Chart Image Generator

A skill for generating publication-quality chart images from research data using Python visualization libraries. Covers chart type selection, styling for academic journals, multi-panel layouts, color accessibility, and export at the correct resolution and format for submission.

Overview

Creating figures for academic publications requires more than just plotting data. Journals have specific requirements for resolution (typically 300-600 DPI), file format (TIFF, EPS, PDF, or high-resolution PNG), font sizes (often 8-12pt in the final printed figure), line weights, and color accessibility. This skill automates the production of figures that meet these standards, reducing the time researchers spend on manual formatting and ensuring consistency across all figures in a manuscript.

The skill supports common chart types used in academic research: scatter plots, bar charts, line plots, box plots, violin plots, heatmaps, forest plots, Kaplan-Meier curves, and multi-panel composite figures. All examples use matplotlib and seaborn with a custom academic styling configuration.

Academic Figure Styling

Journal-Ready Style Configuration

import matplotlib.pyplot as plt
import matplotlib as mpl

def set_academic_style():