Documentation updates

7 files changed, 263 insertions, 349 deletions

diff --git a/doc/source/index.rst b/doc/source/index.rst
index 34bf38d..46129ef 100644
--- a/doc/source/index.rst
+++ b/doc/source/index.rst
@@ -41,8 +41,7 @@ Subscribe to the mailing list for update announcements!
 .. toctree::
    :maxdepth: 2
 
-   quick_install
-   install
+   install/overview
    tour
    geometry
    surface
diff --git a/doc/source/install.rst b/doc/source/install.rst
deleted file mode 100644
index 3fde4c3..0000000
--- a/doc/source/install.rst
+++ /dev/null
@@ -1,260 +0,0 @@
-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.
diff --git a/doc/source/install/macosx.rst b/doc/source/install/macosx.rst
new file mode 100644
index 0000000..8b02b77
--- /dev/null
+++ b/doc/source/install/macosx.rst
@@ -0,0 +1,42 @@
+Mac OS X Installation
+=====================
+
+Most Mac systems lack the GPU required to run Chroma, with the notable exception of the current 15" MacBook Pro (both Retina and Standard) models, which use an NVIDIA GeForce GT 650M GPU.  These instructions have been tested on OS X 10.8, which ships with the above systems.
+
+.. warning: We have only tested Chroma on the 15" MacBook Pro with Retina display and 1 GB of GPU memory.  Models with 512 MB of video memory may have difficulty running Chroma depending on how much video memory is used by the driver and GUI.
+
+Step 1: Install Xcode
+^^^^^^^^^^^^^^^^^^^^^
+
+Xcode can be `installed from the Mac App Store <http://itunes.apple.com/us/app/xcode/id497799835?ls=1&mt=12>`_.  Once installed, it is important to start Xcode and accept the license agreement.  Once started, open the Preferences window and go to the Downloads pane.  There should be a "Command Line Tools" component listed.  Click the "Install" button next to it if it is not already listed as "Installed."
+
+Step 2: Install XQuartz
+^^^^^^^^^^^^^^^^^^^^^^^
+
+XQuartz is the OS X port of the X.Org X Window system, and is a prerequisite for some packages used by Chroma.  It is much more up to date than the X11.app that has been shipped with OS X in the past.  Download XQuartz from `here <http://xquartz.macosforge.org/landing/>`_ and install it.
+
+Step 3: Install CUDA
+^^^^^^^^^^^^^^^^^^^^
+
+CUDA on OS X requires a special driver.  It can be downloaded from the `CUDA Downloads Page <https://developer.nvidia.com/cuda-downloads>`_.  The package also includes the CUDA compiler and sample programs, installed to /Developer/NVIDIA/CUDA-5.0.
+
+Once installed, you can ensure the CUDA compiler and libraries are in your path by adding the following lines to your bash login script ($HOME/.bashrc)::
+
+  export PATH=/Developer/NVIDIA/CUDA-5.0/bin:$PATH
+  export DYLD_LIBRARY_PATH=/Developer/NVIDIA/CUDA-5.0/lib/:$DYLD_LIBRARY_PATH
+
+
+Step 4: Install MacPorts
+^^^^^^^^^^^^^^^^^^^^^^^^
+
+There are several packaging systems that simplify the installation of Open Source software on the Mac.  We have tested and recommend `MacPorts <http://www.macports.org/>`_, but other systems like `Fink <http://www.finkproject.org>`_ and `Homebrew <http://mxcl.github.com/homebrew/>`_ should also work if you install the same packages.  We will assume MacPorts below.
+
+Follow the `MacPorts installation instructions <http://www.macports.org/install.php>`_.  Once installed, open a terminal and run the following command::
+
+    sudo port install py27-matplotlib mercurial py27-game py27-virtualenv Xft2 xpm
+    sudo port select virtualenv virtualenv27
+
+Step 5: Continue to Common Installation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The rest of the installation process is described in :ref:`common-install`.
