Metadata-Version: 2.1
Name: nasa-pymms
Version: 0.3.0
Summary: Access data from the MMS mission via its API.
Home-page: https://github.com/argallmr/pymms
Author: Matthew R. Argall
Author-email: argallmr@gmail.com
License: MIT
Description: ## 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.
        
        The pymms.gls package includes two user-runnable console commands: gls-mp and gls-mp-data. 
        
        **gls-mp**
        
        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.
        ```
        
        **gls-mp-data**
        
        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.
Keywords: physics space-physics MMS
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Classifier: Development Status :: 4 - Beta
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: gls
