import pyfstat import numpy as np import matplotlib.pyplot as plt F0 = 30.0 F1 = -1e-10 F2 = 0 Alpha = np.radians(83.6292) Delta = np.radians(22.0144) # Properties of the GW data sqrtSX = 1e-23 tstart = 1000000000 duration = 100*86400 tend = tstart+duration tref = .5*(tstart+tend) depth = 40 label = 'semicoherent_directed_follow_up' outdir = 'data' h0 = sqrtSX / depth 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) # Search VF0 = VF1 = 1e5 DeltaF0 = np.sqrt(VF0) * np.sqrt(3)/(np.pi*duration) DeltaF1 = np.sqrt(VF1) * np.sqrt(180)/(np.pi*duration**2) 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 = 3 log10beta_min = -0.5 nwalkers = 100 nsteps = [100, 100] mcmc = pyfstat.MCMCFollowUpSearch( label=label, outdir=outdir, sftfilepattern='{}/*{}*sft'.format(outdir, label), theta_prior=theta_prior, tref=tref, minStartTime=tstart, maxStartTime=tend, nwalkers=nwalkers, nsteps=nsteps, ntemps=ntemps, log10beta_min=log10beta_min) NstarMax = 1000 Nsegs0 = 100 fig, axes = plt.subplots(nrows=2, figsize=(3.4, 3.5)) fig, axes = mcmc.run( NstarMax=NstarMax, Nsegs0=Nsegs0, labelpad=0.01, plot_det_stat=False, return_fig=True, fig=fig, axes=axes) for ax in axes: ax.grid() ax.set_xticks(np.arange(0, 600, 100)) ax.set_xticklabels([str(s) for s in np.arange(0, 700, 100)]) axes[-1].set_xlabel(r'$\textrm{Number of steps}$', labelpad=0.1) fig.tight_layout() fig.savefig('{}/{}_walkers.png'.format(mcmc.outdir, mcmc.label), dpi=400)