Normalise Python typing syntax to 3.8+

examples/python/api_demo/main.py

@@ -8,6 +8,7 @@
 * Run all demos: `examples/python/api_demo/main.py`
 * Run specific demo: `examples/python/api_demo/main.py --demo rects`
 """
+from __future__ import annotations
 
 import argparse
 import logging

examples/python/arkitscenes/download_dataset.py

@@ -1,11 +1,13 @@
 # Copied from https://github.com/apple/ARKitScenes/blob/main/download_data.py
 # Licensing information: https://github.com/apple/ARKitScenes/blob/main/LICENSE
+from __future__ import annotations
+
 import math
 import os
 import subprocess
 import zipfile
 from pathlib import Path
-from typing import Final, List, Optional
+from typing import Final
 
 import pandas as pd
 
@@ -62,7 +64,7 @@
 ]
 
 
-def raw_files(video_id: str, assets: List[str], metadata: pd.DataFrame) -> List[str]:
+def raw_files(video_id: str, assets: list[str], metadata: pd.DataFrame) -> list[str]:
     file_names = []
     for asset in assets:
         if HIGRES_DEPTH_ASSET_NAME == asset:
@@ -153,7 +155,7 @@ def download_laser_scanner_point_clouds_for_video(video_id: str, metadata: pd.Da
         download_laser_scanner_point_clouds(point_cloud_id, visit_id, download_dir)
 
 
-def laser_scanner_point_clouds_for_visit_id(visit_id: int, download_dir: Path) -> List[str]:
+def laser_scanner_point_clouds_for_visit_id(visit_id: int, download_dir: Path) -> list[str]:
     point_cloud_to_visit_id_mapping_filename = "laser_scanner_point_clouds_mapping.csv"
     if not os.path.exists(point_cloud_to_visit_id_mapping_filename):
         point_cloud_to_visit_id_mapping_url = (
@@ -209,11 +211,11 @@ def get_metadata(dataset: str, download_dir: Path) -> pd.DataFrame:
 
 def download_data(
     dataset: str,
-    video_ids: List[str],
-    dataset_splits: List[str],
+    video_ids: list[str],
+    dataset_splits: list[str],
     download_dir: Path,
     keep_zip: bool,
-    raw_dataset_assets: Optional[List[str]] = None,
+    raw_dataset_assets: list[str] | None = None,
     should_download_laser_scanner_point_cloud: bool = False,
 ) -> None:
     """
@@ -307,15 +309,20 @@ def ensure_recording_available(video_id: str, include_highres: bool) -> Path:
     Returns the path to the recording for a given video_id.
 
     Args:
-        video_id (str): Identifier for the recording.
+    ----
+    video_id (str):
+        Identifier for the recording.
+    include_highres (bool):
+        Whether to include the high resolution recording.
 
     Returns
     -------
-        Path: Path object representing the path to the recording.
+    Path: Path object representing the path to the recording.
 
     Raises
     ------
-        AssertionError: If the recording path does not exist.
+    AssertionError:
+        If the recording path does not exist.
     """
     recording_path = ensure_recording_downloaded(video_id, include_highres)
     assert recording_path.exists(), f"Recording path {recording_path} does not exist."

examples/python/arkitscenes/main.py

@@ -1,9 +1,11 @@
 #!/usr/bin/env python3
+from __future__ import annotations
+
 import argparse
 import json
 import os
 from pathlib import Path, PosixPath
-from typing import Any, Dict, List, Tuple
+from typing import Any, Tuple
 
 import cv2
 import matplotlib.pyplot as plt
@@ -30,13 +32,13 @@
 assert set(ORIENTATION.keys()) == set(AVAILABLE_RECORDINGS)
 
 
-def load_json(js_path: Path) -> Dict[str, Any]:
-    with open(js_path, "r") as f:
-        json_data = json.load(f)  # type: Dict[str, Any]
+def load_json(js_path: Path) -> dict[str, Any]:
+    with open(js_path) as f:
+        json_data: dict[str, Any] = json.load(f)
     return json_data
 
 
-def log_annotated_bboxes(annotation: Dict[str, Any]) -> Tuple[npt.NDArray[np.float64], List[str], List[Color]]:
+def log_annotated_bboxes(annotation: dict[str, Any]) -> tuple[npt.NDArray[np.float64], list[str], list[Color]]:
     """
     Logs annotated oriented bounding boxes to Rerun.
 
@@ -229,11 +231,11 @@ def project_3d_bboxes_to_2d_keypoints(
 def log_camera(
     intri_path: Path,
     frame_id: str,
-    poses_from_traj: Dict[str, rr.TranslationRotationScale3D],
+    poses_from_traj: dict[str, rr.TranslationRotationScale3D],
     entity_id: str,
     bboxes: npt.NDArray[np.float64],
-    bbox_labels: List[str],
-    colors: List[Color],
+    bbox_labels: list[str],
+    colors: list[Color],
 ) -> None:
     """Logs camera transform and 3D bounding boxes in the image frame."""
     w, h, fx, fy, cx, cy = np.loadtxt(intri_path)
@@ -256,12 +258,14 @@ def log_camera(
     rr.log_pinhole(f"{entity_id}", child_from_parent=intrinsic, width=w, height=h)
 
 
-def read_camera_from_world(traj_string: str) -> Tuple[str, rr.TranslationRotationScale3D]:
+def read_camera_from_world(traj_string: str) -> tuple[str, rr.TranslationRotationScale3D]:
     """
     Reads out camera_from_world transform from trajectory string.
 
