def load_and_interpolate_plasma_params(time_start,
time_end,
probe,
pad,
nsec=None,
rbsp_path='G://DATA//RBSP//',
HM_filter_mhz=None):
'''
Outputs as SI units: B0 in T, densities in /m3, temperatures in eV (pseudo SI)
nsec is cadence of interpolated array in seconds. If None, defaults to using den_times
as interpolant.
If HOPE or RBSPICE data does not exist, file retrieval returns NoneType - have to deal
with that.
'''
print('Loading and interpolating satellite data')
# Cold (total?) electron plasma density
den_times, edens, dens_err = rfr.retrieve_RBSP_electron_density_data(
rbsp_path, time_start, time_end, probe, pad=pad)
# Magnetic magnitude
mag_times, raw_mags = rfl.load_magnetic_field(rbsp_path,
time_start,
time_end,
probe,
return_raw=True,
pad=3600)
# Filter out EMIC waves (background plus HM)
if HM_filter_mhz is not None:
filt_mags = np.zeros(raw_mags.shape)
for ii in range(3):
filt_mags[:, ii] = ascr.clw_low_pass(raw_mags[:, ii],
HM_filter_mhz,
1. / 64.,
filt_order=4)
else:
filt_mags = raw_mags
# HOPE data
itime, etime, pdict, perr = rfr.retrieve_RBSP_hope_moment_data(rbsp_path,
time_start,
time_end,
padding=pad,
probe=probe)
hope_dens = np.array(
[pdict['Dens_p_30'], pdict['Dens_he_30'], pdict['Dens_o_30']])
hope_temp = np.array(
[pdict['Tperp_p_30'], pdict['Tperp_he_30'], pdict['Tperp_o_30']])
hope_anis = np.array([
pdict['Tperp_Tpar_p_30'], pdict['Tperp_Tpar_he_30'],
pdict['Tperp_Tpar_o_30']
]) - 1
# SPICE data
spice_dens = []
spice_temp = []
spice_anis = []
spice_times = []
for product, spec in zip(['TOFxEH', 'TOFxEHe', 'TOFxEO'],
['P', 'He', 'O']):
spice_epoch, spice_dict = rfr.retrieve_RBSPICE_data(rbsp_path,
time_start,
time_end,
product,
padding=pad,
probe=probe)
# Collect all times (don't assume every file is good)
spice_times.append(spice_epoch)
if spice_dict is not None:
this_dens = spice_dict['F{}DU_Density'.format(spec)]
this_anis = spice_dict['F{}DU_PerpPressure'.format(
spec)] / spice_dict['F{}DU_ParaPressure'.format(spec)] - 1
# Perp Temperature - Calculate as T = P/nk
kB = 1.381e-23
q = 1.602e-19
t_perp_kelvin = 1e-9 * spice_dict['F{}DU_PerpPressure'.format(
spec)] / (kB * 1e6 * spice_dict['F{}DU_Density'.format(spec)])
this_temp = kB * t_perp_kelvin / q # Convert from K to eV
spice_dens.append(this_dens)
spice_temp.append(this_temp)
spice_anis.append(this_anis)
else:
spice_dens.append(None)
spice_temp.append(None)
spice_anis.append(None)
# Interpolation step
if nsec is None:
time_array = den_times.copy()
iedens = edens.copy()
else:
time_array = np.arange(time_start,
time_end,
np.timedelta64(nsec, 's'),
dtype='datetime64[us]')
iedens = interpolate_ne(time_array, den_times, edens)
ihope_dens, ihope_temp, ihope_anis = HOPE_interpolate_to_time(
time_array, itime, hope_dens, hope_temp, hope_anis)
ispice_dens, ispice_temp, ispice_anis = SPICE_interpolate_to_time(
time_array, spice_times, np.array(spice_dens), np.array(spice_temp),
np.array(spice_anis))
Bi = interpolate_B(time_array, mag_times, filt_mags, nsec, LP_filter=False)
# Subtract energetic components from total electron density (assuming each is singly charged)
cold_dens = iedens - ihope_dens.sum(axis=0) - ispice_dens.sum(axis=0)
return time_array, Bi * 1e-9, cold_dens * 1e6, ihope_dens * 1e6, ihope_temp, ihope_anis, ispice_dens * 1e6, ispice_temp, ispice_anis
def load_and_interpolate_plasma_params(time_start,
time_end,
probe,
pad,
rbsp_path='G://DATA//RBSP//',
cold_composition=np.array([70, 20,
10])):
