from __future__ import annotations

import json
from pathlib import Path
from typing import List

import pandas as pd

from .utils import (
    family_label,
    fmt_float,
    fmt_sci,
    fmt_text,
    safe_read_csv,
    split_flags,
    warning_badges,
)


def export_results(df: pd.DataFrame, out_dir: str | Path) -> None:
    out_dir = Path(out_dir)
    out_dir.mkdir(parents=True, exist_ok=True)

    df.to_csv(out_dir / "rf_mode_pack_v5_1_all.csv", index=False)

    passed = df[df["hard_pass"]].copy()
    passed.sort_values(["balanced_v5", "delivered"], ascending=False).to_csv(
        out_dir / "rf_mode_pack_v5_1_passed.csv",
        index=False,
    )

    shortlist = passed[passed["candidate_role"] != ""].copy()
    shortlist_cols = [
        "candidate_id", "type", "family", "candidate_role",
        "freq_GHz", "Eacc_MVpm", "L_m", "Vc_MV",
        "delivered", "P_wall_peak_kW", "P_wall_avg_kW",
        "pulse_length_ns", "rep_rate_Hz", "duty_cycle",
        "deltaT_pulse", "thermal_margin",
        "BDR_eff", "BDR_margin",
        "R_f", "R_phi", "R_c", "R_tol",
        "C_src", "C_cool",
        "mat_rf_wall", "mat_insulator", "mat_thermal_shield", "mat_structural",
        "suggested_primary_material",
        "regime", "c_capture", "c_loss",
        "balanced_v5", "frontier_v5",
        "hard_pass", "warning_flags",
    ]
    shortlist[shortlist_cols].sort_values(
        ["family", "candidate_role", "balanced_v5"],
        ascending=[True, True, False],
    ).to_csv(out_dir / "rf_mode_pack_v5_1_shortlist_3x3.csv", index=False)

    family_summary = passed.groupby("family").agg(
        count=("family", "count"),
        median_delivered=("delivered", "median"),
        median_P_wall_avg_kW=("P_wall_avg_kW", "median"),
        median_deltaT=("deltaT_pulse", "median"),
        median_BDR=("BDR_eff", "median"),
        median_R_f=("R_f", "median"),
        median_R_phi=("R_phi", "median"),
        median_R_c=("R_c", "median"),
        median_R_tol=("R_tol", "median"),
        median_C_src=("C_src", "median"),
        median_C_cool=("C_cool", "median"),
        median_balanced_v5=("balanced_v5", "median"),
        median_frontier_v5=("frontier_v5", "median"),
    ).reset_index()
    family_summary.to_csv(out_dir / "rf_mode_pack_v5_1_family_summary.csv", index=False)

    rejected = df[~df["hard_pass"]].copy()
    reject_counts = (
        rejected.assign(fail_reasons_split=rejected["fail_reasons"].str.split("|"))
        .explode("fail_reasons_split")
        .dropna(subset=["fail_reasons_split"])
        .query("fail_reasons_split != ''")
        .groupby(["family", "fail_reasons_split"])
        .size()
        .reset_index(name="count")
        .rename(columns={"fail_reasons_split":  "reason"})
    )
    reject_counts.to_csv(out_dir / "rf_mode_pack_v5_1_reject_reasons.csv", index=False)

    with open(out_dir / "rf_mode_pack_v5_1_meta.json", "w", encoding="utf-8") as f:
        json.dump({
            "rows_total": int(len(df)),
            "rows_passed": int(df["hard_pass"].sum()),
            "rows_failed": int((~df["hard_pass"]).sum()),
        }, f, ensure_ascii=False, indent=2)


# =========================================================
# GLOBAL VIEWS
# =========================================================

def deduplicate_global_best(df: pd.DataFrame, score_col="balanced_v5") -> pd.DataFrame:
    if df.empty:
        return df.copy()
    s = df.sort_values([score_col, "delivered"], ascending=False).copy()
    return s.drop_duplicates(subset=["candidate_id"], keep="first")


def deduplicate_global_safest(df: pd.DataFrame) -> pd.DataFrame:
    if df.empty:
        return df.copy()
    s = df.sort_values(["R_tol", "C_cool", "C_src", "balanced_v5"], ascending=[False, False, False, False]).copy()
    return s.drop_duplicates(subset=["candidate_id"], keep="first")


def export_global_views(all_class_outputs: List[pd.DataFrame], out_dir: Path, reference_class: str) -> None:
    out_dir.mkdir(parents=True, exist_ok=True)

