Static data 2: static-aware Python SDK (#5536)

Just exposing all the new static stuff to the Python SDK, and trying to
kill the "timeless" terminology in the process.

---

Part of a PR series that removes the concept of timeless data in favor
of the much simpler concept of static data:
- #5534
- #5535
- #5536
- #5537
- #5540

crates/re_query_cache/tests/latest_at.rs

@@ -314,7 +314,7 @@ fn invalidation() {
 
 // Test the following scenario:
 // ```py
-// rr.log("points", rr.Points3D([1, 2, 3]), timeless=True)
+// rr.log("points", rr.Points3D([1, 2, 3]), static=True)
 //
 // # Do first query here: LatestAt(+inf)
 // # Expected: points=[[1,2,3]] colors=[]

crates/re_query_cache/tests/range.rs

@@ -439,7 +439,7 @@ fn invalidation() {
 
 // Test the following scenario:
 // ```py
-// rr.log("points", rr.Points3D([1, 2, 3]), timeless=True)
+// rr.log("points", rr.Points3D([1, 2, 3]), static=True)
 //
 // # Do first query here: LatestAt(+inf)
 // # Expected: points=[[1,2,3]] colors=[]

docs/content/concepts/spaces-and-transforms.md

@@ -110,7 +110,7 @@ Note that none of the names in the paths are special.
 
 You can use [`rr.ViewCoordinates`](https://ref.rerun.io/docs/python/stable/common/archetypes/#rerun.archetypes.ViewCoordinates) to set your preferred view coordinate systems, giving semantic meaning to the XYZ axes of the space.
 
-For 3D spaces it can be used to log what the up-axis is in your coordinate system. This will help Rerun set a good default view of your 3D scene, as well as make the virtual eye interactions more natural. This can be done with `rr.log("world", rr.ViewCoordinates(up="+Z"), timeless=True)`.
+For 3D spaces it can be used to log what the up-axis is in your coordinate system. This will help Rerun set a good default view of your 3D scene, as well as make the virtual eye interactions more natural. This can be done with `rr.log("world", rr.ViewCoordinates(up="+Z"), static=True)`.
 
 You can also use this `log_view_coordinates` for pinhole entities, but it is encouraged that you instead use [`rr.log(…, rr.Pinhole(camera_xyz=…))`](https://ref.rerun.io/docs/python/stable/common/archetypes/#rerun.archetypes.Pinhole) for this. The default coordinate system for pinhole entities is `RDF` (X=Right, Y=Down, Z=Forward).
 

docs/content/concepts/timelines.md

@@ -28,11 +28,11 @@ An _event_ refer to an instance of logging one or more component batches to one
 </picture>
 
 
-## Timeless data
+## Static data
 
-The [`rr.log()`](https://ref.rerun.io/docs/python/stable/common/logging_functions/#rerun.log) function has a `timeless=False` default argument.
-If `timeless=True` is used instead, the entity become *timeless*. Timeless entities belong to all timelines (existing ones, and ones not yet created) and are shown leftmost in the time panel in the viewer.
-This is useful for entities that aren't part of normal data capture, but set the scene for how they are shown.
-For instance, if you are logging cars on a street, perhaps you want to always show a street mesh as part of the scenery, and for that it makes sense for that data to be timeless.
+The [`rr.log()`](https://ref.rerun.io/docs/python/stable/common/logging_functions/#rerun.log) function has a `static=False` default argument.
+If `static=True` is used instead, the data logged becomes *static*. Static data belongs to all timelines (existing ones, and ones not yet created) and shadows any temporal data of the same type on the same entity.
+This is useful for data that isn't part of normal data capture, but sets the scene for how it should be shown.
+For instance, if you are logging cars on a street, perhaps you want to always show a street mesh as part of the scenery, and for that it makes sense for that data to be static.
 
