path: root/numpy/core/multiarray.py

"""
Create the numpy.core.multiarray namespace for backward compatibility. In v1.16
the multiarray and umath c-extension modules were merged into a single
_multiarray_umath extension module. So we replicate the old namespace
by importing from the extension module.

"""

from . import _multiarray_umath
from .overrides import array_function_dispatch
import numpy as np
from numpy.core._multiarray_umath import *
from numpy.core._multiarray_umath import (
    _fastCopyAndTranspose, _flagdict, _insert, _reconstruct, _vec_string,
    _ARRAY_API, _monotonicity
    )

__all__ = [
    '_ARRAY_API', 'ALLOW_THREADS', 'BUFSIZE', 'CLIP', 'DATETIMEUNITS',
    'ITEM_HASOBJECT', 'ITEM_IS_POINTER', 'LIST_PICKLE', 'MAXDIMS',
    'MAY_SHARE_BOUNDS', 'MAY_SHARE_EXACT', 'NEEDS_INIT', 'NEEDS_PYAPI',
    'RAISE', 'USE_GETITEM', 'USE_SETITEM', 'WRAP', '_fastCopyAndTranspose',
    '_flagdict', '_insert', '_reconstruct', '_vec_string', '_monotonicity',
    'add_docstring', 'arange', 'array', 'bincount', 'broadcast',
    'busday_count', 'busday_offset', 'busdaycalendar', 'can_cast',
    'compare_chararrays', 'concatenate', 'copyto', 'correlate', 'correlate2',
    'count_nonzero', 'c_einsum', 'datetime_as_string', 'datetime_data',
    'digitize', 'dot', 'dragon4_positional', 'dragon4_scientific', 'dtype',
    'empty', 'empty_like', 'error', 'flagsobj', 'flatiter', 'format_longfloat',
    'frombuffer', 'fromfile', 'fromiter', 'fromstring', 'getbuffer', 'inner',
    'int_asbuffer', 'interp', 'interp_complex', 'is_busday', 'lexsort',
    'matmul', 'may_share_memory', 'min_scalar_type', 'ndarray', 'nditer',
    'nested_iters', 'newbuffer', 'normalize_axis_index', 'packbits',
    'promote_types', 'putmask', 'ravel_multi_index', 'result_type', 'scalar',
    'set_datetimeparse_function', 'set_legacy_print_mode', 'set_numeric_ops',
    'set_string_function', 'set_typeDict', 'shares_memory', 'test_interrupt',
    'tracemalloc_domain', 'typeinfo', 'unpackbits', 'unravel_index', 'vdot',
    'where', 'zeros']


def _empty_like_dispatcher(prototype, dtype=None, order=None, subok=None):
    return (prototype,)


@array_function_dispatch(_empty_like_dispatcher)
def empty_like(prototype, dtype=None, order='K', subok=True):
    """Return a new array with the same shape and type as a given array.

    Parameters
    ----------
    prototype : array_like
        The shape and data-type of `prototype` define these same attributes
        of the returned array.
    dtype : data-type, optional
        Overrides the data type of the result.

        .. versionadded:: 1.6.0
    order : {'C', 'F', 'A', or 'K'}, optional
        Overrides the memory layout of the result. 'C' means C-order,
        'F' means F-order, 'A' means 'F' if ``prototype`` is Fortran
        contiguous, 'C' otherwise. 'K' means match the layout of ``prototype``
        as closely as possible.

        .. versionadded:: 1.6.0
    subok : bool, optional.
        If True, then the newly created array will use the sub-class
        type of 'a', otherwise it will be a base-class array. Defaults
        to True.

    Returns
    -------
    out : ndarray
        Array of uninitialized (arbitrary) data with the same
        shape and type as `prototype`.

    See Also
    --------
    ones_like : Return an array of ones with shape and type of input.
    zeros_like : Return an array of zeros with shape and type of input.
    full_like : Return a new array with shape of input filled with value.
    empty : Return a new uninitialized array.

