cleanfig 1.1.0

Small vector-first scientific plotting with clean publication-style defaults.

cleanfig

cleanfig is a small Rust/Python plotting package for clean scientific figures with vector-first export.

It is intentionally narrow: simple publication-style defaults, light visual clutter, compact labeling, and a small public API. The focus is on figures that should look close to final output without extensive styling code.

Useful links:

• Gallery: https://adakite.github.io/cleanfig/gallery/
• Documentation landing page: https://adakite.github.io/cleanfig/
• Repository: https://github.com/adakite/cleanfig

Installation

Install the latest GitHub version:

pip install git+https://github.com/adakite/cleanfig.git

Planned future PyPI install:

pip install cleanfig

Quick Start

import numpy as np
import cleanfig as cf

x = np.linspace(0, 10, 200)
y = np.sin(x)

fig = cf.figure(width="single", height=3.4, panel_labels=False)
ax = fig.panel(0, 0)
ax.line(x, y, label="signal")
ax.scatter(x[::20], y[::20], size=5)
ax.xlabel("x")
ax.ylabel("y")

fig.save("basic_line.svg")
fig.save("basic_line.html")
fig.save("basic_line.pdf")

Public API

The package is designed to be used as:

import cleanfig as cf

Current public entry points:

• cf.figure(...)
• Figure.panel(row, col)
• Figure.save(path)
• Panel.scatter(...)
• Panel.line(...)
• Panel.bar(...)
• Panel.histogram(...)
• Panel.field(...)
• Panel.violin(...)
• Panel.box(...)
• Panel.colorbar(...)
• Panel.legend()
• Panel.xlabel(...)
• Panel.ylabel(...)
• Panel.right_ylabel(...)
• Panel.xscale(...)
• Panel.yscale(...)
• Panel.limits(...)
• Panel.right_limits(...)

API Reference

cf.figure(width="single", height=4.0, grid=(1, 1), panel_labels=False, font=None, theme="publication")

• width: "single" or "double"
• height: figure height in inches
• grid: (rows, cols)
• panel_labels: add panel letters
• font: custom font family string
• theme: "publication" / "nature" / "light" alias, or "dark"

Axis labels, limits, and scales

• ax.xlabel(label)
• ax.ylabel(label)
• ax.right_ylabel(label): label for a secondary right Y axis
• ax.limits(x=None, y=None): explicit limits for the main X/Y axes
• ax.right_limits(y=None): explicit limits for the right Y axis
• ax.xscale("linear" | "log")
• ax.yscale("linear" | "log", axis="left" | "right")

Log scales require strictly positive values and strictly positive limits.

ax.scatter(x, y, color=None, size=6.0, alpha=0.8, label=None, cmap=None, yaxis="left")

• x, y: same-length numeric arrays
• color: named/hex color or numeric array for colormap mapping
• size: marker diameter in points
• alpha: opacity
• label: legend entry
• cmap: colormap name for mapped colors
• yaxis: "left" or "right" for dual-Y figures

Returns a PlotHandle when color mapping is used.

ax.line(x, y, color=None, width=1.2, alpha=1.0, label=None, yaxis="left")

• color: named/hex color
• width: stroke width in points
• alpha: opacity
• label: legend entry
• yaxis: "left" or "right"

ax.bar(labels, values, yaxis="left", color=None, alpha=1.0)

• labels: categorical X labels
• values: numeric heights
• yaxis: "left" or "right"
• color: named/hex color
• alpha: opacity

ax.histogram(data, bins=12, range=None, density=False, color=None, alpha=1.0, label=None, yaxis="left")

• data: numeric samples
• bins: number of bins
• range: optional (min, max) binning range
• density: normalize to probability density instead of counts
• color: named/hex fill color
• alpha: opacity
• label: legend entry
• yaxis: "left" or "right"

ax.field(grid, cmap=None, cell_edges=False)

• grid: 2D numeric array
• cmap: colormap name
• cell_edges: draw subtle cell borders when True

Returns a PlotHandle for optional colorbar creation.

ax.colorbar(handle, label=None, placement=None, style=None)

• handle: result of a mapped scatter, field, or mapped-point violin
• label: colorbar label
• placement: "right" or "inside-left"
• style: "binned" or "continuous"

Current default is "binned".

ax.violin(data, labels=None, show_median=False, points=False, point_color=None, point_size=4.0, point_alpha=0.75, point_cmap=None)

• data: grouped numeric data
• labels: category labels
• show_median: draw median segment
• points: overlay individual points
• point_color: constant color, flat array, or grouped arrays
• point_size: point diameter
• point_alpha: point opacity
• point_cmap: colormap for mapped points

Returns a PlotHandle when mapped point colors are used.

ax.box(data, labels=None)

• data: grouped numeric data
• labels: category labels

ax.legend()

Creates a compact frameless legend from labeled layers.

Current Feature Status

• Supported: line, scatter, bar, histogram, violin, box, small vector field plots
• Supported: light/dark themes, log X/Y axes, dual Y axes, SVG/HTML/PDF export
• Not supported yet: ax.spectrogram(), logarithmic colorbars, raster rendering, geographic projections

Examples

Useful example scripts are provided in examples/:

• basic_line.py
• four_panels.py
• violin_box.py
• esec_dual_y_light.py for a light-theme dual-Y example using a pandas.DataFrame loaded from a bundled ESEC catalog extract in examples/Data/
• theme-specific wrappers for light/dark example output

Export Formats

Supported export targets:

• SVG
• HTML with embedded SVG
• PDF through SVG conversion in the Rust backend

Design Philosophy

• vector-first output
• clean left/bottom axes by default
• minimal plot constructors
• no GUI, dashboards, or heavyweight plotting state
• useful scientific defaults over maximum flexibility

Fallback Behavior

cleanfig prefers the compiled Rust extension.

If the extension is unavailable, it falls back to a pure Python implementation. The fallback is intended for graceful local use and testing, but it is not feature-complete. In particular, PDF export is only available when the Rust backend is loaded.

You can inspect the active backend with:

import cleanfig as cf
print(cf.BACKEND)

Current Limitations

• no ax.spectrogram() yet
• no logarithmic colorbars
• no raster backend
• no geographic projections
• visual styling is intentionally constrained
• field rendering is intended for small grids only

Development Install

python -m venv .venv
source .venv/bin/activate
python -m pip install -U pip
python -m pip install -e ".[dev]"
maturin develop
pytest -q

The esec_dual_y_light.py example additionally expects pandas, which is included in the dev extra. The bundled ESEC source file and citation notes are stored under examples/Data/.

Citation, License, Contact

• License: MIT, see LICENSE
• Changelog: CHANGELOG.md
• Release checklist: RELEASE.md
• Contact: Antoine Lucas