Advice on how to work with Python

FluidDyn should be used also by scientists that are not experienced in Python. We provide some advice on how to work with Python and how to get a good Python environment.

Use an up-to-date Python environment!

Python is an old language but the strong dynamics in scientific Python is quite young. The base packages have greatly improved these last years so it is really better to use recent versions. Therefore, it is not a good idea to use scientific python libraries packaged in not very recent Linux versions.

Get a good scientific Python environment

Read and watch!

If you have only basic knowledge on Python, start with the the official Python tutorial.
It is always useful to have a look at the official Python documentation.
If you already know Python, it could be useful to check out some best practices and to have a look at the famous book Dive in Python 3.
If you begin with Python and even if you have some experience with this language, understanding Python variables is important and can avoid some bugs.
http://www.scipy-lectures.org/

There are also plenty interesting Python videos on the web…

Don’t use Python 2.7 and try to use Python >= 3.6

If you still use Python 2.7 in 2019, it is time to stop! In few months, Python 2.7 won’t be supported anymore by the CPython developers (https://pythonclock.org/) and a number of critical Python projects have pledged to stop supporting Python 2 soon.

We even strongly advice to stop using Python < 3.6. Conda and pyenv are very convenient to use recent versions of Python in most systems (see Get a good scientific Python environment).

With Python 3.6, one can use f-strings and the support for pathlib is much better. These are the reasons why we decided to support only Python >= 3.6 for the FluidDyn packages.

Use a good editor with fly checks

Python is a very dynamics language. It is very nice but it is also very dangerous. A silly error (a misspell for example) is very easy and there is no compiler to tell you that something is wrong. Automatic checking of the code is enough to avoid most of these silly errors so anyone has to use it.

The style in Python is also really important (see below) so any Python developer has to get used to code properly. The best way is to code with a fly checker that tells you as soon as you do something wrong.

Most experienced Python programmers use an good Python editor with fly checking and it is really very useful. So of course beginners have to use a good Python editor running fly checks!

For scientists and for simpler programming (only scripts), you can for example use Spyder (Scientific PYthon Development EnviRonment). Note that Spyder has to be setup correctly to use fly checks.

Visual studio code is great for more advanced programming (with at least the Python and Settings Sync extensions). For developping a Python package, it is indeed very good and it really improves productivity and even the quality of the code, see for example this blog post by Kenneth Reitz. On Unix, one may want to avoid using a binary built by Microsoft so we can use vscodium.

Another GNUer solution is Emacs, but it should be setup correctly (for example with Flycheck, see my Emacs configuration).

The style is important

Most of the time, we have to follow the Style Guide for Python Code, the so-called “pep 8”). It is not just for fun. On the long term, it really helps.

In particular,

limit all lines to a maximum of 79 characters.
most of the time, comments before the code. At least not in very long lines of more than 79 characters.
names of the modules in lower case.
names of the classes in CamelCase, i.e. LikeThis.
no space before a comma and a space after.
no tabulation! four spaces.
no trailing white space.
documentation of the functions in a docstring.
Python is in English. It is a good idea to write Python modules all in English.

For FluidDyn packages, we use black with the command black -l 82.

For Matlab users

If you begin with Python and you really like Matlab, I would advice to add in your .bashrc the line:

alias ipython='ipython --pylab'

With the option --pylab, Ipython imports the module matplolib.pylab with the command from matplotlib.pylab import * and runs matplotlib.pylab.ion() (this can take a few seconds), so the iterative Python console will behave much more like in Matlab than the standard ipython console without pylab imported.

In contrast, in you script, do not use the devil line from matplotlib.pylab import *. It is much better to learn how to use matplotlib with the import import matplotlib.pyplot as plt.