data_tamer_tools

Set of tools for using Foxglove with data tamer

README

Data Tamer Tools

Tools for working with DataTamer data in ROS2, providing Foxglove integration and MCAP data storage capabilities.

Overview

This package provides five main tools:

Foxglove Relay: Real-time visualization of DataTamer data through Foxglove Studio
MCAP Sink: Store DataTamer data in MCAP (MessagePack) format for efficient storage and playback
MCAP Converter: Convert DataTamer-encoded MCAP files to Protobuf-encoded MCAP files for Foxglove compatibility
Rosout Logger: Record ROS log messages (/rosout) to MCAP files with Foxglove Log schema
Log Rotation Coordinator: Synchronize log directory rotation across multiple MCAP loggers

Tools

1. Foxglove Relay (`foxglove_relay`)

A ROS2 component that bridges DataTamer data to Foxglove Studio for real-time visualization.

Features:

Automatically discovers DataTamer topics (/data_tamer/schema, /data_tamer/schemas, /data_tamer/snapshot)
Automatically discovers sensor_msgs/msg/NavSatFix topics and relays each source as its own foxglove.LocationFix channel (Map track)
Automatically discovers geographic_msgs/msg/GeoPath topics and relays each source as foxglove.GeoJSON (Map overlay)
Optional MCAP recording of selected Foxglove schemas (foxglove.LocationFix + foxglove.GeoJSON) for offline playback in Foxglove
Supports both JSON and Protocol Buffers encoding for Foxglove visualization
Converts DataTamer schemas to JSON Schema or Protocol Buffers descriptor format
Creates Foxglove WebSocket server for real-time data streaming
Supports soft eviction of idle channels (closes channels but keeps schemas)
Configurable eviction policies and discovery intervals
Optional ROS log relaying to Foxglove

Prerequisites:

Your DataTamer channels must use DataTamer::ROS2PublisherSink to publish data to ROS2 topics
The relay automatically discovers and subscribes to the standard DataTamer ROS2 topics

Usage:

# Launch as ROS2 component
ros2 run data_tamer_tools foxglove_relay

# Or with custom parameters
ros2 run data_tamer_tools foxglove_relay --ros-args \
  -p host:=127.0.0.1 \
  -p port:=8765 \
  -p use_protobuf:=true \
  -p eviction_ttl_sec:=900 \
  -p eviction_period_sec:=30 \
  -p discovery_sec:=5 \
  -p enable_rosout:=true

Parameters:

WebSocket Server Configuration:

host (string, default: “127.0.0.1”): WebSocket server host address
port (int, default: 8765): WebSocket server port number

Data Encoding:

use_protobuf (bool, default: true): Use Protocol Buffers encoding instead of JSON
- true: Data is serialized using Protocol Buffers for better performance and smaller payloads
- false: Data is serialized as JSON strings for human readability

Channel Management:

eviction_ttl_sec (int, default: 900): Time-to-live for idle channels in seconds
- Channels that haven’t received data for this duration will be soft-evicted (closed but schema kept)
eviction_period_sec (int, default: 30): Interval in seconds for checking stale channels
- How often the relay checks for channels that should be evicted
discovery_sec (int, default: 5): Interval in seconds for topic discovery
- How often the relay scans for new DataTamer topics to subscribe to
navsat_ttl_sec (int, default: 30): Time-to-live for NavSatFix sources; stale sources are pruned from the relay’s discovered LocationFix tracks
geopath_ttl_sec (int, default: 0): Time-to-live for GeoPath sources (0 disables pruning)
navsat_qos (string, default: “sensor”): QoS profile for NavSatFix subscriptions. Accepts sensor (or sensor_data), system_default (or default), best_effort, or reliable.
location_fix_prefix (string, default: “/locations”): Prefix for relayed per-source foxglove.LocationFix topics
geojson_prefix (string, default: “/geojson”): Prefix for relayed per-source foxglove.GeoJSON topics

Map Topic Mapping:

A discovered sensor_msgs/msg/NavSatFix topic like /gps/fix is relayed as a per-source foxglove.LocationFix topic at /locations/gps/fix (prefix is configurable via location_fix_prefix)
A discovered geographic_msgs/msg/GeoPath topic like /planner/path is relayed as foxglove.GeoJSON at /geojson/planner/path (prefix is configurable via geojson_prefix)
Each GeoPath is rendered as GeoJSON (FeatureCollection) containing a LineString for the path and Point features for start/goal; colors are deterministic per topic

