Documentation overhaul

.github/workflows/docs.yml

@@ -23,6 +23,7 @@ jobs:
     - name: Checkout repository
       uses: actions/checkout@v4
       with:
+        repository: 'StochasticTree/stochtree'
         submodules: 'recursive'
 
     - name: Setup Python 3.10
@@ -34,22 +35,22 @@ jobs:
     - name: Install Package with Relevant Dependencies
       run: |
         pip install --upgrade pip
-        pip install -r docs/requirements.txt
+        pip install -r python_docs/requirements.txt
         pip install .
 
     - name: Build HTML
       run: |
-        sphinx-build -M html docs/source/ docs/_build/
+        sphinx-build -M html python_docs/source/ python_docs/_build/
 
     - name: Upload Artifact
       uses: actions/upload-artifact@v4
       with:
         path:
-          docs/_build/html/
+          python_docs/_build/html/
 
     - name: Deploy to GitHub Pages
       uses: peaceiris/actions-gh-pages@v4
       if: github.ref == 'refs/heads/main'
       with:
         github_token: ${{ secrets.GITHUB_TOKEN }}
-        publish_dir: docs/_build/html
+        publish_dir: python_docs/_build/html

LICENSE

@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2024 StochasticTree
+Copyright (c) 2024 stochtree authors
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

README.md

@@ -1,52 +1,5 @@
-# StochasticTree Python Package
+# StochasticTree Python Package Documentation
 
-## Getting started
-
-The python package can be installed from source. Before you begin, make sure you have [conda](https://www.anaconda.com/download) installed.
-Clone the repo recursively (including git submodules) by running 
-
-```{bash}
-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
-
-```{bash}
-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
-
-```{bash}
-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
-
-```{bash}
-python -m venv venv
-source venv/bin/activate
-```
-
-Install all of the package (and demo notebook) dependencies
-
-```{bash}
-pip install numpy scipy pytest pandas scikit-learn pybind11 matplotlib seaborn jupyterlab
-```
-
-Then install stochtree via
-
-```{bash}
-pip install .
-```
+This is documentation repository, which builds and publishes 
+docs for the [stochtree python package](https://stochastictree.github.io/stochtree-python/). 
+The source code for the python (and R and C++) is available at https://github.com/StochasticTree/stochtree