1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
|
==========================
NumPy 1.17.0 Release Notes
==========================
Highlights
==========
* NumPy's FFT implementation has switched to pocketfft
New functions
=============
Deprecations
============
Future Changes
==============
Expired deprecations
====================
Compatibility notes
===================
C API changes
=============
New Features
============
``np.ufunc.reduce`` and related functions now accept a ``where`` mask
---------------------------------------------------------------------
``np.ufunc.reduce``, ``np.sum``, ``np.prod``, ``np.min``, ``np.max`` all
now accept a ``where`` keyword argument, which can be used to tell which
elements to include in the reduction. For reductions that do not have an
identity, it is necessary to also pass in an initial value (e.g.,
``initial=np.inf`` for ``np.min``). For instance, the equivalent of
``nansum`` would be, ``np.sum(a, where=~np.isnan(a))``.
Improvements
============
Array comparison assertions include maximum differences
-------------------------------------------------------
Error messages from array comparison tests such as
`np.testing.assert_allclose` now include "max absolute difference" and
"max relative difference," in addition to the previous "mismatch" percentage.
This information makes it easier to update absolute and relative error
tolerances.
Replacement of the `fftpack`-based FFT module by the `pocketfft` library
------------------------------------------------------------------------
Both implementations have the same ancestor (Fortran77 `FFTPACK` by Paul N.
Swarztrauber), but `pocketfft` contains additional modifications which
improve both accuracy and performance in some circumstances. For FFT lengths
containing large prime factors, `pocketfft` uses Bluestein's algorithm, which
maintains `O(N log N)` run time complexity instead of deteriorating towards
`O(N*N)` for prime lengths. Also, accuracy for real-valued FFTs with near-prime
lengths has improved and is on par with complex-valued FFTs.
Changes
=======
|