Relay MCAP Recording (Foxglove schemas only):

enable_mcap (bool, default: false): Record foxglove.LocationFix + foxglove.GeoJSON to an MCAP file
logdir (string, default: “.”): Directory for the initial relay MCAP file
mcap_filename (string, default: “foxglove_relay.mcap”): Relay MCAP filename
mcap_append_timestamp (bool, default: true): Prefix timestamp like YYYY-MM-DD_HH-MM-SS_ to the relay MCAP filename
mcap_profile (string, default: “protobuf”): MCAP profile string passed to the Foxglove SDK writer
mcap_truncate (bool, default: false): If true, overwrite an existing relay MCAP file on name collision
mcap_use_chunks (bool, default: true): Enable chunking (batch writes) in the relay MCAP output
mcap_chunk_size (int, default: 0): Chunk size in bytes (0 = SDK default)
mcap_compression (string, default: “zstd”): Compression type: none, zstd, or lz4
rotate_dir_topic (string, default: “”): Rotation topic override (empty = auto-discover data_tamer_tools/msg/LogDir by type)

ROS Log Integration:

enable_rosout (bool, default: true): Enable relaying of ROS logs to Foxglove
- true: ROS logs from /rosout topic are relayed to Foxglove Log channel
- false: Disable ROS log relaying
rosout_topic (string, default: “/rosout”): ROS topic name for log messages

Foxglove Studio Connection (Live):

Open Foxglove Studio
Connect to ws://localhost:8765 (or your configured host:port)
Add a Map panel to visualize:
- GPS tracks from foxglove.LocationFix topics (default prefix: /locations/...)
- Paths/overlays from foxglove.GeoJSON topics (default prefix: /geojson/...)
Add a Raw Messages panel to inspect the relayed DataTamer channels

Foxglove Studio Connection (Offline):

Enable relay MCAP recording (enable_mcap:=true) or use the bringup launch file options
In Foxglove Studio: File → Open local file → select the relay MCAP file
Add a Map panel (GeoJSON overlays + LocationFix tracks) and any other panels you need

Encoding Notes:

Protocol Buffers mode (use_protobuf:=true): More efficient, smaller payloads, better performance
JSON mode (use_protobuf:=false): Human-readable data, easier to debug, larger payloads
Both modes provide the same data visualization capabilities in Foxglove Studio

2. MCAP Sink

Stores DataTamer data in MCAP (MessagePack) format for efficient storage and playback.

Features:

Multiple compression options (None, LZ4, Zstd)
Configurable chunk sizes
Thread-safe operations
Integration with DataTamer sink system
Log rotation support: When initialized with a ROS node, automatically subscribes to rotation topic
Synchronized rotation: Multiple MCAP sinks can rotate to new directories simultaneously

Basic Usage:

#include <data_tamer_tools/sinks/mcap_sink.hpp>

// Create MCAP sink (standalone)
auto mcap_sink = std::make_shared<data_tamer_tools::McapSink>(
    "/path/to/file.mcap", 
    data_tamer_tools::Format::Protobuf,
    data_tamer_tools::McapSink::Compression::Zstd, 
    1024  // chunk size
);

// Add to DataTamer channel
auto channel = DataTamer::ChannelsRegistry::Global().getChannel("my_channel");
channel->addDataSink(mcap_sink);

Log Rotation Usage:

#include <data_tamer_tools/sinks/mcap_sink.hpp>

// Create MCAP sink with ROS node for rotation support
auto node = std::make_shared<rclcpp::Node>("my_node");
auto mcap_sink = std::make_shared<data_tamer_tools::McapSink>(
    node,
    "/initial/path/data.mcap",
    data_tamer_tools::Format::Protobuf,
    data_tamer_tools::McapSink::Compression::Zstd
);

// Add to DataTamer channel
auto channel = DataTamer::ChannelsRegistry::Global().getChannel("my_channel");
channel->addDataSink(mcap_sink);

// The sink now listens to the rotation topic (default: /data_tamer/rotate_dir)
// When a LogDir message is received, it will:
// 1. Close the current MCAP file
// 2. Create the new directory if needed
// 3. Open a new MCAP file at <new_dir>/<base_filename>
// 4. Re-register all channels and continue recording

Parameters:

rotate_topic (string, default: “/data_tamer/rotate_dir”): Topic to subscribe for rotation commands

Compression Options:

Compression::None: No compression
Compression::LZ4: Fast compression
Compression::Zstd: High compression ratio

Format Options:

Format::Json: JSON encoding with JSON schema
Format::Protobuf: Protocol Buffers encoding (recommended for Foxglove compatibility)

3. MCAP Converter (`mcap_converter`)

Converts DataTamer-encoded MCAP files to Protobuf-encoded MCAP files for Foxglove Studio compatibility.

