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-Installation
-============
-
-Chroma development tends to live on the bleeding-edge.  Installation
-of Chroma requires a more significant hardware and software investment
-than other packages, but we think the rewards are worth it!
-
-.. _hardware-prerequisites:
-
-Hardware Prerequisites
-----------------------
-
-At a minimum, Chroma requires:
-
-* An x86 or x86-64 CPU.
-* A NVIDIA GPU that supports CUDA compute capability 2.0 or later.
-
-However, Chroma can be quite demanding with large detector geometries.
-Both the CPU and GPU will need sufficient memory hold the detector
-geometry and related data structures.  For example, a detector
-represented with 60.1 million triangles requires 2.2 GB of CUDA device
-memory, and more than 6 GB of host memory during detector
-construction.  Chroma also requires the use of multiple CPU cores to
-generate photon vertices with GEANT4 at a rate sufficient to keep up
-with GPU propagation.
-
-We highly recommend that you run Chroma with:
-
-* An x86-64 CPU with at least four cores.
-* 8 GB or more of system RAM
-* An NVIDIA GPU that supports CUDA compute capability 2.0 or later,
-  and has at least 1.25 GB of device memory.  For detectors with more
-  than 20,000 photomultiplier tubes, you will need a GeForce GTX 580
-  with 3 GB of device memory, or a Tesla C2050 or higher.
-
-.. note:: The Chroma interactive renderer includes optional support for
-  the `Space Navigator 3D mouse <http://www.3dconnexion.com/products/spacenavigator.html>`_, which makes it 10x more fun to fly
-  through the detector geometry!
-
-Software Prerequisites
-----------------------
-
-Chroma depends on several software packages:
-
-* Python 2.6 or later
-* The CUDA 4.1 Toolkit and NVIDIA driver. (You may use drivers newer than the developer driver listed on the CUDA 4.1 website.)
-* Boost::Python
-* Numpy 1.6 or later
-* Pygame
-* Matplotlib
-* uncertainties
-* PyCUDA 2011.2 or later
-* PyUblas
-* ZeroMQ
-* GEANT4.9.5 or later
-* `Patched version of g4py <http://bitbucket.org/seibert/g4py/>`_
-* ROOT 5.32 or later
-
-Optional Space Navigator support requires:
-
-* spacenavd (daemon running on the computer with the 3D mouse)
-* libspnav (client library on the system running Chroma)
-* spnav python module
-
-Space Navigator control works over ssh when X Forwarding is enabled.
-
-For development, we also recommend:
-
-* nose (to run unit tests)
-* coverage (to measure the source coverage of the tests)
-* pylint (to check for common problems in Python code)
-* sphinx 1.1 dev or later (to generate the documentation with mathjax support)
-
-We will explain how to install all of these packages in the following section.
-
-Step-by-Step Installation: Ubuntu 11.04
----------------------------------------
-
-Although Chroma can run on any CUDA-supported Linux distribution or
-Mac OS X, we strongly recommend the use of Ubuntu 11.04 for Chroma
-testing and development.  For these instructions, we will assume you
-are starting with a fresh Ubuntu 11.04 installation.
-
-Steps 1 and 2 will require root privileges, but the remaining steps
-will be done in your home directory without administrator
-intervention.
-
-.. warning:: There is very little support for CUDA inside virtual machines, so you cannot use VirtualBox/VMWare/Parallels to setup your Chroma environment.  Amazon EC2 is able to virtualize Tesla devices with Xen, but setting that up for yourself is beyond the scope of this document.
-
-.. _ubuntu_11.04_step1:
-
-Step 1: ``apt-get`` packages from Ubuntu package manager
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Many packages are required to setup your build environment to compile
-GEANT4 and ROOT.  Fortunately, they can be installed with one very
-long ``apt-get`` line.  Although this line may wrap in your browser,
-it should be executed as one line::
-
-  sudo apt-get install build-essential xorg-dev python-dev \
-       python-virtualenv python-numpy python-pygame libglu1-mesa-dev \
-       glutg3-dev cmake uuid-dev liblapack-dev mercurial git subversion \
-       python-matplotlib libboost-all-dev libatlas-base-dev
-
-To be able to generate the documentation, we also need these tools::
-
-  sudo apt-get install texlive dvipng
-
-.. _ubuntu_11.04_step2:
-
-Step 2: CUDA Toolkit and Driver
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-CUDA requires the use of the official NVIDIA graphics driver, rather
-than the open source Nouveau driver that is included with Ubuntu.  The
-NVIDIA driver can be installed by going to the `CUDA 4.1 Download Page
-<http://developer.nvidia.com/cuda-toolkit-41>`_ and downloading the 64-bit Linux
-developer drivers.  (Newer drivers than those listed on this page will
-also work.)  To install the NVIDIA drivers, you will need to switch to a text console (Ctrl-Alt-F1) and shut down the X server::
-
-  # This next will kill everything running on your graphical desktop!
-  sudo service gdm stop
-  chmod +x NVIDIA-Linux-x86_64-285.05.33.run
-  sudo ./NVIDIA-Linux-x86_64-285.05.33.run
-  # Accept the license and pick the default option for the other questions
-  sudo service gdm start
-
-After the driver is installed, you need to download the CUDA 4.1
-toolkit for Ubuntu Linux 11.04 (probably 64-bit) on `this page
-<http://developer.nvidia.com/cuda-toolkit-41>`_.  Once this file has
-been downloaded, run the following commands in the download
-directory::
-
-  chmod +x cudatoolkit_4.1.28_linux_64_ubuntu11.04.run
-  sudo ./cudatoolkit_4.1.28_linux_64_ubuntu11.04.run
-
-Accept the default installation location ``/usr/local/cuda``.  We will
-add the CUDA ``bin`` and ``lib`` directories to the path in a few
-steps.
