Function_Warp improvements and unit tests #77
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This adds unit tests for function_warp testing various data types and shapes, and validates shape and quick sanity check of data. Additionally extended the function_warp functionality itself to be able to handle data of higher dimensionality than the provided warp map, with it broadcasting the map over the higher dimensions. So as an example if the data provided was of shape (8,4,32,32) and the warp map was (32,32) it broadcasts it applying the warp map to each data dimension correctly for each of the sequence/scan dimensions.
Added is_channel_data to specify whether data has the last dimension as channel (rgb/rgba) and so to correctly validate the warped shape. Missed return type, Adding typing to validate_warp
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cmeyer
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Jan 16, 2026
cmeyer
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Jan 16, 2026
cmeyer
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Overall code looks good other than the few nitpicks I put in comments.
cmeyer
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Jan 16, 2026
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Btw - I can make all these changes when I merge it, if you want. |
Removed test-test code. Adds function description comment. Utilise better properties to identify if data is channelled.
cmeyer
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Jan 20, 2026
| def function_warp(data_and_metadata_in: _DataAndMetadataLike, coordinates_in: typing.Sequence[ | ||
| _DataAndMetadataLike], order: int = 1) -> DataAndMetadata.DataAndMetadata: | ||
| def function_warp(data_and_metadata_in: _DataAndMetadataLike, coordinates_in: typing.Sequence[_DataAndMetadataLike], order: int = 1) -> DataAndMetadata.DataAndMetadata: | ||
| """ |
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I'll fix this during the merge, but this is the wrong format for a Python function comment.
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This adds unit tests for function_warp testing various data types and shapes, and validates shape and quick sanity check of data. Additionally extended the function_warp functionality itself to be able to handle data of higher dimensionality than the provided warp map, with it broadcasting the map over the higher dimensions. So as an example if the data provided was of shape (8,4,32,32) and the warp map was (32,32) it broadcasts it applying the warp map to each data dimension correctly for each of the sequence/scan dimensions.