==========================
NumPy 1.14.1 Release Notes

This is a bugfix release for some problems reported following the 1.14.0 release. The major
problems fixed are the following.

• Problems with the new array printing, particularly the printing of complex
  values, Please report any additional problems that may turn up.
• Problems with np.einsum due to the new optimized=True default. Some
  fixes for optimization have been applied and optimize=False is now the
  default.
• The sort order in np.unique when axis=<some-number> will now always
  be lexicographic in the subarray elements. In previous NumPy versions there
  was an optimization that could result in sorting the subarrays as unsigned
  byte strings.
• The change in 1.14.0 that multi-field indexing of structured arrays returns a
  view instead of a copy has been reverted but remains on track for NumPy 1.15.
  Affected users should read the 1.14.1 Numpy User Guide section
  "basics/structured arrays/accessing multiple fields" for advice on how to
  manage this transition.

The Python versions supported in this release 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 source releases were
cythonized with Cython 0.26.1, which is known to not support the upcoming
Python 3.7 release. People who wish to run Python 3.7 should check out the
NumPy repo and try building with the, as yet, unreleased master branch of
Cython.

Contributors

A total of 14 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Charles Harris
• Daniel Smith
• Dennis Weyland +
• Eric Larson
• Eric Wieser
• Jarrod Millman
• Kenichi Maehashi +
• Marten van Kerkwijk
• Mathieu Lamarre
• Sebastian Berg
• Simon Conseil
• Simon Gibbons
• xoviat

Pull requests merged

A total of 36 pull requests were merged for this release.

• #10339: BUG: restrict the config modifications to win32
• #10368: MAINT: Adjust type promotion in linalg.norm
• #10375: BUG: add missing paren and remove quotes from repr of fieldless...
• #10395: MAINT: Update download URL in setup.py.
• #10396: BUG: fix einsum issue with unicode input and py2
• #10397: BUG: fix error message not formatted in einsum
• #10398: DOC: add documentation about how to handle new array printing
• #10403: BUG: Set einsum optimize parameter default to False.
• #10424: ENH: Fix repr of np.record objects to match np.void types #10412
• #10425: MAINT: Update zesty to artful for i386 testing
• #10431: REL: Add 1.14.1 release notes template
• #10435: MAINT: Use ValueError for duplicate field names in lookup (backport)
• #10534: BUG: Provide a better error message for out-of-order fields
• #10536: BUG: Resize bytes_ columns in genfromtxt (backport of #10401)
• #10537: BUG: multifield-indexing adds padding bytes: revert for 1.14.1
• #10539: BUG: fix np.save issue with python 2.7.5
• #10540: BUG: Add missing DECREF in Py2 int() cast
• #10541: TST: Add circleci document testing to maintenance/1.14.x
• #10542: BUG: complex repr has extra spaces, missing + (1.14 backport)
• #10550: BUG: Set missing exception after malloc
• #10557: BUG: In numpy.i, clear CARRAY flag if wrapped buffer is not C_CONTIGUOUS.
• #10558: DEP: Issue FutureWarning when malformed records detected.
• #10559: BUG: Fix einsum optimize logic for singleton dimensions
• #10560: BUG: Fix calling ufuncs with a positional output argument.
• #10561: BUG: Fix various Big-Endian test failures (ppc64)
• #10562: BUG: Make dtype.descr error for out-of-order fields.
• #10563: BUG: arrays not being flattened in union1d
• #10607: MAINT: Update sphinxext submodule hash.
• #10608: BUG: Revert sort optimization in np.unique.
• #10609: BUG: infinite recursion in str of 0d subclasses
• #10610: BUG: Align type definition with generated lapack
• #10612: BUG/ENH: Improve output for structured non-void types
• #10622: BUG: deallocate recursive closure in arrayprint.py (1.14 backport)
• #10624: BUG: Correctly identify comma seperated dtype strings
• #10629: BUG: deallocate recursive closure in arrayprint.py (backport...
• #10630: REL: Prepare for 1.14.1 release.

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==========================
NumPy 1.14.0 Release Notes

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP_.

This release supports Python 2.7 and 3.4 - 3.6.

.. _NEP: https://github.com/numpy/numpy/blob/master/doc/neps/dropping-python2.7-proposal.rst

Highlights

• The np.einsum function uses BLAS when possible

• genfromtxt, loadtxt, fromregex and savetxt can now handle
  files with arbitrary Python supported encoding.

• Major improvements to printing of NumPy arrays and scalars.

New functions

• parametrize: decorator added to numpy.testing

• chebinterpolate: Interpolate function at Chebyshev points.

• format_float_positional and format_float_scientific : format
  floating-point scalars unambiguously with control of rounding and padding.

• PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
  new C-API functions useful in achieving PyPy compatibity.

Deprecations

• Using np.bool_ objects in place of integers is deprecated. Previously
  operator.index(np.bool_) was legal and allowed constructs such as
  [1, 2, 3][np.True_]. That was misleading, as it behaved differently from
  np.array([1, 2, 3])[np.True_].

• Truth testing of an empty array is deprecated. To check if an array is not
  empty, use array.size > 0.

• Calling np.bincount with minlength=None is deprecated.
  minlength=0 should be used instead.

• Calling np.fromstring with the default value of the sep argument is
  deprecated. When that argument is not provided, a broken version of
  np.frombuffer is used that silently accepts unicode strings and -- after
  encoding them as either utf-8 (python 3) or the default encoding
  (python 2) -- treats them as binary data. If reading binary data is
  desired, np.frombuffer should be used directly.

• The style option of array2string is deprecated in non-legacy printing mode.

• PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
  use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
  more details.

• The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
  for details. We will not be dropping support for those arrays, but they are
  not compatible with PyPy.

Future Changes

• np.issubdtype will stop downcasting dtype-like arguments.
  It might be expected that issubdtype(np.float32, 'float64') and
  issubdtype(np.float32, np.float64) mean the same thing - however, there
  was an undocumented special case that translated the former into
  issubdtype(np.float32, np.floating), giving the surprising result of True.

  This translation now gives a warning that explains what translation is
  occurring. In the future, the translation will be disabled, and the first
  example will be made equivalent to the second.

• np.linalg.lstsq default for rcond will be changed. The rcond
  parameter to np.linalg.lstsq will change its default to machine precision
  times the largest of the input array dimensions. A FutureWarning is issued
  when rcond is not passed explicitly.

• a.flat.__array__() will return a writeable copy of a when a is
  non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
  writeable. Currently it returns a non-writeable copy. See gh-7054 for a
  discussion of the issue.

• Unstructured void array's .item method will return a bytes object. In the
  future, calling .item() on arrays or scalars of np.void datatype will
  return a bytes object instead of a buffer or int array, the same as
  returned by bytes(void_scalar). This may affect code which assumed the
  return value was mutable, which will no longer be the case. A
  FutureWarning is now issued when this would occur.

Compatibility notes

The mask of a masked array view is also a view rather than a copy

There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.

np.ma.masked is no longer writeable

Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

# emulating a function that sometimes returns np.ma.masked
val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1  # now errors, previously changed np.ma.masked.data

np.ma functions producing fill_values have changed

Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.

Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.

a.flat.__array__() returns non-writeable arrays when a is non-contiguous

The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.

np.tensordot now returns zero array when contracting over 0-length dimension

Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.

numpy.testing reorganized

This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.

np.asfarray no longer accepts non-dtypes through the dtype argument

This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.

1D np.linalg.norm preserves float input types, even for arbitrary orders

Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
dtype('float32')
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64')  # numpy 1.13
dtype('float32')  # numpy 1.14

This change affects only float32 and float16 arrays.

count_nonzero(arr, axis=()) now counts over no axes, not all axes

Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.

__init__.py files added to test directories

This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.

.astype(bool) on unstructured void arrays now calls bool on each element

On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.

MaskedArray.squeeze never returns np.ma.masked

np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().

Renamed first parameter of can_cast from from to from_

The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.

isnat raises TypeError when passed wrong type

The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.

dtype.__getitem__ raises TypeError when passed wrong type

When indexed with a float, the dtype object used to raise ValueError.

User-defined types now need to implement __str__ and __repr__

Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.

Many changes to array printing, disableable with the new "legacy" printing mode

The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').

In summary, the major changes are:

• For floating-point types:

  • The repr of float arrays often omits a space previously printed
    in the sign position. See the new sign option to np.set_printoptions.
  • Floating-point arrays and scalars use a new algorithm for decimal
    representations, giving the shortest unique representation. This will
    usually shorten float16 fractional output, and sometimes float32 and
    float128 output. float64 should be unaffected. See the new
    floatmode option to np.set_printoptions.
  • Float arrays printed in scientific notation no longer use fixed-precision,
    and now instead show the shortest unique representation.
  • The str of floating-point scalars is no longer truncated in python2.

• For other data types:

  • Non-finite complex scalars print like nanj instead of nan*j.
  • NaT values in datetime arrays are now properly aligned.
  • Arrays and scalars of np.void datatype are now printed using hex
    notation.

• For line-wrapping:

  • The "dtype" part of ndarray reprs will now be printed on the next line
    if there isn't space on the last line of array output.
  • The linewidth format option is now always respected.
    The repr or str of an array will never exceed this, unless a single
    element is too wide.
  • The last line of an array string will never have more elements than earlier
    lines.
  • An extra space is no longer inserted on the first line if the elements are
    too wide.

• For summarization (the use of ... to shorten long arrays):

  • A trailing comma is no longer inserted for str.
    Previously, str(np.arange(1001)) gave
    '[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
  • For arrays of 2-D and beyond, when ... is printed on its own line in
    order to summarize any but the last axis, newlines are now appended to that
    line to match its leading newlines and a trailing space character is
    removed.

• MaskedArray arrays now separate printed elements with commas, always
  print the dtype, and correctly wrap the elements of long arrays to multiple
  lines. If there is more than 1 dimension, the array attributes are now
  printed in a new "left-justified" printing style.

• recarray arrays no longer print a trailing space before their dtype, and
  wrap to the right number of columns.

• 0d arrays no longer have their own idiosyncratic implementations of str
  and repr. The style argument to np.array2string is deprecated.

• Arrays of bool datatype will omit the datatype in the repr.

• User-defined dtypes (subclasses of np.generic) now need to
  implement __str__ and __repr__.

Some of these changes are described in more detail below. If you need to retain
the previous behavior for doctests or other reasons, you may want to do
something like::

# FIXME: We need the str/repr formatting used in Numpy < 1.14.
try:
    np.set_printoptions(legacy='1.13')
except TypeError:
    pass

C API changes

PyPy compatible alternative to UPDATEIFCOPY arrays

UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

• PyArray_SetWritebackIfCopyBase
• PyArray_ResolveWritebackIfCopy,

have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
NPY_ARRAY_INOUT_ARRAY should be replaced by NPY_ARRAY_INOUT_ARRAY2 and
NPY_ARRAY_INOUT_FARRAY should be replaced by NPY_ARRAY_INOUT_FARRAY2.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst

New Features

Encoding argument for text IO functions

genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

External nose plugins are usable by numpy.testing.Tester

numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.

parametrize decorator added to numpy.testing

A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.

The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.

chebinterpolate function added to numpy.polynomial.chebyshev

The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.

Support for reading lzma compressed text files in Python 3

With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.

sign option added to np.setprintoptions and np.array2string

This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

hermitian option added tonp.linalg.matrix_rank

The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

threshold and edgeitems options added to np.array2string

These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.

concatenate and stack gained an out argument

A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows

The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.

Improvements

Numerator degrees of freedom in random.noncentral_f need only be positive.

Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.

The GIL is released for all np.einsum variations

Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.

The np.einsum function will use BLAS when possible and optimize by default

The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.

f2py now handles arrays of dimension 0

f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.

numpy.distutils supports using MSVC and mingw64-gfortran together

Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.

np.linalg.pinv now works on stacked matrices

Previously it was limited to a single 2d array.

numpy.save aligns data to 64 bytes instead of 16

Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.

NPZ files now can be written without using temporary files

In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types

Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

• arr.sort()
• arr.view(bytes)
• arr.resize(...)
• pickle.dumps(arr)

Support for decimal.Decimal in np.lib.financial

Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation

The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.

New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.

A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.

The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.

void datatype elements are now printed in hex notation

A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for void datatypes is now independently customizable

The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.

Reduced memory usage of np.loadtxt

np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.

Changes

Multiple-field indexing/assignment of structured arrays

The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

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

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.

Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::

x = dtype({'names': ['A', 'B'], 'formats': ['i4', 'f4'], 'offsets': [0, 4]})
y = dtype({'names': ['B', 'A'], 'formats': ['f4', 'i4'], 'offsets': [4, 0]})

the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.

Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.

Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.

Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.

The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.

Integer and Void scalars are now unaffected by np.set_string_function

Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.

0d array printing changed, style arg of array2string deprecated

Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.

Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.

This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.

Seeding RandomState using an array requires a 1-d array

RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.

MaskedArray objects show a more useful repr

The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.

The repr of np.polynomial classes is more explicit

It now shows the domain and window parameters as keyword arguments to make
them more clear::

>>> np.polynomial.Polynomial(range(4))
Polynomial([0.,  1.,  2.,  3.], domain=[-1,  1], window=[-1,  1])

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==========================
NumPy 1.14.0 Release Notes

Numpy 1.14.0 is the result of seven months of work and contains a large number
of bug fixes and new features, along with several changes with potential
compatibility issues. The major change that users will notice are the
stylistic changes in the way numpy arrays and scalars are printed, a change
that will affect doctests. See below for details on how to preserve the
old style printing when needed.

A major decision affecting future development concerns the schedule for
dropping Python 2.7 support in the runup to 2020. The decision has been made to
support 2.7 for all releases made in 2018, with the last release being
designated a long term release with support for bug fixes extending through
2019. In 2019 support for 2.7 will be dropped in all new releases. More details
can be found in the relevant NEP.

This release supports Python 2.7 and 3.4 - 3.6.

Highlights

• The np.einsum function uses BLAS when possible

• genfromtxt, loadtxt, fromregex and savetxt can now handle
  files with arbitrary Python supported encoding.

• Major improvements to printing of NumPy arrays and scalars.

New functions

• parametrize: decorator added to numpy.testing

• chebinterpolate: Interpolate function at Chebyshev points.

• format_float_positional and format_float_scientific : format
  floating-point scalars unambiguously with control of rounding and padding.

• PyArray_ResolveWritebackIfCopy and PyArray_SetWritebackIfCopyBase,
  new C-API functions useful in achieving PyPy compatibity.

Deprecations

• Using np.bool_ objects in place of integers is deprecated. Previously
  operator.index(np.bool_) was legal and allowed constructs such as
  [1, 2, 3][np.True_]. That was misleading, as it behaved differently from
  np.array([1, 2, 3])[np.True_].

• Truth testing of an empty array is deprecated. To check if an array is not
  empty, use array.size > 0.

• Calling np.bincount with minlength=None is deprecated.
  minlength=0 should be used instead.

• Calling np.fromstring with the default value of the sep argument is
  deprecated. When that argument is not provided, a broken version of
  np.frombuffer is used that silently accepts unicode strings and -- after
  encoding them as either utf-8 (python 3) or the default encoding
  (python 2) -- treats them as binary data. If reading binary data is
  desired, np.frombuffer should be used directly.

• The style option of array2string is deprecated in non-legacy printing mode.

• PyArray_SetUpdateIfCopyBase has been deprecated. For NumPy versions >= 1.14
  use PyArray_SetWritebackIfCopyBase instead, see C API changes below for
  more details.

• The use of UPDATEIFCOPY arrays is deprecated, see C API changes below
  for details. We will not be dropping support for those arrays, but they are
  not compatible with PyPy.

Future Changes

• np.issubdtype will stop downcasting dtype-like arguments.
  It might be expected that issubdtype(np.float32, 'float64') and
  issubdtype(np.float32, np.float64) mean the same thing - however, there
  was an undocumented special case that translated the former into
  issubdtype(np.float32, np.floating), giving the surprising result of True.

  This translation now gives a warning that explains what translation is
  occurring. In the future, the translation will be disabled, and the first
  example will be made equivalent to the second.

• np.linalg.lstsq default for rcond will be changed. The rcond
  parameter to np.linalg.lstsq will change its default to machine precision
  times the largest of the input array dimensions. A FutureWarning is issued
  when rcond is not passed explicitly.

• a.flat.__array__() will return a writeable copy of a when a is
  non-contiguous. Previously it returned an UPDATEIFCOPY array when a was
  writeable. Currently it returns a non-writeable copy. See gh-7054 for a
  discussion of the issue.

• Unstructured void array's .item method will return a bytes object. In the
  future, calling .item() on arrays or scalars of np.void datatype will
  return a bytes object instead of a buffer or int array, the same as
  returned by bytes(void_scalar). This may affect code which assumed the
  return value was mutable, which will no longer be the case. A
  FutureWarning is now issued when this would occur.

Compatibility notes

The mask of a masked array view is also a view rather than a copy

There was a FutureWarning about this change in NumPy 1.11.x. In short, it is
now the case that, when changing a view of a masked array, changes to the mask
are propagated to the original. That was not previously the case. This change
affects slices in particular. Note that this does not yet work properly if the
mask of the original array is nomask and the mask of the view is changed.
See gh-5580 for an extended discussion. The original behavior of having a copy
of the mask can be obtained by calling the unshare_mask method of the view.

np.ma.masked is no longer writeable

Attempts to mutate the masked constant now error, as the underlying arrays
are marked readonly. In the past, it was possible to get away with::

# emulating a function that sometimes returns np.ma.masked
val = random.choice([np.ma.masked, 10])
var_arr = np.asarray(val)
val_arr += 1  # now errors, previously changed np.ma.masked.data

np.ma functions producing fill_values have changed

Previously, np.ma.default_fill_value would return a 0d array, but
np.ma.minimum_fill_value and np.ma.maximum_fill_value would return a
tuple of the fields. Instead, all three methods return a structured np.void
object, which is what you would already find in the .fill_value attribute.

Additionally, the dtype guessing now matches that of np.array - so when
passing a python scalar x, maximum_fill_value(x) is always the same as
maximum_fill_value(np.array(x)). Previously x = long(1) on Python 2
violated this assumption.

a.flat.__array__() returns non-writeable arrays when a is non-contiguous

The intent is that the UPDATEIFCOPY array previously returned when a was
non-contiguous will be replaced by a writeable copy in the future. This
temporary measure is aimed to notify folks who expect the underlying array be
modified in this situation that that will no longer be the case. The most
likely places for this to be noticed is when expressions of the form
np.asarray(a.flat) are used, or when a.flat is passed as the out
parameter to a ufunc.

np.tensordot now returns zero array when contracting over 0-length dimension

Previously np.tensordot raised a ValueError when contracting over 0-length
dimension. Now it returns a zero array, which is consistent with the behaviour
of np.dot and np.einsum.

numpy.testing reorganized

This is not expected to cause problems, but possibly something has been left
out. If you experience an unexpected import problem using numpy.testing
let us know.

np.asfarray no longer accepts non-dtypes through the dtype argument

This previously would accept dtype=some_array, with the implied semantics
of dtype=some_array.dtype. This was undocumented, unique across the numpy
functions, and if used would likely correspond to a typo.

