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
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
|
=========================
NumPy 1.4.0 Release Notes
=========================
This minor includes numerous bug fixes, as well as a few new features. It
is backward compatible with 1.3.0 release.
Highlights
==========
* New datetime dtype support to deal with dates in arrays
* Faster import time
* Extended array wrapping mechanism for ufuncs
* New Neighborhood iterator (C-level only)
* C99-like complex functions in npymath
New features
============
Extended array wrapping mechanism for ufuncs
--------------------------------------------
An __array_prepare__ method has been added to ndarray to provide subclasses
greater flexibility to interact with ufuncs and ufunc-like functions. ndarray
already provided __array_wrap__, which allowed subclasses to set the array type
for the result and populate metadata on the way out of the ufunc (as seen in
the implementation of MaskedArray). For some applications it is necessary to
provide checks and populate metadata *on the way in*. __array_prepare__ is
therefore called just after the ufunc has initialized the output array but
before computing the results and populating it. This way, checks can be made
and errors raised before operations which may modify data in place.
Automatic detection of forward incompatibilities
------------------------------------------------
Previously, if an extension was built against a version N of NumPy, and used on
a system with NumPy M < N, the import_array was successful, which could cause
crashes because the version M does not have a function in N. Starting from
NumPy 1.4.0, this will cause a failure in import_array, so the error will be
caught early on.
New iterators
-------------
A new neighborhood iterator has been added to the C API. It can be used to
iterate over the items in a neighborhood of an array, and can handle boundaries
conditions automatically. Zero and one padding are available, as well as
arbitrary constant value, mirror and circular padding.
New polynomial support
----------------------
New modules chebyshev and polynomial have been added. The new polynomial module
is not compatible with the current polynomial support in numpy, but is much
like the new chebyshev module. The most noticeable difference to most will
be that coefficients are specified from low to high power, that the low
level functions do *not* work with the Chebyshev and Polynomial classes as
arguments, and that the Chebyshev and Polynomial classes include a domain.
Mapping between domains is a linear substitution and the two classes can be
converted one to the other, allowing, for instance, a Chebyshev series in
one domain to be expanded as a polynomial in another domain. The new classes
should generally be used instead of the low level functions, the latter are
provided for those who wish to build their own classes.
The new modules are not automatically imported into the numpy namespace,
they must be explicitly brought in with an "import numpy.polynomial"
statement.
New C API
---------
The following C functions have been added to the C API:
#. PyArray_GetNDArrayCFeatureVersion: return the *API* version of the
loaded numpy.
#. PyArray_Correlate2 - like PyArray_Correlate, but implements the usual
definition of correlation. Inputs are not swapped, and conjugate is
taken for complex arrays.
#. PyArray_NeighborhoodIterNew - a new iterator to iterate over a
neighborhood of a point, with automatic boundaries handling. It is
documented in the iterators section of the C-API reference, and you can
find some examples in the multiarray_test.c.src file in numpy.core.
New ufuncs
----------
The following ufuncs have been added to the C API:
#. copysign - return the value of the first argument with the sign copied
from the second argument.
#. nextafter - return the next representable floating point value of the
first argument toward the second argument.
New defines
-----------
The alpha processor is now defined and available in numpy/npy_cpu.h. The
failed detection of the PARISC processor has been fixed. The defines are:
#. NPY_CPU_HPPA: PARISC
#. NPY_CPU_ALPHA: Alpha
Testing
-------
#. deprecated decorator: this decorator may be used to avoid cluttering
testing output while testing DeprecationWarning is effectively raised by
the decorated test.
#. assert_array_almost_equal_nulp: new method to compare two arrays of
floating point values. With this function, two values are considered
close if there are not many representable floating point values in
between, thus being more robust than assert_array_almost_equal when the
values fluctuate a lot.
#. assert_array_max_ulp: raise an assertion if there are more than N
representable numbers between two floating point values.
#. assert_warns: raise an AssertionError if a callable does not generate a
warning of the appropriate class, without altering the warning state.
Reusing npymath
---------------
In 1.3.0, we started putting portable C math routines in npymath library, so
that people can use those to write portable extensions. Unfortunately, it was
not possible to easily link against this library: in 1.4.0, support has been
added to numpy.distutils so that 3rd party can reuse this library. See coremath
documentation for more information.
Improved set operations
-----------------------
In previous versions of NumPy some set functions (intersect1d,
setxor1d, setdiff1d and setmember1d) could return incorrect results if
the input arrays contained duplicate items. These now work correctly
for input arrays with duplicates. setmember1d has been renamed to
in1d, as with the change to accept arrays with duplicates it is
no longer a set operation, and is conceptually similar to an
elementwise version of the Python operator 'in'. All of these
functions now accept the boolean keyword assume_unique. This is False
by default, but can be set True if the input arrays are known not
to contain duplicates, which can increase the functions' execution
speed.
Improvements
============
#. numpy import is noticeably faster (from 20 to 30 % depending on the
platform and computer)
#. The sort functions now sort nans to the end.
* Real sort order is [R, nan]
* Complex sort order is [R + Rj, R + nanj, nan + Rj, nan + nanj]
Complex numbers with the same nan placements are sorted according to
the non-nan part if it exists.
#. The type comparison functions have been made consistent with the new
sort order of nans. Searchsorted now works with sorted arrays
containing nan values.
#. Complex division has been made more resistant to overflow.
#. Complex floor division has been made more resistant to overflow.
Deprecations
============
The following functions are deprecated:
#. correlate: it takes a new keyword argument old_behavior. When True (the
default), it returns the same result as before. When False, compute the
conventional correlation, and take the conjugate for complex arrays. The
old behavior will be removed in NumPy 1.5, and raises a
DeprecationWarning in 1.4.
#. unique1d: use unique instead. unique1d raises a deprecation
warning in 1.4, and will be removed in 1.5.
#. intersect1d_nu: use intersect1d instead. intersect1d_nu raises
a deprecation warning in 1.4, and will be removed in 1.5.
#. setmember1d: use in1d instead. setmember1d raises a deprecation
warning in 1.4, and will be removed in 1.5.
The following raise errors:
#. When operating on 0-d arrays, ``numpy.max`` and other functions accept
only ``axis=0``, ``axis=-1`` and ``axis=None``. Using an out-of-bounds
axes is an indication of a bug, so Numpy raises an error for these cases
now.
#. Specifying ``axis > MAX_DIMS`` is no longer allowed; Numpy raises now an
error instead of behaving similarly as for ``axis=None``.
Internal changes
================
Use C99 complex functions when available
----------------------------------------
The numpy complex types are now guaranteed to be ABI compatible with C99
complex type, if available on the platform. Moreover, the complex ufunc now use
the platform C99 functions instead of our own.
split multiarray and umath source code
--------------------------------------
The source code of multiarray and umath has been split into separate logic
compilation units. This should make the source code more amenable for
newcomers.
Separate compilation
--------------------
By default, every file of multiarray (and umath) is merged into one for
compilation as was the case before, but if NPY_SEPARATE_COMPILATION env
variable is set to a non-negative value, experimental individual compilation of
each file is enabled. This makes the compile/debug cycle much faster when
working on core numpy.
Separate core math library
--------------------------
New functions which have been added:
* npy_copysign
* npy_nextafter
* npy_cpack
* npy_creal
* npy_cimag
* npy_cabs
* npy_cexp
* npy_clog
* npy_cpow
* npy_csqr
* npy_ccos
* npy_csin
|
