pySPEDAS Documentation

pySPEDAS is an implementation of the Space Physics Environment Data Analysis Software (SPEDAS) framework in Python.

The SPEDAS framework is written in IDL and contains data loading, data analysis and data plotting tools for various scientific missions (NASA, NOAA, etc.) and ground magnetometers.

pySPEDAS and pyTplot make creating multi-mission, multi-instrument figures simple, e.g., to create a figure showing magnetometer data from Solar Orbiter, Parker Solar Probe, MMS, and THEMIS,

import pyspedas
from pytplot import tplot

time_range = ['2020-04-20/06:00', '2020-04-20/08:00']

pyspedas.solo.mag(trange=time_range, time_clip=True)
pyspedas.psp.fields(trange=time_range, time_clip=True)
pyspedas.mms.fgm(trange=time_range, time_clip=True, probe=2)
pyspedas.themis.fgm(trange=time_range, time_clip=True, probe='d')

tplot(['B_RTN', 'psp_fld_l2_mag_RTN', 'mms2_fgm_b_gsm_srvy_l2_bvec', 'thd_fgs_gsm'])

Getting Started

pySPEDAS supports Windows, macOS and Linux.

Requirements

Python 3.7 or later is required.

We recommend Anaconda, which comes with a suite of packages useful for scientific data analysis. Step-by-step instructions for installing Anaconda can be found at: Windows, macOS, Linux

Installation

To get started, install the pyspedas package using PyPI:

pip install pyspedas

To upgrade to the latest version of pySPEDAS, include the ‘–upgrade’ option when calling pip, e.g.,

pip install pyspedas --upgrade

Local Data Directories

By default, the data are stored in your pyspedas directory in a folder named ‘pydata’. The recommended way of setting your local data directory is to set the SPEDAS_DATA_DIR environment variable. SPEDAS_DATA_DIR acts as a root data directory for all missions, and will also be used by IDL (if you’re running a recent copy of the bleeding edge).

Mission specific data directories (e.g., MMS_DATA_DIR for MMS, THM_DATA_DIR for THEMIS) can also be set, and these will override SPEDAS_DATA_DIR.

Loading and Plotting Data

You can load data into tplot variables by calling pyspedas.mission.instrument(), e.g.,

import pyspedas
pyspedas.mms.fgm()

The load routines support several keywords to control which data products are loaded (datatype, level, etc).

To plot the tplot variables that were loaded, use tplot from pytplot, e.g.,

from pytplot import tplot
tplot(['mms1_fgm_b_gse_srvy_l2_btot', 'mms1_fgm_b_gse_srvy_l2_bvec'])

Accessing the Data and Timestamps

Once the data are loaded into tplot variables, you can access them using the get_data function from pytplot. e.g.,

from pytplot import get_data

mag_data = get_data('mms1_fgm_b_gse_srvy_l2_bvec')

# get_data returns a namedtuple with 'times' and 'y':
mag_data.times # the unix times, stored as a numpy array
mag_data.y # the data values

Note: some types of data (spectrograms, DFs) have higher dimensions; e.g., spectra have a ‘v’ with the y-axis values for the data stored in ‘y’, and some data can have several dimensions: ‘v1’, ‘v2’, and ‘v3’

Load Routines

Advanced Composition Explorer (ACE)

The routines in this module can be used to load data from the Advanced Composition Explorer (ACE) mission.

Magnetometer (MFI)

pyspedas.ace.mfi(trange=['2018-11-5', '2018-11-6'], datatype='h3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Fluxgate Magnetometer

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:

  h0: 16-Second Level 2 Data h1: 4-Minute Level 2 Data h2: 1-Hour Level 2 Data h3: (default) 1-Second Level 2 Data k0: 5-Minute Key Parameters [PRELIM] k1: 16-Second Key Parameters [PRELIM] k2: 1-Hour Key Parameters [PRELIM]

• 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
mfi_vars = pyspedas.ace.mfi(trange=['2018-11-5', '2018-11-6'])
tplot(['BGSEc', 'Magnitude'])

Solar Wind Electron, Proton and Alpha Monitor (SWEPAM)

pyspedas.ace.swe(trange=['2018-11-5', '2018-11-6'], datatype='h0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Wind Electron, Proton and Alpha Monitor (SWEPAM)

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:

  h0: (default) 64-Second Level 2 Data h2: 1-Hour Level 2 Data k0: 5-Minute Key Parameters [PRELIM] k1: 1-Hour Key Parameters [PRELIM]

• 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
swe_vars = pyspedas.ace.swe(trange=['2018-11-5', '2018-11-6'])
tplot(['Vp', 'Tpr'])

Electron, Proton, and Alpha-particle Monitor (EPAM)

pyspedas.ace.epam(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electron Proton Alpha Monitor (EPAM)

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:

  h1: 5-Minute Level 2 Data h2: 1-Hour Level 2 Data h3: 12-second Level 2 Data k0: (default) 5-Minute Key Parameters k1: 1-Hour Key Parameters

• 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
epam_vars = pyspedas.ace.epam(trange=['2018-11-5', '2018-11-6'])
tplot(['H_lo', 'Ion_very_lo', 'Ion_lo', 'Ion_mid', 'Ion_hi', 'Electron_lo', 'Electron_hi'])

Cosmic Ray Isotope Spectrometer (CRIS)

pyspedas.ace.cris(trange=['2018-11-5', '2018-11-6'], datatype='h2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Cosmic Ray Isotope Spectrometer (CRIS)

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:

  h2: (default) 1-Hour Level 2 Data h3: Daily-averaged Level 2 Data

• 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
cris_vars = pyspedas.ace.cris(trange=['2018-11-5', '2018-11-6'])
tplot(['flux_B', 'flux_C', 'flux_N', 'flux_O', 'flux_F', 'flux_Ne'])

Solar Isotope Spectrometer (SIS)

pyspedas.ace.sis(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Isotope Spectrometer (SIS)

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:

  h1: (default) 256-sec Level 2 Data h2: 1-Hour Level 2 Data k0: 1-Hour Key Parameters

• 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
sis_vars = pyspedas.ace.sis(trange=['2018-11-5', '2018-11-6'])
tplot(['H_lo', 'H_hi', 'CNO_lo', 'CNO_hi', 'Z_ge_10'])

Ultra Low Energy Isotope Spectrometer (ULEIS)

pyspedas.ace.uleis(trange=['2018-11-5', '2018-11-6'], datatype='h2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Ultra Low Energy Isotope Spectrometer (ULEIS)

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:

  h2: 1-Hour Level 2 Data

• 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
uleis_vars = pyspedas.ace.uleis(trange=['2018-11-5', '2018-11-6'])
tplot(['H_S1', 'H_S2', 'H_S3', 'H_S4', 'H_S5'])

Solar Energetic Particle Ionic Charge Analyzer (SEPICA)

pyspedas.ace.sepica(trange=['2004-11-5', '2004-11-6'], datatype='h2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Energetic Particle Ionic Charge Analyzer (SEPICA)

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:

  h2: 1-Hour Level 2 Data

• 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
sepica_vars = pyspedas.ace.sepica(trange=['2004-11-5', '2004-11-6'])
tplot(['H1', 'H2', 'H3'])

Solar Wind Ion Composition Spectrometer (SWICS)

pyspedas.ace.swics(trange=['2018-11-5', '2018-11-6'], datatype='sw2_h3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Wind Ion Composition Spectrometer (SWICS)

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:

  sw2_h3: (default) SWICS 2.0 Solar Wind 2-Hour Level 2 Data swi_h2: SWICS 1.1 Solar Wind 1-Hour Level 2 Data swi_h3: SWICS 1.1 Solar Wind 2-Hour Level 2 Data swi_h4: SWICS 1.1 Solar Wind 1-Day Level 2 Data swi_h5: SWICS 1.1 Solar Wind 2-Hour Level 2 Q-state distributions swi_h6: Solar Wind Protons 12-min Level 2 Data

• 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
swi_vars = pyspedas.ace.swics(trange=['2018-11-5', '2018-11-6'])
tplot(['vHe2', 'vthHe2'])

Arase (ERG)

The routines in this module can be used to load data from the Arase mission.

Magnetic Field Experiment (MGF)

pyspedas.erg.mgf(trange=['2017-03-27', '2017-03-28'], datatype='8sec', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True, coord='dsi', version=None)

This function loads data from the MGF experiment from the Arase mission

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:

• level – str Data level; 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.

• 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

• ror – bool If set, print PI info and rules of the road

• coord – str “sm”, “dsi”, “gse”, “gsm”, “sgi”

• version – str Set this value to specify the version of cdf files (such as “v03.03”, “v03.04”, …)

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
pyspedas.erg.mgf(trange=['2017-03-27', '2017-03-28'])
tplot('erg_mgf_l2_mag_8sec_sm')

Extremely High-energy electrons (XEP-e)

pyspedas.erg.xep(trange=['2017-06-01', '2017-06-02'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the XEP-e experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.xep(trange=['2017-03-27', '2017-03-28'])
tplot('erg_xep_l2_FEDO_SSD')

High-energy Particles – electrons (HEP-e)

pyspedas.erg.hep(trange=['2017-03-27', '2017-03-28'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True, version=None)

This function loads data from the HEP experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

• version – str Set this value to specify the version of cdf files (such as “v01_02”, “v01_03”, …)

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.hep(trange=['2017-03-27', '2017-03-28'])
tplot(['erg_hep_l2_FEDO_L', 'erg_hep_l2_FEDO_H'])

Medium-energy Particles - electrons (MEP-e)

pyspedas.erg.mepe(trange=['2017-03-27', '2017-03-28'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the MEP-e experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.mepe(trange=['2017-03-27', '2017-03-28'])
tplot('erg_mepe_l2_omniflux_FEDO')

Low-energy Particles – electrons (LEP-e)

pyspedas.erg.lepe(trange=['2017-04-04', '2017-04-05'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True, version=None, only_fedu=False, et_diagram=False)

This function loads data from the LEP-e experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

• version – str Set this value to specify the version of cdf files (such as “v02_02”)

• only_fedu – bool If set, not make erg_lepe_l3_pa_enech_??(??:01,01,..32)_FEDU Tplot Variables

• et_diagram – bool If set, make erg_lepe_l3_pa_pabin_??(??:01,01,..16)_FEDU Tplot Variables

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.lepe(trange=['2017-03-27', '2017-03-28'])
tplot('erg_lepe_l2_omniflux_FEDO')

Medium-energy Particles – ion (MEP-i)

pyspedas.erg.mepi_nml(trange=['2017-03-27', '2017-03-28'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the MEP-i experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.mepi_nml(trange=['2017-03-27', '2017-03-28'])
tplot('erg_mepi_l2_omniflux_FODO')

pyspedas.erg.mepi_tof(trange=['2017-03-27', '2017-03-28'], datatype='flux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the MEP-i experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

Low-energy Particles – ion (LEP-i)

pyspedas.erg.lepi(trange=['2017-07-01', '2017-07-02'], datatype='omniflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True, version=None)

This function loads data from the LEP-i experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

• version – str Set this value to specify the version of cdf files (such as “v03_00”)

Returns

List of tplot variables created.

