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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) | |