def lyra_ts():
# Create sample TimeSeries
lyra_ts = timeseries.TimeSeries(os.path.join(
rootdir, 'lyra_20150101-000000_lev3_std_truncated.fits.gz'),
source='LYRA')
data = pandas.DataFrame(index=TIME,
data={
"CHANNEL1": CHANNELS[0],
"CHANNEL2": CHANNELS[1],
"CHANNEL3": CHANNELS[0],
"CHANNEL4": CHANNELS[1]
})
lyra_ts = timeseries.TimeSeries(data, lyra_ts.meta)
return lyra_ts
def other():
goes = ts.TimeSeries('go1520140611.fits')
filename = '11jun14.lis.gz'
rstn = np.genfromtxt(filename, delimiter=2*[4]+5*[2]+8*[7], dtype=('|S10', int, int, int, int, int, int,
float, float,float, float,float, float,float, float),
names = ['sta','year','mon','day','hour','min','sec','f1','f2','f3','f4','f5','f6',
'f7','f8'])
times = list(map(datetime.datetime,rstn['year'],rstn['mon'],rstn['day'],
rstn['hour'],rstn['min'],rstn['sec']))
data = np.transpose([rstn['f1'],rstn['f2'],rstn['f3'],rstn['f4'],rstn['f5'],rstn['f6'],rstn['f7'],rstn['f8']])
df = pd.DataFrame(data, columns=['245 MHz','410 MHz','610 MHz','1.4 GHz',
'2.7 GHz','4.9 GHz','8.8 GHz','15.4 GHz'], index = times)
# save data to a csv
df.sort_index(inplace=True)
df.to_csv('san_vito_rstn_11062014.csv', header=True, index=True)
new_df = df.truncate(flare_ts, flare_te)
short_goes = goes.truncate(flare_ts, flare_te)
gl = short_goes.data['xrsb']
gs = short_goes.data['xrsa']
def units_attach(data, units, warn_missing_units=True):
"""
Takes the units defined by the user and attaches them to the TimeSeries.
Parameters
----------
data : :class:`pandas.DataFrame`
Input data. Takes the DataFrame which needs to have units attached.
units : :class:`collections.OrderedDict`
The units manually defined by the user.
Returns
-------
out : :class:`~sunpy.timeseries.TimeSeries`
DataFrame converted into TimeSeries with units attached.
"""
unit_key = list(units.keys())
for column_name in data.columns:
if column_name not in unit_key:
units[column_name] = u.dimensionless_unscaled
if warn_missing_units:
message = "{} column has missing units.".format(column_name)
warnings.warn(message, Warning)
with warnings.catch_warnings():
warnings.simplefilter('ignore',
'Discarding nonzero nanoseconds in conversion')
timeseries_data = ts.TimeSeries(data, units)
return timeseries_data
def test_calculate_temperature_em():
# Create XRSTimeSeries object, then create new one with
# temperature & EM using with calculate_temperature_em().
goeslc = timeseries.TimeSeries(get_test_filepath("go1520110607.fits"))
goeslc_new = goes.calculate_temperature_em(goeslc)
# Test correct exception is raised if a XRSTimeSeries object is
# not inputted.
with pytest.raises(TypeError):
goes.calculate_temperature_em([])
# Find temperature and EM manually with _goes_chianti_tem()
temp, em = goes._goes_chianti_tem(
goeslc.quantity("xrsb"),
goeslc.quantity("xrsa"),
satellite=int(goeslc.meta.metas[0]["TELESCOP"].split()[1]),
date="2014-01-01")
# Check that temperature and EM arrays from _goes_chianti_tem()
# are same as those in new XRSTimeSeries object.
assert goeslc_new.data.temperature.all() == temp.value.all()
assert goeslc_new.data.em.all() == em.value.all()
# Check rest of data frame of new XRSTimeSeries object is same
# as that in original object.
goeslc_revert = copy.deepcopy(goeslc_new)
del goeslc_revert.data["temperature"]
del goeslc_revert.data["em"]
assert_frame_equal(goeslc_revert.data, goeslc.data)
def test_calculate_radiative_loss_rate():
# Define input variables.
goeslc_input = timeseries.TimeSeries(get_test_filepath("go1520110607.fits"))
not_goeslc = []
goeslc_no_em = goes.calculate_temperature_em(goeslc_input)
del goeslc_no_em.data["em"]
# Check correct exceptions are raised to incorrect inputs
with pytest.raises(TypeError):
goes_test = goes.calculate_radiative_loss_rate(not_goeslc)
# Check function gives correct results.
# Test case 1: GOESLightCurve object with only flux data
goeslc_test = goes.calculate_radiative_loss_rate(goeslc_input)
exp_data = np.array([1.78100055e+19, 1.66003113e+19, 1.71993065e+19,
1.60171768e+19, 1.71993065e+19])
np.testing.assert_allclose(goeslc_test.data.rad_loss_rate[:5],
exp_data)
# Test case 2: GOESLightCurve object with flux and temperature
# data, but no EM data.
goes_test = goes.calculate_radiative_loss_rate(goeslc_no_em)
# we test that the column has been added
assert "rad_loss_rate" in goes_test.columns
# Compare every 50th value to save on filesize
return np.array(goes_test.data[::50])
def units_attach(data, units, warn_missing_units=True):
"""
Takes the units defined by the user and attaches them to the TimeSeries.
Parameters
----------
data : :class:`pandas.DataFrame`
Input data. Takes the DataFrame which needs to have units attached.
units : :class:`collections.OrderedDict`
The units manually defined by the user.
Returns
-------
out : :class:`~sunpy.timeseries.TimeSeries`
DataFrame converted into TimeSeries with units attached.
