NumPy 1.13.2

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA1

==========================
NumPy 1.13.2 Release Notes

This is a bugfix release for some problems found since 1.13.1. The most
important fixes are for CVE-2017-12852 and temporary elision. Users of earlier
versions of 1.13 should upgrade.

The Python versions supported are 2.7 and 3.4 - 3.6. The Python 3.6 wheels
available from PIP are built with Python 3.6.2 and should be compatible with
all previous versions of Python 3.6. The Windows wheels are now built
with OpenBlas instead ATLAS, which should improve the performance of the linear
algebra functions.

Contributors

A total of 12 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Brandon Carter
• Charles Harris
• Eric Wieser
• Iryna Shcherbina +
• James Bourbeau +
• Jonathan Helmus
• Julian Taylor
• Matti Picus
• Michael Lamparski +
• Michael Seifert
• Ralf Gommers

Pull requests merged

A total of 20 pull requests were merged for this release.

• #9390 BUG: Return the poly1d coefficients array directly
• #9555 BUG: Fix regression in 1.13.x in distutils.mingw32ccompiler.
• #9556 BUG: Fix true_divide when dtype=np.float64 specified.
• #9557 DOC: Fix some rst markup in numpy/doc/basics.py.
• #9558 BLD: Remove -xhost flag from IntelFCompiler.
• #9559 DOC: Removes broken docstring example (source code, png, pdf)...
• #9580 BUG: Add hypot and cabs functions to WIN32 blacklist.
• #9732 BUG: Make scalar function elision check if temp is writeable.
• #9736 BUG: Various fixes to np.gradient
• #9742 BUG: Fix np.pad for CVE-2017-12852
• #9744 BUG: Check for exception in sort functions, add tests
• #9745 DOC: Add whitespace after "versionadded::" directive so it actually...
• #9746 BUG: Memory leak in np.dot of size 0
• #9747 BUG: Adjust gfortran version search regex
• #9757 BUG: Cython 0.27 breaks NumPy on Python 3.
• #9764 BUG: Ensure _npy_scaled_cexp{,f,l} is defined when needed.
• #9765 BUG: PyArray_CountNonzero does not check for exceptions
• #9766 BUG: Fixes histogram monotonicity check for unsigned bin values
• #9767 BUG: Ensure consistent result dtype of count_nonzero
• #9771 BUG, MAINT: Fix mtrand for Cython 0.27.

Checksums

MD5

---

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SHA256

---

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v1.13.1

==========================
NumPy 1.13.1 Release Notes

This is a bugfix release for problems found in 1.13.0. The major changes are
fixes for the new memory overlap detection and temporary elision as well as
reversion of the removal of the boolean binary - operator. Users of 1.13.0
should upgrade.

Thr Python versions supported are 2.7 and 3.4 - 3.6. Note that the Python 3.6
wheels available from PIP are built against 3.6.1, hence will not work when
used with 3.6.0 due to Python bug 29943_. NumPy 1.13.2 will be released shortly
after Python 3.6.2 is out to fix that problem. If you are using 3.6.0 the
workaround is to upgrade to 3.6.1 or use an earlier Python version.

.. _#29943: https://bugs.python.org/issue29943

Pull requests merged

A total of 19 pull requests were merged for this release.

• #9240 DOC: BLD: fix lots of Sphinx warnings/errors.
• #9255 Revert "DEP: Raise TypeError for subtract(bool_, bool_)."
• #9261 BUG: don't elide into readonly and updateifcopy temporaries for...
• #9262 BUG: fix missing keyword rename for common block in numpy.f2py
• #9263 BUG: handle resize of 0d array
• #9267 DOC: update f2py front page and some doc build metadata.
• #9299 BUG: Fix Intel compilation on Unix.
• #9317 BUG: fix wrong ndim used in empty where check
• #9319 BUG: Make extensions compilable with MinGW on Py2.7
• #9339 BUG: Prevent crash if ufunc doc string is null
• #9340 BUG: umath: un-break ufunc where= when no out= is given
• #9371 DOC: Add isnat/positive ufunc to documentation
• #9372 BUG: Fix error in fromstring function from numpy.core.records...
• #9373 BUG: ')' is printed at the end pointer of the buffer in numpy.f2py.
• #9374 DOC: Create NumPy 1.13.1 release notes.
• #9376 BUG: Prevent hang traversing ufunc userloop linked list
• #9377 DOC: Use x1 and x2 in the heaviside docstring.
• #9378 DOC: Add $PARAMS to the isnat docstring
• #9379 DOC: Update the 1.13.1 release notes

Contributors

A total of 12 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Andras Deak +
• Bob Eldering +
• Charles Harris
• Daniel Hrisca +
• Eric Wieser
• Joshua Leahy +
• Julian Taylor
• Michael Seifert
• Pauli Virtanen
• Ralf Gommers
• Roland Kaufmann
• Warren Weckesser

Checksums

MD5

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SHA256

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v1.13.0

==========================
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.
• New __array_ufunc__ attribute provides improved ability for classes to
  override default ufunc behavior.
• New np.block function for creating blocked arrays.

