"""
==============================
Flare times on a GOES XRS plot
==============================

How to plot flare times as provided by the HEK on a GOES XRS plot.
"""
import matplotlib.pyplot as plt

from sunpy.net import Fido
from sunpy.net import attrs as a
from sunpy.time import parse_time
from sunpy.timeseries import TimeSeries

###############################################################################
# Let's grab GOES XRS data for a particular time of interest and the HEK flare
# data for this time from the NOAA Space Weather Prediction Center (SWPC).

tr = a.Time('2011-06-07 04:00', '2011-06-07 12:00')
results = Fido.search(tr, a.Instrument.xrs & a.goes.SatelliteNumber(15) | a.hek.FL & (a.hek.FRM.Name == 'SWPC'))  # NOQA

###############################################################################
# Then download the XRS data and load it into a TimeSeries.

files = Fido.fetch(results)
goes = TimeSeries(files)

###############################################################################
# Next let's retrieve `~sunpy.net.hek.HEKTable` from the Fido result
# and then load the first row from HEK results into ``flares_hek``.

hek_results = results['hek']
flares_hek = hek_results[0]

###############################################################################
# Lets plot everything together.

plt.figure()
goes.plot()
plt.axvline(parse_time(flares_hek['event_peaktime']).datetime)
plt.axvspan(parse_time(flares_hek['event_starttime']).datetime,
            parse_time(flares_hek['event_endtime']).datetime,
            alpha=0.2, label=flares_hek['fl_goescls'])
plt.legend(loc=2)
plt.yscale('log')

plt.show()

"""
==============================
Flare times on a GOES XRS plot
==============================

How to plot flare times as provided by the HEK on a GOES XRS plot.
"""
import matplotlib.pyplot as plt

from sunpy.net import Fido
from sunpy.net import attrs as a
from sunpy.time import parse_time
from sunpy.timeseries import TimeSeries

###############################################################################
# Let's grab GOES XRS data for a particular time of interest and the HEK flare
# data for this time from the NOAA Space Weather Prediction Center (SWPC).

tr = a.Time('2011-06-07 04:00', '2011-06-07 12:00')
results = Fido.search(tr, a.Instrument.xrs & a.goes.SatelliteNumber(15) | a.hek.FL & (a.hek.FRM.Name == 'SWPC'))  # NOQA

###############################################################################
# Then download the XRS data and load it into a TimeSeries.

files = Fido.fetch(results)
goes = TimeSeries(files)

###############################################################################
# Next let's retrieve `~sunpy.net.hek.HEKTable` from the Fido result
# and then load the first row from HEK results into ``flares_hek``.

hek_results = results['hek']
flares_hek = hek_results[0]

###############################################################################
# Lets plot everything together.

plt.figure()
goes.plot()
plt.axvline(parse_time(flares_hek['event_peaktime']).datetime)
plt.axvspan(parse_time(flares_hek['event_starttime']).datetime,
            parse_time(flares_hek['event_endtime']).datetime,
            alpha=0.2, label=flares_hek['fl_goescls'])
plt.legend(loc=2)
plt.yscale('log')

plt.show()

============================== Flare times on a GOES XRS plot ==============================  How to plot flare times as provided by the HEK on a GOES XRS plot. ############################################################################### # Let's grab GOES XRS data for a particular time of interest and the HEK flare # data for this time from the NOAA Space Weather Prediction Center (SWPC). # NOQA ############################################################################### # Then download the XRS data and load it into a TimeSeries. ############################################################################### # Next let's retrieve `~sunpy.net.hek.HEKTable` from the Fido result # and then load the first row from HEK results into ``flares_hek``. ############################################################################### # Lets plot everything together.