==========================
NumPy 1.17.0 Release Notes

This NumPy release contains a number of new features that should substantially
improve its performance and usefulness, see Highlights below for a summary. The
Python versions supported are 3.5-3.7, note that Python 2.7 has been dropped.
Python 3.8b1 should work with the released source packages, but there are no
future guarantees.

Downstream developers should use Cython >= 0.29.10 for Python 3.8 support and
OpenBLAS >= 3.7 (not currently out) to avoid problems on the Skylake
architecture. The NumPy wheels on PyPI are built from the OpenBLAS development
branch in order to avoid those problems.

Highlights

• A new extensible random module along with four selectable random number
  generators and improved seeding designed for use in parallel processes has
  been added. The currently available bit generators are MT19937, PCG64,
  Philox, and SFC64. See below under New Features.

• NumPy's FFT implementation was changed from fftpack to pocketfft, resulting
  in faster, more accurate transforms and better handling of datasets of
  prime length. See below under Improvements.

• New radix sort and timsort sorting methods. It is currently not possible to
  choose which will be used, but they are hardwired to the datatype and used
  when either stable or mergesort is passed as the method. See below
  under Improvements.

• Overriding numpy functions is now possible by default,
  see __array_function__ below.

New functions

• numpy.errstate is now also a function decorator

Deprecations

np.polynomial functions warn when passed float in place of int

Previously functions in this module would accept float values provided they
were integral (1.0, 2.0, etc). For consistency with the rest of numpy,
doing so is now deprecated, and in future will raise a TypeError.

Similarly, passing a float like 0.5 in place of an integer will now raise a
TypeError instead of the previous ValueError.

Deprecate numpy.distutils.exec_command and numpy.distutils.temp_file_name

The internal use of these functions has been refactored and there are better
alternatives. Relace exec_command with subprocess.Popen and
temp_file_name with tempfile.mkstemp.

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only
indication we have of the read-write nature of the data is the writeable
flag set during creation. It is dangerous to force the flag to writeable.
In the future it will not be possible to switch the writeable flag to True
from python. This deprecation should not affect many users since arrays created in such
a manner are very rare in practice and only available through the NumPy C-API.

numpy.nonzero should no longer be called on 0d arrays

The behavior of nonzero on 0d arrays was surprising, making uses of it almost
always incorrect. If the old behavior was intended, it can be preserved without
a warning by using nonzero(atleast_1d(arr)) instead of nonzero(arr).
In a future release, it is most likely this will raise a ValueError.

Writing to the result of numpy.broadcast_arrays will warn

Commonly numpy.broadcast_arrays returns a writeable array with internal
overlap, making it unsafe to write to. A future version will set the
writeable flag to False, and require users to manually set it to
True if they are sure that is what they want to do. Now writing to it will
emit a deprecation warning with instructions to set the writeable flag
True. Note that if one were to inspect the flag before setting it, one
would find it would already be True. Explicitly setting it, though, as one
will need to do in future versions, clears an internal flag that is used to
produce the deprecation warning. To help alleviate confusion, an additional
FutureWarning will be emitted when accessing the writeable flag state to
clarify the contradiction.

Future Changes

Shape-1 fields in dtypes won't be collapsed to scalars in a future version

Currently, a field specified as [(name, dtype, 1)] or "1type" is
interpreted as a scalar field (i.e., the same as [(name, dtype)] or
[(name, dtype, ()]). This now raises a FutureWarning; in a future version,
it will be interpreted as a shape-(1,) field, i.e. the same as
[(name,dtype, (1,))] or "(1,)type" (consistent with
[(name, dtype, n)] / "ntype" for n > 1, which is already equivalent to
[(name, dtype,(n,)] / "(n,)type").

Compatibility notes

float16 subnormal rounding

Casting from a different floating point precision to float16 used incorrect
rounding in some edge cases. This means in rare cases, subnormal results will
now be rounded up instead of down, changing the last bit (ULP) of the result.

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example:

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero:

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

MaskedArray.mask now returns a view of the mask, not the mask itself

Returning the mask itself was unsafe, as it could be reshaped in place which
would violate expectations of the masked array code. It's behavior is now
consistent with the .data attribute, which also returns a view.

The underlying mask can still be accessed with ._mask if it is needed.
Tests that contain assert x.mask is not y.mask or similar will need to be
updated.

Do not lookup __buffer__ attribute in numpy.frombuffer

Looking up __buffer__ attribute in numpy.frombuffer was undocumented and
non-functional. This code was removed. If needed, use
frombuffer(memoryview(obj), ...) instead.

outis buffered for memory overlaps in np.take, np.choose, np.put

If the out argument to these functions is provided and has memory overlap with
the other arguments, it is now buffered to avoid order-dependent behavior.

Unpickling while loading requires explicit opt-in

The functions np.load, and np.lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Potential changes to the random stream in old random module

Due to bugs in the application of log to random floating point numbers,
the stream may change when sampling from np.random.beta, np.random.binomial,
np.random.laplace, np.random.logistic, np.random.logseries or
np.random.multinomial if a 0 is generated in the underlying MT19937 random stream.
There is a 1 in :math:10^{53} chance of this occurring, and so the probability that
the stream changes for any given seed is extremely small. If a 0 is encountered in the
underlying generator, then the incorrect value produced (either np.inf
or np.nan) is now dropped.

i0 now always returns a result with the same shape as the input

Previously, the output was squeezed, such that, e.g., input with just a single
element would lead to an array scalar being returned, and inputs with shapes
such as (10, 1) would yield results that would not broadcast against the
input.

Note that we generally recommend the SciPy implementation over the numpy one:
it is a proper ufunc written in C, and more than an order of magnitude faster.

np.can_cast no longer assumes all unsafe casting is allowed

Previously, can_cast returned True for almost all inputs for
casting='unsafe', even for cases where casting was not possible, such as
from a structured dtype to a regular one. This has been fixed, making it
more consistent with actual casting using, e.g., the .astype method.

arr.writeable can be switched to true slightly more often

In rare cases, it was not possible to switch an array from not writeable
to writeable, although a base array is writeable. This can happen if an
intermediate arr.base object is writeable. Previously, only the deepest
base object was considered for this decision. However, in rare cases this
object does not have the necessary information. In that case switching to
writeable was never allowed. This has now been fixed.

C API changes

dimension or stride input arguments are now passed by npy_intp const*

Previously these function arguments were declared as the more strict
npy_intp*, which prevented the caller passing constant data.
This change is backwards compatible, but now allows code like::

    npy_intp const fixed_dims[] = {1, 2, 3};
    // no longer complains that the const-qualifier is discarded
    npy_intp size = PyArray_MultiplyList(fixed_dims, 3);

New Features

New extensible random module with selectable random number generators

A new extensible random module along with four selectable random number
generators and improved seeding designed for use in parallel processes has been
added. The currently available bit generators are MT19937, PCG64, Philox, and
SFC64. PCG64 is the new default while MT19937 is retained for backwards
compatibility. Note that the legacy random module is unchanged and is now
frozen, your current results will not change. Extensive documentation for the
new module is available online at
NumPy devdocs.

libFLAME

Support for building NumPy with the libFLAME linear algebra package as the LAPACK,
implementation, see
libFLAME for details.

User-defined BLAS detection order

numpy.distutils now uses an environment variable, comma-separated and case
insensitive, to determine the detection order for BLAS libraries.
By default NPY_BLAS_ORDER=mkl,blis,openblas,atlas,accelerate,blas.
However, to force the use of OpenBLAS simply do::

NPY_BLAS_ORDER=openblas python setup.py build

which forces the use of OpenBLAS.
This may be helpful for users which have a MKL installation but wishes to try
out different implementations.

User-defined LAPACK detection order

numpy.distutils now uses an environment variable, comma-separated and case
insensitive, to determine the detection order for LAPACK libraries.
By default NPY_BLAS_ORDER=mkl,openblas,flame,atlas,accelerate,lapack.
However, to force the use of OpenBLAS simply do::

NPY_LAPACK_ORDER=openblas python setup.py build

which forces the use of OpenBLAS.
This may be helpful for users which have a MKL installation but wishes to try
out different implementations.

np.ufunc.reduce and related functions now accept a where mask

np.ufunc.reduce, np.sum, np.prod, np.min, np.max all
now accept a where keyword argument, which can be used to tell which
elements to include in the reduction. For reductions that do not have an
identity, it is necessary to also pass in an initial value (e.g.,
initial=np.inf for np.min). For instance, the equivalent of
nansum would be, np.sum(a, where=~np.isnan(a)).

Timsort and radix sort have replaced mergesort for stable sorting

Both radix sort and timsort have been implemented and are now used in place of
mergesort. Due to the need to maintain backward compatibility, the sorting
kind options "stable" and "mergesort" have been made aliases of
each other with the actual sort implementation depending on the array type.
Radix sort is used for small integer types of 16 bits or less and timsort for
the remaining types. Timsort features improved performace on data containing
already or nearly sorted data and performs like mergesort on random data and
requires O(n/2) working space. Details of the timsort algorithm can be found
at CPython listsort.txt.

np.unpackbits now accepts a count parameter

count allows subsetting the number of bits that will be unpacked up-front,
rather than reshaping and subsetting later, making the packbits operation
invertible, and the unpacking less wasteful. Counts larger than the number of
available bits add zero padding. Negative counts trim bits off the end instead
of counting from the beginning. None counts implement the existing behavior of
unpacking everything.

np.linalg.svd and np.linalg.pinv can be faster on hermitian inputs

These functions now accept a hermitian argument, matching the one added
to np.linalg.matrix_rank in 1.14.0.

divmod operation is now supported for two timedelta64 operands

The divmod operator now handles two np.timedelta64 operands, with
type signature mm->qm.

np.fromfile now takes an offset argument

This function now takes an offset keyword argument for binary files,
which specifics the offset (in bytes) from the file's current position.
Defaults to 0.

New mode "empty" for np.pad

This mode pads an array to a desired shape without initializing the new
entries.

np.empty_like and related functions now accept a shape argument

np.empty_like, np.full_like, np.ones_like and np.zeros_like now
accept a shape keyword argument, which can be used to create a new array
as the prototype, overriding its shape as well. This is particularly useful
when combined with the __array_function__ protocol, allowing the creation
of new arbitrary-shape arrays from NumPy-like libraries when such an array
is used as the prototype.

Floating point scalars implement as_integer_ratio to match the builtin float

This returns a (numerator, denominator) pair, which can be used to construct a
fractions.Fraction.

Structured dtype objects can be indexed with multiple fields names

arr.dtype[['a', 'b']] now returns a dtype that is equivalent to
arr[['a', 'b']].dtype, for consistency with
arr.dtype['a'] == arr['a'].dtype.

Like the dtype of structured arrays indexed with a list of fields, this dtype
has the same itemsize as the original, but only keeps a subset of the fields.

This means that arr[['a', 'b']] and arr.view(arr.dtype[['a', 'b']]) are
equivalent.

.npy files support unicode field names

A new format version of 3.0 has been introduced, which enables structured types
with non-latin1 field names. This is used automatically when needed.

numpy.packbits and numpy.unpackbits accept an order keyword

The order keyword defaults to big, and will order the bits
accordingly. For 'big' 3 will become [0, 0, 0, 0, 0, 0, 1, 1], and
[1, 1, 0, 0, 0, 0, 0, 0] for little

Improvements

Array comparison assertions include maximum differences

Error messages from array comparison tests such as
np.testing.assert_allclose now include "max absolute difference" and
"max relative difference," in addition to the previous "mismatch" percentage.
This information makes it easier to update absolute and relative error
tolerances.

Replacement of the fftpack based FFT module by the pocketfft library

Both implementations have the same ancestor (Fortran77 FFTPACK by Paul N.
Swarztrauber), but pocketfft contains additional modifications which improve
both accuracy and performance in some circumstances. For FFT lengths containing
large prime factors, pocketfft uses Bluestein's algorithm, which maintains
O(N log N) run time complexity instead of deteriorating towards O(N*N)
for prime lengths. Also, accuracy for real valued FFTs with near prime lengths
has improved and is on par with complex valued FFTs.

Further improvements to ctypes support in numpy.ctypeslib

A new numpy.ctypeslib.as_ctypes_type function has been added, which can be
used to converts a dtype into a best-guess ctypes type. Thanks to this
new function, numpy.ctypeslib.as_ctypes now supports a much wider range of
array types, including structures, booleans, and integers of non-native
endianness.

numpy.errstate is now also a function decorator

Currently, if you have a function like::

def foo():
    pass

and you want to wrap the whole thing in errstate, you have to rewrite it
like so::

def foo():
    with np.errstate(...):
        pass

but with this change, you can do::

@np.errstate(...)
def foo():
    pass

thereby saving a level of indentation

numpy.exp and numpy.log speed up for float32 implementation

float32 implementation of numpy.exp and numpy.log now benefit from AVX2/AVX512
instruction set which are detected during runtime. numpy.exp has a max ulp
error of 2.52 and numpy.log has a max ulp error or 3.83.

Improve performance of numpy.pad

The performance of the function has been improved for most cases by filling in
a preallocated array with the desired padded shape instead of using
concatenation.

numpy.interp handles infinities more robustly

In some cases where np.interp would previously return np.nan, it now
returns an appropriate infinity.

Pathlib support for np.fromfile, ndarray.tofile and ndarray.dump

np.fromfile, np.ndarray.tofile and np.ndarray.dump now support
the pathlib.Path type for the file/fid parameter.

Specialized np.isnan, np.isinf, and np.isfinite ufuncs for bool and int types

The boolean and integer types are incapable of storing np.nan and
np.inf values, which allows us to provide specialized ufuncs that are up to
250x faster than the current approach.

np.isfinite supports datetime64 and timedelta64 types

Previously, np.isfinite used to raise a TypeError on being used on these
two types.

New keywords added to np.nan_to_num

np.nan_to_num now accepts keywords nan, posinf and neginf
allowing the user to define the value to replace the nan, positive and
negative np.inf values respectively.

MemoryErrors caused by allocated overly large arrays are more descriptive

Often the cause of a MemoryError is incorrect broadcasting, which results in a
very large and incorrect shape. The message of the error now includes this
shape to help diagnose the cause of failure.

floor, ceil, and trunc now respect builtin magic methods

These ufuncs now call the __floor__, __ceil__, and __trunc__
methods when called on object arrays, making them compatible with
decimal.Decimal and fractions.Fraction objects.

quantile now works on fraction.Fraction and decimal.Decimal objects

In general, this handles object arrays more gracefully, and avoids floating-
point operations if exact arithmetic types are used.

Support of object arrays in np.matmul

It is now possible to use np.matmul (or the @ operator) with object arrays.
For instance, it is now possible to do::

from fractions import Fraction
a = np.array([[Fraction(1, 2), Fraction(1, 3)], [Fraction(1, 3), Fraction(1, 2)]])
b = a @ a

Changes

median and percentile family of functions no longer warn about nan

numpy.median, numpy.percentile, and numpy.quantile used to emit a
RuntimeWarning when encountering an numpy.nan. Since they return the
nan value, the warning is redundant and has been removed.

timedelta64 % 0 behavior adjusted to return NaT

The modulus operation with two np.timedelta64 operands now returns
NaT in the case of division by zero, rather than returning zero

NumPy functions now always support overrides with __array_function__

NumPy now always checks the __array_function__ method to implement overrides
of NumPy functions on non-NumPy arrays, as described in NEP 18_. The feature
was available for testing with NumPy 1.16 if appropriate environment variables
are set, but is now always enabled.

.. _NEP 18 : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

numpy.lib.recfunctions.structured_to_unstructured does not squeeze single-field views

Previously structured_to_unstructured(arr[['a']]) would produce a squeezed
result inconsistent with structured_to_unstructured(arr[['a', b']]). This
was accidental. The old behavior can be retained with
structured_to_unstructured(arr[['a']]).squeeze(axis=-1) or far more simply,
arr['a'].

clip now uses a ufunc under the hood

This means that registering clip functions for custom dtypes in C via
descr->f->fastclip is deprecated - they should use the ufunc registration
mechanism instead, attaching to the np.core.umath.clip ufunc.

