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
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Assets
6
- v1.19.0rc2
-
c063b7d -
Verified
This tag was signed with a verified signature.charris Charles Harris
- v1.19.0rc2
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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.compatwill 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.fromarraysnumpy.core.records.fromrecordsnumpy.core.records.fromstringnumpy.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_GetArrayParamsFromObjectPyUFunc_GenericFunctionPyUFunc_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_BroadcastToShapePyArray_IntTupleFromIntpPyArray_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.compatwill 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.fromarraysnumpy.core.records.fromrecordsnumpy.core.records.fromstringnumpy.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_GetArrayParamsFromObjectPyUFunc_GenericFunctionPyUFunc_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_BroadcastToShapePyArray_IntTupleFromIntpPyArray_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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Assets
6
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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Assets
6
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'andmethod='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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Assets
6
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
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Assets
6
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.
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Assets
6
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.
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Assets
6
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.utilsfunctions have been updated from 1.19.0-dev0.
This improves the function documentation and error messages as well
extending theassert_array_comparefunction 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.
Checksums
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Assets
6
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.randomhas 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_As1DandPyArray_As2Dhave been removed in favor of
PyArray_AsCArray
(gh-14036)np.rankhas been removed. This was deprecated in NumPy 1.10 and
has been replaced bynp.ndim.
(gh-14039)- The deprecation of
expand_dimsout-of-range axes in 1.13.0 has
expired. (gh-14051) PyArray_FromDimsAndDataAndDescrandPyArray_FromDimshave been
removed (they will always raise an error). Use
PyArray_NewFromDescrandPyArray_SimpleNewinstead.
(gh-14100)numeric.loads,numeric.load,np.ma.dump,np.ma.dumps,
np.ma.load,np.ma.loadsare removed, usepicklemethods
instead (gh-14256)arrayprint.FloatFormat,arrayprint.LongFloatFormathas been
removed, useFloatingFormatinsteadarrayprint.ComplexFormat,arrayprint.LongComplexFormathas been
removed, useComplexFloatingFormatinsteadarrayprint.StructureFormathas been removed, use
StructureVoidFormatinstead
(gh-14259)np.testing.randhas been removed. This was deprecated in NumPy
1.11 and has been replaced bynp.random.rand.
(gh-14325)- Class
SafeEvalinnumpy/lib/utils.pyhas been removed. This was
deprecated in NumPy 1.10. Usenp.safe_evalinstead.
(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
nummust be an integer. Deprecated in NumPy
1.12. (gh-14620) - UFuncs with multiple outputs must use a tuple for the
outkwarg.
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.
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