Arase (ERG) Analysis Tools

Coordinate transformations

The ERG mission defines coordinate systems SGA, SGI, and DSI. The J2000 system serves as a bridge between the ERG systems and the geophysical systems handled by pyspedas.cotrans().

The top-level routine for converting between SGA, SGI, DSI, and J2000 is erg_cotrans.

pyspedas.projects.erg.erg_cotrans(in_name='', out_name='', in_coord='', out_coord='', noload=False)[source]
Parameters:

  • in_name – str name of input tplot variable to be transformed

  • out_name – str Name of output tplot variable in which the transformed data are stored. If not explicitly provided, out_name is automatically generated from “in_name” by replacing the coordinate name with a new one. For example, if you runs erg_cotrans(in_name=’xxxx_sgi’, out_coord=’sga’) then the result is stored in a newly created tplot variable ‘xxxx_sga’.

  • in_coord – str ‘sga’, ‘sgi’, ‘dsi’, or ‘j2000’ Set to explicitly give the coordinate system name for the input variable. If not given, this routine tries to guess it from the name of the input tplot variable (in_name).

  • out_coord – str ‘sga’, ‘sgi’, ‘dsi’, or ‘j2000’ Set to explicitly give the coordinate system name for the output variable. If not given, the coordinate system name is obtained from the variable name as done for in_coord.

Returns:

None

pyspedas.projects.erg.dsi2j2000(name_in=None, name_out=None, no_orb=False, J20002DSI=False, noload=False)[source]

This function transform a time series data between the DSI and J2000 coordinate systems

Parameters:

  • name_in – str input tplot variable to be transformed

  • name_out – str Name of the tplot variable in which the transformed data is stored

  • J20002DSI – bool Set to transform data from J2000 to DSI. If not set, it transforms data from DSI to J2000.

Returns:

None

pyspedas.projects.erg.sga2sgi(name_in=None, name_out=None, SGI2SGA=False, noload=False)[source]

This transform a time series data between the SGA and SGI coordinate systems.

Parameters:

  • name_in – str input tplot variable to be transformed

  • name_out – str Name of the tplot variable in which the transformed data is stored

  • SGI2SGA – bool Set to transform data from SGI to SGA. If not set, it transforms data from SGA to SGI.

Returns:

None

pyspedas.projects.erg.sgi2dsi(name_in=None, name_out=None, DSI2SGI=False, noload=False)[source]

This transform a time series data between the SGI and DSI coordinate systems.

Parameters:

  • name_in – str input tplot variable to be transformed

  • name_out – str Name of the tplot variable in which the transformed data is stored

  • DSI2SGI – bool Set to transform data from DSI to SGI (despun coord –> spinning coord). If not set, it transforms data from SGI to DSI (spinning coord –> despun coord).

Returns:

None

Particle Tools

pyspedas.projects.erg.erg_hep_part_products(in_tvarname, species=None, outputs=['energy'], no_ang_weighting=True, suffix='', units='flux', datagap=8.1, regrid=[16, 16], pitch=[0.0, 180.0], theta=[-90.0, 90.0], phi_in=[0.0, 360.0], gyro=[0.0, 360.0], energy=None, fac_type='phigeo', trange=None, mag_name=None, pos_name=None, relativistic=False, no_regrid=True, include_allazms=False, muconv=False)[source]
pyspedas.projects.erg.erg_lep_part_products(in_tvarname, species=None, outputs=['energy'], no_ang_weighting=True, suffix='', units='flux', datagap=32.1, regrid=[16, 16], pitch=[0.0, 180.0], theta=[-90.0, 90.0], phi_in=[0.0, 360.0], gyro=[0.0, 360.0], energy=None, fac_type='mphism', trange=None, mag_name=None, pos_name=None, relativistic=False, no_regrid=False)[source]
pyspedas.projects.erg.erg_mep_part_products(in_tvarname, species=None, outputs=['energy'], no_ang_weighting=True, suffix='', units='flux', datagap=16.1, regrid=[32, 16], pitch=[0.0, 180.0], theta=[-90.0, 90.0], phi_in=[0.0, 360.0], gyro=[0.0, 360.0], energy=None, fac_type='mphism', trange=None, mag_name=None, pos_name=None, relativistic=False, no_regrid=False)[source]
Parameters:

