Python SDK spring cleaning: 3.9, no more monkey patching, more lints

crates/build/re_types_builder/src/codegen/python/views.rs

@@ -58,8 +58,8 @@ fn init_method(reporter: &Reporter, objects: &Objects, obj: &Object) -> String {
     contents: ViewContentsLike = "$origin/**",
     name: Utf8Like | None = None,
     visible: datatypes.BoolLike | None = None,
-    defaults: list[Union[AsComponents, ComponentBatchLike]] = [],
-    overrides: dict[EntityPathLike, list[ComponentBatchLike]] = {},
+    defaults: list[Union[AsComponents, ComponentBatchLike]] | None = None,
+    overrides: dict[EntityPathLike, list[ComponentBatchLike]] | None = None,
     "#
     .to_owned();
 

docs/snippets/compare_snippet_output.py

@@ -205,7 +205,7 @@ def run_example(example: Example, language: str, args: argparse.Namespace) -> No
         rust_output_path = run_prebuilt_rust(example, args.release, args.target, args.target_dir)
         check_non_empty_rrd(rust_output_path)
     else:
-        assert False, f"Unknown language: {language}"
+        raise AssertionError(f"Unknown language: {language}")
 
 
 def build_rust_snippets(build_env: dict[str, str], release: bool, target: str | None, target_dir: str | None):

examples/python/air_traffic_data/air_traffic_data.py

@@ -93,7 +93,7 @@ def download_eu_map_data() -> None:
     # cspell:disable-next-line
     map_data = gpd.read_file(MAP_DATA_DIR / f"NUTS_RG_01M_2021_4326_LEVL_{level}.json").set_crs("epsg:4326").to_crs(crs)
 
-    for i, row in map_data[map_data.CNTR_CODE == country_code].iterrows():
+    for _i, row in map_data[map_data.CNTR_CODE == country_code].iterrows():
         entity_path = f"region_boundaries/{country_code}/{level}/{row.NUTS_ID}"
         lines = shapely_geom_to_numpy(row.geometry)
         rr.log(entity_path + "/2D", rr.LineStrips2D(lines, colors=color), static=True)

examples/python/all_examples/all_examples/__init__.py

@@ -1,9 +1,10 @@
 from __future__ import annotations
 
 import platform
+from collections.abc import Iterable
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Any, Iterable, cast
+from typing import Any, cast
 
 import tomli
 from pyproject_metadata import StandardMetadata

examples/python/all_examples/hatch_build.py

@@ -11,7 +11,7 @@
 from all_examples import active_examples
 
 
-class MetadataHook(MetadataHookInterface):  # type: ignore[misc]
+class MetadataHook(MetadataHookInterface):
     def update(self, metadata: dict[str, Any]) -> None:
         """
         Use our very own package to list the examples we depend on.

examples/python/arkit_scenes/arkit_scenes/__main__.py

@@ -5,7 +5,7 @@
 import json
 import os
 from pathlib import Path
-from typing import Any, Tuple
+from typing import Any
 
 import cv2
 import numpy as np
@@ -26,7 +26,7 @@
 [on GitHub](https://github.com/rerun-io/rerun/blob/latest/examples/python/arkit_scenes).
 """.strip()
 
-Color = Tuple[float, float, float, float]
+Color = tuple[float, float, float, float]
 
 # hack for now since dataset does not provide orientation information, only known after initial visual inspection
 ORIENTATION = {

examples/python/controlnet/controlnet.py

@@ -34,8 +34,8 @@ def controlnet_callback(
     rr.set_index("timestep", timedelta=timestep)
     latents = callback_kwargs["latents"]
 
-    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]  # type: ignore[attr-defined]
-    image = pipe.image_processor.postprocess(image, output_type="np").squeeze()  # type: ignore[attr-defined]
+    image = pipe.vae.decode(latents / pipe.vae.config.scaling_factor, return_dict=False)[0]
+    image = pipe.image_processor.postprocess(image, output_type="np").squeeze()
     rr.log("output", rr.Image(image))
     rr.log("latent", rr.Tensor(latents.squeeze(), dim_names=["channel", "height", "width"]))
 

examples/python/detect_and_track_objects/detect_and_track_objects.py

@@ -7,9 +7,10 @@
 import json
 import logging
 import os
+from collections.abc import Sequence
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Final, Sequence
+from typing import Any, Final
 
 import cv2
 import numpy as np
@@ -176,7 +177,7 @@ def __init__(self, tracking_id: int, detection: Detection, bgr: cv2.typing.MatLi
         self.tracked = detection.scaled_to_fit_image(bgr)
         self.num_recent_undetected_frames = 0
 
