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
label = 'fully_coherent_search_using_MCMC'
outdir = 'data'

data = pyfstat.Writer(
    label=label, outdir=outdir, 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 = 2
log10beta_min = -0.5
nwalkers = 100
nsteps = [300, 300]

mcmc = pyfstat.MCMCSearch(
    label=label, outdir=outdir,
    sftfilepattern='{}/*{}*sft'.format(outdir, label), theta_prior=theta_prior,
    tref=tref, minStartTime=tstart, maxStartTime=tend, nsteps=nsteps,
    nwalkers=nwalkers, ntemps=ntemps, log10beta_min=log10beta_min)
mcmc.transform_dictionary = dict(
    F0=dict(subtractor=F0, symbol='$f-f^\mathrm{s}$'),
    F1=dict(subtractor=F1, symbol='$\dot{f}-\dot{f}^\mathrm{s}$'))
mcmc.run()
mcmc.plot_corner(add_prior=True)
mcmc.print_summary()