def subsample()

in point_e/util/point_cloud.py [0:0]

• 17 lines of code
• 6 McCabe index (conditional complexity)

    def subsample(self, indices: np.ndarray, average_neighbors: bool = False) -> "PointCloud":
        if not average_neighbors:
            return PointCloud(
                coords=self.coords[indices],
                channels={k: v[indices] for k, v in self.channels.items()},
            )

        new_coords = self.coords[indices]
        neighbor_indices = PointCloud(coords=new_coords, channels={}).nearest_points(self.coords)

        # Make sure every point points to itself, which might not
        # be the case if points are duplicated or there is rounding
        # error.
        neighbor_indices[indices] = np.arange(len(indices))

        new_channels = {}
        for k, v in self.channels.items():
            v_sum = np.zeros_like(v[: len(indices)])
            v_count = np.zeros_like(v[: len(indices)])
            np.add.at(v_sum, neighbor_indices, v)
            np.add.at(v_count, neighbor_indices, 1)
            new_channels[k] = v_sum / v_count
        return PointCloud(coords=new_coords, channels=new_channels)