NumPy 1.19.1 Release Notes

NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces
several functions deprecated in the upcoming Python-3.9 release, has
improved support for AIX, and has a number of development related
updates to keep CI working with recent upstream changes.

This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
used when building with Python 3.9 for testing purposes.

Contributors

A total of 15 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Abhinav Reddy +
• Anirudh Subramanian
• Antonio Larrosa +
• Charles Harris
• Chunlin Fang
• Eric Wieser
• Etienne Guesnet +
• Kevin Sheppard
• Matti Picus
• Raghuveer Devulapalli
• Roman Yurchak
• Ross Barnowski
• Sayed Adel
• Sebastian Berg
• Tyler Reddy

Pull requests merged

A total of 25 pull requests were merged for this release.

• #16649: MAINT, CI: disable Shippable cache
• #16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
• #16654: REL: Fix outdated docs link
• #16656: BUG: raise IEEE exception on AIX
• #16672: BUG: Fix bug in AVX complex absolute while processing array of...
• #16693: TST: Add extra debugging information to CPU features detection
• #16703: BLD: Add CPU entry for Emscripten / WebAssembly
• #16705: TST: Disable Python 3.9-dev testing.
• #16714: MAINT: Disable use_hugepages in case of ValueError
• #16724: BUG: Fix PyArray_SearchSorted signature.
• #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
• #16772: MAINT: Remove unneeded call to PyUnicode_READY
• #16776: MAINT: Fix deprecated functions in scalarapi.c
• #16779: BLD, ENH: Add RPATH support for AIX
• #16780: BUG: Fix default fallback in genfromtxt
• #16784: BUG: Added missing return after raising error in methods.c
• #16795: BLD: update cython to 0.29.21
• #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
• #16872: BUG: Validate output size in bin- and multinomial
• #16875: BLD, MAINT: Pin setuptools
• #16904: DOC: Reconstruct Testing Guideline.
• #16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
• #16906: BUG, DOC: Fix bad MPL kwarg.
• #16916: BUG: Fix string/bytes to complex assignment
• #16922: REL: Prepare for NumPy 1.19.1 release

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NumPy 1.19.0 Release Notes

This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.

The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

• Code compatibility with Python versions < 3.6 (including Python 2)
  was dropped from both the python and C code. The shims in
  numpy.compat will remain to support third-party packages, but they
  may be deprecated in a future release. Note that 1.19.x will not
  compile with earlier versions of Python due to the use of f-strings.

  (gh-15233)

Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
used as:

try:
    ...
except Exception:
    e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

SeedSequence with small seeds no longer conflicts with spawning

Small seeds (less than 2**96) were previously implicitly 0-padded out
to 128 bits, the size of the internal entropy pool. When spawned, the
spawn key was concatenated before the 0-padding. Since the first spawn
key is (0,), small seeds before the spawn created the same states as
the first spawned SeedSequence. Now, the seed is explicitly 0-padded
out to the internal pool size before concatenating the spawn key.
Spawned SeedSequences will produce different results than in the
previous release. Unspawned SeedSequences will still produce the same
results.

(gh-16551)

Deprecations

Deprecate automatic dtype=object for ragged input

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.

(gh-15119)

Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:

• numpy.core.records.fromarrays
• numpy.core.records.fromrecords
• numpy.core.records.fromstring
• numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
deprecated:

• PyArray_GetArrayParamsFromObject
• PyUFunc_GenericFunction
• PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

numpy.ndarray.tostring() is deprecated in favor of tobytes()

~numpy.ndarray.tobytes has existed since the 1.9 release, but until
this release ~numpy.ndarray.tostring emitted no warning. The change to
emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:

• PyArray_BroadcastToShape
• PyArray_IntTupleFromIntp
• PyArray_OverflowMultiplyList

Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
incompatible-pointer-types warning forcing users to either ignore the
compiler warnings or to const qualify their own loop signatures.

(gh-15355)

New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
np.copy(arr, subok=True). This addition better documents that the
default behavior of numpy.copy differs from the numpy.ndarray.copy
method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

Improvements

Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

Use AVX512 intrinsic to implement np.exp when input is np.float64

Use AVX512 intrinsic to implement np.exp when input is np.float64,
which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

On Linux NumPy has previously added support for madavise hugepages which
can improve performance for very large arrays. Unfortunately, on older
Kernel versions this led to peformance regressions, thus by default the
support has been disabled on kernels before version 4.6. To override the
default, you can use the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

Changes

Remove handling of extra argument to __array__

A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__(dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op.__array__ existed. However that variant is not documented, and it
is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
distribution. This is now fixed.

(gh-15872)

Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
that are directly seeded.

The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

Checksums

MD5

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SHA256

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NumPy 1.18.5 Release Notes

This is a short release to allow pickle protocol=5 to be used in
Python3.5. It is motivated by the recent backport of pickle5 to
Python3.5.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Contributors

A total of 3 people contributed to this release. People with a "+" by
their names contributed a patch for the first time.

• Charles Harris
• Matti Picus
• Siyuan Zhuang +

Pull requests merged

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

• #16439: ENH: enable pickle protocol 5 support for python3.5
• #16441: BUG: relpath fails for different drives on windows

Checksums

MD5

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SHA256

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NumPy 1.19.0 Release Notes

This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.

The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

• Code compatibility with Python versions < 3.6 (including Python 2)
  was dropped from both the python and C code. The shims in
  numpy.compat will remain to support third-party packages, but they
  may be deprecated in a future release. Note that 1.19.x will not
  compile with earlier versions of Python due to the use of f-strings.

  (gh-15233)

Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
used as:

try:
    ...
except Exception:
    e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

Deprecations

Deprecate automatic dtype=object for ragged input

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.

(gh-15119)

Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:

• numpy.core.records.fromarrays
• numpy.core.records.fromrecords
• numpy.core.records.fromstring
• numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
deprecated:

• PyArray_GetArrayParamsFromObject
• PyUFunc_GenericFunction
• PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

numpy.ndarray.tostring() is deprecated in favor of tobytes()

~numpy.ndarray.tobytes has existed since the 1.9 release, but until
this release ~numpy.ndarray.tostring emitted no warning. The change to
emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:

• PyArray_BroadcastToShape
• PyArray_IntTupleFromIntp
• PyArray_OverflowMultiplyList

Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
incompatible-pointer-types warning forcing users to either ignore the
compiler warnings or to const qualify their own loop signatures.

(gh-15355)

New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
np.copy(arr, subok=True). This addition better documents that the
default behavior of numpy.copy differs from the numpy.ndarray.copy
method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

Improvements

Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

Use AVX512 intrinsic to implement np.exp when input is np.float64

Use AVX512 intrinsic to implement np.exp when input is np.float64,
which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

On Linux NumPy has previously added support for madavise hugepages which
can improve performance for very large arrays. Unfortunately, on older
Kernel versions this led to peformance regressions, thus by default the
support has been disabled on kernels before version 4.6. To override the
default, you can use the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

Changes

Remove handling of extra argument to __array__

A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__(dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op.__array__ existed. However that variant is not documented, and it
is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
distribution. This is now fixed.

(gh-15872)

Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
that are directly seeded.

The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

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NumPy 1.19.0 Release Notes

This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.

The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython >= 0.29.16 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid problems on the Skylake architecture.

Highlights

• Code compatibility with Python versions < 3.5 (including Python 2)
  was dropped from both the python and C code. The shims in
  numpy.compat will remain to support third-party packages, but they
  may be deprecated in a future release.

  (gh-15233)

Expired deprecations

numpy.insert and numpy.delete can no longer be passed an axis on 0d arrays

This concludes a deprecation from 1.9, where when an axis argument was
passed to a call to ~numpy.insert and ~numpy.delete on a 0d array,
the axis and obj argument and indices would be completely ignored.
In these cases, insert(arr, "nonsense", 42, axis=0) would actually
overwrite the entire array, while delete(arr, "nonsense", axis=0)
would be arr.copy()

Now passing axis on a 0d array raises ~numpy.AxisError.

(gh-15802)

numpy.delete no longer ignores out-of-bounds indices

This concludes deprecations from 1.8 and 1.9, where np.delete would
ignore both negative and out-of-bounds items in a sequence of indices.
This was at odds with its behavior when passed a single index.

Now out-of-bounds items throw IndexError, and negative items index
from the end.

(gh-15804)

numpy.insert and numpy.delete no longer accept non-integral indices

This concludes a deprecation from 1.9, where sequences of non-integers
indices were allowed and cast to integers. Now passing sequences of
non-integral indices raises IndexError, just like it does when passing
a single non-integral scalar.

(gh-15805)

numpy.delete no longer casts boolean indices to integers

This concludes a deprecation from 1.8, where np.delete would cast
boolean arrays and scalars passed as an index argument into integer
indices. The behavior now is to treat boolean arrays as a mask, and to
raise an error on boolean scalars.

(gh-15815)

Compatibility notes

Changed random variate stream from numpy.random.Generator.dirichlet

A bug in the generation of random variates for the Dirichlet
distribution with small 'alpha' values was fixed by using a different
algorithm when max(alpha) < 0.1. Because of the change, the stream of
variates generated by dirichlet in this case will be different from
previous releases.

(gh-14924)

Scalar promotion in PyArray_ConvertToCommonType

The promotion of mixed scalars and arrays in
PyArray_ConvertToCommonType has been changed to adhere to those used
by np.result_type. This means that input such as
(1000, np.array([1], dtype=np.uint8))) will now return uint16
dtypes. In most cases the behaviour is unchanged. Note that the use of
this C-API function is generally discouraged. This also fixes
np.choose to behave the same way as the rest of NumPy in this respect.

(gh-14933)

Fasttake and fastputmask slots are deprecated and NULL'ed

The fasttake and fastputmask slots are now never used and must always be
set to NULL. This will result in no change in behaviour. However, if a
user dtype should set one of these a DeprecationWarning will be given.

