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548 lines
18 KiB
548 lines
18 KiB
"""
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Utilities that manipulate strides to achieve desirable effects.
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An explanation of strides can be found in the "ndarray.rst" file in the
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NumPy reference guide.
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"""
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import numpy as np
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from numpy.core.numeric import normalize_axis_tuple
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from numpy.core.overrides import array_function_dispatch, set_module
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__all__ = ['broadcast_to', 'broadcast_arrays', 'broadcast_shapes']
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class DummyArray:
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"""Dummy object that just exists to hang __array_interface__ dictionaries
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and possibly keep alive a reference to a base array.
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"""
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def __init__(self, interface, base=None):
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self.__array_interface__ = interface
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self.base = base
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def _maybe_view_as_subclass(original_array, new_array):
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if type(original_array) is not type(new_array):
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# if input was an ndarray subclass and subclasses were OK,
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# then view the result as that subclass.
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new_array = new_array.view(type=type(original_array))
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# Since we have done something akin to a view from original_array, we
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# should let the subclass finalize (if it has it implemented, i.e., is
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# not None).
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if new_array.__array_finalize__:
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new_array.__array_finalize__(original_array)
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return new_array
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def as_strided(x, shape=None, strides=None, subok=False, writeable=True):
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"""
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Create a view into the array with the given shape and strides.
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.. warning:: This function has to be used with extreme care, see notes.
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Parameters
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----------
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x : ndarray
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Array to create a new.
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shape : sequence of int, optional
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The shape of the new array. Defaults to ``x.shape``.
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strides : sequence of int, optional
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The strides of the new array. Defaults to ``x.strides``.
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subok : bool, optional
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.. versionadded:: 1.10
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If True, subclasses are preserved.
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writeable : bool, optional
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.. versionadded:: 1.12
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If set to False, the returned array will always be readonly.
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Otherwise it will be writable if the original array was. It
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is advisable to set this to False if possible (see Notes).
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Returns
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-------
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view : ndarray
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See also
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--------
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broadcast_to : broadcast an array to a given shape.
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reshape : reshape an array.
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lib.stride_tricks.sliding_window_view :
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userfriendly and safe function for the creation of sliding window views.
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Notes
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-----
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``as_strided`` creates a view into the array given the exact strides
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and shape. This means it manipulates the internal data structure of
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ndarray and, if done incorrectly, the array elements can point to
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invalid memory and can corrupt results or crash your program.
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It is advisable to always use the original ``x.strides`` when
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calculating new strides to avoid reliance on a contiguous memory
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layout.
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Furthermore, arrays created with this function often contain self
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overlapping memory, so that two elements are identical.
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Vectorized write operations on such arrays will typically be
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unpredictable. They may even give different results for small, large,
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or transposed arrays.
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Since writing to these arrays has to be tested and done with great
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care, you may want to use ``writeable=False`` to avoid accidental write
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operations.
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For these reasons it is advisable to avoid ``as_strided`` when
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possible.
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"""
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# first convert input to array, possibly keeping subclass
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x = np.array(x, copy=False, subok=subok)
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interface = dict(x.__array_interface__)
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if shape is not None:
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interface['shape'] = tuple(shape)
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if strides is not None:
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interface['strides'] = tuple(strides)
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array = np.asarray(DummyArray(interface, base=x))
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# The route via `__interface__` does not preserve structured
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# dtypes. Since dtype should remain unchanged, we set it explicitly.
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array.dtype = x.dtype
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view = _maybe_view_as_subclass(x, array)
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if view.flags.writeable and not writeable:
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view.flags.writeable = False
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return view
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def _sliding_window_view_dispatcher(x, window_shape, axis=None, *,
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subok=None, writeable=None):
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return (x,)
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@array_function_dispatch(_sliding_window_view_dispatcher)
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def sliding_window_view(x, window_shape, axis=None, *,
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subok=False, writeable=False):
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"""
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Create a sliding window view into the array with the given window shape.
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Also known as rolling or moving window, the window slides across all
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dimensions of the array and extracts subsets of the array at all window
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positions.
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.. versionadded:: 1.20.0
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Parameters
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----------
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x : array_like
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Array to create the sliding window view from.
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window_shape : int or tuple of int
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Size of window over each axis that takes part in the sliding window.
