Directed grid search: Monochromatic source

Search for a monochromatic (no spindown) signal using a parameter space grid (i.e. no MCMC).

  9 import os
 10
 11 import matplotlib.pyplot as plt
 12 import numpy as np
 13
 14 import pyfstat
 15
 16 label = "PyFstatExampleGridSearchF0"
 17 outdir = os.path.join("PyFstat_example_data", label)
 18 logger = pyfstat.set_up_logger(label=label, outdir=outdir)
 19
 20 # Properties of the GW data
 21 sqrtS = "1e-23"
 22 IFOs = "H1"
 23 # IFOs = "H1,L1"
 24 sqrtSX = ",".join(np.repeat(sqrtS, len(IFOs.split(","))))
 25 tstart = 1000000000
 26 duration = 100 * 86400
 27 tend = tstart + duration
 28 tref = 0.5 * (tstart + tend)
 29
 30 # parameters for injected signals
 31 depth = 70
 32 inj = {
 33     "tref": tref,
 34     "F0": 30.0,
 35     "F1": 0,
 36     "F2": 0,
 37     "Alpha": 1.0,
 38     "Delta": 1.5,
 39     "h0": float(sqrtS) / depth,
 40     "cosi": 0.0,
 41 }
 42
 43 data = pyfstat.Writer(
 44     label=label,
 45     outdir=outdir,
 46     tstart=tstart,
 47     duration=duration,
 48     sqrtSX=sqrtSX,
 49     detectors=IFOs,
 50     **inj,
 51 )
 52 data.make_data()
 53
 54 m = 0.001
 55 dF0 = np.sqrt(12 * m) / (np.pi * duration)
 56 DeltaF0 = 800 * dF0
 57 F0s = [inj["F0"] - DeltaF0 / 2.0, inj["F0"] + DeltaF0 / 2.0, dF0]
 58 F1s = [inj["F1"]]
 59 F2s = [inj["F2"]]
 60 Alphas = [inj["Alpha"]]
 61 Deltas = [inj["Delta"]]
 62 search = pyfstat.GridSearch(
 63     label=label,
 64     outdir=outdir,
 65     sftfilepattern=data.sftfilepath,
 66     F0s=F0s,
 67     F1s=F1s,
 68     F2s=F2s,
 69     Alphas=Alphas,
 70     Deltas=Deltas,
 71     tref=tref,
 72     minStartTime=tstart,
 73     maxStartTime=tend,
 74 )
 75 search.run()
 76
 77 # report details of the maximum point
 78 max_dict = search.get_max_twoF()
 79 logger.info(
 80     "max2F={:.4f} from GridSearch, offsets from injection: {:s}.".format(
 81         max_dict["twoF"],
 82         ", ".join(
 83             [
 84                 "{:.4e} in {:s}".format(max_dict[key] - inj[key], key)
 85                 for key in max_dict.keys()
 86                 if not key == "twoF"
 87             ]
 88         ),
 89     )
 90 )
 91 search.generate_loudest()
 92
 93 logger.info("Plotting 2F(F0)...")
 94 fig, ax = plt.subplots()
 95 frequencies = search.data["F0"]
 96 twoF = search.data["twoF"]
 97 # mismatch = np.sign(x-inj["F0"])*(duration * np.pi * (x - inj["F0"]))**2 / 12.0
 98 ax.plot(frequencies, twoF, "k", lw=1)
 99 DeltaF = frequencies - inj["F0"]
100 sinc = np.sin(np.pi * DeltaF * duration) / (np.pi * DeltaF * duration)
101 A = np.abs((np.max(twoF) - 4) * sinc**2 + 4)
102 ax.plot(frequencies, A, "-r", lw=1)
103 ax.set_ylabel("$\\widetilde{2\\mathcal{F}}$")
104 ax.set_xlabel("Frequency")
105 ax.set_xlim(F0s[0], F0s[1])
106 dF0 = np.sqrt(12 * 1) / (np.pi * duration)
107 xticks = [inj["F0"] - 10 * dF0, inj["F0"], inj["F0"] + 10 * dF0]
108 ax.set_xticks(xticks)
109 xticklabels = ["$f_0 {-} 10\\Delta f$", "$f_0$", "$f_0 {+} 10\\Delta f$"]
110 ax.set_xticklabels(xticklabels)
111 plt.tight_layout()
112 fig.savefig(os.path.join(outdir, label + "_1D.png"), dpi=300)

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