    concat_all = pd.concat(all_class_outputs, axis=0, ignore_index=True)
    passing = concat_all[concat_all["hard_pass"]].copy()
    passing.to_csv(out_dir / "rf_mode_pack_v5_1_global_concat_passed.csv", index=False)

    dedup_balanced = deduplicate_global_best(passing, score_col="balanced_v5")
    dedup_frontier = deduplicate_global_best(passing, score_col="frontier_v5")
    dedup_safest = deduplicate_global_safest(passing)

    dedup_balanced.to_csv(out_dir / "rf_mode_pack_v5_1_global_dedup_balanced.csv", index=False)
    dedup_frontier.to_csv(out_dir / "rf_mode_pack_v5_1_global_dedup_frontier.csv", index=False)
    dedup_safest.to_csv(out_dir / "rf_mode_pack_v5_1_global_dedup_safest.csv", index=False)

    ref = concat_all[
        (concat_all["feasibility_class"] == reference_class) &
        (concat_all["hard_pass"])
    ].copy()
    ref.to_csv(out_dir / f"rf_mode_pack_v5_1_reference_{reference_class}.csv", index=False)

    fam_bal = (
        dedup_balanced.groupby("family").agg(
            count=("family", "count"),
            median_delivered=("delivered", "median"),
            median_P_wall_avg_kW=("P_wall_avg_kW", "median"),
            median_deltaT=("deltaT_pulse", "median"),
            median_BDR=("BDR_eff", "median"),
            median_R_f=("R_f", "median"),
            median_R_phi=("R_phi", "median"),
            median_R_c=("R_c", "median"),
            median_R_tol=("R_tol", "median"),
            median_C_src=("C_src", "median"),
            median_C_cool=("C_cool", "median"),
            median_balanced_v5=("balanced_v5", "median"),
            median_frontier_v5=("frontier_v5", "median"),
        ).reset_index().sort_values("median_balanced_v5", ascending=False)
        if not dedup_balanced.empty else pd.DataFrame()
    )
    fam_bal.to_csv(out_dir / "rf_mode_pack_v5_1_global_family_summary_balanced.csv", index=False)

    with open(out_dir / "rf_mode_pack_v5_1_global_meta.json", "w", encoding="utf-8") as f:
        json.dump({
            "reference_class": reference_class,
            "rows_concat_passed": int(len(passing)),
            "rows_dedup_balanced": int(len(dedup_balanced)),
            "rows_dedup_frontier": int(len(dedup_frontier)),
            "rows_dedup_safest": int(len(dedup_safest)),
            "rows_reference_class": int(len(ref)),
        }, f, ensure_ascii=False, indent=2)


# =========================================================
# REPORT
# =========================================================

def top_line(df: pd.DataFrame, score_col: str, title: str) -> str:
    if df.empty:
        return f"- No candidate for {title}."
    row = df.sort_values([score_col, "delivered"], ascending=False).iloc[0]
    return (
        f"-  {title}: **{row['type']}** ({family_label(row['family'])}), "
        f"f={fmt_float(row['freq_GHz'],3)} GHz, "
        f"yield={fmt_sci(row['delivered'],2)}, "
        f"Pavg={fmt_float(row['P_wall_avg_kW'],2)} kW, "
        f"material={fmt_text(row.get('suggested_primary_material', ''))}, "
        f"regime={fmt_text(row['regime'])}, "
        f"warnings={warning_badges(row['warning_flags'])}."
    )


def safest_line(df: pd.DataFrame) -> str:
    if df.empty:
        return "- No safest candidate available."
    row = df.sort_values(["R_tol", "C_cool", "C_src", "balanced_v5"], ascending=[False, False, False, False]).iloc[0]
    return (
        f"- Global safest (deduplicated): **{row['type']}** ({family_label(row['family'])}), "
        f"Rtol={fmt_float(row['R_tol'],2)}, "
        f"Csrc={fmt_float(row['C_src'],2)}, "
        f"Ccool={fmt_float(row['C_cool'],2)}, "
        f"material={fmt_text(row.get('suggested_primary_material', ''))}, "
        f"warnings={warning_badges(row['warning_flags'])}."
    )


def family_summary_table(df: pd.DataFrame) -> List[str]:
    if df.empty:
        return ["_No family summary available._"]

    s = df.copy()
    if "median_balanced_v5" in s.columns:
        s = s.sort_values("median_balanced_v5", ascending=False)
    elif "count" in s.columns:
        s = s.sort_values("count", ascending=False)

