Python API: Rename sequence time parameter to seq

README.md

@@ -37,7 +37,7 @@ rr.connect()  # Connect to a remote viewer
 # rr.save("recording.rrd")  # Stream all logs to disk
 
 # Associate subsequent data with 42 on the “frame” timeline
-rr.set_index("frame", sequence=42)
+rr.set_index("frame", seq=42)
 
 # Log colored 3D points to the entity at `path/to/points`
 rr.log("path/to/points", rr.Points3D(positions, colors=colors))

crates/store/re_types_core/src/id.rs

@@ -140,7 +140,7 @@ crate::delegate_arrow_tuid!(ChunkId as "rerun.controls.ChunkId"); // Used in the
 ///
 /// In pseudo-code:
 /// ```text
-/// rr.set_index("frame", sequence=10)
+/// rr.set_index("frame", seq=10)
 ///
 /// rr.log("my_entity", point1, row_id=#1)
 /// rr.log("my_entity", point2, row_id=#0)

docs/content/concepts/chunks.md

@@ -87,7 +87,7 @@ But if you're handing a bunch of rows of data over to Rerun, how does it end up
 #### How are these rows turned into columns?
 
 Before logging data, you can use the `rr.set_time_` APIs to update the SDK's time context with timestamps for custom timelines.
-For example, `rr.set_index("frame", sequence=42)` will set the "frame" timeline's current value to 42 in the time context.
+For example, `rr.set_index("frame", seq=42)` will set the "frame" timeline's current value to 42 in the time context.
 
 When you later call `rr.log`, the SDK will generate a row id and values for the built-in timelines `log_time` and `log_tick`.
 It will also grab the current values for any custom timelines from the time context.

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

@@ -46,7 +46,7 @@ Here is how to send the data as a scalar:
 ```python
 rr.send_columns(
     "/jaw_open_state",
-    indexes=[rr.IndexColumn("frame_nr", sequence=df["frame_nr"])],
+    indexes=[rr.IndexColumn("frame_nr", seq=df["frame_nr"])],
     columns=rr.Scalar.columns(scalar=df["jawOpenState"]),
 )
 ```
@@ -60,7 +60,7 @@ target_entity = "/video/detector/faces/0/bbox"
 rr.log(target_entity, rr.Boxes2D.update_fields(show_labels=True), static=True)
 rr.send_columns(
     target_entity,
-    indexes=[rr.IndexColumn("frame_nr", sequence=df["frame_nr"])],
+    indexes=[rr.IndexColumn("frame_nr", seq=df["frame_nr"])],
     columns=rr.Boxes2D.columns(labels=np.where(df["jawOpenState"], "OPEN", "CLOSE")),
 )
 ```

docs/content/howto/integrations/embed-notebooks.md

@@ -143,7 +143,7 @@ STEPS = 100
 twists = math.pi * np.sin(np.linspace(0, math.tau, STEPS)) / 4
 for t in range(STEPS):
     sleep(0.05)  # delay to simulate a long-running computation
-    rr.set_index("step", sequence=t)
+    rr.set_index("step", seq=t)
     cube = build_color_grid(10, 10, 10, twist=twists[t])
     rr.log("cube", rr.Points3D(cube.positions, colors=cube.colors, radii=0.5))
 ```

docs/content/howto/logging/clears.md

@@ -13,7 +13,7 @@ For example, if you have an object tracking application, your code might look so
 …
 for frame in sensors.read():
     # Associate the following logs with `frame == frame.id`
-    rr.set_index("frame", sequence=frame.id)
+    rr.set_index("frame", seq=frame.id)
     # Do the actual tracking update
     tracker.update(frame)
     if tracker.is_lost:
@@ -33,7 +33,7 @@ In some cases, the best approach may be to rethink how you log data to better ex
 …
 for frame in sensors.read():
     # Associate the following logs with `frame = frame.id`
-    rr.set_index("frame", sequence=frame.id)
+    rr.set_index("frame", seq=frame.id)
     # Log every image that comes in
     rr.log("input/image", rr.Image(frame.image))
     if frame.id % 10 == 0:
@@ -63,7 +63,7 @@ class Detector:
 …
 for frame in sensors.read():
     # Associate the following logs with `frame = frame.id`
-    rr.set_index("frame", sequence=frame.id)
+    rr.set_index("frame", seq=frame.id)
     # Log every image that comes in
     rr.log("input/image", rr.Image(frame.image))
     if frame.id % 10 == 0:

