This example demonstrates how to log multi dimensional signals with the Rerun SDK, using the [TUM VI Benchmark](https://cvg.cit.tum.de/data/datasets/visual-inertial-dataset). ## Background This example shows how to log multi-dimensional signals efficiently using the [`rr.send_columns()`](https://ref.rerun.io/docs/python/0.22.1/common/columnar_api/#rerun.send_columns) API. The API automatically selects the right partition sizes, making it simple to log scalar signals like this: ```py # Load IMU data from CSV into a dataframe imu_data = pd.read_csv( cwd / DATASET_NAME / "dso/imu.txt", sep=" ", header=0, names=["timestamp", "gyro.x", "gyro.y", "gyro.z", "accel.x", "accel.y", "accel.z"], comment="#", ) times = rr.TimeColumn("timestamp", timestamp=imu_data["timestamp"]) # Extract gyroscope data (x, y, z axes) and log it to a single entity. gyro = imu_data[["gyro.x", "gyro.y", "gyro.z"]] rr.send_columns("/gyroscope", indexes=[times], columns=rr.Scalars.columns(scalars=gyro)) # Extract accelerometer data (x, y, z axes) and log it to a single entity. accel = imu_data[["accel.x", "accel.y", "accel.z"]] rr.send_columns("/accelerometer", indexes=[times], columns=rr.Scalars.columns(scalars=accel)) ``` ## Running Install the example package: ```bash pip install -e examples/python/imu_signals ``` To experiment with the provided example, simply execute the main Python script: ```bash python -m imu_signals ``` ## Attribution This example uses a scene from the **TUM VI Benchmark dataset**, originally provided by [Technical University of Munich (TUM)](https://cvg.cit.tum.de/data/datasets/visual-inertial-dataset). The dataset is licensed under **Creative Commons Attribution 4.0 (CC BY 4.0)**. - Original dataset: [TUM VI Benchmark](https://cvg.cit.tum.de/data/datasets/visual-inertial-dataset) - License details: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)