Note
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Follow up example
Multi-stage MCMC follow up of a CW signal produced by an isolated source using a ladder of coherent times.
8 import os
9
10 import matplotlib.pyplot as plt
11 import numpy as np
12
13 import pyfstat
14
15 label = "PyFstat_example_semi_coherent_directed_follow_up"
16 outdir = os.path.join("PyFstat_example_data", label)
17
18 # Properties of the GW data
19 data_parameters = {
20 "sqrtSX": 1e-23,
21 "tstart": 1000000000,
22 "duration": 100 * 86400,
23 "detectors": "H1",
24 }
25 tend = data_parameters["tstart"] + data_parameters["duration"]
26 mid_time = 0.5 * (data_parameters["tstart"] + tend)
27
28 # Properties of the signal
29 depth = 40
30 signal_parameters = {
31 "F0": 30.0,
32 "F1": -1e-10,
33 "F2": 0,
34 "Alpha": np.radians(83.6292),
35 "Delta": np.radians(22.0144),
36 "tref": mid_time,
37 "h0": data_parameters["sqrtSX"] / depth,
38 "cosi": 1.0,
39 }
40
41 data = pyfstat.Writer(
42 label=label, outdir=outdir, **data_parameters, **signal_parameters
43 )
44 data.make_data()
45
46 # The predicted twoF, given by lalapps_predictFstat can be accessed by
47 twoF = data.predict_fstat()
48 print("Predicted twoF value: {}\n".format(twoF))
49
50 # Search
51 VF0 = VF1 = 1e5
52 DeltaF0 = np.sqrt(VF0) * np.sqrt(3) / (np.pi * data_parameters["duration"])
53 DeltaF1 = np.sqrt(VF1) * np.sqrt(180) / (np.pi * data_parameters["duration"] ** 2)
54 theta_prior = {
55 "F0": {
56 "type": "unif",
57 "lower": signal_parameters["F0"] - DeltaF0 / 2.0,
58 "upper": signal_parameters["F0"] + DeltaF0 / 2,
59 },
60 "F1": {
61 "type": "unif",
62 "lower": signal_parameters["F1"] - DeltaF1 / 2.0,
63 "upper": signal_parameters["F1"] + DeltaF1 / 2,
64 },
65 }
66 for key in "F2", "Alpha", "Delta":
67 theta_prior[key] = signal_parameters[key]
68
69
70 ntemps = 3
71 log10beta_min = -0.5
72 nwalkers = 100
73 nsteps = [100, 100]
74
75 mcmc = pyfstat.MCMCFollowUpSearch(
76 label=label,
77 outdir=outdir,
78 sftfilepattern=os.path.join(outdir, "*{}*sft".format(label)),
79 theta_prior=theta_prior,
80 tref=mid_time,
81 minStartTime=data_parameters["tstart"],
82 maxStartTime=tend,
83 nwalkers=nwalkers,
84 nsteps=nsteps,
85 ntemps=ntemps,
86 log10beta_min=log10beta_min,
87 )
88
89 NstarMax = 1000
90 Nsegs0 = 100
91 walkers_fig, walkers_axes = plt.subplots(nrows=2, figsize=(3.4, 3.5))
92 mcmc.run(
93 NstarMax=NstarMax,
94 Nsegs0=Nsegs0,
95 plot_walkers=True,
96 walker_plot_args={
97 "labelpad": 0.01,
98 "plot_det_stat": False,
99 "fig": walkers_fig,
100 "axes": walkers_axes,
101 "injection_parameters": signal_parameters,
102 },
103 )
104 walkers_fig.savefig(os.path.join(outdir, label + "_walkers.png"))
105 plt.close(walkers_fig)
106
107 mcmc.print_summary()
108 mcmc.plot_corner(add_prior=True, truths=signal_parameters)
109 mcmc.plot_prior_posterior(injection_parameters=signal_parameters)
Total running time of the script: ( 0 minutes 0.000 seconds)