How to use sortednp - 3 common examples

sorted_s1_indexes, sorted_s2_indexes = find_indexes_of_next_arrival_times(
            sorted_s1_trip_values,
            sorted_s1_departure_time_values,
            sorted_s2_trip_values,
            sorted_s2_arrival_time_values
        )

    else:
        # for non-loop routes, there should be at most 1 departure/arrival for a particular trip ID.
        # sort by trip ID, then use snp for better performance to find the indexes of matching trip IDs
        sort_order = np.argsort(s1_trip_values)
        sorted_s1_trip_values = s1_trip_values[sort_order]

        sorted_s2_trip_values, sorted_s2_arrival_time_values = sort_parallel(s2_trip_values, s2_arrival_time_values)

        _, (sorted_s1_indexes, sorted_s2_indexes) = snp.intersect(sorted_s1_trip_values, sorted_s2_trip_values, indices=True)

    # start with an array of all nans
    s1_s2_arrival_time_values = np.full(len(s1_trip_values), np.nan)

    # find original s1 indexes corresponding to sorted s1 indexes
    result_indexes = sort_order[sorted_s1_indexes]

    s1_s2_arrival_time_values[result_indexes] = sorted_s2_arrival_time_values[sorted_s2_indexes]

    trip_min = (s1_s2_arrival_time_values - s1_departure_time_values) / 60

    return trip_min, s1_s2_arrival_time_values

# If s1 and s2 are the same stop, this will compute the time to complete 1 full loop

        if not assume_sorted:
            s1_departure_time_values, s1_trip_values = sort_parallel(s1_departure_time_values, s1_trip_values)
            s2_arrival_time_values, s2_trip_values = sort_parallel(s2_arrival_time_values, s2_trip_values)

        s1_indexes, s2_indexes = find_indexes_of_next_arrival_times(
            s1_trip_values, s1_departure_time_values,
            s2_trip_values, s2_arrival_time_values
        )
    else:
        if not assume_sorted:
            s1_trip_values, s1_departure_time_values = sort_parallel(s1_trip_values, s1_departure_time_values)
            s2_trip_values, s2_arrival_time_values = sort_parallel(s2_trip_values, s2_arrival_time_values)

        _, (s1_indexes, s2_indexes) = snp.intersect(s1_trip_values, s2_trip_values, indices=True)

    return (s2_arrival_time_values[s2_indexes] - s1_departure_time_values[s1_indexes]) / 60

def match_keypoints(self, kps_0, kps_1, ids_0, ids_1, ratio, reprojThresh):
        """Match keypoints between 2 frames and find the homography matrix."""
        intersection, indices = snp.intersect(
            ids_0.reshape(-1), ids_1.reshape(-1), indices=True)
        kps_0 = kps_0.reshape(-1, 2)  # 2D keypoints
        kps_1 = kps_1.reshape(-1, 2)  # 2D keypoints

        # At least 4 matches to compute an homography
        if ids_0[indices[0]].shape[0] > 12:
            # Homography between points in kps_1 and kps_0
            homography, status = cv2.findHomography(
                kps_1[indices[1]], kps_0[indices[0]], cv2.RANSAC)
            # reprojThresh)

            if status.sum() != kps_0[indices[0]].shape[0]:
                print('WARNING: cv2.findHomography can not match all detected'
                      ' keypoints (%d/%d matches).' % (
                        status.sum(), kps_0[indices[0]].shape[0]))
            return homography