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Redesigned metatensor-based Clebsch Gordan iterations #237
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* computation of dense clebsch gordan matrices * passing use_sparse argumentation through whole computation * adding test file that compares output of sparse and dense
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small comments, I'll do a full review later
python/rascaline/rascaline/utils/clebsch_gordan/cg_coefficients.py
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@Luthaf In order to make the CI happy I had to add some dependencies in the min-deps env - for CG tests we need |
yes! https://docs.pytest.org/en/7.1.x/how-to/skipping.html#skipping-on-a-missing-import-dependency |
Thanks for the review @Luthaf ! I've made the changes re: your suggestions :) |
Closes #178 and #185 . New API design for performing metatensor-based Clebsch Gordan iterations.
This PR:
correlate_density
andcorrelate_density_metadata
, which perform iterative CG tensor products of a density with itself, returning the a density correlation descriptor of specifiedcorrelation_order
, or just the metadata that would wrap it..rst
from Added cg_utils to Rascaline #185 (@jwa7, @curiosity54, @ecignoni)Not in this PR (see #239):
correlate_tensors
for doing a single CG product step on 2 arbitrary TensorMaps with the same samplescorrelate_tensors_different_samples
for doing a single CG product step on 2 arbitrary TensorMaps with different samples.Authored by @jwa7 , @agoscinski , @curiosity54, @frostedoyster , @DivyaSuman14 , @Luthaf , @ecignoni
📚 Documentation preview 📚: https://rascaline--237.org.readthedocs.build/en/237/