pymcap-cli

A high-performance Python CLI for MCAP file processing with advanced recovery, filtering, and optimization capabilities.

Installation

# Run directly without installing
uvx pymcap-cli info data.mcap

# Or add to your project
uv add pymcap-cli

# With video support (for video generation and ROS image compression)
uv add "pymcap-cli[video]"

# With ROS image and point-cloud compression support
uv add "pymcap-cli[video,pointcloud]"

# Add Draco point-cloud compression support
uv add "pymcap-cli[video,pointcloud,draco]"

The base install includes the CLI framework, MCAP compression support, ROS schema parsing/decoding, configuration paths, and YAML handling because those are used across the core inspect and transform commands. Feature-specific binary and web stacks remain optional:

Each extra is tested from the built wheel in an isolated environment. Adding a new optional dependency requires assigning it to a feature module in the import contracts and adding its promised command to that wheel matrix.

Why pymcap-cli over the official Go CLI?

• Advanced Recovery — handles corrupt MCAP files with intelligent chunk-level recovery and MessageIndex validation
• Smart Chunk Copying — fast chunk copying without decompression when possible, up to 10x faster for filtering operations
• Unified Processing — single process command combines recovery + filtering + compression in one optimized pass
• Precise Filtering — regex topic filtering, time range filtering, and content type filtering with deferred schema/channel writing
• Broad Format Coverage — converts ROS 1 .bag and ROS 2 .db3 to MCAP, exports to NDJSON, CSV, Parquet, PCD, GeoJSON/KML/GPX, and image/video files
• Rich Terminal Output — colored topics, Unicode distribution histograms, tree views, and responsive layouts
• Robust Error Handling — graceful degradation with detailed error reporting and recovery statistics

Commands

info — File Information

Display detailed MCAP file information including schemas, channels, message counts, time ranges, and per-topic distribution histograms.

pymcap-cli info data.mcap

Use --tree to group topics into a hierarchical tree view:

# Multiple files
pymcap-cli info file1.mcap file2.mcap file3.mcap

# JSON output
pymcap-cli info-json data.mcap

Common Message Filters

File-reading commands use the same selectors, including cat, filter, process, roscompress, the exporters, and the diagnostic/TF readers:

# Exact topics (repeatable)
pymcap-cli export-json data.mcap -o ./json -t /odom -t /imu

# Regex selectors use full-match semantics; exclusions always win
pymcap-cli cat data.mcap -t '/camera/.*' -x '.*/debug'

# Inclusive start, exclusive end; recording-relative values are supported
pymcap-cli export-images data.mcap -o ./images --start @10s --end end-5s

Every --topic / --exclude-topic value is a regular expression evaluated with full-match semantics. Thus /camera/front matches only that topic, /camera/.* matches the camera namespace, and .*camera.* performs a substring-style match. Escape regex metacharacters in arbitrary non-ROS MCAP topic names when you mean them literally. See each command's --help for domain-specific options.

Time-filter cheat sheet

--start / -S is inclusive. --end / -E is exclusive.

# Keep [10s, 20s) relative to recording start
-S +10s -E +20s

# Keep everything except the final 30 seconds
-E=-30s

# Keep the final minute
-S=-1m

# Absolute RFC3339 window
-S 2026-07-13T12:00:00Z -E 2026-07-13T12:10:00Z

Use = with negative shorthand so it is not mistaken for another option:

--start=-1m
--end=-30s

cat — Stream Messages

Stream MCAP messages to stdout. Outputs as Rich tables when interactive, JSONL when piped.

Use --query to extract nested fields from deeply structured ROS messages with JSONPath-like syntax:

# Display messages in a table
pymcap-cli cat recording.mcap

# Filter an exact topic
pymcap-cli cat recording.mcap --topic /camera/image

# Filter by recording-relative time range
pymcap-cli cat recording.mcap --start @10s --end @20s

# Limit output
pymcap-cli cat recording.mcap --limit 100

# Query specific field using message path
pymcap-cli cat recording.mcap --query '/odom.pose.position.x'

# Filter array elements
pymcap-cli cat recording.mcap --query '/detections.objects[:]{confidence>0.8}'

# Reduce an array in each message to one scalar
pymcap-cli cat recording.mcap --query '/joint_states.position.@max'

# Supply MessagePath variables on the command line
pymcap-cli cat recording.mcap --query '/temperature{>=$minimum}' --var minimum=-40

# Or reuse variables from the environment; --var overrides matching names
PYMCAP_VAR_minimum=-40 PYMCAP_VAR_maximum=125 \
  pymcap-cli cat recording.mcap --query '/temperature{>=$minimum && <=$maximum}'

# Pipe to file as JSONL
pymcap-cli cat recording.mcap > messages.jsonl

# Write to file with progress bar
pymcap-cli cat recording.mcap -o messages.jsonl

# Control binary field serialization
pymcap-cli cat recording.mcap --bytes base64   # base64-encoded
pymcap-cli cat recording.mcap --bytes skip     # omit binary fields

check — Recording Contract Validation

Check the topics, schemas, encodings, timing, and decoded values in a recording against a strict versioned YAML spec. Topic selectors are case-insensitive regular expressions matched against the whole topic name. Warnings are shown without causing a non-zero exit; errors exit with status 1.

pymcap-cli check recording.mcap --spec recording.yaml

version: 1

topics:
  imu:
    topic: /imu
    schema:
      name: sensor_msgs/msg/Imu
      encoding: ros2msg
    message_encoding: cdr
    frequency:
      min: 95
      max: 105
      tolerance: 0.05
      window: 1s
    timeout: 50ms
    values:
      - '.linear_acceleration.@norm{<=30}'
      - '.header.frame_id{=="imu_link"}'
    live:
      publishers:
        min: 1
        max: 1
        node: /imu_driver
      subscribers:
        min: 1

  forbidden_front_radar:
    topic: /RADAR_FRONT
    expected: false
    severity: error

live:
  nodes:
    localization:
      node: /localization
      expected: true

The spec format is described by schemas/mcap_check_spec.json; point your editor at it for validation and completion:

# yaml-language-server: $schema=https://raw.githubusercontent.com/mrkbac/robotic-tools/main/pymcap-cli/schemas/mcap_check_spec.json

expected defaults to true, severity defaults to error, and frequency tolerance defaults to zero. Predicate-ending MessagePaths are the preferred value-check form: a matching value passes and an empty result fails. Mapping rules with inclusive min/max, equals, or one_of remain available when a predicate is not convenient or when reports need the rejected scalar value:

values:
  - '.fields[:]{name == "z"}.@length{==1}'
  - '.@product(width, height){>=1000 && <=100000}'
  - path: .temperature
    min: -40
    max: 85

Cross-message modifiers use @@. Their state is isolated per concrete topic:

values:
  - '.temperature.@@mean{>=15 && <=35}'
  - '.status{=="OK"}.@@timedelta.@@max{<=0.5}'
  - '.header.stamp.@to_nsec.@@unchanged_for.@@max{<=0.5}'
  - '.@@timedelta.@@stddev{<=0.005}'

Recorded stream timing uses MCAP log time; live checks use monotonic local arrival time. Select a timestamp field and use @@delta when checking that clock instead, such as .header.stamp.@to_nsec.@@delta.@@max{<=200000000}.

Checks may reference $log_time_ns, $publish_time_ns, $recording_start_ns, and $recording_end_ns as evaluation variables. On files without summary statistics (e.g. torn, in-progress recordings) the recording end is unknown while messages stream by, so rules referencing $recording_end_ns report an evaluation error instead of a guessed value.

