Installation and getting started

To install PySPH, you need a working Python environment with the required dependencies installed. You may use any of the available Python distributions. PySPH is currently tested with Python-2.7.x and 3.x. If you are new to Python we recommend Enthought Canopy or EDM. PySPH will work fine with Miniconda_, Anaconda or other environments like WinPython. The following instructions should help you get started.

Since there is a lot of information here, we suggest that you skim the section on Dependencies and then directly jump to one of the “Installing the dependencies on xxx” sections below depending on your operating system. Depending on your chosen Python distribution, simply follow the instructions and links referred therein.

Quick installation

If you are reasonably experienced with installing Python packages, already have a C++ compiler setup on your machine, and are not immediately interested in running PySPH on multiple CPUs (using MPI), then installing PySPH is simple. Simply running pip like so:

$ pip install PySPH

should do the trick. You may do this in a virtualenv if you chose to. The important examples are packaged with the sources, you should be able to run those immediately. If you wish to download the sources and explore them, you can download the sources either using the tarball/ZIP or from git, see Downloading PySPH.

The above will install the latest released version of PySPH, you can install the development version using:

$ pip install

If you wish to track the development of the package, clone the repository (as described in Downloading PySPH and do the following:

$ pip install -r requirements.txt
$ python develop

The following instructions are more detailed and also show how optional dependencies can be installed. Instructions on how to set things up on Windows is also available below.

If you are running into strange issues when you are setting up an installation with ZOLTAN, see here, pip-cache-issues.


Core dependencies

The core dependencies are:

The project’s requirements.txt lists all the required core dependencies.

These packages can be installed from your Python distribution’s package manager, or using pip. For more detailed instructions on how to do this for different distributions, see below.

Running PySPH requires a working C/C++ compiler on your machine. On Linux/OS X the gcc toolchain will work well. On Windows, you will need to have a suitable MSVC compiler installed, see for specific details.

On Python 2.7 for example, you will need Microsoft Visual C++ Compiler for Python 2.7 or an equivalent compiler. More details are available below.


PySPH generates high-performance code and compiles it on the fly. This requires a working C/C++ compiler even after installing PySPH.

Optional dependencies

The optional dependencies are:

  • OpenMP: PySPH can use OpenMP if it is available. Installation instructions are available below.
  • PyOpenCL: PySPH can use OpenCL if it is available. This requires installing PyOpenCL.
  • Mayavi: PySPH provides a convenient viewer to visualize the output of simulations. This viewer can be launched using the command pysph view and requires Mayavi to be installed. Since this is only a viewer it is optional for use, however, it is highly recommended that you have it installed as the viewer is very convenient.
  • mpi4py and Zoltan: If you want to use PySPH in parallel, you will need mpi4py and the Zoltan data management library along with the PyZoltan package. PySPH will work in serial without mpi4py or Zoltan. Simple build instructions for Zoltan are included below.

Mayavi is packaged with all the major distributions and is easy to install. Zoltan is very unlikely to be already packaged and will need to be compiled.

Building and linking PyZoltan on OSX/Linux

If you want to use PySPH in parallel you will need to install PyZoltan. PyZoltan requires the Zoltan library to be available. We’ve provided a simple Zoltan build script in the PyZoltan repository. This works on Linux and OS X but not on Windows. It can be used as:


where the $INSTALL_PREFIX is where the library and includes will be installed (remember, this script is in the PyZoltan repository and not in PySPH). You may edit and tweak the build to suit your installation. However, this script is what we use to build Zoltan on our continuous integration servers on Travis-CI and Shippable.

After Zoltan is build, set the environment variable ZOLTAN to point to the $INSTALL_PREFIX that you used above:


Note that replace $INSTALL_PREFIX with the directory you specified above. After this, follow the instructions to build PyZoltan. The PyZoltan wrappers will be compiled and available.

Now, when you build PySPH, it too needs to know where to link to Zoltan and you should keep the ZOLTAN environment variable set. This is only needed until PySPH is compiled, thereafter we do not need the environment variable.

If you are running into strange issues when you are setting up pysph with ZOLTAN, see here, pip-cache-issues.


