schiebung-rerun 0.5.1

Python bindings for schiebung with Rerun visualization

Schiebung Rerun - Python Bindings

Python bindings for schiebung's Rerun visualization adapter — every transform you push is bulk-logged to a Rerun recording stream so you can visualize the graph live.

Installation

pip install schiebung-rerun

Quick Start

import time
from schiebung_rerun import RerunBufferTree, StampedIsometry, TransformType

# application_id, recording_id, timeline name, publish_static_transforms.
# Spawns a Rerun viewer by default; pass spawn=False (optionally with
# connect_addr="rerun+http://host:port/proxy") to connect to one that is
# already running instead.
tree = RerunBufferTree("my_app", "recording_1", "stable_time", True)

# stamp accepts int (ns) or float (s).
transform = StampedIsometry([1.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0], time.time())

# Updates go through tree.buffer; the underlying observer logs each batch to Rerun.
tree.buffer.update("world", "robot", transform, TransformType.Dynamic)
tree.buffer.update_batch([("world", "robot", transform, TransformType.Dynamic)])

StampedIsometry, BufferTree, TransformType, TfError and UrdfLoader are re-exported from the schiebung package — they are the same types, so values pass freely between the two packages (schiebung_rerun.StampedIsometry is schiebung.StampedIsometry).

Tuning the recording stream

RerunBufferTree / RerunObserver take an optional batcher_config — a rerun.ChunkBatcherConfig (including its DEFAULT / LOW_LATENCY / ALWAYS / NEVER presets) controlling the chunk-batcher flush thresholds:

import rerun as rr
tree = RerunBufferTree("my_app", "recording_1", "stable_time", True,
                       batcher_config=rr.ChunkBatcherConfig.LOW_LATENCY())

The RERUN_FLUSH_TICK_SECS / RERUN_FLUSH_NUM_BYTES / RERUN_FLUSH_NUM_ROWS / RERUN_CHUNK_MAX_ROWS_IF_UNSORTED environment variables still override it.

Documentation

Full documentation: https://maximaerz.github.io/schiebung/ · API reference: docs.rs/schiebung-rerun