Rerun

Any scalar

A 6-minute narrated walkthrough of using the Rerun UI to plot arbitrary scalar data from a dataset (MCAP) is available on Youtube.

Overview overview

This example demonstrates how to visualize arbitrary data, even when it was not logged with specific Rerun-semantics. With the "Any Scalar" feature, you can log complex data structures (like dictionaries or structs) once and use Selectors in the Blueprint to "pick" which internal fields to plot.

Key Benefits:

  • Decoupled Logging: You no longer need to log separate scalar entities for every value you want to graph.
  • Selective Visualization: Use a single data stream to power multiple different views by targeting specific component fields (e.g., .position or .close).

Run the code run-the-code

To run this example, make sure you have the required Python version, the Rerun repository checked out and the latest SDK installed:

pip install --upgrade rerun-sdk  # install the latest Rerun SDK
git clone git@github.com:rerun-io/rerun.git  # Clone the repository
cd rerun
git checkout latest  # Check out the commit matching the latest SDK release

Install the necessary libraries specified in the requirements file:

pip install -e examples/python/any_scalar

To experiment with the provided example, simply execute the main Python script:

python -m any_scalar --demo robotics # Simulated PID control
python -m any_scalar --demo market   # Real-time stock performance

If you wish to explore additional features, use the CLI with the --help option for guidance:

python -m any_scalar --help

Guided demos guided-demos

1. Robotics: PID controller telemetry 1-robotics-pid-controller-telemetry

Goal: Visualize a control loop's internal state without logging separate scalars for every field.

In robotics_demo.py, we simulate a joint controller. Instead of logging error, effort, and position as individual Rerun entities, we log a single Telemetry struct per time step.

Tutorial highlights:

  • Decoupled logging: We log one ControllerTelemetry object. Later, in the Blueprint, we "pick" which parts to see.
  • Visualizer mapping: Notice how the Error field is plotted twice: once as a Line (to see trends) and once as Points (to see individual sample timing).
  • Step interpolation: The Effort signal uses StepAfter interpolation, which accurately reflects how a digital controller holds its output constant between steps.
  • Boolean plotting: The is_stable flag is visualized as a step-function scalar (0/1).

What you should see when running python -m any_scalar --demo robotics:

2. Market data: relative performance 2-market-data-relative-performance

Goal: Compare multiple live data streams (tickers) using a centralized selector.

In market_demo.py, we fetch real stock data. We log the raw prices and a "normalized" % change field.

Tutorial highlights:

  • Dynamic normalization: We log the price relative to the morning opening.
  • Dynamic archetype: We log the stock data as a dictionary using rerun.DynamicArchetype.
  • Selectors: We use jq-style selectors, like .prices.normalized and .prices.close, to power different parts of the dashboard.

What you should see when running python -m any_scalar --demo market:

3. Load datasets directly into the viewer 3-load-datasets-directly-into-the-viewer

Goal: Plot values from dataset files without writing any code.

Because Rerun can now plot Any Scalar, you can drag an .mcap or .rrd file into the viewer and create a Time Series view. Use the UI in the viewer to drill into nested ROS messages or telemetry logs and start plotting immediately.

[!TIP] Watch the video at the top of this page to see a step-by-step walkthrough of how to use the UI to plot any field from an MCAP/RRD file.

The "Magic": component mapping the-magic-component-mapping

What is "Any Scalar"? what-is-any-scalar

Traditionally, to plot a graph, you had to log data specifically as one of Rerun's Scalar archetypes. With Any Scalar, you can log complex blobs (Dictionaries, TypedDicts, Arrow Structs) and Rerun will let you "map" internal fields to visualizers.

Selectors (jq-style) selectors-jqstyle

Rerun uses a path syntax inspired by jq to reach into your data:

  • .state.position -> reaches into the state dict and finds position.
  • .prices.normalized -> pulls the calculated performance from the market tick.

Benefits benefits

  1. Developer velocity: Log your entire state object once; decide what to plot later in the UI.
  2. Smaller files: Less metadata overhead than logging 50 separate entities.
  3. Flexibility: Change what you are visualizing in the Blueprint without restarting your simulation or re-running your data pipeline.

Resources resources

Used Rerun types used-rerun-types

DynamicArchetype, SeriesLines, SeriesPoints

DocsBlogExamplesTeamCareers
Privacy policy