The installation will use $ZOLTAN/include and $ZOLTAN/lib to find the actual directories, if these do not work for your particular installation for whatever reason, set the environment variables ZOLTAN_INCLUDE and ZOLTAN_LIBRARY explicitly without setting up ZOLTAN. If you used the above script, this would be:


Installing the dependencies on GNU/Linux

If you are using Enthought Canopy EDM or Anaconda the instructions in the section Installing the dependencies on Mac OS X will be useful as the instructions are the same. The following are for the case where you wish to use the native Python packages distributed with the Linux distribution you are using.

If you are running into trouble, note that it is very easy to install using EDM (see Using EDM) or conda (see Using Anaconda) and you may make your lives easier going that route.

GNU/Linux is probably the easiest platform to install PySPH. On Ubuntu one may install the dependencies using:

$ sudo apt-get install build-essential python-dev python-numpy \
    python-mako cython python-pytest mayavi2 python-qt4 python-virtualenv

OpenMP is typically available but if it is not, it can be installed with:

$ sudo apt-get install libomp-dev

If you need parallel support:

$ sudo apt-get install libopenmpi-dev python-mpi4py
$ ./ ~/zoltan # Replace ~/zoltan with what you want
$ export ZOLTAN=~/zoltan

On Linux it is probably best to install PySPH into its own virtual environment. This will allow you to install PySPH as a user without any superuser priviledges. See the section below on Using a virtualenv for PySPH. In short do the following:

$ virtualenv --system-site-packages pysph_env
$ source pysph_env/bin/activate
$ pip install cython --upgrade # if you have an old version.

If you wish to use a compiler which is not currently your default compiler, simply update the CC and CXX environment variables. For example, to use icc run the following commands before building PySPH:

$ export CC=icc
$ export CXX=icpc


In this case, you will additionally have to ensure that the relevant intel shared libraries can be found when running PySPH code. Most intel installations come along with shell scripts that load relevant environment variables with the right values automatically. This shell script is generally named and can be found in /path/to/icc/bin. If you didn’t get this file along with your installation, you can try running export LD_LIBRARY_PATH=/path/to/icc/lib.

You should be set now and should skip to Downloading PySPH and Building and Installing PySPH.

On recent versions of Ubuntu (16.10 and 18.04) there may be problems with Mayavi viewer, and pysph view may not work correctly. To see how to resolve these, please look at Possible issues with the viewer.


If you wish to see a working build/test script please see our shippable.yml.

Installing the dependencies on Ubuntu 18.04

On Ubuntu 18.04 it should be relatively simple to install PySPH with ZOLTAN as follows:

# For OpenMP
$ sudo apt-get install libomp-dev

# For Zoltan
$ sudo apt-get install openmpi-bin libopenmpi-dev libtrilinos-zoltan-dev

$ export ZOLTAN_INCLUDE=/usr/include/trilinos
$ export ZOLTAN_LIBRARY=/usr/lib/x86_64-linux-gnu
$ export USE_TRILINOS=1

Now depending on your setup you can install the Python related dependencies. For example with conda_ you can do:

$ conda install -c conda-forge cython mako matplotlib jupyter pyside pytest \
                   mock numpy-stl pytools

$ conda install -c conda-forge mpi4py

Then you should be able to install pyzoltan and its dependency cyarray using:

$ pip install pyzoltan

Finally, install PySPH with

$ pip install pysph

Or with:

$ pip install --no-cache-dir pysph

If you are having trouble due to pip’s cache as discussed in pip-cache-issues.

You should be all set now and should next consider Running the tests.

Installing the dependencies on Mac OS X

On OS X, your best bet is to install Enthought Canopy, EDM, or Anaconda or some other Python distribution. Ensure that you have gcc or clang installed by installing XCode. See this if you installed XCode but can’t find clang or gcc.

If you are getting strange errors of the form:

lang: warning: libstdc++ is deprecated; move to libc++ with a minimum deployment target of OS X 10.9 [-Wdeprecated]
ld: library not found for -lstdc++
clang: error: linker command failed with exit code 1 (use -v to see invocation)

Then try this (on a bash shell):


And run your command again (replace the above with a suitable line on other shells). This is necessary because your Python was compiled with an older deployment target and the current version of XCode that you have installed is not compatible with that. By setting the environment variable you allow compyle to use a newer version. If this works, it is a good idea to set this in your default environment (.bashrc for bash shells) so you do not have to do this every time.