Features:

Converts DataTamer’s custom message encoding to standard Protobuf encoding
Preserves all message timestamps and metadata
Uses Zstd compression for efficient output files
Maintains channel and schema information
Enables visualization of DataTamer data in Foxglove Studio

Usage:

# Convert a DataTamer MCAP file to Foxglove-compatible format
ros2 run data_tamer_tools mcap_converter input.mcap output.mcap

# With custom compression and chunk size
ros2 run data_tamer_tools mcap_converter input.mcap output.mcap \
  --compression lz4 \
  --chunk-size 1048576

Command-line Options:

--compression <type>: Compression type (zstd, lz4, or none). Default: zstd
--chunk-size <size>: Chunk size in bytes (positive integer). Default: writer default

What it does:

Reads DataTamer MCAP files: Parses MCAP files created with DataTamer’s custom encoding
Converts schemas: Transforms DataTamer schemas to Protobuf descriptors
Re-encodes messages: Converts DataTamer snapshot data to Protobuf format
Preserves metadata: Maintains timestamps, channel names, and message sequences
Outputs Foxglove-compatible MCAP: Creates MCAP files that can be opened directly in Foxglove Studio

Use Cases:

Post-processing: Convert recorded DataTamer sessions for analysis in Foxglove
Data sharing: Share DataTamer recordings with team members who use Foxglove
Visualization: Enable rich visualization of DataTamer data using Foxglove’s plotting capabilities
Debugging: Inspect DataTamer data using Foxglove’s message inspection tools

Example Workflow:

# 1. Record data with DataTamer (creates data_tamer_encoded.mcap)
ros2 run your_package your_node

# 2. Convert to Foxglove-compatible format
ros2 run data_tamer_tools mcap_converter data_tamer_encoded.mcap foxglove_compatible.mcap

# 3. Open in Foxglove Studio
# - File -> Open Local File -> Select foxglove_compatible.mcap
# - Add panels to visualize your data

Technical Details:

Input: MCAP files with DataTamer’s custom message encoding (JSON Schema + binary snapshots)
Output: MCAP files with Protobuf encoding (Protocol Buffers descriptors + Protobuf messages)
Compression: Output files use Zstd compression for efficiency
Schema conversion: DataTamer schemas are converted to Protobuf FileDescriptorSets
Message conversion: DataTamer snapshots are re-encoded as Protobuf messages

4. Rosout Logger (`rosout_logger`)

Records ROS log messages (/rosout) to MCAP files using the Foxglove Log schema for seamless playback in Foxglove Studio.

Features:

Subscribes to ROS /rosout topic and records all log messages
Encodes logs using Foxglove Log schema (Protobuf format)
Supports log rotation via LogDir topic
Configurable compression and chunk size
Thread-safe MCAP writing
Creates timestamped log files on startup
Synchronized rotation with other MCAP loggers

Usage:

# Launch as ROS2 component with defaults
ros2 run data_tamer_tools rosout_logger

# Or with custom parameters
ros2 run data_tamer_tools rosout_logger --ros-args \
  -p rosout_topic:=/rosout \
  -p output_base:=rosout \
  -p output_dir:=/logs \
  -p compression:=zstd \
  -p chunk_size:=0 \
  -p rotate_dir_topic:=''

Parameters:

rosout_topic (string, default: “/rosout”): ROS topic to subscribe for log messages
output_base (string, default: “rosout”): Base filename for MCAP files
output_dir (string, default: “.”): Directory for initial MCAP file
compression (string, default: “zstd”): Compression type (none, zstd, or lz4)
chunk_size (int, default: 0): Chunk size in bytes (0 = no chunking)
append_timestamp (bool, default: true): Append a timestamp to the output filename
rotate_dir_topic (string, default: “”): Rotation topic override (empty = auto-discover data_tamer_tools/msg/LogDir by type)

How it works:

On startup, creates a timestamped MCAP file: <output_dir>/<output_base>_YYYYMMDD_HHMMSS.mcap
Subscribes to /rosout and records all log messages in Foxglove Log format
Listens to the rotation topic (latched/transient_local), auto-discovered by type if rotate_dir_topic is empty
When a LogDir message is received with a new directory:
- Closes the current MCAP file
- Creates the new directory if needed
- Opens a new file at <new_dir>/<output_base>_YYYYMMDD_HHMMSS.mcap (or <new_dir>/<output_base>.mcap if append_timestamp is false)
- Continues recording with monotonic sequence numbers

Foxglove Integration: The recorded MCAP files can be opened directly in Foxglove Studio and will display logs in the Log panel with proper severity levels, timestamps, and message details.

