functional.py 文件源码

def find_spikes(data, spike_thresh):
    data -= np.median(np.median(np.median(data, axis=0), axis=0), axis=0)
    slice_mean = np.median(np.median(data, axis=0), axis=0)
    t_z = _robust_zscore(slice_mean)
    spikes = np.abs(t_z) > spike_thresh
    spike_inds = np.transpose(spikes.nonzero())
    # mask out the spikes and recompute z-scores using variance uncontaminated with spikes.
    # This will catch smaller spikes that may have been swamped by big
    # ones.
    data.mask[:, :, spike_inds[:, 0], spike_inds[:, 1]] = True
    slice_mean2 = np.median(np.median(data, axis=0), axis=0)
    t_z = _robust_zscore(slice_mean2)

    spikes = np.logical_or(spikes, np.abs(t_z) > spike_thresh)
    spike_inds = [tuple(i) for i in np.transpose(spikes.nonzero())]
    return spike_inds, t_z