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transient_search_using_MCMC_make_simulated_data.py
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Gregory Ashton authoredGregory Ashton authored
generate_data.py 3.19 KiB
import pyfstat
import numpy as np
import os
import sys
ID = sys.argv[1]
outdir = sys.argv[2]
label = 'run_{}'.format(ID)
data_label = '{}_data'.format(label)
results_file_name = '{}/MCResults_{}.txt'.format(outdir, ID)
# Properties of the GW data
sqrtSX = 2e-23
tstart = 1000000000
Tspan = 100*86400
tend = tstart + Tspan
# Fixed properties of the signal
F0_center = 30
F1_center = 1e-10
F2 = 0
tref = .5*(tstart+tend)
VF0 = VF1 = 100
DeltaF0 = VF0 * np.sqrt(3)/(np.pi*Tspan)
DeltaF1 = VF1 * np.sqrt(45/4.)/(np.pi*Tspan**2)
depths = np.linspace(100, 400, 7)
run_setup = [((100, 0), 27, False),
((100, 0), 15, False),
((100, 0), 8, False),
((100, 0), 4, False),
((50, 50), 1, False)]
DeltaAlpha = 0.05
DeltaDelta = 0.05
for depth in depths:
h0 = sqrtSX / float(depth)
r = np.random.uniform(0, 1)
theta = np.random.uniform(0, 2*np.pi)
F0 = F0_center + 3*np.sqrt(r)*np.cos(theta)/(np.pi**2 * Tspan**2)
F1 = F1_center + 45*np.sqrt(r)*np.sin(theta)/(4*np.pi**2 * Tspan**4)
Alpha = np.random.uniform(0, 2*np.pi)
Delta = np.arccos(2*np.random.uniform(0, 1)-1)-np.pi/2
fAlpha = np.random.uniform(0, 1)
Alpha_min = Alpha - DeltaAlpha*(1-fAlpha)
Alpha_max = Alpha + DeltaAlpha*fAlpha
fDelta = np.random.uniform(0, 1)
Delta_min = Delta - DeltaDelta*(1-fDelta)
Delta_max = Delta + DeltaDelta*fDelta
psi = np.random.uniform(-np.pi/4, np.pi/4)
phi = np.random.uniform(0, 2*np.pi)
cosi = np.random.uniform(-1, 1)
data = pyfstat.Writer(
label=data_label, outdir=outdir, tref=tref,
tstart=tstart, F0=F0, F1=F1, F2=F2, duration=Tspan, Alpha=Alpha,
Delta=Delta, h0=h0, sqrtSX=sqrtSX, psi=psi, phi=phi, cosi=cosi,
detector='H1,L1')
data.make_data()
predicted_twoF = data.predict_fstat()
theta_prior = {'F0': {'type': 'unif',
'lower': F0-DeltaF0/2.,
'upper': F0+DeltaF0/2.},
'F1': {'type': 'unif',
'lower': F1-DeltaF1/2.,
'upper': F1+DeltaF1/2.},
'F2': F2,
'Alpha': {'type': 'unif',
'lower': Alpha_min,
'upper': Alpha_max},
'Delta': {'type': 'unif',
'lower': Delta_min,
'upper': Delta_max},
}
ntemps = 1
log10temperature_min = -1
nwalkers = 100
mcmc = pyfstat.MCMCFollowUpSearch(
label=label, outdir=outdir,
sftfilepath='{}/*{}*sft'.format(outdir, data_label),
theta_prior=theta_prior,
tref=tref, minStartTime=tstart, maxStartTime=tend,
nwalkers=nwalkers, ntemps=ntemps,
log10temperature_min=log10temperature_min)
mcmc.run(run_setup=run_setup, create_plots=False, log_table=False,
gen_tex_table=False)
d, maxtwoF = mcmc.get_max_twoF()
dF0 = F0 - d['F0']
dF1 = F1 - d['F1']
with open(results_file_name, 'a') as f:
f.write('{} {:1.8e} {:1.8e} {:1.8e} {:1.8e} {:1.8e}\n'
.format(depth, h0, dF0, dF1, predicted_twoF, maxtwoF))
os.system('rm {}/*{}*'.format(outdir, label))