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  • v1.21.0.dev0

    December 02, 2020
  • v1.20.0.rc1 Changes

    December 03, 2020

    ๐Ÿš€ NumPy 1.20.0 Release Notes

    ๐Ÿš€ This NumPy release is the largest so made to date, some 654 PRs
    ๐Ÿ‘€ contributed by 182 people have been merged. See the list of highlights
    ๐Ÿš€ below for more details. The Python versions supported for this release
    ๐Ÿ‘ are 3.7-3.9, support for Python 3.6 has been dropped. Highlights are

    • Annotations for NumPy functions. This work is ongoing and
      ๐Ÿ‘Œ improvements can be expected pending feedback from users.
    • Wider use of SIMD to increase execution speed of ufuncs. Much work
      has been done in introducing universal functions that will ease use
      of modern features across different hardware platforms. This work is
      ongoing.
    • Preliminary work in changing the dtype and casting implementations
      in order to provide an easier path to extending dtypes. This work is
      ongoing but enough has been done to allow experimentation and
      feedback.
    • ๐Ÿ“š Extensive documentation improvements comprising some 185 PR merges.
      This work is ongoing and part of the larger project to improve
      NumPy's online presence and usefulness to new users.
    • Further cleanups related to removing Python 2.7. This improves code
      ๐Ÿšš readability and removes technical debt.
    • ๐Ÿ‘ Preliminary support for the upcoming Cython 3.0.

    ๐Ÿ†• New functions

    The random.Generator class has a new permuted function.

    The new function differs from shuffle and permutation in that the
    subarrays indexed by an axis are permuted rather than the axis being
    treated as a separate 1-D array for every combination of the other
    indexes. For example, it is now possible to permute the rows or columns
    of a 2-D array.

    (gh-15121)

    sliding_window_view provides a sliding window view for numpy arrays

    [numpy.lib.stride_tricks.sliding_window_view]{.title-ref} constructs
    views on numpy arrays that offer a sliding or moving window access to
    the array. This allows for the simple implementation of certain
    algorithms, such as running means.

    (gh-17394)

    [numpy.broadcast_shapes]{.title-ref} is a new user-facing function

    [~numpy.broadcast_shapes]{.title-ref} gets the resulting shape from
    broadcasting the given shape tuples against each other.

    >>> np.broadcast_shapes((1, 2), (3, 1))
    (3, 2)
    
    >>> np.broadcast_shapes(2, (3, 1))
    (3, 2)
    
    >>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
    (5, 6, 7)
    

    (gh-17535)

    ๐Ÿ—„ Deprecations

    ๐Ÿ—„ Using the aliases of builtin types like np.int is deprecated

    For a long time, np.int has been an alias of the builtin int. This
    is repeatedly a cause of confusion for newcomers, and is also simply not
    ๐Ÿ‘‰ useful.

    ๐Ÿ—„ These aliases have been deprecated. The table below shows the full list
    ๐Ÿ—„ of deprecated aliases, along with their exact meaning. Replacing uses of
    items in the first column with the contents of the second column will
    ๐Ÿ—„ work identically and silence the deprecation warning.

    In many cases, it may have been intended to use the types from the third
    column. Be aware that use of these types may result in subtle but
    desirable behavior changes.

    ๐Ÿ—„ Deprecated name Identical to Possibly intended numpy type


    numpy.boolbool [numpy.bool_]{.title-ref}
    0๏ธโƒฃ numpy.intint [numpy.int_]{.title-ref} (default int dtype), [numpy.cint]{.title-ref} (C int)
    numpy.floatfloat [numpy.float_]{.title-ref}, [numpy.double]{.title-ref} (equivalent)
    numpy.complexcomplex [numpy.complex_]{.title-ref}, [numpy.cdouble]{.title-ref} (equivalent)
    numpy.objectobject [numpy.object_]{.title-ref}
    numpy.strstr [numpy.str_]{.title-ref}
    numpy.longint (long on Python 2) [numpy.int_]{.title-ref} (C long), [numpy.longlong]{.title-ref} (largest integer type)
    numpy.unicodestr (unicode on Python 2) [numpy.unicode_]{.title-ref}

    ๐Ÿ—„ Note that for technical reasons these deprecation warnings will only be
    emitted on Python 3.7 and above.

    (gh-14882)

    ๐Ÿ—„ Passing shape=None to functions with a non-optional shape argument is deprecated

    ๐Ÿ—„ Previously, this was an alias for passing shape=(). This deprecation
    is emitted by [PyArray_IntpConverter]{.title-ref} in the C API. If your
    ๐Ÿ‘ API is intended to support passing None, then you should check for
    None prior to invoking the converter, so as to be able to distinguish
    None and ().

    (gh-15886)

    Indexing errors will be reported even when index result is empty

    In the future, NumPy will raise an IndexError when an integer array
    index contains out of bound values even if a non-indexed dimension is of
    ๐Ÿ—„ length 0. This will now emit a DeprecationWarning. This can happen when
    the array is previously empty, or an empty slice is involved:

    arr1 = np.zeros((5, 0))
    arr1[[20]]
    arr2 = np.zeros((5, 5))
    arr2[[20], :0]
    

    Previously the non-empty index [20] was not checked for correctness.
    ๐Ÿ—„ It will now be checked causing a deprecation warning which will be
    turned into an error. This also applies to assignments.

    (gh-15900)

    ๐Ÿ—„ Inexact matches for mode and searchside are deprecated

    Inexact and case insensitive matches for mode and searchside were
    ๐Ÿ—„ valid inputs earlier and will give a DeprecationWarning now. For
    ๐Ÿ—„ example, below are some example usages which are now deprecated and will
    ๐Ÿ—„ give a DeprecationWarning:

    import numpy as np
    arr = np.array([[3, 6, 6], [4, 5, 1]])
    # mode: inexact match
    np.ravel_multi_index(arr, (7, 6), mode="clap") # should be "clip"
    # searchside: inexact match
    np.searchsorted(arr[0], 4, side='random') # should be "right"
    

    (gh-16056)

    ๐Ÿ—„ Deprecation of [numpy.dual]{.title-ref}

    ๐Ÿ—„ The module [numpy.dual]{.title-ref} is deprecated. Instead of importing
    functions from [numpy.dual]{.title-ref}, the functions should be
    imported directly from NumPy or SciPy.

    (gh-16156)

    ๐Ÿ—„ outer and ufunc.outer deprecated for matrix

    np.matrix use with [~numpy.outer]{.title-ref} or generic ufunc outer
    calls such as numpy.add.outer. Previously, matrix was converted to an
    array here. This will not be done in the future requiring a manual
    conversion to arrays.

    (gh-16232)

    ๐Ÿ’… Further Numeric Style types Deprecated

    ๐Ÿ’… The remaining numeric-style type codes Bytes0, Str0, Uint32,
    ๐Ÿ—„ Uint64, and Datetime64 have been deprecated. The lower-case variants
    should be used instead. For bytes and string "S" and "U" are further
    alternatives.

    (gh-16554)

    ๐Ÿ—„ The ndincr method of ndindex is deprecated

    ๐Ÿ“š The documentation has warned against using this function since NumPy
    1.8. Use next(it) instead of it.ndincr().