    Notes
    -----
    This function does *not* initialize the returned array; to do that use
    `zeros_like` or `ones_like` instead.  It may be marginally faster than
    the functions that do set the array values.

    Examples
    --------
    >>> a = ([1,2,3], [4,5,6])                         # a is array-like
    >>> np.empty_like(a)
    array([[-1073741821, -1073741821,           3],    #random
           [          0,           0, -1073741821]])
    >>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
    >>> np.empty_like(a)
    array([[ -2.00000715e+000,   1.48219694e-323,  -2.00000572e+000],#random
           [  4.38791518e-305,  -2.00000715e+000,   4.17269252e-309]])

    """
    return _multiarray_umath.empty_like(prototype, dtype, order, subok)


def _concatenate_dispatcher(arrays, axis=None, out=None):
    for array in arrays:
        yield array
    yield out


@array_function_dispatch(_concatenate_dispatcher)
def concatenate(arrays, axis=0, out=None):
    """
    concatenate((a1, a2, ...), axis=0, out=None)

    Join a sequence of arrays along an existing axis.

    Parameters
    ----------
    a1, a2, ... : sequence of array_like
        The arrays must have the same shape, except in the dimension
        corresponding to `axis` (the first, by default).
    axis : int, optional
        The axis along which the arrays will be joined.  If axis is None,
        arrays are flattened before use.  Default is 0.
    out : ndarray, optional
        If provided, the destination to place the result. The shape must be
        correct, matching that of what concatenate would have returned if no
        out argument were specified.

    Returns
    -------
    res : ndarray
        The concatenated array.

    See Also
    --------
    ma.concatenate : Concatenate function that preserves input masks.
    array_split : Split an array into multiple sub-arrays of equal or
                  near-equal size.
    split : Split array into a list of multiple sub-arrays of equal size.
    hsplit : Split array into multiple sub-arrays horizontally (column wise)
    vsplit : Split array into multiple sub-arrays vertically (row wise)
    dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
    stack : Stack a sequence of arrays along a new axis.
    hstack : Stack arrays in sequence horizontally (column wise)
    vstack : Stack arrays in sequence vertically (row wise)
    dstack : Stack arrays in sequence depth wise (along third dimension)
    block : Assemble arrays from blocks.

    Notes
    -----
    When one or more of the arrays to be concatenated is a MaskedArray,
    this function will return a MaskedArray object instead of an ndarray,
    but the input masks are *not* preserved. In cases where a MaskedArray
    is expected as input, use the ma.concatenate function from the masked
    array module instead.

    Examples
    --------
    >>> a = np.array([[1, 2], [3, 4]])
    >>> b = np.array([[5, 6]])
    >>> np.concatenate((a, b), axis=0)
    array([[1, 2],
           [3, 4],
           [5, 6]])
    >>> np.concatenate((a, b.T), axis=1)
    array([[1, 2, 5],
           [3, 4, 6]])
    >>> np.concatenate((a, b), axis=None)
    array([1, 2, 3, 4, 5, 6])

    This function will not preserve masking of MaskedArray inputs.

    >>> a = np.ma.arange(3)
    >>> a[1] = np.ma.masked
    >>> b = np.arange(2, 5)
    >>> a
    masked_array(data=[0, --, 2],
                 mask=[False,  True, False],
           fill_value=999999)
    >>> b
    array([2, 3, 4])
    >>> np.concatenate([a, b])
    masked_array(data=[0, 1, 2, 2, 3, 4],
                 mask=False,
           fill_value=999999)
    >>> np.ma.concatenate([a, b])
    masked_array(data=[0, --, 2, 2, 3, 4],
                 mask=[False,  True, False, False, False, False],
           fill_value=999999)

    """
    return _multiarray_umath.concatenate(arrays, axis, out)


def _inner_dispatcher(a, b):
    return (a, b)


@array_function_dispatch(_inner_dispatcher)
def inner(a, b):
    """
    Inner product of two arrays.

