IGRF-14 Evaluation

Python code and Gauss coefficients from the 14 generation of the IGRF candidate evaluation. Check the Book and Binder links above to view and execute the notebooks.

Based on: Alken, P., Thébault, E., Beggan, C.D. et al. Evaluation of candidate models for the 13th generation International Geomagnetic Reference Field. Earth Planets Space 73, 48 (2021). https://doi.org/10.1186/s40623-020-01281-4

Tests are:

0) Check files are correctly formatted, then: 1) Lowes-Mauerberger power spectra plots 2) Root-mean-square differences between candidates 3) Degree correlation plot of differences between one candidate and all others 4) Azimuthal spectra plot of the differences between one candidate and the others 5) Triangle plot of differences between one candidate and the others 6) Spatial maps of comparison in X, Y, Z, Br, Bt, Bp between one candidate and the others

Code based on GitHub repos: IAGA Summer School Python exercises and ChaosMagPy

Submission guide

1. Prepare your model coefficients in the .cof format
2. Test them by running the notebooks:
   a) Using the Binder link
   b) Locally on your machine (see below)
3. Open a Pull Request to add your model coefficients. You can do this through the GitHub web interface (see instructions in docs/submission_guide_GitHub_web.pdf), or using git yourself:
   a) Fork this repository
   b) Clone your fork onto your machine
   c) Create a branch and add your coefficients

     git checkout -b <branch-name>
     git add data/coefficients/DGRF/DGRF_<candidate-name>.cof
     git add data/coefficients/IGRF/IGRF_<candidate-name>.cof
     git add data/coefficients/SV/SV_<candidate-name>.cof
     git commit -m "Add coefficients for ..."
     git push

   d) Open the Pull Request here

Local development

Setting up the environment:

• This can be done manually without using conda if you provide numpy, pandas, matplotlib, pyshp (check environment-base.yml for specific versions).
• (Recommended) Use the conda lockfile for your platform. Run one of:

  conda create --name igrf --file lockfiles/conda-linux-64.lock
  conda create --name igrf --file lockfiles/conda-osx-64.lock
  conda create --name igrf --file lockfiles/conda-osx-arm64.lock
  conda create --name igrf --file lockfiles/conda-win-64.lock

  (and activate it with conda activate igrf when you need to use it)

Running the code:

The code is provided as three notebooks/scripts in the /notebooks/ directory. There are two copies of each: in .py format and in Jupyter notebook .ipynb format. You can use whichever you are more comfortable with.

JupyterLab is provided in the conda environment so you can run it with jupyter-lab to use the notebooks.