    (gh-17233)

    Future Changes

    Arrays cannot be using subarray dtypes

    Array creation and casting using np.array(arr, dtype) and
    arr.astype(dtype) will use different logic when dtype is a subarray
    dtype such as np.dtype("(2)i,").

    For such a dtype the following behaviour is true:

    res = np.array(arr, dtype)
    
    res.dtype is not dtype
    res.dtype is dtype.base
    res.shape == arr.shape + dtype.shape
    

    But res is filled using the logic:

    res = np.empty(arr.shape + dtype.shape, dtype=dtype.base)
    res[...] = arr
    

    which uses incorrect broadcasting (and often leads to an error). In the
    future, this will instead cast each element individually, leading to the
    same result as:

    res = np.array(arr, dtype=np.dtype(["f", dtype]))["f"]
    

    Which can normally be used to opt-in to the new behaviour.

    This change does not affect np.array(list, dtype="(2)i,") unless the
    list itself includes at least one array. In particular, the behaviour
    is unchanged for a list of tuples.

    (gh-17596)

    ๐Ÿ—„ Expired deprecations

    ๐Ÿ’… The deprecation of numeric style type-codes np.dtype("Complex64")
    (with upper case spelling), is expired. "Complex64" corresponded
    to "complex128" and "Complex32" corresponded to "complex64".

    ๐Ÿ—„ The deprecation of np.sctypeNA and np.typeNA is expired. Both
    ๐Ÿšš have been removed from the public API. Use np.typeDict instead.

    (gh-16554)

    The 14-year deprecation of np.ctypeslib.ctypes_load_library is
    expired. Use ~numpy.ctypeslib.load_library{.interpreted-text
    role="func"} instead, which is identical.

    (gh-17116)

    ๐Ÿšš Financial functions removed

    ๐Ÿšš In accordance with NEP 32, the financial functions are removed from
    ๐Ÿšš NumPy 1.20. The functions that have been removed are fv, ipmt,
    irr, mirr, nper, npv, pmt, ppmt, pv, and rate. These
    functions are available in the
    numpy_financial library.

    (gh-17067)

    Compatibility notes

    Same kind casting in concatenate with axis=None

    When [~numpy.concatenate]{.title-ref} is called with axis=None, the
    flattened arrays were cast with unsafe. Any other axis choice uses
    ๐Ÿ—„ "same kind". That different default has been deprecated and "same
    kind" casting will be used instead. The new casting keyword argument
    can be used to retain the old behaviour.

    (gh-16134)

    NumPy Scalars are cast when assigned to arrays

    When creating or assigning to arrays, in all relevant cases NumPy
    scalars will now be cast identically to NumPy arrays. In particular this
    ๐Ÿ”„ changes the behaviour in some cases which previously raised an error:

    np.array([np.float64(np.nan)], dtype=np.int64)
    

    will succeed and return an undefined result (usually the smallest
    possible integer). This also affects assignments:

    arr[0] = np.float64(np.nan)
    

    At this time, NumPy retains the behaviour for:

    np.array(np.float64(np.nan), dtype=np.int64)
    

    The above changes do not affect Python scalars:

    np.array([float("NaN")], dtype=np.int64)
    

    remains unaffected (np.nan is a Python float, not a NumPy one).
    Unlike signed integers, unsigned integers do not retain this special
    case, since they always behaved more like casting. The following code
    stops raising an error:

    np.array([np.float64(np.nan)], dtype=np.uint64)
    

    To avoid backward compatibility issues, at this time assignment from
    ๐Ÿ‘ datetime64 scalar to strings of too short length remains supported.
    This means that np.asarray(np.datetime64("2020-10-10"), dtype="S5")
    succeeds now, when it failed before. In the long term this may be
    ๐Ÿ—„ deprecated or the unsafe cast may be allowed generally to make
    assignment of arrays and scalars behave consistently.

    Array coercion changes when Strings and other types are mixed

    When strings and other types are mixed, such as:

    np.array(["string", np.float64(3.)], dtype="S")
    

    The results will change, which may lead to string dtypes with longer
    strings in some cases. In particularly, if dtype="S" is not provided
    any numerical value will lead to a string results long enough to hold
    all possible numerical values. (e.g. "S32" for floats). Note that you
    should always provide dtype="S" when converting non-strings to
    strings.

    If dtype="S" is provided the results will be largely identical to
    before, but NumPy scalars (not a Python float like 1.0), will still
    enforce a uniform string length:

    np.array([np.float64(3.)], dtype="S") # gives "S32"
    np.array([3.0], dtype="S") # gives "S3"
    

    Previously the first version gave the same result as the second.

    Array coercion restructure

    Array coercion has been restructured. In general, this should not affect
    ๐Ÿ‘‰ users. In extremely rare corner cases where array-likes are nested:

    np.array([array_like1])
    

    Things will now be more consistent with:

    np.array([np.array(array_like1)])
    

    This could potentially subtly change output for badly defined
    array-likes. We are not aware of any such case where the results were
    not clearly incorrect previously.

    (gh-16200)

    Writing to the result of [numpy.broadcast_arrays]{.title-ref} will export readonly buffers

    โš  In NumPy 1.17 [numpy.broadcast_arrays]{.title-ref} started warning when
    โš  the resulting array was written to. This warning was skipped when the
    array was used through the buffer interface (e.g. memoryview(arr)).
    The same thing will now occur for the two protocols
    __array_interface__, and __array_struct__ returning read-only
    โš  buffers instead of giving a warning.

    (gh-16350)

    ๐Ÿ’… Numeric-style type names have been removed from type dictionaries

    ๐Ÿ”€ To stay in sync with the deprecation for np.dtype("Complex64") and
    ๐Ÿ’… other numeric-style (capital case) types. These were removed from
    np.sctypeDict and np.typeDict. You should use the lower case
    ๐Ÿ”– versions instead. Note that "Complex64" corresponds to "complex128"
    ๐Ÿ’… and "Complex32" corresponds to "complex64". The numpy style (new)
    ๐Ÿ”– versions, denote the full size and not the size of the real/imaginary
    part.

    (gh-16554)

    The operator.concat function now raises TypeError for array arguments

    The previous behavior was to fall back to addition and add the two
    arrays, which was thought to be unexpected behavior for a concatenation
    function.

    (gh-16570)

    ๐Ÿšš nickname attribute removed from ABCPolyBase

    ๐Ÿšš An abstract property nickname has been removed from ABCPolyBase as
    it was no longer used in the derived convenience classes. This may
    affect users who have derived classes from ABCPolyBase and overridden
    the methods for representation and display, e.g. __str__, __repr__,
    _repr_latex, etc.

    (gh-16589)

    float->timedelta and uint64->timedelta promotion will raise a TypeError

    Float and timedelta promotion consistently raises a TypeError.
    np.promote_types("float32", "m8") aligns with
    np.promote_types("m8", "float32") now and both raise a TypeError.
    Previously, np.promote_types("float32", "m8") returned "m8" which
    was considered a bug.

    Uint64 and timedelta promotion consistently raises a TypeError.
    np.promote_types("uint64", "m8") aligns with
    np.promote_types("m8", "uint64") now and both raise a TypeError.
    Previously, np.promote_types("uint64", "m8") returned "m8" which was
    considered a bug.

