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"""
=====================================================
Retrieving and analyzing GOES X-Ray Sensor (XRS) data
=====================================================
The X-ray Sensor (XRS) on board the GOES series of satellites
have provided soft X-ray measurements in two broadband energy
ranges 0.5-4 and 1-8 angstrom since 1975. The GOES 16 and 17
satellites are the latest in line. The flux levels in the GOES
1-8 angstrom channel are used to report flares and determine
their size (i.e. their GOES class).
In this example we are going to look at how you can query and
retrieve the GOES XRS data using `~sunpy.net.Fido` and load it
into a `~sunpy.timeseries.TimeSeries`.
Some things to note: NOAA have recently re-processed the GOES 13,
14 and 15 XRS science quality data, such that the SWPC scaling factor
has been removed. This means that the fluxes will have a different values,
and so will flare peak fluxes from previous 13, 14 and 15 XRS data. See
`here <https://www.ncei.noaa.gov/data/goes-space-environment-monitor/access/science/xrs/GOES_1-15_XRS_Science-Quality_Data_Readme.pdf>`__
for more details. The sunpy GOES XRS client for Fido now provides this
new re-processed data. We now also provide the data for GOES 16 and 17.
Another thing to note is that the GOES XRS client `~sunpy.net.Fido` now
returns all available GOES data for the specific timerange queried. For
example, there are times when GOES 13, 14 and 15 overlap and such data is
available from each satellite. Similarly there are times when GOES 16 and 17 overlap.
"""
import matplotlib.pyplot as plt
import numpy as np
from astropy.visualization import time_support
from sunpy import timeseries as ts
from sunpy.net import Fido
from sunpy.net import attrs as a
#############################################################
# Lets first define our start and end times and query using the
# `~sunpy.net.Fido`.
tstart = "2015-06-21 01:00"
tend = "2015-06-21 23:00"
result = Fido.search(a.Time(tstart, tend), a.Instrument("XRS"))
print(result)
#############################################################
# As we can see this now returns six results, two files for each GOES
# 13, one for GOES 14 and one for GOES 15, which can be identified
# by the ``SatelliteNumber`` column.
# The GOES data provided can either be high-cadence (1s/2s/3s based on GOES satellite) or averaged over 1 minute.
# This can be noted in the ``Resolution`` column, where the ``avg1m`` and ``flx1s`` attributes are the 1 minute average and the high-cadence data, respectively.
# However, we probably will only want one of these files for our analysis, so we can query by the `sunpy.net.attrs`:
# `sunpy.net.dataretriever.attrs.goes.SatelliteNumber` to specify what GOES satellite number we want
# to use, and `sunpy.net.attrs.Resolution` for the resolution. Here we will use the high-cadence observations.
result_goes15 = Fido.search(a.Time(tstart, tend), a.Instrument("XRS"), a.goes.SatelliteNumber(15), a.Resolution("flx1s"))
print(result_goes15)
#############################################################
# Now we can see that this returns just one file for the GOES 15 data.
# Lets now download this data using `~sunpy.net.fido_factory.UnifiedDownloaderFactory.fetch`.
file_goes15 = Fido.fetch(result_goes15)
#############################################################
# Lets now load this data into a `~sunpy.timeseries.TimeSeries`,
# and inspect the data using `~sunpy.timeseries.GenericTimeSeries.peek()`.
goes_15 = ts.TimeSeries(file_goes15)
goes_15.peek()
#############################################################
# The resulting `~sunpy.timeseries.TimeSeries` can be filtered by GOES quality flags. For more information
# refer to the `GOES Data Guide <https://www.ncei.noaa.gov/data/goes-space-environment-monitor/access/science/xrs/GOES_1-15_XRS_Science-Quality_Data_Readme.pdf>`__.
df = goes_15.to_dataframe()
df = df[(df["xrsa_quality"] == 0) & (df["xrsb_quality"] == 0)]
goes_15 = ts.TimeSeries(df, goes_15.meta, goes_15.units)
###############################################################
# We can also pull out the individual GOES channels and plot. The 0.5-4 angstrom
# channel is known as the "xrsa" channel and the 1-8 angstrom channel is known
# as the "xrsb" channel.
fig, ax = plt.subplots()
goes_15.plot(axes=ax, columns=["xrsb"])
plt.show()
###############################################################
# We can also truncate the data for the time of the large flare,
# and analyze the different channels. For example, we can plot the
# derivative which is useful in terms of the Neupert effect when analyzing
# flares.
goes_flare = goes_15.truncate("2015-06-21 09:35", "2015-06-21 10:30")
time_support()
fig, ax = plt.subplots(figsize=(10, 8))
ax.plot(goes_flare.time, np.gradient(goes_flare.quantity("xrsb")))
ax.set_ylabel("Flux (Wm$^{-2}$$s^{-1}$)")
fig.autofmt_xdate()
plt.show()
###############################################################
# GOES 16 and 17 data
# -------------------
# Since March 2020, data prior to GOES 15 (incl) is no longer supported
# by NOAA and GOES 16 and 17 data is now provided. See
# `here <https://data.ngdc.noaa.gov/platforms/solar-space-observing-satellites/goes/goes16/l2/docs/GOES-R_XRS_L2_Data_Users_Guide.pdf>`__
# for more details. GOES 16 and 17 are part of the GOES-R series and provide
# XRS data at a better time resolution (1s). sunpy now supports this data also.
# GOES 16 has been taking observations from 2017, and GOES 17 since 2018, both of
# which are now and its now available through sunpy.net.Fido.
###############################################################
# Lets query for some data over two days.
results = Fido.search(a.Time("2020-11-20 00:00", "2020-11-21 23:00"), a.Instrument("XRS"), a.Resolution("flx1s"))
print(results)
###############################################################
# We can see that we are provided with 4 results, two files for GOES 16
# and two for GOES 17. Again we can make the query only specifying one
# GOES satellite number.
results_16 = Fido.search(a.Time("2020-11-20 00:00", "2020-11-21 23:00"), a.Instrument("XRS"),
a.goes.SatelliteNumber(16))
print(results_16)
###############################################################
# Lets now download this data and load into a
# `~sunpy.timeseries.TimeSeries`.
files = Fido.fetch(results_16)
# We use the `concatenate=True` keyword argument in TimeSeries, as
# we have two files and want to create one timeseries from them.
goes_16 = ts.TimeSeries(files, concatenate=True)
goes_16.peek()
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