-Similarly, [coordinate systems](spaces-and-transforms.md) or [annotation context](annotation-context.md) are typically timeless.
+Similarly, [coordinate systems](spaces-and-transforms.md) or [annotation context](annotation-context.md) are typically static.

docs/content/howto/ros2-nav-turtlebot.md

@@ -421,7 +421,7 @@ def urdf_callback(self, urdf_msg: String) -> None:
     rerun_urdf.log_scene(scene=scaled,
                          node=urdf.base_link,
                          path="map/robot/urdf",
-                         timeless=True)
+                         static=True)
 ```
 
 Back in `rerun_urdf.log_scene` all the code is doing is recursively walking through

docs/content/reference/data-loaders/overview.md

@@ -62,9 +62,9 @@ When the viewer and/or SDK executes an external loader, it will pass to it a set
 
     Recommended prefix to prepend to all entity paths.
 
-* `--timeless` (optional)
+* `--static` (optional)
 
-    The data is expected to be logged timelessly.
+    The data is expected to be logged as static.
 
 * `--time <timeline1>=<time1> <timeline2>=<time2> …` (optional)
 

docs/snippets/all/annotation-context/example.py

@@ -9,7 +9,7 @@
             rr.AnnotationInfo(id=1, label="Person", color=(255, 0, 0)),
         ],
     ),
-    timeless=True,
+    static=True,
 )
 
 # Annotation context with simple keypoints & keypoint connections.
@@ -20,5 +20,5 @@
         keypoint_annotations=[rr.AnnotationInfo(id=i, color=(0, 255 / 9 * i, 0)) for i in range(10)],
         keypoint_connections=[(i, i + 1) for i in range(9)],
     ),
-    timeless=True,
+    static=True,
 )

docs/snippets/all/annotation_context_connections.py

@@ -21,7 +21,7 @@
             )
         ]
     ),
-    timeless=True,
+    static=True,
 )
 
 rr.log(

docs/snippets/all/annotation_context_rects.py

@@ -3,7 +3,7 @@
 rr.init("rerun_example_annotation_context_rects", spawn=True)
 
 # Log an annotation context to assign a label and color to each class
-rr.log("/", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), timeless=True)
+rr.log("/", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), static=True)
 
 # Log a batch of 2 rectangles with different `class_ids`
 rr.log("detections", rr.Boxes2D(mins=[[-2, -2], [0, 0]], sizes=[[3, 3], [2, 2]], class_ids=[1, 2]))

docs/snippets/all/annotation_context_segmentation.py

@@ -11,6 +11,6 @@
 image[100:180, 130:280] = 2
 
 # Log an annotation context to assign a label and color to each class
-rr.log("segmentation", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), timeless=True)
+rr.log("segmentation", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), static=True)
 
 rr.log("segmentation/image", rr.SegmentationImage(image))

docs/snippets/all/asset3d_out_of_tree.py

@@ -12,7 +12,7 @@
 
 rr.init("rerun_example_asset3d_out_of_tree", spawn=True)
 
-rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, timeless=True)  # Set an up-axis
+rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, static=True)  # Set an up-axis
 
 rr.set_time_sequence("frame", 0)
 rr.log("world/asset", rr.Asset3D(path=sys.argv[1]))

docs/snippets/all/asset3d_simple.py

@@ -9,5 +9,5 @@
 
 rr.init("rerun_example_asset3d", spawn=True)
 
-rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, timeless=True)  # Set an up-axis
+rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, static=True)  # Set an up-axis
 rr.log("world/asset", rr.Asset3D(path=sys.argv[1]))

docs/snippets/all/scalar_multiple_plots.py

@@ -9,12 +9,12 @@
 lcg_state = np.int64(0)
 
 # Set up plot styling:
-# They are logged timeless as they don't change over time and apply to all timelines.
+# They are logged as static as they don't change over time and apply to all timelines.
 # Log two lines series under a shared root so that they show in the same plot by default.
-rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)"), timeless=True)
-rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)"), timeless=True)
+rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)"), static=True)
+rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)"), static=True)
 # Log scattered points under a different root so that they show in a different plot by default.
-rr.log("scatter/lcg", rr.SeriesPoint(), timeless=True)
+rr.log("scatter/lcg", rr.SeriesPoint(), static=True)
 
 # Log the data on a timeline called "step".
 for t in range(0, int(tau * 2 * 100.0)):

docs/snippets/all/segmentation_image_simple.py

@@ -11,6 +11,6 @@
 rr.init("rerun_example_segmentation_image", spawn=True)
 
 # Assign a label and color to each class
-rr.log("/", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), timeless=True)
+rr.log("/", rr.AnnotationContext([(1, "red", (255, 0, 0)), (2, "green", (0, 255, 0))]), static=True)
 
 rr.log("image", rr.SegmentationImage(image))

docs/snippets/all/series_line_style.py

@@ -7,10 +7,10 @@
 rr.init("rerun_example_series_line_style", spawn=True)
 
 # Set up plot styling:
-# They are logged timeless as they don't change over time and apply to all timelines.
+# They are logged as static as they don't change over time and apply to all timelines.
 # Log two lines series under a shared root so that they show in the same plot by default.
-rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)", width=2), timeless=True)
-rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)", width=4), timeless=True)
+rr.log("trig/sin", rr.SeriesLine(color=[255, 0, 0], name="sin(0.01t)", width=2), static=True)
+rr.log("trig/cos", rr.SeriesLine(color=[0, 255, 0], name="cos(0.01t)", width=4), static=True)
 
 # Log the data on a timeline called "step".
 for t in range(0, int(tau * 2 * 100.0)):

docs/snippets/all/series_point_style.py

@@ -7,7 +7,7 @@
 rr.init("rerun_example_series_point_style", spawn=True)
 
 # Set up plot styling:
-# They are logged timeless as they don't change over time and apply to all timelines.
+# They are logged as static as they don't change over time and apply to all timelines.
 # Log two point series under a shared root so that they show in the same plot by default.
 rr.log(
     "trig/sin",
@@ -17,7 +17,7 @@
         marker="circle",
         marker_size=4,
     ),
-    timeless=True,
+    static=True,
 )
 rr.log(
     "trig/cos",
@@ -27,7 +27,7 @@
         marker="cross",
         marker_size=2,
     ),
-    timeless=True,
+    static=True,
 )
 
 # Log the data on a timeline called "step".

docs/snippets/all/view_coordinates_simple.py

@@ -4,7 +4,7 @@
 
 rr.init("rerun_example_view_coordinates", spawn=True)
 
-rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, timeless=True)  # Set an up-axis
+rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, static=True)  # Set an up-axis
 rr.log(
     "world/xyz",
     rr.Arrows3D(

examples/python/arkit_scenes/main.py

@@ -70,10 +70,10 @@
         vertex_colors=mesh.visual.vertex_colors,
         indices=mesh.faces,
     ),
-    timeless=True,
+    static=True,
 )
 ```
-Here, the mesh is logged to the [world/mesh entity](recording://world/mesh) and is marked as timeless, since it does not
+Here, the mesh is logged to the [world/mesh entity](recording://world/mesh) and is marked as static, since it does not
 change in the context of this visualization.
 
