def runningstd(t, data, width):
    """Compute the running standard deviation of a time series.

    Returns `t_new`, `std_r`.
    """
    ne = len(t) - width
    t_new = np.zeros(ne)
    std_r = np.zeros(ne)
    for i in range(ne):
        t_new[i] = np.mean(t[i:i+width+1])
        std_r[i] = scipy.stats.nanstd(data[i:i+width+1])
    return t_new, std_r
def runningstd ( t , data , width ) : ne = len ( t ) - width t_new = np . zeros ( ne ) std_r = np . zeros ( ne ) for i in range ( ne ) : t_new [ i ] = np . mean ( t [ i : i + width + 1 ] ) std_r [ i ] = scipy . stats . nanstd ( data [ i : i + width + 1 ] ) return t_new , std_r
Compute the running standard deviation of a time series.

    Returns `t_new`, `std_r`.