Python is a general purpose programming language that has
been embraced by the earth science community for analysis,
visualization and data exploration. Geoscience professionals are
replacing collections of poorly integrated software tools and
languages with this general purpose programming language that can
handle remote data requests, statistics, analysis, and
visualization. As a result, the Unidata
2018 Proposal highlights the Python programming language and
ecosystem as an area where Unidata should focus efforts to benefit the
core community. To that end, we have initiated Python training and
software projects centered around existing Unidata technology.
Online Python Training
With support from the National Science Foundation, Unidata is working to create a collection of online training materials focused on the use of Python in the atmospheric sciences. While our examples and scenarios may feature Unidata tools and data technologies, our aim is to present a generic set of freely available tools that are generally useful to scientists, educators, and students in the geosciences, broadly defined.
Visit Unidata's Online Python Training
Siphon is a collection of Python utilities for downloading data from Unidata data technologies. Siphon's current functionality focuses on access to data hosted on a THREDDS Data Server. Siphon is still in an early stage of development, but you can read more here.
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, but you can read more here.
Jupyter Notebook Gallery
Unidata staff have put together a selection of interesting Jupyter notebooks featuring techniques used in MetPy and elsewhere. Check it out.
Unidata Python Workshop
Unidata Program Center staff have been conducting python training workshops as part of Unidata's annual software training workshops since 2013. Python notebooks used in the workshop are available on Github.
Unidata Program Center staff assist author Jeff Whitaker in maintaining the netcdf4-python module, which is a Python interface to the netCDF C library. The module can read and write files in both the netCDF 4 and netCDF 3 formats, and can create files that are readable by HDF5 clients. The API modelled after Scientific.IO.NetCDF, and should be familiar to users of that module. More information about netcdf4-python is available on Github.
AWIPS II Python Data Access Framework
The AWIPS II Python Data Access Framework (DAF) provides access to an AWIPS II Environmental Data EXchange (EDEX) server directly from Python code. The DAF strengthens Unidata's AWIPS II offerings by making it easier to retrieve data from the AWIPS II data storage system from outside the Common AWIPS Visualization Environment (CAVE).