A simple crib sheet for loading and plotting Arase satellite data¶

(latest updated: Mar., 2024)

This notebook shows how to load and plot Arase satellite data with pySPEDAS and pyTplot.

Please refer to the following website for the details of the data.

https://ergsc.isee.nagoya-u.ac.jp/data_info/erg.shtml.en

Get started¶

If you are using the Google Colaboratory and have not had the PySPEDAS module installed, please run the following !pip install pyspedas command to get it ready. If you run your own Python session on your local PC, you have to install the PySPEDAS module beforehand, with the same pip command (without "!").

The second pip command with a github URL is to install the bleeding-edge distribution of the ERG submodule developed by ERG-SC. This distribution always delivers the latest development version of only the ERG-SC plug-in module for ERG satellite and ground data. The downside, however, is that some modules are in an highly experimental phase and not fully tested, possibly containing unresolved bugs. You should use it at your own risk. Because this module is installed as ergpyspedas module, you can load it with import ergpyspedas.

If you would like to use the stable distribution of the ERG-SC submodule, you only have to install the official PySPEDAS module using the first pip command: PySPEDAS always contain a stable version of the ERG-SC plug-in. So just skip the second one.

In [1]:
!pip install pyspedas
!pip install git+https://github.com/ergsc-devel/pyspedas_plugin.git

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Installing collected packages: isodate, cftime, cdflib, netCDF4, geopack, cdasws, hapiclient, viresclient, pytplot-mpl-temp, pyspedas
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The following commands import some necessary modules for loading and plotting the data.

In [2]:
import pyspedas
import pytplot
from pytplot.tplot import tplot     # As a shortcut for pytplot.tplot()

For example, the data-load module for the MGF data can be imported with the following command:

In [3]:
from pyspedas.erg import mgf

Basic commands of pyTplot and pySPEDAS¶

With MGF data, let us introduce some basic commands of pyTplot and pySPEDAS, which are used commonly for loading and visualizing data. Also see the official document of the pyTplot module at:

https://pytplot.readthedocs.io/en/latest/index.html

Load data and plot them with "tplot"¶

In [4]:
from pyspedas.erg import mgf
tr=['2017-09-07', '2017-09-09']  # Set time range to load MGF data.
vars = mgf( trange=tr )          # load MGF Lv.2 8-s data for 7-9 September, 2017.
tplot('erg_mgf_l2_mag_8sec_sm' ) # Plot MGF Lv.2 8-s data.

11-Mar-24 09:03:17: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/
11-Mar-24 09:03:18: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170907_v03.04.cdf to erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170907_v03.04.cdf
11-Mar-24 09:03:19: Download complete: erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170907_v03.04.cdf
11-Mar-24 09:03:20: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170908_v03.04.cdf to erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170908_v03.04.cdf
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**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Magnetic Field Experiment (MGF) Level 2 spin-averaged magnetic field data

Information about ERG MGF

PI:  Ayako Matsuoka
Affiliation: Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake Cho, Sakyo-ku Kyoto 606-8502, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf
Contact: erg_mgf_info at isee.nagoya-u.ac.jp
**************************************************************************

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _PyDrive2ImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _PyDriveImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _GenerativeAIImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _OpenCVImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: APICoreClientInfoImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _BokehImportHook.find_spec() not found; falling back to find_module()

11-Mar-24 09:03:22: <frozen importlib._bootstrap>:914: ImportWarning: _AltairImportHook.find_spec() not found; falling back to find_module()

Plot multiple tplot variables verticaly in a row on a window¶

In [5]:
tplot(['erg_mgf_l2_mag_8sec_sm', 'erg_mgf_l2_mag_8sec_gsm'])

Limit the time range of a plot: timespan()¶

Notes:Because the 'timespan' module does not work in the older version of PySPEDAS, please install the latest version of PySPEDAS if you use the older version.

In [6]:
pytplot.timespan( '2017-09-07 20:00:00', 6, keyword='hours') # Set time span to create the plot of MGF Lv.2 8-s data.
tplot(['erg_mgf_l2_mag_8sec_sm', 'erg_mgf_l2_mag_8sec_gsm'])

Change the vertical scale of a plot: ylim¶

In [7]:
pytplot.options( 'erg_mgf_l2_mag_8sec_sm', 'yrange', [-200., 200.] ) # Set the vertical scale to create the plot of MGF Lv.2 8-s data.
#pytplot.ylim( 'erg_mgf_l2_mag_8sec_sm', -200., 200. ) # There is a bug in pytplot.ylim. It will be fixed.
tplot(['erg_mgf_l2_mag_8sec_sm'])

Change the title of the vertical axis: options()¶

In [ ]:
pytplot.options( 'erg_mgf_l2_mag_8sec_sm', 'ytitle', 'MGF Lv.2 8 s') # Change the title of the vertical axis.
tplot(['erg_mgf_l2_mag_8sec_sm'])

Change the font size of axes: tplot_options()¶

In [ ]:
pytplot.tplot_options('axis_font_size', 15) # Change the font size of axes. The default is 10.
tplot(['erg_mgf_l2_mag_8sec_sm'])

Change the labels and line colors: options()¶

In [ ]:
pytplot.options('erg_mgf_l2_mag_8sec_sm', 'legend_names', ['Bx (SM)','By (SM)','Bz (SM)'])  # Change the label.
pytplot.options('erg_mgf_l2_mag_8sec_sm', 'color', ['green','red','blue'])                  # Change the color.
tplot(['erg_mgf_l2_mag_8sec_sm'])

Split the vector data to each component: split_vec()¶

In [ ]:
pytplot.split_vec('erg_mgf_l2_mag_8sec_sm')
Out[ ]:
['erg_mgf_l2_mag_8sec_sm_x',
 'erg_mgf_l2_mag_8sec_sm_y',
 'erg_mgf_l2_mag_8sec_sm_z']
In [ ]:
#For Bx component.
pytplot.options('erg_mgf_l2_mag_8sec_sm_x', 'ytitle', 'MGF Bx Lv.2 8 s')      # Set the title of y-axis.
pytplot.options('erg_mgf_l2_mag_8sec_sm_x', 'ysubtitle', '[nT]')              # Set the subtitle of y-axis (for unit).
pytplot.options('erg_mgf_l2_mag_8sec_sm_x', 'color', 'green')                 # Set the line color.
pytplot.options('erg_mgf_l2_mag_8sec_sm_x', 'charsize', 8)                    # Set the character size.
pytplot.options( 'erg_mgf_l2_mag_8sec_sm_x', 'yrange', [-400., 400.] )        # Set the vertical scale.
pytplot.options('erg_mgf_l2_mag_8sec_sm_x', 'legend_names', ['Bx (SM)'])      # Set the label of the Bx componenet.

#For By component.
pytplot.options('erg_mgf_l2_mag_8sec_sm_y', 'ytitle', 'MGF By Lv.2 8 s')
pytplot.options('erg_mgf_l2_mag_8sec_sm_y', 'ysubtitle', '[nT]')
pytplot.options('erg_mgf_l2_mag_8sec_sm_y', 'color', 'red')
pytplot.options('erg_mgf_l2_mag_8sec_sm_y', 'charsize', 8)
pytplot.options( 'erg_mgf_l2_mag_8sec_sm_y', 'yrange', [-400., 400.] )
pytplot.options('erg_mgf_l2_mag_8sec_sm_y', 'legend_names', ['By (SM)'])

#For Bz component.
pytplot.options('erg_mgf_l2_mag_8sec_sm_z', 'ytitle', 'MGF Bz Lv.2 8 s')
pytplot.options('erg_mgf_l2_mag_8sec_sm_z', 'ysubtitle', '[nT]')
pytplot.options('erg_mgf_l2_mag_8sec_sm_z', 'color', 'blue')
pytplot.options( 'erg_mgf_l2_mag_8sec_sm_z', 'yrange', [-400., 400.] )
pytplot.options('erg_mgf_l2_mag_8sec_sm_z', 'charsize', 8)
pytplot.options('erg_mgf_l2_mag_8sec_sm_z', 'legend_names', ['Bz (SM)'])

tplot(['erg_mgf_l2_mag_8sec_sm_x','erg_mgf_l2_mag_8sec_sm_y','erg_mgf_l2_mag_8sec_sm_z'])

Save the tplot variables and restore them: pytplot.tplot_save(), and pytplot.tplot_restore()¶

In [ ]:
pytplot.tplot_save('erg_mgf_l2_mag_8sec_sm_x', filename='erg_mgf_l2_mag_8sec_sm_x.dat') # Save the tplot variable 'erg_mgf_l2_mag_8sec_sm_x'
pytplot.del_data('erg_mgf_l2_mag_8sec_sm_x')                                            # Delete the tplot variable 'erg_mgf_l2_mag_8sec_sm_x'
pytplot.tplot_names()                                                                   # Check tplot variables

0 : erg_mgf_l2_epoch_8sec
1 : erg_mgf_l2_mag_8sec_dsi
2 : erg_mgf_l2_mag_8sec_gse
3 : erg_mgf_l2_mag_8sec_gsm
4 : erg_mgf_l2_mag_8sec_sm
5 : erg_mgf_l2_magt_8sec
6 : erg_mgf_l2_rmsd_8sec_dsi
7 : erg_mgf_l2_rmsd_8sec_gse
8 : erg_mgf_l2_rmsd_8sec_gsm
9 : erg_mgf_l2_rmsd_8sec_sm
10 : erg_mgf_l2_rmsd_8sec
11 : erg_mgf_l2_n_rmsd_8sec
12 : erg_mgf_l2_dyn_rng_8sec
13 : erg_mgf_l2_quality_8sec
14 : erg_mgf_l2_quality_8sec_gc
15 : erg_mgf_l2_igrf_8sec_dsi
16 : erg_mgf_l2_igrf_8sec_gse
17 : erg_mgf_l2_igrf_8sec_gsm
18 : erg_mgf_l2_igrf_8sec_sm
19 : erg_mgf_l2_mag_8sec_sm_y
20 : erg_mgf_l2_mag_8sec_sm_z
Out[ ]:
['erg_mgf_l2_epoch_8sec',
 'erg_mgf_l2_mag_8sec_dsi',
 'erg_mgf_l2_mag_8sec_gse',
 'erg_mgf_l2_mag_8sec_gsm',
 'erg_mgf_l2_mag_8sec_sm',
 'erg_mgf_l2_magt_8sec',
 'erg_mgf_l2_rmsd_8sec_dsi',
 'erg_mgf_l2_rmsd_8sec_gse',
 'erg_mgf_l2_rmsd_8sec_gsm',
 'erg_mgf_l2_rmsd_8sec_sm',
 'erg_mgf_l2_rmsd_8sec',
 'erg_mgf_l2_n_rmsd_8sec',
 'erg_mgf_l2_dyn_rng_8sec',
 'erg_mgf_l2_quality_8sec',
 'erg_mgf_l2_quality_8sec_gc',
 'erg_mgf_l2_igrf_8sec_dsi',
 'erg_mgf_l2_igrf_8sec_gse',
 'erg_mgf_l2_igrf_8sec_gsm',
 'erg_mgf_l2_igrf_8sec_sm',
 'erg_mgf_l2_mag_8sec_sm_y',
 'erg_mgf_l2_mag_8sec_sm_z']
In [ ]:
pytplot.tplot_restore(filename='erg_mgf_l2_mag_8sec_sm_x.dat') # Restore the tplot variable 'erg_mgf_l2_mag_8sec_sm_x'
pytplot.tplot_names()                                          # Check tplot variables

0 : erg_mgf_l2_epoch_8sec
1 : erg_mgf_l2_mag_8sec_dsi
2 : erg_mgf_l2_mag_8sec_gse
3 : erg_mgf_l2_mag_8sec_gsm
4 : erg_mgf_l2_mag_8sec_sm
5 : erg_mgf_l2_magt_8sec
6 : erg_mgf_l2_rmsd_8sec_dsi
7 : erg_mgf_l2_rmsd_8sec_gse
8 : erg_mgf_l2_rmsd_8sec_gsm
9 : erg_mgf_l2_rmsd_8sec_sm
10 : erg_mgf_l2_rmsd_8sec
11 : erg_mgf_l2_n_rmsd_8sec
12 : erg_mgf_l2_dyn_rng_8sec
13 : erg_mgf_l2_quality_8sec
14 : erg_mgf_l2_quality_8sec_gc
15 : erg_mgf_l2_igrf_8sec_dsi
16 : erg_mgf_l2_igrf_8sec_gse
17 : erg_mgf_l2_igrf_8sec_gsm
18 : erg_mgf_l2_igrf_8sec_sm
19 : erg_mgf_l2_mag_8sec_sm_y
20 : erg_mgf_l2_mag_8sec_sm_z
21 : erg_mgf_l2_mag_8sec_sm_x
Out[ ]:
['erg_mgf_l2_epoch_8sec',
 'erg_mgf_l2_mag_8sec_dsi',
 'erg_mgf_l2_mag_8sec_gse',
 'erg_mgf_l2_mag_8sec_gsm',
 'erg_mgf_l2_mag_8sec_sm',
 'erg_mgf_l2_magt_8sec',
 'erg_mgf_l2_rmsd_8sec_dsi',
 'erg_mgf_l2_rmsd_8sec_gse',
 'erg_mgf_l2_rmsd_8sec_gsm',
 'erg_mgf_l2_rmsd_8sec_sm',
 'erg_mgf_l2_rmsd_8sec',
 'erg_mgf_l2_n_rmsd_8sec',
 'erg_mgf_l2_dyn_rng_8sec',
 'erg_mgf_l2_quality_8sec',
 'erg_mgf_l2_quality_8sec_gc',
 'erg_mgf_l2_igrf_8sec_dsi',
 'erg_mgf_l2_igrf_8sec_gse',
 'erg_mgf_l2_igrf_8sec_gsm',
 'erg_mgf_l2_igrf_8sec_sm',
 'erg_mgf_l2_mag_8sec_sm_y',
 'erg_mgf_l2_mag_8sec_sm_z',
 'erg_mgf_l2_mag_8sec_sm_x']

