"""
07_pipeline_health.py – Performance-Analyse der internen Pipeline-Stufen
=======================================================================
Extrahiert Metriken aus pipeline.log:
  - Latenz pro Stage (avg_latency_ms)
  - Durchsatz pro Stage (throughput_eps)
  - Events verarbeitet/verworfen
"""

import json
import re
import sys
from pathlib import Path

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

sys.path.insert(0, ".")
from config import *

LOG_PATTERN = re.compile(r"^(\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}) \[(.*?)\] (\{.*\})$")


def parse_pipeline_log(run_key: str) -> pd.DataFrame:
    """Liest pipeline.log eines Laufs und extrahiert die Health-Metriken."""
    _, run_dir = RUNS[run_key]
    log_path = ROOT / run_dir / "pipeline.log"

    if not log_path.exists():
        return pd.DataFrame()

    records = []
    with open(log_path, "r") as f:
        for line in f:
            match = LOG_PATTERN.match(line.strip())
            if match:
                log_ts, stage_label, json_str = match.groups()
                try:
                    data = json.loads(json_str)
                    data["log_timestamp"] = pd.to_datetime(
                        log_ts, format="%Y/%m/%d %H:%M:%S"
                    )
                    data["run"] = run_key
                    records.append(data)
                except json.JSONDecodeError:
                    continue

    return pd.DataFrame(records)


def main():
    print_section("07 – Pipeline Health & Latency Analysis")

    all_data = []

    for run_key in RUNS:
        df_run = parse_pipeline_log(run_key)
        if not df_run.empty:
            print(f"  {run_key}: {len(df_run)} Health-Einträge extrahiert.")
            csv_path = OUTPUT_DIR / f"health_{run_key}.csv"
            df_run.to_csv(csv_path, index=False)
            all_data.append(df_run)

    if not all_data:
        print("  Keine pipeline.log Dateien gefunden.")
        return

    df = pd.concat(all_data, ignore_index=True)

    # --- Statistik berechnen ---
    stats_df = (
        df.groupby("stage_name")
        .agg(
            {
                "avg_latency_ms": ["median", "mean", "std", "max"],
                "throughput_eps": ["median", "max"],
                "events_processed": "sum",
                "events_dropped": "sum",
            }
        )
        .round(3)
    )

    print("\nStatistik der Pipeline-Stages (Latenz in ms):")
    print(stats_df.to_string())

    stats_df.to_csv(OUTPUT_DIR / "07_pipeline_health_stats.csv")

    # --- Visualisierung 1: Latenzen pro Stage ---
    plt.figure(figsize=(12, 7))
    sns.boxplot(data=df, x="stage_name", y="avg_latency_ms", palette="viridis")
    plt.yscale("symlog", linthresh=1)
    plt.title("Latenz-Verteilung der Pipeline-Komponenten (REQ-NF06)")
    plt.ylabel("Durchschnittliche Latenz [ms] (Log-Skala)")
    plt.xlabel("Pipeline Stage")
    plt.xticks(rotation=15)
    plt.grid(True, which="both", axis="y", alpha=0.3)
    save_fig("07_pipeline_latency_boxplot")

    # --- Visualisierung 2: Durchsatz pro Stage ---
    plt.figure(figsize=(12, 6))
    sns.barplot(
        data=df,
        x="stage_name",
        y="throughput_eps",
        estimator="median",
        errorbar="sd",
        palette="magma",
    )
    plt.title("Medianer Durchsatz der Pipeline-Komponenten")
    plt.ylabel("Events pro Sekunde [EPS]")
    plt.xlabel("Pipeline Stage")
    plt.xticks(rotation=15)
    plt.grid(True, axis="y", alpha=0.3)
    save_fig("07_pipeline_throughput_bar")

    with open(OUTPUT_DIR / "07_health_interpretation.txt", "w") as f:
        f.write("Pipeline Health & Performance Summary\n")
        f.write("=" * 40 + "\n\n")
        f.write(stats_df.to_string())
        f.write("\n\nInterpretation:\n")
        f.write(
            "- Die Stage mit der höchsten Latenz ist der Flaschenhals der Pipeline.\n"
        )
        f.write(
            "- 'events_dropped' > 0 deutet auf aktives Load-Shedding hin (REQ-NF04).\n"
        )

    print(f"\n→ Ergebnisse gespeichert in {OUTPUT_DIR}/07_pipeline_*")


if __name__ == "__main__":
    main()