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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
|
=========================
NumPy 1.3.0 Release Notes
=========================
This minor includes numerous bug fixes, official python 2.6 support, and
several new features such as generalized ufuncs.
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
~~~~~~~~~~~~~~~~~
Float formatting is now handled by numpy instead of the C runtime: this enables
locale independent formatting, more robust fromstring and related methods.
Special values (inf and nan) are also more consistent across platforms (nan vs
IND/NaN, etc...), and more consistent with recent python formatting work (in
2.6 and later).
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
=====================
TODO
New C API
=========
Multiarray API
~~~~~~~~~~~~~~
The following functions have been added to the multiarray C API:
* PyArray_GetEndianness: to get runtime endianness
Ufunc API
~~~~~~~~~~~~~~
The following functions have been added to the ufunc API:
* TODO
New defines
~~~~~~~~~~~
New public C defines are available for ARCH specific code through numpy/npy_cpu.h:
* NPY_CPU_X86: x86 arch (32 bits)
* NPY_CPU_AMD64: amd64 arch (x86_64, NOT Itanium)
* NPY_CPU_PPC: 32 bits ppc
* NPY_CPU_PPC64: 64 bits ppc
* NPY_CPU_SPARC: 32 bits sparc
* NPY_CPU_SPARC64: 64 bits sparc
* NPY_CPU_S390: S390
* NPY_CPU_PARISC: PARISC
New macros for CPU endianness has been added as well (see internal changes
below for details):
* NPY_BYTE_ORDER: integer
* NPY_LITTLE_ENDIAN/NPY_BIG_ENDIAN defines
Those provide portable alternatives to glibc endian.h macros for platforms
without it.
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).
The core library will be made available to any extension in 1.4.0.
CPU arch detection
~~~~~~~~~~~~~~~~~~
npy_cpu.h defines numpy specific CPU defines, such as NPY_CPU_X86, etc...
Those are portable across OS and toolchains, and set up when the header is
parsed, so that they can be safely used even in the case of cross-compilation
(the values is not set when numpy is built), or for multi-arch binaries (e.g.
fat binaries on Max OS X).
npy_endian.h defines numpy specific endianness defines, modeled on the glibc
endian.h. NPY_BYTE_ORDER is equivalent to BYTE_ORDER, and one of
NPY_LITTLE_ENDIAN or NPY_BIG_ENDIAN is defined. As for CPU archs, those are set
when the header is parsed by the compiler, and as such can be used for
cross-compilation and multi-arch binaries.
|
