rerun-mcap

name	rerun-mcap
description	Ingest MCAP files into Rerun chunk streams with rerun.experimental.McapReader. Read when converting an MCAP recording, selecting topics or decoders, decoding custom protobuf messages, or when an MCAP-derived stream comes out empty. Builds on rerun-chunk-processing (stream mechanics) and rerun-data-model (what the topics should become).
user_invocable	true
allowed-tools	Read, Grep, Bash, WebFetch

Rerun MCAP ingestion

McapReader turns an MCAP file into a lazy chunk stream: one entity per topic at the topic's path, message payloads decoded by pluggable decoders. This skill covers the reader's options, what each topic becomes, and the failure modes that yield an empty stream with no error. Stream mechanics (filter, drop, lenses, merge, write) are in rerun-chunk-processing.

The API

from rerun.experimental import McapReader

reader = McapReader(mcap_path)  # see help(McapReader) for the full option set
stream = reader.stream()

A URDF embedded in the MCAP can be ingested as well (then see rerun-urdf).

What a topic becomes

• Entity path = topic name (/sensors/joint_states stays /sensors/joint_states). Filter early: McapReader(path).stream().filter(content="/sensors/**").
• A reflection-decoded message lands as one struct component named <fully.qualified.MessageName>:message. Navigate it with Selector (Selector(".joint_positions")) inside lenses; this is how custom messages get Rerun semantics attached (see the DeriveLens patterns in rerun-chunk-processing).
• Topic regexes use RE2 syntax and are not anchored: cam matches /external/cam_low and /camera_info. Anchor explicitly (^/external/cam) when it matters. Prefer reader-level topic filtering over .filter(...) when you can, so excluded topics are never decoded at all.

When to use the low-level mcap package instead

McapReader keeps payloads in columnar chunk streams; that is almost always what you want. Drop to mcap.reader.make_reader only when you need raw record metadata without payloads, or when you need to rewrite the container itself (re-registering schemas, channels, and messages).

Gotchas

1. Empty stream, no error: a topic regex that matched nothing, or a channel whose decoder produced no rows. Check Chunk.format() on a few chunks of reader.stream().to_chunks() against a tiny test file, or compare topic names with the mcap CLI / package first.
2. Topic regexes are unanchored RE2; excludes run after includes.
3. timeline_type="timestamp" interprets MCAP log times as wall-clock ns since epoch. If the recording's clock is wrong, fix it at the reader with timestamp_offset_ns rather than mutating timestamps downstream.
4. Decoder subsets silently skip topics no decoder claims; when a topic is missing, retry with decoders=None to rule out decoder selection.

References

• End-to-end MCAP pipeline: https://github.com/rerun-io/rerun/tree/main/examples/python/robot_data_preprocessing
• rerun-chunk-processing (stream/lens mechanics), rerun-urdf (FK from joint-state topics), rerun-data-model (modeling decisions)