plotstyle

Publication-ready scientific figures — one line of code.

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PlotStyle configures Matplotlib (and optionally Seaborn) so your figures match the exact typographic, dimensional, and export requirements of major academic journals — out of the box.

Getting a figure accepted often means matching a journal's precise column width, font size range, line weight, DPI, and export format. PlotStyle encodes those requirements as TOML specs and applies them automatically, so you spend time on your science rather than your figure settings.

Current release: v1.0.0 (stable)

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Table of Contents

• Why PlotStyle?
• Features
• Supported Journals
• Installation
• Quick Start
• Usage
• CLI
• Examples
• Documentation
• Contributing
• Citation
• License

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Why PlotStyle?

Most journals reject figures that don't meet their formatting requirements — wrong column width, incorrect font sizes, missing font embedding, insufficient DPI, or incompatible file formats. Fixing these manually is tedious and error-prone, especially when targeting multiple journals.

PlotStyle solves this by encoding each journal's exact requirements into machine-readable specs and applying them automatically. You focus on your data; PlotStyle handles the formatting.

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Features

• One-line journal presets — plotstyle.use("nature") sets fonts, sizes, line widths, and export parameters in Matplotlib's rcParams. Wrap it in a with block and everything is restored automatically when the block exits.
• Correctly-sized figures — plotstyle.figure() and plotstyle.subplots() create figures at the exact column width and maximum height specified by each journal.
• Auto panel labels — multi-panel figures get (a), (b), (c), … labels placed and styled according to each journal's conventions.
• Colorblind-safe palettes — built-in Okabe–Ito, Tol Bright/Vibrant/Muted, and grayscale-safe palettes via plotstyle.palette().
• Accessibility previews — simulate deuteranopia, protanopia, and tritanopia; preview grayscale rendering.
• Pre-submission validation — check figure dimensions, font sizes, line weights, color accessibility, and export settings against the target journal's spec before you submit.
• Submission-ready export — plotstyle.savefig() enforces TrueType font embedding and minimum DPI; plotstyle.export_submission() batch-exports to every format the journal requires.
• Spec diffing & migration — compare two journal specs side-by-side; re-target a figure from one journal to another with plotstyle.migrate().
• Seaborn compatibility — a patch layer ensures PlotStyle's rcParams survive sns.set_theme() calls.
• Typed, schema-validated specs — journal requirements are stored as TOML files validated by immutable typed dataclasses.
• CLI — plotstyle list, plotstyle info, plotstyle validate, and more — no Python script needed.

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Supported Journals

Key	Journal	Publisher
acs	ACS (JACS)	American Chemical Society
cell	Cell	Cell Press
elsevier	Elsevier	Elsevier
ieee	IEEE Transactions	IEEE
nature	Nature	Springer Nature
plos	PLOS ONE	Public Library of Science
prl	Physical Review Letters	American Physical Society
science	Science	AAAS
springer	Springer	Springer
wiley	Wiley	Wiley

Need another journal? See CONTRIBUTING.md for how to add one.

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Installation

Requires Python 3.10+ and Matplotlib ≥ 3.9.

pip install plotstyle

Optional extras

# Colorblind / grayscale preview (needs Pillow)
pip install "plotstyle[color]"

# Font subsetting / inspection
pip install "plotstyle[fonttools]"

# Seaborn integration
pip install "plotstyle[seaborn]"

# Everything
pip install "plotstyle[all]"

Development install

git clone https://github.com/rahulkaushal04/plotstyle.git
cd plotstyle
pip install -e ".[dev]"

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Quick Start

import numpy as np
import plotstyle

# plotstyle.use() applies the journal's rcParams (fonts, sizes, line widths).
# The `with` block ensures they are restored automatically when plotting is done.
with plotstyle.use("nature"):
    # Creates a figure at Nature's exact single-column width (89 mm).
    # Use columns=2 for double-column (full text width).
    fig, ax = plotstyle.figure("nature", columns=1)

    x = np.linspace(0, 2 * np.pi, 200)
    ax.plot(x, np.sin(x), label="sin(x)")
    ax.plot(x, np.cos(x), label="cos(x)")
    ax.set_xlabel("Phase (rad)")
    ax.set_ylabel("Amplitude (a.u.)")
    ax.legend()

    # Enforces Nature's minimum DPI (300) and embeds TrueType fonts.
    plotstyle.savefig(fig, "quickstart_nature.pdf", journal="nature")

The with block is the recommended pattern — rcParams are always restored on exit, even if an exception occurs inside the block.

If you need to manage the style manually:

style = plotstyle.use("ieee")
try:
    fig, ax = plotstyle.figure("ieee", columns=1)
    ax.plot([1, 2, 3])
    plotstyle.savefig(fig, "fig_ieee.eps", journal="ieee")
finally:
    style.restore()  # always restore, even on error

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Usage

Multi-panel figures

plotstyle.subplots() works like plt.subplots() but sizes the figure to the journal spec and adds panel labels automatically.