    (gh-16592)

    numpy.genfromtxt now correctly unpacks structured arrays

    Previously, [numpy.genfromtxt]{.title-ref} failed to unpack if it was
    called with unpack=True and a structured datatype was passed to the
    dtype argument (or dtype=None was passed and a structured datatype
    was inferred). For example:

    >>> data = StringIO("21 58.0\n35 72.0")
    >>> np.genfromtxt(data, dtype=None, unpack=True)
    array([(21, 58.), (35, 72.)], dtype=[('f0', '<i8'), ('f1', '<f8')])
    

    Structured arrays will now correctly unpack into a list of arrays, one
    for each column:

    >>> np.genfromtxt(data, dtype=None, unpack=True)
    [array([21, 35]), array([58., 72.])]
    

    (gh-16650)

    0๏ธโƒฃ mgrid, r_, etc. consistently return correct outputs for non-default precision input

    Previously,
    np.mgrid[np.float32(0.1):np.float32(0.35):np.float32(0.1),] and
    np.r_[0:10:np.complex64(3j)] failed to return meaningful output. This
    ๐Ÿ› bug potentially affects [~numpy.mgrid]{.title-ref},
    [~numpy.ogrid]{.title-ref}, [~numpy.r_]{.title-ref}, and
    [~numpy.c_]{.title-ref} when an input with dtype other than the
    0๏ธโƒฃ default float64 and complex128 and equivalent Python types were
    ๐Ÿ›  used. The methods have been fixed to handle varying precision correctly.

    (gh-16815)

    Boolean array indices with mismatching shapes now properly give IndexError

    Previously, if a boolean array index matched the size of the indexed
    array but not the shape, it was incorrectly allowed in some cases. In
    other cases, it gave an error, but the error was incorrectly a
    ValueError with a message about broadcasting instead of the correct
    IndexError.

    For example, the following used to incorrectly give
    ValueError: operands could not be broadcast together with shapes (2,2) (1,4):

    np.empty((2, 2))[np.array([[True, False, False, False]])]
    

    And the following used to incorrectly return array([], dtype=float64):

    np.empty((2, 2))[np.array([[False, False, False, False]])]
    

    Both now correctly give
    IndexError: boolean index did not match indexed array along dimension 0; dimension is 2 but corresponding boolean dimension is 1.

    (gh-17010)

    Casting errors interrupt Iteration

    When iterating while casting values, an error may stop the iteration
    earlier than before. In any case, a failed casting operation always
    returned undefined, partial results. Those may now be even more
    undefined and partial. For users of the NpyIter C-API such cast errors
    will now cause the [iternext()]{.title-ref} function to return 0 and
    thus abort iteration. Currently, there is no API to detect such an error
    directly. It is necessary to check PyErr_Occurred(), which may be
    problematic in combination with NpyIter_Reset. These issues always
    existed, but new API could be added if required by users.

    (gh-17029)

    f2py generated code may return unicode instead of byte strings

    Some byte strings previously returned by f2py generated code may now be
    unicode strings. This results from the ongoing Python2 -> Python3
    cleanup.

    (gh-17068)

    The first element of the __array_interface__ ["data"] tuple must be an integer

    This has been the documented interface for many years, but there was
    still code that would accept a byte string representation of the pointer
    โž• address. That code has been removed, passing the address as a byte
    string will now raise an error.

    (gh-17241)

    poly1d respects the dtype of all-zero argument

    Previously, constructing an instance of poly1d with all-zero
    coefficients would cast the coefficients to np.float64. This affected
    the output dtype of methods which construct poly1d instances
    internally, such as np.polymul.

    (gh-17577)

    The numpy.i file for swig is Python 3 only.

    โšก๏ธ Uses of Python 2.7 C-API functions have been updated to Python 3 only.
    ๐Ÿ‘‰ Users who need the old version should take it from an older version of
    NumPy.

    (gh-17580)

    Void dtype discovery in np.array

    In calls using np.array(..., dtype="V"), arr.astype("V"), and
    similar a TypeError will now be correctly raised unless all elements
    have the identical void length. An example for this is:

    np.array([b"1", b"12"], dtype="V")
    

    Which previously returned an array with dtype "V2" which cannot
    represent b"1" faithfully.

    (gh-17706)

    C API changes

    Size of np.ndarray and np.void_ changed

    The size of the PyArrayObject and PyVoidScalarObject structures have
    ๐Ÿ”„ changed. The following header definition has been removed:

    #define NPY_SIZEOF_PYARRAYOBJECT (sizeof(PyArrayObject_fields))
    

    since the size must not be considered a compile time constant: it will
    ๐Ÿ”„ change for different runtime versions of NumPy.

    The most likely relevant use are potential subclasses written in C which
    โšก๏ธ will have to be recompiled and should be updated. Please see the
    ๐Ÿ“š documentation for :cPyArrayObject{.interpreted-text role="type"} for
    more details and contact the NumPy developers if you are affected by
    this change.

    NumPy will attempt to give a graceful error but a program expecting a
    ๐Ÿ›  fixed structure size may have undefined behaviour and likely crash.

    (gh-16938)

    ๐Ÿ†• New Features

    where keyword argument for numpy.all and numpy.any functions

    The keyword argument where is added and allows to only consider
    specified elements or subaxes from an array in the Boolean evaluation of
    all and any. This new keyword is available to the functions all
    and any both via numpy directly or in the methods of
    numpy.ndarray.

    Any broadcastable Boolean array or a scalar can be set as where. It
    0๏ธโƒฃ defaults to True to evaluate the functions for all elements in an
    array if where is not set by the user. Examples are given in the
    ๐Ÿ“š documentation of the functions.

    where keyword argument for numpy functions mean, std, var

    The keyword argument where is added and allows to limit the scope in
    the calculation of mean, std and var to only a subset of elements.
    It is available both via numpy directly or in the methods of
    numpy.ndarray.

    Any broadcastable Boolean array or a scalar can be set as where. It
    0๏ธโƒฃ defaults to True to evaluate the functions for all elements in an
    array if where is not set by the user. Examples are given in the
    ๐Ÿ“š documentation of the functions.

    (gh-15852)

    norm=backward, forward keyword options for numpy.fft functions

    The keyword argument option norm=backward is added as an alias for
    0๏ธโƒฃ None and acts as the default option; using it has the direct
    transforms unscaled and the inverse transforms scaled by 1/n.

    Using the new keyword argument option norm=forward has the direct
    transforms scaled by 1/n and the inverse transforms unscaled (i.e.
    0๏ธโƒฃ exactly opposite to the default option norm=backward).

    (gh-16476)

    NumPy is now typed

    Type annotations have been added for large parts of NumPy. There is also
    a new [numpy.typing]{.title-ref} module that contains useful types for
    end-users. The currently available types are

    • ArrayLike: for objects that can be coerced to an array
    • DtypeLike: for objects that can be coerced to a dtype

    (gh-16515)

    numpy.typing is accessible at runtime

    The types in numpy.typing can now be imported at runtime. Code like
    the following will now work:

    from numpy.typing import ArrayLike
    x: ArrayLike = [1, 2, 3, 4]
    

    (gh-16558)

    New __f2py_numpy_version__ attribute for f2py generated modules.

