Installation and advice¶
Python 2.7 or >= 3.4
a C++11 compiler (for example GCC 4.9 or clang)
Make sure to correctly install numpy before anything.
Be careful, the wheels install with pip install numpy can be slow. You might get something more efficient by compiling from source using:
pip install numpy --no-binary numpy python -c 'import numpy; numpy.test()'
In anaconda (or miniconda), Numpy installed with conda install numpy is built and linked with MKL (an Intel library). This can be a real plus for performance since MKL replaces fftw functions by (usually) faster ones but it has a drawback for fft using the library fftw3_mpi (an implementation of parallel fft using 1D decomposition by fftw). MKL implements some fftw functions but not all the functions defined in fftw3_mpi. Since the libraries are loaded dynamically, if numpy is imported before the fftw_mpi libraries, this can lead to very bad issues (segmentation fault, only if numpy is imported before the class!). For security, we prefer to automatically disable the building of the fft classes using fftw3_mpi when it is detected that numpy uses the MKL library where some fftw symbols are defined.
We choose to use the new static Python compiler Pythran for some functions of the operators. Our microbenchmarks show that the performances are as good as what we are able to get with Fortran or C++!
To reach good performance, we advice to try to put in the file ~/.pythranrc the lines (it seems to work well on Linux, see the Pythran documentation):
[pythran] complex_hook = True
The compilation of C++ files produced by Pythran can be long and can consume a lot of memory. If you encounter any problems, you can try to use clang (for example with
conda install clangdev) and to enable its use in the file ~/.pythranrc with:
[compiler] CXX=clang++ CC=clang
mpi4py (optional, only for mpi runs),
pyfftw: FluidFFT can of course use pyfftw and it is often a very fast solution for undistributed FFT. However, pyfftw is just an optional dependency.
And of course FFT libraries!
The libraries are used if they are installed so you shouldn’t have any error if you build-install FluidFFT without FFT libraries! However, nothing will be built and it’s not very interesting. So you have to install at least one of the supported libraries, let’s say at least fftw!
FluidFFT builds its binaries in parallel. It speedups the build process a lot on
most computers. However, it can be a very bad idea on computers with not enough
memory. If you encounter problems, you can force the number of processes used
during the build using the environment variable
FluidFFT is also sensible to the environment variable
Basic installation with pip¶
If you are in a hurry and that you are not really concerned about performance, you can use pip directly without any configuration file:
pip install fluidfft
pip install fluidfft --user
However, it better to configure the installation of FluidFFT by creating the file
~/.fluidfft-site.cfg and modify it to fit your requirements before the
installation with pip:
wget https://bitbucket.org/fluiddyn/fluidfft/raw/default/site.cfg.default -O ~/.fluidfft-site.cfg
Install from the repository (recommended)¶
Get the source code¶
For FluidFFT, we use the revision control software Mercurial and the main repository is hosted here in Bitbucket. Download the source with something like:
hg clone https://bitbucket.org/fluiddyn/fluidfft
If you are new with Mercurial and Bitbucket, you can also read this short tutorial.
For particular installation setup, copy the default configuration file:
cp site.cfg.default site.cfg
and modify it to fit your requirements.
Build/install in development mode (with a virtualenv):
python setup.py develop
or (without virtualenv):
python setup.py develop --user
Of course you can also install FluidDyn with the install command
After the installation, it is a good practice to run the unit tests by running
python -m unittest discover from the root directory or from any of the
“test” directories (or just
make tests or