Note: Unlike plt.subplots(), plotstyle.subplots() always returns a 2-D NumPy array of axes — even for a single panel. Use axes[0, 0] to access a single axes, or axes.flat to iterate over all panels.

import plotstyle

with plotstyle.use("science"):
    fig, axes = plotstyle.subplots("science", nrows=2, ncols=2, columns=2)
    # axes has shape (2, 2); each panel is labelled (a), (b), (c), (d)
    for ax in axes.flat:
        ax.plot([1, 2, 3])
    plotstyle.savefig(fig, "multipanel.pdf", journal="science")

Pass panels=False to suppress the automatic labels.

Color palettes

# A list of 4 hex color strings from Nature's recommended palette
colors = plotstyle.palette("nature", n=4)

# With linestyles and markers — useful for accessible line plots
styled = plotstyle.palette("ieee", n=3, with_markers=True)
# styled is a list of (color, linestyle, marker) tuples
for color, ls, marker in styled:
    ax.plot(x, y, color=color, linestyle=ls, marker=marker)

Validation

report = plotstyle.validate(fig, journal="nature")
print(report)           # formatted table of all checks
print(report.passed)    # True if no checks failed

for failure in report.failures:
    print(failure.message)          # what failed
    print(failure.fix_suggestion)   # how to fix it

Submission export

export_submission() writes the figure in every format the journal requires (PDF, TIFF, EPS, etc.) and applies journal-specific naming conventions.

paths = plotstyle.export_submission(
    fig,
    "figure1",
    journal="ieee",
    author_surname="Kaushal",   # IEEE prepends the first 5 chars of the surname
    output_dir="submission_ieee",
)
# Produces: submission_ieee/kaush_figure1.pdf (and any other IEEE-required formats)

Spec diffing and migration

# Compare two journals — useful when retargeting a figure
result = plotstyle.diff("nature", "science")
print(result)           # aligned two-column table of differences

# Re-target a figure to a different journal in place
plotstyle.migrate(fig, from_journal="nature", to_journal="science")
plotstyle.savefig(fig, "figure_science.pdf", journal="science")

Accessibility previews

# Simulate how a figure looks under three types of color blindness
comp = plotstyle.preview_colorblind(fig)
comp.savefig("colorblind_check.png", dpi=150)

# Preview grayscale rendering
gs = plotstyle.preview_grayscale(fig)
gs.savefig("grayscale_check.png", dpi=150)

Seaborn integration

sns.set_theme() normally overwrites the rcParams that PlotStyle set. Pass seaborn_compatible=True to prevent that:

import seaborn as sns
import plotstyle

with plotstyle.use("nature", seaborn_compatible=True):
    fig, ax = plotstyle.figure("nature", columns=1)
    sns.lineplot(x=[1, 2, 3], y=[4, 5, 6], ax=ax)
    plotstyle.savefig(fig, "seaborn_figure.pdf", journal="nature")

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CLI

plotstyle list                                 # List all journal presets
plotstyle info <journal>                       # Show spec details
plotstyle diff <journal_a> <journal_b>         # Compare two journals
plotstyle fonts --journal <journal>            # Check font availability
plotstyle validate <file> --journal <journal>  # Validate a saved figure
plotstyle export <file> --journal <journal>    # Re-export in required formats

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Examples

Working examples are in the examples/ directory:

#	File	Topic
01	01_quickstart.py	Basic publication-ready figure
02	02_multi_panel_figure.py	2×2 subplot grid with auto panel labels
03	03_color_palettes.py	Journal-specific and universal palettes
04	04_accessibility_checks.py	Colorblind and grayscale previews
05	05_validation.py	Pre-submission validation
06	06_export_submission.py	Batch submission export
07	07_spec_diff_and_migrate.py	Spec comparison and figure migration
08	08_gallery_preview.py	Gallery preview generation
09	09_registry_and_spec.py	Registry and spec access
10	10_context_manager_patterns.py	Context manager usage patterns

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Documentation

Full documentation is available at plotstyle.readthedocs.io:

• Installation guide
• Quick start tutorial
• API reference
• How-to guides
• CLI reference
• FAQ

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Contributing

See CONTRIBUTING.md for development setup, adding journal specs, code style, and pull request guidelines.

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Citation

If PlotStyle helps your research, a citation or star is appreciated:

@software{plotstyle,
  author  = {Kaushal, Rahul},
  title   = {PlotStyle: Publication-ready scientific figure presets for Matplotlib},
  year    = {2026},
  url     = {https://github.com/rahulkaushal04/plotstyle},
  license = {MIT},
}

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License

MIT © 2026 Rahul Kaushal