[pull] main from numpy:main#566
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pull[bot] merged 4 commits intoishandutta2007:mainfrom Mar 6, 2026
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Since NumPy 2.0, code like the following will cause a scalar value to be truncated:
>>> np.where([False], np.array([1], dtype=np.uint8), 1000)
array([232], dtype=uint8)
(in NumPy 1.x, it would instead output a uint16 array)
This is dangerous, and inconsistent with other NumPy functions, e.g.:
>>> np.multiply(np.array([1], dtype=np.uint8), 1000)
Traceback (most recent call last):
File "<python-input-3>", line 1, in <module>
np.multiply(np.array([1], dtype=np.uint8), 1000)
~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
OverflowError: Python integer 1000 out of bounds for uint8
Use `npy_update_operand_for_scalar` to bring the behavior of `where` in line with other functions.
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
Refactor things (slightly) to use a pattern of PyArray_DTypeFromObject and NPY_DT_CALL_ensure_canonical to avoid relying on type numbers (except for final BLAS dispatching). This is because type numbers are not reliable representation for user dtypes (and in general for parametric ones). A future iteration may want to use normal promotion (but this very subtly changes things for empty list inputs). Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
Co-authored-by: Sebastian Berg <sebastian@sipsolutions.net>
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