# AI use cases for Dalaran (Flaminis/Dalaran)

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Use cases: 5 | Page: https://dedupedai.com/use-cases/flaminis-dalaran/

Dalaran is an Apache-2.0 robotics visualization and data tool that logs, stores, replays, and queries multimodal time-series data such as ROS 2 topics, LiDAR, camera, IMU, and TF.

This file holds every use case of this repo. Each one starts with a "## " heading and ends with its URL.

- Open an existing .rrd recording in the Dalaran viewer: https://dedupedai.com/use-cases/flaminis-dalaran/replay-rrd-recording-in-viewer/
- Save a Python sensor run to a .dlr file for later review: https://dedupedai.com/use-cases/flaminis-dalaran/log-python-run-to-dlr-file/
- Check a workstation for viewer and driver problems before a robot session: https://dedupedai.com/use-cases/flaminis-dalaran/check-robot-workstation-with-doctor/
- Show nav2 costmaps from a rosbag2 replay next to robot poses: https://dedupedai.com/use-cases/flaminis-dalaran/visualize-nav2-costmaps-from-ros2/
- Export recording data to dataframes for computation: https://dedupedai.com/use-cases/flaminis-dalaran/pull-recording-data-into-dataframes/

## How to open an existing .rrd recording in the Dalaran viewer with Dalaran

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Replay an upstream .rrd recording on one timeline without converting it first.

**Who it is for:** A robotics engineer who already has .rrd recordings from earlier runs and wants to review them in Dalaran.

**The problem:** Old recordings are stored in a format that other tools cannot read in a synchronized way, so reviewing a run takes extra conversion work.

**The outcome:** The recording opens directly in the viewer with entity paths, timelines, and data from all sensors visible together.

### Steps

1. Run `pip install dalaran-sdk` to install the Python package that ships the viewer.
2. Run `dalaran recording.rrd` in the folder that holds your recording, replacing recording.rrd with your file name.
3. Scrub the timeline and check that the point clouds, camera frames, and transforms line up with each other.
4. If you want a normalized copy, run `dalaran convert` as described in the README and open the new .dlr file.

### Prompt to paste into your agent

```text
Using the Flaminis/Dalaran repository, install dalaran-sdk with pip and open <recording_path> with the dalaran command. Report whether the viewer opens the file and list the entity paths you can see. Verify that the timeline shows data from more than one sensor.
```

### Caveat

The README says blueprints saved in upstream recordings may be ignored while the data still loads, so the saved layout may not appear.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/flaminis-dalaran/replay-rrd-recording-in-viewer/

## How to save a Python sensor run to a .dlr file for later review with Dalaran

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Difficulty: Intermediate | Time: 30 minutes | Basis: From the README

Log data from a script to a .dlr file and open it in the viewer afterward.

**Who it is for:** A developer or researcher who runs a simulator or offline pipeline in Python and wants a replayable file to share.

**The problem:** Live viewing ends when the script ends, so the run cannot be inspected later or sent to a colleague.

**The outcome:** A session.dlr file that can be opened in the viewer at any time, with the logged frames on a sequence timeline.

### Steps

1. Run `pip install dalaran-sdk` to install the Python SDK.
2. Write a script that calls dl.init with your app name, sets a frame timeline with dl.set_time, and logs points with dl.log.
3. In the same script, call dl.save("session.dlr") in place of spawning a live viewer.
4. Run `dalaran session.dlr` to open the saved recording and scrub through the frames.

### Prompt to paste into your agent

```text
In the Flaminis/Dalaran repository, install dalaran-sdk with pip, then write a Python script that logs 100 frames of a point cloud to session.dlr using dl.save. Expected output is a session.dlr file. Verify it by running dalaran session.dlr and confirming the frame timeline appears.
```

### Caveat

The README calls the API still evolving with breaking changes between releases, so check CHANGELOG.md if a call fails after an upgrade.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/flaminis-dalaran/log-python-run-to-dlr-file/

## How to check a workstation for viewer and driver problems before a robot session with Dalaran

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Difficulty: Intermediate | Time: 15 minutes | Basis: From the README

Run the built-in doctor command to find setup problems in plain language.

**Who it is for:** A robotics operator who must start a viewer session on a new or changed machine and needs to know it will work.

