This module is used as an example for shifting scientific code from quick&dirty solutions towards modular, tested code that could be published on PyPI. It was created for a micro-lecture at the chair for hydrology at the Karlsruhe Institute for Technology (KIT).
It includes three different modules:
- 01_script.py
- 02_functional.py
- diff2d module
All three modules solve the same problem: a two dimensional diffusion calculated iteratively on a regular grid with the border conditions rolling over to the opposite edge. This example was implemented as an easy to understand usecase, most natural scientists can easily understand and reflect to thier problems. The example was taken from the great book: High Performance Python by Micha Gorelick and Ian Ozsvald (O'Reilly). [Copyright 2014 Micha Gorelick and Ian Ozsvald, 978-1-449-36159-4].
The first script is a quick&dirty solution for only one use case creating a static output. A number of flaws I experienced as a environmental scientists, when digging into foreign code are implemented.
The second example represents a version of 01_script.py, that does not include any static paths, parameters or global variables the user would have to change. The code was broken down into different functions, that can be imported by an application script. Additionally, the script is a command line tool as well, using passed arguments to run the diffusion function as an example.
diff2d is a modular version with only some minor changes. Now the simulate and evolve function do not plot anymore on their own, but return the numpy.array containing the new diffusion state. This package can be installed using pip and is in fact already published on PyPI. As an straightforward example the functionality was shifted into a core module, while the application is present in the diffusion.py command line tool. One could now easily extend the functionality or add new command line tools. The most important change compared to 02_functional.py is the usage of ņosetests and coverage.py, that scientists should get more familiar with.