5. Log Rotation Coordinator (`log_rotation_coordinator`)

Coordinates synchronized log directory rotation across multiple MCAP loggers (data_tamer_tools::McapSink sinks and Rosout logger).

Features:

Provides a ROS service to trigger rotation
Publishes rotation commands on a latched topic
Ensures all loggers rotate simultaneously to the same directory
Simple service-based API for external control

Usage:

# Launch the coordinator
ros2 run data_tamer_tools log_rotation_coordinator

# Or with custom parameters
ros2 run data_tamer_tools log_rotation_coordinator --ros-args \
  -p rotate_topic:=/data_tamer/rotate_dir \
  -p service_name:=/data_tamer/loggers/rotate

Parameters:

rotate_topic (string, default: “/data_tamer/rotate_dir”): Topic name for publishing rotation commands
service_name (string, default: “/data_tamer/loggers/rotate”): Service name for rotation requests

Triggering Rotation:

# Rotate all loggers to a new directory
ros2 service call /data_tamer/loggers/rotate data_tamer_tools/srv/Rotate "{directory: '/logs/session_001'}"

How it works:

The coordinator provides a ROS service that accepts a directory path
When the service is called, it creates the new logdir if it doesn’t xist and publishes a LogDir message on the rotation topic
The topic uses transient_local QoS (latched), so:
- All current subscribers receive the message immediately
- Late-joining subscribers (loggers started after rotation) also receive the last message
All MCAP sinks and Rosout loggers subscribed to this topic will:
- Close their current files
- Create the new directory if needed
- Open new files in the specified directory
- Continue recording seamlessly

Synchronized Logging Architecture:

┌─────────────────────────┐
│  Log Rotation           │
│  Coordinator            │
│  (Service Provider)     │
└───────────┬─────────────┘
            │ publishes LogDir (latched)
            ├──────────────┐──────────────┐
            ↓              ↓              ↓
    /data_tamer/rotate_dir topic         topic
            ↓              ↓              ↓
    ┌───────┴────┐   ┌────┴──────┐   ┌──────────┐
    │ McapSink 1 │   │ McapSink 2│   │  Rosout  │
    │ (DataTamer)│   │(DataTamer)│   │  Logger  │
    └────────────┘   └───────────┘   └──────────┘
         ↓                 ↓               ↓
    data1.mcap        data2.mcap      rosout.mcap
         (all in the same directory)

Use Cases:

Session-based logging: Organize logs by test session or experiment
Time-based rotation: Rotate to hourly/daily directories
Event-triggered rotation: Switch directories on specific events
Multi-robot systems: Coordinate logging across multiple robots

Launch Files

Bringup Launch File

The package provides a comprehensive launch file (bringup.launch.py) that starts all three components (Foxglove Relay, Rosout Logger, and Log Rotation Coordinator) in a single composable node container for efficient execution.

Quick Start:

# Launch with all default settings
ros2 launch data_tamer_tools bringup.launch.py

# Launch with custom log directory
ros2 launch data_tamer_tools bringup.launch.py logdir:=/tmp/my_logs

# Launch with custom Foxglove port
ros2 launch data_tamer_tools bringup.launch.py relay_port:=9000

# Disable specific components
ros2 launch data_tamer_tools bringup.launch.py relay:=False log_rosout:=False

Architecture: The launch file creates a multi-threaded composable node container (component_container_mt) that hosts all three components, providing:

Efficient inter-component communication
Shared namespace configuration
Coordinated lifecycle management
Lower resource overhead compared to separate nodes

Launch Arguments:

General Configuration:

ns (string, default: “data_tamer_tools”): Namespace for all nodes
logdir (string, default: “.”): Output directory for log files

Component Enable/Disable:

relay (bool, default: True): Enable/disable Foxglove Relay component
log_rosout (bool, default: True): Enable/disable Rosout Logger component
rotation_coordinator (bool, default: True): Enable/disable Log Rotation Coordinator component

