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diff --git a/doc/source/release.rst b/doc/source/release.rst index 6d208d395..a2dece94b 100644 --- a/doc/source/release.rst +++ b/doc/source/release.rst @@ -5,6 +5,7 @@ Release Notes .. toctree:: :maxdepth: 3 + 1.22.0 <release/1.22.0-notes> 1.21.0 <release/1.21.0-notes> 1.20.3 <release/1.20.3-notes> 1.20.2 <release/1.20.2-notes> diff --git a/doc/source/release/1.21.0-notes.rst b/doc/source/release/1.21.0-notes.rst index 5fda1f631..ac65b8fd0 100644 --- a/doc/source/release/1.21.0-notes.rst +++ b/doc/source/release/1.21.0-notes.rst @@ -4,3 +4,563 @@ NumPy 1.21.0 Release Notes ========================== + +New functions +============= + +.. currentmodule:: numpy.random + +Add `PCG64DXSM` `BitGenerator` +------------------------------ + +Uses of the `PCG64` `BitGenerator` in a massively-parallel context have been +shown to have statistical weaknesses that were not apparent at the first +release in numpy 1.17. Most users will never observe this weakness and are +safe to continue to use `PCG64`. We have introduced a new `PCG64DXSM` +`BitGenerator` that will eventually become the new default `BitGenerator` +implementation used by `default_rng` in future releases. `PCG64DXSM` solves +the statistical weakness while preserving the performance and the features of +`PCG64`. + +See :ref:`upgrading-pcg64` for more details. + +.. currentmodule:: numpy + +(`gh-18906 <https://github.com/numpy/numpy/pull/18906>`__) + + +Deprecations +============ + +Inexact matches for `numpy.convolve` and `numpy.correlate` are deprecated +------------------------------------------------------------------------- + +`numpy.convolve` and `numpy.correlate` now emits a warning when there are case +insensitive and/or inexact matches found for ``mode`` argument in the functions. +Pass full ``"same"``, ``"valid"``, ``"full"`` strings instead of +``"s"``, ``"v"``, ``"f"`` for the ``mode`` argument. + +(`gh-17492 <https://github.com/numpy/numpy/pull/17492>`__) + +``np.typeDict`` has been formally deprecated +-------------------------------------------- +``np.typeDict`` is a deprecated alias for ``np.sctypeDict`` and +has been so for over 14 years (6689502_). +A deprecation warning will now be issued whenever getting ``np.typeDict``. + +.. _6689502: https://github.com/numpy/numpy/commit/668950285c407593a368336ff2e737c5da84af7d + +(`gh-17586 <https://github.com/numpy/numpy/pull/17586>`__) + +Exceptions will be raised during array-like creation +---------------------------------------------------- +When an object raised an exception during access of the special +attributes ``__array__`` or ``__array_interface__``, this exception +was usually ignored. +A warning is now given when the exception is anything but AttributeError. +To silence the warning, the type raising the exception has to be adapted +to raise an ``AttributeError``. + +(`gh-19001 <https://github.com/numpy/numpy/pull/19001>`__) + +Four `ndarray.ctypes` methods have been deprecated +-------------------------------------------------- +Four methods of the `ndarray.ctypes` object have been deprecated, +as they are (undocumentated) implementation artifacts of their respective +properties. + +The methods in question are: + +* ``_ctypes.get_data`` (use ``_ctypes.data`` instead) +* ``_ctypes.get_shape`` (use ``_ctypes.shape`` instead) +* ``_ctypes.get_strides`` (use ``_ctypes.strides`` instead) +* ``_ctypes.get_as_parameter`` (use ``_ctypes._as_parameter_`` instead) + +(`gh-19031 <https://github.com/numpy/numpy/pull/19031>`__) + + +Future Changes +============== + +Promotion of strings with numbers and bools is deprecated +--------------------------------------------------------- +Any promotion of numbers and strings is deprecated and will +give a ``FutureWarning`` the main affected functionalities +are: + +* `numpy.promote_types` and `numpy.result_type` which will raise + an error in this case in the future. +* `numpy.concatenate` will raise an error when concatenating a string + and numeric array. You can use ``dtype="S"`` to explicitly request + a string result. +* `numpy.array` and related functions will start returning ``object`` + arrays because these functions use ``object`` as a fallback when + no common dtype can be found. However, it may happen that future + releases of NumPy will generally error in these cases. + +This will mainly affect code such as:: + + np.asarray(['string', 0]) + +and:: + + np.concatenate((['string'], [0])) + +in both cases adding ``dtype="U"`` or ``dtype="S"`` will give the +previous (string) result, while ``dtype=object`` will ensure an array with +object dtype is returned. + +Comparisons, universal functions, and casting are not affected by this. + +(`gh-18116 <https://github.com/numpy/numpy/pull/18116>`__) + + +Expired deprecations +==================== + +* The ``shape`` argument `numpy.unravel_index` cannot be passed + as ``dims`` keyword argument anymore. (Was deprecated in NumPy 1.16.) + + (`gh-17900 <https://github.com/numpy/numpy/pull/17900>`__) + +* The function ``PyUFunc_GenericFunction`` has been disabled. + It was deprecated in NumPy 1.19. Users should call the ufunc + directly using the Python API. +* The function ``PyUFunc_SetUsesArraysAsData`` has been disabled. + It was deprecated in NumPy 1.19. + + (`gh-18697 <https://github.com/numpy/numpy/pull/18697>`__) + +Remove deprecated ``PolyBase`` and unused ``PolyError`` and ``PolyDomainError`` +------------------------------------------------------------------------------- + +The class ``PolyBase`` has been removed (deprecated in numpy 1.9.0). Please use +the abstract ``ABCPolyBase`` class instead. + +Furthermore, the unused ``PolyError`` and ``PolyDomainError`` exceptions are +removed from the `numpy.polynomial`. + +(`gh-18963 <https://github.com/numpy/numpy/pull/18963>`__) + + +Compatibility notes +=================== + +Error type changes in universal functions +----------------------------------------- +The universal functions may now raise different errors +on invalid input in some cases. +The main changes should be that a ``RuntimeError`` was +replaced with a more fitting ``TypeError``. +When multiple errors were present in the same call, +NumPy may now raise a different one. + + +``__array_ufunc__`` argument validation +--------------------------------------- +NumPy will now partially validate arguments before calling +``__array_ufunc__``. Previously, it was possible to pass +on invalid arguments (such as a non-existing keyword +argument) when dispatch was known to occur. + + +``__array_ufunc__`` and additional positional arguments +------------------------------------------------------- +Previously, all positionally passed arguments were checked for +``__array_ufunc__`` support. In the case of ``reduce``, +``accumulate``, and ``reduceat`` all arguments may be passed by +position. This means that when they were passed by +position, they could previously have been asked to handle +the ufunc call via ``__array_ufunc__``. +Since this depended on the way the arguments were passed +(by position or by keyword), NumPy will now only dispatch +on the input and output array. +For example, NumPy will never dispatch on the ``where`` array +in a reduction such as ``np.add.reduce``. + +(`gh-15271 <https://github.com/numpy/numpy/pull/15271>`__) + +Validate input values in ``Generator.uniform`` +---------------------------------------------- +Checked that ``high - low >= 0`` in ``np.random.Generator.uniform``. Raises +``ValueError`` if ``low > high``. Previously out-of-order inputs were accepted +and silently swapped, so that if ``low > high``, the value generated was +``high + (low - high) * random()``. + +(`gh-17921 <https://github.com/numpy/numpy/pull/17921>`__) + +``/usr/include`` removed from default include paths +--------------------------------------------------- +The default include paths when building a package with ``numpy.distutils`` no +longer include ``/usr/include``. This path is normally added by the compiler, +and hardcoding it can be problematic. In case this causes a problem, please +open an issue. A workaround is documented in PR 18658. + +(`gh-18658 <https://github.com/numpy/numpy/pull/18658>`__) + +Changes to comparisons with ``dtype=...`` +----------------------------------------- +When the ``dtype=`` (or ``signature``) arguments to comparison +ufuncs (``equal``, ``less``, etc.) is used, this will denote +the desired output dtype in the future. +This means that: + + np.equal(2, 3, dtype=object) + +will give a ``FutureWarning`` that it will return an ``object`` +array in the future, which currently happens for: + + np.equal(None, None, dtype=object) + +due to the fact that ``np.array(None)`` is already an object +array. (This also happens for some other dtypes.) + +Since comparisons normally only return boolean arrays, providing +any other dtype will always raise an error in the future and +give a ``DeprecationWarning`` now. + + +Changes to ``dtype`` and ``signature`` arguments in ufuncs +---------------------------------------------------------- +The universal function arguments ``dtype`` and ``signature`` +which are also valid for reduction such as ``np.add.reduce`` +(which is the implementation for ``np.sum``) will now issue +a warning when the ``dtype`` provided is not a "basic" dtype. + +NumPy almost always ignored metadata, byteorder or time units +on these inputs. NumPy will now always ignore it and raise an +error if byteorder or