It also means that clip accepts where and casting arguments,
and can be override with __array_ufunc__.

A consequence of this change is that some behaviors of the old clip have
been deprecated:

• Passing nan to mean "do not clip" as one or both bounds. This didn't work
  in all cases anyway, and can be better handled by passing infinities of the
  appropriate sign.
• Using "unsafe" casting by default when an out argument is passed. Using
  casting="unsafe" explicitly will silence this warning.

Additionally, there are some corner cases with behavior changes:

• Padding max < min has changed to be more consistent across dtypes, but
  should not be relied upon.
• Scalar min and max take part in promotion rules like they do in all
  other ufuncs.

__array_interface__ offset now works as documented

The interface may use an offset value that was mistakenly ignored.

Pickle protocol in np.savez set to 3 for force zip64 flag

np.savez was not using the force_zip64 flag, which limited the size of
the archive to 2GB. But using the flag requires us to use pickle protocol 3 to
write object arrays. The protocol used was bumped to 3, meaning the archive
will be unreadable by Python2.

Structured arrays indexed with non-existent fields raise KeyError not ValueError

arr['bad_field'] on a structured type raises KeyError, for consistency
with dict['bad_field'].

.. _NEP 18 : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

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==========================
NumPy 1.16.4 Release Notes

The NumPy 1.16.4 release fixes bugs reported against the 1.16.3 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.7-dev, which should fix issues on Skylake series
cpus.

Downstream developers building this release should use Cython >= 0.29.2 and,
if using OpenBLAS, OpenBLAS > v0.3.7. The supported Python versions are 2.7 and
3.5-3.7.

New deprecations

Writeable flag of C-API wrapped arrays

When an array is created from the C-API to wrap a pointer to data, the only
indication we have of the read-write nature of the data is the writeable
flag set during creation. It is dangerous to force the flag to writeable. In
the future it will not be possible to switch the writeable flag to True
from python. This deprecation should not affect many users since arrays
created in such a manner are very rare in practice and only available through
the NumPy C-API.

Compatibility notes

Potential changes to the random stream

Due to bugs in the application of log to random floating point numbers,
the stream may change when sampling from np.random.beta, np.random.binomial,
np.random.laplace, np.random.logistic, np.random.logseries or
np.random.multinomial if a 0 is generated in the underlying MT19937 random stream.
There is a 1 in :math:10^{53} chance of this occurring, and so the probability that
the stream changes for any given seed is extremely small. If a 0 is encountered in the
underlying generator, then the incorrect value produced (either np.inf
or np.nan) is now dropped.

Changes

numpy.lib.recfunctions.structured_to_unstructured does not squeeze single-field views

Previously structured_to_unstructured(arr[['a']]) would produce a squeezed
result inconsistent with structured_to_unstructured(arr[['a', b']]). This
was accidental. The old behavior can be retained with
structured_to_unstructured(arr[['a']]).squeeze(axis=-1) or far more simply,
arr['a'].

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
• Dennis Zollo +
• Hunter Damron +
• Jingbei Li +
• Kevin Sheppard
• Matti Picus
• Nicola Soranzo +
• Sebastian Berg
• Tyler Reddy

Pull requests merged

A total of 16 pull requests were merged for this release.

• #13392: BUG: Some PyPy versions lack PyStructSequence_InitType2.
• #13394: MAINT, DEP: Fix deprecated assertEquals()
• #13396: BUG: Fix structured_to_unstructured on single-field types (backport)
• #13549: BLD: Make CI pass again with pytest 4.5
• #13552: TST: Register markers in conftest.py.
• #13559: BUG: Removes ValueError for empty kwargs in arraymultiter_new
• #13560: BUG: Add TypeError to accepted exceptions in crackfortran.
• #13561: BUG: Handle subarrays in descr_to_dtype
• #13562: BUG: Protect generators from log(0.0)
• #13563: BUG: Always return views from structured_to_unstructured when...
• #13564: BUG: Catch stderr when checking compiler version
• #13565: BUG: longdouble(int) does not work
• #13587: BUG: distutils/system_info.py fix missing subprocess import (#13523)
• #13620: BUG,DEP: Fix writeable flag setting for arrays without base
• #13641: MAINT: Prepare for the 1.16.4 release.
• #13644: BUG: special case object arrays when printing rel-, abs-error

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==========================
NumPy 1.16.3 Release Notes

The NumPy 1.16.3 release fixes bugs reported against the 1.16.2 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29.2 and,
if using OpenBLAS, OpenBLAS > v0.3.4.

The most noticeable change in this release is that unpickling object arrays
when loading *.npy or *.npz files now requires an explicit opt-in.
This backwards incompatible change was made in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Compatibility notes

Unpickling while loading requires explicit opt-in

The functions np.load, and np.lib.format.read_array take an
allow_pickle keyword which now defaults to False in response to
CVE-2019-6446 <https://nvd.nist.gov/vuln/detail/CVE-2019-6446>_.

Improvements

Covariance in random.mvnormal cast to double

This should make the tolerance used when checking the singular values of the
covariance matrix more meaningful.

Changes

__array_interface__ offset now works as documented

The interface may use an offset value that was previously mistakenly
ignored.

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==========================
NumPy 1.16.2 Release Notes

NumPy 1.16.2 is a quick release fixing several problems encountered on Windows.
The Python versions supported are 2.7 and 3.5-3.7. The Windows problems
addressed are:

• DLL load problems for NumPy wheels on Windows,
• distutils command line parsing on Windows.

There is also a regression fix correcting signed zeros produced by divmod, see
below for details.

Downstream developers building this release should use Cython >= 0.29.2 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

If you are installing using pip, you may encounter a problem with older
installed versions of NumPy that pip did not delete becoming mixed with the
current version, resulting in an ImportError. That problem is particularly
common on Debian derived distributions due to a modified pip. The fix is to
make sure all previous NumPy versions installed by pip have been removed. See
#12736 <https://github.com/numpy/numpy/issues/12736>__ for discussion of the
issue.

Compatibility notes

Signed zero when using divmod

Starting in version 1.12.0, numpy incorrectly returned a negatively signed zero
when using the divmod and floor_divide functions when the result was
zero. For example:

   >>> np.zeros(10)//1
   array([-0., -0., -0., -0., -0., -0., -0., -0., -0., -0.])

With this release, the result is correctly returned as a positively signed
zero:

   >>> np.zeros(10)//1
   array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

Contributors

A total of 5 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Eric Wieser
• Matti Picus
• Tyler Reddy
• Tony LaTorre +

Pull requests merged

A total of 7 pull requests were merged for this release.

• #12909: TST: fix vmImage dispatch in Azure
• #12923: MAINT: remove complicated test of multiarray import failure mode
• #13020: BUG: fix signed zero behavior in npy_divmod
• #13026: MAINT: Add functions to parse shell-strings in the platform-native...
• #13028: BUG: Fix regression in parsing of F90 and F77 environment variables
• #13038: BUG: parse shell escaping in extra_compile_args and extra_link_args
• #13041: BLD: Windows absolute path DLL loading

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==========================
NumPy 1.16.1 Release Notes

The NumPy 1.16.1 release fixes bugs reported against the 1.16.0 release, and
also backports several enhancements from master that seem appropriate for a
release series that is the last to support Python 2.7. The wheels on PyPI are
linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29.2 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

If you are installing using pip, you may encounter a problem with older
installed versions of NumPy that pip did not delete becoming mixed with the
current version, resulting in an ImportError. That problem is particularly
common on Debian derived distributions due to a modified pip. The fix is to
make sure all previous NumPy versions installed by pip have been removed. See
#12736 <https://github.com/numpy/numpy/issues/12736>__ for discussion of the
issue. Note that previously this problem resulted in an AttributeError.

Contributors

A total of 16 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Antoine Pitrou
• Arcesio Castaneda Medina +
• Charles Harris
• Chris Markiewicz +
• Christoph Gohlke
• Christopher J. Markiewicz +
• Daniel Hrisca +
• EelcoPeacs +
• Eric Wieser
• Kevin Sheppard
• Matti Picus
• OBATA Akio +
• Ralf Gommers
• Sebastian Berg
• Stephan Hoyer
• Tyler Reddy

Enhancements

• #12767: ENH: add mm->q floordiv
• #12768: ENH: port np.core.overrides to C for speed
• #12769: ENH: Add np.ctypeslib.as_ctypes_type(dtype), improve np.ctypeslib.as_ctypes
• #12773: ENH: add "max difference" messages to np.testing.assert_array_equal...
• #12820: ENH: Add mm->qm divmod
• #12890: ENH: add _dtype_ctype to namespace for freeze analysis

Compatibility notes

• The changed error message emited by array comparison testing functions may
  affect doctests. See below for detail.

• Casting from double and single denormals to float16 has been corrected. In
  some rare cases, this may result in results being rounded up instead of down,
  changing the last bit (ULP) of the result.

New Features

divmod operation is now supported for two timedelta64 operands

The divmod operator now handles two np.timedelta64 operands, with
type signature mm->qm.

Improvements

Further improvements to ctypes support in np.ctypeslib

A new np.ctypeslib.as_ctypes_type function has been added, which can be
used to converts a dtype into a best-guess ctypes type. Thanks to this
new function, np.ctypeslib.as_ctypes now supports a much wider range of
array types, including structures, booleans, and integers of non-native
endianness.

Array comparison assertions include maximum differences

Error messages from array comparison tests such as
np.testing.assert_allclose now include "max absolute difference" and
"max relative difference," in addition to the previous "mismatch" percentage.
This information makes it easier to update absolute and relative error
tolerances.

Changes

timedelta64 % 0 behavior adjusted to return NaT

The modulus operation with two np.timedelta64 operands now returns
NaT in the case of division by zero, rather than returning zero

Checksums

MD5

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==========================
NumPy 1.16.0 Release Notes

This NumPy release is the last one to support Python 2.7 and will be maintained
as a long term release with bug fixes until 2020. Support for Python 3.4 been
dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI
are linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

This release has seen a lot of refactoring and features many bug fixes, improved
code organization, and better cross platform compatibility. Not all of these
improvements will be visible to users, but they should help make maintenance
easier going forward.

Highlights

• Experimental support for overriding numpy functions,
  see __array_function__ below.

• The matmul function is now a ufunc. This provides better
  performance and allows overriding with __array_ufunc__.

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

• New functions added to the numpy.lib.recfuntions module to ease the
  structured assignment changes:

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

  See the user guide at https://docs.scipy.org/doc/numpy/user/basics.rec.html
  for more info.

New deprecations

• The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are
  deprecated. They were buggy and not documented and will be removed in the
  1.18 release. Usenumpy.sctypeDict instead.

• The numpy.asscalar function is deprecated. It is an alias to the more
  powerful numpy.ndarray.item, not tested, and fails for scalars.

• The numpy.set_array_ops and numpy.get_array_ops functions are deprecated.
  As part of NEP 15, they have been deprecated along with the C-API functions
  :c:func:PyArray_SetNumericOps and :c:func:PyArray_GetNumericOps. Users
  who wish to override the inner loop functions in built-in ufuncs should use
  :c:func:PyUFunc_ReplaceLoopBySignature.

• The numpy.unravel_index keyword argument dims is deprecated, use
  shape instead.

• The numpy.histogram normed argument is deprecated. It was deprecated
  previously, but no warning was issued.

• The positive operator (+) applied to non-numerical arrays is
  deprecated. See below for details.

• Passing an iterator to the stack functions is deprecated

Expired deprecations

• NaT comparisons now return False without a warning, finishing a
  deprecation cycle begun in NumPy 1.11.

• np.lib.function_base.unique was removed, finishing a deprecation cycle
  begun in NumPy 1.4. Use numpy.unique instead.

• multi-field indexing now returns views instead of copies, finishing a
  deprecation cycle begun in NumPy 1.7. The change was previously attempted in
  NumPy 1.14 but reverted until now.

• np.PackageLoader and np.pkgload have been removed. These were
  deprecated in 1.10, had no tests, and seem to no longer work in 1.15.

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

On Windows, the installed script for running f2py is now an .exe file
rather than a *.py file and should be run from the command line as f2py
whenever the Scripts directory is in the path. Running f2py as a module
python -m numpy.f2py [...] will work without path modification in any
version of NumPy.

NaT comparisons

Consistent with the behavior of NaN, all comparisons other than inequality
checks with datetime64 or timedelta64 NaT ("not-a-time") values now always
return False, and inequality checks with NaT now always return True.
This includes comparisons beteween NaT values. For compatibility with the
old behavior, use np.isnat to explicitly check for NaT or convert
datetime64/timedelta64 arrays with .astype(np.int64) before making
comparisons.

complex64/128 alignment has changed

The memory alignment of complex types is now the same as a C-struct composed of
two floating point values, while before it was equal to the size of the type.
For many users (for instance on x64/unix/gcc) this means that complex64 is now
4-byte aligned instead of 8-byte aligned. An important consequence is that
aligned structured dtypes may now have a different size. For instance,
np.dtype('c8,u1', align=True) used to have an itemsize of 16 (on x64/gcc)
but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-struct
struct {float r, i;}, according to the compiler used to compile numpy, and
similarly for the complex128 and complex256 types.

nd_grid len removal

len(np.mgrid) and len(np.ogrid) are now considered nonsensical
and raise a TypeError.

np.unravel_index now accepts shape keyword argument

Previously, only the dims keyword argument was accepted
for specification of the shape of the array to be used
for unraveling. dims remains supported, but is now deprecated.

multi-field views return a view instead of a copy

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 often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

To help users update their code to account for these changes, a number of
functions have been added to the numpy.lib.recfunctions module which
safely allow such operations. For instance, the code above can be replaced
with structured_to_unstructured(arr[['f1', 'f3']], dtype='float64').
See the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html#accessing-multiple-fields>__.

C API changes

The :c:data:NPY_API_VERSION was incremented to 0x0000D, due to the addition
of:

• :c:member:PyUFuncObject.core_dim_flags
• :c:member:PyUFuncObject.core_dim_sizes
• :c:member:PyUFuncObject.identity_value
• :c:function:PyUFunc_FromFuncAndDataAndSignatureAndIdentity

New Features

Integrated squared error (ISE) estimator added to histogram

This method (bins='stone') for optimizing the bin number is a
generalization of the Scott's rule. The Scott's rule assumes the distribution
is approximately Normal, while the ISE_ is a non-parametric method based on
cross-validation.

.. _ISE: https://en.wikipedia.org/wiki/Histogram#Minimizing_cross-validation_estimated_squared_error

max_rows keyword added for np.loadtxt

New keyword max_rows in numpy.loadtxt sets the maximum rows of the
content to be read after skiprows, as in numpy.genfromtxt.

modulus operator support added for np.timedelta64 operands

The modulus (remainder) operator is now supported for two operands
of type np.timedelta64. The operands may have different units
and the return value will match the type of the operands.

Improvements

no-copy pickling of numpy arrays

Up to protocol 4, numpy array pickling created 2 spurious copies of the data
being serialized. With pickle protocol 5, and the PickleBuffer API, a
large variety of numpy arrays can now be serialized without any copy using
out-of-band buffers, and with one less copy using in-band buffers. This
results, for large arrays, in an up to 66% drop in peak memory usage.

build shell independence

NumPy builds should no longer interact with the host machine
shell directly. exec_command has been replaced with
subprocess.check_output where appropriate.

np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks

When used in a front-end that supports it, Polynomial instances are now
rendered through LaTeX. The current format is experimental, and is subject to
change.

randint and choice now work on empty distributions

Even when no elements needed to be drawn, np.random.randint and
np.random.choice raised an error when the arguments described an empty
distribution. This has been fixed so that e.g.
np.random.choice([], 0) == np.array([], dtype=float64).

linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays

Previously, a LinAlgError would be raised when an empty matrix/empty
matrices (with zero rows and/or columns) is/are passed in. Now outputs of
appropriate shapes are returned.

