chart-image-generator

name	chart-image-generator
description	Generate publication-quality chart images from research data
metadata	{"openclaw":{"emoji":"📉","category":"analysis","subcategory":"dataviz","keywords":["chart generation","data visualization","matplotlib","publication figures","scientific plots","graph export"],"source":"wentor-research-plugins"}}

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():
    """
    Configure matplotlib for publication-quality figures.
    Matches common requirements for Nature, Science, PLOS, IEEE journals.
    """
    plt.rcParams.update({
        # Font settings
        'font.family': 'sans-serif',
        'font.sans-serif': ['Arial', 'Helvetica', 'DejaVu Sans'],
        'font.size': 8,
        'axes.titlesize': 9,
        'axes.labelsize': 8,
        'xtick.labelsize': 7,
        'ytick.labelsize': 7,
        'legend.fontsize': 7,

        # Line and marker settings
        'lines.linewidth': 1.0,
        'lines.markersize': 4,
        'axes.linewidth': 0.5,
        'xtick.major.width': 0.5,
        'ytick.major.width': 0.5,

        # Grid and background
        'axes.grid': False,
        'axes.facecolor': 'white',
        'figure.facecolor': 'white',

        # Legend
        'legend.frameon': False,
        'legend.borderpad': 0.3,

        # Save settings
        'savefig.dpi': 300,
        'savefig.bbox': 'tight',
        'savefig.pad_inches': 0.05,

        # Use Type 1 fonts for EPS/PDF (required by many journals)
        'pdf.fonttype': 42,
        'ps.fonttype': 42,
    })

# Common journal figure widths (in inches):
SINGLE_COLUMN = 3.5   # ~89mm (Nature, Science, PLOS)
DOUBLE_COLUMN = 7.0   # ~178mm
ONE_AND_HALF = 5.5    # ~140mm

Accessible Color Palettes

# Colorblind-safe palettes for academic figures
PALETTES = {
    'categorical_8': [
        '#332288', '#88CCEE', '#44AA99', '#117733',
        '#999933', '#DDCC77', '#CC6677', '#882255'
    ],  # Tol's qualitative palette

    'sequential': 'viridis',  # Perceptually uniform

    'diverging': 'RdBu_r',   # Red-Blue diverging

    'binary': ['#0072B2', '#D55E00'],  # Blue and vermilion
}

Chart Type Selection Guide

Data Pattern	Recommended Chart	When to Use
Distribution of one variable	Histogram, KDE, violin	Showing data spread
Comparing groups	Box plot, violin, bar + error bars	Group differences
Two continuous variables	Scatter plot	Correlation, regression
Trends over time	Line plot	Time series, longitudinal
Proportions	Stacked bar, pie (sparingly)	Composition
Correlation matrix	Heatmap	Many variable pairs
Effect sizes + CIs	Forest plot	Meta-analysis, multi-model
Survival data	Kaplan-Meier curve	Time-to-event

Generating Common Academic Charts

Scatter Plot with Regression Line

import numpy as np
import seaborn as sns

def scatter_with_regression(x, y, xlabel, ylabel, title, output_path,
                            groups=None, group_label=None):
    """
    Create a scatter plot with regression line and confidence interval.
    """
    set_academic_style()
    fig, ax = plt.subplots(figsize=(SINGLE_COLUMN, SINGLE_COLUMN * 0.8))

    if groups is not None:
        for group_val in sorted(set(groups)):
            mask = groups == group_val
            ax.scatter(x[mask], y[mask], s=15, alpha=0.7, label=group_val)
        ax.legend(title=group_label)
    else:
        ax.scatter(x, y, s=15, alpha=0.7, color=PALETTES['binary'][0])

    # Add regression line
    from scipy import stats
    slope, intercept, r, p, se = stats.linregress(x, y)
    x_line = np.linspace(x.min(), x.max(), 100)
    ax.plot(x_line, slope * x_line + intercept, color='#CC6677',
            linewidth=1.0, linestyle='--')

    # Annotate with statistics
    ax.text(0.05, 0.95, f'r = {r:.3f}\np = {p:.3f}',
            transform=ax.transAxes, verticalalignment='top', fontsize=)

    ax.set_xlabel(xlabel)
    ax.set_ylabel(ylabel)
    ax.set_title(title)

    fig.savefig(output_path, dpi=, bbox_inches=)
    plt.close(fig)
     output_path

Multi-Panel Composite Figure

def create_multipanel_figure(panels: list, ncols: int = 2,
                              output_path: str = 'figure.pdf'):
    """
    Create a multi-panel figure with automatic panel labels (A, B, C, ...).

    Args:
        panels: List of dicts with 'plot_func', 'args', 'title'
        ncols: Number of columns
        output_path: Output file path
    """
    set_academic_style()
    nrows = int(np.ceil(len(panels) / ncols))
    fig, axes = plt.subplots(nrows, ncols,
                              figsize=(DOUBLE_COLUMN, 3.0 * nrows))
    axes = axes.flatten() if hasattr(axes, 'flatten') else [axes]

    for i, (ax, panel) in enumerate(zip(axes, panels)):
        panel['plot_func'](ax, **panel.get('args', {}))
        # Add panel label (A, B, C, ...)
        ax.text(-0.15, 1.08, chr(65 + i), transform=ax.transAxes,
                fontsize=11, fontweight='bold', va='top')
        if 'title' in panel:
            ax.set_title(panel['title'])

    # Hide unused panels
    for ax in axes[len(panels):]:
        ax.set_visible(False)

    fig.tight_layout()
    fig.savefig(output_path, dpi=300, bbox_inches='tight')
    plt.close(fig)
    return output_path

Export Specifications by Journal

Journal / Publisher	Format	DPI	Max Width	Color Mode
Nature	TIFF, EPS, PDF	300	180mm	RGB
Science	EPS, PDF	300	174mm	RGB
PLOS	TIFF, EPS	300	174mm	RGB
IEEE	EPS, PDF, PNG	300	3.5in (1-col)	RGB or CMYK
Elsevier	TIFF, EPS, PDF	300-600	190mm	RGB or CMYK
Springer	TIFF, EPS, PDF	300	174mm	RGB or CMYK

Export Function

def export_figure(fig, basename: str, formats=('pdf', 'png', 'tiff'), dpi=300):
    """Export a figure in multiple formats for journal submission."""
    paths = []
    for fmt in formats:
        path = f"{basename}.{fmt}"
        fig.savefig(path, format=fmt, dpi=dpi, bbox_inches='tight',
                    facecolor='white', edgecolor='none')
        paths.append(path)
    return paths

Best Practices

• Always use vector formats (PDF, EPS) for line art and plots; raster (TIFF, PNG) only when required.
• Set figure dimensions to the exact column width of your target journal.
• Use the same font and size across all figures in a manuscript for consistency.
• Test figures in grayscale to ensure they remain readable without color.
• Include all figure generation code in your supplementary materials for reproducibility.
• Label axes with units (e.g., "Temperature (K)") and avoid abbreviations unless defined.

References

• Rougier, N. P., Droettboom, M., & Borne, P. E. (2014). Ten Simple Rules for Better Figures. PLoS Computational Biology, 10(9).
• Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
• Wong, B. (2011). Color Blindness. Nature Methods, 8(6), 441.