The repository includes a complete contract for its nuScenes fixture. From the workspace root, run:

pymcap-cli check data/data/nuScenes-v1.0-mini-scene-0061-ros2.mcap \
  --spec pymcap-cli/examples/check/nuscenes.yaml

Use the same contract as a live preflight before recording:

pymcap-cli bridge check localhost --spec recording.yaml --duration 5

The recording command validates the shared topic rules and skips live constraints. bridge check validates advertised topics, schemas, publisher/subscriber counts and node identities, and samples only topics with frequency, timeout, or value rules. Live graph constraints require the bridge connectionGraph capability. A node is considered present when it publishes, subscribes, or provides a service in that graph.

doctor — MCAP Container Validation

Check an MCAP file structure against the MCAP container specification, with summary, index, chunk, message-order, and advisory findings.

pymcap-cli doctor data.mcap
pymcap-cli doctor data.mcap --strict-message-order --show-all

tftree — TF Transform Tree

Visualize the ROS TF transform tree with colored static/dynamic transforms, translation and rotation values.

# Show complete TF tree (both /tf and /tf_static)
pymcap-cli tftree data.mcap

# Show only static transforms
pymcap-cli tftree data.mcap --static-only

tf-get — TF Transform Lookup

Resolve the transform from a source frame into a target frame using /tf_static and /tf. Without --at, dynamic edges use their latest sample.

pymcap-cli tf-get data.mcap map base_link
pymcap-cli tf-get data.mcap odom base_link --at 2024-01-01T10:00:00Z

tf-export — TF Tree to URDF / SDF / JSON

Reconstruct robot description files from /tf_static (and optionally /tf at a snapshot timestamp). Useful when the original .urdf is missing — Foxglove Studio and rviz can render the static skeleton from the exported file.

# Write a URDF for the static tree
pymcap-cli tf-export data.mcap -o robot.urdf

# SDF or JSON instead
pymcap-cli tf-export data.mcap --format sdf -o robot.sdf
pymcap-cli tf-export data.mcap --format json

# Capture a dynamic snapshot from /tf at a given time
pymcap-cli tf-export data.mcap --include-dynamic-at 2024-01-01T10:00:00Z -o snapshot.urdf

# Pick a subtree when the recording has multiple disconnected roots
pymcap-cli tf-export data.mcap --root base_link -o robot.urdf

diag — ROS2 Diagnostics

Inspect ROS2 diagnostics with per-component health overview, sparkline timelines, frequency stats, and time-in-state tracking.

# Show components with issues (WARN/ERROR/STALE)
pymcap-cli diag recording.mcap

# Show all components including OK
pymcap-cli diag recording.mcap --all

# Detailed inspection of specific components
pymcap-cli diag recording.mcap --inspect "encoder"

# Hierarchical tree view
pymcap-cli diag recording.mcap --tree

# JSON output for scripting
pymcap-cli diag recording.mcap --json

plot — Time-Series And Distribution Visualization

Plot message fields as time series, value histograms, categorical frequency bars, or XY trajectories using Plotly. Supports named labels, LTTB downsampling, interactive HTML, and static image output. Requires the plot extra (uv add pymcap-cli[plot]).

# Plot a single field
pymcap-cli plot recording.mcap /odom.pose.position.x

# Named series
pymcap-cli plot recording.mcap "Vel X=/odom.twist.twist.linear.x"

# XY trajectory plot
pymcap-cli plot recording.mcap --kind xy /odom.pose.position.x /odom.pose.position.y

# Numeric histogram with at most 40 bins
pymcap-cli plot recording.mcap /imu.linear_acceleration.x \
  --kind histogram --bins 40

# Categorical frequencies as probabilities
pymcap-cli plot recording.mcap /system.mode \
  --kind histogram --normalize probability

# Downsample to 1000 points and save to file
pymcap-cli plot recording.mcap /odom.pose.position.x -d 1000 -o plot.html

process — Unified Processing

The most powerful command — combines recovery, filtering, and optimization in a single pass.

