Chart Image Generator

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

# 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
}

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=7)

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

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

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