"""
missing_msg = ('If you are trying to auomatically download data '
'with HelioPy this is a bug, please report it at '
'https://github.com/heliopython/heliopy/issues')
unit_key = list(units.keys())
for column_name in data.columns:
if column_name not in unit_key:
units[column_name] = u.dimensionless_unscaled
if warn_missing_units:
message = (f"{column_name} column has missing units."
f"\n{missing_msg}")
warnings.warn(message, Warning)
with warnings.catch_warnings():
warnings.simplefilter('ignore',
'Discarding nonzero nanoseconds in conversion')
timeseries_data = ts.TimeSeries(data, units)
return timeseries_data
def AIATimeSeries():
startDate = request.args.get('a', 0, type=str)
endDate = request.args.get('b', 0, type=str)
result = Fido.search(a.Time(startDate, endDate), a.Instrument('XRS'))
try:
downloaded_files = Fido.fetch(result)
combined_goes_ts = ts.TimeSeries(downloaded_files,
source='XRS',
concatenate=True)
combined_goes_ts.peek()
filename = (os.path.basename(downloaded_files[0]))
zipf = zipfile.ZipFile('app/static/TSFits/' + filename + '.zip', 'w',
zipfile.ZIP_DEFLATED)
for files in downloaded_files:
zipf.write(files, os.path.basename(files))
zipf.close()
plt.savefig('app/static/images/' + filename + '_timeseries.png')
return jsonify(
result='<img id="img" src="static/images/' + filename +
'_timeseries.png" style="width: inherit; padding-bottom: 6px;">',
download='<a href="static/images/' + filename +
'_timeseries.png" download="" id="btn-down" '
'class="glyphicon glyphicon-floppy-save" style="font-size: 20px; color: black; text-decoration: none; '
'data-toggle="tooltip" data-placement="top" title="Download PNG file""></a>'
'<a href="static/TSFits/' + filename +
'.zip" download="" id="btn-down" class="glyphicon '
'glyphicon glyphicon-save-file" style="font-size: 20px; color: black; text-decoration: none;'
'data-toggle="tooltip" data-placement="top" title="Download FITS file""></a>'
)
except HTTPError:
import pdb
pdb.set_trace()
result = "error"
return jsonify(result=result)
def plot_flares(i, transmitter='NRK'):
tt = parse_time(events_to_download[i]).strftime("%Y%m%d")
files_vlf = glob.glob("./vlf_bas_files/{:s}{:s}*".format(transmitter, tt))
if len(files_vlf)==0:
print("No VLF data")
return
goes_file = goes_data_dir + "go15" + tt + ".fits"
if not Path(goes_file).exists():
print("No goes data")
return
goes_data = ts.TimeSeries(goes_file)
gl = goes_data.data["xrsb"]
gs = goes_data.data["xrsa"]
flares_ind = np.where(daytime_flares["event_date"].isin([events_to_download[i]])==True)[0]
flares = daytime_flares.iloc[flares_ind]
vlf_amp, vlf_phase = read_vlf_data(files_vlf[0], tt)
fig, ax = plt.subplots(3, sharex=True, figsize=(8, 10))
ax1 = ax[0]
ax2 = ax[1]
ax3 = ax[2]
ax1.plot(gl, color="r", label="1-8 $\mathrm{\AA}$")
ax1.plot(gs, color="b", label="0.5-4 $\mathrm{\AA}$")
ax1.set_ylim(1e-9, 1e-3)
ax1.set_yscale("log")
ax1.tick_params(which="both", direction="in", right=True, top=True)
ax1.set_ylabel("Flux (Wm$^{-2}$)")
ax1.legend(loc="upper right")
ax2.plot(vlf_amp, label='NAA', color='grey')
ax2.set_ylabel('VLF Amplitude (dB)')
ax3.plot(vlf_phase, label='NAA', color='k')
ax3.set_ylabel('Phase (degrees)')
ax3.set_xlabel("Time {:s} UT".format(events_to_download[i]))
ax3.set_xlim(events_to_download[i] + " 00:00", events_to_download[i] + " 23:59")
ax3.xaxis.set_major_locator(dates.HourLocator(interval=3))
ax3.xaxis.set_minor_locator(dates.HourLocator(interval=1))
ax3.xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
ax3.tick_params(which="both", direction="in", right=True, top=True)
for f in flares["peak_time"]:
ax1.axvline(parse_time(f).datetime, color="k", ls="dashed")
ax2.axvline(parse_time(f).datetime, color="k", ls="dashed")
ax3.axvline(parse_time(f).datetime, color="k", ls="dashed")
ax1.grid()
ax2.grid()
ax3.grid()
plt.tight_layout()
plt.subplots_adjust(hspace=0.05)
plt.savefig(save_dir + transmitter + parse_time(events_to_download[i]).strftime("%Y%m%d.png"), dpi=200)
plt.close()
def plot_flare(new_df, i, save=False):
"""
Function to plot a flare from a pandas DataFrame.