1D np.linalg.norm preserves float input types, even for arbitrary orders

Previously, this would promote to float64 when arbitrary orders were
passed, despite not doing so under the simple cases::

>>> f32 = np.float32([1, 2])
>>> np.linalg.norm(f32, 2.0).dtype
dtype('float32')
>>> np.linalg.norm(f32, 2.0001).dtype
dtype('float64')  # numpy 1.13
dtype('float32')  # numpy 1.14

This change affects only float32 and float16 arrays.

count_nonzero(arr, axis=()) now counts over no axes, not all axes

Elsewhere, axis==() is always understood as "no axes", but
count_nonzero had a special case to treat this as "all axes". This was
inconsistent and surprising. The correct way to count over all axes has always
been to pass axis == None.

__init__.py files added to test directories

This is for pytest compatibility in the case of duplicate test file names in
the different directories. As a result, run_module_suite no longer works,
i.e., python <path-to-test-file> results in an error.

.astype(bool) on unstructured void arrays now calls bool on each element

On Python 2, void_array.astype(bool) would always return an array of
True, unless the dtype is V0. On Python 3, this operation would usually
crash. Going forwards, astype matches the behavior of bool(np.void),
considering a buffer of all zeros as false, and anything else as true.
Checks for V0 can still be done with arr.dtype.itemsize == 0.

MaskedArray.squeeze never returns np.ma.masked

np.squeeze is documented as returning a view, but the masked variant would
sometimes return masked, which is not a view. This has been fixed, so that
the result is always a view on the original masked array.
This breaks any code that used masked_arr.squeeze() is np.ma.masked, but
fixes code that writes to the result of .squeeze().

Renamed first parameter of can_cast from from to from_

The previous parameter name from is a reserved keyword in Python, which made
it difficult to pass the argument by name. This has been fixed by renaming
the parameter to from_.

isnat raises TypeError when passed wrong type

The ufunc isnat used to raise a ValueError when it was not passed
variables of type datetime or timedelta. This has been changed to
raising a TypeError.

dtype.__getitem__ raises TypeError when passed wrong type

When indexed with a float, the dtype object used to raise ValueError.

User-defined types now need to implement __str__ and __repr__

Previously, user-defined types could fall back to a default implementation of
__str__ and __repr__ implemented in numpy, but this has now been
removed. Now user-defined types will fall back to the python default
object.__str__ and object.__repr__.

Many changes to array printing, disableable with the new "legacy" printing mode

The str and repr of ndarrays and numpy scalars have been changed in
a variety of ways. These changes are likely to break downstream user's
doctests.

These new behaviors can be disabled to mostly reproduce numpy 1.13 behavior by
enabling the new 1.13 "legacy" printing mode. This is enabled by calling
np.set_printoptions(legacy="1.13"), or using the new legacy argument to
np.array2string, as np.array2string(arr, legacy='1.13').

In summary, the major changes are:

• For floating-point types:

  • The repr of float arrays often omits a space previously printed
    in the sign position. See the new sign option to np.set_printoptions.
  • Floating-point arrays and scalars use a new algorithm for decimal
    representations, giving the shortest unique representation. This will
    usually shorten float16 fractional output, and sometimes float32 and
    float128 output. float64 should be unaffected. See the new
    floatmode option to np.set_printoptions.
  • Float arrays printed in scientific notation no longer use fixed-precision,
    and now instead show the shortest unique representation.
  • The str of floating-point scalars is no longer truncated in python2.

• For other data types:

  • Non-finite complex scalars print like nanj instead of nan*j.
  • NaT values in datetime arrays are now properly aligned.
  • Arrays and scalars of np.void datatype are now printed using hex
    notation.

• For line-wrapping:

  • The "dtype" part of ndarray reprs will now be printed on the next line
    if there isn't space on the last line of array output.
  • The linewidth format option is now always respected.
    The repr or str of an array will never exceed this, unless a single
    element is too wide.
  • The last line of an array string will never have more elements than earlier
    lines.
  • An extra space is no longer inserted on the first line if the elements are
    too wide.

• For summarization (the use of ... to shorten long arrays):

  • A trailing comma is no longer inserted for str.
    Previously, str(np.arange(1001)) gave
    '[ 0 1 2 ..., 998 999 1000]', which has an extra comma.
  • For arrays of 2-D and beyond, when ... is printed on its own line in
    order to summarize any but the last axis, newlines are now appended to that
    line to match its leading newlines and a trailing space character is
    removed.

• MaskedArray arrays now separate printed elements with commas, always
  print the dtype, and correctly wrap the elements of long arrays to multiple
  lines. If there is more than 1 dimension, the array attributes are now
  printed in a new "left-justified" printing style.

• recarray arrays no longer print a trailing space before their dtype, and
  wrap to the right number of columns.

• 0d arrays no longer have their own idiosyncratic implementations of str
  and repr. The style argument to np.array2string is deprecated.

• Arrays of bool datatype will omit the datatype in the repr.

• User-defined dtypes (subclasses of np.generic) now need to
  implement __str__ and __repr__.

Some of these changes are described in more detail below.

C API changes

PyPy compatible alternative to UPDATEIFCOPY arrays

UPDATEIFCOPY arrays are contiguous copies of existing arrays, possibly with
different dimensions, whose contents are copied back to the original array when
their refcount goes to zero and they are deallocated. Because PyPy does not use
refcounts, they do not function correctly with PyPy. NumPy is in the process of
eliminating their use internally and two new C-API functions,

• PyArray_SetWritebackIfCopyBase
• PyArray_ResolveWritebackIfCopy,

have been added together with a complimentary flag,
NPY_ARRAY_WRITEBACKIFCOPY. Using the new functionality also requires that
some flags be changed when new arrays are created, to wit:
NPY_ARRAY_INOUT_ARRAY should be replaced by NPY_ARRAY_INOUT_ARRAY2 and
NPY_ARRAY_INOUT_FARRAY should be replaced by NPY_ARRAY_INOUT_FARRAY2.
Arrays created with these new flags will then have the WRITEBACKIFCOPY
semantics.

If PyPy compatibility is not a concern, these new functions can be ignored,
although there will be a DeprecationWarning. If you do wish to pursue PyPy
compatibility, more information on these functions and their use may be found
in the c-api_ documentation and the example in how-to-extend_.

.. _c-api: https://github.com/numpy/numpy/blob/master/doc/source/reference/c-api.array.rst
.. _how-to-extend: https://github.com/numpy/numpy/blob/master/doc/source/user/c-info.how-to-extend.rst

New Features

Encoding argument for text IO functions

genfromtxt, loadtxt, fromregex and savetxt can now handle files
with arbitrary encoding supported by Python via the encoding argument.
For backward compatibility the argument defaults to the special bytes value
which continues to treat text as raw byte values and continues to pass latin1
encoded bytes to custom converters.
Using any other value (including None for system default) will switch the
functions to real text IO so one receives unicode strings instead of bytes in
the resulting arrays.

External nose plugins are usable by numpy.testing.Tester

numpy.testing.Tester is now aware of nose plugins that are outside the
nose built-in ones. This allows using, for example, nose-timer like
so: np.test(extra_argv=['--with-timer', '--timer-top-n', '20']) to
obtain the runtime of the 20 slowest tests. An extra keyword timer was
also added to Tester.test, so np.test(timer=20) will also report the 20
slowest tests.

parametrize decorator added to numpy.testing

A basic parametrize decorator is now available in numpy.testing. It is
intended to allow rewriting yield based tests that have been deprecated in
pytest so as to facilitate the transition to pytest in the future. The nose
testing framework has not been supported for several years and looks like
abandonware.

The new parametrize decorator does not have the full functionality of the
one in pytest. It doesn't work for classes, doesn't support nesting, and does
not substitute variable names. Even so, it should be adequate to rewrite the
NumPy tests.

chebinterpolate function added to numpy.polynomial.chebyshev

The new chebinterpolate function interpolates a given function at the
Chebyshev points of the first kind. A new Chebyshev.interpolate class
method adds support for interpolation over arbitrary intervals using the scaled
and shifted Chebyshev points of the first kind.

Support for reading lzma compressed text files in Python 3

With Python versions containing the lzma module the text IO functions can
now transparently read from files with xz or lzma extension.

sign option added to np.setprintoptions and np.array2string

This option controls printing of the sign of floating-point types, and may be
one of the characters '-', '+' or ' '. With '+' numpy always prints the sign of
positive values, with ' ' it always prints a space (whitespace character) in
the sign position of positive values, and with '-' it will omit the sign
character for positive values. The new default is '-'.

This new default changes the float output relative to numpy 1.13. The old
behavior can be obtained in 1.13 "legacy" printing mode, see compatibility
notes above.

hermitian option added tonp.linalg.matrix_rank

The new hermitian option allows choosing between standard SVD based matrix
rank calculation and the more efficient eigenvalue based method for
symmetric/hermitian matrices.

threshold and edgeitems options added to np.array2string

These options could previously be controlled using np.set_printoptions, but
now can be changed on a per-call basis as arguments to np.array2string.

concatenate and stack gained an out argument

A preallocated buffer of the desired dtype can now be used for the output of
these functions.

Support for PGI flang compiler on Windows

The PGI flang compiler is a Fortran front end for LLVM released by NVIDIA under
the Apache 2 license. It can be invoked by ::

python setup.py config --compiler=clang --fcompiler=flang install

There is little experience with this new compiler, so any feedback from people
using it will be appreciated.

Improvements

Numerator degrees of freedom in random.noncentral_f need only be positive.

Prior to NumPy 1.14.0, the numerator degrees of freedom needed to be > 1, but
the distribution is valid for values > 0, which is the new requirement.

The GIL is released for all np.einsum variations

Some specific loop structures which have an accelerated loop version
did not release the GIL prior to NumPy 1.14.0. This oversight has been
fixed.

The np.einsum function will use BLAS when possible and optimize by default

The np.einsum function will now call np.tensordot when appropriate.
Because np.tensordot uses BLAS when possible, that will speed up execution.
By default, np.einsum will also attempt optimization as the overhead is
small relative to the potential improvement in speed.

f2py now handles arrays of dimension 0

f2py now allows for the allocation of arrays of dimension 0. This allows
for more consistent handling of corner cases downstream.

numpy.distutils supports using MSVC and mingw64-gfortran together

Numpy distutils now supports using Mingw64 gfortran and MSVC compilers
together. This enables the production of Python extension modules on Windows
containing Fortran code while retaining compatibility with the
binaries distributed by Python.org. Not all use cases are supported,
but most common ways to wrap Fortran for Python are functional.

Compilation in this mode is usually enabled automatically, and can be
selected via the --fcompiler and --compiler options to
setup.py. Moreover, linking Fortran codes to static OpenBLAS is
supported; by default a gfortran compatible static archive
openblas.a is looked for.

np.linalg.pinv now works on stacked matrices

Previously it was limited to a single 2d array.

numpy.save aligns data to 64 bytes instead of 16

Saving NumPy arrays in the npy format with numpy.save inserts
padding before the array data to align it at 64 bytes. Previously
this was only 16 bytes (and sometimes less due to a bug in the code
for version 2). Now the alignment is 64 bytes, which matches the
widest SIMD instruction set commonly available, and is also the most
common cache line size. This makes npy files easier to use in
programs which open them with mmap, especially on Linux where an
mmap offset must be a multiple of the page size.

NPZ files now can be written without using temporary files

In Python 3.6+ numpy.savez and numpy.savez_compressed now write
directly to a ZIP file, without creating intermediate temporary files.

Better support for empty structured and string types

Structured types can contain zero fields, and string dtypes can contain zero
characters. Zero-length strings still cannot be created directly, and must be
constructed through structured dtypes::

str0 = np.empty(10, np.dtype([('v', str, N)]))['v']
void0 = np.empty(10, np.void)

It was always possible to work with these, but the following operations are
now supported for these arrays:

• arr.sort()
• arr.view(bytes)
• arr.resize(...)
• pickle.dumps(arr)

Support for decimal.Decimal in np.lib.financial

Unless otherwise stated all functions within the financial package now
support using the decimal.Decimal built-in type.

Float printing now uses "dragon4" algorithm for shortest decimal representation

The str and repr of floating-point values (16, 32, 64 and 128 bit) are
now printed to give the shortest decimal representation which uniquely
identifies the value from others of the same type. Previously this was only
true for float64 values. The remaining float types will now often be shorter
than in numpy 1.13. Arrays printed in scientific notation now also use the
shortest scientific representation, instead of fixed precision as before.

Additionally, the str of float scalars scalars will no longer be truncated
in python2, unlike python2 floats. np.double scalars now have a str
and repr identical to that of a python3 float.

New functions np.format_float_scientific and np.format_float_positional
are provided to generate these decimal representations.

A new option floatmode has been added to np.set_printoptions and
np.array2string, which gives control over uniqueness and rounding of
printed elements in an array. The new default is floatmode='maxprec' with
precision=8, which will print at most 8 fractional digits, or fewer if an
element can be uniquely represented with fewer. A useful new mode is
floatmode="unique", which will output enough digits to specify the array
elements uniquely.

Numpy complex-floating-scalars with values like inf*j or nan*j now
print as infj and nanj, like the pure-python complex type.

The FloatFormat and LongFloatFormat classes are deprecated and should
both be replaced by FloatingFormat. Similarly ComplexFormat and
LongComplexFormat should be replaced by ComplexFloatingFormat.

void datatype elements are now printed in hex notation

A hex representation compatible with the python bytes type is now printed
for unstructured np.void elements, e.g., V4 datatype. Previously, in
python2 the raw void data of the element was printed to stdout, or in python3
the integer byte values were shown.

printing style for void datatypes is now independently customizable

The printing style of np.void arrays is now independently customizable
using the formatter argument to np.set_printoptions, using the
'void' key, instead of the catch-all numpystr key as before.

Reduced memory usage of np.loadtxt

np.loadtxt now reads files in chunks instead of all at once which decreases
its memory usage significantly for large files.

Changes

Multiple-field indexing/assignment of structured arrays

The indexing and assignment of structured arrays with multiple fields has
changed in a number of ways, as warned about in previous releases.

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

Second, assignment between structured arrays will now occur "by position"
instead of "by field name". The Nth field of the destination will be set to the
Nth field of the source regardless of field name, unlike in numpy versions 1.6
to 1.13 in which fields in the destination array were set to the
identically-named field in the source array or to 0 if the source did not have
a field.

Correspondingly, the order of fields in a structured dtypes now matters when
computing dtype equality. For example, with the dtypes ::

x = dtype({'names': ['A', 'B'], 'formats': ['i4', 'f4'], 'offsets': [0, 4]})
y = dtype({'names': ['B', 'A'], 'formats': ['f4', 'i4'], 'offsets': [4, 0]})

the expression x == y will now return False, unlike before.
This makes dictionary based dtype specifications like
dtype({'a': ('i4', 0), 'b': ('f4', 4)}) dangerous in python < 3.6
since dict key order is not preserved in those versions.

Assignment from a structured array to a boolean array now raises a ValueError,
unlike in 1.13, where it always set the destination elements to True.

Assignment from structured array with more than one field to a non-structured
array now raises a ValueError. In 1.13 this copied just the first field of the
source to the destination.

Using field "titles" in multiple-field indexing is now disallowed, as is
repeating a field name in a multiple-field index.

The documentation for structured arrays in the user guide has been
significantly updated to reflect these changes.

Integer and Void scalars are now unaffected by np.set_string_function

Previously, unlike most other numpy scalars, the str and repr of
integer and void scalars could be controlled by np.set_string_function.
This is no longer possible.

0d array printing changed, style arg of array2string deprecated

Previously the str and repr of 0d arrays had idiosyncratic
implementations which returned str(a.item()) and 'array(' + repr(a.item()) + ')' respectively for 0d array a, unlike both numpy
scalars and higher dimension ndarrays.

Now, the str of a 0d array acts like a numpy scalar using str(a[()])
and the repr acts like higher dimension arrays using formatter(a[()]),
where formatter can be specified using np.set_printoptions. The
style argument of np.array2string is deprecated.

This new behavior is disabled in 1.13 legacy printing mode, see compatibility
notes above.

Seeding RandomState using an array requires a 1-d array

RandomState previously would accept empty arrays or arrays with 2 or more
dimensions, which resulted in either a failure to seed (empty arrays) or for
some of the passed values to be ignored when setting the seed.

MaskedArray objects show a more useful repr

The repr of a MaskedArray is now closer to the python code that would
produce it, with arrays now being shown with commas and dtypes. Like the other
formatting changes, this can be disabled with the 1.13 legacy printing mode in
order to help transition doctests.

The repr of np.polynomial classes is more explicit

It now shows the domain and window parameters as keyword arguments to make
them more clear::

>>> np.polynomial.Polynomial(range(4))
Polynomial([0.,  1.,  2.,  3.], domain=[-1,  1], window=[-1,  1])

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

==========================
NumPy 1.13.3 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. It was cythonized with Cython 0.26.1,
which should be free of the bugs found in 0.27 while also being compatible with
Python 3.7-dev. The Windows wheels were built with OpenBlas instead ATLAS,
which should improve the performance of the linear algebra functions.

The NumPy 1.13.3 release is a re-release of 1.13.2, which suffered from a
bug in Cython 0.27.0.

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 22 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.
• #9772 DOC: Create the 1.13.2 release notes.
• #9794 DOC: Create 1.13.3 release notes.

Checksums

MD5

---

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SHA256

---

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2875e8055a1ea8d933b1c9d0f8714c0aa11c097bfadfcb8564c4d868fbf09a41  numpy-1.13.3-cp34-none-win_amd64.whl
09b87d652c03508447d0f618e1d3ae57595acd3e0f0c11ac91bf68ed7bdb3a28  numpy-1.13.3-cp35-cp35m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
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-----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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9160e9c633bac901caf90d249aa1a745  numpy-1.13.2-cp36-none-win_amd64.whl

SHA256

---

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==========================
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

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==========================
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 = 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.

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.

Temporary elision

On platforms providing the backtrace function NumPy will try to avoid
creating temporaries in expression involving basic numeric types.
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).

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]])]

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).

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 environment 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

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.

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.

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

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.

assert_array_ and masked arrays assert_equal hide less warnings

Some warnings that were previously hidden by the assert_array_
functions are not hidden anymore. In most cases the warnings should be
correct and, should they occur, will require changes to the tests using
these functions.
For the masked array assert_equal version, warnings may occur when
comparing NaT. The function presently does not handle NaT or NaN
specifically and it may be best to avoid it at this time should a warning
show up due to this change.

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.

Output arguments to ufuncs can be tuples also for ufunc methods

For calls to ufuncs, it was already possible, and recommended, to use an
out argument with a tuple for ufuncs with multiple outputs. This has now
been extended to output arguments in the reduce, accumulate, and
reduceat methods. This is mostly for compatibility with __array_ufunc;
there are no ufuncs yet that have more than one output.

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==========================
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 = 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.