     Args:
-        traj_string: A space-delimited file where each line represents a camera position at a particular timestamp.
+    ----
+    traj_string:
+        A space-delimited file where each line represents a camera position at a particular timestamp.
             The file has seven columns:
             * Column 1: timestamp
             * Columns 2-4: rotation (axis-angle representation in radians)
@@ -276,7 +280,8 @@ def read_camera_from_world(traj_string: str) -> Tuple[str, rr.TranslationRotatio
 
     Raises
     ------
-        AssertionError: If the input string does not contain 7 tokens.
+    AssertionError:
+        If the input string does not contain 7 tokens.
     """
     tokens = traj_string.split()  # Split the input string into tokens
     assert len(tokens) == 7, f"Input string must have 7 tokens, but found {len(tokens)}."
@@ -295,7 +300,7 @@ def read_camera_from_world(traj_string: str) -> Tuple[str, rr.TranslationRotatio
     return (ts, camera_from_world)
 
 
-def find_closest_frame_id(target_id: str, frame_ids: Dict[str, Any]) -> str:
+def find_closest_frame_id(target_id: str, frame_ids: dict[str, Any]) -> str:
     """Finds the closest frame id to the target id."""
     target_value = float(target_id)
     closest_id = min(frame_ids.keys(), key=lambda x: abs(float(x) - target_value))
@@ -307,11 +312,16 @@ def log_arkit(recording_path: Path, include_highres: bool) -> None:
     Logs ARKit recording data using Rerun.
 
     Args:
-        recording_path (Path): The path to the ARKit recording.
+    ----
+    recording_path (Path):
+        The path to the ARKit recording.
+
+    include_highres (bool):
+        Whether to include high resolution data.
 
     Returns
     -------
-        None
+    None
     """
     video_id = recording_path.stem
     lowres_image_dir = recording_path / "lowres_wide"
@@ -330,7 +340,7 @@ def log_arkit(recording_path: Path, include_highres: bool) -> None:
 
     # dict of timestamp to pose which is a tuple of translation and quaternion
     camera_from_world_dict = {}
-    with open(traj_path, "r", encoding="utf-8") as f:
+    with open(traj_path, encoding="utf-8") as f:
         trajectory = f.readlines()
 
     for line in trajectory:

examples/python/blueprint/main.py

@@ -1,4 +1,5 @@
 """Example of using the blueprint APIs to configure Rerun."""
+from __future__ import annotations
 
 import argparse
 

examples/python/car/main.py

@@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 """Shows how to use the Rerun SDK."""
+from __future__ import annotations
 
 import argparse
 from dataclasses import dataclass
-from typing import Iterator, Tuple
+from typing import Iterator
 
 import cv2
 import numpy as np
@@ -57,8 +58,8 @@ class DummyCar:
 
     def __init__(
         self,
-        center: Tuple[int, int],
-        size: Tuple[int, int],
+        center: tuple[int, int],
+        size: tuple[int, int],
         distance_mm: float,
     ):
         self.center: npt.NDArray[np.int32] = np.array(center, dtype=np.int32)
@@ -157,7 +158,7 @@ class SampleFrame:
     camera: CameraParameters
     depth_image_mm: npt.NDArray[np.float32]
     rgb_image: npt.NDArray[np.float32]
-    car_bbox: Tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]
+    car_bbox: tuple[npt.NDArray[np.int32], npt.NDArray[np.int32]]
 
 
 class SimpleDepthCamera:

examples/python/clock/main.py

@@ -1,14 +1,14 @@
 #!/usr/bin/env python3
-
 """
 An example showing usage of `log_arrow`.
 
 An analog clock is built with Rerun Arrow3D primitives.
 """
+from __future__ import annotations
 
 import argparse
 import math
-from typing import Final, Tuple
+from typing import Final
 
 import numpy as np
 import rerun as rr  # pip install rerun-sdk
@@ -23,7 +23,7 @@
 
 
 def log_clock(steps: int) -> None:
-    def rotate(angle: float, len: float) -> Tuple[float, float, float]:
+    def rotate(angle: float, len: float) -> tuple[float, float, float]:
         return (
             len * math.sin(angle),
             len * math.cos(angle),

examples/python/colmap/main.py

@@ -1,12 +1,14 @@
 #!/usr/bin/env python3
 """Example of using Rerun to log and visualize the output of COLMAP's sparse reconstruction."""
+from __future__ import annotations
+
 import io
 import os
 import re
 import zipfile
 from argparse import ArgumentParser
 from pathlib import Path
-from typing import Any, Final, Optional, Tuple
+from typing import Any, Final
 
 import cv2
 import numpy as np
@@ -22,7 +24,7 @@
 FILTER_MIN_VISIBLE: Final = 500
 