Chain exceptions to give better error messages for invalid PEP3118 format strings

This should help track down problems.

Einsum optimization path updates and efficiency improvements

Einsum was synchronized with the current upstream work.

numpy.angle and numpy.expand_dims now work on ndarray subclasses

In particular, they now work for masked arrays.

NPY_NO_DEPRECATED_API compiler warning suppression

Setting NPY_NO_DEPRECATED_API to a value of 0 will suppress the current compiler
warnings when the deprecated numpy API is used.

np.diff Added kwargs prepend and append

New kwargs prepend and append, allow for values to be inserted on
either end of the differences. Similar to options for ediff1d. Now the
inverse of cumsum can be obtained easily via prepend=0.

ARM support updated

Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets,
and also big and little endian byte ordering. AARCH32 memory alignment issues
have been addressed. CI testing has been expanded to include AARCH64 targets
via the services of shippable.com.

Appending to build flags

numpy.distutils has always overridden rather than appended to LDFLAGS and
other similar such environment variables for compiling Fortran extensions.
Now, if the NPY_DISTUTILS_APPEND_FLAGS environment variable is set to 1, the
behavior will be appending. This applied to: LDFLAGS, F77FLAGS,
F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. See gh-11525 for more
details.

Generalized ufunc signatures now allow fixed-size dimensions

By using a numerical value in the signature of a generalized ufunc, one can
indicate that the given function requires input or output to have dimensions
with the given size. E.g., the signature of a function that converts a polar
angle to a two-dimensional cartesian unit vector would be ()->(2); that
for one that converts two spherical angles to a three-dimensional unit vector
would be (),()->(3); and that for the cross product of two
three-dimensional vectors would be (3),(3)->(3).

Note that to the elementary function these dimensions are not treated any
differently from variable ones indicated with a name starting with a letter;
the loop still is passed the corresponding size, but it can now count on that
size being equal to the fixed one given in the signature.

Generalized ufunc signatures now allow flexible dimensions

Some functions, in particular numpy's implementation of @ as matmul,
are very similar to generalized ufuncs in that they operate over core
dimensions, but one could not present them as such because they were able to
deal with inputs in which a dimension is missing. To support this, it is now
allowed to postfix a dimension name with a question mark to indicate that the
dimension does not necessarily have to be present.

With this addition, the signature for matmul can be expressed as
(m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand
has only one dimension, for the purposes of the elementary function it will be
treated as if that input has core shape (n, 1), and the output has the
corresponding core shape of (m, 1). The actual output array, however, has
the flexible dimension removed, i.e., it will have shape (..., m).
Similarly, if both arguments have only a single dimension, the inputs will be
presented as having shapes (1, n) and (n, 1) to the elementary
function, and the output as (1, 1), while the actual output array returned
will have shape (). In this way, the signature allows one to use a
single elementary function for four related but different signatures,
(m,n),(n,p)->(m,p), (n),(n,p)->(p), (m,n),(n)->(m) and
(n),(n)->().

np.clip and the clip method check for memory overlap

The out argument to these functions is now always tested for memory overlap
to avoid corrupted results when memory overlap occurs.

New value unscaled for option cov in np.polyfit

A further possible value has been added to the cov parameter of the
np.polyfit function. With cov='unscaled' the scaling of the covariance
matrix is disabled completely (similar to setting absolute_sigma=True in
scipy.optimize.curve_fit). This would be useful in occasions, where the
weights are given by 1/sigma with sigma being the (known) standard errors of
(Gaussian distributed) data points, in which case the unscaled matrix is
already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types

The help function, when applied to numeric types such as numpy.intc,
numpy.int_, and numpy.longlong, now lists all of the aliased names for that
type, distinguishing between platform -dependent and -independent aliases.

__module__ attribute now points to public modules

The __module__ attribute on most NumPy functions has been updated to refer
to the preferred public module from which to access a function, rather than
the module in which the function happens to be defined. This produces more
informative displays for functions in tools such as IPython, e.g., instead of
<function 'numpy.core.fromnumeric.sum'> you now see
<function 'numpy.sum'>.

Large allocations marked as suitable for transparent hugepages

On systems that support transparent hugepages over the madvise system call
numpy now marks that large memory allocations can be backed by hugepages which
reduces page fault overhead and can in some fault heavy cases improve
performance significantly. On Linux the setting for huge pages to be used,
/sys/kernel/mm/transparent_hugepage/enabled, must be at least madvise.
Systems which already have it set to always will not see much difference as
the kernel will automatically use huge pages where appropriate.

Users of very old Linux kernels (~3.x and older) should make sure that
/sys/kernel/mm/transparent_hugepage/defrag is not set to always to avoid
performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support

We now default to use fenv.h for floating point status error reporting.
Previously we had a broken default that sometimes would not report underflow,
overflow, and invalid floating point operations. Now we can support non-glibc
distrubutions like Alpine Linux as long as they ship fenv.h.

Speedup np.block for large arrays

Large arrays (greater than 512 * 512) now use a blocking algorithm based on
copying the data directly into the appropriate slice of the resulting array.
This results in significant speedups for these large arrays, particularly for
arrays being blocked along more than 2 dimensions.

arr.ctypes.data_as(...) holds a reference to arr

Previously the caller was responsible for keeping the array alive for the
lifetime of the pointer.

Speedup ``np.take`` for read-only arrays
----------------------------------------
The implementation of ``np.take`` no longer makes an unnecessary copy of the
source array when its ``writeable`` flag is set to ``False``.

Support path-like objects for more functions
--------------------------------------------
The ``np.core.records.fromfile`` function now supports ``pathlib.Path``
and other path-like objects in addition to a file object. Furthermore, the
``np.load`` function now also supports path-like objects when using memory
mapping (``mmap_mode`` keyword argument).

Better behaviour of ufunc identities during reductions
------------------------------------------------------
Universal functions have an ``.identity`` which is used when ``.reduce`` is
called on an empty axis.

As of this release, the logical binary ufuncs, `logical_and`, `logical_or`,
and `logical_xor`, now have ``identity`` s of type `bool`, where previously they
were of type `int`. This restores the 1.14 behavior of getting ``bool`` s when
reducing empty object arrays with these ufuncs, while also keeping the 1.15
behavior of getting ``int`` s when reducing empty object arrays with arithmetic
ufuncs like ``add`` and ``multiply``.

Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be
called on empty sequences, where previously it could not be.

This is possible thanks to the new
:c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`, which allows
arbitrary values to be used as identities now.

Improved conversion from ctypes objects
---------------------------------------
Numpy has always supported taking a value or type from ``ctypes`` and
converting it into an array or dtype, but only behaved correctly for simpler
types. As of this release, this caveat is lifted - now:

* The ``_pack_`` attribute of ``ctypes.Structure``, used to emulate C's
  ``__attribute__((packed))``, is respected.
* Endianness of all ctypes objects is preserved
* ``ctypes.Union`` is supported
* Non-representable constructs raise exceptions, rather than producing
  dangerously incorrect results:

  * Bitfields are no longer interpreted as sub-arrays
  * Pointers are no longer replaced with the type that they point to

A new ``ndpointer.contents`` member
-----------------------------------
This matches the ``.contents`` member of normal ctypes arrays, and can be used
to construct an ``np.array`` around the pointers contents.  This replaces
``np.array(some_nd_pointer)``, which stopped working in 1.15.  As a side effect
of this change, ``ndpointer`` now supports dtypes with overlapping fields and
padding.

``matmul`` is now a ``ufunc``
-----------------------------
`numpy.matmul` is now a ufunc which means that both the function and the
``__matmul__`` operator can now be overridden by ``__array_ufunc__``. Its
implementation has also changed. It uses the same BLAS routines as
`numpy.dot`, ensuring its performance is similar for large matrices.

Start and stop arrays for ``linspace``, ``logspace`` and ``geomspace``
----------------------------------------------------------------------
These functions used to be limited to scalar stop and start values, but can
now take arrays, which will be properly broadcast and result in an output
which has one axis prepended.  This can be used, e.g., to obtain linearly
interpolated points between sets of points.

CI extended with additional services
------------------------------------
We now use additional free CI services, thanks to the companies that provide:

* Codecoverage testing via codecov.io
* Arm testing via shippable.com
* Additional test runs on azure pipelines

These are in addition to our continued use of travis, appveyor (for wheels) and
LGTM


Changes
=======

Comparison ufuncs will now error rather than return NotImplemented
------------------------------------------------------------------
Previously, comparison ufuncs such as ``np.equal`` would return
`NotImplemented` if their arguments had structured dtypes, to help comparison
operators such as ``__eq__`` deal with those.  This is no longer needed, as the
relevant logic has moved to the comparison operators proper (which thus do
continue to return `NotImplemented` as needed). Hence, like all other ufuncs,
the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays
----------------------------------------------------------------------
Previously, ``+array`` unconditionally returned a copy. Now, it will
raise a ``DeprecationWarning`` if the array is not numerical (i.e.,
if ``np.positive(array)`` raises a ``TypeError``. For ``ndarray``
subclasses that override the default ``__array_ufunc__`` implementation,
the ``TypeError`` is passed on.

``NDArrayOperatorsMixin`` now implements matrix multiplication
--------------------------------------------------------------
Previously, ``np.lib.mixins.NDArrayOperatorsMixin`` did not implement the
special methods for Python's matrix multiplication operator (``@``). This has
changed now that ``matmul`` is a ufunc and can be overridden using
``__array_ufunc__``.

The scaling of the covariance matrix in ``np.polyfit`` is different
-------------------------------------------------------------------
So far, ``np.polyfit`` used a non-standard factor in the scaling of the the
covariance matrix. Namely, rather than using the standard ``chisq/(M-N)``, it
scaled it with ``chisq/(M-N-2)`` where M is the number of data points and N is the
number of parameters.  This scaling is inconsistent with other fitting programs
such as e.g. ``scipy.optimize.curve_fit`` and was changed to ``chisq/(M-N)``.

``maximum`` and ``minimum`` no longer emit warnings
---------------------------------------------------
As part of code introduced in 1.10,  ``float32`` and ``float64`` set invalid
float status when a Nan is encountered in `numpy.maximum` and `numpy.minimum`,
when using SSE2 semantics. This caused a `RuntimeWarning` to sometimes be
emitted. In 1.15 we fixed the inconsistencies which caused the warnings to
become more conspicuous. Now no warnings will be emitted.

Umath and multiarray c-extension modules merged into a single module
--------------------------------------------------------------------
The two modules were merged, according to `NEP 15`_. Previously `np.core.umath`
and `np.core.multiarray` were seperate c-extension modules. They are now python
wrappers to the single `np.core/_multiarray_math` c-extension module.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html

``getfield`` validity checks extended
-------------------------------------
`numpy.ndarray.getfield` now checks the dtype and offset arguments to prevent
accessing invalid memory locations.

NumPy functions now support overrides with ``__array_function__``
-----------------------------------------------------------------
It is now possible to override the implementation of almost all NumPy functions
on non-NumPy arrays by defining a ``__array_function__`` method, as described
in `NEP 18`_. The sole exception are functions for explicitly casting to NumPy
arrays such as ``np.array``. As noted in the NEP, this feature remains
experimental and the details of how to implement such overrides may change in
the future.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html
.. _`NEP 18` : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

Arrays based off readonly buffers cannot be set ``writeable``
-------------------------------------------------------------
We now disallow setting the ``writeable`` flag True on arrays created
from ``fromstring(readonly-buffer)``.

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==========================
NumPy 1.16.0 Release Notes

This NumPy release is the last one to support Python 2.7 and will be maintained
as a long term release with bug fixes until 2020. Support for Python 3.4 been
dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI
are linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

This release has seen a lot of refactoring and features many bug fixes, improved
code organization, and better cross platform compatibility. Not all of these
improvements will be visible to users, but they should help make maintenance
easier going forward.

Highlights

• Experimental support for overriding numpy functions,
  see __array_function__ below.

• The matmul function is now a ufunc. This provides better
  performance and allows overriding with __array_ufunc__.

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

• New functions added to the numpy.lib.recfuntions module to ease the
  structured assignment changes:

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

  See the user guide at https://docs.scipy.org/doc/numpy/user/basics.rec.html
  for more info.

New deprecations

• The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are
  deprecated. They were buggy and not documented and will be removed in the
  1.18 release. Usenumpy.sctypeDict instead.

• The numpy.asscalar function is deprecated. It is an alias to the more
  powerful numpy.ndarray.item, not tested, and fails for scalars.

• The numpy.set_array_ops and numpy.get_array_ops functions are deprecated.
  As part of NEP 15, they have been deprecated along with the C-API functions
  :c:func:PyArray_SetNumericOps and :c:func:PyArray_GetNumericOps. Users
  who wish to override the inner loop functions in built-in ufuncs should use
  :c:func:PyUFunc_ReplaceLoopBySignature.

• The numpy.unravel_index keyword argument dims is deprecated, use
  shape instead.

• The numpy.histogram normed argument is deprecated. It was deprecated
  previously, but no warning was issued.

• The positive operator (+) applied to non-numerical arrays is
  deprecated. See below for details.

• Passing an iterator to the stack functions is deprecated

Expired deprecations

• NaT comparisons now return False without a warning, finishing a
  deprecation cycle begun in NumPy 1.11.

• np.lib.function_base.unique was removed, finishing a deprecation cycle
  begun in NumPy 1.4. Use numpy.unique instead.

• multi-field indexing now returns views instead of copies, finishing a
  deprecation cycle begun in NumPy 1.7. The change was previously attempted in
  NumPy 1.14 but reverted until now.

• np.PackageLoader and np.pkgload have been removed. These were
  deprecated in 1.10, had no tests, and seem to no longer work in 1.15.

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

On Windows, the installed script for running f2py is now an .exe file
rather than a *.py file and should be run from the command line as f2py
whenever the Scripts directory is in the path. Running f2py as a module
python -m numpy.f2py [...] will work without path modification in any
version of NumPy.

NaT comparisons

Consistent with the behavior of NaN, all comparisons other than inequality
checks with datetime64 or timedelta64 NaT ("not-a-time") values now always
return False, and inequality checks with NaT now always return True.
This includes comparisons beteween NaT values. For compatibility with the
old behavior, use np.isnat to explicitly check for NaT or convert
datetime64/timedelta64 arrays with .astype(np.int64) before making
comparisons.

complex64/128 alignment has changed

The memory alignment of complex types is now the same as a C-struct composed of
two floating point values, while before it was equal to the size of the type.
For many users (for instance on x64/unix/gcc) this means that complex64 is now
4-byte aligned instead of 8-byte aligned. An important consequence is that
aligned structured dtypes may now have a different size. For instance,
np.dtype('c8,u1', align=True) used to have an itemsize of 16 (on x64/gcc)
but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-struct
struct {float r, i;}, according to the compiler used to compile numpy, and
similarly for the complex128 and complex256 types.

nd_grid len removal

len(np.mgrid) and len(np.ogrid) are now considered nonsensical
and raise a TypeError.

np.unravel_index now accepts shape keyword argument

Previously, only the dims keyword argument was accepted
for specification of the shape of the array to be used
for unraveling. dims remains supported, but is now deprecated.

multi-field views return a view instead of a copy

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 often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

To help users update their code to account for these changes, a number of
functions have been added to the numpy.lib.recfunctions module which
safely allow such operations. For instance, the code above can be replaced
with structured_to_unstructured(arr[['f1', 'f3']], dtype='float64').
See the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html#accessing-multiple-fields>__.