time unit changed. +The following are the most important examples of changes which +will give the error. In some cases previously the information +stored was not ignored, in all of these an error is now raised:: + + # Previously ignored the byte-order (affect if non-native) + np.add(3, 5, dtype=">i32") + + # The biggest impact is for timedelta or datetimes: + arr = np.arange(10, dtype="m8[s]") + # The examples always ignored the time unit "ns": + np.add(arr, arr, dtype="m8[ns]") + np.maximum.reduce(arr, dtype="m8[ns]") + + # The following previously did use "ns" (as opposed to `arr.dtype`) + np.add(3, 5, dtype="m8[ns]") # Now return generic time units + np.maximum(arr, arr, dtype="m8[ns]") # Now returns "s" (from `arr`) + +The same applies for functions like ``np.sum`` which use these internally. +This change is necessary to achieve consistent handling within NumPy. + +If you run into these, in most cases pass for example ``dtype=np.timedelta64`` +which clearly denotes a general ``timedelta64`` without any unit or byte-order +defined. If you need to specify the output dtype precisely, you may do so +by either casting the inputs or providing an output array using `out=`. + +NumPy may choose to allow providing an exact output ``dtype`` here in the +future, which would be preceded by a ``FutureWarning``. + +(`gh-18718 <https://github.com/numpy/numpy/pull/18718>`__) + +Ufunc ``signature=...`` and ``dtype=`` generalization and ``casting`` +--------------------------------------------------------------------- +The behaviour for ``np.ufunc(1.0, 1.0, signature=...)`` or +``np.ufunc(1.0, 1.0, dtype=...)`` can now yield different loops in 1.21 +compared to 1.20 because of changes in promotion. +When ``signature`` was previously used, the casting check on inputs +was relaxed, which could lead to downcasting inputs unsafely especially +if combined with ``casting="unsafe"``. + +Casting is now guaranteed to be safe. If a signature is only +partially provided, for example using ``signature=("float64", None, None)``, +this could lead to no loop being found (an error). +In that case, it is necessary to provide the complete signature +to enforce casting the inputs. +If ``dtype="float64"`` is used or only outputs are set (e.g. +``signature=(None, None, "float64")`` the is unchanged. +We expect that very few users are affected by this change. + +Further, the meaning of ``dtype="float64"`` has been slightly modified and +now strictly enforces only the correct output (and not input) DTypes. +This means it is now always equivalent to:: + + signature=(None, None, "float64") + +(If the ufunc has two inputs and one output). Since this could lead +to no loop being found in some cases, NumPy will normally also search +for the loop:: + + signature=("float64", "float64", "float64") + +if the first search failed. +In the future, this behaviour may be customized to achieve the expected +results for more complex ufuncs. (For some universal functions such as +``np.ldexp`` inputs can have different DTypes.) + +(`gh-18880 <https://github.com/numpy/numpy/pull/18880>`__) + +Distutils forces strict floating point model on clang +----------------------------------------------------- +NumPy distutils will now always add the ``-ffp-exception-behavior=strict`` +compiler flag when compiling with clang. Clang defaults to a non-strict +version, which allows the compiler to generate code that does not set +floating point warnings/errors correctly. + +(`gh-19049 <https://github.com/numpy/numpy/pull/19049>`__) + + +C API changes +============= + +Use of ``ufunc->type_resolver`` and "type tuple" +------------------------------------------------ +NumPy now normalizes the "type tuple" argument to the +type resolver functions before calling it. Note that in +the use of this type resolver is legacy behaviour and NumPy +will not do so when possible. +Calling ``ufunc->type_resolver`` or ``PyUFunc_DefaultTypeResolver`` +is strongly discouraged and will now enforce a normalized +type tuple if done. +Note that this does not affect providing a type resolver, which +is expected to keep working in most circumstances. +If you have an unexpected use-case for calling the type resolver, +please inform the NumPy developers so that a solution can be found. + +(`gh-18718 <https://github.com/numpy/numpy/pull/18718>`__) + + +New Features +============ + +Added a mypy plugin for handling platform-specific `numpy.number` precisions +---------------------------------------------------------------------------- + +A mypy_ plugin is now available for automatically assigning the (platform-dependent) +precisions of certain `~numpy.number` subclasses, including the likes of +`~numpy.int_`, `~numpy.intp` and `~numpy.longlong`. See the