- 01: Geo-effectiveness (2020)
- A full-earth ground magnetic perturbation forecasting model using deep learning.
Each notebook is contained within its own folder:
.
└── notebooks
└── ##_<project>_<year> # Each project has its own folder named sequentially, with the project name, and year of the project
├── README.md
├── <project>_colab.ipynb # A Jupyter notebook designed to be executed on Google Colab.
├── <project>.ipynb # The corresponding local development version of the colab notebook.
├── environment.yml # Conda environment file
└── requirements.txt # Requirements file
For local development, the necessary environment can be created as follows (under the assumption that an anaconda installation exists).
cd notebooks/<project>
conda env create -f environment.yml
conda activate <environment>
# start the jupyter notebook app
jupyter notebook
Contributions are welcome as pull requests to the main branch, and should mirror the structure of existing projects.
-
A requirements file can be produced with
pip freeze > requirements.txt
, however, to minimize the number of redundant packages in that list, first create a virtual environment, andpip install
packages there (Anaconda is popular among scientists).conda create --name <name> conda activate <name> conda list #this should be empty
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Formatting with Black (https://black.readthedocs.io/en/stable/) is preferred; see https://github.com/drillan/jupyter-black for the Jupyter notebook integration:
pip install black jupyter nbextension install https://github.com/drillan/jupyter-black/archive/master.zip --user jupyter nbextension enable jupyter-black-master/jupyter-black