Parameters:
----------
new_df : ~`pd.DataFrame
DataFrame with each row a flare
i : ~`int`
row index to plot
"""
sid_file = glob.glob(pd.to_datetime(new_df["event_starttime"].iloc[i]).strftime(sid_file_dir))[0]
new_ts = pd.to_datetime(new_df["event_starttime"].iloc[i])-datetime.timedelta(minutes=5)
new_te = pd.to_datetime(new_df["event_endtime"].iloc[i])+datetime.timedelta(minutes=5)
sid_data = sid_to_series(sid_file).truncate(new_ts, new_te)
sid_resample = pd.Series(savgol_filter(sid_data, 2*60+1, 3), index=sid_data.index)
if len(sid_data)>300:
sid_resample2 = pd.Series(savgol_filter(sid_data, 5*60+1, 3), index=sid_data.index)
tmax_sid = sid_resample.index[np.argmax(sid_resample)]
goes_file = glob.glob(pd.to_datetime(new_df["event_starttime"].iloc[i]).strftime(goes_file_dir))[0]
goes = ts.TimeSeries(goes_file).truncate(new_ts, new_te)
gl = goes.to_dataframe()["xrsb"]
gs = goes.to_dataframe()["xrsa"]
fig, ax = plt.subplots(2, sharex=True)
ax[0].plot(gl, color="r", label="1-8$\mathrm{\AA}$")
ax[0].plot(gs, color="b", label="0.5-4$\mathrm{\AA}$")
ax[0].set_ylabel("Flux (Wm$^{-2}$)")
ax[0].legend(loc="upper left")
ax[0].set_yscale("log")
ax[1].plot(sid_data, color="grey", lw=0.5, label="raw")
ax[1].plot(sid_resample, color="k", label="2 minute smooth")
if len(sid_data)>300:
ax[1].plot(sid_resample2, color="g", label="5 minute smooth")
ax[1].legend(loc="upper left")
for a in ax:
a.axvline(pd.to_datetime(new_df["event_peaktime"].iloc[i]), color="k", lw=0.8, ls="dashed")
a.axvline(pd.to_datetime(new_df["event_starttime"].iloc[i]), color="k", lw=0.8, ls="dashed" )
a.axvline(pd.to_datetime(new_df["event_endtime"].iloc[i]), color="k", lw=0.8, ls="dashed")
a.axvline(tmax_sid, color="r")
ax[0].xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
tstart_str = pd.to_datetime(new_df["event_starttime"].iloc[i]).strftime("%Y-%m-%dT%H:%M")
ax[1].set_xlabel(new_df["event_peaktime"].iloc[i])
plt.tight_layout()
plt.subplots_adjust(hspace=0.01)
if save:
plt.savefig("./final_stats_study_tests/flare2_{:d}_{:s}.png".format(i, tstart_str))
plt.close()
# for i in range(len(vlf_flares)):
# plot_flare(vlf_flares, i, save=True)
# print(i)
def plot(i):
tt = parse_time(days_to_plot[i]).strftime("%Y%m%d")
files_vlf = glob.glob(vlf_data_dir + tt + '*.csv')
if len(files_vlf) == 0:
print("No VLF data")
return
goes_file = goes_data_dir + "go15" + tt + ".fits"
if not Path(goes_file).exists():
print("No goes data")
return
data_vlf = read_files(files_vlf)
goes_data = ts.TimeSeries(goes_file).to_dataframe()
flares_ind = np.where(daytime_flares["event_date"].isin([days_to_plot[i]])==True)[0]
flares = daytime_flares.iloc[flares_ind]
fig, ax = plt.subplots(2, figsize=(8,6), sharex=True)
ax[0].plot(goes_data['xrsb'], color='b', label='1-8$\mathrm{\AA}$')
ax[0].plot(goes_data['xrsa'], color='r', label='0.5-4$\mathrm{\AA}$')
ax[0].set_yscale('log')
ax[0].set_xlim(days_to_plot[i] + " 00:00", days_to_plot[i] + " 23:59")
ax[1].plot(pd.to_datetime(data_vlf['time']), data_vlf['volts'], color='grey')
for f in flares["peak_time"]:
ax[0].axvline(parse_time(f).datetime, color="k", ls="dashed")
ax[1].axvline(parse_time(f).datetime, color="k", ls="dashed")
ax[1].xaxis.set_major_locator(dates.HourLocator(interval=3))
ax[1].xaxis.set_minor_locator(dates.HourLocator(interval=1))
ax[1].xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
ax[0].set_ylim(1e-9, 1e-3)
#ax[1].set_ylim(-5, 5)
for a in ax:
a.tick_params(which='both', direction='in')
ax[0].set_ylabel('Flux Wm$^{-2}$')
ax[1].set_ylabel('Volts')
ax[1].set_xlabel('Time ' + days_to_plot[i] + ' UT')
plt.tight_layout()
plt.subplots_adjust(hspace=0.05)
plt.savefig(save_dir + 'birr_vlf_' + days_to_plot[i] + '.png', dpi=100)
plt.close()
def second_runthrough():
gg = glob.glob(
"/Users/laurahayes/ionospheric_work/ionospheric-analysis/stats_study/plots_that_work/*.png"
)
gg.sort()
flare_unique_day = [x.split("/")[-1][6:16] for x in gg]
new_df = flare_list[flare_list["unique_day"].isin([flare_unique_day[0]])]
for i in range(1, len(flare_unique_day)):
new_df = new_df.append(flare_list[flare_list["unique_day"].isin(
[flare_unique_day[i]])])
new_df.reset_index(inplace=True)
for i in range(len(new_df)):
print(i)
sid_file = glob.glob(
pd.to_datetime(
new_df["event_starttime"].iloc[i]).strftime(sid_file_dir))[0]
new_ts = pd.to_datetime(
new_df["event_starttime"].iloc[i]) - datetime.timedelta(minutes=10)
new_te = pd.to_datetime(
new_df["event_endtime"].iloc[i]) + datetime.timedelta(minutes=10)
sid_data = sid_to_series(sid_file).truncate(new_ts, new_te)
goes_file = glob.glob(
pd.to_datetime(
new_df["event_starttime"].iloc[i]).strftime(goes_file_dir))[0]
goes = ts.TimeSeries(goes_file).truncate(new_ts, new_te)
gl = goes.to_dataframe()["xrsb"]
fig, ax = plt.subplots(2, sharex=True)
ax[0].plot(gl)
ax[1].plot(sid_data)
for a in ax:
a.axvline(pd.to_datetime(new_df["event_peaktime"].iloc[i]))
a.axvline(pd.to_datetime(new_df["event_starttime"].iloc[i]))
a.axvline(pd.to_datetime(new_df["event_endtime"].iloc[i]))
ax[0].xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
tstart_str = pd.to_datetime(
new_df["event_starttime"].iloc[i]).strftime("%Y-%m-%dT%H:%M")
ax[1].set_xlabel(new_df["event_peaktime"].iloc[i])
plt.tight_layout()
plt.savefig("./all_flares/flare_{:s}.png".format(tstart_str))
plt.close()
def test_calculate_xray_luminosity():
# Check correct exceptions are raised to incorrect inputs
not_goeslc = []
with pytest.raises(TypeError):
goes_test = goes.calculate_xray_luminosity(not_goeslc)
