Skip to content

Instantly share code, notes, and snippets.

Embed
What would you like to do?
def sliding_window(data, size, stepsize=1, padded=False, axis=-1, copy=True):
"""
Calculate a sliding window over a signal
Parameters
----------
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.
Returns
-------
data : numpy array
A matrix where row in last dimension consists of one instance
of the sliding window.
Notes
-----
- Be wary of setting `copy` to `False` as undesired sideffects with the
output values may occurr.
Examples
--------
>>> 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)
shape.append(size)
strides = list(data.strides)
strides[axis] *= stepsize
strides.append(data.strides[axis])
strided = numpy.lib.stride_tricks.as_strided(
data, shape=shape, strides=strides
)
if copy:
return strided.copy()
else:
return strided
@delaram19

This comment has been minimized.

Copy link

delaram19 commented Jun 22, 2017

Tnx for sharing, it helped me a lot!

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.