# Filter by topic regex
pymcap-cli process data.mcap -o filtered.mcap \
  -t '/camera/.*' -t '/lidar/.*'

# Time range filtering (nanoseconds or RFC3339)
pymcap-cli process data.mcap -o subset.mcap -S "2022-01-01T00:00:00Z" -E "2022-01-01T01:00:00Z"

# Exclude topics and metadata
pymcap-cli process data.mcap -o clean.mcap \
  -x '/debug/.*' --metadata exclude

# Change compression with filtering
pymcap-cli process zstd.mcap -o lz4.mcap --compression lz4 \
  -t '/important/.*'

# Convert Jazzy QoS policy names to Humble-compatible integer codes
pymcap-cli process jazzy.mcap -o humble.mcap --qos-format numeric

# Embed standard ROS 2 per-topic QoS overrides
pymcap-cli process data.mcap -o qos-fixed.mcap --qos-override qos.yaml

# Apply repeatable regex overrides, then convert the result for Humble
pymcap-cli process data.mcap -o qos-fixed.mcap \
  --qos-set '/camera/.*:reliability=best_effort' \
  --qos-set '/camera/front:depth=3' --qos-format numeric

# Recovery mode with filtering (handles corrupt files)
pymcap-cli process corrupt.mcap -o recovered.mcap \
  -t '/camera/.*' --recovery-mode

recover — Advanced Recovery

Recover data from potentially corrupt MCAP files with intelligent error handling.

# Basic recovery
pymcap-cli recover corrupt.mcap -o fixed.mcap

# Force chunk decoding for maximum recovery
pymcap-cli recover corrupt.mcap -o fixed.mcap --always-decode-chunk

recover-inplace — In-Place Recovery

Rebuild an MCAP file's summary and footer in place without creating a new file.

# Rebuild summary/footer in place
pymcap-cli recover-inplace data.mcap

# With exact size calculation
pymcap-cli recover-inplace data.mcap --exact-sizes

# Skip confirmation prompt
pymcap-cli recover-inplace data.mcap --force

merge — Merge Files

Merge multiple MCAP files chronologically into a single output file.

# Merge two files
pymcap-cli merge recording1.mcap recording2.mcap -o combined.mcap

# Merge with compression
pymcap-cli merge *.mcap -o all_recordings.mcap --compression lz4

# Exclude metadata/attachments
pymcap-cli merge file1.mcap file2.mcap -o merged.mcap --metadata exclude

# Drop duplicate messages (same channel, log_time, payload) from overlapping inputs
pymcap-cli merge a.mcap b.mcap -o merged.mcap --dedup-identical

convert — Convert DB3 to MCAP

Convert ROS2 DB3 (SQLite) bag files to MCAP format.

# Basic conversion
pymcap-cli convert input.db3 -o output.mcap

# Specify ROS distro
pymcap-cli convert input.db3 -o output.mcap --distro jazzy

# With custom message definitions
pymcap-cli convert input.db3 -o output.mcap --extra-path /path/to/msgs

bag2mcap — Convert ROS 1 Bag to MCAP

Convert ROS 1 .bag files to MCAP using the ros1 profile. Message bytes are preserved as raw ROS 1 serialization and schemas use ros1msg encoding with the full message definition from the bag.

# Basic conversion
pymcap-cli bag2mcap recording.bag -o recording.mcap

# Pick a different compression / chunk size
pymcap-cli bag2mcap recording.bag -o recording.mcap --compression lz4 --chunk-size 8388608

split — Split into Segments

Split an MCAP file into multiple output segments by duration, explicit timestamps, value-change of a message-path expression, or a byte budget per segment.

# Split every 60 seconds
pymcap-cli split data.mcap --duration 60s -t "out_{index:03d}.mcap"

# Split at specific RFC3339 timestamps
pymcap-cli split data.mcap --split-at "2024-01-01T10:00:00Z" --split-at "2024-01-01T10:30:00Z"

# Start a new segment when /gps/fix.status.status changes value
pymcap-cli split data.mcap -E "/gps/fix.status.status"

# Predicate trigger — split on match/no-match transitions
pymcap-cli split data.mcap -E "/detections.objects[:]{confidence>0.8}"

