Installation

Table of Contents

• Prerequisites
  • Getting prerequisites on a Mac
  • Getting prerequisites on Windows
• Optional prerequisites
• Where to go next

Building and installing basics

IMP is available in a variety of different ways. If you are just planning on using existing IMP code and run on a standard platform, you may be able to install a pre-built binary. See the download page.

If you are planning on contributing to IMP, you should download and build the source. See the next section for more information.

Building IMP from source is straightforward if the prerequisites are already installed.

git clone git://github.com/salilab/imp.git
cd imp
./setup_git.py
mkdir ../imp_release
cd ../imp_release
cmake ../imp -DCMAKE_BUILD_TYPE=Release
make -j 8

See Building IMP with CMake for more information.

Prerequisites

In order to obtain and compile IMP, you will need:

• cmake (2.8 or later)
• Boost (1.40 or later)
• HDF5 (1.8 or later)
• Python (2.6 or later)
• SWIG (1.3.40 or later)
• Developers will also need a git client to access the repository

Getting prerequisites on a Mac

Mac users must first install Xcode (previously known as Developer Tools) which is not installed by default with OS X, but is available from the App store (or from the Mac OS install DVD for old versions of Mac OS). They will also need the Xcode command line tools (install by going to Xcode Preferences, then Downloads, then Components, and select "Command Line Tools").

Then Mac users should use one of the available collections of Unix tools, either

• Homebrew (recommended) Once you installed homebrew do

  brew tap homebrew/science brew tap salilab/salilab brew install boost gmp google-perftools cgal graphviz gsl cmake doxygen hdf5 swig fftw mpfr libtau

  to install everything IMP finds useful (or that you will want for installing various useful Python libs that IMP finds useful). On older Macs, you may also need to brew install git if you want to use git (newer Macs include git).

• Macports If you use MacPorts, you must add /opt/local/bin to your path (either by modifying your shell's config file or by making an environment.plist file) and then do

  sudo port install boost cmake swig-python

  to install the needed libraries and tools. When installing HDF5 with MacPorts, be sure to install hdf5-18 (version 1.8), rather than the older hdf5 (version 1.6.9).

• or Fink

Mac OS X 10.5 and 10.6

These versions of Mac OS X include a 'swig' binary, but it is too old to use with IMP. You need to make sure that the newer version of swig is found first in your PATH.

Getting prerequisites on Windows

We recommend Linux or Mac for developing with IMP, as obtaining the prerequisites on Windows is much more involved. However, we do test IMP on Windows, built with the Microsoft Visual Studio compilers (we use Visual Studio Express 2010 SP1 for 32-bit Windows, and VS Express 2012 for 64-bit). One complication is that different packages are compiled with different versions of Visual Studio, and mixing the different runtimes (msvc*.dll) can cause odd behavior; therefore, we recommend building most of the dependencies from source code using the same version of Visual Studio that you're going to use to build IMP. The basic procedure is as follows:

• Install Microsoft Visual Studio Express (it is free, but registration with Microsoft is required).
• Get and install cmake.
• Get Python 2 (not Python 3) (make sure you get the 32-bit version if you're going to build IMP for 32-bit Windows).
• Get and install the zlib package (both the "complete package, except sources" and the "sources" installers).
  • The package without sources can be installed anywhere; we chose the default location of C:\Program Files\GnuWin32. The sources, however, must be installed in a path that doesn't contain spaces (otherwise the Boost build will fail). We chose C:\zlib.
  • We found that the zconf.h header included with zlib erroneously includes unistd.h, which doesn't exist on Windows, so we commented out that line (in both packages).
• Download the Boost source code (we extracted it into C:\Program Files\boost_1_53_0), then
  • Open a Visual Studio Command Prompt, and cd into the directory where Boost was extracted
  • Run bootstrap.bat
  • You may need to help the compiler find the zlib header file with set INCLUDE=C:\Program Files\GnuWin32\include
  • Run bjam link=shared runtime-link=shared -sNO_ZLIB=0 -sZLIB_SOURCE=C:\zlib\1.2.3\zlib-1.2.3
• Get and install SWIG for Windows
• Get the HDF5 source code
  • Make a 'build' subdirectory, then run from a command prompt in that subdirectory something similar to cmake.exe -G "Visual Studio 10" -DHDF5_ENABLE_SZIP_SUPPORT:BOOL=OFF -DHDF5_ENABLE_Z_LIB_SUPPORT:BOOL=ON -DHDF5_BUILD_HL_LIB:BOOL=ON -DZLIB_INCLUDE_DIR="C:\\Program Files\\GnuWin32\\include" -DZLIB_LIBRARY="C:\\Program Files\\GnuWin32\\lib\\zlib.lib" -DBUILD_SHARED_LIBS:BOOL=ON ..
  • Open the resulting HDF5 solution file in Visual Studio, change to Release configuration, then build the hdf5 project.
• (Optional) Build CGAL from source code.
• (Optional) Download the FFTW DLLs and follow the instructions at that website to make .lib import libraries needed for Visual Studio.
  • Copy libfftw3-3.lib to fftw3.lib to help cmake find it
• (Optional) Get the GSL source code and build it:
  • Open the libgsl project file in the src\gsl\1.8\gsl-1.8\VC8 subdirectory
  • Build in Release-DLL configuration
  • Copy the generated libgsl.dll and libgslcblas.dll to a suitable location (we used C:\Program Files\gsl-1.8\lib)
  • Copy the corresponding .lib files, libgsl_dll.lib and libgslcblas_dll.lib (we recommend removing the _dll suffix and the lib prefix when you do this so that cmake has an easier time finding them, i.e. call them gsl.lib and gslcblas.lib).
• (Optional) Get numpy and scipy to match your Python version.
• (Optional) Get and install libTAU
  • Copy libTAU.lib to TAU.lib to help cmake find it.
• (Optional) Get the OpenCV source code and build it by following these instructions
  • Copy each opencv_*.lib to a similar file without the version extension (e.g. copy opencv_ml244.lib to opencv_ml.lib) to help cmake find it
• Set PATH, INCLUDE, and/or LIB environment variables so that the compiler can find all of the dependencies. (We wrote a little batch file.)

• Set up IMP by running something similar to

  cmake <imp_source_directory> -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS="/DBOOST_ALL_DYN_LINK /EHsc /D_HDF5USEDLL_ /DWIN32 /DGSL_DLL" -G "NMake Makefiles"

• Note: if building for 64-bit Windows, you may need to add /bigobj to CMAKE_CXX_CFLAGS.
• Then use simply 'nmake' (instead of 'make', as on Linux or Mac) to build IMP. (cmake can also generate Visual Studio project files, but we recommend nmake.)
• To use IMP or run tests, first run the setup_environment.bat file to set up the environment so all the programs and Python modules can be found. (This batch file needs to be run only once, not for each test.)

Getting prerequisites on Linux

All of the prerequisites should be available as pre-built packages for your Linux distribution of choice.

Optional prerequisites

IMP can make use of a variety of external tools to provide more or better functionality.

• Doxygen and Graphviz: Required for building documentation. They are available as part of most Unix tool sets (HomeBrew, all Linux distributions etc.).
• Modeller
• CGAL
• Google perf tools
• ANN
• GSL
• OpenCV is needed for IMP.em2d
• libTAU is needed for IMP.cnmultifit
• MPI

Where to go next

You are now ready to use IMP within Python and C++.

Everyone should read the introduction and developers should then move on to the Developer Guide.