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import h5py, numpy | ||
from matplotlib import pyplot | ||
from pycbc.results import str_utils | ||
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# 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 | ||
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# 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'][()] | ||
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# 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) | ||
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#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)) | ||
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fig = pyplot.figure(); | ||
ax = fig.add_subplot(111) | ||
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# 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) | ||
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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') | ||
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fig.set_dpi(250) | ||
fig.savefig('Figure4a.png') |
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import h5py, numpy | ||
from matplotlib import pyplot | ||
from pycbc.results import str_utils | ||
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# 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 | ||
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# 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'][()] | ||
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# 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) | ||
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#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)) | ||
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fig = pyplot.figure(); ax = fig.add_subplot(111) | ||
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amp330_color = 'navy' | ||
fillcolor = 'lightsteelblue' | ||
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# 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'])) | ||
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# 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) | ||
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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') | ||
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fig.set_dpi(250) | ||
fig.savefig('Figure4b.png') |