docs/content/reference/migration/migration-0-17.md

@@ -41,7 +41,7 @@ For example, setting color and enabling the `SeriesPoint` visualizer was previou
 rr.log("data", rr.SeriesPoint(color=[255, 255, 0]), static=True)
 
 for t in range(1000):
-    rr.set_index("frame_nr", sequence=t)
+    rr.set_time_sequence("frame_nr", t)
     rr.log("data",rr.Scalar(get_data(t))),
 
 rr.send_blueprint(
@@ -53,7 +53,7 @@ Now the override can be specified from the blueprint, removing the need to inclu
 
 ```python
 for t in range(1000):
-    rr.set_index("frame_nr", sequence=t)
+    rr.set_time_sequence("frame_nr", t)
     rr.log("data",rr.Scalar(get_data(t))),
 
 rr.send_blueprint(

docs/content/reference/migration/migration-0-23.md

@@ -14,7 +14,7 @@ We're moving towards a more explicit API for setting time, where you need to exp
 Previously we would infer the user intent at runtime based on the value: if it was large enough, it was interpreted as time since the Unix epoch, otherwise it was interpreted as a timedelta.
 
 To this end, we're deprecated `rr.set_time_seconds`, `rr.set_time_nanos`, as well as `rr.set_time_sequence` and replaced them with `rr.set_index`.
-`set_index` takes either a `sequence=`, `timedelta=` or `datetime=` argument.
+`set_index` takes either a `seq=`, `timedelta=` or `datetime=` argument.
 
 `timedelta` must be either:
 * seconds as `int` or `float`
@@ -28,7 +28,7 @@ To this end, we're deprecated `rr.set_time_seconds`, `rr.set_time_nanos`, as wel
 
 ### Migrating
 ##### `rr.set_sequence("foo", 42)`
-New: `rr.set_index("foo", sequence=42)`
+New: `rr.set_index("foo", seq=42)`
 
 ##### `rr.set_time_seconds("foo", duration_seconds)`
 When using relative times (durations/timedeltas): `rr.set_index("foo", timedelta=duration_seconds)`
@@ -59,7 +59,7 @@ The migration is very similar to the above.
 
 ### Migration
 ##### `rr.TimeSequenceColumn("foo", values)`
-New: `rr.IndexColumn("foo", sequence=values)`
+New: `rr.IndexColumn("foo", seq=values)`
 
 ##### `rr.TimeSecondsColumn("foo", duration_seconds)`
 New: `rr.IndexColumn("foo", timedelta=duration_seconds)`

docs/snippets/all/archetypes/image_column_updates.py

@@ -26,7 +26,7 @@
 # Send all images at once.
 rr.send_columns(
     "images",
-    indexes=[rr.IndexColumn("step", sequence=times)],
+    indexes=[rr.IndexColumn("step", seq=times)],
     # Reshape the images so `Image` can tell that this is several blobs.
     #
     # Note that the `Image` consumes arrays of bytes, so we should ensure that we take a

docs/snippets/all/archetypes/image_row_updates.py

@@ -10,7 +10,7 @@
 rr.init("rerun_example_image_row_updates", spawn=True)
 
 for t in range(20):
-    rr.set_index("time", sequence=t)
+    rr.set_index("time", seq=t)
 
     image = np.zeros((200, 300, 3), dtype=np.uint8)
     image[:, :, 2] = 255

docs/snippets/all/archetypes/instance_poses3d_combined.py

@@ -5,14 +5,14 @@
 
 rr.init("rerun_example_instance_pose3d_combined", spawn=True)
 
-rr.set_index("frame", sequence=0)
+rr.set_index("frame", seq=0)
 
 # Log a box and points further down in the hierarchy.
 rr.log("world/box", rr.Boxes3D(half_sizes=[[1.0, 1.0, 1.0]]))
