numpy.lib.stride

module documentation

(source)

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.

Class	`DummyArray`	Dummy object that just exists to hang __array_interface__ dictionaries and possibly keep alive a reference to a base array.
Function	`as_strided`	Create a view into the array with the given shape and strides.
Function	`broadcast_arrays`	Broadcast any number of arrays against each other.
Function	`broadcast_shapes`	Broadcast the input shapes into a single shape.
Function	`broadcast_to`	Broadcast an array to a new shape.
Function	`sliding_window_view`	Create a sliding window view into the array with the given window shape.
Function	`_broadcast_arrays_dispatcher`	Undocumented
Function	`_broadcast_shape`	Returns the shape of the arrays that would result from broadcasting the supplied arrays against each other.
Function	`_broadcast_to`	Undocumented
Function	`_broadcast_to_dispatcher`	Undocumented
Function	`_maybe_view_as_subclass`	Undocumented
Function	`_sliding_window_view_dispatcher`	Undocumented

def as_strided(x, shape=None, strides=None, subok=False, writeable=True): (source) ¶

Create a view into the array with the given shape and strides.

Warning

This function has to be used with extreme care, see notes.

See Also

broadcast_to: broadcast an array to a given shape.
reshape: reshape an array.
lib.stride_tricks.sliding_window_view: userfriendly and safe function for the creation of sliding window views.

Notes

as_strided creates a view into the array given the exact strides and shape. This means it manipulates the internal data structure of ndarray and, if done incorrectly, the array elements can point to invalid memory and can corrupt results or crash your program. It is advisable to always use the original x.strides when calculating new strides to avoid reliance on a contiguous memory layout.

Furthermore, arrays created with this function often contain self overlapping memory, so that two elements are identical. Vectorized write operations on such arrays will typically be unpredictable. They may even give different results for small, large, or transposed arrays.

Since writing to these arrays has to be tested and done with great care, you may want to use writeable=False to avoid accidental write operations.

For these reasons it is advisable to avoid as_strided when possible.

Parameters
x:`ndarray`	Array to create a new.
shape:`sequence` of `int`, optional	The shape of the new array. Defaults to `x.shape`.
strides:`sequence` of `int`, optional	The strides of the new array. Defaults to `x.strides`.
subok:`bool`, optional	New in version 1.10. If True, subclasses are preserved.
writeable:`bool`, optional	New in version 1.12. If set to False, the returned array will always be readonly. Otherwise it will be writable if the original array was. It is advisable to set this to False if possible (see Notes).
Returns
`ndarray`	view

@array_function_dispatch(_broadcast_arrays_dispatcher, module='numpy')
def broadcast_arrays(*args, subok=False): (source) ¶

Broadcast any number of arrays against each other.

See Also

broadcast, broadcast_to, broadcast_shapes

Examples

>>> x = np.array([[1,2,3]])
>>> y = np.array([[4],[5]])
>>> np.broadcast_arrays(x, y)
[array([[1, 2, 3],
       [1, 2, 3]]), array([[4, 4, 4],
       [5, 5, 5]])]

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]]), array([[4, 4, 4],
       [5, 5, 5]])]

Parameters
*args:`array_likes`	The arrays to broadcast.
subok:`bool`, optional	If True, then sub-classes will be passed-through, otherwise the returned arrays will be forced to be a base-class array (default).
Returns
`list` of `arrays`	broadcasted - 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. While you can set the `writable` flag True, writing to a single output value may end up changing more than one location in the output array. Deprecated since version 1.17: The output is currently marked so that if written to, a deprecation warning will be emitted. A future version will set the `writable` flag False so writing to it will raise an error.

@set_module('numpy')
def broadcast_shapes(*args): (source) ¶

Broadcast the input shapes into a single shape.

:ref:`Learn more about broadcasting here <basics.broadcasting>`.

New in version 1.20.0.

See Also

broadcast, broadcast_arrays, broadcast_to

Examples

>>> np.broadcast_shapes((1, 2), (3, 1), (3, 2))
(3, 2)

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

Parameters
*args:`tuples` of `ints`, or `ints`	The shapes to be broadcast against each other.
Returns
`tuple`	Broadcasted shape.
Raises
`ValueError`	If the shapes are not compatible and cannot be broadcast according to NumPy's broadcasting rules.

@array_function_dispatch(_broadcast_to_dispatcher, module='numpy')
def broadcast_to(array, shape, subok=False): (source) ¶

Broadcast an array to a new shape.

See Also

broadcast, broadcast_arrays, broadcast_shapes

Notes

New in version 1.10.0.

Examples

>>> x = np.array([1, 2, 3])
>>> np.broadcast_to(x, (3, 3))
array([[1, 2, 3],
       [1, 2, 3],
       [1, 2, 3]])

Parameters
array:`array_like`	The array to broadcast.
shape:`tuple` or `int`	The shape of the desired array. A single integer `i` is interpreted as `(i,)`.
subok:`bool`, optional	If True, then sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).
Returns
`array`	broadcast - A readonly view on the original array with the given shape. It is typically not contiguous. Furthermore, more than one element of a broadcasted array may refer to a single memory location.
Raises
`ValueError`	If the array is not compatible with the new shape according to NumPy's broadcasting rules.

@array_function_dispatch(_sliding_window_view_dispatcher)
def sliding_window_view(x, window_shape, axis=None, *, subok=False, writeable=False): (source) ¶

Create a sliding window view into the array with the given window shape.