\ No newline at end of file
diff --git a/doc/source/install/overview.rst b/doc/source/install/overview.rst
new file mode 100644
index 0000000..554675d
--- /dev/null
+++ b/doc/source/install/overview.rst
@@ -0,0 +1,106 @@
+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-requirements:
+
+Hardware Requirements
+---------------------
+
+At a minimum, Chroma requires:
+
+* An x86 or x86-64 CPU.
+* A NVIDIA GPU that supports CUDA compute capability 2.0 or later.
+
+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 GB of device memory.
+
+Memory requirements on the CPU and GPU scale with the complexity of your
+model.  A detector represented with 60.1 million triangles (corresponding to
+20,000 detailed photomultipler tubes) requires 2.2 GB of CUDA device memory,
+and more than 6 GB of host memory during detector construction.  Chroma can
+take advantage of multiple CPU cores to generate Cherenkov light with GEANT4.
+
+.. 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!
+
+OS Specific Prerequisites
+-------------------------
+
+First, follow one of the following OS-specific guides to install the system-level prerequisites for Chroma:
+
+.. toctree::
+    :maxdepth: 1
+
+    ubuntu
+    rhel
+    macosx
+
+
+.. _common-install:
+
+Common Installation Guide
+-------------------------
+
+We have tried to streamline the Chroma installation process to be portable to
+many platforms.  If you have problems following these instructions, please
+`open an issue
+<http://bitbucket.org/chroma/chroma/issues?status=new&status=open>`_.
+
+
+Step 1: Create virtualenv
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Chroma should never be installed into your system Python directories.  Instead
+create a self-contained virtualenv::
+
+  virtualenv --system-site-package chroma_env
+  source chroma_env/bin/activate
+
+You only need to delete the chroma_env directory to completely remove Chroma from your system.
+
+
+Step 2: Install Chroma Dependencies
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Chroma depends on several C and C++ libraries that are not typically included
+in the package managers of many platforms.  Using `shrinkwrap
+<http://shrinkwrap.rtfd.org>`_, we have automated the installation of these
+libraries into the virtualenv, isolating them from the rest of your system::
+
+  # Create configuration file for PyCUDA
+  echo -e "import os\nvirtual_env = os.environ['VIRTUAL_ENV']\nBOOST_INC_DIR = [os.path.join(virtual_env, 'include')]\nBOOST_LIB_DIR = [os.path.join(virtual_env, 'lib')]\nBOOST_PYTHON_LIBNAME = ['boost_python']" > ~/.aksetup-defaults.py
+  # Search this site for shrinkwrap packages used by Chroma
+  export PIP_EXTRA_INDEX_URL=http://mtrr.org/chroma_pkgs/
+
+  # On RHEL/Centos/Scientific Linux ONLY, uncomment and run the following commands
+  # pip install -U numpy
+  # easy_install pygame
+
+  # This will take a LONG time.
+  # If interrupted, run the command again and it will resume where it left off
+  pip install chroma_deps
+
+  # Refresh environment variables
+  source $VIRTUAL_ENV/bin/activate
+
+Step 3: Install Chroma
+^^^^^^^^^^^^^^^^^^^^^^
+
+Now we can checkout a copy of Chroma and install it.  By default, we will put it into the $VIRTUAL_ENV/src directory, but anywhere is fine::
+
+  cd $VIRTUAL_ENV/src
+  hg clone https://
+  hg clone https://bitbucket.org/chroma/chroma
+  cd chroma
+  python setup.py develop
+
+If everything has succeeded, you are ready to move onto the :ref:`tour`!
\ No newline at end of file
diff --git a/doc/source/install/rhel.rst b/doc/source/install/rhel.rst
new file mode 100644
index 0000000..644202d
--- /dev/null
+++ b/doc/source/install/rhel.rst
@@ -0,0 +1,64 @@
+RHEL/Centos/Scientific Linux 6 Installation
+===========================================
+
+Chroma only supports RHEL-derived distributions if they are based on version 6 or later.  The packages included with RHEL 5 are too old to run Chroma.