(gh-14942)

np.ediff1d casting behaviour with to_end and to_begin

np.ediff1d now uses the "same_kind" casting rule for its additional
to_end and to_begin arguments. This ensures type safety except when
the input array has a smaller integer type than to_begin or to_end.
In rare cases, the behaviour will be more strict than it was previously
in 1.16 and 1.17. This is necessary to solve issues with floating point
NaN.

(gh-14981)

Converting of empty array-like objects to NumPy arrays

Objects with len(obj) == 0 which implement an "array-like"
interface, meaning an object implementing obj.__array__(),
obj.__array_interface__, obj.__array_struct__, or the python buffer
interface and which are also sequences (i.e. Pandas objects) will now
always retain there shape correctly when converted to an array. If such
an object has a shape of (0, 1) previously, it could be converted into
an array of shape (0,) (losing all dimensions after the first 0).

(gh-14995)

Removed multiarray.int_asbuffer

As part of the continued removal of Python 2 compatibility,
multiarray.int_asbuffer was removed. On Python 3, it threw a
NotImplementedError and was unused internally. It is expected that
there are no downstream use cases for this method with Python 3.

(gh-15229)

numpy.distutils.compat has been removed

This module contained only the function get_exception(), which was
used as:

try:
    ...
except Exception:
    e = get_exception()

Its purpose was to handle the change in syntax introduced in Python 2.6,
from except Exception, e: to except Exception as e:, meaning it was
only necessary for codebases supporting Python 2.5 and older.

(gh-15255)

issubdtype no longer interprets float as np.floating

numpy.issubdtype had a FutureWarning since NumPy 1.14 which has
expired now. This means that certain input where the second argument was
neither a datatype nor a NumPy scalar type (such as a string or a python
type like int or float) will now be consistent with passing in
np.dtype(arg2).type. This makes the result consistent with
expectations and leads to a false result in some cases which previously
returned true.

(gh-15773)

Change output of round on scalars to be consistent with Python

Output of the __round__ dunder method and consequently the Python
built-in round has been changed to be a Python int to be consistent
with calling it on Python float objects when called with no arguments.
Previously, it would return a scalar of the np.dtype that was passed
in.

(gh-15840)

The numpy.ndarray constructor no longer interprets strides=() as strides=None

The former has changed to have the expected meaning of setting
numpy.ndarray.strides to (), while the latter continues to result in
strides being chosen automatically.

(gh-15882)

C-Level string to datetime casts changed

The C-level casts from strings were simplified. This changed also fixes
string to datetime and timedelta casts to behave correctly (i.e. like
Python casts using string_arr.astype("M8") while previously the cast
would behave like string_arr.astype(np.int_).astype("M8"). This only
affects code using low-level C-API to do manual casts (not full array
casts) of single scalar values or using e.g. PyArray_GetCastFunc, and
should thus not affect the vast majority of users.

(gh-16068)

Deprecations

Deprecate automatic dtype=object for ragged input

Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
per NEP 34. Users should
explicitly use dtype=object to avoid the warning.

(gh-15119)

Passing shape=0 to factory functions in numpy.rec is deprecated

0 is treated as a special case and is aliased to None in the
functions:

• numpy.core.records.fromarrays
• numpy.core.records.fromrecords
• numpy.core.records.fromstring
• numpy.core.records.fromfile

In future, 0 will not be special cased, and will be treated as an
array length like any other integer.

(gh-15217)

Deprecation of probably unused C-API functions

The following C-API functions are probably unused and have been
deprecated:

• PyArray_GetArrayParamsFromObject
• PyUFunc_GenericFunction
• PyUFunc_SetUsesArraysAsData

In most cases PyArray_GetArrayParamsFromObject should be replaced by
converting to an array, while PyUFunc_GenericFunction can be replaced
with PyObject_Call (see documentation for details).

(gh-15427)

Converting certain types to dtypes is Deprecated

The super classes of scalar types, such as np.integer, np.generic,
or np.inexact will now give a deprecation warning when converted to a
dtype (or used in a dtype keyword argument). The reason for this is that
np.integer is converted to np.int_, while it would be expected to
represent any integer (e.g. also int8, int16, etc. For example,
dtype=np.floating is currently identical to dtype=np.float64, even
though also np.float32 is a subclass of np.floating.

(gh-15534)

Deprecation of round for np.complexfloating scalars

Output of the __round__ dunder method and consequently the Python
built-in round has been deprecated on complex scalars. This does not
affect np.round.

(gh-15840)

numpy.ndarray.tostring() is deprecated in favor of tobytes()

~numpy.ndarray.tobytes has existed since the 1.9 release, but until
this release ~numpy.ndarray.tostring emitted no warning. The change to
emit a warning brings NumPy in line with the builtin array.array
methods of the same name.

(gh-15867)

C API changes

Better support for const dimensions in API functions

The following functions now accept a constant array of npy_intp:

• PyArray_BroadcastToShape
• PyArray_IntTupleFromIntp
• PyArray_OverflowMultiplyList

Previously the caller would have to cast away the const-ness to call
these functions.

(gh-15251)

Const qualify UFunc inner loops

UFuncGenericFunction now expects pointers to const dimension and
strides as arguments. This means inner loops may no longer modify
either dimension or strides. This change leads to an
incompatible-pointer-types warning forcing users to either ignore the
compiler warnings or to const qualify their own loop signatures.

(gh-15355)

New Features

numpy.frompyfunc now accepts an identity argument

This allows the `numpy.ufunc.identity{.interpreted-text
role="attr"}[ attribute to be set on the resulting ufunc, meaning it can
be used for empty and multi-dimensional calls to
:meth:]{.title-ref}[numpy.ufunc.reduce]{.title-ref}`.

(gh-8255)

np.str_ scalars now support the buffer protocol

np.str_ arrays are always stored as UCS4, so the corresponding scalars
now expose this through the buffer interface, meaning
memoryview(np.str_('test')) now works.

(gh-15385)

subok option for numpy.copy

A new kwarg, subok, was added to numpy.copy to allow users to toggle
the behavior of numpy.copy with respect to array subclasses. The
default value is False which is consistent with the behavior of
numpy.copy for previous numpy versions. To create a copy that
preserves an array subclass with numpy.copy, call
np.copy(arr, subok=True). This addition better documents that the
default behavior of numpy.copy differs from the numpy.ndarray.copy
method which respects array subclasses by default.

(gh-15685)

numpy.linalg.multi_dot now accepts an out argument

out can be used to avoid creating unnecessary copies of the final
product computed by numpy.linalg.multidot.

(gh-15715)

keepdims parameter for numpy.count_nonzero

The parameter keepdims was added to numpy.count_nonzero. The
parameter has the same meaning as it does in reduction functions such as
numpy.sum or numpy.mean.

(gh-15870)

equal_nan parameter for numpy.array_equal

The keyword argument equal_nan was added to numpy.array_equal.
equal_nan is a boolean value that toggles whether or not nan values
are considered equal in comparison (default is False). This matches
API used in related functions such as numpy.isclose and
numpy.allclose.

(gh-16128)

Improvements

Improve detection of CPU features

Replace npy_cpu_supports which was a gcc specific mechanism to test
support of AVX with more general functions npy_cpu_init and
npy_cpu_have, and expose the results via a NPY_CPU_HAVE c-macro as
well as a python-level __cpu_features__ dictionary.

(gh-13421)

Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

Use 64-bit integer size on 64-bit platforms in the fallback LAPACK
library, which is used when the system has no LAPACK installed, allowing
it to deal with linear algebra for large arrays.

(gh-15218)

Use AVX512 intrinsic to implement np.exp when input is np.float64

Use AVX512 intrinsic to implement np.exp when input is np.float64,
which can improve the performance of np.exp with np.float64 input
5-7x faster than before. The _multiarray_umath.so module has grown
about 63 KB on linux64.

(gh-15648)

Ability to disable madvise hugepages

On Linux NumPy has previously added support for madavise hugepages which
can improve performance for very large arrays. Unfortunately, on older
Kernel versions this led to peformance regressions, thus by default the
support has been disabled on kernels before version 4.6. To override the
default, you can use the environment variable:

NUMPY_MADVISE_HUGEPAGE=0

or set it to 1 to force enabling support. Note that this only makes a
difference if the operating system is set up to use madvise transparent
hugepage.

(gh-15769)

numpy.einsum accepts NumPy int64 type in subscript list

There is no longer a type error thrown when numpy.einsum is passed a
NumPy int64 array as its subscript list.

(gh-16080)

np.logaddexp2.identity changed to -inf

The ufunc ~numpy.logaddexp2 now has an identity of -inf, allowing it
to be called on empty sequences. This matches the identity of
~numpy.logaddexp.

(gh-16102)

Changes

Remove handling of extra argument to __array__

A code path and test have been in the code since NumPy 0.4 for a
two-argument variant of __array__(dtype=None, context=None). It was
activated when calling ufunc(op) or ufunc.reduce(op) if
op.__array__ existed. However that variant is not documented, and it
is not clear what the intention was for its use. It has been removed.

(gh-15118)

numpy.random._bit_generator moved to numpy.random.bit_generator

In order to expose numpy.random.BitGenerator and
numpy.random.SeedSequence to Cython, the _bitgenerator module is now
public as numpy.random.bit_generator

Cython access to the random distributions is provided via a pxd file

c_distributions.pxd provides access to the c functions behind many of
the random distributions from Cython, making it convenient to use and
extend them.

(gh-15463)

Fixed eigh and cholesky methods in numpy.random.multivariate_normal

Previously, when passing method='eigh' or method='cholesky',
numpy.random.multivariate_normal produced samples from the wrong
distribution. This is now fixed.

(gh-15872)

Fixed the jumping implementation in MT19937.jumped

This fix changes the stream produced from jumped MT19937 generators. It
does not affect the stream produced using RandomState or MT19937
that are directly seeded.

The translation of the jumping code for the MT19937 contained a reversed
loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
implementation of the Horner and Sliding Window jump methods.

