nasa-pymms


Namenasa-pymms JSON
Version 0.4.8 PyPI version JSON
download
home_pagehttps://github.com/argallmr/pymms
SummaryAccess data from the MMS mission via its API.
upload_time2024-06-13 19:42:50
maintainerNone
docs_urlNone
authorMatthew R. Argall
requires_python>=3.6
licenseMIT
keywords physics space-physics mms
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            [![DOI](https://zenodo.org/badge/124706809.svg)](https://zenodo.org/badge/latestdoi/124706809)

# PyMMS

## Installation

For development purposes, install the package using
```bash
$ 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:
```bash
$ 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](https://doi.org/10.5281/zenodo.3765993) to see the citations/DOIs of other releases.

            

Raw data

            {
    "_id": null,
    "home_page": "https://github.com/argallmr/pymms",
    "name": "nasa-pymms",
    "maintainer": null,
    "docs_url": null,
    "requires_python": ">=3.6",
    "maintainer_email": null,
    "keywords": "physics space-physics MMS",
    "author": "Matthew R. Argall",
    "author_email": "argallmr@gmail.com",
    "download_url": "https://files.pythonhosted.org/packages/49/6f/2e260d19f9722207fa97cd8cc092a86636ff66e60e25570d268106462f42/nasa-pymms-0.4.8.tar.gz",
    "platform": null,
    "description": "[![DOI](https://zenodo.org/badge/124706809.svg)](https://zenodo.org/badge/latestdoi/124706809)\n\n# PyMMS\n\n## Installation\n\nFor development purposes, install the package using\n```bash\n$ python3 setup.py develop --user\n```\nThis installation will reflect any changes made in the pymms development directory without the need to reinstall the package every single time.\n\n## Scripts\n\n### gls\n\nThe `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.\n\n```\n$ gls-mp -h\nusage: gls-mp [-h] [-g] [-t] [-c C] [-temp] start end sc\n\npositional arguments:\n  start            Start date of data interval, formatted as either '%Y-%m-%d'\n                   or '%Y-%m-%dT%H:%M:%S'. Optionally an integer, interpreted\n                   as an orbit number.\n  end              Start date of data interval, formatted as either '%Y-%m-%d'\n                   or '%Y-%m-%dT%H:%M:%S'. Optionally an integer, interpreted\n                   as an orbit number.\n  sc               Spacecraft IDs ('mms1', 'mms2', 'mms3', 'mms4')\n\noptional arguments:\n  -h, --help       show this help message and exit\n  -g, -gpu         Enables use of GPU-accelerated model for faster\n                   predictions. Requires CUDA installed.\n  -t, -test        Runs a test routine on the model.\n  -c C, -chunks C  Break up the processing of the date interval in C chunks.\n  -temp            If running the job in chunks, deletes the contents of the\n                   MMS root data folder after each chunk.\n```\n\nCalling `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.\n\n```\n$ gls-mp-data -h\nusage: gls-mp-data [-h] [-is] [-ip] [-v] sc level start end output\n\npositional arguments:\n  sc                    Spacecraft IDs ('mms1', 'mms2', 'mms3', 'mms4')\n  level                 Data quality level ('l1a', 'l1b', 'sitl', 'l2pre',\n                        'l2', 'l3')\n  start                 Start date of data interval, formatted as either\n                        '%Y-%m-%d' or '%Y-%m-%dT%H:%M:%S'. Optionally an\n                        integer, interpreted as an orbit number.\n  end                   Start date of data interval, formatted as either\n                        '%Y-%m-%d' or '%Y-%m-%dT%H:%M:%S'. Optionally an\n                        integer, interpreted as an orbit number.\n  output                Path the output CSV file, including the CSV file's\n                        name.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  -is, --include-selections\n                        Includes SITL selections in the output data.\n  -ip, --include-partials\n                        Includes partial magnetopause crossings in SITL\n                        selections.\n  -v, --verbose         If true, prints out optional information about\n                        downloaded variables.\n```\n\nIf PyMMS is installed with the ``--user`` flag and PyMMS is used from a unix system, you must call:\n```bash\n$ export PATH=~/.local/bin$PATH\n$ source ~/.bash_profile\n```\nbefore calling `gls-mp` or `gls-mp-data`.\n\n## Citation\n\nIf 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](https://doi.org/10.5281/zenodo.3765993) to see the citations/DOIs of other releases.\n",
    "bugtrack_url": null,
    "license": "MIT",
    "summary": "Access data from the MMS mission via its API.",
    "version": "0.4.8",
    "project_urls": {
        "Homepage": "https://github.com/argallmr/pymms"
    },
    "split_keywords": [
        "physics",
        "space-physics",
        "mms"
    ],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "14f6e3f76e49910dce56e54b9cb79505bf7970b698c5d8f7e15044a3568cda8f",
                "md5": "9716b837eb1b9d358d641a1977c4e19b",
                "sha256": "94f1638c1e115351851033722848b356b6984b81ff8f1c56bd372bdb5e75f2de"
            },
            "downloads": -1,
            "filename": "nasa_pymms-0.4.8-py3-none-any.whl",
            "has_sig": false,
            "md5_digest": "9716b837eb1b9d358d641a1977c4e19b",
            "packagetype": "bdist_wheel",
            "python_version": "py3",
            "requires_python": ">=3.6",
            "size": 137674,
            "upload_time": "2024-06-13T19:42:48",
            "upload_time_iso_8601": "2024-06-13T19:42:48.357130Z",
            "url": "https://files.pythonhosted.org/packages/14/f6/e3f76e49910dce56e54b9cb79505bf7970b698c5d8f7e15044a3568cda8f/nasa_pymms-0.4.8-py3-none-any.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "496f2e260d19f9722207fa97cd8cc092a86636ff66e60e25570d268106462f42",
                "md5": "f9f818340ec78e1bf9a989a274e87ee1",
                "sha256": "c4c701e3bfb269e31fc5e2e963bbb71864738484c0e606f0ebf9a5b63f471672"
            },
            "downloads": -1,
            "filename": "nasa-pymms-0.4.8.tar.gz",
            "has_sig": false,
            "md5_digest": "f9f818340ec78e1bf9a989a274e87ee1",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.6",
            "size": 112225,
            "upload_time": "2024-06-13T19:42:50",
            "upload_time_iso_8601": "2024-06-13T19:42:50.411998Z",
            "url": "https://files.pythonhosted.org/packages/49/6f/2e260d19f9722207fa97cd8cc092a86636ff66e60e25570d268106462f42/nasa-pymms-0.4.8.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-06-13 19:42:50",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "argallmr",
    "github_project": "pymms",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": false,
    "lcname": "nasa-pymms"
}
        
Elapsed time: 1.51634s