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def sliding_window(data, size, stepsize=1, padded=False, axis=-1, copy=True):
Calculate a sliding window over a signal
data : numpy array
The array to be slided over.
size : int
The sliding window size
stepsize : int
The sliding window stepsize. Defaults to 1.
axis : int
The axis to slide over. Defaults to the last axis.
copy : bool
Return strided array as copy to avoid sideffects when manipulating the
output array.
data : numpy array
A matrix where row in last dimension consists of one instance
of the sliding window.
- Be wary of setting `copy` to `False` as undesired sideffects with the
output values may occurr.
>>> a = numpy.array([1, 2, 3, 4, 5])
>>> sliding_window(a, size=3)
array([[1, 2, 3],
[2, 3, 4],
[3, 4, 5]])
>>> sliding_window(a, size=3, stepsize=2)
array([[1, 2, 3],
[3, 4, 5]])
See Also
pieces : Calculate number of pieces available by sliding
if axis >= data.ndim:
raise ValueError(
"Axis value out of range"
if stepsize < 1:
raise ValueError(
"Stepsize may not be zero or negative"
if size > data.shape[axis]:
raise ValueError(
"Sliding window size may not exceed size of selected axis"
shape = list(data.shape)
shape[axis] = numpy.floor(data.shape[axis] / stepsize - size / stepsize + 1).astype(int)
strides = list(data.strides)
strides[axis] *= stepsize
strided = numpy.lib.stride_tricks.as_strided(
data, shape=shape, strides=strides
if copy:
return strided.copy()
return strided

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delaram19 commented Jun 22, 2017

Tnx for sharing, it helped me a lot!

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