OpenMP on OSX

The default clang compiler available on MacOS uses an LLVM backend and does not support OpenMP. There are two ways to support OpenMP. The first involves installing the OpenMP support for clang. This can be done with brew using:

$ brew install libomp

Once that is done, it should “just work”. If you get strange errors, try setting the MACOSX_DEPLOYMENT_TARGET as shown above.

Another option is to install GCC for MacOS available on brew using

$ brew install gcc

Once this is done, you need to use this as your default compiler. The gcc formula on brew currently ships with gcc version 9. Therefore, you can tell Python to use the GCC installed by brew by setting:

$ export CC=gcc-9
$ export CXX=g++-9

Note that you still do need to have the command-line-tools for XCode installed, otherwise the important header files are not available. See how-to-install-xcode-command-line-tools for more details. You may also want to set these environment variables in your .bashrc so you don’t have to do this every time.

Once you do this, compyle will automatically use this version of GCC and will also work with OpenMP. Note that on some preliminary benchmarks, GCC’s OpenMP implementation seems about 10% or so faster than the LLVM version. Your mileage may vary.

Using EDM

It is very easy to install all the dependencies with the Enthought Deployment Manager (EDM).

  • Download the EDM installer if you do not already have it installed. Install the appropriate installer package for your system.

  • Once you have installed EDM, run the following:

    $ edm install mayavi pyside cython matplotlib jupyter pytest mock pip
    $ edm shell
    $ pip install mako
  • With this done, you should be able to install PySPH relatively easily, see Building and Installing PySPH.

Using Canopy

Download the Canopy express installer for your platform (the full installer is also fine). Launch Canopy after you install it so it initializes your user environment. If you have made Canopy your default Python, all should be well, otherwise launch the Canopy terminal from the Tools menu of the Canopy editor before typing your commands below.

NumPy ships by default but Cython does not. Mako and Cython can be installed with pip easily (pip will be available in your Canopy environment):

$ pip install cython mako

Mayavi is best installed with the Canopy package manager:

$ enpkg mayavi


If you are a subscriber you can also enpkg cython to install Enthought’s build.

If you need parallel support, please see Installing mpi4py and Zoltan on OS X, otherwise, skip to Downloading PySPH and Building and Installing PySPH.

Using Anaconda

After installing Anaconda or miniconda_, you will need to make sure the dependencies are installed. You can create a separate environment as follows:

$ conda create -n pysph_env
$ source activate pysph_env

Now you can install the necessary packages:

$ conda install -c conda-forge cython mako matplotlib jupyter pyside pytest mock
$ conda install -c menpo mayavi

If you need parallel support, please see Installing mpi4py and Zoltan on OS X, otherwise, skip to Downloading PySPH and Building and Installing PySPH.

Installing mpi4py and Zoltan on OS X

In order to build/install mpi4py one first has to install the MPI library. This is easily done with Homebrew as follows (you need to have brew installed for this but that is relatively easy to do):

$ sudo brew install open-mpi

After this is done, one can install mpi4py by hand. First download mpi4py from here. Then run the following (modify these to suit your XCode installation and version of mpi4py):

$ cd /tmp
$ tar xvzf ~/Downloads/mpi4py-1.3.1.tar.gz
$ cd mpi4py-1.3.1
$ export SDKROOT=/Applications/
$ python install

Change the above environment variables to suite your SDK version. If this installs correctly, mpi4py should be available.

You can then follow the instructions on how to build/install Zoltan and PyZoltan given above. You should be set now and should move to Building and Installing PySPH. Just make sure you have set the ZOLTAN environment variable so PySPH knows where to find it.

Installing the dependencies on Windows

While it should be possible to use mpi4py and Zoltan on Windows, we do not at this point have much experience with this. Feel free to experiment and let us know if you’d like to share your instructions. The following instructions are all without parallel support.

Using EDM

It is very easy to install all the dependencies with the Enthought Deployment Manager (EDM).

  • Download the EDM installer if you do not already have it installed. Install the appropriate installer package for your system.

  • Once you have installed EDM, run the following:

    > edm install mayavi pyside cython matplotlib jupyter pytest mock pip
    > edm shell
    > pip install mako

Once you are done with this, please skip ahead to Installing Visual C++ Compiler for Python.

