Skip to content
Snippets Groups Projects
Commit 0d37d2db authored by Yifan Wang's avatar Yifan Wang
Browse files

upload gw190521 plot notebook

parent 5022785a
No related branches found
No related tags found
No related merge requests found
Hide whitespace changes
Inline Side-by-side
Showing
with 276 additions and 0 deletions
GW190521plot.ipynb 0 → 100644
+ 276
− 0
View file @ 0d37d2db
%% 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
```
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment

AEI Privacy Policy Imprint