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If `axis` is not present, must have same length as the number of input
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array dimensions. Single integers `i` are treated as if they were the
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tuple `(i,)`.
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axis : int or tuple of int, optional
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Axis or axes along which the sliding window is applied.
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By default, the sliding window is applied to all axes and
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`window_shape[i]` will refer to axis `i` of `x`.
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If `axis` is given as a `tuple of int`, `window_shape[i]` will refer to
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the axis `axis[i]` of `x`.
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Single integers `i` are treated as if they were the tuple `(i,)`.
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subok : bool, optional
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If True, sub-classes will be passed-through, otherwise the returned
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array will be forced to be a base-class array (default).
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writeable : bool, optional
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When true, allow writing to the returned view. The default is false,
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as this should be used with caution: the returned view contains the
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same memory location multiple times, so writing to one location will
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cause others to change.
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Returns
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-------
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view : ndarray
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Sliding window view of the array. The sliding window dimensions are
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inserted at the end, and the original dimensions are trimmed as
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required by the size of the sliding window.
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That is, ``view.shape = x_shape_trimmed + window_shape``, where
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``x_shape_trimmed`` is ``x.shape`` with every entry reduced by one less
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than the corresponding window size.
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See Also
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--------
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lib.stride_tricks.as_strided: A lower-level and less safe routine for
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creating arbitrary views from custom shape and strides.
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broadcast_to: broadcast an array to a given shape.
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Notes
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-----
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For many applications using a sliding window view can be convenient, but
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potentially very slow. Often specialized solutions exist, for example:
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- `scipy.signal.fftconvolve`
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- filtering functions in `scipy.ndimage`
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- moving window functions provided by
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`bottleneck <https://github.com/pydata/bottleneck>`_.
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As a rough estimate, a sliding window approach with an input size of `N`
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and a window size of `W` will scale as `O(N*W)` where frequently a special
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algorithm can achieve `O(N)`. That means that the sliding window variant
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for a window size of 100 can be a 100 times slower than a more specialized
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version.
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Nevertheless, for small window sizes, when no custom algorithm exists, or
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as a prototyping and developing tool, this function can be a good solution.
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Examples
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--------
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>>> x = np.arange(6)
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>>> x.shape
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(6,)
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>>> v = sliding_window_view(x, 3)
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>>> v.shape
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(4, 3)
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>>> v
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array([[0, 1, 2],
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[1, 2, 3],
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[2, 3, 4],
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[3, 4, 5]])
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This also works in more dimensions, e.g.
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>>> i, j = np.ogrid[:3, :4]
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>>> x = 10*i + j
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>>> x.shape
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(3, 4)
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>>> x
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array([[ 0, 1, 2, 3],
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[10, 11, 12, 13],
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[20, 21, 22, 23]])
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>>> shape = (2,2)
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>>> v = sliding_window_view(x, shape)
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>>> v.shape
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(2, 3, 2, 2)
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>>> v
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array([[[[ 0, 1],
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[10, 11]],
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[[ 1, 2],
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[11, 12]],
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[[ 2, 3],
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[12, 13]]],
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[[[10, 11],
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[20, 21]],
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[[11, 12],
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[21, 22]],
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[[12, 13],
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[22, 23]]]])
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The axis can be specified explicitly:
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>>> v = sliding_window_view(x, 3, 0)
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>>> v.shape
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(1, 4, 3)
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>>> v
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array([[[ 0, 10, 20],
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[ 1, 11, 21],
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[ 2, 12, 22],
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[ 3, 13, 23]]])
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The same axis can be used several times. In that case, every use reduces
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the corresponding original dimension:
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>>> v = sliding_window_view(x, (2, 3), (1, 1))
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>>> v.shape
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(3, 1, 2, 3)
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>>> v
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array([[[[ 0, 1, 2],
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[ 1, 2, 3]]],
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[[[10, 11, 12],
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[11, 12, 13]]],
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[[[20, 21, 22],
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[21, 22, 23]]]])
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Combining with stepped slicing (`::step`), this can be used to take sliding
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views which skip elements:
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>>> x = np.arange(7)
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>>> sliding_window_view(x, 5)[:, ::2]
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array([[0, 2, 4],
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[1, 3, 5],
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[2, 4, 6]])
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or views which move by multiple elements
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>>> x = np.arange(7)
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>>> sliding_window_view(x, 3)[::2, :]
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array([[0, 1, 2],
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[2, 3, 4],
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[4, 5, 6]])
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A common application of `sliding_window_view` is the calculation of running
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statistics. The simplest example is the
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`moving average <https://en.wikipedia.org/wiki/Moving_average>`_:
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>>> x = np.arange(6)
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>>> x.shape
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(6,)
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>>> v = sliding_window_view(x, 3)
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>>> v.shape
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(4, 3)
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>>> v
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array([[0, 1, 2],
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[1, 2, 3],
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[2, 3, 4],
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[3, 4, 5]])
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>>> moving_average = v.mean(axis=-1)
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>>> moving_average
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array([1., 2., 3., 4.])