Plasma Wave Experiment (PWE)

pyspedas.erg.pwe_ofa(trange=['2017-04-01', '2017-04-02'], datatype='spec', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the PWE experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.pwe_ofa(trange=['2017-03-27', '2017-03-28'])
tplot('erg_pwe_ofa_l2_spec_E_spectra_132')

pyspedas.erg.pwe_hfa(trange=['2017-04-01', '2017-04-02'], datatype='spec', mode='low', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the PWE experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.pwe_hfa(trange=['2017-03-27', '2017-03-28'])
tplot('erg_pwe_hfa_l2_low_spectra_esum')

pyspedas.erg.pwe_efd(trange=['2017-04-01', '2017-04-02'], datatype='E_spin', level='l2', suffix='', coord='dsi', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, ror=True)

This function loads data from the PWE experiment from the Arase mission

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:

• level – str Data level; 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

• ror – bool If set, print PI info and rules of the road

Returns

List of tplot variables created.

Orbit data

pyspedas.erg.orb(trange=['2017-03-27', '2017-03-28'], datatype='def', level='l2', model='op', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, uname=None, passwd=None, time_clip=False, version=None, ror=True)

This function loads orbit data from the Arase mission

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:

• level – str Data level; 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

• version – str Set this value to specify the version of cdf files (such as “v03”)

Returns

List of tplot variables created.

import pyspedas
from pytplot import tplot
pyspedas.erg.orb(trange=['2017-03-27', '2017-03-28'])
tplot(['erg_orb_l2_pos_gsm', 'erg_orb_l2_vel_gsm'])

Cluster

The routines in this module can be used to load data from the Cluster mission.

Fluxgate Magnetometer (FGM)

pyspedas.cluster.fgm(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='up', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Fluxgate Magnetometer

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
fgm_vars = pyspedas.cluster.fgm(trange=['2018-11-5', '2018-11-6'])
tplot('B_xyz_gse__C1_UP_FGM')

Active Spacecraft Potential Control experiment (ASPOC)

pyspedas.cluster.aspoc(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Active Spacecraft Potential Control experiment

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
asp_vars = pyspedas.cluster.aspoc(trange=['2004-10-01', '2004-10-2'])
tplot('I_ion__C1_PP_ASP')

Cluster Ion Spectroscopy experiment (CIS)

pyspedas.cluster.cis(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Cluster Ion Spectroscopy experiment

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
cis_vars = pyspedas.cluster.cis(trange=['2004-10-01', '2004-10-2'])
tplot(['N_p__C1_PP_CIS', 'V_p_xyz_gse__C1_PP_CIS', 'T_p_par__C1_PP_CIS', 'T_p_perp__C1_PP_CIS'])

Digital Wave Processing instrument (DWP)

pyspedas.cluster.dwp(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Digital Wave Processing instrument

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
dwp_vars = pyspedas.cluster.dwp(trange=['2004-10-01', '2004-10-2'])
tplot('Correl_Ivar__C1_PP_DWP')

Electron Drift Instrument (EDI)

pyspedas.cluster.edi(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electron Drift Instrument

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
edi_vars = pyspedas.cluster.edi(trange=['2004-10-01', '2004-10-2'])
tplot(['V_ed_xyz_gse__C1_PP_EDI', 'E_xyz_gse__C1_PP_EDI'])

Electric Field and Wave experiment (EFW)

pyspedas.cluster.efw(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electric Field and Wave experiment

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
efw_vars = pyspedas.cluster.efw(trange=['2004-10-01', '2004-10-2'])
tplot('E_dusk__C1_PP_EFW')

Plasma Electron and Current Experiment (PEACE)

pyspedas.cluster.peace(trange=['2016-11-5', '2016-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Plasma Electron and Current Experiment

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
peace_vars = pyspedas.cluster.peace(trange=['2004-10-01', '2004-10-2'])
tplot(['N_e_den__C1_PP_PEA', 'V_e_xyz_gse__C1_PP_PEA', 'T_e_par__C1_PP_PEA', 'T_e_perp__C1_PP_PEA'])

Research with Adaptive Particle Imaging Detectors (RAPID)

pyspedas.cluster.rapid(trange=['2016-11-5', '2016-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Research with Adaptive Particle Imaging Detectors

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
rap_vars = pyspedas.cluster.rapid(trange=['2004-10-01', '2004-10-2'])
tplot(['J_e_lo__C1_PP_RAP', 'J_e_hi__C1_PP_RAP', 'J_p_lo__C1_PP_RAP', 'J_p_hi__C1_PP_RAP'])

Spatio-Temporal Analysis of Field Fluctuation experiment (STAFF)

pyspedas.cluster.staff(trange=['2012-11-5', '2012-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Spatio-Temporal Analysis of Field Fluctuation experiment

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
sta_vars = pyspedas.cluster.staff(trange=['2004-10-01', '2004-10-02'])
tplot('B_par_f1__C1_PP_STA')

Wide Band Data receiver (WBD)

pyspedas.cluster.wbd(trange=['2012-11-6', '2012-11-7'], probe='1', datatype='waveform', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Wide Band Data receiver

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
wbd_vars = pyspedas.cluster.wbd(trange=['2012-11-06/02:10', '2012-11-06/02:20'])
tplot('WBD_Elec')

Waves of High Frequency and Sounder for Probing of Density by Relaxation (WHISPER)

pyspedas.cluster.whi(trange=['2012-11-5', '2012-11-6'], probe='1', datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Waves of High Frequency and Sounder for Probing of Density by Relaxation instrument

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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
whi_vars = pyspedas.cluster.whi()
tplot('N_e_res__C1_PP_WHI')

Colorado Student Space Weather Experiment (CSSWE)

The routines in this module can be used to load data from the Colorado Student Space Weather Experiment (CSSWE) mission.

Relativistic Electron and Proton Telescope integrated little experiment (REPTile)

pyspedas.csswe.reptile(trange=['2013-11-5', '2013-11-6'], datatype='flux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Relativistic Electron and Proton Telescope integrated little experiment (REPTile)

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:

  ‘counts’ for L1 data ‘flux’ for L2 data

• level (str) – Data level; options: ‘l1’, ‘l2’ (default: l2)

• 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
reptile_vars = pyspedas.csswe.reptile(trange=['2013-11-5', '2013-11-6'])
tplot(['E1flux', 'E2flux', 'E3flux', 'P1flux', 'P2flux', 'P3flux'])

Deep Space Climate Observatory (DSCOVR)

The routines in this module can be used to load data from the Deep Space Climate Observatory (DSCOVR) mission.

Magnetometer (MAG)

pyspedas.dscovr.mag(trange=['2018-10-16', '2018-10-17'], datatype='h0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads DSCOVR Fluxgate Magnetometer 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:

  ‘h0’: 1-sec Definitive Data (default)

• 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
mag_vars = pyspedas.dscovr.mag(trange=['2018-11-5', '2018-11-6'])
tplot('dsc_h0_mag_B1GSE')

Faraday cup (FC)

pyspedas.dscovr.fc(trange=['2018-10-16', '2018-10-17'], datatype='h1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads DSCOVR Faraday 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:

  ‘h1’: 1-minute Isotropic Maxwellian parameters for solar wind protons (default)

• 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
fc_vars = pyspedas.dscovr.fc(trange=['2018-11-5', '2018-11-6'])
tplot(['dsc_h1_fc_V_GSE', 'dsc_h1_fc_THERMAL_SPD', 'dsc_h1_fc_Np', 'dsc_h1_fc_THERMAL_TEMP'])

Orbit data

pyspedas.dscovr.orb(trange=['2018-10-16', '2018-10-17'], datatype='orbit', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads DSCOVR Ephemeris 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’]

• 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
orb_vars = pyspedas.dscovr.orb(trange=['2018-11-5', '2018-11-6'])
tplot(['dsc_orbit_SUN_R', 'dsc_orbit_GCI_POS', 'dsc_orbit_GCI_VEL', 'dsc_orbit_GSE_POS', 'dsc_orbit_MOON_GSE_POS'])

Attitude data

pyspedas.dscovr.att(trange=['2018-10-16', '2018-10-17'], datatype='orbit', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads DSCOVR Attitude 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’]

• 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
att_vars = pyspedas.dscovr.att(trange=['2018-11-5', '2018-11-6'])
tplot(['dsc_att_GSE_Yaw', 'dsc_att_GSE_Pitch', 'dsc_att_GSE_Roll'])

Load all data at once

pyspedas.dscovr.all(trange=['2018-10-16', '2018-10-17'], downloadonly=False, suffix='', no_update=False, time_clip=False)

This function loads all DSCOVR 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’]

• downloadonly (bool) – Set this flag to download the CDF files, but not load them into 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
all_vars = pyspedas.dscovr.all(trange=['2018-11-5', '2018-11-6'])
tplot(['dsc_h0_mag_B1GSE', 'dsc_h1_fc_V_GSE', 'dsc_h1_fc_THERMAL_SPD', 'dsc_h1_fc_Np', 'dsc_orbit_GSE_POS'])

Equator-S

The routines in this module can be used to load data from the Equator-S mission.

Fluxgate magnetometer (MAM)

pyspedas.equator_s.mam(trange=['1998-04-06', '1998-04-07'], datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Fluxgate magnetometer

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
mam_vars = pyspedas.equator_s.mam(trange=['1998-04-06', '1998-04-07'])
tplot('B_xyz_gse%eq_pp_mam')

Electron beam sensing instrument (EDI)

pyspedas.equator_s.edi(trange=['1998-04-06', '1998-04-07'], datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electron beam sensing instrument

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
edi_vars = pyspedas.equator_s.edi(trange=['1998-04-06', '1998-04-07'])
tplot('E_xyz_gse%eq_pp_edi')

Solid state detector (EPI)

pyspedas.equator_s.epi(trange=['1998-04-06', '1998-04-07'], datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solid state detector

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.equator_s.epi(trange=['1998-04-06', '1998-04-30'])
tplot(['J_e_1%eq_pp_epi', 'J_e_2%eq_pp_epi', 'J_e_3%eq_pp_epi'])

Time-of-fight spectrometer (ICI)

pyspedas.equator_s.ici(trange=['1998-04-06', '1998-04-07'], datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Time-of-fight spectrometer

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
ici_vars = pyspedas.equator_s.ici(trange=['1998-04-06', '1998-04-07'])
tplot('V_p_xyz_gse%eq_pp_ici')

Ion emitter (PCD)

pyspedas.equator_s.pcd(trange=['1998-04-06', '1998-04-07'], datatype='pp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Ion emitter

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
pcd_vars = pyspedas.equator_s.pcd(trange=['1998-04-06', '1998-04-07'])
tplot('I_ion%eq_pp_pcd')

Scintillating fiber detector (SFD)

pyspedas.equator_s.sfd(trange=['1998-01-26', '1998-01-27'], datatype='sp', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Scintillating fiber detector

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
sfd_vars = pyspedas.equator_s.sfd()
tplot('F_e>0.26%eq_sp_sfd')

Fast Auroral Snapshot Explorer (FAST)

The routines in this module can be used to load data from the Fast Auroral Snapshot Explorer (FAST) mission.

Fluxgate Magnetometer (DCB)

pyspedas.fast.dcb(trange=['2001-09-05', '2001-09-06'], datatype='', level='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Fluxgate Magnetometer

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
dcb_vars = pyspedas.fast.dcb(trange=['1998-09-05', '1998-09-06'])
tplot('')

Search-coil Magnetometer (ACB)

pyspedas.fast.acb(trange=['1998-01-05', '1998-01-06'], datatype='', level='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Search-coil Magnetometer

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
acb_vars = pyspedas.fast.acb()
tplot('HF_E_SPEC')

Time-of-flight Energy Angle Mass Spectrograph (TEAMS)

pyspedas.fast.teams(trange=['1998-09-05', '1998-09-06'], datatype='', level='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Time-of-flight Energy Angle Mass Spectrograph (TEAMS)

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
teams_vars = pyspedas.fast.teams(trange=['1998-09-05', '1998-09-06'])
tplot(['H+', 'H+_low', 'H+_high'])

Geotail

The routines in this module can be used to load data from the Geotail mission.