+
+Step 1: Install packages with Yum package manager
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+As the root user, run the following commands::
+
+    yum groupinstall "Development tools"
+    yum install python-matplotlib python-devel uuid-devel lapack-devel atlas-devel \
+        mercurial git subversion mesa-libGLU-devel freeglut-devel SDL-devel gtk2-devel \
+        libXpm-devel libXft-devel libXext-devel libXlibX11-devel expat-devel bzip2-devel \
+        libXt-devel
+    easy_install virtualenv
+
+
+Step 2: Disable the Nouveau Graphics Driver
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+CUDA requires the use of the official NVIDIA graphics driver, rather
+than the open source Nouveau driver that is included with RHEL. 
+
+Edit /etc/grub.conf and add ``rdblacklist=nouveau`` to the end of the kernel line::
+
+    kernel /vmlinuz-2.6.32-279.11.1.el6.x86_64 ... rdblacklist=nouveau
+
+Create the file /etc/modprobe.d/blacklist-nouveau.conf with the line::
+
+    blacklist nouveau
+
+Step 3: Install the CUDA Driver and Toolkit
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The NVIDIA driver can be installed by going to the `CUDA Downloads
+<https://developer.nvidia.com/cuda-downloads>`_ and downloading the package
+corresponding to your operating system.This single package includes a current
+NVIDIA driver, the CUDA compiler toolkit, and sample programs.
+
+Drop to a console terminal by pressing `CTRL+ALT+F1` and then enter::
+
+    su -
+    init 3
+
+Login again at the prompt and enter the following commands::
+
+    su -
+    cd /home/user/Downloads
+    chmod +x cuda_5.0.35_linux_64_ubuntu11.10-1.run
+    .cuda_5.0.35_linux_64_ubuntu11.10-1.run
+
+During the installation you can pick all the default options.
+
+Once installed, you can ensure the CUDA compiler and libraries are in your path by adding the following lines to your bash login script (usually $HOME/.bashrc)::
+
+  export PATH=/usr/local/cuda/bin:$PATH
+  export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
+
+.. warning:: Non-bash shells will need to be adjusted appropriately.  If you are using a 32-bit distribution, then lib64/ should be changed to lib/.
+
+Step 4: Continue to Common Installation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The rest of the installation process is described in :ref:`common-install`.
\ No newline at end of file
diff --git a/doc/source/install/ubuntu.rst b/doc/source/install/ubuntu.rst
new file mode 100644
index 0000000..ffd7260
--- /dev/null
+++ b/doc/source/install/ubuntu.rst
@@ -0,0 +1,50 @@
+Ubuntu Installation
+===================
+
+Step 1: ``apt-get`` packages with Ubuntu package manager
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+We need to install several system packages::
+
+    sudo apt-get install python-pygame python-matplotlib python-virtualenv \
+        build-essential xorg-dev python-dev libglu1-mesa-dev  freeglut3-dev \
+        uuid-dev liblapack-dev mercurial git subversion libatlas-base-dev \
+        libbz2-dev
+
+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 Downloads <https://developer.nvidia.com
+/cuda-downloads>`_ and downloading the package corresponding to your Ubuntu
+version. This single package includes a current NVIDIA driver, the CUDA
+compiler toolkit, and sample programs.
+
+.. note:: Although NVIDIA only lists support up to Ubuntu 11.10 in CUDA 5, we have found the package to also work with Ubuntu 12.04 LTS.
+
+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!
+
+  # On Ubuntu 12.04: sudo service lightdm stop 
+  sudo service gdm stop 
+
+  chmod +x cuda_5.0.35_linux_64_ubuntu11.10-1.run
+  sudo ./cuda_5.0.35_linux_64_ubuntu11.10-1.run
+  # Accept the license and pick the defaults
+
+  # On Ubuntu 12.04: sudo service lightdm start
+  sudo service gdm start
+
+Once installed, you can ensure the CUDA compiler and libraries are in your path by adding the following lines to your bash login script (usually $HOME/.bashrc)::
+
+  export PATH=/usr/local/cuda/bin:$PATH
+  export LD_LIBRARY_PATH=/usr/local/cuda/lib64/:$LD_LIBRARY_PATH
+
+.. warning:: Non-bash shells will need to be adjusted appropriately.  If you are using a 32-bit distribution, then lib64/ should be changed to lib/.
