ROS2Bag Reader

A lightweight Python utility for reading ROS 2 (rosbag2) recordings stored with the default sqlite3 backend.

ROS2BagReader provides:

• Direct access to deserialized ROS messages.
• Convenient NumPy array extraction from message fields.
• Flexible timestamp handling (raw, bag-relative, topic-relative).
• Automatic support for split bag files.
• Simple bag metadata summary.

The reader operates at the bag folder level (the directory containing metadata.yaml), not individual .db3 files.

Description

ROS 2 recordings created with:

ros2 bag record ...

generate a folder containing:

recording/
├── metadata.yaml
├── recording_0.db3
├── recording_1.db3
└── ...

ROS2BagReader:

• Parses metadata.yaml
• Opens all .db3 files automatically
• Deserializes messages using rosidl_runtime_py
• Provides NumPy-ready data extraction utilities

It is designed for data analysis, plotting, and post-processing workflows, especially inside Jupyter notebooks.

Installation

Python 3.10 or greater is required.

You can intall the module with pip using.

pip install ros2bag-reader

Or you can copy the module into your project or install it as a local package.

Dependencies

The library requires:

dependencies = [
    "numpy>=1.24.0",
    "rosidl-runtime-py>=0.9.3",
    "rclpy>=3.3.16",
    "PyYAML>=6.0.3",
    "setuptools>=82.0.0",
    "typeguard>=4.5.0",
    "jinja>=3.1.6",
]

Simple Usage Example

Example using a turtlesim recording:

from ros2bag_reader import ROS2BagReader

reader = ROS2BagReader("recording")

reader.summary()

pose = reader.get_messages("/turtle1/pose")

pose_data = reader.get_field_array(
    "/turtle1/pose",
    ["x", "y", "theta"]
)

timestamps = reader.get_time_array(
    "/turtle1/pose",
    reference="topic"
)

reader.close()

For a complete example including plotting and data visualization, see the Jupyter notebook in the examples/ folder.

Class Reference

ROS2BagReader(bag_folder: str)

Initialize the reader from a ROS 2 bag folder.

Parameters

• bag_folder — Path to the directory containing metadata.yaml

---

close()

Closes all open SQLite database connections.

Call this when finished reading the bag to release file handles.

---

get_messages(topic_name: str)

Retrieve all messages from a topic.

Returns

list[(timestamp_ns: int, message: ROS message instance)]

Timestamps are returned in nanoseconds since epoch.

---

summary()

Print a human-readable summary of the bag:

• Storage backend
• Duration
• Total message count
• Number of database files
• Per-topic type and message count

---

get_time_array(topic_name: str, unit="s", reference="topic")

Return timestamps as a NumPy array.

Parameters

• unit

  • "s" → seconds (float64)
  • "ns" → nanoseconds (int64)

• reference

  • "topic" → relative to first message of the topic
  • "bag" → relative to bag start time
  • "raw" → no normalization (epoch time)

Returns

np.ndarray

---

get_field_array(topic_name: str, fields: list[str], return_timestamps=False)

Extract selected numeric fields into a NumPy array.

Supports:

• Nested attributes using dot notation
• Indexed access for array fields

Examples

["x", "y", "theta"]

["pose.position.x", "pose.position.y"]

["ranges[0]", "ranges[10]"]

Returns

If return_timestamps=False:

np.ndarray shape (N, len(fields))

If return_timestamps=True:

timestamps_ns, data_array