(gh-16153)

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---

title: 'NumPy 1.18.4 Release Notes'

This is that last planned release in the 1.18.x series. It reverts the
bool("0") behavior introduced in 1.18.3 and fixes a bug in
Generator.integers. There is also improved help in the error message
emitted when numpy import fails due to a link to a new troubleshooting
section in the documentation that is now included.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

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
• Warren Weckesser

Pull requests merged

A total of 6 pull requests were merged for this release.

• #16055 BLD: add i686 for 1.18 builds
• #16090 BUG: random: Generator.integers(2**32) always returned 0.
• #16091 BLD: fix path to libgfortran on macOS
• #16109 REV: Reverts side-effect changes to casting
• #16114 BLD: put openblas library in local directory on windows
• #16132 DOC: Change import error "howto" to link to new troubleshooting...

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NumPy 1.18.3 Release Notes

This release contains various bug/regression fixes.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

• Fix for the method='eigh' and method='cholesky' options in
  numpy.random.multivariate_normal. Those were producing samples
  from the wrong distribution.

Contributors

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

• Charles Harris
• Max Balandat +
• @Mibu287 +
• Pan Jan +
• Sebastian Berg
• @panpiort8 +

Pull requests merged

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

• #15916: BUG: Fix eigh and cholesky methods of numpy.random.multivariate_normal
• #15929: BUG,MAINT: Remove incorrect special case in string to number...
• #15930: BUG: Guarantee array is in valid state after memory error occurs...
• #15954: BUG: Check that [pvals]{.title-ref} is 1D in _generator.multinomial.
• #16017: BUG: Alpha parameter must be 1D in _generator.dirichlet

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5ec887ba38cd99775666f3493d82ea7c  numpy-1.18.3-cp38-cp38-manylinux1_i686.whl
88ce81bc31dec4c14bf835dc466308ed  numpy-1.18.3-cp38-cp38-manylinux1_x86_64.whl
5afe9a5f3c21299da599210ff5b76834  numpy-1.18.3-cp38-cp38-win32.whl
205364093300906654debbe3beb13359  numpy-1.18.3-cp38-cp38-win_amd64.whl
cd631c761f141d382b4e1b31c8232fc0  numpy-1.18.3.tar.gz
91314710fe9d29d80b6ccc9629e4532b  numpy-1.18.3.zip

SHA256

a6bc9432c2640b008d5f29bad737714eb3e14bb8854878eacf3d7955c4e91c36  numpy-1.18.3-cp35-cp35m-macosx_10_9_intel.whl
48e15612a8357393d176638c8f68a19273676877caea983f8baf188bad430379  numpy-1.18.3-cp35-cp35m-manylinux1_i686.whl
eb2286249ebfe8fcb5b425e5ec77e4736d53ee56d3ad296f8947f67150f495e3  numpy-1.18.3-cp35-cp35m-manylinux1_x86_64.whl
1e37626bcb8895c4b3873fcfd54e9bfc5ffec8d0f525651d6985fcc5c6b6003c  numpy-1.18.3-cp35-cp35m-win32.whl
163c78c04f47f26ca1b21068cea25ed7c5ecafe5f5ab2ea4895656a750582b56  numpy-1.18.3-cp35-cp35m-win_amd64.whl
3d9e1554cd9b5999070c467b18e5ae3ebd7369f02706a8850816f576a954295f  numpy-1.18.3-cp36-cp36m-macosx_10_9_x86_64.whl
40c24960cd5cec55222963f255858a1c47c6fa50a65a5b03fd7de75e3700eaaa  numpy-1.18.3-cp36-cp36m-manylinux1_i686.whl
a551d8cc267c634774830086da42e4ba157fa41dd3b93982bc9501b284b0c689  numpy-1.18.3-cp36-cp36m-manylinux1_x86_64.whl
0aa2b318cf81eb1693fcfcbb8007e95e231d7e1aa24288137f3b19905736c3ee  numpy-1.18.3-cp36-cp36m-win32.whl
a41f303b3f9157a31ce7203e3ca757a0c40c96669e72d9b6ee1bce8507638970  numpy-1.18.3-cp36-cp36m-win_amd64.whl
e607b8cdc2ae5d5a63cd1bec30a15b5ed583ac6a39f04b7ba0f03fcfbf29c05b  numpy-1.18.3-cp37-cp37m-macosx_10_9_x86_64.whl
fdee7540d12519865b423af411bd60ddb513d2eb2cd921149b732854995bbf8b  numpy-1.18.3-cp37-cp37m-manylinux1_i686.whl
6725d2797c65598778409aba8cd67077bb089d5b7d3d87c2719b206dc84ec05e  numpy-1.18.3-cp37-cp37m-manylinux1_x86_64.whl
4847f0c993298b82fad809ea2916d857d0073dc17b0510fbbced663b3265929d  numpy-1.18.3-cp37-cp37m-win32.whl
46f404314dbec78cb342904f9596f25f9b16e7cf304030f1339e553c8e77f51c  numpy-1.18.3-cp37-cp37m-win_amd64.whl
264fd15590b3f02a1fbc095e7e1f37cdac698ff3829e12ffdcffdce3772f9d44  numpy-1.18.3-cp38-cp38-macosx_10_9_x86_64.whl
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99f0ba97e369f02a21bb95faa3a0de55991fd5f0ece2e30a9e2eaebeac238921  numpy-1.18.3-cp38-cp38-win32.whl
c60175d011a2e551a2f74c84e21e7c982489b96b6a5e4b030ecdeacf2914da68  numpy-1.18.3-cp38-cp38-win_amd64.whl
93ee59ec38f3bf8f9a42d5f4301f60e6825a4a6385a145f70badcd2bf2a11134  numpy-1.18.3.tar.gz
e46e2384209c91996d5ec16744234d1c906ab79a701ce1a26155c9ec890b8dc8  numpy-1.18.3.zip

NumPy 1.18.2 Release Notes

This small elease contains a fix for a performance regression in
numpy/random and several bug/maintenance updates.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.15 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

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
• Ganesh Kathiresan +
• Matti Picus
• Sebastian Berg
• przemb +

Pull requests merged

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

• #15675: TST: move _no_tracing to testing._private
• #15676: MAINT: Large overhead in some random functions
• #15677: TST: Do not create gfortran link in azure Mac testing.
• #15679: BUG: Added missing error check in ndarray.__contains__
• #15722: MAINT: use list-based APIs to call subprocesses
• #15729: REL: Prepare for 1.18.2 release.
• #15734: BUG: fix logic error when nm fails on 32-bit

Checksums

MD5

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52601ac4cfbd513218bc088b74715098  numpy-1.18.2.tar.gz
511010c9fbd2516fe5a24aabcb76a56d  numpy-1.18.2.zip

SHA256

a1baa1dc8ecd88fb2d2a651671a84b9938461e8a8eed13e2f0a812a94084d1fa  numpy-1.18.2-cp35-cp35m-macosx_10_9_x86_64.whl
a244f7af80dacf21054386539699ce29bcc64796ed9850c99a34b41305630286  numpy-1.18.2-cp35-cp35m-manylinux1_i686.whl
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b5ad0adb51b2dee7d0ee75a69e9871e2ddfb061c73ea8bc439376298141f77f5  numpy-1.18.2-cp35-cp35m-win32.whl
87902e5c03355335fc5992a74ba0247a70d937f326d852fc613b7f53516c0963  numpy-1.18.2-cp35-cp35m-win_amd64.whl
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6d205249a0293e62bbb3898c4c2e1ff8a22f98375a34775a259a0523111a8f6c  numpy-1.18.2-cp36-cp36m-manylinux1_x86_64.whl
a35af656a7ba1d3decdd4fae5322b87277de8ac98b7d9da657d9e212ece76a61  numpy-1.18.2-cp36-cp36m-win32.whl
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cd77d58fb2acf57c1d1ee2835567cd70e6f1835e32090538f17f8a3a99e5e34b  numpy-1.18.2-cp37-cp37m-manylinux1_i686.whl
b1fe1a6f3a6f355f6c29789b5927f8bd4f134a4bd9a781099a7c4f66af8850f5  numpy-1.18.2-cp37-cp37m-manylinux1_x86_64.whl
2e40be731ad618cb4974d5ba60d373cdf4f1b8dcbf1dcf4d9dff5e212baf69c5  numpy-1.18.2-cp37-cp37m-win32.whl
4ba59db1fcc27ea31368af524dcf874d9277f21fd2e1f7f1e2e0c75ee61419ed  numpy-1.18.2-cp37-cp37m-win_amd64.whl
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1b0ece94018ae21163d1f651b527156e1f03943b986188dd81bc7e066eae9d1c  numpy-1.18.2-cp38-cp38-manylinux1_i686.whl
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5e0feb76849ca3e83dd396254e47c7dba65b3fa9ed3df67c2556293ae3e16de3  numpy-1.18.2-cp38-cp38-win32.whl
ba3c7a2814ec8a176bb71f91478293d633c08582119e713a0c5351c0f77698da  numpy-1.18.2-cp38-cp38-win_amd64.whl
da204ce460aa4247e595b7c7189d2fb2ed5f796bc03197055de01dac61d0125e  numpy-1.18.2.tar.gz
e7894793e6e8540dbeac77c87b489e331947813511108ae097f1715c018b8f3d  numpy-1.18.2.zip

NumPy 1.18.1 Release Notes

This release contains fixes for bugs reported against NumPy 1.18.0. Two
bugs in particular that caused widespread problems downstream were:

• The cython random extension test was not using a temporary directory
  for building, resulting in a permission violation. Fixed.
• Numpy distutils was appending [-std=c99]{.title-ref} to all C
  compiler runs, leading to changed behavior and compile problems
  downstream. That flag is now only applied when building numpy C
  code.

The Python versions supported in this release are 3.5-3.8. Downstream
developers should use Cython >= 0.29.14 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

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
• Matti Picus
• Maxwell Aladago
• Pauli Virtanen
• Ralf Gommers
• Tyler Reddy
• Warren Weckesser

Pull requests merged

A total of 13 pull requests were merged for this release.