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.

Temporary elision

On platforms providing the backtrace function NumPy will try to avoid
creating temporaries in expression involving basic numeric types.
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).

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]])]

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).

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 environment 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

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.

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.

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

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.

assert_array_ and masked arrays assert_equal hide less warnings

Some warnings that were previously hidden by the assert_array_
functions are not hidden anymore. In most cases the warnings should be
correct and, should they occur, will require changes to the tests using
these functions.
For the masked array assert_equal version, warnings may occur when
comparing NaT. The function presently does not handle NaT or NaN
specifically and it may be best to avoid it at this time should a warning
show up due to this change.

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.

Output arguments to ufuncs can be tuples also for ufunc methods

For calls to ufuncs, it was already possible, and recommended, to use an
out argument with a tuple for ufuncs with multiple outputs. This has now
been extended to output arguments in the reduce, accumulate, and
reduceat methods. This is mostly for compatibility with __array_ufunc;
there are no ufuncs yet that have more than one output.

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==========================
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 for creating block matrices.

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.

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).

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]])]

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).

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 environment 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

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.

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.

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

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.

assert_array_ and masked arrays assert_equal hide less warnings

Some warnings that were previously hidden by the assert_array_
functions are not hidden anymore. In most cases the warnings should be
correct and, should they occur, will require changes to the tests using
these functions.
For the masked array assert_equal version, warnings may occur when
comparing NaT. The function presently does not handle NaT or NaN
specifically and it may be best to avoid it at this time should a warning
show up due to this change.

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.

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==========================
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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==========================
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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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 nancumprod added

Nan-functions nancumsum and nancumprod have been added to
compute cumsum and cumprod by ignoring nans.

np.interp can now interpolate complex values

np.lib.interp(x, xp, fp) now allows the interpolated array fp
to be complex and will interpolate at complex128 precision.

New polynomial evaluation function polyvalfromroots added

The new function polyvalfromroots evaluates a polynomial at given points
from the roots of the polynomial. This is useful for higher order polynomials,
where expansion into polynomial coefficients is inaccurate at machine
precision.

New array creation function geomspace added

The new function geomspace generates a geometric sequence. It is similar
to logspace, but with start and stop specified directly:
geomspace(start, stop) behaves the same as
logspace(log10(start), log10(stop)).

New context manager for testing warnings

A new context manager suppress_warnings has been added to the testing
utils. This context manager is designed to help reliably test warnings.
Specifically to reliably filter/ignore warnings. Ignoring warnings
by using an "ignore" filter in Python versions before 3.4.x can quickly
result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar
to the catch_warnings context, but allows for easier specificity.
Also printing warnings that have not been filtered or nesting the
context manager will work as expected. Additionally, it is possible
to use the context manager as a decorator which can be useful when
multiple tests give need to hide the same warning.

New masked array functions ma.convolve and ma.correlate added

These functions wrapped the non-masked versions, but propagate through masked
values. There are two different propagation modes. The default causes masked
values to contaminate the result with masks, but the other mode only outputs
masks if there is no alternative.

New float_power ufunc

The new float_power ufunc is like the power function except all
computation is done in a minimum precision of float64. There was a long
discussion on the numpy mailing list of how to treat integers to negative
integer powers and a popular proposal was that the __pow__ operator should
always return results of at least float64 precision. The float_power
function implements that option. Note that it does not support object arrays.

np.loadtxt now supports a single integer as usecol argument

Instead of using usecol=(n,) to read the nth column of a file
it is now allowed to use usecol=n. Also the error message is
more user friendly when a non-integer is passed as a column index.

Improved automated bin estimators for histogram

Added 'doane' and 'sqrt' estimators to histogram via the bins
argument. Added support for range-restricted histograms with automated
bin estimation.

np.roll can now roll multiple axes at the same time

The shift and axis arguments to roll are now broadcast against each
other, and each specified axis is shifted accordingly.

The __complex__ method has been implemented for the ndarrays

Calling complex() on a size 1 array will now cast to a python
complex.

pathlib.Path objects now supported

The standard np.load, np.save, np.loadtxt, np.savez, and similar
functions can now take pathlib.Path objects as an argument instead of a
filename or open file object.

New bits attribute for np.finfo

This makes np.finfo consistent with np.iinfo which already has that
attribute.

New signature argument to np.vectorize

This argument allows for vectorizing user defined functions with core
dimensions, in the style of NumPy's
:ref:generalized universal functions<c-api.generalized-ufuncs>. This allows
for vectorizing a much broader class of functions. For example, an arbitrary
distance metric that combines two vectors to produce a scalar could be
vectorized with signature='(n),(n)->()'. See np.vectorize for full
details.

Emit py3kwarnings for division of integer arrays

To help people migrate their code bases from Python 2 to Python 3, the
python interpreter has a handy option -3, which issues warnings at runtime.
One of its warnings is for integer division::

$ python -3 -c "2/3"

-c:1: DeprecationWarning: classic int division

In Python 3, the new integer division semantics also apply to numpy arrays.
With this version, numpy will emit a similar warning::

$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"

-c:1: DeprecationWarning: numpy: classic int division

numpy.sctypes now includes bytes on Python3 too

Previously, it included str (bytes) and unicode on Python2, but only str
(unicode) on Python3.

Improvements

bitwise_and identity changed

The previous identity was 1 with the result that all bits except the LSB were
masked out when the reduce method was used. The new identity is -1, which
should work properly on twos complement machines as all bits will be set to
one.

Generalized Ufuncs will now unlock the GIL

Generalized Ufuncs, including most of the linalg module, will now unlock
the Python global interpreter lock.

Caches in np.fft are now bounded in total size and item count

The caches in np.fft that speed up successive FFTs of the same length can no
longer grow without bounds. They have been replaced with LRU (least recently
used) caches that automatically evict no longer needed items if either the
memory size or item count limit has been reached.

Improved handling of zero-width string/unicode dtypes

Fixed several interfaces that explicitly disallowed arrays with zero-width
string dtypes (i.e. dtype('S0') or dtype('U0'), and fixed several
bugs where such dtypes were not handled properly. In particular, changed
ndarray.__new__ to not implicitly convert dtype('S0') to
dtype('S1') (and likewise for unicode) when creating new arrays.

Integer ufuncs vectorized with AVX2

If the cpu supports it at runtime the basic integer ufuncs now use AVX2
instructions. This feature is currently only available when compiled with GCC.

Order of operations optimization in np.einsum

np.einsum now supports the optimize argument which will optimize the
order of contraction. For example, np.einsum would complete the chain dot
example np.einsum(‘ij,jk,kl->il’, a, b, c) in a single pass which would
scale like N^4; however, when optimize=True np.einsum will create
an intermediate array to reduce this scaling to N^3 or effectively
np.dot(a, b).dot(c). Usage of intermediate tensors to reduce scaling has
been applied to the general einsum summation notation. See np.einsum_path
for more details.

quicksort has been changed to an introsort

The quicksort kind of np.sort and np.argsort is now an introsort which
is regular quicksort but changing to a heapsort when not enough progress is
made. This retains the good quicksort performance while changing the worst case
runtime from O(N^2) to O(N*log(N)).

ediff1d improved performance and subclass handling

The ediff1d function uses an array instead on a flat iterator for the
subtraction. When to_begin or to_end is not None, the subtraction is performed
in place to eliminate a copy operation. A side effect is that certain
subclasses are handled better, namely astropy.Quantity, since the complete
array is created, wrapped, and then begin and end values are set, instead of
using concatenate.

Improved precision of ndarray.mean for float16 arrays

The computation of the mean of float16 arrays is now carried out in float32 for
improved precision. This should be useful in packages such as Theano
where the precision of float16 is adequate and its smaller footprint is
desireable.

Changes

All array-like methods are now called with keyword arguments in fromnumeric.py

Internally, many array-like methods in fromnumeric.py were being called with
positional arguments instead of keyword arguments as their external signatures
were doing. This caused a complication in the downstream 'pandas' library
that encountered an issue with 'numpy' compatibility. Now, all array-like
methods in this module are called with keyword arguments instead.

Operations on np.memmap objects return numpy arrays in most cases

Previously operations on a memmap object would misleadingly return a memmap
instance even if the result was actually not memmapped. For example,
arr + 1 or arr + arr would return memmap instances, although no memory
from the output array is memmaped. Version 1.12 returns ordinary numpy arrays
from these operations.

Also, reduction of a memmap (e.g. .sum(axis=None) now returns a numpy
scalar instead of a 0d memmap.

stacklevel of warnings increased

The stacklevel for python based warnings was increased so that most warnings
will report the offending line of the user code instead of the line the
warning itself is given. Passing of stacklevel is now tested to ensure that
new warnings will receive the stacklevel argument.

This causes warnings with the "default" or "module" filter to be shown once
for every offending user code line or user module instead of only once. On
python versions before 3.4, this can cause warnings to appear that were falsely
ignored before, which may be surprising especially in test suits.

Contributors

A total of 139 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Aditya Panchal +
• Ales Erjavec +
• Alex Griffing
• Alexandr Shadchin +
• Alistair Muldal
• Allan Haldane
• Amit Aronovitch +
• Andrei Kucharavy +
• Antony Lee
• Antti Kaihola +
• Arne de Laat +
• Auke Wiggers +
• AustereCuriosity +
• Badhri Narayanan Krishnakumar +
• Ben North +
• Ben Rowland +
• Bertrand Lefebvre
• Boxiang Sun
• CJ Carey
• Charles Harris
• Christoph Gohlke
• Daniel Ching +
• Daniel Rasmussen +
• Daniel Smith +
• David Schaich +
• Denis Alevi +
• Devin Jeanpierre +
• Dmitry Odzerikho
• Dongjoon Hyun +
• Edward Richards +
• Ekaterina Tuzova +
• Emilien Kofman +
• Endolith
• Eren Sezener +
• Eric Moore
• Eric Quintero +
• Eric Wieser +
• Erik M. Bray
• Frederic Bastien
• Friedrich Dunne +
• Gerrit Holl
• Golnaz Irannejad +
• Graham Markall +
• Greg Knoll +
• Greg Young
• Gustavo Serra Scalet +
• Ines Wichert +
• Irvin Probst +
• Jaime Fernandez
• James Sanders +
• Jan David Mol +
• Jan Schlüter
• Jeremy Tuloup +
• John Kirkham
• John Zwinck +
• Jonathan Helmus
• Joseph Fox-Rabinovitz
• Josh Wilson +
• Joshua Warner +
• Julian Taylor
• Ka Wo Chen +
• Kamil Rytarowski +
• Kelsey Jordahl +
• Kevin Deldycke +
• Khaled Ben Abdallah Okuda +
• Lion Krischer +
• Loïc Estève +
• Luca Mussi +
• Mads Ohm Larsen +
• Manoj Kumar +
• Mario Emmenlauer +
• Marshall Bockrath-Vandegrift +
• Marshall Ward +
• Marten van Kerkwijk
• Mathieu Lamarre +
• Matthew Brett
• Matthew Harrigan +
• Matthias Geier
• Matti Picus +
• Meet Udeshi +
• Michael Felt +
• Michael Goerz +
• Michael Martin +
• Michael Seifert +
• Mike Nolta +
• Nathaniel Beaver +
• Nathaniel J. Smith
• Naveen Arunachalam +
• Nick Papior
• Nikola Forró +
• Oleksandr Pavlyk +
• Olivier Grisel
• Oren Amsalem +
• Pauli Virtanen
• Pavel Potocek +
• Pedro Lacerda +
• Peter Creasey +
• Phil Elson +
• Philip Gura +
• Phillip J. Wolfram +
• Pierre de Buyl +
• Raghav RV +
• Ralf Gommers
• Ray Donnelly +
• Rehas Sachdeva
• Rob Malouf +
• Robert Kern
• Samuel St-Jean
• Sanchez Gonzalez Alvaro +
• Saurabh Mehta +
• Scott Sanderson +
• Sebastian Berg
• Shayan Pooya +
• Shota Kawabuchi +
• Simon Conseil
• Simon Gibbons
• Sorin Sbarnea +
• Stefan van der Walt
• Stephan Hoyer
• Steven J Kern +
• Stuart Archibald
• Tadeu Manoel +
• Takuya Akiba +
• Thomas A Caswell
• Tom Bird +
• Tony Kelman +
• Toshihiro Kamishima +
• Valentin Valls +
• Varun Nayyar
• Victor Stinner +
• Warren Weckesser
• Wendell Smith
• Wojtek Ruszczewski +
• Xavier Abellan Ecija +
• Yaroslav Halchenko
• Yash Shah +
• Yinon Ehrlich +
• Yu Feng +
• nevimov +

Pull requests merged

A total of 418 pull requests were merged for this release.

