chore: import upstream snapshot with attribution
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<!--[metadata]
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title = "Clock"
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tags = ["3D", "API example"]
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thumbnail = "https://static.rerun.io/clock/8c49e25f5cac4d6a1d7d0490b14cf6881bdb707b/480w.png"
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thumbnail_dimensions = [480, 480]
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-->
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<picture>
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/clock/05e69dc20c9a28005f1ffe7f0f2ac9eeaa95ba3b/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/clock/05e69dc20c9a28005f1ffe7f0f2ac9eeaa95ba3b/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/clock/05e69dc20c9a28005f1ffe7f0f2ac9eeaa95ba3b/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/clock/05e69dc20c9a28005f1ffe7f0f2ac9eeaa95ba3b/1200w.png">
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<img src="https://static.rerun.io/clock/05e69dc20c9a28005f1ffe7f0f2ac9eeaa95ba3b/full.png" alt="Clock example screenshot">
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</picture>
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An example visualizing an analog clock with hour, minute and seconds hands using Rerun Arrow3D primitives.
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## Used Rerun types
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[`Boxes3D`](https://www.rerun.io/docs/reference/types/archetypes/boxes3d), [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d), [`Arrows3D`](https://www.rerun.io/docs/reference/types/archetypes/arrows3d)
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## Logging and visualizing with Rerun
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The visualizations in this example were created with the following Rerun code:
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The clock's frame is logged as a 3D box using [`Boxes3D`](https://www.rerun.io/docs/reference/types/archetypes/boxes3d) archetype.
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```python
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rr.log(
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"world/frame",
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rr.Boxes3D(half_sizes=[LENGTH_S, LENGTH_S, 1.0], centers=[0.0, 0.0, 0.0]),
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static=True,
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)
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```
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Then, the positions and colors of points and arrows representing the hands of a clock for seconds, minutes, and hours are logged in each simulation time.
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It first sets the simulation time using [`timelines`](https://www.rerun.io/docs/concepts/logging-and-ingestion/timelines), calculates the data for each hand, and logs it using [`Points3D`](https://www.rerun.io/docs/reference/types/archetypes/points3d) and [`Arrows3D`](https://www.rerun.io/docs/reference/types/archetypes/arrows3d) archetypes.
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This enables the visualization of the clock's movement over time.
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```python
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for step in range(steps):
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rr.set_time("sim_time", duration=t_secs)
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# … calculating seconds …
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rr.log("world/seconds_pt", rr.Points3D(positions=point_s, colors=color_s))
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rr.log("world/seconds_hand", rr.Arrows3D(vectors=point_s, colors=color_s, radii=WIDTH_S))
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# … calculating minutes …
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rr.log("world/minutes_pt", rr.Points3D(positions=point_m, colors=color_m))
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rr.log("world/minutes_hand", rr.Arrows3D(vectors=point_m, colors=color_m, radii=WIDTH_M))
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# … calculating hours …
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rr.log("world/hours_pt", rr.Points3D(positions=point_h, colors=color_h))
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rr.log("world/hours_hand", rr.Arrows3D(vectors=point_h, colors=color_h, radii=WIDTH_H))
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```
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## Run the code
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To run this example, make sure you have the Rerun repository checked out and the latest SDK installed:
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```bash
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pip install --upgrade rerun-sdk # install the latest Rerun SDK
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git clone git@github.com:rerun-io/rerun.git # Clone the repository
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cd rerun
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git checkout latest # Check out the commit matching the latest SDK release
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```
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Install the necessary libraries specified in the requirements file:
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```bash
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pip install -e examples/python/clock
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```
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To experiment with the provided example, simply execute the main Python script:
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```bash
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python -m clock # run the example
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```
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If you wish to customize it, explore additional features, or save it use the CLI with the `--help` option for guidance:
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```bash
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python -m clock --help
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```
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