  • in_tvarname (str) – The name of a tplot variable containing ERG/MEP 3dflux data

  • species (str)

  • outputs (list of str) –

    The output quantities to generate. Valid options are:

    energy
theta
phi
gyro
pa
moments
fac_energy
fac_moments

  • no_ang_weighting (bool)

  • suffix (str) – A suffix to be appended to the output tplot variable names

  • units (str)

  • datagap (float) – A time in seconds; data gaps longer than this time will be marked with NaNs

  • regrid (list of int) – Dimensions to use for regridded data

  • pitch (list of float) – Lower and upper pitch angle limits to use

  • theta (list of float) – Lower and upper theta angle limits to use

  • phi_in (list of float) – Lower and upper phi angle limits to use

  • gyro (list of float) – Lower and upper gyro angle limits to use

  • energy (list of float) – Lower and upper energu limits to use when cleaning the data

  • fac_type (str) – Type of field-aligned coordinate frame to use. Default: ‘mphism’

  • trange (list of str) – A list containing the start and end times to use for generating the products

  • mag_name (str) – tplot name of the magnetic field variable

  • pos_name (str) – tplot name of the orbit position variable

  • relativistic (bool)

  • no_regrid (bool)

Returns:

A list of tplot variables created

Return type:

list of str

Example

import pyspedas
from pyspedas.projects.erg import erg_mep_part_products
from pyspedas import timespan, tplot
timespan('2017-04-05 21:45:00', 2.25, keyword='hours')
pyspedas.projects.erg.mepe( trange=[ '2017-04-05 21:45:00', '2017-04-05 23:59:59'], datatype='3dflux' )
vars = pyspedas.projects.erg.mgf(trange=['2017-04-05 21:45:00', '2017-04-05 23:59:59'])  # Load necessary B-field data
vars = pyspedas.projects.erg.orb(trange=['2017-04-05 21:45:00', '2017-04-05 23:59:59'])  # Load necessary orbit data
mag_vn = 'erg_mgf_l2_mag_8sec_dsi'
pos_vn = 'erg_orb_l2_pos_gse'
# Calculate energy-time spectra of electron flux for limited pitch-angle (PA) ranges
## Here we calculate energy-time spectra for PA = 0-10 deg and PA = 80-100 deg.
vars = erg_mep_part_products('erg_mepe_l2_3dflux_FEDU', outputs='fac_energy', pitch=[80., 100.],
                             fac_type='xdsi', mag_name=mag_vn, pos_name=pos_vn,
                             trange=['2017-04-05 21:45:00', '2017-04-05 23:59:59'], suffix='_pa80-100')
vars = erg_mep_part_products('erg_mepe_l2_3dflux_FEDU', outputs='fac_energy', pitch=[0., 10.], fac_type='xdsi',
                             mag_name=mag_vn, pos_name=pos_vn,
                             trange=['2017-04-05 21:45:00', '2017-04-05 23:59:59'], suffix='_pa0-10')

## Decorate the obtained spectrum variables
pyspedas.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'ytitle', 'MEP-e flux\nPA: 80-100\n\n[eV]')
pyspedas.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10', 'ytitle', 'MEP-e flux\nPA: 0-10\n\n[eV]')
tplot(['erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10'], save_png='erg_mep_en_pa_limit.png')
_images/erg_mep_en_pa_limit.png
pyspedas.projects.erg.erg_xep_part_products(in_tvarname, species=None, outputs=['energy'], no_ang_weighting=True, suffix='', units='flux', datagap=15.0, regrid=[16, 12], pitch=[0.0, 180.0], theta=[-90.0, 90.0], phi_in=[0.0, 360.0], gyro=[0.0, 360.0], energy=None, fac_type='mphism', trange=None, mag_name=None, pos_name=None, relativistic=False, no_regrid=False)[source]