generate_data.py

import pyfstat
import numpy as np
import os
import sys
import time

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)

nsteps = 50
run_setup = [((nsteps, 0), 20, False),
             ((nsteps, 0), 11, False),
             ((nsteps, 0), 6, False),
             ((nsteps, 0), 3, False),
             ((nsteps, nsteps), 1, False)]

DeltaAlpha = 0.05
DeltaDelta = 0.05

h0 = 0
F0 = F0_center + np.random.uniform(-0.5, 0.5)*DeltaF0
F1 = F1_center + np.random.uniform(-0.5, 0.5)*DeltaF1
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()

startTime = time.time()
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']
runTime = time.time() - startTime
with open(results_file_name, 'a') as f:
    f.write('{:1.8e} {:1.8e} {:1.8e} {:1.8e} {}\n'
            .format(dF0, dF1, predicted_twoF, maxtwoF, runTime))
os.system('rm {}/*{}*'.format(outdir, label))