New functions

• New np.positive ufunc.
• New np.divmod ufunc provides more efficient divmod.
• New np.isnat ufunc tests for NaT special values.
• New np.heaviside ufunc computes the Heaviside function.
• New np.isin function, improves on in1d.
• New np.block function for creating blocked arrays.
• New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.

See below for details.

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, np.ma.minimum.reduce, and np.ma.maximum.reduce
  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, 0, and 0, respectively).
• np.ma.MaskedArray.mini is deprecated, as it almost duplicates the
  functionality of np.MaskedArray.min. Exactly equivalent behaviour
  can be obtained with np.ma.minimum.reduce.
• The single-argument form of np.ma.minimum and np.ma.maximum is
  deprecated. np.maximum. np.ma.minimum(x) should now be spelt
  np.ma.minimum.reduce(x), which is consistent with how this would be done
  with np.minimum.
• Calling ndarray.conjugate on non-numeric dtypes is deprecated (it
  should match the behavior of np.conjugate, which throws an error).
• Calling expand_dims when the axis keyword does not satisfy
  -a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped,
  is deprecated.

Future Changes

• Assignment between structured arrays with different field names will change
  in NumPy 1.14. Previously, fields in the dst would be set to the value of the
  identically-named field in the src. In numpy 1.14 fields will instead be
  assigned 'by position': The n-th field of the dst will be set to the n-th
  field of the src array. Note that the FutureWarning raised in NumPy 1.12
  incorrectly reported this change as scheduled for NumPy 1.13 rather than
  NumPy 1.14.

Build System Changes

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

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
invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will
now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be
used instead.

Indexing MaskedArrays/Constants with ... (ellipsis) now returns MaskedArray

This behavior mirrors that of np.ndarray, and accounts for nested arrays in
MaskedArrays of object dtype, and ellipsis combined with other forms of
indexing.

C API changes

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.

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.

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. See the NEP_ and
documentation_ for more details.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst
.. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst

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, and is used to implement
divmod when called on numpy arrays. np.divmod(x, y) calculates a result
equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is
approximately twice as fast as calling the functions separately.

np.isnat ufunc tests for NaT special datetime and timedelta values

The new ufunc np.isnat finds the positions of special NaT values
within datetime and timedelta arrays. This is analogous to np.isnan.

np.heaviside ufunc 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.

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 ...

v1.13.0rc2

==========================
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.
• New __array_ufunc__ attribute provides improved ability for classes to
  override default ufunc behavior.
• New np.block function for creating blocked arrays.

New functions

• New np.positive ufunc.
• New np.divmod ufunc provides more efficient divmod.
• New np.isnat ufunc tests for NaT special values.
• New np.heaviside ufunc computes the Heaviside function.
• New np.isin function, improves on in1d.
• New np.block function for creating blocked arrays.
• New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.

See below for details.

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, np.ma.minimum.reduce, and np.ma.maximum.reduce
  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, 0, and 0, respectively).
• np.ma.MaskedArray.mini is deprecated, as it almost duplicates the
  functionality of np.MaskedArray.min. Exactly equivalent behaviour
  can be obtained with np.ma.minimum.reduce.
• The single-argument form of np.ma.minimum and np.ma.maximum is
  deprecated. np.maximum. np.ma.minimum(x) should now be spelt
  np.ma.minimum.reduce(x), which is consistent with how this would be done
  with np.minimum.
• Calling ndarray.conjugate on non-numeric dtypes is deprecated (it
  should match the behavior of np.conjugate, which throws an error).
• Calling expand_dims when the axis keyword does not satisfy
  -a.ndim - 1 <= axis <= a.ndim, where a is the array being reshaped,
  is deprecated.

Future Changes

• Assignment between structured arrays with different field names will change
  in NumPy 1.14. Previously, fields in the dst would be set to the value of the
  identically-named field in the src. In numpy 1.14 fields will instead be
  assigned 'by position': The n-th field of the dst will be set to the n-th
  field of the src array. Note that the FutureWarning raised in NumPy 1.12
  incorrectly reported this change as scheduled for NumPy 1.13 rather than
  NumPy 1.14.

Build System Changes

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

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
invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will
now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be
used instead.

Indexing MaskedArrays/Constants with ... (ellipsis) now returns MaskedArray

This behavior mirrors that of np.ndarray, and accounts for nested arrays in
MaskedArrays of object dtype, and ellipsis combined with other forms of
indexing.

C API changes

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.

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.

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. See the NEP_ and
documentation_ for more details.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst
.. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst

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, and is used to implement
divmod when called on numpy arrays. np.divmod(x, y) calculates a result
equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is
approximately twice as fast as calling the functions separately.

np.isnat ufunc tests for NaT special datetime and timedelta values

The new ufunc np.isnat finds the positions of special NaT values
within datetime and timedelta arrays. This is analogous to np.isnan.

np.heaviside ufunc 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.