C API changes

The :c:data:NPY_API_VERSION was incremented to 0x0000D, due to the addition
of:

• :c:member:PyUFuncObject.core_dim_flags
• :c:member:PyUFuncObject.core_dim_sizes
• :c:member:PyUFuncObject.identity_value
• :c:function:PyUFunc_FromFuncAndDataAndSignatureAndIdentity

New Features

Integrated squared error (ISE) estimator added to histogram

This method (bins='stone') for optimizing the bin number is a
generalization of the Scott's rule. The Scott's rule assumes the distribution
is approximately Normal, while the ISE_ is a non-parametric method based on
cross-validation.

.. _ISE: https://en.wikipedia.org/wiki/Histogram#Minimizing_cross-validation_estimated_squared_error

max_rows keyword added for np.loadtxt

New keyword max_rows in numpy.loadtxt sets the maximum rows of the
content to be read after skiprows, as in numpy.genfromtxt.

modulus operator support added for np.timedelta64 operands

The modulus (remainder) operator is now supported for two operands
of type np.timedelta64. The operands may have different units
and the return value will match the type of the operands.

Improvements

no-copy pickling of numpy arrays

Up to protocol 4, numpy array pickling created 2 spurious copies of the data
being serialized. With pickle protocol 5, and the PickleBuffer API, a
large variety of numpy arrays can now be serialized without any copy using
out-of-band buffers, and with one less copy using in-band buffers. This
results, for large arrays, in an up to 66% drop in peak memory usage.

build shell independence

NumPy builds should no longer interact with the host machine
shell directly. exec_command has been replaced with
subprocess.check_output where appropriate.

np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks

When used in a front-end that supports it, Polynomial instances are now
rendered through LaTeX. The current format is experimental, and is subject to
change.

randint and choice now work on empty distributions

Even when no elements needed to be drawn, np.random.randint and
np.random.choice raised an error when the arguments described an empty
distribution. This has been fixed so that e.g.
np.random.choice([], 0) == np.array([], dtype=float64).

linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays

Previously, a LinAlgError would be raised when an empty matrix/empty
matrices (with zero rows and/or columns) is/are passed in. Now outputs of
appropriate shapes are returned.

Chain exceptions to give better error messages for invalid PEP3118 format strings

This should help track down problems.

Einsum optimization path updates and efficiency improvements

Einsum was synchronized with the current upstream work.

numpy.angle and numpy.expand_dims now work on ndarray subclasses

In particular, they now work for masked arrays.

NPY_NO_DEPRECATED_API compiler warning suppression

Setting NPY_NO_DEPRECATED_API to a value of 0 will suppress the current compiler
warnings when the deprecated numpy API is used.

np.diff Added kwargs prepend and append

New kwargs prepend and append, allow for values to be inserted on
either end of the differences. Similar to options for ediff1d. Now the
inverse of cumsum can be obtained easily via prepend=0.

ARM support updated

Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets,
and also big and little endian byte ordering. AARCH32 memory alignment issues
have been addressed. CI testing has been expanded to include AARCH64 targets
via the services of shippable.com.

Appending to build flags

numpy.distutils has always overridden rather than appended to LDFLAGS and
other similar such environment variables for compiling Fortran extensions.
Now, if the NPY_DISTUTILS_APPEND_FLAGS environment variable is set to 1, the
behavior will be appending. This applied to: LDFLAGS, F77FLAGS,
F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. See gh-11525 for more
details.

Generalized ufunc signatures now allow fixed-size dimensions

By using a numerical value in the signature of a generalized ufunc, one can
indicate that the given function requires input or output to have dimensions
with the given size. E.g., the signature of a function that converts a polar
angle to a two-dimensional cartesian unit vector would be ()->(2); that
for one that converts two spherical angles to a three-dimensional unit vector
would be (),()->(3); and that for the cross product of two
three-dimensional vectors would be (3),(3)->(3).

Note that to the elementary function these dimensions are not treated any
differently from variable ones indicated with a name starting with a letter;
the loop still is passed the corresponding size, but it can now count on that
size being equal to the fixed one given in the signature.

Generalized ufunc signatures now allow flexible dimensions

Some functions, in particular numpy's implementation of @ as matmul,
are very similar to generalized ufuncs in that they operate over core
dimensions, but one could not present them as such because they were able to
deal with inputs in which a dimension is missing. To support this, it is now
allowed to postfix a dimension name with a question mark to indicate that the
dimension does not necessarily have to be present.

With this addition, the signature for matmul can be expressed as
(m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand
has only one dimension, for the purposes of the elementary function it will be
treated as if that input has core shape (n, 1), and the output has the
corresponding core shape of (m, 1). The actual output array, however, has
the flexible dimension removed, i.e., it will have shape (..., m).
Similarly, if both arguments have only a single dimension, the inputs will be
presented as having shapes (1, n) and (n, 1) to the elementary
function, and the output as (1, 1), while the actual output array returned
will have shape (). In this way, the signature allows one to use a
single elementary function for four related but different signatures,
(m,n),(n,p)->(m,p), (n),(n,p)->(p), (m,n),(n)->(m) and
(n),(n)->().

np.clip and the clip method check for memory overlap

The out argument to these functions is now always tested for memory overlap
to avoid corrupted results when memory overlap occurs.

New value unscaled for option cov in np.polyfit

A further possible value has been added to the cov parameter of the
np.polyfit function. With cov='unscaled' the scaling of the covariance
matrix is disabled completely (similar to setting absolute_sigma=True in
scipy.optimize.curve_fit). This would be useful in occasions, where the
weights are given by 1/sigma with sigma being the (known) standard errors of
(Gaussian distributed) data points, in which case the unscaled matrix is
already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types

The help function, when applied to numeric types such as numpy.intc,
numpy.int_, and numpy.longlong, now lists all of the aliased names for that
type, distinguishing between platform -dependent and -independent aliases.

__module__ attribute now points to public modules

The __module__ attribute on most NumPy functions has been updated to refer
to the preferred public module from which to access a function, rather than
the module in which the function happens to be defined. This produces more
informative displays for functions in tools such as IPython, e.g., instead of
<function 'numpy.core.fromnumeric.sum'> you now see
<function 'numpy.sum'>.

Large allocations marked as suitable for transparent hugepages

On systems that support transparent hugepages over the madvise system call
numpy now marks that large memory allocations can be backed by hugepages which
reduces page fault overhead and can in some fault heavy cases improve
performance significantly. On Linux the setting for huge pages to be used,
/sys/kernel/mm/transparent_hugepage/enabled, must be at least madvise.
Systems which already have it set to always will not see much difference as
the kernel will automatically use huge pages where appropriate.

Users of very old Linux kernels (~3.x and older) should make sure that
/sys/kernel/mm/transparent_hugepage/defrag is not set to always to avoid
performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support

We now default to use fenv.h for floating point status error reporting.
Previously we had a broken default that sometimes would not report underflow,
overflow, and invalid floating point operations. Now we can support non-glibc
distrubutions like Alpine Linux as long as they ship fenv.h.

Speedup np.block for large arrays

Large arrays (greater than 512 * 512) now use a blocking algorithm based on
copying the data directly into the appropriate slice of the resulting array.
This results in significant speedups for these large arrays, particularly for
arrays being blocked along more than 2 dimensions.

arr.ctypes.data_as(...) holds a reference to arr

Previously the caller was responsible for keeping the array alive for the
lifetime of the pointer.

Speedup ``np.take`` for read-only arrays
----------------------------------------
The implementation of ``np.take`` no longer makes an unnecessary copy of the
source array when its ``writeable`` flag is set to ``False``.

Support path-like objects for more functions
--------------------------------------------
The ``np.core.records.fromfile`` function now supports ``pathlib.Path``
and other path-like objects in addition to a file object. Furthermore, the
``np.load`` function now also supports path-like objects when using memory
mapping (``mmap_mode`` keyword argument).

Better behaviour of ufunc identities during reductions
------------------------------------------------------
Universal functions have an ``.identity`` which is used when ``.reduce`` is
called on an empty axis.

As of this release, the logical binary ufuncs, `logical_and`, `logical_or`,
and `logical_xor`, now have ``identity`` s of type `bool`, where previously they
were of type `int`. This restores the 1.14 behavior of getting ``bool`` s when
reducing empty object arrays with these ufuncs, while also keeping the 1.15
behavior of getting ``int`` s when reducing empty object arrays with arithmetic
ufuncs like ``add`` and ``multiply``.

Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be
called on empty sequences, where previously it could not be.

This is possible thanks to the new
:c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`, which allows
arbitrary values to be used as identities now.

Improved conversion from ctypes objects
---------------------------------------
Numpy has always supported taking a value or type from ``ctypes`` and
converting it into an array or dtype, but only behaved correctly for simpler
types. As of this release, this caveat is lifted - now:

* The ``_pack_`` attribute of ``ctypes.Structure``, used to emulate C's
  ``__attribute__((packed))``, is respected.
* Endianness of all ctypes objects is preserved
* ``ctypes.Union`` is supported
* Non-representable constructs raise exceptions, rather than producing
  dangerously incorrect results:

  * Bitfields are no longer interpreted as sub-arrays
  * Pointers are no longer replaced with the type that they point to

A new ``ndpointer.contents`` member
-----------------------------------
This matches the ``.contents`` member of normal ctypes arrays, and can be used
to construct an ``np.array`` around the pointers contents.  This replaces
``np.array(some_nd_pointer)``, which stopped working in 1.15.  As a side effect
of this change, ``ndpointer`` now supports dtypes with overlapping fields and
padding.

``matmul`` is now a ``ufunc``
-----------------------------
`numpy.matmul` is now a ufunc which means that both the function and the
``__matmul__`` operator can now be overridden by ``__array_ufunc__``. Its
implementation has also changed. It uses the same BLAS routines as
`numpy.dot`, ensuring its performance is similar for large matrices.

Start and stop arrays for ``linspace``, ``logspace`` and ``geomspace``
----------------------------------------------------------------------
These functions used to be limited to scalar stop and start values, but can
now take arrays, which will be properly broadcast and result in an output
which has one axis prepended.  This can be used, e.g., to obtain linearly
interpolated points between sets of points.

CI extended with additional services
------------------------------------
We now use additional free CI services, thanks to the companies that provide:

* Codecoverage testing via codecov.io
* Arm testing via shippable.com
* Additional test runs on azure pipelines

These are in addition to our continued use of travis, appveyor (for wheels) and
LGTM


Changes
=======

Comparison ufuncs will now error rather than return NotImplemented
------------------------------------------------------------------
Previously, comparison ufuncs such as ``np.equal`` would return
`NotImplemented` if their arguments had structured dtypes, to help comparison
operators such as ``__eq__`` deal with those.  This is no longer needed, as the
relevant logic has moved to the comparison operators proper (which thus do
continue to return `NotImplemented` as needed). Hence, like all other ufuncs,
the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays
----------------------------------------------------------------------
Previously, ``+array`` unconditionally returned a copy. Now, it will
raise a ``DeprecationWarning`` if the array is not numerical (i.e.,
if ``np.positive(array)`` raises a ``TypeError``. For ``ndarray``
subclasses that override the default ``__array_ufunc__`` implementation,
the ``TypeError`` is passed on.

``NDArrayOperatorsMixin`` now implements matrix multiplication
--------------------------------------------------------------
Previously, ``np.lib.mixins.NDArrayOperatorsMixin`` did not implement the
special methods for Python's matrix multiplication operator (``@``). This has
changed now that ``matmul`` is a ufunc and can be overridden using
``__array_ufunc__``.

The scaling of the covariance matrix in ``np.polyfit`` is different
-------------------------------------------------------------------
So far, ``np.polyfit`` used a non-standard factor in the scaling of the the
covariance matrix. Namely, rather than using the standard ``chisq/(M-N)``, it
scaled it with ``chisq/(M-N-2)`` where M is the number of data points and N is the
number of parameters.  This scaling is inconsistent with other fitting programs
such as e.g. ``scipy.optimize.curve_fit`` and was changed to ``chisq/(M-N)``.

``maximum`` and ``minimum`` no longer emit warnings
---------------------------------------------------
As part of code introduced in 1.10,  ``float32`` and ``float64`` set invalid
float status when a Nan is encountered in `numpy.maximum` and `numpy.minimum`,
when using SSE2 semantics. This caused a `RuntimeWarning` to sometimes be
emitted. In 1.15 we fixed the inconsistencies which caused the warnings to
become more conspicuous. Now no warnings will be emitted.

Umath and multiarray c-extension modules merged into a single module
--------------------------------------------------------------------
The two modules were merged, according to `NEP 15`_. Previously `np.core.umath`
and `np.core.multiarray` were seperate c-extension modules. They are now python
wrappers to the single `np.core/_multiarray_math` c-extension module.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html

``getfield`` validity checks extended
-------------------------------------
`numpy.ndarray.getfield` now checks the dtype and offset arguments to prevent
accessing invalid memory locations.

NumPy functions now support overrides with ``__array_function__``
-----------------------------------------------------------------
It is now possible to override the implementation of almost all NumPy functions
on non-NumPy arrays by defining a ``__array_function__`` method, as described
in `NEP 18`_. The sole exception are functions for explicitly casting to NumPy
arrays such as ``np.array``. As noted in the NEP, this feature remains
experimental and the details of how to implement such overrides may change in
the future.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html
.. _`NEP 18` : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

Arrays based off readonly buffers cannot be set ``writeable``
-------------------------------------------------------------
We now disallow setting the ``writeable`` flag True on arrays created
from ``fromstring(readonly-buffer)``.

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==========================
NumPy 1.16.0 Release Notes

This NumPy release is the last one to support Python 2.7 and will be maintained
as a long term release with bug fixes until 2020. Support for Python 3.4 been
dropped, the supported Python versions are 2.7 and 3.5-3.7. The wheels on PyPI
are linked with OpenBLAS v0.3.4+, which should fix the known threading issues
found in previous OpenBLAS versions.

Downstream developers building this release should use Cython >= 0.29 and, if
using OpenBLAS, OpenBLAS > v0.3.4.

This release has seen a lot of refactoring and features many bug fixes, improved
code organization, and better cross platform compatibility. Not all of these
improvements will be visible to users, but they should help make maintenance
easier going forward.

Highlights

• Experimental support for overriding numpy functions,
  see __array_function__ below.

• The matmul function is now a ufunc. This provides better
  performance and allows overriding with __array_ufunc__.

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

• New functions added to the numpy.lib.recfuntions module to ease the
  structured assignment changes:

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

  See the user guide at https://docs.scipy.org/doc/numpy/user/basics.rec.html
  for more info.

New deprecations

• The type dictionaries numpy.core.typeNA and numpy.core.sctypeNA are
  deprecated. They were buggy and not documented and will be removed in the
  1.18 release. Usenumpy.sctypeDict instead.

• The numpy.asscalar function is deprecated. It is an alias to the more
  powerful numpy.ndarray.item, not tested, and fails for scalars.

• The numpy.set_array_ops and numpy.get_array_ops functions are deprecated.
  As part of NEP 15, they have been deprecated along with the C-API functions
  :c:func:PyArray_SetNumericOps and :c:func:PyArray_GetNumericOps. Users
  who wish to override the inner loop functions in built-in ufuncs should use
  :c:func:PyUFunc_ReplaceLoopBySignature.

• The numpy.unravel_index keyword argument dims is deprecated, use
  shape instead.

• The numpy.histogram normed argument is deprecated. It was deprecated
  previously, but no warning was issued.

• The positive operator (+) applied to non-numerical arrays is
  deprecated. See below for details.

• Passing an iterator to the stack functions is deprecated

Expired deprecations

• NaT comparisons now return False without a warning, finishing a
  deprecation cycle begun in NumPy 1.11.

• np.lib.function_base.unique was removed, finishing a deprecation cycle
  begun in NumPy 1.4. Use numpy.unique instead.

• multi-field indexing now returns views instead of copies, finishing a
  deprecation cycle begun in NumPy 1.7. The change was previously attempted in
  NumPy 1.14 but reverted until now.

