qlat_utils.get_threshold_i_arr¶
- qlat_utils.get_threshold_i_arr(data_arr, threshold_arr, axis=-1)[source]¶
return
i_arr# letshape=np.moveaxis(data_arr, axis, -1)[..., 0].shape`` threshold_arr = np.broadcast_to(threshold_arr, shape) `` such that `` for index in np.ndindex(shape):q.interp(data_arr[index], i_arr[index]) approx threshold_arr[index]