**The problem:** Graphics drivers, environment variables, or a broken Python install can stop the viewer just before a test run.

**The outcome:** A plain-language report of build info, graphics adapters, DALARAN_* variables, and recording integrity.

### Steps

1. Run `cargo install dalaran-cli --locked` to install the viewer binary on its own.
2. Run `dalaran --help` to confirm the binary is on your path.
3. Run `dalaran doctor` and read the report for graphics adapter, driver, and environment problems.
4. Fix each reported issue, then run `dalaran --serve` to confirm the web viewer starts.

### Prompt to paste into your agent

```text
In the Flaminis/Dalaran repository, install the dalaran-cli binary and run dalaran doctor on this machine. Expected output is a list of findings about build info, graphics adapters, and environment variables. Verify that the report names no failing item before the robot session starts.
```

### Caveat

The README says doctor lives in the viewer binary, so it only helps once cargo install has succeeded; the Python-side check is dalaran-doctor.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/flaminis-dalaran/check-robot-workstation-with-doctor/

## How to show nav2 costmaps from a rosbag2 replay next to robot poses with Dalaran

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Difficulty: Advanced | Time: 60 minutes | Basis: From the README

Replay a rosbag2 with nav2 costmap topics and inspect the layers on one timeline.

**Who it is for:** A ROS 2 navigation engineer debugging why a robot took a path, using a recorded rosbag2 from a nav2 run.

**The problem:** Costmaps flattened into plain images hide which cells are lethal obstacles and which are inflated, making path failures hard to explain.

**The outcome:** The global and local costmaps appear as grid map entities with origin and resolution handled, with cost categories drawn distinctly.

### Steps

1. Locate a rosbag2 or MCAP file that contains /global_costmap or /local_costmap topics from your nav2 run.
2. Replay the rosbag2 into Dalaran with the ROS 2 bridge described in the README, saving the output as a .dlr file. (inferred, check the README)
3. Run `dalaran session.dlr` to open the saved recording, replacing session.dlr with your file name.
4. Step through the timeline and check that the costmap layers stack with their own opacity alongside the robot pose.

### Prompt to paste into your agent

```text
In the Flaminis/Dalaran repository, replay <bag_path> into a .dlr file using the ROS 2 bridge and open it with the dalaran command. Expected output is a viewer showing nav2 costmap grids on the timeline. Verify that the global and local costmap entities appear and that lethal and inflated obstacle cells are shown as separate categories.
```

### Caveat

The README does not give the exact ROS 2 bridge command, so this use case needs the BUILD.md, docs, or examples to confirm the replay steps.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/flaminis-dalaran/visualize-nav2-costmaps-from-ros2/

## How to export recording data to dataframes for computation with Dalaran

Repo: [Flaminis/Dalaran](https://github.com/Flaminis/Dalaran) | Difficulty: Advanced | Time: 45 minutes | Basis: From the README

Load a Dalaran recording into a dataframe to compute on it instead of viewing it.

**Who it is for:** A data analyst or robotics researcher who needs sensor values from a run in a table for statistics or plotting.

**The problem:** Viewing a run cannot answer questions about averages, drift, or timing across many frames.

**The outcome:** The recording's entity paths and timelines are available as a dataframe for analysis in a script.

### Steps

1. Run `pip install dalaran-sdk` to install the Python package.
2. Record or save a run to a file such as session.dlr using the logging SDK.
3. Use the dataframe view described in the dl_dataframe crate (crates/store/dl_dataframe) to load the recording into a dataframe. (inferred, check the README)
4. Compute the summary you need, such as per-frame point counts or timing gaps, and check the row count against the frames you logged.

### Prompt to paste into your agent

```text
In the Flaminis/Dalaran repository, install dalaran-sdk and load <recording_path> into a dataframe using the dl_dataframe view. Expected output is a table of rows for each logged entity and timeline value. Verify that the row count matches the number of frames logged in the source run.
```

### Caveat

The README names the dl_dataframe crate but does not show the Python call that exposes it, so confirm the exact API in docs/content before relying on it.

Disclaimer: Third-party repos we don't own or vouch for. AI-generated use cases can be wrong: review everything and run at your own risk. Full text: https://dedupedai.com/disclaimer/

URL: https://dedupedai.com/use-cases/flaminis-dalaran/pull-recording-data-into-dataframes/