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 ...

v1.13.0rc1

==========================
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.
• New __array_ufunc__ attribute provides improved ability for classes to
  override default ufunc behavior.
• New np.block function for creating blocked arrays.

New functions

• New np.positive ufunc.
• New np.divmod ufunc provides more efficient divmod.
• New np.isnat ufunc tests for NaT special values.
• New np.heaviside ufunc computes the Heaviside function.
• New np.isin function, improves on in1d.
• New np.block function for creating blocked arrays.
• New PyArray_MapIterArrayCopyIfOverlap added to NumPy C-API.

See below for details.

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, np.ma.minimum.reduce, and np.ma.maximum.reduce
  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, 0, and 0, respectively).
• np.ma.MaskedArray.mini is deprecated, as it almost duplicates the
  functionality of np.MaskedArray.min. Exactly equivalent behaviour
  can be obtained with np.ma.minimum.reduce.
• The single-argument form of np.ma.minimum and np.ma.maximum is
  deprecated. np.maximum. np.ma.minimum(x) should now be spelt
  np.ma.minimum.reduce(x), which is consistent with how this would be done
  with np.minimum.
• Calling ndarray.conjugate on non-numeric dtypes is deprecated (it
  should match the behavior of np.conjugate, which throws an error).

Future Changes

• Assignment between structured arrays with different field names will change
  in NumPy 1.14. Previously, fields in the dst would be set to the value of the
  identically-named field in the src. In numpy 1.14 fields will instead be
  assigned 'by position': The n-th field of the dst will be set to the n-th
  field of the src array. Note that the FutureWarning raised in NumPy 1.12
  incorrectly reported this change as scheduled for NumPy 1.13 rather than
  NumPy 1.14.

Build System Changes

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

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
invokesndarray.__getslice__ (e.g. through super(...).__getslice__) will
now issue a DeprecationWarning - .__getitem__(slice(start, end)) should be
used instead.

C API changes

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.

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.

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. See the NEP_ and
documentation_ for more details.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/ufunc-overrides.rst
.. _documentation: https://github.com/charris/numpy/blob/master/doc/source/reference/arrays.classes.rst

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, and is used to implement
divmod when called on numpy arrays. np.divmod(x, y) calculates a result
equivalent to (np.floor_divide(x, y), np.remainder(x, y)) but is
approximately twice as fast as calling the functions separately.

np.isnat ufunc tests for NaT special datetime and timedelta values

The new ufunc np.isnat finds the positions of special NaT values
within datetime and timedelta arrays. This is analogous to np.isnan.

np.heaviside ufunc 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.

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 ...

v1.12.1

==========================
NumPy 1.12.1 Release Notes

NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions
found in NumPy 1.12.0. In particular, the regression in f2py constant parsing
is fixed. Wheels for Linux, Windows, and OSX can be found on pypi,

Contributors

A total of 10 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Eric Wieser
• Greg Young
• Joerg Behrmann +
• John Kirkham
• Julian Taylor
• Marten van Kerkwijk
• Matthew Brett
• Shota Kawabuchi
• Jean Utke +

Fixes Backported

• #8483: BUG: Fix wrong future nat warning and equiv type logic error...
• #8489: BUG: Fix wrong masked median for some special cases
• #8490: DOC: Place np.average in inline code
• #8491: TST: Work around isfinite inconsistency on i386
• #8494: BUG: Guard against replacing constants without '_' spec in f2py.
• #8524: BUG: Fix mean for float 16 non-array inputs for 1.12
• #8571: BUG: Fix calling python api with error set and minor leaks for...
• #8602: BUG: Make iscomplexobj compatible with custom dtypes again
• #8618: BUG: Fix undefined behaviour induced by bad __array_wrap__
• #8648: BUG: Fix MaskedArray.__setitem__
• #8659: BUG: PPC64el machines are POWER for Fortran in f2py
• #8665: BUG: Look up methods on MaskedArray in _frommethod
• #8674: BUG: Remove extra digit in binary_repr at limit
• #8704: BUG: Fix deepcopy regression for empty arrays.
• #8707: BUG: Fix ma.median for empty ndarrays

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SHA256

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v1.12.1rc1

==========================
NumPy 1.12.1 Release Notes

NumPy 1.12.1 supports Python 2.7 and 3.4 - 3.6 and fixes bugs and regressions
found in NumPy 1.12.0. In particular, the regression in f2py constant parsing
is fixed. Wheels for Linux, Windows, and OSX can be found on pypi,

Contributors

A total of 10 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Eric Wieser
• Greg Young
• Joerg Behrmann +
• John Kirkham
• Julian Taylor
• Marten van Kerkwijk
• Matthew Brett
• Shota Kawabuchi
• Jean Utke +