• #4073: BUG: change real output checking to test if all imaginary parts...
• #4619: BUG : np.sum silently drops keepdims for sub-classes of ndarray
• #5488: ENH: add contract: optimizing numpy's einsum expression
• #5706: ENH: make some masked array methods behave more like ndarray...
• #5822: Allow many distributions to have a scale of 0.
• #6054: WIP: MAINT: Add deprecation warning to views of multi-field indexes
• #6298: Check lower base limit in base_repr.
• #6430: Fix issues with zero-width string fields
• #6656: ENH: usecols now accepts an int when only one column has to be...
• #6660: Added pathlib support for several functions
• #6872: ENH: linear interpolation of complex values in lib.interp
• #6997: MAINT: Simplify mtrand.pyx helpers
• #7003: BUG: Fix string copying for np.place
• #7026: DOC: Clarify behavior in np.random.uniform
• #7055: BUG: One Element Array Inputs Return Scalars in np.random
• #7063: REL: Update master branch after 1.11.x branch has been made.
• #7073: DOC: Update the 1.11.0 release notes.
• #7076: MAINT: Update the git .mailmap file.
• #7082: TST, DOC: Added Broadcasting Tests in test_random.py
• #7087: BLD: fix compilation on non glibc-Linuxes
• #7088: BUG: Have norm cast non-floating point arrays to 64-bit float...
• #7090: ENH: Added 'doane' and 'sqrt' estimators to np.histogram in numpy.function_base
• #7091: Revert "BLD: fix compilation on non glibc-Linuxes"
• #7092: BLD: fix compilation on non glibc-Linuxes
• #7099: TST: Suppressed warnings
• #7102: MAINT: Removed conditionals that are always false in datetime_strings.c
• #7105: DEP: Deprecate as_strided returning a writable array as default
• #7109: DOC: update Python versions requirements in the install docs
• #7114: MAINT: Fix typos in docs
• #7116: TST: Fixed f2py test for win32 virtualenv
• #7118: TST: Fixed f2py test for non-versioned python executables
• #7119: BUG: Fixed mingw.lib error
• #7125: DOC: Updated documentation wording and examples for np.percentile.
• #7129: BUG: Fixed 'midpoint' interpolation of np.percentile in odd cases.
• #7131: Fix setuptools sdist
• #7133: ENH: savez: temporary file alongside with target file and improve...
• #7134: MAINT: Fix some typos in a code string and comments
• #7141: BUG: Unpickled void scalars should be contiguous
• #7144: MAINT: Change call_fortran into callfortran in comments.
• #7145: BUG: Fixed regressions in np.piecewise in ref to #5737 and #5729.
• #7147: Temporarily disable __numpy_ufunc__
• #7148: ENH,TST: Bump stacklevel and add tests for warnings
• #7149: TST: Add missing suffix to temppath manager
• #7152: BUG: mode kwargs passed as unicode to np.pad raises an exception
• #7156: BUG: Reascertain that linspace respects ndarray subclasses in...
• #7167: DOC: Update Wikipedia references for mtrand.pyx
• #7171: TST: Fixed f2py test for Anaconda non-win32
• #7174: DOC: Fix broken pandas link in release notes
• #7177: ENH: added axis param for np.count_nonzero
• #7178: BUG: Fix binary_repr for negative numbers
• #7180: BUG: Fixed previous attempt to fix dimension mismatch in nanpercentile
• #7181: DOC: Updated minor typos in function_base.py and test_function_base.py
• #7191: DOC: add vstack, hstack, dstack reference to stack documentation.
• #7193: MAINT: Removed supurious assert in histogram estimators
• #7194: BUG: Raise a quieter MaskedArrayFutureWarning for mask changes.
• #7195: STY: Drop some trailing spaces in numpy.ma.core.
• #7196: Revert "DOC: add vstack, hstack, dstack reference to stack documentation."
• #7197: TST: Pin virtualenv used on Travis CI.
• #7198: ENH: Unlock the GIL for gufuncs
• #7199: MAINT: Cleanup for histogram bin estimator selection
• #7201: Raise IOError on not a file in python2
• #7202: MAINT: Made iterable return a boolean
• #7209: TST: Bump virtualenv to 14.0.6
• #7211: DOC: Fix fmin examples
• #7215: MAINT: Use PySlice_GetIndicesEx instead of custom reimplementation
• #7229: ENH: implement __complex__
• #7231: MRG: allow distributors to run custom init
• #7232: BLD: Switch order of test for lapack_mkl and openblas_lapack
• #7239: DOC: Removed residual merge markup from previous commit
• #7240: Change 'pubic' to 'public'.
• #7241: MAINT: update doc/sphinxext to numpydoc 0.6.0, and fix up some...
• #7243: ENH: Adding support to the range keyword for estimation of the...
• #7246: DOC: metion writeable keyword in as_strided in release notes
• #7247: TST: Fail quickly on AppVeyor for superseded PR builds
• #7248: DOC: remove link to documentation wiki editor from HOWTO_DOCUMENT.
• #7250: DOC,REL: Update 1.11.0 notes.
• #7251: BUG: only benchmark complex256 if it exists
• #7252: Forward port a fix and enhancement from 1.11.x
• #7253: DOC: note in h/v/dstack points users to stack/concatenate
• #7254: BUG: Enforce dtype for randint singletons
• #7256: MAINT: Use is None or is not None instead of == None or...
• #7257: DOC: Fix mismatched variable names in docstrings.
• #7258: ENH: Make numpy floor_divide and remainder agree with Python...
• #7260: BUG/TST: Fix #7259, do not "force scalar" for already scalar...
• #7261: Added self to mailmap
• #7266: BUG: Segfault for classes with deceptive __len__
• #7268: ENH: add geomspace function
• #7274: BUG: Preserve array order in np.delete
• #7275: DEP: Warn about assigning 'data' attribute of ndarray
• #7276: DOC: apply_along_axis missing whitespace inserted (before colon)
• #7278: BUG: Make returned unravel_index arrays writeable
• #7279: TST: Fixed elements being shuffled
• #7280: MAINT: Remove redundant trailing semicolons.
• #7285: BUG: Make Randint Backwards Compatible with Pandas
• #7286: MAINT: Fix typos in docs/comments of ma and polynomial modules.
• #7292: Clarify error on repr failure in assert_equal.
• #7294: ENH: add support for BLIS to numpy.distutils
• #7295: DOC: understanding code and getting started section to dev doc
• #7296: Revert part of #3907 which incorrectly propogated MaskedArray...
• #7299: DOC: Fix mismatched variable names in docstrings.
• #7300: DOC: dev: stop recommending keeping local master updated with...
• #7301: DOC: Update release notes
• #7305: BUG: Remove data race in mtrand: two threads could mutate the...
• #7307: DOC: Missing some characters in link.
• #7308: BUG: Incrementing the wrong reference on return
• #7310: STY: Fix GitHub rendering of ordered lists >9
• #7311: ENH: Make _pointer_type_cache functional
• #7313: DOC: corrected grammatical error in quickstart doc
• #7325: BUG, MAINT: Improve fromnumeric.py interface for downstream compatibility
• #7328: DEP: Deprecated using a float index in linspace
• #7331: Add comment, TST: fix MemoryError on win32
• #7332: Check for no solution in np.irr Fixes #6744
• #7338: TST: Install pytz in the CI.
• #7340: DOC: Fixed math rendering in tensordot docs.
• #7341: TST: Add test for #6469
• #7344: DOC: Fix more typos in docs and comments.
• #7346: Generalized flip
• #7347: ENH Generalized rot90
• #7348: Maint: Removed extra space from ureduce
• #7349: MAINT: Hide nan warnings for masked internal MA computations
• #7350: BUG: MA ufuncs should set mask to False, not array([False])
• #7351: TST: Fix some MA tests to avoid looking at the .data attribute
• #7358: BUG: pull request related to the issue #7353
• #7359: Update 7314, DOC: Clarify valid integer range for random.seed...
• #7361: MAINT: Fix copy and paste oversight.
• #7363: ENH: Make no unshare mask future warnings less noisy
• #7366: TST: fix #6542, add tests to check non-iterable argument raises...
• #7373: ENH: Add bitwise_and identity
• #7378: added NumPy logo and separator
• #7382: MAINT: cleanup np.average
• #7385: DOC: note about wheels / windows wheels for pypi
• #7386: Added label icon to Travis status
• #7397: BUG: incorrect type for objects whose __len__ fails
• #7398: DOC: fix typo
• #7404: Use PyMem_RawMalloc on Python 3.4 and newer
• #7406: ENH ufunc called on memmap return a ndarray
• #7407: BUG: Fix decref before incref for in-place accumulate
• #7410: DOC: add nanprod to the list of math routines
• #7414: Tweak corrcoef
• #7415: DOC: Documention fixes
• #7416: BUG: Incorrect handling of range in histogram with automatic...
• #7418: DOC: Minor typo fix, hermefik -> hermefit.
• #7421: ENH: adds np.nancumsum and np.nancumprod
• #7423: BUG: Ongoing fixes to PR#7416
• #7430: DOC: Update 1.11.0-notes.
• #7433: MAINT: FutureWarning for changes to np.average subclass handling
• #7437: np.full now defaults to the filling value's dtype.
• #7438: Allow rolling multiple axes at the same time.
• #7439: BUG: Do not try sequence repeat unless necessary
• #7442: MANT: Simplify diagonal length calculation logic
• #7445: BUG: reference count leak in bincount, fixes #6805
• #7446: DOC: ndarray typo fix
• #7447: BUG: scalar integer negative powers gave wrong results.
• #7448: DOC: array "See also" link to full and full_like instead of fill
• #7456: BUG: int overflow in reshape, fixes #7455, fixes #7293
• #7463: BUG: fix array too big error for wide dtypes.
• #7466: BUG: segfault inplace object reduceat, fixes #7465
• #7468: BUG: more on inplace reductions, fixes #615
• #7469: MAINT: Update git .mailmap
• #7472: MAINT: Update .mailmap.
• #7477: MAINT: Yet more .mailmap updates for recent contributors.
• #7481: BUG: Fix segfault in PyArray_OrderConverter
• #7482: BUG: Memory Leak in _GenericBinaryOutFunction
• #7489: Faster real_if_close.
• #7491: DOC: Update subclassing doc regarding downstream compatibility
• #7496: BUG: don't use pow for integer power ufunc loops.
• #7504: DOC: remove "arr" from keepdims docstrings
• #7505: MAIN: fix to #7382, make scl in np.average writeable
• #7507: MAINT: Remove nose.SkipTest import.
• #7508: DOC: link frompyfunc and vectorize
• #7511: numpy.power(0, 0) should return 1
• #7515: BUG: MaskedArray.count treats negative axes incorrectly
• #7518: BUG: Extend glibc complex trig functions blacklist to glibc <...
• #7521: DOC: rephrase writeup of memmap changes
• #7522: BUG: Fixed iteration over additional bad commands
• #7526: DOC: Removed an extra :const:
• #7529: BUG: Floating exception with invalid axis in np.lexsort
• #7534: MAINT: Update setup.py to reflect supported python versions.
• #7536: MAINT: Always use PyCapsule instead of PyCObject in mtrand.pyx
• #7539: MAINT: Cleanup of random stuff
• #7549: BUG: allow graceful recovery for no Liux compiler
• #7562: BUG: Fix test_from_object_array_unicode (test_defchararray.TestBasic)…
• #7565: BUG: Fix test_ctypeslib and test_indexing for debug interpreter
• #7566: MAINT: use manylinux1 wheel for cython
• #7568: Fix a false positive OverflowError in Python 3.x when value above...
• #7579: DOC: clarify purpose of Attributes section
• #7584: BUG: fixes #7572, percent in path
• #7586: Make np.ma.take works on scalars
• #7587: BUG: linalg.norm(): Don't convert object arrays to float
• #7598: Cast array size to int64 when loading from archive
• #7602: DOC: Remove isreal and iscomplex from ufunc list
• #7605: DOC: fix incorrect Gamma distribution parameterization comments
• #7609: BUG: Fix TypeError when raising TypeError
• #7611: ENH: expose test runner raise_warnings option
• #7614: BLD: Avoid using os.spawnve in favor of os.spawnv in exec_command
• #7618: BUG: distance arg of np.gradient must be scalar, fix docstring
• #7626: DOC: RST definition list fixes
• #7627: MAINT: unify tup processing, move tup use to after all PyTuple_SetItem...
• #7630: MAINT: add ifdef around PyDictProxy_Check macro
• #7631: MAINT: linalg: fix comment, simplify math
• #7634: BLD: correct C compiler customization in system_info.py Closes...
• #7635: BUG: ma.median alternate fix for #7592
• #7636: MAINT: clean up testing.assert_raises_regexp, 2.6-specific code...
• #7637: MAINT: clearer exception message when importing multiarray fails.
• #7639: TST: fix a set of test errors in master.
• #7643: DOC : minor changes to linspace docstring
• #7651: BUG: one to any power is still 1. Broken edgecase for int arrays
• #7655: BLD: Remove Intel compiler flag -xSSE4.2
• #7658: BUG: fix incorrect printing of 1D masked arrays
• #7659: BUG: Temporary fix for str(mvoid) for object field types
• #7664: BUG: Fix unicode with byte swap transfer and copyswap
• #7667: Restore histogram consistency
• #7668: ENH: Do not check the type of module.dict explicit in test.
• #7669: BUG: boolean assignment no GIL release when transfer needs API
• #7673: DOC: Create Numpy 1.11.1 release notes.
• #7675: BUG: fix handling of right edge of final bin.
• #7678: BUG: Fix np.clip bug NaN handling for Visual Studio 2015
• #7679: MAINT: Fix up C++ comment in arraytypes.c.src.
• #7681: DOC: Update 1.11.1 release notes.
• #7686: ENH: Changing FFT cache to a bounded LRU cache
• #7688: DOC: fix broken genfromtxt examples in user guide. Closes gh-7662.
• #7689: BENCH: add correlate/convolve benchmarks.
• #7696: DOC: update wheel build / upload instructions
• #7699: BLD: preserve library order
• #7704: ENH: Add bits attribute to np.finfo
• #7712: BUG: Fix race condition with new FFT cache
• #7715: BUG: Remove memory leak in np.place
• #7719: BUG: Fix segfault in np.random.shuffle for arrays of different...
• #7723: Change mkl_info.dir_env_var from MKL to MKLROOT
• #7727: DOC: Corrections in Datetime Units-arrays.datetime.rst
• #7729: DOC: fix typo in savetxt docstring (closes #7620)
• #7733: Update 7525, DOC: Fix order='A' docs of np.array.
• #7734: Update 7542, ENH: Add polyrootval to numpy.polynomial
• #7735: BUG: fix issue on OS X with Python 3.x where npymath.ini was...
• #7739: DOC: Mention the changes of #6430 in the release notes.
• #7740: DOC: add reference to poisson rng
• #7743: Update 7476, DEP: deprecate Numeric-style typecodes, closes #2148
• #7744: DOC: Remove "ones_like" from ufuncs list (it is not)
• #7746: DOC: Clarify the effect of rcond in numpy.linalg.lstsq.
• #7747: Update 7672, BUG: Make sure we don't divide by zero
• #7748: DOC: Update float32 mean example in docstring
• #7754: Update 7612, ENH: Add broadcast.ndim to match code elsewhere.
• #7757: Update 7175, BUG: Invalid read of size 4 in PyArray_FromFile
• #7759: BUG: Fix numpy.i support for numpy API < 1.7.
• #7760: ENH: Make assert_almost_equal & assert_array_almost_equal consistent.
• #7766: fix an English typo
• #7771: DOC: link geomspace from logspace
• #7773: DOC: Remove a redundant the
• #7777: DOC: Update Numpy 1.11.1 release notes.
• #7785: DOC: update wheel building procedure for release
• #7789: MRG: add note of 64-bit wheels on Windows
• #7791: f2py.compile issues (#7683)
• #7799: "lambda" is not allowed to use as keyword arguments in a sample...
• #7803: BUG: interpret 'c' PEP3118/struct type as 'S1'.
• #7807: DOC: Misplaced parens in formula
• #7817: BUG: Make sure npy_mul_with_overflow_ detects overflow.
• #7818: numpy/distutils/misc_util.py fix for #7809: check that _tmpdirs...
• #7820: MAINT: Allocate fewer bytes for empty arrays.
• #7823: BUG: Fixed masked array behavior for scalar inputs to np.ma.atleast_*d
• #7834: DOC: Added an example
• #7839: Pypy fixes
• #7840: Fix ATLAS version detection
• #7842: Fix versionadded tags
• #7848: MAINT: Fix remaining uses of deprecated Python imp module.
• #7853: BUG: Make sure numpy globals keep identity after reload.
• #7863: ENH: turn quicksort into introsort
• #7866: Document runtests extra argv
• #7871: BUG: handle introsort depth limit properly
• #7879: DOC: fix typo in documentation of loadtxt (closes #7878)
• #7885: Handle NetBSD specific <sys/endian.h>
• #7889: DOC: #7881. Fix link to record arrays
• #7894: fixup-7790, BUG: construct ma.array from np.array which contains...
• #7898: Spelling and grammar fix.
• #7903: BUG: fix float16 type not being called due to wrong ordering
• #7908: BLD: Fixed detection for recent MKL versions
• #7911: BUG: fix for issue#7835 (ma.median of 1d)
• #7912: ENH: skip or avoid gc/objectmodel differences btwn pypy and cpython
• #7918: ENH: allow numpy.apply_along_axis() to work with ndarray subclasses
• #7922: ENH: Add ma.convolve and ma.correlate for #6458
• #7925: Monkey-patch _msvccompile.gen_lib_option like any other compilators
• #7931: BUG: Check for HAVE_LDOUBLE_DOUBLE_DOUBLE_LE in npy_math_complex.
• #7936: ENH: improve duck typing inside iscomplexobj
• #7937: BUG: Guard against buggy comparisons in generic quicksort.
• #7938: DOC: add cbrt to math summary page
• #7941: BUG: Make sure numpy globals keep identity after reload.
• #7943: DOC: #7927. Remove deprecated note for memmap relevant for Python...
• #7952: BUG: Use keyword arguments to initialize Extension base class.
• #7956: BLD: remove __NUMPY_SETUP__ from builtins at end of setup.py
• #7963: BUG: MSVCCompiler grows 'lib' & 'include' env strings exponentially.
• #7965: BUG: cannot modify tuple after use
• #7976: DOC: Fixed documented dimension of return value
• #7977: DOC: Create 1.11.2 release notes.
• #7979: DOC: Corrected allowed keywords in add_(installed_)library
• #7980: ENH: Add ability to runtime select ufunc loops, add AVX2 integer...
• #7985: Rebase 7763, ENH: Add new warning suppression/filtering context
• #7987: DOC: See also np.load and np.memmap in np.lib.format.open_memmap
• #7988: DOC: Include docstring for cbrt, spacing and fabs in documentation
• #7999: ENH: add inplace cases to fast ufunc loop macros
• #8006: DOC: Update 1.11.2 release notes.
• #8008: MAINT: Remove leftover imp module imports.
• #8009: DOC: Fixed three typos in the c-info.ufunc-tutorial
• #8011: DOC: Update 1.11.2 release notes.
• #8014: BUG: Fix fid.close() to use os.close(fid)
• #8016: BUG: Fix numpy.ma.median.
• #8018: BUG: Fixes return for np.ma.count if keepdims is True and axis...
• #8021: DOC: change all non-code instances of Numpy to NumPy
• #8027: ENH: Add platform indepedent lib dir to PYTHONPATH
• #8028: DOC: Update 1.11.2 release notes.
• #8030: BUG: fix np.ma.median with only one non-masked value and an axis...
• #8038: MAINT: Update error message in rollaxis.
• #8040: Update add_newdocs.py
• #8042: BUG: core: fix bug in NpyIter buffering with discontinuous arrays
• #8045: DOC: Update 1.11.2 release notes.
• #8050: remove refcount semantics, now a.resize() almost always requires...
• #8051: Clear signaling NaN exceptions
• #8054: ENH: add signature argument to vectorize for vectorizing like...
• #8057: BUG: lib: Simplify (and fix) pad's handling of the pad_width
• #8061: BUG : financial.pmt modifies input (issue #8055)
• #8064: MAINT: Add PMIP files to .gitignore
• #8065: BUG: Assert fromfile ending earlier in pyx_processing
• #8066: BUG, TST: Fix python3-dbg bug in Travis script
• #8071: MAINT: Add Tempita to randint helpers
• #8075: DOC: Fix description of isinf in nan_to_num
• #8080: BUG: non-integers can end up in dtype offsets
• #8081: Update outdated Nose URL to nose.readthedocs.io
• #8083: ENH: Deprecation warnings for / integer division when running...
• #8084: DOC: Fix erroneous return type description for np.roots.
• #8087: BUG: financial.pmt modifies input #8055
• #8088: MAINT: Remove duplicate randint helpers code.
• #8093: MAINT: fix assert_raises_regex when used as a context manager
• #8096: ENH: Vendorize tempita.
• #8098: DOC: Enhance description/usage for np.linalg.eig*h
• #8103: ENH: Pypy fixes
• #8104: Fix test code on cpuinfo's main function
• #8107: BUG: Fix array printing with precision=0.
• #8109: Fix bug in ravel_multi_index for big indices (Issue #7546)
• #8110: BUG: distutils: fix issue with rpath in fcompiler/gnu.py
• #8111: ENH: Add a tool for release authors and PRs.
• #8112: DOC: Fix "See also" links in linalg.
• #8114: BUG: core: add missing error check after PyLong_AsSsize_t
• #8121: DOC: Improve histogram2d() example.
• #8122: BUG: Fix broken pickle in MaskedArray when dtype is object (Return...
• #8124: BUG: Fixed build break
• #8125: Rebase, BUG: Fixed deepcopy of F-order object arrays.
• #8127: BUG: integers to a negative integer powers should error.
• #8141: improve configure checks for broken systems
• #8142: BUG: np.ma.mean and var should return scalar if no mask
• #8148: BUG: import full module path in npy_load_module
• #8153: MAINT: Expose void-scalar "base" attribute in python
• #8156: DOC: added example with empty indices for a scalar, #8138
• #8160: BUG: fix _array2string for structured array (issue #5692)
• #8164: MAINT: Update mailmap for NumPy 1.12.0
• #8165: Fixup 8152, BUG: assert_allclose(..., equal_nan=False) doesn't...
• #8167: Fixup 8146, DOC: Clarify when PyArray_{Max, Min, Ptp} return...
• #8168: DOC: Minor spelling fix in genfromtxt() docstring.
• #8173: BLD: Enable build on AIX
• #8174: DOC: warn that dtype.descr is only for use in PEP3118
• #8177: MAINT: Add python 3.6 support to suppress_warnings
• #8178: MAINT: Fix ResourceWarning new in Python 3.6.
• #8180: FIX: protect stolen ref by PyArray_NewFromDescr in array_empty
• #8181: ENH: Improve announce to find github squash-merge commits.
• #8182: MAINT: Update .mailmap
• #8183: MAINT: Ediff1d performance
• #8184: MAINT: make assert_allclose behavior on nans match pre 1.12
• #8188: DOC: 'highest' is exclusive for randint()
• #8189: BUG: setfield should raise if arr is not writeable
• #8190: ENH: Add a float_power function with at least float64 precision.
• #8197: DOC: Add missing arguments to np.ufunc.outer
• #8198: DEP: Deprecate the keepdims argument to accumulate
• #8199: MAINT: change path to env in distutils.system_info. Closes gh-8195.
• #8200: BUG: Fix structured array format functions
• #8202: ENH: specialize name of dev package by interpreter
• #8205: DOC: change development instructions from SSH to HTTPS access.
• #8216: DOC: Patch doc errors for atleast_nd and frombuffer
• #8218: BUG: ediff1d should return subclasses
• #8219: DOC: Turn SciPy references into links.
• #8222: ENH: Make numpy.mean() do more precise computation
• #8227: BUG: Better check for invalid bounds in np.random.uniform.
• #8231: ENH: Refactor numpy ** operators for numpy scalar integer powers
• #8234: DOC: Clarified when a copy is made in numpy.asarray
• #8236: DOC: Fix documentation pull requests.
• #8238: MAINT: Update pavement.py
• #8239: ENH: Improve announce tool.
• #8240: REL: Prepare for 1.12.x branch
• #8243: BUG: Update operator ** tests for new behavior.
• #8246: REL: Reset strides for RELAXED_STRIDE_CHECKING for 1.12 releases.
• #8265: BUG: np.piecewise not working for scalars
• #8272: TST: Path test should resolve symlinks when comparing
• #8282: DOC: Update 1.12.0 release notes.
• #8286: BUG: Fix pavement.py write_release_task.
• #8296: BUG: Fix iteration over reversed subspaces in mapiter_@name@.
• #8304: BUG: Fix PyPy crash in PyUFunc_GenericReduction.
• #8319: BLD: blacklist powl (longdouble power function) on OS X.
• #8320: BUG: do not link to Accelerate if OpenBLAS, MKL or BLIS are found.
• #8322: BUG: fixed kind specifications for parameters
• #8336: BUG: fix packbits and unpackbits to correctly handle empty arrays
• #8338: BUG: fix test_api test that fails intermittently in python 3
• #8339: BUG: Fix ndarray.tofile large file corruption in append mode.
• #8359: BUG: Fix suppress_warnings (again) for Python 3.6.
• #8372: BUG: Fixes for ma.median and nanpercentile.
• #8373: BUG: correct letter case
• #8379: DOC: Update 1.12.0-notes.rst.
• #8390: ENH: retune apply_along_axis nanmedian cutoff in 1.12
• #8391: DEP: Fix escaped string characters deprecated in Python 3.6.
• #8394: DOC: create 1.11.3 release notes.
• #8399: BUG: Fix author search in announce.py
• #8402: DOC, MAINT: Update 1.12.0 notes and mailmap.
• #8418: BUG: Fix ma.median even elements for 1.12
• #8424: DOC: Fix tools and release notes to be more markdown compatible.
• #8427: BUG: Add a lock to assert_equal and other testing functions
• #8431: BUG: Fix apply_along_axis() for when func1d() returns a non-ndarray.
• #8432: BUG: Let linspace accept input that has an array_interface.
• #8437: TST: Update 3.6-dev tests to 3.6 after Python final release.
• #8439: DOC: Update 1.12.0 release notes.
• #8466: MAINT: Update mailmap entries.
• #8467: DOC: Back-port the missing part of gh-8464.
• #8476: DOC: Update 1.12.0 release notes.
• #8477: DOC: Update 1.12.0 release notes.