    Because f2py is released together with NumPy, __f2py_numpy_version__
    provides a way to track the version f2py used to generate the module.

    (gh-16594)

    โœ… mypy tests can be run via runtests.py

    ๐Ÿ”ง Currently running mypy with the NumPy stubs configured requires either:

    • Installing NumPy
    • โž• Adding the source directory to MYPYPATH and linking to the
      mypy.ini

    Both options are somewhat inconvenient, so add a --mypy option to
    โœ… runtests that handles setting things up for you. This will also be
    ๐Ÿ‘‰ useful in the future for any typing codegen since it will ensure the
    project is built before type checking.

    (gh-17123)

    Negation of user defined BLAS/LAPACK detection order

    [~numpy.distutils]{.title-ref} allows negation of libraries when
    ๐Ÿšš determining BLAS/LAPACK libraries. This may be used to remove an item
    from the library resolution phase, i.e. to disallow NetLIB libraries one
    could do:

    NPY_BLAS_ORDER='^blas' NPY_LAPACK_ORDER='^lapack' python setup.py build
    

    That will use any of the accelerated libraries instead.

    (gh-17219)

    ๐Ÿ‘ Allow passing optimizations arguments to asv build

    It is now possible to pass -j, --cpu-baseline, --cpu-dispatch and
    ๐Ÿ— --disable-optimization flags to ASV build when the --bench-compare
    argument is used.

    (gh-17284)

    ๐Ÿ‘ The NVIDIA HPC SDK nvfortran compiler is now supported

    ๐Ÿ‘Œ Support for the nvfortran compiler, a version of pgfortran, has been
    โž• added.

    (gh-17344)

    dtype option for cov and corrcoef

    The dtype option is now available for [numpy.cov]{.title-ref} and
    [numpy.corrcoef]{.title-ref}. It specifies which data-type the returned
    0๏ธโƒฃ result should have. By default the functions still return a
    [numpy.float64]{.title-ref} result.

    (gh-17456)

    ๐Ÿ‘Œ Improvements

    Improved string representation for polynomials (__str__)

    The string representation (__str__) of all six polynomial types in
    โšก๏ธ [numpy.polynomial]{.title-ref} has been updated to give the polynomial
    as a mathematical expression instead of an array of coefficients. Two
    ๐Ÿ“ฆ package-wide formats for the polynomial expressions are available - one
    using Unicode characters for superscripts and subscripts, and another
    using only ASCII characters.

    (gh-15666)

    โœ‚ Remove the Accelerate library as a candidate LAPACK library

    ๐Ÿšš Apple no longer supports Accelerate. Remove it.

    (gh-15759)

    Object arrays containing multi-line objects have a more readable repr

    If elements of an object array have a repr containing new lines, then
    the wrapped lines will be aligned by column. Notably, this improves the
    repr of nested arrays:

    >>> np.array([np.eye(2), np.eye(3)], dtype=object)
    array([array([[1., 0.],
                  [0., 1.]]),
           array([[1., 0., 0.],
                  [0., 1., 0.],
                  [0., 0., 1.]])], dtype=object)
    

    (gh-15997)

    ๐Ÿ‘ Concatenate supports providing an output dtype

    ๐Ÿ‘Œ Support was added to [~numpy.concatenate]{.title-ref} to provide an
    output dtype and casting using keyword arguments. The dtype
    argument cannot be provided in conjunction with the out one.

    (gh-16134)

    Thread safe f2py callback functions

    Callback functions in f2py are now thread safe.

    (gh-16519)

    ๐Ÿ‘ [numpy.core.records.fromfile]{.title-ref} now supports file-like objects

    [numpy.rec.fromfile]{.title-ref} can now use file-like objects, for
    instance :pyio.BytesIO{.interpreted-text role="class"}

    (gh-16675)

    ๐Ÿ‘ RPATH support on AIX added to distutils

    This allows SciPy to be built on AIX.

    (gh-16710)

    ๐Ÿ‘‰ Use f90 compiler specified by the command line args

    The compiler command selection for Fortran Portland Group Compiler is
    ๐Ÿ”„ changed in [numpy.distutils.fcompiler]{.title-ref}. This only affects
    the linking command. This forces the use of the executable provided by
    ๐Ÿ’ป the command line option (if provided) instead of the pgfortran
    ๐Ÿ’ป executable. If no executable is provided to the command line option it
    0๏ธโƒฃ defaults to the pgf90 executable, wich is an alias for pgfortran
    ๐Ÿ“š according to the PGI documentation.

    (gh-16730)

    โž• Add NumPy declarations for Cython 3.0 and later

    The pxd declarations for Cython 3.0 were improved to avoid using
    ๐Ÿ—„ deprecated NumPy C-API features. Extension modules built with Cython
    3.0+ that use NumPy can now set the C macro
    NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings
    ๐Ÿ—„ about deprecated API usage.

    (gh-16986)

    ๐Ÿ‘‰ Make the window functions exactly symmetric

    ๐Ÿ‘‰ Make sure the window functions provided by NumPy are symmetric. There
    were previously small deviations from symmetry due to numerical
    ๐Ÿ‘ precision that are now avoided by better arrangement of the computation.

    (gh-17195)

    ๐ŸŽ Performance improvements and changes

    Enable multi-platform SIMD compiler optimizations

    A series of improvements for NumPy infrastructure to pave the way to
    NEP-38 , that can be summarized as follow:

    ๐Ÿ†• New Build Arguments

    • --cpu-baseline to specify the minimal set of required
      0๏ธโƒฃ optimizations, default value is min which provides the minimum
      CPU features that can safely run on a wide range of users
      platforms.
    • --cpu-dispatch to specify the dispatched set of additional
      0๏ธโƒฃ optimizations, default value is max -xop -fma4 which enables
      all CPU features, except for AMD legacy features.
    • --disable-optimization to explicitly disable the whole new
      improvements, It also adds a new C compiler #definition
      called NPY_DISABLE_OPTIMIZATION which it can be used as guard
      for any SIMD code.

    Advanced CPU dispatcher

    A flexible cross-architecture CPU dispatcher built on the top of
    ๐Ÿ‘ Python/Numpy distutils, support all common compilers with a wide
    range of CPU features.

    The new dispatcher requires a special file extension *.dispatch.c
    to mark the dispatch-able C sources. These sources have the
    ability to be compiled multiple times so that each compilation
    ๐Ÿ–จ process represents certain CPU features and provides different

    definitions and flags that affect the code paths.

    New auto-generated C header
    core/src/common/\_cpu\_dispatch.h

    This header is generated by the distutils module ccompiler_opt,
    and contains all the #definitions and headers of instruction sets,
    ๐Ÿ”ง that had been configured through command arguments
    '--cpu-baseline' and '--cpu-dispatch'.

    ๐Ÿ†• New C header core/src/common/npy\_cpu\_dispatch.h

    This header contains all utilities that required for the whole CPU
    dispatching process, it also can be considered as a bridge linking
    the new infrastructure work with NumPy CPU runtime detection.

