Python: remove legacy send_columns and update everything left (#8799)

Title.

Author: teh-cmc

crates/store/re_types/definitions/rerun/components/video_timestamp.fbs

@@ -4,6 +4,7 @@ namespace rerun.components;
 /// Timestamp inside a [archetypes.AssetVideo].
 struct VideoTimestamp (
   "attr.arrow.transparent",
+  "attr.python.array_aliases": "npt.NDArray[np.int64]",
   "attr.rust.derive": "Copy, PartialEq, Eq, Default",
   "attr.rust.repr": "transparent"
 ) {

crates/store/re_types/definitions/rerun/datatypes/video_timestamp.fbs

@@ -7,6 +7,7 @@ namespace rerun.datatypes;
 struct VideoTimestamp (
   "attr.arrow.transparent",
   "attr.python.aliases": "int",
+  "attr.python.array_aliases": "npt.NDArray[np.int64]",
   "attr.rust.derive": "Default, Copy, PartialEq, Eq, PartialOrd, Ord",
   "attr.rust.tuple_struct"
 ) {

docs/content/getting-started/data-out/analyze-and-send.md

@@ -46,10 +46,8 @@ Here is how to send the data as a scalar:
 ```python
 rr.send_columns(
     "/jaw_open_state",
-    times=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
-    components=[
-        rr.components.ScalarBatch(df["jawOpenState"]),
-    ],
+    indexes=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
+    columns=rr.Scalar.columns(scalar=df["jawOpenState"]),
 )
 ```
 
@@ -59,17 +57,15 @@ Next, let's send the same data as `Text` component:
 
 ```python
 target_entity = "/video/detector/faces/0/bbox"
-rr.log(target_entity, [rr.components.ShowLabels(True)], static=True)
+rr.log(target_entity, rr.Boxes2D.update_fields(show_labels=True), static=True)
 rr.send_columns(
     target_entity,
-    times=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
-    components=[
-        rr.components.TextBatch(np.where(df["jawOpenState"], "OPEN", "CLOSE")),
-    ],
+    indexes=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
+    columns=rr.Boxes2D.columns(labels=np.where(df["jawOpenState"], "OPEN", "CLOSE")),
 )
 ```
 
-Here we first log the [`ShowLabel`](../../reference/types/components/show_labels.md) component as static to enable the display of the label. Then, we use `rr.send_column()` again to send an entire batch of text labels. We use the [`np.where()`](https://numpy.org/doc/stable/reference/generated/numpy.where.html) to produce a label matching the state for each timestamp.
+Here we first log the [`ShowLabel`](../../reference/types/components/show_labels.md) component as static to enable the display of the label. Then, we use `rr.send_column()` again to send an entire batch of text labels. We use [`np.where()`](https://numpy.org/doc/stable/reference/generated/numpy.where.html) to produce a label matching the state for each timestamp.
 
 ### Final result
 

docs/snippets/INDEX.md

@@ -17,16 +17,16 @@
 
 # Log the ImageFormat and indicator once, as static.
 format_static = rr.components.ImageFormat(width=width, height=height, color_model="RGB", channel_datatype="U8")
-rr.log("images", [format_static, rr.Image.indicator()], static=True)
+rr.send_columns("images", indexes=[], columns=rr.Image.columns(format=format_static))
 
 # Send all images at once.
 rr.send_columns(
     "images",
-    times=[rr.TimeSequenceColumn("step", times)],
-    # Reshape the images so `ImageBufferBatch` can tell that this is several blobs.
+    indexes=[rr.TimeSequenceColumn("step", times)],
+    # Reshape the images so `Image` can tell that this is several blobs.
     #
-    # Note that the `ImageBufferBatch` consumes arrays of bytes,
-    # so if you have a different channel datatype than `U8`, you need to make sure
-    # that the data is converted to arrays of bytes before passing it to `ImageBufferBatch`.
-    components=[rr.components.ImageBufferBatch(images.reshape(len(times), -1))],
+    # Note that the `Image` consumes arrays of bytes, so if you have a different channel
+    # datatype than `U8`, you need to make sure that the data is converted to arrays of bytes
+    # before passing it to `Image`.
+    columns=rr.Image.columns(buffer=images.reshape(len(times), -1)),
 )

docs/snippets/all/archetypes/image_send_columns.py

@@ -2,7 +2,6 @@
 
 import numpy as np
 import rerun as rr
-from rerun.components import Position3DBatch
 
 rr.init("rerun_example_mesh3d_partial_updates", spawn=True)
 