Get the data from tplot variables: pytplot.get_data()¶

In [ ]:
data=pytplot.get_data('erg_mgf_l2_mag_8sec_sm_x')   #Get the data from 'erg_mgf_l2_mag_8sec_sm_x' tplot variable.
time = data[0]                                      #Time data
ydata = data[1]                                     #Magnetic field data
In [ ]:
pyspedas.time_string(time)                          #Print the time in YYYY-MM-DD HH:MM:SS format.
Out[ ]:
['2017-09-06 23:59:59.137643',
 '2017-09-07 00:00:02.026645',
 '2017-09-07 00:00:10.022048',
 '2017-09-07 00:00:18.001724',
 '2017-09-07 00:00:25.997128',
 '2017-09-07 00:00:33.992530',
 '2017-09-07 00:00:41.987906',
 '2017-09-07 00:00:49.983309',
 '2017-09-07 00:00:57.978686',
 '2017-09-07 00:01:05.974089',
 '2017-09-07 00:01:13.969492',
 '2017-09-07 00:01:21.964768',
 '2017-09-07 00:01:29.960071',
 '2017-09-07 00:01:37.955549',
 '2017-09-07 00:01:45.950852',
 '2017-09-07 00:01:53.946253',
 '2017-09-07 00:02:01.926030',
 '2017-09-07 00:02:09.921434',
 '2017-09-07 00:02:17.916711',
 '2017-09-07 00:02:25.912214',
 '2017-09-07 00:02:33.907516',
 '2017-09-07 00:02:41.902892',
 '2017-09-07 00:02:49.898296',
 '2017-09-07 00:02:57.893572',
 '2017-09-07 00:03:05.889076',
 '2017-09-07 00:03:13.884478',
 '2017-09-07 00:03:21.879855',
 '2017-09-07 00:03:29.875157',
 '2017-09-07 00:03:37.854860',
 '2017-09-07 00:03:45.850138',
 '2017-09-07 00:03:53.845539',
 '2017-09-07 00:04:01.840817',
 '2017-09-07 00:04:09.836320',
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 ...]
In [ ]:
ydata    #Print magnetic field data.
Out[ ]:
array([          nan, -589.49727521, -620.5628498 , ..., -604.23949429,
       -601.68811175, -598.66261866])
In [ ]:
ydata_sq = ydata*ydata    #Calculate a squre of ydata.
In [ ]:
ydata_sq   #Print a squre of magnetic field data.
Out[ ]:
array([            nan, 347507.03747429, 385098.2505468 , ...,
       365105.3664615 , 362028.58381858, 358396.93097956])
In [ ]:
# Store the data into tplot variable 'erg_mgf_l2_mag_8sec_sm_x_sq'.
pytplot.store_data('erg_mgf_l2_mag_8sec_sm_x_sq', data={'x':time,'y':ydata_sq})
pytplot.tplot_names()

0 : erg_mgf_l2_epoch_8sec
1 : erg_mgf_l2_mag_8sec_dsi
2 : erg_mgf_l2_mag_8sec_gse
3 : erg_mgf_l2_mag_8sec_gsm
4 : erg_mgf_l2_mag_8sec_sm
5 : erg_mgf_l2_magt_8sec
6 : erg_mgf_l2_rmsd_8sec_dsi
7 : erg_mgf_l2_rmsd_8sec_gse
8 : erg_mgf_l2_rmsd_8sec_gsm
9 : erg_mgf_l2_rmsd_8sec_sm
10 : erg_mgf_l2_rmsd_8sec
11 : erg_mgf_l2_n_rmsd_8sec
12 : erg_mgf_l2_dyn_rng_8sec
13 : erg_mgf_l2_quality_8sec
14 : erg_mgf_l2_quality_8sec_gc
15 : erg_mgf_l2_igrf_8sec_dsi
16 : erg_mgf_l2_igrf_8sec_gse
17 : erg_mgf_l2_igrf_8sec_gsm
18 : erg_mgf_l2_igrf_8sec_sm
19 : erg_mgf_l2_mag_8sec_sm_y
20 : erg_mgf_l2_mag_8sec_sm_z
21 : erg_mgf_l2_mag_8sec_sm_x
22 : erg_mgf_l2_mag_8sec_sm_x_sq
Out[ ]:
['erg_mgf_l2_epoch_8sec',
 'erg_mgf_l2_mag_8sec_dsi',
 'erg_mgf_l2_mag_8sec_gse',
 'erg_mgf_l2_mag_8sec_gsm',
 'erg_mgf_l2_mag_8sec_sm',
 'erg_mgf_l2_magt_8sec',
 'erg_mgf_l2_rmsd_8sec_dsi',
 'erg_mgf_l2_rmsd_8sec_gse',
 'erg_mgf_l2_rmsd_8sec_gsm',
 'erg_mgf_l2_rmsd_8sec_sm',
 'erg_mgf_l2_rmsd_8sec',
 'erg_mgf_l2_n_rmsd_8sec',
 'erg_mgf_l2_dyn_rng_8sec',
 'erg_mgf_l2_quality_8sec',
 'erg_mgf_l2_quality_8sec_gc',
 'erg_mgf_l2_igrf_8sec_dsi',
 'erg_mgf_l2_igrf_8sec_gse',
 'erg_mgf_l2_igrf_8sec_gsm',
 'erg_mgf_l2_igrf_8sec_sm',
 'erg_mgf_l2_mag_8sec_sm_y',
 'erg_mgf_l2_mag_8sec_sm_z',
 'erg_mgf_l2_mag_8sec_sm_x',
 'erg_mgf_l2_mag_8sec_sm_x_sq']
In [ ]:
tplot(['erg_mgf_l2_mag_8sec_sm_x', 'erg_mgf_l2_mag_8sec_sm_x_sq'])

Analysis of Fourier spectrum: pyspedas.tdpwrspc()¶

In [ ]:
pyspedas.tdpwrspc('erg_mgf_l2_mag_8sec_sm_x',nboxpoints=128, nshiftpoints=8)  #Calculate Fourier spectra of MGF data with a window of 128*8 sec and a time shift of 8 sec.
pyspedas.tdpwrspc('erg_mgf_l2_mag_8sec_sm_y',nboxpoints=128, nshiftpoints=8)  #Calculate Fourier spectra of MGF data with a window of 128*8 sec and a time shift of 8 sec.
pyspedas.tdpwrspc('erg_mgf_l2_mag_8sec_sm_z',nboxpoints=128, nshiftpoints=8)  #Calculate Fourier spectra of MGF data with a window of 128*8 sec and a time shift of 8 sec.
Out[ ]:
'erg_mgf_l2_mag_8sec_sm_z_dpwrspc'
In [ ]:
#Add the plot option of x and y no sample for usage of google coab.
pytplot.options(['erg_mgf_l2_mag_8sec_sm_x_dpwrspc','erg_mgf_l2_mag_8sec_sm_y_dpwrspc','erg_mgf_l2_mag_8sec_sm_z_dpwrspc'],'x_no_resample', 1)
pytplot.options(['erg_mgf_l2_mag_8sec_sm_x_dpwrspc','erg_mgf_l2_mag_8sec_sm_y_dpwrspc','erg_mgf_l2_mag_8sec_sm_z_dpwrspc'],'y_no_resample', 1)

#Add the option of y-axsis title.
pytplot.options(['erg_mgf_l2_mag_8sec_sm_x_dpwrspc'],'ytitle', 'Frequency (Bx)')
pytplot.options(['erg_mgf_l2_mag_8sec_sm_y_dpwrspc'],'ytitle', 'Frequency (By)')
pytplot.options(['erg_mgf_l2_mag_8sec_sm_z_dpwrspc'],'ytitle', 'Frequency (Bz)')

#Plot the three magnetic field components and thier Fourier spectra with x and y sizes of 10 and 20, respectively..
tplot(['erg_mgf_l2_mag_8sec_sm_x','erg_mgf_l2_mag_8sec_sm_x_dpwrspc','erg_mgf_l2_mag_8sec_sm_y','erg_mgf_l2_mag_8sec_sm_y_dpwrspc','erg_mgf_l2_mag_8sec_sm_z','erg_mgf_l2_mag_8sec_sm_z_dpwrspc'], xsize = 10, ysize = 20)
#tplot(['erg_mgf_l2_mag_8sec_sm_x_dpwrspc','erg_mgf_l2_mag_8sec_sm_y_dpwrspc','erg_mgf_l2_mag_8sec_sm_z_dpwrspc'], xsize = 10, ysize = 20)
#tplot(['erg_mgf_l2_mag_8sec_sm_x','erg_mgf_l2_mag_8sec_sm_y','erg_mgf_l2_mag_8sec_sm_z'], xsize = 10, ysize = 20)

Change the contour scale for a spectrum-type plot: zlim()¶

In [ ]:
from pyspedas.erg import pwe_ofa                                                #Import pwe_ofa module from pyspedas.erg.
pwe_ofa( trange=['2017-09-07 00:00:00', '2017-09-08 00:00:00'] )                #Load PWE_OFA data for a period from 2017-09-07 00:00:00 to 2017-09-08 00:00:00.
pytplot.timespan( '2017-09-07 00:00:00', 1)                                     #Set time span from 2017-09-07 00:00:00 to 2017-09-08 00:00:00.
pytplot.zlim( 'erg_pwe_ofa_l2_spec_E_spectra_132', 1e-7, 1e-2 )                 #Change the contour range.
pytplot.options('erg_pwe_ofa_l2_spec_E_spectra_132','x_no_resample', 1)
pytplot.options('erg_pwe_ofa_l2_spec_E_spectra_132','y_no_resample', 1)
pytplot.options('erg_pwe_ofa_l2_spec_E_spectra_132','data_gap', 60)             #This option is no plot of the data for a period when there are no data for more than 60 seconds.
tplot( 'erg_pwe_ofa_l2_spec_E_spectra_132' )

08-Mar-24 00:43:21: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/
08-Mar-24 00:43:22: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170907_v02_03.cdf to erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170907_v02_03.cdf
08-Mar-24 00:43:26: Download complete: erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170907_v02_03.cdf

 
 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Plasma Wave Experiment (PWE) Onboard Frequency Analyzer (OFA) Level 2 spectrum data

Information about ERG PWE OFA

PI:  Yoshiya Kasahara
Affiliation: Kanazawa University

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of PWE/OFA: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Ofa

Contact: erg_pwe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _PyDrive2ImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _PyDriveImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _GenerativeAIImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _OpenCVImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: APICoreClientInfoImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _BokehImportHook.find_spec() not found; falling back to find_module()

08-Mar-24 00:43:30: <frozen importlib._bootstrap>:914: ImportWarning: _AltairImportHook.find_spec() not found; falling back to find_module()

Show the list of loaded tplot variables: tplot_names()¶

In [ ]:
vars = pytplot.tplot_names()

0 : erg_mgf_l2_epoch_8sec
1 : erg_mgf_l2_mag_8sec_dsi
2 : erg_mgf_l2_mag_8sec_gse
3 : erg_mgf_l2_mag_8sec_gsm
4 : erg_mgf_l2_mag_8sec_sm
5 : erg_mgf_l2_magt_8sec
6 : erg_mgf_l2_rmsd_8sec_dsi
7 : erg_mgf_l2_rmsd_8sec_gse
8 : erg_mgf_l2_rmsd_8sec_gsm
9 : erg_mgf_l2_rmsd_8sec_sm
10 : erg_mgf_l2_rmsd_8sec
11 : erg_mgf_l2_n_rmsd_8sec
12 : erg_mgf_l2_dyn_rng_8sec
13 : erg_mgf_l2_quality_8sec
14 : erg_mgf_l2_quality_8sec_gc
15 : erg_mgf_l2_igrf_8sec_dsi
16 : erg_mgf_l2_igrf_8sec_gse
17 : erg_mgf_l2_igrf_8sec_gsm
18 : erg_mgf_l2_igrf_8sec_sm
19 : erg_mgf_l2_mag_8sec_sm_y
20 : erg_mgf_l2_mag_8sec_sm_z
21 : erg_mgf_l2_mag_8sec_sm_x
22 : erg_mgf_l2_mag_8sec_sm_x_sq
23 : erg_mgf_l2_mag_8sec_sm_x_dpwrspc
24 : erg_mgf_l2_mag_8sec_sm_y_dpwrspc
25 : erg_mgf_l2_mag_8sec_sm_z_dpwrspc
26 : erg_pwe_ofa_l2_spec_epoch_e132
27 : erg_pwe_ofa_l2_spec_E_spectra_132
28 : erg_pwe_ofa_l2_spec_quality_flag_e132
29 : erg_pwe_ofa_l2_spec_epoch_b132
30 : erg_pwe_ofa_l2_spec_B_spectra_132
31 : erg_pwe_ofa_l2_spec_quality_flag_b132