 
-def scale_camera(camera: Camera, resize: Tuple[int, int]) -> Tuple[Camera, npt.NDArray[np.float_]]:
+def scale_camera(camera: Camera, resize: tuple[int, int]) -> tuple[Camera, npt.NDArray[np.float_]]:
     """Scale the camera intrinsics to match the resized image."""
     assert camera.model == "PINHOLE"
     new_width = resize[0]
@@ -88,9 +90,7 @@ def download_with_progress(url: str) -> io.BytesIO:
     return zip_file
 
 
-def read_and_log_sparse_reconstruction(
-    dataset_path: Path, filter_output: bool, resize: Optional[Tuple[int, int]]
-) -> None:
+def read_and_log_sparse_reconstruction(dataset_path: Path, filter_output: bool, resize: tuple[int, int] | None) -> None:
     print("Reading sparse COLMAP reconstruction")
     cameras, images, points3D = read_model(dataset_path / "sparse", ext=".bin")
     print("Building visualization by logging to Rerun")

examples/python/colmap/read_write_model.py

@@ -1,6 +1,5 @@
 # This file is adapted from
 # https://github.com/colmap/colmap/blob/bf3e19140f491c3042bfd85b7192ef7d249808ec/scripts/python/read_write_model.py
-
 # Copyright (c) 2023, ETH Zurich and UNC Chapel Hill.
 # All rights reserved.
 #
@@ -31,8 +30,8 @@
 # POSSIBILITY OF SUCH DAMAGE.
 #
 # Author: Johannes L. Schoenberger (jsch-at-demuc-dot-de)
-
 #  type: ignore
+from __future__ import annotations
 
 import argparse
 import collections
@@ -110,7 +109,7 @@ def read_cameras_text(path: Path):
         void Reconstruction::ReadCamerasText(const std::string& path)
     """
     cameras = {}
-    with open(path, "r") as fid:
+    with open(path) as fid:
         while True:
             line = fid.readline()
             if not line:
@@ -161,7 +160,7 @@ def write_cameras_text(cameras, path):
     HEADER = (
         "# Camera list with one line of data per camera:\n"
         + "#   CAMERA_ID, MODEL, WIDTH, HEIGHT, PARAMS[]\n"
-        + "# Number of cameras: {}\n".format(len(cameras))
+        + f"# Number of cameras: {len(cameras)}\n"
     )
     with open(path, "w") as fid:
         fid.write(HEADER)
@@ -195,7 +194,7 @@ def read_images_text(path: Path):
         void Reconstruction::WriteImagesText(const std::string& path)
     """
     images = {}
-    with open(path, "r") as fid:
+    with open(path) as fid:
         while True:
             line = fid.readline()
             if not line:
@@ -273,7 +272,7 @@ def write_images_text(images, path):
         "# Image list with two lines of data per image:\n"
         + "#   IMAGE_ID, QW, QX, QY, QZ, TX, TY, TZ, CAMERA_ID, NAME\n"
         + "#   POINTS2D[] as (X, Y, POINT3D_ID)\n"
-        + "# Number of images: {}, mean observations per image: {}\n".format(len(images), mean_observations)
+        + f"# Number of images: {len(images)}, mean observations per image: {mean_observations}\n"
     )
 
     with open(path, "w") as fid:
@@ -317,7 +316,7 @@ def read_points3D_text(path):
         void Reconstruction::WritePoints3DText(const std::string& path)
     """
     points3D = {}
-    with open(path, "r") as fid:
+    with open(path) as fid:
         while True:
             line = fid.readline()
             if not line:
@@ -375,7 +374,7 @@ def write_points3D_text(points3D, path):
     HEADER = (
         "# 3D point list with one line of data per point:\n"
         + "#   POINT3D_ID, X, Y, Z, R, G, B, ERROR, TRACK[] as (IMAGE_ID, POINT2D_IDX)\n"
-        + "# Number of points: {}, mean track length: {}\n".format(len(points3D), mean_track_length)
+        + f"# Number of points: {len(points3D)}, mean track length: {mean_track_length}\n"
     )
 
     with open(path, "w") as fid:

examples/python/deep_sdf/download_dataset.py

@@ -1,10 +1,11 @@
 #!/usr/bin/env python3
+from __future__ import annotations
 
 import io
 import os
 import zipfile
 from pathlib import Path
-from typing import Final, Optional
+from typing import Final
 
 import requests
 
@@ -49,7 +50,7 @@ def download_mesh(name: str) -> Path:
     raise RuntimeError(f"Unknown mesh named: {name}")
 
 
-def find_mesh_path_if_downloaded(name: str) -> Optional[Path]:
+def find_mesh_path_if_downloaded(name: str) -> Path | None:
     for mesh_format in ("obj", "glb"):
         for path in DOWNLOADED_DIR.glob(f"{name}/**/*.{mesh_format}"):
             return path