 rr.log("world/box/points", rr.Points3D(np.vstack([xyz.ravel() for xyz in np.mgrid[3 * [slice(-10, 10, 10j)]]]).T))
 
 for i in range(0, 180):
-    rr.set_index("frame", sequence=i)
+    rr.set_index("frame", seq=i)
 
     # Log a regular transform which affects both the box and the points.
     rr.log("world/box", rr.Transform3D(rotation_axis_angle=rr.RotationAxisAngle([0, 0, 1], angle=rr.Angle(deg=i * 2))))

docs/snippets/all/archetypes/mesh3d_instancing.py

@@ -3,7 +3,7 @@
 import rerun as rr
 
 rr.init("rerun_example_mesh3d_instancing", spawn=True)
-rr.set_index("frame", sequence=0)
+rr.set_index("frame", seq=0)
 
 rr.log(
     "shape",
@@ -20,7 +20,7 @@
 )
 
 for i in range(0, 100):
-    rr.set_index("frame", sequence=i)
+    rr.set_index("frame", seq=i)
     rr.log(
         "shape",
         rr.InstancePoses3D(

docs/snippets/all/archetypes/mesh3d_partial_updates.py

@@ -8,7 +8,7 @@
 vertex_positions = np.array([[-1.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 1.0, 0.0]], dtype=np.float32)
 
 # Log the initial state of our triangle
-rr.set_index("frame", sequence=0)
+rr.set_index("frame", seq=0)
 rr.log(
     "triangle",
     rr.Mesh3D(
@@ -21,5 +21,5 @@
 # Only update its vertices' positions each frame
 factors = np.abs(np.sin(np.arange(1, 300, dtype=np.float32) * 0.04))
 for i, factor in enumerate(factors):
-    rr.set_index("frame", sequence=i)
+    rr.set_index("frame", seq=i)
     rr.log("triangle", rr.Mesh3D.from_fields(vertex_positions=vertex_positions * factor))

docs/snippets/all/archetypes/points3d_partial_updates.py

@@ -6,17 +6,17 @@
 
 positions = [[i, 0, 0] for i in range(0, 10)]
 
-rr.set_index("frame", sequence=0)
+rr.set_index("frame", seq=0)
 rr.log("points", rr.Points3D(positions))
 
 for i in range(0, 10):
     colors = [[20, 200, 20] if n < i else [200, 20, 20] for n in range(0, 10)]
     radii = [0.6 if n < i else 0.2 for n in range(0, 10)]
 
     # Update only the colors and radii, leaving everything else as-is.
-    rr.set_index("frame", sequence=i)
+    rr.set_index("frame", seq=i)
     rr.log("points", rr.Points3D.from_fields(radii=radii, colors=colors))
 
 # Update the positions and radii, and clear everything else in the process.
-rr.set_index("frame", sequence=20)
+rr.set_index("frame", seq=20)
 rr.log("points", rr.Points3D.from_fields(clear_unset=True, positions=positions, radii=0.3))

docs/snippets/all/archetypes/scalar_column_updates.py

@@ -16,6 +16,6 @@
 
 rr.send_columns(
     "scalars",
-    indexes=[rr.IndexColumn("step", sequence=times)],
+    indexes=[rr.IndexColumn("step", seq=times)],
     columns=rr.Scalar.columns(scalar=scalars),
 )

docs/snippets/all/archetypes/scalar_multiple_plots.py

@@ -18,7 +18,7 @@
 
 # Log the data on a timeline called "step".
 for t in range(0, int(tau * 2 * 100.0)):
-    rr.set_index("step", sequence=t)
+    rr.set_index("step", seq=t)
 
     rr.log("trig/sin", rr.Scalar(sin(float(t) / 100.0)))
     rr.log("trig/cos", rr.Scalar(cos(float(t) / 100.0)))

docs/snippets/all/archetypes/scalar_row_updates.py

