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 .