Using Canopy

Download and install Canopy Express for you Windows machine (32 or 64 bit). Launch the Canopy editor at least once so it sets up your user environment. Make the Canopy Python the default Python when it prompts you. If you have already skipped that option, you may enable it in the Edit->Preferences menu. With that done you may install the required dependencies. You can either use the Canopy package manager or use the command line. We will use the command line for the rest of the instructions. To start a command line, click on “Start” and navigate to the All Programs/Enthought Canopy menu. Select the “Canopy command prompt”, if you made Canopy your default Python, just starting a command prompt (via cmd.exe) will also work.

On the command prompt, Mako and Cython can be installed with pip easily (pip should be available in your Canopy environment):

> pip install cython mako

Mayavi is best installed with the Canopy package manager:

> enpkg mayavi

Once you are done with this, please skip ahead to Installing Visual C++ Compiler for Python.


If you are a subscriber you can also enpkg cython to install Enthought’s build.

Using WinPython

Instead of Canopy or Anaconda you could try WinPython 2.7.x.x. To obtain the core dependencies, download the corresponding binaries from Christoph Gohlke’s Unofficial Windows Binaries for Python Extension Packages. Mayavi is available through the binary ETS.

You can now add these binaries to your WinPython installation by going to WinPython Control Panel. The option to add packages is available under the section Install/upgrade packages.

Make sure to set your system PATH variable pointing to the location of the scripts as required. If you have installed WinPython 2.7.6 64-bit, make sure to set your system PATH variables to <path to installation folder>/python-2.7.6.amd64 and <path to installation folder>/python-2.7.6.amd64/Scripts/.

Once you are done with this, please skip ahead to Installing Visual C++ Compiler for Python.

Using Anaconda

Install Anaconda for your platform, make it the default and then install the required dependencies:

$ conda install cython mayavi
$ pip install mako

Once you are done with this, please skip ahead to Installing Visual C++ Compiler for Python.

Installing Visual C++ Compiler for Python

For all of the above Python distributions, it is highly recommended that you build PySPH with Microsoft’s Visual C++ for Python. See see for specific details for each version of Python. Note that different Python versions may have different compiler requirements.

On Python 3.6 and above you should use Microsoft’s Build Tools for Visual Studio 2017.

On Python 2.7 for example use Microsoft’s Visual C++ for Python 2.7. We recommend that you download and install the VCForPython27.msi available from the link. Make sure you install the system requirements specified on that page. For example, you will need to install the Microsoft Visual C++ 2008 SP1 Redistributable Package for your platform (x86 for 32 bit or x64 for 64 bit) and on Windows 8 and above you will need to install the .NET framework 3.5. Please look at the link given above, it should be fairly straightforward. Note that doing this will also get OpenMP working for you.

After you do this, you will find a “Microsoft Visual C++ Compiler Package for Python” in your Start menu. Choose a suitable command prompt from this menu for your architecture and start it (we will call this the MSVC command prompt). You may make a short cut to it as you will need to use this command prompt to build PySPH and also run any of the examples.

After this is done, see section Downloading PySPH and get a copy of PySPH. Thereafter, you may follow section Building and Installing PySPH.


On 64 bit Windows, do not build PySPH with mingw64 as it does not work reliably at all and frequently crashes. YMMV with mingw32 but it is safer and just as easy to use the MS VC++ compiler.

Using a virtualenv for PySPH

A virtualenv allows you to create an isolated environment for PySPH and its related packages. This is useful in a variety of situations.

  • Your OS does not provide a recent enough Cython version (say you are running Debian stable).
  • You do not have root access to install any packages PySPH requires.
  • You do not want to mess up your system files and wish to localize any installations inside directories you control.
  • You wish to use other packages with conflicting requirements.
  • You want PySPH and its related packages to be in an “isolated” environment.

You can either install virtualenv (or ask your system administrator to) or just download the script and use it (run python after you download the script).

Create a virtualenv like so:

$ virtualenv --system-site-packages pysph_env

This creates a directory called pysph_env which contains all the relevant files for your virtualenv, this includes any new packages you wish to install into it. You can delete this directory if you don’t want it anymore for some reason. This virtualenv will also “inherit” packages from your system. Hence if your system administrator already installed NumPy it may be imported from your virtual environment and you do not need to install it. This is very useful for large packages like Mayavi, Qt etc.


If your version of virtualenv does not support the --system-site-packages option, please use the script mentioned above.