Fixes Backported

• #8483: BUG: Fix wrong future nat warning and equiv type logic error...
• #8489: BUG: Fix wrong masked median for some special cases
• #8490: DOC: Place np.average in inline code
• #8491: TST: Work around isfinite inconsistency on i386
• #8494: BUG: Guard against replacing constants without '_' spec in f2py.
• #8524: BUG: Fix mean for float 16 non-array inputs for 1.12
• #8571: BUG: Fix calling python api with error set and minor leaks for...
• #8602: BUG: Make iscomplexobj compatible with custom dtypes again
• #8618: BUG: Fix undefined behaviour induced by bad __array_wrap__
• #8648: BUG: Fix MaskedArray.__setitem__
• #8659: BUG: PPC64el machines are POWER for Fortran in f2py
• #8665: BUG: Look up methods on MaskedArray in _frommethod
• #8674: BUG: Remove extra digit in binary_repr at limit
• #8704: BUG: Fix deepcopy regression for empty arrays.
• #8707: BUG: Fix ma.median for empty ndarrays

Checksums

MD5

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SHA256

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v1.12.0

NumPy 1.12.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

The NumPy 1.12.0 release contains a large number of fixes and improvements, but
few that stand out above all others. That makes picking out the highlights
somewhat arbitrary but the following may be of particular interest or indicate
areas likely to have future consequences.

• Order of operations in np.einsum can now be optimized for large speed improvements.
• New signature argument to np.vectorize for vectorizing with core dimensions.
• The keepdims argument was added to many functions.
• New context manager for testing warnings
• Support for BLIS in numpy.distutils
• Much improved support for PyPy (not yet finished)

Dropped Support

• Support for Python 2.6, 3.2, and 3.3 has been dropped.

Added Support

• Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer
  updateifcopy is not supported yet), this is a milestone for PyPy's
  C-API compatibility layer.

Build System Changes

• Library order is preserved, instead of being reordered to match that of
  the directories.

Deprecations

Assignment of ndarray object's data attribute

Assigning the 'data' attribute is an inherently unsafe operation as pointed
out in gh-7083. Such a capability will be removed in the future.

Unsafe int casting of the num attribute in linspace

np.linspace now raises DeprecationWarning when num cannot be safely
interpreted as an integer.

Insufficient bit width parameter to binary_repr

If a 'width' parameter is passed into binary_repr that is insufficient to
represent the number in base 2 (positive) or 2's complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the
future.

Future Changes

• In 1.13 NAT will always compare False except for NAT != NAT,
  which will be True. In short, NAT will behave like NaN
• In 1.13 np.average will preserve subclasses, to match the behavior of most
  other numpy functions such as np.mean. In particular, this means calls which
  returned a scalar may return a 0-d subclass object instead.

Multiple-field manipulation of structured arrays

In 1.13 the behavior of structured arrays involving multiple fields will change
in two ways:

First, indexing a structured array with multiple fields (eg,
arr[['f1', 'f3']]) will return a view into the original array in 1.13,
instead of a copy. Note the returned view will have extra padding bytes
corresponding to intervening fields in the original array, unlike the copy in
1.12, which will affect code such as arr[['f1', 'f3']].view(newdtype).

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays
occurs "by field name": Fields in the destination array are set to the
identically-named field in the source array or to 0 if the source does not have
a field::

>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')])
>>> b[:] = a
>>> b
array([(0, 2, 1), (0, 4, 3)],
      dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])

In 1.13 assignment will instead occur "by position": The Nth field of the
destination will be set to the Nth field of the source regardless of field
name. The old behavior can be obtained by using indexing to reorder the fields
before
assignment, e.g., b[['x', 'y']] = a[['y', 'x']].

Compatibility notes

DeprecationWarning to error

• Indexing with floats raises IndexError,
  e.g., a[0, 0.0].
• Indexing with non-integer array_like raises IndexError,
  e.g., a['1', '2']
• Indexing with multiple ellipsis raises IndexError,
  e.g., a[..., ...].
• Non-integers used as index values raise TypeError,
  e.g., in reshape, take, and specifying reduce axis.

FutureWarning to changed behavior

• np.full now returns an array of the fill-value's dtype if no dtype is
  given, instead of defaulting to float.
• np.average will emit a warning if the argument is a subclass of ndarray,
  as the subclass will be preserved starting in 1.13. (see Future Changes)

power and ** raise errors for integer to negative integer powers

The previous behavior depended on whether numpy scalar integers or numpy
integer arrays were involved.

For arrays

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers returned zero when raised to negative integer powers.

For scalars

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers sometimes returned zero, sometimes the
  correct float depending on the integer type combination.

All of these cases now raise a ValueError except for those integer
combinations whose common type is float, for instance uint64 and int8. It was
felt that a simple rule was the best way to go rather than have special
exceptions for the integer units. If you need negative powers, use an inexact
type.

