For ETS's SKLL project, we found out the hard way that Travis-CI's support for numpy and scipy is pretty abysmal. There are pre-installed versions of numpy for some versions of Python, but those are seriously out of date, and scipy is not there are at all. The two most popular approaches for working around this are to (1) build everything from scratch, or (2) use apt-get to install more recent (but still out of date) versions of numpy and scipy. Both of these approaches lead to longer build times, and with the second approach, you still don't have the most recent versions of anything. To circumvent these issues, we've switched to using Miniconda (Anaconda's lightweight cousin) to install everything.
A template for installing a simple Python package that relies on numpy and scipy using Miniconda is provided below. Since it's a common setup, I've also included steps to use Coveralls for calculating test coverage.
All you need to do is replace the
YOUR_PACKAGE_NAME_HERE parts with your package's name in both
.travis.yml, and include both files in your project's root directory. Then just activate Travis and you should be set.
You may also consider adding the latest Miniconda shell script to your repository, and modifying your
.travis.yml to just use that instead of downloading it every time. It can make building slightly faster, and is more resistent to server issues on Continuum's side (although you still are downloading the packages from them).