v1.16.1

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

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

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

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

Contributors

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

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

Enhancements

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

Compatibility notes

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

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

New Features

divmod operation is now supported for two timedelta64 operands

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

Improvements

Further improvements to ctypes support in np.ctypeslib

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

Array comparison assertions include maximum differences

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

Changes

timedelta64 % 0 behavior adjusted to return NaT

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

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v1.16.0

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

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

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

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

Highlights

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

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

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

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

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

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

New deprecations

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

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

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

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

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

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

• Passing an iterator to the stack functions is deprecated

Expired deprecations

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

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

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

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

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

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

NaT comparisons

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

complex64/128 alignment has changed

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

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

nd_grid len removal

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

np.unravel_index now accepts shape keyword argument

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

multi-field views return a view instead of a copy

Indexing a structured array with multiple fields, e.g., arr[['f1', 'f3']],
returns a view into the original array instead of a copy. The returned view
will often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

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

C API changes

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

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

New Features

Integrated squared error (ISE) estimator added to histogram

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

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

max_rows keyword added for np.loadtxt

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

modulus operator support added for np.timedelta64 operands

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

Improvements

no-copy pickling of numpy arrays

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

build shell independence

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

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

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

randint and choice now work on empty distributions

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

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

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

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

v1.16.0rc2

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

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

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

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

Highlights

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

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

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

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

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

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

New deprecations

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

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

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

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

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

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

• Passing an iterator to the stack functions is deprecated

Expired deprecations

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

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

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

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

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

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

NaT comparisons

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

complex64/128 alignment has changed

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

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

nd_grid len removal

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

np.unravel_index now accepts shape keyword argument

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

multi-field views return a view instead of a copy

Indexing a structured array with multiple fields, e.g., arr[['f1', 'f3']],
returns a view into the original array instead of a copy. The returned view
will often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

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

C API changes

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

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

New Features

Integrated squared error (ISE) estimator added to histogram

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

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

max_rows keyword added for np.loadtxt

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

modulus operator support added for np.timedelta64 operands

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

Improvements

no-copy pickling of numpy arrays

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

build shell independence

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

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

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

randint and choice now work on empty distributions

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

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

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

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

v1.16.0rc1

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

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

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

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

Highlights

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

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

• Improved support for the ARM and POWER architectures.

• Improved support for AIX and PyPy.

• Improved interop with ctypes.

• Improved support for PEP 3118.

New functions

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

  • assign_fields_by_name
  • structured_to_unstructured
  • unstructured_to_structured
  • apply_along_fields
  • require_fields

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

New deprecations

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

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

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

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

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

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

• Passing an iterator to the stack functions is deprecated

Expired deprecations

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

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

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

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

Future changes

• NumPy 1.17 will drop support for Python 2.7.

Compatibility notes

f2py script on Windows

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

NaT comparisons

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

complex64/128 alignment has changed

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

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

nd_grid len removal

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

np.unravel_index now accepts shape keyword argument

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

multi-field views return a view instead of a copy

Indexing a structured array with multiple fields, e.g., arr[['f1', 'f3']],
returns a view into the original array instead of a copy. The returned view
will often have extra padding bytes corresponding to intervening fields in the
original array, unlike before, which will affect code such as
arr[['f1', 'f3']].view('float64'). This change has been planned since numpy
1.7. Operations hitting this path have emitted FutureWarnings since then.
Additional FutureWarnings about this change were added in 1.12.

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

C API changes

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

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

New Features

Integrated squared error (ISE) estimator added to histogram

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

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

max_rows keyword added for np.loadtxt

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

modulus operator support added for np.timedelta64 operands

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

Improvements

no-copy pickling of numpy arrays

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

build shell independence

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

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

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

randint and choice now work on empty distributions

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

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

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

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

v1.15.4

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

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

Compatibility Note

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

Contributors

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

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

Pull requests merged

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

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

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v1.15.3

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

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

Compatibility Note

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

Contributors

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

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

Pull requests merged

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

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

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274dd6db3a13c6b6c47a05b5365e1749  numpy-1.15.3.tar.gz
7f1b9e521c2a662cecf3708026e8bdad  numpy-1.15.3.zip

SHA256

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d263f8f14f2da0c079c0297e829e550d8f2c4e0ffef215506bd1d0ddd2bff3de  numpy-1.15.3-cp27-none-win32.whl
b12fe6f31babb9477aa0f9692730654b3ee0e71f33b4568170dfafd439caf0a2  numpy-1.15.3-cp27-none-win_amd64.whl
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ce3622b73ccd844ba301c1aea65d36cf9d8331e7c25c16b1725d0f14db99aaf4  numpy-1.15.3-cp35-none-win32.whl
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094f8a83e5bd0a44a7557fa24a46db6ba7d5299c389ddbc9e0e18722f567fb63  numpy-1.15.3-cp37-none-win32.whl
a245464ddf6d90e2d6287e9cef6bcfda2a99467fdcf1b677b99cd0b6c7b43de2  numpy-1.15.3-cp37-none-win_amd64.whl
4656ea0d66a3724fd88aafa39a0c5cef216d1257a71b40534fe589abd46ba77b  numpy-1.15.3.tar.gz
1c0c80e74759fa4942298044274f2c11b08c86230b25b8b819e55e644f5ff2b6  numpy-1.15.3.zip

v1.15.2

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

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

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

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

Compatibility Note

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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d40b15478148a48ec324327578de4583  numpy-1.15.2.tar.gz
5a55a994eca6095b1e82d44600217ece  numpy-1.15.2.zip

SHA256

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v1.14.6

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

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

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

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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v1.15.1

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

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

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

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

Compatibility Note

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

Contributors

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

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

Pull requests merged

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

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

Checksums

MD5

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SHA256

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v1.15.0

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

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

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

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

Highlights

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

New functions

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

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

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

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

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

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

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

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

Deprecations

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

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

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

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

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

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

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

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

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

Future Changes

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

Compatibility notes

Compiled testing modules renamed and made private

The following compiled modules have been renamed and made private:

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

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

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

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

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

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

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

Numpy has switched to using pytest instead of nose for testing

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

Numpy no longer monkey-patches ctypes with __array_interface__

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

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

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

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

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

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

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

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

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

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

Multifield Indexing of Structured Arrays will still return a copy

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

C API changes

New functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier

Functions npy_get_floatstatus_barrier and npy_clear_floatstatus_barrier
have been added and should be used in place of the npy_get_floatstatusand
npy_clear_status functions. Optimizing compilers like GCC 8.1 and Cla...