# note that the keywords are the same as in IDL
mms_load_fgm(probe='4',
data_rate='brst',
trange=['2015-10-16/13:06', '2015-10-16/13:07'],
time_clip=True)
# find which variables were loaded
from pytplot import tplot_names
tplot_names()
from pyspedas.mms import mms_load_edp
# load some burst mode electric field data
mms_load_edp(probe='4',
data_rate='brst',
trange=['2015-10-16/13:06', '2015-10-16/13:07'],
time_clip=True)
from pyspedas import tnames
# the tnames function supports filtering with wild cards, e.g.,
# to find the E-field variables:
dce_vars = tnames('*_edp_dce_*')
# trange also accepts datetime objects
# note: be aware of potential time zone issues
from pyspedas.mms import mms_load_fpi
from datetime import datetime
from datetime import timezone as tz
start_time = datetime(year=2015,
def test_load_brst_data(self):
data = mms_load_edp(data_rate='brst',
trange=['2015-10-16/13:06', '2015-10-16/13:10'])
self.assertTrue(data_exists('mms1_edp_dce_gse_brst_l2'))
def test_load_spdf_data(self):
data = mms_load_edp(trange=['2015-10-16', '2015-10-16/01:00'],
spdf=True)
self.assertTrue(data_exists('mms1_edp_dce_gse_fast_l2'))
def test_load_suffix(self):
data = mms_load_edp(trange=['2015-10-16', '2015-10-16/01:00'],
suffix='_test')
self.assertTrue(data_exists('mms1_edp_dce_gse_fast_l2'))
def test_load_hfesp_data(self):
data = mms_load_edp(trange=['2015-10-16', '2015-10-16/01:00'],
datatype='hfesp',
data_rate='srvy')
self.assertTrue(data_exists('mms1_edp_hfesp_srvy_l2'))