• #15158: MAINT: Update pavement.py for towncrier.
• #15159: DOC: add moved modules to 1.18 release note
• #15161: MAINT, DOC: Minor backports and updates for 1.18.x
• #15176: TST: Add assert_array_equal test for big integer arrays
• #15184: BUG: use tmp dir and check version for cython test (#15170)
• #15220: BUG: distutils: fix msvc+gfortran openblas handling corner case
• #15221: BUG: remove -std=c99 for c++ compilation (#15194)
• #15222: MAINT: unskip test on win32
• #15223: TST: add BLAS ILP64 run in Travis & Azure
• #15245: MAINT: only add --std=c99 where needed
• #15246: BUG: lib: Fix handling of integer arrays by gradient.
• #15247: MAINT: Do not use private Python function in testing
• #15250: REL: Prepare for the NumPy 1.18.1 release.

Checksums

MD5

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18787d6482681c85a66629a781fb84c3  numpy-1.18.1.zip

SHA256

20b26aaa5b3da029942cdcce719b363dbe58696ad182aff0e5dcb1687ec946dc  numpy-1.18.1-cp35-cp35m-macosx_10_6_intel.whl
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17aa7a81fe7599a10f2b7d95856dc5cf84a4eefa45bc96123cbbc3ebc568994e  numpy-1.18.1-cp35-cp35m-manylinux1_x86_64.whl
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590355aeade1a2eaba17617c19edccb7db8d78760175256e3cf94590a1a964f3  numpy-1.18.1-cp38-cp38-win32.whl
39d2c685af15d3ce682c99ce5925cc66efc824652e10990d2462dfe9b8918c6a  numpy-1.18.1-cp38-cp38-win_amd64.whl
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b6ff59cee96b454516e47e7721098e6ceebef435e3e21ac2d6c3b8b02628eb77  numpy-1.18.1.zip

NumPy 1.17.5 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.4 along
with some build improvements. The Python versions supported in this
release are 3.5-3.8.

Downstream developers should use Cython >= 0.29.14 for Python 3.8
support and OpenBLAS >= 3.7 to avoid errors on the Skylake
architecture.

It is recommended that developers interested in the new random bit
generators upgrade to the NumPy 1.18.x series, as it has updated
documentation and many small improvements.

Contributors

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

• Charles Harris
• Eric Wieser
• Ilhan Polat
• Matti Picus
• Michael Hudson-Doyle
• Ralf Gommers

Pull requests merged

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

• #14593: MAINT:
  backport Cython API cleanup to 1.17.x, remove docs
• #14937: BUG: fix
  integer size confusion in handling array's ndmin argument
• #14939: BUILD: remove
  SSE2 flag from numpy.random builds
• #14993: MAINT: Added
  Python3.8 branch to dll lib discovery
• #15038: BUG: Fix
  refcounting in ufunc object loops
• #15067: BUG:
  Exceptions tracebacks are dropped
• #15175: ENH: Backport
  improvements to testing functions.
• #15213: REL: Prepare
  for the NumPy 1.17.5 release.

Checksums

MD5

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NumPy 1.16.6 Release Notes

The NumPy 1.16.6 release fixes bugs reported against the 1.16.5 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, which should fix
errors 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.

Highlights

• The np.testing.utils functions have been updated from 1.19.0-dev0.
  This improves the function documentation and error messages as well
  extending the assert_array_compare function to additional types.

New functions

Allow matmul (@) to work with object arrays.

This is an enhancement that was added in NumPy 1.17 and seems reasonable
to include in the LTS 1.16 release series.

Compatibility notes

Fix regression in matmul (@) for boolean types

Booleans were being treated as integers rather than booleans, which was
a regression from previous behavior.

Improvements

Array comparison assertions include maximum differences

Error messages from array comparison tests such as
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.

Contributors

A total of 10 people contributed to this release.

• CakeWithSteak
• Charles Harris
• Chris Burr
• Eric Wieser
• Fernando Saravia
• Lars Grueter
• Matti Picus
• Maxwell Aladago
• Qiming Sun
• Warren Weckesser

Pull requests merged

A total of 14 pull requests were merged for this release.

• #14211: BUG: Fix
  uint-overflow if padding with linear_ramp and negative...
• #14275: BUG: fixing to
  allow unpickling of PY3 pickles from PY2
• #14340: BUG: Fix
  misuse of .names and .fields in various places (backport...
• #14423: BUG: test, fix
  regression in converting to ctypes.
• #14434: BUG: Fixed
  maximum relative error reporting in assert_allclose
• #14509: BUG: Fix
  regression in boolean matmul.
• #14686: BUG: properly
  define PyArray_DescrCheck
• #14853: BLD: add 'apt
  update' to shippable
• #14854: BUG: Fix
  _ctypes class circular reference. (#13808)
• #14856: BUG: Fix
  [np.einsum]{.title-ref} errors on Power9 Linux and z/Linux
• #14863: BLD: Prevent
  -flto from optimising long double representation...
• #14864: BUG: lib: Fix
  histogram problem with signed integer arrays.
• #15172: ENH: Backport
  improvements to testing functions.
• #15191: REL: Prepare
  for 1.16.6 release.

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NumPy NumPy 1.18.0 Release Notes

In addition to the usual bug fixes, this NumPy release cleans up and
documents the new random C-API, expires a large number of old
deprecations, and improves the appearance of the documentation. The
Python versions supported are 3.5-3.8. This is the last NumPy release
series that will support Python 3.5.

Downstream developers should use Cython >= 0.29.14 for Python 3.8
support and OpenBLAS >= 3.7 to avoid problems on the Skylake
architecture.

Highlights

• The C-API for numpy.random has been defined and documented.
• Basic infrastructure for linking with 64 bit BLAS and LAPACK
  libraries.
• Many documentation improvements.

New functions

Multivariate hypergeometric distribution added to numpy.random

The method multivariate_hypergeometric has been added to the class
[numpy.random.Generator]{.title-ref}. This method generates random
variates from the multivariate hypergeometric probability distribution.
(gh-13794)

Deprecations

np.fromfile and np.fromstring will error on bad data

In future numpy releases, the functions np.fromfile and
np.fromstring will throw an error when parsing bad data. This will now
give a DeprecationWarning where previously partial or even invalid
data was silently returned. This deprecation also affects the C defined
functions PyArray_FromString and PyArray_FromFile
(gh-13605)

Deprecate non-scalar arrays as fill values in ma.fill_value

Setting a MaskedArray.fill_value to a non-scalar array is deprecated
since the logic to broadcast the fill value to the array is fragile,
especially when slicing.
(gh-13698)

Deprecate PyArray_As1D, PyArray_As2D

PyArray_As1D, PyArray_As2D are deprecated, use PyArray_AsCArray
instead (gh-14036)

Deprecate np.alen

np.alen was deprecated. Use len instead.
(gh-14181)

Deprecate the financial functions

In accordance with
NEP-32,
the financial functions fv ipmt, irr, mirr, nper, npv,
pmt, ppmt, pv and rate are deprecated, and will be removed from
NumPy 1.20.The replacement for these functions is the Python package
numpy-financial.
(gh-14720)

The axis argument to numpy.ma.mask_cols and numpy.ma.mask_row is deprecated

This argument was always ignored.
(gh-14996)

Expired deprecations

• PyArray_As1D and PyArray_As2D have been removed in favor of
  PyArray_AsCArray
  (gh-14036)
• np.rank has been removed. This was deprecated in NumPy 1.10 and
  has been replaced by np.ndim.
  (gh-14039)
• The deprecation of expand_dims out-of-range axes in 1.13.0 has
  expired. (gh-14051)
• PyArray_FromDimsAndDataAndDescr and PyArray_FromDims have been
  removed (they will always raise an error). Use
  PyArray_NewFromDescr and PyArray_SimpleNew instead.
  (gh-14100)
• numeric.loads, numeric.load, np.ma.dump, np.ma.dumps,
  np.ma.load, np.ma.loads are removed, use pickle methods
  instead (gh-14256)
• arrayprint.FloatFormat, arrayprint.LongFloatFormat has been
  removed, use FloatingFormat instead
• arrayprint.ComplexFormat, arrayprint.LongComplexFormat has been
  removed, use ComplexFloatingFormat instead
• arrayprint.StructureFormat has been removed, use
  StructureVoidFormat instead
  (gh-14259)
• np.testing.rand has been removed. This was deprecated in NumPy
  1.11 and has been replaced by np.random.rand.
  (gh-14325)
• Class SafeEval in numpy/lib/utils.py has been removed. This was
  deprecated in NumPy 1.10. Use np.safe_eval instead.
  (gh-14335)
• Remove deprecated support for boolean and empty condition lists in
  np.select (gh-14583)
• Array order only accepts 'C', 'F', 'A', and 'K'. More
  permissive options were deprecated in NumPy 1.11.
  (gh-14596)
• np.linspace parameter num must be an integer. Deprecated in NumPy
  1.12. (gh-14620)
• UFuncs with multiple outputs must use a tuple for the out kwarg.
  This finishes a deprecation started in NumPy 1.10.
  (gh-14682)

The files numpy/testing/decorators.py, numpy/testing/noseclasses.py
and numpy/testing/nosetester.py have been removed. They were never
meant to be public (all relevant objects are present in the
numpy.testing namespace), and importing them has given a deprecation
warning since NumPy 1.15.0
(gh-14567)

Compatibility notes

[numpy.lib.recfunctions.drop_fields]{.title-ref} can no longer return None

If drop_fields is used to drop all fields, previously the array would
be completely discarded and None returned. Now it returns an array of
the same shape as the input, but with no fields. The old behavior can be
retained with:

dropped_arr = drop_fields(arr, ['a', 'b'])
if dropped_arr.dtype.names == ():
    dropped_arr = None

converting the empty recarray to None
(gh-14510)

numpy.argmin/argmax/min/max returns NaT if it exists in array

numpy.argmin, numpy.argmax, numpy.min, and numpy.max will return
NaT if it exists in the array.
(gh-14717)

np.can_cast(np.uint64, np.timedelta64, casting='safe') is now False

Previously this was True - however, this was inconsistent with
uint64 not being safely castable to int64, and resulting in strange
type resolution.

