fix: gridify() IndexError from numpy float // int staying float64 - #30
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BaseFeature2D.gridify() computed grid cell indices as f.p[0] // binwidth / f.p[1] // binheight. f.p is a numpy float array (feature point coordinates, shape (2,1)), and floor-dividing a numpy float array by a Python int still yields a float64 result -- so bins[iy, ix] always raised IndexError: arrays used as indices must be of integer (or boolean) type. This affected every call to gridify() regardless of whether nbins was passed as a tuple or scalar. Fix: extract the scalar value with .item() before floor-dividing, then cast to int explicitly. Added regression tests (gridify() had zero prior coverage) that fail with the exact reported IndexError against the unfixed code and pass against the fix -- verified both directions directly before committing.
Up to standards ✅🟢 Issues
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| Metric | Results |
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| Complexity | 0 |
| Duplication | 0 |
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Summary
BaseFeature2D.gridify()crashed withIndexError: arrays used as indices must be of integer (or boolean) typeon every call, regardless of whethernbinswas passed as a tuple or scalar.f.p[0] // binwidth/f.p[1] // binheight—f.pis a numpy float array (shape(2,1)), and floor-dividing a numpy float array by a Python int still yields afloat64result, not an int.bins[iy, ix]then rejects the float-typed index.int(f.p[0].item() // binwidth)— extract the scalar with.item()first, then cast.gridify()had zero test coverage before this. Added two regression tests (scalar and tuplenbins). Verified genuinely: reverted just the source fix (kept the test), confirmed both tests fail with the exact reportedIndexError, then restored the fix and confirmed they pass.main(confirmed viagit stash), so it's pre-existing, not something that fix introduced.Test plan