text: """sgementaxis code.
  
  This code has been implemented by Anne Archibald, and has been discussed on the
  ML."""
  
  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)
  