# Omit neutral runs and name files with the typed expression value
pymcap-cli split data.mcap \
  -E '/sensor/aramine/drive_state.drive_direction' \
  --skip-value 0 \
  -t 'drive_{value:+d}_{index:03d}.mcap'

# Split when each output reaches roughly 1 GB
pymcap-cli split data.mcap --max-size 1G -t "shard_{index:03d}.mcap"

Expression extractors must resolve to a primitive (bool, int, float, or str). Filter expressions normalize to a boolean match/no-match value. Output templates accept normal Python format specifications for typed fields such as {value:+d} and {index:03d}.

rechunk — Topic-Based Rechunking

Reorganize MCAP messages into separate chunk groups based on topic patterns for optimized playback.

# Group camera and lidar topics into separate chunks
pymcap-cli rechunk data.mcap -o rechunked.mcap -p "/camera.*" -p "/lidar.*"

# Multiple patterns — each gets its own chunk group
pymcap-cli rechunk data.mcap -o rechunked.mcap \
  -p "/camera/front.*" \
  -p "/camera/rear.*" \
  -p "/lidar.*" \
  -p "/radar.*"

filter — Message Filtering

Filter messages by topic and time (simpler version of process).

# Include specific topics
pymcap-cli filter data.mcap -o filtered.mcap \
  -t /camera/image -t /lidar/points

# Exclude topics
pymcap-cli filter data.mcap -o filtered.mcap \
  -x '/debug/.*' -x '/test/.*'

compress — Compression Tool

Change MCAP file compression.

pymcap-cli compress input.mcap -o output.mcap --compression zstd
pymcap-cli compress input.mcap -o output.mcap --compression lz4

# Compress in place: write to a temp file, validate it, then replace the source
pymcap-cli compress input.mcap --in-place --compression zstd

# Trade a little ratio for throughput: --fast (zstd fast mode), or pick a level
pymcap-cli compress input.mcap -o output.mcap --fast
pymcap-cli compress input.mcap -o output.mcap --compression-level -5

du — Disk Usage Analysis

Analyze MCAP file size breakdown by chunks, schemas, channels, and message counts.

pymcap-cli du large.mcap

list — List Records

List various record types in an MCAP file.

pymcap-cli list channels data.mcap
pymcap-cli list chunks data.mcap
pymcap-cli list schemas data.mcap
pymcap-cli list schema data.mcap --name sensor_msgs/msg/Image
pymcap-cli list attachments data.mcap
pymcap-cli list metadata data.mcap

msg — ROS2 Message Definitions

Resolve, list, and browse ROS2 .msg definitions. msg def prints complete definitions including dependencies; msg list lists package message types; and msg serve starts a local browser UI.

# Resolve a standard ROS2 message
pymcap-cli msg def sensor_msgs/msg/Image --distro humble

# Include custom package roots before AMENT_PREFIX_PATH and the user cache
pymcap-cli msg def my_robot_msgs/msg/Status -I ./install/share

# List messages in a package or browse definitions locally
pymcap-cli msg list sensor_msgs --distro jazzy
pymcap-cli msg serve --distro jazzy --no-browser

Missing standard packages are resolved from rosdistro/GitHub and cached under the pymcap_cli_msg_def user cache.

get — Extract Attachments and Metadata

Extract a single attachment's bytes or a metadata record's key/value map.

# Write attachment bytes to a file (or pipe stdout)
pymcap-cli get attachment --name calib.bin --output calib.bin data.mcap
pymcap-cli get attachment -n calib.bin data.mcap > calib.bin

# Disambiguate when multiple attachments share a name
pymcap-cli get attachment --name notes.txt --offset 1234 -o notes.txt data.mcap

# Print a metadata record as JSON (records sharing a name are merged)
pymcap-cli get metadata --name session data.mcap

diff — Compare Files

Compare MCAP files using summary and message-index timestamps. Reads through the footer/summary first and falls back to rebuilding metadata from the data section when the summary is missing.

