Fast Estimation of Effective Migration Surfaces (feems) is a python package implementing a statistical method for inferring and visualizing gene-flow in spatial population genetic data.

The feems method and software was developed by Joe Marcus and Wooseok Ha and advised by Rina Foygel Barber and John Novembre. We also used code from Benjamin M. Peter to help construct the spatial graphs.

For details on the method see our pre-print. Note that feems is in review so the method could be subject to change.

We've found that the easiest way to get started is to setup a conda environment:

conda create -n=feems_e python=3.8.3 
conda activate feems_e

Some of the plotting utilities in the feems package require geos as a dependency which can be installed on mac with brew as follows:

brew install geos

Unfortunately some of the other dependencies for feems are not easily installed by pip so we recommend getting started using conda:

conda install -c conda-forge suitesparse=5.7.2 scikit-sparse=0.4.4 cartopy=0.18.0 jupyter=1.0.0 jupyterlab=2.1.5 sphinx=3.1.2 sphinx_rtd_theme=0.5.0 nbsphinx=0.7.1 sphinx-autodoc-typehints

We added jupyter and jupyterlab to explore some example notebooks but these are not necessary for the feems package. Once the conda environment has been setup with these tricky dependencies we can install feems:

pip install git+https://github.com/jhmarcus/feems

You can also install feems locally by:

git clone https://github.com/jhmarcus/feems
cd feems/
pip install .