+
+Step 3: Continue to Common Installation
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+The rest of the installation process is described in :ref:`common-install`.
\ No newline at end of file
diff --git a/doc/source/quick_install.rst b/doc/source/quick_install.rst
deleted file mode 100644
index 6ed939f..0000000
--- a/doc/source/quick_install.rst
+++ /dev/null
@@ -1,87 +0,0 @@
-Quick Installation
-==================
-
-Make sure you're computer meets the :ref:`hardware-prerequisites`!
-
-.. _ubuntu11.04_quick:
-
-Ubuntu 11.04
-------------
-
-Andy Mastbaum has provided a shell script that downloads and compiles
-all of the Chroma prerequisites.  It has been tested to work with
-Ubuntu 11.04.  To use this script, first go perform
-:ref:`ubuntu_11.04_step1` and :ref:`ubuntu_11.04_step2` in the
-Step-by-Step Installation guide.  Then download
-:download:`chroma-setup.sh` and run the following::
-
-  chmod +x chroma-setup.sh
-  ./chroma-setup -j4 -n ~/chroma_env
-
-This will download and compile (with 4 CPU cores; increase the ``-j``
-option if you have more cores) all the source code required to create
-a self-contained Chroma environment in ``$HOME/chroma_env``.  To setup
-your environment to use Chroma, just run ``source
-$HOME/chroma_env/bin/activate``.
-
-.. _rhel6_quick:
-
-Red Hat Enterprise Linux 6
---------------------------
-
-The following procedure has been tested on a basic RHEL6 install.
-
-Disable the nouveau graphics driver
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-Edit /etc/grub.conf and add ``rdblacklist=nouveau`` to the end of the kernel line::
-
-    kernel /vmlinuz-2.6.32-279.11.1.el6.x86_64 ... rdblacklist=nouveau
-
-Create the file /etc/modprobe.d/blacklist-nouveau.conf with the line::
-
-    blacklist nouveau
-
-Install the CUDA Driver and Toolkit
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-First, download the NVIDIA driver appropriate to your machine at the `NVIDIA Unix Drivers Download Page <http://www.nvidia.com/object/unix.html>`_.
-
-Dropt to a full terminal by pressing `CTRL+ALT+F1` and then enter::
-
-    su -
-    init 3
-
-Login again at the prompt and enter the following commands::
-
-    su -
-    cd /home/user/Downloads
-    chmod +x NVIDIA-Linux-x86_64-304.64.run
-    ./NVIDIA-Linux-x86_64-304.64.run
-
-During the installation you can just pick all the default options.
-
-Download the CUDA toolkit from the `CUDA Toolkit Archive <https://developer.nvidia.com/cuda-toolkit-41-archive>`_, and run the following commands to install it::
-
-    su -
-    cd /home/user/Downloads
-    chmod +x cudatoolkit_4.1.28_linux_64_rhel6.x.run
-    ./cudatoolkit_4.1.28_linux_64_rhel6.x.run
-
-Download and run `chroma-setup.py <http://chroma.bitbucket.org/_downloads/chroma-setup.py>`_::
-
-    python chroma-setup.py /home/user/chroma_env
-
-where ``/home/user/chroma_env`` is where you would like all of the packages installed. This should preferably be an empty directory.
-
-.. note:: You must be able to run the ``sudo`` command. Near the end of the installation as it is trying to install pygame, the install sort of halts. Just press enter and it will continue.
-
-Now, go get some lunch! The installation takes hours.
-
-If the installation went successfully, chroma and many of the other packages were installed into a `virtual environment <http://www.virtualenv.org/en/latest/>`_ created during the setup. To start using chroma just activate the virtual environment by running::
-
-    source /home/user/chroma_env/bin/activate
-
-Try the following to see if everything worked::
-
-    chroma-cam @chroma.models.lionsolid