MetPy 0.3.1

Collection of tools for reading, visualizing andperforming calculations with weather data.

MetPy is a collection of tools in Python for reading, visualizing and performing calculations with weather data.

MetPy is still in an early stage of development, and as such no APIs are considered stable. While we won’t break things just for fun, many things may still change as we work through design issues.

We support Python 2.7 as well as Python >= 3.3.

Important Links

• Source code repository: https://github.com/MetPy/MetPy

• HTML Documentation (stable release): http://metpy.readthedocs.org/en/stable/

• HTML Documentation (development): http://metpy.readthedocs.org/en/latest/

• Issue tracker: http://github.com/Metpy/MetPy/issues

• Gitter chat room: https://gitter.im/metpy/MetPy

Dependencies

Other required packages:

• Numpy

• Scipy

• Matplotlib

• Pint

Python versions older than 3.4 require the enum34 package, which is a backport of the standard library enum module.

There is also an optional dependency on the pyproj library for geographic projections (used with CDM interface).

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.