Generalize (F)(L)ODOG normalization#3
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Also properly document `padval` default
Where possible compare components to MATLAB output; else, compare to RHS transplementation
Easier to test against subcomponents of LODOG in transplementation
(Rather than no padding)
... for example stimulus, replicates exactly what the MATLAB code produces Merge branch 'dev_LODOG' into dev
extract normalizers to subroutine reuse LODOG gaussian kernel separate spatial masks reuse LODOG RMS function extract local RMS subroutine separate read out from normalization default spatial averaging mask is circular rename fixture
Brings more in line with other doc pages
(and pseudo-implementation)
Reorganizes and removes some existing normalization Better specification of shape for spatial normalization
Closes #3 (Gitlab)
Since it is used to prevent DivideByZero errors, makes sense to use it in divisive_normalization() Default eps=0.0 Takes on different values in F/L/ODOG tests
feat(models): `norm_energies` method to replace `normalizers_to_RMS`
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normalization-module, and subsequently ofmodels-module