• np.PackageLoader and np.pkgload have been removed. These were
  deprecated in 1.10, had no tests, and seem to no longer work in 1.15.

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

On Windows, the installed script for running f2py is now an .exe file
rather than a *.py file and should be run from the command line as f2py
whenever the Scripts directory is in the path. Running f2py as a module
python -m numpy.f2py [...] will work without path modification in any
version of NumPy.

NaT comparisons

Consistent with the behavior of NaN, all comparisons other than inequality
checks with datetime64 or timedelta64 NaT ("not-a-time") values now always
return False, and inequality checks with NaT now always return True.
This includes comparisons beteween NaT values. For compatibility with the
old behavior, use np.isnat to explicitly check for NaT or convert
datetime64/timedelta64 arrays with .astype(np.int64) before making
comparisons.

complex64/128 alignment has changed

The memory alignment of complex types is now the same as a C-struct composed of
two floating point values, while before it was equal to the size of the type.
For many users (for instance on x64/unix/gcc) this means that complex64 is now
4-byte aligned instead of 8-byte aligned. An important consequence is that
aligned structured dtypes may now have a different size. For instance,
np.dtype('c8,u1', align=True) used to have an itemsize of 16 (on x64/gcc)
but now it is 12.

More in detail, the complex64 type now has the same alignment as a C-struct
struct {float r, i;}, according to the compiler used to compile numpy, and
similarly for the complex128 and complex256 types.

nd_grid len removal

len(np.mgrid) and len(np.ogrid) are now considered nonsensical
and raise a TypeError.

np.unravel_index now accepts shape keyword argument

Previously, only the dims keyword argument was accepted
for specification of the shape of the array to be used
for unraveling. dims remains supported, but is now deprecated.

multi-field views return a view instead of a copy

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 often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

To help users update their code to account for these changes, a number of
functions have been added to the numpy.lib.recfunctions module which
safely allow such operations. For instance, the code above can be replaced
with structured_to_unstructured(arr[['f1', 'f3']], dtype='float64').
See the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html#accessing-multiple-fields>__.

C API changes

The :c:data:NPY_API_VERSION was incremented to 0x0000D, due to the addition
of:

• :c:member:PyUFuncObject.core_dim_flags
• :c:member:PyUFuncObject.core_dim_sizes
• :c:member:PyUFuncObject.identity_value
• :c:function:PyUFunc_FromFuncAndDataAndSignatureAndIdentity

New Features

Integrated squared error (ISE) estimator added to histogram

This method (bins='stone') for optimizing the bin number is a
generalization of the Scott's rule. The Scott's rule assumes the distribution
is approximately Normal, while the ISE_ is a non-parametric method based on
cross-validation.

.. _ISE: https://en.wikipedia.org/wiki/Histogram#Minimizing_cross-validation_estimated_squared_error

max_rows keyword added for np.loadtxt

New keyword max_rows in numpy.loadtxt sets the maximum rows of the
content to be read after skiprows, as in numpy.genfromtxt.

modulus operator support added for np.timedelta64 operands

The modulus (remainder) operator is now supported for two operands
of type np.timedelta64. The operands may have different units
and the return value will match the type of the operands.

Improvements

no-copy pickling of numpy arrays

Up to protocol 4, numpy array pickling created 2 spurious copies of the data
being serialized. With pickle protocol 5, and the PickleBuffer API, a
large variety of numpy arrays can now be serialized without any copy using
out-of-band buffers, and with one less copy using in-band buffers. This
results, for large arrays, in an up to 66% drop in peak memory usage.

build shell independence

NumPy builds should no longer interact with the host machine
shell directly. exec_command has been replaced with
subprocess.check_output where appropriate.

np.polynomial.Polynomial classes render in LaTeX in Jupyter notebooks

When used in a front-end that supports it, Polynomial instances are now
rendered through LaTeX. The current format is experimental, and is subject to
change.

randint and choice now work on empty distributions

Even when no elements needed to be drawn, np.random.randint and
np.random.choice raised an error when the arguments described an empty
distribution. This has been fixed so that e.g.
np.random.choice([], 0) == np.array([], dtype=float64).

linalg.lstsq, linalg.qr, and linalg.svd now work with empty arrays

Previously, a LinAlgError would be raised when an empty matrix/empty
matrices (with zero rows and/or columns) is/are passed in. Now outputs of
appropriate shapes are returned.

Chain exceptions to give better error messages for invalid PEP3118 format strings

This should help track down problems.

Einsum optimization path updates and efficiency improvements

Einsum was synchronized with the current upstream work.

numpy.angle and numpy.expand_dims now work on ndarray subclasses

In particular, they now work for masked arrays.

NPY_NO_DEPRECATED_API compiler warning suppression

Setting NPY_NO_DEPRECATED_API to a value of 0 will suppress the current compiler
warnings when the deprecated numpy API is used.

np.diff Added kwargs prepend and append

New kwargs prepend and append, allow for values to be inserted on
either end of the differences. Similar to options for ediff1d. Now the
inverse of cumsum can be obtained easily via prepend=0.

ARM support updated

Support for ARM CPUs has been updated to accommodate 32 and 64 bit targets,
and also big and little endian byte ordering. AARCH32 memory alignment issues
have been addressed. CI testing has been expanded to include AARCH64 targets
via the services of shippable.com.

Appending to build flags

numpy.distutils has always overridden rather than appended to LDFLAGS and
other similar such environment variables for compiling Fortran extensions.
Now, if the NPY_DISTUTILS_APPEND_FLAGS environment variable is set to 1, the
behavior will be appending. This applied to: LDFLAGS, F77FLAGS,
F90FLAGS, FREEFLAGS, FOPT, FDEBUG, and FFLAGS. See gh-11525 for more
details.

Generalized ufunc signatures now allow fixed-size dimensions

By using a numerical value in the signature of a generalized ufunc, one can
indicate that the given function requires input or output to have dimensions
with the given size. E.g., the signature of a function that converts a polar
angle to a two-dimensional cartesian unit vector would be ()->(2); that
for one that converts two spherical angles to a three-dimensional unit vector
would be (),()->(3); and that for the cross product of two
three-dimensional vectors would be (3),(3)->(3).

Note that to the elementary function these dimensions are not treated any
differently from variable ones indicated with a name starting with a letter;
the loop still is passed the corresponding size, but it can now count on that
size being equal to the fixed one given in the signature.

Generalized ufunc signatures now allow flexible dimensions

Some functions, in particular numpy's implementation of @ as matmul,
are very similar to generalized ufuncs in that they operate over core
dimensions, but one could not present them as such because they were able to
deal with inputs in which a dimension is missing. To support this, it is now
allowed to postfix a dimension name with a question mark to indicate that the
dimension does not necessarily have to be present.

With this addition, the signature for matmul can be expressed as
(m?,n),(n,p?)->(m?,p?). This indicates that if, e.g., the second operand
has only one dimension, for the purposes of the elementary function it will be
treated as if that input has core shape (n, 1), and the output has the
corresponding core shape of (m, 1). The actual output array, however, has
the flexible dimension removed, i.e., it will have shape (..., m).
Similarly, if both arguments have only a single dimension, the inputs will be
presented as having shapes (1, n) and (n, 1) to the elementary
function, and the output as (1, 1), while the actual output array returned
will have shape (). In this way, the signature allows one to use a
single elementary function for four related but different signatures,
(m,n),(n,p)->(m,p), (n),(n,p)->(p), (m,n),(n)->(m) and
(n),(n)->().

np.clip and the clip method check for memory overlap

The out argument to these functions is now always tested for memory overlap
to avoid corrupted results when memory overlap occurs.

New value unscaled for option cov in ``np.polyfit''

A further possible value has been added to the cov parameter of the
np.polyfit function. With cov='unscaled' the scaling of the covariance
matrix is disabled completely (similar to setting absolute_sigma=True'' in scipy.optimize.curve_fit``). This would be useful in occasions, where the
weights are given by 1/sigma with sigma being the (known) standard errors of
(Gaussian distributed) data points, in which case the unscaled matrix is
already a correct estimate for the covariance matrix.

Detailed docstrings for scalar numeric types

The help function, when applied to numeric types such as numpy.intc,
numpy.int_, and numpy.longlong, now lists all of the aliased names for that
type, distinguishing between platform -dependent and -independent aliases.

__module__ attribute now points to public modules

The __module__ attribute on most NumPy functions has been updated to refer
to the preferred public module from which to access a function, rather than
the module in which the function happens to be defined. This produces more
informative displays for functions in tools such as IPython, e.g., instead of
<function 'numpy.core.fromnumeric.sum'> you now see
<function 'numpy.sum'>.

Large allocations marked as suitable for transparent hugepages

On systems that support transparent hugepages over the madvise system call
numpy now marks that large memory allocations can be backed by hugepages which
reduces page fault overhead and can in some fault heavy cases improve
performance significantly. On Linux the setting for huge pages to be used,
/sys/kernel/mm/transparent_hugepage/enabled, must be at least madvise.
Systems which already have it set to always will not see much difference as
the kernel will automatically use huge pages where appropriate.

Users of very old Linux kernels (~3.x and older) should make sure that
/sys/kernel/mm/transparent_hugepage/defrag is not set to always to avoid
performance problems due concurrency issues in the memory defragmentation.

Alpine Linux (and other musl c library distros) support

We now default to use fenv.h for floating point status error reporting.
Previously we had a broken default that sometimes would not report underflow,
overflow, and invalid floating point operations. Now we can support non-glibc
distrubutions like Alpine Linux as long as they ship fenv.h.

Speedup np.block for large arrays

Large arrays (greater than 512 * 512) now use a blocking algorithm based on
copying the data directly into the appropriate slice of the resulting array.
This results in significant speedups for these large arrays, particularly for
arrays being blocked along more than 2 dimensions.

arr.ctypes.data_as(...) holds a reference to arr

Previously the caller was responsible for keeping the array alive for the
lifetime of the pointer.

Speedup ``np.take`` for read-only arrays
----------------------------------------
The implementation of ``np.take`` no longer makes an unnecessary copy of the
source array when its ``writeable`` flag is set to ``False``.

Support path-like objects for more functions
--------------------------------------------
The ``np.core.records.fromfile`` function now supports ``pathlib.Path``
and other path-like objects in addition to a file object. Furthermore, the
``np.load`` function now also supports path-like objects when using memory
mapping (``mmap_mode`` keyword argument).

Better behaviour of ufunc identities during reductions
------------------------------------------------------
Universal functions have an ``.identity`` which is used when ``.reduce`` is
called on an empty axis.

As of this release, the logical binary ufuncs, `logical_and`, `logical_or`,
and `logical_xor`, now have ``identity`` s of type `bool`, where previously they
were of type `int`. This restores the 1.14 behavior of getting ``bool`` s when
reducing empty object arrays with these ufuncs, while also keeping the 1.15
behavior of getting ``int`` s when reducing empty object arrays with arithmetic
ufuncs like ``add`` and ``multiply``.

Additionally, `logaddexp` now has an identity of ``-inf``, allowing it to be
called on empty sequences, where previously it could not be.

This is possible thanks to the new
:c:function:`PyUFunc_FromFuncAndDataAndSignatureAndIdentity`, which allows
arbitrary values to be used as identities now.

Improved conversion from ctypes objects
---------------------------------------
Numpy has always supported taking a value or type from ``ctypes`` and
converting it into an array or dtype, but only behaved correctly for simpler
types. As of this release, this caveat is lifted - now:

* The ``_pack_`` attribute of ``ctypes.Structure``, used to emulate C's
  ``__attribute__((packed))``, is respected.
* Endianness of all ctypes objects is preserved
* ``ctypes.Union`` is supported
* Non-representable constructs raise exceptions, rather than producing
  dangerously incorrect results:

  * Bitfields are no longer interpreted as sub-arrays
  * Pointers are no longer replaced with the type that they point to

A new ``ndpointer.contents`` member
-----------------------------------
This matches the ``.contents`` member of normal ctypes arrays, and can be used
to construct an ``np.array`` around the pointers contents.  This replaces
``np.array(some_nd_pointer)``, which stopped working in 1.15.  As a side effect
of this change, ``ndpointer`` now supports dtypes with overlapping fields and
padding.

``matmul`` is now a ``ufunc``
-----------------------------
`numpy.matmul` is now a ufunc which means that both the function and the
``__matmul__`` operator can now be overridden by ``__array_ufunc__``. Its
implementation has also changed. It uses the same BLAS routines as
`numpy.dot`, ensuring its performance is similar for large matrices.

Start and stop arrays for ``linspace``, ``logspace`` and ``geomspace``
----------------------------------------------------------------------
These functions used to be limited to scalar stop and start values, but can
now take arrays, which will be properly broadcast and result in an output
which has one axis prepended.  This can be used, e.g., to obtain linearly
interpolated points between sets of points.

CI extended with additional services
------------------------------------
We now use additional free CI services, thanks to the companies that provide:

* Codecoverage testing via codecov.io
* Arm testing via shippable.com
* Additional test runs on azure pipelines

These are in addition to our continued use of travis, appveyor (for wheels) and
LGTM


Changes
=======

Comparison ufuncs will now error rather than return NotImplemented
------------------------------------------------------------------
Previously, comparison ufuncs such as ``np.equal`` would return
`NotImplemented` if their arguments had structured dtypes, to help comparison
operators such as ``__eq__`` deal with those.  This is no longer needed, as the
relevant logic has moved to the comparison operators proper (which thus do
continue to return `NotImplemented` as needed). Hence, like all other ufuncs,
the comparison ufuncs will now error on structured dtypes.

Positive will now raise a deprecation warning for non-numerical arrays
----------------------------------------------------------------------
Previously, ``+array`` unconditionally returned a copy. Now, it will
raise a ``DeprecationWarning`` if the array is not numerical (i.e.,
if ``np.positive(array)`` raises a ``TypeError``. For ``ndarray``
subclasses that override the default ``__array_ufunc__`` implementation,
the ``TypeError`` is passed on.

``NDArrayOperatorsMixin`` now implements matrix multiplication
--------------------------------------------------------------
Previously, ``np.lib.mixins.NDArrayOperatorsMixin`` did not implement the
special methods for Python's matrix multiplication operator (``@``). This has
changed now that ``matmul`` is a ufunc and can be overridden using
``__array_ufunc__``.

The scaling of the covariance matrix in ``np.polyfit`` is different
-------------------------------------------------------------------
So far, ``np.polyfit`` used a non-standard factor in the scaling of the the
covariance matrix. Namely, rather than using the standard ``chisq/(M-N)``, it
scaled it with ``chisq/(M-N-2)`` where M is the number of data points and N is the
number of parameters.  This scaling is inconsistent with other fitting programs
such as e.g. ``scipy.optimize.curve_fit`` and was changed to ``chisq/(M-N)``.

``maximum`` and ``minimum`` no longer emit warnings
---------------------------------------------------
As part of code introduced in 1.10,  ``float32`` and ``float64`` set invalid
float status when a Nan is encountered in `numpy.maximum` and `numpy.minimum`,
when using SSE2 semantics. This caused a `RuntimeWarning` to sometimes be
emitted. In 1.15 we fixed the inconsistencies which caused the warnings to
become more conspicuous. Now no warnings will be emitted.

Umath and multiarray c-extension modules merged into a single module
--------------------------------------------------------------------
The two modules were merged, according to `NEP 15`_. Previously `np.core.umath`
and `np.core.multiarray` were seperate c-extension modules. They are now python
wrappers to the single `np.core/_multiarray_math` c-extension module.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html

``getfield`` validity checks extended
-------------------------------------
`numpy.ndarray.getfield` now checks the dtype and offset arguments to prevent
accessing invalid memory locations.

NumPy functions now support overrides with ``__array_function__``
-----------------------------------------------------------------
It is now possible to override the implementation of almost all NumPy functions
on non-NumPy arrays by defining a ``__array_function__`` method, as described
in `NEP 18`_. The sole exception are functions for explicitly casting to NumPy
arrays such as ``np.array``. As noted in the NEP, this feature remains
experimental and the details of how to implement such overrides may change in
the future.