Remove tplot variables that have been loaded¶

In [ ]:
pytplot.del_data( 'erg_*' )
vars = pytplot.tplot_names()

Load Arase satellite data¶

In [ ]:
from pyspedas.erg import pwe_hfa, pwe_ofa, pwe_efd, mgf, xep, hep, mepe, lepe, mepi_nml, lepi, orb             #Import several modules from pyspedas.erg.
trng = ['2017-09-07 00:00:00', '2017-09-08 00:00:00']                                                          #Set time range from 2017-09-07 00:00:00 to 2017-09-08 00:00:00.
pwe_hfa( trange=trng, datatype='spec' )                                                                        #PWE_HFA  (The defults are mode:low and level:l2)
pwe_ofa( trange=trng, datatype='spec' )                                                                        #PWE_OGA  (The defult is level:l2)
pwe_efd( trange=trng, datatype='spec' )                                                                        #PWE_EFD  (The defult is level:l2)
mgf( trange=trng )                                                                                             #MGF
xep( trange=trng, datatype='omniflux' )                                                                        #XEP
hep( trange=trng, datatype='omniflux' )                                                                        #HEP
mepe( trange=trng, datatype='omniflux' )                                                                       #MEP-e
lepe( trange=trng, datatype='omniflux' )                                                                       #LEP-e
mepi_nml( trange=trng, datatype='omniflux' )                                                                   #MEP-i
lepi( trange=trng, datatype='omniflux' )                                                                       #LEP-i

08-Mar-24 00:44:19: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/hfa/l2/spec/low/2017/09/
08-Mar-24 00:44:20: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/hfa/l2/spec/low/2017/09/erg_pwe_hfa_l2_spec_low_20170907_v01_02.cdf to erg_data/satellite/erg/pwe/hfa/l2/spec/low/2017/09/erg_pwe_hfa_l2_spec_low_20170907_v01_02.cdf
08-Mar-24 00:44:23: Download complete: erg_data/satellite/erg/pwe/hfa/l2/spec/low/2017/09/erg_pwe_hfa_l2_spec_low_20170907_v01_02.cdf
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_er contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_el contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_e_ar contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 00:44:27: Conflicting size for at least one dimension for variable erg_pwe_hfa_l2_low_spectra_eu_ev
08-Mar-24 00:44:27: Could not create coordinate v1_dim for variable erg_pwe_hfa_l2_low_spectra_eu_ev
08-Mar-24 00:44:27: Could not create coordinate v2_dim for variable erg_pwe_hfa_l2_low_spectra_eu_ev
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_eu_ev does not contain coordinates for spectrogram plotting.  Continuing...
08-Mar-24 00:44:27: Conflicting size for at least one dimension for variable erg_pwe_hfa_l2_low_spectra_eu_bg
08-Mar-24 00:44:27: Could not create coordinate v1_dim for variable erg_pwe_hfa_l2_low_spectra_eu_bg
08-Mar-24 00:44:27: Could not create coordinate v2_dim for variable erg_pwe_hfa_l2_low_spectra_eu_bg
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_eu_bg does not contain coordinates for spectrogram plotting.  Continuing...
08-Mar-24 00:44:27: Conflicting size for at least one dimension for variable erg_pwe_hfa_l2_low_spectra_ev_bg
08-Mar-24 00:44:27: Could not create coordinate v1_dim for variable erg_pwe_hfa_l2_low_spectra_ev_bg
08-Mar-24 00:44:27: Could not create coordinate v2_dim for variable erg_pwe_hfa_l2_low_spectra_ev_bg
08-Mar-24 00:44:27: erg_pwe_hfa_l2_low_spectra_ev_bg does not contain coordinates for spectrogram plotting.  Continuing...
08-Mar-24 00:44:28: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/

 
 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Plasma Wave Experiment (PWE) Electric Field Data (HFA) Level 2 spectrum data

Information about ERG PWE HFA

PI:  Yoshiya Kasahara
Affiliation: Kanazawa University

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of PWE/HFA: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Hfa

Contact: erg_pwe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:29: File is current: erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170907_v02_03.cdf
08-Mar-24 00:44:33: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/efd/l2/spec/2017/09/

 
 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Plasma Wave Experiment (PWE) Onboard Frequency Analyzer (OFA) Level 2 spectrum data

Information about ERG PWE OFA

PI:  Yoshiya Kasahara
Affiliation: Kanazawa University

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of PWE/OFA: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Ofa

Contact: erg_pwe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:34: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/efd/l2/spec/2017/09/erg_pwe_efd_l2_spec_20170907_v02_02.cdf to erg_data/satellite/erg/pwe/efd/l2/spec/2017/09/erg_pwe_efd_l2_spec_20170907_v02_02.cdf
08-Mar-24 00:44:36: Download complete: erg_data/satellite/erg/pwe/efd/l2/spec/2017/09/erg_pwe_efd_l2_spec_20170907_v02_02.cdf
08-Mar-24 00:44:37: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/

 
 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Plasma Wave Experiment (PWE) Electric Field Data (EFD) Level 2 spectrum data

Information about ERG PWE EFD

PI:  Yoshiya Kasahara
Affiliation: Kanazawa University

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of PWE/EFD: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Efd

Contact: erg_pwe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:39: File is current: erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170907_v03.04.cdf
08-Mar-24 00:44:39: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/xep/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Magnetic Field Experiment (MGF) Level 2 spin-averaged magnetic field data

Information about ERG MGF

PI:  Ayako Matsuoka
Affiliation: Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake Cho, Sakyo-ku Kyoto 606-8502, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf
Contact: erg_mgf_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:40: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/xep/l2/omniflux/2017/09/erg_xep_l2_omniflux_20170907_v01_00.cdf to erg_data/satellite/erg/xep/l2/omniflux/2017/09/erg_xep_l2_omniflux_20170907_v01_00.cdf
08-Mar-24 00:44:40: Download complete: erg_data/satellite/erg/xep/l2/omniflux/2017/09/erg_xep_l2_omniflux_20170907_v01_00.cdf
08-Mar-24 00:44:41: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/hep/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Extremely High-Energy Electron Experiment (XEP) Level 2 extremely high energy electron data

Information about ERG XEP

PI:  Nana Higashio
Affiliation: Space Environment Group, Aerospace Research and Development Directorate, Tsukuba Space Center, Japan Aerospace Exploration Agency, 2-1-1 Sengen, Tsukuba, Ibaraki 305-8505, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of XEP: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Xep

Contact: erg_xep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:42: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/hep/l2/omniflux/2017/09/erg_hep_l2_omniflux_20170907_v03_01.cdf to erg_data/satellite/erg/hep/l2/omniflux/2017/09/erg_hep_l2_omniflux_20170907_v03_01.cdf
08-Mar-24 00:44:43: Download complete: erg_data/satellite/erg/hep/l2/omniflux/2017/09/erg_hep_l2_omniflux_20170907_v03_01.cdf
08-Mar-24 00:44:43: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) High-energy electron experiments (HEP) Level-2 omni flux data

PI:  Takefumi Mitani
Affiliation: ISAS, JAXA

- The rules of the road (RoR) common to the ERG project:
       https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for HEP data: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Hep

Contact: erg_hep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:44: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170907_v01_02.cdf to erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170907_v01_02.cdf
08-Mar-24 00:44:44: Download complete: erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170907_v01_02.cdf
08-Mar-24 00:44:44: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepe/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Medium Energy Particle experiments - electron analyzer (MEP-e) electron omni flux data

PI:  Satoshi Kasahara
Affiliation: The University of Tokyo

- The rules of the road (RoR) common to the ERG project:
      https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for MEP-e data:  https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mepe

Contact: erg_mep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:46: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170907_v04_01.cdf to erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170907_v04_01.cdf
08-Mar-24 00:44:46: Download complete: erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170907_v04_01.cdf
08-Mar-24 00:44:47: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Low-Energy Particle experiments - electron analyzer (LEP-e) Level 2 omni electron flux data

Information about ERG LEPe

PI:  Shiang-Yu Wang
Affiliation: Academia Sinica, Taiwan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of LEPe L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepe

Contact: erg_lepe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:48: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170907_v02_01.cdf to erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170907_v02_01.cdf
08-Mar-24 00:44:48: Download complete: erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170907_v02_01.cdf
08-Mar-24 00:44:49: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepi/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Medium Energy Particle experiments - ion mass analyzer (MEP-i) 3D ion omni flux data

PI:  Shoichiro Yokota
Affiliation: Osaka University

- The rules of the road (RoR) common to the ERG project:
      https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for MEP-i data: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mepi

Contact: erg_mep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:44:50: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170907_v03_00.cdf to erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170907_v03_00.cdf
08-Mar-24 00:44:51: Download complete: erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170907_v03_00.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Low Energy Particle Ion (LEPi) Experiment 3D ion flux data

Information about ERG LEPi

PI:  Kazushi Asamura
Affiliation: ISAS, Jaxa

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of LEPi L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi
RoR of ERG/LEPi: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi#Rules_of_the_Road

Contact: erg_lepi_info at isee.nagoya-u.ac.jp
**************************************************************************
Out[ ]:
['erg_lepi_l2_omniflux_Epoch',
 'erg_lepi_l2_omniflux_FPDO',
 'erg_lepi_l2_omniflux_FHEDO',
 'erg_lepi_l2_omniflux_FODO',
 'erg_lepi_l2_omniflux_FPDO_raw',
 'erg_lepi_l2_omniflux_FHEDO_raw',
 'erg_lepi_l2_omniflux_FODO_raw']
In [ ]:
vars = pytplot.tplot_names()

0 : erg_pwe_hfa_l2_low_Epoch
1 : erg_pwe_hfa_l2_low_spectra_eu
2 : erg_pwe_hfa_l2_low_spectra_ev
3 : erg_pwe_hfa_l2_low_spectra_bgamma
4 : erg_pwe_hfa_l2_low_spectra_esum
5 : erg_pwe_hfa_l2_low_spectra_er
6 : erg_pwe_hfa_l2_low_spectra_el
7 : erg_pwe_hfa_l2_low_spectra_e_mix
8 : erg_pwe_hfa_l2_low_spectra_e_ar
9 : erg_pwe_hfa_l2_low_spectra_eu_ev
10 : erg_pwe_hfa_l2_low_spectra_eu_bg
11 : erg_pwe_hfa_l2_low_spectra_ev_bg
12 : erg_pwe_hfa_l2_low_quality_flag
13 : erg_pwe_ofa_l2_spec_epoch_e132
14 : erg_pwe_ofa_l2_spec_E_spectra_132
15 : erg_pwe_ofa_l2_spec_quality_flag_e132
16 : erg_pwe_ofa_l2_spec_epoch_b132
17 : erg_pwe_ofa_l2_spec_B_spectra_132
18 : erg_pwe_ofa_l2_spec_quality_flag_b132
19 : erg_pwe_efd_l2_spec_spectra
20 : erg_pwe_efd_l2_spec_quality_flag
21 : erg_mgf_l2_epoch_8sec
22 : erg_mgf_l2_mag_8sec_dsi
23 : erg_mgf_l2_mag_8sec_gse
24 : erg_mgf_l2_mag_8sec_gsm
25 : erg_mgf_l2_mag_8sec_sm
26 : erg_mgf_l2_magt_8sec
27 : erg_mgf_l2_rmsd_8sec_dsi
28 : erg_mgf_l2_rmsd_8sec_gse
29 : erg_mgf_l2_rmsd_8sec_gsm
30 : erg_mgf_l2_rmsd_8sec_sm
31 : erg_mgf_l2_rmsd_8sec
32 : erg_mgf_l2_n_rmsd_8sec
33 : erg_mgf_l2_dyn_rng_8sec
34 : erg_mgf_l2_quality_8sec
35 : erg_mgf_l2_quality_8sec_gc
36 : erg_mgf_l2_igrf_8sec_dsi
37 : erg_mgf_l2_igrf_8sec_gse
38 : erg_mgf_l2_igrf_8sec_gsm
39 : erg_mgf_l2_igrf_8sec_sm
40 : erg_xep_l2_FEDO_SSD
41 : erg_hep_l2_FEDO_L
42 : erg_hep_l2_FEDO_H
43 : erg_mepe_l2_omniflux_epoch
44 : erg_mepe_l2_omniflux_FEDO
45 : erg_lepe_l2_omniflux_FEDO
46 : erg_mepi_l2_omniflux_epoch
47 : erg_mepi_l2_omniflux_epoch_tof
48 : erg_mepi_l2_omniflux_FIDO_Energy
49 : erg_mepi_l2_omniflux_FPDO
50 : erg_mepi_l2_omniflux_FHE2DO
51 : erg_mepi_l2_omniflux_FHEDO
52 : erg_mepi_l2_omniflux_FOPPDO
53 : erg_mepi_l2_omniflux_FODO
54 : erg_mepi_l2_omniflux_FO2PDO
55 : erg_mepi_l2_omniflux_FPDO_tof
56 : erg_mepi_l2_omniflux_FHE2DO_tof
57 : erg_mepi_l2_omniflux_FHEDO_tof
58 : erg_mepi_l2_omniflux_FOPPDO_tof
59 : erg_mepi_l2_omniflux_FODO_tof
60 : erg_mepi_l2_omniflux_FO2PDO_tof
61 : erg_lepi_l2_omniflux_Epoch
62 : erg_lepi_l2_omniflux_FPDO_raw
63 : erg_lepi_l2_omniflux_FHEDO_raw
64 : erg_lepi_l2_omniflux_FODO_raw
65 : erg_lepi_l2_omniflux_FPDO
66 : erg_lepi_l2_omniflux_FHEDO
67 : erg_lepi_l2_omniflux_FODO
In [ ]:
#For usage of google colab.
pytplot.options(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm', 'erg_xep_l2_FEDO_SSD', 'erg_hep_l2_FEDO_H', 'erg_hep_l2_FEDO_L', 'erg_mepe_l2_omniflux_FEDO', 'erg_lepe_l2_omniflux_FEDO', 'erg_mepi_l2_omniflux_FPDO', 'erg_lepi_l2_omniflux_FPDO'], 'x_no_resample', 1)
pytplot.options(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm', 'erg_xep_l2_FEDO_SSD', 'erg_hep_l2_FEDO_H', 'erg_hep_l2_FEDO_L', 'erg_mepe_l2_omniflux_FEDO', 'erg_lepe_l2_omniflux_FEDO', 'erg_mepi_l2_omniflux_FPDO', 'erg_lepi_l2_omniflux_FPDO'], 'y_no_resample', 1)