-        self.tracker = cv2.legacy.TrackerCSRT_create()  # type: ignore[attr-defined]
+        self.tracker = cv2.legacy.TrackerCSRT_create()
         bbox_xywh_rounded = [int(val) for val in self.tracked.bbox_xywh]
         self.tracker.init(bgr, bbox_xywh_rounded)
         self.log_tracked()
@@ -218,7 +219,7 @@ def log_tracked(self) -> None:
     def update_with_detection(self, detection: Detection, bgr: cv2.typing.MatLike) -> None:
         self.num_recent_undetected_frames = 0
         self.tracked = detection.scaled_to_fit_image(bgr)
-        self.tracker = cv2.TrackerCSRT_create()  # type: ignore[attr-defined]
+        self.tracker = cv2.TrackerCSRT_create()
         bbox_xywh_rounded = [int(val) for val in self.tracked.bbox_xywh]
         self.tracker.init(bgr, bbox_xywh_rounded)
         self.log_tracked()

examples/python/dicom_mri/dicom_mri.py

@@ -7,8 +7,9 @@
 import io
 import os
 import zipfile
+from collections.abc import Iterable
 from pathlib import Path
-from typing import Final, Iterable
+from typing import Final
 
 import dicom_numpy
 import numpy as np
@@ -37,7 +38,7 @@
 def extract_voxel_data(
     dicom_files: Iterable[Path],
 ) -> tuple[npt.NDArray[np.int16], npt.NDArray[np.float32]]:
-    slices = [dicom.read_file(f) for f in dicom_files]  # type: ignore[misc]
+    slices = [dicom.read_file(f) for f in dicom_files]
     try:
         voxel_ndarray, ijk_to_xyz = dicom_numpy.combine_slices(slices)
     except dicom_numpy.DicomImportException:

examples/python/face_tracking/face_tracking.py

@@ -8,8 +8,9 @@
 import logging
 import math
 import os
+from collections.abc import Iterable
 from pathlib import Path
-from typing import Final, Iterable
+from typing import Final
 
 import cv2
 import mediapipe as mp

examples/python/gesture_detection/gesture_detection.py

@@ -7,8 +7,9 @@
 import itertools
 import logging
 import os
+from collections.abc import Iterable
 from pathlib import Path
-from typing import Final, Iterable
+from typing import Final
 
 import cv2
 import mediapipe as mp
@@ -114,7 +115,7 @@ def detect_and_log(self, image: cv2.typing.MatLike, frame_time_nano: int) -> Non
         for log_key in ["hand2d/points", "hand2d/connections", "hand3d/points"]:
             rr.log(log_key, rr.Clear(recursive=True))
 
-        for i, gesture in enumerate(recognition_result.gestures):
+        for gesture in recognition_result.gestures:
             # Get the top gesture from the recognition result
             gesture_category = gesture[0].category_name if recognition_result.gestures else "None"
             self.present_detected_gesture(gesture_category)  # Log the detected gesture

examples/python/human_pose_tracking/human_pose_tracking.py

@@ -6,10 +6,11 @@
 import argparse
 import logging
 import os
+from collections.abc import Iterator
 from contextlib import closing
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Final, Iterator
+from typing import Any, Final
 
 import cv2
 import mediapipe as mp

examples/python/live_depth_sensor/live_depth_sensor.py

@@ -73,7 +73,7 @@ def run_realsense(num_frames: int | None) -> None:
             frame_nr += 1
 
             frames = pipe.wait_for_frames()
-            for f in frames:
+            for _f in frames:
                 # Log the depth frame
                 depth_frame = frames.get_depth_frame()
                 depth_units = depth_frame.get_units()

examples/python/live_scrolling_plot/live_scrolling_plot.py

@@ -5,7 +5,7 @@
 
 import argparse
 import time
-from typing import Iterator
+from collections.abc import Iterator
 