.. _`NEP 15` : http://www.numpy.org/neps/nep-0015-merge-multiarray-umath.html
.. _`NEP 18` : http://www.numpy.org/neps/nep-0018-array-function-protocol.html

Arrays based off readonly buffers cannot be set ``writeable``
-------------------------------------------------------------
We now disallow setting the ``writeable`` flag True on arrays created
from ``fromstring(readonly-buffer)``.

Checksums
=========

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    439fefdac5de6e80c0213aa5dd3aed83  numpy-1.16.0rc1-cp37-cp37m-manylinux1_i686.whl
    a238bdbdaece30f8bdd68cb17b8fc0f3  numpy-1.16.0rc1-cp37-cp37m-manylinux1_x86_64.whl
    f27e4791cc0696451572350a64fc3509  numpy-1.16.0rc1-cp37-cp37m-win32.whl
    b90d6fefb38650b5a343171dfabe1e38  numpy-1.16.0rc1-cp37-cp37m-win_amd64.whl
    acfd5138cd9ba5a0ee434c112866e1d1  numpy-1.16.0rc1.tar.gz
    f9a9356b066174b1375d509d7fdf03a6  numpy-1.16.0rc1.zip

SHA256
------

    1d94a5649fb8412784d9edda539b266801ee87fc89b812ebf5ec0fdab96444a3  numpy-1.16.0rc1-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
    f901fa2232562abf9250e4a86d2b50f008b2fe0f9d91bc1ca6a3ffd17c81d2a4  numpy-1.16.0rc1-cp27-cp27m-manylinux1_i686.whl
    4c725b6557eb043b963a8b4b5da560fec4500b5e2a9c4794adb408de838aeb94  numpy-1.16.0rc1-cp27-cp27m-manylinux1_x86_64.whl
    867ed40b1bebb50f8b060a1b2a76c227a436a7d9fc73bf873dbc8e79862a56af  numpy-1.16.0rc1-cp27-cp27m-win32.whl
    70279fa434b878692c15bdb9b3376765ff63a91d5f1f6e5973eb34b0bddd7aa5  numpy-1.16.0rc1-cp27-cp27m-win_amd64.whl
    c5db80fc344977d3df5435ded1a8ce05719283aeacaaf60a818a4baf63c756e4  numpy-1.16.0rc1-cp27-cp27mu-manylinux1_i686.whl
    026a3b7c46e01a736bdf27853ce2a4f56a4feb3147ff879dbd463ed4da0a511b  numpy-1.16.0rc1-cp27-cp27mu-manylinux1_x86_64.whl
    2a17337e1980e753d4bac3dd4859bc1300e995ddf05fc09828b8d60d932ce4d3  numpy-1.16.0rc1-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
    0efbe5f61044e7b0ed5583380aca26995c10cd625266f09b2d6ccef243499f2e  numpy-1.16.0rc1-cp35-cp35m-manylinux1_i686.whl
    60dd924257899a44d55a7e548aba2a41c12e5d963b900ec138e0b15f3e6f3ef5  numpy-1.16.0rc1-cp35-cp35m-manylinux1_x86_64.whl
    078efe4fc53079acb4a70f378a1fbf97f22a1f864321788ae1fd9ca4c952bb10  numpy-1.16.0rc1-cp35-cp35m-win32.whl
    3dd416deba0e6b734e653961354ac2bee2888c832835304853b14299a8c3169a  numpy-1.16.0rc1-cp35-cp35m-win_amd64.whl
    ff9859fffc4a03b196d742fe683684921e3d3819aa97d935b92d573b7ff0bf7b  numpy-1.16.0rc1-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
    ad0a948011cafe20323944aea7570cea77c3659cf970ee4e94993a75fdf1bf3a  numpy-1.16.0rc1-cp36-cp36m-manylinux1_i686.whl
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    e299aaed551f16c3822e261adc6d5685ad7067536ae6af00e53837b637dc1270  numpy-1.16.0rc1-cp36-cp36m-win32.whl
    9c76c0f114b729606081b0e4c49d2f7887946004872576f51b8bd50461f4110b  numpy-1.16.0rc1-cp36-cp36m-win_amd64.whl
    bcf57e4694e32d7ebb9402cd3fcd9c4fd913a29f372fbefc8edd675a32262558  numpy-1.16.0rc1-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
    c953969e4b9a65998be1d7e895613be77bd42d438b54f9ce8c2453616ca8f5c9  numpy-1.16.0rc1-cp37-cp37m-manylinux1_i686.whl
    09d327e93f2b13dfbfd6a7c2534441478896a24e9521f59f1ec64c775eff0066  numpy-1.16.0rc1-cp37-cp37m-manylinux1_x86_64.whl
    ff73c826d81442f00b38360e52618d94f811c8db9047546d135c0e56117f3812  numpy-1.16.0rc1-cp37-cp37m-win32.whl
    f735dad0d72598ca0e19f50a0891e83e496288038628addd86c05650fab49181  numpy-1.16.0rc1-cp37-cp37m-win_amd64.whl
    90aa92d9c1bf0cbde34c0eb3964b87d15f2a6de01da99af18615925e3b7d8a05  numpy-1.16.0rc1.tar.gz
    6e638a8d56af542aebf625efdacdfb555943c08044795ee7550789407f5b905c  numpy-1.16.0rc1.zip

==========================
NumPy 1.15.4 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.3
release. The Python versions supported by this release are 2.7, 3.4-3.7. The
wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg
problems reported for NumPy 1.14.

Compatibility Note

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries. That will also be the case in future releases. See
#11625 <https://github.com/numpy/numpy/issues/11625>__ for the related
discussion. Those needing 32-bit support should look elsewhere or build
from source.

Contributors

A total of 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Matti Picus
• Sebastian Berg
• bbbbbbbbba +

Pull requests merged

A total of 4 pull requests were merged for this release.

• #12296: BUG: Dealloc cached buffer info
• #12297: BUG: Fix fill value in masked array '==' and '!=' ops.
• #12307: DOC: Correct the default value of optimize in numpy.einsum
• #12320: REL: Prepare for the NumPy 1.15.4 release

Checksums

MD5

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b3626fec2f39ab01cad8bbb63a103742  numpy-1.15.4.tar.gz
219ac537d12cf06ed14f478662096ebc  numpy-1.15.4.zip

SHA256

18e84323cdb8de3325e741a7a8dd4a82db74fde363dce32b625324c7b32aa6d7  numpy-1.15.4-cp27-cp27m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
154c35f195fd3e1fad2569930ca51907057ae35e03938f89a8aedae91dd1b7c7  numpy-1.15.4-cp27-cp27m-manylinux1_i686.whl
4d8d3e5aa6087490912c14a3c10fbdd380b40b421c13920ff468163bc50e016f  numpy-1.15.4-cp27-cp27m-manylinux1_x86_64.whl
c857ae5dba375ea26a6228f98c195fec0898a0fd91bcf0e8a0cae6d9faf3eca7  numpy-1.15.4-cp27-cp27mu-manylinux1_i686.whl
0df89ca13c25eaa1621a3f09af4c8ba20da849692dcae184cb55e80952c453fb  numpy-1.15.4-cp27-cp27mu-manylinux1_x86_64.whl
36e36b6868e4440760d4b9b44587ea1dc1f06532858d10abba98e851e154ca70  numpy-1.15.4-cp27-none-win32.whl
99d59e0bcadac4aa3280616591fb7bcd560e2218f5e31d5223a2e12a1425d495  numpy-1.15.4-cp27-none-win_amd64.whl
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db9814ff0457b46f2e1d494c1efa4111ca089e08c8b983635ebffb9c1573361f  numpy-1.15.4-cp34-cp34m-manylinux1_x86_64.whl
df04f4bad8a359daa2ff74f8108ea051670cafbca533bb2636c58b16e962989e  numpy-1.15.4-cp34-none-win32.whl
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561ef098c50f91fbac2cc9305b68c915e9eb915a74d9038ecf8af274d748f76f  numpy-1.15.4-cp35-none-win32.whl
4f41fd159fba1245e1958a99d349df49c616b133636e0cf668f169bce2aeac2d  numpy-1.15.4-cp35-none-win_amd64.whl
416a2070acf3a2b5d586f9a6507bb97e33574df5bd7508ea970bbf4fc563fa52  numpy-1.15.4-cp36-cp36m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
24fd645a5e5d224aa6e39d93e4a722fafa9160154f296fd5ef9580191c755053  numpy-1.15.4-cp36-cp36m-manylinux1_i686.whl
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b1853df739b32fa913cc59ad9137caa9cc3d97ff871e2bbd89c2a2a1d4a69451  numpy-1.15.4-cp36-none-win32.whl
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1e8956c37fc138d65ded2d96ab3949bd49038cc6e8a4494b1515b0ba88c91565  numpy-1.15.4-cp37-cp37m-macosx_10_6_intel.macosx_10_9_intel.macosx_10_9_x86_64.macosx_10_10_intel.macosx_10_10_x86_64.whl
a4cc09489843c70b22e8373ca3dfa52b3fab778b57cf81462f1203b0852e95e3  numpy-1.15.4-cp37-cp37m-manylinux1_i686.whl
4a22dc3f5221a644dfe4a63bf990052cc674ef12a157b1056969079985c92816  numpy-1.15.4-cp37-cp37m-manylinux1_x86_64.whl
b1f44c335532c0581b77491b7715a871d0dd72e97487ac0f57337ccf3ab3469b  numpy-1.15.4-cp37-none-win32.whl
a61dc29cfca9831a03442a21d4b5fd77e3067beca4b5f81f1a89a04a71cf93fa  numpy-1.15.4-cp37-none-win_amd64.whl
766e09248298e3ad4ae4a805159f358610bbe7dcc7b4a14e5df2128c05655b80  numpy-1.15.4.tar.gz
3d734559db35aa3697dadcea492a423118c5c55d176da2f3be9c98d4803fc2a7  numpy-1.15.4.zip

==========================
NumPy 1.15.3 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.2
release. The Python versions supported by this release are 2.7, 3.4-3.7. The
wheels are linked with OpenBLAS v0.3.0, which should fix some of the linalg
problems reported for NumPy 1.14.

Compatibility Note

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries. That will also be the case in future releases. See
#11625 <https://github.com/numpy/numpy/issues/11625>__ for the related
discussion. Those needing 32-bit support should look elsewhere or build
from source.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Charles Harris
• Jeroen Demeyer
• Kevin Sheppard
• Matthew Bowden +
• Matti Picus
• Tyler Reddy

Pull requests merged

A total of 12 pull requests were merged for this release.

• #12080: MAINT: Blacklist some MSVC complex functions.
• #12083: TST: Add azure CI testing to 1.15.x branch.
• #12084: BUG: test_path() now uses Path.resolve()
• #12085: TST, MAINT: Fix some failing tests on azure-pipelines mac and...
• #12187: BUG: Fix memory leak in mapping.c
• #12188: BUG: Allow boolean subtract in histogram
• #12189: BUG: Fix in-place permutation
• #12190: BUG: limit default for get_num_build_jobs() to 8
• #12191: BUG: OBJECT_to_* should check for errors
• #12192: DOC: Prepare for NumPy 1.15.3 release.
• #12237: BUG: Fix MaskedArray fill_value type conversion.
• #12238: TST: Backport azure-pipeline testing fixes for Mac

Checksums

MD5

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SHA256

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094f8a83e5bd0a44a7557fa24a46db6ba7d5299c389ddbc9e0e18722f567fb63  numpy-1.15.3-cp37-none-win32.whl
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4656ea0d66a3724fd88aafa39a0c5cef216d1257a71b40534fe589abd46ba77b  numpy-1.15.3.tar.gz
1c0c80e74759fa4942298044274f2c11b08c86230b25b8b819e55e644f5ff2b6  numpy-1.15.3.zip

==========================
NumPy 1.14.6 Release Notes

This is a bugfix release for bugs reported following the 1.14.5 release. The
most significant fixes are:

• Fix for behavior change in ma.masked_values(shrink=True)
• Fix the new cached allocations machinery to be thread safe.

The Python versions supported in this release are 2.7 and 3.4 - 3.7. The Python
3.6 wheels on PyPI should be compatible with all Python 3.6 versions.

Contributors

A total of 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Eric Wieser
• Julian Taylor
• Matti Picus

Pull requests merged

A total of 4 pull requests were merged for this release.

• #11985: BUG: fix cached allocations without the GIL
• #11986: BUG: Undo behavior change in ma.masked_values(shrink=True)
• #11987: BUG: fix refcount leak in PyArray_AdaptFlexibleDType
• #11995: TST: Add Python 3.7 testing to NumPy 1.14.

Checksums

MD5

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SHA256

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==========================
NumPy 1.15.2 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.1
release.

• The matrix PendingDeprecationWarning is now suppressed in pytest 3.8.
• The new cached allocations machinery has been fixed to be thread safe.
• The boolean indexing of subclasses now works correctly.
• A small memory leak in PyArray_AdaptFlexibleDType has been fixed.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Compatibility Note

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries. That will also be the case in future releases. See
#11625 <https://github.com/numpy/numpy/issues/11625>__ for the related
discussion. Those needing 32-bit support should look elsewhere or build
from source.

Contributors

A total of 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Julian Taylor
• Marten van Kerkwijk
• Matti Picus

Pull requests merged

A total of 4 pull requests were merged for this release.

• #11902: BUG: Fix matrix PendingDeprecationWarning suppression for pytest...
• #11981: BUG: fix cached allocations without the GIL for 1.15.x
• #11982: BUG: fix refcount leak in PyArray_AdaptFlexibleDType
• #11992: BUG: Ensure boolean indexing of subclasses sets base correctly.

Checksums

MD5

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SHA256

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==========================
NumPy 1.15.1 Release Notes

This is a bugfix release for bugs and regressions reported following the 1.15.0
release.

• The annoying but harmless RuntimeWarning that "numpy.dtype size changed" has
  been suppressed. The long standing suppression was lost in the transition to
  pytest.
• The update to Cython 0.28.3 exposed a problematic use of a gcc attribute used
  to prefer code size over speed in module initialization, possibly resulting in
  incorrect compiled code. This has been fixed in latest Cython but has been
  disabled here for safety.
• Support for big-endian and ARMv8 architectures has been improved.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Compatibility Note

The NumPy 1.15.x OS X wheels released on PyPI no longer contain 32-bit
binaries. That will also be the case in future releases. See
#11625 <https://github.com/numpy/numpy/issues/11625>__ for the related
discussion. Those needing 32-bit support should look elsewhere or build
from source.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris
• Chris Billington
• Elliott Sales de Andrade +
• Eric Wieser
• Jeremy Manning +
• Matti Picus
• Ralf Gommers

Pull requests merged

A total of 24 pull requests were merged for this release.

• #11647: MAINT: Filter Cython warnings in __init__.py
• #11648: BUG: Fix doc source links to unwrap decorators
• #11657: BUG: Ensure singleton dimensions are not dropped when converting...
• #11661: BUG: Warn on Nan in minimum,maximum for scalars
• #11665: BUG: cython sometimes emits invalid gcc attribute
• #11682: BUG: Fix regression in void_getitem
• #11698: BUG: Make matrix_power again work for object arrays.
• #11700: BUG: Add missing PyErr_NoMemory after failing malloc
• #11719: BUG: Fix undefined functions on big-endian systems.
• #11720: MAINT: Make einsum optimize default to False.
• #11746: BUG: Fix regression in loadtxt for bz2 text files in Python 2.
• #11757: BUG: Revert use of console_scripts.
• #11758: BUG: Fix Fortran kind detection for aarch64 & s390x.
• #11759: BUG: Fix printing of longdouble on ppc64le.
• #11760: BUG: Fixes for unicode field names in Python 2
• #11761: BUG: Increase required cython version on python 3.7
• #11763: BUG: check return value of _buffer_format_string
• #11775: MAINT: Make assert_array_compare more generic.
• #11776: TST: Fix urlopen stubbing.
• #11777: BUG: Fix regression in intersect1d.
• #11779: BUG: Fix test sensitive to platform byte order.
• #11781: BUG: Avoid signed overflow in histogram
• #11785: BUG: Fix pickle and memoryview for datetime64, timedelta64 scalars
• #11786: BUG: Deprecation triggers segfault

Checksums

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==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Highlights

• NumPy has switched to pytest for testing.
• A new numpy.printoptions context manager.
• Many improvements to the histogram functions.
• Support for unicode field names in python 2.7.
• Improved support for PyPy.
• Fixes and improvements to numpy.einsum.

