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1. Information is derived with the help of `threadpoolctl` library.
2. In case `threadpoolctl` is not installed, a message is displayed
with help on how to install it.
3. SIMD related information is derived from `__cpu_features__`,
`__cpu_baseline__` and `__cpu_dispatch__`
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[ci skip]
Co-authored-by: h-vetinari <h.vetinari@gmx.com>
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CI: Test NumPy build against old versions of GCC(6, 7, 8)
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[skip actions] [skip travis] [skip azp]
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Closes gh-7958.
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MAINT,DOC: Remove sphinx-panels in favor of sphinx-design
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Since Sphinx 5.0.0, some intersphinx links need explicit inventory names or they won't be resolved correctly. See https://www.sphinx-doc.org/en/master/changes.html\#release-5-0-0-released-may-30-2022 and https://github.com/sphinx-doc/sphinx/issues/2068.
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DEV: supporting IBM i system
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DOC: Fix documentation for percentile and quantile
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Co-authored-by: deego <deego3@gmail.com>
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Developer-Ecosystem-Engineering/improve_tobits_pack_apple_silicon
BUG: Missed a case in the diff merge for #22110
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Developer-Ecosystem-Engineering/improve_tobits_pack_apple_silicon
BUG: Address failures in aarch64 gcc builds due to #22096
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Fix typos and wrap length
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Add a new version or seed that supports seeding any bit gen
Add set/get_bit_generator as explicity methodds to support swapping
closes #21808
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The lerp test compared the output of the original and the symmetric
functions by using basic assertion. Double precision errors could
accumulate in a way that the 2 outputs mismatch by epsilon.
For more information on the precision issue, see #22073
Fix: use assert_allclose instead for float comparison.
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https://github.com/numpy/numpy/issues/9968 was fixed. workaround no
longer appropriate.
Also inefficient once https://github.com/numpy/numpy/issues/22098 is
implemented.
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BUG: Fix skip condition for test_loss_of_precision[complex256]
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The check code needs to use another constant to check for an affected
GLIBC on PPC.
See #15763
Fixes #15243
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PERF: Remove numpy.compat._pep440 from default imports
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The submoudle numpy.compat._pep440 is removed from the default import of
numpy to reduce the import time. See #22061
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Improvements:
- up to 1.25x faster on pack_bits()
- up to 1.63x faster for comparison loops
Apple M1 native (arm64):
```
before after ratio
[da6297b9] [1d330fc2]
<main> <tobits-pack-intrinsics/upstream-pr>
+ 67.2±0.6μs 74.9±6μs 1.11 bench_indexing.Indexing.time_op('indexes_', 'I', '')
+ 38.6±0.06μs 42.8±1μs 1.11 bench_ufunc_strides.Unary.time_ufunc(<ufunc 'invert'>, 4, 4, 'i')
+ 7.68±0.02μs 8.47±0.01μs 1.10 bench_function_base.Sort.time_argsort('merge', 'uint32', ('uniform',))
- 42.5±0.3μs 38.6±0.03μs 0.91 bench_ufunc_strides.Unary.time_ufunc(<ufunc 'invert'>, 2, 2, 'l')
- 147±1μs 133±1μs 0.91 bench_function_base.Histogram1D.time_small_coverage
- 4.62±0.04μs 4.17±0.04μs 0.90 bench_ufunc_strides.AVX_cmplx_funcs.time_ufunc('conjugate', 1, 'D')
- 1.11±0μs 992±2ns 0.90 bench_core.PackBits.time_packbits_little(<class 'bool'>)
- 2.60±0.01μs 2.33±0μs 0.90 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'float16')
- 2.89±0μs 2.59±0μs 0.90 bench_itemselection.Take.time_contiguous((1000, 2), 'raise', 'int32')
- 2.90±0μs 2.59±0μs 0.89 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'complex64')
- 2.90±0μs 2.59±0μs 0.89 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'float64')
