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# -*- coding: utf-8 -*-
# Copyright 2014 João Felipe Santos, jfsantos@emt.inrs.ca
#
# This file is part of the SRMRpy library, and is licensed under the
# MIT license: https://github.com/jfsantos/SRMRpy/blob/master/LICENSE
"""segmentaxis code, originally in scikits.talkbox (https://pypi.python.org/pypi/scikits.talkbox)
This code has been implemented by Anne Archibald, and has been discussed on the
ML."""
from __future__ import division
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)
if end in ['pad','wrap']:
b[..., :l] = a
if end == 'pad':
b[..., l:] = endvalue
elif end == 'wrap':
b[..., l:] = a[..., :roundup-l]
a = b
elif end == 'delay':
s = list(a.shape)
l_orig = l
l += overlap
# if l not divisible by length, pad last frame with zeros
if l_orig % (length-overlap):
roundup = length + (1+(l-length)//(length-overlap))*(length-overlap)
else:
roundup = l
s[-1] = roundup
b = np.empty(s,dtype=a.dtype)
b[..., :(overlap)] = endvalue
b[..., (overlap):(l_orig+overlap)] = a
b[..., (l_orig+overlap):] = endvalue
a = b
else:
raise ValueError("end has to be either 'cut', 'pad', 'wrap', or 'delay'.")
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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