data chunking with overlapping windows! uses stride tricks so no data is copied; it just produces a view!

chunk_data.py

from __future__ import division
import numpy as np
from numpy.lib.stride_tricks import as_strided as ast

def chunk_data(data,window_size,overlap_size=0,flatten_inside_window=True):
    assert data.ndim == 1 or data.ndim == 2
    if data.ndim == 1:
        data = data.reshape((-1,1))

    # get the number of overlapping windows that fit into the data
    num_windows = (data.shape[0] - window_size) // (window_size - overlap_size) + 1
    overhang = data.shape[0] - (num_windows*window_size - (num_windows-1)*overlap_size)

    # if there's overhang, need an extra window and a zero pad on the data
    # (numpy 1.7 has a nice pad function I'm not using here)
    if overhang != 0:
        num_windows += 1
        newdata = np.zeros((num_windows*window_size - (num_windows-1)*overlap_size,data.shape[1]))
        newdata[:data.shape[0]] = data
        data = newdata

    sz = data.dtype.itemsize
    ret = ast(
            data,
            shape=(num_windows,window_size*data.shape[1]),
            strides=((window_size-overlap_size)*data.shape[1]*sz,sz)
            )

    if flatten_inside_window:
        return ret
    else:
        return ret.reshape((num_windows,-1,data.shape[1]))

In [53]: chunk_data.chunk_data(arange(5),3,1)
Out[53]:
array([[0, 1, 2],
       [2, 3, 4]])

In [54]: chunk_data.chunk_data(arange(5),3,2)
Out[54]:
array([[0, 1, 2],
       [1, 2, 3],
       [2, 3, 4]])
In [55]: chunk_data.chunk_data(arange(12).reshape((-1,2)),3,2)
Out[55]:
array([[ 0,  1,  2,  3,  4,  5],
       [ 2,  3,  4,  5,  6,  7],
       [ 4,  5,  6,  7,  8,  9],
       [ 6,  7,  8,  9, 10, 11]])

In the last one, the first row contains the first three vectors (which are pairs), etc.