# Compare two recordings
pymcap-cli diff a.mcap b.mcap

# Hide channels with identical timestamps
pymcap-cli diff a.mcap b.mcap --skip-identical

# Show more timestamp ranges per channel
pymcap-cli diff a.mcap b.mcap --max-ranges 10

duplicates — Find Duplicate Recordings

Scan files and directories for likely duplicate MCAP recordings using summary and message-index fingerprints.

# Scan a directory tree
pymcap-cli duplicates /data/recordings

# Include singleton groups
pymcap-cli duplicates /data/recordings --all

# Rebuild summaries for files missing them
pymcap-cli duplicates /data/recordings --rebuild-missing

index — Sidecar Catalog

Maintain a sidecar SQLite catalog of MCAP summaries for fast lookup across large recording trees. Requires the xxhash extra.

# Scan a tree and skip unchanged files on later runs
pymcap-cli index scan /data/recordings

# Coverage and directory-level rollups
pymcap-cli index status /data/recordings
pymcap-cli index tree /data/recordings --max-depth 3

# Query by topic/schema/time and inspect catalog-wide topics
pymcap-cli index query /data/recordings --topic /camera/front --format json
pymcap-cli index topics /camera --sort-by messages

# Apply pending schema migrations to an existing catalog
pymcap-cli index migrate

# Browse the catalog in a local Datasette web UI (dashboards, charts, cross-links)
pymcap-cli index serve

index serve launches Datasette against the sidecar DB with bundled dashboards, canned queries, and a render plugin. It needs the serve extra:

uvx --from 'pymcap-cli[serve]' pymcap-cli index serve

Datasette runs from the same environment so the plugin resolves. Use --db to point at a non-default catalog, --port to change the port (default 8001), and --no-browser to skip auto-open.

records — Raw Record Dump

Print every MCAP record in file order using its repr. Useful for inspecting raw file structure when debugging readers/writers.

pymcap-cli records data.mcap

topic-chunks — Topic/Chunk Layout

Show which topics appear in which chunks, sorted by chunk count and percentage of total chunks. Helps identify topics that would benefit from rechunk.

pymcap-cli topic-chunks data.mcap

video — Video Generation

Generate one MP4 per image topic using hardware-accelerated encoding. Requires the video extra.

# Basic video generation
pymcap-cli video data.mcap --topic /camera/front --output ./videos

# With quality preset
pymcap-cli video data.mcap --topic /camera/rear --output ./videos --quality high

# Use specific codec and encoder
pymcap-cli video data.mcap --topic /lidar/image --output ./videos --codec h265 --encoder videotoolbox

roscompress — ROS Image and Point-Cloud Compression

Compress ROS MCAP files by converting CompressedImage/Image topics to CompressedVideo format and PointCloud2 topics to Cloudini or Draco compressed point clouds. Requires the video and pointcloud extras; Draco compression also requires the draco extra.

# Basic compression
pymcap-cli roscompress data.mcap -o compressed.mcap

# Specify quality and codec
pymcap-cli roscompress data.mcap -o compressed.mcap --quality 28 --codec h265

# Draco point cloud compression using the Foxglove compressed point cloud schema
pymcap-cli roscompress data.mcap -o compressed.mcap --pc-format draco --pc-schema foxglove

rosdecompress — ROS Decompression

Decompress CompressedVideo, CompressedPointCloud2, and Foxglove CompressedPointCloud topics back to standard ROS formats. Requires the video and pointcloud extras.

# Decompress to CompressedImage (JPEG)
pymcap-cli rosdecompress input.mcap output.mcap

# Decompress to raw Image
pymcap-cli rosdecompress input.mcap output.mcap --video-format raw

# Skip point cloud decompression
pymcap-cli rosdecompress input.mcap output.mcap --no-pointcloud

export-images — Image Files

Export image topics to per-topic folders of image files. CompressedImage payloads keep their original encoding by default (--format native); set --format to a Pillow format (e.g. jpeg, png, webp) to re-encode. Raw Image messages always use --raw-format (default png). Requires the image extra.