If this impacts your code, cast uint64 to int64 first.
(gh-14718)

Changed random variate stream from numpy.random.Generator.integers

There was a bug in numpy.random.Generator.integers that caused biased
sampling of 8 and 16 bit integer types. Fixing that bug has changed the
output stream from what it was in previous releases.
(gh-14777)

Add more ufunc loops for datetime64, timedelta64

np.datetime('NaT') should behave more like float('Nan'). Add needed
infrastructure so np.isinf(a) and np.isnan(a) will run on
datetime64 and timedelta64 dtypes. Also added specific loops for
numpy.fmin and numpy.fmax that mask NaT. This may require
adjustment to user- facing code. Specifically, code that either
disallowed the calls to numpy.isinf or numpy.isnan or checked that
they raised an exception will require adaptation, and code that
mistakenly called numpy.fmax and numpy.fmin instead of
numpy.maximum or numpy.minimum respectively will requre adjustment.
This also affects numpy.nanmax and numpy.nanmin.
(gh-14841)

C API changes

PyDataType_ISUNSIZED(descr) now returns False for structured datatypes

Previously this returned True for any datatype of itemsize 0, but now
this returns false for the non-flexible datatype with itemsize 0,
np.dtype([]). (gh-14393)

New Features

Add our own *.pxd cython import file

Added a numpy/__init__.pxd file. It will be used for cimport numpy
(gh-12284)

A tuple of axes can now be input to expand_dims

The numpy.expand_dims axis keyword can now accept a tuple of axes.
Previously, axis was required to be an integer.
(gh-14051)

Support for 64-bit OpenBLAS

Added support for 64-bit (ILP64) OpenBLAS. See site.cfg.example for
details. (gh-15012)

Add --f2cmap option to F2PY

Allow specifying a file to load Fortran-to-C type map customizations
from. (gh-15113)

Improvements

Different C numeric types of the same size have unique names

On any given platform, two of np.intc, np.int_, and np.longlong
would previously appear indistinguishable through their repr, despite
their corresponding dtype having different properties. A similar
problem existed for the unsigned counterparts to these types, and on
some platforms for np.double and np.longdouble

These types now always print with a unique __name__.
(gh-10151)

argwhere now produces a consistent result on 0d arrays

On N-d arrays, numpy.argwhere now always produces an array of shape
(n_non_zero, arr.ndim), even when arr.ndim == 0. Previously, the
last axis would have a dimension of 1 in this case.
(gh-13610)

Add axis argument for random.permutation and random.shuffle

Previously the random.permutation and random.shuffle functions can
only shuffle an array along the first axis; they now have a new argument
axis which allows shuffle along a specified axis.
(gh-13829)

method keyword argument for np.random.multivariate_normal

A method keyword argument is now available for
np.random.multivariate_normal with possible values
{'svd', 'eigh', 'cholesky'}. To use it, write
np.random.multivariate_normal(..., method=<method>).
(gh-14197)

Add complex number support for numpy.fromstring

Now numpy.fromstring can read complex numbers.
(gh-14227)

numpy.unique has consistent axes order when axis is not None

Using moveaxis instead of swapaxes in numpy.unique, so that the
ordering of axes except the axis in arguments will not be broken.
(gh-14255)

numpy.matmul with boolean output now converts to boolean values

Calling numpy.matmul where the output is a boolean array would fill
the array with uint8 equivalents of the result, rather than 0/1. Now it
forces the output to 0 or 1 (NPY_TRUE or NPY_FALSE).
(gh-14464)

numpy.random.randint produced incorrect value when the range was 2**32

The implementation introduced in 1.17.0 had an incorrect check when
determining whether to use the 32-bit path or the full 64-bit path that
incorrectly redirected random integer generation with a high - low range
of 2**32 to the 64-bit generator.
(gh-14501)

Add complex number support for numpy.fromfile

Now numpy.fromfile can read complex numbers.
(gh-14730)

std=c99 added if compiler is named gcc

GCC before version 5 requires the -std=c99 command line argument.
Newer compilers automatically turn on C99 mode. The compiler setup code
will automatically add the code if the compiler name has gcc in it.
(gh-14771)

Changes

NaT now sorts to the end of arrays

NaT is now effectively treated as the largest integer for sorting
purposes, so that it sorts to the end of arrays. This change is for
consistency with NaN sorting behavior.
(gh-12658)
(gh-15068)

Incorrect threshold in np.set_printoptions raises TypeError or ValueError

Previously an incorrect threshold raised ValueError; it now raises
TypeError for non-numeric types and ValueError for nan values.
(gh-13899)

Warn when saving a dtype with metadata

A UserWarning will be emitted when saving an array via numpy.save
with metadata. Saving such an array may not preserve metadata, and if
metadata is preserved, loading it will cause a ValueError. This
shortcoming in save and load will be addressed in a future release.
(gh-14142)

numpy.distutils append behavior changed for LDFLAGS and similar

[numpy.distutils]{.title-ref} has always overridden rather than appended
to LDFLAGS and other similar such environment variables for compiling
Fortran extensions. Now the default behavior has changed to appending -
which is the expected behavior in most situations. To preserve the old
(overwriting) behavior, set the NPY_DISTUTILS_APPEND_FLAGS environment
variable to 0. This applies to: LDFLAGS, F77FLAGS, F90FLAGS,
FREEFLAGS, FOPT, FDEBUG, and FFLAGS. NumPy 1.16 and 1.17 gave
build warnings in situations where this change in behavior would have
affected the compile flags used.
(gh-14248)

Remove numpy.random.entropy without a deprecation

numpy.random.entropy was added to the numpy.random namespace in
1.17.0. It was meant to be a private c-extension module, but was exposed
as public. It has been replaced by numpy.random.SeedSequence so the
module was completely removed.
(gh-14498)

Add options to quiet build configuration and build with -Werror

Added two new configuration options. During the build_src subcommand,
as part of configuring NumPy, the files _numpyconfig.h and config.h
are created by probing support for various runtime functions and
routines. Previously, the very verbose compiler output during this stage
clouded more important information. By default the output is silenced.
Running runtests.py --debug-info will add --verbose-cfg to the
build_src subcommand, which will restore the previous behaviour.

Adding CFLAGS=-Werror to turn warnings into errors would trigger
errors during the configuration. Now runtests.py --warn-error will add
--warn-error to the build subcommand, which will percolate to the
build_ext and build_lib subcommands. This will add the compiler flag
to those stages and turn compiler warnings into errors while actually
building NumPy itself, avoiding the build_src subcommand compiler
calls.

(gh-14527)
(gh-14518)

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NumPy NumPy 1.18.0 Release Notes

In addition to the usual bug fixes, this NumPy release cleans up and
documents the new random C-API, expires a large number of old
deprecations, and improves the appearance of the documentation. The
Python versions supported are 3.5-3.8. This is the last NumPy release
series that will support Python 3.5.

Downstream developers should use Cython >= 0.29.14 for Python 3.8
support and OpenBLAS >= 3.7 to avoid problems on the Skylake
architecture.

Highlights

• The C-API for numpy.random has been defined and documented.
• Basic infrastructure for linking with 64 bit BLAS and LAPACK
  libraries.
• Many documentation improvements.

New functions

Multivariate hypergeometric distribution added to numpy.random

The method multivariate_hypergeometric has been added to the class
numpy.random.Generator. This method generates random variates from the
multivariate hypergeometric probability distribution.
(gh-13794 <https://github.com/numpy/numpy/pull/13794>__)

Deprecations

np.fromfile and np.fromstring will error on bad data

In future numpy releases, the functions np.fromfile and
np.fromstring will throw an error when parsing bad data. This will now
give a DeprecationWarning where previously partial or even invalid
data was silently returned. This deprecation also affects the C defined
functions PyArray_FromString and PyArray_FromFile
(gh-13605 <https://github.com/numpy/numpy/pull/13605>__)

Deprecate non-scalar arrays as fill values in ma.fill_value

Setting a MaskedArray.fill_value to a non-scalar array is deprecated
since the logic to broadcast the fill value to the array is fragile,
especially when slicing.
(gh-13698 <https://github.com/numpy/numpy/pull/13698>__)

Deprecate PyArray_As1D, PyArray_As2D

PyArray_As1D, PyArray_As2D are deprecated, use PyArray_AsCArray
instead (gh-14036 <https://github.com/numpy/numpy/pull/14036>__)

Deprecate np.alen

np.alen was deprecated. Use len instead.
(gh-14181 <https://github.com/numpy/numpy/pull/14181>__)

Deprecate the financial functions

In accordance with
NEP-32 <https://numpy.org/neps/nep-0032-remove-financial-functions.html>,
the financial functions fv, ipmt, irr, mirr, nper, npv,
pmt, ppmt, pv and rate are deprecated, and will be removed from
NumPy 1.20.The replacement for these functions is the Python package
numpy-financial <https://pypi.org/project/numpy-financial>*.
(gh-14720 <https://github.com/numpy/numpy/pull/14720>__)

The axis argument to numpy.ma.mask_cols and numpy.ma.mask_row is deprecated

This argument was always ignored.
(gh-14996 <https://github.com/numpy/numpy/pull/14996>__)

Expired deprecations

• PyArray_As1D and PyArray_As2D have been removed in favor of
  PyArray_AsCArray
  (gh-14036 <https://github.com/numpy/numpy/pull/14036>__)

• np.rank has been removed. This was deprecated in NumPy 1.10 and
  has been replaced by np.ndim.
  (gh-14039 <https://github.com/numpy/numpy/pull/14039>__)

• The deprecation of expand_dims out-of-range axes in 1.13.0 has
  expired.
  (gh-14051 <https://github.com/numpy/numpy/pull/14051>__)

• PyArray_FromDimsAndDataAndDescr and PyArray_FromDims have been
  removed (they will always raise an error). Use
  PyArray_NewFromDescr and PyArray_SimpleNew instead.
  (gh-14100 <https://github.com/numpy/numpy/pull/14100>__)

