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Commit 0d37d2db authored by Yifan Wang's avatar Yifan Wang
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upload gw190521 plot notebook

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%% Cell type:code id:expressed-speed tags:
``` python
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
from pycbc.inference import io, models
from pycbc import waveform
from pycbc.detector import Detector
# PLOTTING OPTIONS
fig_width_pt = 3*246.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (np.sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = { 'axes.labelsize': 24,
'font.family': 'serif',
'font.serif': 'Computer Modern Raman',
'font.size': 24,
'legend.fontsize': 20,
'xtick.labelsize': 24,
'ytick.labelsize': 24,
'axes.grid' : True,
'text.usetex': True,
'savefig.dpi' : 100,
'lines.markersize' : 14,
'figure.figsize': fig_size}
mpl.rcParams.update(params)
```
%% Cell type:code id:settled-motel tags:
``` python
def fn_to_waveform(fn):
# read in the raw posterior file and extract the max loglikelihood parameter value
with io.loadfile(fn, 'r') as fp:
data = fp.read_data()
psds = fp.read_psds()
cp = fp.read_config_file()
samples = fp.read_samples(list(fp['samples'].keys()))
idx = samples['loglikelihood'].argmax()
params = {p: samples[p][idx] for p in samples.fieldnames}
# set up the model
model = models.read_from_config(cp, data=data, psds=psds)
model.update(**params)
_ = model.loglikelihood
pol = model.current_stats['maxl_polarization']
#compute the max loglikelihood waveform
wfs = model.waveform_generator.generate(polarization=pol,**model.current_params)
whdata = {}
for det in model.data:
d = model.data[det]
d = d.copy()
d *= model.weight[det] / (4*model.psds[det].delta_f)**0.5
whdata[det] = d.to_timeseries()
waveforms = {}
whitened_waveforms = {}
for detname in wfs:
hs = {}
whitend_hs = {}
det = Detector(detname)
fp, fc = det.antenna_pattern(model.current_params['ra'],
model.current_params['dec'],
pol,
model.current_params['tc'])
hp, hc = wfs[detname]
h = fp*hp + fc*hc
h.resize(len(model.weight[detname]))
wh = h.copy()
wh *= model.weight[detname] / (4*model.psds[detname].delta_f)**0.5
whitened_waveforms[detname] = wh.to_timeseries()
return whdata, whitened_waveforms,model
```
%% Cell type:code id:8691a3c5 tags:
``` python
parity_path = '/work/yifan.wang/gw190521_xrerun/t3-localrun/0_550/result.hdf'
parity_whdata, parity_whwaveform, parity_model = fn_to_waveform(parity_path)
#gr_path = '/work/yifan.wang/gw190521_xrerun/t1-mpi-gr/mpirun_output/config_files/result.hdf'
gr_path = '/work/yifan.wang/gw190521_xrerun/t1-mpi-gr/mpirun_output/config_files/result.hdf'
_, gr_whwaveform,gr_model = fn_to_waveform(gr_path)
```
%% Output
WARNING:root:No sampling_params section read from config file
WARNING:root:WARNING: The following args are not being used for waveform generation: q, parity_mpvinverse, srcmchirp, trigger_time, comoving_volume, baseapprox, redshift, delta_tc
WARNING:root:No sampling_params section read from config file
WARNING:root:WARNING: The following args are not being used for waveform generation: q, comoving_volume, srcmchirp, trigger_time, redshift, delta_tc
%% Cell type:code id:d27e743c tags:
``` python
xlim=(-0.1, 0.1)
fig = plt.figure(figsize=(10.211706102117061, 6.311181454233049*1.6))
fig.subplots_adjust(hspace=.2)
ax = fig.add_subplot(311);
tc = parity_model.static_params['trigger_time']
det = list(gr_whwaveform.keys())
wdata = parity_whdata[det[0]]
ax.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
wwf = parity_whwaveform[det[0]]
ax.plot(wwf.sample_times-tc, wwf,label='birefringence')
ax.plot(gr_whwaveform[det[0]].sample_times-tc, gr_whwaveform[det[0]],label='GR')
ax.grid(True)
ax.set_xticklabels([])
ax.set_xlim(*xlim)
ax.set_ylim(-100,100)
ax.text(0.054, 70, 'LIGO Hanford', style='italic',fontsize=20,
bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})
################
bx = fig.add_subplot(312);
wdata = parity_whdata[det[1]]
bx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
wwf = parity_whwaveform[det[1]]
bx.plot(wwf.sample_times-tc, wwf,label='birefringence')
bx.plot(gr_whwaveform[det[1]].sample_times-tc, gr_whwaveform[det[1]],label='GR')
bx.set_ylabel('Whitened Strain')
bx.set_xlim(*xlim)
bx.set_ylim(-100,100)
bx.text(0.0515, 70, 'LIGO Livingston', style='italic',fontsize=20,
bbox={'facecolor':'white', 'alpha':0.5, 'pad':0,'edgecolor':'none'})
bx.grid(True)
bx.set_xticklabels([])
################
cx = fig.add_subplot(313);
wdata = parity_whdata[det[2]]
cx.plot(wdata.sample_times-tc, wdata, c='k', alpha=0.2, label='whitened data')
wwf = parity_whwaveform[det[2]]
cx.plot(wwf.sample_times-tc, wwf,label='birefringence')
cx.plot(gr_whwaveform[det[2]].sample_times-tc, gr_whwaveform[det[2]],label='GR')
cx.text(0.07, 70, 'Virgo', style='italic',fontsize=20,
bbox={'facecolor':'white', 'alpha':0.5, 'pad':5,'edgecolor':'none'})
cx.set_xlim(*xlim)
cx.set_ylim(-100,100)
cx.set_xlabel('Time [s]')
cx.legend(loc='lower right', ncol=3,framealpha=0)
fig.savefig('1124_gw190521.pdf',bbox_inches='tight')
```
%% Output
%% Cell type:code id:a2769cdb tags:
``` python
parity_model.current_params
```
%% Output
{'loglikelihood': -27862.587034617434,
'spin1_a': 0.905760370062339,
'q': 4.604661012059659,
'parity_mpvinverse': 524.2753151337122,
'ra': 3.3298846773746726,
'logwt': -27907.29942400331,
'spin1_polar': 1.9165479311929257,
'spin2_azimuthal': 2.5418720868611406,
'comoving_volume': 5977742169.868624,
'dec': 0.512136845042128,
'srcmchirp': 60.215369361157165,
'spin2_polar': 2.399028465257018,
'delta_tc': 0.006884290349553457,
'spin1_azimuthal': 2.3867100805570907,
'coa_phase': 3.053881460939539,
'spin2_a': 0.7737867301521402,
'inclination': 1.33582259332741,
'approximant': 'mpvnosmall',
'baseapprox': 'IMRPhenomXPHM',
'f_lower': 20.0,
'f_ref': 20.0,
'trigger_time': 1242442967.4473,
'spin1x': -0.6206712642453288,
'spin1y': 0.583901598182653,
'spin1z': -0.30696572016320367,
'spin2x': -0.4319134341549804,
'spin2y': 0.29531073260830837,
'spin2z': -0.5700774161132298,
'tc': 1242442967.4541843,
'redshift': 0.27219564064922364,
'distance': 1432.3102047089094,
'mass1': 199.18204787598563,
'mass2': 43.25661484185819}
%% Cell type:code id:aacabce7 tags:
``` python
```
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