# Native passthrough for CompressedImage; PNG for raw Image
pymcap-cli export-images data.mcap -o ./images -t /camera/front

# Force re-encoding to JPEG for everything
pymcap-cli export-images data.mcap -o ./images --format jpeg

export-csv — CSV Files

Export an MCAP file to a directory of CSV files (one per topic). Nested fields are flattened with dot notation (pose.position.x); arrays remain JSON strings to preserve row counts. Schemas with raw media payloads (Image, CompressedImage, …) are skipped unless --include-blobs is set.

pymcap-cli export-csv data.mcap -o ./csv
pymcap-cli export-csv data.mcap -o ./csv -t /odom -t /imu

export-json — NDJSON / Per-Message JSON

Export an MCAP file to NDJSON (one line per message) or per-message JSON files. Default writes one <topic>.ndjson per topic; with --per-message each topic gets a directory of <log_time_ns>.json files — handy for downstream tools that expect one record per file.

# One NDJSON per topic
pymcap-cli export-json data.mcap -o ./ndjson

# One JSON file per message
pymcap-cli export-json data.mcap -o ./json --per-message

export-parquet — Parquet Files

Export an MCAP file to a directory of Parquet files (one per topic). Requires the parquet extra.

pymcap-cli export-parquet data.mcap -o ./parquet
pymcap-cli export-parquet data.mcap -o ./parquet --compression snappy

export-pcd — Point Cloud Files

Export sensor_msgs/PointCloud2 topics to ASCII PCD v0.7 files (<output>/<safe_topic>/<log_time_ns>.pcd) — readable by pcl_viewer, Open3D, and CloudCompare. Requires the pointcloud extra.

pymcap-cli export-pcd data.mcap -o ./pcd
pymcap-cli export-pcd data.mcap -o ./pcd -t /lidar/points

export-geo — Map Formats

Export geographic topics (NavSatFix, geographic_msgs/*) to GeoJSON, KML, or GPX. GeoJSON writes one <topic>.geojson per topic; KML and GPX produce a single export.{kml,gpx} covering all topics. Local-frame poses (Odometry, geometry_msgs/Pose*) are out of scope — they need a datum.

# Default GeoJSON, track + points per topic
pymcap-cli export-geo data.mcap -o ./geo

# GPX track every 5th sample
pymcap-cli export-geo data.mcap -o ./geo --format gpx --mode track --stride 5

# Keep NO_FIX samples too
pymcap-cli export-geo data.mcap -o ./geo --include-no-fix

bridge — Live Foxglove Bridge

Inspect, stream, or record live topics from a Foxglove WebSocket bridge. Requires the bridge extra.

# Inspect advertised channels
pymcap-cli bridge localhost:8765

# Validate the live system before recording
pymcap-cli bridge check localhost --spec recording.yaml --duration 5

# Stream decoded messages
pymcap-cli bridge cat localhost:8765 --topics /tf --limit 10

# Record all advertised topics to MCAP
pymcap-cli bridge record localhost:8765 --all -o live.mcap

# Chronologically merge and play MCAP files into an existing bridge
pymcap-cli bridge play first.mcap second.mcap --target localhost --speed 2

# The bridge target can come from the environment
PYMCAP_BRIDGE=localhost pymcap-cli bridge play recording.mcap -t '/camera/.*'

# Host an MCAP through the minimal Foxglove launcher
pymcap-cli bridge serve recording.mcap --port 8765

# Browse a directory and open one or more recordings in Foxglove
pymcap-cli bridge serve /data/recordings --port 9090

# Compress images and point clouds just in time while serving (no temporary MCAP)
pymcap-cli bridge serve recording.mcap --transform roscompress --port 8765

# Publish a compressed recording as standard JPEG images and PointCloud2 messages
pymcap-cli bridge play compressed.mcap --target localhost --transform rosdecompress

# Avoid JIT work for topics without consumers (target must support connectionGraph)
pymcap-cli bridge play recording.mcap --target localhost \
  --transform roscompress --only-subscribed

bridge serve only transforms messages on channels that currently have subscribers, and releases per-channel codec state after the last subscriber leaves. For bridge play, --only-subscribed uses the target's connectionGraph capability to follow consumers dynamically and pauses playback while no selected topic has one. bridge serve always serves the same minimal recording launcher. Passing explicit files shows only those files; passing a directory discovers recordings beneath it. Opening one file or a multi-file selection launches Foxglove Desktop through foxglove://open. Pass --no-browser for headless operation. Playback loops by default; pass --no-loop to play each connection once.