Magnetic Field Experiment (MGF)

pyspedas.geotail.mgf(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the MGF instrument

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
mgf_vars = pyspedas.geotail.mgf(trange=['2018-11-5', '2018-11-6'])
tplot(['IB', 'IB_vector'])

Electric Field Detector (EFD)

pyspedas.geotail.efd(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the EFD instrument

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
efd_vars = pyspedas.geotail.efd(trange=['2018-11-5', '2018-11-6'])
tplot(['Es', 'Ss', 'Bs', 'Vs', 'Ew', 'Sw', 'Bw', 'Vw'])

Low Energy Particle experiment (LEP)

pyspedas.geotail.lep(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the LEP instrument

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
lep_vars = pyspedas.geotail.lep(trange=['2018-11-5/05:00', '2018-11-5/06:00'], time_clip=True)
tplot(['N0', 'V0'])

Comprehensive Plasma Instrumentation (CPI)

pyspedas.geotail.cpi(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the CPI instrument

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
cpi_vars = pyspedas.geotail.cpi(trange=['2018-11-5/15:00', '2018-11-5/18:00'], time_clip=True)
tplot(['SW_P_Den', 'SW_P_AVGE', 'SW_V', 'HP_P_Den'])

Energetic Particles and Ion Composition Instrument (EPIC)

pyspedas.geotail.epic(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the EPIC instrument

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
epic_vars = pyspedas.geotail.epic(trange=['2018-11-5', '2018-11-6'])
tplot('IDiffI_I')

Plasma Wave Instrument (PWI)

pyspedas.geotail.pwi(trange=['2018-11-5', '2018-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the PWI instrument

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
pwi_vars = pyspedas.geotail.pwi(trange=['2018-11-5/06:00', '2018-11-5/07:00'], time_clip=True)
tplot(['MCAE_AVE', 'MCAB_AVE'])

Geostationary Operational Environmental Satellite (GOES)

The routines in this module can be used to load data from the Geostationary Operational Environmental Satellite (GOES) mission.

Magnetometer (FGM)

pyspedas.goes.fgm(trange=['2013-11-5', '2013-11-6'], probe='15', datatype='1min', suffix='', downloadonly=False, no_update=False, time_clip=False)

This function loads data from the GOES Magnetometer

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’]

• probe (str/int or list of strs/ints) – GOES spacecraft #, e.g., probe=15

• datatype (str) – Data type; Valid options:

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• downloadonly (bool) – Set this flag to download the CDF files, but not load them into 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
mag_vars = pyspedas.goes.fgm(trange=['2013-11-5', '2013-11-6'], datatype='512ms')
tplot(['BX_1', 'BY_1', 'BZ_1'])

Imager for Magnetopause-to-Aurora Global Exploration (IMAGE)

The routines in this module can be used to load data from the IMAGE ission.

Low-Energy Neutral Atom (LENA) imager

pyspedas.image.lena(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE LENA 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.

Medium-Energy Neutral Atom (MENA) imager

pyspedas.image.mena(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE MENA 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.

High-Energy Neutral Atom (HENA) imager

pyspedas.image.hena(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE HENA 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.

Radio Plasma Imaging (RPI)

pyspedas.image.rpi(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE RPI 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.

Extreme Ultraviolet Imager (EUV)

pyspedas.image.euv(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE EUV 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.

Far Ultraviolet Imager (FUV)

pyspedas.image.fuv(trange=['2004-11-5', '2004-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE FUV 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.

Orbit data

pyspedas.image.orbit(trange=['2004-11-5', '2004-11-6'], datatype='def_or', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads IMAGE orbit 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.

Kyoto Dst

The routines in this module can be used to load Kyoto Dst data from the World Data Center for Geomagnetism, Kyoto.

Load the data

pyspedas.kyoto.dst(trange=None, time_clip=True, remote_data_dir='http://wdc.kugi.kyoto-u.ac.jp/', suffix='')

Loads Dst data from the Kyoto servers.

Parameters

• trange (list of str) – Time range to load

• time_clip (bool) – If set, time the data to the requested trange

• remote_data_dir (str) – Remote data server at Kyoto

• suffix (str) – Suffix to append to the output variable’s name

The DST data are provided by the World Data Center for Geomagnetism, Kyoto, and are not for redistribution (http://wdc.kugi.kyoto-u.ac.jp/). Furthermore, we thank the geomagnetic observatories (Kakioka [JMA], Honolulu and San Juan [USGS], Hermanus [RSA], Alibag [IIG]), NiCT, INTERMAGNET, and many others for their cooperation to make the Dst index available.

Return type

Name of the tplot variable created.

Example

import pyspedas
from pytplot import tplot
dst_vars = pyspedas.kyoto.dst(trange=['2018-11-5', '2018-11-6'])
tplot('kyoto_dst')

Mars Atmosphere and Volatile Evolution (MAVEN)

The routines in this module can be used to load data from the Mars Atmosphere and Volatile Evolution (MAVEN) mission.

Magnetometer (MAG)

pyspedas.maven.mag(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='ss', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
mag_vars = pyspedas.maven.mag(trange=['2014-10-18', '2014-10-19'])
tplot('OB_B')

Solar Wind Electron Analyzer (SWEA)

pyspedas.maven.swea(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='svyspec', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
swe_vars = pyspedas.maven.swea(trange=['2014-10-18', '2014-10-19'])
tplot('diff_en_fluxes_svyspec')

Solar Wind Ion Analyzer (SWIA)

pyspedas.maven.swia(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='onboardsvyspec', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
swi_vars = pyspedas.maven.swia(trange=['2014-10-18', '2014-10-19'])
tplot('spectra_diff_en_fluxes_onboardsvyspec')

SupraThermal And Thermal Ion Composition (STATIC)

pyspedas.maven.sta(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='2a', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
sta_vars = pyspedas.maven.sta(trange=['2014-10-18', '2014-10-19'])
tplot('hkp_2a-hkp')

Solar Energetic Particle (SEP)

pyspedas.maven.sep(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='s2-cal-svy-full', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
sep_vars = pyspedas.maven.sep(trange=['2014-10-18', '2014-10-19'])
tplot('f_ion_flux_tot_s2-cal-svy-full')

Langmuir Probe and Waves (LPW)

pyspedas.maven.lpw(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='lpiv', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
lpw_vars = pyspedas.maven.lpw(trange=['2014-10-18', '2014-10-19'])
tplot('mvn_lpw_lp_iv_l2_lpiv')

Extreme Ultraviolet Monitor (EUV)

pyspedas.maven.euv(trange=['2016-01-01', '2016-01-02'], level='l2', datatype='bands', varformat=None, get_support_data=False, auto_yes=True, downloadonly=False, varnames=[])

Example

import pyspedas
from pytplot import tplot
euv_vars = pyspedas.maven.euv(trange=['2014-10-18', '2014-10-19'])
tplot('mvn_euv_calib_bands_bands')

Magnetic Induction Coil Array (MICA)

The routines in this module can be used to load data from the Magnetic Induction Coil Array (MICA) mission.

Induction data

pyspedas.mica.induction(site=None, trange=['2019-02-01', '2019-02-02'], suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Magnetic Induction Coil Array (MICA)

Parameters

• site (str) – abbreviated name of station. sites include: NAL, LYR, LOR, ISR, SDY, IQA, SNK, MCM, SPA, JBS, NEV, HAL, PG2[3,4,5]

• 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’]

• 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
nal_vars = pyspedas.mica.induction(site='NAL', trange=['2019-02-01','2019-02-02'])
tplot('spectra_x_1Hz_NAL')

Magnetospheric Multiscale (MMS)

The routines in this module can be used to load data from the Magnetospheric Multiscale (MMS) mission.

Fluxgate Magnetometer (FGM)

pyspedas.mms.fgm(*args, **kwargs)

This function loads FGM data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for FGM include ‘brst’ ‘fast’ ‘slow’ ‘srvy’. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – no datatype for FGM instrument (all science data are loaded)

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• keep_flagged (bool) – If True, don’t remove flagged data (flagged data are set to NaNs by default, this keyword turns this off)

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

• get_fgm_ephemeris (bool) – Keep the ephemeris variables in the FGM files

Returns

List of tplot variables created.

FGM Example

import pyspedas
from pytplot import tplot
pyspedas.mms.fgm(trange=['2015-10-16/12:45', '2015-10-16/13:00'], time_clip=True)
tplot(['mms1_fgm_b_gsm_srvy_l2_btot', 'mms1_fgm_b_gsm_srvy_l2_bvec'])

Search-coil Magnetometer (SCM)

pyspedas.mms.scm(*args, **kwargs)

This function loads SCM data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for SCM include [‘brst’ ‘fast’ ‘slow’ ‘srvy’]. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for SCM are: [‘scsrvy’, ‘cal’, ‘scb’, ‘scf’, ‘schb’, ‘scm’, ‘scs’] If no value is given the default is scsrvy for srvy data, and scb for brst data.

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

SCM Example

import pyspedas
from pytplot import tplot
pyspedas.mms.scm(trange=['2015-10-16/13:06', '2015-10-16/13:07'], time_clip=True)
tplot('mms1_scm_acb_gse_scsrvy_srvy_l2')

Level 3 FGM+SCM Data (FSM)

pyspedas.mms.fsm(*args, **kwargs)

This function loads FSM data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – the current instrument data rate for FSM is ‘brst’

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatype for FSM is: 8khz

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

FSM Example

import pyspedas
from pytplot import tplot
pyspedas.mms.fsm(trange=['2015-10-16/13:06', '2015-10-16/13:07'], time_clip=True)
tplot(['mms1_fsm_b_mag_brst_l3', 'mms1_fsm_b_gse_brst_l3'])

Electric field Double Probe (EDP)

pyspedas.mms.edp(*args, **kwargs)

This function loads EDP data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for EDP include [‘brst’, ‘fast’, ‘slow’, ‘srvy’]. The default is ‘fast’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for EDP are: [‘dce’, ‘dcv’, ‘ace’, ‘hmfe’]; default is ‘dce’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

EDP Example

import pyspedas
from pytplot import tplot
pyspedas.mms.edp(trange=['2015-10-16/13:06', '2015-10-16/13:07'], time_clip=True)
tplot('mms1_edp_dce_gse_fast_l2')

Electron Drift Instrument (EDI)

pyspedas.mms.edi(*args, **kwargs)

This function loads EDI data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for EDI include [‘brst’, ‘fast’, ‘slow’, ‘srvy’]. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for EDI are: [‘efield’, ‘amb’]; default is ‘efield’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

EDI Example

import pyspedas
from pytplot import tplot
pyspedas.mms.edi(trange=['2016-10-16/21:00', '2016-10-16/22:00'], time_clip=True)
tplot(['mms1_edi_vdrift_gse_srvy_l2', 'mms1_edi_e_gse_srvy_l2'])

Fly’s Eye Energetic Particle Sensor (FEEPS)

pyspedas.mms.feeps(*args, **kwargs)

This function loads FEEPS data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for FEEPS include [‘brst’, ‘srvy’]. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) –

  Valid datatypes for FEEPS are:

