| Commit message (Collapse) | Author | Age | Files | Lines |
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of NumPy build
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performance tests
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MAINT: Add exception chaining where appropriate
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Co-authored-by: Eric Wieser <wieser.eric@gmail.com>
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avo-exceptions-chaining
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ENH, SIMD: Dispatch for unsigned floor division
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DEP: Formally deprecate `np.typeDict`
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* Upload docs to main branch of neps/devdocs
* Update `master` to `main` to be consistent with current state of renaming
* Remove badges from README
* Replace remaining instances of `master` with `main`
* Add back logo
* Fix CircleCI branch checks
* Fix GA branch name
* master->main in issue templates.
* master->main for 2 links to numpy archive.
* Change master to main in NEP 23
Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>
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MAINT: Optimize numpy.count_nonzero for int types using SIMD operations
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benchmarks
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* benchmark bench_lib.Unique added
* extended test_unique_1d
* modify _unique1d
* extend test with return_index, return_inverse and return_counts parameters
* documentation updated
* Update numpy/lib/arraysetops.py
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
* full coverage of nan types
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
* added tests for the datetime like dtypes
* nan as vector of length 1
* use aux[-1] as nan, ..versionchanged, release note
* for complex arrays all NaN values are considered equivalent
Co-authored-by: filip_trojan <Tarantula2018>
Co-authored-by: Bas van Beek <43369155+BvB93@users.noreply.github.com>
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This change speeds up taking the median of 1001 floats by ~20%, as
measured by
`python -mtimeit -s 'import numpy as np; x = np.random.randn(1001)' -- 'np.median(x)'`
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... or when the input isn't/cannot be a set. I left a few usages, e.g.
in random sampling, where "set" is reasonable as informal description of
an array as the order doesn't matter; however, for e.g. np.gradient the
order of the returned list is clearly important, so "set" is wrong.
Also some other minor doc edits noticed during the grepping: using
`shape` instead of `form` in `cov` is consistent with most other places;
the wording in `Polynomial.trim` now matches other methods on the same
class.
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This test should not be exclusive to AVX. this patch also
extends unary test to cover different sets of output strides.
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SIMD: Optimize the performance of np.packbits using USIMD
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BUG, Benchmark: fix passing optimization build options to asv
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This patch allows passing `-j`, `--cpu-baseline`, `--cpu-dispatch` and `--disable-optimization`
to ASV build when argument `--bench-compare` is used.
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* Added a benchmark for `trim_zeros()`
* Improve the performance of `np.trim_zeros()`
* Increase the variety of the tests
Fall back to the old `np.trim_zeros()` implementation if an exception is encountered.
Emit a `DeprecationWarning` in such case.
* DEP,REL: Added a deprecation release note
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MAINT: Avx512 intrinsics implementation for float64 input np.log
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* BENCH: Add basic benchmarks for scalar indexing and assignment
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BENCH: Expand array-creation benchmarks
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This adds some simple additional tests, one for a list of NumPy
scalars. Another for generally lists, both integers and floats
as one common cases (floats previously had some super-fast paths).
As well as a test with the dtype given, since that may make
some differences.
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optimization
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ENH: Hardcode buffer handling for simple scalars
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* Neon implementation with intrinsic for bool argmax
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* TST: Adding tests to validate strided np.ldexp and np.frexp
* ENH: Use AVX-512 for float and double np.ldexp
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* ENH: Use AVX-512 for np.isnan, np.infinite, np.isinf and np.signbit
* TST: Add tests to validate isnan, isfinite, signbit and isinf ufuncs
* BENCH: Adding benchmarks for isnan, isinf, isfinite and signbit
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asv executes the benchmarks of the current branch building the code
specified in "branches". Previously this was "master" instead of "HEAD"
(the code currently worked on). This PR changes "master" -> "HEAD" as it
seems more likely for a user to be generating benchmarks for the code
they are currently working on.
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MAINT: AVX512 implementation with intrinsic for float64 input np.exp()
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