"""
Utilities that manipulate strides to achieve desirable effects.

An explanation of strides can be found in the "ndarray.rst" file in the
NumPy reference guide.

"""
from __future__ import division, absolute_import, print_function

import numpy as np

__all__ = ['broadcast_arrays']

class DummyArray(object):
    """Dummy object that just exists to hang __array_interface__ dictionaries
    and possibly keep alive a reference to a base array.
    """

    def __init__(self, interface, base=None):
        self.__array_interface__ = interface
        self.base = base

def as_strided(x, shape=None, strides=None):
    """ Make an ndarray from the given array with the given shape and strides.
    """
    interface = dict(x.__array_interface__)
    if shape is not None:
        interface['shape'] = tuple(shape)
    if strides is not None:
        interface['strides'] = tuple(strides)
    array = np.asarray(DummyArray(interface, base=x))
    # Make sure dtype is correct in case of custom dtype
    if array.dtype.kind == 'V':
        array.dtype = x.dtype
    return array

def broadcast_arrays(*args):
    """
    Broadcast any number of arrays against each other.

    Parameters
    ----------
    `*args` : array_likes
        The arrays to broadcast.

    Returns
    -------
    broadcasted : list of arrays
        These arrays are views on the original arrays.  They are typically
        not contiguous.  Furthermore, more than one element of a
        broadcasted array may refer to a single memory location.  If you
        need to write to the arrays, make copies first.

    Examples
    --------
    >>> x = np.array([[1,2,3]])
    >>> y = np.array([[1],[2],[3]])
    >>> np.broadcast_arrays(x, y)
    [array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]]), array([[1, 1, 1],
           [2, 2, 2],
           [3, 3, 3]])]

    Here is a useful idiom for getting contiguous copies instead of
    non-contiguous views.

    >>> [np.array(a) for a in np.broadcast_arrays(x, y)]
    [array([[1, 2, 3],
           [1, 2, 3],
           [1, 2, 3]]), array([[1, 1, 1],
           [2, 2, 2],
           [3, 3, 3]])]

    """
    args = [np.asarray(_m) for _m in args]
    shapes = [x.shape for x in args]
    if len(set(shapes)) == 1:
        # Common case where nothing needs to be broadcasted.
        return args
    shapes = [list(s) for s in shapes]
    strides = [list(x.strides) for x in args]
    nds = [len(s) for s in shapes]
    biggest = max(nds)
    # Go through each array and prepend dimensions of length 1 to each of
    # the shapes in order to make the number of dimensions equal.
    for i in range(len(args)):
        diff = biggest - nds[i]
        if diff > 0:
            shapes[i] = [1] * diff + shapes[i]
            strides[i] = [0] * diff + strides[i]
    # Chech each dimension for compatibility. A dimension length of 1 is
    # accepted as compatible with any other length.
    common_shape = []
    for axis in range(biggest):
        lengths = [s[axis] for s in shapes]
        unique = set(lengths + [1])
        if len(unique) > 2:
            # There must be at least two non-1 lengths for this axis.
            raise ValueError("shape mismatch: two or more arrays have "
                "incompatible dimensions on axis %r." % (axis,))
        elif len(unique) == 2:
            # There is exactly one non-1 length. The common shape will take
            # this value.
            unique.remove(1)
            new_length = unique.pop()
            common_shape.append(new_length)
            # For each array, if this axis is being broadcasted from a
            # length of 1, then set its stride to 0 so that it repeats its
            # data.
            for i in range(len(args)):
                if shapes[i][axis] == 1:
                    shapes[i][axis] = new_length
                    strides[i][axis] = 0
        else:
            # Every array has a length of 1 on this axis. Strides can be
            # left alone as nothing is broadcasted.
            common_shape.append(1)

    # Construct the new arrays.
    broadcasted = [as_strided(x, shape=sh, strides=st) for (x, sh, st) in
                   zip(args, shapes, strides)]
    return broadcasted