path: root/doc/release/1.13.0-notes.rst

==========================
NumPy 1.13.0 Release Notes
==========================

This release supports Python 2.7 and 3.4 - 3.6.

Highlights
==========

 * Operations like ``a + b + c`` will reuse temporaries on some platforms,
   resulting in less memory use and faster execution.
 * Inplace operations check if inputs overlap outputs and create temporaries
   to avoid problems.
 * Improved ability for classes to override default ufunc behavior. See
   ``__array_ufunc__`` below.


Dropped Support
===============


Deprecations
============

* Calling ``np.fix``, ``np.isposinf``, and ``np.isneginf`` with ``f(x, y=out)``
  is deprecated - the argument should be passed as ``f(x, out=out)``, which
  matches other ufunc-like interfaces.
* Use of the C-API ``NPY_CHAR`` type number deprecated since version 1.7 will
  now raise deprecation warnings at runtime. Extensions built with older f2py
  versions need to be recompiled to remove the warning.
* ``np.ma.argsort`` should be called with an explicit `axis` argument when
  applied to arrays with more than 2 dimensions, as the default value of
  this argument (``None``) is inconsistent with the rest of numpy (``-1``).


Build System Changes
====================

 * ``numpy.distutils`` now automatically determines C-file dependencies with
   GCC compatible compilers.

Future Changes
==============


Compatibility notes
===================

Error type changes
------------------

* ``numpy.hstack()`` now throws ``ValueError`` instead of ``IndexError`` when
  input is empty.
* Functions taking an axis argument, when that argument is out of range, now
  throw ``np.AxisError`` instead of a mixture of ``IndexError`` and
  ``ValueError``. For backwards compatibility, ``AxisError`` subclasses both of
  these.

Tuple object dtypes
-------------------

Support has been removed for certain obscure dtypes that were unintentionally
allowed, of the form ``(old_dtype, new_dtype)``, where either of the dtypes
is or contains the ``object`` dtype. As an exception, dtypes of the form
``(object, [('name', object)])`` are still supported due to evidence of
existing use.

DeprecationWarning to error
---------------------------
See Changes section for more detail.

* ``partition``, TypeError when non-integer partition index is used.
* ``NpyIter_AdvancedNew``, ValueError when ``oa_ndim == 0`` and ``op_axes`` is NULL
* ``negative(bool_)``, TypeError when negative applied to booleans.
* ``subtract(bool_, bool_)``, TypeError when subtracting boolean from boolean.
* ``np.equal, np.not_equal``, object identity doesn't override failed comparison.
* ``np.equal, np.not_equal``, object identity doesn't override non-boolean comparison.
* Deprecated boolean indexing behavior dropped. See Changes below for details.
* Deprecated ``np.alterdot()`` and ``np.restoredot()`` removed.

FutureWarning to changed behavior
---------------------------------
See Changes section for more detail.

* ``numpy.average`` preserves subclasses
* ``array == None`` and ``array != None`` do element-wise comparison.
* ``np.equal, np.not_equal``, object identity doesn't override comparison result.

dtypes are now always true
--------------------------

Previously ``bool(dtype)`` would fall back to the default python
implementation, which checked if ``len(dtype) > 0``. Since ``dtype`` objects
implement ``__len__`` as the number of record fields, ``bool`` of scalar dtypes
would evaluate to ``False``, which was unintuitive. Now ``bool(dtype) == True``
for all dtypes.

``__getslice__`` and ``__setslice__`` are no longer needed in ``ndarray`` subclasses
------------------------------------------------------------------------------------
When subclassing np.ndarray in Python 2.7, it is no longer _necessary_ to
implement ``__*slice__`` on the derived class, as ``__*item__`` will intercept
these calls correctly.

Any code that did implement these will work exactly as before. Code that
invokes``ndarray.__getslice__`` (e.g. through ``super(...).__getslice__``) will
now issue a DeprecationWarning - ``.__getitem__(slice(start, end))`` should be
used instead.