Relaxed stride checking is the default

This will have some impact on code that assumed that F_CONTIGUOUS and
C_CONTIGUOUS were mutually exclusive and could be set to determine the
default order for arrays that are now both.

The np.percentile 'midpoint' interpolation method fixed for exact indices

The 'midpoint' interpolator now gives the same result as 'lower' and 'higher' when
the two coincide. Previous behavior of 'lower' + 0.5 is fixed.

keepdims kwarg is passed through to user-class methods

numpy functions that take a keepdims kwarg now pass the value
through to the corresponding methods on ndarray sub-classes. Previously the
keepdims keyword would be silently dropped. These functions now have
the following behavior:

1. If user does not provide keepdims, no keyword is passed to the underlying
   method.
2. Any user-provided value of keepdims is passed through as a keyword
   argument to the method.

This will raise in the case where the method does not support a
keepdims kwarg and the user explicitly passes in keepdims.

The following functions are changed: sum, product,
sometrue, alltrue, any, all, amax, amin,
prod, mean, std, var, nanmin, nanmax,
nansum, nanprod, nanmean, nanmedian, nanvar,
nanstd

bitwise_and identity changed

The previous identity was 1, it is now -1. See entry in Improvements_ for
more explanation.

ma.median warns and returns nan when unmasked invalid values are encountered

Similar to unmasked median the masked median ma.median now emits a Runtime
warning and returns NaN in slices where an unmasked NaN is present.

Greater consistancy in assert_almost_equal

The precision check for scalars has been changed to match that for arrays. It
is now::

abs(actual - desired) < 1.5 * 10**(-decimal)

Note that this is looser than previously documented, but agrees with the
previous implementation used in assert_array_almost_equal. Due to the
change in implementation some very delicate tests may fail that did not
fail before.

NoseTester behaviour of warnings during testing

When raise_warnings="develop" is given, all uncaught warnings will now
be considered a test failure. Previously only selected ones were raised.
Warnings which are not caught or raised (mostly when in release mode)
will be shown once during the test cycle similar to the default python
settings.

assert_warns and deprecated decorator more specific

The assert_warns function and context manager are now more specific
to the given warning category. This increased specificity leads to them
being handled according to the outer warning settings. This means that
no warning may be raised in cases where a wrong category warning is given
and ignored outside the context. Alternatively the increased specificity
may mean that warnings that were incorrectly ignored will now be shown
or raised. See also the new suppress_warnings context manager.
The same is true for the deprecated decorator.

C API

No changes.

New Features

Writeable keyword argument for as_strided

np.lib.stride_tricks.as_strided now has a writeable
keyword argument. It can be set to False when no write operation
to the returned array is expected to avoid accidental
unpredictable writes.

axes keyword argument for rot90

The axes keyword argument in rot90 determines the plane in which the
array is rotated. It defaults to axes=(0,1) as in the originial function.

Generalized flip

flipud and fliplr reverse the elements of an array along axis=0 and
axis=1 respectively. The newly added flip function reverses the elements of
an array along any given axis.

• np.count_nonzero now has an axis parameter, allowing
  non-zero counts to be generated on more than just a flattened
  array object.

BLIS support in numpy.distutils

Building against the BLAS implementation provided by the BLIS library is now
supported. See the [blis] section in site.cfg.example (in the root of
the numpy repo or source distribution).

Hook in numpy/__init__.py to run distribution-specific checks

Binary distributions of numpy may need to run specific hardware checks or load
specific libraries during numpy initialization. For example, if we are
distributing numpy with a BLAS library that requires SSE2 instructions, we
would like to check the machine on which numpy is running does have SSE2 in
order to give an informative error.

Add a hook in numpy/__init__.py to import a numpy/_distributor_init.py
file that will remain empty (bar a docstring) in the standard numpy source,
but that can be overwritten by people making binary distributions of numpy.

New nanfunctions nancumsum and `n...

v1.12.0rc2

NumPy 1.12.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

The NumPy 1.12.0 release contains a large number of fixes and improvements, but
few that stand out above all others. That makes picking out the highlights
somewhat arbitrary but the following may be of particular interest or indicate
areas likely to have future consequences.

• Order of operations in np.einsum can now be optimized for large speed improvements.
• New signature argument to np.vectorize for vectorizing with core dimensions.
• The keepdims argument was added to many functions.
• New context manager for testing warnings
• Support for BLIS in numpy.distutils
• Much improved support for PyPy (not yet finished)

Dropped Support

• Support for Python 2.6, 3.2, and 3.3 has been dropped.

Added Support

• Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer
  updateifcopy is not supported yet), this is a milestone for PyPy's
  C-API compatibility layer.

Build System Changes

• Library order is preserved, instead of being reordered to match that of
  the directories.

Deprecations

Assignment of ndarray object's data attribute

Assigning the 'data' attribute is an inherently unsafe operation as pointed
out in gh-7083. Such a capability will be removed in the future.

