Installation

Dependencies

dadi depends on a number of Python libraries. The absolute dependencies are

  • Python 3
  • NumPy
  • SciPy
  • nlopt

It is also recommended that you install

  • matplotlib
  • IPython

The easiest way to obtain all these dependencies to install the Anaconda Python Distribution. You'll need to separately install nlopt from conda-forge. And the easiest way to install dadi is then via conda, using the command conda install -c conda-forge dadi. dadi can also be installed via pip, using the command python3 -m pip install dadi.

GPU computing

dadi can be sped up substantially by running on a CUDA-enabled Nvidia GPU. To enable this functionality, you will need to install the CUDA Toolkit. After install the CUDA Toolkit, you will then need to install PyCUDA and scikit-cuda. Both of these can be installed from the Python Package Index using pip, python3 -m pip install pycuda and python3 -m pip install scikit-cuda.

Installing from source

dadi can be easily installed from source code, as long as you have an appropriate C compiler installed. (On OS X, you'll need to install the Developer Tools to get gcc. On Windows, you'll need to install the Microsoft Visual Studio to get C/C++ builder.) To do so, first unpack the source code tarball, unzip dadi-<version>.zip In the dadi-<version> directory, run python setup.py install. This will compile the C modules dadi uses and install those plus all dadi Python files in your Python installation's site-packages directory. A (growing) series of tests can be run in the tests directory, via python run_tests.py.

If you want the latest and greatest from dadi, clone the Bitbucket repository. You can then create a local install using python setup.py develop. This will ensure that when you edit pull revisions or edit the source code, changes are reflected immediately, without requiring a separate install step.