#Set the option of data gap with 60 seconds.
pytplot.options(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm', 'erg_xep_l2_FEDO_SSD', 'erg_hep_l2_FEDO_H', 'erg_mepe_l2_omniflux_FEDO', 'erg_lepe_l2_omniflux_FEDO', 'erg_mepi_l2_omniflux_FPDO', 'erg_lepi_l2_omniflux_FPDO'], 'data_gap', 60.0)

#Plot all the data with x and ysizes of 10 and 30, respectively.
tplot(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm', 'erg_xep_l2_FEDO_SSD', 'erg_hep_l2_FEDO_H', 'erg_hep_l2_FEDO_L', 'erg_mepe_l2_omniflux_FEDO', 'erg_mepi_l2_omniflux_FPDO', 'erg_lepi_l2_omniflux_FPDO'], xsize = 10, ysize = 30)

Change the color bar for F-t and E-t diagrams (colormaps optimized for color-blind individuals)¶

Perceptually uniform colormaps optimized for color-blined individuals are usually recommended for publications and presentations.

Perceptually uniform colormaps

'viridis', 'inferno', 'plasma', 'magma', 'cividis'

Diverging colormaps in which a reddish color and a greenish color are not used at the same time are also good especially for indicating larger or smaller values relative to a reference value.

'seismic', 'bwr', 'coolwarm', 'RdYlBu', 'RdBu', 'RdGy'

Append _r to the name of any colormap to get the reversed version.

Example: 'seismic_r', 'RdYlBu_r'

In [ ]:
pytplot.options('erg_pwe_hfa_l2_low_spectra_e_mix', 'colormap', 'inferno')       #Chage the colormap for HFA data again.
pytplot.options('erg_pwe_ofa_l2_spec_E_spectra_132', 'colormap', 'inferno' )     #Chage the colormap for OFA-E data again.
pytplot.options('erg_pwe_ofa_l2_spec_B_spectra_132', 'colormap', 'inferno' )     #Chage the colormap for OFA-B data again.
pytplot.options('erg_pwe_efd_l2_spec_spectra', 'colormap', 'inferno' )           #Chage the colormap for EFD-Spectrum data again.
#pytplot.options(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra'], 'colormap', 'inferno' )
pytplot.options('erg_xep_l2_FEDO_SSD', 'colormap', 'viridis')                    #Chage the colormap for XEP data again.
pytplot.options('erg_hep_l2_FEDO_H', 'colormap', 'viridis' )                     #Chage the colormap for HEP-H data again.
pytplot.options('erg_hep_l2_FEDO_L', 'colormap', 'viridis' )                     #Chage the colormap for HEP-L data again.
pytplot.options('erg_mepe_l2_omniflux_FEDO', 'colormap', 'viridis' )             #Chage the colormap for MEP-e data again.
pytplot.options('erg_mepi_l2_omniflux_FPDO', 'colormap', 'viridis' )             #Chage the colormap for MEP-i data again.
pytplot.options('erg_lepe_l2_omniflux_FEDO', 'colormap', 'viridis' )             #Chage the colormap for LEP-e data again.
pytplot.options('erg_lepi_l2_omniflux_FPDO', 'colormap', 'viridis' )             #Chage the colormap for LEP-i data again.
#pytplot.options(['erg_xep_l2_FEDO_SSD','erg_hep_l2_FEDO_H','erg_hep_l2_FEDO_L','erg_mepe_l2_omniflux_FEDO','erg_mepi_l2_omniflux_FPDO','erg_lepe_l2_omniflux_FEDO','erg_lepi_l2_omniflux_FPDO'], 'colormap', 'viridis')

#Plot all the data with x and ysizes of 10 and 30, respectively.
tplot(['erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm', 'erg_xep_l2_FEDO_SSD', 'erg_hep_l2_FEDO_H', 'erg_hep_l2_FEDO_L', 'erg_mepe_l2_omniflux_FEDO', 'erg_mepi_l2_omniflux_FPDO', 'erg_lepi_l2_omniflux_FPDO'], xsize = 10, ysize = 30)

Add some extra Xaxes to the bottom of the plot¶

In [ ]:
orb( trange=['2017-09-07 00:00:00', '2017-09-08 00:00:00'])                      #Load Arase orbit data.
labels = pytplot.split_vec( 'erg_orb_l2_pos_rmlatmlt' )                          #Select label data to show at a bottom plot.
pytplot.options( 'erg_orb_l2_pos_rmlatmlt_x', 'ytitle', 'R [Re]' )               #Set title of altitude data.
pytplot.options( 'erg_orb_l2_pos_rmlatmlt_y', 'ytitle', 'MLat [deg]' )           #Set title of magnetic latitude data.
pytplot.options( 'erg_orb_l2_pos_rmlatmlt_z', 'ytitle', 'MLT [h]' )              #Set title of magnetic local time data.

#Plot all the data.
tplot( [ 'erg_pwe_hfa_l2_low_spectra_e_mix', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132', 'erg_pwe_efd_l2_spec_spectra', 'erg_mgf_l2_mag_8sec_sm'], var_label=labels, xsize = 10, ysize = 20 )

08-Mar-24 00:47:40: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/
08-Mar-24 00:47:42: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/erg_orb_l2_20170907_v03.cdf to erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170907_v03.cdf
08-Mar-24 00:47:42: Download complete: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170907_v03.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Level-2 orbit data

Information about ERG orbit


RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en

Contact: erg-sc-core at isee.nagoya-u.ac.jp
**************************************************************************

Remove tplot variables that have been loaded¶

In [ ]:
pytplot.del_data( 'erg_*' )
vars = pytplot.tplot_names()

Use the part_products library to obtain particle spectra¶

An experimental version of the part_products library has just been implemented to the ERG-SC plug-in. So far only the bleeding-edge distribution of the plug-in contains the part_products. In near future, after fully tested, the ERG part_products will be merged to the main distribution of pySPEDAS.

As of Mar., 2022, the following modules are released experimentally:

  • erg_xep_part_products()
  • erg_hep_part_products()
  • erg_mep_part_products() for MEP-e and MEP-i Normal mode data
  • erg_lep_part_products() for LEP-e and LEP-i Normal mode data

They can be used with common arguments and options, similar to those of the (original) IDL version. Several spectrum plots using part_products are demonstrated below to show how to use the library for Arase's particle data.

Generate a tplot variable containing energy-time spectra¶

In [ ]:
# Load MEP-e Lv.2 3-D flux data
pytplot.timespan( '2017-09-08 20:00:00', 4, keyword='hours' )
mepe( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], datatype='3dflux' )

08-Mar-24 00:49:34: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/3dflux/2017/09/
08-Mar-24 00:49:35: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/3dflux/2017/09/erg_mepe_l2_3dflux_20170908_v01_01.cdf to erg_data/satellite/erg/mepe/l2/3dflux/2017/09/erg_mepe_l2_3dflux_20170908_v01_01.cdf
08-Mar-24 00:49:56: Download complete: erg_data/satellite/erg/mepe/l2/3dflux/2017/09/erg_mepe_l2_3dflux_20170908_v01_01.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Medium-Energy Particle experiments - electron analyzer (MEP-e) Level 2 3D electron flux data

PI:  Satoshi Kasahara
Affiliation: The University of Tokyo

- The rules of the road (RoR) common to the ERG project:
      https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for MEP-e data:  https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mepe

Contact: erg_mep_info at isee.nagoya-u.ac.jp
**************************************************************************
Out[ ]:
['erg_mepe_l2_3dflux_FEDU',
 'erg_mepe_l2_3dflux_FEDU_n',
 'erg_mepe_l2_3dflux_FEEDU',
 'erg_mepe_l2_3dflux_count_raw',
 'erg_mepe_l2_3dflux_spin_phase']
In [ ]:
# Calculate energy-time spectra of the omni-dir. electron flux based on MEP-e data
from pyspedas.erg import erg_mep_part_products
vars = erg_mep_part_products( 'erg_mepe_l2_3dflux_FEDU', outputs='energy', trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] )
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy', 'y_no_resample', 1)
tplot( 'erg_mepe_l2_3dflux_FEDU_energy' )
In [ ]:
# Calculate pitch-angle-time spectra of electron flux based on MEP-e data

vars = mgf( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] ) # Load necessary B-field data
vars = orb( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] ) # Load necessary orbit data
mag_vn = 'erg_mgf_l2_mag_8sec_dsi'
pos_vn = 'erg_orb_l2_pos_gse'

vars = erg_mep_part_products( 'erg_mepe_l2_3dflux_FEDU', outputs='pa', energy=[15000., 22000.], fac_type='xdsi', mag_name=mag_vn, pos_name=pos_vn, trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] )
pytplot.options('erg_mepe_l2_3dflux_FEDU_pa', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_pa', 'y_no_resample', 1)
tplot( 'erg_mepe_l2_3dflux_FEDU_pa' )

08-Mar-24 00:51:18: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/
08-Mar-24 00:51:19: File is current: erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170908_v03.04.cdf
08-Mar-24 00:51:19: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Magnetic Field Experiment (MGF) Level 2 spin-averaged magnetic field data

Information about ERG MGF

PI:  Ayako Matsuoka
Affiliation: Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake Cho, Sakyo-ku Kyoto 606-8502, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf
Contact: erg_mgf_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:51:21: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/erg_orb_l2_20170908_v03.cdf to erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170908_v03.cdf
08-Mar-24 00:51:21: Download complete: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170908_v03.cdf
08-Mar-24 00:51:22: erg_mgf_l2_mag_8sec_dsi_shifted copied to erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:51:22: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:51:22: erg_orb_l2_pos_gse copied to erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:51:22: tinterpol (linear) was applied to: erg_orb_l2_pos_gse_pgs_temp

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Level-2 orbit data

Information about ERG orbit


RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en

Contact: erg-sc-core at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 00:51:27: erg_mepe_l2_3dflux_FEDU is 42% done.
08-Mar-24 00:51:32: erg_mepe_l2_3dflux_FEDU is 70% done.
In [ ]:
# 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=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], suffix='_pa80-100' )
vars = erg_mep_part_products( 'erg_mepe_l2_3dflux_FEDU', outputs='fac_energy', pitch=[40., 50.], fac_type='xdsi', mag_name=mag_vn, pos_name=pos_vn, trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], suffix='_pa40-50' )
vars = erg_mep_part_products( 'erg_mepe_l2_3dflux_FEDU', outputs='fac_energy', pitch=[10., 20.], fac_type='xdsi', mag_name=mag_vn, pos_name=pos_vn, trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], suffix='_pa10-20' )
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=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], suffix='_pa0-10' )