Once you create a virtualenv you can activate it as follows (on a bash shell):

$ source pysph_env/bin/activate

On Windows you run a bat file as follows:

$ pysph_env/bin/activate

This sets up the PATH to point to your virtualenv’s Python. You may now run any normal Python commands and it will use your virtualenv’s Python. For example you can do the following:

$ virtualenv myenv
$ source myenv/bin/activate
(myenv) $ pip install Cython mako pytest
(myenv) $ cd pysph
(myenv) $ python install

Now PySPH will be installed into myenv. You may deactivate your virtualenv using the deactivate command:

(myenv) $ deactivate

On Windows, use myenv\Scripts\activate.bat and myenv\Scripts\deactivate.bat.

If for whatever reason you wish to delete myenv just remove the entire directory:

$ rm -rf myenv


With a virtualenv, one should be careful while running things like ipython or pytest as these are sometimes also installed on the system in /usr/bin. If you suspect that you are not running the correct Python, you could simply run (on Linux/OS X):

$ python `which ipython`

to be absolutely sure.

Using Virtualenv on Canopy

If you are using Enthought Canopy, it already bundles virtualenv for you but you should use the venv script. For example:

$ venv --help
$ venv --system-site-packages myenv
$ source myenv/bin/activate

The rest of the steps are the same as above.

Downloading PySPH

One way to install PySPH is to use pip

$ pip install PySPH

This will install PySPH, and you should be able to import it and use the modules with your Python scripts that use PySPH. This will also provide the standard set of PySPH examples. If you want to take a look at the PySPH sources you can get it from git or download a tarball or ZIP as described below.

To get PySPH using git type the following

$ git clone

If you do not have git or do not wish to bother with it, you can get a ZIP or tarball from the pysph site. You can unzip/untar this and use the sources.

In the instructions, we assume that you have the pysph sources in the directory pysph and are inside the root of this directory. For example:

$ unzip pysph-pysph-*.zip
$ cd pysph-pysph-1ce*

or if you cloned the repository:

$ git clone
$ cd pysph

Once you have downloaded PySPH you should be ready to build and install it, see Building and Installing PySPH.

Building and Installing PySPH

Once you have the dependencies installed you can install PySPH with:

$ pip install PySPH

You can install the development version using:

$ pip install

If you downloaded PySPH using git or used a tarball you can do:

$ python install

You could also do:

$ python develop

This is useful if you are tracking the latest version of PySPH via git. With git you can update the sources and rebuild using:

$ git pull
$ python develop

You should be all set now and should next consider Running the tests.

Note that pip caches any packages it has built and installed earlier. So if you installed PySPH without Zoltan support, say and then uninstalled PySPH using:

$ pip uninstall pysph

then if you try a pip install pysph again (and the PySPH version has not changed), pip will simply re-use the old build it made. You do not want this and want it to re-build PySPH to use ZOLTAN say, then you can do the following:

$ pip install --no-cache-dir pysph

In this case, pip will disregard its default cache and freshly download and build PySPH. This is often handy.

Running the tests

Once you install PySPH you can run the tests using the pysph script that is installed:

$ pysph test

If you see errors while running the tests, you might want more verbose reporting which you can get with:

$ pysph test -v

This should run all the tests that do not take a long while to complete. If this fails, please contact the pysph-users mailing list or send us email.

There are a few additional test dependencies that need to be installed when running the tests. These can be installed using:

$ pip install -r requirements-test.txt

Once you run the tests, you should see the section on Running the examples.


Internally, we use the pytest package to run the tests.

For more information on what you can do with the pysph script try this:

$ pysph -h

Running the examples

You can verify the installation by exploring some examples. The examples are actually installed along with the PySPH library in the pysph.examples package. You can list and choose the examples to run by doing:

$ pysph run

This will list all the available examples and allow you to run any of them. If you wish to run a particular one, like say elliptical_drop, you may do:

$ pysph run elliptical_drop

This can also be run as:

$ pysph run pysph.examples.elliptical_drop

To see the options available, try this:

$ pysph run elliptical_drop -h


Technically you can run the examples using python -m pysph.examples.elliptical_drop. The pysph run command is a lot more convenient as it allows a much shorter command

You can view the data generated by the simulation (after the simulation is complete or during the simulation) by running pysph view command. To view the simulated data you may do:

$ pysph view elliptical_drop_output

If you have Mayavi installed this should show a UI that looks like:

PySPH viewer

If the viewer does not start, you may want to see Possible issues with the viewer.

