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The function uses the numeric scalar common dtype/promotion rules.
These are subtly different from the typical NumPy rules defined by
`np.result_type`.
Mainly, there is no good reason to have two subtly different rules
exposed and `find_common_type` is less reliable, slower, and not really
maintainable when it comes to NEP 50.
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Rather, use `result_type` instead. There are some exceedingly small
theoretical changes, since `result_type` currently uses value-inspection
logic.
`find_common_type` did not, because it pre-dates the value inspection
logic.
(I.e. in theory, this switches it to value-based promotion, just to
partially undo that in NEP 50; although more changes there.)
The only place where it is fathomable to matter is if someone is using
`np.c_[uint8_arr, -1]` to append 255 to an unsigned integer array.
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* Ensure raw dlpack deleter works when called without the GIL
* PR comments
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BUG: -unsigned_int(0) no overflow warning
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TST: Make test_partial_iteration_cleanup robust but require leak checker
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This makes sure the test is not flaky, but the test now requires
a leak checker (both valgrind or reference count based should work
in CPython at least).
Closes gh-21169
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MAINT: change subprocess arguments from Python>=3.7
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Not new things, but in touched lines...
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In some cases, the replacement is clearly not what is intended,
in those (where setup was called explicitly), I mostly renamed
`setup` to `_setup`.
The `test_ccompile_opt` is a bit confusing, so left it right now
(this will probably fail)
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MAINT: remove code specific to Python 2
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BUG: Fix boundschecking for random.logseries
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Logseries previously did not enforce bounds to be strictly exclusive
for the upper bound, where it leads to incorrect behavior.
The NOT_NAN check is removed, since it was never used: The current bounded
version always excludes NaNs.
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MAINT: Ensure that fmin loops do not show up multiple times
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This is not problematic for NumPy, because fmin/fmax have a custom
type resolver, however, Numba relies on the order (they should not
ideally, but probably have little choice currently).
Even without Numba, having the multiple loops is not nice though.
Closes gh-22376
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DOC: remove reference to Python 2
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Remove reference to Visual Studio version required by old versions
of Python. Python >= 3.5 is built with Microsoft Visual C++ 14.0 /
Visual Studio 2015:
https://wiki.python.org/moin/WindowsCompilers
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ENH: Allow all allocated operands in nditer/NpyIter
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This allows all operands to be allocated, it is necessary to
provide the dtype in this case, if missing the error changes
from ``ValueError`` to ``TypeError``
Closes gh-13934, gh-15140
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MAINT: refactor mandatory npymath functions to #define macros
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DEP: Proposal to deprecate the `msort` convenience function
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Add date to deprecation in comment
Co-authored-by: Matti Picus <matti.picus@gmail.com>
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I don't think this has worked in the past 10 years.
Also update because now we do have multiline past in the terminal.
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BUG: Do not allow nditer to reduce the mask
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Previously this was not checked correctly when `op_axes` were
provided. Note that we may want to disallow writing to a mask
completely.
This also continues some maintanence started earlier, which was
missing here due to the mask confusion.
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Developer-Ecosystem-Engineering/remove_Avx_when_not_used
ENH: Remove AVX related functions from non x86 based builds
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Apple silicon builds of NumPy have extra functions in them for AVX2/AVX512. The changes here remove those implementations if we're not building for x86.
Apple silicon:
- original size: 3946035 bytes
- new size: 3657731 bytes
- savings: 288304 bytes (7.31%)
Changes pass all tests on M1 native, M1 Rosetta, and iMacPro (AVX512). We've verified performance is the same before/after for Rosetta and iMacPro. We've also verified that binaries are exactly the same size and have the same number of symbols in them.
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Improve docstring to clarify the relationship between np.round and np.around
Co-authored-by: Mukulika Pahari <mukulikapahari@gmail.com>, Aparna Bushan <aparna.bushan@auto-grid.com>
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
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Before:
>>> field = unstructured_to_structured(np.zeros((20, 2)), dtype=[('x', float), ('y', float)]) # failed
>>> field = unstructured_to_structured(np.zeros((20, 2)), dtype=np.dtype([('x', float), ('y', float)])) # success
After:
>>> field = unstructured_to_structured(np.zeros((20, 2)), dtype=[('x', float), ('y', float)]) # success
>>> field = unstructured_to_structured(np.zeros((20, 2)), dtype=np.dtype([('x', float), ('y', float)])) # success
Closes gh-22428
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ENH: Implement correct scalar and integer overflow errors for NEP 50
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Especially adding coverage also for the power operator which
does not share its code perfectly.
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This should be fixed in `choose` to not do the unnecessary cast,
see gh-22237.
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This is the *actual* correct fix for the test adaptations. The test
adaptations should only be necessary when running in weak-promotion mode,
but they are NOT doing that currently.
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