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Name	Size	Permission	Date
benchmarks	[ DIR ]	drwxr-xr-x	2021-09-16 10:54
tests	[ DIR ]	drwxr-xr-x	2021-09-16 10:54
__init__.py	972 B	-rw-r--r--	2013-04-07 01:04
__init__.pyc	1.03 KB	-rw-r--r--	2018-04-10 19:40
__init__.pyo	1.03 KB	-rw-r--r--	2018-04-10 19:40
_compiled_base.so	27.96 KB	-rwxr-xr-x	2018-04-10 19:40
_datasource.py	20.13 KB	-rw-r--r--	2013-04-07 01:04
_datasource.pyc	20.46 KB	-rw-r--r--	2018-04-10 19:40
_datasource.pyo	20.46 KB	-rw-r--r--	2018-04-10 19:40
_iotools.py	29.54 KB	-rw-r--r--	2013-04-07 01:04
_iotools.pyc	28.3 KB	-rw-r--r--	2018-04-10 19:40
_iotools.pyo	28.3 KB	-rw-r--r--	2018-04-10 19:40
arraypad.py	26.12 KB	-rw-r--r--	2013-04-07 01:04
arraypad.pyc	24.33 KB	-rw-r--r--	2018-04-10 19:40
arraypad.pyo	24.33 KB	-rw-r--r--	2018-04-10 19:40
arraysetops.py	11.67 KB	-rw-r--r--	2013-04-07 01:04
arraysetops.pyc	11.45 KB	-rw-r--r--	2018-04-10 19:40
arraysetops.pyo	11.45 KB	-rw-r--r--	2018-04-10 19:40
arrayterator.py	7.07 KB	-rw-r--r--	2013-04-07 01:04
arrayterator.pyc	7.56 KB	-rw-r--r--	2018-04-10 19:40
arrayterator.pyo	7.56 KB	-rw-r--r--	2018-04-10 19:40
financial.py	22.97 KB	-rw-r--r--	2013-04-07 01:04
financial.pyc	23.53 KB	-rw-r--r--	2018-04-10 19:40
financial.pyo	23.53 KB	-rw-r--r--	2018-04-10 19:40
format.py	19.31 KB	-rw-r--r--	2013-04-07 01:04
format.pyc	17.33 KB	-rw-r--r--	2018-04-10 19:40
format.pyo	17.33 KB	-rw-r--r--	2018-04-10 19:40
function_base.py	112.61 KB	-rw-r--r--	2013-04-07 01:04
function_base.pyc	109.34 KB	-rw-r--r--	2018-04-10 19:40
function_base.pyo	109.3 KB	-rw-r--r--	2018-04-10 19:40
index_tricks.py	25.51 KB	-rw-r--r--	2013-04-07 01:04
index_tricks.pyc	26.43 KB	-rw-r--r--	2018-04-10 19:40
index_tricks.pyo	26.43 KB	-rw-r--r--	2018-04-10 19:40
info.py	6.09 KB	-rw-r--r--	2013-04-07 01:04
info.pyc	6.27 KB	-rw-r--r--	2018-04-10 19:40
info.pyo	6.27 KB	-rw-r--r--	2018-04-10 19:40
npyio.py	63.79 KB	-rw-r--r--	2013-04-07 01:04
npyio.pyc	51.81 KB	-rw-r--r--	2018-04-10 19:40
npyio.pyo	51.81 KB	-rw-r--r--	2018-04-10 19:40
polynomial.py	36.57 KB	-rw-r--r--	2013-04-07 01:04
polynomial.pyc	38.57 KB	-rw-r--r--	2018-04-10 19:40
polynomial.pyo	38.57 KB	-rw-r--r--	2018-04-10 19:40
recfunctions.py	34.06 KB	-rw-r--r--	2013-04-07 01:04
recfunctions.pyc	29.82 KB	-rw-r--r--	2018-04-10 19:40
recfunctions.pyo	29.82 KB	-rw-r--r--	2018-04-10 19:40
scimath.py	13.68 KB	-rw-r--r--	2013-04-07 01:04
scimath.pyc	15.5 KB	-rw-r--r--	2018-04-10 19:40
scimath.pyo	15.5 KB	-rw-r--r--	2018-04-10 19:40
setup.py	588 B	-rw-r--r--	2013-04-07 01:04
setup.pyc	916 B	-rw-r--r--	2018-04-10 19:40
setup.pyo	916 B	-rw-r--r--	2018-04-10 19:40
setupscons.py	470 B	-rw-r--r--	2013-04-07 01:04
setupscons.pyc	822 B	-rw-r--r--	2018-04-10 19:40
setupscons.pyo	822 B	-rw-r--r--	2018-04-10 19:40
shape_base.py	23.79 KB	-rw-r--r--	2013-04-07 01:04
shape_base.pyc	24.56 KB	-rw-r--r--	2018-04-10 19:40
shape_base.pyo	24.56 KB	-rw-r--r--	2018-04-10 19:40
stride_tricks.py	3.89 KB	-rw-r--r--	2013-04-07 01:04
stride_tricks.pyc	3.66 KB	-rw-r--r--	2018-04-10 19:40
stride_tricks.pyo	3.66 KB	-rw-r--r--	2018-04-10 19:40
twodim_base.py	23.08 KB	-rw-r--r--	2013-04-07 01:04
twodim_base.pyc	25.48 KB	-rw-r--r--	2018-04-10 19:40
twodim_base.pyo	25.48 KB	-rw-r--r--	2018-04-10 19:40
type_check.py	15.44 KB	-rw-r--r--	2013-04-07 01:04
type_check.pyc	16.49 KB	-rw-r--r--	2018-04-10 19:40
type_check.pyo	16.49 KB	-rw-r--r--	2018-04-10 19:40
ufunclike.py	4.67 KB	-rw-r--r--	2013-04-07 01:04
ufunclike.pyc	5.34 KB	-rw-r--r--	2018-04-10 19:40
ufunclike.pyo	5.34 KB	-rw-r--r--	2018-04-10 19:40
user_array.py	7.3 KB	-rw-r--r--	2013-04-07 01:04
user_array.pyc	15.35 KB	-rw-r--r--	2018-04-10 19:40
user_array.pyo	15.35 KB	-rw-r--r--	2018-04-10 19:40
utils.py	35.92 KB	-rw-r--r--	2013-04-07 01:04
utils.pyc	32.34 KB	-rw-r--r--	2018-04-10 19:40
utils.pyo	32.34 KB	-rw-r--r--	2018-04-10 19:40

Rename

"""
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.

"""
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)
    return np.asarray(DummyArray(interface, base=x))

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.

>>> map(np.array, 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 = map(np.asarray, 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