Skip to main content

DOI

PyMMS

Installation

For development purposes, install the package using

$ python3 setup.py develop --user

This installation will reflect any changes made in the pymms development directory without the need to reinstall the package every single time.

Scripts

gls

The pymms.gls package includes two user-runnable console commands: gls-mp and gls-mp-data. Calling gls-mp runs the mp-dl-unh model to generate predicted SITL selections over a date range.

$ gls-mp -h
usage: gls-mp [-h] [-g] [-t] [-c C] [-temp] start end sc

positional arguments:
  start            Start date of data interval, formatted as either '%Y-%m-%d'
                   or '%Y-%m-%dT%H:%M:%S'. Optionally an integer, interpreted
                   as an orbit number.
  end              Start date of data interval, formatted as either '%Y-%m-%d'
                   or '%Y-%m-%dT%H:%M:%S'. Optionally an integer, interpreted
                   as an orbit number.
  sc               Spacecraft IDs ('mms1', 'mms2', 'mms3', 'mms4')

optional arguments:
  -h, --help       show this help message and exit
  -g, -gpu         Enables use of GPU-accelerated model for faster
                   predictions. Requires CUDA installed.
  -t, -test        Runs a test routine on the model.
  -c C, -chunks C  Break up the processing of the date interval in C chunks.
  -temp            If running the job in chunks, deletes the contents of the
                   MMS root data folder after each chunk.

Calling gls-mp-data generates a CSV file containing data formatted and preprocessed for gls-mp. This can be used when training your own version of mp-dl-unh.

$ gls-mp-data -h
usage: gls-mp-data [-h] [-is] [-ip] [-v] sc level start end output

positional arguments:
  sc                    Spacecraft IDs ('mms1', 'mms2', 'mms3', 'mms4')
  level                 Data quality level ('l1a', 'l1b', 'sitl', 'l2pre',
                        'l2', 'l3')
  start                 Start date of data interval, formatted as either
                        '%Y-%m-%d' or '%Y-%m-%dT%H:%M:%S'. Optionally an
                        integer, interpreted as an orbit number.
  end                   Start date of data interval, formatted as either
                        '%Y-%m-%d' or '%Y-%m-%dT%H:%M:%S'. Optionally an
                        integer, interpreted as an orbit number.
  output                Path the output CSV file, including the CSV file's
                        name.

optional arguments:
  -h, --help            show this help message and exit
  -is, --include-selections
                        Includes SITL selections in the output data.
  -ip, --include-partials
                        Includes partial magnetopause crossings in SITL
                        selections.
  -v, --verbose         If true, prints out optional information about
                        downloaded variables.

If PyMMS is installed with the --user flag and PyMMS is used from a unix system, you must call:

$ export PATH=~/.local/bin$PATH
$ source ~/.bash_profile

before calling gls-mp or gls-mp-data.

Citation

If you make use of this software to analyze MMS use or data, please consider citing the software. Follow the Zenodo DOI at the top for a citation to the most recent release, or head to Zenodo to see the citations/DOIs of other releases.

Metadata

Release files for nasa-pymms 0.4.8

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for nasa-pymms 0.4.8
File Size Uploaded Provenance Actions
nasa-pymms-0.4.8.tar.gz 112.2 kB Without provenance Details for nasa-pymms-0.4.8.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for nasa-pymms 0.4.8
File Interpreter ABI Platform Actions
nasa_pymms-0.4.8-py3-none-any.whl Python 3 none any Details for nasa_pymms-0.4.8-py3-none-any.whl

Total release size: 249.9 kB

Release files / nasa-pymms-0.4.8.tar.gz

Download URL nasa-pymms-0.4.8.tar.gz
Size 112.2 kB
Tags Source
SHA-256 checksum
How to use checksums
c4c701e3bfb269e31fc5e2e963bbb71864738484c0e606f0ebf9a5b63f471672
BLAKE2b-256 checksum
How to use checksums
496f2e260d19f9722207fa97cd8cc092a86636ff66e60e25570d268106462f42
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.6.10

Release files / nasa_pymms-0.4.8-py3-none-any.whl

Download URL nasa_pymms-0.4.8-py3-none-any.whl
Size 137.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
94f1638c1e115351851033722848b356b6984b81ff8f1c56bd372bdb5e75f2de
BLAKE2b-256 checksum
How to use checksums
14f6e3f76e49910dce56e54b9cb79505bf7970b698c5d8f7e15044a3568cda8f
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/2.0.0 pkginfo/1.5.0.1 requests/2.23.0 setuptools/46.0.0 requests-toolbelt/0.9.1 tqdm/4.45.0 CPython/3.6.10

Security for nasa-pymms 0.4.8

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page