Installation

FluidSim is part of the FluidDyn project. Some issues regarding the installation of Python packages are discussed in the main documentation of the project.

Dependencies

  • Python 2.7 or >= 3.4

  • Numpy

  • fluiddyn

    The base package of the FluidDyn project is a pure Python package that will be installed automatically so you should not need to worry about this dependency.

  • fluidfft

    fluidsim needs fluidfft. If you don’t install it before carefully, it will be installed automatically and you won’t be able to use fancy FFT libraries (using for example MPI with 2D decomposition or GPU with CUDA). If you are not too concerned about performance, no problem. Otherwise, install fluidfft as explained here.

  • h5py (optionally, with MPI support, but only if you know what you do)

    Warning

    Prebuilt installations (for eg. via h5py wheels) may lack MPI support. Most of the time, this is what you want. However, you can install h5py from source and link it to a hdf5 built with MPI support, as follows:

    $ CC="mpicc" HDF5_MPI="ON" HDF5_DIR=/path/to/parallel-hdf5 pip install --no-deps --no-binary=h5py h5py
    $ python -c 'import h5py; h5py.run_tests()'
    

    See the h5py documentation for more details.

  • A C++11 compiler (for example GCC 4.9 or clang)

  • Pythran

    We choose to use the new static Python compiler Pythran for some functions. Our microbenchmarks show that the performances are as good as what we are able to get with Fortran or C++!

    Warning

    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
    

    Warning

    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
    
  • Optionally (for MPI runs), mpi4py (which depends on a MPI implementation).

  • Optionally (for some command-line tools), Pandas.

Basic installation with pip

If you are in a hurry and that you are not really concerned about performance, you can use pip:

pip install fluidsim

or:

pip install fluidsim --user

You can also configure the installation of fluidsim by creating the file ~/.fluidsim-site.cfg and modify it to fit your requirements before the installation with pip:

wget https://bitbucket.org/fluiddyn/fluidsim/raw/default/site.cfg.default -O ~/.fluidsim-site.cfg

Run the tests!

You can run some unit tests by running make tests (shortcut for fluidsim-test -v) or make tests_mpi (shortcut for mpirun -np 2 fluidsim-test -v). Alternatively, you can also run python -m unittest discover from the root directory or from any of the “test” directories.

Environment variables

Fluidsim 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 FLUIDDYN_NUM_PROCS_BUILD:

export FLUIDDYN_NUM_PROCS_BUILD=2

Fluidsim is also sensible to the environment variables:

  • FLUIDSIM_PATH: path where the simulation results are saved.

    In Unix systems, you can for example put this line in your ~/.bashrc:

    export FLUIDSIM_PATH=$HOME/Data
    
  • FLUIDDYN_PATH_SCRATCH: working directory (can be useful on some clusters).