Python Space Physics Environment Data Analysis Software (pySPEDAS)
Project description
PySPEDAS
PySPEDAS is an implementation of the SPEDAS framework for Python.
The Space Physics Environment Data Analysis Software (SPEDAS) framework is written in IDL and contains data loading, data analysis and data plotting tools for various scientific missions (NASA, NOAA, etc.) and ground magnetometers.
Please see our documentation at:
https://pyspedas.readthedocs.io/
Projects Supported
- Advanced Composition Explorer (ACE)
- Arase (ERG)
- Cluster
- Colorado Student Space Weather Experiment (CSSWE)
- Deep Space Climate Observatory (DSCOVR)
- Equator-S
- Fast Auroral Snapshot Explorer (FAST)
- Geotail
- Geostationary Operational Environmental Satellite (GOES)
- Imager for Magnetopause-to-Aurora Global Exploration (IMAGE)
- Kyoto Dst Index
- 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 Orbiter (SOLO)
- Solar Terrestrial Relations Observatory (STEREO)
- 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 3.7+ 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
Setup your 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 formatstime_clip: if set, clip the variables to the exact time range specified by thetrangekeywordsuffix: string specifying a suffix to append to the loaded variablesvarformat: 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 CDFsdownloadonly: if set, download the files but do not load them into tplotno_update: if set, only load the data from the local cachenotplot: if set, load the variables into dictionaries containing numpy arrays (instead of creating the tplot variables)
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
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/
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