delta/python-packages/numpy.git/numpy/core/include, branch main github.com: numpy/numpy.git Merge pull request #23505 from cbrt64/fix-2256 2023-05-17T17:38:41+00:00 Matti Picus matti.picus@gmail.com 2023-05-17T17:38:41+00:00 d9b38d687cd513aa6688f7fba805a908c0ac3979 BUG: Use 2GiB chunking code for fwrite() on mingw32/64 
BUG: Use 2GiB chunking code for fwrite() on mingw32/64
 Merge pull request #23528 from seberg/allow-backcomp-builds 2023-04-28T11:02:40+00:00 Matti Picus matti.picus@gmail.com 2023-04-28T11:02:40+00:00 46055823a517d9e53918cb9b4495d05633ff6f35 ENH: Allow, and default to, downstream building with old API 
ENH: Allow, and default to, downstream building with old API
 ENH: float64 sin/cos using Numpy intrinsics (#23399) 2023-04-25T16:56:37+00:00 Christopher Sidebottom chris.sidebottom@arm.com 2023-04-25T16:56:37+00:00 fe5472fa4eae131ff9646d7c980c6c4081c10386 This takes the [sin](https://github.com/ARM-software/optimized-routines/blob/master/math/v_sin.c) and [cos](https://github.com/ARM-software/optimized-routines/blob/master/math/v_cos.c) algorithms from Optimized Routines under MIT license, and converts them to Numpy intrinsics. The routines are within the ULP boundaries of other vectorised math routines (<4ULP). The routines reduce performance in some special cases but improves normal cases. Comparing to the SVML implementation, these routines are more performant in special cases, we're therefore safe to assume the performance is acceptable for AArch64 as well. | performance ratio (lower is better) | benchmark | | ---- | ---- | | 1.8 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 4 2 'd') | | 1.79 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 4 4 'd') | | 1.77 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 4 1 'd') | | 1.74 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 2 2 'd') | | 1.74 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 2 4 'd') | | 1.72 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 2 1 'd') | | 1.6 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 1 2 'd') | | 1.6 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 1 4 'd') | | 1.56 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'cos'> 1 1 'd') | | 1.42 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 2 2 'd') | | 1.41 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 2 4 'd') | | 1.37 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 2 1 'd') | | 1.26 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 4 2 'd') | | 1.26 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 4 4 'd') | | 1.2 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 4 1 'd') | | 1.18 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 1 2 'd') | | 1.18 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 1 4 'd') | | 1.12 | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc 'sin'> 1 1 'd') | | 0.65 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 4 2 'd') | | 0.64 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 2 4 'd') | | 0.64 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 4 4 'd') | | 0.64 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 2 2 'd') | | 0.61 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 1 4 'd') | | 0.61 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 1 2 'd') | | 0.6 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 2 1 'd') | | 0.6 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 4 1 'd') | | 0.56 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'cos'> 1 1 'd') | | 0.52 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 4 2 'd') | | 0.52 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 4 4 'd') | | 0.52 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 2 4 'd') | | 0.52 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 2 2 'd') | | 0.47 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 1 4 'd') | | 0.47 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 1 2 'd') | | 0.46 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 4 1 'd') | | 0.46 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 2 1 'd') | | 0.42 | bench_ufunc_strides.UnaryFP.time_unary(<ufunc 'sin'> 1 1 'd') | Co-authored-by: Pierre Blanchard <Pierre.Blanchard@arm.com> 
This takes the [sin](https://github.com/ARM-software/optimized-routines/blob/master/math/v_sin.c) and [cos](https://github.com/ARM-software/optimized-routines/blob/master/math/v_cos.c) algorithms from Optimized Routines under MIT license, and converts them to Numpy intrinsics.

The routines are within the ULP boundaries of other vectorised math routines (<4ULP). The routines reduce performance in some special cases but improves normal cases. Comparing to the SVML implementation, these routines are more performant in special cases, we're therefore safe to assume the performance is acceptable for AArch64 as well.