## Decorate the obtained spectrum variables
pytplot.options( 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'ytitle', 'MEP-e flux\nPA: 80-100\n\n[eV]')
pytplot.options( 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50', 'ytitle', 'MEP-e flux\nPA: 40-50\n\n[eV]')
pytplot.options( 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20', 'ytitle', 'MEP-e flux\nPA: 10-20\n\n[eV]')
pytplot.options( 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10', 'ytitle', 'MEP-e flux\nPA: 0-10\n\n[eV]')

pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'y_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50', 'y_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20', 'y_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10', 'x_no_resample', 1)
pytplot.options('erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10', 'y_no_resample', 1)

tplot( ['erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100', 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50', 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20', 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10' ] , xsize = 10, ysize = 20)

08-Mar-24 00:59:29: erg_mgf_l2_mag_8sec_dsi_shifted copied to erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:29: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:29: erg_orb_l2_pos_gse copied to erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:29: tinterpol (linear) was applied to: erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:29: /usr/local/lib/python3.10/dist-packages/pyspedas/erg/satellite/erg/particle/erg_pgs_make_e_spec.py:30: RuntimeWarning: invalid value encountered in divide
  ave = data_array.sum(axis=1) / data['bins'].sum(axis=1)

08-Mar-24 00:59:34: erg_mepe_l2_3dflux_FEDU is 50% done.
08-Mar-24 00:59:39: erg_mgf_l2_mag_8sec_dsi_shifted copied to erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:39: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:39: erg_orb_l2_pos_gse copied to erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:39: tinterpol (linear) was applied to: erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:39: /usr/local/lib/python3.10/dist-packages/pyspedas/erg/satellite/erg/particle/erg_pgs_make_e_spec.py:30: RuntimeWarning: invalid value encountered in divide
  ave = data_array.sum(axis=1) / data['bins'].sum(axis=1)

08-Mar-24 00:59:44: erg_mepe_l2_3dflux_FEDU is 56% done.
08-Mar-24 00:59:48: erg_mgf_l2_mag_8sec_dsi_shifted copied to erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:48: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:48: erg_orb_l2_pos_gse copied to erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:48: tinterpol (linear) was applied to: erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:48: /usr/local/lib/python3.10/dist-packages/pyspedas/erg/satellite/erg/particle/erg_pgs_make_e_spec.py:30: RuntimeWarning: invalid value encountered in divide
  ave = data_array.sum(axis=1) / data['bins'].sum(axis=1)

08-Mar-24 00:59:53: erg_mepe_l2_3dflux_FEDU is 58% done.
08-Mar-24 00:59:57: erg_mgf_l2_mag_8sec_dsi_shifted copied to erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:57: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
08-Mar-24 00:59:57: erg_orb_l2_pos_gse copied to erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:57: tinterpol (linear) was applied to: erg_orb_l2_pos_gse_pgs_temp
08-Mar-24 00:59:57: /usr/local/lib/python3.10/dist-packages/pyspedas/erg/satellite/erg/particle/erg_pgs_make_e_spec.py:30: RuntimeWarning: invalid value encountered in divide
  ave = data_array.sum(axis=1) / data['bins'].sum(axis=1)

08-Mar-24 01:00:02: erg_mepe_l2_3dflux_FEDU is 57% done.
In [ ]:
# Derivation of the ion velocity moments from MEP-i proton data

## First load the necessary datasets
from pyspedas.erg import mgf, orb, mepi_nml, erg_mep_part_products
vars = mepi_nml( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'], datatype='3dflux' ) # Load necessary B-field data
vars = mgf( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] ) # Load necessary B-field data
vars = orb( trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'] ) # Load necessary orbit data
mag_vn = 'erg_mgf_l2_mag_8sec_dsi'
pos_vn = 'erg_orb_l2_pos_gse'

08-Mar-24 01:03:17: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/3dflux/2017/09/
08-Mar-24 01:03:19: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/3dflux/2017/09/erg_mepi_l2_3dflux_20170908_v01_03.cdf to erg_data/satellite/erg/mepi/l2/3dflux/2017/09/erg_mepi_l2_3dflux_20170908_v01_03.cdf
08-Mar-24 01:03:23: Download complete: erg_data/satellite/erg/mepi/l2/3dflux/2017/09/erg_mepi_l2_3dflux_20170908_v01_03.cdf
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FPDU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FHE2DU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FHEDU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FOPPDU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FODU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_FO2PDU contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_P contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_HE2 contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_HE contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_OPP contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_O contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: erg_mepi_l2_3dflux_count_raw_O2P contains negative values; setting the z-axis to log scale will cause the negative values to be ignored on figures.
08-Mar-24 01:04:09: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/

printing PI info and rules of the road was failed

08-Mar-24 01:04:10: File is current: erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170908_v03.04.cdf
08-Mar-24 01:04:10: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Magnetic Field Experiment (MGF) Level 2 spin-averaged magnetic field data

Information about ERG MGF

PI:  Ayako Matsuoka
Affiliation: Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake Cho, Sakyo-ku Kyoto 606-8502, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf
Contact: erg_mgf_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:04:11: File is current: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170908_v03.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Level-2 orbit data

Information about ERG orbit


RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en

Contact: erg-sc-core at isee.nagoya-u.ac.jp
**************************************************************************
In [ ]:
## Calculate a set of velocity moments with the "part_products" library.
vars = erg_mep_part_products( 'erg_mepi_l2_3dflux_FPDU', outputs='moments', mag_name=mag_vn, pos_name=pos_vn, trange=['2017-09-08 20:00:00', '2017-09-08 24:00:00'])

08-Mar-24 01:04:37: tinterpol (linear) was applied to: erg_mgf_l2_mag_8sec_dsi_shifted
08-Mar-24 01:04:40: /usr/local/lib/python3.10/dist-packages/pyspedas/particles/moments/moments_3d.py:78: RuntimeWarning: overflow encountered in multiply
  tmp = data['data']*de_e*weight*e_inf**1.5/energy

08-Mar-24 01:04:40: /usr/local/lib/python3.10/dist-packages/numpy/core/fromnumeric.py:88: RuntimeWarning: invalid value encountered in reduce
  return ufunc.reduce(obj, axis, dtype, out, **passkwargs)

08-Mar-24 01:04:40: /usr/local/lib/python3.10/dist-packages/pyspedas/particles/moments/moments_3d.py:90: RuntimeWarning: overflow encountered in multiply
  tmp = data['data']*de_e*weight*e_inf**2/energy

08-Mar-24 01:04:42: erg_mepi_l2_3dflux_FPDU is 80% done.
08-Mar-24 01:04:43: erg_mepi_l2_3dflux_FPDU_eflux is currently not in pytplot.
08-Mar-24 01:04:43: erg_mepi_l2_3dflux_FPDU_eflux is currently not in pytplot.
In [ ]:
pytplot.tplot_names()

0 : erg_mepe_l2_3dflux_FEDU
1 : erg_mepe_l2_3dflux_FEDU_n
2 : erg_mepe_l2_3dflux_FEEDU
3 : erg_mepe_l2_3dflux_count_raw
4 : erg_mepe_l2_3dflux_spin_phase
5 : erg_mepe_l2_3dflux_FEDU_energy
6 : erg_mgf_l2_epoch_8sec
7 : erg_mgf_l2_mag_8sec_dsi
8 : erg_mgf_l2_mag_8sec_gse
9 : erg_mgf_l2_mag_8sec_gsm
10 : erg_mgf_l2_mag_8sec_sm
11 : erg_mgf_l2_magt_8sec
12 : erg_mgf_l2_rmsd_8sec_dsi
13 : erg_mgf_l2_rmsd_8sec_gse
14 : erg_mgf_l2_rmsd_8sec_gsm
15 : erg_mgf_l2_rmsd_8sec_sm
16 : erg_mgf_l2_rmsd_8sec
17 : erg_mgf_l2_n_rmsd_8sec
18 : erg_mgf_l2_dyn_rng_8sec
19 : erg_mgf_l2_quality_8sec
20 : erg_mgf_l2_quality_8sec_gc
21 : erg_mgf_l2_igrf_8sec_dsi
22 : erg_mgf_l2_igrf_8sec_gse
23 : erg_mgf_l2_igrf_8sec_gsm
24 : erg_mgf_l2_igrf_8sec_sm
25 : erg_orb_l2_pos_llr
26 : erg_orb_l2_pos_gse
27 : erg_orb_l2_pos_gsm
28 : erg_orb_l2_pos_sm
29 : erg_orb_l2_pos_rmlatmlt
30 : erg_orb_l2_pos_eq
31 : erg_orb_l2_pos_iono_north
32 : erg_orb_l2_pos_iono_south
33 : erg_orb_l2_pos_blocal
34 : erg_orb_l2_pos_blocal_mag
35 : erg_orb_l2_pos_beq
36 : erg_orb_l2_pos_beq_mag
37 : erg_orb_l2_pos_Lm
38 : erg_orb_l2_vel_gse
39 : erg_orb_l2_vel_gsm
40 : erg_orb_l2_vel_sm
41 : erg_orb_l2_spn_num
42 : erg_orb_l2_man_prep_flag
43 : erg_orb_l2_man_on_flag
44 : erg_orb_l2_eclipse_flag
45 : erg_mgf_l2_mag_8sec_dsi_shifted
46 : erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
47 : erg_orb_l2_pos_gse_pgs_temp
48 : erg_mepe_l2_3dflux_FEDU_pa
49 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100
50 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10
51 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50
52 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20
53 : erg_mepi_l2_3dflux_spin_phase
54 : erg_mepi_l2_3dflux_FPDU
55 : erg_mepi_l2_3dflux_FPEDU
56 : erg_mepi_l2_3dflux_count_raw_P
57 : erg_mepi_l2_3dflux_FPDU_Energy
58 : erg_mepi_l2_3dflux_FHE2DU
59 : erg_mepi_l2_3dflux_FHE2EDU
60 : erg_mepi_l2_3dflux_count_raw_HE2
61 : erg_mepi_l2_3dflux_FHE2DU_Energy
62 : erg_mepi_l2_3dflux_FHEDU
63 : erg_mepi_l2_3dflux_FHEEDU
64 : erg_mepi_l2_3dflux_count_raw_HE
65 : erg_mepi_l2_3dflux_FHEDU_Energy
66 : erg_mepi_l2_3dflux_FOPPDU
67 : erg_mepi_l2_3dflux_FOPPEDU
68 : erg_mepi_l2_3dflux_count_raw_OPP
69 : erg_mepi_l2_3dflux_FOPPDU_Energy
70 : erg_mepi_l2_3dflux_FODU
71 : erg_mepi_l2_3dflux_FOEDU
72 : erg_mepi_l2_3dflux_count_raw_O
73 : erg_mepi_l2_3dflux_FODU_Energy
74 : erg_mepi_l2_3dflux_FO2PDU
75 : erg_mepi_l2_3dflux_FO2PEDU
76 : erg_mepi_l2_3dflux_count_raw_O2P
77 : erg_mepi_l2_3dflux_FO2PDU_Energy
78 : erg_mepi_l2_3dflux_FPDU_density
79 : erg_mepi_l2_3dflux_FPDU_flux
80 : erg_mepi_l2_3dflux_FPDU_mftens
81 : erg_mepi_l2_3dflux_FPDU_velocity
82 : erg_mepi_l2_3dflux_FPDU_ptens
83 : erg_mepi_l2_3dflux_FPDU_ttens
84 : erg_mepi_l2_3dflux_FPDU_vthermal
85 : erg_mepi_l2_3dflux_FPDU_avgtemp
Out[ ]:
['erg_mepe_l2_3dflux_FEDU',
 'erg_mepe_l2_3dflux_FEDU_n',
 'erg_mepe_l2_3dflux_FEEDU',
 'erg_mepe_l2_3dflux_count_raw',
 'erg_mepe_l2_3dflux_spin_phase',
 'erg_mepe_l2_3dflux_FEDU_energy',
 'erg_mgf_l2_epoch_8sec',
 'erg_mgf_l2_mag_8sec_dsi',
 'erg_mgf_l2_mag_8sec_gse',
 'erg_mgf_l2_mag_8sec_gsm',
 'erg_mgf_l2_mag_8sec_sm',
 'erg_mgf_l2_magt_8sec',
 'erg_mgf_l2_rmsd_8sec_dsi',
 'erg_mgf_l2_rmsd_8sec_gse',
 'erg_mgf_l2_rmsd_8sec_gsm',
 'erg_mgf_l2_rmsd_8sec_sm',
 'erg_mgf_l2_rmsd_8sec',
 'erg_mgf_l2_n_rmsd_8sec',
 'erg_mgf_l2_dyn_rng_8sec',
 'erg_mgf_l2_quality_8sec',
 'erg_mgf_l2_quality_8sec_gc',
 'erg_mgf_l2_igrf_8sec_dsi',
 'erg_mgf_l2_igrf_8sec_gse',
 'erg_mgf_l2_igrf_8sec_gsm',
 'erg_mgf_l2_igrf_8sec_sm',
 'erg_orb_l2_pos_llr',
 'erg_orb_l2_pos_gse',
 'erg_orb_l2_pos_gsm',
 'erg_orb_l2_pos_sm',
 'erg_orb_l2_pos_rmlatmlt',
 'erg_orb_l2_pos_eq',
 'erg_orb_l2_pos_iono_north',
 'erg_orb_l2_pos_iono_south',
 'erg_orb_l2_pos_blocal',
 'erg_orb_l2_pos_blocal_mag',
 'erg_orb_l2_pos_beq',
 'erg_orb_l2_pos_beq_mag',
 'erg_orb_l2_pos_Lm',
 'erg_orb_l2_vel_gse',
 'erg_orb_l2_vel_gsm',
 'erg_orb_l2_vel_sm',
 'erg_orb_l2_spn_num',
 'erg_orb_l2_man_prep_flag',
 'erg_orb_l2_man_on_flag',
 'erg_orb_l2_eclipse_flag',
 'erg_mgf_l2_mag_8sec_dsi_shifted',
 'erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp',
 'erg_orb_l2_pos_gse_pgs_temp',
 'erg_mepe_l2_3dflux_FEDU_pa',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20',
 'erg_mepi_l2_3dflux_spin_phase',
 'erg_mepi_l2_3dflux_FPDU',
 'erg_mepi_l2_3dflux_FPEDU',
 'erg_mepi_l2_3dflux_count_raw_P',
 'erg_mepi_l2_3dflux_FPDU_Energy',
 'erg_mepi_l2_3dflux_FHE2DU',
 'erg_mepi_l2_3dflux_FHE2EDU',
 'erg_mepi_l2_3dflux_count_raw_HE2',
 'erg_mepi_l2_3dflux_FHE2DU_Energy',
 'erg_mepi_l2_3dflux_FHEDU',
 'erg_mepi_l2_3dflux_FHEEDU',
 'erg_mepi_l2_3dflux_count_raw_HE',
 'erg_mepi_l2_3dflux_FHEDU_Energy',
 'erg_mepi_l2_3dflux_FOPPDU',
 'erg_mepi_l2_3dflux_FOPPEDU',
 'erg_mepi_l2_3dflux_count_raw_OPP',
 'erg_mepi_l2_3dflux_FOPPDU_Energy',
 'erg_mepi_l2_3dflux_FODU',
 'erg_mepi_l2_3dflux_FOEDU',
 'erg_mepi_l2_3dflux_count_raw_O',
 'erg_mepi_l2_3dflux_FODU_Energy',
 'erg_mepi_l2_3dflux_FO2PDU',
 'erg_mepi_l2_3dflux_FO2PEDU',
 'erg_mepi_l2_3dflux_count_raw_O2P',
 'erg_mepi_l2_3dflux_FO2PDU_Energy',
 'erg_mepi_l2_3dflux_FPDU_density',
 'erg_mepi_l2_3dflux_FPDU_flux',
 'erg_mepi_l2_3dflux_FPDU_mftens',
 'erg_mepi_l2_3dflux_FPDU_velocity',
 'erg_mepi_l2_3dflux_FPDU_ptens',
 'erg_mepi_l2_3dflux_FPDU_ttens',
 'erg_mepi_l2_3dflux_FPDU_vthermal',
 'erg_mepi_l2_3dflux_FPDU_avgtemp']
In [ ]:
## Then plot the derived partial density, temperature, and pressure tensor for MEP-i protons.