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Note that a sliding window approach is often **not** optimal (see Notes).
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"""
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window_shape = (tuple(window_shape)
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if np.iterable(window_shape)
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else (window_shape,))
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# first convert input to array, possibly keeping subclass
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x = np.array(x, copy=False, subok=subok)
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window_shape_array = np.array(window_shape)
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if np.any(window_shape_array < 0):
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raise ValueError('`window_shape` cannot contain negative values')
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if axis is None:
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axis = tuple(range(x.ndim))
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if len(window_shape) != len(axis):
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raise ValueError(f'Since axis is `None`, must provide '
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f'window_shape for all dimensions of `x`; '
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f'got {len(window_shape)} window_shape elements '
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f'and `x.ndim` is {x.ndim}.')
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else:
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axis = normalize_axis_tuple(axis, x.ndim, allow_duplicate=True)
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if len(window_shape) != len(axis):
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raise ValueError(f'Must provide matching length window_shape and '
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f'axis; got {len(window_shape)} window_shape '
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f'elements and {len(axis)} axes elements.')
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out_strides = x.strides + tuple(x.strides[ax] for ax in axis)
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# note: same axis can be windowed repeatedly
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x_shape_trimmed = list(x.shape)
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for ax, dim in zip(axis, window_shape):
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if x_shape_trimmed[ax] < dim:
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raise ValueError(
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'window shape cannot be larger than input array shape')
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x_shape_trimmed[ax] -= dim - 1
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out_shape = tuple(x_shape_trimmed) + window_shape
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return as_strided(x, strides=out_strides, shape=out_shape,
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subok=subok, writeable=writeable)
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def _broadcast_to(array, shape, subok, readonly):
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shape = tuple(shape) if np.iterable(shape) else (shape,)
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array = np.array(array, copy=False, subok=subok)
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if not shape and array.shape:
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raise ValueError('cannot broadcast a non-scalar to a scalar array')
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if any(size < 0 for size in shape):
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raise ValueError('all elements of broadcast shape must be non-'
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'negative')
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extras = []
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it = np.nditer(
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(array,), flags=['multi_index', 'refs_ok', 'zerosize_ok'] + extras,
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op_flags=['readonly'], itershape=shape, order='C')
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with it:
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# never really has writebackifcopy semantics
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broadcast = it.itviews[0]
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result = _maybe_view_as_subclass(array, broadcast)
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# In a future version this will go away
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if not readonly and array.flags._writeable_no_warn:
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result.flags.writeable = True
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result.flags._warn_on_write = True
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return result
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def _broadcast_to_dispatcher(array, shape, subok=None):
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return (array,)
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@array_function_dispatch(_broadcast_to_dispatcher, module='numpy')
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def broadcast_to(array, shape, subok=False):
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"""Broadcast an array to a new shape.
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Parameters
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----------
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array : array_like
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The array to broadcast.
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shape : tuple or int
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The shape of the desired array. A single integer ``i`` is interpreted
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as ``(i,)``.
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subok : bool, optional
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If True, then sub-classes will be passed-through, otherwise
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the returned array will be forced to be a base-class array (default).
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Returns
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-------
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broadcast : array
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A readonly view on the original array with the given shape. It is
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typically not contiguous. Furthermore, more than one element of a
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broadcasted array may refer to a single memory location.
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Raises
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------
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ValueError
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If the array is not compatible with the new shape according to NumPy's
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broadcasting rules.
