fully_coherent_search_using_MCMC.py

import pyfstat
import numpy as np

# Properties of the GW data
sqrtSX = 1e-23
tstart = 1000000000
duration = 100*86400
tend = tstart + duration

# Properties of the signal
F0 = 30.0
F1 = -1e-10
F2 = 0
Alpha = np.radians(83.6292)
Delta = np.radians(22.0144)
tref = .5*(tstart+tend)

depth = 10
h0 = sqrtSX / depth
data_label = 'fully_coherent_search_using_MCMC'

data = pyfstat.Writer(
    label=data_label, outdir='data', tref=tref,
    tstart=tstart, F0=F0, F1=F1, F2=F2, duration=duration, Alpha=Alpha,
    Delta=Delta, h0=h0, sqrtSX=sqrtSX)
data.make_data()

# The predicted twoF, given by lalapps_predictFstat can be accessed by
twoF = data.predict_fstat()
print 'Predicted twoF value: {}\n'.format(twoF)

DeltaF0 = 1e-7
DeltaF1 = 1e-13
VF0 = (np.pi * duration * DeltaF0)**2 / 3.0
VF1 = (np.pi * duration**2 * DeltaF1)**2 * 4/45.
print '\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n'.format(VF0*VF1, VF0, VF1)

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': Alpha,
               'Delta': Delta
               }

ntemps = 1
log10temperature_min = -1
nwalkers = 100
nsteps = [1000, 1000]

mcmc = pyfstat.MCMCSearch(
    label='fully_coherent_search_using_MCMC', outdir='data',
    sftfilepattern='data/*'+data_label+'*sft', theta_prior=theta_prior, tref=tref,
    minStartTime=tstart, maxStartTime=tend, nsteps=nsteps, nwalkers=nwalkers,
    ntemps=ntemps, log10temperature_min=log10temperature_min)
mcmc.run(context='paper', subtractions=[30, -1e-10], c=2)
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()