@opaliss will take the lead on this. Essentially this will involve writing a new transformer class, perhaps WaveshiftTransformer? See opinf.pre._shiftscale.ShiftScaleTransformer for another transformer to compare to. Implementation steps:
[ ] Create a new file in /src/opinf/pre/.
[ ] Define the class so it inherits from opinf.pre.TransformerTemplate and implements fit(), transform(), and inverse_transform().
[ ] Import the new class in /src/opinf/pre/__init__.py.
[ ] Write unit tests for the new class in a new file in /tests/pre/.
[ ] Compile the docs (make docs) and check that the automatically generated documentation page looks good.
[ ] If possible, demonstrate using the class in a new Jupyter notebook tutorial in docs/source/tutorials/.
New feature: Shifted Operator Inference from the paper Predicting solar wind streams from the inner-heliosphere to Earth via shifted operator inference by Opal Issan (@opaliss) and Boris Kramer (@bokramer). This strategy shifts the state snapshots to a moving coordinate frame. In the paper, this is notated in Eq. (16),
where
@opaliss will take the lead on this. Essentially this will involve writing a new transformer class, perhaps
WaveshiftTransformer
? Seeopinf.pre._shiftscale.ShiftScaleTransformer
for another transformer to compare to. Implementation steps:/src/opinf/pre/
.opinf.pre.TransformerTemplate
and implementsfit()
,transform()
, andinverse_transform()
./src/opinf/pre/__init__.py
./tests/pre/
.make docs
) and check that the automatically generated documentation page looks good.docs/source/tutorials/
.