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scientific-visualization

Create publication-ready scientific figures with matplotlib, seaborn, and plotly for journal submission.

Updated Oct 7, 2022
Search Tags:#plotly#matplotlib#publication figures#journal submission#seaborn#scientific visualization#colorblind palette
One-click install
npx skills add https://github.com/tamagusko/linux-cfg --skill scientific-visualization-tamagusko
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scientific-visualization
Source: https://github.com/tamagusko/linux-cfg/tree/main/dotfiles/claude/skills/scientific-visualization
Command: npx skills add https://github.com/tamagusko/linux-cfg --skill scientific-visualization-tamagusko

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, numpy, seaborn, plotly, pandas, scipy, PyPDF2, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve? Preparing figures that meet journal submission standards is tedious: wrong DPI, non-colorblind-safe palettes, incorrect column widths, and missing error bars cause desk rejections and revision cycles. This Skill encodes the technical requirements of major journals (Nature, Science, Cell, PLOS, ACS, IEEE) and applies them automatically to matplotlib, seaborn, and plotly figures. ## Core Features & Use Cases - Journal-specific export: Save figures with correct formats, DPI, and dimensions using save_for_journal() and verify compliance with check_figure_size(). - Colorblind-safe palettes: Apply Okabe-Ito, Wong, and Paul Tol palettes via assets/color_palettes.py, plus guidance on perceptually uniform and diverging colormaps. - Publication styling: Pre-configured matplotlib style files and style_presets.py helpers for fonts, sizes, and spine removal matching journal specs. - Use Case: A researcher finishing a Nature manuscript needs a multi-panel figure at 89 mm single-column width with lowercase bold panel labels, 1000 DPI line art, and colorblind-safe colors — the Skill configures the style, validates the size, and exports PDF/EPS in one workflow. ## Quick Start Ask the AI to create a publication-ready multi-panel figure for Nature using colorblind-safe colors and export it as PDF at the correct size and resolution.

Frequently Asked Questions about scientific-visualization

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I make a publication-ready figure in matplotlib?▼

Apply a publication style preset, size the figure to the target journal's column width (e.g., 3.5 inches for Nature single column), use colorblind-safe colors, label axes with units, remove top and right spines, and save as PDF at 300+ DPI using save_publication_figure().

What DPI and file format do journals require for figures?▼

Most journals require 300 DPI for photographs, 600 DPI for combination figures, and 1000 DPI for line art. Vector formats PDF or EPS are preferred for graphs, while TIFF or PNG are used for images; JPEG should never be used for plots.

Which color palettes are colorblind-friendly for scientific plots?▼

The Okabe-Ito palette is the most widely recommended categorical palette, distinguishable by all common forms of color blindness. For continuous data use perceptually uniform colormaps like viridis or cividis, and avoid red-green diverging maps and jet.

Should I use matplotlib, seaborn, or plotly for journal figures?▼

Matplotlib gives the most control for complex multi-panel figures, seaborn is best for statistical plots with automatic confidence intervals and faceting, and plotly suits interactive exploration with static export via write_image at scale=3 for roughly 300 DPI.

How do I check if my figure size meets Nature requirements?▼

Use the check_figure_size() function with journal='nature' to compare your figure against Nature's 89 mm single-column, 183 mm double-column, and 247 mm maximum height specifications. It reports compliance and the detected column type.

Why does my figure look blurry or unreadable after submission?▼

Blurriness usually comes from saving raster images below 300 DPI or using JPEG compression. Unreadable text means fonts were below 6-7 pt at final print size; always design figures at their exact final dimensions and export as vector PDF when possible.

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