import pytplot
pytplot.tplot(['erg_mepi_l2_3dflux_FPDU_density','erg_mepi_l2_3dflux_FPDU_avgtemp','erg_mepi_l2_3dflux_FPDU_ptens'], xsize = 10, ysize=12)

Load and plot OMNI data¶

This is an example to show how to load the OMNI data, which include the solar wind parameters as well as some frequently used geomagnetic indices, and to manipulate and plot the data.

In [ ]:
pyspedas.omni.data( trange=['2017-09-06', '2017-09-09'], time_clip=False )  ## Download and then load OMNI data for 2 days from September 7, 2017.

08-Mar-24 01:06:17: Downloading remote index: https://spdf.gsfc.nasa.gov/pub/data/omni/omni_cdaweb/hro2_1min/2017/
08-Mar-24 01:06:17: Downloading https://spdf.gsfc.nasa.gov/pub/data/omni/omni_cdaweb/hro2_1min/2017/omni_hro2_1min_20170901_v01.cdf to omni_data/hro2_1min/2017/omni_hro2_1min_20170901_v01.cdf
08-Mar-24 01:06:17: Download complete: omni_data/hro2_1min/2017/omni_hro2_1min_20170901_v01.cdf
Out[ ]:
['IMF',
 'PLS',
 'IMF_PTS',
 'PLS_PTS',
 'percent_interp',
 'Timeshift',
 'RMS_Timeshift',
 'RMS_phase',
 'Time_btwn_obs',
 'F',
 'BX_GSE',
 'BY_GSE',
 'BZ_GSE',
 'BY_GSM',
 'BZ_GSM',
 'RMS_SD_B',
 'RMS_SD_fld_vec',
 'flow_speed',
 'Vx',
 'Vy',
 'Vz',
 'proton_density',
 'T',
 'NaNp_Ratio',
 'Pressure',
 'E',
 'Beta',
 'Mach_num',
 'Mgs_mach_num',
 'x',
 'y',
 'z',
 'BSN_x',
 'BSN_y',
 'BSN_z',
 'AE_INDEX',
 'AL_INDEX',
 'AU_INDEX',
 'SYM_D',
 'SYM_H',
 'ASY_D',
 'ASY_H']

Set a time range for which plots are made using timespan(), and plot several tplot variables with tplot().

In [ ]:
pytplot.timespan( "2017-09-06 12:00:00", 2.5, keyword='days')    #Set time span from 2017-09-06 12:00:00 to 2017-09-09 00:00:00
tplot(['BX_GSE', 'BY_GSM', 'BZ_GSM', 'flow_speed', 'proton_density', 'Pressure','AU_INDEX', 'AL_INDEX','SYM_H',], xsize=10, ysize=20 )

he keywords xsize and ysize can be used to resize a plot. The default size is approximately (xsize, ysize) = (8, 9). The example right below thus produces a vertically-long plot.

In [ ]:
tplot( ['flow_speed', 'Pressure','SYM_H'], xsize=4, ysize=10 )

pytplot.options() module changes various attributes for a tplot variable. The example below changes the type of the vertical axis for the tplot variable Pressure to logarithmic. The resultant plot shows the dynamic pressure (bottom panel) with a log scale.

In [ ]:
pytplot.options('Pressure', 'ylog', True )
tplot( ['flow_speed', 'Pressure','SYM_H'], xsize=12, ysize=4 )  ## This pair of (xsize, ysize) would lead to a horizontally-long plot.
In [ ]:
pytplot.options('Pressure', 'ylog', False )

The following example changes the title of the tplot variable Pressure. ytitle attribute holds a string that is to be shown by the vertical axis of a tplot variable. '\n' (backslash + n) is replaced with a newline.

In [ ]:
pytplot.options('Pressure', 'ytitle', 'Dynamic\npressure' )
tplot( ['flow_speed', 'Pressure','SYM_H'] , xsize=10, ysize=10 )

yrange attribute should be set to be a two-element array determining the range of a vertical axis.

timebar() can draw a vertical line at a designated date&time. Setting delete keyword to be True, the command erases the vertical lines previously drawn at the given date.

In [ ]:
pytplot.options( 'AL_INDEX', 'yrange', [-1000,0])
t_lines = pyspedas.time_double('2017-09-07 23:00:00')
pytplot.timebar( t_lines )
tplot(['Pressure','AL_INDEX','SYM_H'], xsize=10, ysize=10)
In [ ]:
pytplot.timebar( t_lines, delete=True )
tplot( ['Pressure','AL_INDEX','SYM_H'], xsize=10, ysize=10 )

Create time-series plots of OMNI and Arase OFA spectra data.¶

In [ ]:
from pyspedas.erg import pwe_ofa
pwe_ofa( trange=['2017-09-06 00:00:00', '2017-09-09 00:00:00'] )
pytplot.timespan( '2017-09-06 12:00:00', 2.5)
pytplot.zlim( 'erg_pwe_ofa_l2_spec_E_spectra_132', 1e-7, 1e-2 )

pytplot.options(['erg_pwe_ofa_l2_spec_E_spectra_132','erg_pwe_ofa_l2_spec_B_spectra_132'], 'data_gap', 8.0)
pytplot.options(['erg_pwe_ofa_l2_spec_E_spectra_132','erg_pwe_ofa_l2_spec_B_spectra_132'], 'x_no_resample', 1)
pytplot.options(['erg_pwe_ofa_l2_spec_E_spectra_132','erg_pwe_ofa_l2_spec_B_spectra_132'], 'y_no_resample', 1)

tplot( ['Pressure', 'AL_INDEX','SYM_H', 'erg_pwe_ofa_l2_spec_E_spectra_132', 'erg_pwe_ofa_l2_spec_B_spectra_132'] , xsize=10, ysize=20)

08-Mar-24 01:10:07: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/
08-Mar-24 01:10:10: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170906_v02_03.cdf to erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170906_v02_03.cdf
08-Mar-24 01:10:14: Download complete: erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170906_v02_03.cdf
08-Mar-24 01:10:16: File is current: erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170907_v02_03.cdf
08-Mar-24 01:10:18: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170908_v02_03.cdf to erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170908_v02_03.cdf
08-Mar-24 01:10:22: Download complete: erg_data/satellite/erg/pwe/ofa/l2/spec/2017/09/erg_pwe_ofa_l2_spec_20170908_v02_03.cdf

 
 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Plasma Wave Experiment (PWE) Onboard Frequency Analyzer (OFA) Level 2 spectrum data

Information about ERG PWE OFA

PI:  Yoshiya Kasahara
Affiliation: Kanazawa University

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of PWE/OFA: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Pwe/Ofa

Contact: erg_pwe_info at isee.nagoya-u.ac.jp
**************************************************************************

Let's reproduce Figure 3 by Kistler et al. (2023) with PySPEDAS¶

This paper has been recently press-released on October 30 for both domestic and international media. Kistler et al. (2023) showed that the plasma sheet source changes from predominantly solar wind to predominantly ionospheric as a storm develops. In this tranning, we try to reproduce Figure 3 by Kistler et al. (2023) with PySPEDAS.

Load Arase particle data with PySPEDAS¶

In [ ]:
from pyspedas.erg import pwe_hfa, pwe_ofa, pwe_efd, mgf, xep, hep, mepe, lepe, mepi_nml, lepi, orb
trng = ['2017-09-06 00:00:00', '2017-09-09 00:00:00']
mepe( trange=trng, datatype='omniflux' )
lepe( trange=trng, datatype='omniflux' )
mepi_nml( trange=trng, datatype='omniflux' )
lepi( trange=trng, datatype='omniflux' )
orb(trange=trng, level = 'l2')
orb(trange=trng, level = 'l3', model ='t89')

08-Mar-24 01:12:31: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/omniflux/2017/09/
08-Mar-24 01:12:32: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170906_v01_02.cdf to erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170906_v01_02.cdf
08-Mar-24 01:12:33: Download complete: erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170906_v01_02.cdf
08-Mar-24 01:12:33: File is current: erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170907_v01_02.cdf
08-Mar-24 01:12:34: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170908_v01_02.cdf to erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170908_v01_02.cdf
08-Mar-24 01:12:35: Download complete: erg_data/satellite/erg/mepe/l2/omniflux/2017/09/erg_mepe_l2_omniflux_20170908_v01_02.cdf
08-Mar-24 01:12:35: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepe/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Medium Energy Particle experiments - electron analyzer (MEP-e) electron omni flux data

PI:  Satoshi Kasahara
Affiliation: The University of Tokyo

- The rules of the road (RoR) common to the ERG project:
      https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for MEP-e data:  https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mepe

Contact: erg_mep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:12:36: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170906_v04_01.cdf to erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170906_v04_01.cdf
08-Mar-24 01:12:37: Download complete: erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170906_v04_01.cdf
08-Mar-24 01:12:37: File is current: erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170907_v04_01.cdf
08-Mar-24 01:12:38: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170908_v04_01.cdf to erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170908_v04_01.cdf
08-Mar-24 01:12:39: Download complete: erg_data/satellite/erg/lepe/l2/omniflux/2017/09/erg_lepe_l2_omniflux_20170908_v04_01.cdf
08-Mar-24 01:12:39: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Low-Energy Particle experiments - electron analyzer (LEP-e) Level 2 omni electron flux data

Information about ERG LEPe

PI:  Shiang-Yu Wang
Affiliation: Academia Sinica, Taiwan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of LEPe L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepe

Contact: erg_lepe_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:12:41: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170906_v02_01.cdf to erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170906_v02_01.cdf
08-Mar-24 01:12:42: Download complete: erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170906_v02_01.cdf
08-Mar-24 01:12:42: File is current: erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170907_v02_01.cdf
08-Mar-24 01:12:43: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170908_v02_01.cdf to erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170908_v02_01.cdf
08-Mar-24 01:12:44: Download complete: erg_data/satellite/erg/mepi/l2/omniflux/2017/09/erg_mepi_l2_omniflux_20170908_v02_01.cdf
08-Mar-24 01:12:44: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepi/l2/omniflux/2017/09/

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Medium Energy Particle experiments - ion mass analyzer (MEP-i) 3D ion omni flux data