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See Also
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--------
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broadcast
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broadcast_arrays
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broadcast_shapes
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Notes
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-----
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.. versionadded:: 1.10.0
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Examples
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--------
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>>> x = np.array([1, 2, 3])
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>>> np.broadcast_to(x, (3, 3))
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array([[1, 2, 3],
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[1, 2, 3],
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[1, 2, 3]])
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"""
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return _broadcast_to(array, shape, subok=subok, readonly=True)
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def _broadcast_shape(*args):
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"""Returns the shape of the arrays that would result from broadcasting the
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supplied arrays against each other.
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"""
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# use the old-iterator because np.nditer does not handle size 0 arrays
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# consistently
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b = np.broadcast(*args[:32])
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# unfortunately, it cannot handle 32 or more arguments directly
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for pos in range(32, len(args), 31):
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# ironically, np.broadcast does not properly handle np.broadcast
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# objects (it treats them as scalars)
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# use broadcasting to avoid allocating the full array
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b = broadcast_to(0, b.shape)
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b = np.broadcast(b, *args[pos:(pos + 31)])
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return b.shape
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@set_module('numpy')
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def broadcast_shapes(*args):
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"""
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Broadcast the input shapes into a single shape.
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:ref:`Learn more about broadcasting here <basics.broadcasting>`.
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.. versionadded:: 1.20.0
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Parameters
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----------
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`*args` : tuples of ints, or ints
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The shapes to be broadcast against each other.
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Returns
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-------
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tuple
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Broadcasted shape.
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Raises
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------
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ValueError
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If the shapes are not compatible and cannot be broadcast according
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to NumPy's broadcasting rules.
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See Also
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--------
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broadcast
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broadcast_arrays
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broadcast_to
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Examples
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--------
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>>> np.broadcast_shapes((1, 2), (3, 1), (3, 2))
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(3, 2)
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>>> np.broadcast_shapes((6, 7), (5, 6, 1), (7,), (5, 1, 7))
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(5, 6, 7)
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"""
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arrays = [np.empty(x, dtype=[]) for x in args]
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return _broadcast_shape(*arrays)
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def _broadcast_arrays_dispatcher(*args, subok=None):
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return args
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@array_function_dispatch(_broadcast_arrays_dispatcher, module='numpy')
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def broadcast_arrays(*args, subok=False):
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"""
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Broadcast any number of arrays against each other.
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Parameters
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----------
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`*args` : array_likes
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The arrays to broadcast.
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subok : bool, optional
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If True, then sub-classes will be passed-through, otherwise
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the returned arrays will be forced to be a base-class array (default).
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Returns
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-------
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broadcasted : list of arrays
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These arrays are views on the original arrays. They are typically
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not contiguous. Furthermore, more than one element of a
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broadcasted array may refer to a single memory location. If you need
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to write to the arrays, make copies first. While you can set the
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``writable`` flag True, writing to a single output value may end up
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changing more than one location in the output array.
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|
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.. deprecated:: 1.17
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The output is currently marked so that if written to, a deprecation
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warning will be emitted. A future version will set the
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``writable`` flag False so writing to it will raise an error.
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See Also
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--------
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broadcast
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broadcast_to
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broadcast_shapes
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|
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Examples
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--------
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>>> x = np.array([[1,2,3]])
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>>> y = np.array([[4],[5]])
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>>> np.broadcast_arrays(x, y)
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[array([[1, 2, 3],
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[1, 2, 3]]), array([[4, 4, 4],
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[5, 5, 5]])]
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Here is a useful idiom for getting contiguous copies instead of
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non-contiguous views.
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>>> [np.array(a) for a in np.broadcast_arrays(x, y)]
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[array([[1, 2, 3],
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[1, 2, 3]]), array([[4, 4, 4],
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[5, 5, 5]])]
|
|
|
|
"""
|
|
# nditer is not used here to avoid the limit of 32 arrays.
|
|
# Otherwise, something like the following one-liner would suffice:
|
|
# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],
|
|
# order='C').itviews
|
|
|
|
args = [np.array(_m, copy=False, subok=subok) for _m in args]
|
|
|
|
shape = _broadcast_shape(*args)
|
|
|
|
if all(array.shape == shape for array in args):
|
|
# Common case where nothing needs to be broadcasted.
|
|
return args
|
|
|
|
return [_broadcast_to(array, shape, subok=subok, readonly=False)
|
|
for array in args]
|