    โž• Add new attributes to NumPy umath module(Python level)

    • __cpu_baseline__ a list contains the minimal set of required
      ๐Ÿ‘ optimizations that supported by the compiler and platform
      according to the specified values to command argument
      '--cpu-baseline'.
    • __cpu_dispatch__ a list contains the dispatched set of
      โž• additional optimizations that supported by the compiler and
      platform according to the specified values to command argument
      '--cpu-dispatch'.

    โœ… Print the supported CPU features during the run of PytestTester

    (gh-13516)

    ๐Ÿ”„ Changes

    ๐Ÿ”„ Changed behavior of divmod(1., 0.) and related functions

    The changes also assure that different compiler versions have the same
    behavior for nan or inf usages in these operations. This was previously
    compiler dependent, we now force the invalid and divide by zero flags,
    making the results the same across compilers. For example, gcc-5, gcc-8,
    or gcc-9 now result in the same behavior. The changes are tabulated
    below:

    ๐ŸŽ Operator Old Warning New Warning Old Result New Result Works on MacOS


    np.divmod(1.0, 0.0) Invalid Invalid and Dividebyzero nan, nan inf, nan Yes
    np.fmod(1.0, 0.0) Invalid Invalid nan nan No? Yes
    np.floor_divide(1.0, 0.0) Invalid Dividebyzero nan inf Yes
    np.remainder(1.0, 0.0) Invalid Invalid nan nan Yes

    : Summary of New Behavior

    (gh-16161)

    np.linspace on integers now uses floor

    When using a int dtype in [numpy.linspace]{.title-ref}, previously
    float values would be rounded towards zero. Now
    [numpy.floor]{.title-ref} is used instead, which rounds toward -inf.
    This changes the results for negative values. For example, the following
    would previously give:

    >>> np.linspace(-3, 1, 8, dtype=int)
    array([-3, -2, -1, -1, 0, 0, 0, 1])
    

    and now results in:

    >>> np.linspace(-3, 1, 8, dtype=int)
    array([-3, -3, -2, -2, -1, -1, 0, 1])
    

    The former result can still be obtained with:

    >>> np.linspace(-3, 1, 8).astype(int)
    array([-3, -2, -1, -1, 0, 0, 0, 1])
    

    (gh-16841)

    Checksums

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    SHA256

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  • v1.19.4 Changes

    November 02, 2020

    ๐Ÿš€ NumPy 1.19.4 Release Notes

    ๐Ÿš€ NumPy 1.19.4 is a quick release to revert the OpenBLAS library version.
    It was hoped that the 0.3.12 OpenBLAS version used in 1.19.3 would work
    ๐Ÿณ around the Microsoft fmod bug, but problems in some docker environments
    turned up. Instead, 1.19.4 will use the older library and run a sanity
    check on import, raising an error if the problem is detected. Microsoft
    โฌ†๏ธ is aware of the problem and has promised a fix, users should upgrade
    when it becomes available.

    ๐Ÿš€ This release supports Python 3.6-3.9

    Contributors

    ๐Ÿš€ A total of 1 people contributed to this release. People with a "+" by
    their names contributed a patch for the first time.

    • Charles Harris

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 2 pull requests were merged for this release.

    • ๐Ÿ #17679: MAINT: Add check for Windows 10 version 2004 bug.
    • โช #17680: REV: Revert OpenBLAS to 1.19.2 version for 1.19.4

    Checksums

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    SHA256

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  • v1.19.3 Changes

    October 29, 2020

    ๐Ÿš€ NumPy 1.19.3 Release Notes

    ๐Ÿš€ NumPy 1.19.3 is a small maintenace release with two major improvements:

    • ๐Ÿ‘ Python 3.9 binary wheels on all supported platforms.
    • ๐Ÿ OpenBLAS fixes for Windows 10 version 2004 fmod bug.

    ๐Ÿš€ This release supports Python 3.6-3.9 and is linked with OpenBLAS 3.7 to
    ๐Ÿ avoid some of the fmod problems on Windows 10 version 2004. Microsoft is
    โฌ†๏ธ aware of the problem and users should upgrade when the fix becomes
    available, the fix here is limited in scope.

    Contributors

    ๐Ÿš€ A total of 8 people contributed to this release. People with a "+" by
    their names contributed a patch for the first time.

    • Charles Harris
    • Chris Brown +
    • Daniel Vanzo +
    • E. Madison Bray +
    • Hugo van Kemenade +
    • Ralf Gommers
    • Sebastian Berg
    • @danbeibei +

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 10 pull requests were merged for this release.

    • ๐Ÿ— #17298: BLD: set upper versions for build dependencies
    • ๐Ÿ—„ #17336: BUG: Set deprecated fields to null in PyArray_InitArrFuncs
    • ๐Ÿ‘ #17446: ENH: Warn on unsupported Python 3.10+
    • โšก๏ธ #17450: MAINT: Update test_requirements.txt.
    • ๐Ÿ‘ #17522: ENH: Support for the NVIDIA HPC SDK nvfortran compiler
    • โ†ช #17568: BUG: Cygwin Workaround for #14787 on affected platforms
    • #17647: BUG: Fix memory leak of buffer-info cache due to relaxed strides
    • ๐Ÿ‘ #17652: MAINT: Backport openblas_support from master.
    • ๐Ÿ #17653: TST: Add Python 3.9 to the CI testing on Windows, Mac.
    • โœ… #17660: TST: Simplify source path names in test_extending.

    Checksums

    MD5

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    SHA256

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  • v1.19.2 Changes

    September 10, 2020

    ๐Ÿš€ NumPy 1.19.2 Release Notes

    ๐Ÿ›  NumPy 1.19.2 fixes several bugs, prepares for the upcoming Cython 3.x
    ๐Ÿš€ release. and pins setuptools to keep distutils working while upstream
    โœ… modifications are ongoing. The aarch64 wheels are built with the latest
    ๐Ÿš€ manylinux2014 release that fixes the problem of differing page sizes
    ๐Ÿง used by different linux distros.

    ๐Ÿš€ This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
    ๐Ÿ— used when building with Python 3.9 for testing purposes.

    ๐Ÿ There is a known problem with Windows 10 version=2004 and OpenBLAS svd
    ๐Ÿ that we are trying to debug. If you are running that Windows version you
    should use a NumPy version that links to the MKL library, earlier
    ๐Ÿ Windows versions are fine.

    ๐Ÿ‘Œ Improvements

    โž• Add NumPy declarations for Cython 3.0 and later

    The pxd declarations for Cython 3.0 were improved to avoid using
    ๐Ÿ—„ deprecated NumPy C-API features. Extension modules built with Cython
    3.0+ that use NumPy can now set the C macro
    NPY_NO_DEPRECATED_API=NPY_1_7_API_VERSION to avoid C compiler warnings
    ๐Ÿ—„ about deprecated API usage.

    Contributors

    ๐Ÿš€ A total of 8 people contributed to this release. People with a "+" by
    their names contributed a patch for the first time.

    • Charles Harris
    • Matti Picus
    • Pauli Virtanen
    • Philippe Ombredanne +
    • Sebastian Berg
    • Stefan Behnel +
    • Stephan Loyd +
    • Zac Hatfield-Dodds

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 9 pull requests were merged for this release.