 ### 3D bounding boxes
@@ -121,7 +121,7 @@ def log_annotated_bboxes(annotation: dict[str, Any]) -> None:
                 rotations=rr.Quaternion(xyzw=rot.as_quat()),
                 labels=label,
             ),
-            timeless=True,
+            static=True,
         )
 
 
@@ -213,7 +213,7 @@ def log_arkit(recording_path: Path, include_highres: bool) -> None:
     None
 
     """
-    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), timeless=True)
+    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True)
 
     video_id = recording_path.stem
     lowres_image_dir = recording_path / "lowres_wide"
@@ -242,7 +242,7 @@ def log_arkit(recording_path: Path, include_highres: bool) -> None:
         timestamp = f"{round(float(timestamp), 3):.3f}"
         camera_from_world_dict[timestamp] = camera_from_world
 
-    rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, timeless=True)
+    rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Z_UP, static=True)
     ply_path = recording_path / f"{recording_path.stem}_3dod_mesh.ply"
     print(f"Loading {ply_path}…")
     assert os.path.isfile(ply_path), f"Failed to find {ply_path}"
@@ -255,7 +255,7 @@ def log_arkit(recording_path: Path, include_highres: bool) -> None:
             vertex_colors=mesh.visual.vertex_colors,
             indices=mesh.faces,
         ),
-        timeless=True,
+        static=True,
     )
 
