.. _installation:

=================================
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-2.6.x, 2.7.x and 3.4.x. If you are new
to Python we recommend `Enthought Canopy`_ or EDM_. PySPH will work fine with
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 :ref:`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.

.. contents::
    :local:
    :depth: 1


.. _Enthought Canopy: https://www.enthought.com/products/canopy/
.. _EDM: https://www.enthought.com/products/edm/
.. _Anaconda: http://continuum.io/downloads


.. _quick-install:

-------------------
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
:ref:`downloading-pysph`.

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

    $ pip install git+https://github.com/pypr/pysph.git#egg=PySPH

If you wish to track the development of the package, clone the repository (as
described in :ref:`downloading-pysph` and do the following::

    $ pip install -r requirements.txt
    $ python setup.py 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.


.. _dependencies:

------------------
Dependencies
------------------

^^^^^^^^^^^^^^^^^^
Core dependencies
^^^^^^^^^^^^^^^^^^

The core dependencies are:

  - NumPy_
  - Cython_ (version 0.20 and above)
  - Mako_
  - pytest_ for running the unit tests.

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

On Python-2.6.x a few additional packages are needed and these are listed in
the project's `requirements-2.6.txt
<https://github.com/pypr/pysph/tree/master/requirements-2.6.txt>`_

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 `Microsoft
Visual C++ Compiler for Python 2.7
<http://www.microsoft.com/en-us/download/details.aspx?id=44266>`_ or an
equivalent compiler.  More details are available below.

.. note::

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


.. _NumPy: http://numpy.scipy.org
.. _Cython: http://www.cython.org
.. _pytest: https://www.pytest.org
.. _Mako: https://pypi.python.org/pypi/Mako
.. _pip: http://www.pip-installer.org

^^^^^^^^^^^^^^^^^^^^^^
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.  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.

.. _Mayavi: http://code.enthought.com/projects/mayavi
.. _mpi4py: http://mpi4py.scipy.org/
.. _Zoltan: http://www.cs.sandia.gov/zoltan/
.. _OpenMP: http://openmp.org/
.. _PyOpenCL: https://documen.tician.de/pyopencl/
.. _OpenCL: https://www.khronos.org/opencl/

Building and linking PyZoltan on OSX/Linux
-------------------------------------------

We've provided a simple Zoltan build script in the repository.  This works on
Linux and OS X but not on Windows.  It can be used as::

    $ ./build_zoltan.sh INSTALL_PREFIX

where the ``INSTALL_PREFIX`` is where the library and includes will be
installed.  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::

    $ export ZOLTAN=$INSTALL_PREFIX

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

.. note::

    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::

        $ export ZOLTAN_INCLUDE=$INSTALL_PREFIX/include
        $ export ZOLTAN_LIBRARY=$INSTALL_PREFIX/lib


-----------------------------------------
Installing the dependencies on GNU/Linux
-----------------------------------------

If you are using `Enthought Canopy`_ EDM_ or Anaconda_ the instructions in the
section :ref:`installing-deps-osx` 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 :ref:`using_edm_osx`) or conda (see :ref:`using_conda_osx`) 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 libgomp1

If you need parallel support::

    $ sudo apt-get install libopenmpi-dev python-mpi4py
    $ ./build_zoltan.sh ~/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 :ref:`using-virtualenv`.  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

.. note::

    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 ``compilervars.sh`` 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 :ref:`downloading-pysph` and
:ref:`building-pysph`.

On recent versions of Ubuntu (16.04 and 16.10) there may be problems with
Mayavi viewer, and ``pysph view`` may not work correctly. To see how to
resolve these, please look at :ref:`viewer-issues`.


.. note::

    If you wish to see a working build/test script please see our
    `shippable.yml
    <https://github.com/pypr/pysph/tree/master/shippable.yml>`_.

.. _Shippable: http://shippable.com
.. _Travis-CI: http://travis-ci.org

.. _installing-deps-osx:

------------------------------------------
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
<http://stackoverflow.com/questions/12228382/after-install-xcode-where-is-clang>`_
if you installed XCode but can't find clang or gcc.

^^^^^^^^^^^^^
OpenMP on OSX
^^^^^^^^^^^^^

The default "gcc" available on OSX uses an LLVM backend and does not support 
OpenMP_. To use OpenMP_ on OSX, you can install the GCC 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 7. Therefore, you can 
tell Python to use the GCC installed by brew by setting::

    $ export CC=gcc-7
    $ export CXX=g++-7

.. _brew: http://brew.sh/


.. _using_edm_osx:

^^^^^^^^^^^
Using EDM
^^^^^^^^^^^

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

- `Download the EDM installer
  <https://www.enthought.com/products/edm/installers>`_ 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
  :ref:`building-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

.. note:: If you are a subscriber you can also ``enpkg cython`` to install
   Enthought's build.

If you need parallel support, please see :ref:`installing-mpi-osx`, otherwise,
skip to :ref:`downloading-pysph` and :ref:`building-pysph`.