@@ -13,5 +13,5 @@
 rr.init("rerun_example_scalar_row_updates", spawn=True)
 
 for step in range(64):
-    rr.set_index("step", sequence=step)
+    rr.set_index("step", seq=step)
     rr.log("scalars", rr.Scalar(math.sin(step / 10.0)))

docs/snippets/all/archetypes/scalar_simple.py

@@ -8,5 +8,5 @@
 
 # Log the data on a timeline called "step".
 for step in range(0, 64):
-    rr.set_index("step", sequence=step)
+    rr.set_index("step", seq=step)
     rr.log("scalar", rr.Scalar(math.sin(step / 10.0)))

docs/snippets/all/archetypes/series_line_style.py

@@ -14,7 +14,7 @@
 
 # Log the data on a timeline called "step".
 for t in range(0, int(tau * 2 * 100.0)):
-    rr.set_index("step", sequence=t)
+    rr.set_index("step", seq=t)
 
     rr.log("trig/sin", rr.Scalar(sin(float(t) / 100.0)))
     rr.log("trig/cos", rr.Scalar(cos(float(t) / 100.0)))

docs/snippets/all/archetypes/series_point_style.py

@@ -32,7 +32,7 @@
 
 # Log the data on a timeline called "step".
 for t in range(0, int(tau * 2 * 10.0)):
-    rr.set_index("step", sequence=t)
+    rr.set_index("step", seq=t)
 
     rr.log("trig/sin", rr.Scalar(sin(float(t) / 10.0)))
     rr.log("trig/cos", rr.Scalar(cos(float(t) / 10.0)))

docs/snippets/all/archetypes/transform3d_axes.py

@@ -4,14 +4,14 @@
 
 rr.init("rerun_example_transform3d_axes", spawn=True)
 
-rr.set_index("step", sequence=0)
+rr.set_index("step", seq=0)
 
 # Set the axis lengths for all the transforms
 rr.log("base", rr.Transform3D(axis_length=1))
 
 # Now sweep out a rotation relative to the base
 for deg in range(360):
-    rr.set_index("step", sequence=deg)
+    rr.set_index("step", seq=deg)
     rr.log(
         "base/rotated",
         rr.Transform3D(

docs/snippets/all/archetypes/transform3d_column_updates.py

@@ -15,7 +15,7 @@ def truncated_radians(deg: float) -> float:
 
 rr.init("rerun_example_transform3d_column_updates", spawn=True)
 
-rr.set_index("tick", sequence=0)
+rr.set_index("tick", seq=0)
 rr.log(
     "box",
     rr.Boxes3D(half_sizes=[4.0, 2.0, 1.0], fill_mode=rr.components.FillMode.Solid),
@@ -24,7 +24,7 @@ def truncated_radians(deg: float) -> float:
 
 rr.send_columns(
     "box",
-    indexes=[rr.IndexColumn("tick", sequence=range(1, 101))],
+    indexes=[rr.IndexColumn("tick", seq=range(1, 101))],
     columns=rr.Transform3D.columns(
         translation=[[0, 0, t / 10.0] for t in range(100)],
         rotation_axis_angle=[

docs/snippets/all/archetypes/transform3d_row_updates.py

@@ -15,15 +15,15 @@ def truncated_radians(deg: float) -> float:
 
 rr.init("rerun_example_transform3d_row_updates", spawn=True)
 
-rr.set_index("tick", sequence=0)
+rr.set_index("tick", seq=0)
 rr.log(
     "box",
     rr.Boxes3D(half_sizes=[4.0, 2.0, 1.0], fill_mode=rr.components.FillMode.Solid),
     rr.Transform3D(clear=False, axis_length=10),
 )
 
 for t in range(100):
-    rr.set_index("tick", sequence=t + 1)
+    rr.set_index("tick", seq=t + 1)
     rr.log(
         "box",
         rr.Transform3D(

docs/snippets/all/concepts/different_data_per_timeline.py

@@ -5,7 +5,7 @@
 
 rr.init("rerun_example_different_data_per_timeline", spawn=True)
 
