split figure4 to two figures (#7)

figures/figure4_split_a.py

@@ -0,0 +1,65 @@
+import h5py, numpy
+from matplotlib import pyplot
+from pycbc.results import str_utils
+
+# Amplitude fits from Borhanian et al., CQG 37 (2020)
+alpha33 = 0.4433
+def q33(amp33):
+    # A_33 = alpha_33 * delta
+    cond = amp33 <= alpha33
+    amp = amp33[cond]
+    massratio = (alpha33 + amp) / (alpha33 - amp)
+    return massratio
+
+# Load data from posterior files
+kerr_file = '../posteriors/kerr/220_330/KERR-220_330-06MS.hdf'
+fp_kerr = h5py.File(kerr_file, 'r')
+amp330 = fp_kerr['samples/amp330'][()]
+
+# Reweighted IMR files for mass ratio
+imr_files = {'phenom':'../posteriors/reweighted/REWEIGHTED_IMR-XPHM.hdf',
+             'nrsur':'../posteriors/reweighted/REWEIGHTED_IMR-NRSUR.hdf'}
+mass_ratios = {}
+for waveform in imr_files:
+    fp = h5py.File(imr_files[waveform])
+    mass_ratios[waveform] = 1./fp['samples/q'][()]
+    fp.close()
+# Reweighted ringdown results for mass ratio
+kerr_reweighted = '../posteriors/reweighted/REWEIGHTED_KERR-220_330-06MS.hdf'
+fp_kerr_reweighted = h5py.File(kerr_reweighted, 'r')
+amp330_reweighted = fp_kerr_reweighted['samples/amp330'][()]
+mass_ratios['kerr'] = 1./q33(amp330_reweighted)
+
+#Make figure
+def plot_percentiles(ax, samples, color):
+    plotp = numpy.percentile(samples, [5, 95])
+    for val in plotp:
+        ax.axvline(x=val, ls='dashed', color=color, lw=2, zorder=5)
+def get_interval(samples):
+    values_min, values_med, values_max = numpy.percentile(samples, [5, 50, 95])
+    negerror = values_med - values_min
+    poserror = values_max - values_med
+    return '${0}$'.format(str_utils.format_value(
+            values_med, negerror, plus_error=poserror))
+
+fig = pyplot.figure(); 
+ax = fig.add_subplot(111)
+
+# Top panel: amplitude distribution
+amp330_color = 'navy'
+fillcolor = 'lightsteelblue'
+ax.hist(amp330, label='Ringdown 330 mode',
+        edgecolor=amp330_color, facecolor=fillcolor,
+        bins=50, range=(0,alpha33), density=True,
+        histtype='stepfilled', lw=2)
+plot_percentiles(ax, amp330, amp330_color)
+
+ax.set_xlabel('amplitude ratio $A_{330}/A_{220}$', fontsize=14)
+ax.set_xlim(0, alpha33)
+ax.set_yticks([])
+ax.set_yticklabels([])
+#ax.xaxis.tick_top()
+ax.legend(loc='upper right')
+
+fig.set_dpi(250)
+fig.savefig('Figure4a.png')

figures/figure4_split_b.py

@@ -0,0 +1,80 @@
+import h5py, numpy
+from matplotlib import pyplot
+from pycbc.results import str_utils
+
+# Amplitude fits from Borhanian et al., CQG 37 (2020)
+alpha33 = 0.4433
+def q33(amp33):
+    # A_33 = alpha_33 * delta
+    cond = amp33 <= alpha33
+    amp = amp33[cond]
+    massratio = (alpha33 + amp) / (alpha33 - amp)
+    return massratio
+
+# Load data from posterior files
+kerr_file = '../posteriors/kerr/220_330/KERR-220_330-06MS.hdf'
+fp_kerr = h5py.File(kerr_file, 'r')
+amp330 = fp_kerr['samples/amp330'][()]
+
+# Reweighted IMR files for mass ratio
+imr_files = {'phenom':'../posteriors/reweighted/REWEIGHTED_IMR-XPHM.hdf',
+             'nrsur':'../posteriors/reweighted/REWEIGHTED_IMR-NRSUR.hdf'}
+mass_ratios = {}
+for waveform in imr_files:
+    fp = h5py.File(imr_files[waveform])
+    mass_ratios[waveform] = 1./fp['samples/q'][()]
+    fp.close()
+# Reweighted ringdown results for mass ratio
+kerr_reweighted = '../posteriors/reweighted/REWEIGHTED_KERR-220_330-06MS.hdf'
+fp_kerr_reweighted = h5py.File(kerr_reweighted, 'r')
+amp330_reweighted = fp_kerr_reweighted['samples/amp330'][()]
+mass_ratios['kerr'] = 1./q33(amp330_reweighted)
+
+#Make figure
+def plot_percentiles(ax, samples, color):
+    plotp = numpy.percentile(samples, [5, 95])
+    for val in plotp:
+        ax.axvline(x=val, ls='dashed', color=color, lw=2, zorder=5)
+def get_interval(samples):
+    values_min, values_med, values_max = numpy.percentile(samples, [5, 50, 95])
+    negerror = values_med - values_min
+    poserror = values_max - values_med
+    return '${0}$'.format(str_utils.format_value(
+            values_med, negerror, plus_error=poserror))
+
+fig = pyplot.figure(); ax = fig.add_subplot(111)
+
+amp330_color = 'navy'
+fillcolor = 'lightsteelblue'
+
+# Bottom panel: mass ratio distributions
+# Mass ratio from 33 mode
+ax.hist(mass_ratios['kerr'], label='Ringdown 330 mode',
+        edgecolor=amp330_color, facecolor=fillcolor,
+        bins=50, range=(0,1), density=True,
+        histtype='stepfilled', lw=2)
+plot_percentiles(ax, mass_ratios['kerr'], amp330_color)
+print('Mass ratio: ', get_interval(mass_ratios['kerr']))
+
+# Mass ratio from NR Surrogate
+color_sur = 'rosybrown'
+ax.hist(mass_ratios['nrsur'], label='IMR NRSurrogate',
+        edgecolor=color_sur, zorder=3,
+        bins=50, range=(0,1), density=True,
+        histtype='step', lw=1.5)
+# Mass ratio from PhenomXPHM
+color_phenom = 'purple'
+ax.hist(mass_ratios['phenom'], label='IMR PhenomXPHM',
+        edgecolor=color_phenom,
+        bins=50, range=(0,1), density=True,
+        histtype='step', lw=1.5)
+
+ax.set_xlabel('mass ratio', fontsize=14)
+ax.set_xlim(0,1)
+ax.invert_xaxis()
+ax.set_yticks([])
+ax.set_yticklabels([])
+ax.legend(loc='upper right')
+
+fig.set_dpi(250)
+fig.savefig('Figure4b.png')