    • #16959: TST: Change aarch64 to arm64 in travis.yml.
    • ๐Ÿ”ง #16998: MAINT: Configure hypothesis in np.test() for determinism,...
    • ๐Ÿ“Œ #17000: BLD: pin setuptools < 49.2.0
    • #17015: ENH: Add NumPy declarations to be used by Cython 3.0+
    • ๐Ÿšš #17125: BUG: Remove non-threadsafe sigint handling from fft calculation
    • #17243: BUG: core: fix ilp64 blas dot/vdot/... for strides > int32 max
    • #17244: DOC: Use SPDX license expressions with correct license
    • #17245: DOC: Fix the link to the quick-start in the old API functions
    • #17272: BUG: fix pickling of arrays larger than 2GiB

    Checksums

    MD5

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    SHA256

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  • v1.19.1 Changes

    July 21, 2020

    ๐Ÿš€ NumPy 1.19.1 Release Notes

    ๐Ÿš€ NumPy 1.19.1 fixes several bugs found in the 1.19.0 release, replaces
    ๐Ÿš€ several functions deprecated in the upcoming Python-3.9 release, has
    ๐Ÿ‘Œ improved support for AIX, and has a number of development related
    โšก๏ธ updates to keep CI working with recent upstream changes.

    ๐Ÿš€ This release supports Python 3.6-3.8. Cython >= 0.29.21 needs to be
    ๐Ÿ— used when building with Python 3.9 for testing purposes.

    Contributors

    ๐Ÿš€ A total of 15 people contributed to this release. People with a "+" by
    their names contributed a patch for the first time.

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

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 25 pull requests were merged for this release.

    • #16649: MAINT, CI: disable Shippable cache
    • #16652: MAINT: Replace PyUString_GET_SIZE with PyUnicode_GetLength.
    • ๐Ÿ“„ #16654: REL: Fix outdated docs link
    • ๐Ÿ‘ป #16656: BUG: raise IEEE exception on AIX
    • #16672: BUG: Fix bug in AVX complex absolute while processing array of...
    • #16693: TST: Add extra debugging information to CPU features detection
    • #16703: BLD: Add CPU entry for Emscripten / WebAssembly
    • โœ… #16705: TST: Disable Python 3.9-dev testing.
    • #16714: MAINT: Disable use_hugepages in case of ValueError
    • #16724: BUG: Fix PyArray_SearchSorted signature.
    • ๐Ÿ—„ #16768: MAINT: Fixes for deprecated functions in scalartypes.c.src
    • ๐Ÿšš #16772: MAINT: Remove unneeded call to PyUnicode_READY
    • ๐Ÿ—„ #16776: MAINT: Fix deprecated functions in scalarapi.c
    • ๐Ÿ‘ #16779: BLD, ENH: Add RPATH support for AIX
    • 0๏ธโƒฃ #16780: BUG: Fix default fallback in genfromtxt
    • #16784: BUG: Added missing return after raising error in methods.c
    • โšก๏ธ #16795: BLD: update cython to 0.29.21
    • โš  #16832: MAINT: setuptools 49.2.0 emits a warning, avoid it
    • #16872: BUG: Validate output size in bin- and multinomial
    • ๐Ÿ“Œ #16875: BLD, MAINT: Pin setuptools
    • โœ… #16904: DOC: Reconstruct Testing Guideline.
    • #16905: TST, BUG: Re-raise MemoryError exception in test_large_zip's...
    • #16906: BUG, DOC: Fix bad MPL kwarg.
    • #16916: BUG: Fix string/bytes to complex assignment
    • ๐Ÿš€ #16922: REL: Prepare for NumPy 1.19.1 release

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  • v1.19.0 Changes

    June 20, 2020

    ๐Ÿš€ NumPy 1.19.0 Release Notes

    ๐Ÿš€ This NumPy release is marked by the removal of much technical debt:
    ๐Ÿ‘Œ support for Python 2 has been removed, many deprecations have been
    ๐Ÿ“š expired, and documentation has been improved. The polishing of the
    ๐Ÿ›  random module continues apace with bug fixes and better usability from
    Cython.

    ๐Ÿš€ The Python versions supported for this release are 3.6-3.8. Downstream
    ๐Ÿ‘ 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
    ๐Ÿ“ฆ numpy.compat will remain to support third-party packages, but they
    may be deprecated in a future release. Note that 1.19.x will not
    compile with earlier versions of Python due to the use of f-strings.

    (gh-15233)

    ๐Ÿ—„ Expired deprecations

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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ”„ Changed random variate stream from numpy.random.Generator.dirichlet

    A bug in the generation of random variates for the Dirichlet
    ๐Ÿ›  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
    ๐Ÿš€ 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
    ๐Ÿ›  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)

    ๐Ÿ—„ 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
    ๐Ÿ—„ 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)

    โœ‚ Removed multiarray.int_asbuffer

    As part of the continued removal of Python 2 compatibility,
    ๐Ÿšš 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)

    ๐Ÿšš numpy.distutils.compat has been removed

    This module contained only the function get_exception(), which was
    ๐Ÿ‘‰ used as:

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

    Its purpose was to handle the change in syntax introduced in Python 2.6,
    ๐Ÿ‘ป from except Exception, e: to except Exception as e:, meaning it was
    ๐Ÿ‘ only necessary for codebases supporting Python 2.5 and older.

    (gh-15255)

    issubdtype no longer interprets float as np.floating

    โš  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)

    ๐Ÿ”„ 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

    ๐Ÿ›  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)

    ๐Ÿ‘€ SeedSequence with small seeds no longer conflicts with spawning

    ๐Ÿ‘€ Small seeds (less than 2**96) were previously implicitly 0-padded out
    to 128 bits, the size of the internal entropy pool. When spawned, the
    spawn key was concatenated before the 0-padding. Since the first spawn
    ๐Ÿ‘€ key is (0,), small seeds before the spawn created the same states as
    ๐Ÿ‘€ the first spawned SeedSequence. Now, the seed is explicitly 0-padded
    out to the internal pool size before concatenating the spawn key.
    ๐Ÿ‘€ Spawned SeedSequences will produce different results than in the
    ๐Ÿš€ previous release. Unspawned SeedSequences will still produce the same
    results.

    (gh-16551)

    ๐Ÿ—„ Deprecations

    ๐Ÿ—„ Deprecate automatic dtype=object for ragged input

    ๐Ÿ—„ Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
    per NEP 34. Users should
    โš  explicitly use dtype=object to avoid the warning.

    (gh-15119)

    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of probably unused C-API functions

    The following C-API functions are probably unused and have been
    ๐Ÿ—„ 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
    ๐Ÿ“š with PyObject_Call (see documentation for details).

    (gh-15427)

    ๐Ÿ—„ Converting certain types to dtypes is Deprecated

    The super classes of scalar types, such as np.integer, np.generic,
    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of round for np.complexfloating scalars

    Output of the __round__ dunder method and consequently the Python
    ๐Ÿ—„ built-in round has been deprecated on complex scalars. This does not
    affect np.round.

