[SciPy-User] [ANN] Numpy 1.4.0 release
Sun Dec 27 19:34:38 CST 2009
I am pleased to announce the release of numpy 1.4.0. The highlights of
this release are:
- Faster import time
- Extended array wrapping mechanism for ufuncs
- New Neighborhood iterator (C-level only)
- C99-like complex functions in npymath, and a lot of portability
fixes for basic floating point math functions
The full release notes are at the end of the email. The sources are
uploaded on Pypi, and the binary installers will soon come on the
Thank you to everyone involved in this release, developers, users who
reported bugs, fix documentation, etc...
the numpy developers.
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.
* Faster import time
* Extended array wrapping mechanism for ufuncs
* New Neighborhood iterator (C-level only)
* C99-like complex functions in npymath
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 successfull, 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
catched early on.
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
arguements, 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"
New C API
The following C functions have been added to the C API:
#. PyArray_GetNDArrayCFeatureVersion: return the *API* version of the
#. 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.
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.
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
#. 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_nulps: 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.
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
#. 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 resistent to overflow.
#. Complex floor division has been made more resistent to overflow.
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
#. Specifying ``axis > MAX_DIMS`` is no longer allowed; Numpy raises now an
error instead of behaving similarly as for ``axis=None``.
Use C99 complex functions when available
The numpy complex types are now guaranteed to be ABI compatible with C99
complex type, if availble on the platform. Moreoever, the complex ufunc now use
the platform C99 functions intead 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
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:
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