# Check function gives correct results.
goeslc_input = timeseries.TimeSeries(get_test_filepath("go1520110607.fits"))
goeslc_test = goes.calculate_xray_luminosity(goeslc_input)
exp_xrsa = u.Quantity([2.8962085e+14, 2.8962085e+14, 2.8962085e+14, 2.8962085e+14,
2.8962085e+14], "W")
exp_xrsb = u.Quantity([5.4654352e+16, 5.3133844e+16, 5.3895547e+16, 5.2375035e+16,
5.3895547e+16], "W")
assert_quantity_allclose(exp_xrsa, goeslc_test.quantity("luminosity_xrsa")[:5])
assert_quantity_allclose(exp_xrsb, goeslc_test.quantity("luminosity_xrsb")[:5])
def get_lyradata(dtype):
if dtype == 'lc':
# Create sample LYRALightCurve
lyrats = lightcurve.LYRALightCurve.create("2014-01-01")
lyrats.data = pandas.DataFrame(index=TIME,
data={"CHANNEL1": CHANNELS[0],
"CHANNEL2": CHANNELS[1],
"CHANNEL3": CHANNELS[0],
"CHANNEL4": CHANNELS[1]})
else:
# Create sample TimeSeries
lyrats = timeseries.TimeSeries(
os.path.join(rootdir, 'lyra_20150101-000000_lev3_std_truncated.fits.gz'),
source='LYRA')
lyrats.data = pandas.DataFrame(index=TIME,
data={"CHANNEL1": CHANNELS[0],
"CHANNEL2": CHANNELS[1],
"CHANNEL3": CHANNELS[0],
"CHANNEL4": CHANNELS[1]})
return lyrats
def units_attach(data, units):
"""
Takes the units defined by the user and attaches them to the TimeSeries.
Parameters
----------
data : :class:`pandas.DataFrame`
Input data. Takes the DataFrame which needs to have units attached.
units : :class:`collections.OrderedDict`
The units manually defined by the user.
Returns
-------
out : sunpy.timeseries.timeseriesbase.GenericTimeSeries
DataFrame converted into TimeSeries with units attached.
"""
unit_key = list(units.keys())
for column_name in data.columns:
if column_name not in unit_key:
units[column_name] = u.dimensionless_unscaled
message = "{} column has missing units.".format(column_name)
warnings.warn(message, Warning)
timeseries_data = ts.TimeSeries(data, units)
return timeseries_data
def plot_and_get_data(save=True) errors = [] results = [] for i in range(len(vlf_flares)): print(i) try: sid_file = glob.glob(vlf_flares.iloc[i]["event_starttime"].strftime(sid_file_dir))[0] new_ts = pd.to_datetime(vlf_flares["event_starttime"].iloc[i])-datetime.timedelta(minutes=5) new_te = pd.to_datetime(vlf_flares["event_endtime"].iloc[i])+datetime.timedelta(minutes=5) sid_data = sid_to_series(sid_file).truncate(new_ts, new_te) goes_file = glob.glob(pd.to_datetime(vlf_flares["event_starttime"].iloc[i]).strftime(goes_file_dir))[0] goes = ts.TimeSeries(goes_file).truncate(new_ts, new_te) gl = goes.to_dataframe()["xrsb"] gs = goes.to_dataframe()["xrsa"] gl_flare = gl.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i]) gs_flare = gs.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i]) window_sec = (sid_data.index[1] - sid_data.index[0]).total_seconds() window = int((2*60)/window_sec) if window%2 ==0: window = window+1 sid_resample = pd.Series(savgol_filter(sid_data, int(window), 3), index=sid_data.index) sid_resample_flare = sid_resample.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i]) p_vlf = np.max(sid_resample_flare) p_vlf2 = np.abs(np.max(sid_resample_flare) - sid_resample_flare[0])
from matplotlib.patches import ConnectionPatch
import numpy as np
import datetime
from astropy.io import fits
import pandas as pd
from sunpy.time import parse_time
from scipy.io import readsav
flare_ts = '2014-06-11 05:30'
flare_te = '2014-06-11 05:40'
pul_ts = '2014-06-11 05:32:30'
pul_te = '2014-06-11 05:36:30'
goes = ts.TimeSeries('go1520140611.fits')
short_goes = goes.truncate(flare_ts, flare_te)
gl = short_goes.data['xrsb']
gs = short_goes.data['xrsa']
norp_df, norp1, norp2, norp3, norp9, norp17, norp35, norp80 = read_norp('norp20140611_0534.xdr')
rhessi_time, rhessi_arr, rhessi_emin, rhessi_emax, atten_state = read_rhessi('./rhessi_data/hsi_spectrum_20140611_052832.fits')
rhessi_atten = pd.Series(atten_state[0], index=rhessi_time)
rhessi_atten[rhessi_atten==0] = 1.1
def df_rhessi_kev(e_low, e_high):
return pd.Series(np.sum(rhessi_arr[find_closest(rhessi_emin, e_low):find_closest(rhessi_emax, e_high)], axis=0), index=rhessi_time)
rhessi_612 = df_rhessi_kev(6, 12)
rhessi_1225 = df_rhessi_kev(12, 25)
rhessi_2550 = df_rhessi_kev(25, 50)
flaremask = (labels==3)
# Creating arrays containing time-serie data
Int_Inc = []
tiempos = []
for i in range(20):
diff = Mfiles[i+1].data-Mfiles[i].data
Mdiff = Map(np.nan_to_num(np.abs(diff)),Mfiles[i].meta)
Mdiffrot = Mdiff.rotate(angle=Mdiff.meta['crota2'] * u.deg)
Int_Inc.append((Mdiffrot.data*flaremask).sum())
tiempos.append(datetime.datetime.strptime(Mdiffrot.meta["date-obs"],'%Y-%m-%dT%H:%M:%S.%f'))
tbl_meta = {'t_key':'t_value'}
table = Table([tiempos, Int_Inc/np.max(Int_Inc)], names=['time', 'Inclination'], meta=Mfiles[i].meta)
table.add_index('time')
ts_table = ts.TimeSeries(table)
# PLOT
fig, ax = plt.subplots(figsize=(10,4))
ts_table.plot(marker='o',linewidth=3)
ax.axvline(tflare, color="gray", linestyle="--")
ax.text(tflare, np.min(Int_Inc/np.max(Int_Inc)), 'flare peak', fontsize=14,color='gray',rotation=90, rotation_mode='anchor')
ax.tick_params(axis='both',labelsize=14)
