Skip to main content

Python3 package and scripts for space weather prediction research.

Project description

PREDSTORM

This is Python3 code for space weather prediction research.

The package is used for predicting the background solar wind, high speed solar wind streams and solar storm magnetic flux ropes based on data from solar wind monitors at the Sun-Earth L1 and L5 points, as well as from any spacecraft positioned east of the Sun-Earth line around or < 1 AU. We also derive predictions of the geomagnetic Dst index, Kp and auroral power.

Status in 2019: work in progress.

If you want to use parts of this code for generating results for peer-reviewed scientific publications, please contact us per email (see contributor biographies) or via twitter @chrisoutofspace (Christian Moestl).

Installation

PREDSTORM is written in Python 3. Most dependencies (numpy, scipy, matplotlib) can be downloaded and used in an anaconda environment (https://www.anaconda.com/distribution/) and can be installed using the following lines:

  conda install scipy numpy matplotlib scikit-learn seaborn requests-ftp beautifulsoup4
  conda install -c conda-forge spiceypy cftime
  conda install -c numba numba

Remaining dependencies (particularly those for CDF handling) can be downloaded using pip:

pip install cdflib spacepy astropy

PREDSTORM also relies on the HelioSat package for all heliospheric data downloads and SPICE kernel handling. Currently it can be downloaded from GitHub:

git clone https://github.com/IWF-helio/HelioSat.git
cd HelioSat
  python setup.py install

HelioSat automatically downloads kernels and all required satellite files (e.g. STEREO, DSCOVR, ...). To set the path where these files are downloaded to, set the following environment variable (in .bashrc on Linux, .bash_profile on Mac):

export HELIOSAT_DATAPATH="/path/to/data/heliosat"

It's a good idea to import the package after first installation to handle first download of all required SPICE kernels.

Running the code

In the command line:

python predstorm_l1.py
python predstorm_l5.py

Use the following option for the Agg backend in matplotlib:

python predstorm_l5.py --server 

Notes

  • Running the scripts creates the local folder /data. OMNI2 data, among other things, are automatically downloaded into this folder.

  • Results are saved in the folder /results.

  • Logs of the scripts are saved in predstorm.log.

Contributors

IWF-Helio Group, Space Research Institute (ÖAW), Graz, Austria:

  • Christian Möstl
  • Rachel L. Bailey

Project details


Download files

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

Source Distribution

predstorm-0.1.1.tar.gz (50.2 kB view details)

Uploaded Source

File details

Details for the file predstorm-0.1.1.tar.gz.

File metadata

  • Download URL: predstorm-0.1.1.tar.gz
  • Upload date:
  • Size: 50.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.1.1 pkginfo/1.5.0.1 requests/2.21.0 setuptools/40.8.0 requests-toolbelt/0.9.1 tqdm/4.32.2 CPython/3.6.8

File hashes

Hashes for predstorm-0.1.1.tar.gz
Algorithm Hash digest
SHA256 fd2e2b9986bb964d2416dd1e3c823b20710a440d54f5d5c7254d5ba084b8a425
MD5 cf6c2b66992cb0c0cce609e3889f0c54
BLAKE2b-256 6458d6aa34ff5014b9c93cdcc1ad21773b2b5200efbcfa0c2fb282bd1e3037f8

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