plotstyle 1.2.1

Matplotlib/seaborn style presets matching scientific journal requirements, with validation, export safety, and preview capabilities.

plotstyle

Matplotlib figures formatted for journal submission, automatically.

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PlotStyle makes it easy to produce Matplotlib figures that meet the exact typographic, dimensional, and export requirements of major academic journals. It also integrates with Seaborn, with more integrations planned. Pick a journal, create your figure, save it. PlotStyle handles the rest.

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Table of Contents

• Installation
• Quick Start
• Examples
  • Multi-panel figures
  • Color palettes
  • Overlays
  • Colorblind and grayscale previews
  • Validation and submission export
• Supported Journals
• CLI
• Documentation
• Contributing
• Citation
• License

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Installation

Requires Python 3.10+ and Matplotlib >= 3.9.

pip install plotstyle

Optional extras:

pip install "plotstyle[color]"     # colorblind / grayscale previews
pip install "plotstyle[seaborn]"   # seaborn integration
pip install "plotstyle[all]"       # everything

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Quick Start

import numpy as np
import plotstyle

with plotstyle.use("nature") as style:
    fig, ax = style.figure(columns=1)   # sized to Nature's single-column width (89 mm)

    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()

    style.savefig(fig, "figure.pdf")    # 300 DPI minimum, TrueType fonts embedded

The with block is the recommended pattern. Matplotlib's rcParams are restored automatically when it exits, even if an exception occurs.

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Examples

Multi-panel figures

style.subplots() works like plt.subplots() but sizes the figure to the journal spec and adds panel labels automatically. All built-in journal specs use bold lowercase labels (a, b, c, …). The label style is driven by each spec's panel_label_case field and can be lower, upper, parens_lower, parens_upper, sentence, or title.

import numpy as np
import plotstyle

rng = np.random.default_rng(42)

with plotstyle.use("science") as style:
    fig, axes = style.subplots(nrows=2, ncols=2, columns=2)

    x = np.linspace(0, 10, 100)
    axes[0, 0].plot(x, np.sin(x), label="sin")
    axes[0, 0].plot(x, np.cos(x), label="cos")
    axes[0, 0].set_xlabel("x")
    axes[0, 0].set_ylabel("f(x)")
    axes[0, 0].legend()

    xs = rng.normal(0, 1, 60)
    ys = 0.7 * xs + rng.normal(0, 0.3, 60)
    axes[0, 1].scatter(xs, ys, s=12, alpha=0.7)
    axes[0, 1].set_xlabel("Variable X")
    axes[0, 1].set_ylabel("Variable Y")

    axes[1, 0].bar(["A", "B", "C", "D"], [3.2, 5.8, 4.1, 6.5])
    axes[1, 0].set_xlabel("Category")
    axes[1, 0].set_ylabel("Count")

    axes[1, 1].hist(rng.normal(0, 1, 500), bins=25, edgecolor="white", linewidth=0.5)
    axes[1, 1].set_xlabel("Value")
    axes[1, 1].set_ylabel("Frequency")

    style.savefig(fig, "multi_panel.pdf")

axes is always a 2-D NumPy array. Use axes[0, 0] to access a single panel or axes.flat to iterate. Pass panels=False to suppress the automatic labels.

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Color palettes

Each journal has a recommended colorblind-safe palette. plotstyle.palette() returns hex color strings, cycling if you need more than the palette length.

import matplotlib.pyplot as plt
import plotstyle

journals = ["nature", "science", "ieee", "acs"]
fig, axes = plt.subplots(len(journals), 1, figsize=(6, 0.6 * len(journals)))

for ax, journal in zip(axes, journals, strict=False):
    pal = plotstyle.palette(journal, n=8)
    for i, color in enumerate(pal):
        ax.barh(0, 1, left=i, color=color, edgecolor="none", height=0.8)
    ax.set_xlim(0, 8)
    ax.set_yticks([])
    ax.set_ylabel(journal, rotation=0, ha="right", va="center")
    ax.set_xticks([])

fig.suptitle("Journal Color Palettes")
fig.tight_layout()
fig.savefig("palette_comparison.png", dpi=150)