C API
-----

GUfuncs on empty arrays and NpyIter axis removal
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
It is now allowed to remove a zero-sized axis from NpyIter. Which may mean
that code removing axes from NpyIter has to add an additional check when
accessing the removed dimensions later on.

The largest followup change is that gufuncs are now allowed to have zero-sized
inner dimensions. This means that a gufunc now has to anticipate an empty inner
dimension, while this was never possible and an error raised instead.

For most gufuncs no change should be necessary. However, it is now possible
for gufuncs with a signature such as ``(..., N, M) -> (..., M)`` to return
a valid result if ``N=0`` without further wrapping code.


New Features
============

``__array_ufunc__`` added
-------------------------
This is the renamed and redesigned ``__numpy_ufunc__``. Any class, ndarray
subclass or not, can define this method or set it to ``None`` in order to
override the behavior of NumPy's ufuncs. This works quite similarly to Python's
``__mul__`` and other binary operation routines. See the documentation for a
more detailed description of the implementation and behavior of this new
option. The API is provisional, we do not yet guarantee backward compatibility
as modifications may be made pending feedback.

``PyArray_MapIterArrayCopyIfOverlap`` added to NumPy C-API
----------------------------------------------------------
Similar to ``PyArray_MapIterArray`` but with an additional ``copy_if_overlap``
argument. If ``copy_if_overlap != 0``,  checks if input has memory overlap with
any of the other arrays and make copies as appropriate to avoid problems if the
input is modified during the iteration. See the documentation for more complete
documentation.

Temporary elision
-----------------
On platforms providing the ``backtrace`` function NumPy will now not create
temporaries in expression when possible.
For example ``d = a + b + c`` is transformed to ``d = a + b; d += c`` which can
improve performance for large arrays as less memory bandwidth is required to
perform the operation.

``axes`` argument for ``unique``
--------------------------------
In an N-dimensional array, the user can now choose the axis along which to look
for duplicate N-1-dimensional elements using ``numpy.unique``. The original
behaviour is recovered if ``axis=None`` (default).

``isin`` function, improving on ``in1d``
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
The new function ``isin`` tests whether each element of an N-dimensonal
array is present anywhere within a second array. It is an enhancement
of ``in1d`` that preserves the shape of the first array.

``np.gradient`` now supports unevenly spaced data
-------------------------------------------------
Users can now specify a not-constant spacing for data.
In particular ``np.gradient`` can now take:

1. A single scalar to specify a sample distance for all dimensions.
2. N scalars to specify a constant sample distance for each dimension.
   i.e. ``dx``, ``dy``, ``dz``, ...
3. N arrays to specify the coordinates of the values along each dimension of F.
   The length of the array must match the size of the corresponding dimension
4. Any combination of N scalars/arrays with the meaning of 2. and 3.

This means that, e.g., it is now possible to do the following::

    >>> f = np.array([[1, 2, 6], [3, 4, 5]], dtype=np.float)
    >>> dx = 2.
    >>> y = [1., 1.5, 3.5]
    >>> np.gradient(f, dx, y)
    [array([[ 1. ,  1. , -0.5], [ 1. ,  1. , -0.5]]),
     array([[ 2. ,  2. ,  2. ], [ 2. ,  1.7,  0.5]])]

``np.heaviside`` computes the Heaviside function
------------------------------------------------
The new function ``np.heaviside(x, h0)`` (a ufunc) computes the Heaviside
function:
.. code::
                       { 0   if x < 0,
    heaviside(x, h0) = { h0  if x == 0,
                       { 1   if x > 0.

Support for returning arrays of arbitrary dimensions in ``apply_along_axis``
----------------------------------------------------------------------------
Previously, only scalars or 1D arrays could be returned by the function passed
to ``apply_along_axis``. Now, it can return an array of any dimensionality
(including 0D), and the shape of this array replaces the axis of the array
being iterated over.

``.ndim`` property added to ``dtype`` to complement ``.shape``
--------------------------------------------------------------
For consistency with ``ndarray`` and ``broadcast``, ``d.ndim`` is a shorthand
for ``len(d.shape)``.

``np.block`` function for creating blocked arrays
-------------------------------------------------
Add a new ``block`` function to the current stacking functions ``vstack``,
``hstack``, and ``stack``. This allows concatenation across multiple axes
simultaneously, with a similar syntax to array creation, but where elements
can themselves be arrays. For instance::

    >>> A = np.eye(2) * 2
    >>> B = np.eye(3) * 3
    >>> np.block([
    ...     [A,               np.zeros((2, 3))],
    ...     [np.ones((3, 2)), B               ]
    ... ])
    array([[ 2.,  0.,  0.,  0.,  0.],
           [ 0.,  2.,  0.,  0.,  0.],
           [ 1.,  1.,  3.,  0.,  0.],
           [ 1.,  1.,  0.,  3.,  0.],
           [ 1.,  1.,  0.,  0.,  3.]])

While primarily useful for block matrices, this works for arbitrary dimensions
of arrays.

It is similar to Matlab's square bracket notation for creating block matrices.

Support for tracemalloc in Python 3.6
-------------------------------------
NumPy now supports memory tracing with tracemalloc_ module of Python 3.6 or
newer. Memory allocations from NumPy are placed into the domain defined by
``numpy.lib.tracemalloc_domain``.
Note that NumPy allocation will not show up in tracemalloc_ of earlier Python
versions.

.. _tracemalloc: https://docs.python.org/3/library/tracemalloc.html

NumPy may be built with relaxed stride checking debugging
---------------------------------------------------------
Setting NPY_RELAXED_STRIDES_DEBUG=1 in the enviroment when relaxed stride
checking is enabled will cause NumPy to be compiled with the affected strides
set to the maximum value of npy_intp in order to help detect invalid usage of
the strides in downstream projects. When enabled, invalid usage often results
in an error being raised, but the exact type of error depends on the details of
the code. TypeError and OverflowError have been observed in the wild.

It was previously the case that this option was disabled for releases and
enabled in master and changing between the two required editing the code. It is
now disabled by default but can be enabled for test builds.


Improvements
============

Partial support for 64-bit f2py extensions with MinGW
-----------------------------------------------------
Extensions that incorporate Fortran libraries can now be built using the free
MinGW_ toolset, also under Python 3.5. This works best for extensions that only
do calculations and uses the runtime modestly (reading and writing from files,
for instance). Note that this does not remove the need for Mingwpy; if you make
extensive use of the runtime, you will most likely run into issues_. Instead,
it should be regarded as a band-aid until Mingwpy is fully functional.

Extensions can also be compiled using the MinGW toolset using the runtime
library from the (moveable) WinPython 3.4 distribution, which can be useful for
programs with a PySide1/Qt4 front-end.

.. _MinGW: https://sf.net/projects/mingw-w64/files/Toolchains%20targetting%20Win64/Personal%20Builds/mingw-builds/6.2.0/threads-win32/seh/

.. _issues: https://mingwpy.github.io/issues.html

Performance improvements for ``packbits`` and ``unpackbits``
------------------------------------------------------------
The functions ``numpy.packbits`` with boolean input and ``numpy.unpackbits`` have
been optimized to be a significantly faster for contiguous data.

Fix for PPC long double floating point information
--------------------------------------------------
In previous versions of NumPy, the ``finfo`` function returned invalid
information about the `double double`_ format of the ``longdouble`` float type
on Power PC (PPC).  The invalid values resulted from the failure of the NumPy
algorithm to deal with the variable number of digits in the significand
that are a feature of `PPC long doubles`.  This release by-passes the failing
algorithm by using heuristics to detect the presence of the PPC double double
format.  A side-effect of using these heuristics is that the ``finfo``
function is faster than previous releases.

.. _PPC long doubles: https://www.ibm.com/support/knowledgecenter/en/ssw_aix_71/com.ibm.aix.genprogc/128bit_long_double_floating-point_datatype.htm

.. _double double: https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic

Better default repr for ``ndarray`` subclasses
----------------------------------------------
Subclasses of ndarray with no ``repr`` specialization now correctly indent
their data and type lines.

More reliable comparisons of masked arrays
------------------------------------------
Comparisons of masked arrays were buggy for masked scalars and failed for
structured arrays with dimension higher than one. Both problems are now
solved. In the process, it was ensured that in getting the result for a
structured array, masked fields are properly ignored, i.e., the result is equal
if all fields that are non-masked in both are equal, thus making the behaviour
identical to what one gets by comparing an unstructured masked array and then
doing ``.all()`` over some axis.

np.matrix with booleans elements can now be created using the string syntax
---------------------------------------------------------------------------
``np.matrix`` failed whenever one attempts to use it with booleans, e.g.,
``np.matrix('True')``. Now, this works as expected.

More ``linalg`` operations now accept empty vectors and matrices
----------------------------------------------------------------
All of the following functions in ``np.linalg`` now work when given input
arrays with a 0 in the last two dimensions: ``det``, ``slogdet``, ``pinv``,
``eigvals``, ``eigvalsh``, ``eig``, ``eigh``.

Bundled version of LAPACK is now 3.2.2
--------------------------------------
NumPy comes bundled with a minimal implementation of lapack for systems without
a lapack library installed, under the name of ``lapack_lite``. This has been
upgraded from LAPACK 3.0.0 (June 30, 1999) to LAPACK 3.2.2 (June 30, 2010). See
the `LAPACK changelogs`_ for details on the all the changes this entails.

While no new features are exposed through ``numpy``, this fixes some bugs
regarding "workspace" sizes, and in some places may use faster algorithms.

.. _`LAPACK changelogs`: http://www.netlib.org/lapack/release_notes.html#_4_history_of_lapack_releases

``reduce`` of ``np.hypot.reduce`` and ``np.logical_xor`` allowed in more cases
------------------------------------------------------------------------------
This now works on empty arrays, returning 0, and can reduce over multiple axes.
Previously, a ``ValueError`` was thrown in these cases.

New ``positive`` ufunc
----------------------
This ufunc corresponds to unary `+`, but unlike `+` on an ndarray it will raise
an error if array values do not support numeric operations.

New ``divmod`` ufunc
--------------------
This ufunc corresponds to the Python builtin `divmod`. ``np.divmod(x, y)``
calculates a result equivalent to
``(np.floor_divide(x, y), np.remainder(x, y))`` but faster.

Better ``repr`` of object arrays
--------------------------------
Object arrays that contain themselves no longer cause a recursion error.

Object arrays that contain ``list`` objects are now printed in a way that makes
clear the difference between a 2d object array, and a 1d object array of lists.

Changes
=======

Ufunc behavior for overlapping inputs
-------------------------------------

Operations where ufunc input and output operands have memory overlap
produced undefined results in previous NumPy versions, due to data
dependency issues. In NumPy 1.13.0, results from such operations are
now defined to be the same as for equivalent operations where there is
no memory overlap.

Operations affected now make temporary copies, as needed to eliminate
data dependency. As detecting these cases is computationally
expensive, a heuristic is used, which may in rare cases result to
needless temporary copies.  For operations where the data dependency
is simple enough for the heuristic to analyze, temporary copies will
not be made even if the arrays overlap, if it can be deduced copies
are not necessary.  As an example,``np.add(a, b, out=a)`` will not
involve copies.