Foxglove Relay Parameters:

relay_host (string, default: “127.0.0.1”): WebSocket server host address
relay_port (int, default: 8765): WebSocket server port (0-65535)
relay_eviction_ttl_sec (int, default: 900): Time-to-live for stale publishers in seconds (15 minutes)
relay_eviction_period_sec (int, default: 30): Interval for checking stale publishers
relay_discover_sec (int, default: 5): Period for rediscovering DataTamer snapshot topics
relay_enable_rosout (bool, default: true): Enable relaying /rosout to Foxglove
relay_use_protobuf (bool, default: true): Use Protobuf encoding instead of JSON
relay_enable_mcap (bool, default: false): Record foxglove.LocationFix + foxglove.GeoJSON to an MCAP file from the relay
relay_mcap_filename (string, default: “foxglove_relay.mcap”): Relay MCAP filename (written under logdir)
relay_mcap_append_timestamp (bool, default: true): Prefix timestamp like YYYY-MM-DD_HH-MM-SS_ to the relay MCAP filename
relay_mcap_truncate (bool, default: false): Overwrite relay MCAP file on collision
relay_mcap_use_chunks (bool, default: true): Enable chunking in relay MCAP output
relay_mcap_chunk_size (int, default: 0): Chunk size in bytes (0 = SDK default)
relay_mcap_compression (string, default: “zstd”): Compression type: none, zstd, or lz4
relay_rotate_dir_topic (string, default: “”): Rotation topic override for relay (empty = auto-discover data_tamer_tools/msg/LogDir)

Rosout Logger Parameters:

logger_output_base (string, default: “rosout”): Base filename for MCAP output files
logger_compression (string, default: “zstd”): Compression type (none, zstd, or lz4)
logger_chunk_size (int, default: 0): MCAP chunk size in bytes (0 = no chunking)
logger_rotate_dir_topic (string, default: “”): Rotation topic override for rosout logger (empty = auto-discover data_tamer_tools/msg/LogDir)

Log Rotation Coordinator Parameters:

coordinator_rotate_topic (string, default: “/data_tamer/rotate_dir”): Topic to publish rotation commands
coordinator_service_name (string, default: “/data_tamer/loggers/rotate”): Service name for rotation requests

Shared Parameters:

rosout_topic (string, default: “/rosout”): ROS topic to subscribe for logs (used by both Foxglove Relay and Rosout Logger)

Common Usage Patterns:

1. Production Deployment with Custom Settings:

ros2 launch data_tamer_tools bringup.launch.py \
  ns:=robot_logger \
  logdir:=/data/robot_logs \
  relay_host:=0.0.0.0 \
  relay_port:=8765 \
  logger_compression:=lz4 \
  relay_use_protobuf:=true

2. Development/Debugging (JSON mode, disable logger):

ros2 launch data_tamer_tools bringup.launch.py \
  relay_use_protobuf:=false \
  log_rosout:=False \
  relay_discover_sec:=2

3. Logging Only (no live visualization):

ros2 launch data_tamer_tools bringup.launch.py \
  relay:=False \
  logdir:=/logs/experiment_001

4. Remote Foxglove Access:

ros2 launch data_tamer_tools bringup.launch.py \
  relay_host:=0.0.0.0 \
  relay_port:=8765
# Now accessible from any machine: ws://<robot_ip>:8765

5. High-Frequency Logging (optimized settings):

ros2 launch data_tamer_tools bringup.launch.py \
  logger_compression:=lz4 \
  logger_chunk_size:=1048576 \
  relay_discover_sec:=10 \
  relay_eviction_period_sec:=60

Typical Workflow with Launch File:

# 1. Start the complete logging system
ros2 launch data_tamer_tools bringup.launch.py logdir:=/logs/initial

# 2. Connect Foxglove Studio to ws://localhost:8765

# 3. Run your application with DataTamer integration
ros2 run your_package your_node

# 4. Trigger log rotation for organized sessions
ros2 service call /data_tamer/loggers/rotate data_tamer_tools/srv/Rotate \
  "{directory: '/logs/test_run_001'}"

# 5. All logs now go to /logs/test_run_001/:
#    - rosout.mcap (ROS logs)
#    - <your_datatamer_files>.mcap (DataTamer data)

Integration with Your Launch Files:

from launch import LaunchDescription
from launch.actions import IncludeLaunchDescription
from launch.launch_description_sources import PythonLaunchDescriptionSource
from launch_ros.substitutions import FindPackageShare
from launch.substitutions import PathJoinSubstitution

def generate_launch_description():
    return LaunchDescription([
        # Include data_tamer_tools bringup
        IncludeLaunchDescription(
            PythonLaunchDescriptionSource([
                PathJoinSubstitution([
                    FindPackageShare('data_tamer_tools'),
                    'launch',
                    'bringup.launch.py'
                ])
            ]),
            launch_arguments={
                'ns': 'my_robot/logging',
                'logdir': '/data/logs',
                'relay_port': '9000',
                'logger_compression': 'lz4',
            }.items()
        ),
        
