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
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
|
=========================
NumPy 1.3.0 Release Notes
=========================
This minor release comes almost four months after the 1.2.0 release.
Highlights
==========
Python 2.6 support
~~~~~~~~~~~~~~~~~~
Python 2.6 is now supported on all previously supported platforms, including
windows.
http://www.python.org/dev/peps/pep-0361/
Generalized ufuncs
~~~~~~~~~~~~~~~~~~
http://projects.scipy.org/numpy/ticket/887
Experimental Windows 64 bits support
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Numpy can now be built on windows 64 bits (amd64 only, not IA64), with both MS
compilers and mingw-w64 compilers:
This is *highly experimental*: DO NOT USE FOR PRODUCTION USE. See INSTALL.txt,
Windows 64 bits section for more information on limitations and how to build it
by yourself.
New features
============
Formatting issues
~~~~~~~~~~~~~~~~~
TODO
Nan handling in max/min
~~~~~~~~~~~~~~~~~~~~~~~
The maximum/minimum ufuncs now reliably propagate nans. If one of the
arguments is a nan, then nan is retured. This affects np.min/np.max, amin/amax
and the array methods max/min. New ufuncs fmax and fmin have been added to deal
with non-propagating nans.
New ufuncs
~~~~~~~~~~
#. fmax - same as maximum for integer types and non-nan floats. Returns the
non-nan argument if one argument is nan and returns nan if both arguments
are nan.
#. fmin - same as minimum for integer types and non-nan floats. Returns the
non-nan argument if one argument is nan and returns nan if both arguments
are nan.
#. deg2rad - converts degrees to radians, same as the radians ufunc.
#. rad2deg - converts radians to degrees, same as the degrees ufunc.
#. log2 - base 2 logarithm.
#. exp2 - base 2 exponential.
#. logaddexp - add numbers stored as logarithms and return the logarithm
of the result.
#. logaddexp2 - add numbers stored as base 2 logarithms and return the base 2
logarithm of the result result.
Masked arrays
~~~~~~~~~~~~~
TODO
gfortran support on windows
~~~~~~~~~~~~~~~~~~~~~~~~~~~
Gfortran can now be used as a fortran compiler for numpy on windows, even when
the C compiler is Visual Studio. Gfortran + Visual studio does not work on
windows 64 bits (but gcc + gfortran does).
Deprecated features
===================
Histogram
~~~~~~~~~
The semantics of histogram has been modified to fix long-standing issues
with outliers handling. The main changes concern
#. the definition of the bin edges, now including the rightmost edge, and
#. the handling of upper outliers, now ignored rather than tallied in the
rightmost bin.
The previous behavior is still accessible using `new=False`, but this is
deprecated, and will be removed entirely in 1.4.0.
Documentation changes
=====================
Internal changes
================
numpy.core math configuration revamp
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
This should make the porting to new platforms easier, and more robust. In
particular, the configuration stage does not need to execute any code on the
target platform, which is a first step toward cross-compilation.
http://projects.scipy.org/numpy/browser/trunk/doc/neps/math_config_clean.txt
umath refactor
~~~~~~~~~~~~~~
A lot of code cleanup for umath/ufunc code (charris).
Improvements to build warnings
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Numpy can now build with -W -Wall without warnings
http://projects.scipy.org/numpy/browser/trunk/doc/neps/warnfix.txt
Separate core math library
~~~~~~~~~~~~~~~~~~~~~~~~~~
The core math functions (sin, cos, etc... for basic C types) have been put into
a separate library; it acts as a compatibility layer, to support most C99 maths
functions (real only for now). The library includes platform-specific fixes for
various maths functions, such as using those versions should be more robust
than using your platform functions directly. The API for existing functions is
exactly the same as the C99 math functions API; the only difference is the npy
prefix (npy_cos vs cos).
|