Skip to main content

Python Space Physics Environment Data Analysis Software (pySPEDAS)

Project description

PySPEDAS

build Coverage Status Version Status License

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

Requirements

Python 3.7+ 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

Loading Data

Plotting

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

Coordinate Transformations

Analysis

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.

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, see: https://github.com/MAVENSDC/PyTplot

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

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

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyspedas-1.4.26.tar.gz (831.0 kB view details)

Uploaded Source

Built Distribution

pyspedas-1.4.26-py3-none-any.whl (1.1 MB view details)

Uploaded Python 3

File details

Details for the file pyspedas-1.4.26.tar.gz.

File metadata

  • Download URL: pyspedas-1.4.26.tar.gz
  • Upload date:
  • Size: 831.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for pyspedas-1.4.26.tar.gz
Algorithm Hash digest
SHA256 b6fbc8d1e257ecd63fc6a9ea5b60c989adc2ae46243a7e46684ea93eb71be22f
MD5 68c2e4d6b00cf8b244d11d4b6985e534
BLAKE2b-256 3e8b355d026db3c0d8841bf4de5a3e2fbdedd24efe4654dac382b8339c4c8b92

See more details on using hashes here.

File details

Details for the file pyspedas-1.4.26-py3-none-any.whl.

File metadata

  • Download URL: pyspedas-1.4.26-py3-none-any.whl
  • Upload date:
  • Size: 1.1 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.0 CPython/3.10.4

File hashes

Hashes for pyspedas-1.4.26-py3-none-any.whl
Algorithm Hash digest
SHA256 6f2864f9d861875a57b43b13a1ed39708027e28da7c0d20099625cd543253a1a
MD5 7ff0b3d476d4a19c9489988c7a9f9ea8
BLAKE2b-256 f6a792a5853837fde09cfac7a8833574b988277bdf7718a6e7cfd3648cffe72b

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page