This week we continue setting up our Python environments by learning about Conda channels. We'll add the Conda Forge channel and see how to install and update packages. We'll round out the screencast by using Conda to install the most recent version of Unidata's MetPy package.

Welcome to MetPy Mondays! This week we're going to start off by getting your Python environment up and running. We will install Miniconda, a product of Continuum Analytics. Conda is a Python environment and package manager that makes managing various versions of Python and packages a breeze.

For a number of years, the Unidata Thredds group has been in the process of "implementing" server-side computation Real-Soon-Now (as the saying goes).

Events have overtaken the previous notion of server-side computing and here we try to codify a replacement that uses a separate server model based on Jupyter (an offshoot of IPython).

From the point of view of Unidata, Jupyter provides a powerful alternative to roll-your-own server-side computing. It supports multiple, "real" programming languages. It is a server itself, so it can be co-located with an existing Thredds server. And, most importantly, it is designed to execute small programs written in any of its supported languages.

We are proposing to implement server-side computing for Thredds by using one or more co-located Jupyter servers. This document elaborates on the capabilities and required support infrastructure to make this proposal operational.

This document proposes an architecture for implementing thread-safe access to the netcdf-c library. Here, the term 'thread-safe' means that multiple threads can access the netcdf-c library safely (i.e. without interference or deadlock or race conditions). This does not mean that the library is itself multi-threaded. Rather, access to the library is serialized so that only one thread at a time is executing the library code.

This post provides some preliminary ideas on the consequences of moving TDS to use OSGI or JigSaw.