Module: NNP-CG Descriptor analysis
This module adds tools to the N2P2 package which allow to assess the quality of atomic environment descriptors. This is particularly useful when designing a neural network potential based coarse-grained model (NNP-CG).
- Create new tool to collect descriptor data for external processing.
- Design and write Python (Jupyter) tools to analyse descriptor quality.
- CI tests for tool.
- Provide example.
- Source code documentation.
- Complete module README.
Module verification checklist (for reviewers)
Checklist when the module is first submitted
- Have the relevant labels been added to the MR
- If submitted on someone elses behalf, has the software author been referenced (if they have a GitLab account)
Checklist when module is no longer "WIP"
- Is the module documentation sufficiently detailed?
- Is it mergeable? (i.e., there should be no merge conflicts)
- Are the build instructions sufficient - source code locations, build instructions, etc.? (If not the MR should be updated)
- Did it pass the tests that were described? (Are there unit/regression tests? Do they pass?)
- Are the tests sufficient?
- If the module introduces new functionality, is it tested? (Unit/regression tests?)
- Is the associated source code well formatted? (typos, line length, brackets,...it should be consistent with existing source)
- Is all new source code sufficiently documented? (functions, their arguments,...)
- Is there a description of any applications the module has? (This is a hard requirement for E-CAM PDRAs)
- Make sure the module appears in a toctree
- Add a link to the final result on https://e-cam.readthedocs.io