     # load the obb annotations and log them in the world frame

examples/python/clock/main.py

@@ -30,12 +30,12 @@ def rotate(angle: float, len: float) -> tuple[float, float, float]:
             0.0,
         )
 
-    rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Y_UP, timeless=True)
+    rr.log("world", rr.ViewCoordinates.RIGHT_HAND_Y_UP, static=True)
 
     rr.log(
         "world/frame",
         rr.Boxes3D(half_sizes=[LENGTH_S, LENGTH_S, 1.0], centers=[0.0, 0.0, 0.0]),
-        timeless=True,
+        static=True,
     )
 
     for step in range(steps):

examples/python/controlnet/main.py

@@ -74,8 +74,8 @@ def run_canny_controlnet(image_path: str, prompt: str, negative_prompt: str) ->
     canny_image = np.concatenate([canny_image, canny_image, canny_image], axis=2)
     canny_image = PIL.Image.fromarray(canny_image)
 
-    rr.log("input/raw", rr.Image(image), timeless=True)
-    rr.log("input/canny", rr.Image(canny_image), timeless=True)
+    rr.log("input/raw", rr.Image(image), static=True)
+    rr.log("input/canny", rr.Image(canny_image), static=True)
 
     controlnet = ControlNetModel.from_pretrained(
         "diffusers/controlnet-canny-sdxl-1.0",
@@ -95,8 +95,8 @@ def run_canny_controlnet(image_path: str, prompt: str, negative_prompt: str) ->
 
     pipeline.enable_model_cpu_offload()
 
-    rr.log("positive_prompt", rr.TextDocument(prompt), timeless=True)
-    rr.log("negative_prompt", rr.TextDocument(negative_prompt), timeless=True)
+    rr.log("positive_prompt", rr.TextDocument(prompt), static=True)
+    rr.log("negative_prompt", rr.TextDocument(negative_prompt), static=True)
 
     images = pipeline(
         prompt,

examples/python/detect_and_track_objects/main.py

@@ -62,7 +62,7 @@
 
 The color and label for each class is determined by the
 [rr.AnnotationContext archetype](https://www.rerun.io/docs/reference/types/archetypes/annotation_context) which is
-logged to the root entity using `rr.log("/", …, timeless=True` as it should apply to the whole sequence and all
+logged to the root entity using `rr.log("/", …, static=True` as it should apply to the whole sequence and all
 entities that have a class id.
 
 ### Detections
@@ -369,7 +369,7 @@ def track_objects(video_path: str, *, max_frame_count: int | None) -> None:
     class_descriptions = [
         rr.AnnotationInfo(id=cat["id"], color=cat["color"], label=cat["name"]) for cat in coco_categories
     ]
-    rr.log("/", rr.AnnotationContext(class_descriptions), timeless=True)
+    rr.log("/", rr.AnnotationContext(class_descriptions), static=True)
 
     detector = Detector(coco_categories=coco_categories)
 
@@ -460,7 +460,7 @@ def main() -> None:
 
     setup_logging()
 
-    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), timeless=True)
+    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True)
 
     video_path: str = args.video_path
     if not video_path:

examples/python/dicom_mri/main.py

@@ -71,7 +71,7 @@ def list_dicom_files(dir: Path) -> Iterable[Path]:
 
 
 def read_and_log_dicom_dataset(dicom_files: Iterable[Path]) -> None:
-    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), timeless=True)
+    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True)
 
     voxels_volume, _ = extract_voxel_data(dicom_files)
 

examples/python/dna/main.py

@@ -47,7 +47,7 @@
 
 
 def log_data() -> None:
-    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), timeless=True)
+    rr.log("description", rr.TextDocument(DESCRIPTION, media_type=rr.MediaType.MARKDOWN), static=True)
 
     rr.set_time_seconds("stable_time", 0)
 