Pass with_markers=True to get (color, linestyle, marker) tuples, useful for journals like IEEE that print in grayscale:

import numpy as np
import plotstyle

x = np.linspace(0, 2 * np.pi, 100)
curves = [np.sin(x + i * 0.5) for i in range(4)]

with plotstyle.use("ieee") as style:
    fig, ax = style.figure(columns=1)
    styled = plotstyle.palette("ieee", n=4, with_markers=True)
    for i, (color, ls, marker) in enumerate(styled):
        ax.plot(x, curves[i], color=color, linestyle=ls, marker=marker,
                markevery=20, label=f"Series {i + 1}")
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.legend()
    style.savefig(fig, "ieee_markers.pdf")

# styled: one (color, linestyle, marker) tuple per series:
[('#000000', '-', 'o'), ('#333333', '--', 's'), ('#666666', '-.', '^'), ('#999999', ':', 'D')]

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Overlays

Overlays are additive patches that layer on top of a journal preset. They let you adjust one aspect of a figure (the colour palette, the context, the chart type) without changing the base journal settings.

Pass overlay names in the same list as the journal key:

import matplotlib.pyplot as plt
import plotstyle

# Strip top/right spines for a clean editorial look
with plotstyle.use(["nature", "minimal"]) as style:
    fig, ax = style.figure(columns=1)
    ax.plot([1, 2, 3], label="data")
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.legend()
    style.savefig(fig, "minimal_figure.pdf")

import matplotlib.pyplot as plt
import plotstyle

# Larger figure and fonts for Jupyter notebooks
with plotstyle.use(["nature", "notebook"]) as style:
    fig, ax = plt.subplots()   # plt.subplots() picks up the notebook figsize
    ax.plot([1, 2, 3], label="data")
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.legend()
    style.savefig(fig, "notebook_figure.pdf")

import matplotlib.pyplot as plt
import plotstyle

# Swap the colour cycle to a specific palette
with plotstyle.use(["ieee", "okabe-ito"]) as style:
    fig, ax = style.figure(columns=1)
    ax.plot([1, 2, 3], label="data")
    ax.set_xlabel("x")
    ax.set_ylabel("y")
    ax.legend()
    style.savefig(fig, "okabe_ito_figure.pdf")

Category	Purpose	Examples
color	Swap the colour cycle	okabe-ito, tol-bright, safe-grayscale
context	Adjust scale for the medium	notebook, presentation, minimal, high-vis
rendering	Control LaTeX and grid rendering	no-latex, grid, latex-sans, pgf
plot-type	Optimise for a chart type	bar, scatter
script	Non-Latin font support	cjk-simplified, russian, turkish

# List all available overlays
plotstyle.list_overlays()
plotstyle.list_overlays(category="context")

# plotstyle.list_overlays()
['bar', 'cjk-japanese', 'cjk-korean', 'cjk-simplified', 'cjk-traditional', 'grid',
 'high-vis', 'latex-sans', 'minimal', 'no-latex', 'notebook', 'okabe-ito', 'pgf',
 'presentation', 'russian', 'safe-grayscale', 'scatter', 'tol-bright',
 'tol-high-contrast', 'tol-light', 'tol-muted', 'tol-rainbow-10', 'tol-rainbow-12',
 'tol-rainbow-4', 'tol-rainbow-6', 'tol-rainbow-8', 'tol-vibrant', 'turkish']

# plotstyle.list_overlays(category="context")
['high-vis', 'minimal', 'notebook', 'presentation']

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Colorblind and grayscale previews

Build a figure, then simulate how it looks under color vision deficiency or grayscale printing before you submit.

import numpy as np
import plotstyle

with plotstyle.use("nature") as style:
    colors = style.palette(n=4)
    fig, ax = style.figure(columns=1)
    x = np.linspace(0, 5, 80)
    for i, c in enumerate(colors):
        ax.plot(x, np.sin(x + i), color=c, linewidth=1.5, label=f"Series {i + 1}")
    ax.set_xlabel("Time (s)")
    ax.set_ylabel("Signal")
    ax.legend()