ax.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M'))
ax.set_xlabel('Time [hour:min]',fontsize=16)
ax.set_ylabel('Normalized B-Inclination diff ',fontsize=14)
ax.set_title(Mfiles[imask].meta['date-obs'],fontsize=24)
plt.legend('',frameon=False) # fix bug with legend
fig.savefig('20131108T0422.pdf',dpi=150,bbox_inches='tight')
plt.show()
def plot_flare(i):
new_ts = pd.to_datetime(
vlf_flares["event_starttime"].iloc[i]) - datetime.timedelta(minutes=5)
new_te = pd.to_datetime(
vlf_flares["event_endtime"].iloc[i]) + datetime.timedelta(minutes=5)
# SID data
sid_file = glob.glob(
vlf_flares.iloc[i]["event_starttime"].strftime(sid_file_dir))[0]
sid_data = sid_to_series(sid_file).truncate(new_ts, new_te)
sid_data_db = sid_to_series(sid_file, amp=True).truncate(new_ts, new_te)
# smoothing window defined in terms of cadence
window_sec = (sid_data.index[1] - sid_data.index[0]).total_seconds()
window = int((3 * 60) / window_sec)
if window % 2 == 0:
window = window + 1
sid_resample = pd.Series(savgol_filter(sid_data, int(window), 3),
index=sid_data.index)
sid_resample_flare = sid_resample.truncate(
vlf_flares["event_starttime"].iloc[i],
vlf_flares["event_endtime"].iloc[i])
sid_resample_db = pd.Series(savgol_filter(sid_data_db, int(window), 3),
index=sid_data_db.index)
sid_resample_flare_db = sid_resample_db.truncate(
vlf_flares["event_starttime"].iloc[i],
vlf_flares["event_endtime"].iloc[i])
# GOES data
goes_file = glob.glob(
pd.to_datetime(
vlf_flares["event_starttime"].iloc[i]).strftime(goes_file_dir))[0]
goes = ts.TimeSeries(goes_file).truncate(new_ts, new_te)
gl = goes.to_dataframe()["xrsb"]
gs = goes.to_dataframe()["xrsa"]
gl_flare = gl.truncate(vlf_flares["event_starttime"].iloc[i],
vlf_flares["event_endtime"].iloc[i])
gs_flare = gs.truncate(vlf_flares["event_starttime"].iloc[i],
vlf_flares["event_endtime"].iloc[i])
fig, ax = plt.subplots(2, figsize=(8, 6), sharex=True)
ax[0].plot(gl, color="r", label="1-8$\mathrm{\AA}$")
ax[0].plot(gs, color="b", label="0.5-4$\mathrm{\AA}$")
ax[0].set_ylabel("Flux (Wm$^{-2}$)")
ax[0].legend(loc="upper left")
ax[0].set_yscale("log")
ax[1].plot(sid_data_db - sid_data_db[0], label="VLF amp", color="grey")
ax[1].plot(sid_resample_flare_db - sid_resample_flare_db[0],
label="Smoothed VLF amp",
color="k")
ax[1].legend(loc="upper left")
for a in ax:
a.axvline(gl_flare.index[np.argmax(gl_flare)], color="r", lw=0.4)
a.axvline(gs_flare.index[np.argmax(gs_flare)], color="b", lw=0.4)
a.axvline(pd.to_datetime(vlf_flares["event_starttime"].iloc[i]),
ls="dashed",
color="grey")
a.axvline(pd.to_datetime(vlf_flares["event_endtime"].iloc[i]),
ls="dashed",
color="grey")
a.axvline(sid_resample_flare.index[np.argmax(sid_resample_flare)],
color="k",
lw=0.4)
tstart_str = pd.to_datetime(
vlf_flares["event_starttime"].iloc[i]).strftime("%Y-%m-%dT%H:%M")
ax[1].set_xlabel("Time {:s}".format(
pd.to_datetime(
vlf_flares["event_starttime"].iloc[i]).strftime("%Y-%m-%d %H:%M")))
ax[1].xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
ax[1].set_xlim(gl.index[0], gl.index[-1])
ax[1].tick_params(which="both", direction="in")
ax[0].tick_params(which="both", direction="in")
ax[1].set_ylabel("VLF amplitude excess (db)")
plt.tight_layout()
ax[1].xaxis.set_major_locator(
dates.MinuteLocator(byminute=[35, 40, 45, 50]))
ax[1].xaxis.set_minor_locator(dates.MinuteLocator(interval=1))
plt.subplots_adjust(hspace=0.01)
plt.savefig("./paper_plots/example_flare_ana.png",
dpi=300,
facecolor="w",
bbox_inches="tight")
plt.close()
def plot_test(i):
tt = parse_time(events_to_download[i]).strftime("%Y%m%d")
files_magno = glob.glob("./magno_files/*{:s}*".format(tt))
if len(files_magno) == 0:
print("No magnetometer data")
#break
goes_file = goes_data_dir + "go15" + tt + ".fits"
if not Path(goes_file).exists():
print("No goes data")
#break
goes_data = ts.TimeSeries(goes_file)
gl = goes_data.data["xrsb"]
gs = goes_data.data["xrsa"]
flares_ind = np.where(
daytime_flares["event_date"].isin([events_to_download[i]]) == True)[0]
flares = daytime_flares.iloc[flares_ind]
filey = files_magno[0]
magno = pd.read_csv(filey,
delim_whitespace=True,
skiprows=1,
names=[
"Date", "Time", "Index", "Bx", "By", "Bz", "E1",
"E2", "E3", "E4", "T(FG)", "T(E)", "volts"
])
magno_time = [
datetime.datetime.strptime(
magno.iloc[i]["Date"] + " " + magno.iloc[i]["Time"],
"%d/%m/%Y %H:%M:%S") for i in range(len(magno))
]
bx = pd.Series(np.array(magno["Bx"]), index=magno_time)
by = pd.Series(np.array(magno["By"]), index=magno_time)
bz = pd.Series(np.array(magno["Bz"]), index=magno_time)
h = np.sqrt(np.array(bx)**2 + np.array(by)**2)
H = pd.Series(h, index=magno_time)
fig, ax = plt.subplots(2, sharex=True, figsize=(10, 8))
ax1 = ax[0]
ax2 = ax[1]
ax1.plot(gl, color="r", label="1-8 $\mathrm{\AA}$")
ax1.plot(gs, color="b", label="0.5-4 $\mathrm{\AA}$")
ax1.set_ylim(1e-9, 1e-3)
ax1.set_yscale("log")
ax1.tick_params(which="both", direction="in", right=True, top=True)
ax1.set_ylabel("Flux (Wm$^{-2}$)")
ax1.legend(loc="upper right")
ax2.plot(magno_time, magno["Bx"], label="Bx", color="k")
ax2.plot(np.nan, color="grey", label="By")
ax2.plot(np.nan, color="green", label="Bx")