Also known as rolling or moving window, the window slides across all dimensions of the array and extracts subsets of the array at all window positions.

New in version 1.20.0.

See Also

lib.stride_tricks.as_strided: A lower-level and less safe routine for creating arbitrary views from custom shape and strides.
broadcast_to: broadcast an array to a given shape.

Notes

For many applications using a sliding window view can be convenient, but potentially very slow. Often specialized solutions exist, for example:

scipy.signal.fftconvolve
filtering functions in scipy.ndimage
moving window functions provided by bottleneck.

As a rough estimate, a sliding window approach with an input size of N and a window size of W will scale as O(N*W) where frequently a special algorithm can achieve O(N). That means that the sliding window variant for a window size of 100 can be a 100 times slower than a more specialized version.

Nevertheless, for small window sizes, when no custom algorithm exists, or as a prototyping and developing tool, this function can be a good solution.

Examples

>>> x = np.arange(6)
>>> x.shape
(6,)
>>> v = sliding_window_view(x, 3)
>>> v.shape
(4, 3)
>>> v
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4],
       [3, 4, 5]])

This also works in more dimensions, e.g.

>>> i, j = np.ogrid[:3, :4]
>>> x = 10*i + j
>>> x.shape
(3, 4)
>>> x
array([[ 0,  1,  2,  3],
       [10, 11, 12, 13],
       [20, 21, 22, 23]])
>>> shape = (2,2)
>>> v = sliding_window_view(x, shape)
>>> v.shape
(2, 3, 2, 2)
>>> v
array([[[[ 0,  1],
         [10, 11]],
        [[ 1,  2],
         [11, 12]],
        [[ 2,  3],
         [12, 13]]],
       [[[10, 11],
         [20, 21]],
        [[11, 12],
         [21, 22]],
        [[12, 13],
         [22, 23]]]])

The axis can be specified explicitly:

>>> v = sliding_window_view(x, 3, 0)
>>> v.shape
(1, 4, 3)
>>> v
array([[[ 0, 10, 20],
        [ 1, 11, 21],
        [ 2, 12, 22],
        [ 3, 13, 23]]])

The same axis can be used several times. In that case, every use reduces the corresponding original dimension:

>>> v = sliding_window_view(x, (2, 3), (1, 1))
>>> v.shape
(3, 1, 2, 3)
>>> v
array([[[[ 0,  1,  2],
         [ 1,  2,  3]]],
       [[[10, 11, 12],
         [11, 12, 13]]],
       [[[20, 21, 22],
         [21, 22, 23]]]])

Combining with stepped slicing (::step), this can be used to take sliding views which skip elements:

>>> x = np.arange(7)
>>> sliding_window_view(x, 5)[:, ::2]
array([[0, 2, 4],
       [1, 3, 5],
       [2, 4, 6]])

or views which move by multiple elements

>>> x = np.arange(7)
>>> sliding_window_view(x, 3)[::2, :]
array([[0, 1, 2],
       [2, 3, 4],
       [4, 5, 6]])

A common application of sliding_window_view is the calculation of running statistics. The simplest example is the moving average:

>>> x = np.arange(6)
>>> x.shape
(6,)
>>> v = sliding_window_view(x, 3)
>>> v.shape
(4, 3)
>>> v
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4],
       [3, 4, 5]])
>>> moving_average = v.mean(axis=-1)
>>> moving_average
array([1., 2., 3., 4.])

Note that a sliding window approach is often not optimal (see Notes).

Parameters
x:`array_like`	Array to create the sliding window view from.
window_shape:`int` or `tuple` of `int`	Size of window over each axis that takes part in the sliding window. If `axis` is not present, must have same length as the number of input array dimensions. Single integers `i` are treated as if they were the tuple `(i,)`.
axis:`int` or `tuple` of `int`, optional	Axis or axes along which the sliding window is applied. By default, the sliding window is applied to all axes and `window_shape[i]` will refer to axis `i` of `x`. If `axis` is given as a `tuple of int`, `window_shape[i]` will refer to the axis `axis[i]` of `x`. Single integers `i` are treated as if they were the tuple `(i,)`.
subok:`bool`, optional	If True, sub-classes will be passed-through, otherwise the returned array will be forced to be a base-class array (default).
writeable:`bool`, optional	When true, allow writing to the returned view. The default is false, as this should be used with caution: the returned view contains the same memory location multiple times, so writing to one location will cause others to change.
Returns
`ndarray`	view - Sliding window view of the array. The sliding window dimensions are inserted at the end, and the original dimensions are trimmed as required by the size of the sliding window. That is, `view.shape = x_shape_trimmed + window_shape`, where `x_shape_trimmed` is `x.shape` with every entry reduced by one less than the corresponding window size.

def _broadcast_arrays_dispatcher(*args, subok=None): (source) ¶

Undocumented

def _broadcast_shape(*args): (source) ¶

Returns the shape of the arrays that would result from broadcasting the supplied arrays against each other.

def _broadcast_to(array, shape, subok, readonly): (source) ¶

Undocumented

def _broadcast_to_dispatcher(array, shape, subok=None): (source) ¶

Undocumented

def _maybe_view_as_subclass(original_array, new_array): (source) ¶

Undocumented

def _sliding_window_view_dispatcher(x, window_shape, axis=None, *, subok=None, writeable=None): (source) ¶

Undocumented

numpy.lib.stride_tricks

`numpy.lib.stride_tricks`