fix: handle numpy integer types in PeriodRange.__getitem__#173
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fix: handle numpy integer types in PeriodRange.__getitem__#173
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The validation code in create-backtest-with-data uses np.flatnonzero which returns numpy.int64 values. When these are used to index into PeriodRange, the isinstance(item, int) check failed because numpy.int64 is not a Python int, causing an AttributeError when trying to access item.step on the numpy integer. This fix extends the type check to also accept np.integer types. CLIM-455
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Summary
PeriodRange.__getitem__now acceptsnp.integertypes in addition to Pythonintnp.flatnonzeroindices to work correctly when reporting validation errorsRoot Cause
The validation code in
create-backtest-with-dataendpoint usesnp.flatnonzero(isnan)to find indices of NaN values. These indices arenumpy.int64, not Pythonint. When used to indexPeriodRange, theisinstance(item, int)check failed, falling through to slice handling code which tried to accessitem.step.Test plan
PeriodRange.__getitem__with numpy integer typesCLIM-455