"""Plot synthesis-feasibility score distributions: pre-BO vs post-BO.

Mirrors the D/L distribution figures but compares two ablation workflows
instead of D vs L:
  * pre-BO  = Chroma+ProteinMPNN, no Bayesian optimization
              (ablation/{target}/chroma_proteinmpnn/all_results.csv)
  * post-BO = full Chroma+ProteinMPNN+BO workflow
              (ablation/{target}/chroma_proteinmpnn_bo/all_bo_results.csv)

x-axis = synthesis feasibility score (bo.scoring.synthesis_metrics, same as
the ablation study). Targets PDL1/MDM2/IL23R. Same buried-pose filter and
Nature style as the other benchmark figures. Per-target sample sizes differ
(process-level), so histograms are density-normalised and both n are shown.
"""
from pathlib import Path
import sys

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd


ROOT = Path(__file__).resolve().parents[2]
if str(ROOT) not in sys.path:
    sys.path.insert(0, str(ROOT))
SCRIPT_DIR = Path(__file__).resolve().parent
if str(SCRIPT_DIR) not in sys.path:
    sys.path.insert(0, str(SCRIPT_DIR))

from plot_ablation_dl_figures import (  # noqa: E402
    BENCH,
    PALETTE,
    TARGETS,
    _load_bad_pose_keys,
    apply_nature_style,
    save_pub_fig,
)


PRE_FLOW, PRE_FNAME = "chroma_proteinmpnn", "all_results.csv"
POST_FLOW, POST_FNAME = "chroma_proteinmpnn_bo", "all_bo_results.csv"


def _load_ablation(target, flow, fname, filter_poses=True):
    path = BENCH / "ablation" / target / flow / fname
    df = pd.read_csv(path)
    if filter_poses:
        # Only meaningful for the full Chroma+ProteinMPNN pool (pose ids 1-50).
        # The BO result table uses its own selected-pose ids (1-7) that do not
        # share the buried-pose inventory's indexing, so it must NOT be filtered.
        bad = _load_bad_pose_keys("D_peptide")
        if bad:
            keys = list(zip(
                [target] * len(df),
                df["length"].astype(int),
                df["pose"].astype(int),
            ))
            df = df.loc[[k not in bad for k in keys]].copy()
    return df.dropna(subset=["synthesis_feasibility_score"])


def plot_bo_synthesis_distribution():
    apply_nature_style(font_size=8)
    from scipy import stats as sp_stats

    fig, axes = plt.subplots(1, len(TARGETS), figsize=(9.2, 2.8))

    for idx, target in enumerate(TARGETS):
        ax = axes[idx]
        pre = _load_ablation(target, PRE_FLOW, PRE_FNAME, filter_poses=True)
        post = _load_ablation(target, POST_FLOW, POST_FNAME, filter_poses=False)
        ps = pre["synthesis_feasibility_score"].to_numpy()
        qs = post["synthesis_feasibility_score"].to_numpy()

        bins = np.linspace(
            min(ps.min(), qs.min()),
            max(np.percentile(ps, 99), np.percentile(qs, 99)),
            50,
        )

        ax.hist(
            ps, bins=bins, density=True, alpha=0.55,
            color=PALETTE["orange"], label="Pre-BO (Chroma+ProteinMPNN)",
        )
        ax.hist(
            qs, bins=bins, density=True, alpha=0.55,
            color=PALETTE["blue_main"], label="Post-BO (+BO)",
        )

        ks_stat, ks_p = sp_stats.ks_2samp(ps, qs)

        ax.axvline(np.median(ps), color=PALETTE["orange"], ls="--", lw=0.8)
        ax.axvline(np.median(qs), color=PALETTE["blue_main"], ls="--", lw=0.8)

        ax.set_xlabel(f"{target} — Synthesis feasibility score")
        if idx == 0:
            ax.set_ylabel("Density")
        ptxt = f"p < {ks_p:.1e}" if ks_p < 0.001 else f"p = {ks_p:.3f}"
        ax.text(
            0.03, 0.95,
            f"pre  $n$ = {len(ps)}\npost $n$ = {len(qs)}\n"
            f"KS = {ks_stat:.3f}\n{ptxt}",
            transform=ax.transAxes, ha="left", va="top",
            fontsize=7, color=PALETTE["neutral_dark"],
            bbox=dict(boxstyle="round,pad=0.3", fc="white",
                      ec=PALETTE["neutral_light"], lw=0.5),
        )
        if idx == 0:
            ax.legend(loc="upper right", fontsize=7)

    save_pub_fig(fig, "fig_bo_synthesis_distribution")


def main():
    plot_bo_synthesis_distribution()


if __name__ == "__main__":
    main()