• numeric.loads, numeric.load, np.ma.dump, np.ma.dumps,
  np.ma.load, np.ma.loads are removed, use pickle methods
  instead (gh-14256 <https://github.com/numpy/numpy/pull/14256>__)

• arrayprint.FloatFormat, arrayprint.LongFloatFormat has been
  removed, use FloatingFormat instead

• arrayprint.ComplexFormat, arrayprint.LongComplexFormat has been
  removed, use ComplexFloatingFormat instead

• arrayprint.StructureFormat has been removed, use
  StructureVoidFormat instead
  (gh-14259 <https://github.com/numpy/numpy/pull/14259>__)

• np.testing.rand has been removed. This was deprecated in NumPy
  1.11 and has been replaced by np.random.rand.
  (gh-14325 <https://github.com/numpy/numpy/pull/14325>__)

• Class SafeEval in numpy/lib/utils.py has been removed. This was
  deprecated in NumPy 1.10. Use np.safe_eval instead.
  (gh-14335 <https://github.com/numpy/numpy/pull/14335>__)

• Remove deprecated support for boolean and empty condition lists in
  np.select
  (gh-14583 <https://github.com/numpy/numpy/pull/14583>__)

• Array order only accepts 'C', 'F', 'A', and 'K'. More permissive
  options were deprecated in NumPy 1.11.
  (gh-14596 <https://github.com/numpy/numpy/pull/14596>__)

• np.linspace parameter num must be an integer. Deprecated in NumPy
  1.12. (gh-14620 <https://github.com/numpy/numpy/pull/14620>__)

• UFuncs with multiple outputs must use a tuple for the out kwarg.
  This finishes a deprecation started in NumPy 1.10.
  (gh-14682 <https://github.com/numpy/numpy/pull/14682>__)

The files numpy/testing/decorators.py, numpy/testing/noseclasses.py
and numpy/testing/nosetester.py have been removed. They were never
meant to be public (all relevant objects are present in the
numpy.testing namespace), and importing them has given a deprecation
warning since NumPy 1.15.0
(gh-14567 <https://github.com/numpy/numpy/pull/14567>__)

Compatibility notes

numpy.lib.recfunctions.drop_fields can no longer return None

If drop_fields is used to drop all fields, previously the array would
be completely discarded and None returned. Now it returns an array of
the same shape as the input, but with no fields. The old behavior can be
retained with::

dropped_arr = drop_fields(arr, ['a', 'b'])
if dropped_arr.dtype.names == ():
    dropped_arr = None

converting the empty recarray to None
(gh-14510 <https://github.com/numpy/numpy/pull/14510>__)

numpy.argmin/argmax/min/max returns NaT if it exists in array

numpy.argmin, numpy.argmax, numpy.min, and numpy.max will return
NaT if it exists in the array.
(gh-14717 <https://github.com/numpy/numpy/pull/14717>__)

np.can_cast(np.uint64, np.timedelta64, casting='safe') is now False

Previously this was True - however, this was inconsistent with
uint64 not being safely castable to int64, and resulting in strange
type resolution.

If this impacts your code, cast uint64 to int64 first.
(gh-14718 <https://github.com/numpy/numpy/pull/14718>__)

Changed random variate stream from numpy.random.Generator.integers

There was a bug in numpy.random.Generator.integers that caused biased
sampling of 8 and 16 bit integer types. Fixing that bug has changed the
output stream from what it was in previous releases.
(gh-14777 <https://github.com/numpy/numpy/pull/14777>__)

Add more ufunc loops for datetime64, timedelta64

np.datetime('NaT') should behave more like float('Nan'). Add needed
infrastructure so np.isinf(a) and np.isnan(a) will run on
datetime64 and timedelta64 dtypes. Also added specific loops for
numpy.fmin and numpy.fmax that mask NaT. This may require
adjustment to user- facing code. Specifically, code that either
disallowed the calls to numpy.isinf or numpy.isnan or checked that
they raised an exception will require adaptation, and code that
mistakenly called numpy.fmax and numpy.fmin instead of
numpy.maximum or numpy.minimum respectively will requre adjustment.
This also affects numpy.nanmax and numpy.nanmin.
(gh-14841 <https://github.com/numpy/numpy/pull/14841>__)

C API changes

PyDataType_ISUNSIZED(descr) now returns False for structured datatypes

Previously this returned True for any datatype of itemsize 0, but now
this returns false for the non-flexible datatype with itemsize 0,
np.dtype([]).
(gh-14393 <https://github.com/numpy/numpy/pull/14393>__)

New Features

Add our own *.pxd cython import file

Added a numpy/__init__.pxd file. It will be used for cimport numpy
(gh-12284 <https://github.com/numpy/numpy/pull/12284>__)

A tuple of axes can now be input to expand_dims

The numpy.expand_dims axis keyword can now accept a tuple of axes.
Previously, axis was required to be an integer.
(gh-14051 <https://github.com/numpy/numpy/pull/14051>__)

Support for 64-bit OpenBLAS with symbol suffix

Added support for 64-bit (ILP64) OpenBLAS compiled with
make INTERFACE64=1 SYMBOLSUFFIX=64_. See site.cfg.example for
details. (gh-15012 <https://github.com/numpy/numpy/pull/15012>__)

Improvements

Different C numeric types of the same size have unique names

On any given platform, two of np.intc, np.int_, and np.longlong
would previously appear indistinguishable through their repr, despite
their corresponding dtype having different properties. A similar
problem existed for the unsigned counterparts to these types, and on
some platforms for np.double and np.longdouble

These types now always print with a unique __name__.
(gh-10151 <https://github.com/numpy/numpy/pull/10151>__)

argwhere now produces a consistent result on 0d arrays

On N-d arrays, numpy.argwhere now always produces an array of shape
(n_non_zero, arr.ndim), even when arr.ndim == 0. Previously, the
last axis would have a dimension of 1 in this case.
(gh-13610 <https://github.com/numpy/numpy/pull/13610>__)

Add axis argument for random.permutation and random.shuffle

Previously the random.permutation and random.shuffle functions can
only shuffle an array along the first axis; they now have a new argument
axis which allows shuffle along a specified axis.
(gh-13829 <https://github.com/numpy/numpy/pull/13829>__)

method keyword argument for np.random.multivariate_normal

A method keyword argument is now available for
np.random.multivariate_normal with possible values
{'svd', 'eigh', 'cholesky'}. To use it, write
np.random.multivariate_normal(..., method=<method>).
(gh-14197 <https://github.com/numpy/numpy/pull/14197>__)

Add complex number support for numpy.fromstring

Now numpy.fromstring can read complex numbers.
(gh-14227 <https://github.com/numpy/numpy/pull/14227>__)

numpy.unique has consistent axes order when axis is not None

Using moveaxis instead of swapaxes in numpy.unique, so that the
ordering of axes except the axis in arguments will not be broken.
(gh-14255 <https://github.com/numpy/numpy/pull/14255>__)

numpy.matmul with boolean output now converts to boolean values

Calling numpy.matmul where the output is a boolean array would fill
the array with uint8 equivalents of the result, rather than 0/1. Now it
forces the output to 0 or 1 (NPY_TRUE or NPY_FALSE).
(gh-14464 <https://github.com/numpy/numpy/pull/14464>__)

numpy.random.randint produced incorrect value when the range was 2**32

The implementation introduced in 1.17.0 had an incorrect check when
determining whether to use the 32-bit path or the full 64-bit path that
incorrectly redirected random integer generation with a high - low range
of 2**32 to the 64-bit generator.
(gh-14501 <https://github.com/numpy/numpy/pull/14501>__)

Add complex number support for numpy.fromfile

Now numpy.fromfile can read complex numbers.
(gh-14730 <https://github.com/numpy/numpy/pull/14730>__)

std=c99 added if compiler is named gcc

GCC before version 5 requires the -std=c99 command line argument.
Newer compilers automatically turn on C99 mode. The compiler setup code
will automatically add the code if the compiler name has gcc in it.
(gh-14771 <https://github.com/numpy/numpy/pull/14771>__)

Changes

NaT now sorts to the end of arrays

NaT is now effectively treated as the largest integer for sorting
purposes, so that it sorts to the end of arrays. This change is for
consistency with NaN sorting behavior.
(gh-12658 <https://github.com/numpy/numpy/pull/12658>__)

Incorrect threshold in np.set_printoptions raises TypeError or ValueError

Previously an incorrect threshold raised ValueError; it now raises
TypeError for non-numeric types and ValueError for nan values.
(gh-13899 <https://github.com/numpy/numpy/pull/13899>__)

Warn when saving a dtype with metadata

A UserWarning will be emitted when saving an array via numpy.save
with metadata. Saving such an array may not preserve metadata, and if
metadata is preserved, loading it will cause a ValueError. This
shortcoming in save and load will be addressed in a future release.
(gh-14142 <https://github.com/numpy/numpy/pull/14142>__)

numpy.distutils append behavior changed for LDFLAGS and similar

numpy.distutils has always overridden rather than appended to
LDFLAGS and other similar such environment variables for compiling
Fortran extensions. Now the default behavior has changed to appending -
which is the expected behavior in most situations. To preserve the old
(overwriting) behavior, set the NPY_DISTUTILS_APPEND_FLAGS environment
variable to 0. This applies to: LDFLAGS, F77FLAGS, F90FLAGS,
FREEFLAGS, FOPT, FDEBUG, and FFLAGS. NumPy 1.16 and 1.17 gave
build warnings in situations where this change in behavior would have
affected the compile flags used.
(gh-14248 <https://github.com/numpy/numpy/pull/14248>__)

Remove numpy.random.entropy without a deprecation

numpy.random.entropy was added to the numpy.random namespace in
1.17.0. It was meant to be a private c-extension module, but was exposed
as public. It has been replaced by numpy.random.SeedSequence so the
module was completely removed.
(gh-14498 <https://github.com/numpy/numpy/pull/14498>__)

Add options to quiet build configuration and build with -Werror

Added two new configuration options. During the build_src subcommand,
as part of configuring NumPy, the files _numpyconfig.h and config.h
are created by probing support for various runtime functions and
routines. Previously, the very verbose compiler output during this stage
clouded more important information. By default the output is silenced.
Running runtests.py --debug-info will add --verbose-cfg to the
build_src subcommand, which will restore the previous behaviour.

