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"""
========================================================================
Creating a TimeSeries from GOES-XRS near real time data with flare times
========================================================================
This example will demonstrate how to make use of GOES XRS Near Real Time data.
This includes the goes XRS timeseries data as well as the flare times.
The real-time datasets can all be found at https://services.swpc.noaa.gov/json/goes/primary/
"""
import matplotlib.pyplot as plt
import pandas as pd
from astropy import units as u
from sunpy import timeseries as ts
from sunpy.time import parse_time
###############################################################################
# We will start by getting reading the GOES-XRS JSON file using :func:`pandas.read_json`.
# This allows us to download the file and load it straight into a `pandas.DataFrame`.
# This file updates every minute and contains the last 7 days worth of data.
# There is also a file with only the last 3 days worth of data.
goes_data = pd.read_json(
"https://services.swpc.noaa.gov/json/goes/primary/xrays-7-day.json"
)
###############################################################################
# The recorded flux values alternate between the two XRS energy channels:
# "0.05-0.4nm" and "0.1-0.8nm". We make a `pivot table <https://en.wikipedia.org/wiki/Pivot_table>`__
# that naturally rearranges the data into two flux columns. We then rename the
# columns.
goes_data = goes_data.pivot(
index='time_tag',
columns='energy',
values='observed_flux'
)
goes_data.rename(columns={'0.05-0.4nm': 'xrsa', '0.1-0.8nm': 'xrsb'}, inplace=True)
###############################################################################
# `sunpy.timeseries.TimeSeries` requires a datetime index, which we can get by
# parsing the time strings.
goes_data.index = parse_time(goes_data.index).datetime
###############################################################################
# `sunpy.timeseries.TimeSeries` requires that there are units for data variables.
# To do this, we will create a dictionary that will map the names of the two columns,
# "xrsa" and "xrsb" (the channel names for GOES XRS), to their corresponding
# physical flux units, ``u.W/u.m**2``.
units = dict([("xrsa", u.W / u.m ** 2), ("xrsb", u.W / u.m ** 2)])
###############################################################################
# We need to create a metadata dictionary for the data.
# Typically, `sunpy.timeseries.TimeSeries` reads the metadata directly from the file.
# However, here we need to define our own metadata and we will keep it fairly simple.
meta = dict(
{"instrument": "GOES X-ray sensor", "measurements": "primary", "type": "quicklook"}
)
###############################################################################
# Now we will create a `sunpy.timeseries.TimeSeries` by passing in the data,
# the metadata and the units.
goes_ts = ts.TimeSeries(goes_data, meta, units, source="xrs")
###############################################################################
# NOAA also provides the past 7 days of flare event identifications which we
# can also parse.
# Note that it is possible that no flares occurred in the last 7 days.
flare_events = pd.read_json(
"https://services.swpc.noaa.gov/json/goes/primary/xray-flares-7-day.json"
)
###############################################################################
# Next we sort the list and retain the five largest flares.
if len(flare_events) > 0:
largest_flares = flare_events.sort_values('max_xrlong', ascending=False)[:5]
else:
largest_flares = []
###############################################################################
# Finally, we plot the timeseries and overlay the largest flares that occurred.
fig, ax = plt.subplots()
goes_ts.plot(axes=ax)
if len(largest_flares) > 0:
for _, this_flare in largest_flares.iterrows():
peak_time = parse_time(this_flare["max_time"])
ax.axvline(peak_time.datetime, label=f'{peak_time.datetime} {this_flare["max_class"]}')
ax.legend()
ax.set_title(f"Last 7 days of GOES XRS data with the largest {len(largest_flares)} flares labeled")
plt.show()
|