.. _using_conda_osx:

^^^^^^^^^^^^^^^
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 :ref:`installing-mpi-osx`, otherwise,
skip to :ref:`downloading-pysph` and :ref:`building-pysph`.


.. _miniconda: http://conda.pydata.org/miniconda.html

.. _installing-mpi-osx:

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
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 <https://pypi.python.org/pypi/mpi4py>`_. 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 MACOSX_DEPLOYMENT_TARGET=10.7
    $ export SDKROOT=/Applications/Xcode.app/Contents/Developer/Platforms/MacOSX.platform/Developer/SDKs/MacOSX10.7.sdk/
    $ python setup.py install

Change the above environment variables to suite your SDK version.  If this
installs correctly, mpi4py should be available.  You can now build Zoltan,
(the script to do this is in the pysph sources, see :ref:`downloading-pysph`)
::

    $ cd pysph
    $ ./build_zoltan.sh ~/zoltan # Replace ~/zoltan with what you want
    $ export ZOLTAN=~/zoltan


You should be set now and should move to :ref:`building-pysph`.

.. _Homebrew: http://brew.sh/


---------------------------------------
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
  <https://www.enthought.com/products/edm/installers>`_ 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
:ref:`installing-visual-c++`.


^^^^^^^^^^^^^^
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
:ref:`installing-visual-c++`.

.. note:: 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
<http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_. 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.

.. _WinPython: http://winpython.sourceforge.net/

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
:ref:`installing-visual-c++`.

^^^^^^^^^^^^^^^
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
:ref:`installing-visual-c++`.

.. _installing-visual-c++:

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Installing Visual C++ Compiler for Python 2.7
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

For all of the above Python distributions, it is highly recommended that you
build PySPH with `Microsoft's Visual C++ for Python 2.7
<http://www.microsoft.com/en-us/download/details.aspx?id=44266>`_. We
recommend that you download and install the ``VCForPython27.msi`` available
from the `link
<http://www.microsoft.com/en-us/download/details.aspx?id=44266>`_. **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 2.7" 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 :ref:`downloading-pysph` and get a copy of
PySPH. Thereafter, you may follow section :ref:`building-pysph`.

.. warning::

    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-virtualenv:

-------------------------------
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 `virtualenv.py
<http://github.com/pypa/virtualenv/tree/master/virtualenv.py>`_ script and use
it (run ``python virtualenv.py`` after you download the script).

.. _virtualenv: http://www.virtualenv.org

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.

.. note:: If your version of ``virtualenv`` does not support the
   ``--system-site-packages`` option, please use the ``virtualenv.py`` 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 setup.py 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

.. note::

    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:

------------------
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 https://github.com/pypr/pysph.git

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 <https://github.com/pypr/pysph/downloads>`_.
You can unzip/untar this and use the sources.

.. _git: http://git-scm.com/

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 https://github.com/pypr/pysph.git
    $ cd pysph

Once you have downloaded PySPH you should be ready to build and install it,
see :ref:`building-pysph`.


.. _building-pysph:

-------------------------------
Building and Installing PySPH
-------------------------------

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

    $ pip install PySPH

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

    $ python setup.py install

You could also do::

    $ python setup.py 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 setup.py develop

You should be all set now and should next consider :ref:`running-the-tests`.



.. _running-the-tests:

------------------
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
<https://groups.google.com/forum/#!forum/pysph-users>`_ or send us `email
<mailto:pysph-users@googlegroups.com>`_.

Once you run the tests, you should see the section on
:ref:`running-the-examples`.

.. note::

    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:

---------------------
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

.. note::

    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:

.. image:: ../Images/pysph_viewer.png
    :width: 800px
    :alt: PySPH viewer

If the viewer does not start, you may want to see :ref:`viewer-issues`.

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:

.. image:: ../Images/ldc-streamlines.png

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.

.. image:: ../Images/rings-collision.png

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
``pysph_script.py`` exists one can run it as::

    $ python pysph_script.py

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::

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


.. _viewer-issues:

-------------------------------
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_ and while Mayavi itself is a Python package and
can be installed with pip_, it depends on VTK_ which is harder to install and
cannot be installed using pip.

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
this case your only option is to not use Qt at all and use the wxPython_
backend for Mayavi.  This can be done on Linux/OS X as follows::

   $ export ETS_TOOLKIT=wx
   $ pysph view ...

The environment variable ``ETS_TOOLKIT`` tells Mayavi to use wxPython. By
default, the Mayavi viewer will use the Qt backend. You will obviously need
wxPython installed to use the wx backend. On Ubuntu this is available via::

   $ sudo apt install python-wxgtk3.0

With EDM_ one can do::

   $ edm install wxpython

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
<https://groups.google.com/forum/#!forum/pysph-users>`_ or send us `email
<mailto:pysph-users@googlegroups.com>`_.

.. _VTK: http://www.vtk.org

.. _wxPython: http://www.wxpython.org
