"""Plot FuzzyScore distributions for D/L-peptide designs.

Mirrors ``plot_dl_synthesis_distribution`` but swaps the x-axis to the
``bo.scoring.FuzzyScore`` geometric-mean desirability score. Per the README
spec, FuzzyScore combines per-property desirability functions (here:
ProteinMPNN NLL and SWI solubility) into a unified score in (0, 1].

Desirability control points are anchored to the combined D+L 10/90
percentiles of each property (per target); weights are equal (importance=1,
i.e. pure desirability in the geometric mean). Same targets (PDL1, MDM2,
IL23R), same buried-pose filter, same D/L down-sampling as the ProteinMPNN
and synthesis figures, so all three are row-for-row comparable.

Tune ``DESIR_WEIGHT`` or the percentile anchors if a different trade-off is
desired.
"""
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
    PALETTE,
    TARGETS,
    _load_filtered_dl_df,
    apply_nature_style,
    save_pub_fig,
)
from bo.scoring import FuzzyScore, swi  # noqa: E402


DESIR_WEIGHT = 1.0          # importance per property (1 = pure desirability)
PCT_LOW, PCT_HIGH = 10, 90  # percentile anchors for the control points


def _desirability_controls(combined):
    """Build MPNN-NLL and SWI desirability control points from combined D+L."""
    mp_low, mp_high = np.percentile(combined["score"], [PCT_LOW, PCT_HIGH])
    sw_low, sw_high = np.percentile(combined["SWI"], [PCT_LOW, PCT_HIGH])
    # NLL: lower is better -> high desirability at the low end
    mpnn_points = [{"x": float(mp_low), "y": 1.0}, {"x": float(mp_high), "y": 0.0}]
    # SWI: higher is better -> high desirability at the high end
    swi_points = [{"x": float(sw_low), "y": 0.0}, {"x": float(sw_high), "y": 1.0}]
    return {"score": mpnn_points, "SWI": swi_points}


def _add_fuzzyscore(df, controls):
    """Attach the bo FuzzyScore to a per-design DataFrame."""
    df = df.copy()
    df["SWI"] = df["sequence"].map(swi)
    fs = FuzzyScore(df)
    out = fs.fuzzy_score(
        score=(controls["score"], DESIR_WEIGHT),
        SWI=(controls["SWI"], DESIR_WEIGHT),
    )
    return out


def plot_dl_fuzzyscore_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))
    rng = np.random.default_rng(42)

    for idx, target in enumerate(TARGETS):
        ax = axes[idx]
        d_df = _load_filtered_dl_df(target, "D_peptide")
        l_df = _load_filtered_dl_df(target, "L_peptide")
        d_df["SWI"] = d_df["sequence"].map(swi)
        l_df["SWI"] = l_df["sequence"].map(swi)

        # Match D count, identical sampling seed to the other D/L figures
        n_d = len(d_df)
        n_l = len(l_df)
        if n_l > n_d:
            l_df = l_df.sample(n=n_d, random_state=rng).copy()

        controls = _desirability_controls(pd.concat([d_df, l_df]))
        d_df = _add_fuzzyscore(d_df, controls)
        l_df = _add_fuzzyscore(l_df, controls)

        d_scores = d_df["FuzzyScore"].to_numpy()
        l_scores = l_df["FuzzyScore"].to_numpy()

        bins = np.linspace(
            min(d_scores.min(), l_scores.min()),
            max(np.percentile(d_scores, 99), np.percentile(l_scores, 99)),
            50,
        )

        ax.hist(
            d_scores, bins=bins, density=True, alpha=0.55,
            color=PALETTE["blue_main"], label="D-peptide",
        )
        ax.hist(
            l_scores, bins=bins, density=True, alpha=0.55,
            color=PALETTE["orange"], label="L-peptide",
        )

        ks_stat, ks_p = sp_stats.ks_2samp(d_scores, l_scores)

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

        ax.set_xlabel(f"{target} — FuzzyScore")
        if idx == 0:
            ax.set_ylabel("Density")
        ax.text(
            0.97, 0.95,
            f"$n$ = {n_d}\nKS = {ks_stat:.3f}\np < {ks_p:.1e}" if ks_p < 0.001
            else f"$n$ = {n_d}\nKS = {ks_stat:.3f}\np = {ks_p:.3f}",
            transform=ax.transAxes, ha="right", 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 left", fontsize=7)

    save_pub_fig(fig, "fig_dl_fuzzyscore_distribution")


def main():
    plot_dl_fuzzyscore_distribution()


if __name__ == "__main__":
    main()