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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 nancumprod added

Nan-functions nancumsum and nancumprod have been added to
compute cumsum and cumprod by ignoring nans.

np.interp can now interpolate complex values

np.lib.interp(x, xp, fp) now allows the interpolated array fp
to be complex and will interpolate at complex128 precision.

New polynomial evaluation function polyvalfromroots added

The new function polyvalfromroots evaluates a polynomial at given points
from the roots of the polynomial. This is useful for higher order polynomials,
where expansion into polynomial coefficients is inaccurate at machine
precision.

New array creation function geomspace added

The new function geomspace generates a geometric sequence. It is similar
to logspace, but with start and stop specified directly:
geomspace(start, stop) behaves the same as
logspace(log10(start), log10(stop)).

New context manager for testing warnings

A new context manager suppress_warnings has been added to the testing
utils. This context manager is designed to help reliably test warnings.
Specifically to reliably filter/ignore warnings. Ignoring warnings
by using an "ignore" filter in Python versions before 3.4.x can quickly
result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar
to the catch_warnings context, but allows for easier specificity.
Also printing warnings that have not been filtered or nesting the
context manager will work as expected. Additionally, it is possible
to use the context manager as a decorator which can be useful when
multiple tests give need to hide the same warning.

New masked array functions ma.convolve and ma.correlate added

These functions wrapped the non-masked versions, but propagate through masked
values. There are two different propagation modes. The default causes masked
values to contaminate the result with masks, but the other mode only outputs
masks if there is no alternative.

New float_power ufunc

The new float_power ufunc is like the power function except all
computation is done in a minimum precision of float64. There was a long
discussion on the numpy mailing list of how to treat integers to negative
integer powers and a popular proposal was that the __pow__ operator should
always return results of at least float64 precision. The float_power
function implements that option. Note that it does not support object arrays.

np.loadtxt now supports a single integer as usecol argument

Instead of using usecol=(n,) to read the nth column of a file
it is now allowed to use usecol=n. Also the error message is
more user friendly when a non-integer is passed as a column index.

Improved automated bin estimators for histogram

Added 'doane' and 'sqrt' estimators to histogram via the bins
argument. Added support for range-restricted histograms with automated
bin estimation.

np.roll can now roll multiple axes at the same time

The shift and axis arguments to roll are now broadcast against each
other, and each specified axis is shifted accordingly.

The __complex__ method has been implemented for the ndarrays

Calling complex() on a size 1 array will now cast to a python
complex.

pathlib.Path objects now supported

The standard np.load, np.save, np.loadtxt, np.savez, and similar
functions can now take pathlib.Path objects as an argument instead of a
filename or open file object.

New bits attribute for np.finfo

This makes np.finfo consistent with np.iinfo which already has that
attribute.

New signature argument to np.vectorize

This argument allows for vectorizing user defined functions with core
dimensions, in the style of NumPy's
:ref:generalized universal functions<c-api.generalized-ufuncs>. This allows
for vectorizing a much broader class of functions. For example, an arbitrary
distance metric that combines two vectors to produce a scalar could be
vectorized with signature='(n),(n)->()'. See np.vectorize for full
details.

Emit py3kwarnings for division of integer arrays

To help people migrate their code bases from Python 2 to Python 3, the
python interpreter has a handy option -3, which issues warnings at runtime.
One of its warnings is for integer division::

$ python -3 -c "2/3"

-c:1: DeprecationWarning: classic int division

In Python 3, the new integer division semantics also apply to numpy arrays.
With this version, numpy will emit a similar warning::

$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"

-c:1: DeprecationWarning: numpy: classic int division

numpy.sctypes now includes bytes on Python3 too

Previously, it included str (bytes) and unicode on Python2, but only str
(unicode) on Python3.

Improvements

bitwise_and identity changed

The previous identity was 1 with the result that all bits except the LSB were
masked out when the reduce method was used. The new identity is -1, which
should work properly on twos complement machines as all bits will be set to
one.

Generalized Ufuncs will now unlock the GIL

Generalized Ufuncs, including most of the linalg module, will now unlock
the Python global interpreter lock.

Caches in np.fft are now bounded in total size and item count

The caches in np.fft that speed up successive FFTs of the same length can no
longer grow without bounds. They have been replaced with LRU (least recently
used) caches that automatically evict no longer needed items if either the
memory size or item count limit has been reached.

Improved handling of zero-width string/unicode dtypes

Fixed several interfaces that explicitly disallowed arrays with zero-width
string dtypes (i.e. dtype('S0') or dtype('U0'), and fixed several
bugs where such dtypes were not handled properly. In particular, changed
ndarray.__new__ to not implicitly convert dtype('S0') to
dtype('S1') (and likewise for unicode) when creating new arrays.

Integer ufuncs vectorized with AVX2

If the cpu supports it at runtime the basic integer ufuncs now use AVX2
instructions. This feature is currently only available when compiled with GCC.

Order of operations optimization in np.einsum

np.einsum now supports the optimize argument which will optimize the
order of contraction. For example, np.einsum would complete the chain dot
example np.einsum(‘ij,jk,kl->il’, a, b, c) in a single pass which would
scale like N^4; however, when optimize=True np.einsum will create
an intermediate array to reduce this scaling to N^3 or effectively
np.dot(a, b).dot(c). Usage of intermediate tensors to reduce scaling has
been applied to the general einsum summation notation. See np.einsum_path
for more details.

quicksort has been changed to an introsort

The quicksort kind of np.sort and np.argsort is now an introsort which
is regular quicksort but changing to a heapsort when not enough progress is
made. This retains the good quicksort performance while changing the worst case
runtime from O(N^2) to O(N*log(N)).

ediff1d improved performance and subclass handling

The ediff1d function uses an array instead on a flat iterator for the
subtraction. When to_begin or to_end is not None, the subtraction is performed
in place to eliminate a copy operation. A side effect is that certain
subclasses are handled better, namely astropy.Quantity, since the complete
array is created, wrapped, and then begin and end values are set, instead of
using concatenate.

Improved precision of ndarray.mean for float16 arrays

The computation of the mean of float16 arrays is now carried out in float32 for
improved precision. This should be useful in packages such as scikit-learn
where the precision of float16 is adequate and its smaller footprint is
desireable.

Changes

All array-like methods are now called with keyword arguments in fromnumeric.py

Internally, many array-like methods in fromnumeric.py were being called with
positional arguments instead of keyword arguments as their external signatures
were doing. This caused a complication in the downstream 'pandas' library
that encountered an issue with 'numpy' compatibility. Now, all array-like
methods in this module are called with keyword arguments instead.

Operations on np.memmap objects return numpy arrays in most cases

Previously operations on a memmap object would misleadingly return a memmap
instance even if the result was actually not memmapped. For example,
arr + 1 or arr + arr would return memmap instances, although no memory
from the output array is memmaped. Version 1.12 returns ordinary numpy arrays
from these operations.

Also, reduction of a memmap (e.g. .sum(axis=None) now returns a numpy
scalar instead of a 0d memmap.

stacklevel of warnings increased

The stacklevel for python based warnings was increased so that most warnings
will report the offending line of the user code instead of the line the
warning itself is given. Passing of stacklevel is now tested to ensure that
new warnings will receive the stacklevel argument.

This causes warnings with the "default" or "module" filter to be shown once
for every offending user code line or user module instead of only once. On
python versions before 3.4, this can cause warnings to appear that were falsely
ignored before, which may be surprising especially in test suits.

Contributors

A total of 139 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Aditya Panchal +
• Ales Erjavec +
• Alex Griffing
• Alexandr Shadchin +
• Alistair Muldal
• Allan Haldane
• Amit Aronovitch +
• Andrei Kucharavy +
• Antony Lee
• Antti Kaihola +
• Arne de Laat +
• Auke Wiggers +
• AustereCuriosity +
• Badhri Narayanan Krishnakumar +
• Ben North +
• Ben Rowland +
• Bertrand Lefebvre
• Boxiang Sun
• CJ Carey
• Charles Harris
• Christoph Gohlke
• Daniel Ching +
• Daniel Rasmussen +
• Daniel Smith +
• David Schaich +
• Denis Alevi +
• Devin Jeanpierre +
• Dmitry Odzerikho
• Dongjoon Hyun +
• Edward Richards +
• Ekaterina Tuzova +
• Emilien Kofman +
• Endolith
• Eren Sezener +
• Eric Moore
• Eric Quintero +
• Eric Wieser +
• Erik M. Bray
• Frederic Bastien +
• Friedrich Dunne +
• Gerrit Holl
• Golnaz Irannejad +
• Graham Markall +
• Greg Knoll +
• Greg Young
• Gustavo Serra Scalet +
• Ines Wichert +
• Irvin Probst +
• Jaime Fernandez
• James Sanders +
• Jan David Mol +
• Jan Schlüter
• Jeremy Tuloup +
• John Kirkham
• John Zwinck +
• Jonathan Helmus
• Joseph Fox-Rabinovitz
• Josh Wilson +
• Joshua Warner +
• Julian Taylor
• Ka Wo Chen +
• Kamil Rytarowski +
• Kelsey Jordahl +
• Kevin Deldycke +
• Khaled Ben Abdallah Okuda +
• Lion Krischer +
• Loïc Estève +
• Luca Mussi +
• Mads Ohm Larsen +
• Manoj Kumar +
• Mario Emmenlauer +
• Marshall Bockrath-Vandegrift +
• Marshall Ward +
• Marten van Kerkwijk
• Mathieu Lamarre +
• Matthew Brett
• Matthew Harrigan +
• Matthias Geier
• Matti Picus +
• Meet Udeshi +
• Michael Felt +
• Michael Goerz +
• Michael Martin +
• Michael Seifert +
• Mike Nolta +
• Nathaniel Beaver +
• Nathaniel J. Smith
• Naveen Arunachalam +
• Nick Papior
• Nikola Forró +
• Oleksandr Pavlyk +
• Olivier Grisel
• Oren Amsalem +
• Pauli Virtanen
• Pavel Potocek +
• Pedro Lacerda +
• Peter Creasey +
• Phil Elson +
• Philip Gura +
• Phillip J. Wolfram +
• Pierre de Buyl +
• Raghav RV +
• Ralf Gommers
• Ray Donnelly +
• Rehas Sachdeva
• Rob Malouf +
• Robert Kern
• Samuel St-Jean
• Sanchez Gonzalez Alvaro +
• Saurabh Mehta +
• Scott Sanderson +
• Sebastian Berg
• Shayan Pooya +
• Shota Kawabuchi +
• Simon Conseil
• Simon Gibbons
• Sorin Sbarnea +
• Stefan van der Walt
• Stephan Hoyer
• Steven J Kern +
• Stuart Archibald
• Tadeu Manoel +
• Takuya Akiba +
• Thomas A Caswell
• Tom Bird +
• Tony Kelman +
• Toshihiro Kamishima +
• Valentin Valls +
• Varun Nayyar
• Victor Stinner +
• Warren Weckesser
• Wendell Smith
• Wojtek Ruszczewski +
• Xavier Abellan Ecija +
• Yaroslav Halchenko
• Yash Shah +
• Yinon Ehrlich +
• Yu Feng +
• nevimov +

Pull requests merged

A total of 413 pull requests were merged for this release.

