MCMC search on data presenting a glitch

Executes a directed MCMC semicoherent F-statistic search on data presenting a glitch. This is intended to show the impact of glitches on vanilla CW searches.

 import numpy as np
 import pyfstat
 from PyFstat_example_make_data_for_search_on_1_glitch import (
     tstart,
     duration,
     tref,
     F0,
     F1,
     F2,
     Alpha,
     Delta,
     outdir,
 )
 import os

 label = "PyFstat_example_standard_directed_MCMC_search_on_1_glitch"

 Nstar = 10000
 F0_width = np.sqrt(Nstar) * np.sqrt(12) / (np.pi * duration)
 F1_width = np.sqrt(Nstar) * np.sqrt(180) / (np.pi * duration ** 2)

 theta_prior = {
     "F0": {"type": "unif", "lower": F0 - F0_width / 2.0, "upper": F0 + F0_width / 2.0},
     "F1": {"type": "unif", "lower": F1 - F1_width / 2.0, "upper": F1 + F1_width / 2.0},
     "F2": F2,
     "Alpha": Alpha,
     "Delta": Delta,
 }

 ntemps = 2
 log10beta_min = -0.5
 nwalkers = 100
 nsteps = [500, 2000]

 mcmc = pyfstat.MCMCSearch(
     label=label,
     outdir=outdir,
     sftfilepattern=os.path.join(outdir, "*1_glitch*sft"),
     theta_prior=theta_prior,
     tref=tref,
     minStartTime=tstart,
     maxStartTime=tstart + duration,
     nsteps=nsteps,
     nwalkers=nwalkers,
     ntemps=ntemps,
     log10beta_min=log10beta_min,
 )

 mcmc.transform_dictionary["F0"] = dict(subtractor=F0, symbol="$f-f^\\mathrm{s}$")
 mcmc.transform_dictionary["F1"] = dict(
     subtractor=F1, symbol="$\\dot{f}-\\dot{f}^\\mathrm{s}$"
 )

 mcmc.run()
 mcmc.print_summary()
 mcmc.plot_corner()
 mcmc.plot_cumulative_max(savefig=True)