# Cold (total?) electron plasma density
den_times, edens, dens_err = rfr.retrieve_RBSP_electron_density_data(
rbsp_path, time_start, time_end, probe, pad=pad)
# Magnetic magnitude
mag_times, B_mag = rfl.load_magnetic_field(rbsp_path,
time_start,
time_end,
probe,
return_raw=True)
# HOPE data
itime, etime, pdict, perr = rfr.retrieve_RBSP_hope_moment_data(rbsp_path,
time_start,
time_end,
padding=pad,
probe=probe)
hope_dens = np.array(
[pdict['Dens_p_30'], pdict['Dens_he_30'], pdict['Dens_o_30']])
hope_temp = np.array(
[pdict['Tperp_p_30'], pdict['Tperp_he_30'], pdict['Tperp_o_30']])
hope_anis = np.array([
pdict['Tperp_Tpar_p_30'], pdict['Tperp_Tpar_he_30'],
pdict['Tperp_Tpar_o_30']
]) - 1
# SPICE data
spice_dens = []
spice_temp = []
spice_anis = []
for product, spec in zip(['TOFxEH', 'TOFxEHe', 'TOFxEO'],
['P', 'He', 'O']):
spice_time, spice_dict = rfr.retrieve_RBSPICE_data(rbsp_path,
time_start,
time_end,
product,
padding=pad,
probe=probe)
this_dens = spice_dict['F{}DU_Density'.format(spec)]
this_anis = spice_dict['F{}DU_PerpPressure'.format(spec)] / spice_dict[
'F{}DU_ParaPressure'.format(spec)] - 1
# Perp Temperature - Calculate as T = P/nk
kB = 1.381e-23
q = 1.602e-19
t_perp_kelvin = 1e-9 * spice_dict['F{}DU_PerpPressure'.format(
spec)] / (kB * 1e6 * spice_dict['F{}DU_Density'.format(spec)])
this_temp = kB * t_perp_kelvin / q # Convert to eV
spice_dens.append(this_dens)
spice_temp.append(this_temp)
spice_anis.append(this_anis)
ihope_dens, ihope_temp, ihope_anis = interpolate_to_edens(
den_times, itime, hope_dens, hope_temp, hope_anis)
ispice_dens, ispice_temp, ispice_anis = interpolate_to_edens(
den_times, spice_time, np.array(spice_dens), np.array(spice_temp),
np.array(spice_anis))
Bi = interpolate_B(den_times, mag_times, B_mag)
# Subtract energetic components from total electron density (assuming each is singly charged)
cold_dens = edens - ihope_dens.sum(axis=0) - ispice_dens.sum(axis=0)
# Calculate cold ion composition. Assumes static percentage composition, but this could be changed.
cold_comp = np.array([
cold_composition[0] * cold_dens * 0.01,
cold_composition[1] * cold_dens * 0.01,
cold_composition[2] * cold_dens * 0.01
])
param_dict = {}
param_dict['times'] = den_times
param_dict['field'] = Bi
param_dict['ndensc'] = cold_comp
param_dict['ndensw'] = ihope_dens
param_dict['temp_perp'] = ihope_temp
param_dict['A'] = ihope_anis
param_dict['ndensw2'] = ispice_dens
param_dict['temp_perp2'] = ispice_temp
param_dict['A2'] = ispice_anis
return param_dict
[pdict['Dens_p_30'], pdict['Dens_he_30'], pdict['Dens_o_30']])
hope_temp = np.array(
[pdict['Tperp_p_30'], pdict['Tperp_he_30'], pdict['Tperp_o_30']])
hope_anis = np.array([
pdict['Tperp_Tpar_p_30'], pdict['Tperp_Tpar_he_30'],
pdict['Tperp_Tpar_o_30']
]) - 1
spice_dens = []
spice_temp = []
spice_anis = []
for product, spec in zip(['TOFxEH', 'TOFxEHe', 'TOFxEO'],
['P', 'He', 'O']):
spice_time, spice_dict = rfr.retrieve_RBSPICE_data(_rbsp_path,
_time_start,
_time_end,
product,
padding=_pad,
probe=_probe)
this_dens = spice_dict['F{}DU_Density'.format(spec)]
this_anis = spice_dict['F{}DU_PerpPressure'.format(
spec)] / spice_dict['F{}DU_ParaPressure'.format(spec)] - 1
# Perp Temperature - Calculate as T = P/nk
kB = 1.381e-23
q = 1.602e-19
t_perp_kelvin = 1e-9 * spice_dict['F{}DU_PerpPressure'.format(
spec)] / (kB * 1e6 * spice_dict['F{}DU_Density'.format(spec)])
this_temp = kB * t_perp_kelvin / q # Convert from K to eV
spice_dens.append(this_dens)