    lines = [
        "| Family | Count | Median Yield | Median Pavg [kW] | Median ΔT [K] | Median BDR | Median Rf | Median Rφ | Median Rc | Median Rtol | Median Csrc | Median Ccool | Median Balanced | Median Frontier |",
        "|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|",
    ]
    for _, r in s.iterrows():
        lines.append(
            f"| {family_label(r['family'])} | {int(r['count'])} | "
            f"{fmt_sci(r['median_delivered'],2)} | "
            f"{fmt_float(r['median_P_wall_avg_kW'],2)} | "
            f"{fmt_float(r['median_deltaT'],2)} | "
            f"{fmt_sci(r['median_BDR'],2)} | "
            f"{fmt_float(r['median_R_f'],2)} | "
            f"{fmt_float(r['median_R_phi'],2)} | "
            f"{fmt_float(r['median_R_c'],2)} | "
            f"{fmt_float(r['median_R_tol'],2)} | "
            f"{fmt_float(r['median_C_src'],2)} | "
            f"{fmt_float(r['median_C_cool'],2)} | "
            f"{fmt_float(r['median_balanced_v5'],3)} | "
            f"{fmt_float(r['median_frontier_v5'],3)} |"
        )
    return lines


def shortlist_table(shortlist: pd.DataFrame) -> List[str]:
    if shortlist.empty:
        return ["_No shortlist candidates available._"]

    s = shortlist.copy().sort_values(["family", "candidate_role", "balanced_v5"], ascending=[True, True, False])
    lines = [
        "| Family | Type | Role | f [GHz] | Eacc [MV/m] | Vc [MV] | Yield | Pavg [kW] | ΔT [K] | BDR | Rtol | Csrc | Ccool | RF Wall | Insulator | Shield | Structural | Primary Material | Score | Regime | Warnings |",
        "|---|---|---|---:|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|---|---|---|---|---:|---|---|",
    ]
    for _, r in s.iterrows():
        score_to_show = r["frontier_v5"] if r["candidate_role"] == "aggressive" else r["balanced_v5"]
        lines.append(
            f"| {family_label(r['family'])} | {r['type']} | {fmt_text(r['candidate_role'])} | "
            f"{fmt_float(r['freq_GHz'],3)} | {fmt_float(r['Eacc_MVpm'],2)} | {fmt_float(r['Vc_MV'],2)} | "
            f"{fmt_sci(r['delivered'],2)} | {fmt_float(r['P_wall_avg_kW'],2)} | {fmt_float(r['deltaT_pulse'],2)} | "
            f"{fmt_sci(r['BDR_eff'],2)} | {fmt_float(r['R_tol'],2)} | {fmt_float(r['C_src'],2)} | {fmt_float(r['C_cool'],2)} | "
            f"{fmt_text(r.get('mat_rf_wall', ''))} | {fmt_text(r.get('mat_insulator', ''))} | "
            f"{fmt_text(r.get('mat_thermal_shield', ''))} | {fmt_text(r.get('mat_structural', ''))} | "
            f"{fmt_text(r.get('suggested_primary_material', ''))} | {fmt_float(score_to_show,3)} | "
            f"{fmt_text(r['regime'])} |  {warning_badges(r['warning_flags'])} |"
        )
    return lines


def reject_reason_table(df: pd.DataFrame, top_n=15) -> List[str]:
    if df.empty:
        return ["_No reject diagnostics available._"]
    s = df.copy().sort_values("count", ascending=False).head(top_n)
    lines = ["| Family | Reason | Count |", "|---|---|---:|"]
    for _, r in s.iterrows():
        lines.append(f"| {family_label(r['family'])} | {r['reason']} | {int(r['count'])} |")
    return lines


def warning_summary_global(passed: pd.DataFrame) -> List[str]:
    if passed.empty:
        return ["- No warning-bearing candidates."]
    warn = passed.copy()
    warn["warning_flags"] = warn["warning_flags"].fillna("")
    warn = warn[warn["warning_flags"] != ""]
    if warn.empty:
        return ["- No yellow warnings among qualified candidates."]

    exploded = (
        warn.assign(flag=warn["warning_flags"].str.split("|"))
        .explode("flag")
        .groupby(["family", "flag"])
        .size()
        .reset_index(name="count")
        .sort_values(["flag", "count"], ascending=[True, False])
    )

    lines, current_flag = [], None
    for _, r in exploded.iterrows():
        if r["flag"] != current_flag:
            current_flag = r["flag"]
            lines.append(f"- {current_flag}:")
        lines.append(f"  - {family_label(r['family'])}: {int(r['count'])}")
    return lines


def class_conclusion(passed: pd.DataFrame) -> str:
    if passed.empty:
        return "No viable candidates in this class."

