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MCMC search: Fully coherent F-statistic
Directed MCMC search for an isolated CW signal using the fully coherent F-statistic.
9 import os
10
11 import numpy as np
12
13 import pyfstat
14 from pyfstat.helper_functions import get_predict_fstat_parameters_from_dict
15
16 label = "PyFstat_example_fully_coherent_MCMC_search"
17 outdir = os.path.join("PyFstat_example_data", label)
18
19 # Properties of the GW data
20 data_parameters = {
21 "sqrtSX": 1e-23,
22 "tstart": 1000000000,
23 "duration": 100 * 86400,
24 "detectors": "H1",
25 }
26 tend = data_parameters["tstart"] + data_parameters["duration"]
27 mid_time = 0.5 * (data_parameters["tstart"] + tend)
28
29 # Properties of the signal
30 depth = 10
31 signal_parameters = {
32 "F0": 30.0,
33 "F1": -1e-10,
34 "F2": 0,
35 "Alpha": np.radians(83.6292),
36 "Delta": np.radians(22.0144),
37 "tref": mid_time,
38 "h0": data_parameters["sqrtSX"] / depth,
39 "cosi": 1.0,
40 }
41
42 data = pyfstat.Writer(
43 label=label, outdir=outdir, **data_parameters, **signal_parameters
44 )
45 data.make_data()
46
47 # The predicted twoF, given by lalapps_predictFstat can be accessed by
48 twoF = data.predict_fstat()
49 print("Predicted twoF value: {}\n".format(twoF))
50
51 DeltaF0 = 1e-7
52 DeltaF1 = 1e-13
53 VF0 = (np.pi * data_parameters["duration"] * DeltaF0) ** 2 / 3.0
54 VF1 = (np.pi * data_parameters["duration"] ** 2 * DeltaF1) ** 2 * 4 / 45.0
55 print("\nV={:1.2e}, VF0={:1.2e}, VF1={:1.2e}\n".format(VF0 * VF1, VF0, VF1))
56
57 theta_prior = {
58 "F0": {
59 "type": "unif",
60 "lower": signal_parameters["F0"] - DeltaF0 / 2.0,
61 "upper": signal_parameters["F0"] + DeltaF0 / 2.0,
62 },
63 "F1": {
64 "type": "unif",
65 "lower": signal_parameters["F1"] - DeltaF1 / 2.0,
66 "upper": signal_parameters["F1"] + DeltaF1 / 2.0,
67 },
68 }
69 for key in "F2", "Alpha", "Delta":
70 theta_prior[key] = signal_parameters[key]
71
72 ntemps = 2
73 log10beta_min = -0.5
74 nwalkers = 100
75 nsteps = [300, 300]
76
77 mcmc = pyfstat.MCMCSearch(
78 label=label,
79 outdir=outdir,
80 sftfilepattern=os.path.join(outdir, "*{}*sft".format(label)),
81 theta_prior=theta_prior,
82 tref=mid_time,
83 minStartTime=data_parameters["tstart"],
84 maxStartTime=tend,
85 nsteps=nsteps,
86 nwalkers=nwalkers,
87 ntemps=ntemps,
88 log10beta_min=log10beta_min,
89 )
90 mcmc.transform_dictionary = dict(
91 F0=dict(subtractor=signal_parameters["F0"], symbol="$f-f^\\mathrm{s}$"),
92 F1=dict(
93 subtractor=signal_parameters["F1"], symbol="$\\dot{f}-\\dot{f}^\\mathrm{s}$"
94 ),
95 )
96 mcmc.run(
97 walker_plot_args={"plot_det_stat": True, "injection_parameters": signal_parameters}
98 )
99 mcmc.print_summary()
100 mcmc.plot_corner(add_prior=True, truths=signal_parameters)
101 mcmc.plot_prior_posterior(injection_parameters=signal_parameters)
102
103 mcmc.generate_loudest()
104
105 # plot cumulative 2F, first building a dict as required for PredictFStat
106 d, maxtwoF = mcmc.get_max_twoF()
107 for key, val in mcmc.theta_prior.items():
108 if key not in d:
109 d[key] = val
110 d["h0"] = data.h0
111 d["cosi"] = data.cosi
112 d["psi"] = data.psi
113 PFS_input = get_predict_fstat_parameters_from_dict(d)
114 mcmc.plot_cumulative_max(PFS_input=PFS_input)
Total running time of the script: ( 0 minutes 0.000 seconds)