New functions

• numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
  common multiple.

• numpy.ma.stack, the numpy.stack array-joining function generalized to
  masked arrays.

• numpy.quantile function, an interface to percentile without factors of
  100

• numpy.nanquantile function, an interface to nanpercentile without
  factors of 100

• numpy.printoptions, a context manager that sets print options temporarily
  for the scope of the with block::

  with np.printoptions(precision=2):
  ... print(np.array([2.0]) / 3)
  [0.67]

• numpy.histogram_bin_edges, a function to get the edges of the bins used by a
  histogram without needing to calculate the histogram.

• C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added to deal with compiler optimization changing the order of
  operations. See below for details.

Deprecations

• Aliases of builtin pickle functions are deprecated, in favor of their
  unaliased pickle.<func> names:

  • numpy.loads
  • numpy.core.numeric.load
  • numpy.core.numeric.loads
  • numpy.ma.loads, numpy.ma.dumps
  • numpy.ma.load, numpy.ma.dump - these functions already failed on
    python 3 when called with a string.

• Multidimensional indexing with anything but a tuple is deprecated. This means
  that the index list in ind = [slice(None), 0]; arr[ind] should be changed
  to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
  arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
  expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
  arr[array([0, 1]), array([0, 1])], that will be interpreted
  as arr[array([[0, 1], [0, 1]])] in the future.

• Imports from the following sub-modules are deprecated, they will be removed
  at some future date.

  • numpy.testing.utils
  • numpy.testing.decorators
  • numpy.testing.nosetester
  • numpy.testing.noseclasses
  • numpy.core.umath_tests

• Giving a generator to numpy.sum is now deprecated. This was undocumented
  behavior, but worked. Previously, it would calculate the sum of the generator
  expression. In the future, it might return a different result. Use
  np.sum(np.from_iter(generator)) or the built-in Python sum instead.

• Users of the C-API should call PyArrayResolveWriteBackIfCopy or
  PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
  flag set, before deallocating the array. A deprecation warning will be
  emitted if those calls are not used when needed.

• Users of nditer should use the nditer object as a context manager
  anytime one of the iterator operands is writeable, so that numpy can
  manage writeback semantics, or should call it.close(). A
  RuntimeWarning may be emitted otherwise in these cases.

• The normed argument of np.histogram, deprecated long ago in 1.6.0,
  now emits a DeprecationWarning.

Future Changes

• NumPy 1.16 will drop support for Python 3.4.
• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

• umath_tests -> _umath_tests
• test_rational -> _rational_tests
• multiarray_tests -> _multiarray_tests
• struct_ufunc_test -> _struct_ufunc_tests
• operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

• Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added and should be used in place of the npy_get_floatstatusand
  npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
  were rearranging the order of operations when the previous functions were
  used in the ufunc SIMD functions, resulting in the floatstatus flags being '
  checked before the operation whose status we wanted to check was run.
  See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.einsum updates

Syncs einsum path optimization tech between numpy and opt_einsum. In
particular, the greedy path has received many enhancements by @jcmgray. A
full list of issues fixed are:

• Arbitrary memory can be passed into the greedy path. Fixes gh-11210.
• The greedy path has been updated to contain more dynamic programming ideas
  preventing a large number of duplicate (and expensive) calls that figure out
  the actual pair contraction that takes place. Now takes a few seconds on
  several hundred input tensors. Useful for matrix product state theories.
• Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be
  a bit earlier in the process.
• Enhances the can_dot functionality that previous missed an edge case (part
  of gh-11308).

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

The normed arguments of histogramdd and histogram2d have been renamed

These arguments are now called density, which is consistent with
histogram. The old argument continues to work, but the new name should be
preferred.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

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==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.7. The wheels are
linked with OpenBLAS v0.3.0, which should fix some of the linalg problems
reported for NumPy 1.14.

Highlights

• NumPy has switched to pytest for testing.
• A new numpy.printoptions context manager.
• Many improvements to the histogram functions.
• Support for unicode field names in python 2.7.
• Improved support for PyPy.
• Fixes and improvements to numpy.einsum.

New functions

• numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
  common multiple.

• numpy.ma.stack, the numpy.stack array-joining function generalized to
  masked arrays.

• numpy.quantile function, an interface to percentile without factors of
  100

• numpy.nanquantile function, an interface to nanpercentile without
  factors of 100

• numpy.printoptions, a context manager that sets print options temporarily
  for the scope of the with block::

  with np.printoptions(precision=2):
  ... print(np.array([2.0]) / 3)
  [0.67]

• numpy.histogram_bin_edges, a function to get the edges of the bins used by a
  histogram without needing to calculate the histogram.

• C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added to deal with compiler optimization changing the order of
  operations. See below for details.

Deprecations

• Aliases of builtin pickle functions are deprecated, in favor of their
  unaliased pickle.<func> names:

  • numpy.loads
  • numpy.core.numeric.load
  • numpy.core.numeric.loads
  • numpy.ma.loads, numpy.ma.dumps
  • numpy.ma.load, numpy.ma.dump - these functions already failed on
    python 3 when called with a string.

• Multidimensional indexing with anything but a tuple is deprecated. This means
  that the index list in ind = [slice(None), 0]; arr[ind] should be changed
  to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
  arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
  expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
  arr[array([0, 1]), array([0, 1])], that will be interpreted
  as arr[array([[0, 1], [0, 1]])] in the future.

• Imports from the following sub-modules are deprecated, they will be removed
  at some future date.

  • numpy.testing.utils
  • numpy.testing.decorators
  • numpy.testing.nosetester
  • numpy.testing.noseclasses
  • numpy.core.umath_tests

• Giving a generator to numpy.sum is now deprecated. This was undocumented
  behavior, but worked. Previously, it would calculate the sum of the generator
  expression. In the future, it might return a different result. Use
  np.sum(np.from_iter(generator)) or the built-in Python sum instead.

• Users of the C-API should call PyArrayResolveWriteBackIfCopy or
  PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
  flag set, before deallocating the array. A deprecation warning will be
  emitted if those calls are not used when needed.

• Users of nditer should use the nditer object as a context manager
  anytime one of the iterator operands is writeable, so that numpy can
  manage writeback semantics, or should call it.close(). A
  RuntimeWarning may be emitted otherwise in these cases.

• The normed argument of np.histogram, deprecated long ago in 1.6.0,
  now emits a DeprecationWarning.

Future Changes

• NumPy 1.16 will drop support for Python 3.4.
• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

• umath_tests -> _umath_tests
• test_rational -> _rational_tests
• multiarray_tests -> _multiarray_tests
• struct_ufunc_test -> _struct_ufunc_tests
• operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

• Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added and should be used in place of the npy_get_floatstatusand
  npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
  were rearranging the order of operations when the previous functions were
  used in the ufunc SIMD functions, resulting in the floatstatus flags being '
  checked before the operation whose status we wanted to check was run.
  See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.einsum updates

Syncs einsum path optimization tech between numpy and opt_einsum. In
particular, the greedy path has received many enhancements by @jcmgray. A
full list of issues fixed are:

• Arbitrary memory can be passed into the greedy path. Fixes gh-11210.
• The greedy path has been updated to contain more dynamic programming ideas
  preventing a large number of duplicate (and expensive) calls that figure out
  the actual pair contraction that takes place. Now takes a few seconds on
  several hundred input tensors. Useful for matrix product state theories.
• Reworks the broadcasting dot error catching found in gh-11218 gh-10352 to be
  a bit earlier in the process.
• Enhances the can_dot functionality that previous missed an edge case (part
  of gh-11308).

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

The normed arguments of histogramdd and histogram2d have been renamed

These arguments are now called density, which is consistent with
histogram. The old argument continues to work, but the new name should be
preferred.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

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==========================
NumPy 1.15.0 Release Notes

NumPy 1.15.0 is a release with an unusual number of cleanups, many deprecations
of old functions, and improvements to many existing functions. Please read the
detailed descriptions below to see if you are affected.

For testing, we have switched to pytest as a replacement for the no longer
maintained nose framework. The old nose based interface remains for downstream
projects who may still be using it.

The Python versions supported by this release are 2.7, 3.4-3.6. The upcoming
3.7 release should also work, but you will need to compile from source using
Cython 0.28.2 or later. The wheels will be linked with OpenBLAS 3.0, which
should fix some of the linalg problems reported for NumPy 1.14.

Highlights

• NumPy has switched to pytest for testing.
• A new numpy.printoptions context manager.
• Many improvements to the histogram functions.
• Support for unicode field names in python 2.7.
• Improved support for PyPy.

New functions

• numpy.gcd and numpy.lcm, to compute the greatest common divisor and least
  common multiple.

• numpy.ma.stack, the numpy.stack array-joining function generalized to
  masked arrays.

• numpy.quantile function, an interface to percentile without factors of
  100

• numpy.nanquantile function, an interface to nanpercentile without
  factors of 100

• numpy.printoptions, a context manager that sets print options temporarily
  for the scope of the with block::

  with np.printoptions(precision=2):
  ... print(np.array([2.0]) / 3)
  [0.67]

• numpy.histogram_bin_edges, a function to get the edges of the bins used by a
  histogram without needing to calculate the histogram.

• C functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added to deal with compiler optimization changing the order of
  operations. See below for details.

Deprecations

• Aliases of builtin pickle functions are deprecated, in favor of their
  unaliased pickle.<func> names:

  • numpy.loads
  • numpy.core.numeric.load
  • numpy.core.numeric.loads
  • numpy.ma.loads, numpy.ma.dumps
  • numpy.ma.load, numpy.ma.dump - these functions already failed on
    python 3 when called with a string.

• Multidimensional indexing with anything but a tuple is deprecated. This means
  that the index list in ind = [slice(None), 0]; arr[ind] should be changed
  to a tuple, e.g., ind = [slice(None), 0]; arr[tuple(ind)] or
  arr[(slice(None), 0)]. That change is necessary to avoid ambiguity in
  expressions such as arr[[[0, 1], [0, 1]]], currently interpreted as
  arr[array([0, 1]), array([0, 1])], that will be interpreted
  as arr[array([[0, 1], [0, 1]])] in the future.

• Imports from the following sub-modules are deprecated, they will be removed
  at some future date.

  • numpy.testing.utils
  • numpy.testing.decorators
  • numpy.testing.nosetester
  • numpy.testing.noseclasses
  • numpy.core.umath_tests

• Giving a generator to numpy.sum is now deprecated. This was undocumented
  behavior, but worked. Previously, it would calculate the sum of the generator
  expression. In the future, it might return a different result. Use
  np.sum(np.from_iter(generator)) or the built-in Python sum instead.

• Users of the C-API should call PyArrayResolveWriteBackIfCopy or
  PyArray_DiscardWritbackIfCopy on any array with the WRITEBACKIFCOPY
  flag set, before deallocating the array. A deprecation warning will be
  emitted if those calls are not used when needed.

• Users of numpy.nditer should use the nditer object as a context manager
  whenever one of the iterator operands is writeable so that numpy can manage
  writeback semantics, or alternately, one can call it.close() to trigger a
  writeback. A RuntimeWarning will otherwise be raised in those cases. Users
  of the C-API should call NpyIter_Close before NpyIter_Deallocate.

• Users of nditer should use the nditer object as a context manager
  anytime one of the iterator operands is writeable, so that numpy can
  manage writeback semantics, or should call it.close(). A
  RuntimeWarning may be emitted otherwise in these cases.

• The normed argument of np.histogram, deprecated long ago in 1.6.0,
  now emits a DeprecationWarning.

Future Changes

• NumPy 1.16 will drop support for Python 3.4.
• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

• umath_tests -> _umath_tests
• test_rational -> _rational_tests
• multiarray_tests -> _multiarray_tests
• struct_ufunc_test -> _struct_ufunc_tests
• operand_flag_tests -> _operand_flag_tests

The umath_tests module is still available for backwards compatibility, but
will be removed in the future.

The NpzFile returned by np.savez is now a collections.abc.Mapping

This means it behaves like a readonly dictionary, and has a new .values()
method and len() implementation.

For python 3, this means that .iteritems(), .iterkeys() have been
deprecated, and .keys() and .items() now return views and not lists.
This is consistent with how the builtin dict type changed between python 2
and python 3.

Under certain conditions, nditer must be used in a context manager

When using an numpy.nditer with the "writeonly" or "readwrite" flags, there
are some circumstances where nditer doesn't actually give you a view of the
writable array. Instead, it gives you a copy, and if you make changes to the
copy, nditer later writes those changes back into your actual array. Currently,
this writeback occurs when the array objects are garbage collected, which makes
this API error-prone on CPython and entirely broken on PyPy. Therefore,
nditer should now be used as a context manager whenever it is used
with writeable arrays, e.g., with np.nditer(...) as it: .... You may also
explicitly call it.close() for cases where a context manager is unusable,
for instance in generator expressions.

Numpy has switched to using pytest instead of nose for testing

The last nose release was 1.3.7 in June, 2015, and development of that tool has
ended, consequently NumPy has now switched to using pytest. The old decorators
and nose tools that were previously used by some downstream projects remain
available, but will not be maintained. The standard testing utilities,
assert_almost_equal and such, are not be affected by this change except for
the nose specific functions import_nose and raises. Those functions are
not used in numpy, but are kept for downstream compatibility.

Numpy no longer monkey-patches ctypes with __array_interface__

Previously numpy added __array_interface__ attributes to all the integer
types from ctypes.

np.ma.notmasked_contiguous and np.ma.flatnotmasked_contiguous always return lists

This is the documented behavior, but previously the result could be any of
slice, None, or list.

All downstream users seem to check for the None result from
flatnotmasked_contiguous and replace it with []. Those callers will
continue to work as before.

np.squeeze restores old behavior of objects that cannot handle an axis argument

Prior to version 1.7.0, numpy.squeeze did not have an axis argument and
all empty axes were removed by default. The incorporation of an axis
argument made it possible to selectively squeeze single or multiple empty axes,
but the old API expectation was not respected because axes could still be
selectively removed (silent success) from an object expecting all empty axes to
be removed. That silent, selective removal of empty axes for objects expecting
the old behavior has been fixed and the old behavior restored.

unstructured void array's .item method now returns a bytes object

.item now returns a bytes object instead of a buffer or byte array.
This may affect code which assumed the return value was mutable, which is no
longer the case.

copy.copy and copy.deepcopy no longer turn masked into an array

Since np.ma.masked is a readonly scalar, copying should be a no-op. These
functions now behave consistently with np.copy().

Multifield Indexing of Structured Arrays will still return a copy

The change that multi-field indexing of structured arrays returns a view
instead of a copy is pushed back to 1.16. A new method
numpy.lib.recfunctions.repack_fields has been introduced to help mitigate
the effects of this change, which can be used to write code compatible with
both numpy 1.15 and 1.16. For more information on how to update code to account
for this future change see the "accessing multiple fields" section of the
user guide <https://docs.scipy.org/doc/numpy/user/basics.rec.html>__.

C API changes

New function NpyIter_Close

The function NpyIter_Close has been added and should be called before
NpyIter_Deallocate to resolve possible writeback-enabled arrays.

