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')