chore: import upstream snapshot with attribution

docs/snippets/all/tutorials/custom_data.py

@@ -0,0 +1,84 @@
+"""Shows how to implement custom archetypes and components."""
+
+from __future__ import annotations
+
+import argparse
+from typing import Any
+
+import numpy as np
+import numpy.typing as npt
+import pyarrow as pa
+
+import rerun as rr
+
+
+class ConfidenceBatch(rr.ComponentBatchMixin):  # type: ignore[misc]
+    """A batch of confidence data."""
+
+    def __init__(self: Any, confidence: npt.ArrayLike) -> None:
+        self.confidence = confidence
+
+    def as_arrow_array(self) -> pa.Array:
+        """The arrow batch representing the custom component."""
+        return pa.array(self.confidence, type=pa.float32())
+
+
+class CustomPoints3D(rr.AsComponents):  # type: ignore[misc]
+    """A custom archetype extending the builtin `Points3D` with extra data."""
+
+    def __init__(
+        self: Any, positions: npt.ArrayLike, confidences: npt.ArrayLike
+    ) -> None:
+        self.points3d = rr.Points3D(positions)
+        self.confidences = ConfidenceBatch(confidences).described(
+            rr.ComponentDescriptor(
+                "user.CustomPoints3D:confidences",
+                archetype="user.CustomPoints3D",
+                component_type="user.Confidence",
+            )
+        )
+
+    def as_component_batches(self) -> list[rr.DescribedComponentBatch]:
+        return [
+            # The components from Points3D
+            *self.points3d.as_component_batches(),
+            # Custom confidence data
+            self.confidences,
+        ]
+
+
+def log_custom_data() -> None:
+    lin = np.linspace(-5, 5, 3)
+    z, y, x = np.meshgrid(lin, lin, lin, indexing="ij")
+    point_grid = np.vstack([x.flatten(), y.flatten(), z.flatten()]).T
+
+    rr.log(
+        "left/my_confident_point_cloud",
+        CustomPoints3D(
+            positions=point_grid,
+            confidences=[42],
+        ),
+    )
+
+    rr.log(
+        "right/my_polarized_point_cloud",
+        CustomPoints3D(
+            positions=point_grid, confidences=np.arange(0, len(point_grid))
+        ),
+    )
+
+
+def main() -> None:
+    parser = argparse.ArgumentParser(
+        description="Logs rich data using the Rerun SDK."
+    )
+    rr.script_add_args(parser)
+    args = parser.parse_args()
+
+    rr.script_setup(args, "rerun_example_custom_data")
+    log_custom_data()
+    rr.script_teardown(args)
+
+
+if __name__ == "__main__":
+    main()