Use pip to install the dependencies as follows, after cloning the repo and opening a terminal in the root folder of the repo.

First we’ll set-up a virtual environment to install the dependencies in:

# Create a virtual environment called geocompy
python -m venv geocompy
# Activate the virtual environment
source geocompy/bin/activate

Then install the dependencies (with the optional python -m prefix specifying the Python version):

# Install dependencies from the requirements.txt file
python -m pip install -r requirements.txt

You can also install packages individually, e.g.:

pip install jupyter-book

Deactivate the virtual environment when you’re done:

deactivate

The environment.yml file contains a list of dependencies that can be installed with a package manager such as conda, mamba or micromamba. The instructions below are for micromamba but should work for any package manager.

# For Linux, the default shell is bash:
curl -L micro.mamba.pm/install.sh | bash
# For macOS, the default shell is zsh:
curl -L micro.mamba.pm/install.sh | zsh

After answering the questions, install dependencies with the following command:

micromamba env create -f environment.yml

Activate the environment as follows:

micromamba activate geocompy

Install kernel, this will allow you to select the environment in vscode or IPython as follows:

python -m ipykernel install --user

You can now reproduce the book (requires quarto to be installed):

micromamba run -n geocompy quarto preview

VS Code’s quarto.quarto plugin can Python code in the chunks in the .qmd files in this book interactively.

However, you can also run any of the chapters in a Jupyter Notebook, e.g. as follows:

cd ipynb
# jupyter notebook . # open a notebook showing all chapters
jupyter notebook 02-spatial-data.ipynb

You should see something like this:

See documentation on running and developing Python code in a Jupyter notebook at docs.jupyter.org.

The Python scripts and IPython notebook files stored in the code and ipynb folders are generated from the .qmd files. To regenerate them, you can use the following commands, to generate .ipynb and .py files for local versions of Chapter 2, for example:

quarto convert 02-spatial-data.qmd # generate .ipynb file
jupytext --to py *.ipynb # generate .py files .ipynb files

Do this for all chapters with the following bash script in the repo:

./convert.sh

We have set-up a GitHub Action to do this automatically: every commit message that contains the text string ‘convert’ will create and push updated .ipynb and .py files.

Running the code chunks in the .qmd files in an IDE such as VSCode or directly with quarto is the main way code in this book is designed to be run interactively, but you can also execute the .py and .ipynb files directly. To run the code for chapter 2, for example, you can run one of the following commands from your system shell:

python code/chapters/02-spatial-data.py # currently requires manual intervention to complete, see #71
ipython ipynb/02-spatial-data.ipynb # currently requires manual intervention to complete, see #71
bash ./run-code.sh # run all .python files

We pin package versions in the environment.yml and requirements.txt files to ensure reproducibility.

To update the requirements.txt run the following:

python -m pip install pur
pur -r requirements.txt
python -m pip install -r requirements.txt

To update the environment.yml file in the same way based on your newly installed packages, run the following:

micromamba list export > environment.yml