def test_solo(descriptor):
with pytest.warns(
UserWarning,
match='Low latency data is not suitable for publication'):
df = solo.download(starttime, endtime, descriptor, 'LL02')
check_data_output(df)
def test_solo_bad_descriptor():
with pytest.raises(RuntimeError,
match='No data files found for descriptor=GARBAGE'):
df = solo.download(starttime, endtime, 'garbage', 'LL02')
from astropy.time import Time from heliopy.data.solo import download import plasmapy.formulary from sunpy.timeseries import GenericTimeSeries ############################################################################### # Set some nice default plotting settings from astropy.visualization import quantity_support quantity_support() matplotlib.rcParams['date.converter'] = 'concise' ############################################################################### # Get 12 hours day of MAG data in normal mode. start_time = "2020-07-07 12:00:00" end_time = "2020-07-08 00:00:00" mag_data = download(start_time, end_time, 'MAG-RTN-NORMAL', 'L2') print(mag_data.columns) ############################################################################### # Get 12 hours of SWA/PAS ground moments. swa_data = download(start_time, end_time, 'SWA-PAS-GRND-MOM', 'L2') print(swa_data.columns) ############################################################################### # Re-index the magnetic field data on to the plasma data timestamps. # To do this the magnetic field `~sunpy.timeseries.GenericTimeSeries` is first # converted to a `~pandas.DataFrame`, so the powerful time-series maniuplation # tools present in `pandas` can be used. Then the data is re-indexed using # a nearest neighbour method, and re-inserted into a # `~sunpy.timeseries.GenericTimeSeries` to preserve the unit information mag_df = mag_data.to_dataframe()
###############################################################################
# Import the required modules
from datetime import datetime
import warnings
import astropy.units as u
import matplotlib.pyplot as plt
from matplotlib import dates as mdates
import numpy as np
from heliopy.data.solo import download
###############################################################################
# Download some magnetic field data
data = download(datetime(2020, 7, 10), datetime(2020, 8, 3), 'MAG', 'LL02')
print(data.columns)
###############################################################################
# Calculate the magnetic field mangitude
modB = np.sqrt(
data.quantity('B_RTN_0')**2 + data.quantity('B_RTN_1')**2 +
data.quantity('B_RTN_2')**2)
data = data.add_column('modB', modB)
###############################################################################
# Plot the data
fig, axs = plt.subplots(nrows=2, sharex=True)
ax = axs[0]
ax.plot(data.index, data.quantity('modB'))
"""
###############################################################################
# Import the required modules
from datetime import datetime
import matplotlib.pyplot as plt
from matplotlib import dates as mdates
import numpy as np
from heliopy.data.solo import download
###############################################################################
# Download some magnetic field data
data = download(datetime(2020, 6, 2), datetime(2020, 6, 3), 'MAG-RTN-NORMAL',
'L2')
print(data.columns)
###############################################################################
# Calculate the magnetic field mangitude
modB = np.sqrt(
data.quantity('B_RTN_0')**2 + data.quantity('B_RTN_1')**2 +
data.quantity('B_RTN_2')**2)
data = data.add_column('modB', modB)
###############################################################################
# Plot the data
fig, axs = plt.subplots(nrows=2, sharex=True)
ax = axs[0]
ax.plot(data.index, data.quantity('modB'))
def test_solo_science():
starttime = datetime(2020, 6, 2)
endtime = datetime(2020, 6, 2, 12)
df = solo.download(starttime, endtime, 'MAG-RTN-NORMAL', 'L2')
check_data_output(df)
def test_solo_science(descriptor):
starttime = datetime(2020, 9, 1)
endtime = datetime(2020, 9, 1, 12)
df = solo.download(starttime, endtime, descriptor, 'L2')
check_data_output(df)