Get a good scientific Python environment

By a “good Python environment”, I mean a recent version of Python with recent versions of the main packages for sciences installed (SciPy, NumPy, Matplotlib, IPython, h5py, etc.) and a good editor with fly checks.

Warning

Announcement: require Python 3. As many other scientific projects, we now require Python 3 for all new feature releases. For science, try to use a recent version of Python (>= 3.6 in 2019).

The easy way: Python distributions (for example Anaconda)

A very simple way to get such environment is to use one of the major science-oriented Python distributions, for example the good Python - Anaconda (for slightly more advanced users, Miniconda is surely better).

To get started with Miniconda (commands for Linux):

wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Then load the conda environment (maybe start a new terminal). It can be good to add the channel conda-forge:

conda config --add channels conda-forge

You are then ready to run typical conda and pip install commands, for example:

conda install numpy matplotlib h5py blas=*=openblas
conda install scipy pandas ipython jupyterlab imageio cython psutil

# if you use Spyder (good idea if you do not use a good Python editor)
conda install spyder

# to use clang to compile C++ files produced by Pythran
conda install clangdev

# it can be better to install mpi4py from source rather than with conda to
# use the native mpi library
pip install mpi4py

# Same for pyfftw
pip install pyfftw

# and for Pythran...
pip install pythran colorlog

# pip is also the good tool to install pure python packages, for example:
pip install h5netcdf yapf future mako

Warning

Note the blas=*=openblas requirement in the first line. This is important if you want to use the library fftw_mpi, with is incompatible with MKL.

Another easy way (slightly more difficult?)

It is now very easy to build the most recent Python versions with pyenv.

With the latest versions of pip and the wheels, it is now easy and fast to install scientific packages without conda, using pip.

But without conda, one needs to get the non-python dependencies with the system package management tool, for example apt for Debian/Ubuntu, as shown here:

Python on Windows

On windows, I use Python - Anaconda.

For FluidDyn, you really need a good terminal. The standard console of Windows 7 (cmd) is just surprisingly bad. DO NOT use it since you could get some silly problems and there are simple alternatives. For example, you could use

For flycheck, I install http://aspell.net/win32/

And on macOS

Warning

As of July 2018, there is a bad bug with clang++ with a pure conda install: clang++ does not find the standard C++ library (see this fluidsim_ocean issue)… One needs to use homebrew to install:

brew install open-mpi
brew install fftw --with-mpi
brew install --with-clang llvm
brew install mercurial

Then, two alternatives. First with the “homebrew” Python and pip:

brew install python

python3 -m pip install virtualenv
virtualenv -p python3 fluid-env
source fluid-env/bin/activate

pip install scipy matplotlib cython h5py ipython imageio pandas

pip install mpi4py
pip install pyfftw
pip install pythran colorlog
pip install h5netcdf mako pulp

Other alternative, using conda and pip:

conda config --add channels conda-forge
conda env create fluid-env blas=*=openblas scipy matplotlib cython h5py ipython imageio pandas
conda activate fluid-env

pip install mpi4py
pip install pyfftw
pip install pythran colorlog
pip install h5netcdf mako pulp

The intermediate way and the hard way: from source

Another (harder) way is to build the packages from source (using the system Python interpreter) or (even harder) to build everything from source (the Python interpreter and then the packages) as explained here: