=========================
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 - 3.3 and is the last
release that supports Python 2.4 - 2.5.

Highlights
==========

* ``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)


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

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 attempting
to write to such an array. See the documentation for array indexing for
details.

In a future version of numpy, the default casting rule for UFunc out=
parameters will be changed from 'unsafe' to 'same_kind'. (This also applies
to in-place operations like a += b, which is equivalent to np.add(a, b,
out=a).) 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. In this version of numpy, such usages will continue to succeed,
but will raise a DeprecationWarning.

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.

New features
============

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.

Datetime support
----------------

.. 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
  convenience extensions
* 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 <https://github.com/numpy/numpy/blob/maintenance/1.7.x/doc/source/reference/arrays.datetime.rst>`_
(also available in the online docs at `arrays.datetime.html
<https://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``
function.

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.

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
``<function>``.


New argument to searchsorted
----------------------------

The function searchsorted now accepts a 'sorter' argument that is a
permutation array that sorts the array to search.

Build system
------------

Added experimental support for the AArch64 architecture.

C API
-----

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``).

Changes
=======

General
-------

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 axis arguments.

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.

The ``.base`` attribute on ndarrays, which is used on views to ensure that the
underlying array owning the memory is not deallocated prematurely, now
collapses out references when you have a view-of-a-view. For example::

    a = np.arange(10)
    b = a[1:]
    c = b[1:]

In numpy 1.6, ``c.base`` is ``b``, and ``c.base.base`` is ``a``. In numpy 1.7,
``c.base`` is ``a``.

To increase backwards compatibility for software which relies on the old
behaviour of ``.base``, we only 'skip over' objects which have exactly the same
type as the newly created view. This makes a difference if you use ``ndarray``
subclasses. For example, if we have a mix of ``ndarray`` and ``matrix`` objects
which are all views on the same original ``ndarray``::

    a = np.arange(10)
    b = np.asmatrix(a)
    c = b[0, 1:]
    d = c[0, 1:]

then ``d.base`` will be ``b``. This is because ``d`` is a ``matrix`` object,
and so the collapsing process only continues so long as it encounters other
``matrix`` objects. It considers ``c``, ``b``, and ``a`` in that order, and
``b`` is the last entry in that list which is a ``matrix`` object.

Casting Rules
-------------

Casting rules have undergone some changes in corner cases, due to the
NA-related work. In particular for combinations of scalar+scalar:

* the `longlong` type (`q`) now stays `longlong` for operations with any other
  number (`? b h i l q p B H I`), previously it was cast as `int_` (`l`). The
  `ulonglong` type (`Q`) now stays as `ulonglong` instead of `uint` (`L`).

* the `timedelta64` type (`m`) can now be mixed with any integer type (`b h i l
  q p B H I L Q P`), previously it raised `TypeError`.

For array + scalar, the above rules just broadcast except the case when
the array and scalars are unsigned/signed integers, then the result gets
converted to the array type (of possibly larger size) as illustrated by the
following examples::

    >>> (np.zeros((2,), dtype=np.uint8) + np.int16(257)).dtype
    dtype('uint16')
    >>> (np.zeros((2,), dtype=np.int8) + np.uint16(257)).dtype
    dtype('int16')
    >>> (np.zeros((2,), dtype=np.int16) + np.uint32(2**17)).dtype
    dtype('int32')

Whether the size gets increased depends on the size of the scalar, for
example::

    >>> (np.zeros((2,), dtype=np.uint8) + np.int16(255)).dtype
    dtype('uint8')
    >>> (np.zeros((2,), dtype=np.uint8) + np.int16(256)).dtype
    dtype('uint16')

Also a ``complex128`` scalar + ``float32`` array is cast to ``complex64``.

In NumPy 1.7 the `datetime64` type (`M`) must be constructed by explicitly
specifying the type as the second argument (e.g. ``np.datetime64(2000, 'Y')``).


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

General
-------

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.

``concatenate`` now raises DepractionWarning for 1D arrays if ``axis != 0``.
Versions of numpy < 1.7.0 ignored axis argument value for 1D arrays. We
allow this for now, but in due course we will raise an error.

C-API
-----

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 adding a line
composed of ``#define NPY_NO_DEPRECATED_API`` and the target version number,
such as ``NPY_1_7_API_VERSION``, before including any NumPy headers.

The ``NPY_CHAR`` member of the ``NPY_TYPES`` enum is deprecated and will be
removed in NumPy 1.8. See the discussion at
`gh-2801 <https://github.com/numpy/numpy/issues/2801>`_ for more details.
