Fix FutureWarning in zscore by casting to float32 #164
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Resolves a pandas FutureWarning that occurs when assigning a float64 result to a float32 DataFrame column. The `zscore` method in `eegdash/features/datasets.py` was performing a calculation that resulted in a float64 dtype. When this was assigned back to the DataFrame, which has a float32 dtype, a FutureWarning was raised. This change explicitly casts the result of the z-score calculation to `float32` before assignment, ensuring dtype compatibility and silencing the warning. A new test was added to `tests/test_features.py` to verify that the `zscore` method correctly maintains the float32 dtype.
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The feature module doesn't work with Python 3.10, but we support Python 3.10. It is mainly because of this type of attribution: ==================================== ERRORS ==================================== |
Thank you for catching this! You are correct, the syntax was not compatible with Python 3.10. I have now updated the code to use a backward-compatible indexing method. This should resolve the |
This commit addresses two separate issues: 1. Resolves a pandas FutureWarning in the `zscore` method by explicitly casting the result of the z-score calculation to `float32` before assignment. This ensures dtype compatibility and silences the warning. A new test was added to verify this fix. 2. Fixes a `SyntaxError` in `eegdash/features/feature_bank/complexity.py` that occurred in Python 3.10. The indexing syntax was updated to be backward-compatible, ensuring the feature module works correctly on all supported Python versions.
This change resolves a pandas
FutureWarningin thezscoremethod of theFeaturesConcatDatasetclass. The warning was triggered by an incompatible dtype assignment. The fix involves explicitly casting the result of the normalization tofloat32before assigning it back to the DataFrame. A targeted unit test has been added to verify the fix and prevent future regressions.PR created automatically by Jules for task 355021486991017514