==========================
This NumPy release is marked by the removal of much technical debt:
support for Python 2 has been removed, many deprecations have been
expired, and documentation has been improved. The polishing of the
random module continues apace with bug fixes and better usability from
Cython.
The Python versions supported for this release are 3.6-3.8. Downstream
developers should use Cython \>= 0.29.16 for Python 3.8 support and
OpenBLAS \>= 3.7 to avoid problems on the Skylake architecture.
Highlights
----------
- Code compatibility with Python versions \< 3.5 (including Python 2)
was dropped from both the python and C code. The shims in
`numpy.compat` will remain to support third-party packages, but they
may be deprecated in a future release.
([gh-15233](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/16068))
Deprecations
------------
Deprecate automatic `dtype=object` for ragged input
Calling `np.array([[1, [1, 2, 3]])` will issue a `DeprecationWarning` as
per [NEP 34](https://numpy.org/neps/nep-0034.html). Users should
explicitly use `dtype=object` to avoid the warning.
([gh-15119](https://github.com/numpy/numpy/pull/15119))
Passing `shape=0` to factory functions in `numpy.rec` is deprecated
`0` is treated as a special case and is aliased to `None` in the
functions:
- `numpy.core.records.fromarrays`
- `numpy.core.records.fromrecords`
- `numpy.core.records.fromstring`
- `numpy.core.records.fromfile`
In future, `0` will not be special cased, and will be treated as an
array length like any other integer.
([gh-15217](https://github.com/numpy/numpy/pull/15217))
Deprecation of probably unused C-API functions
The following C-API functions are probably unused and have been
deprecated:
- `PyArray_GetArrayParamsFromObject`
- `PyUFunc_GenericFunction`
- `PyUFunc_SetUsesArraysAsData`
In most cases `PyArray_GetArrayParamsFromObject` should be replaced by
converting to an array, while `PyUFunc_GenericFunction` can be replaced
with `PyObject_Call` (see documentation for details).
([gh-15427](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/15867))
C API changes
-------------
Better support for `const` dimensions in API functions
The following functions now accept a constant array of `npy_intp`:
- `PyArray_BroadcastToShape`
- `PyArray_IntTupleFromIntp`
- `PyArray_OverflowMultiplyList`
Previously the caller would have to cast away the const-ness to call
these functions.
([gh-15251](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/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](https://github.com/numpy/numpy/pull/16153))
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