timitdataset/segmentaxis.py at master · davidtob/timitdataset · GitHub

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import numpy as np
from numpy.lib.stride_tricks import as_strided

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.

    Parameters
    ----------
    a : array-like
        The array to segment
    length : int
        The length of each frame
    overlap : int, optional
        The number of array elements by which the frames should overlap
    axis : int, optional
        The axis to operate on; if None, act on the flattened array
    end : {'cut', 'wrap', 'end'}, optional
        What to do with the last frame, if the array is not evenly
        divisible into pieces.

            - 'cut'   Simply discard the extra values
            - 'wrap'  Copy values from the beginning of the array
            - 'pad'   Pad with a constant value

    endvalue : object
        The value to use for end='pad'


    Examples
    --------
    >>> segment_axis(arange(10), 4, 2)
    array([[0, 1, 2, 3],
           [2, 3, 4, 5],
           [4, 5, 6, 7],
           [6, 7, 8, 9]])

    Notes
    -----
    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').

    use as_strided

    """

    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 as_strided(a, strides=newstrides, shape=newshape)
    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 as_strided(a, strides=newstrides, shape=newshape)