Unsafe int casting of the num attribute in linspace

np.linspace now raises DeprecationWarning when num cannot be safely
interpreted as an integer.

Insufficient bit width parameter to binary_repr

If a 'width' parameter is passed into binary_repr that is insufficient to
represent the number in base 2 (positive) or 2's complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the
future.

Future Changes

• In 1.13 NAT will always compare False except for NAT != NAT,
  which will be True. In short, NAT will behave like NaN
• In 1.13 np.average will preserve subclasses, to match the behavior of most
  other numpy functions such as np.mean. In particular, this means calls which
  returned a scalar may return a 0-d subclass object instead.

Multiple-field manipulation of structured arrays

In 1.13 the behavior of structured arrays involving multiple fields will change
in two ways:

First, indexing a structured array with multiple fields (eg,
arr[['f1', 'f3']]) will return a view into the original array in 1.13,
instead of a copy. Note the returned view will have extra padding bytes
corresponding to intervening fields in the original array, unlike the copy in
1.12, which will affect code such as arr[['f1', 'f3']].view(newdtype).

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays
occurs "by field name": Fields in the destination array are set to the
identically-named field in the source array or to 0 if the source does not have
a field::

>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')])
>>> b[:] = a
>>> b
array([(0, 2, 1), (0, 4, 3)],
      dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])

In 1.13 assignment will instead occur "by position": The Nth field of the
destination will be set to the Nth field of the source regardless of field
name. The old behavior can be obtained by using indexing to reorder the fields
before
assignment, e.g., b[['x', 'y']] = a[['y', 'x']].

Compatibility notes

DeprecationWarning to error

• Indexing with floats raises IndexError,
  e.g., a[0, 0.0].
• Indexing with non-integer array_like raises IndexError,
  e.g., a['1', '2']
• Indexing with multiple ellipsis raises IndexError,
  e.g., a[..., ...].
• Non-integers used as index values raise TypeError,
  e.g., in reshape, take, and specifying reduce axis.

FutureWarning to changed behavior

• np.full now returns an array of the fill-value's dtype if no dtype is
  given, instead of defaulting to float.
• np.average will emit a warning if the argument is a subclass of ndarray,
  as the subclass will be preserved starting in 1.13. (see Future Changes)

power and ** raise errors for integer to negative integer powers

The previous behavior depended on whether numpy scalar integers or numpy
integer arrays were involved.

For arrays

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers returned zero when raised to negative integer powers.

For scalars

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers sometimes returned zero, sometimes the
  correct float depending on the integer type combination.

All of these cases now raise a ValueError except for those integer
combinations whose common type is float, for instance uint64 and int8. It was
felt that a simple rule was the best way to go rather than have special
exceptions for the integer units. If you need negative powers, use an inexact
type.

Relaxed stride checking is the default

This will have some impact on code that assumed that F_CONTIGUOUS and
C_CONTIGUOUS were mutually exclusive and could be set to determine the
default order for arrays that are now both.

The np.percentile 'midpoint' interpolation method fixed for exact indices

The 'midpoint' interpolator now gives the same result as 'lower' and 'higher' when
the two coincide. Previous behavior of 'lower' + 0.5 is fixed.

keepdims kwarg is passed through to user-class methods

numpy functions that take a keepdims kwarg now pass the value
through to the corresponding methods on ndarray sub-classes. Previously the
keepdims keyword would be silently dropped. These functions now have
the following behavior:

1. If user does not provide keepdims, no keyword is passed to the underlying
   method.
2. Any user-provided value of keepdims is passed through as a keyword
   argument to the method.

This will raise in the case where the method does not support a
keepdims kwarg and the user explicitly passes in keepdims.

The following functions are changed: sum, product,
sometrue, alltrue, any, all, amax, amin,
prod, mean, std, var, nanmin, nanmax,
nansum, nanprod, nanmean, nanmedian, nanvar,
nanstd

bitwise_and identity changed

The previous identity was 1, it is now -1. See entry in Improvements_ for
more explanation.

ma.median warns and returns nan when unmasked invalid values are encountered

Similar to unmasked median the masked median ma.median now emits a Runtime
warning and returns NaN in slices where an unmasked NaN is present.

Greater consistancy in assert_almost_equal

The precision check for scalars has been changed to match that for arrays. It
is now::

abs(actual - desired) < 1.5 * 10**(-decimal)

Note that this is looser than previously documented, but agrees with the
previous implementation used in assert_array_almost_equal. Due to the
change in implementation some very delicate tests may fail that did not
fail before.

NoseTester behaviour of warnings during testing

When raise_warnings="develop" is given, all uncaught warnings will now
be considered a test failure. Previously only selected ones were raised.
Warnings which are not caught or raised (mostly when in release mode)
will be shown once during the test cycle similar to the default python
settings.

assert_warns and deprecated decorator more specific

The assert_warns function and context manager are now more specific
to the given warning category. This increased specificity leads to them
being handled according to the outer warning settings. This means that
no warning may be raised in cases where a wrong category warning is given
and ignored outside the context. Alternatively the increased specificity
may mean that warnings that were incorrectly ignored will now be shown
or raised. See also the new suppress_warnings context manager.
The same is true for the deprecated decorator.

