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#
# Copyright (C) 2016-2020 by Nathan Lovato, Daniel Oakey, Razvan Radulescu, and contributors
#
# This file is part of Power Sequencer.
#
# Power Sequencer is free software: you can redistribute it and/or modify it under the terms of the
# GNU General Public License as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# Power Sequencer is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with Power Sequencer. If
# not, see <https://www.gnu.org/licenses/>.
#
import numpy as np
import warnings
def segment_axis(a, length, overlap=0, axis=None, end="cut", endvalue=0):
"""Generate a new array that chops the given array along the given axis
into overlapping frames.
example:
>>> segment_axis(arange(10), 4, 2)
array([[0, 1, 2, 3],
[2, 3, 4, 5],
[4, 5, 6, 7],
[6, 7, 8, 9]])
arguments:
a The array to segment
length The length of each frame
overlap The number of array elements by which the frames should overlap
axis The axis to operate on; if None, act on the flattened array
end What to do with the last frame, if the array is not evenly
divisible into pieces. Options are:
'cut' Simply discard the extra values
'wrap' Copy values from the beginning of the array
'pad' Pad with a constant value
endvalue The value to use for end='pad'
The array is not copied unless necessary (either because it is unevenly
strided and being flattened or because end is set to 'pad' or 'wrap').
"""
if axis is None:
a = np.ravel(a) # may copy
axis = 0
l = a.shape[axis]
if overlap >= length:
raise ValueError("frames cannot overlap by more than 100%")
if overlap < 0 or length <= 0:
raise ValueError("overlap must be nonnegative and length must " "be positive")
if l < length or (l - length) % (length - overlap):
if l > length:
roundup = length + (1 + (l - length) // (length - overlap)) * (length - overlap)
rounddown = length + ((l - length) // (length - overlap)) * (length - overlap)
else:
roundup = length
rounddown = 0
assert rounddown < l < roundup
assert roundup == rounddown + (length - overlap) or (roundup == length and rounddown == 0)
a = a.swapaxes(-1, axis)
if end == "cut":
a = a[..., :rounddown]
elif end in ["pad", "wrap"]: # copying will be necessary
s = list(a.shape)
s[-1] = roundup
b = np.empty(s, dtype=a.dtype)
b[..., :l] = a
if end == "pad":
b[..., l:] = endvalue
elif end == "wrap":
b[..., l:] = a[..., : roundup - l]
a = b
a = a.swapaxes(-1, axis)
l = a.shape[axis]
if l == 0:
raise ValueError(
"Not enough data points to segment array in 'cut' mode; " "try 'pad' or 'wrap'"
)
assert l >= length
assert (l - length) % (length - overlap) == 0
n = 1 + (l - length) // (length - overlap)
s = a.strides[axis]
newshape = a.shape[:axis] + (n, length) + a.shape[axis + 1 :]
newstrides = a.strides[:axis] + ((length - overlap) * s, s) + a.strides[axis + 1 :]
try:
return np.ndarray.__new__(
np.ndarray, strides=newstrides, shape=newshape, buffer=a, dtype=a.dtype
)
except TypeError:
warnings.warn("Problem with ndarray creation forces copy.")
a = a.copy()
# Shape doesn't change but strides does
newstrides = a.strides[:axis] + ((length - overlap) * s, s) + a.strides[axis + 1 :]
return np.ndarray.__new__(
np.ndarray, strides=newstrides, shape=newshape, buffer=a, dtype=a.dtype
)
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