qlat_utils.get_threshold_x_arr

qlat_utils.get_threshold_x_arr(data_arr, data_x_arr, threshold_arr, axis=-1)

return x_arrn ::n

data_x_arr.shape == (data_arr.shape[axis],)n

let shape = np.moveaxis(data_arr, axis, -1)[…, 0].shapen ::n

threshold_arr = np.broadcast_to(threshold_arr, shape)n

such thatn ::n

for index in np.ndindex(shape):

q.interp_x(data_arr[index], data_x_arr, x_arr[index]) approx threshold_arr[index]