    (gh-15840)

    ๐Ÿ—„ numpy.ndarray.tostring() is deprecated in favor of tobytes()

    ๐Ÿš€ ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
    ๐Ÿš€ this release ~numpy.ndarray.tostring emitted no warning. The change to
    โš  emit a warning brings NumPy in line with the builtin array.array
    methods of the same name.

    (gh-15867)

    C API changes

    ๐Ÿ‘ Better support for const dimensions in API functions

    The following functions now accept a constant array of npy_intp:

    • PyArray_BroadcastToShape
    • PyArray_IntTupleFromIntp
    • PyArray_OverflowMultiplyList

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

    (gh-15251)

    Const qualify UFunc inner loops

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

    (gh-15355)

    ๐Ÿ†• 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)

    ๐Ÿ‘ 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
    โœ… 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
    0๏ธโƒฃ 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
    ๐Ÿ‘ np.copy(arr, subok=True). This addition better documents that the
    0๏ธโƒฃ default behavior of numpy.copy differs from the numpy.ndarray.copy
    0๏ธโƒฃ 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
    0๏ธโƒฃ are considered equal in comparison (default is False). This matches
    API used in related functions such as numpy.isclose and
    numpy.allclose.

    (gh-16128)

    ๐Ÿ‘Œ Improvements

    ๐Ÿ‘Œ 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)

    ๐Ÿ‘‰ Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

    ๐Ÿ‘‰ 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)

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64,
    ๐ŸŽ which can improve the performance of np.exp with np.float64 input
    5-7x faster than before. The _multiarray_umath.so module has grown
    ๐Ÿง about 63 KB on linux64.

    (gh-15648)

    Ability to disable madvise hugepages

    ๐Ÿง On Linux NumPy has previously added support for madavise hugepages which
    ๐ŸŽ can improve performance for very large arrays. Unfortunately, on older
    0๏ธโƒฃ Kernel versions this led to peformance regressions, thus by default the
    ๐Ÿ‘Œ support has been disabled on kernels before version 4.6. To override the
    0๏ธโƒฃ default, you can use the environment variable:

    NUMPY_MADVISE_HUGEPAGE=0
    

    ๐Ÿ‘ 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)

    ๐Ÿ”„ Changes

    Remove handling of extra argument to __array__

    โœ… 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
    ๐Ÿšš 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
    ๐Ÿ‘€ 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)

    ๐Ÿ›  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
    ๐Ÿ›  distribution. This is now fixed.

    (gh-15872)

    ๐Ÿ›  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
    ๐Ÿ‘€ that are directly seeded.

    ๐ŸŒ 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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  • v1.19.0.rc2 Changes

    May 31, 2020

    ๐Ÿš€ NumPy 1.19.0 Release Notes

    ๐Ÿš€ This NumPy release is marked by the removal of much technical debt:
    ๐Ÿ‘Œ support for Python 2 has been removed, many deprecations have been
    ๐Ÿ“š expired, and documentation has been improved. The polishing of the
    ๐Ÿ›  random module continues apace with bug fixes and better usability from
    Cython.

    ๐Ÿš€ The Python versions supported for this release are 3.6-3.8. Downstream
    ๐Ÿ‘ 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
    ๐Ÿ“ฆ numpy.compat will remain to support third-party packages, but they
    may be deprecated in a future release. Note that 1.19.x will not
    compile with earlier versions of Python due to the use of f-strings.

    (gh-15233)

    ๐Ÿ—„ Expired deprecations

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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ”„ Changed random variate stream from numpy.random.Generator.dirichlet

    A bug in the generation of random variates for the Dirichlet
    ๐Ÿ›  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
    ๐Ÿš€ 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
    ๐Ÿ›  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)

    ๐Ÿ—„ 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
    ๐Ÿ—„ 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)

    โœ‚ Removed multiarray.int_asbuffer

    As part of the continued removal of Python 2 compatibility,
    ๐Ÿšš 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)

    ๐Ÿšš numpy.distutils.compat has been removed

    This module contained only the function get_exception(), which was
    ๐Ÿ‘‰ used as:

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

    Its purpose was to handle the change in syntax introduced in Python 2.6,
    ๐Ÿ‘ป from except Exception, e: to except Exception as e:, meaning it was
    ๐Ÿ‘ only necessary for codebases supporting Python 2.5 and older.

    (gh-15255)

    issubdtype no longer interprets float as np.floating

    โš  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)

    ๐Ÿ”„ 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

    ๐Ÿ›  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)

    ๐Ÿ—„ Deprecations

    ๐Ÿ—„ Deprecate automatic dtype=object for ragged input

    ๐Ÿ—„ Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
    per NEP 34. Users should
    โš  explicitly use dtype=object to avoid the warning.

    (gh-15119)

    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of probably unused C-API functions

    The following C-API functions are probably unused and have been
    ๐Ÿ—„ 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
    ๐Ÿ“š with PyObject_Call (see documentation for details).

    (gh-15427)

    ๐Ÿ—„ Converting certain types to dtypes is Deprecated

    The super classes of scalar types, such as np.integer, np.generic,
    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of round for np.complexfloating scalars

    Output of the __round__ dunder method and consequently the Python
    ๐Ÿ—„ built-in round has been deprecated on complex scalars. This does not
    affect np.round.

    (gh-15840)

    ๐Ÿ—„ numpy.ndarray.tostring() is deprecated in favor of tobytes()

    ๐Ÿš€ ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
    ๐Ÿš€ this release ~numpy.ndarray.tostring emitted no warning. The change to
    โš  emit a warning brings NumPy in line with the builtin array.array
    methods of the same name.

    (gh-15867)

    C API changes

    ๐Ÿ‘ Better support for const dimensions in API functions

    The following functions now accept a constant array of npy_intp:

    • PyArray_BroadcastToShape
    • PyArray_IntTupleFromIntp
    • PyArray_OverflowMultiplyList

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

    (gh-15251)

    Const qualify UFunc inner loops

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

    (gh-15355)

    ๐Ÿ†• 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)

    ๐Ÿ‘ 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
    โœ… 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
    0๏ธโƒฃ 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
    ๐Ÿ‘ np.copy(arr, subok=True). This addition better documents that the
    0๏ธโƒฃ default behavior of numpy.copy differs from the numpy.ndarray.copy
    0๏ธโƒฃ 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
    0๏ธโƒฃ are considered equal in comparison (default is False). This matches
    API used in related functions such as numpy.isclose and
    numpy.allclose.

    (gh-16128)

    ๐Ÿ‘Œ Improvements

    ๐Ÿ‘Œ 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)

    ๐Ÿ‘‰ Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

    ๐Ÿ‘‰ 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)

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64,
    ๐ŸŽ which can improve the performance of np.exp with np.float64 input
    5-7x faster than before. The _multiarray_umath.so module has grown
    ๐Ÿง about 63 KB on linux64.

    (gh-15648)

    Ability to disable madvise hugepages

    ๐Ÿง On Linux NumPy has previously added support for madavise hugepages which
    ๐ŸŽ can improve performance for very large arrays. Unfortunately, on older
    0๏ธโƒฃ Kernel versions this led to peformance regressions, thus by default the
    ๐Ÿ‘Œ support has been disabled on kernels before version 4.6. To override the
    0๏ธโƒฃ default, you can use the environment variable:

    NUMPY_MADVISE_HUGEPAGE=0
    

    ๐Ÿ‘ 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)

    ๐Ÿ”„ Changes

    Remove handling of extra argument to __array__

    โœ… 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
    ๐Ÿšš 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
    ๐Ÿ‘€ 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)

    ๐Ÿ›  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
    ๐Ÿ›  distribution. This is now fixed.

