Cookiecutter Climate Science

A trimmped down version of cookiecutter data science for publishable climate science projects.

Project homepage

Requirements to use the cookiecutter template:

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• Python 3.8
• Cookiecutter Python package >= 1.4.0: This can be installed with pip by or conda depending on how you manage your Python packages:

$ pip install cookiecutter

or

$ conda config --add channels conda-forge
$ conda install cookiecutter

To start a new project, run:

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cookiecutter https://github.com/bradyrx/cookiecutter-climate-science

Then move into the new folder and run make repo, which will automatically create the Github repo for your project. You'll have to enter your Github password.

Following this, run make create_environment to create a conda environment specifically for this project. Follow that with make requirements to install the default package requirements. You can update environment.yml to add/remove packages to this environment.

Finally, run make pre-commit to install the pre-commit rules. pre-commit is an awesome service that runs a bunch of tests/formatting scripts prior to the user being able to commit their code. This will ensure that it looks good stylistically, passes flake8 tests, etc.

For Travis to work, I believe you'll need to go to https://travis-ci.com/ and connect Travis to your repository. You can also ensure that Travis runs on your Pull Requests. I haven't tested this just yet.

The resulting directory structure

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The directory structure of your new project looks like this:

    ├── LICENSE
    ├── Makefile           <- Makefile with commands like `make repo` or `make data`
    ├── README.md          <- The top-level README for developers using this project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   └── raw            <- The original, immutable data dump.
    │
    ├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
    │                         the creator's initials, and a short `-` delimited description, e.g.
    │                         `1.0-rxb-whats-an-ocean`.
    │
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    │
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    │
    ├── environment.yml    <- The requirements file for reproducing the analysis environment,
    │                         installed via `conda env update -f environment.yml`
    │
    ├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
    ├── project_name    <- Source code for use in this project.
    │   ├── __init__.py    <- Makes project_name a Python module
    │   │
    │   ├── data           <- Scripts to download or generate data
    │   │   └── make_dataset.py
    │   │
    │   └── visualization  <- Scripts to create exploratory and results oriented visualizations
    │       └── visualize.py
    |
    ├── .gitignore         <- Tells Github which files to ignore when adding to repository.
    |
    ├── .pre-commit-config.yaml <- Configuration file for `pre-commit`.
    |
    ├── .travis.yml           <- Configuration file for Travis Continuous Integration.
    |
    ├── create_repo.sh    <- Script to create Github repository for this folder.