(txt_list[i])):
data[i].append([])
labels[i].append([])
ylabels[i].append(None)
txt_list[i][j] = txt_list[i][j].split(',')
for k in range(len(txt_list[i][j])):
data[i][j].append(np.loadtxt(txt_list[i][j][k]).transpose())
......@@ -85,140 +84,140 @@ for i in range(len(txt_list)):
if 'tst' in txt_list[i][j][k] or 'TST' in txt_list[i][j][k]:
if 'imag' in txt_list[i][j][k] or 'IMAG' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Im(\kappa_{\rm T})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(-0.1)
data_max.append(0.1)
elif 'real' in txt_list[i][j][k] or 'REAL' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Re(\kappa_{\rm T})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'tau' in txt_list[i][j][k] or 'TAU' in txt_list[i][j][k]:
labels[i][j][k] += r': $\tau_{\rm T}$'
ylabels[i][j] = "Time ($\mu$s)"
ylabels[i] = "Time ($\mu$s)"
if j == 0 and k == 0:
data_min.append(-1000)
data_max.append(1000)
else:
# Assume it's the magnitude
labels[i][j][k] += r': $\kappa_{\rm T}$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'pum' in txt_list[i][j][k] or 'PUM' in txt_list[i][j][k]:
if 'imag' in txt_list[i][j][k] or 'IMAG' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Im(\kappa_{\rm P})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(-0.1)
data_max.append(0.1)
elif 'real' in txt_list[i][j][k] or 'REAL' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Re(\kappa_{\rm P})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'tau' in txt_list[i][j][k] or 'TAU' in txt_list[i][j][k]:
labels[i][j][k] += r': $\tau_{\rm P}$'
ylabels[i][j] = "Time ($\mu$s)"
ylabels[i] = "Time ($\mu$s)"
if j == 0 and k == 0:
data_min.append(-1000)
data_max.append(1000)
else:
# Assume it's the magnitude
labels[i][j][k] += r': $\kappa_{\rm P}$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'uim' in txt_list[i][j][k] or 'UIM' in txt_list[i][j][k]:
if 'imag' in txt_list[i][j][k] or 'IMAG' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Im(\kappa_{\rm U})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(-0.1)
data_max.append(0.1)
elif 'real' in txt_list[i][j][k] or 'REAL' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Re(\kappa_{\rm U})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'tau' in txt_list[i][j][k] or 'TAU' in txt_list[i][j][k]:
labels[i][j][k] += r': $\tau_{\rm U}$'
ylabels[i][j] = "Time ($\mu$s)"
ylabels[i] = "Time ($\mu$s)"
if j == 0 and k == 0:
data_min.append(-1000)
data_max.append(1000)
else:
# Assume it's the magnitude
labels[i][j][k] += r': $\kappa_{\rm U}$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'pu' in txt_list[i][j][k] or 'PU' in txt_list[i][j][k]:
if 'imag' in txt_list[i][j][k] or 'IMAG' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Im(\kappa_{\rm PU})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(-0.1)
data_max.append(0.1)
elif 'real' in txt_list[i][j][k] or 'REAL' in txt_list[i][j][k]:
labels[i][j][k] += r': $\Re(\kappa_{\rm PU})$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'tau' in txt_list[i][j][k] or 'TAU' in txt_list[i][j][k]:
labels[i][j][k] += r': $\tau_{\rm PU}$'
ylabels[i][j] = "Time ($\mu$s)"
ylabels[i] = "Time ($\mu$s)"
if j == 0 and k == 0:
data_min.append(-1000)
data_max.append(1000)
else:
# Assume it's the magnitude
labels[i][j][k] += r': $\kappa_{\rm PU}$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.9)
data_max.append(1.1)
elif 'kc' in txt_list[i][j][k] or 'KC' in txt_list[i][j][k] or 'kappac' in txt_list[i][j][k] or 'KAPPAC' in txt_list[i][j][k] or 'kappa_c' in txt_list[i][j][k] or 'KAPPA_C' in txt_list[i][j][k] or 'kappa_C' in txt_list[i][j][k]:
labels[i][j][k] += r': $\kappa_{\rm C}$'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(0.95)
data_max.append(1.05)
elif 'fc' in txt_list[i][j][k] or 'FC' in txt_list[i][j][k] or 'f_c' in txt_list[i][j][k] or 'F_C' in txt_list[i][j][k]:
labels[i][j][k] += r': $f_{\rm cc}$'
ylabels[i][j] = "Frequency (Hz)"
ylabels[i] = "Frequency (Hz)"
if j == 0 and k == 0:
data_min.append(390)
data_max.append(450)
elif ('fs_over_Q' in txt_list[i][j][k] or 'FS_OVER_Q' in txt_list[i][j][k] or 'f_s_over_Q' in txt_list[i][j][k] or 'F_S_OVER_Q' in txt_list[i][j][k]):
labels[i][j][k] += r': $f_{\rm s} / Q$'
ylabels[i][j] = "Frequency (Hz)"
ylabels[i] = "Frequency (Hz)"
if j == 0 and k == 0:
data_min.append(-2)
data_max.append(0)
elif 'fs' in txt_list[i][j][k] or 'FS' in txt_list[i][j][k] or 'f_s' in txt_list[i][j][k] or 'F_S' in txt_list[i][j][k]:
labels[i][j][k] += r': $f_{\rm s}^2$'
ylabels[i][j] = "Square Frequency (Hz$^2$)"
ylabels[i] = "Square Frequency (Hz$^2$)"
if j == 0 and k == 0:
data_min.append(-10)
data_max.append(200)
elif 'Q' in txt_list[i][j][k] or 'q' in txt_list[i][j][k]:
labels[i][j][k] += r': $Q^{-1}$'
ylabels[i][j] = "Inverse Quality Factor"
ylabels[i] = "Inverse Quality Factor"
if j == 0 and k == 0:
data_min.append(-1)
data_max.append(1)
else:
labels[i][j][k] += 'TDCF'
ylabels[i][j] = "Correction"
ylabels[i] = "Correction"
if j == 0 and k == 0:
data_min.append(min(data[i][j][k][1]))
data_max.append(max(data[i][j][k][1]))
......@@ -245,12 +244,14 @@ elif dur > 100:
plt.figure(figsize = (18, len(data) * 6))
for i in range(len(data)):
ax = plt.subplot(len(data), 1, i + 1)
num_legend = 0
for j in range(len(data[i])):
if ylabels[i][j] is not None:
plt.ylabel(ylabels[i][j])
for k in range(len(data[i][j])):
plt.plot((data[i][j][k][0] - t_start) / sec_per_t_unit, data[i][j][k][1], colors[j % 3][k % 3], linewidth = 2.0, label = labels[i][j][k])
leg = plt.legend(fancybox = True)
num_legend += 1
if ylabels[i] is not None:
plt.ylabel(ylabels[i])
leg = plt.legend(fancybox = True, loc = 'upper right', ncol = max(1, num_legend // 3))
leg.get_frame().set_alpha(0.5)
if i == 0 and options.plot_title is not None:
......
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