documentation on +:ref:`scalar types <arrays.scalars.built-in>` for a comprehensive overview +of the affected classes. + +Note that while usage of the plugin is completely optional, without it the +precision of above-mentioned classes will be inferred as `~typing.Any`. + +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html + +(`gh-17843 <https://github.com/numpy/numpy/pull/17843>`__) + +Let the mypy plugin manage extended-precision `numpy.number` subclasses +----------------------------------------------------------------------- + +The mypy_ plugin, introduced in `numpy/numpy#17843`_, has been expanded: +the plugin now removes annotations for platform-specific extended-precision +types that are not available to the platform in question. +For example, it will remove `~numpy.float128` when not available. + +Without the plugin *all* extended-precision types will, as far as mypy is concerned, +be available on all platforms. + +To enable the plugin, one must add it to their mypy `configuration file`_: + +.. code-block:: ini + + [mypy] + plugins = numpy.typing.mypy_plugin + + +.. _mypy: http://mypy-lang.org/ +.. _configuration file: https://mypy.readthedocs.io/en/stable/config_file.html +.. _`numpy/numpy#17843`: https://github.com/numpy/numpy/pull/17843 + +(`gh-18322 <https://github.com/numpy/numpy/pull/18322>`__) + +New ``min_digits`` argument for printing float values +----------------------------------------------------- +A new ``min_digits`` argument has been added to the dragon4 float printing +functions `np.format_float_positional` and `np.format_float_scientific` . This +kwd guarantees that at least the given number of digits will be printed when +printing in unique=True mode, even if the extra digits are unnecessary to +uniquely specify the value. It is the counterpart to the precision argument +which sets the maximum number of digits to be printed. When unique=False in +fixed precision mode, it has no effect and the precision argument fixes the +number of digits. + +(`gh-18629 <https://github.com/numpy/numpy/pull/18629>`__) + +f2py now recognizes Fortran abstract interface blocks +----------------------------------------------------- +`np.f2py` can now parse abstract interface blocks. + +(`gh-18695 <https://github.com/numpy/numpy/pull/18695>`__) + +BLAS and LAPACK configuration via environment variables +------------------------------------------------------- +Autodetection of installed BLAS and LAPACK libraries can be bypassed by using +the ``NPY_BLAS_LIBS`` and ``NPY_LAPACK_LIBS`` environment variables. Instead, +the link flags in these environment variables will be used directly, and the +language is assumed to be F77. This is especially useful in automated builds +where the BLAS and LAPACK that are installed are known exactly. A use case is +replacing the actual implementation at runtime via stub library links. + +If ``NPY_CBLAS_LIBS`` is set (optional in addition to ``NPY_BLAS_LIBS``), this +will be used as well, by defining ``HAVE_CBLAS`` and appending the environment +variable content to the link flags. + +(`gh-18737 <https://github.com/numpy/numpy/pull/18737>`__) + +A runtime-subcriptable alias has been added for `ndarray` +--------------------------------------------------------- +`numpy.typing.NDArray` has been added, a runtime-subscriptable alias for +``np.ndarray[Any, np.dtype[~Scalar]]``. The new type alias can be used +for annotating arrays with a given dtype and unspecified shape. :sup:`1` + +:sup:`1` NumPy does not support the annotating of array shapes as of 1.21, +this is expected to change in the future though (see :pep:`646`). + +Examples +~~~~~~~~ + +.. code-block:: python + + >>> import numpy as np + >>> import numpy.typing as npt + + >>> print(npt.NDArray) + numpy.ndarray[typing.Any, numpy.dtype[~ScalarType]] + + >>> print(npt.NDArray[np.float64]) + numpy.ndarray[typing.Any, numpy.dtype[numpy.float64]] + + >>> NDArrayInt = npt.NDArray[np.int_] + >>> a: NDArrayInt = np.arange(10) + + >>> def func(a: npt.ArrayLike) -> npt.NDArray[Any]: + ... return np.array(a) + +(`gh-18935 <https://github.com/numpy/numpy/pull/18935>`__) + + +Improvements +============ + +Arbitrary ``period`` option for `numpy.unwrap` +---------------------------------------------- +The size of the interval over which phases are unwrapped is no longer restricted to ``2 * pi``. +This is especially useful for unwrapping degrees, but can also be used for other intervals. + +.. code:: python + + >>> phase_deg = np.mod(np.linspace(0,720,19), 360) - 180 + >>> phase_deg + array([-180., -140., -100., -60., -20., 20., 60., 100., 140., + -180., -140., -100., -60., -20., 20., 60., 100., 140., + -180.]) + + >>> unwrap(phase_deg, period=360) + array([-180., -140., -100., -60., -20., 