Hi,

I noticed a type-checking inconsistency between assignments using slicing and fancy-indexing. The first will happily cast on assignment (regardless of type), while the second will throw a type error if there's reason to believe the casting will be unsafe. I'm not sure which would be the "correct" behavior, but the inconsistency is surprising.

Best,
-Tony

Example:

>>> import numpy as np
>>> a = np.arange(10)
>>> b = np.ones(10, dtype=np.uint8)

# this runs without error
>>> b[:5] = a[:5]

>>> mask = a < 5
>>> b[mask] = b[mask]
TypeError: array cannot be safely cast to required type

And I just noticed that 1D arrays behave differently than 2D arrays. If you replace the above definitions of a, b with:

>>> a = np.arange(10)[:, np.newaxis]
>>> b = np.ones((10, 1), dtype=np.uint8)

The rest of the code will run without error.