timeshuffle_bootstrap.py

def timeshuffle_bootstrap(dview, statistic, data, axis=-1, n_boot=1000):
    """Perform bootstrap resampling to assess the significance of a statistic by reshuffling in time.
    
    Args:
        dview: IPython client (i.e. dview = IPython.parallel.Client()[:]) for parallelization.
        statistic: Function that takes the dataset and computes some statistic (or array of statistics).
        data: Data from which the statistic is computed.
        axis: Axis in the data the shuffle along (usually time).
    """
    T = data.shape[axis]
    shufflers = [np.random.permutation(T) for _ in xrange(n_boot)]
    dview.push({'data': data, 'statistic':statistic, 'axis':axis}, block=True)
    
    def shuffle_and_compute(shuffler):
        global data, statistic, axis
        shuffled = data.take(shuffler, axis)
        return statistic(shuffled)
    
    shuffles = dview.map_sync(shuffle_and_compute, shufflers)
    original = statistic(data)
    shuffle_arr = np.concatenate([shuffle[...,None] for shuffle in shuffles], axis=-1)
    mean = shuffle_arr.mean(axis=-1)
    sd = np.sqrt(shuffle_arr.var(axis=-1, ddof=1))                
    zscore = (original - mean) / sdå
    return zscore