| performance ratio (lower is better)  | benchmark |
| ----  | ---- |
| 1.8   | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	4	2	'd') |
| 1.79  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	4	4	'd') |
| 1.77  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	4	1	'd') |
| 1.74  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	2	2	'd') |
| 1.74  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	2	4	'd') |
| 1.72  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	2	1	'd') |
| 1.6   | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	1	2	'd') |
| 1.6   | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	1	4	'd') |
| 1.56  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'cos'>	1	1	'd') |
| 1.42  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	2	2	'd') |
| 1.41  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	2	4	'd') |
| 1.37  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	2	1	'd') |
| 1.26  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	4	2	'd') |
| 1.26  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	4	4	'd') |
| 1.2   | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	4	1	'd') |
| 1.18  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	1	2	'd') |
| 1.18  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	1	4	'd') |
| 1.12  | bench_ufunc_strides.UnaryFPSpecial.time_unary(<ufunc	'sin'>	1	1	'd') |
| 0.65  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	4	2	'd') |
| 0.64  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	2	4	'd') |
| 0.64  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	4	4	'd') |
| 0.64  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	2	2	'd') |
| 0.61  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	1	4	'd') |
| 0.61  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	1	2	'd') |
| 0.6   | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	2	1	'd') |
| 0.6   | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	4	1	'd') |
| 0.56  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'cos'>	1	1	'd') |
| 0.52  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	4	2	'd') |
| 0.52  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	4	4	'd') |
| 0.52  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	2	4	'd') |
| 0.52  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	2	2	'd') |
| 0.47  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	1	4	'd') |
| 0.47  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	1	2	'd') |
| 0.46  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	4	1	'd') |
| 0.46  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	2	1	'd') |
| 0.42  | bench_ufunc_strides.UnaryFP.time_unary(<ufunc	'sin'>	1	1	'd') |

Co-authored-by: Pierre Blanchard <Pierre.Blanchard@arm.com>
 DOC: Align default with what the NEP draft says 2023-04-25T09:18:04+00:00 Sebastian Berg sebastianb@nvidia.com 2023-04-25T08:49:19+00:00 c62881fa55fc830d16ac46bb1b91663266a8d94f We have three choices: 1. Be compatible with limited API (oldest supported Python version) 2. Be compatible with everything on the same Python version (this) 3. Be strict and default to NEP 29 This just rephrases things to be 2. Because our API version was not bumped over the relevant time frame, it is actually also compatible with the first. 
We have three choices:
1. Be compatible with limited API (oldest supported Python version)
2. Be compatible with everything on the same Python version (this)
3. Be strict and default to NEP 29

This just rephrases things to be 2.  Because our API version was not
bumped over the relevant time frame, it is actually also compatible
with the first.
MAINT: refactor zero-filling to use a traversal loop 2023-04-18T17:11:51+00:00 Nathan Goldbaum nathan.goldbaum@gmail.com 2023-04-18T14:58:21+00:00 f8babe2788a8be941e4b1588d4f3c0bd9b0621c3 






ENH: Refactor special zero-filling to be managed by the DType
2023-04-18T17:03:54+00:00

Nathan Goldbaum
nathan.goldbaum@gmail.com

2023-04-14T19:20:47+00:00

726676a21ac4661e81ecdb4a236324f5740500ae












MAINT: correct typos in dtype API documentation comments
2023-04-18T17:03:54+00:00

Nathan Goldbaum
nathan.goldbaum@gmail.com

2023-04-14T18:44:42+00:00

8a428fdef4933edc0a74528bf51fedd3aab3f7c1












Tweak to just warn for low versions
2023-04-06T15:43:37+00:00

Sebastian Berg
sebastianb@nvidia.com

2023-04-06T15:43:37+00:00

66ccd52bb19093be6725a3fc672feeaf727ac299












BUG: Fix typo in new version checks
2023-04-04T15:43:45+00:00

Sebastian Berg
sebastianb@nvidia.com

2023-04-04T15:43:45+00:00

5c09dea8dc68bd54d7743557cae1d6409b07da92












ENH: Allow compiling compatibly to old NumPy versions
2023-04-04T15:17:17+00:00

Sebastian Berg
sebastianb@nvidia.com

2023-04-04T14:46:56+00:00

3a811358830c324b7b6819b88dec4e4bcd91444a

The default compiles compatibly with 1.17.x, we allow going back to
1.15 (mainly because it is easy).

There were few additions in this time, a few structs grew and very
few API functions were added.  Added a way to mark API functions
as requiring a specific target version.

If a user wishes to use the *new* API, they have to add the definition:

    #define NPY_TARGET_VERSION NPY_1_22_API_VERSION

Before importing NumPy.  (Our version numbering is a bit funny
I first thought to use a hex version of the main NumPy version,
but since we already have the `NPY_1_22_API_VERSION` defines...)





The default compiles compatibly with 1.17.x, we allow going back to
1.15 (mainly because it is easy).

There were few additions in this time, a few structs grew and very
few API functions were added.  Added a way to mark API functions
as requiring a specific target version.

If a user wishes to use the *new* API, they have to add the definition:

    #define NPY_TARGET_VERSION NPY_1_22_API_VERSION

Before importing NumPy.  (Our version numbering is a bit funny
I first thought to use a hex version of the main NumPy version,
but since we already have the `NPY_1_22_API_VERSION` defines...)