The launcher's links contain the raw WebSocket URL. Clicking one opens Foxglove, while copying its address produces a connection URL that can be shared or pasted directly into Foxglove:

Foxglove connects to /ws with one repeated file parameter per selected recording, for example:

ws://localhost:9090/ws?file=run-1.mcap&file=run-2.mcap

An optional preset overrides the server's transform configuration for that connection only. Without it, the server's CLI configuration remains the default:

# Original messages, even when the server has a compression preset
ws://localhost:9090/ws?file=run.mcap&preset=none

# Full-resolution or reduced-bandwidth JIT compression
wss://recordings.example/ws?file=run.mcap&preset=compress
wss://recordings.example/ws?file=run.mcap&preset=fast
wss://recordings.example/ws?file=run.mcap&preset=low

# Decode roscompress messages for Foxglove
ws://localhost:9090/ws?file=run.mcap&preset=decompress

Files selected within one connection are merged chronologically. Every Foxglove connection receives an independent playhead, controls, and MCAP cache. The playback clock advances even when no topics are subscribed, while message transforms and publishing remain subscriber-aware. Closing the Foxglove connection closes its session. The playback clock remains authoritative at high speeds: messages more than 100 ms late are counted and dropped before further JIT transform work instead of building a backlog. The server has no authentication, so expose it remotely only through a trusted network or an authenticated reverse proxy.

Shell Autocompletion

# Automatically install completion for your current shell
pymcap-cli --install-completion

# Or manually for a specific shell
eval "$(pymcap-cli --show-completion bash)"   # bash
eval "$(pymcap-cli --show-completion zsh)"    # zsh
pymcap-cli --show-completion fish | source    # fish

Common Use Cases

# Remove debug topics and compress
pymcap-cli process raw.mcap -o clean.mcap \
  -x '/debug/.*' -x '/test/.*' \
  --metadata exclude --compression zstd

# Extract camera data with time range
pymcap-cli process full_log.mcap -o camera.mcap \
  -t '/camera/.*' \
  -S "2024-01-01T10:00:00Z" -E "2024-01-01T11:00:00Z"

# Recover corrupt file and compress in one pass
pymcap-cli process corrupt.mcap -o recovered.mcap --recovery-mode --compression lz4

# Fast filtering with automatic chunk copying when possible
pymcap-cli process 100gb_file.mcap -o filtered.mcap \
  -t '/lidar/.*' --compression zstd

# Optimize for topic-specific playback
pymcap-cli rechunk robot_log.mcap -o optimized.mcap \
  -p "/camera.*" -p "/lidar.*" -p "/imu.*" -p "/gps.*"

Technical Details

• Smart Chunk Processing — automatically chooses between fast chunk copying and individual record processing based on filter criteria
• MessageIndex Validation — validates and rebuilds MessageIndexes when necessary for data integrity
• Deferred Schema Writing — only writes schemas and channels that are actually used by included messages
• Compression Support — zstd, lz4, and uncompressed formats with configurable chunk sizes
• Memory Efficient — streams processing with configurable buffer sizes for handling large files
• Error Recovery — multiple fallback strategies for handling corrupt or incomplete MCAP files

Development

# Setup development environment
uv sync --all-groups --all-extras --all-packages

# Run locally during development
uv run pymcap-cli --help

# Format and lint code
pre-commit run --all-files

# Run tests
uv run pytest pymcap-cli/tests -m "not benchmark" --no-cov -q