    fam_counts = passed["family"].value_counts()
    top_family_count = fam_counts.index[0]
    top_count = int(fam_counts.iloc[0])

    fam_score = passed.groupby("family")["balanced_v5"].median().sort_values(ascending=False)
    top_family_score = fam_score.index[0]

    best = passed.sort_values(["balanced_v5", "delivered"], ascending=False).iloc[0]
    return (
        f"Dominant family by count: **{family_label(top_family_count)}** ({top_count} qualified). "
        f"Top family by median balanced score: **{family_label(top_family_score)}**. "
        f"Best candidate: **{best['type']}**, "
        f"yield={fmt_sci(best['delivered'],2)}, "
        f"Pavg={fmt_float(best['P_wall_avg_kW'],2)} kW, "
        f"material={fmt_text(best.get('suggested_primary_material', ''))}, "
        f"regime={fmt_text(best['regime'])}."
    )


def build_class_section(base_dir: Path, class_name: str) -> List[str]:
    class_dir = base_dir / class_name
    passed = safe_read_csv(class_dir / "rf_mode_pack_v5_1_passed.csv")
    shortlist = safe_read_csv(class_dir / "rf_mode_pack_v5_1_shortlist_3x3.csv")
    family = safe_read_csv(class_dir / "rf_mode_pack_v5_1_family_summary.csv")
    rejects = safe_read_csv(class_dir / "rf_mode_pack_v5_1_reject_reasons.csv")

    lines = [
        f"## {class_name}",
        "",
        f"- Qualified designs: **{len(passed)}**",
        f"- Conclusion: {class_conclusion(passed)}",
        "",
        "### Shortlist (safest / balanced / aggressive)",
        "",
    ]
    lines.extend(shortlist_table(shortlist))
    lines += ["", "### Family summary", ""]
    lines.extend(family_summary_table(family))
    lines += ["", "### Warning diagnostics", ""]
    lines.extend(warning_summary_global(passed))
    lines += ["", "### Main rejection reasons", ""]
    lines.extend(reject_reason_table(rejects))
    lines.append("")
    return lines


def build_global_section(base_dir: Path, reference_class: str) -> List[str]:
    gdir = base_dir / "global_views"

    dedup_bal = safe_read_csv(gdir / "rf_mode_pack_v5_1_global_dedup_balanced.csv")
    dedup_front = safe_read_csv(gdir / "rf_mode_pack_v5_1_global_dedup_frontier.csv")
    dedup_safe = safe_read_csv(gdir / "rf_mode_pack_v5_1_global_dedup_safest.csv")
    fam_bal = safe_read_csv(gdir / "rf_mode_pack_v5_1_global_family_summary_balanced.csv")
    ref = safe_read_csv(gdir / f"rf_mode_pack_v5_1_reference_{reference_class}.csv")

    lines = [
        "# RF Mode Pack v5.1 — Engineering  Pass Report",
        "",
        "## Executive summary",
        "",
        top_line(dedup_bal, "balanced_v5", "Global best balanced (deduplicated)"),
        top_line(dedup_front, "frontier_v5", "Global best frontier (deduplicated)"),
        safest_line(dedup_safe),
        "",
        f"- Reference class for stable engineering view: **{reference_class}**",
        "",
        "## Global family ranking (deduplicated balanced view)",
        "",
    ]
    lines.extend(family_summary_table(fam_bal))
    lines.append("")

    if not ref.empty:
        lines += [
            f"## Reference-class snapshot ({reference_class})",
            "",
            top_line(ref, "balanced_v5", "Best balanced inside reference class"),
            top_line(ref, "frontier_v5", "Best frontier-scored candidate inside reference class"),
            "",
            "### Warning diagnostics in reference class",
            "",
        ]
        lines.extend(warning_summary_global(ref))
        lines.append("")

    return lines


def generate_report(base_dir: str | Path, output_path: str | Path, class_order: List[str], reference_class: str) -> None:
    base_dir = Path(base_dir)
    output_path = Path(output_path)

    lines = []
    lines.extend(build_global_section(base_dir, reference_class))
    for class_name in class_order:
        lines.extend(build_class_section(base_dir, class_name))

    output_path.write_text("\n".join(lines), encoding="utf-8")