Adding CFLAGS=-Werror to turn warnings into errors would trigger
errors during the configuration. Now runtests.py --warn-error will add
--warn-error to the build subcommand, which will percolate to the
build_ext and build_lib subcommands. This will add the compiler flag
to those stages and turn compiler warnings into errors while actually
building NumPy itself, avoiding the build_src subcommand compiler
calls.

(gh-14527 <https://github.com/numpy/numpy/pull/14527>)
(gh-14518 <https://github.com/numpy/numpy/pull/14518>)

Checksums

MD5

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2708c1b940227b917c088ad3b79183bf  numpy-1.18.0rc1.zip

SHA256

76d92b0b86227c6dbc3f9030b8b865f41ae04f73daa161e4e3c5566e03d6ed41  numpy-1.18.0rc1-cp35-cp35m-macosx_10_6_intel.whl
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2bc9c62dfc893626cdd50dce30f597f387c4aca11cd2cdffe3e7e06a4fe19ee6  numpy-1.18.0rc1-cp35-cp35m-manylinux1_x86_64.whl
73abd855401b9da6efb67490f4dd82226cf95f47d66b8dc9ebe4df523baaaeb2  numpy-1.18.0rc1-cp35-cp35m-win32.whl
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f676739da486d1c7de2d9450dafca6dee04f4d3e881b8761b795865ef1872eaf  numpy-1.18.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl
787781333c1d69c7c23ccd85165cca732a5f3fd9d997b8ee40829b7c0c38db86  numpy-1.18.0rc1-cp36-cp36m-manylinux1_i686.whl
e9907b5ea505dcd2cfaeb6ab08f241047ba651611f3974c9d624a7c5066ab3a6  numpy-1.18.0rc1-cp36-cp36m-manylinux1_x86_64.whl
78ca6befab03c682bcb013241801e3b750e9de9b60664e9839e5b2098b5580ee  numpy-1.18.0rc1-cp36-cp36m-win32.whl
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fa8851d10af0739adcae54acf5706953e2e45752f4a550006f6f3aff92335566  numpy-1.18.0rc1-cp38-cp38-manylinux1_i686.whl
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3e9b05aa444adbd2511e5646541cf3aa64ef7aa923d48350aa8db8099a0a3800  numpy-1.18.0rc1.tar.gz
7b0b915190cf60e691c17147f5d955e273d4c482b795a7bb168ad4a2fe2fb180  numpy-1.18.0rc1.zip

NumPy 1.17.4 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.3 along with
some build improvements. The Python versions supported in this release
are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

• Fixed np.random.random_integers biased generation of 8 and 16 bit integers.
• Fixed np.einsum regression on Power9 and z/Linux.
• Fixed histogram problem with signed integer arrays.

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
• Chris Burr +
• Matti Picus
• Qiming Sun +
• Warren Weckesser

Pull requests merged

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

• #14758: BLD: declare support for python 3.8
• #14781: BUG: random: biased samples from integers() with 8 or 16 bit...
• #14851: BUG: Fix _ctypes class circular reference. (#13808)
• #14852: BLD: add 'apt update' to shippable
• #14855: BUG: Fix np.einsum errors on Power9 Linux and z/Linux
• #14857: BUG: lib: Fix histogram problem with signed integer arrays.
• #14858: BLD: Prevent -flto from optimising long double representation...
• #14866: MAINT: move buffer.h -> npy_buffer.h to avoid conflicts

Checksums

MD5

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d7d3563cca0b99ba68a3f064a9e46ebe  numpy-1.17.4.zip

SHA256

ede47b98de79565fcd7f2decb475e2dcc85ee4097743e551fe26cfc7eb3ff143  numpy-1.17.4-cp35-cp35m-macosx_10_6_intel.whl
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c7354e8f0eca5c110b7e978034cd86ed98a7a5ffcf69ca97535445a595e07b8e  numpy-1.17.4-cp35-cp35m-manylinux1_x86_64.whl
64874913367f18eb3013b16123c9fed113962e75d809fca5b78ebfbb73ed93ba  numpy-1.17.4-cp35-cp35m-win32.whl
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0c0763787133dfeec19904c22c7e358b231c87ba3206b211652f8cbe1241deb6  numpy-1.17.4-cp37-cp37m-win_amd64.whl
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0a7a1dd123aecc9f0076934288ceed7fd9a81ba3919f11a855a7887cbe82a02f  numpy-1.17.4-cp38-cp38-win32.whl
ada4805ed51f5bcaa3a06d3dd94939351869c095e30a2b54264f5a5004b52170  numpy-1.17.4-cp38-cp38-win_amd64.whl
fb0415475e673cb9a6dd816df999e0ab9f86fa3af2b1770944e7288d2bea4ac9  numpy-1.17.4.tar.gz
f58913e9227400f1395c7b800503ebfdb0772f1c33ff8cb4d6451c06cabdf316  numpy-1.17.4.zip

NumPy 1.17.3 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.2 along with a
some documentation improvements. The Python versions supported in this release
are 3.5-3.8.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

Highlights

• Wheels for Python 3.8
• Boolean matmul fixed to use booleans instead of integers.

Compatibility notes

• The seldom used PyArray_DescrCheck macro has been changed/fixed.

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
• Kevin Sheppard
• Matti Picus
• Ralf Gommers
• Sebastian Berg
• Warren Weckesser

Pull requests merged

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

• #14456: MAINT: clean up pocketfft modules inside numpy.fft namespace.
• #14463: BUG: random.hypergeometic assumes npy_long is npy_int64, hung...
• #14502: BUG: random: Revert gh-14458 and refix gh-14557.
• #14504: BUG: add a specialized loop for boolean matmul.
• #14506: MAINT: Update pytest version for Python 3.8
• #14512: DOC: random: fix doc linking, was referencing private submodules.
• #14513: BUG,MAINT: Some fixes and minor cleanup based on clang analysis
• #14515: BUG: Fix randint when range is 2**32
• #14519: MAINT: remove the entropy c-extension module
• #14563: DOC: remove note about Pocketfft license file (non-existing here).
• #14578: BUG: random: Create a legacy implementation of random.binomial.
• #14687: BUG: properly define PyArray_DescrCheck

Checksums

MD5

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a3195ccbbd97b0366f0c46e36a62717a  numpy-1.17.3.zip

SHA256

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NumPy 1.17.2 Release Notes

This release contains fixes for bugs reported against NumPy 1.17.1 along with a
some documentation improvements. The most important fix is for lexsort when the
keys are of type (u)int8 or (u)int16. If you are currently using 1.17 you
should upgrade.

The Python versions supported in this release are 3.5-3.7, Python 2.7 has been
dropped. Python 3.8b4 should work with the released source packages, but there
are no future guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 to avoid errors on the Skylake architecture. The NumPy wheels
on PyPI are built from the OpenBLAS development branch in order to avoid those
errors.

Contributors

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

• CakeWithSteak +
• Charles Harris
• Dan Allan
• Hameer Abbasi
• Lars Grueter
• Matti Picus
• Sebastian Berg

Pull requests merged

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

• #14418: BUG: Fix aradixsort indirect indexing.
• #14420: DOC: Fix a minor typo in dispatch documentation.
• #14421: BUG: test, fix regression in converting to ctypes
• #14430: BUG: Do not show Override module in private error classes.
• #14432: BUG: Fixed maximum relative error reporting in assert_allclose.
• #14433: BUG: Fix uint-overflow if padding with linear_ramp and negative...
• #14436: BUG: Update 1.17.x with 1.18.0-dev pocketfft.py.
• #14446: REL: Prepare for NumPy 1.17.2 release.

Checksums

MD5

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SHA256

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NumPy 1.16.5 Release Notes

The NumPy 1.16.5 release fixes bugs reported against the 1.16.4 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 errors 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.

Contributors

A total of 18 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Alexander Shadchin
• Allan Haldane
• Bruce Merry +
• Charles Harris
• Colin Snyder +
• Dan Allan +
• Emile +
• Eric Wieser
• Grey Baker +
• Maksim Shabunin +
• Marten van Kerkwijk
• Matti Picus
• Peter Andreas Entschev +
• Ralf Gommers
• Richard Harris +
• Sebastian Berg
• Sergei Lebedev +
• Stephan Hoyer

Pull requests merged

A total of 23 pull requests were merged for this release.

• #13742: ENH: Add project URLs to setup.py
• #13823: TEST, ENH: fix tests and ctypes code for PyPy
• #13845: BUG: use npy_intp instead of int for indexing array
• #13867: TST: Ignore DeprecationWarning during nose imports
• #13905: BUG: Fix use-after-free in boolean indexing
• #13933: MAINT/BUG/DOC: Fix errors in _add_newdocs
• #13984: BUG: fix byte order reversal for datetime64[ns]
• #13994: MAINT,BUG: Use nbytes to also catch empty descr during allocation
• #14042: BUG: np.array cleared errors occured in PyMemoryView_FromObject
• #14043: BUG: Fixes for Undefined Behavior Sanitizer (UBSan) errors.
• #14044: BUG: ensure that casting to/from structured is properly checked.
• #14045: MAINT: fix histogram*d dispatchers
• #14046: BUG: further fixup to histogram2d dispatcher.
• #14052: BUG: Replace contextlib.suppress for Python 2.7
• #14056: BUG: fix compilation of 3rd party modules with Py_LIMITED_API...
• #14057: BUG: Fix memory leak in dtype from dict contructor
• #14058: DOC: Document array_function at a higher level.
• #14084: BUG, DOC: add new recfunctions to __all__
• #14162: BUG: Remove stray print that causes a SystemError on python 3.7
• #14297: TST: Pin pytest version to 5.0.1.
• #14322: ENH: Enable huge pages in all Linux builds
• #14346: BUG: fix behavior of structured_to_unstructured on non-trivial...
• #14382: REL: Prepare for the NumPy 1.16.5 release.

