def test_center_brst_electron_data(self):
data = mms_load_fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'], data_rate='brst')
centered = mms_load_fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'], data_rate='brst', center_measurement=True, suffix='_centered')
t, d = get_data('mms1_des_bulkv_gse_brst')
c, d = get_data('mms1_des_bulkv_gse_brst_centered')
self.assertTrue(np.round(c[0]-t[0], decimals=3) == 0.015)
def test_center_fast_electron_data(self):
data = mms_load_fpi(trange=['2015-10-16/14:00', '2015-10-16/15:00'])
centered = mms_load_fpi(trange=['2015-10-16/14:00', '2015-10-16/15:00'], center_measurement=True, suffix='_centered')
t, d = get_data('mms1_des_bulkv_gse_fast')
c, d = get_data('mms1_des_bulkv_gse_fast_centered')
self.assertTrue(np.round(c[0]-t[0], decimals=3) == 2.25)
def test_center_fast_ion_data_notplot(self):
data = mms_load_fpi(trange=['2015-10-16/14:00', '2015-10-16/15:00'],
notplot=True)
centered = mms_load_fpi(
trange=['2015-10-16/14:00', '2015-10-16/15:00'],
center_measurement=True,
suffix='_centered',
notplot=True)
self.assertTrue(
np.round(centered['mms1_dis_bulkv_gse_fast_centered']['x'][0] -
data['mms1_dis_bulkv_gse_fast']['x'][0],
decimals=3) == 2.25)
def test_errorflag_compression_bars(self):
data = mms_load_fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'],
data_rate='brst',
datatype=['des-dist', 'des-moms'])
data = mms_load_fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'],
data_rate='brst',
datatype=['dis-dist', 'dis-moms'])
mms_fpi_make_errorflagbars('mms1_des_errorflags_brst_moms', level='l2')
mms_fpi_make_errorflagbars('mms1_dis_errorflags_brst_moms', level='l2')
mms_fpi_make_errorflagbars('mms1_des_errorflags_brst_dist', level='l2')
mms_fpi_make_errorflagbars('mms1_dis_errorflags_brst_dist', level='l2')
mms_fpi_make_compressionlossbars('mms1_des_compressionloss_brst_moms')
mms_fpi_make_compressionlossbars('mms1_dis_compressionloss_brst_moms')
mms_fpi_make_compressionlossbars('mms1_des_compressionloss_brst_dist')
mms_fpi_make_compressionlossbars('mms1_dis_compressionloss_brst_dist')
self.assertTrue(
data_exists('mms1_des_errorflags_brst_moms_flagbars_full'))
self.assertTrue(
data_exists('mms1_des_errorflags_brst_moms_flagbars_main'))
self.assertTrue(
data_exists('mms1_des_errorflags_brst_moms_flagbars_mini'))
self.assertTrue(
data_exists('mms1_dis_errorflags_brst_moms_flagbars_full'))
self.assertTrue(
data_exists('mms1_dis_errorflags_brst_moms_flagbars_main'))
self.assertTrue(
data_exists('mms1_dis_errorflags_brst_moms_flagbars_mini'))
self.assertTrue(
data_exists('mms1_des_errorflags_brst_dist_flagbars_dist'))
self.assertTrue(
data_exists('mms1_dis_errorflags_brst_dist_flagbars_dist'))
self.assertTrue(
data_exists('mms1_des_compressionloss_brst_moms_flagbars'))
self.assertTrue(
data_exists('mms1_dis_compressionloss_brst_moms_flagbars'))
self.assertTrue(
data_exists('mms1_des_compressionloss_brst_dist_flagbars'))
self.assertTrue(
data_exists('mms1_dis_compressionloss_brst_dist_flagbars'))
start_time = datetime(year=2015,
month=10,
day=16,
hour=13,
minute=6,
tzinfo=tz.utc)
end_time = datetime(year=2015,
month=10,
day=16,
hour=13,
minute=7,
tzinfo=tz.utc)
mms_load_fpi(trange=[start_time, end_time],
probe='4',
datatype='des-moms',
data_rate='brst')
# to return the actual data values, use get_data
from pytplot import get_data
times, fgm_data = get_data('mms4_fgm_b_gsm_brst_l2')
# times are unix time (seconds since 1 January 1970)
print(times[0])
# FGM data include the magnitude
fgm_data[0]
# you can convert the unix time to a string with time_string
from pyspedas import time_string
def test_load_small_brst_interval(self):
data = mms_load_fpi(trange=['2015-10-16/13:06', '2015-10-16/13:07'],
data_rate='brst',
datatype=['dis-moms', 'dis-dist'],
time_clip=True)
self.assertTrue(data_exists('mms1_dis_energyspectr_omni_brst'))
def test_load_spdf_data(self):
data = mms_load_fpi(trange=['2015-10-16/14:00', '2015-10-16/15:00'],
spdf=True)
self.assertTrue(data_exists('mms1_dis_energyspectr_omni_fast'))
###############################################################
# 3. plotting MMS data
from pytplot import tplot
tplot('mms1_fgm_b_gsm_srvy_l2')
tplot([
'mms4_epd_eis_brst_phxtof_proton_flux_omni',
'mms4_epd_eis_brst_extof_proton_flux_omni'
])
from pyspedas.mms import mms_load_fpi
mms_load_fpi(probe='4',
data_rate='brst',
datatype='des-moms',
trange=['2015-10-16/13:00', '2015-10-16/13:10'])
tplot([
'mms4_des_energyspectr_omni_brst', 'mms4_des_pitchangdist_miden_brst',
'mms4_des_bulkv_gse_brst', 'mms4_des_numberdensity_brst'
])
###############################################################
# 4. working with the data vales, timestamps and metadata
from pytplot import get_data, store_data
# extract the data from a tplot variable
times, data = get_data('mms1_fgm_b_gsm_srvy_l2')
