Adding `plotkit_ndarray` library as a dependency

Run this command in a terminal, in your project's directory:

cargo add plotkit-ndarray

To add it manually, edit your project's Cargo.toml file and add to the [dependencies] section:

plotkit-ndarray = "1.0.0"

The plotkit_ndarray library will be automatically available globally. Read the plotkit_ndarray library documentation.

Back to the crate overview.

Readme

plotkit

Publication-quality plots in 3 lines of Rust

Rust finally has a plotting library that looks as good as matplotlib — and it's faster.

No Python subprocess. No JavaScript runtime. No system dependencies. Just add plotkit to your Cargo.toml and start making figures that belong in a journal paper.

Quick Start

plotkit::plot(&x, &y)?;
plotkit::title("sin(x)");
plotkit::savefig("plot.png")?;

Three lines. Anti-aliased text, clean axes, and a Tableau-10 color palette out of the box.

Installation

[dependencies]
plotkit = "0.4"

Optional features:

plotkit = { version = "0.4", features = ["svg", "jupyter", "ndarray", "polars"] }

Feature	Description
`png`	PNG rasterization (enabled by default)
`svg`	SVG vector output
`jupyter`	Inline display in Evcxr notebooks
`ndarray`	Plot directly from `ndarray::Array1`
`polars`	Plot directly from Polars `Series`/`DataFrame`

Chart Types

plotkit supports 16 chart types with a matplotlib-familiar API:

Chart Type	Method	Description
Line	`ax.plot(x, y)`	Connected data points with optional markers
Scatter	`ax.scatter(x, y)`	Individual data points with colormap support
Bar	`ax.bar(cats, vals)`	Vertical bars with stacking and grouping
Horizontal Bar	`ax.barh(cats, vals)`	Horizontal bars
Grouped Bar	`ax.bar_group(cats, series)`	Side-by-side bars for multiple series
Histogram	`ax.hist(data, bins)`	Frequency distribution
Fill Between	`ax.fill_between(x, y1, y2)`	Shaded region between curves
Step	`ax.step(x, y)`	Staircase function
Stem	`ax.stem(x, y)`	Lollipop chart
Box Plot	`ax.boxplot(datasets)`	Statistical box-and-whisker
Violin	`ax.violin(datasets)`	Kernel density distribution
Error Bar	`ax.errorbar(x, y)`	Data with uncertainty ranges
Heatmap	`ax.heatmap(data)`	2D color matrix with colorbar
Pie	`ax.pie(sizes)`	Proportional wedge chart
Contour	`ax.contour(x, y, z)`	Iso-line contour plot
Filled Contour	`ax.contourf(x, y, z)`	Filled contour regions

Two APIs

pyplot-style (quick scripts)

use plotkit::prelude::*;

let x: Vec<f64> = (0..100).map(|i| i as f64 * 0.1).collect();
let y: Vec<f64> = x.iter().map(|&v| v.sin()).collect();

plotkit::plot(&x, &y)?;
plotkit::title("sin(x)");
plotkit::xlabel("x");
plotkit::ylabel("y");
plotkit::savefig("sine.png")?;

Figure/Axes (full control)

use plotkit::prelude::*;
use plotkit::FigureExt;

let mut fig = Figure::with_size(1200, 500);

let ax1 = fig.add_subplot(1, 2, 1);
ax1.plot(&x, &sin_y)?.label("sin(x)");
ax1.plot(&x, &cos_y)?.label("cos(x)");
ax1.set_title("Trigonometric Functions");
ax1.set_xlabel("x");
ax1.set_ylabel("y");
ax1.legend();

let ax2 = fig.add_subplot(1, 2, 2);
ax2.scatter(&x, &y)?;
ax2.set_title("Scatter Plot");

fig.save("subplots.png")?;