There are other examples that use the transport velocity formulation:

$ pysph run cavity

This runs the driven cavity problem using the transport velocity formulation of Adami et al. The example also performs post-processing of the results and the cavity_output will contain a few PNG images with these. You may view these results using pysph view cavity_output.

For example for example the file streamlines.png may look like what is shown below:


If you want to use PySPH for elastic dynamics, you can try some of the examples from Gray et al., Comput. Methods Appl. Mech. Engrg. 190 (2001), 6641-6662:

$ pysph run solid_mech.rings

Which runs the problem of the collision of two elastic rings. View the results like so:

$ pysph view rings_output

This should produce something that may look like the image below.


The auto-generated high-performance code for the example resides in the directory ~/.pysph/source. A note of caution however, it’s not for the faint hearted.

Running the examples with OpenMP

If you have OpenMP available run any of the examples as follows:

$ pysph run elliptical_drop --openmp

This should run faster if you have multiple cores on your machine. If you wish to change the number of threads to run simultaneously, you can try the following:

$ OMP_NUM_THREADS=8 pysph run elliptical_drop --openmp

You may need to set the number of threads to about 4 times the number of physical cores on your machine to obtain the most scale-up. If you wish to time the actual scale up of the code with and without OpenMP you may want to disable any output (which will be serial), you can do this like:

$ pysph run elliptical_drop --disable-output --openmp

Note that one may run example scripts directly with Python but this requires access to the location of the script. For example, if a script exists one can run it as:

$ python

The pysph run command is just a convenient way to run the pre-installed examples that ship with PySPH.

Running the examples with OpenCL

If you have PyOpenCL installed and working with an appropriate device setup, then you can transparently use OpenCL as well with PySPH. This feature is very new and still fairly experimental. You may run into issues but using it is simple. You may run any of the supported examples as follows:

$ pysph run elliptical_drop --opencl

Yes, thats it, just use the --opencl option and code will be auto-generated and run for you. By default it uses single-precision but you can also run the code with double precision using:

$ pysph run elliptical_drop --opencl --use-double

Currently inlets and outlets are not supported, periodicity is slow and many optimizations still need to be made but this is rapidly improving. If you want to see an example that runs pretty fast, try the cube example:

$ pysph run cube --disable-output --np 1e6 --opencl

You may compare the execution time with that of OpenMP.

Running the examples with MPI

If you compiled PySPH with Zoltan and have mpi4py installed you may run any of the examples with MPI as follows (here we choose 4 processors with --np 4, change this to suit your needs):

$ mpirun -np 4 pysph run dam_break_3d

This may not give you significant speedup if the problem is too small. You can also combine OpenMP and MPI if you wish. You should take care to setup the MPI host information suitably to utilize the processors effectively.


Note that again we are using pysph run here but for any other scripts, one could do mpirun -np python

Possible issues with the viewer

Often users are able to install PySPH and run the examples but are unable to run pysph view for a variety of reasons. This section discusses how these could be resolved.

The PySPH viewer uses Mayavi. Mayavi can be installed via pip. Mayavi depends on VTK which can also be installed via pip if your package manager does not have a suitable version.

If you are using Ubuntu 16.04 or 16.10 or a VTK version built with Qt5, it is possible that you will see a strange segmentation fault when starting the viewer. This is because Mayavi uses Qt4 and the VTK build has linked to Qt5. In these cases it may be best to use to use the latest VTK wheels that are now available on pypi. If you have VTK installed but you want a more recent version of Mayavi, you can always use pip to install Mayavi.

For the very specific case of Mayavi on Ubuntu 16.04 and its derivatives, you can use Ubuntu’s older VTK package like so:

$ sudo apt remove mayavi2 python-vtk6
$ sudo apt install python-vtk
$ pip install mayavi

What this does is to remove the system Mayavi and the VTK-6.x package which is linked to Qt5 and instead install the older python-vtk package. Then using pip to install Mayavi against this version of VTK. If the problem persists remember that by default pip caches any previous installations of Mayavi and you may need to install Mayavi like this:

$ pip --no-cache-dir install mayavi

If you are using EDM or Anaconda, things should work most of the time. However, there may be problems and in this case please report the issues to the pysph-users mailing list or send us email.