PI:  Shoichiro Yokota
Affiliation: Osaka University

- The rules of the road (RoR) common to the ERG project:
      https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
- RoR for MEP-i data: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mepi

Contact: erg_mep_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:12:46: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170906_v03_00.cdf to erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170906_v03_00.cdf
08-Mar-24 01:12:46: Download complete: erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170906_v03_00.cdf
08-Mar-24 01:12:47: File is current: erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170907_v03_00.cdf
08-Mar-24 01:12:48: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170908_v03_00.cdf to erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170908_v03_00.cdf
08-Mar-24 01:12:49: Download complete: erg_data/satellite/erg/lepi/l2/omniflux/2017/09/erg_lepi_l2_omniflux_20170908_v03_00.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Low Energy Particle Ion (LEPi) Experiment 3D ion flux data

Information about ERG LEPi

PI:  Kazushi Asamura
Affiliation: ISAS, Jaxa

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of LEPi L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi
RoR of ERG/LEPi: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Lepi#Rules_of_the_Road

Contact: erg_lepi_info at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:12:49: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/
08-Mar-24 01:12:51: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/def/2017/erg_orb_l2_20170906_v03.cdf to erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170906_v03.cdf
08-Mar-24 01:12:51: Download complete: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170906_v03.cdf
08-Mar-24 01:12:52: File is current: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170907_v03.cdf
08-Mar-24 01:12:53: File is current: erg_data/satellite/erg/orb/def/2017/erg_orb_l2_20170908_v03.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Level-2 orbit data

Information about ERG orbit


RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en

Contact: erg-sc-core at isee.nagoya-u.ac.jp
**************************************************************************

08-Mar-24 01:12:57: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/l3/t89/2017/09/
08-Mar-24 01:12:57: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170906_v02.cdf to erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170906_v02.cdf
08-Mar-24 01:12:58: Download complete: erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170906_v02.cdf
08-Mar-24 01:12:59: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170907_v02.cdf to erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170907_v02.cdf
08-Mar-24 01:12:59: Download complete: erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170907_v02.cdf
08-Mar-24 01:13:00: Downloading https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170908_v02.cdf to erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170908_v02.cdf
08-Mar-24 01:13:00: Download complete: erg_data/satellite/erg/orb/l3/t89/2017/09/erg_orb_l3_t89_20170908_v02.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Level-3 (t89) orbit data

Information about ERG L3 orbit


RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en

Contact: erg-sc-core at isee.nagoya-u.ac.jp
**************************************************************************
Out[ ]:
['erg_orb_l3_pos_eq_t89',
 'erg_orb_l3_pos_iono_north_t89',
 'erg_orb_l3_pos_iono_south_t89',
 'erg_orb_l3_pos_lmc_t89',
 'erg_orb_l3_pos_lstar_t89',
 'erg_orb_l3_pos_I_t89',
 'erg_orb_l3_pos_blocal_t89',
 'erg_orb_l3_pos_beq_t89']
In [ ]:
pytplot.split_vec('erg_orb_l3_pos_eq_t89')
pytplot.tplot_names()

0 : erg_mepe_l2_3dflux_FEDU
1 : erg_mepe_l2_3dflux_FEDU_n
2 : erg_mepe_l2_3dflux_FEEDU
3 : erg_mepe_l2_3dflux_count_raw
4 : erg_mepe_l2_3dflux_spin_phase
5 : erg_mepe_l2_3dflux_FEDU_energy
6 : erg_mgf_l2_epoch_8sec
7 : erg_mgf_l2_mag_8sec_dsi
8 : erg_mgf_l2_mag_8sec_gse
9 : erg_mgf_l2_mag_8sec_gsm
10 : erg_mgf_l2_mag_8sec_sm
11 : erg_mgf_l2_magt_8sec
12 : erg_mgf_l2_rmsd_8sec_dsi
13 : erg_mgf_l2_rmsd_8sec_gse
14 : erg_mgf_l2_rmsd_8sec_gsm
15 : erg_mgf_l2_rmsd_8sec_sm
16 : erg_mgf_l2_rmsd_8sec
17 : erg_mgf_l2_n_rmsd_8sec
18 : erg_mgf_l2_dyn_rng_8sec
19 : erg_mgf_l2_quality_8sec
20 : erg_mgf_l2_quality_8sec_gc
21 : erg_mgf_l2_igrf_8sec_dsi
22 : erg_mgf_l2_igrf_8sec_gse
23 : erg_mgf_l2_igrf_8sec_gsm
24 : erg_mgf_l2_igrf_8sec_sm
25 : erg_orb_l2_pos_llr
26 : erg_orb_l2_pos_gse
27 : erg_orb_l2_pos_gsm
28 : erg_orb_l2_pos_sm
29 : erg_orb_l2_pos_rmlatmlt
30 : erg_orb_l2_pos_eq
31 : erg_orb_l2_pos_iono_north
32 : erg_orb_l2_pos_iono_south
33 : erg_orb_l2_pos_blocal
34 : erg_orb_l2_pos_blocal_mag
35 : erg_orb_l2_pos_beq
36 : erg_orb_l2_pos_beq_mag
37 : erg_orb_l2_pos_Lm
38 : erg_orb_l2_vel_gse
39 : erg_orb_l2_vel_gsm
40 : erg_orb_l2_vel_sm
41 : erg_orb_l2_spn_num
42 : erg_orb_l2_man_prep_flag
43 : erg_orb_l2_man_on_flag
44 : erg_orb_l2_eclipse_flag
45 : erg_mgf_l2_mag_8sec_dsi_shifted
46 : erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp
47 : erg_orb_l2_pos_gse_pgs_temp
48 : erg_mepe_l2_3dflux_FEDU_pa
49 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100
50 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10
51 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50
52 : erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20
53 : erg_mepi_l2_3dflux_spin_phase
54 : erg_mepi_l2_3dflux_FPDU
55 : erg_mepi_l2_3dflux_FPEDU
56 : erg_mepi_l2_3dflux_count_raw_P
57 : erg_mepi_l2_3dflux_FPDU_Energy
58 : erg_mepi_l2_3dflux_FHE2DU
59 : erg_mepi_l2_3dflux_FHE2EDU
60 : erg_mepi_l2_3dflux_count_raw_HE2
61 : erg_mepi_l2_3dflux_FHE2DU_Energy
62 : erg_mepi_l2_3dflux_FHEDU
63 : erg_mepi_l2_3dflux_FHEEDU
64 : erg_mepi_l2_3dflux_count_raw_HE
65 : erg_mepi_l2_3dflux_FHEDU_Energy
66 : erg_mepi_l2_3dflux_FOPPDU
67 : erg_mepi_l2_3dflux_FOPPEDU
68 : erg_mepi_l2_3dflux_count_raw_OPP
69 : erg_mepi_l2_3dflux_FOPPDU_Energy
70 : erg_mepi_l2_3dflux_FODU
71 : erg_mepi_l2_3dflux_FOEDU
72 : erg_mepi_l2_3dflux_count_raw_O
73 : erg_mepi_l2_3dflux_FODU_Energy
74 : erg_mepi_l2_3dflux_FO2PDU
75 : erg_mepi_l2_3dflux_FO2PEDU
76 : erg_mepi_l2_3dflux_count_raw_O2P
77 : erg_mepi_l2_3dflux_FO2PDU_Energy
78 : erg_mepi_l2_3dflux_FPDU_density
79 : erg_mepi_l2_3dflux_FPDU_flux
80 : erg_mepi_l2_3dflux_FPDU_mftens
81 : erg_mepi_l2_3dflux_FPDU_velocity
82 : erg_mepi_l2_3dflux_FPDU_ptens
83 : erg_mepi_l2_3dflux_FPDU_ttens
84 : erg_mepi_l2_3dflux_FPDU_vthermal
85 : erg_mepi_l2_3dflux_FPDU_avgtemp
86 : IMF
87 : PLS
88 : IMF_PTS
89 : PLS_PTS
90 : percent_interp
91 : Timeshift
92 : RMS_Timeshift
93 : RMS_phase
94 : Time_btwn_obs
95 : F
96 : BX_GSE
97 : BY_GSE
98 : BZ_GSE
99 : BY_GSM
100 : BZ_GSM
101 : RMS_SD_B
102 : RMS_SD_fld_vec
103 : flow_speed
104 : Vx
105 : Vy
106 : Vz
107 : proton_density
108 : T
109 : NaNp_Ratio
110 : Pressure
111 : E
112 : Beta
113 : Mach_num
114 : Mgs_mach_num
115 : x
116 : y
117 : z
118 : BSN_x
119 : BSN_y
120 : BSN_z
121 : AE_INDEX
122 : AL_INDEX
123 : AU_INDEX
124 : SYM_D
125 : SYM_H
126 : ASY_D
127 : ASY_H
128 : erg_pwe_ofa_l2_spec_epoch_e132
129 : erg_pwe_ofa_l2_spec_E_spectra_132
130 : erg_pwe_ofa_l2_spec_quality_flag_e132
131 : erg_pwe_ofa_l2_spec_epoch_b132
132 : erg_pwe_ofa_l2_spec_B_spectra_132
133 : erg_pwe_ofa_l2_spec_quality_flag_b132
134 : erg_mepe_l2_omniflux_epoch
135 : erg_mepe_l2_omniflux_FEDO
136 : erg_lepe_l2_omniflux_FEDO
137 : erg_mepi_l2_omniflux_epoch
138 : erg_mepi_l2_omniflux_epoch_tof
139 : erg_mepi_l2_omniflux_FIDO_Energy
140 : erg_mepi_l2_omniflux_FPDO
141 : erg_mepi_l2_omniflux_FHE2DO
142 : erg_mepi_l2_omniflux_FHEDO
143 : erg_mepi_l2_omniflux_FOPPDO
144 : erg_mepi_l2_omniflux_FODO
145 : erg_mepi_l2_omniflux_FO2PDO
146 : erg_mepi_l2_omniflux_FPDO_tof
147 : erg_mepi_l2_omniflux_FHE2DO_tof
148 : erg_mepi_l2_omniflux_FHEDO_tof
149 : erg_mepi_l2_omniflux_FOPPDO_tof
150 : erg_mepi_l2_omniflux_FODO_tof
151 : erg_mepi_l2_omniflux_FO2PDO_tof
152 : erg_lepi_l2_omniflux_Epoch
153 : erg_lepi_l2_omniflux_FPDO_raw
154 : erg_lepi_l2_omniflux_FHEDO_raw
155 : erg_lepi_l2_omniflux_FODO_raw
156 : erg_lepi_l2_omniflux_FPDO
157 : erg_lepi_l2_omniflux_FHEDO
158 : erg_lepi_l2_omniflux_FODO
159 : erg_orb_l3_pos_eq_t89
160 : erg_orb_l3_pos_iono_north_t89
161 : erg_orb_l3_pos_iono_south_t89
162 : erg_orb_l3_pos_lmc_t89
163 : erg_orb_l3_pos_lstar_t89
164 : erg_orb_l3_pos_I_t89
165 : erg_orb_l3_pos_blocal_t89
166 : erg_orb_l3_pos_beq_t89
167 : erg_orb_l3_pos_eq_t89_0
168 : erg_orb_l3_pos_eq_t89_1
Out[ ]:
['erg_mepe_l2_3dflux_FEDU',
 'erg_mepe_l2_3dflux_FEDU_n',
 'erg_mepe_l2_3dflux_FEEDU',
 'erg_mepe_l2_3dflux_count_raw',
 'erg_mepe_l2_3dflux_spin_phase',
 'erg_mepe_l2_3dflux_FEDU_energy',
 'erg_mgf_l2_epoch_8sec',
 'erg_mgf_l2_mag_8sec_dsi',
 'erg_mgf_l2_mag_8sec_gse',
 'erg_mgf_l2_mag_8sec_gsm',
 'erg_mgf_l2_mag_8sec_sm',
 'erg_mgf_l2_magt_8sec',
 'erg_mgf_l2_rmsd_8sec_dsi',
 'erg_mgf_l2_rmsd_8sec_gse',
 'erg_mgf_l2_rmsd_8sec_gsm',
 'erg_mgf_l2_rmsd_8sec_sm',
 'erg_mgf_l2_rmsd_8sec',
 'erg_mgf_l2_n_rmsd_8sec',
 'erg_mgf_l2_dyn_rng_8sec',
 'erg_mgf_l2_quality_8sec',
 'erg_mgf_l2_quality_8sec_gc',
 'erg_mgf_l2_igrf_8sec_dsi',
 'erg_mgf_l2_igrf_8sec_gse',
 'erg_mgf_l2_igrf_8sec_gsm',
 'erg_mgf_l2_igrf_8sec_sm',
 'erg_orb_l2_pos_llr',
 'erg_orb_l2_pos_gse',
 'erg_orb_l2_pos_gsm',
 'erg_orb_l2_pos_sm',
 'erg_orb_l2_pos_rmlatmlt',
 'erg_orb_l2_pos_eq',
 'erg_orb_l2_pos_iono_north',
 'erg_orb_l2_pos_iono_south',
 'erg_orb_l2_pos_blocal',
 'erg_orb_l2_pos_blocal_mag',
 'erg_orb_l2_pos_beq',
 'erg_orb_l2_pos_beq_mag',
 'erg_orb_l2_pos_Lm',
 'erg_orb_l2_vel_gse',
 'erg_orb_l2_vel_gsm',
 'erg_orb_l2_vel_sm',
 'erg_orb_l2_spn_num',
 'erg_orb_l2_man_prep_flag',
 'erg_orb_l2_man_on_flag',
 'erg_orb_l2_eclipse_flag',
 'erg_mgf_l2_mag_8sec_dsi_shifted',
 'erg_mgf_l2_mag_8sec_dsi_shifted_pgs_temp',
 'erg_orb_l2_pos_gse_pgs_temp',
 'erg_mepe_l2_3dflux_FEDU_pa',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa80-100',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa0-10',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa40-50',
 'erg_mepe_l2_3dflux_FEDU_energy_mag_pa10-20',
 'erg_mepi_l2_3dflux_spin_phase',
 'erg_mepi_l2_3dflux_FPDU',
 'erg_mepi_l2_3dflux_FPEDU',
 'erg_mepi_l2_3dflux_count_raw_P',
 'erg_mepi_l2_3dflux_FPDU_Energy',
 'erg_mepi_l2_3dflux_FHE2DU',
 'erg_mepi_l2_3dflux_FHE2EDU',
 'erg_mepi_l2_3dflux_count_raw_HE2',
 'erg_mepi_l2_3dflux_FHE2DU_Energy',
 'erg_mepi_l2_3dflux_FHEDU',
 'erg_mepi_l2_3dflux_FHEEDU',
 'erg_mepi_l2_3dflux_count_raw_HE',
 'erg_mepi_l2_3dflux_FHEDU_Energy',
 'erg_mepi_l2_3dflux_FOPPDU',
 'erg_mepi_l2_3dflux_FOPPEDU',
 'erg_mepi_l2_3dflux_count_raw_OPP',
 'erg_mepi_l2_3dflux_FOPPDU_Energy',
 'erg_mepi_l2_3dflux_FODU',
 'erg_mepi_l2_3dflux_FOEDU',
 'erg_mepi_l2_3dflux_count_raw_O',
 'erg_mepi_l2_3dflux_FODU_Energy',
 'erg_mepi_l2_3dflux_FO2PDU',
 'erg_mepi_l2_3dflux_FO2PEDU',
 'erg_mepi_l2_3dflux_count_raw_O2P',
 'erg_mepi_l2_3dflux_FO2PDU_Energy',
 'erg_mepi_l2_3dflux_FPDU_density',
 'erg_mepi_l2_3dflux_FPDU_flux',
 'erg_mepi_l2_3dflux_FPDU_mftens',
 'erg_mepi_l2_3dflux_FPDU_velocity',
 'erg_mepi_l2_3dflux_FPDU_ptens',
 'erg_mepi_l2_3dflux_FPDU_ttens',
 'erg_mepi_l2_3dflux_FPDU_vthermal',
 'erg_mepi_l2_3dflux_FPDU_avgtemp',
 'IMF',
 'PLS',
 'IMF_PTS',
 'PLS_PTS',
 'percent_interp',
 'Timeshift',
 'RMS_Timeshift',
 'RMS_phase',
 'Time_btwn_obs',
 'F',
 'BX_GSE',
 'BY_GSE',
 'BZ_GSE',
 'BY_GSM',
 'BZ_GSM',
 'RMS_SD_B',
 'RMS_SD_fld_vec',
 'flow_speed',
 'Vx',
 'Vy',
 'Vz',
 'proton_density',
 'T',
 'NaNp_Ratio',
 'Pressure',
 'E',
 'Beta',
 'Mach_num',
 'Mgs_mach_num',
 'x',
 'y',
 'z',
 'BSN_x',
 'BSN_y',
 'BSN_z',
 'AE_INDEX',
 'AL_INDEX',
 'AU_INDEX',
 'SYM_D',
 'SYM_H',
 'ASY_D',
 'ASY_H',
 'erg_pwe_ofa_l2_spec_epoch_e132',
 'erg_pwe_ofa_l2_spec_E_spectra_132',
 'erg_pwe_ofa_l2_spec_quality_flag_e132',
 'erg_pwe_ofa_l2_spec_epoch_b132',
 'erg_pwe_ofa_l2_spec_B_spectra_132',
 'erg_pwe_ofa_l2_spec_quality_flag_b132',
 'erg_mepe_l2_omniflux_epoch',
 'erg_mepe_l2_omniflux_FEDO',
 'erg_lepe_l2_omniflux_FEDO',
 'erg_mepi_l2_omniflux_epoch',
 'erg_mepi_l2_omniflux_epoch_tof',
 'erg_mepi_l2_omniflux_FIDO_Energy',
 'erg_mepi_l2_omniflux_FPDO',
 'erg_mepi_l2_omniflux_FHE2DO',
 'erg_mepi_l2_omniflux_FHEDO',
 'erg_mepi_l2_omniflux_FOPPDO',
 'erg_mepi_l2_omniflux_FODO',
 'erg_mepi_l2_omniflux_FO2PDO',
 'erg_mepi_l2_omniflux_FPDO_tof',
 'erg_mepi_l2_omniflux_FHE2DO_tof',
 'erg_mepi_l2_omniflux_FHEDO_tof',
 'erg_mepi_l2_omniflux_FOPPDO_tof',
 'erg_mepi_l2_omniflux_FODO_tof',
 'erg_mepi_l2_omniflux_FO2PDO_tof',
 'erg_lepi_l2_omniflux_Epoch',
 'erg_lepi_l2_omniflux_FPDO_raw',
 'erg_lepi_l2_omniflux_FHEDO_raw',
 'erg_lepi_l2_omniflux_FODO_raw',
 'erg_lepi_l2_omniflux_FPDO',
 'erg_lepi_l2_omniflux_FHEDO',
 'erg_lepi_l2_omniflux_FODO',
 'erg_orb_l3_pos_eq_t89',
 'erg_orb_l3_pos_iono_north_t89',
 'erg_orb_l3_pos_iono_south_t89',
 'erg_orb_l3_pos_lmc_t89',
 'erg_orb_l3_pos_lstar_t89',
 'erg_orb_l3_pos_I_t89',
 'erg_orb_l3_pos_blocal_t89',
 'erg_orb_l3_pos_beq_t89',
 'erg_orb_l3_pos_eq_t89_0',
 'erg_orb_l3_pos_eq_t89_1']
In [ ]:
#Set time span (from 2017-09-06 12:00:00 to 2017-09-09 00:00:00)
pytplot.timespan( '2017-09-06 14:00:00', 2.417, keyword='days')

