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MCMC search: Semicoherent F-statisticΒΆ
Directed MCMC search for an isolated CW signal using the semicoherent F-statistic.
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 | import pyfstat
import numpy as np
import os
label = "PyFstat_example_semi_coherent_MCMC_search"
outdir = os.path.join("PyFstat_example_data", label)
# Properties of the GW data
data_parameters = {
"sqrtSX": 1e-23,
"tstart": 1000000000,
"duration": 100 * 86400,
"detectors": "H1",
}
tend = data_parameters["tstart"] + data_parameters["duration"]
mid_time = 0.5 * (data_parameters["tstart"] + tend)
# Properties of the signal
depth = 10
signal_parameters = {
"F0": 30.0,
"F1": -1e-10,
"F2": 0,
"Alpha": np.radians(83.6292),
"Delta": np.radians(22.0144),
"tref": mid_time,
"h0": data_parameters["sqrtSX"] / depth,
"cosi": 1.0,
}
data = pyfstat.Writer(
label=label, outdir=outdir, **data_parameters, **signal_parameters
)
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 * data_parameters["duration"] * DeltaF0) ** 2 / 3.0
VF1 = (np.pi * data_parameters["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": signal_parameters["F0"] - DeltaF0 / 2.0,
"upper": signal_parameters["F0"] + DeltaF0 / 2.0,
},
"F1": {
"type": "unif",
"lower": signal_parameters["F1"] - DeltaF1 / 2.0,
"upper": signal_parameters["F1"] + DeltaF1 / 2.0,
},
}
for key in "F2", "Alpha", "Delta":
theta_prior[key] = signal_parameters[key]
ntemps = 1
log10beta_min = -1
nwalkers = 100
nsteps = [300, 300]
mcmc = pyfstat.MCMCSemiCoherentSearch(
label=label,
outdir=outdir,
nsegs=10,
sftfilepattern=os.path.join(outdir, "*{}*sft".format(label)),
theta_prior=theta_prior,
tref=mid_time,
minStartTime=data_parameters["tstart"],
maxStartTime=tend,
nsteps=nsteps,
nwalkers=nwalkers,
ntemps=ntemps,
log10beta_min=log10beta_min,
)
mcmc.transform_dictionary = dict(
F0=dict(subtractor=signal_parameters["F0"], symbol="$f-f^\\mathrm{s}$"),
F1=dict(
subtractor=signal_parameters["F1"], symbol="$\\dot{f}-\\dot{f}^\\mathrm{s}$"
),
)
mcmc.run(
walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters}
)
mcmc.print_summary()
mcmc.plot_corner(add_prior=True, truths=signal_parameters)
mcmc.plot_prior_posterior(injection_parameters=signal_parameters)
mcmc.plot_chainconsumer(truth=signal_parameters)
mcmc.plot_cumulative_max(
savefig=True,
custom_ax_kwargs={"title": "Cumulative 2F for the best MCMC candidate"},
)
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Total running time of the script: ( 0 minutes 0.000 seconds)