-
-
-Step 3: virtualenv
-^^^^^^^^^^^^^^^^^^
-
-.. tip:: All the remaining installation steps can be performed using a shell script.  See :ref:`ubuntu11.04_quick`.
-
-The excellent `virtualenv <http://www.virtualenv.org/>`_ tool
-allows you to create an isolated Python environment, independent from
-your system environment. We will keep all of the python modules for
-Chroma (with a few exceptions) and libraries compiled from source
-inside of a virtualenv in your ``$HOME`` directory::
-
-  virtualenv $HOME/chroma_env
-  cd $HOME/chroma_env/bin/
-
-Next, append the following lines to the end of
-``$HOME/chroma_env/bin/activate`` to add the CUDA tools to the path::
-
-  export PATH=/usr/local/cuda/bin:$PATH
-  export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$VIRTUAL_ENV/lib:$LD_LIBRARY_PATH
-
-
-Finally, we can enable the virtual environment::
-
-  source $HOME/chroma_env/bin/activate
-
-This will put the appropriate version of python in the path and also
-set the ``$VIRTUAL_ENV`` environment variable we will use in the
-remainder of the directions.
-
-Step 4: ROOT
-^^^^^^^^^^^^
-
-Chroma uses the ROOT I/O system to record event information to disk
-for access later.  In addition, we expect many Chroma users will
-want to use ROOT to analyze the output of Chroma.
-
-Begin by downloading the `ROOT 5.32.02 tarball
-<ftp://root.cern.ch/root/root_v5.32.02.source.tar.gz>`_.  Then, from
-the download directory, execute the following commands::
-
-  tar xvf root_v5.32.02.source.tar.gz
-  mkdir $VIRTUAL_ENV/src/
-  mv root $VIRTUAL_ENV/src/root-5.32.02
-  cd $VIRTUAL_ENV/src/root-5.32.02
-  ./configure
-  make
-
-We also need to append a ``source`` line to ``$VIRTUAL_ENV/bin/activate``::
-
-  source $VIRTUAL_ENV/src/root-5.32.02/bin/thisroot.sh
-
-
-Step 5: GEANT4
-^^^^^^^^^^^^^^
-
-Chroma uses GEANT4 to propagate particles other than optical photons
-and create the initial photon vertices propagated on the GPU.  These
-instructions describe how to compile GEANT4 using the new CMake-based
-build system which uses a bundled version of CLHEP and automatically
-downloads data files.  This requires at least GEANT4.9.5.
-
-Download the `GEANT4.9.5.p01 source code
-<http://geant4.cern.ch/support/source/geant4.9.5.p01.tar.gz>`_ and run
-the following::
-
-  tar xvf geant4.9.5.p01.tar.gz
-  mv geant4.9.5.p01 $VIRTUAL_ENV/src/
-  cd $VIRTUAL_ENV/src/
-  mkdir geant4.9.5.p01-build
-  cd geant4.9.5.p01-build
-  cmake -DCMAKE_INSTALL_PREFIX=$VIRTUAL_ENV -DGEANT4_INSTALL_DATA=ON ../geant4.9.5.p01
-  make install
-
-GEANT4 requires several environment variables to locate data files.  Set
-these by appending the following line to ``$VIRTUAL_ENV/bin/activate``::
-
-  source $VIRTUAL_ENV/bin/geant4.sh
-
-
-Step 6: g4py
-^^^^^^^^^^^^
-
-To access GEANT4 from Python, Chroma uses the g4py wrappers.  We have
-had to fix a few bugs and add wrapper a few additional classes for
-Chroma, so for now you will need to use our fork of g4py::
-
-  cd $VIRTUAL_ENV/src
-  hg clone https://bitbucket.org/seibert/g4py#geant4.9.5.p01
-  cd g4py
-  # select system name from linux, linux64, macosx as appropriate
-  ./configure linux64 --prefix=$VIRTUAL_ENV --with-g4-incdir=$VIRTUAL_ENV/include/geant4 --with-g4-libdir=$VIRTUAL_ENV/lib --libdir=$VIRTUAL_ENV/lib/python2.7/site-packages/
-  make install
-
-Step 7: Chroma
-^^^^^^^^^^^^^^
-
-Finally, we are getting close to being able to use ``pip`` to do the
-rest of the installation.  In order for PyUblas to find boost, we have
-to create a file in your ``$HOME`` directory called
-``.aksetup-defaults.py`` that contains the following lines::
-
-  BOOST_INC_DIR = ['/usr/include/boost']
-  BOOST_LIB_DIR = ['/usr/lib64']
-  BOOST_PYTHON_LIBNAME = ['boost_python-mt-py27']
-
-Some of the python dependencies of Chroma have fiddly installation
-scripts, so we need to add them individually before doing the final
-install of the Chroma package::
-
-  pip install -U distribute
-  pip install pyublas
-  # Bug workaround for Numpy 1.6.1
-  mkdir $VIRTUAL_ENV/local
-  ln -s $VIRTUAL_ENV/lib $VIRTUAL_ENV/local/lib
-  pip install -e hg+http://bitbucket.org/chroma/chroma#egg=Chroma
-
-Now you can enable the Chroma environment whenever you want by typing
-``source $HOME/chroma_env/bin/activate``, or by placing that line in the
-``.bashrc`` login script.