    Ordinary inner product of vectors for 1-D arrays (without complex
    conjugation), in higher dimensions a sum product over the last axes.

    Parameters
    ----------
    a, b : array_like
        If `a` and `b` are nonscalar, their last dimensions must match.

    Returns
    -------
    out : ndarray
        `out.shape = a.shape[:-1] + b.shape[:-1]`

    Raises
    ------
    ValueError
        If the last dimension of `a` and `b` has different size.

    See Also
    --------
    tensordot : Sum products over arbitrary axes.
    dot : Generalised matrix product, using second last dimension of `b`.
    einsum : Einstein summation convention.

    Notes
    -----
    For vectors (1-D arrays) it computes the ordinary inner-product::

        np.inner(a, b) = sum(a[:]*b[:])

    More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::

        np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))

    or explicitly::

        np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
             = sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])

    In addition `a` or `b` may be scalars, in which case::

       np.inner(a,b) = a*b

    Examples
    --------
    Ordinary inner product for vectors:

    >>> a = np.array([1,2,3])
    >>> b = np.array([0,1,0])
    >>> np.inner(a, b)
    2

    A multidimensional example:

    >>> a = np.arange(24).reshape((2,3,4))
    >>> b = np.arange(4)
    >>> np.inner(a, b)
    array([[ 14,  38,  62],
           [ 86, 110, 134]])

    An example where `b` is a scalar:

    >>> np.inner(np.eye(2), 7)
    array([[ 7.,  0.],
           [ 0.,  7.]])

    """
    return _multiarray_umath.inner(a, b)


def _where_dispatcher(condition, x=np._NoValue, y=np._NoValue):
    return (condition, x, y)


@array_function_dispatch(_where_dispatcher)
def where(condition, x=np._NoValue, y=np._NoValue):
    """
    where(condition, [x, y])

    Return elements chosen from `x` or `y` depending on `condition`.

    .. note::
        When only `condition` is provided, this function is a shorthand for
        ``np.asarray(condition).nonzero()``. Using `nonzero` directly should be
        preferred, as it behaves correctly for subclasses. The rest of this
        documentation covers only the case where all three arguments are
        provided.

    Parameters
    ----------
    condition : array_like, bool
        Where True, yield `x`, otherwise yield `y`.
    x, y : array_like
        Values from which to choose. `x`, `y` and `condition` need to be
        broadcastable to some shape.

    Returns
    -------
    out : ndarray
        An array with elements from `x` where `condition` is True, and elements
        from `y` elsewhere.

    See Also
    --------
    choose
    nonzero : The function that is called when x and y are omitted

    Notes
    -----
    If all the arrays are 1-D, `where` is equivalent to::

        [xv if c else yv
         for c, xv, yv in zip(condition, x, y)]

    Examples
    --------
    >>> a = np.arange(10)
    >>> a
    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
    >>> np.where(a < 5, a, 10*a)
    array([ 0,  1,  2,  3,  4, 50, 60, 70, 80, 90])

    This can be used on multidimensional arrays too:

    >>> np.where([[True, False], [True, True]],
    ...          [[1, 2], [3, 4]],
    ...          [[9, 8], [7, 6]])
    array([[1, 8],
           [3, 4]])

    The shapes of x, y, and the condition are broadcast together:

    >>> x, y = np.ogrid[:3, :4]
    >>> np.where(x < y, x, 10 + y)  # both x and 10+y are broadcast
    array([[10,  0,  0,  0],
           [10, 11,  1,  1],
           [10, 11, 12,  2]])

    >>> a = np.array([[0, 1, 2],
    ...               [0, 2, 4],
    ...               [0, 3, 6]])
    >>> np.where(a < 4, a, -1)  # -1 is broadcast
    array([[ 0,  1,  2],
           [ 0,  2, -1],
           [ 0,  3, -1]])
    """
    # _multiarray_umath.where only accepts positional arguments
    args = tuple(a for a in (x, y) if a is not np._NoValue)
    return _multiarray_umath.where(condition, *args)