    # Simulate colour vision deficiency (deuteranopia, protanopia, tritanopia)
    cvd_fig = plotstyle.preview_colorblind(fig)
    cvd_fig.savefig("accessibility_colorblind.png", dpi=150, bbox_inches="tight")

    # Simulate grayscale print
    gray_fig = plotstyle.preview_grayscale(fig)
    gray_fig.savefig("accessibility_grayscale.png", dpi=150, bbox_inches="tight")

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Validation and submission export

Validate a figure against the journal's requirements, then export in all required formats at once.

import numpy as np
import matplotlib.pyplot as plt
import plotstyle

x = np.linspace(0, 2 * np.pi, 100)

# Outside plotstyle.use() — some checks will fail
fig, ax = plt.subplots()
ax.plot(x, np.sin(x), label="sin(x)")
ax.set_xlabel("Phase (rad)")
ax.set_ylabel("Amplitude")
ax.legend()

report = plotstyle.validate(fig, journal="nature")
print(report)          # formatted compliance table
print(report.passed)   # False — rcParams not configured

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

# Inside plotstyle.use() — all checks pass
with plotstyle.use("nature") as style:
    fig, ax = style.figure(columns=1)
    ax.plot(x, np.sin(x), label="sin(x)")
    ax.set_xlabel("Phase (rad)")
    ax.set_ylabel("Amplitude")
    ax.legend()

    report = plotstyle.validate(fig, journal="nature")
    print(report)
    print(report.passed)   # True

┌──────────────────────────────────────────────────────┐
│         PlotStyle Validation Report: Nature          │
├──────────┬───────────────────────────────────────────┤
│ ✓ PASS   │ Figure width 89.0mm matches single colu...│
│ ✓ PASS   │ Figure height 55.0mm is within the Natu...│
│ ✗ FAIL   │ pdf.fonttype = 3; must be 42 for TrueTy...│
│ ✗ FAIL   │ ps.fonttype = 3; must be 42 for TrueTyp...│
│ ⚠ WARN   │ savefig.dpi = 'figure'; Nature requires...│
│ ✓ PASS   │ All plotted lines and spines meet the N...│
│ ✗ FAIL   │ 14 text element(s) outside the Nature r...│
└──────────┴───────────────────────────────────────────┘
3/7 checks passed, 1 warning(s), 3 failure(s)

passed: False

failure.message:       pdf.fonttype = 3; must be 42 for TrueType font embedding.
failure.fix_suggestion: Call plotstyle.use() to apply all required rcParams, or set
                        mpl.rcParams['pdf.fonttype'] = 42 manually.

When called inside plotstyle.use(), all checks pass:

┌──────────────────────────────────────────────────────┐
│         PlotStyle Validation Report: Nature          │
├──────────┬───────────────────────────────────────────┤
│ ✓ PASS   │ Figure width 89.0mm matches single colu...│
│ ✓ PASS   │ Figure height 55.0mm is within the Natu...│
│ ✓ PASS   │ pdf.fonttype = 42 (TrueType fonts will ...│
│ ✓ PASS   │ ps.fonttype = 42 (TrueType fonts will b...│
│ ✓ PASS   │ savefig.dpi = 300.0 meets the Nature mi...│
│ ✓ PASS   │ All plotted lines and spines meet the N...│
│ ✓ PASS   │ All text elements are within the Nature...│
└──────────┴───────────────────────────────────────────┘
7/7 checks passed, 0 warning(s), 0 failure(s)

passed: True

import numpy as np
import os
import plotstyle

os.makedirs("submission", exist_ok=True)

with plotstyle.use("ieee") as style:
    fig, ax = style.figure(columns=1)
    x = np.linspace(0, 2 * np.pi, 100)
    ax.plot(x, np.sin(x), label="sin(x)")
    ax.set_xlabel("Phase (rad)")
    ax.set_ylabel("Amplitude")
    ax.legend()

    paths = plotstyle.export_submission(
        fig,
        "figure1",
        journal="ieee",
        author_surname="Smith",     # IEEE prepends the surname prefix to filenames
        output_dir="submission/",
    )
    print(paths)

[PosixPath('submission/smith_figure1.tiff'),
 PosixPath('submission/smith_figure1.eps'),
 PosixPath('submission/smith_figure1.pdf'),
 PosixPath('submission/smith_figure1.png')]

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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.