C API

No changes.

New Features

Writeable keyword argument for as_strided

np.lib.stride_tricks.as_strided now has a writeable
keyword argument. It can be set to False when no write operation
to the returned array is expected to avoid accidental
unpredictable writes.

axes keyword argument for rot90

The axes keyword argument in rot90 determines the plane in which the
array is rotated. It defaults to axes=(0,1) as in the originial function.

Generalized flip

flipud and fliplr reverse the elements of an array along axis=0 and
axis=1 respectively. The newly added flip function reverses the elements of
an array along any given axis.

• np.count_nonzero now has an axis parameter, allowing
  non-zero counts to be generated on more than just a flattened
  array object.

BLIS support in numpy.distutils

Building against the BLAS implementation provided by the BLIS library is now
supported. See the [blis] section in site.cfg.example (in the root of
the numpy repo or source distribution).

Hook in numpy/__init__.py to run distribution-specific checks

Binary distributions of numpy may need to run specific hardware checks or load
specific libraries during numpy initialization. For example, if we are
distributing numpy with a BLAS library that requires SSE2 instructions, we
would like to check the machine on which numpy is running does have SSE2 in
order to give an informative error.

Add a hook in numpy/__init__.py to import a numpy/_distributor_init.py
file that will remain empty (bar a docstring) in the standard numpy source,
but that can be overwritten by people making binary distributions of numpy.

New nanfunctions nancumsum and `n...

v1.12.0rc1

NumPy 1.12.0 Release Notes

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

The NumPy 1.12.0 release contains a large number of fixes and improvements, but
few that stand out above all others. That makes picking out the highlights
somewhat arbitrary but the following may be of particular interest or indicate
areas likely to have future consequences.

• Order of operations in np.einsum can now be optimized for large speed improvements.
• New signature argument to np.vectorize for vectorizing with core dimensions.
• The keepdims argument was added to many functions.
• New context manager for testing warnings
• Support for BLIS in numpy.distutils
• Much improved support for PyPy (not yet finished)

Dropped Support

• Support for Python 2.6, 3.2, and 3.3 has been dropped.

Added Support

• Support for PyPy 2.7 v5.6.0 has been added. While not complete (nditer
  updateifcopy is not supported yet), this is a milestone for PyPy's
  C-API compatibility layer.

Build System Changes

• Library order is preserved, instead of being reordered to match that of
  the directories.

Deprecations

Assignment of ndarray object's data attribute

Assigning the 'data' attribute is an inherently unsafe operation as pointed
out in gh-7083. Such a capability will be removed in the future.

Unsafe int casting of the num attribute in linspace

np.linspace now raises DeprecationWarning when num cannot be safely
interpreted as an integer.

Insufficient bit width parameter to binary_repr

If a 'width' parameter is passed into binary_repr that is insufficient to
represent the number in base 2 (positive) or 2's complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the
future.

Future Changes

• In 1.13 NAT will always compare False except for NAT != NAT,
  which will be True. In short, NAT will behave like NaN
• In 1.13 np.average will preserve subclasses, to match the behavior of most
  other numpy functions such as np.mean. In particular, this means calls which
  returned a scalar may return a 0-d subclass object instead.

Multiple-field manipulation of structured arrays

In 1.13 the behavior of structured arrays involving multiple fields will change
in two ways:

First, indexing a structured array with multiple fields (eg,
arr[['f1', 'f3']]) will return a view into the original array in 1.13,
instead of a copy. Note the returned view will have extra padding bytes
corresponding to intervening fields in the original array, unlike the copy in
1.12, which will affect code such as arr[['f1', 'f3']].view(newdtype).

Second, for numpy versions 1.6 to 1.12 assignment between structured arrays
occurs "by field name": Fields in the destination array are set to the
identically-named field in the source array or to 0 if the source does not have
a field::

>>> a = np.array([(1,2),(3,4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> b = np.ones(2, dtype=[('z', 'i4'), ('y', 'i4'), ('x', 'i4')])
>>> b[:] = a
>>> b
array([(0, 2, 1), (0, 4, 3)],
      dtype=[('z', '<i4'), ('y', '<i4'), ('x', '<i4')])

In 1.13 assignment will instead occur "by position": The Nth field of the
destination will be set to the Nth field of the source regardless of field
name. The old behavior can be obtained by using indexing to reorder the fields
before
assignment, e.g., b[['x', 'y']] = a[['y', 'x']].

Compatibility notes

DeprecationWarning to error

• Indexing with floats raises IndexError,
  e.g., a[0, 0.0].
• Indexing with non-integer array_like raises IndexError,
  e.g., a['1', '2']
• Indexing with multiple ellipsis raises IndexError,
  e.g., a[..., ...].
• Non-integers used as index values raise TypeError,
  e.g., in reshape, take, and specifying reduce axis.

