"""
=========================================
Making a power spectrum from a TimeSeries
=========================================

How to estimate the power spectrum of a TimeSeries.
"""
import matplotlib.pyplot as plt
from scipy import signal

import astropy.units as u

import sunpy.timeseries
from sunpy.data.sample import RHESSI_TIMESERIES

###############################################################################
# Let's first load a RHESSI TimeSeries from sunpy's sample data.
# This data contains 9 columns, which are evenly sampled with a time step of 4
# seconds.

ts = sunpy.timeseries.TimeSeries(RHESSI_TIMESERIES)

###############################################################################
# We now use SciPy's `~scipy.signal.periodogram` to estimate the
# power spectra of the first column of the Timeseries. The first column contains
# X-Ray emissions in the range of 3-6 keV. An alternative version is Astropy's
# `~astropy.timeseries.LombScargle` periodogram.

x_ray = ts.columns[0]
# The suitable value for fs would be 0.25 Hz as the time step is 4 s.
freq, spectra = signal.periodogram(ts.quantity(x_ray), fs=0.25)

###############################################################################
# Let's plot the results.

fig, ax = plt.subplots()
ax.semilogy(freq, spectra)
ax.set_title(f'Power Spectrum of {x_ray}')
ax.set_ylabel('Power Spectral Density [{:LaTeX}]'.format(ts.units[x_ray] ** 2 / u.Hz))
ax.set_xlabel('Frequency [Hz]')
plt.show()
