# Copyright (c) 2014-2017 Louisiana State University
#               2017-2025 Cardiff University
#
# This file is part of GWpy.
#
# GWpy is free software: you can redistribute it and/or modify
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#
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"""
.. sectionauthor:: Duncan Macleod <duncan.macleod@ligo.org>
.. currentmodule:: gwpy.timeseries

Plotting a `Spectrogram` of the Rayleigh statistic
##################################################

As described in :ref:`sphx_glr_examples_frequencyseries_rayleigh.py`, the Rayleigh
statistic can be used to study non-Gaussianity in a timeseries.
We can study the time variance of these features by plotting a
time-frequency spectrogram where we calculate the Rayleigh statistic for
each time bin.
"""

# %%
# To demonstate this, we can load some data from the LIGO Livingston
# interferometer around the time of the GW151226 gravitational wave detection:

from gwpy.timeseries import TimeSeries
gwdata = TimeSeries.get(
    "L1",
    "Dec 26 2015 03:37",
    "Dec 26 2015 03:47",
)

# %%
# Next, we can calculate a Rayleigh statistic `Spectrogram` using the
# :meth:`~gwpy.timeseries.TimeSeries.rayleigh_spectrogram` method of the
# `~gwpy.timeseries.TimeSeries` and a 5-second stride with a 2-second FFT and
# 1-second overlap (50%):
rayleigh = gwdata.rayleigh_spectrogram(5, fftlength=2, overlap=1)

# %%
# and can make a plot using the :meth:`~Spectrogram.plot` method
plot = rayleigh.plot(norm="log", vmin=0.25, vmax=4)
ax = plot.gca()
ax.set_yscale("log")
ax.set_ylim(30, 1500)
ax.set_title("Sensitivity of LIGO-Livingston around GW151226")
ax.colorbar(cmap="coolwarm", label="Rayleigh statistic")
plot.show()