Krill inversion component sketch #321
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…nversion_extension
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| from typing import Any, Callable, Dict, Literal, Union | ||
| from numpy.typing import ArrayLike | ||
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| def mae( |
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should this be mad? it's used below in prepare_optimization
| # RETURNS: Dictionary with optimization parameters and minimizer | ||
| return {"parameters": params, "minimizer": minim} | ||
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| def optimize_scattering_model( |
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This should be renamed to something like optimize_model_parameters, since it is not the model that gets optimized, but the parameters.
| pass | ||
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| class validate_pcdwba(BaseModel): |
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I think it'll be more efficient to have build a validator that works with a model class - let's talk more about this when we meet, because I had some thought on existing scattering model packages and had a discussion on this with Sven some time in Dec.
| from numpy.typing import ArrayLike | ||
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| def normalize_scattering_model_parameters( |
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I think this can go into optimize.py since the output of the optimizer is doing the "inversion" so can just be packaged there.
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| def invert_population( |
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This invert_population and Sv_prediction_error are not strictly part of the "inversion" but are derived computations. Let me read through the rest of doc and see where these might go.
| from ...survey import Survey | ||
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| def inversion_pipeline( |
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The way things are designed now are completely separate from the hake component, which is not what I envisioned. I think it is fine as an intermediate step, but ultimately a more modularized framework will be needed to make the package more useful as an open-source project, and also remove the need for most "patcher" type of functions. Let's talk more about this when we meet.
leewujung
changed the title
Creating this so that it is easier to comment on code/pseudocode.