ax2.legend(loc="lower right")
ax3 = ax2.twinx()
ax3.plot(magno_time, magno["By"], label="By", color="grey")
ax4 = ax2.twinx()
ax4.plot(magno_time, magno["Bz"], label="Bz", color="green")
ax2.set_xlabel("Time {:s} UT".format(events_to_download[i]))
ax2.set_xlim(events_to_download[i] + " 00:00",
events_to_download[i] + " 23:59")
ax2.xaxis.set_major_locator(dates.HourLocator(interval=3))
ax2.xaxis.set_minor_locator(dates.HourLocator(interval=1))
ax2.xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
ax2.tick_params(which="both", direction="in", right=True, top=True)
for f in flares["peak_time"]:
ax1.axvline(parse_time(f).datetime, color="k", ls="dashed")
ax2.axvline(parse_time(f).datetime, color="k", ls="dashed")
ax1.grid()
ax2.grid()
plt.tight_layout()
plt.subplots_adjust(hspace=0.05)
plt.savefig(save_dir +
parse_time(events_to_download[i]).strftime("%Y%m%d.png"),
dpi=200)
plt.close()
from matplotlib import dates
import numpy as np
from sunpy import timeseries as ts
from sunpy.time import parse_time
from read_files import euve_to_series, mag_to_series, sid_to_series
# tstart = '2017-09-10 15:00'
# tend = '2017-09-10 22:00'
tstart = '2015-11-04 08:00'
tend = '2015-11-04 20:00'
euve_data = euve_to_series("./magno_codes/euve_data/g15_euve_{:s}.txt".format(
parse_time(tstart).strftime('%Y%m%d')))
goes_data = ts.TimeSeries(
"/Users/laurahayes/QPP/stats_study/TEBBS/goes_rawdata/go15{:s}.fits".
format(parse_time(tstart).strftime('%Y%m%d')))
magno_data = mag_to_series(
"./magno_codes/magno_files/birr_mag_{:s}_000001.txt".format(
parse_time(tstart).strftime('%Y%m%d')))
sid_data = sid_to_series(
"./vlf_codes/vlf_files/BIR_sid_{:s}_000000.txt".format(
parse_time(tstart).strftime('%Y%m%d')))
#euve_flare = euve_data.truncate(tstart, tend)
bx, by, bz = magno_data[0].truncate(tstart, tend), magno_data[1].truncate(
tstart, tend), magno_data[2].truncate(tstart, tend)
gl, gs = goes_data.to_dataframe().truncate(
tstart, tend)['xrsb'], goes_data.to_dataframe().truncate(tstart,
tend)['xrsa']
euve_data = euve_data.truncate(tstart, tend)
===============
This example shows the current and possible next solar cycle.
"""
import datetime
import matplotlib.pyplot as plt
import sunpy.timeseries as ts
from sunpy.data.sample import NOAAINDICES_TIMESERIES, NOAAPREDICT_TIMESERIES
###############################################################################
# For this example we will use the SunPy sample data. This code snippet grabs
# the most current NOAA solar cycle data as a ``TimeSeries``
# (see :ref:`timeseries_code_ref`).
noaa = ts.TimeSeries(NOAAINDICES_TIMESERIES, source='noaaindices')
noaa_predict = ts.TimeSeries(NOAAPREDICT_TIMESERIES,
source='noaapredictindices')
###############################################################################
# Next, we grab a new copy of the data and shift it forward 12 years to
# simulate the next solar cycle. We will also truncate the data to ensure
# that we only plot what is necessary.
noaa2 = ts.TimeSeries(NOAAINDICES_TIMESERIES, source='noaaindices')
noaa2.data = noaa2.data.shift(2, freq=datetime.timedelta(days=365 * 12))
noaa2 = noaa2.truncate('2021/04/01', '2030/01/01')
###############################################################################
# Finally, we plot both ``noaa`` and ``noaa2`` together, with an arbitrary
# range for the strength of the next solar cycle.
def plot_event(tstart,
tend,
path="./bst_files/",
plot_goes=False,
goes_path="./goes_files/",
background_sub=False,
save_plot=None,
**kwargs):
"""
Function to plot the dynamic spectrum for a given date.
Parameters
----------
tstart : ~`datetime.datetime`, ~`str`
start time
tend : ~`datetime.datetime`, ~`str`
end time
plot_goes : ~`boolean`, optional
if True overplot the GOES XRS lightcurves
"""
if isinstance(tstart, str):
tstart = parse_time(tstart).datetime
if isinstance(tend, str):
tend = parse_time(tend).datetime
file = glob.glob(path + tstart.strftime("%Y%m%d*.dat"))
if len(file) == 0:
return
spec_data, times, freq = read_bst_data(file[0])
dynamic_spec = dynamic_spectra(spec_data, times,
freq).crop_time(tstart, tend)
if background_sub:
dynamic_spec = dynamic_spec.background_sub1()
if plot_goes:
goes_file = Path(goes_path + tstart.strftime("go15%Y%m%d.fits"))
if goes_file.exists():
goes_ts = ts.TimeSeries(os.fspath(goes_file)).truncate(
tstart, tend)
else:
try:
goes_file = get_goes(tstart, tend)
goes_ts = ts.TimeSeries(goes_file).truncate(tstart, tend)
except:
print("cant get GOES XRS data")
return
fig, ax = plt.subplots(figsize=(10, 6))
im = dynamic_spec.plot(**kwargs)
if plot_goes:
ax2 = ax.twinx()
ax2.plot(goes_ts.to_dataframe()["xrsb"],
color="k",
label="1-8 $\mathrm{\AA}$")
ax2.plot(goes_ts.to_dataframe()["xrsa"],
color="k",
ls="dashed",
label="0.5-4 $\mathrm{\AA}$")
ax2.set_ylabel("Flux Wm$^{-2}$")
ax2.set_yscale("log")
ax2.legend(loc='lower right')
ax2.set_ylim(1e-9, 1e-3)
ax.xaxis.set_major_formatter(dates.DateFormatter('%H:%M'))
#fig.colorbar(im)
fig.autofmt_xdate(rotation=45)
plt.tight_layout()
if save_plot is not None:
plt.savefig(save_plot, dpi=200)
plt.close()
plt.show()
