Parker Solar Probe (PSP)

The routines in this module can be used to load data from the Parker Solar Probe (PSP) mission.

Electromagnetic Fields Investigation (FIELDS)

pyspedas.psp.fields(trange=['2018-11-5', '2018-11-6'], datatype='mag_rtn', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe FIELDS data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
fields_vars = pyspedas.psp.fields(trange=['2018-11-5', '2018-11-5/06:00'], datatype='mag_rtn', level='l2', time_clip=True)
tplot('psp_fld_l2_mag_RTN')

Solar Probe Cup

pyspedas.psp.spc(trange=['2018-11-5', '2018-11-6'], datatype='l3i', level='l3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe Solar Probe Cup data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
spc_vars = pyspedas.psp.spc(trange=['2018-11-5', '2018-11-6'], datatype='l3i', level='l3')
tplot(['np_fit', 'vp_fit_RTN'])

SWEAP/SPAN-e

pyspedas.psp.spe(trange=['2018-11-5', '2018-11-6'], datatype='spa_sf1_32e', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe SWEAP/SPAN-e data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
spe_vars = pyspedas.psp.spe(trange=['2018-11-5', '2018-11-5/06:00'], datatype='spa_sf1_32e', level='l2', time_clip=True)
tplot('EFLUX')

SWEAP/SPAN-i

pyspedas.psp.spi(trange=['2018-11-5', '2018-11-6'], datatype='spi_sf0a_mom_inst', level='l3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe SWEAP/SPAN-i data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
spi_vars = pyspedas.psp.spi(trange=['2018-11-5', '2018-11-5/06:00'], datatype='spi_sf0a_mom_inst', level='l3', time_clip=True)
tplot(['DENS', 'VEL', 'T_TENSOR', 'TEMP', 'EFLUX_VS_ENERGY', 'EFLUX_VS_THETA', 'EFLUX_VS_PHI'])

IS☉IS/EPI-Hi

pyspedas.psp.epihi(trange=['2018-11-5', '2018-11-6'], datatype='let1_rates1h', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe ISoIS/EPI-Hi data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
epihi_vars = pyspedas.psp.epihi(trange=['2018-11-5', '2018-11-5/06:00'], datatype='let1_rates1h', level='l2', time_clip=True)
tplot(['B_He_Rate', 'A_He_Flux', 'A_S_Rate'])

IS☉IS/EPI (merged summary data)

pyspedas.psp.epi(trange=['2018-11-5', '2018-11-6'], datatype='summary', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Parker Solar Probe ISoIS/EPI (merged summary) data

Parameters

trange (list of str) – time range of interest [starttime, endtime] with the format ‘YYYY-MM-DD’,’YYYY-MM-DD’] or to specify more or less than a day [‘YYYY-MM-DD/hh:mm:ss’,’YYYY-MM-DD/hh:mm:ss’]
datatype (str) – Data type; Valid options:
suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.
get_support_data (bool) – Data with an attribute “VAR_TYPE” with a value of “support_data” will be loaded into tplot. By default, only loads in data with a “VAR_TYPE” attribute of “data”.
varformat (str) – The file variable formats to load into tplot. Wildcard character “*” is accepted. By default, all variables are loaded in.
varnames (list of str) – List of variable names to load (if not specified, all data variables are loaded)
downloadonly (bool) – Set this flag to download the CDF files, but not load them into tplot variables
notplot (bool) – Return the data in hash tables instead of creating tplot variables
no_update (bool) – If set, only load data from your local cache
time_clip (bool) – Time clip the variables to exactly the range specified in the trange keyword

Return type

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
epi_vars = pyspedas.psp.epi(trange=['2018-11-5', '2018-11-5/06:00'], datatype='summary', level='l2', time_clip=True)
tplot(['A_H_Rate_TS', 'H_CountRate_ChanT_SP', 'Electron_CountRate_ChanE', 'HET_A_H_Rate_TS', 'HET_A_Electrons_Rate_TS'])