    (gh-15872)

    ๐Ÿ›  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
    ๐Ÿ‘€ that are directly seeded.

    ๐ŸŒ 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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  • v1.19.0.rc1 Changes

    May 18, 2020

    ๐Ÿš€ NumPy 1.19.0 Release Notes

    ๐Ÿš€ This NumPy release is marked by the removal of much technical debt:
    ๐Ÿ‘Œ support for Python 2 has been removed, many deprecations have been
    ๐Ÿ“š expired, and documentation has been improved. The polishing of the
    ๐Ÿ›  random module continues apace with bug fixes and better usability from
    Cython.

    ๐Ÿš€ The Python versions supported for this release are 3.6-3.8. Downstream
    ๐Ÿ‘ 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
    ๐Ÿ“ฆ numpy.compat will remain to support third-party packages, but they
    ๐Ÿš€ may be deprecated in a future release.

    (gh-15233)

    ๐Ÿ—„ Expired deprecations

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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ—„ 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

    ๐Ÿ”„ Changed random variate stream from numpy.random.Generator.dirichlet

    A bug in the generation of random variates for the Dirichlet
    ๐Ÿ›  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
    ๐Ÿš€ 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
    ๐Ÿ›  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)

    ๐Ÿ—„ 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
    ๐Ÿ—„ 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)

    โœ‚ Removed multiarray.int_asbuffer

    As part of the continued removal of Python 2 compatibility,
    ๐Ÿšš 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)

    ๐Ÿšš numpy.distutils.compat has been removed

    This module contained only the function get_exception(), which was
    ๐Ÿ‘‰ used as:

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

    Its purpose was to handle the change in syntax introduced in Python 2.6,
    ๐Ÿ‘ป from except Exception, e: to except Exception as e:, meaning it was
    ๐Ÿ‘ only necessary for codebases supporting Python 2.5 and older.

    (gh-15255)

    issubdtype no longer interprets float as np.floating

    โš  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)

    ๐Ÿ”„ 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

    ๐Ÿ›  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)

    ๐Ÿ—„ Deprecations

    ๐Ÿ—„ Deprecate automatic dtype=object for ragged input

    ๐Ÿ—„ Calling np.array([[1, [1, 2, 3]]) will issue a DeprecationWarning as
    per NEP 34. Users should
    โš  explicitly use dtype=object to avoid the warning.

    (gh-15119)

    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of probably unused C-API functions

    The following C-API functions are probably unused and have been
    ๐Ÿ—„ 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
    ๐Ÿ“š with PyObject_Call (see documentation for details).

    (gh-15427)

    ๐Ÿ—„ Converting certain types to dtypes is Deprecated

    The super classes of scalar types, such as np.integer, np.generic,
    ๐Ÿ—„ 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)

    ๐Ÿ—„ Deprecation of round for np.complexfloating scalars

    Output of the __round__ dunder method and consequently the Python
    ๐Ÿ—„ built-in round has been deprecated on complex scalars. This does not
    affect np.round.

    (gh-15840)

    ๐Ÿ—„ numpy.ndarray.tostring() is deprecated in favor of tobytes()

    ๐Ÿš€ ~numpy.ndarray.tobytes has existed since the 1.9 release, but until
    ๐Ÿš€ this release ~numpy.ndarray.tostring emitted no warning. The change to
    โš  emit a warning brings NumPy in line with the builtin array.array
    methods of the same name.

    (gh-15867)

    C API changes

    ๐Ÿ‘ Better support for const dimensions in API functions

    The following functions now accept a constant array of npy_intp:

    • PyArray_BroadcastToShape
    • PyArray_IntTupleFromIntp
    • PyArray_OverflowMultiplyList

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

    (gh-15251)

    Const qualify UFunc inner loops

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

    (gh-15355)

    ๐Ÿ†• 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)

    ๐Ÿ‘ 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
    โœ… 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
    0๏ธโƒฃ 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
    ๐Ÿ‘ np.copy(arr, subok=True). This addition better documents that the
    0๏ธโƒฃ default behavior of numpy.copy differs from the numpy.ndarray.copy
    0๏ธโƒฃ 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
    0๏ธโƒฃ are considered equal in comparison (default is False). This matches
    API used in related functions such as numpy.isclose and
    numpy.allclose.

    (gh-16128)

    ๐Ÿ‘Œ Improvements

    ๐Ÿ‘Œ 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)

    ๐Ÿ‘‰ Use 64-bit integer size on 64-bit platforms in fallback lapack_lite

    ๐Ÿ‘‰ 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)

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64

    ๐Ÿ‘‰ Use AVX512 intrinsic to implement np.exp when input is np.float64,
    ๐ŸŽ which can improve the performance of np.exp with np.float64 input
    5-7x faster than before. The _multiarray_umath.so module has grown
    ๐Ÿง about 63 KB on linux64.

    (gh-15648)

    Ability to disable madvise hugepages

    ๐Ÿง On Linux NumPy has previously added support for madavise hugepages which
    ๐ŸŽ can improve performance for very large arrays. Unfortunately, on older
    0๏ธโƒฃ Kernel versions this led to peformance regressions, thus by default the
    ๐Ÿ‘Œ support has been disabled on kernels before version 4.6. To override the
    0๏ธโƒฃ default, you can use the environment variable:

    NUMPY_MADVISE_HUGEPAGE=0
    

    ๐Ÿ‘ 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)

    ๐Ÿ”„ Changes

    Remove handling of extra argument to __array__

    โœ… 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
    ๐Ÿšš 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
    ๐Ÿ‘€ 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)

    ๐Ÿ›  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
    ๐Ÿ›  distribution. This is now fixed.

    (gh-15872)

    ๐Ÿ›  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
    ๐Ÿ‘€ that are directly seeded.

    ๐ŸŒ The translation of the jumping code for the MT19937 contained a reversed
    loop ordering. MT19937.jumped matches the Makoto Matsumoto's original
    implementation of the Horner and Sliding Window jump methods.

    (gh-16153)

    Checksums

    MD5

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    SHA256

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  • v1.18.5 Changes

    June 04, 2020

    ๐Ÿš€ NumPy 1.18.5 Release Notes

    ๐Ÿš€ 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.

    ๐Ÿš€ The Python versions supported in this release are 3.5-3.8. Downstream
    ๐Ÿ‘ developers should use Cython >= 0.29.15 for Python 3.8 support and
    OpenBLAS >= 3.7 to avoid errors on the Skylake architecture.

    Contributors

    ๐Ÿš€ 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 +

    ๐Ÿ”€ Pull requests merged

    ๐Ÿš€ A total of 2 pull requests were merged for this release.

    • ๐Ÿ‘ #16439: ENH: enable pickle protocol 5 support for python3.5
    • ๐Ÿ #16441: BUG: relpath fails for different drives on windows

    Checksums

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