        # Your other nodes...
    ])

Benefits of Using the Launch File:

Single command: Launch all logging infrastructure at once
Coordinated configuration: Shared parameters ensure consistency
Resource efficiency: Composable nodes reduce overhead
Easy customization: Override any parameter without modifying code
Production ready: Sensible defaults for most use cases
Modular: Disable components you don’t need

Examples

Example: Basic MCAP Recording

// See examples/json_mcap_sink.cpp
#include <data_tamer_tools/sinks/mcap_sink.hpp>

auto mcap_sink = std::make_shared<data_tamer_tools::McapSink>(
    "/tmp/example.mcap", 
    data_tamer_tools::Format::Protobuf,
    data_tamer_tools::McapSink::Compression::None, 
    1024
);

auto channel = DataTamer::ChannelsRegistry::Global().getChannel("sensor_data");
channel->addDataSink(mcap_sink);

// Register and log data
double temperature = 22.5;
channel->registerValue("temperature", &temperature);
channel->takeSnapshot();

Example: Synchronized Log Rotation System

// In your DataTamer-enabled node
#include <data_tamer_tools/sinks/mcap_sink.hpp>
#include <rclcpp/rclcpp.hpp>

class MyRobotNode : public rclcpp::Node {
public:
    MyRobotNode() : Node("my_robot") {
        // Create MCAP sink with rotation support
        auto mcap_sink = std::make_shared<data_tamer_tools::McapSink>(
            shared_from_this(),  // Pass the node for rotation
            "/initial/logs/robot_data.mcap",
            data_tamer_tools::Format::Protobuf,
            data_tamer_tools::McapSink::Compression::Zstd
        );
        
        // Set up DataTamer channel
        auto channel = DataTamer::ChannelsRegistry::Global().getChannel("robot_state");
        channel->addDataSink(mcap_sink);
        
        // Register values
        channel->registerValue("position_x", &position_x_);
        channel->registerValue("velocity", &velocity_);
        
        // Timer to take snapshots
        timer_ = create_wall_timer(
            std::chrono::milliseconds(100),
            [channel]() { channel->takeSnapshot(); }
        );
    }
    
private:
    double position_x_{0.0};
    double velocity_{0.0};
    rclcpp::TimerBase::SharedPtr timer_;
};

Complete System Launch:

# Terminal 1: Launch the rotation coordinator
ros2 run data_tamer_tools log_rotation_coordinator

# Terminal 2: Launch rosout logger
ros2 run data_tamer_tools rosout_logger --ros-args -p output_dir:=/logs/initial

# Terminal 3: Launch your robot node with DataTamer
ros2 run my_package my_robot_node

# Terminal 4: Trigger rotation to organize logs by session
ros2 service call /data_tamer/loggers/rotate data_tamer_tools/srv/Rotate "{directory: '/logs/test_session_001'}"

# Now all loggers (DataTamer MCAP sinks + Rosout logger) write to /logs/test_session_001/
# - /logs/test_session_001/robot_data.mcap
# - /logs/test_session_001/rosout.mcap

# Rotate again for a new session
ros2 service call /data_tamer/loggers/rotate data_tamer_tools/srv/Rotate "{directory: '/logs/test_session_002'}"

Building

Install dependencies:

rosdep install --from-paths src --ignore-src -r -y

Build with colcon:

cd /path/to/your/workspace
colcon build --packages-select data_tamer_tools

License

MIT License - see LICENSE file for details.

Support

If you find this project useful, consider buying me a coffee! ☕

data_tamer_tools

README

Data Tamer Tools

Overview

Tools

1. Foxglove Relay (foxglove_relay)

2. MCAP Sink

3. MCAP Converter (mcap_converter)

4. Rosout Logger (rosout_logger)

5. Log Rotation Coordinator (log_rotation_coordinator)

Launch Files

Bringup Launch File

Examples

Example: Basic MCAP Recording

Example: Synchronized Log Rotation System

Building

License

Support

1. Foxglove Relay (`foxglove_relay`)

3. MCAP Converter (`mcap_converter`)

4. Rosout Logger (`rosout_logger`)

5. Log Rotation Coordinator (`log_rotation_coordinator`)