A toolbox for galactic simulations.

pip install PySpace==0.1.0



A python-based toolbox for galactic simulations

Build Status Documentation Status Coverage


Galaxy collision simulation done using PySpace


The documentation for this project can be found at


  • A python interface for high performance C++ implementation of N-body simulation algorithms.
  • PySpace has a numpy friendly API which makes it easier to use.
  • Parallel support using OpenMP.
  • GPU support using CUDA
  • Dumps vtk output which allows users to take advantage of tools like ParaView, MayaVi, etc. for visualization.


  • Brute Force O(n^2)
  • Barnes-Hut O(nlogn)



  • Numpy
  • PyEVTK (pip install pyevtk)
  • gcc compiler
  • OpenMP (optional)
  • ParaView / MayaVi or any other vtk rendering tool (optional)

Linux and OSX

To install the latest stable version, run:

$ pip install pyspace

To install development version, clone this repository by:

$ git clone

To install, run:

$ python install

To install without OpenMP, set USE_OPENMP environment variable to 0 and then install:

$ export USE_OPENMP=0
$ python install

To install without GPU support, set USE_CUDA environment variable to 0 and then install:

$ export USE_CUDA=0
$ python install


If you run into any issues regarding installation or otherwise, please report here.

Some common issues are addressed below

CUDA not found

Make sure if the CUDA toolkit is installed. If you still get this message after installation, follow the instructions given below.

Add CUDA it to PATH environmental variable and try again

Or, set CUDAHOME environmental variable to path of the CUDA installation by:

$ export CUDAHOME=/usr/local/cuda

Image not found

If your code compiles and you get this error at runtime, make sure you have a CUDA compatible device installed.

If you don't, install without GPU support (see Installation)

PySpace doesn't support Windows currently

Running the tests

For running the tests you will need to install nose, install using:

$ pip install nose

To run the tests, from project's root directory run:

$ make test

Running the benchmarks

For running benchmarks you will need to install pandas, install using:

$ pip install pandas

To run the benchmarks, cd to benchmarks directory and run:

$ python


Use PEP 8 coding standard for python and follow this for C++.