• #4073: BUG: change real output checking to test if all imaginary parts...
• #4619: BUG : np.sum silently drops keepdims for sub-classes of ndarray
• #5488: ENH: add contract: optimizing numpy's einsum expression
• #5706: ENH: make some masked array methods behave more like ndarray...
• #5822: Allow many distributions to have a scale of 0.
• #6054: WIP: MAINT: Add deprecation warning to views of multi-field indexes
• #6298: Check lower base limit in base_repr.
• #6430: Fix issues with zero-width string fields
• #6656: ENH: usecols now accepts an int when only one column has to be...
• #6660: Added pathlib support for several functions
• #6872: ENH: linear interpolation of complex values in lib.interp
• #6997: MAINT: Simplify mtrand.pyx helpers
• #7003: BUG: Fix string copying for np.place
• #7026: DOC: Clarify behavior in np.random.uniform
• #7055: BUG: One Element Array Inputs Return Scalars in np.random
• #7063: REL: Update master branch after 1.11.x branch has been made.
• #7073: DOC: Update the 1.11.0 release notes.
• #7076: MAINT: Update the git .mailmap file.
• #7082: TST, DOC: Added Broadcasting Tests in test_random.py
• #7087: BLD: fix compilation on non glibc-Linuxes
• #7088: BUG: Have norm cast non-floating point arrays to 64-bit float...
• #7090: ENH: Added 'doane' and 'sqrt' estimators to np.histogram in numpy.function_base
• #7091: Revert "BLD: fix compilation on non glibc-Linuxes"
• #7092: BLD: fix compilation on non glibc-Linuxes
• #7099: TST: Suppressed warnings
• #7102: MAINT: Removed conditionals that are always false in datetime_strings.c
• #7105: DEP: Deprecate as_strided returning a writable array as default
• #7109: DOC: update Python versions requirements in the install docs
• #7114: MAINT: Fix typos in docs
• #7116: TST: Fixed f2py test for win32 virtualenv
• #7118: TST: Fixed f2py test for non-versioned python executables
• #7119: BUG: Fixed mingw.lib error
• #7125: DOC: Updated documentation wording and examples for np.percentile.
• #7129: BUG: Fixed 'midpoint' interpolation of np.percentile in odd cases.
• #7131: Fix setuptools sdist
• #7133: ENH: savez: temporary file alongside with target file and improve...
• #7134: MAINT: Fix some typos in a code string and comments
• #7141: BUG: Unpickled void scalars should be contiguous
• #7144: MAINT: Change call_fortran into callfortran in comments.
• #7145: BUG: Fixed regressions in np.piecewise in ref to #5737 and #5729.
• #7147: Temporarily disable __numpy_ufunc__
• #7148: ENH,TST: Bump stacklevel and add tests for warnings
• #7149: TST: Add missing suffix to temppath manager
• #7152: BUG: mode kwargs passed as unicode to np.pad raises an exception
• #7156: BUG: Reascertain that linspace respects ndarray subclasses in...
• #7167: DOC: Update Wikipedia references for mtrand.pyx
• #7171: TST: Fixed f2py test for Anaconda non-win32
• #7174: DOC: Fix broken pandas link in release notes
• #7177: ENH: added axis param for np.count_nonzero
• #7178: BUG: Fix binary_repr for negative numbers
• #7180: BUG: Fixed previous attempt to fix dimension mismatch in nanpercentile
• #7181: DOC: Updated minor typos in function_base.py and test_function_base.py
• #7191: DOC: add vstack, hstack, dstack reference to stack documentation.
• #7193: MAINT: Removed supurious assert in histogram estimators
• #7194: BUG: Raise a quieter MaskedArrayFutureWarning for mask changes.
• #7195: STY: Drop some trailing spaces in numpy.ma.core.
• #7196: Revert "DOC: add vstack, hstack, dstack reference to stack documentation."
• #7197: TST: Pin virtualenv used on Travis CI.
• #7198: ENH: Unlock the GIL for gufuncs
• #7199: MAINT: Cleanup for histogram bin estimator selection
• #7201: Raise IOError on not a file in python2
• #7202: MAINT: Made iterable return a boolean
• #7209: TST: Bump virtualenv to 14.0.6
• #7211: DOC: Fix fmin examples
• #7215: MAINT: Use PySlice_GetIndicesEx instead of custom reimplementation
• #7229: ENH: implement __complex__
• #7231: MRG: allow distributors to run custom init
• #7232: BLD: Switch order of test for lapack_mkl and openblas_lapack
• #7239: DOC: Removed residual merge markup from previous commit
• #7240: Change 'pubic' to 'public'.
• #7241: MAINT: update doc/sphinxext to numpydoc 0.6.0, and fix up some...
• #7243: ENH: Adding support to the range keyword for estimation of the...
• #7246: DOC: metion writeable keyword in as_strided in release notes
• #7247: TST: Fail quickly on AppVeyor for superseded PR builds
• #7248: DOC: remove link to documentation wiki editor from HOWTO_DOCUMENT.
• #7250: DOC,REL: Update 1.11.0 notes.
• #7251: BUG: only benchmark complex256 if it exists
• #7252: Forward port a fix and enhancement from 1.11.x
• #7253: DOC: note in h/v/dstack points users to stack/concatenate
• #7254: BUG: Enforce dtype for randint singletons
• #7256: MAINT: Use is None or is not None instead of == None or...
• #7257: DOC: Fix mismatched variable names in docstrings.
• #7258: ENH: Make numpy floor_divide and remainder agree with Python...
• #7260: BUG/TST: Fix #7259, do not "force scalar" for already scalar...
• #7261: Added self to mailmap
• #7266: BUG: Segfault for classes with deceptive __len__
• #7268: ENH: add geomspace function
• #7274: BUG: Preserve array order in np.delete
• #7275: DEP: Warn about assigning 'data' attribute of ndarray
• #7276: DOC: apply_along_axis missing whitespace inserted (before colon)
• #7278: BUG: Make returned unravel_index arrays writeable
• #7279: TST: Fixed elements being shuffled
• #7280: MAINT: Remove redundant trailing semicolons.
• #7285: BUG: Make Randint Backwards Compatible with Pandas
• #7286: MAINT: Fix typos in docs/comments of ma and polynomial modules.
• #7292: Clarify error on repr failure in assert_equal.
• #7294: ENH: add support for BLIS to numpy.distutils
• #7295: DOC: understanding code and getting started section to dev doc
• #7296: Revert part of #3907 which incorrectly propogated MaskedArray...
• #7299: DOC: Fix mismatched variable names in docstrings.
• #7300: DOC: dev: stop recommending keeping local master updated with...
• #7301: DOC: Update release notes
• #7305: BUG: Remove data race in mtrand: two threads could mutate the...
• #7307: DOC: Missing some characters in link.
• #7308: BUG: Incrementing the wrong reference on return
• #7310: STY: Fix GitHub rendering of ordered lists >9
• #7311: ENH: Make _pointer_type_cache functional
• #7313: DOC: corrected grammatical error in quickstart doc
• #7325: BUG, MAINT: Improve fromnumeric.py interface for downstream compatibility
• #7328: DEP: Deprecated using a float index in linspace
• #7331: Add comment, TST: fix MemoryError on win32
• #7332: Check for no solution in np.irr Fixes #6744
• #7338: TST: Install pytz in the CI.
• #7340: DOC: Fixed math rendering in tensordot docs.
• #7341: TST: Add test for #6469
• #7344: DOC: Fix more typos in docs and comments.
• #7346: Generalized flip
• #7347: ENH Generalized rot90
• #7348: Maint: Removed extra space from ureduce
• #7349: MAINT: Hide nan warnings for masked internal MA computations
• #7350: BUG: MA ufuncs should set mask to False, not array([False])
• #7351: TST: Fix some MA tests to avoid looking at the .data attribute
• #7358: BUG: pull request related to the issue #7353
• #7359: Update 7314, DOC: Clarify valid integer range for random.seed...
• #7361: MAINT: Fix copy and paste oversight.
• #7363: ENH: Make no unshare mask future warnings less noisy
• #7366: TST: fix #6542, add tests to check non-iterable argument raises...
• #7373: ENH: Add bitwise_and identity
• #7378: added NumPy logo and separator
• #7382: MAINT: cleanup np.average
• #7385: DOC: note about wheels / windows wheels for pypi
• #7386: Added label icon to Travis status
• #7397: BUG: incorrect type for objects whose __len__ fails
• #7398: DOC: fix typo
• #7404: Use PyMem_RawMalloc on Python 3.4 and newer
• #7406: ENH ufunc called on memmap return a ndarray
• #7407: BUG: Fix decref before incref for in-place accumulate
• #7410: DOC: add nanprod to the list of math routines
• #7414: Tweak corrcoef
• #7415: DOC: Documention fixes
• #7416: BUG: Incorrect handling of range in histogram with automatic...
• #7418: DOC: Minor typo fix, hermefik -> hermefit.
• #7421: ENH: adds np.nancumsum and np.nancumprod
• #7423: BUG: Ongoing fixes to PR#7416
• #7430: DOC: Update 1.11.0-notes.
• #7433: MAINT: FutureWarning for changes to np.average subclass handling
• #7437: np.full now defaults to the filling value's dtype.
• #7438: Allow rolling multiple axes at the same time.
• #7439: BUG: Do not try sequence repeat unless necessary
• #7442: MANT: Simplify diagonal length calculation logic
• #7445: BUG: reference count leak in bincount, fixes #6805
• #7446: DOC: ndarray typo fix
• #7447: BUG: scalar integer negative powers gave wrong results.
• #7448: DOC: array "See also" link to full and full_like instead of fill
• #7456: BUG: int overflow in reshape, fixes #7455, fixes #7293
• #7463: BUG: fix array too big error for wide dtypes.
• #7466: BUG: segfault inplace object reduceat, fixes #7465
• #7468: BUG: more on inplace reductions, fixes #615
• #7469: MAINT: Update git .mailmap
• #7472: MAINT: Update .mailmap.
• #7477: MAINT: Yet more .mailmap updates for recent contributors.
• #7481: BUG: Fix segfault in PyArray_OrderConverter
• #7482: BUG: Memory Leak in _GenericBinaryOutFunction
• #7489: Faster real_if_close.
• #7491: DOC: Update subclassing doc regarding downstream compatibility
• #7496: BUG: don't use pow for integer power ufunc loops.
• #7504: DOC: remove "arr" from keepdims docstrings
• #7505: MAIN: fix to #7382, make scl in np.average writeable
• #7507: MAINT: Remove nose.SkipTest import.
• #7508: DOC: link frompyfunc and vectorize
• #7511: numpy.power(0, 0) should return 1
• #7515: BUG: MaskedArray.count treats negative axes incorrectly
• #7518: BUG: Extend glibc complex trig functions blacklist to glibc <...
• #7521: DOC: rephrase writeup of memmap changes
• #7522: BUG: Fixed iteration over additional bad commands
• #7526: DOC: Removed an extra :const:
• #7529: BUG: Floating exception with invalid axis in np.lexsort
• #7534: MAINT: Update setup.py to reflect supported python versions.
• #7536: MAINT: Always use PyCapsule instead of PyCObject in mtrand.pyx
• #7539: MAINT: Cleanup of random stuff
• #7549: BUG: allow graceful recovery for no Liux compiler
• #7562: BUG: Fix test_from_object_array_unicode (test_defchararray.TestBasic)…
• #7565: BUG: Fix test_ctypeslib and test_indexing for debug interpreter
• #7566: MAINT: use manylinux1 wheel for cython
• #7568: Fix a false positive OverflowError in Python 3.x when value above...
• #7579: DOC: clarify purpose of Attributes section
• #7584: BUG: fixes #7572, percent in path
• #7586: Make np.ma.take works on scalars
• #7587: BUG: linalg.norm(): Don't convert object arrays to float
• #7598: Cast array size to int64 when loading from archive
• #7602: DOC: Remove isreal and iscomplex from ufunc list
• #7605: DOC: fix incorrect Gamma distribution parameterization comments
• #7609: BUG: Fix TypeError when raising TypeError
• #7611: ENH: expose test runner raise_warnings option
• #7614: BLD: Avoid using os.spawnve in favor of os.spawnv in exec_command
• #7618: BUG: distance arg of np.gradient must be scalar, fix docstring
• #7626: DOC: RST definition list fixes
• #7627: MAINT: unify tup processing, move tup use to after all PyTuple_SetItem...
• #7630: MAINT: add ifdef around PyDictProxy_Check macro
• #7631: MAINT: linalg: fix comment, simplify math
• #7634: BLD: correct C compiler customization in system_info.py Closes...
• #7635: BUG: ma.median alternate fix for #7592
• #7636: MAINT: clean up testing.assert_raises_regexp, 2.6-specific code...
• #7637: MAINT: clearer exception message when importing multiarray fails.
• #7639: TST: fix a set of test errors in master.
• #7643: DOC : minor changes to linspace docstring
• #7651: BUG: one to any power is still 1. Broken edgecase for int arrays
• #7655: BLD: Remove Intel compiler flag -xSSE4.2
• #7658: BUG: fix incorrect printing of 1D masked arrays
• #7659: BUG: Temporary fix for str(mvoid) for object field types
• #7664: BUG: Fix unicode with byte swap transfer and copyswap
• #7667: Restore histogram consistency
• #7668: ENH: Do not check the type of module.__dict__ explicit in test.
• #7669: BUG: boolean assignment no GIL release when transfer needs API
• #7673: DOC: Create Numpy 1.11.1 release notes.
• #7675: BUG: fix handling of right edge of final bin.
• #7678: BUG: Fix np.clip bug NaN handling for Visual Studio 2015
• #7679: MAINT: Fix up C++ comment in arraytypes.c.src.
• #7681: DOC: Update 1.11.1 release notes.
• #7686: ENH: Changing FFT cache to a bounded LRU cache
• #7688: DOC: fix broken genfromtxt examples in user guide. Closes gh-7662.
• #7689: BENCH: add correlate/convolve benchmarks.
• #7696: DOC: update wheel build / upload instructions
• #7699: BLD: preserve library order
• #7704: ENH: Add bits attribute to np.finfo
• #7712: BUG: Fix race condition with new FFT cache
• #7715: BUG: Remove memory leak in np.place
• #7719: BUG: Fix segfault in np.random.shuffle for arrays of different...
• #7723: Change mkl_info.dir_env_var from MKL to MKLROOT
• #7727: DOC: Corrections in Datetime Units-arrays.datetime.rst
• #7729: DOC: fix typo in savetxt docstring (closes #7620)
• #7733: Update 7525, DOC: Fix order='A' docs of np.array.
• #7734: Update 7542, ENH: Add polyrootval to numpy.polynomial
• #7735: BUG: fix issue on OS X with Python 3.x where npymath.ini was...
• #7739: DOC: Mention the changes of #6430 in the release notes.
• #7740: DOC: add reference to poisson rng
• #7743: Update 7476, DEP: deprecate Numeric-style typecodes, closes #2148
• #7744: DOC: Remove "ones_like" from ufuncs list (it is not)
• #7746: DOC: Clarify the effect of rcond in numpy.linalg.lstsq.
• #7747: Update 7672, BUG: Make sure we don't divide by zero
• #7748: DOC: Update float32 mean example in docstring
• #7754: Update 7612, ENH: Add broadcast.ndim to match code elsewhere.
• #7757: Update 7175, BUG: Invalid read of size 4 in PyArray_FromFile
• #7759: BUG: Fix numpy.i support for numpy API < 1.7.
• #7760: ENH: Make assert_almost_equal & assert_array_almost_equal consistent.
• #7766: fix an English typo
• #7771: DOC: link geomspace from logspace
• #7773: DOC: Remove a redundant the
• #7777: DOC: Update Numpy 1.11.1 release notes.
• #7785: DOC: update wheel building procedure for release
• #7789: MRG: add note of 64-bit wheels on Windows
• #7791: f2py.compile issues (#7683)
• #7799: "lambda" is not allowed to use as keyword arguments in a sample...
• #7803: BUG: interpret 'c' PEP3118/struct type as 'S1'.
• #7807: DOC: Misplaced parens in formula
• #7817: BUG: Make sure npy_mul_with_overflow_ detects overflow.
• #7818: numpy/distutils/misc_util.py fix for #7809: check that _tmpdirs...
• #7820: MAINT: Allocate fewer bytes for empty arrays.
• #7823: BUG: Fixed masked array behavior for scalar inputs to np.ma.atleast_*d
• #7834: DOC: Added an example
• #7839: Pypy fixes
• #7840: Fix ATLAS version detection
• #7842: Fix versionadded tags
• #7848: MAINT: Fix remaining uses of deprecated Python imp module.
• #7853: BUG: Make sure numpy globals keep identity after reload.
• #7863: ENH: turn quicksort into introsort
• #7866: Document runtests extra argv
• #7871: BUG: handle introsort depth limit properly
• #7879: DOC: fix typo in documentation of loadtxt (closes #7878)
• #7885: Handle NetBSD specific <sys/endian.h>
• #7889: DOC: #7881. Fix link to record arrays
• #7894: fixup-7790, BUG: construct ma.array from np.array which contains...
• #7898: Spelling and grammar fix.
• #7903: BUG: fix float16 type not being called due to wrong ordering
• #7908: BLD: Fixed detection for recent MKL versions
• #7911: BUG: fix for issue#7835 (ma.median of 1d)
• #7912: ENH: skip or avoid gc/objectmodel differences btwn pypy and cpython
• #7918: ENH: allow numpy.apply_along_axis() to work with ndarray subclasses
• #7922: ENH: Add ma.convolve and ma.correlate for #6458
• #7925: Monkey-patch _msvccompile.gen_lib_option like any other compilators
• #7931: BUG: Check for HAVE_LDOUBLE_DOUBLE_DOUBLE_LE in npy_math_complex.
• #7936: ENH: improve duck typing inside iscomplexobj
• #7937: BUG: Guard against buggy comparisons in generic quicksort.
• #7938: DOC: add cbrt to math summary page
• #7941: BUG: Make sure numpy globals keep identity after reload.
• #7943: DOC: #7927. Remove deprecated note for memmap relevant for Python...
• #7952: BUG: Use keyword arguments to initialize Extension base class.
• #7956: BLD: remove __NUMPY_SETUP__ from builtins at end of setup.py
• #7963: BUG: MSVCCompiler grows 'lib' & 'include' env strings exponentially.
• #7965: BUG: cannot modify tuple after use
• #7976: DOC: Fixed documented dimension of return value
• #7977: DOC: Create 1.11.2 release notes.
• #7979: DOC: Corrected allowed keywords in add_(installed_)library
• #7980: ENH: Add ability to runtime select ufunc loops, add AVX2 integer...
• #7985: Rebase 7763, ENH: Add new warning suppression/filtering context
• #7987: DOC: See also np.load and np.memmap in np.lib.format.open_memmap
• #7988: DOC: Include docstring for cbrt, spacing and fabs in documentation
• #7999: ENH: add inplace cases to fast ufunc loop macros
• #8006: DOC: Update 1.11.2 release notes.
• #8008: MAINT: Remove leftover imp module imports.
• #8009: DOC: Fixed three typos in the c-info.ufunc-tutorial
• #8011: DOC: Update 1.11.2 release notes.
• #8014: BUG: Fix fid.close() to use os.close(fid)
• #8016: BUG: Fix numpy.ma.median.
• #8018: BUG: Fixes return for np.ma.count if keepdims is True and axis...
• #8021: DOC: change all non-code instances of Numpy to NumPy
• #8027: ENH: Add platform indepedent lib dir to PYTHONPATH
• #8028: DOC: Update 1.11.2 release notes.
• #8030: BUG: fix np.ma.median with only one non-masked value and an axis...
• #8038: MAINT: Update error message in rollaxis.
• #8040: Update add_newdocs.py
• #8042: BUG: core: fix bug in NpyIter buffering with discontinuous arrays
• #8045: DOC: Update 1.11.2 release notes.
• #8050: remove refcount semantics, now a.resize() almost always requires...
• #8051: Clear signaling NaN exceptions
• #8054: ENH: add signature argument to vectorize for vectorizing like...
• #8057: BUG: lib: Simplify (and fix) pad's handling of the pad_width
• #8061: BUG : financial.pmt modifies input (issue #8055)
• #8064: MAINT: Add PMIP files to .gitignore
• #8065: BUG: Assert fromfile ending earlier in pyx_processing
• #8066: BUG, TST: Fix python3-dbg bug in Travis script
• #8071: MAINT: Add Tempita to randint helpers
• #8075: DOC: Fix description of isinf in nan_to_num
• #8080: BUG: non-integers can end up in dtype offsets
• #8081: Update outdated Nose URL to nose.readthedocs.io
• #8083: ENH: Deprecation warnings for / integer division when running...
• #8084: DOC: Fix erroneous return type description for np.roots.
• #8087: BUG: financial.pmt modifies input #8055
• #8088: MAINT: Remove duplicate randint helpers code.
• #8093: MAINT: fix assert_raises_regex when used as a context manager
• #8096: ENH: Vendorize tempita.
• #8098: DOC: Enhance description/usage for np.linalg.eig*h
• #8103: Pypy fixes
• #8104: Fix test code on cpuinfo's main function
• #8107: BUG: Fix array printing with precision=0.
• #8109: Fix bug in ravel_multi_index for big indices (Issue #7546)
• #8110: BUG: distutils: fix issue with rpath in fcompiler/gnu.py
• #8111: ENH: Add a tool for release authors and PRs.
• #8112: DOC: Fix "See also" links in linalg.
• #8114: BUG: core: add missing error check after PyLong_AsSsize_t
• #8121: DOC: Improve histogram2d() example.
• #8122: BUG: Fix broken pickle in MaskedArray when dtype is object (Return...
• #8124: BUG: Fixed build break
• #8125: Rebase, BUG: Fixed deepcopy of F-order object arrays.
• #8127: BUG: integers to a negative integer powers should error.
• #8141: improve configure checks for broken systems
• #8142: BUG: np.ma.mean and var should return scalar if no mask
• #8148: BUG: import full module path in npy_load_module
• #8153: MAINT: Expose void-scalar "base" attribute in python
• #8156: DOC: added example with empty indices for a scalar, #8138
• #8160: BUG: fix _array2string for structured array (issue #5692)
• #8164: MAINT: Update mailmap for NumPy 1.12.0
• #8165: Fixup 8152, BUG: assert_allclose(..., equal_nan=False) doesn't...
• #8167: Fixup 8146, DOC: Clarify when PyArray_{Max, Min, Ptp} return...
• #8168: DOC: Minor spelling fix in genfromtxt() docstring.
• #8173: BLD: Enable build on AIX
• #8174: DOC: warn that dtype.descr is only for use in PEP3118
• #8177: MAINT: Add python 3.6 support to suppress_warnings
• #8178: MAINT: Fix ResourceWarning new in Python 3.6.
• #8180: FIX: protect stolen ref by PyArray_NewFromDescr in array_empty
• #8181: ENH: Improve announce to find github squash-merge commits.
• #8182: MAINT: Update .mailmap
• #8183: MAINT: Ediff1d performance
• #8184: MAINT: make assert_allclose behavior on nans match pre 1.12
• #8188: DOC: 'highest' is exclusive for randint()
• #8189: BUG: setfield should raise if arr is not writeable
• #8190: ENH: Add a float_power function with at least float64 precision.
• #8197: DOC: Add missing arguments to np.ufunc.outer
• #8198: DEP: Deprecate the keepdims argument to accumulate
• #8199: MAINT: change path to env in distutils.system_info. Closes gh-8195.
• #8200: BUG: Fix structured array format functions
• #8202: ENH: specialize name of dev package by interpreter
• #8205: DOC: change development instructions from SSH to HTTPS access.
• #8216: DOC: Patch doc errors for atleast_nd and frombuffer
• #8218: BUG: ediff1d should return subclasses
• #8219: DOC: Turn SciPy references into links.
• #8222: ENH: Make numpy.mean() do more precise computation
• #8227: BUG: Better check for invalid bounds in np.random.uniform.
• #8231: ENH: Refactor numpy ** operators for numpy scalar integer powers
• #8234: DOC: Clarified when a copy is made in numpy.asarray
• #8236: DOC: Fix documentation pull requests.
• #8238: MAINT: Update pavement.py
• #8239: ENH: Improve announce tool.
• #8240: REL: Prepare for 1.12.x branch
• #8243: BUG: Update operator ** tests for new behavior.
• #8246: REL: Reset strides for RELAXED_STRIDE_CHECKING for 1.12 releases.
• #8265: BUG: np.piecewise not working for scalars
• #8272: TST: Path test should resolve symlinks when comparing
• #8282: DOC: Update 1.12.0 release notes.
• #8286: BUG: Fix pavement.py write_release_task.
• #8296: BUG: Fix iteration over reversed subspaces in mapiter_@name@.
• #8304: BUG: Fix PyPy crash in PyUFunc_GenericReduction.
• #8319: BLD: blacklist powl (longdouble power function) on OS X.
• #8320: BUG: do not link to Accelerate if OpenBLAS, MKL or BLIS are found.
• #8322: BUG: fixed kind specifications for parameters
• #8336: BUG: fix packbits and unpackbits to correctly handle empty arrays
• #8338: BUG: fix test_api test that fails intermittently in python 3
• #8339: BUG: Fix ndarray.tofile large file corruption in append mode.
• #8359: BUG: Fix suppress_warnings (again) for Python 3.6.
• #8372: BUG: Fixes for ma.median and nanpercentile.
• #8373: BUG: correct letter case
• #8379: DOC: Update 1.12.0-notes.rst.
• #8390: ENH: retune apply_along_axis nanmedian cutoff in 1.12
• #8391: DEP: Fix escaped string characters deprecated in Python 3.6.
• #8394: DOC: create 1.11.3 release notes.
• #8399: BUG: Fix author search in announce.py
• #8402: DOC, MAINT: Update 1.12.0 notes and mailmap.
• #8418: BUG: Fix ma.median even elements for 1.12
• #8424: DOC: Fix tools and release notes to be more markdown compatible.
• #8427: BUG: Add a lock to assert_equal and other testing functions
• #8431: BUG: Fix apply_along_axis() for when func1d() returns a non-ndarray.
• #8432: BUG: Let linspace accept input that has an array_interface.
• #8437: TST: Update 3.6-dev tests to 3.6 after Python final release.
• #8439: DOC: Update 1.12.0 release notes.

