If you are comfortable with GitHub and git, the command-line program, you can download the workshop materials by cloning my repository:

git clone https://github.com/ljwolf/agile-2023-spatialopt
cd agile-2023-spatialopt

If you are not comfortable with GitHub, download the repository to your computer as a zip file from the workshop website. To do this, go to https://github.com/ljwolf/agile-2023-spatialopt, click on the green button labelled "< > Code ", then click the " Download Zip" option.

To follow along with the workshop material, it is best if you install Anaconda, a scientific python computing environment. If you do not yet have Anaconda installed, you can install miniconda (https://conda.io/miniconda.html) or the (larger) Anaconda distribution (https://www.anaconda.com/download/).

Using the conda program from your command line or within the anaconda prompt, we recommend using the following terminal commands to set up your environment:

# we want to use only the community-distributed packages on "conda-forge" 
conda config --add channels conda-forge
conda config --set channel_priority strict
# and, we want to use the faster implementation of conda, called "mamba"
conda install mamba
# Then, we want to create our actual environment
mamba env create --file environment.yml
# and finally, activate the environment
conda activate spatialopt

If you are using the "Anaconda Navigator" interface, you can import the workshop environment using the steps described in its documentation.

If you would prefer not to install everything on your own computer, you can follow along with the materials online using Binder to open the notebooks without having to install any software.

To start your analysis environment locally, you first must activate the spatialopt environment:

conda activate spatialopt

And then, you must start Juptyer Lab, the next-generation user interface for Project Jupyter. The workshop code is implemented in Jupyter Notebooks, a literate and interactive programming environment for scientific computing.

jupyter lab