How To Use

1. Insert description here.
2. Rename repo with a 4 digit year-of-initiation prefix, e.g., "2022-". Convention is to use hyphens between words and all lower case.
3. Create a conda environment for this project. First modify conda-env.yml to include the relevant repositories and dependencies needed; also give the environment a good name (e.g., similar or same as this repo) - the default is "project-env". Then create the environment (see below).
4. If you do not want to work in a development container, skip to "Local Installation" to use a conda environment on your local machine.
5. Otherwise, a Docker dev container template for VS Code is provided in the .devcontainer/ folder. This creates a miniconda container and installs the environment specified in conda-env.yml into the default IPython kernel in the container. To use:
   • Change the UID and GID in .devcontainer/Dockerfile if needed.
   • Change the name of the conda environment (default="project-env") in the conda-env.yml and files in .devcontainer/.
   • Install the "Dev Containers" Extension in VS Code.
   • First git clone this repo, then open the folder in the container by selecting "Dev Containers: Open Folder in Container" from the Command Palette.
   • From a terminal in VS Code, (1) navigate to your desired starting point (data/analysis is recommended), then (2) run $ bash /path/to/.devcontainer/start_jupyter.sh to launch a Jupyter server (forwarded on port 1234 by default) from the head of the repo. The default kernel contains the conda-env.yml packages but is not renamed.

Local Installation

Set up the conda environment for this project. You will need to install the environment in your Jupyter to use it (third command below). Change the name of the conda environment (default="project-env") in the conda-env.yml if you wish.

$ conda env create -f conda-env.yml
$ conda activate project-env
$ python -m ipykernel install --user --name=project-env

At the end of a project it is good practice to export the entire conda environment for posterity, especially if not working in a development container.

$ conda env export > environment.yml

This environment can be recreated later; the conda-env.yml file can also be exchanged for this, but I prefer to keep both as a record.

$ conda env create -f environment.yml

This works for many cases, but if you need an exactly reproducible environment use conda-lock instead.

Contributors

Update the CITATION.cff file to enable appropriate citations.

The logo for this repository (logo.png) was generated using Google Gemini 2.0 Flash (Imagen 3) on Feb. 19, 2025 with the prompt "Create a logo of a robotic bird being designed and templated by a robot in a factory."

Versioning

• Use the public-template to create a fresh repo to release the code and details after a project is finished, tag the release, then use zenodo to capture changes to future changes/releases made to that repo, if a public record is desired. That serves as the primary public repo which is shared with external parties.
• In addition, create a "published" branch on this repo to correspond to when the associated results/paper/report was first published or shared. This repo is retained as the primary private version where future work can be performed. Subsequent branches, such as "revision-YYYY-MM-DD" can be created later and similarly reflected in the public-template version if revisions are necessary.

Associated Publications

LINK TO REPORT