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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 nancumprod added

Nan-functions nancumsum and nancumprod have been added to
compute cumsum and cumprod by ignoring nans.

np.interp can now interpolate complex values

np.lib.interp(x, xp, fp) now allows the interpolated array fp
to be complex and will interpolate at complex128 precision.

New polynomial evaluation function polyvalfromroots added

The new function polyvalfromroots evaluates a polynomial at given points
from the roots of the polynomial. This is useful for higher order polynomials,
where expansion into polynomial coefficients is inaccurate at machine
precision.

New array creation function geomspace added

The new function geomspace generates a geometric sequence. It is similar
to logspace, but with start and stop specified directly:
geomspace(start, stop) behaves the same as
logspace(log10(start), log10(stop)).

New context manager for testing warnings

A new context manager suppress_warnings has been added to the testing
utils. This context manager is designed to help reliably test warnings.
Specifically to reliably filter/ignore warnings. Ignoring warnings
by using an "ignore" filter in Python versions before 3.4.x can quickly
result in these (or similar) warnings not being tested reliably.

The context manager allows to filter (as well as record) warnings similar
to the catch_warnings context, but allows for easier specificity.
Also printing warnings that have not been filtered or nesting the
context manager will work as expected. Additionally, it is possible
to use the context manager as a decorator which can be useful when
multiple tests give need to hide the same warning.

New masked array functions ma.convolve and ma.correlate added

These functions wrapped the non-masked versions, but propagate through masked
values. There are two different propagation modes. The default causes masked
values to contaminate the result with masks, but the other mode only outputs
masks if there is no alternative.

New float_power ufunc

The new float_power ufunc is like the power function except all
computation is done in a minimum precision of float64. There was a long
discussion on the numpy mailing list of how to treat integers to negative
integer powers and a popular proposal was that the __pow__ operator should
always return results of at least float64 precision. The float_power
function implements that option. Note that it does not support object arrays.

np.loadtxt now supports a single integer as usecol argument

Instead of using usecol=(n,) to read the nth column of a file
it is now allowed to use usecol=n. Also the error message is
more user friendly when a non-integer is passed as a column index.

Improved automated bin estimators for histogram

Added 'doane' and 'sqrt' estimators to histogram via the bins
argument. Added support for range-restricted histograms with automated
bin estimation.

np.roll can now roll multiple axes at the same time

The shift and axis arguments to roll are now broadcast against each
other, and each specified axis is shifted accordingly.

The __complex__ method has been implemented for the ndarrays

Calling complex() on a size 1 array will now cast to a python
complex.

pathlib.Path objects now supported

The standard np.load, np.save, np.loadtxt, np.savez, and similar
functions can now take pathlib.Path objects as an argument instead of a
filename or open file object.

New bits attribute for np.finfo

This makes np.finfo consistent with np.iinfo which already has that
attribute.

New signature argument to np.vectorize

This argument allows for vectorizing user defined functions with core
dimensions, in the style of NumPy's
:ref:generalized universal functions<c-api.generalized-ufuncs>. This allows
for vectorizing a much broader class of functions. For example, an arbitrary
distance metric that combines two vectors to produce a scalar could be
vectorized with signature='(n),(n)->()'. See np.vectorize for full
details.

Emit py3kwarnings for division of integer arrays

To help people migrate their code bases from Python 2 to Python 3, the
python interpreter has a handy option -3, which issues warnings at runtime.
One of its warnings is for integer division::

$ python -3 -c "2/3"

-c:1: DeprecationWarning: classic int division

In Python 3, the new integer division semantics also apply to numpy arrays.
With this version, numpy will emit a similar warning::

$ python -3 -c "import numpy as np; np.array(2)/np.array(3)"

-c:1: DeprecationWarning: numpy: classic int division

numpy.sctypes now includes bytes on Python3 too

Previously, it included str (bytes) and unicode on Python2, but only str
(unicode) on Python3.

Improvements

bitwise_and identity changed

The previous identity was 1 with the result that all bits except the LSB were
masked out when the reduce method was used. The new identity is -1, which
should work properly on twos complement machines as all bits will be set to
one.

Generalized Ufuncs will now unlock the GIL

Generalized Ufuncs, including most of the linalg module, will now unlock
the Python global interpreter lock.

Caches in np.fft are now bounded in total size and item count

The caches in np.fft that speed up successive FFTs of the same length can no
longer grow without bounds. They have been replaced with LRU (least recently
used) caches that automatically evict no longer needed items if either the
memory size or item count limit has been reached.

Improved handling of zero-width string/unicode dtypes

Fixed several interfaces that explicitly disallowed arrays with zero-width
string dtypes (i.e. dtype('S0') or dtype('U0'), and fixed several
bugs where such dtypes were not handled properly. In particular, changed
ndarray.__new__ to not implicitly convert dtype('S0') to
dtype('S1') (and likewise for unicode) when creating new arrays.

Integer ufuncs vectorized with AVX2

If the cpu supports it at runtime the basic integer ufuncs now use AVX2
instructions. This feature is currently only available when compiled with GCC.

Order of operations optimization in np.einsum

np.einsum now supports the optimize argument which will optimize the
order of contraction. For example, np.einsum would complete the chain dot
example np.einsum(‘ij,jk,kl->il’, a, b, c) in a single pass which would
scale like N^4; however, when optimize=True np.einsum will create
an intermediate array to reduce this scaling to N^3 or effectively
np.dot(a, b).dot(c). Usage of intermediate tensors to reduce scaling has
been applied to the general einsum summation notation. See np.einsum_path
for more details.

quicksort has been changed to an introsort

The quicksort kind of np.sort and np.argsort is now an introsort which
is regular quicksort but changing to a heapsort when not enough progress is
made. This retains the good quicksort performance while changing the worst case
runtime from O(N^2) to O(N*log(N)).

ediff1d improved performance and subclass handling

The ediff1d function uses an array instead on a flat iterator for the
subtraction. When to_begin or to_end is not None, the subtraction is performed
in place to eliminate a copy operation. A side effect is that certain
subclasses are handled better, namely astropy.Quantity, since the complete
array is created, wrapped, and then begin and end values are set, instead of
using concatenate.

Improved precision of ndarray.mean for float16 arrays

The computation of the mean of float16 arrays is now carried out in float32 for
improved precision. This should be useful in packages such as scikit-learn
where the precision of float16 is adequate and its smaller footprint is
desireable.

Changes

All array-like methods are now called with keyword arguments in fromnumeric.py

Internally, many array-like methods in fromnumeric.py were being called with
positional arguments instead of keyword arguments as their external signatures
were doing. This caused a complication in the downstream 'pandas' library
that encountered an issue with 'numpy' compatibility. Now, all array-like
methods in this module are called with keyword arguments instead.

Operations on np.memmap objects return numpy arrays in most cases

Previously operations on a memmap object would misleadingly return a memmap
instance even if the result was actually not memmapped. For example,
arr + 1 or arr + arr would return memmap instances, although no memory
from the output array is memmaped. Version 1.12 returns ordinary numpy arrays
from these operations.

Also, reduction of a memmap (e.g. .sum(axis=None) now returns a numpy
scalar instead of a 0d memmap.

stacklevel of warnings increased

The stacklevel for python based warnings was increased so that most warnings
will report the offending line of the user code instead of the line the
warning itself is given. Passing of stacklevel is now tested to ensure that
new warnings will receive the stacklevel argument.

This causes warnings with the "default" or "module" filter to be shown once
for every offending user code line or user module instead of only once. On
python versions before 3.4, this can cause warnings to appear that were falsely
ignored before, which may be surprising especially in test suits.

Contributors to maintenance/1.12.x

A total of 139 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Aditya Panchal
• Ales Erjavec +
• Alex Griffing
• Alexandr Shadchin +
• Alistair Muldal
• Allan Haldane
• Amit Aronovitch +
• Andrei Kucharavy +
• Antony Lee
• Antti Kaihola +
• Arne de Laat +
• Auke Wiggers +
• AustereCuriosity +
• Badhri Narayanan Krishnakumar +
• Ben North +
• Ben Rowland +
• Bertrand Lefebvre
• Boxiang Sun
• CJ Carey
• Charles Harris
• Christoph Gohlke
• Daniel Ching +
• Daniel Rasmussen +
• Daniel Smith +
• David Schaich +
• Denis Alevi +
• Devin Jeanpierre +
• Dmitry Odzerikho
• Dongjoon Hyun +
• Edward Richards +
• Ekaterina Tuzova +
• Emilien Kofman +
• Endolith
• Eren Sezener +
• Eric Moore
• Eric Quintero +
• Eric Wieser +
• Erik M. Bray
• Frederic Bastien +
• Friedrich Dunne +
• Gerrit Holl
• Golnaz Irannejad +
• Graham Markall +
• Greg Knoll +
• Greg Young
• Gustavo Serra Scalet +
• Ines Wichert +
• Irvin Probst +
• Jaime Fernandez
• James Sanders +
• Jan David Mol +
• Jan Schlüter
• Jeremy Tuloup +
• John Kirkham
• John Zwinck +
• Jonathan Helmus
• Joseph Fox-Rabinovitz
• Josh Wilson +
• Joshua Warner +
• Julian Taylor
• Ka Wo Chen +
• Kamil Rytarowski +
• Kelsey Jordahl +
• Kevin Deldycke +
• Khaled Ben Abdallah Okuda +
• Lion Krischer +
• Loïc Estève +
• Luca Mussi +
• Mads Ohm Larsen +
• Manoj Kumar +
• Mario Emmenlauer +
• Marshall Bockrath-Vandegrift +
• Marshall Ward +
• Marten van Kerkwijk
• Mathieu Lamarre +
• Matthew Brett
• Matthew Harrigan +
• Matthias Geier
• Matti Picus +
• Meet Udeshi +
• Michael Felt +
• Michael Goerz +
• Michael Martin +
• Michael Seifert +
• Mike Nolta +
• Nathaniel Beaver +
• Nathaniel J. Smith
• Naveen Arunachalam +
• Nick Papior
• Nikola Forró +
• Oleksandr Pavlyk +
• Olivier Grisel
• Oren Amsalem +
• Pauli Virtanen
• Pavel Potocek +
• Pedro Lacerda +
• Peter Creasey +
• Phil Elson +
• Philip Gura +
• Phillip J. Wolfram +
• Pierre de Buyl +
• Raghav RV +
• Ralf Gommers
• Ray Donnelly +
• Rehas Sachdeva
• Rob Malouf +
• Robert Kern
• Samuel St-Jean
• Sanchez Gonzalez Alvaro +
• Saurabh Mehta +
• Scott Sanderson +
• Sebastian Berg
• Shayan Pooya +
• Shota Kawabuchi +
• Simon Conseil
• Simon Gibbons
• Sorin Sbarnea +
• Stefan van der Walt
• Stephan Hoyer
• Steven J Kern +
• Stuart Archibald
• Tadeu Manoel +
• Takuya Akiba +
• Thomas A Caswell
• Tom Bird +
• Tony Kelman +
• Toshihiro Kamishima +
• Valentin Valls +
• Varun Nayyar
• Victor Stinner +
• Warren Weckesser
• Wendell Smith
• Wojtek Ruszczewski +
• Xavier Abellan Ecija +
• Yaroslav Halchenko
• Yash Shah +
• Yinon Ehrlich +
• Yu Feng +
• nevimov +

Pull requests merged for maintenance/1.12.x

A total of 406 pull requests were merged for this release.

