Installing PyMultiNest and PyCuba

You need to install the python module and put the libraries it uses into your library path.

1. Installing the Python Module

Installing the python module from PyPI is easy:

$ pip install pymultinest
  • Use the “–user” switch if you only want to install the software locally

  • On older systems, you may need to use easy_install instead of “pip install”

To get the latest version, download the source directly from the Code repository and install it locally:

$ git clone
$ cd PyMultiNest
$ python install

However, installing pymultinest is not enough; MultiNest and Cuba are not included, so you will see an error like:

ERROR:   Could not load MultiNest library ""
ERROR:   You have to build it first, and point the LD_LIBRARY_PATH environment variable to it!


OSError: cannot open shared object file: No such file or directory

You need to install the libraries themselves! The next section explains how.

2. Prerequisites for building the libraries

To use PyMultiNest and PyCuba, you need to install the relevant libraries. If you only want to use one of them, skip the others according.

  • Prerequisites: numpy, scipy, matplotlib, c and fortran compilers (e.g. gcc, gfortran)

  • Prerequisites for MultiNest: cmake, blas, lapack, atlas (and their development versions)

  • Recommended: git

On Ubuntu Linux, install with:

$ sudo apt-get install python-{scipy,numpy,matplotlib,progressbar} ipython libblas{3,-dev} liblapack{3,-dev} libatlas{3-base,-dev} cmake build-essential git gfortran

On Mac OSX:

  • As in the instructions above, you need cmake (e.g. with “brew install cmake”), a Fortran compiler (e.g. with “brew install gcc”) and possibly MPI (e.g. with “brew install open-mpi” and then “pip install mpi4py”)

  • If you google for “MultiNest Mac OSX” or “PyMultiNest Mac OSX” you will find installation instructions.

2. Building the libraries

  • If you want to use PyMultiNest:

    • Get and compile MultiNest (use the cmake version from The goal is to create lib/

      git clone
      cd MultiNest/build
      cmake ..
    • On e.g. Mac OSX, make sure the correct compilers are used by calling cmake using -DCMAKE_C_COMPILER=/path/to/gcc -DCMAKE_CXX_COMPILER=/path/to/g++

    • Include the lib/ directory in your LD_LIBRARY_PATH

    • More detailed install instructions for MultiNest are available in the tutorial.

  • If you want to use PyCuba: Get and compile Cuba. The goal is to create the file

    git clone
    cd cuba

    Include the containing directory your LD_LIBRARY_PATH.

  • To install on Mac OSX, make sure the multinest/cuba libraries are in your library path. For that, it is simplest to copy them, e.g.:

    $ cp -v ~/Downloads/MultiNest/lib/lib* /anaconda3/lib/

A discussion on installing on Mac can be found in issue 10. Compiling with MPI support on Mac is discussed in issue 45

3. Running some code

PyMultiNest and PyCuba have to be able to find the corresponding libraries. So put the three directories in the dynamic library load path:

$ export LD_LIBRARY_PATH=$HOME/Downloads/MultiNest/lib:$HOME/Downloads/cuba/directory/:$LD_LIBRARY_PATH
  • On Mac OSX, do the same for DYLD_LIBRARY_PATH.

  • Replace the above with your actual path.

  • Consider putting this line into your shell startup script (e.g. ~/.bashrc).

Test importing the libraries:

$ python -c 'import pymultinest'
$ python -c 'import pycuba'

Try out the demo programs distributed in the package:

$ python $OLDPWD/
   Acceptance Rate:                        0.690765
   Replacements:                               3650
   Total Samples:                              5284
   Nested Sampling ln(Z):                235.562844
   Importance Nested Sampling ln(Z):     236.164929 +/-  0.147246
   Acceptance Rate:                        0.690809
   Replacements:                               3653
   Total Samples:                              5288
   Nested Sampling ln(Z):                235.565469
   Importance Nested Sampling ln(Z):     236.165091 +/-  0.147221
    ln(ev)=   235.91594564793959      +/-  0.12311459261215110
    Total Likelihood Evaluations:         5288
    Sampling finished. Exiting MultiNest
     analysing data from chains/3-.txt

   evidence: 235.9 +- 0.1

   parameter values:
                 x : 15.968 +- 8.548
                 y : 15.165 +- 9.195

$ python $OLDPWD/

$ python $OLDPWD/

Congratulations! You are now ready to run your own code. Copy the demo files as starting points, play with the functions and analysis, and integrate it to your own code. The documentation should help you:

Getting help

Try searching the error message. Search through the existing questions.

Generating the documentation

Go in the doc directory and run make:

$ cd doc && make html

Point your web browser to _build/html/index.html in doc.