Multi-Panel Patchwork Composite Figure

#!/usr/bin/env python3
"""FigureCraft Chart Engine: Multi-Panel Patchwork Composite Figure (c15_patchwork)."""

import os
import sys
import argparse
from pathlib import Path
from typing import List, Dict, Optional
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import stats

try:
    from engines.styles import apply_style, get_palette
except ImportError:
    sys.path.insert(0, str(Path(__file__).resolve().parents[2]))
    from engines.styles import apply_style, get_palette

CHART_ID = "c15_patchwork"


def add_panel_letter(ax, letter: str):
    """Add bold publication subpanel letter (a, b, c) in top-left corner."""
    ax.text(
        -0.12,
        1.08,
        letter,
        transform=ax.transAxes,
        fontsize=11.0,
        fontweight="bold",
        va="top",
        ha="left",
        color="#111827",
    )


def render(
    data_path: Optional[str] = None,
    style: str = "nature",
    output_dir: str = "output",
    formats: Optional[List[str]] = None,
    dpi: int = 600,
) -> Dict[str, str]:
    if formats is None:
        formats = ["svg", "pdf", "png", "tiff"]
    if data_path is None:
        data_path = str(Path(__file__).resolve().parents[1] / "data" / f"{CHART_ID}.csv")

    apply_style(style)
    palette = get_palette(style)
    df = pd.read_csv(data_path)

    fig = plt.figure(figsize=(7.5, 6.2))
    gs = fig.add_gridspec(nrows=2, ncols=2, height_ratios=[1.0, 1.2], hspace=0.36, wspace=0.30)

    # Panel A: Summary Bar / Point Comparison (Top-Left)
    ax_a = fig.add_subplot(gs[0, 0])
    df_a = df[df["panel"] == "A"]
    groups_a = list(df_a["category"].unique())
    for i, grp in enumerate(groups_a):
        vals = df_a[df_a["category"] == grp]["y_val"].values
        mean_v = np.mean(vals)
        sem_v = stats.sem(vals) if len(vals) > 1 else 0.0
        color = palette[i % len(palette)]
        ax_a.bar(i, mean_v, yerr=sem_v, color=color, alpha=0.65, edgecolor=color, width=0.5, capsize=3.0)
        np.random.seed(42 + i)
        jitter = np.random.uniform(-0.12, 0.12, len(vals))
        ax_a.scatter(i + jitter, vals, color=color, s=20, edgecolors="#FFFFFF", lw=0.4, zorder=3)
    ax_a.set_xticks(range(len(groups_a)))
    ax_a.set_xticklabels([g.replace("_", " ") for g in groups_a], fontsize=7.5)
    ax_a.set_ylabel("Response Signal (a.u.)", fontsize=7.5)
    ax_a.set_title("Target Expression", fontsize=8.5, pad=6)
    add_panel_letter(ax_a, "a")

    # Panel B: Correlation / Multi-Variable Fit (Top-Right)
    ax_b = fig.add_subplot(gs[0, 1])
    df_b = df[df["panel"] == "B"]
    cats_b = list(df_b["category"].unique())
    for i, cat in enumerate(cats_b):
        sub_b = df_b[df_b["category"] == cat]
        x_b = sub_b["x_val"].values
        y_b = sub_b["y_val"].values
        color = palette[(i + 2) % len(palette)]
        ax_b.scatter(x_b, y_b, color=color, label=cat.replace("_", " "), s=24, alpha=0.8, edgecolors="#FFFFFF", lw=0.4)
        if len(x_b) >= 2:
            sl, inter, _, _, _ = stats.linregress(x_b, y_b)
            x_seq = np.linspace(x_b.min(), x_b.max(), 50)
            ax_b.plot(x_seq, sl * x_seq + inter, color=color, lw=1.2, linestyle="--")
    ax_b.set_xlabel("Biomarker X ($μM$)", fontsize=7.5)
    ax_b.set_ylabel("Activity Metric ($Y$)", fontsize=7.5)
    ax_b.set_title("Dose-Dependent Correlation", fontsize=8.5, pad=6)
    ax_b.legend(loc="upper right", frameon=False, fontsize=6.5)
    add_panel_letter(ax_b, "b")

    # Panel C: Longitudinal Progression Curve (Bottom Span)
    ax_c = fig.add_subplot(gs[1, :])
    df_c = df[df["panel"] == "C"]
    groups_c = list(df_c["group"].unique())
    for i, grp in enumerate(groups_c):
        sub_c = df_c[df_c["group"] == grp].sort_values("x_val")
        x_c = sub_c["x_val"].values
        y_c = sub_c["y_val"].values
        color = palette[(i + 4) % len(palette)]
        ax_c.plot(x_c, y_c, marker="o" if i == 0 else "s", color=color, lw=1.8, label=grp.replace("_", " "), markersize=4.5)
        # Add 95% CI synthetic band
        ci_w = 2.5
        ax_c.fill_between(x_c, y_c - ci_w, y_c + ci_w, color=color, alpha=0.15)
    ax_c.set_xlabel("Time Progression (Hours)", fontsize=7.5)
    ax_c.set_ylabel("Efficacy Score (%)", fontsize=7.5)
    ax_c.set_title("Longitudinal Trajectory Dynamics Across Experimental Cohorts", fontsize=8.5, pad=6)
    ax_c.legend(loc="lower right", frameon=False, fontsize=7.0)
    add_panel_letter(ax_c, "c")

    fig.suptitle("Main Manuscript Multi-Panel Composite Figure", fontsize=10.0, fontweight="bold", y=0.99)

    os.makedirs(output_dir, exist_ok=True)
    generated_files = {}
    for fmt in formats:
        out_path = os.path.join(output_dir, f"{CHART_ID}_{style}.{fmt}")
        fig.savefig(out_path, format=fmt, dpi=dpi, bbox_inches="tight")
        generated_files[fmt] = str(Path(out_path).resolve())

    plt.close(fig)
    return generated_files


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Render Multi-Panel Patchwork Composite Figure")
    parser.add_argument("--data", type=str, default=None, help="Path to input CSV dataset")
    parser.add_argument("--style", type=str, default="nature", choices=["nature", "cell", "lancet", "ieee", "economist"], help="Journal style pack")
    parser.add_argument("--output-dir", type=str, default="output", help="Output directory")
    parser.add_argument("--formats", type=str, default="svg,pdf,png,tiff", help="Comma-separated export formats")
    parser.add_argument("--dpi", type=int, default=600, help="Raster DPI")
    args = parser.parse_args()

    fmt_list = [f.strip() for f in args.formats.split(",") if f.strip()]
    results = render(args.data, style=args.style, output_dir=args.output_dir, formats=fmt_list, dpi=args.dpi)
    for fmt, path in results.items():
        print(f"Generated {fmt.upper()}: {path}")