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
were rearranging the order of operations when the previous functions were used
in the ufunc SIMD functions, resulting in the floatstatus flags being checked
before the operation whose status we wanted to check was run. See #10339 <https://github.com/numpy/numpy/issues/10370>__.

Changes to PyArray_GetDTypeTransferFunction

PyArray_GetDTypeTransferFunction now defaults to using user-defined
copyswapn / copyswap for user-defined dtypes. If this causes a
significant performance hit, consider implementing copyswapn to reflect the
implementation of PyArray_GetStridedCopyFn. See #10898 <https://github.com/numpy/numpy/pull/10898>__.

• Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
  have been added and should be used in place of the npy_get_floatstatusand
  npy_clear_status functions. Optimizing compilers like GCC 8.1 and Clang
  were rearranging the order of operations when the previous functions were
  used in the ufunc SIMD functions, resulting in the floatstatus flags being '
  checked before the operation whose status we wanted to check was run.
  See #10339 <https://github.com/numpy/numpy/issues/10370>__.

New Features

np.gcd and np.lcm ufuncs added for integer and objects types

These compute the greatest common divisor, and lowest common multiple,
respectively. These work on all the numpy integer types, as well as the
builtin arbitrary-precision Decimal and long types.

Support for cross-platform builds for iOS

The build system has been modified to add support for the
_PYTHON_HOST_PLATFORM environment variable, used by distutils when
compiling on one platform for another platform. This makes it possible to
compile NumPy for iOS targets.

This only enables you to compile NumPy for one specific platform at a time.
Creating a full iOS-compatible NumPy package requires building for the 5
architectures supported by iOS (i386, x86_64, armv7, armv7s and arm64), and
combining these 5 compiled builds products into a single "fat" binary.

return_indices keyword added for np.intersect1d

New keyword return_indices returns the indices of the two input arrays
that correspond to the common elements.

np.quantile and np.nanquantile

Like np.percentile and np.nanpercentile, but takes quantiles in [0, 1]
rather than percentiles in [0, 100]. np.percentile is now a thin wrapper
around np.quantile with the extra step of dividing by 100.

Build system

Added experimental support for the 64-bit RISC-V architecture.

Improvements

np.ufunc.reduce and related functions now accept an initial value

np.ufunc.reduce, np.sum, np.prod, np.min and np.max all
now accept an initial keyword argument that specifies the value to start
the reduction with.

np.flip can operate over multiple axes

np.flip now accepts None, or tuples of int, in its axis argument. If
axis is None, it will flip over all the axes.

histogram and histogramdd functions have moved to np.lib.histograms

These were originally found in np.lib.function_base. They are still
available under their un-scoped np.histogram(dd) names, and
to maintain compatibility, aliased at np.lib.function_base.histogram(dd).

Code that does from np.lib.function_base import * will need to be updated
with the new location, and should consider not using import * in future.

histogram will accept NaN values when explicit bins are given

Previously it would fail when trying to compute a finite range for the data.
Since the range is ignored anyway when the bins are given explicitly, this error
was needless.

Note that calling histogram on NaN values continues to raise the
RuntimeWarning s typical of working with nan values, which can be silenced
as usual with errstate.

histogram works on datetime types, when explicit bin edges are given

Dates, times, and timedeltas can now be histogrammed. The bin edges must be
passed explicitly, and are not yet computed automatically.

histogram "auto" estimator handles limited variance better

No longer does an IQR of 0 result in n_bins=1, rather the number of bins
chosen is related to the data size in this situation.

The edges retuned by `histogramandhistogramdd`` now match the data float type

When passed np.float16, np.float32, or np.longdouble data, the
returned edges are now of the same dtype. Previously, histogram would only
return the same type if explicit bins were given, and histogram would
produce float64 bins no matter what the inputs.

histogramdd allows explicit ranges to be given in a subset of axes

The range argument of numpy.histogramdd can now contain None values to
indicate that the range for the corresponding axis should be computed from the
data. Previously, this could not be specified on a per-axis basis.

np.r_ works with 0d arrays, and np.ma.mr_ works with np.ma.masked

0d arrays passed to the r_ and mr_ concatenation helpers are now treated as
though they are arrays of length 1. Previously, passing these was an error.
As a result, numpy.ma.mr_ now works correctly on the masked constant.

np.ptp accepts a keepdims argument, and extended axis tuples

np.ptp (peak-to-peak) can now work over multiple axes, just like np.max
and np.min.

MaskedArray.astype now is identical to ndarray.astype

This means it takes all the same arguments, making more code written for
ndarray work for masked array too.

Enable AVX2/AVX512 at compile time

Change to simd.inc.src to allow use of AVX2 or AVX512 at compile time. Previously
compilation for avx2 (or 512) with -march=native would still use the SSE
code for the simd functions even when the rest of the code got AVX2.

nan_to_num always returns scalars when receiving scalar or 0d inputs

Previously an array was returned for integer scalar inputs, which is
inconsistent with the behavior for float inputs, and that of ufuncs in general.
For all types of scalar or 0d input, the result is now a scalar.

np.flatnonzero works on numpy-convertible types

np.flatnonzero now uses np.ravel(a) instead of a.ravel(), so it
works for lists, tuples, etc.

np.interp returns numpy scalars rather than builtin scalars

Previously np.interp(0.5, [0, 1], [10, 20]) would return a float, but
now it returns a np.float64 object, which more closely matches the behavior
of other functions.

Additionally, the special case of np.interp(object_array_0d, ...) is no
longer supported, as np.interp(object_array_nd) was never supported anyway.

As a result of this change, the period argument can now be used on 0d
arrays.

Allow dtype field names to be unicode in Python 2

Previously np.dtype([(u'name', float)]) would raise a TypeError in
Python 2, as only bytestrings were allowed in field names. Now any unicode
string field names will be encoded with the ascii codec, raising a
UnicodeEncodeError upon failure.

This change makes it easier to write Python 2/3 compatible code using
from __future__ import unicode_literals, which previously would cause
string literal field names to raise a TypeError in Python 2.

Comparison ufuncs accept dtype=object, overriding the default bool

This allows object arrays of symbolic types, which override == and other
operators to return expressions, to be compared elementwise with
np.equal(a, b, dtype=object).

sort functions accept kind='stable'

Up until now, to perform a stable sort on the data, the user must do:

>>> np.sort([5, 2, 6, 2, 1], kind='mergesort')
[1, 2, 2, 5, 6]

because merge sort is the only stable sorting algorithm available in
NumPy. However, having kind='mergesort' does not make it explicit that
the user wants to perform a stable sort thus harming the readability.

This change allows the user to specify kind='stable' thus clarifying
the intent.

Do not make temporary copies for in-place accumulation

When ufuncs perform accumulation they no longer make temporary copies because
of the overlap between input an output, that is, the next element accumulated
is added before the accumulated result is stored in its place, hence the
overlap is safe. Avoiding the copy results in faster execution.

linalg.matrix_power can now handle stacks of matrices

Like other functions in linalg, matrix_power can now deal with arrays
of dimension larger than 2, which are treated as stacks of matrices. As part
of the change, to further improve consistency, the name of the first argument
has been changed to a (from M), and the exceptions for non-square
matrices have been changed to LinAlgError (from ValueError).

Increased performance in random.permutation for multidimensional arrays

permutation uses the fast path in random.shuffle for all input
array dimensions. Previously the fast path was only used for 1-d arrays.

Generalized ufuncs now accept axes, axis and keepdims arguments

One can control over which axes a generalized ufunc operates by passing in an
axes argument, a list of tuples with indices of particular axes. For
instance, for a signature of (i,j),(j,k)->(i,k) appropriate for matrix
multiplication, the base elements are two-dimensional matrices and these are
taken to be stored in the two last axes of each argument. The corresponding
axes keyword would be [(-2, -1), (-2, -1), (-2, -1)]. If one wanted to
use leading dimensions instead, one would pass in [(0, 1), (0, 1), (0, 1)].

For simplicity, for generalized ufuncs that operate on 1-dimensional arrays
(vectors), a single integer is accepted instead of a single-element tuple, and
for generalized ufuncs for which all outputs are scalars, the (empty) output
tuples can be omitted. Hence, for a signature of (i),(i)->() appropriate
for an inner product, one could pass in axes=[0, 0] to indicate that the
vectors are stored in the first dimensions of the two inputs arguments.

As a short-cut for generalized ufuncs that are similar to reductions, i.e.,
that act on a single, shared core dimension such as the inner product example
above, one can pass an axis argument. This is equivalent to passing in
axes with identical entries for all arguments with that core dimension
(e.g., for the example above, axes=[(axis,), (axis,)]).

Furthermore, like for reductions, for generalized ufuncs that have inputs that
all have the same number of core dimensions and outputs with no core dimension,
one can pass in keepdims to leave a dimension with size 1 in the outputs,
thus allowing proper broadcasting against the original inputs. The location of
the extra dimension can be controlled with axes. For instance, for the
inner-product example, keepdims=True, axes=[-2, -2, -2] would act on the
inner-product example, keepdims=True, axis=-2 would act on the
one-but-last dimension of the input arguments, and leave a size 1 dimension in
that place in the output.

float128 values now print correctly on ppc systems

Previously printing float128 values was buggy on ppc, since the special
double-double floating-point-format on these systems was not accounted for.
float128s now print with correct rounding and uniqueness.

Warning to ppc users: You should upgrade glibc if it is version <=2.23,
especially if using float128. On ppc, glibc's malloc in these version often
misaligns allocated memory which can crash numpy when using float128 values.

New np.take_along_axis and np.put_along_axis functions

When used on multidimensional arrays, argsort, argmin, argmax, and
argpartition return arrays that are difficult to use as indices.
take_along_axis provides an easy way to use these indices to lookup values
within an array, so that::

np.take_along_axis(a, np.argsort(a, axis=axis), axis=axis)

is the same as::

np.sort(a, axis=axis)

np.put_along_axis acts as the dual operation for writing to these indices
within an array.

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ff5045d88b409bfeff664b13a110400e  numpy-1.15.0rc1.zip

SHA256

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9870ddc0055dbe5d77f1a7f5abd493737f0728f88e15fdafc459ea5e64a82efa  numpy-1.15.0rc1-cp27-cp27mu-manylinux1_x86_64.whl
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8a211172894d6b21e8d2133dbdced490b95492707982cffce32b97fcada718ca  numpy-1.15.0rc1-cp36-cp36m-manylinux1_i686.whl
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b1dcad0333986cbbbfa547d57807047d9777d5e1fd56c47eb47c59050e4485b1  numpy-1.15.0rc1-cp36-none-win32.whl
0e1871bb9307fbc3173c198f5536792185d20750cd5ad9907a14d49f767464b5  numpy-1.15.0rc1-cp36-none-win_amd64.whl
c57f33f83f61ad819e3fcb41afa54d224d03377d6f33be57289924cc193ead63  numpy-1.15.0rc1.tar.gz
592657828982b13ff48a56c5f75fb2f286709f7b830ec211029ac7970027b54d  numpy-1.15.0rc1.zip

NumPy 1.14.5 Release Notes

This is a bugfix release for bugs reported following the 1.14.4 release. The
most significant fixes are:

• fixes for compilation errors on alpine and NetBSD

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 0.28.2 and should work for the upcoming Python 3.7.

Contributors

A total of 1 person contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Charles Harris

Pull requests merged

A total of 2 pull requests were merged for this release.

• #11274 <https://github.com/numpy/numpy/pull/11274>__: BUG: Correct use of NPY_UNUSED.
• #11294 <https://github.com/numpy/numpy/pull/11294>__: BUG: Remove extra trailing parentheses.

Checksums

MD5

---

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02d940a6931703de2c41fa5590ac7e98  numpy-1.14.5.zip

SHA256

---

e1864a4e9f93ddb2dc6b62ccc2ec1f8250ff4ac0d3d7a15c8985dd4e1fbd6418  numpy-1.14.5-cp27-cp27m-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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==========================
NumPy 1.14.4 Release Notes

This is a bugfix release for bugs reported following the 1.14.3 release. The
most significant fixes are:

• fixes for compiler instruction reordering that resulted in NaN's not being
  properly propagated in np.max and np.min,

• fixes for bus faults on SPARC and older ARM due to incorrect alignment
  checks.

There are also improvements to printing of long doubles on PPC platforms. All
is not yet perfect on that platform, the whitespace padding is still incorrect
and is to be fixed in numpy 1.15, consequently NumPy still fails some
printing-related (and other) unit tests on ppc systems. However, the printed
values are now correct.

Note that NumPy will error on import if it detects incorrect float32 dot
results. This problem has been seen on the Mac when working in the Anaconda
enviroment and is due to a subtle interaction between MKL and PyQt5. It is not
strictly a NumPy problem, but it is best that users be aware of it. See the
gh-8577 NumPy issue for more information.

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 0.28.2 and should work for the upcoming Python 3.7.

Contributors

A total of 7 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Charles Harris
• Marten van Kerkwijk
• Matti Picus
• Pauli Virtanen
• Ryan Soklaski +
• Sebastian Berg

Pull requests merged

A total of 11 pull requests were merged for this release.

• #11104: BUG: str of DOUBLE_DOUBLE format wrong on ppc64
• #11170: TST: linalg: add regression test for gh-8577
• #11174: MAINT: add sanity-checks to be run at import time
• #11181: BUG: void dtype setup checked offset not actual pointer for alignment
• #11194: BUG: Python2 doubles don't print correctly in interactive shell.
• #11198: BUG: optimizing compilers can reorder call to npy_get_floatstatus
• #11199: BUG: reduce using SSE only warns if inside SSE loop
• #11203: BUG: Bytes delimiter/comments in genfromtxt should be decoded
• #11211: BUG: Fix reference count/memory leak exposed by better testing
• #11219: BUG: Fixes einsum broadcasting bug when optimize=True
• #11251: DOC: Document 1.14.4 release.

Checksums

MD5

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SHA256

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==========================
NumPy 1.14.3 Release Notes

This is a bugfix release for a few bugs reported following the 1.14.2 release:

• np.lib.recfunctions.fromrecords accepts a list-of-lists, until 1.15
• In python2, float types use the new print style when printing to a file
• style arg in "legacy" print mode now works for 0d arrays

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 0.28.2.

Contributors

A total of 6 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Charles Harris
• Jonathan March +
• Malcolm Smith +
• Matti Picus
• Pauli Virtanen

Pull requests merged

A total of 8 pull requests were merged for this release.

• #10862: BUG: floating types should override tp_print (1.14 backport)
• #10905: BUG: for 1.14 back-compat, accept list-of-lists in fromrecords
• #10947: BUG: 'style' arg to array2string broken in legacy mode (1.14...
• #10959: BUG: test, fix for missing flags['WRITEBACKIFCOPY'] key
• #10960: BUG: Add missing underscore to prototype in check_embedded_lapack
• #10961: BUG: Fix encoding regression in ma/bench.py (Issue #10868)
• #10962: BUG: core: fix NPY_TITLE_KEY macro on pypy
• #10974: BUG: test, fix PyArray_DiscardWritebackIfCopy...

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==========================
NumPy 1.14.2 Release Notes

This is a bugfix release for some bugs reported following the 1.14.1 release. The major
problems dealt with are as follows.

• Residual bugs in the new array printing functionality.
• Regression resulting in a relocation problem with shared library.
• Improved PyPy compatibility.

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 4 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Allan Haldane
• Charles Harris
• Eric Wieser
• Pauli Virtanen

Pull requests merged

A total of 5 pull requests were merged for this release.

• #10674: BUG: Further back-compat fix for subclassed array repr
• #10725: BUG: dragon4 fractional output mode adds too many trailing zeros
• #10726: BUG: Fix f2py generated code to work on PyPy
• #10727: BUG: Fix missing NPY_VISIBILITY_HIDDEN on npy_longdouble_to_PyLong
• #10729: DOC: Create 1.14.2 notes and changelog.

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