20., 60., 100., 140., + 180., 220., 260., 300., 340., 380., 420., 460., 500., + 540.]) + +(`gh-16987 <https://github.com/numpy/numpy/pull/16987>`__) + +``np.unique`` now returns single ``NaN`` +---------------------------------------- +When ``np.unique`` operated on an array with multiple ``NaN`` entries, +its return included a ``NaN`` for each entry that was ``NaN`` in the original array. +This is now improved such that the returned array contains just one ``NaN`` as the +last element. + +Also for complex arrays all ``NaN`` values are considered equivalent +(no matter whether the ``NaN`` is in the real or imaginary part). As the +representant for the returned array the smallest one in the +lexicographical order is chosen - see ``np.sort`` for how the lexicographical +order is defined for complex arrays. + +(`gh-18070 <https://github.com/numpy/numpy/pull/18070>`__) + +``Generator.rayleigh`` and ``Generator.geometric`` performance improved +----------------------------------------------------------------------- +The performance of Rayleigh and geometric random variate generation +in ``Generator`` has improved. These are both transformation of exponential +random variables and the slow log-based inverse cdf transformation has +been replaced with the Ziggurat-based exponential variate generator. + +This change breaks the stream of variates generated when variates from +either of these distributions are produced. + +(`gh-18666 <https://github.com/numpy/numpy/pull/18666>`__) + +Placeholder annotations have been improved +------------------------------------------ +All placeholder annotations, that were previously annotated as ``typing.Any``, +have been improved. Where appropiate they have been replaced with explicit +function definitions, classes or other miscellaneous objects. + +(`gh-18934 <https://github.com/numpy/numpy/pull/18934>`__) + + +Performance improvements and changes +==================================== + +Improved performance in integer division of NumPy arrays +-------------------------------------------------------- +Integer division of NumPy arrays now uses `libdivide <https://libdivide.com/>` +when the divisor is a constant. With the usage of libdivide and +other minor optimizations, there is a large speedup. +The ``//`` operator and ``np.floor_divide`` makes use +of the new changes. + +(`gh-17727 <https://github.com/numpy/numpy/pull/17727>`__) + +Improve performance of ``np.save`` and ``np.load`` for small arrays +------------------------------------------------------------------- +``np.save`` is now a lot faster for small arrays. + +``np.load`` is also faster for small arrays, +but only when serializing with a version >= `(3, 0)`. + +Both are done by removing checks that are only relevant for Python 2, +while still maintaining compatibility with arrays +which might have been created by Python 2. + +(`gh-18657 <https://github.com/numpy/numpy/pull/18657>`__) + + +Changes +======= + +`numpy.piecewise` output class now matches the input class +---------------------------------------------------------- +When `numpy.ndarray` subclasses are used on input to `numpy.piecewise`, +they are passed on to the functions. The output will now be of the +same subclass as well. + +(`gh-18110 <https://github.com/numpy/numpy/pull/18110>`__) + +Enable Accelerate Framework +---------------------------- +With the release of macOS 11.3, several different issues that +numpy was encountering when using Accelerate Framework's +implementation of BLAS and LAPACK should be resolved. This +change enables the Accelerate Framework as an option on macOS. +If additional issues are found, please file a bug report +against Accelerate using the developer feedback assistant +tool (https://developer.apple.com/bug-reporting/). We +intend to address issues promptly and plan to continue +supporting and updating our BLAS and LAPACK libraries. + +(`gh-18874 <https://github.com/numpy/numpy/pull/18874>`__) + + +.. currentmodule:: numpy + +========================== +NumPy 1.21.0 Release Notes +========================== + diff --git a/doc/source/release/1.22.0-notes.rst b/doc/source/release/1.22.0-notes.rst new file mode 100644 index 000000000..0760a3dd7 --- /dev/null +++ b/doc/source/release/1.22.0-notes.rst @@ -0,0 +1,45 @@ +.. currentmodule:: numpy + +========================== +NumPy 1.22.0 Release Notes +========================== + + +Highlights +========== + + +New functions +============= + + +Deprecations +============ + + +Future Changes +============== + + +Expired deprecations +==================== + + +Compatibility notes +=================== + + +C API changes +============= + + +New Features +============ + + +Improvements +============ + + +Changes +======= |