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BUG: np.unicode_ scalars misbehave on narrow builds #3258
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Need to track this down with a narrow build. |
To avoid having two issue about the "that unicode scalar bug", #7227 notes that there is the same bug with |
Another example from #10015: >>> a = np.zeros(2, dtype='U3')
>>> a[:] = np.unicode_('xxx')
>>> a.view(np.void)
array([b'\x78\x00\x78\x00\x78\x00\x00\x00\xF0\xE6\xD4\x68',
b'\x78\x00\x78\x00\x78\x00\x00\x00\xF0\xE6\xD4\x68'], dtype='|V12')
# '\x78' == 'x' |
Problem seems to be within Specifically, |
Whats the status of this? Any plans to fix it? |
Nothing specific right now unfortunately @OmerJog if you have some time to look into things, that is always appreciated! |
#15363 is related |
These APIs work with either UCS2 or UCS4, depending on the value of `Py_UNICODE_WIDE`. After python 3.3, there's a better way to handle this type of thing, which means we no longer have to care about this. Fixes numpygh-3258 Fixes numpygh-15363
Follows on from #1123
I'm assigning a numpy.unicode_ to a slice of array of numpy.unicode_ and see the following:
On a wide unicode build I can do the same with no problem:
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