ruviz 0.4.18

Python bindings and notebook widget support for ruviz

ruviz for Python

ruviz for Python wraps the Rust plotting runtime with a fluent Python API, static export helpers, native desktop show(), and notebook widget support. It requires Python 3.10 or newer and installs the runtime dependencies needed for NumPy inputs, static rendering, and AnyWidget notebooks.

Install

pip install ruviz

If you want pandas or Polars dataframe inputs, install the matching optional extra:

pip install "ruviz[dataframes]"
# or only one dataframe backend:
pip install "ruviz[pandas]"
pip install "ruviz[polars]"

Quick Start

import numpy as np
import ruviz

x = np.linspace(0.0, 4.0, 50)
y = x**2

(
    ruviz.plot()
    .line(x, y)
    .title("Quadratic")
    .xlabel("x")
    .ylabel("y = x^2")
    .save("quadratic.png")
)

Notebook and Desktop Usage

• In Jupyter, plot.show() displays a static PNG in the cell output.
• Use plot.widget() when you want the synced WASM-backed notebook widget.
• plot.size_px(width, height) also controls the widget's displayed size and aspect ratio.
• Without size_px(...), the widget uses the default PNG size (640x480) and shrinks proportionally if the notebook column is narrower.
• Drag the widget's bottom-right handle to resize the display freely.
• Hold Shift or Ctrl while dragging the handle to preserve the current aspect ratio.
• In the widget, right click opens the export menu and right drag performs box zoom.
• Outside notebooks, plot.show() opens the native interactive window.
• The published Linux wheel focuses on static rendering and notebook widgets. Install from source on Linux if you need the native desktop plot.show() window.
• plot.render_png() returns PNG bytes and plot.render_svg() returns an SVG string.
• plot.save(path) writes PNG, SVG, or PDF according to the file extension and returns the output Path.

Reactive Notebook Data

Use ruviz.observable(...) for notebook-driven updates that keep explicit widgets in sync:

import numpy as np
import ruviz

x = np.linspace(0.0, 6.0, 200)
y = ruviz.observable(np.sin(x))

plot = ruviz.plot().size_px(640, 360).line(x, y).title("Live Sine Wave")
widget = plot.widget()

ObservableSeries supports elementwise arithmetic and NumPy ufuncs. Derived observables stay live until you write to them directly. Live observable series are supported by line, scatter, bar, histogram, boxplot, error_bars, and error_bars_xy; other plot types snapshot their inputs.

import numpy as np

scaled = np.sin(y * 2.0 + 0.25)
plot.line(x, scaled)
y.replace(np.cos(x))

deepcopy(plot) creates an independent live copy with fresh observables, while plot.clone() remains a static snapshot copy.

Documentation

• Python docs source: python/docs/
• Python examples: python/examples/
• Root project README: https://github.com/Ameyanagi/ruviz/blob/main/README.md

Contributor Workflow

cd python
uv sync
uv run maturin develop
uv run python scripts/generate_gallery.py
uv run mkdocs serve

Rebuild the bundled widget frontend from the repository root when you change the web SDK or packages/ruviz-web/src/python-widget.ts:

bun run build:python-widget