Unlike many methods in population genetics, ∂a∂i is more than just a single command-line program. This allows us to offer more features (such as plotting) and allows you greater flexibility. On the other hand, it may require adjusting to a new workflow and getting a little comfortable with Python.

Luckily, Python is very easy to read, and you can probably learn most of what you need just by looking at the provided in the source distribution of dadi. If you'd like to dig more into Python, there are many good tutorials and books.

To get your feet wet, start with the YRI_CEU example, which fits a model to EGP data from the YRI and CEU populations.

One important tip is to take advantage of Python's interactivity. My preferred workflow involves one window editing a Python script (e.g. script.py) and another running an IPython session. In the IPython session I can interactively use ∂a∂i to explore my data, while I record my work in script.py. IPython's %run script.py magic command lets me apply changes I've made to script.py to my interactive session. (Note that you will need to reload other Python used by your script if you change them.) Once I'm sure I've defined my model correctly and have a useful script, I run that from the command line (python script.py) for extended optimizations and other long computations.