-rr.set_index("blue timeline", sequence=0)
+rr.set_index("blue timeline", seq=0)
 rr.set_index("red timeline", timedelta=0.0)
 rr.log("points", rr.Points2D([[0, 0], [1, 1]], radii=rr.Radius.ui_points(10.0)))
 
@@ -16,7 +16,7 @@
 
 # And a blue color on the other.
 rr.reset_time()  # Clears all set timeline info.
-rr.set_index("blue timeline", sequence=1)
+rr.set_index("blue timeline", seq=1)
 rr.log("points", [rr.components.Color(0x0000FFFF)])
 
 

docs/snippets/all/concepts/indices.py

@@ -7,7 +7,7 @@
 
 rr.init("rerun_example_different_indices", spawn=True)
 
-rr.set_index("frame_nr", sequence=42)
+rr.set_index("frame_nr", seq=42)
 rr.set_index("elapsed", timedelta=12)  # elapsed seconds
 rr.set_index("time", datetime=1_741_017_564)  # Seconds since unix epoch
 rr.set_index("time", datetime=datetime.fromisoformat("2025-03-03T15:59:24"))

docs/snippets/all/concepts/static/log_temporal_10x.py

@@ -1,3 +1,3 @@
-rr.set_index("frame", sequence=4)
+rr.set_index("frame", seq=4)
 for _ in range(10):
     rr.log("camera/image", camera.save_current_frame())

docs/snippets/all/howto/any_batch_value_column_updates.py

@@ -14,7 +14,7 @@
 
 rr.send_columns(
     "/",
-    indexes=[rr.IndexColumn("step", sequence=timestamps)],
+    indexes=[rr.IndexColumn("step", seq=timestamps)],
     columns=[
         # log one value per timestamp
         rr.AnyBatchValue.column("custom_component_single", one_per_timestamp),

docs/snippets/all/howto/any_values_column_updates.py

@@ -15,6 +15,6 @@
 
 rr.send_columns(
     "/",
-    indexes=[rr.IndexColumn("step", sequence=timestamps)],
+    indexes=[rr.IndexColumn("step", seq=timestamps)],
     columns=rr.AnyValues.columns(sin=np.sin(timestamps / 10.0), cos=np.cos(timestamps / 10.0)),
 )

docs/snippets/all/howto/any_values_row_updates.py

@@ -13,5 +13,5 @@
 rr.init("rerun_example_any_values_row_updates", spawn=True)
 
 for step in range(64):
-    rr.set_index("step", sequence=step)
+    rr.set_index("step", seq=step)
     rr.log("/", rr.AnyValues(sin=math.sin(step / 10.0), cos=math.cos(step / 10.0)))

docs/snippets/all/tutorials/data_out.py

@@ -32,7 +32,7 @@
 # log the jaw open state signal as a scalar
 rr.send_columns(
     "/jaw_open_state",
-    indexes=[rr.IndexColumn("frame_nr", sequence=df["frame_nr"])],
+    indexes=[rr.IndexColumn("frame_nr", seq=df["frame_nr"])],
     columns=rr.Scalar.columns(scalar=df["jawOpenState"]),
 )
 
@@ -41,6 +41,6 @@
 rr.log(target_entity, rr.Boxes2D.from_fields(show_labels=True), static=True)
 rr.send_columns(
     target_entity,
-    indexes=[rr.IndexColumn("frame_nr", sequence=df["frame_nr"])],
+    indexes=[rr.IndexColumn("frame_nr", seq=df["frame_nr"])],
     columns=rr.Boxes2D.columns(labels=np.where(df["jawOpenState"], "OPEN", "CLOSE")),
 )

docs/snippets/all/tutorials/timelines_example.py

@@ -1,5 +1,5 @@
 for frame in read_sensor_frames():
-    rr.set_index("frame_idx", sequence=frame.idx)
+    rr.set_index("frame_idx", seq=frame.idx)
     rr.set_index("sensor_time", datetime=frame.timestamp)
 
     rr.log("sensor/points", rr.Points3D(frame.points))