  L2, L1b: [‘electron’, ‘ion’] L1a: [‘electron-bottom’, ‘electron-top’, ‘ion-bottom’, ‘ion-top’]

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

FEEPS Example

import pyspedas
from pytplot import tplot
pyspedas.mms.feeps(trange=['2015-10-16', '2015-10-17'])
tplot('mms1_epd_feeps_srvy_l2_electron_intensity_omni_spin')

Energetic Ion Spectrometer (EIS)

pyspedas.mms.eis(*args, **kwargs)

This function loads EIS data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for EIS include [‘brst’, ‘srvy’]. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for EIS are: [‘extof’, ‘phxtof’, and ‘electronenergy’]; default is ‘extof’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

EIS Example

import pyspedas
from pytplot import tplot
pyspedas.mms.eis(trange=['2015-10-16', '2015-10-17'])
tplot(['mms1_epd_eis_srvy_l2_extof_proton_flux_omni', 'mms1_epd_eis_srvy_l2_extof_proton_flux_omni_spin'])

Active Spacecraft Potential Control (ASPOC)

pyspedas.mms.aspoc(*args, **kwargs)

This function loads ASPOC data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for ASPOC include ‘srvy’, ‘sitl’. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for ASPOC are: [‘asp1’, ‘asp2’, ‘aspoc’]; default is ‘aspoc’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

ASPOC Example

import pyspedas
from pytplot import tplot
pyspedas.mms.aspoc(trange=['2015-10-16', '2015-10-17'])
tplot(['mms1_aspoc_ionc_l2', 'mms1_asp1_ionc_l2', 'mms1_asp2_ionc_l2'])

Fast Plasma Investigation (FPI)

pyspedas.mms.fpi(*args, **kwargs)

This function loads FPI data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for FPI include ‘brst’, ‘fast’. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) –

  Valid datatypes for FPI are:

  ‘des-moms’, ‘dis-moms’ (default) ‘des-dist’, ‘dis-dist’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• center_measurement (bool) – If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

FPI Example

import pyspedas
from pytplot import tplot
pyspedas.mms.fpi(trange=['2015-10-16', '2015-10-17'], datatype='des-moms')
tplot(['mms1_des_energyspectr_omni_fast', 'mms1_des_bulkv_gse_fast', 'mms1_des_numberdensity_fast'])

Hot Plasma Composition Analyzer (HPCA)

pyspedas.mms.hpca(*args, **kwargs)

This function loads HPCA data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for HPCA include ‘brst’, ‘srvy’. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for HPCA are ‘moments’ and ‘ion’; the default is ‘moments’

• 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)

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• center_measurement (bool) – If True, the CDF epoch variables are time-shifted to the middle of the accumulation interval by their DELTA_PLUS_VAR and DELTA_MINUS_VAR variable attributes

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Returns

List of tplot variables created.

HPCA Example

import pyspedas
from pytplot import tplot
pyspedas.mms.hpca(trange=['2015-10-16', '2015-10-17'], datatype='moments')
tplot(['mms1_hpca_hplus_number_density', 'mms1_hpca_hplus_ion_bulk_velocity', 'mms1_hpca_hplus_scalar_temperature'])

Magnetic Ephemeris Coordinates (MEC)

pyspedas.mms.mec(*args, **kwargs)

This function loads MEC data into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• data_rate (str or list of str) – instrument data rates for MEC include [‘brst’, ‘srvy’]. The default is ‘srvy’.

• level (str) – indicates level of data processing. the default if no level is specified is ‘l2’

• datatype (str or list of str) – Valid datatypes for MEC are: [‘ephts04d’, ‘epht89q’, ‘epht89d’]; default is ‘epht89q’

• 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”.

• time_clip (bool) – Data will be clipped to the exact trange specified by the trange keyword.

• 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)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• notplot (bool) – If True, then data are returned in a hash table instead of being stored in tplot variables (useful for debugging, and access to multi-dimensional data products)

• available (bool) – If True, simply return the available data files (without downloading) for the requested paramters

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

• cdf_version (str) – Specify a specific CDF version # to load (e.g., cdf_version=’4.3.0’)

• min_version (str) – Specify a minimum CDF version # to load

• latest_version (bool) – Only grab the latest CDF version in the requested time interval

• major_version (bool) – Only open the latest major CDF version (e.g., X in vX.Y.Z) in the requested time interval

• always_prompt (bool) – Set this keyword to always prompt for the user’s username and password; useful if you accidently save an incorrect password, or if your SDC password has changed

• spdf (bool) – If True, download the data from the SPDF instead of the SDC

Notes

The default datatype was changed to ‘epht89q’ on 15Nov2021. There are sometimes issues with creating the Tsyganenko 04 data products, which leads to the ‘epht04d’ files not being available. The ‘epht89d’ files contain the same ephemeris data - the only difference are the data products that rely on the field model.

Returns

List of tplot variables created.

MEC Example

import pyspedas
from pytplot import tplot
pyspedas.mms.mec(trange=['2015-10-16', '2015-10-17'])
tplot(['mms1_mec_r_gsm', 'mms1_mec_v_gsm'])

pyspedas.mms.state(*args, **kwargs)

This function loads the state (ephemeris and attitude) data from the ASCII files into tplot variables

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’]

• probe (str or list of str) – list of probes, valid values for MMS probes are [‘1’,’2’,’3’,’4’].

• level (str) – indicates level of data (options: ‘def’ (definitive), ‘pred’ (predicted); default: def)

• datatypes (str or list of str) – no datatype for state data (options: ‘pos’, ‘vel’, ‘spinras’, ‘spindec’)

• suffix (str) – The tplot variable names will be given this suffix. By default, no suffix is added.

• no_update (bool) – Set this flag to preserve the original data. if not set and newer data is found the existing data will be overwritten

Returns

List of tplot variables created.

State Example

import pyspedas
from pytplot import tplot
pyspedas.mms.state(trange=['2020-12-16', '2020-12-17'])
tplot(['mms1_defeph_pos', 'mms1_defeph_vel'])

OMNI data

The routines in this module can be used to load data from the OMNI data mission.

pyspedas.omni.data(trange=['2013-11-5', '2013-11-6'], datatype='1min', level='hro2', suffix='', get_support_data=False, get_ignore_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=True)

This function loads OMNI (Combined 1AU IP Data; Magnetic and Solar Indices) 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’]

• level (str) – Data level; valid options: hro, hro2

• datatype (str) – Data type; valid options: 1min, 5min, hourly (1 hour)

• 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
omni_vars = pyspedas.omni.data(trange=['2013-11-5', '2013-11-6'])
tplot(['BX_GSE', 'BY_GSE', 'BZ_GSE', 'flow_speed', 'Vx', 'Vy', 'Vz', 'SYM_H'])

Polar Orbiting Environmental Satellites (POES) Mission

The routines in this module can be used to load data from the Polar Orbiting Environmental Satellites (POES) Mission mission.

Space Environment Monitor (SEM)

pyspedas.poes.sem(trange=['2018-11-5', '2018-11-6'], probe=['noaa19'], datatype='*', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads POES Space Environment Monitor (SEM) 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
sem_vars = pyspedas.poes.sem(trange=['2013-11-5', '2013-11-6'])
tplot('ted_ele_tel30_low_eflux')

Polar

The routines in this module can be used to load data from the Polar mission.

Magnetic Field Experiment (MFE)

pyspedas.polar.mfe(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Magnetic Field Experiment

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
mfe_vars = pyspedas.polar.mfe(trange=['2003-10-28', '2003-10-29'])
tplot(['B_GSE', 'B_GSM'])

Electric Fields Instrument (EFI)

pyspedas.polar.efi(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electric Fields Instrument

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
efi_vars = pyspedas.polar.efi(trange=['2003-10-28', '2003-10-29'])
tplot(['ESPIN', 'EXY12G', 'EZ12G'])

Plasma Wave Instrument (PWI)

pyspedas.polar.pwi(trange=['1997-01-03', '1997-01-04'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Plasma Wave Instrument

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
pwi_vars = pyspedas.polar.pwi()
tplot(['Fce', 'Fcp', 'FcO'])

Hot Plasma Analyzer Experiment (HYDRA)

pyspedas.polar.hydra(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Hot Plasma Analyzer Experiment

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
hydra_vars = pyspedas.polar.hydra(trange=['2003-10-28', '2003-10-29'])
tplot('ELE_DENSITY')

Thermal Ion Dynamics Experiment (TIDE)

pyspedas.polar.tide(trange=['1997-01-03', '1997-01-04'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Thermal Ion Dynamics Experiment / Plasma Source Investigation

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
tide_vars = pyspedas.polar.tide()
tplot(['total_den', 'total_v', 'total_t'])

Toroidal Imaging Mass Angle Spectrograph (TIMAS)

pyspedas.polar.timas(trange=['1997-01-03', '1997-01-04'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Toroidal Imaging Mass Angle Spectrograph

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
timas_vars = pyspedas.polar.timas(trange=['1997-01-03/6:00', '1997-01-03/7:00'], time_clip=True)
tplot(['Density_H', 'Density_O'])

Charge and Mass Magnetospheric Ion Composition Experiment (CAMMICE)

pyspedas.polar.cammice(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Charge and Mass Magnetospheric Ion Composition Experiment

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
cammice_vars = pyspedas.polar.cammice(trange=['2003-10-28', '2003-10-29'])
tplot('Protons')

Comprehensive Energetic Particle-Pitch Angle Distribution (CEPPAD)

pyspedas.polar.ceppad(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Comprehensive Energetic Particle-Pitch Angle Distribution

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
cep_vars = pyspedas.polar.ceppad(trange=['2003-10-28', '2003-10-29'])
tplot(['IPS_10_ERR', 'IPS_30_ERR', 'IPS_50_ERR'])

Orbit data

pyspedas.polar.orbit(trange=['2003-10-28', '2003-10-29'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Polar orbit 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
orb_vars = pyspedas.polar.orbit(trange=['2003-10-28', '2003-10-29'])
tplot(['SPIN_PHASE', 'AVG_SPIN_RATE'])

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'])

Solar Orbiter (SOLO)

The routines in this module can be used to load data from the Solar Orbiter (SOLO) mission.

