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https://github.com/M3Works/metloom

Location Oriented Observed Meteorology
https://github.com/M3Works/metloom

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Location Oriented Observed Meteorology

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README 

        
========

metloom

========



.. image:: https://img.shields.io/pypi/v/metloom.svg

        :target: https://pypi.python.org/pypi/metloom

.. image:: https://github.com/M3Works/metloom/actions/workflows/testing.yml/badge.svg

        :target: https://github.com/M3Works/metloom/actions/workflows/testing.yml

        :alt: Testing Status

.. image:: https://readthedocs.org/projects/metloom/badge/?version=latest

        :target: https://metloom.readthedocs.io/en/latest/?version=latest

        :alt: Documentation Status

.. image:: https://img.shields.io/endpoint?url=https://gist.githubusercontent.com/micah-prime/04da387b53bdb4a3aa31253789550a9f/raw/metloom__heads_main.json

        :target: https://github.com/M3Works/metloom

        :alt: Code Coverage



Location Oriented Observed Meteorology



metloom is a python library created with the goal of consistent, simple sampling of

meteorology and snow related point measurments from a variety of datasources is developed by `M3 Works `_ as a tool for validating

computational hydrology model results. Contributions welcome!



Warning - This software is provided as is (see the license), so use at your own risk.

This is an opensource package with the goal of making data wrangling easier. We make

no guarantees about the quality or accuracy of the data and any interpretation of the meaning

of the data is up to you.



* Free software: BSD license



Features

--------



* Sampling of daily, hourly, and snow course data

* Searching for stations from a datasource within a shapefile

* Current data sources:

    * `CDEC `_

    * `SNOTEL `_

    * `MESOWEST `_

    * `USGS `_

    * `NWS FORECAST `_

    * `GEOSPHERE AUSTRIA `_

    * `UCSB CUES `_

    * `MET NORWAY `_

    * `SNOWEX MET STATIONS `_

    * `CENTER FOR SNOW AND AVALANCHE STUDIES (CSAS) `_



Requirements

------------

python >= 3.7



Install

-------

.. code-block:: bash



    python3 -m pip install metloom



* Common install issues:

    * Macbook M1 and M2 chips: some python packages run into issues with the new M chips

        * ``error : from lxml import etree in utils.py ((mach-o file, but is an incompatible architecture (have 'x86_64', need 'arm64)``

            The solution is the following



            .. code-block:: bash



                pip uninstall lxml

                pip install --no-binary lxml lxml



Local install for dev

---------------------

The recommendation is to use virtualenv, but other local python

environment isolation tools will work (pipenv, conda)



.. code-block:: bash



    python3 -m pip install --upgrade pip

    python3 -m pip install -r requirements_dev

    python3 -m pip install .



Testing

-------



.. code-block:: bash



    pytest



If contributing to the codebase, code coverage should not decrease

from the contributions. Make sure to check code coverage before

opening a pull request.



.. code-block:: bash



    pytest --cov=metloom



Documentation

-------------

readthedocs coming soon



https://metloom.readthedocs.io.



Usage

-----

See usage documentation https://metloom.readthedocs.io/en/latest/usage.html



**NOTES:**

PointData methods that get point data return a GeoDataFrame indexed

on *both* datetime and station code. To reset the index simply run

``df.reset_index(inplace=True)``



Simple usage examples are provided in this readme and in the docs. See

our `examples `_

for code walkthroughs and more complicated use cases.



Usage Examples

==============



Use metloom to find data for a station



.. code-block:: python



    from datetime import datetime

    from metloom.pointdata import SnotelPointData



    snotel_point = SnotelPointData("713:CO:SNTL", "MyStation")

    df = snotel_point.get_daily_data(

        datetime(2020, 1, 2), datetime(2020, 1, 20),

        [snotel_point.ALLOWED_VARIABLES.SWE]

    )

    print(df)



Use metloom to find snow courses within a geometry



.. code-block:: python



    from metloom.pointdata import CDECPointData

    from metloom.variables import CdecStationVariables



    import geopandas as gpd



    fp = 

    obj = gpd.read_file(fp)



    vrs = [

        CdecStationVariables.SWE,

        CdecStationVariables.SNOWDEPTH

    ]

    points = CDECPointData.points_from_geometry(obj, vrs, snow_courses=True)

    df = points.to_dataframe()

    print(df)



Tutorials

---------

In the ``Examples`` folder, there are multiple Jupyter notbook based

tutorials. You can edit and run these notebooks by running Jupyter Lab

from the command line



.. code-block:: bash



    pip install jupyterlab

    jupyter lab



This will open a Jupyter Lab session in your default browser.



Credits

-------



This package was created with Cookiecutter_ and the `audreyr/cookiecutter-pypackage`_ project template.



.. _Cookiecutter: https://github.com/audreyr/cookiecutter

.. _`audreyr/cookiecutter-pypackage`: https://github.com/audreyr/cookiecutter-pypackage