#No resample of X and Y components
pytplot.options(['erg_mepi_l2_omniflux_FPDO','erg_mepi_l2_omniflux_FODO','erg_lepi_l2_omniflux_FPDO','erg_lepi_l2_omniflux_FODO'],'x_no_resample',1)
pytplot.options(['erg_mepi_l2_omniflux_FPDO','erg_mepi_l2_omniflux_FODO','erg_lepi_l2_omniflux_FPDO','erg_lepi_l2_omniflux_FODO'],'y_no_resample',1)

#Set data gap.
#Parts of data gap of more than 64 sec are not plotted
pytplot.options(['erg_mepi_l2_omniflux_FPDO','erg_mepi_l2_omniflux_FODO','erg_lepi_l2_omniflux_FPDO','erg_lepi_l2_omniflux_FODO'],'data_gap',64)

#Set yrange of each panel.
pytplot.options('F','yrange', [0.0, 40.0])
pytplot.options('BZ_GSM','yrange', [-40, 20.0])
pytplot.options('Pressure','yrange', [0.0, 15.0])
pytplot.options('proton_density','yrange', [0.1, 100.0])
pytplot.options('flow_speed','yrange', [0.0, 1000.0])
pytplot.options('SYM_H','yrange', [-150.0, 100.0])
pytplot.options('erg_mepi_l2_omniflux_FPDO','yrange', [10.0, 200.0])
pytplot.options('erg_mepi_l2_omniflux_FODO','yrange', [10.0, 200.0])
pytplot.options('erg_lepi_l2_omniflux_FPDO','yrange', [1.0, 25.0])
pytplot.options('erg_lepi_l2_omniflux_FODO','yrange', [1.0, 25.0])

#Set zrange of particle data.
pytplot.zlim('erg_mepi_l2_omniflux_FPDO', 10e0, 10e6)
pytplot.zlim('erg_mepi_l2_omniflux_FODO', 10e0, 10e6)
pytplot.zlim('erg_lepi_l2_omniflux_FPDO', 10e0, 10e6)
pytplot.zlim('erg_lepi_l2_omniflux_FODO', 10e0, 10e6)

#Change the tile of y-axis of each panel.
pytplot.options('F','ytitle', 'IMF\nBtot')
pytplot.options('BZ_GSM','ytitle', 'IMF\nBz\ngsm')
pytplot.options('Pressure','ytitle', 'SW\nflow\npressure')
pytplot.options('proton_density','ytitle', 'SW\nproton\ndensity')
pytplot.options('flow_speed','ytitle', 'SW\nflow\nspeed')
pytplot.options('SYM_H','ytitle', 'SYM\nH')
pytplot.options('erg_mepi_l2_omniflux_FPDO','ytitle', 'MEP-i\nH+')
pytplot.options('erg_mepi_l2_omniflux_FODO','ytitle', 'MEP-i\nO+')
pytplot.options('erg_lepi_l2_omniflux_FPDO','ytitle', 'LEP-i\nH+')
pytplot.options('erg_lepi_l2_omniflux_FODO','ytitle', 'LEP-i\nO+')
pytplot.options(['erg_orb_l3_pos_eq_t89_0'],'ytitle','Arase\nEquatorial\nCrossing\nT89')
pytplot.options(['erg_orb_l3_pos_eq_t89_0'],'ysubtitle','[Re]')

#Plot the data.
tplot(['F','BZ_GSM','Pressure','proton_density','flow_speed','SYM_H','erg_mepi_l2_omniflux_FPDO','erg_lepi_l2_omniflux_FPDO','erg_mepi_l2_omniflux_FODO','erg_lepi_l2_omniflux_FODO', 'erg_orb_l3_pos_eq_t89_0'],xsize = 10, ysize = 30)

Let's use line colors optimized for color-blind individuals¶

Usually the use of line colors optimized for color-blind individuals are recommended for publications and presentations in conferences and seminars. The default line color frequently uses red and green lines at the same time, making it difficult for people with color blindness to distinguish between the two lines.

Colors optimized for color-blind individuals [Wong, Nature Methods, 2011]

Black '#000000'

Orange '#E69F00'

Sky blue '#56B4E9'

Bluish green '#009E73'

Yellow '#F0E442'

Blue '#0072B2'

Vermilion '#B55E00'

Reddish purple '#CC79A7'

In [ ]:
pytplot.del_data( 'erg_*' )
In [ ]:
from pyspedas.erg import mgf
tr=['2017-09-07', '2017-09-08']
vars = mgf( trange=tr )
pytplot.timespan( '2017-09-06 14:00:00', 2.417, keyword='days')
pytplot.options( 'erg_mgf_l2_mag_8sec_gsm', 'color', ['#E69F00','#56B4E9','#009E73'] )
pytplot.options( 'erg_mgf_l2_mag_8sec_gsm', 'data_gap', 8.5 ) #Set the option of data gap with 8.5 seconds.
pytplot.options( 'erg_mgf_l2_mag_8sec_gsm', 'yrange', [-1000., 1000.] ) # Set the vertical scale to create the plot of MGF Lv.2 8-s data.
#pytplot.ylim( 'erg_mgf_l2_mag_8sec_gsm', -500., 500. ) # There is a bug in pytplot.ylim. It will be fixed.
tplot( 'erg_mgf_l2_mag_8sec_gsm' , xsize = 10, ysize = 10 )

08-Mar-24 01:14:29: Downloading remote index: https://ergsc.isee.nagoya-u.ac.jp/data/ergsc/satellite/erg/mgf/l2/8sec/2017/09/
08-Mar-24 01:14:30: File is current: erg_data/satellite/erg/mgf/l2/8sec/2017/09/erg_mgf_l2_8sec_20170907_v03.04.cdf

 
**************************************************************************
Exploration of Energization and Radiation in Geospace (ERG) Magnetic Field Experiment (MGF) Level 2 spin-averaged magnetic field data

Information about ERG MGF

PI:  Ayako Matsuoka
Affiliation: Data Analysis Center for Geomagnetism and Space Magnetism, Graduate School of Science, Kyoto University, Kitashirakawa-Oiwake Cho, Sakyo-ku Kyoto 606-8502, Japan

RoR of ERG project common: https://ergsc.isee.nagoya-u.ac.jp/data_info/rules_of_the_road.shtml.en
RoR of MGF L2: https://ergsc.isee.nagoya-u.ac.jp/mw/index.php/ErgSat/Mgf
Contact: erg_mgf_info at isee.nagoya-u.ac.jp
**************************************************************************