Magnetometer (MAG)

pyspedas.solo.mag(trange=['2020-06-01', '2020-06-02'], datatype='rtn-normal', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Magnetometer (MAG)

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:

  ‘rtn-normal’: RTN Coordinates in Normal Mode ‘rtn-normal-1-minute’: Same as above, but at 1-min resolution ‘rtn-burst’: RTN Coordinates in Burst Mode ‘srf-normal’: Spacecraft Reference Frame in Normal Mode ‘srf-burst’: Spacecraft Reference Frame in Burst Mode

• level (str) – Data level (default: l2)

• 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
mag_vars = pyspedas.solo.mag(trange=['2020-06-01', '2020-06-02'], datatype='rtn-normal')
tplot('B_RTN')

Solar Wind Plasma Analyser (SWA)

pyspedas.solo.swa(trange=['2020-07-22', '2020-07-23'], datatype='pas-eflux', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Wind Plasma Analyser (SWA)

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:

• level (str) – Data level (default: l2)

• 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
swa_vars = pyspedas.solo.swa(trange=['2020-07-22', '2020-07-23'], datatype='pas-eflux')
tplot('eflux')

Energetic Particle Detector (EPD)

pyspedas.solo.epd(trange=['2020-06-14', '2020-06-15'], datatype='step', mode='hcad', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Energetic Particle Detector (EPD)

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:

• mode (str) – EPD mode; Valid options:

• level (str) – Data level (default: l2)

• 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
epd_vars = pyspedas.solo.epd(trange=['2020-06-01', '2020-06-02'], datatype='step', mode='rates')
tplot(['Magnet_Flux', 'Integral_Flux'])

Radio and Plasma Waves (RPW)

pyspedas.solo.rpw(trange=['2020-06-15', '2020-06-16'], datatype='hfr-surv', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Radio and Plasma Waves (RPW) instrument

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: Level 1:

  ‘hfr-surv’, ‘lfr-surv-asm’, ‘lfr-surv-bp1’, ‘lfr-surv-bp2’, ‘lfr-surv-cwf’, ‘lfr-surv-swf’, ‘tds-surv-hist1d’, ‘tds-surv-hist2d’, ‘tds-surv-mamp’, ‘tds-surv-rswf’, ‘tds-surv-stat’, ‘tds-surv-tswf’, ‘tnr-surv’ (see below for definitions)

  Level 2:

  High Frequency Receiver (HFR):

  ‘hfr-surv’: High Frequency Receiver (HFR) Data in Survey Mode

  Low Frequency Receiver (LFR):

  ‘lfr-surv-asm’: Averaged Spectral Matrix (ASM) Data in Survey Mode ‘lfr-surv-bp1’: Basic Parameters Set 1 (BP1) Data in Survey Mode ‘lfr-surv-bp2’: Basic Parameter Set 2 (BP2) Data in Survey Mode ‘lfr-surv-cwf-b’: Continuous Magnetic Waveform (CWF-B) in Survey Mode ‘lfr-surv-cwf-e’: Continuous Electric Waveform (CWF-E) in Survey Mode ‘lfr-surv-swf-b’: Snapshot Magnetic Waveform (SWF-B) in Survey Mode ‘lfr-surv-swf-e’: Snapshot Electric Waveform (SWF-E) in Survey Mode

  Time Domain Sampler (TDS):

  ‘tds-surv-hist1d’: Histogram Set 1 (HIST1D) Data in Survey Mode ‘tds-surv-hist2d’: Histogram Set 2 (HIST2D) Data in Survey Mode ‘tds-surv-mamp’: Maximum Amplitude (MAMP) Data in Survey Mode ‘tds-surv-rswf-b’: Regular Snapshot Waveform (RSWF) Magnetic Field Data in Survey Mode ‘tds-surv-rswf-e’: Regular Snapshot Waveform (RSWF) Electric Field Data in Survey Mode ‘tds-surv-stat’: Statistical (STAT) Data in Survey Mode ‘tds-surv-tswf-b’: Triggered Snapshot Magnetic Waveform (TSWF-B) in Survey Mode ‘tds-surv-tswf-e’: Triggered Snapshot Electric Waveform (TSWF-E) in Survey Mode

  Level 3:

  ‘bia-density’: Plasma density derived from probe-to-spacecraft potential and electron plasma frequency ‘bia-density-10-seconds’: same as above, but median value over 10 s interval ‘bia-efield-10-seconds’: Electric field vector in SRF. Median value over 10 s interval ‘bia-scpot-10-seconds’: Spacecraft potential with respect to plasma. Median value over 10 s interval ‘tnr-fp’: Plasma frequency value derived by the plasma peak tracking (Thermal Noise Receiver (TNR))

• level (str) – Data level (default: l2)

• 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
rpw_vars = pyspedas.solo.rpw(trange=['2020-06-15', '2020-06-16'], datatype='hfr-surv')
tplot(['AVERAGE_NR', 'TEMPERATURE', 'FLUX_DENSITY1', 'FLUX_DENSITY2'])

Solar Terrestrial Relations Observatory (STEREO)

The routines in this module can be used to load data from the Solar Terrestrial Relations Observatory (STEREO) mission.

Magnetometer (MAG)

pyspedas.stereo.mag(trange=['2013-11-5', '2013-11-6'], probe='a', datatype='8hz', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the magnetometer

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: 8hz, 32hz

• 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
mag_vars = pyspedas.stereo.mag(trange=['2013-11-5', '2013-11-6'])
tplot('BFIELD')

PLAsma and SupraThermal Ion Composition (PLASTIC)

pyspedas.stereo.plastic(trange=['2013-11-5', '2013-11-6'], probe='a', datatype='1min', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the PLASTIC instrument

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: 1min

• 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
plastic_vars = pyspedas.stereo.plastic(trange=['2013-11-5', '2013-11-6'])
tplot(['proton_number_density', 'proton_bulk_speed', 'proton_temperature', 'proton_thermal_speed'])

Time History of Events and Macroscale Interactions during Substorms (THEMIS)

The routines in this module can be used to load data from the Time History of Events and Macroscale Interactions during Substorms (THEMIS) mission.

Fluxgate magnetometer (FGM)

pyspedas.themis.fgm(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, coord=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Fluxgate magnetometer (FGM) 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str or list of str Data type; Valid options: ‘l1’, ‘l2’

• 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.

• coord – str Coordinate system

• 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. If set, this function returns a list of the files downloaded.

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
fgm_vars = pyspedas.themis.fgm(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_fgs_btotal', 'thd_fgs_gse'])

Search-coil magnetometer (SCM)

pyspedas.themis.scm(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Search-coil magnetometer (SCM) 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
scm_vars = pyspedas.themis.scm(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_scf_btotal', 'thd_scf_gse'])

Electric Field Instrument (EFI)

pyspedas.themis.efi(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Electric Field Instrument (EFI) 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
efi_vars = pyspedas.themis.efi(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot('thd_efs_dot0_gse')

Electrostatic Analyzer (ESA)

pyspedas.themis.esa(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Electrostatic Analyzer (ESA) 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
esa_vars = pyspedas.themis.esa(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_peif_density', 'thd_peif_vthermal'])

Solid State Telescope (SST)

pyspedas.themis.sst(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Solid State Telescope (SST) 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
sst_vars = pyspedas.themis.sst(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot('thd_psif_density')

Moments data

pyspedas.themis.mom(trange=['2008-03-23', '2008-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads THEMIS moments 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
mom_vars = pyspedas.themis.mom(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_peim_velocity_gsm', 'thd_peim_density'])

Ground computed moments data

pyspedas.themis.gmom(trange=['2007-03-23', '2007-03-24'], probe='c', level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads THEMIS Level 2 ground calculated combined ESA+SST moments.

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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l2’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
gmom_vars = pyspedas.themis.gmom(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_ptiff_velocity_gse', 'thd_pteff_density', 'thd_pteff_avgtemp'])

State data

pyspedas.themis.state(trange=['2007-03-23', '2007-03-24'], probe='c', level='l1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads THEMIS state 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’]

• probe – str or list of str Spacecraft probe letter(s) (‘a’, ‘b’, ‘c’, ‘d’ and/or ‘e’)

• level – str Data type; Valid options: ‘l1’

• 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

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
state_vars = pyspedas.themis.state(probe='d', trange=['2013-11-5', '2013-11-6'])
tplot(['thd_pos', 'thd_vel'])

Ground magnetometer data

pyspedas.themis.gmag(trange=['2007-03-23', '2007-03-24'], sites=None, group=None, level='l2', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads ground magnetometer 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’]

• level – str Data type; Valid options: ‘l1’, ‘l2’

• 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

• sites – str/list of str GMAG station names to load (e.g. ‘bmls’).

• group – str GMAG group of stations (eg. ‘epo’). If specified, stations is ignored.

Returns

List of tplot variables created.

Example

import pyspedas
from pytplot import tplot
gmag_vars = pyspedas.themis.gmag(sites='ccnv', trange=['2013-11-5', '2013-11-6'])
tplot('thg_mag_ccnv')

Two Wide-Angle Imaging Neutral-Atom Spectrometers (TWINS) Mission

The routines in this module can be used to load data from the Two Wide-Angle Imaging Neutral-Atom Spectrometers (TWINS) Mission mission.

Energetic Neutral Atom (ENA) imager

pyspedas.twins.imager(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads TWINS imager 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.

Lyman-alpha Detector (LAD)

pyspedas.twins.lad(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the LAD instrument

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
lad_vars = pyspedas.twins.lad(trange=['2018-11-5/6:00', '2018-11-5/6:20'], time_clip=True)
tplot(['lad1_data', 'lad2_data'])

Ephemeris

pyspedas.twins.ephemeris(trange=['2018-11-5', '2018-11-6'], probe='1', datatype='or', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads TWINS ephemeris 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
ephem_vars = pyspedas.twins.ephemeris(trange=['2018-11-5', '2018-11-6'])
tplot('FSCGSM')

Ulysses

The routines in this module can be used to load data from the Ulysses mission.

Magnetic field (VHM)

pyspedas.ulysses.vhm(trange=['2009-01-01', '2009-01-02'], datatype='1min', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=True)

This function loads data from the VHM/FGM experiment from the Ulysses mission

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:

• level (str) – Data level; 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
vhm_vars = pyspedas.ulysses.vhm()
tplot('B_MAG')

Solar wind plasma (SWOOPS)

pyspedas.ulysses.swoops(trange=['2009-01-01', '2009-01-02'], datatype='bai_m0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the SWOOPS experiment from the Ulysses mission

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:

• level (str) – Data level; 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
swoops_vars = pyspedas.ulysses.swoops()
tplot(['Density', 'Temperature', 'Velocity'])

Solar wind ion composition (SWICS)

pyspedas.ulysses.swics(trange=['2009-01-01', '2009-01-02'], datatype='scs_m1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the SWICS experiment from the Ulysses mission

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:

• level (str) – Data level; 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
swics_vars = pyspedas.ulysses.swics()
tplot('Velocity')

Energetic particles (EPAC)

pyspedas.ulysses.epac(trange=['1996-01-01', '1996-01-02'], datatype='epac_m1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the EPAC experiment from the Ulysses mission

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:

• level (str) – Data level; 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
epac_vars = pyspedas.ulysses.epac()
tplot('Omni_Protons')

Low-energy ions and electrons (HI-SCALE)

pyspedas.ulysses.hiscale(trange=['2003-01-01', '2003-01-02'], datatype='lmde_m1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the HI-SCALE experiment from the Ulysses mission

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:

• level (str) – Data level; 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
hiscale_vars = pyspedas.ulysses.hiscale()
tplot('Electrons')

Solar X-rays and cosmic gamma-ray bursts (GRB)

pyspedas.ulysses.grb(trange=['2003-01-01', '2003-01-02'], datatype='grb_m0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the GRB experiment from the Ulysses mission

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:

• level (str) – Data level; 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
grb_vars = pyspedas.ulysses.grb()
tplot('Count_Rate')

Van Allen Probes (RBSP)

The routines in this module can be used to load data from the Van Allen Probes (RBSP) mission.

Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS)

pyspedas.rbsp.emfisis(trange=['2018-11-5', '2018-11-6'], probe='a', datatype='magnetometer', level='l3', cadence='4sec', coord='sm', wavetype='waveform', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electric and Magnetic Field Instrument Suite and Integrated Science (EMFISIS) instrument

For information on the EMFISIS data products, see:

https://emfisis.physics.uiowa.edu/data/level_descriptions

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• cadence (str) – Data cadence (default: 4sec); other options: ‘1sec’, ‘hires’

• coord (str) – Data coordinate system (default: sm)

• level (str) – Data level; options: ‘l1’, ‘l2’, ‘l3’, l4’

• datatype (str) – Data type; valid options: Level 1:

  ‘magnetometer’ ‘hfr’ ‘housekeeping’ ‘sc-hk’ ‘spaceweather’ ‘wfr’ ‘wna’

  Level 2:

  ‘magnetometer’ ‘wfr’ ‘hfr’ ‘housekeeping’

  Level 3:

  ‘magnetometer’

  Level 4:

  ‘density’ ‘wna-survey’

• wavetype (str) –

  Type of level 2 waveform data; valid options:

  For WFR data: ‘waveform’ (default) ‘waveform-continuous-burst’ ‘spectral-matrix’ ‘spectral-matrix-diagonal’ ‘spectral-matrix-diagonal-merged’

  For HFR data: ‘waveform’ ‘spectra’ ‘spectra-burst’ ‘spectra-merged’

  For descriptions of these data, see:

  https://emfisis.physics.uiowa.edu/data/L2_products

• 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
emfisis_vars = pyspedas.rbsp.emfisis(trange=['2018-11-5/10:00', '2018-11-5/15:00'], datatype='magnetometer', level='l3', time_clip=True)
tplot(['Mag', 'Magnitude'])

Electric Field and Waves Suite (EFW)

pyspedas.rbsp.efw(trange=['2015-11-5', '2015-11-6'], probe='a', datatype='spec', level='l3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Electric Field and Waves Suite (EFW)

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• 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
efw_vars = pyspedas.rbsp.efw(trange=['2015-11-5', '2015-11-6'], level='l3')
tplot(['efield_in_inertial_frame_spinfit_mgse', 'spacecraft_potential'])

Radiation Belt Storm Probes Ion Composition Experiment (RBSPICE)

pyspedas.rbsp.rbspice(trange=['2018-11-5', '2018-11-6'], probe='a', datatype='tofxeh', level='l3', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Radiation Belt Storm Probes Ion Composition Experiment (RBSPICE) instrument

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• datatype (str) – Data type (default: tofxeh); 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
rbspice_vars = pyspedas.rbsp.rbspice(trange=['2018-11-5', '2018-11-6'], datatype='tofxeh', level='l3')
tplot('Alpha')

Energetic Particle, Composition, and Thermal Plasma Suite (ECT) - MagEIS

pyspedas.rbsp.mageis(trange=['2015-11-5', '2015-11-6'], probe='a', datatype='', level='l3', rel='rel04', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Energetic Particle, Composition, and Thermal Plasma Suite (ECT)

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• 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
mageis_vars = pyspedas.rbsp.mageis(trange=['2018-11-5', '2018-11-6'], level='l3', rel='rel04')
tplot('I')

Energetic Particle, Composition, and Thermal Plasma Suite (ECT) - HOPE

pyspedas.rbsp.hope(trange=['2015-11-5', '2015-11-6'], probe='a', datatype='moments', level='l3', rel='rel04', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Energetic Particle, Composition, and Thermal Plasma Suite (ECT)

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• 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
hope_vars = pyspedas.rbsp.hope(trange=['2018-11-5', '2018-11-6'], datatype='moments', level='l3', rel='rel04')
tplot('Ion_density')

Energetic Particle, Composition, and Thermal Plasma Suite (ECT) - REPT

pyspedas.rbsp.rept(trange=['2015-11-5', '2015-11-6'], probe='a', datatype='', level='l3', rel='rel03', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Energetic Particle, Composition, and Thermal Plasma Suite (ECT)

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• 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
rept_vars = pyspedas.rbsp.rept(trange=['2018-11-5', '2018-11-6'], level='l3', rel='rel03')
tplot('Tperp_e_200')

Relativistic Proton Spectrometer (RPS)

pyspedas.rbsp.rps(trange=['2015-11-5', '2015-11-6'], probe='a', datatype='rps-1min', level='l2', suffix='', get_support_data=True, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Relativistic Proton Spectrometer (RPS)

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’]

• probe (str or list of str) – Spacecraft probe name (‘a’ or ‘b’); default: a

• 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
rps_vars = pyspedas.rbsp.rps(trange=['2018-11-5', '2018-11-6'], datatype='rps', level='l2')
tplot('DOSE1')

Wind

The routines in this module can be used to load data from the Wind mission.

Magnetic Field Investigation (MFI)

pyspedas.wind.mfi(trange=['2018-11-5', '2018-11-6'], datatype='h0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Fluxgate Magnetometer

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
mfi_vars = pyspedas.wind.mfi(trange=['2013-11-5', '2013-11-6'])
tplot('BGSE')

Solar Wind Experiment (SWE)

pyspedas.wind.swe(trange=['2018-11-5', '2018-11-6'], datatype='h5', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the SWE instrument

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
swe_vars = pyspedas.wind.swe(trange=['2013-11-5', '2013-11-6'])
tplot(['N_elec', 'T_elec'])

Radio and Plasma Waves (WAVES)

pyspedas.wind.waves(trange=['2018-11-5', '2018-11-6'], datatype='h1', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads Radio/Plasma Wave (WAVES) 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
waves_vars = pyspedas.wind.waves(trange=['2013-11-5', '2013-11-6'])
tplot(['E_VOLTAGE_RAD2', 'E_VOLTAGE_RAD1', 'E_VOLTAGE_TNR'])

3D Plasma Analyzer (3DP)

pyspedas.wind.threedp(trange=['1999-11-5', '1999-11-6'], datatype='3dp_emfits_e0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads 3DP 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
threedp_vars = pyspedas.wind.threedp()
tplot(['V_e_xyz_gse_wi_3dp', 'N_e_dens_wi_3dp', 'T_e_par_wi_3dp'])

Solar Wind and Suprathermal Ion Composition Experiment (SMS)

pyspedas.wind.sms(trange=['1999-11-5', '1999-11-6'], datatype='k0', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads data from the Solar Wind and Suprathermal Ion Composition Instrument

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
sms_vars = pyspedas.wind.sms()
tplot(['C_ion_temp', 'O_ion_temp'])

Orbit data

pyspedas.wind.orbit(trange=['1999-11-5', '1999-11-6'], datatype='pre_or', suffix='', get_support_data=False, varformat=None, varnames=[], downloadonly=False, notplot=False, no_update=False, time_clip=False)

This function loads orbit 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
orb_vars = pyspedas.wind.orbit(trange=['2013-11-5', '2013-11-6'])
tplot(['GSE_POS', 'GSE_VEL', 'GSM_POS', 'GSM_VEL'])

Analysis Tools

Average data

pyspedas.avg_data(names, dt=None, width=60, noremainder=False, new_names=None, suffix=None, overwrite=None)

Get a new tplot variable with averaged data.

Parameters

• names (str/list of str) – List of pytplot names.

• dt (float, optional) – Time window in seconds for averaging data. It can be less than 1 sec.

• width (int, optional) – Number of values for the averaging window. Default is 60 points (usually this means 60 seconds). If dt is set, then width is ignored.

• noremainder (boolean, optional) – If True, the remainter (last part of data) will not be included. If False. the remainter will be included.

• new_names (str/list of str, optional) – List of new_names for pytplot variables. If not given, then a suffix is applied.

• suffix (str, optional) – A suffix to apply. Default is ‘-avg’.

• overwrite (bool, optional) – Replace the existing tplot name.

Return type

None.

Clean spikes

pyspedas.clean_spikes(names, nsmooth=10, thresh=0.3, sub_avg=False, new_names=None, suffix=None, overwrite=None)

Clean spikes from data.

Parameters

• names (str/list of str) – List of pytplot names.

• new_names (str/list of str, optional) – List of new_names for pytplot variables. If not given, then a suffix is applied.

• suffix (str, optional) – A suffix to apply. Default is ‘-avg’.

• overwrite (bool, optional) – Replace the existing tplot name.

• nsmooth (int, optional) – the number of data points for smoothing

• thresh (float, optional) – threshold value

• sub_avg (bool, optional) – if set, subtract the average value of the data prior to checking for spikes

Return type

None.

Cross products

pyspedas.tcrossp(v1, v2, newname=None, return_data=False)

Calculates the cross product of two tplot varibles

v1: str

First tplot variable

v2: str

Second tplot variable

Parameters

• newname (str) – Name of the output variable

• return_data (bool) – Returns the data as an ndarray instead of creating a tplot variable

Return type

Name of the tplot variable

Dot products

pyspedas.tdotp(variable1, variable2, newname=None)

Routine to calculate the dot product of two tplot variables containing arrays of vectors and storing the results in a tplot variable

variable1: str

First tplot variable

variable2: str

Second tplot variable

Parameters

newname (str) – Name of the output variable

Return type

Name of the tplot variable

Dynamic power spectra

pyspedas.tdpwrspc(varname, newname=None, nboxpoints=256, nshiftpoints=128, binsize=3, nohanning=False, noline=False, notperhz=False, notmvariance=False)

Compute power spectra for a tplot variable.

Parameters

• varname (str) – Name of pytplot variable.

• newname (str, optional) – Name of new pytplot variable to save data to.

• nboxpoints (int, optional) – The number of points to use for the hanning window. The default is 256.

• nshiftpoints (int, optional) – The number of points to shift for each spectrum. The default is 128.

• binsize (int, optional) – Size for binning of the data along the frequency domain. The default is 3.

• nohanning (bool, optional) – If True, no hanning window is applied to the input. The default is False.

• noline (bool, optional) – If True, no straight line is subtracted. The default is False.

• notperhz (bool, optional) – If True, the output units are the square of the input units. The default is False.

• notmvariance (bool, optional) – If True, replace output spectrum for any windows that have variable. cadence with NaNs. The default is False.

Returns

Name of new pytplot variable.

Return type

str

pyspedas.dpwrspc(time, quantity, nboxpoints=256, nshiftpoints=128, binsize=3, nohanning=False, noline=False, notperhz=False, notmvariance=False, tm_sensitivity=None)

Compute power spectra.

Parameters

• time (list of float) – Time array.

• quantity (list of float) – Data array.

• nboxpoints (int, optional) – The number of points to use for the hanning window. The default is 256.

• nshiftpoints (int, optional) – The number of points to shift for each spectrum. The default is 128.

• binsize (int, optional) – Size for binning of the data along the frequency domain. The default is 3.

• nohanning (bool, optional) – If True, no hanning window is applied to the input. The default is False.

• noline (bool, optional) – If True, no straight line is subtracted. The default is False.

• notperhz (bool, optional) – If True, the output units are the square of the input units. The default is False.

• notmvariance (bool, optional) – If True, replace output spectrum for any windows that have variable cadence with NaNs. The default is False.

• tm_sensitivity (float, optional) – If noTmVariance is set, this number controls how much of a dt anomaly is accepted. The default is None.

Returns

• tdps (array of float) – The time array for the dynamic power spectrum, the center time of the interval used for the spectrum.

• fdps (array of float) – The frequency array (units =1/time units).

• dps (array of float) – The power spectrum, (units of quantity)^2/frequency_units.

Interpolation

pyspedas.tinterpol(names, interp_to, method=None, newname=None, suffix=None)

Interpolate data to times in interp_to.

Parameters

• names (str/list of str) – List of variables to interpolate.

• interp_to (str) –

  String containing the variable

  containing the time stamps to interpolate to

• method (str, optional) – Interpolation method. Default is ‘linear’. Specifies the kind of interpolation as a string (‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’) where ‘zero’, ‘slinear’, ‘quadratic’ and ‘cubic’ refer to a spline interpolation of zeroth, first, second or third order; ‘previous’ and ‘next’ simply return the previous or next value of the point) or as an integer specifying the order of the spline interpolator to use.

