Metadata-Version: 2.1
Name: MetPy
Version: 1.0.0rc2
Summary: Collection of tools for reading, visualizing and performing calculations with weather data.
Home-page: https://github.com/Unidata/MetPy
Author: Ryan May, Patrick Marsh, Sean Arms, Eric Bruning, John Leeman, Zach Bruick
Author-email: support-python@unidata.ucar.edu
Maintainer: MetPy Developers
Maintainer-email: support-python@unidata.ucar.edu
License: BSD 3-Clause
Project-URL: Documentation, https://unidata.github.io/MetPy/
Project-URL: Release Notes, https://github.com/Unidata/MetPy/releases
Project-URL: Bug Tracker, https://github.com/Unidata/MetPy/issues
Project-URL: Source Code, https://github.com/Unidata/MetPy
Description: MetPy
        =====
        
        [![License](https://img.shields.io/pypi/l/metpy.svg)](https://pypi.python.org/pypi/MetPy/)
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        [![Latest Docs](https://github.com/Unidata/MetPy/workflows/Build%20Docs/badge.svg)](http://unidata.github.io/MetPy)
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        [![Travis Build Status](https://travis-ci.com/Unidata/MetPy.svg?branch=master)](https://travis-ci.com/Unidata/MetPy)
        [![Code Coverage Status](https://codecov.io/github/Unidata/MetPy/coverage.svg?branch=master)](https://codecov.io/github/Unidata/MetPy?branch=master)
        
        [![Codacy issues](https://api.codacy.com/project/badge/Grade/e1ea0937eb4942e79a44bc9bb2de616d)](https://www.codacy.com/app/dopplershift/MetPy)
        [![Code Climate](https://codeclimate.com/github/Unidata/MetPy/badges/gpa.svg)](https://codeclimate.com/github/Unidata/MetPy)
        
        MetPy is a collection of tools in Python for reading, visualizing and
        performing calculations with weather data.
        
        MetPy follows [semantic versioning](https://semver.org) in its version number. With our
        current 0.x version, that implies that MetPy's APIs (application programming interfaces) are
        still evolving (we won't break things just for fun, but many things are still changing as we
        work through design issues). Also, for a version `0.x.y`, we change `x` when we
        release new features, and `y` when we make a release with only bug fixes.
        
        For additional MetPy examples not included in this repository, please see the [Unidata Python
        Gallery](https://unidata.github.io/python-gallery/).
        
        We support Python >= 3.6.
        
        0.12 is the first version to drop support for Python 2.7 in Fall 2019. See
        [here](https://github.com/Unidata/MetPy/blob/master/docs/installguide.rst) for more
        information.
        
        Need Help?
        ----------
        Need help using MetPy? Found an issue? Have a feature request? Checkout our
        [support page](https://github.com/Unidata/MetPy/blob/master/SUPPORT.md).
        
        Important Links
        ---------------
        
        - [HTML Documentation](http://unidata.github.io/MetPy)
        - [Unidata Python Gallery](https://unidata.github.io/python-gallery/)
        - "metpy" tagged questions on [Stack Overflow](https://stackoverflow.com/questions/tagged/metpy)
        - [Gitter chat room](https://gitter.im/Unidata/MetPy)
        - [Say Thanks!](https://saythanks.io/to/unidata)
        
        Dependencies
        ------------
        Other required packages:
        
        - Numpy
        - Scipy
        - Matplotlib
        - Pandas
        - Pint
        - Xarray
        
        There is also an optional dependency on the pyproj library for geographic
        projections (used with cross sections, grid spacing calculation, and the GiniFile interface).
        
        See the [installation guide](https://unidata.github.io/MetPy/dev/installguide.html)
        for more information.
        
        Code of Conduct
        ---------------
        We want everyone to feel welcome to contribute to MetPy and participate in discussions. In that
        spirit please have a look at our [Code of Conduct](https://github.com/Unidata/MetPy/blob/master/CODE_OF_CONDUCT.md).
        
        Contributing
        ------------
        **Imposter syndrome disclaimer**: We want your help. No, really.
        
        There may be a little voice inside your head that is telling you that you're not ready to be
        an open source contributor; that your skills aren't nearly good enough to contribute. What
        could you possibly offer a project like this one?
        
        We assure you - the little voice in your head is wrong. If you can write code at all,
        you can contribute code to open source. Contributing to open source projects is a fantastic
        way to advance one's coding skills. Writing perfect code isn't the measure of a good developer
        (that would disqualify all of us!); it's trying to create something, making mistakes, and
        learning from those mistakes. That's how we all improve, and we are happy to help others learn.
        
        Being an open source contributor doesn't just mean writing code, either. You can help out by
        writing documentation, tests, or even giving feedback about the project (and yes - that
        includes giving feedback about the contribution process). Some of these contributions may be
        the most valuable to the project as a whole, because you're coming to the project with fresh
        eyes, so you can see the errors and assumptions that seasoned contributors have glossed over.
        
        For more information, please read the see the [contributing guide](https://github.com/Unidata/MetPy/blob/master/CONTRIBUTING.md).
        
        Philosophy
        ----------
        The space MetPy aims for is GEMPAK (and maybe NCL)-like functionality, in a way that plugs
        easily into the existing scientific Python ecosystem (numpy, scipy, matplotlib). So, if you
        take the average GEMPAK script for a weather map, you need to:
        
        - read data
        - calculate a derived field
        - show on a map/skew-T
        
        One of the benefits hoped to achieve over GEMPAK is to make it easier to use these routines for
        any meteorological Python application; this means making it easy to pull out the LCL
        calculation and just use that, or re-use the Skew-T with your own data code. MetPy also prides
        itself on being well-documented and well-tested, so that on-going maintenance is easily
        manageable.
        
        The intended audience is that of GEMPAK: researchers, educators, and any one wanting to script
        up weather analysis. It doesn't even have to be scripting; all python meteorology tools are
        hoped to be able to benefit from MetPy. Conversely, it's hoped to be the meteorological
        equivalent of the audience of scipy/scikit-learn/skimage.
        
Keywords: meteorology,weather
Platform: any
Classifier: Development Status :: 5 - Production/Stable
Classifier: Framework :: Matplotlib
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Atmospheric Science
Classifier: Intended Audience :: Science/Research
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: BSD License
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: doc
Provides-Extra: examples
Provides-Extra: test