#############################################################
# 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)
#############################################################
# Also just to note, if this will download the file to the
# ``~/sunpy/data/`` directory on your local machine. You can also
# define where you want this to download to using the ``path`` keyword
# argument in `~sunpy.net.sunpy.net.fido_factory.UnifiedDownloaderFactory.fetch`
# (e.g. ``Fido.fetch(result, path=".\")``).
#############################################################
# 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()
###############################################################
# We can also pull out the individual GOES chanels 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()
ax.plot(goes_15.index, goes_15.quantity("xrsb"))
ax.set_ylabel("Flux (Wm$^{-2}$)")
ax.set_xlabel("Time")
fig.autofmt_xdate()
plt.show()
###############################################################
# We can also truncate the data for the time of the large flare,
# sunspot number and radio flux will evolve. Predicted values are based on the
# consensus of the Solar Cycle 24 Prediction Panel.
#
# We will first search for and then download the data.
time_range = TimeRange("2008-06-01 00:00", Time.now())
result = Fido.search(a.Time(time_range), a.Instrument('noaa-indices'))
f_noaa_indices = Fido.fetch(result)
result = Fido.search(a.Time(time_range.end, time_range.end + TimeDelta(4 * u.year)),
a.Instrument('noaa-predict'))
f_noaa_predict = Fido.fetch(result)
###############################################################################
# We then load them into individual `~sunpy.timeseries.TimeSeries` objects.
noaa = ts.TimeSeries(f_noaa_indices, source='noaaindices').truncate(time_range)
noaa_predict = ts.TimeSeries(f_noaa_predict, source='noaapredictindices')
###############################################################################
# Finally, we plot both ``noaa`` and ``noaa_predict`` for the sunspot number.
# In this case we use the S.I.D.C. Brussels International Sunspot Number (RI).
# The predictions provide both a high and low values, which we plot below as
# ranges.
time_support()
plt.figure()
plt.plot(noaa.time, noaa.quantity('sunspot RI'), label='Sunspot Number')
plt.plot(noaa_predict.time, noaa_predict.quantity('sunspot'),
color='grey', label='Near-term Prediction')
plt.fill_between(noaa_predict.time, noaa_predict.quantity('sunspot low'),
noaa_predict.quantity('sunspot high'), alpha=0.3, color='grey')
save_dir = "/Users/laurahayes/ionospheric_work/ionospheric-analysis/magno_codes/plots_x_flares/"
i = 0
for i in range(len(x_flares)):
print("analyzing {:d}".format(i))
try:
file_euve = glob.glob(euve_data_dir + parse_time(
x_flares.iloc[i]['event_date']).strftime("*%Y%m%d.txt"))[0]
file_magno = glob.glob(magno_data_dir + parse_time(
x_flares.iloc[i]['event_date']).strftime("*%Y%m%d*.txt"))[0]
goes_file = glob.glob(goes_data_dir + parse_time(
x_flares.iloc[i]['event_date']).strftime("go15%Y%m%d.fits"))[0]
data_euve = euve_to_series(file_euve)
data_goes = ts.TimeSeries(goes_file)
data_mag_bx, data_mag_by, data_mag_bz = mag_to_series(file_magno)
gl = data_goes.to_dataframe()['xrsb'].truncate(
x_flares.iloc[i]['start_time'], x_flares.iloc[i]['end_time'])
gs = data_goes.to_dataframe()['xrsa'].truncate(
x_flares.iloc[i]['start_time'], x_flares.iloc[i]['end_time'])
data_mag_bx = data_mag_bx.truncate(x_flares.iloc[i]['start_time'],
x_flares.iloc[i]['end_time'])
data_mag_by = data_mag_by.truncate(x_flares.iloc[i]['start_time'],
x_flares.iloc[i]['end_time'])
data_mag_bz = data_mag_bz.truncate(x_flares.iloc[i]['start_time'],
x_flares.iloc[i]['end_time'])
data_euve = data_euve.truncate(x_flares.iloc[i]['start_time'],
x_flares.iloc[i]['end_time'])
import matplotlib.pyplot as plt
import matplotlib
import pylab
import sunpy.map
import sunpy.data.sample
import sunpy.timeseries as ts
from sunpy.time import parse_time
from astropy import units as u
from astropy.coordinates import SkyCoord
from sunpy.net import hek
client = hek.HEKClient()
# GOES data for timeseries
goes = ts.TimeSeries(sunpy.data.sample.GOES_XRS_TIMESERIES, source='XRS')
flares_hek = client.search(hek.attrs.Time('2011-06-07 00:00', '2011-06-07 23:59'),
hek.attrs.FL, hek.attrs.FRM.Name == 'SWPC')
# AIA data for map
my_map = sunpy.map.Map(sunpy.data.sample.AIA_171_IMAGE)
top_right = SkyCoord(1200 * u.arcsec, 0 * u.arcsec, frame=my_map.coordinate_frame)
bottom_left = SkyCoord(500 * u.arcsec, -700 * u.arcsec, frame=my_map.coordinate_frame)
my_submap = my_map.submap(bottom_left, top_right)
# plot figure
fig = plt.figure(figsize=(13,6))
ax0 = pylab.axes([0.05, 0.09, 0.42, 0.8])
ax0.plot(goes.data['xrsb'], color='r',label='1-8 $\mathrm{\AA}$')
ax0.plot(goes.data['xrsa'], color='b',label='0.5-4 $\mathrm{\AA}$')
sid = pd.read_csv(file, comment="#", names=["times", "data"])
tt = parse_time(sid["times"]).datetime
if amp:
ser = pd.Series(calc_amp(sid["data"].values), index=tt)
else:
ser = pd.Series(sid["data"].values, index=tt)
ser.sort_index(inplace=True)
return ser
flare_start = "2013-05-22 12:00:00"
flare_end = "2013-05-22 15:00:00"
sid_data = sid_to_series(sid_file).truncate(flare_start, flare_end)
goes_data = ts.TimeSeries(goes_file).truncate(flare_start, flare_end)
gl = goes_data.to_dataframe()["xrsb"]
gs = goes_data.to_dataframe()["xrsa"]
def make_rhessi_lc(file):
#file = 'hsi_spectrum_20130515_012024.fits'
a = fits.open(file)
start_time = a[0].header['DATE_OBS']
t_start = datetime.datetime.strptime(start_time[0:10] + ' '+start_time[11:], '%Y-%m-%d %H:%M:%S.%f')
start_time_day = datetime.datetime.strptime(str(t_start)[0:10]+' 00:00:00', '%Y-%m-%d %H:%M:%S')
#print a[1].data.columns
time = a[1].data['TIME']
time_array = []
for i in range(len(time)):
import sunpy.timeseries as ts
from collections import OrderedDict
import astropy.units as u
# The index of the SunPy Timeseries is always datetime
base = datetime.datetime.today()
times = [base - datetime.timedelta(minutes=x) for x in range(24*60, 0, -1)]
intensity = np.sin(np.arange(0, 12 * np.pi, ((12 * np.pi) / (24*60))))
# This example shows how a TimeSeries object is made from a Pandas DataFrame
data = pd.DataFrame(intensity, index=times, columns=['intensity'])