• newname (str/list of str, optional) – List of new_names for pytplot variables. If ‘’, then pytplot variables are replaced. If not given, then a suffix is applied.

• suffix (str, optional) – A suffix to apply. Default is ‘-itrp’.

Return type

None.

Normalize vectors

pyspedas.tnormalize(variable, newname=None, return_data=False)

Normalize all the vectors stored in a tplot variable

variable: str or np.ndarray

tplot variable (or numpy array) containing the vectors to be normalized

Parameters

• newname (str) – name of the output variable; default: variable_normalized

• return_data (bool) – return the normalized vectors instead of creating a tplot variable

Returns

• name of the tplot variable created or normalized vectors if return_data

• is set

Subtract average

pyspedas.subtract_average(names, new_names=None, suffix=None, overwrite=None, median=None)

Subtracts the average or the median from data.

Parameters

• names (str/list of str) – List of pytplot names.

• new_names (str/list of str, optional) – List of new_names for pytplot variables. If not given, then a suffix is applied.

• suffix (str, optional) – A suffix to apply. Default is ‘-d’.

• overwrite (bool, optional) – If set, then pytplot variables are replaced.

• median (float, optional) – If it is 0 or not set, then it computes the mean. Otherwise, it computes the median.

Return type

None.

Subtract median

pyspedas.subtract_median(names, new_names=None, suffix=None, overwrite=None)

Subtracts the median from data.

Parameters

• names (str/list of str) – List of pytplot names.

• new_names (str/list of str, optional) – List of new_names for pytplot variables. If not given, then a suffix is applied.

• suffix (str, optional) – A suffix to apply. Default is ‘-d’.

• overwrite (bool, optional) – If set, then pytplot variables are replaced.

Return type

None.

Wave polarization

pyspedas.twavpol(tvarname, prefix='', nopfft=- 1, steplength=- 1, bin_freq=- 1)

Apply wavpol to a pytplot variable.

Creates multiple pytplot variables: ‘_powspec’,’_degpol’, ‘_waveangle’, ‘_elliptict’, ‘_helict’, ‘_pspec3_x’, ‘_pspec3_y’, ‘_pspec3_z’

Parameters

• tvarname (string) – Name of pytplot variable.

• prefix (string, optional) – Prefix for pytplot variables created.

• nopfft (int, optional) – Number of points in FFT. The default is 256.

• steplength (int, optional) – The amount of overlap between successive FFT intervals. The default is -1 which means nopfft/2.

• bin_freq (int, optional) – Number of bins in frequency domain. The default is 3.

Returns

result – Returns 1 if completed successfully. Returns 0 if it encountered problems and exited.

Return type

bool

pyspedas.analysis.twavpol.wavpol(ct, bx, by, bz, nopfft=256, steplength=- 1, bin_freq=3)

Perform polarisation analysis of Bx, By, Bz time series data.

Parameters

• ct (list of float) – Time.

• b1 (list of float) – Bx field.

• b2 (list of float) – By field.

• b3 (list of float) – Bz field.

• nopfft (int, optional) – Number of points in FFT. The default is 256.

• steplength (int, optional) – The amount of overlap between successive FFT intervals. The default is -1 which means nopfft/2.

• bin_freq (int, optional) – Number of bins in frequency domain. The default is 3.

Returns

• result (tuple with 9 items)

• timeline (list of float) – Times.

• freqline (list of float) – Frequencies.

• powspec (2-dim array of float) – Wave power.

• degpol (2-dim array of float) – Degree of Polarisation.

• waveangle (2-dim array of float) – Wavenormal Angle.

• elliptict (2-dim array of float) – Ellipticity.

• helict (2-dim array of float) – Helicity.

• pspec3 (3-dim array of float) – Power spectra.

• err_flag (bool) – Error flag. The default is 0. Returns 1 if there are large number of batches and aborts.

Magnetic Field Models

The routines in this module can be used to calculate Tsyganenko magnetic field models using Sheng Tian’s implementation of the geopack library (https://github.com/tsssss/geopack).

Tsyganenko 89 (T89)

pyspedas.geopack.tt89(pos_var_gsm, iopt=3, suffix='', igrf_only=False)

tplot wrapper for the functional interface to Sheng Tian’s implementation of the Tsyganenko 96 and IGRF model:

https://github.com/tsssss/geopack

pos_gsm_tvar: str

tplot variable containing the position data (km) in GSM coordinates

Parameters

• iopt (int) –

  Specifies the ground disturbance level:

  iopt= 1 2 3 4 5 6 7

  correspond to:

  kp= 0,0+ 1-,1,1+ 2-,2,2+ 3-,3,3+ 4-,4,4+ 5-,5,5+ &gt =6-

• suffix (str) – Suffix to append to the tplot output variable

Return type

Name of the tplot variable containing the model data

T89 Example

# load some spacecraft position data
import pyspedas
pyspedas.mms.mec(trange=['2015-10-16', '2015-10-17'])

# calculate the field using the T89 model
from pyspedas.geopack import tt89
tt89('mms1_mec_r_gsm')

from pytplot import tplot
tplot('mms1_mec_r_gsm_bt89')

Tsyganenko 96 (T96)

pyspedas.geopack.tt96(pos_var_gsm, parmod=None, suffix='')

tplot wrapper for the functional interface to Sheng Tian’s implementation of the Tsyganenko 96 and IGRF model:

https://github.com/tsssss/geopack

pos_gsm_tvar: str

tplot variable containing the position data (km) in GSM coordinates

Parameters

• parmod (ndarray) –

  10-element array (vs. time), but only the first 4 elements are used

  1. solar wind pressure pdyn (nanopascals)

  2. dst (nanotesla)

  3. byimf (nanotesla)

  4. bzimf (nanotesla)

• suffix (str) – Suffix to append to the tplot output variable

Return type

Name of the tplot variable containing the model data

T96 Example

# load some spacecraft position data
import pyspedas
pyspedas.mms.mec(trange=['2015-10-16', '2015-10-17'])

# calculate the params using the solar wind data; see the "Solar Wind Parameters" section below for an example

# interpolate the MEC timestamps to the solar wind timestamps
from pyspedas import tinterpol
tinterpol('mms1_mec_r_gsm', 'proton_density')

# calculate the field using the T96 model
from pyspedas.geopack import tt96
tt96('mms1_mec_r_gsm-itrp', parmod=params)

from pytplot import tplot
tplot('mms1_mec_r_gsm-itrp_bt96')

Tsyganenko 2001 (T01)

pyspedas.geopack.tt01(pos_var_gsm, parmod=None, suffix='')

tplot wrapper for the functional interface to Sheng Tian’s implementation of the Tsyganenko 2001 and IGRF model:

https://github.com/tsssss/geopack

pos_gsm_tvar: str

tplot variable containing the position data (km) in GSM coordinates

Parameters

• parmod (ndarray) –

  10-element array (vs. time), but only the first 6 elements are used

  1. solar wind pressure pdyn (nanopascals),

  2. dst (nanotesla)

  3. byimf (nanotesla)

  4. bzimf (nanotesla)

  5. g1-index

  6. g2-index (see Tsyganenko [2001] for an exact definition of these two indices)

• suffix (str) – Suffix to append to the tplot output variable

Return type

Name of the tplot variable containing the model data

T01 Example

# load some spacecraft position data
import pyspedas
pyspedas.mms.mec(trange=['2015-10-16', '2015-10-17'])

# calculate the params using the solar wind data; see the "Solar Wind Parameters" section below for an example

# interpolate the MEC timestamps to the solar wind timestamps
from pyspedas import tinterpol
tinterpol('mms1_mec_r_gsm', 'proton_density')

# calculate the field using the T01 model
from pyspedas.geopack import tt01
tt01('mms1_mec_r_gsm-itrp', parmod=params)

from pytplot import tplot
tplot('mms1_mec_r_gsm-itrp_bt01')

Tsyganenko-Sitnov 2004 (TS04)

pyspedas.geopack.tts04(pos_var_gsm, parmod=None, suffix='')

tplot wrapper for the functional interface to Sheng Tian’s implementation of the Tsyganenko-Sitnov (2004) storm-time geomagnetic field model

https://github.com/tsssss/geopack

pos_gsm_tvar: str

tplot variable containing the position data (km) in GSM coordinates

Parameters

• parmod (ndarray) –

  10-element array (vs. time):

  1. solar wind pressure pdyn (nanopascals),

  2. dst (nanotesla),

  3. byimf,

  (4) bzimf (nanotesla) (5-10) indices w1 - w6, calculated as time integrals from the beginning of a storm

  see the reference (3) below, for a detailed definition of those variables

• suffix (str) – Suffix to append to the tplot output variable

Return type

Name of the tplot variable containing the model data

TS04 Example

# load some spacecraft position data
import pyspedas
pyspedas.mms.mec(trange=['2015-10-16', '2015-10-17'])

# calculate the params using the solar wind data; see the "Solar Wind Parameters" section below for an example

# interpolate the MEC timestamps to the solar wind timestamps
from pyspedas import tinterpol
tinterpol('mms1_mec_r_gsm', 'proton_density')

# calculate the field using the TS04 model
from pyspedas.geopack import tts04
tts04('mms1_mec_r_gsm-itrp', parmod=params)

from pytplot import tplot
tplot('mms1_mec_r_gsm-itrp_bts04')

Solar Wind Parameters

To generate the “parmod” variable using Dst and solar wind data, use the get_tsy_params routine.

pyspedas.geopack.get_tsy_params.get_tsy_params(dst_tvar, imf_tvar, Np_tvar, Vp_tvar, model, pressure_tvar=None, newname=None, speed=False, g_variables=None)

This procedure will interpolate inputs, generate Tsyganenko model parameters and store them in a tplot variable that can be passed directly to the model procedure.

dst_tvar: str

tplot variable containing the Dst index

imf_tvar: str

tplot variable containing the interplanetary magnetic field vector in GSM coordinates

Np_tvar: str

tplot variable containing the solar wind ion density (cm**-3)

Vp_tvar: str

tplot variable containing the proton velocity

model: str

Tsyganenko model; should be: ‘T89’, T96’, ‘T01’,’TS04’

Parameters

• newname (str) – name of the output variable; default: t96_par, ‘t01_par’ or ‘ts04_par’, depending on the model

• speed (bool) – Flag to indicate Vp_tvar is speed, and not velocity (defaults to False)

• pressure_tvar (str) – Set this to specify a tplot variable containing solar wind dynamic pressure data. If not supplied, it will be calculated internally from proton density and proton speed.

Returns

• Name of the tplot variable containing the parameters.

• The parameters are –

  1. solar wind pressure pdyn (nanopascals),

  2. dst (nanotesla),

  3. byimf,

  (4) bzimf (nanotesla) (5-10) indices w1 - w6, calculated as time integrals from the beginning of a storm

  see the reference (3) below, for a detailed definition of those variables

get_tsy_params Example

# load Dst and solar wind data
import pyspedas
pyspedas.kyoto.dst(trange=['2015-10-16', '2015-10-17'])
pyspedas.omni.data(trange=['2015-10-16', '2015-10-17'])

# join the components of B into a single variable
# BX isn't used
from pytplot import join_vec
join_vec(['BX_GSE', 'BY_GSM', 'BZ_GSM'])

from pyspedas.geopack.get_tsy_params import get_tsy_params
params = get_tsy_params('kyoto_dst',
                     'BX_GSE-BY_GSM-BZ_GSM_joined',
                     'proton_density',
                     'flow_speed',
                     't96', # or 't01', 'ts04'
                     pressure_tvar='Pressure',
                     speed=True)

Coordinate Systems

Transformations

pyspedas.cotrans(name_in=None, name_out=None, time_in=None, data_in=None, coord_in=None, coord_out=None)

Transform data from coord_in to coord_out.

