wehub-resource-sync
143 lines
7.3 KiB
Markdown
143 lines
7.3 KiB
Markdown
<!--[metadata]
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title = "Plots"
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tags = ["2D", "Plots", "API example"]
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thumbnail = "https://static.rerun.io/plots/e8e51071f6409f61dc04a655d6b9e1caf8179226/480w.png"
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thumbnail_dimensions = [480, 480]
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channel = "main"
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include_in_manifest = true
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-->
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This example demonstrates how to log simple plots with the Rerun SDK. Charts can be created from 1-dimensional tensors, or from time-varying scalars.
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<picture data-inline-viewer="examples/plots">
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<source media="(max-width: 480px)" srcset="https://static.rerun.io/plots/c5b91cf0bf2eaf91c71d6cdcd4fe312d4aeac572/480w.png">
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<source media="(max-width: 768px)" srcset="https://static.rerun.io/plots/c5b91cf0bf2eaf91c71d6cdcd4fe312d4aeac572/768w.png">
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<source media="(max-width: 1024px)" srcset="https://static.rerun.io/plots/c5b91cf0bf2eaf91c71d6cdcd4fe312d4aeac572/1024w.png">
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<source media="(max-width: 1200px)" srcset="https://static.rerun.io/plots/c5b91cf0bf2eaf91c71d6cdcd4fe312d4aeac572/1200w.png">
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<img src="https://static.rerun.io/plots/c5b91cf0bf2eaf91c71d6cdcd4fe312d4aeac572/full.png" alt="Plots example screenshot">
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</picture>
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## Used Rerun types
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[`BarChart`](https://www.rerun.io/docs/reference/types/archetypes/bar_chart), [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars), [`SeriesPoints`](https://www.rerun.io/docs/reference/types/archetypes/series_points), [`SeriesLines`](https://www.rerun.io/docs/reference/types/archetypes/series_lines), [`TextDocument`](https://www.rerun.io/docs/reference/types/archetypes/text_document)
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## Logging and visualizing with Rerun
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This example shows various plot types that you can create using Rerun. Common usecases for such plots would be logging
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losses or metrics over time, histograms, or general function plots.
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The bar chart is created by logging the [`BarChart`](https://www.rerun.io/docs/reference/types/archetypes/bar_chart) archetype.
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All other plots are created using the [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars) archetype.
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Each plot is created by logging scalars at different time steps (i.e., the x-axis).
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Additionally, the plots are styled using the [`SeriesLines`](https://www.rerun.io/docs/reference/types/archetypes/series_lines) and
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[`SeriesPoints`](https://www.rerun.io/docs/reference/types/archetypes/series_points) archetypes respectively.
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The visualizations in this example were created with the following Rerun code:
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### Bar chart
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The `log_bar_chart` function logs a bar chat.
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It generates data for a Gaussian bell curve and logs it using [`BarChart`](https://www.rerun.io/docs/reference/types/archetypes/bar_chart) archetype.
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```python
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def log_bar_chart() -> None:
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# … existing code …
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rr.log("bar_chart", rr.BarChart(y))
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```
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### Curves
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The `log_parabola` function logs a parabola curve (sine and cosine functions) as a time series.
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It first sets up a time sequence using [`timelines`](https://www.rerun.io/docs/concepts/logging-and-ingestion/timelines), then calculates the y-value of the parabola at each time step, and logs it using [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars) archetype.
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It also adjusts the width and color of the plotted line based on the calculated y value using [`SeriesLines`](https://www.rerun.io/docs/reference/types/archetypes/series_lines) archetype.
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```python
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def log_parabola() -> None:
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# Name never changes, log it only once.
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rr.log("curves/parabola", rr.SeriesLines(name="f(t) = (0.01t - 3)³ + 1"), static=True)
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# Log a parabola as a time series
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for t in range(0, 1000, 10):
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rr.set_time("frame_nr", sequence=t)
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# … existing code …
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rr.log(
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"curves/parabola",
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rr.Scalars(f_of_t),
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rr.SeriesLines(width=width, color=color),
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)
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```
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### Trig
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The `log_trig` function logs sin and cos functions as time series. Sin and cos are logged with the same parent entity (i.e.,`trig/{cos,sin}`) which will put them in the same view by default.
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It first logs the styling properties of the sin and cos plots using [`SeriesLines`](https://www.rerun.io/docs/reference/types/archetypes/series_lines) archetype.
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Then, it iterates over a range of time steps, calculates the sin and cos values at each time step, and logs them using [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars) archetype.
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```python
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def log_trig() -> None:
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# Styling doesn't change over time, log it once with static=True.
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rr.log("trig/sin", rr.SeriesLines(color=[255, 0, 0], name="sin(0.01t)"), static=True)
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rr.log("trig/cos", rr.SeriesLines(color=[0, 255, 0], name="cos(0.01t)"), static=True)
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for t in range(0, int(tau * 2 * 100.0)):
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rr.set_time("frame_nr", sequence=t)
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sin_of_t = sin(float(t) / 100.0)
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rr.log("trig/sin", rr.Scalars(sin_of_t))
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cos_of_t = cos(float(t) / 100.0)
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rr.log("trig/cos", rr.Scalars(cos_of_t))
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```
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### Classification
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The `log_classification` function simulates a classification problem by logging a line function and randomly generated samples around that line.
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It first logs the styling properties of the line plot using [`SeriesLines`](https://www.rerun.io/docs/reference/types/archetypes/series_lines) archetype.
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Then, it iterates over a range of time steps, calculates the y value of the line function at each time step, and logs it as a scalars using [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars) archetype.
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Additionally, it generates random samples around the line function and logs them using [`Scalars`](https://www.rerun.io/docs/reference/types/archetypes/scalars) and [`SeriesPoints`](https://www.rerun.io/docs/reference/types/archetypes/series_points) archetypes.
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```python
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def log_classification() -> None:
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# Log components that don't change only once:
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rr.log("classification/line", rr.SeriesLines(colors=[255, 255, 0], widths=3.0), static=True)
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for t in range(0, 1000, 2):
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rr.set_time("frame_nr", sequence=t)
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# … existing code …
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rr.log("classification/line", rr.Scalars(f_of_t))
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# … existing code …
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rr.log("classification/samples", rr.Scalars(g_of_t), rr.SeriesPoints(colors=color, marker_sizes=marker_size))
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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/plots
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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 plots # 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 plots --help
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```
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## Advanced time series - [`send_columns`](https://ref.rerun.io/docs/python/stable/common/columnar_api/#rerun.send_columns)
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Logging many scalars individually can be slow.
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The [`send_columns`](https://ref.rerun.io/docs/python/stable/common/columnar_api/#rerun.send_columns) API can be used to log many scalars at once.
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Check the [`Scalars` `send_columns` snippet](https://rerun.io/docs/reference/types/archetypes/scalars#update-a-scalar-over-time-in-a-single-operation) to learn more.
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