[Numpy-discussion] ANN: NumPy 1.7.0b1 release
ondrej.certik at gmail.com
Tue Aug 21 12:24:21 EDT 2012
I'm pleased to announce the availability of the first beta release of
Sources and binary installers can be found at
Please test this release and report any issues on the numpy-discussion
mailing list. The following problems are known and
we'll work on fixing them before the final release:
I would like to thank Ralf for a lot of help with creating binaries
and other help for this release.
NumPy 1.7.0 Release Notes
This release includes several new features as well as numerous bug fixes and
refactorings. It supports Python 2.4 - 2.7 and 3.1 - 3.2.
* ``where=`` parameter to ufuncs (allows the use of boolean arrays to choose
where a computation should be done)
* ``vectorize`` improvements (added 'excluded' and 'cache' keyword, general
cleanup and bug fixes)
* ``numpy.random.choice`` (random sample generating function)
In a future version of numpy, the functions np.diag, np.diagonal, and
the diagonal method of ndarrays will return a view onto the original
array, instead of producing a copy as they do now. This makes a
difference if you write to the array returned by any of these
functions. To facilitate this transition, numpy 1.7 produces a
FutureWarning if it detects that you may be attempting to write to
such an array. See the documentation for np.diagonal for details.
Similar to np.diagonal above, in a future version of numpy, indexing
a record array by a list of field names will return a view onto the
original array, instead of producing a copy as they do now. As with
np.diagonal, numpy 1.7 produces a FutureWarning if it detects
that you may be attemping to write to such an array. See the documentation
for array indexing for details.
The default casting rule for UFunc out= parameters has been changed from
'unsafe' to 'same_kind'. Most usages which violate the 'same_kind'
rule are likely bugs, so this change may expose previously undetected
errors in projects that depend on NumPy.
Full-array boolean indexing has been optimized to use a different,
optimized code path. This code path should produce the same results,
but any feedback about changes to your code would be appreciated.
Attempting to write to a read-only array (one with
``arr.flags.writeable`` set to ``False``) used to raise either a
RuntimeError, ValueError, or TypeError inconsistently, depending on
which code path was taken. It now consistently raises a ValueError.
The <ufunc>.reduce functions evaluate some reductions in a different
order than in previous versions of NumPy, generally providing higher
performance. Because of the nature of floating-point arithmetic, this
may subtly change some results, just as linking NumPy to a different
BLAS implementations such as MKL can.
If upgrading from 1.5, then generally in 1.6 and 1.7 there have been
substantial code added and some code paths altered, particularly in
the areas of type resolution and buffered iteration over universal
functions. This might have an impact on your code particularly if
you relied on accidental behavior in the past.
Reduction UFuncs Generalize axis= Parameter
Any ufunc.reduce function call, as well as other reductions like
sum, prod, any, all, max and min support the ability to choose
a subset of the axes to reduce over. Previously, one could say
axis=None to mean all the axes or axis=# to pick a single axis.
Now, one can also say axis=(#,#) to pick a list of axes for reduction.
Reduction UFuncs New keepdims= Parameter
There is a new keepdims= parameter, which if set to True, doesn't
throw away the reduction axes but instead sets them to have size one.
When this option is set, the reduction result will broadcast correctly
to the original operand which was reduced.
.. note:: The datetime API is *experimental* in 1.7.0, and may undergo changes
in future versions of NumPy.
There have been a lot of fixes and enhancements to datetime64 compared
to NumPy 1.6:
* the parser is quite strict about only accepting ISO 8601 dates, with a few
* converts between units correctly
* datetime arithmetic works correctly
* business day functionality (allows the datetime to be used in contexts where
only certain days of the week are valid)
The notes in `doc/source/reference/arrays.datetime.rst
(also available in the online docs at `arrays.datetime.html
<http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html>`_) should be
consulted for more details.
Custom formatter for printing arrays
See the new ``formatter`` parameter of the ``numpy.set_printoptions``
New function numpy.random.choice
A generic sampling function has been added which will generate samples from
a given array-like. The samples can be with or without replacement, and
with uniform or given non-uniform probabilities.
New function isclose
Returns a boolean array where two arrays are element-wise equal within a
tolerance. Both relative and absolute tolerance can be specified. The
function is NA aware.
Preliminary multi-dimensional support in the polynomial package
Axis keywords have been added to the integration and differentiation
functions and a tensor keyword was added to the evaluation functions.
These additions allow multi-dimensional coefficient arrays to be used in
those functions. New functions for evaluating 2-D and 3-D coefficient
arrays on grids or sets of points were added together with 2-D and 3-D
pseudo-Vandermonde matrices that can be used for fitting.
Ability to pad rank-n arrays
A pad module containing functions for padding n-dimensional arrays has
been added. The various private padding functions are exposed as options to
a public 'pad' function. Example::
pad(a, 5, mode='mean')
Current modes are ``constant``, ``edge``, ``linear_ramp``, ``maximum``,
``mean``, ``median``, ``minimum``, ``reflect``, ``symmetric``, ``wrap``, and
New argument to searchsorted
The function searchsorted now accepts a 'sorter' argument that is a
permuation array that sorts the array to search.
New function ``PyArray_RequireWriteable`` provides a consistent
interface for checking array writeability -- any C code which works
with arrays whose WRITEABLE flag is not known to be True a priori,
should make sure to call this function before writing.
NumPy C Style Guide added (``doc/C_STYLE_GUIDE.rst.txt``).
The function np.concatenate tries to match the layout of its input
arrays. Previously, the layout did not follow any particular reason,
and depended in an undesirable way on the particular axis chosen for
concatenation. A bug was also fixed which silently allowed out of bounds
The ufuncs logical_or, logical_and, and logical_not now follow Python's
behavior with object arrays, instead of trying to call methods on the
objects. For example the expression (3 and 'test') produces the string
'test', and now np.logical_and(np.array(3, 'O'), np.array('test', 'O'))
produces 'test' as well.
Specifying a custom string formatter with a `_format` array attribute is
deprecated. The new `formatter` keyword in ``numpy.set_printoptions`` or
``numpy.array2string`` can be used instead.
The deprecated imports in the polynomial package have been removed.
Direct access to the fields of PyArrayObject* has been deprecated. Direct
access has been recommended against for many releases. Expect similar
deprecations for PyArray_Descr* and other core objects in the future as
preparation for NumPy 2.0.
The macros in old_defines.h are deprecated and will be removed in the next
major release (>= 2.0). The sed script tools/replace_old_macros.sed can
be used to replace these macros with the newer versions.
You can test your code against the deprecated C API by #defining
NPY_NO_DEPRECATED_API to the target version number, for example
NPY_1_7_API_VERSION, before including any NumPy headers.
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