Twin Axes

Overlay two datasets with independent y-scales:

let mut fig = Figure::with_size(800, 600);
let ax1 = fig.add_subplot(1, 1, 1);
ax1.plot(&time, &temperature)?.label("Temperature");
ax1.set_ylabel("Temperature (°C)");

let ax2 = fig.twinx(0);
ax2.plot(&time, &pressure)?.label("Pressure");
ax2.set_ylabel("Pressure (hPa)");

ax1.legend();

Colormaps & Colorbar

14 built-in colormaps: Viridis, Plasma, Inferno, Magma, Cividis, Turbo, Coolwarm, Spectral, RdYlBu, RdYlGn, PiYG, BrBG, PRGn, RdBu.

ax.heatmap(data)?.colormap(Colormap::Viridis).colorbar(true);
ax.scatter(&x, &y)?.colormap(Colormap::Plasma).c(&values);

Scales & Axis Control

ax.set_xscale(Scale::Log10);
ax.set_yscale(Scale::SymLog { linthresh: 1.0 });
ax.set_xlim(0.1, 1000.0);
ax.set_xticks(&[0.1, 1.0, 10.0, 100.0, 1000.0]);
ax.invert_yaxis();
ax.grid(true);

Annotations

ax.text(2.0, 0.8, "Peak", None);
ax.annotate(
    "Important point",
    (3.0, 0.5),   // data point
    (4.5, 0.8),   // text position
    ArrowStyle::Arrow,
);

Themes

fig.set_theme(Theme::dark());   // Dark background, light text
fig.set_theme(Theme::default()); // Clean white background

Custom themes:

let theme = Theme {
    figure_background: Color::rgb(0xF5, 0xF5, 0xF5),
    font_family: Some("Helvetica".to_string()),
    ..Theme::default()
};
fig.set_theme(theme);

DataFrame Integration

Polars

use plotkit::prelude::*;

let df = polars::df! {
    "x" => [1.0, 2.0, 3.0, 4.0],
    "y" => [1.0, 4.0, 9.0, 16.0],
}?;

ax.plot(df.column("x")?, df.column("y")?)?;

ndarray

use ndarray::Array1;

let x = Array1::linspace(0.0, 10.0, 100);
let y = x.mapv(f64::sin);
ax.plot(&x, &y)?;

Output Formats

Format	Method	Notes
PNG	`fig.save("out.png")`	CPU-rendered, deterministic
SVG	`fig.save("out.svg")`	Scalable vector, feature `svg`
Bytes	`fig.to_png_bytes()`	For embedding / streaming
String	`fig.to_svg_string()`	For web / templating

Architecture

plotkit is a multi-crate workspace:

Crate	Purpose
`plotkit`	Umbrella crate with pyplot API
`plotkit-core`	Figure, Axes, Artists, Renderer trait
`plotkit-render-skia`	PNG backend (tiny-skia + cosmic-text)
`plotkit-render-svg`	SVG backend
`plotkit-ndarray`	ndarray integration
`plotkit-polars`	Polars integration

The Renderer trait is the abstraction boundary — core logic never depends on a specific backend. Adding a new output format means implementing one trait.

Performance

Pure-Rust CPU renderer. No GPU, no system dependencies, no surprises.

plotkit compiles to a single static binary with zero runtime dependencies. It renders plots entirely on the CPU using optimized rasterization. There is no OpenGL context to initialize, no system font lookup to fail, and no shared library to go missing on a CI server at 2 AM.

Embedded Inter font (Regular + Bold) ensures deterministic, cross-platform text rendering — the same plot looks identical on Linux, macOS, and Windows.

Roadmap

Version	Focus
v0.5	WASM support — render in the browser
v0.6	3D surface plots
v0.7	Animation / frame sequences
v1.0	Stable API, full documentation

Contributing

Contributions are welcome. Whether it's a bug report, a feature request, or a pull request — we appreciate it.

Please read CONTRIBUTING.md before getting started.

License

Licensed under either of

at your option.

Adding plotkit_ndarray library as a dependency

Adding `plotkit_ndarray` library as a dependency