Parameters

• name_in (str, optional) – Pytplot name for input data.

• name_out (str, optional) – Pytplot name for output data.

• time_in (list of float, optional) – Time array. Ignored if name_in is provided.

• data_in (list of float, optional) – Data in the coord_in system. Ignored if name_in is provided.

• coord_in (str) – Name of input coordinate system.

• coord_out (str) – Name of output coordinate system.

Returns

Fills a new pytplot variable with data in the coord_out system.

Return type

Returns 1 for suggesful completion.

Examples

import pyspedas
pyspedas.themis.state(trange=['2015-10-16', '2015-10-17'], probe='c')

from pyspedas import cotrans
cotrans(name_in='thc_pos_gse', name_out='gsm_data', coord_in='gse', coord_out='gsm')
cotrans(name_in='thc_pos_gse', name_out='sm_data', coord_in='gse', coord_out='sm')
cotrans(name_in='thc_pos_gse', name_out='geo_data', coord_in='gse', coord_out='geo')

from pytplot import tplot
tplot(['gsm_data', 'sm_data', 'geo_data'])

LMN Coordinates

pyspedas.cotrans.gsm2lmn.gsm2lmn(times, Rxyz, Bxyz, swdata=None)

Transforms vector field from GSM to LMN (boundary-normal) coordinate system for the magnetopause using the Shue et al. (1998) magnetopause model

Getting/Setting the Coordinate System

pyspedas.cotrans_get_coord(name)

This function returns the coordinate system of a tplot variable

Parameters

name – str name of the tplot variable

Notes

The coordinate system is stored in the variable’s metadata at:

metadata[‘data_att’][‘coord_sys’]

See cotrans_set_coord to update the coordinate system

Returns

Coordinate system of the tplot variable or None if the coordinate system isn’t set

pyspedas.cotrans_set_coord(name, coord)

This function sets the coordinate system of a tplot variable

Parameters

name – str name of the tplot variable

Notes

The coordinate system is stored in the variable’s metadata at:

metadata[‘data_att’][‘coord_sys’]

See cotrans_get_coord to return the coordinate system

Returns

True/False depending on if the operation was successful

Return type

bool

Support Routines

pyspedas.cotrans.cotrans_lib.get_time_parts(time_in)

Split time into year, doy, hours, minutes, seconds.fsec.

Parameters

time_in (list of float) – Time array.

Returns

• iyear (array of int) – Year.

• idoy (array of int) – Day of year.

• ih (array of int) – Hours.

• im (array of int) – Minutes.

• isec (array of float) – Seconds and milliseconds.

pyspedas.cotrans.cotrans_lib.csundir_vect(time_in)

Calculate the direction of the sun.

Parameters

time_in (list of float) – Time array.

Returns

• gst (list of float) – Greenwich mean sideral time (radians).

• slong (list of float) – Longitude along ecliptic (radians).

• sra (list of float) – Right ascension (radians).

• sdec (list of float) – Declination of the sun (radians).

• obliq (list of float) – Inclination of Earth’s axis (radians).

pyspedas.cotrans.cotrans_lib.cdipdir(time_in=None, iyear=None, idoy=None)

Compute dipole direction in GEO coordinates.

Parameters

• time_in (float)

• iyear (int)

• idoy (int)

Return type

list of float

Notes

Compute geodipole axis direction from International Geomagnetic Reference Field (IGRF-13) model for time interval 1970 to 2020. For time out of interval, computation is made for nearest boundary. Same as SPEDAS cdipdir.

pyspedas.cotrans.cotrans_lib.cdipdir_vect(time_in=None, iyear=None, idoy=None)

Compute dipole direction in GEO coordinates.

Similar to cdipdir but for arrays.

Parameters

• time_in (list of floats)

• iyear (list of int)

• idoy (list of int)

Return type

list of float

Notes

Same as SPEDAS cdipdir_vec.

pyspedas.cotrans.cotrans_lib.tgeigse_vect(time_in, data_in)

GEI to GSE transformation.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xgei, ygei, zgei cartesian GEI coordinates.

Returns

• xgse (list of float) – Cartesian GSE coordinates.

• ygse (list of float) – Cartesian GSE coordinates.

• zgse (list of float) – Cartesian GSE coordinates.

pyspedas.cotrans.cotrans_lib.subgei2gse(time_in, data_in)

Transform data from GEI to GSE.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GEI.

Returns

Coordinates in GSE.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.tgsegei_vect(time_in, data_in)

GSE to GEI transformation.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xgei, ygei, zgei cartesian GEI coordinates.

Returns

• xgei (list of float) – Cartesian GEI coordinates.

• ygei (list of float) – Cartesian GEI coordinates.

• zgei (list of float) – Cartesian GEI coordinates.

pyspedas.cotrans.cotrans_lib.subgse2gei(time_in, data_in)

Transform data from GSE to GEI.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GSE.

Returns

Coordinates in GEI.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.tgsegsm_vect(time_in, data_in)

Transform data from GSE to GSM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xgse, ygse, zgse cartesian GSE coordinates.

Returns

• xgsm (list of float) – Cartesian GSM coordinates.

• ygsm (list of float) – Cartesian GSM coordinates.

• zgsm (list of float) – Cartesian GSM coordinates.

pyspedas.cotrans.cotrans_lib.subgse2gsm(time_in, data_in)

Transform data from GSE to GSM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GSE.

Returns

Coordinates in GSM.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.tgsmgse_vect(time_in, data_in)

Transform data from GSM to GSE.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xgsm, ygsm, zgsm GSM coordinates.

Returns

• xgse (list of float) – Cartesian GSE coordinates.

• ygse (list of float) – Cartesian GSE coordinates.

• zgse (list of float) – Cartesian GSE coordinates.

pyspedas.cotrans.cotrans_lib.subgsm2gse(time_in, data_in)

Transform data from GSM to GSE.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GSE.

Returns

Coordinates in GSE.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.tgsmsm_vect(time_in, data_in)

Transform data from GSM to SM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xgsm, ygsm, zgsm GSM coordinates.

Returns

• xsm (list of float) – Cartesian SM coordinates.

• ysm (list of float) – Cartesian SM coordinates.

• zsm (list of float) – Cartesian SM coordinates.

pyspedas.cotrans.cotrans_lib.subgsm2sm(time_in, data_in)

Transform data from GSM to SM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GSM.

Returns

Coordinates in SM.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.tsmgsm_vect(time_in, data_in)

Transform data from SM to GSM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – xsm, ysm, zsm SM coordinates.

Returns

• xsm (list of float) – GSM coordinates.

• ysm (list of float) – GSM coordinates.

• zsm (list of float) – GSM coordinates.

pyspedas.cotrans.cotrans_lib.subsm2gsm(time_in, data_in)

Transform data from SM to GSM.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in SM.

Returns

Coordinates in GSM.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.subgei2geo(time_in, data_in)

Transform data from GEI to GEO.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GEI.

Returns

Coordinates in GEO.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.subgeo2gei(time_in, data_in)

Transform data from GEO to GEI.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GEO.

Returns

Coordinates in GEI.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.subgeo2mag(time_in, data_in)

Transform data from GEO to MAG.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GEO.

Returns

Coordinates in MAG.

Return type

Array of float

Notes

Adapted from spedas IDL file geo2mag.pro.

pyspedas.cotrans.cotrans_lib.submag2geo(time_in, data_in)

Transform data from MAG to GEO.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in MAG.

Returns

Coordinates in GEO.

Return type

Array of float

Notes

Adapted from spedas IDL file mag2geo.pro.

pyspedas.cotrans.cotrans_lib.ctv_mm_mult(m1, m2)

Vectorized multiplication of two lists of 3x3 matrices.

Parameters

• m1 (array of float) – Array (3, 3, n). List of n 3x3 matrices.

• m2 (array of float) – Array (3, 3, n). List of n 3x3 matrices.

Returns

Array (3, 3, n). List of n 3x3 matrices.

Return type

Array of float

Notes

Adapted from spedas IDL file matrix_array_lib.pro.

pyspedas.cotrans.cotrans_lib.j2000_matrix_vec(time_in)

Get the conversion matrix for J2000 coordinates.

Gives a matrix that transforms from mean earth equator and equinox of J2000 into the true earth equator and equinox for the dates and times.

Parameters

time_in (list of float) – Time array.

Returns

Transformation matrix.

Return type

Matrix of float

Notes

Adapted from spedas IDL file spd_make_j2000_matrix_vec.pro.

pyspedas.cotrans.cotrans_lib.ctv_mx_vec_rot(m, v)

Vectorized multiplication of n matrices by n vectors.

Parameters

• m (array of float) – Array (k, k, n). List of n kxk matrices. Unually, it is 3x3 matrices, ie. k=3.

• v (array of float) – Array (n, k). List of n vectors.

Returns

Array (n, k). List of n vectors.

Return type

Array of float

Notes

Adapted from spedas IDL file matrix_array_lib.pro.

pyspedas.cotrans.cotrans_lib.subgei2j2000(time_in, data_in)

Transform data from GEI to J2000.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in GEI.

Returns

Coordinates in J2000.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.subj20002gei(time_in, data_in)

Transform data from J2000 to GEI.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in J2000.

Returns

Coordinates in GEI.

Return type

Array of float

pyspedas.cotrans.cotrans_lib.subcotrans(time_in, data_in, coord_in, coord_out)

Transform data from coord_in to coord_out.

Calls the other sub functions in this file.

Parameters

• time_in (list of float) – Time array.

• data_in (list of float) – Coordinates in coord_in.

• coord_in (string) – One of GSE, GSM, SM, GEI, GEO, MAG, J2000.

• coord_out (string) – One of GSE, GSM, SM, GEI, GEO, MAG, J2000.

Returns

Coordinates in coord_out.

Return type

Array of float

Utilities

Time Conversions

Convert from unix time to a string

pyspedas.time_string(float_time=None, fmt=None)

Transform a list of float daytime values to a list of strings.

Parameters

• float_time (float/list of floats, optional) – Input time. The default is None, which returns the time now.

• fmt (str, optional) – Time format. The default is None, which uses ‘%Y-%m-%d %H:%M:%S.%f’.

Returns

Datetimes as string.

Return type

list of str

Example

from pyspedas import time_string
time_string(1444953600.0)

'2015-10-16 00:00:00.000000'

Convert from a string to unix time

pyspedas.time_double(str_time=None)

Transform a list of datetimes from string to decimal.

Same as time_float.

Parameters

str_time (str/list of str, optional) – Input times. The default is None.

Returns

Output times as floats.

Return type

list of float

Example

from pyspedas import time_double
time_double('2015-10-16/14:00')

1445004000.0

Convert from a string or unix time to a datetime object

pyspedas.time_datetime(time=None, tz=None)

Find python datetime.

Transform a list of float daytime values to a list of pythonic

‘datetime.datetime’ values.

Parameters

time (float/list of floats or str/list of str, optional) – Input time. The default is None, which returns the time now.

Returns

Datetimes as datetime.datetime.

Return type

list of datetime.datetime

Example

from pyspedas import time_datetime
time_datetime('2015-10-16/14:00')

datetime.datetime(2015, 10, 16, 14, 0, tzinfo=datetime.timezone.utc)