examples/python/external_data_loader/main.py

@@ -34,9 +34,8 @@
 parser.add_argument("--application-id", type=str, help="optional recommended ID for the application")
 parser.add_argument("--recording-id", type=str, help="optional recommended ID for the recording")
 parser.add_argument("--entity-path-prefix", type=str, help="optional prefix for all entity paths")
-parser.add_argument(
-    "--timeless", action="store_true", default=False, help="optionally mark data to be logged as timeless"
-)
+parser.add_argument("--timeless", action="store_true", default=False, help="deprecated: alias for `--static`")
+parser.add_argument("--static", action="store_true", default=False, help="optionally mark data to be logged as static")
 parser.add_argument(
     "--time",
     type=str,
@@ -77,7 +76,9 @@ def main() -> None:
     with open(args.filepath) as file:
         body = file.read()
         text = f"""## Some Python code\n```python\n{body}\n```\n"""
-        rr.log(entity_path, rr.TextDocument(text, media_type=rr.MediaType.MARKDOWN), timeless=args.timeless)
+        rr.log(
+            entity_path, rr.TextDocument(text, media_type=rr.MediaType.MARKDOWN), static=args.static or args.timeless
+        )
 
 
 def set_time_from_args() -> None:

examples/python/face_tracking/main.py

@@ -120,7 +120,7 @@ def __init__(self, video_mode: bool = False):
             rr.ClassDescription(
                 info=rr.AnnotationInfo(id=0), keypoint_connections=[(0, 1), (1, 2), (2, 0), (2, 3), (0, 4), (1, 5)]
             ),
-            timeless=True,
+            static=True,
         )
 
     def detect_and_log(self, image: npt.NDArray[np.uint8], frame_time_nano: int) -> None:
@@ -223,11 +223,11 @@ def __init__(self, video_mode: bool = False, num_faces: int = 1):
                 )
             )
 
-        rr.log("video/landmarker", rr.AnnotationContext(class_descriptions), timeless=True)
-        rr.log("reconstruction", rr.AnnotationContext(class_descriptions), timeless=True)
+        rr.log("video/landmarker", rr.AnnotationContext(class_descriptions), static=True)
+        rr.log("reconstruction", rr.AnnotationContext(class_descriptions), static=True)
 
         # properly align the 3D face in the viewer
-        rr.log("reconstruction", rr.ViewCoordinates.RDF, timeless=True)
+        rr.log("reconstruction", rr.ViewCoordinates.RDF, static=True)
 
     def detect_and_log(self, image: npt.NDArray[np.uint8], frame_time_nano: int) -> None:
         height, width, _ = image.shape

examples/python/gesture_detection/README.md

@@ -58,7 +58,7 @@ Meanwhile, the 3D points allows the creation of a 3D model of the hand for a mor
 
 The 2D and 3D points are logged through a combination of two archetypes.
 For the 2D points, the Points2D and LineStrips2D archetypes are utilized. These archetypes help visualize the points and connect them with lines, respectively.
-As for the 3D points, the logging process involves two steps. First, a timeless [`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description) is logged, that contains the information which maps keypoint ids to labels and how to connect
+As for the 3D points, the logging process involves two steps. First, a static [`ClassDescription`](https://www.rerun.io/docs/reference/types/datatypes/class_description) is logged, that contains the information which maps keypoint ids to labels and how to connect
 the keypoints. Defining these connections automatically renders lines between them. Mediapipe provides the `HAND_CONNECTIONS` variable which contains the list of `(from, to)` landmark indices that define the connections.
 Second, the actual keypoint positions are logged in 3D [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d) archetype.
 
@@ -73,10 +73,10 @@ rr.log(
             keypoint_connections=mp.solutions.hands.HAND_CONNECTIONS,
         )
     ),
-    timeless=True,
+    static=True,
 )
 
-rr.log("Hand3D", rr.ViewCoordinates.LEFT_HAND_Y_DOWN, timeless=True)
+rr.log("Hand3D", rr.ViewCoordinates.LEFT_HAND_Y_DOWN, static=True)
 ```
 
 ### 2D Points