StochasticTree Python Package

NOTE: we are in the process of refactoring this project so that the R, Python, and C++ source code sits in the same repo.

Getting started

The python package can be installed from source. Before you begin, make sure you have conda installed. Clone the repo recursively (including git submodules) by running

git clone --recursive https://github.com/StochasticTree/stochtree-python.git

Conda-based installation

Conda provides a straightforward experience in managing python dependencies, avoiding version conflicts / ABI issues / etc.

To build stochtree using a conda based workflow, first create and activate a conda environment with the requisite dependencies

conda create -n stochtree-dev -c conda-forge python=3.10 numpy scipy pytest pandas pybind11 scikit-learn matplotlib seaborn
conda activate stochtree-dev
pip install jupyterlab

Then, navigate to the main stochtree-python project folder (i.e. cd /path/to/stochtree-python) and install the package locally via pip

pip install .

Pip-based installation

If you would rather avoid installing and setting up conda, you can alternatively setup the dependencies and install stochtree using only pip (caveat: this has not been extensively tested across platforms and python versions).

First, navigate to the main stochtree-python project folder (i.e. cd /path/to/stochtree-python) and create and activate a virtual environment as a subfolder of the repo

python -m venv venv
source venv/bin/activate

Install all of the package (and demo notebook) dependencies

pip install numpy scipy pytest pandas scikit-learn pybind11 matplotlib seaborn jupyterlab

Then install stochtree via

pip install .