Checksums

MD5

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SHA256

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NumPy 1.17.1 Release Notes

This release contains a number of fixes for bugs reported against NumPy 1.17.0
along with a few documentation and build improvements. The Python versions
supported are 3.5-3.7, note that Python 2.7 has been dropped. Python 3.8b3
should work with the released source packages, but there are no future
guarantees.

Downstream developers should use Cython >= 0.29.13 for Python 3.8 support and
OpenBLAS >= 3.7 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.

Contributors

A total of 17 people contributed to this release. People with a "+" by their
names contributed a patch for the first time.

• Alexander Jung +
• Allan Haldane
• Charles Harris
• Eric Wieser
• Giuseppe Cuccu +
• Hiroyuki V. Yamazaki
• Jérémie du Boisberranger
• Kmol Yuan +
• Matti Picus
• Max Bolingbroke +
• Maxwell Aladago +
• Oleksandr Pavlyk
• Peter Andreas Entschev
• Sergei Lebedev
• Seth Troisi +
• Vladimir Pershin +
• Warren Weckesser

Pull requests merged

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

• #14156: TST: Allow fuss in testing strided/non-strided exp/log loops
• #14157: BUG: avx2_scalef_ps must be static
• #14158: BUG: Remove stray print that causes a SystemError on python 3.7.
• #14159: BUG: Fix DeprecationWarning in python 3.8.
• #14160: BLD: Add missing gcd/lcm definitions to npy_math.h
• #14161: DOC, BUILD: cleanups and fix (again) 'build dist'
• #14166: TST: Add 3.8-dev to travisCI testing.
• #14194: BUG: Remove the broken clip wrapper (Backport)
• #14198: DOC: Fix hermitian argument docs in svd.
• #14199: MAINT: Workaround for Intel compiler bug leading to failing test
• #14200: TST: Clean up of test_pocketfft.py
• #14201: BUG: Make advanced indexing result on read-only subclass writeable...
• #14236: BUG: Fixed default BitGenerator name
• #14237: ENH: add c-imported modules for freeze analysis in np.random
• #14296: TST: Pin pytest version to 5.0.1
• #14301: BUG: Fix leak in the f2py-generated module init and PyMem_Del...
• #14302: BUG: Fix formatting error in exception message
• #14307: MAINT: random: Match type of SeedSequence.pool_size to DEFAULT_POOL_SIZE.
• #14308: BUG: Fix numpy.random bug in platform detection
• #14309: ENH: Enable huge pages in all Linux builds
• #14330: BUG: Fix segfault in random.permutation(x) when x is a string.
• #14338: BUG: don't fail when lexsorting some empty arrays (#14228)
• #14339: BUG: Fix misuse of .names and .fields in various places (backport...
• #14345: BUG: fix behavior of structured_to_unstructured on non-trivial...
• #14350: REL: Prepare 1.17.1 release

Checksums

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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 3.8b2 should work with the released source packages, but there are no
future guarantees.

Downstream developers should use Cython >= 0.29.11 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 <random.BitGenerators> and improved seeding designed for use in parallel
  processes has been added. The currently available bit generators are MT19937 <random.mt19937.MT19937>, PCG64 <random.pcg64.PCG64>, Philox <random.philox.Philox>, and SFC64 <random.sfc64.SFC64>. See below under
  New Features.

• NumPy's FFT <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. 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

numpy.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 temp_file_name

The internal use of these functions has been refactored and there are better
alternatives. Replace exec_command with subprocess.Popen and
temp_file_name <numpy.distutils.exec_command> 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 numpy.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.

Note that for the C-side buffer protocol such an array will return a
readonly buffer immediately unless a writable buffer is requested. If
a writeable buffer is requested a warning will be given. When using
cython, the const qualifier should be used with such arrays to avoid
the warning (e.g. cdef const double[::1] view).

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" (consistently with [(name, dtype, n)]
/ "ntype" with 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. The behavior of mask <ma.MaskedArray.mask> is now consistent with data <ma.MaskedArray.data>,
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.

out is buffered for memory overlaps in take, choose, 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 load, and 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 ~RandomState.beta, ~RandomState.binomial,
~RandomState.laplace, ~RandomState.logistic, ~RandomState.logseries or
~RandomState.multinomial if a 0 is generated in the underlying MT19937 <~numpy.random.mt11937.MT19937> random stream. There is a 1 in
:math:10^{53} chance of this occurring, 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 numpy.inf or
numpy.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.

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 <ndarray.astype>
method.

ndarray.flags.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 ndarray.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 numpy.random module with selectable random number generators

A new extensible numpy.random module along with four selectable random number
generators and improved seeding designed for use in parallel processes has been
added. The currently available :ref:Bit Generators <bit_generator> are
~mt19937.MT19937, ~pcg64.PCG64, ~philox.Philox, and ~sfc64.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. More information is available in
the :ref:API change description <new-or-different> and in the top-level view <numpy.random> documentation.

libFLAME

Support for building NumPy with the libFLAME linear algebra package as the LAPACK,
implementation, see
libFLAME <https://www.cs.utexas.edu/~flame/web/libFLAME.html>_ for details.

User-defined BLAS detection order

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_LAPACK_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.

ufunc.reduce and related functions now accept a where mask

ufunc.reduce, sum, prod, min, 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 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 :math:O(n/2) working space. Details of the timsort algorithm can be
found at CPython listsort.txt <https://github.com/python/cpython/blob/3.7/Objects/listsort.txt>_.

packbits and unpackbits accept an order keyword

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

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.

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

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

divmod operation is now supported for two timedelta64 operands

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

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 pad

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

empty_like and related functions now accept a shape argument

empty_like, full_like, ones_like and 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.

Improvements

Array comparison assertions include maximum differences

Error messages from array comparison tests such as
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
:math:O(N log N) run time complexity instead of deteriorating towards
:math: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 exp and log now benefit from AVX2/AVX512
instruction set which are detected during runtime. exp has a max ulp
error of 2.52 and 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 interp would previously return nan, it now
returns an appropriate infinity.

Pathlib support for fromfile, tofile and ndarray.dump

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

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

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

isfinite supports datetime64 and timedelta64 types

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

New keywords added to nan_to_num

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 matmul

It is now possible to use 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 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

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 savez set to 3 for force zip64 flag

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'].

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.. currentmodule:: numpy

==========================
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 3.8b2 should work with the released source packages, but there are no
future guarantees.

Downstream developers should use Cython >= 0.29.11 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 <random.BitGenerators> and improved seeding designed for use in parallel
  processes has been added. The currently available bit generators are MT19937 <random.mt19937.MT19937>, PCG64 <random.pcg64.PCG64>, Philox <random.philox.Philox>, and SFC64 <random.sfc64.SFC64>. See below under
  New Features.

• NumPy's FFT <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. 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

numpy.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 temp_file_name

The internal use of these functions has been refactored and there are better
alternatives. Replace exec_command with subprocess.Popen and
temp_file_name <numpy.distutils.exec_command> 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 numpy.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.

Note that for the C-side buffer protocol such an array will return a
readonly buffer immediately unless a writable buffer is requested. If
a writeable buffer is requested a warning will be given. When using
cython, the const qualifier should be used with such arrays to avoid
the warning (e.g. cdef const double[::1] view).

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" (consistently with [(name, dtype, n)]
/ "ntype" with 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. The behavior of mask <ma.MaskedArray.mask> is now consistent with data <ma.MaskedArray.data>,
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.

out is buffered for memory overlaps in take, choose, 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 load, and 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>_.

.. currentmodule:: numpy.random.mtrand

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 ~RandomState.beta, ~RandomState.binomial,
~RandomState.laplace, ~RandomState.logistic, ~RandomState.logseries or
~RandomState.multinomial if a 0 is generated in the underlying MT19937 <~numpy.random.mt11937.MT19937> random stream. There is a 1 in
:math:10^{53} chance of this occurring, 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 numpy.inf or
numpy.nan) is now dropped.

.. currentmodule:: numpy

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.

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 <ndarray.astype>
method.

ndarray.flags.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 ndarray.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

.. currentmodule:: numpy.random

New extensible numpy.random module with selectable random number generators

A new extensible numpy.random module along with four selectable random number
generators and improved seeding designed for use in parallel processes has been
added. The currently available :ref:Bit Generators <bit_generator> are
~mt19937.MT19937, ~pcg64.PCG64, ~philox.Philox, and ~sfc64.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. More information is available in
the :ref:API change description <new-or-different> and in the top-level view <numpy.random> documentation.

.. currentmodule:: numpy

libFLAME

Support for building NumPy with the libFLAME linear algebra package as the LAPACK,
implementation, see
libFLAME <https://www.cs.utexas.edu/~flame/web/libFLAME.html>_ for details.

User-defined BLAS detection order

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_LAPACK_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.

ufunc.reduce and related functions now accept a where mask

ufunc.reduce, sum, prod, min, 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 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 :math:O(n/2) working space. Details of the timsort algorithm can be
found at CPython listsort.txt <https://github.com/python/cpython/blob/3.7/Objects/listsort.txt>_.

packbits and unpackbits accept an order keyword

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

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.

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

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

divmod operation is now supported for two timedelta64 operands

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

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 pad

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

empty_like and related functions now accept a shape argument

empty_like, full_like, ones_like and 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.

Improvements

Array comparison assertions include maximum differences

Error messages from array comparison tests such as
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
:math:O(N log N) run time complexity instead of deteriorating towards
:math: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 exp and log now benefit from AVX2/AVX512
instruction set which are detected during runtime. exp has a max ulp
error of 2.52 and 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 interp would previously return nan, it now
returns an appropriate infinity.

Pathlib support for fromfile, tofile and ndarray.dump

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

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

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

isfinite supports datetime64 and timedelta64 types

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

New keywords added to nan_to_num

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 matmul

It is now possible to use 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 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

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 savez set to 3 for force zip64 flag

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'].

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