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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 overlays [--category <category>]    # list available overlays
plotstyle overlay-info <overlay>               # show overlay details
plotstyle validate <file> --journal <journal>  # validate a saved figure
plotstyle export <file> --journal <journal>    # print snippet for re-exporting

plotstyle list

  acs             American Chemical Society
  cell            Cell Press
  elsevier        Elsevier
  ieee            IEEE
  nature          Springer Nature
  plos            Public Library of Science
  prl             American Physical Society
  science         AAAS
  springer        Springer Nature
  wiley           Wiley

plotstyle info nature

Journal: Nature
Publisher: Springer Nature
Source: https://www.nature.com/documents/nature-final-artwork.pdf
Last Verified: 2026-04-22
──────────────────────────
Dimensions:
  Single column: 89.0mm (3.50in)
  Double column: 183.0mm (7.20in)
  Max height:    247.0mm
Typography:
  Font:          Helvetica, Arial (fallback: sans-serif)
  Size range:    5.0-7.0pt
  Panel labels:  5.0pt bold lower (a, b, c)
Export:
  Formats:  ai, eps, pdf
  Min DPI:  300
  Color:    rgb
Accessibility:
  Colorblind safe: Not required
  Grayscale safe:  Not required

plotstyle diff nature science

Nature → Science
──────────────────────────────────────────────────
Column Width (single):  89.0mm → 86.4mm
Column Width (double):  183.0mm → 177.8mm
Max Height:             247.0mm → -
Font Family:            Helvetica, Arial → Minion Pro, Benton Sans Condensed
Min Font Size:          5.0pt → 7.5pt
Max Font Size:          7.0pt → 10.0pt
Panel Label Size:       5.0pt → 7.5pt
Preferred Formats:      ai, eps, pdf → ai, eps, pdf, tiff
Colorblind Required:    No → Yes

plotstyle fonts --journal nature

Font check for: Nature
Required:        Helvetica, Arial
Available:       Helvetica, Arial
Selected:        Helvetica
Exact match:     Yes

plotstyle overlays

  bar             [plot-type]  Optimised rcParams for bar charts.
  cjk-simplified  [script]     Font configuration for Simplified Chinese labels.
  grid            [rendering]  Enable major grid lines with a subtle dashed style.
  high-vis        [context]    Maximum contrast, bold lines, and oversized ticks.
  latex-sans      [rendering]  Enable LaTeX rendering with a sans-serif font family.
  minimal         [context]    Stripped-down axes with no top/right spines.
  no-latex        [rendering]  Disable LaTeX text rendering; use Matplotlib MathText.
  notebook        [context]    Enlarged figures and larger fonts for Jupyter.
  okabe-ito       [color]      Colorblind-safe 8-color qualitative palette.
  pgf             [rendering]  Use the PGF LaTeX backend for vector output.
  presentation    [context]    Large text and thick lines for slide decks.
  safe-grayscale  [color]      6-step grayscale palette for black-and-white print.
  scatter         [plot-type]  Optimised rcParams for scatter plots.
  tol-bright      [color]      Paul Tol's bright 7-color qualitative palette.
  ...

plotstyle overlay-info minimal

Overlay: Minimal
Key:     minimal
Category: context
Description: Stripped-down axes with no top/right spines for editorial and blog use.
──────────────────────────
rcParams:
  axes.spines.top = False
  axes.spines.right = False
  xtick.top = False
  ytick.right = False
  axes.grid = False
  axes.linewidth = 0.8

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Documentation

Full documentation at plotstyle.readthedocs.io:

• Installation guide
• Quick start tutorial
• API reference
• CLI reference
• FAQ

Working examples are in the examples/ directory.

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Contributing

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

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Citation

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

@misc{plotstyle,
  author  = {Kaushal, Rahul},
  title   = {PlotStyle: Publication-ready scientific figure presets for Matplotlib},
  year    = {2026},
  url     = {https://github.com/rahulkaushal04/plotstyle},
  note    = {Version 1.2.1},
}

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License

MIT © 2026 Rahul Kaushal