parameters: (data, window_length, hop_length)
return_statement: return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
docstring: Convert array into a sequence of successive possibly overlapping frames.
  
    An n-dimensional array of shape (num_samples, ...) is converted into an
    (n+1)-D array of shape (num_frames, window_length, ...), where each frame
    starts hop_length points after the preceding one.
  
    This is accomplished using stride_tricks, so the original data is not
    copied.  However, there is no zero-padding, so any incomplete frames at the
    end are not included.
  
    Args:
      data: np.array of dimension N >= 1.
      window_length: Number of samples in each frame.
      hop_length: Advance (in samples) between each window.
  
    Returns:
      (N+1)-D np.array with as many rows as there are complete frames that can be
      extracted.
docstring_summary: Convert array into a sequence of successive possibly overlapping frames.
docstring_tokens: Convert array into a sequence of successive possibly overlapping frames .
function: def frame(data, window_length, hop_length):
      """Convert array into a sequence of successive possibly overlapping frames.
  
    An n-dimensional array of shape (num_samples, ...) is converted into an
    (n+1)-D array of shape (num_frames, window_length, ...), where each frame
    starts hop_length points after the preceding one.
  
    This is accomplished using stride_tricks, so the original data is not
    copied.  However, there is no zero-padding, so any incomplete frames at the
    end are not included.
  
    Args:
      data: np.array of dimension N >= 1.
      window_length: Number of samples in each frame.
      hop_length: Advance (in samples) between each window.
  
    Returns:
      (N+1)-D np.array with as many rows as there are complete frames that can be
      extracted.
    """
      num_samples = data.shape[0]
      num_frames = 1 + int(np.floor((num_samples - window_length) / hop_length))
      shape = (num_frames, window_length) + data.shape[1:]
      strides = (data.strides[0] * hop_length,) + data.strides
      return np.lib.stride_tricks.as_strided(data, shape=shape, strides=strides)
function_tokens: def frame ( data , window_length , hop_length ) : num_samples = data . shape [ 0 ] num_frames = 1 + int ( np . floor ( ( num_samples - window_length ) / hop_length ) ) shape = ( num_frames , window_length ) + data . shape [ 1 : ] strides = ( data . strides [ 0 ] * hop_length , ) + data . strides return np . lib . stride_tricks . as_strided ( data , shape = shape , strides = strides )