To illustrate a previously undefined operation::

    >>> x = np.arange(16).astype(float)
    >>> np.add(x[1:], x[:-1], out=x[1:])

In NumPy 1.13.0 the last line is guaranteed to be equivalent to::

    >>> np.add(x[1:].copy(), x[:-1].copy(), out=x[1:])

A similar operation with simple non-problematic data dependence is::

    >>> x = np.arange(16).astype(float)
    >>> np.add(x[1:], x[:-1], out=x[:-1])

It will continue to produce the same results as in previous NumPy
versions, and will not involve unnecessary temporary copies.

The change applies also to in-place binary operations, for example::

    >>> x = np.random.rand(500, 500)
    >>> x += x.T

This statement is now guaranteed to be equivalent to ``x[...] = x + x.T``,
whereas in previous NumPy versions the results were undefined.

``argsort`` on masked arrays takes the same default arguments as ``sort``
-------------------------------------------------------------------------
By default, ``argsort`` now places the masked values at the end of the sorted
array, in the same way that ``sort`` already did. Additionally, the
``end_with`` argument is added to ``argsort``, for consistency with ``sort``.
Note that this argument is not added at the end, so breaks any code that
passed ``fill_value`` as a positional argument.

``average`` now preserves subclasses
------------------------------------
For ndarray subclasses, ``numpy.average`` will now return an instance of the
subclass, matching the behavior of most other NumPy functions such as ``mean``.
As a consequence, also calls that returned a scalar may now return a subclass
array scalar.

``array == None`` and ``array != None`` do element-wise comparison
------------------------------------------------------------------
Previously these operations returned scalars ``False`` and ``True`` respectively.

``np.equal, np.not_equal`` for object arrays ignores object identity
--------------------------------------------------------------------
Previously, these functions always treated identical objects as equal. This had
the effect of overriding comparison failures, comparison of objects that did
not return booleans, such as np.arrays, and comparison of objects where the
results differed from object identity, such as NaNs.

Boolean indexing changes
------------------------
* Boolean array-likes (such as lists of python bools) are always treated as
  boolean indexes.

* Boolean scalars (including python ``True``) are legal boolean indexes and
  never treated as integers.

* Boolean indexes must match the dimension of the axis that they index.

* Boolean indexes used on the lhs of an assignment must match the dimensions of
  the rhs.

* Boolean indexing into scalar arrays return a new 1-d array.  This means that
  ``array(1)[array(True)]`` gives ``array([1])`` and not the original array.

``np.random.multivariate_normal`` behavior with bad covariance matrix
---------------------------------------------------------------------

It is now possible to adjust the behavior the function will have when dealing
with the covariance matrix by using two new keyword arguments:

* ``tol`` can be used to specify a tolerance to use when checking that
  the covariance matrix is positive semidefinite.

* ``check_valid`` can be used to configure what the function will do in the
  presence of a matrix that is not positive semidefinite. Valid options are
  ``ignore``, ``warn`` and ``raise``. The default value, ``warn`` keeps the
  the behavior used on previous releases.

``assert_array_less`` compares ``np.inf`` and ``-np.inf`` now
-------------------------------------------------------------
Previously, ``np.testing.assert_array_less`` ignored all infinite values. This
is not the expected behavior both according to documentation and intuitively.
Now, -inf < x < inf is considered ``True`` for any real number x and all
other cases fail.

``offset`` attribute value in ``memmap`` objects
------------------------------------------------
The ``offset`` attribute in a ``memmap`` object is now set to the
offset into the file. This is a behaviour change only for offsets
greater than ``mmap.ALLOCATIONGRANULARITY``.

``np.real`` and ``np.imag`` return scalars for scalar inputs
------------------------------------------------------------
Previously, ``np.real`` and ``np.imag`` used to return array objects when
provided a scalar input, which was inconsistent with other functions like
``np.angle`` and ``np.conj``.

The polynomial convenience classes cannot be passed to ufuncs
-------------------------------------------------------------
The ABCPolyBase class, from which the convenience classes are derived, sets
``__array_ufun__ = None`` in order of opt out of ufuncs. If a polynomial
convenience class instance is passed as an argument to a ufunc, a ``TypeError``
will now be raised.