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 = 0.5 * (tstart + tend) depth = 10 h0 = sqrtSX / depth label = "using_initialisation" 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 / 45.0 print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1)) theta_prior = { "F0": {"type": "unif", "lower": F0 - DeltaF0 / 2.0, "upper": F0 + DeltaF0 / 2.0}, "F1": {"type": "unif", "lower": F1 - DeltaF1 / 2.0, "upper": F1 + DeltaF1 / 2.0}, "F2": F2, "Alpha": Alpha, "Delta": Delta, } ntemps = 1 log10beta_min = -1 nwalkers = 100 nsteps = [100, 100] 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.setup_initialisation(100, scatter_val=1e-10) mcmc.run() mcmc.plot_corner(add_prior=True) mcmc.print_summary()