 import numpy as np
 import rerun as rr  # pip install rerun-sdk

examples/python/nuscenes_dataset/nuscenes_dataset/export_gps.py

@@ -6,7 +6,7 @@
 from __future__ import annotations
 
 import math
-from typing import Sequence
+from collections.abc import Sequence
 
 EARTH_RADIUS_METERS = 6.378137e6
 REFERENCE_COORDINATES = {

examples/python/objectron/objectron/__main__.py

@@ -9,9 +9,9 @@
 import os
 import sys
 import time
+from collections.abc import Iterable, Iterator
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Iterable, Iterator
 
 import numpy as np
 import rerun as rr  # pip install rerun-sdk

examples/python/ocr/ocr.py

@@ -6,9 +6,10 @@
 import argparse
 import logging
 import os
+from collections.abc import Iterable
 from enum import Enum
 from pathlib import Path
-from typing import Any, Final, Iterable, Optional
+from typing import Any, Final, Optional
 
 import cv2 as cv2
 import numpy as np
@@ -373,7 +374,7 @@ def detect_and_log_layouts(file_path: str) -> None:
     layouts: list[Layout] = []
     page_numbers = [i + 1 for i in range(len(images))]
     processed_layouts: list[LayoutStructure] = []
-    for i, (image, page_number) in enumerate(zip(images, page_numbers)):
+    for image, page_number in zip(images, page_numbers):
         layouts.append(detect_and_log_layout(image, page_number))
         page_path = f"page_{page_number}"
 

examples/python/raw_mesh/raw_mesh/__main__.py

@@ -88,7 +88,7 @@ def log_scene(scene: trimesh.Scene, node: str, path: str | None = None) -> None:
                 rr.Mesh3D(
                     vertex_positions=mesh.vertices,
                     vertex_colors=vertex_colors,
-                    vertex_normals=mesh.vertex_normals,  # type: ignore[arg-type]
+                    vertex_normals=mesh.vertex_normals,
                     vertex_texcoords=vertex_texcoords,
                     albedo_texture=albedo_texture,
                     triangle_indices=mesh.faces,

examples/python/rgbd/rgbd.py

@@ -141,13 +141,16 @@ def download_progress(url: str, dst: Path) -> None:
     total = int(resp.headers.get("content-length", 0))
     chunk_size = 1024 * 1024
     # Can also replace 'file' with a io.BytesIO object
-    with open(dst, "wb") as file, tqdm(
-        desc=dst.name,
-        total=total,
-        unit="iB",
-        unit_scale=True,
-        unit_divisor=1024,
-    ) as bar:
+    with (
+        open(dst, "wb") as file,
+        tqdm(
+            desc=dst.name,
+            total=total,
+            unit="iB",
+            unit_scale=True,
+            unit_divisor=1024,
+        ) as bar,
+    ):
         for data in resp.iter_content(chunk_size=chunk_size):
             size = file.write(data)
             bar.update(size)

examples/python/rrt_star/rrt_star.py

@@ -15,7 +15,8 @@
 from __future__ import annotations
 
 import argparse
-from typing import Annotated, Generator, Literal
+from collections.abc import Generator
+from typing import Annotated, Literal
 
 import numpy as np
 import numpy.typing as npt

examples/python/segment_anything_model/segment_anything_model.py

@@ -102,7 +102,7 @@ def run_segmentation(mask_generator: SamAutomaticMaskGenerator, image: cv2.typin
     # TODO(jleibs): we could instead draw each mask as a separate image layer, but the current layer-stacking
     # does not produce great results.
     masks_with_ids = list(enumerate(masks, start=1))
-    masks_with_ids.sort(key=(lambda x: x[1]["area"]), reverse=True)  # type: ignore[no-any-return]
+    masks_with_ids.sort(key=(lambda x: x[1]["area"]), reverse=True)
 
     # Layer all of the masks together, using the id as class-id in the segmentation
     segmentation_img = np.zeros((image.shape[0], image.shape[1]))

examples/python/structure_from_motion/structure_from_motion/__main__.py

@@ -103,7 +103,7 @@ def read_and_log_sparse_reconstruction(dataset_path: Path, filter_output: bool,
     rr.log("plot/avg_reproj_err", rr.SeriesLine(color=[240, 45, 58]), static=True)
 
     # Iterate through images (video frames) logging data related to each frame.
-    for image in sorted(images.values(), key=lambda im: im.name):  # type: ignore[no-any-return]
+    for image in sorted(images.values(), key=lambda im: im.name):
         image_file = dataset_path / "images" / image.name
 
         if not os.path.exists(image_file):