- 2.90±0μs 2.58±0μs 0.89 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'int64')
- 2.59±0μs 2.31±0.02μs 0.89 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'int16')
- 2.90±0μs 2.58±0μs 0.89 bench_itemselection.Take.time_contiguous((1000, 2), 'raise', 'float32')
- 2.90±0μs 2.58±0μs 0.89 bench_itemselection.Take.time_contiguous((1000, 1), 'raise', 'longfloat')
- 4.78±0μs 4.25±0μs 0.89 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'int16')
- 4.78±0μs 4.25±0μs 0.89 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'float16')
- 5.41±0μs 4.79±0μs 0.89 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'float64')
- 5.41±0μs 4.78±0μs 0.88 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'complex64')
- 5.41±0μs 4.78±0μs 0.88 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'longfloat')
- 5.41±0μs 4.78±0μs 0.88 bench_itemselection.Take.time_contiguous((2, 1000, 1), 'raise', 'int64')
- 10.8±0.02μs 9.42±0.03μs 0.87 bench_function_base.Sort.time_argsort('merge', 'uint32', ('reversed',))
- 90.0±0.08μs 78.4±0.4μs 0.87 bench_function_base.Sort.time_argsort('quick', 'int32', ('reversed',))
- 2.53±0.02μs 2.20±0.02μs 0.87 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.uint16'>)
- 2.53±0.01μs 2.18±0μs 0.86 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.uint16'>)
- 2.54±0.02μs 2.18±0.01μs 0.86 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.int16'>)
- 2.54±0.02μs 2.16±0.02μs 0.85 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.int16'>)
- 10.8±0.1μs 8.69±0.01μs 0.81 bench_core.PackBits.time_packbits_axis1(<class 'bool'>)
- 57.4±10μs 44.5±0.02μs 0.77 bench_core.CountNonzero.time_count_nonzero(1, 10000, <class 'str'>)
- 60.9±0.01μs 46.7±0.03μs 0.77 bench_function_base.Sort.time_argsort('quick', 'int32', ('ordered',))
- 5.41±1μs 4.15±0μs 0.77 bench_itemselection.Take.time_contiguous((1000, 2), 'wrap', 'complex128')
- 4.77±0.01μs 3.51±0.01μs 0.74 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.int32'>)
- 4.84±0.03μs 3.51±0.02μs 0.72 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.uint32'>)
- 4.87±0.01μs 3.50±0.03μs 0.72 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.float32'>)
- 4.86±0μs 3.50±0.01μs 0.72 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.uint32'>)
- 4.82±0.06μs 3.46±0.04μs 0.72 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.int32'>)
- 4.86±0.01μs 3.48±0.03μs 0.72 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.float32'>)
- 9.26±0.07μs 5.73±0.03μs 0.62 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.int64'>)
- 9.34±0μs 5.76±0.02μs 0.62 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.uint64'>)
- 9.34±0.07μs 5.75±0.02μs 0.62 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.float64'>)
- 9.31±0.04μs 5.73±0.04μs 0.61 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.float64'>)
- 9.28±0.09μs 5.67±0.04μs 0.61 bench_ufunc.CustomComparison.time_less_than_scalar1(<class 'numpy.int64'>)
- 9.18±0.02μs 5.60±0.02μs 0.61 bench_ufunc.CustomComparison.time_less_than_scalar2(<class 'numpy.uint64'>)
```
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TST: Add tests for FP16 umath functions
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PERF: Improve import time of numpy
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* Refactor add_newdoc_for_scalar_type
* Eliminate call to platform.system()
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BUG/ENH: Allow bit generators to supply their own constructor
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Allow bit generators to supply their own constructors to enable Generator
objects using arbitrary bit generators to be supported
closes #22012
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ENH: reorder includes for testing on top of system installations of NumPy
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argument (#22022)
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This reverts commit e2efced9bdfc773f5aca2487f12ab1cb2bd11833.
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This reverts commit 6b8d55e66e532d66e1701ad039b4cda306839b3f.
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This reverts commit 8cd6f4ca00b6e0da3833fc267d50067b2ddbc069.
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This reverts commit db1a98bf194771a677dbc0c2e06bc47f4a9947a8.
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