• #4073: BUG: change real output checking to test if all imaginary parts...
• #4619: BUG : np.sum silently drops keepdims for sub-classes of ndarray
• #5488: ENH: add contract: optimizing numpy's einsum expression
• #5706: ENH: make some masked array methods behave more like ndarray...
• #5822: Allow many distributions to have a scale of 0.
• #6054: WIP: MAINT: Add deprecation warning to views of multi-field indexes
• #6298: Check lower base limit in base_repr.
• #6430: Fix issues with zero-width string fields
• #6656: ENH: usecols now accepts an int when only one column has to be...
• #6660: Added pathlib support for several functions
• #6872: ENH: linear interpolation of complex values in lib.interp
• #6997: MAINT: Simplify mtrand.pyx helpers
• #7003: BUG: Fix string copying for np.place
• #7026: DOC: Clarify behavior in np.random.uniform
• #7055: BUG: One Element Array Inputs Return Scalars in np.random
• #7063: REL: Update master branch after 1.11.x branch has been made.
• #7073: DOC: Update the 1.11.0 release notes.
• #7076: MAINT: Update the git .mailmap file.
• #7082: TST, DOC: Added Broadcasting Tests in test_random.py
• #7087: BLD: fix compilation on non glibc-Linuxes
• #7088: BUG: Have norm cast non-floating point arrays to 64-bit float...
• #7090: ENH: Added 'doane' and 'sqrt' estimators to np.histogram in numpy.function_base
• #7091: Revert "BLD: fix compilation on non glibc-Linuxes"
• #7092: BLD: fix compilation on non glibc-Linuxes
• #7099: TST: Suppressed warnings
• #7102: MAINT: Removed conditionals that are always false in datetime_strings.c
• #7105: DEP: Deprecate as_strided returning a writable array as default
• #7109: DOC: update Python versions requirements in the install docs
• #7114: MAINT: Fix typos in docs
• #7116: TST: Fixed f2py test for win32 virtualenv
• #7118: TST: Fixed f2py test for non-versioned python executables
• #7119: BUG: Fixed mingw.lib error
• #7125: DOC: Updated documentation wording and examples for np.percentile.
• #7129: BUG: Fixed 'midpoint' interpolation of np.percentile in odd cases.
• #7131: Fix setuptools sdist
• #7133: ENH: savez: temporary file alongside with target file and improve...
• #7134: MAINT: Fix some typos in a code string and comments
• #7141: BUG: Unpickled void scalars should be contiguous
• #7144: MAINT: Change call_fortran into callfortran in comments.
• #7145: BUG: Fixed regressions in np.piecewise in ref to #5737 and #5729.
• #7147: Temporarily disable numpy_ufunc
• #7148: ENH,TST: Bump stacklevel and add tests for warnings
• #7149: TST: Add missing suffix to temppath manager
• #7152: BUG: mode kwargs passed as unicode to np.pad raises an exception
• #7156: BUG: Reascertain that linspace respects ndarray subclasses in...
• #7167: DOC: Update Wikipedia references for mtrand.pyx
• #7171: TST: Fixed f2py test for Anaconda non-win32
• #7174: DOC: Fix broken pandas link in release notes
• #7177: ENH: added axis param for np.count_nonzero
• #7178: BUG: Fix binary_repr for negative numbers
• #7180: BUG: Fixed previous attempt to fix dimension mismatch in nanpercentile
• #7181: DOC: Updated minor typos in function_base.py and test_function_base.py
• #7191: DOC: add vstack, hstack, dstack reference to stack documentation.
• #7193: MAINT: Removed supurious assert in histogram estimators
• #7194: BUG: Raise a quieter MaskedArrayFutureWarning for mask changes.
• #7195: STY: Drop some trailing spaces in numpy.ma.core.
• #7196: Revert "DOC: add vstack, hstack, dstack reference to stack documentation."
• #7197: TST: Pin virtualenv used on Travis CI.
• #7198: ENH: Unlock the GIL for gufuncs
• #7199: MAINT: Cleanup for histogram bin estimator selection
• #7201: Raise IOError on not a file in python2
• #7202: MAINT: Made iterable return a boolean
• #7209: TST: Bump virtualenv to 14.0.6
• #7211: DOC: Fix fmin examples
• #7215: MAINT: Use PySlice_GetIndicesEx instead of custom reimplementation
• #7229: ENH: implement complex
• #7231: MRG: allow distributors to run custom init
• #7232: BLD: Switch order of test for lapack_mkl and openblas_lapack
• #7239: DOC: Removed residual merge markup from previous commit
• #7240: Change 'pubic' to 'public'.
• #7241: MAINT: update doc/sphinxext to numpydoc 0.6.0, and fix up some...
• #7243: ENH: Adding support to the range keyword for estimation of the...
• #7246: DOC: metion writeable keyword in as_strided in release notes
• #7247: TST: Fail quickly on AppVeyor for superseded PR builds
• #7248: DOC: remove link to documentation wiki editor from HOWTO_DOCUMENT.
• #7250: DOC,REL: Update 1.11.0 notes.
• #7251: BUG: only benchmark complex256 if it exists
• #7252: Forward port a fix and enhancement from 1.11.x
• #7253: DOC: note in h/v/dstack points users to stack/concatenate
• #7254: BUG: Enforce dtype for randint singletons
• #7256: MAINT: Use is None or is not None instead of == None or...
• #7257: DOC: Fix mismatched variable names in docstrings.
• #7258: ENH: Make numpy floor_divide and remainder agree with Python...
• #7260: BUG/TST: Fix #7259, do not "force scalar" for already scalar...
• #7261: Added self to mailmap
• #7266: BUG: Segfault for classes with deceptive len
• #7268: ENH: add geomspace function
• #7274: BUG: Preserve array order in np.delete
• #7275: DEP: Warn about assigning 'data' attribute of ndarray
• #7276: DOC: apply_along_axis missing whitespace inserted (before colon)
• #7278: BUG: Make returned unravel_index arrays writeable
• #7279: TST: Fixed elements being shuffled
• #7280: MAINT: Remove redundant trailing semicolons.
• #7285: BUG: Make Randint Backwards Compatible with Pandas
• #7286: MAINT: Fix typos in docs/comments of ma and polynomial modules.
• #7292: Clarify error on repr failure in assert_equal.
• #7294: ENH: add support for BLIS to numpy.distutils
• #7295: DOC: understanding code and getting started section to dev doc
• #7296: Revert part of #3907 which incorrectly propogated MaskedArray...
• #7299: DOC: Fix mismatched variable names in docstrings.
• #7300: DOC: dev: stop recommending keeping local master updated with...
• #7301: DOC: Update release notes
• #7305: BUG: Remove data race in mtrand: two threads could mutate the...
• #7307: DOC: Missing some characters in link.
• #7308: BUG: Incrementing the wrong reference on return
• #7310: STY: Fix GitHub rendering of ordered lists >9
• #7311: ENH: Make _pointer_type_cache functional
• #7313: DOC: corrected grammatical error in quickstart doc
• #7325: BUG, MAINT: Improve fromnumeric.py interface for downstream compatibility
• #7328: DEP: Deprecated using a float index in linspace
• #7331: Add comment, TST: fix MemoryError on win32
• #7332: Check for no solution in np.irr Fixes #6744
• #7338: TST: Install pytz in the CI.
• #7340: DOC: Fixed math rendering in tensordot docs.
• #7341: TST: Add test for #6469
• #7344: DOC: Fix more typos in docs and comments.
• #7346: Generalized flip
• #7347: ENH Generalized rot90
• #7348: Maint: Removed extra space from ureduce
• #7349: MAINT: Hide nan warnings for masked internal MA computations
• #7350: BUG: MA ufuncs should set mask to False, not array([False])
• #7351: TST: Fix some MA tests to avoid looking at the .data attribute
• #7358: BUG: pull request related to the issue #7353
• #7359: Update 7314, DOC: Clarify valid integer range for random.seed...
• #7361: MAINT: Fix copy and paste oversight.
• #7363: ENH: Make no unshare mask future warnings less noisy
• #7366: TST: fix #6542, add tests to check non-iterable argument raises...
• #7373: ENH: Add bitwise_and identity
• #7378: added NumPy logo and separator
• #7382: MAINT: cleanup np.average
• #7385: DOC: note about wheels / windows wheels for pypi
• #7386: Added label icon to Travis status
• #7397: BUG: incorrect type for objects whose len fails
• #7398: DOC: fix typo
• #7404: Use PyMem_RawMalloc on Python 3.4 and newer
• #7406: ENH ufunc called on memmap return a ndarray
• #7407: BUG: Fix decref before incref for in-place accumulate
• #7410: DOC: add nanprod to the list of math routines
• #7414: Tweak corrcoef
• #7415: DOC: Documention fixes
• #7416: BUG: Incorrect handling of range in histogram with automatic...
• #7418: DOC: Minor typo fix, hermefik -> hermefit.
• #7421: ENH: adds np.nancumsum and np.nancumprod
• #7423: BUG: Ongoing fixes to PR#7416
• #7430: DOC: Update 1.11.0-notes.
• #7433: MAINT: FutureWarning for changes to np.average subclass handling
• #7437: np.full now defaults to the filling value's dtype.
• #7438: Allow rolling multiple axes at the same time.
• #7439: BUG: Do not try sequence repeat unless necessary
• #7442: MANT: Simplify diagonal length calculation logic
• #7445: BUG: reference count leak in bincount, fixes #6805
• #7446: DOC: ndarray typo fix
• #7447: BUG: scalar integer negative powers gave wrong results.
• #7448: DOC: array "See also" link to full and full_like instead of fill
• #7456: BUG: int overflow in reshape, fixes #7455, fixes #7293
• #7463: BUG: fix array too big error for wide dtypes.
• #7466: BUG: segfault inplace object reduceat, fixes #7465
• #7468: BUG: more on inplace reductions, fixes #615
• #7469: MAINT: Update git .mailmap
• #7472: MAINT: Update .mailmap.
• #7477: MAINT: Yet more .mailmap updates for recent contributors.
• #7481: BUG: Fix segfault in PyArray_OrderConverter
• #7482: BUG: Memory Leak in _GenericBinaryOutFunction
• #7489: Faster real_if_close.
• #7491: DOC: Update subclassing doc regarding downstream compatibility
• #7496: BUG: don't use pow for integer power ufunc loops.
• #7504: DOC: remove "arr" from keepdims docstrings
• #7505: MAIN: fix to #7382, make scl in np.average writeable
• #7507: MAINT: Remove nose.SkipTest import.
• #7508: DOC: link frompyfunc and vectorize
• #7511: numpy.power(0, 0) should return 1
• #7515: BUG: MaskedArray.count treats negative axes incorrectly
• #7518: BUG: Extend glibc complex trig functions blacklist to glibc <...
• #7521: DOC: rephrase writeup of memmap changes
• #7522: BUG: Fixed iteration over additional bad commands
• #7526: DOC: Removed an extra :const:
• #7529: BUG: Floating exception with invalid axis in np.lexsort
• #7534: MAINT: Update setup.py to reflect supported python versions.
• #7536: MAINT: Always use PyCapsule instead of PyCObject in mtrand.pyx
• #7539: MAINT: Cleanup of random stuff
• #7549: BUG: allow graceful recovery for no Liux compiler
• #7562: BUG: Fix test_from_object_array_unicode (test_defchararray.TestBasic)…
• #7565: BUG: Fix test_ctypeslib and test_indexing for debug interpreter
• #7566: MAINT: use manylinux1 wheel for cython
• #7568: Fix a false positive OverflowError in Python 3.x when value above...
• #7579: DOC: clarify purpose of Attributes section
• #7584: BUG: fixes #7572, percent in path
• #7586: Make np.ma.take works on scalars
• #7587: BUG: linalg.norm(): Don't convert object arrays to float
• #7598: Cast array size to int64 when loading from archive
• #7602: DOC: Remove isreal and iscomplex from ufunc list
• #7605: DOC: fix incorrect Gamma distribution parameterization comments
• #7609: BUG: Fix TypeError when raising TypeError
• #7611: ENH: expose test runner raise_warnings option
• #7614: BLD: Avoid using os.spawnve in favor of os.spawnv in exec_command
• #7618: BUG: distance arg of np.gradient must be scalar, fix docstring
• #7626: DOC: RST definition list fixes
• #7627: MAINT: unify tup processing, move tup use to after all PyTuple_SetItem...
• #7630: MAINT: add ifdef around PyDictProxy_Check macro
• #7631: MAINT: linalg: fix comment, simplify math
• #7634: BLD: correct C compiler customization in system_info.py Closes...
• #7635: BUG: ma.median alternate fix for #7592
• #7636: MAINT: clean up testing.assert_raises_regexp, 2.6-specific code...
• #7637: MAINT: clearer exception message when importing multiarray fails.
• #7639: TST: fix a set of test errors in master.
• #7643: DOC : minor changes to linspace docstring
• #7651: BUG: one to any power is still 1. Broken edgecase for int arrays
• #7655: BLD: Remove Intel compiler flag -xSSE4.2
• #7658: BUG: fix incorrect printing of 1D masked arrays
• #7659: BUG: Temporary fix for str(mvoid) for object field types
• #7664: BUG: Fix unicode with byte swap transfer and copyswap
• #7667: Restore histogram consistency
• #7668: ENH: Do not check the type of module.dict explicit in test.
• #7669: BUG: boolean assignment no GIL release when transfer needs API
• #7673: DOC: Create Numpy 1.11.1 release notes.
• #7675: BUG: fix handling of right edge of final bin.
• #7678: BUG: Fix np.clip bug NaN handling for Visual Studio 2015
• #7679: MAINT: Fix up C++ comment in arraytypes.c.src.
• #7681: DOC: Update 1.11.1 release notes.
• #7686: ENH: Changing FFT cache to a bounded LRU cache
• #7688: DOC: fix broken genfromtxt examples in user guide. Closes gh-7662.
• #7689: BENCH: add correlate/convolve benchmarks.
• #7696: DOC: update wheel build / upload instructions
• #7699: BLD: preserve library order
• #7704: ENH: Add bits attribute to np.finfo
• #7712: BUG: Fix race condition with new FFT cache
• #7715: BUG: Remove memory leak in np.place
• #7719: BUG: Fix segfault in np.random.shuffle for arrays of different...
• #7723: Change mkl_info.dir_env_var from MKL to MKLROOT
• #7727: DOC: Corrections in Datetime Units-arrays.datetime.rst
• #7729: DOC: fix typo in savetxt docstring (closes #7620)
• #7733: Update 7525, DOC: Fix order='A' docs of np.array.
• #7734: Update 7542, ENH: Add polyrootval to numpy.polynomial
• #7735: BUG: fix issue on OS X with Python 3.x where npymath.ini was...
• #7739: DOC: Mention the changes of #6430 in the release notes.
• #7740: DOC: add reference to poisson rng
• #7743: Update 7476, DEP: deprecate Numeric-style typecodes, closes #2148
• #7744: DOC: Remove "ones_like" from ufuncs list (it is not)
• #7746: DOC: Clarify the effect of rcond in numpy.linalg.lstsq.
• #7747: Update 7672, BUG: Make sure we don't divide by zero
• #7748: DOC: Update float32 mean example in docstring
• #7754: Update 7612, ENH: Add broadcast.ndim to match code elsewhere.
• #7757: Update 7175, BUG: Invalid read of size 4 in PyArray_FromFile
• #7759: BUG: Fix numpy.i support for numpy API < 1.7.
• #7760: ENH: Make assert_almost_equal & assert_array_almost_equal consistent.
• #7766: fix an English typo
• #7771: DOC: link geomspace from logspace
• #7773: DOC: Remove a redundant the
• #7777: DOC: Update Numpy 1.11.1 release notes.
• #7785: DOC: update wheel building procedure for release
• #7789: MRG: add note of 64-bit wheels on Windows
• #7791: f2py.compile issues (#7683)
• #7799: "lambda" is not allowed to use as keyword arguments in a sample...
• #7803: BUG: interpret 'c' PEP3118/struct type as 'S1'.
• #7807: DOC: Misplaced parens in formula
• #7817: BUG: Make sure npy_mul_with_overflow_ detects overflow.
• #7818: numpy/distutils/misc_util.py fix for #7809: check that _tmpdirs...
• #7820: MAINT: Allocate fewer bytes for empty arrays.
• #7823: BUG: Fixed masked array behavior for scalar inputs to np.ma.atleast_*d
• #7834: DOC: Added an example
• #7839: Pypy fixes
• #7840: Fix ATLAS version detection
• #7842: Fix versionadded tags
• #7848: MAINT: Fix remaining uses of deprecated Python imp module.
• #7853: BUG: Make sure numpy globals keep identity after reload.
• #7863: ENH: turn quicksort into introsort
• #7866: Document runtests extra argv
• #7871: BUG: handle introsort depth limit properly
• #7879: DOC: fix typo in documentation of loadtxt (closes #7878)
• #7885: Handle NetBSD specific <sys/endian.h>
• #7889: DOC: #7881. Fix link to record arrays
• #7894: fixup-7790, BUG: construct ma.array from np.array which contains...
• #7898: Spelling and grammar fix.
• #7903: BUG: fix float16 type not being called due to wrong ordering
• #7908: BLD: Fixed detection for recent MKL versions
• #7911: BUG: fix for issue#7835 (ma.median of 1d)
• #7912: ENH: skip or avoid gc/objectmodel differences btwn pypy and cpython
• #7918: ENH: allow numpy.apply_along_axis() to work with ndarray subclasses
• #7922: ENH: Add ma.convolve and ma.correlate for #6458
• #7925: Monkey-patch _msvccompile.gen_lib_option like any other compilators
• #7931: BUG: Check for HAVE_LDOUBLE_DOUBLE_DOUBLE_LE in npy_math_complex.
• #7936: ENH: improve duck typing inside iscomplexobj
• #7937: BUG: Guard against buggy comparisons in generic quicksort.
• #7938: DOC: add cbrt to math summary page
• #7941: BUG: Make sure numpy globals keep identity after reload.
• #7943: DOC: #7927. Remove deprecated note for memmap relevant for Python...
• #7952: BUG: Use keyword arguments to initialize Extension base class.
• #7956: BLD: remove NUMPY_SETUP from builtins at end of setup.py
• #7963: BUG: MSVCCompiler grows 'lib' & 'include' env strings exponentially.
• #7965: BUG: cannot modify tuple after use
• #7976: DOC: Fixed documented dimension of return value
• #7977: DOC: Create 1.11.2 release notes.
• #7979: DOC: Corrected allowed keywords in add_(installed_)library
• #7980: ENH: Add ability to runtime select ufunc loops, add AVX2 integer...
• #7985: Rebase 7763, ENH: Add new warning suppression/filtering context
• #7987: DOC: See also np.load and np.memmap in np.lib.format.open_memmap
• #7988: DOC: Include docstring for cbrt, spacing and fabs in documentation
• #7999: ENH: add inplace cases to fast ufunc loop macros
• #8006: DOC: Update 1.11.2 release notes.
• #8008: MAINT: Remove leftover imp module imports.
• #8009: DOC: Fixed three typos in the c-info.ufunc-tutorial
• #8011: DOC: Update 1.11.2 release notes.
• #8014: BUG: Fix fid.close() to use os.close(fid)
• #8016: BUG: Fix numpy.ma.median.
• #8018: BUG: Fixes return for np.ma.count if keepdims is True and axis...
• #8021: DOC: change all non-code instances of Numpy to NumPy
• #8027: ENH: Add platform indepedent lib dir to PYTHONPATH
• #8028: DOC: Update 1.11.2 release notes.
• #8030: BUG: fix np.ma.median with only one non-masked value and an axis...
• #8038: MAINT: Update error message in rollaxis.
• #8040: Update add_newdocs.py
• #8042: BUG: core: fix bug in NpyIter buffering with discontinuous arrays
• #8045: DOC: Update 1.11.2 release notes.
• #8050: remove refcount semantics, now a.resize() almost always requires...
• #8051: Clear signaling NaN exceptions
• #8054: ENH: add signature argument to vectorize for vectorizing like...
• #8057: BUG: lib: Simplify (and fix) pad's handling of the pad_width
• #8061: BUG : financial.pmt modifies input (issue #8055)
• #8064: MAINT: Add PMIP files to .gitignore
• #8065: BUG: Assert fromfile ending earlier in pyx_processing
• #8066: BUG, TST: Fix python3-dbg bug in Travis script
• #8071: MAINT: Add Tempita to randint helpers
• #8075: DOC: Fix description of isinf in nan_to_num
• #8080: BUG: non-integers can end up in dtype offsets
• #8081: Update outdated Nose URL to nose.readthedocs.io
• #8083: ENH: Deprecation warnings for / integer division when running...
• #8084: DOC: Fix erroneous return type description for np.roots.
• #8087: BUG: financial.pmt modifies input #8055
• #8088: MAINT: Remove duplicate randint helpers code.
• #8093: MAINT: fix assert_raises_regex when used as a context manager
• #8096: ENH: Vendorize tempita.
• #8098: DOC: Enhance description/usage for np.linalg.eig*h
• #8103: Pypy fixes
• #8104: Fix test code on cpuinfo's main function
• #8107: BUG: Fix array printing with precision=0.
• #8109: Fix bug in ravel_multi_index for big indices (Issue #7546)
• #8110: BUG: distutils: fix issue with rpath in fcompiler/gnu.py
• #8111: ENH: Add a tool for release authors and PRs.
• #8112: DOC: Fix "See also" links in linalg.
• #8114: BUG: core: add missing error check after PyLong_AsSsize_t
• #8121: DOC: Improve histogram2d() example.
• #8122: BUG: Fix broken pickle in MaskedArray when dtype is object (Return...
• #8124: BUG: Fixed build break
• #8125: Rebase, BUG: Fixed deepcopy of F-order object arrays.
• #8127: BUG: integers to a negative integer powers should error.
• #8141: improve configure checks for broken systems
• #8142: BUG: np.ma.mean and var should return scalar if no mask
• #8148: BUG: import full module path in npy_load_module
• #8153: MAINT: Expose void-scalar "base" attribute in python
• #8156: DOC: added example with empty indices for a scalar, #8138
• #8160: BUG: fix _array2string for structured array (issue #5692)
• #8164: MAINT: Update mailmap for NumPy 1.12.0
• #8165: Fixup 8152, BUG: assert_allclose(..., equal_nan=False) doesn't...
• #8167: Fixup 8146, DOC: Clarify when PyArray_{Max, Min, Ptp} return...
• #8168: DOC: Minor spelling fix in genfromtxt() docstring.
• #8173: BLD: Enable build on AIX
• #8174: DOC: warn that dtype.descr is only for use in PEP3118
• #8177: MAINT: Add python 3.6 support to suppress_warnings
• #8178: MAINT: Fix ResourceWarning new in Python 3.6.
• #8180: FIX: protect stolen ref by PyArray_NewFromDescr in array_empty
• #8181: ENH: Improve announce to find github squash-merge commits.
• #8182: MAINT: Update .mailmap
• #8183: MAINT: Ediff1d performance
• #8184: MAINT: make assert_allclose behavior on nans match pre 1.12
• #8188: DOC: 'highest' is exclusive for randint()
• #8189: BUG: setfield should raise if arr is not writeable
• #8190: ENH: Add a float_power function with at least float64 precision.
• #8197: DOC: Add missing arguments to np.ufunc.outer
• #8198: DEP: Deprecate the keepdims argument to accumulate
• #8199: MAINT: change path to env in distutils.system_info. Closes gh-8195.
• #8200: BUG: Fix structured array format functions
• #8202: ENH: specialize name of dev package by interpreter
• #8205: DOC: change development instructions from SSH to HTTPS access.
• #8216: DOC: Patch doc errors for atleast_nd and frombuffer
• #8218: BUG: ediff1d should return subclasses
• #8219: DOC: Turn SciPy references into links.
• #8222: ENH: Make numpy.mean() do more precise computation
• #8227: BUG: Better check for invalid bounds in np.random.uniform.
• #8231: ENH: Refactor numpy ** operators for numpy scalar integer powers
• #8234: DOC: Clarified when a copy is made in numpy.asarray
• #8236: DOC: Fix documentation pull requests.
• #8238: MAINT: Update pavement.py
• #8239: ENH: Improve announce tool.
• #8240: REL: Prepare for 1.12.x branch
• #8243: BUG: Update operator ** tests for new behavior.
• #8246: REL: Reset strides for RELAXED_STRIDE_CHECKING for 1.12 releases.
• #8265: BUG: np.piecewise not working for scalars
• #8272: TST: Path test should resolve symlinks when comparing
• #8282: DOC: Update 1.12.0 release notes.
• #8286: BUG: Fix pavement.py write_release_task.
• #8296: BUG: Fix iteration over reversed subspaces in mapiter_@name@.
• #8304: BUG: Fix PyPy crash in PyUFunc_GenericReduction.
• #8319: BLD: blacklist powl (longdouble power function) on OS X.
• #8320: BUG: do not link to Accelerate if OpenBLAS, MKL or BLIS are found.
• #8322: BUG: fixed kind specifications for parameters
• #8336: BUG: fix packbits and unpackbits to correctly handle empty arrays
• #8338: BUG: fix test_api test that fails intermittently in python 3
• #8339: BUG: Fix ndarray.tofile large file corruption in append mode.
• #8359: BUG: Fix suppress_warnings (again) for Python 3.6.
• #8372: BUG: Fixes for ma.median and nanpercentile.
• #8373: BUG: correct letter case
• #8379: DOC: Update 1.12.0-notes.rst.
• #8390: ENH: retune apply_along_axis nanmedian cutoff in 1.12
• #8391: DEP: Fix escaped string characters deprecated in Python 3.6.
• #8394: DOC: create 1.11.3 release notes.
• #8399: BUG: Fix author search in announce.py
• #8402: DOC, MAINT: Update 1.12.0 notes and mailmap.

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