# TimeSeries can have a metadata attached to it.
meta = OrderedDict({'key':'value'})
# AstroPy Units are attached to the TimeSeries by passing it alongside the data.
# The units are stored in an OrderedDict object.
# Each key is the unit, and the value is the astropy representation of the same.
units = OrderedDict([('intensity', u.W/u.m**2)])
ts_custom = ts.TimeSeries(data, meta, units)
# Using sunpy.timeseries.TimeSeries.data will return a Pandas DataFrame of the TimeSeries object.
print(ts_custom.data)
# To view the units, sunpy.timeserise.TimeSeries.units can be used.
print(ts_custom.units)
# The values can be extracted along with their units as well.
#sunpy.timeseries.TimeSeries.quantity(column_name)[index]
print(ts_custom.quantity('intensity')[1])
def plot_one(i):
new_ts = pd.to_datetime(vlf_flares["event_starttime"].iloc[i])-datetime.timedelta(minutes=5)
new_te = pd.to_datetime(vlf_flares["event_endtime"].iloc[i])+datetime.timedelta(minutes=5)
# SID data
sid_file = glob.glob(vlf_flares.iloc[i]["event_starttime"].strftime(sid_file_dir))[0]
sid_data = sid_to_series(sid_file).truncate(new_ts, new_te)
sid_data_db = sid_to_series(sid_file, amp=True).truncate(new_ts, new_te)
# smoothing window defined in terms of cadence
window_sec = (sid_data.index[1] - sid_data.index[0]).total_seconds()
window = int((3*60)/window_sec)
if window%2 == 0:
window = window+1
sid_resample = pd.Series(savgol_filter(sid_data, int(window), 3), index=sid_data.index)
sid_resample_flare = sid_resample.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i])
sid_resample_db = pd.Series(savgol_filter(sid_data_db, int(window), 3), index=sid_data_db.index)
sid_resample_flare_db = sid_resample_db.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i])
# GOES data
goes_file = glob.glob(pd.to_datetime(vlf_flares["event_starttime"].iloc[i]).strftime(goes_file_dir))[0]
goes = ts.TimeSeries(goes_file).truncate(new_ts, new_te)
gl = goes.to_dataframe()["xrsb"]
gs = goes.to_dataframe()["xrsa"]
gl_flare = gl.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i])
gs_flare = gs.truncate(vlf_flares["event_starttime"].iloc[i], vlf_flares["event_endtime"].iloc[i])
euvs_flare = euvs_df.truncate(new_ts, new_te)
## plots
fig, ax = plt.subplots(3, sharex=True)
## GOES XRS
ax[0].plot(gl, color="r", label="1-8$\mathrm{\AA}$")
ax[0].plot(gs, color="b", label="0.5-4$\mathrm{\AA}$")
ax[0].set_ylabel("Flux (Wm$^{-2}$)")
ax[0].legend(loc="upper left")
ax[0].set_yscale("log")
## GOES EUVS
ax[1].plot(euvs_flare.irrad_ly)
## VLF data
ax[2].plot(sid_data_db - sid_data_db[0], label="raw data", color="grey")
ax[2].plot(sid_resample_flare_db - sid_resample_flare_db[0], label="2min resample", color="k")
ax[2].legend(loc="upper left")
tstart_str = pd.to_datetime(vlf_flares["event_starttime"].iloc[i]).strftime("%Y-%m-%dT%H:%M")
ax[2].set_xlabel("Time {:s}".format(pd.to_datetime(vlf_flares["event_starttime"].iloc[i]).strftime("%Y-%m-%d %H:%M")))
ax[2].xaxis.set_major_formatter(dates.DateFormatter("%H:%M"))
for a in ax:
a.axvline(gl_flare.index[np.argmax(gl_flare)], color="r")
a.axvline(gs_flare.index[np.argmax(gs_flare)], color="b")
a.axvline(pd.to_datetime(vlf_flares["event_starttime"].iloc[i]), ls="dashed", color="grey")
a.axvline(pd.to_datetime(vlf_flares["event_endtime"].iloc[i]), ls="dashed", color="grey")
a.axvline(sid_resample_flare.index[np.argmax(sid_resample_flare)], color="k")
plt.tight_layout()
plt.subplots_adjust(hspace=0.01)
plt.savefig("./test_plots/lyman_alpha_{:d}_{:s}.png".format(i, tstart_str))
plt.close()
result_goes15 = Fido.search(a.Time(tstart, tend), a.Instrument("XRS"),
a.goes.SatelliteNumber(15))
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://satdat.ngdc.noaa.gov/sem/goes/data/science/xrs/GOES_13-15_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 chanels 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.
