Rerun blueprint

A blueprint decides how a recording is shown; the recording decides what exists. Read the data, design a first layout, then iterate from screenshots until it reads at a glance. The constructors are discoverable (dir(rrb), help(rrb.Spatial3DView)); this skill covers what you can't guess. Import as import rerun.blueprint as rrb.

1. Read the data

Enumerate every (entity_path, archetype) pair first — the archetype picks the view, the entity path scopes it. For a dataset (see rerun-catalog-queries):

for c in dataset.schema().component_columns():
    print(c.entity_path, c.archetype, c.component_name)

For a local recording, stream it with RrdReader and read entity_path and the archetype off each chunk (see rerun-chunk-processing).

2. Map archetype to view

3. Group entities into views

This step is just deciding which entities share a view. Sizing and placement are mechanics you tune by eye in the screenshot loop, not rules to settle up front.

• Group by shared path prefix. Entities under a common prefix usually belong in one view. Don't give every entity its own pane: dozens of raw message entities go in one DataframeView, or get left out, rather than a pane each.
• One spatial frame per spatial view. A whole Transform3D tree goes in one Spatial3DView at the common ancestor; a Pinhole camera gets its own Spatial2DView rooted at the camera so images inherit the projection.
• Collapse scalars. Many Scalars under a prefix → one TimeSeriesView over the prefix, not one each. Split only when value ranges clash.
• column_shares/row_shares are relative weights on a container's children, equal by default. Tune them from the screenshots.

4. Construct it

Containers (Grid, Horizontal, Vertical, Tabs) hold views. Default a flat set to Grid; use the others for a deliberate split, Tabs for alternatives competing for one slot (left/right/depth cameras). Always set an explicit origin and name — origin defaults to /, which dumps the whole tree into one view (the usual cause of an unreadable blob).

blueprint = rrb.Blueprint(
    rrb.Horizontal(
        rrb.Spatial3DView(origin="/world", name="Scene"),
        rrb.Vertical(
            rrb.Spatial2DView(origin="/world/camera", name="Camera"),
            rrb.TimeSeriesView(origin="/sensors", name="Sensors"),
        ),
        column_shares=[3, 2],
    ),
    rrb.TextLogView(origin="/logs", name="Logs"),
    collapse_panels=True,
)

contents defaults to "$origin/**". Scope a view with include/exclude rules, e.g. contents=["+ $origin/**", "- $origin/internal/**"]. A bare line is an include; /** is the only wildcard (matches a subtree). Most-specific rule wins, ties go to the last, unmatched paths are excluded.

Coordinate frames. A spatial view only renders entities it can place relative to its target frame. Transform3D/Pinhole logged on entities compose down the tree, so origin is enough. But named frames (CoordinateFrame(frame=...), common in ROS/MCAP) live in a separate frame graph — point the view at a frame the data occupies via spatial_information=rrb.SpatialInformation(target_frame="<frame>"). The tell is an empty 3D view with "No transform path from <frame>…" errors: origin="/" targets the root tf#/, which connects to nothing if the tf tree was never materialized. Read the CoordinateFrame:frame values, target the one the main 3D content sits in, and exclude entities in unconnected frames.

5. Iterate from screenshots

Spawn the viewer and load the recording once, then re-send blueprints into the same viewer; each send + screenshot is one cheap iteration. Render, look, revise.

The blueprint binds to the data by store identity, both application id and recording id. A loaded .rrd keeps its own identity, so build the stream from the rrd's values (set both; recording_id otherwise defaults to a random one). Mismatch either and the viewer keeps blueprint and data as separate recordings and never applies the blueprint.

import time
import rerun as rr
import rerun.blueprint as rrb
from rerun.experimental import ViewerClient, RrdReader

store = RrdReader("segment.rrd").recordings()[0]  # the rrd's application id + recording id

with ViewerClient.spawn(headless=True, port=9879) as viewer:
    rec = rr.RecordingStream(store.application_id, recording_id=store.recording_id)
    rec.connect_grpc(url=viewer.url)
    rec.log_file_from_path("segment.rrd")

    def shot(blueprint, path):
        rec.send_blueprint(blueprint, make_active=True, make_default=True)
        time.sleep(2)  # let the import finish and a frame render; bump if a view is blank
        viewer.save_screenshot(path)  # view_id=view.id for a single view

    shot(blueprint_v1, "bp_v1.png")
    # Read bp_v1.png, revise, send the next one into the same viewer.
    shot(blueprint_v2, "bp_v2.png")

To bake a finished blueprint in instead, pass default_blueprint= to rr.init / spawn / connect_grpc / save, or blueprint= to notebook_show.

Gotchas

• A DataframeView shows "Unknown timeline" without a query: query=rrb.archetypes.DataframeQuery(timeline="<timeline>", apply_latest_at=True).
• A view back blank? The importer may not have finished (bump the settle) or the cursor sits before the data (add rrb.TimePanel(play_state=rrb.components.PlayState.Following)).
• rrb views and ViewerClient (rerun.experimental) are unstable. Check help() if a constructor argument is rejected.

See also

• rerun-data-model — entities, archetypes, timelines.
• rerun-catalog-queries — enumerate entities in a dataset.