@@ -23,4 +22,4 @@
 factors = np.abs(np.sin(np.arange(1, 300, dtype=np.float32) * 0.04))
 for i, factor in enumerate(factors):
     rr.set_time_sequence("frame", i)
-    rr.log("triangle", [Position3DBatch(vertex_positions * factor)])
+    rr.log("triangle", rr.Mesh3D.update_fields(vertex_positions=vertex_positions * factor))

docs/snippets/all/archetypes/mesh3d_partial_updates.py

@@ -23,7 +23,7 @@
 colors = [0xFF0000FF, 0x00FF00FF, 0x0000FFFF, 0xFFFF00FF, 0x00FFFFFF]
 radii = [0.05, 0.01, 0.2, 0.1, 0.3]
 
-rr.send_columns_v2(
+rr.send_columns(
     "points",
     indexes=[rr.TimeSecondsColumn("time", times)],
     columns=[

docs/snippets/all/archetypes/points3d_partial_updates_legacy.cpp

@@ -10,7 +10,7 @@
 times = np.arange(0, 64)
 scalars = np.sin(times / 10.0)
 
-rr.send_columns_v2(
+rr.send_columns(
     "scalars",
     indexes=[rr.TimeSequenceColumn("step", times)],
     columns=rr.Scalar.columns(scalar=scalars),

docs/snippets/all/archetypes/points3d_partial_updates_legacy.py

@@ -1,5 +1,4 @@
 """Log a video asset using automatically determined frame references."""
-# TODO(#7298): ⚠️ Video is currently only supported in the Rerun web viewer.
 
 import sys
 
@@ -21,6 +20,6 @@
 rr.send_columns(
     "video",
     # Note timeline values don't have to be the same as the video timestamps.
-    times=[rr.TimeNanosColumn("video_time", frame_timestamps_ns)],
-    components=[rr.VideoFrameReference.indicator(), rr.components.VideoTimestamp.nanoseconds(frame_timestamps_ns)],
+    indexes=[rr.TimeNanosColumn("video_time", frame_timestamps_ns)],
+    columns=rr.VideoFrameReference.columns_nanoseconds(frame_timestamps_ns),
 )

docs/snippets/all/archetypes/points3d_partial_updates_legacy.rs

@@ -1 +1 @@
-rr.send_columns("skybox", times=[], components=generate_skybox_mesh())
+rr.send_columns("skybox", indexes=[], columns=generate_skybox_mesh())

docs/snippets/all/archetypes/points3d_send_columns.py

@@ -12,7 +12,7 @@
 one_per_timestamp = np.sin(timestamps / 10.0)
 ten_per_timestamp = np.cos(np.arange(0, N * 10) / 100.0)
 
-rr.send_columns_v2(
+rr.send_columns(
     "/",
     indexes=[rr.TimeSequenceColumn("step", timestamps)],
     columns=[

docs/snippets/all/archetypes/scalar_send_columns.py

@@ -10,7 +10,7 @@
 timestamps = np.arange(0, 64)
 
 # Log two component columns, named "sin" and "cos", with the corresponding values
-rr.send_columns_v2(
+rr.send_columns(
     "/",
     indexes=[rr.TimeSequenceColumn("step", timestamps)],
     columns=rr.AnyValues.columns(sin=np.sin(timestamps / 10.0), cos=np.cos(timestamps / 10.0)),

docs/snippets/all/archetypes/video_auto_frames.py

@@ -32,19 +32,15 @@
 # log the jaw open state signal as a scalar
 rr.send_columns(
     "/jaw_open_state",
-    times=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
-    components=[
-        rr.components.ScalarBatch(df["jawOpenState"]),
-    ],
+    indexes=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
+    columns=rr.Scalar.columns(scalar=df["jawOpenState"]),
 )
 
 # log a `Label` component to the face bounding box entity
 target_entity = "/video/detector/faces/0/bbox"
-rr.log(target_entity, [rr.components.ShowLabels(True)], static=True)
+rr.log(target_entity, rr.Boxes2D.update_fields(show_labels=True), static=True)
 rr.send_columns(
     target_entity,
-    times=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
-    components=[
-        rr.components.TextBatch(np.where(df["jawOpenState"], "OPEN", "CLOSE")),
-    ],
+    indexes=[rr.TimeSequenceColumn("frame_nr", df["frame_nr"])],
+    columns=rr.Boxes2D.columns(labels=np.where(df["jawOpenState"], "OPEN", "CLOSE")),
 )

docs/snippets/all/concepts/static/send_static.py

@@ -279,7 +279,6 @@ quick_start = [ # These examples don't have exactly the same implementation.
 ]
 "archetypes/image_send_columns" = [ # This mixes `log` and `send_columns`. Since `log` is suspect to delays by the batcher, this test gets flaky.
   "cpp",
-  "py",
   "rust",
 ]
 "archetypes/mesh3d_instancing" = [ # TODO(#3235): Slight floating point differences in deg to rad conversion.