FutureWarning to changed behavior

• np.full now returns an array of the fill-value's dtype if no dtype is
  given, instead of defaulting to float.
• np.average will emit a warning if the argument is a subclass of ndarray,
  as the subclass will be preserved starting in 1.13. (see Future Changes)

power and ** raise errors for integer to negative integer powers

The previous behavior depended on whether numpy scalar integers or numpy
integer arrays were involved.

For arrays

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers returned zero when raised to negative integer powers.

For scalars

• Zero to negative integer powers returned least integral value.
• Both 1, -1 to negative integer powers returned correct values.
• The remaining integers sometimes returned zero, sometimes the
  correct float depending on the integer type combination.

All of these cases now raise a ValueError except for those integer
combinations whose common type is float, for instance uint64 and int8. It was
felt that a simple rule was the best way to go rather than have special
exceptions for the integer units. If you need negative powers, use an inexact
type.

Relaxed stride checking is the default

This will have some impact on code that assumed that F_CONTIGUOUS and
C_CONTIGUOUS were mutually exclusive and could be set to determine the
default order for arrays that are now both.

The np.percentile 'midpoint' interpolation method fixed for exact indices

The 'midpoint' interpolator now gives the same result as 'lower' and 'higher' when
the two coincide. Previous behavior of 'lower' + 0.5 is fixed.

keepdims kwarg is passed through to user-class methods

numpy functions that take a keepdims kwarg now pass the value
through to the corresponding methods on ndarray sub-classes. Previously the
keepdims keyword would be silently dropped. These functions now have
the following behavior:

1. If user does not provide keepdims, no keyword is passed to the underlying
   method.
2. Any user-provided value of keepdims is passed through as a keyword
   argument to the method.

This will raise in the case where the method does not support a
keepdims kwarg and the user explicitly passes in keepdims.

The following functions are changed: sum, product,
sometrue, alltrue, any, all, amax, amin,
prod, mean, std, var, nanmin, nanmax,
nansum, nanprod, nanmean, nanmedian, nanvar,
nanstd

bitwise_and identity changed

The previous identity was 1, it is now -1. See entry in Improvements_ for
more explanation.

ma.median warns and returns nan when unmasked invalid values are encountered

Similar to unmasked median the masked median ma.median now emits a Runtime
warning and returns NaN in slices where an unmasked NaN is present.

Greater consistancy in assert_almost_equal

The precision check for scalars has been changed to match that for arrays. It
is now::

abs(actual - desired) < 1.5 * 10**(-decimal)

Note that this is looser than previously documented, but agrees with the
previous implementation used in assert_array_almost_equal. Due to the
change in implementation some very delicate tests may fail that did not
fail before.

NoseTester behaviour of warnings during testing

When raise_warnings="develop" is given, all uncaught warnings will now
be considered a test failure. Previously only selected ones were raised.
Warnings which are not caught or raised (mostly when in release mode)
will be shown once during the test cycle similar to the default python
settings.

assert_warns and deprecated decorator more specific

The assert_warns function and context manager are now more specific
to the given warning category. This increased specificity leads to them
being handled according to the outer warning settings. This means that
no warning may be raised in cases where a wrong category warning is given
and ignored outside the context. Alternatively the increased specificity
may mean that warnings that were incorrectly ignored will now be shown
or raised. See also the new suppress_warnings context manager.
The same is true for the deprecated decorator.

C API

No changes.

New Features

Writeable keyword argument for as_strided

np.lib.stride_tricks.as_strided now has a writeable
keyword argument. It can be set to False when no write operation
to the returned array is expected to avoid accidental
unpredictable writes.

axes keyword argument for rot90

The axes keyword argument in rot90 determines the plane in which the
array is rotated. It defaults to axes=(0,1) as in the originial function.

Generalized flip

flipud and fliplr reverse the elements of an array along axis=0 and
axis=1 respectively. The newly added flip function reverses the elements of
an array along any given axis.

• np.count_nonzero now has an axis parameter, allowing
  non-zero counts to be generated on more than just a flattened
  array object.

BLIS support in numpy.distutils

Building against the BLAS implementation provided by the BLIS library is now
supported. See the [blis] section in site.cfg.example (in the root of
the numpy repo or source distribution).

Hook in numpy/__init__.py to run distribution-specific checks

Binary distributions of numpy may need to run specific hardware checks or load
specific libraries during numpy initialization. For example, if we are
distributing numpy with a BLAS library that requires SSE2 instructions, we
would like to check the machine on which numpy is running does have SSE2 in
order to give an informative error.

Add a hook in numpy/__init__.py to import a numpy/_distributor_init.py
file that will remain empty (bar a docstring) in the standard numpy source,
but that can be overwritten by people making binary distributions of numpy.

New nanfunctions nancumsum and `n...