Compute a spectrogram

Compute the spectrogram of a set of SFTs. This is useful to produce visualizations of the Doppler modulation of a CW signal.

 import os
 import matplotlib.pyplot as plt

 import pyfstat

 # not github-action compatible
 # plt.rcParams["font.family"] = "serif"
 # plt.rcParams["font.size"] = 18
 # plt.rcParams["text.usetex"] = True

 label = "PyFstat_example_spectrogram"
 outdir = os.path.join("PyFstat_example_data", label)

 depth = 5

 data_parameters = {
     "sqrtSX": 1e-23,
     "tstart": 1000000000,
     "duration": 2 * 365 * 86400,
     "detectors": "H1",
     "Tsft": 1800,
 }

 signal_parameters = {
     "F0": 100.0,
     "F1": 0,
     "F2": 0,
     "Alpha": 0.0,
     "Delta": 0.5,
     "tp": data_parameters["tstart"],
     "asini": 25.0,
     "period": 50 * 86400,
     "tref": data_parameters["tstart"],
     "h0": data_parameters["sqrtSX"] / depth,
     "cosi": 1.0,
 }

 # making data
 data = pyfstat.BinaryModulatedWriter(
     label=label, outdir=outdir, **data_parameters, **signal_parameters
 )
 data.make_data()

 print("Loading SFT data and computing normalized power...")
 times, freqs, sft_data = pyfstat.helper_functions.get_sft_array(data.sftfilepath)
 normalized_power = (
     2 * sft_data ** 2 / (data_parameters["Tsft"] * data_parameters["sqrtSX"] ** 2)
 )

 plotfile = os.path.join(outdir, label + ".png")
 print(f"Plotting to file: {plotfile}")
 fig, ax = plt.subplots(figsize=(0.8 * 16, 0.8 * 9))
 ax.grid(which="both")
 ax.set(xlabel="Time [days]", ylabel="Frequency [Hz]", ylim=(99.98, 100.02))
 c = ax.pcolormesh(
     (times - times[0]) / 86400,
     freqs,
     normalized_power,
     cmap="inferno_r",
     shading="nearest",
 )
 fig.colorbar(c, label="Normalized Power")
 plt.tight_layout()
 fig.savefig(plotfile)