pyspedas 1.6.3

Python Space Physics Environment Data Analysis Software (pySPEDAS)

PySPEDAS

The Python-based Space Physics Environment Data Analysis Software (PySPEDAS) framework supports multi-mission, multi-instrument retrieval, analysis, and visualization of heliophysics time series data.

Projects Supported

• Advanced Composition Explorer (ACE)
• Akebono
• Arase (ERG)
• Cluster
• Colorado Student Space Weather Experiment (CSSWE)
• Communications/Navigation Outage Forecasting System (C/NOFS)
• Deep Space Climate Observatory (DSCOVR)
• Dynamics Explorer 2 (DE2)
• Equator-S
• Fast Auroral Snapshot Explorer (FAST)
• Geotail
• Geostationary Operational Environmental Satellite (GOES)
• Imager for Magnetopause-to-Aurora Global Exploration (IMAGE)
• Kyoto Dst Index
• LANL
• Mars Atmosphere and Volatile Evolution (MAVEN)
• Magnetic Induction Coil Array (MICA)
• Magnetospheric Multiscale (MMS)
• OMNI
• Polar Orbiting Environmental Satellites (POES)
• Polar
• Parker Solar Probe (PSP)
• Solar & Heliospheric Observatory (SOHO)
• Solar Orbiter (SOLO)
• Solar Terrestrial Relations Observatory (STEREO)
• Space Technology 5 (ST5)
• Spherical Elementary Currents (SECS)
• Swarm
• Time History of Events and Macroscale Interactions during Substorms (THEMIS)
• Two Wide-Angle Imaging Neutral-Atom Spectrometers (TWINS)
• Ulysses
• Van Allen Probes (RBSP)
• Wind

Requirements

Python 3.9+ is required.

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

Installation

Virtual Environment

To avoid potential dependency issues with other Python packages, we suggest creating a virtual environment for PySPEDAS; you can create a virtual environment in your terminal with:

python -m venv pyspedas

To enter your virtual environment, run the 'activate' script:

Windows

.\pyspedas\Scripts\activate

macOS and Linux

source pyspedas/bin/activate

Using Jupyter notebooks with your virtual environment

To get virtual environments working with Jupyter, in the virtual environment, type:

pip install ipykernel
python -m ipykernel install --user --name pyspedas --display-name "Python (pySPEDAS)"

(note: "pyspedas" is the name of your virtual environment)

Then once you open the notebook, go to "Kernel" then "Change kernel" and select the one named "Python (PySPEDAS)"

Install

PySPEDAS supports Windows, macOS and Linux. To get started, install the pyspedas package using PyPI:

pip install pyspedas

Upgrade

To upgrade to the latest version of PySPEDAS:

pip install pyspedas --upgrade

Local Data Directories

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

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

Usage

To get started, import pyspedas and pytplot:

import pyspedas
from pytplot import tplot

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

To load and plot 1 day of THEMIS FGM data for probe 'd':

thm_fgm = pyspedas.themis.fgm(trange=['2015-10-16', '2015-10-17'], probe='d')

tplot(['thd_fgs_gse', 'thd_fgs_gsm'])

To load and plot 2 minutes of MMS burst mode FGM data:

mms_fgm = pyspedas.mms.fgm(trange=['2015-10-16/13:05:30', '2015-10-16/13:07:30'], data_rate='brst')

tplot(['mms1_fgm_b_gse_brst_l2', 'mms1_fgm_b_gsm_brst_l2'])

Note: by default, PySPEDAS loads all data contained in CDFs found within the requested time range; this can potentially load data outside of your requested trange. To remove the data outside of your requested trange, set the time_clip keyword to True

To load and plot 6 hours of PSP SWEAP/SPAN-i data:

spi_vars = pyspedas.psp.spi(trange=['2018-11-5', '2018-11-5/06:00'], time_clip=True)

tplot(['DENS', 'VEL', 'T_TENSOR', 'TEMP'])

To download 5 days of STEREO magnetometer data (but not load them into tplot variables):

stereo_files = pyspedas.stereo.mag(trange=['2013-11-1', '2013-11-6'], downloadonly=True)

Standard Options

• trange: two-element list specifying the time range of interest. This keyword accepts a wide range of formats
• time_clip: if set, clip the variables to the exact time range specified by the trange keyword
• suffix: string specifying a suffix to append to the loaded variables
• varformat: string specifying which CDF variables to load; accepts the wild cards * and ?
• varnames: string specifying which CDF variables to load (exact names)
• get_support_data: if set, load the support variables from the CDFs
• downloadonly: if set, download the files but do not load them into tplot
• no_update: if set, only load the data from the local cache
• notplot: if set, load the variables into dictionaries containing numpy arrays (instead of creating the tplot variables)

Examples

Please see the following notebooks for examples of using PySPEDAS

PyTplot Basics

• Introduction to PyTplot

Loading Data

• MMS examples
• THEMIS examples
• Load data from HAPI servers
• Exploring the Heliosphere with Python

Plotting

• Annotations
• Range options
• Spectrogram options
• Legend options
• Markers and symbols
• Error bars
• Pseudo variables
• Highlight intervals and vertical bars

Additional examples of loading and plotting data can be found in the documentation for the project you're interested in (PySPEDAS projects), as well as the project's README file.

Dates and Times

• Working with dates and times

Coordinate Transformations

• Coordinate transformations
• Boundary normal (LMN) coordinates
• Quaternion transformations with SpacePy

Analysis

• Plasma calculations with PlasmaPy
• Poynting flux with MMS data
• Plasma beta with MMS data (note: the PlasmaPy notebook above shows a much easier method)
• Curlometer calculations
• Neutral sheet models
• Wave polarization calculations
• Dynamic power spectra calculations
• 2D slices of MMS distribution functions
• Generating spectrograms and moments from MMS distribution functions

Documentation

For more information, please see our HTML documentation at:

https://pyspedas.readthedocs.io/

Getting Help

To find the options supported, call help on the instrument function you're interested in:

help(pyspedas.themis.fgm)

You can ask questions by creating an issue or by joining the SPEDAS mailing list.

PyTplot

Pytplot is a separate project, that replicates the IDL "tplot" functionality. Pyspedas uses a modified version of pytplot with matplotlib as the plotting library.

Contributing

We welcome contributions to PySPEDAS; to learn how you can contribute, please see our Contributing Guide

Plug-in Development

An introduction to PySPEDAS plug-in development can be found here:

Introduction to PySPEDAS plug-in development

Code of Conduct

In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. To learn more, please see our Code of Conduct.

Additional Information

For examples of pyspedas, see: https://github.com/spedas/pyspedas_examples

For MMS examples, see: https://github.com/spedas/mms-examples

For pytplot (matplotlib version), see: https://github.com/MAVENSDC/PyTplot/tree/matplotlib-backend

For cdflib, see: https://github.com/MAVENSDC/cdflib

For SPEDAS, see http://spedas.org/