"""
Report Analyzer
Reads the CSV output from ip_geo_report.py and generates:
  1. Monthly total visit count with MoM change
  2. Monthly unique country count with MoM change
  3. Top countries per month
  4. Export to CSV + print to console
"""

import os
import csv
import argparse
from datetime import datetime
from collections import defaultdict
from typing import Dict, List, Tuple, Optional

try:
    import matplotlib.pyplot as plt
    import matplotlib.ticker as mticker
    from matplotlib.gridspec import GridSpec
    HAS_MATPLOTLIB = True
except ImportError:
    HAS_MATPLOTLIB = False

# ─── Config ───────────────────────────────────────────────────────────────────

REPORT_DIR      = "./report"
OUTPUT_DIR      = "./analysis"
TOP_N_COUNTRIES = 10        # top N countries to show per month

# ─── Data Loader ──────────────────────────────────────────────────────────────

def load_report(path: str) -> List[dict]:
    """
    Load report CSV generated by ip_geo_report.py
    Expected columns: Country, Date, Visit Count
    """
    rows = []
    with open(path, newline="", encoding="utf-8") as f:
        reader = csv.DictReader(f)
        for row in reader:
            try:
                rows.append({
                    "country":    row["Country"].strip(),
                    "date":       row["Date"].strip(),
                    "count":      int(row["Visit Count"]),
                    "year_month": parse_year_month(row["Date"]),
                })
            except (KeyError, ValueError):
                continue
    print(f"Loaded {len(rows):,} rows from {path}")
    return rows


def parse_year_month(date_str: str) -> str:
    """
    Parse date string to YYYY-MM regardless of format:
    - 2023-08-01  → 2023-08
    - 20230801    → 2023-08
    - 2023/08/01  → 2023-08
    - 2023-08-01 00:00:00 → 2023-08
    """
    date_str = date_str.strip().split(" ")[0]   # strip time part
    date_str = date_str.replace("/", "-")        # normalize slashes

    if "-" in date_str:
        # YYYY-MM-DD
        return date_str[:7]
    elif len(date_str) == 8:
        # YYYYMMDD
        return f"{date_str[:4]}-{date_str[4:6]}"
    else:
        # fallback — try common formats
        for fmt in ("%Y%m%d", "%Y-%m-%d", "%d/%m/%Y", "%m/%d/%Y"):
            try:
                return datetime.strptime(date_str, fmt).strftime("%Y-%m")
            except ValueError:
                continue
        return date_str[:7]   # last resort
    """Find the most recent report_*.csv in report dir."""
    files = [
        f for f in os.listdir(report_dir)
        if f.startswith("report_") and f.endswith(".csv")
    ]
    if not files:
        return None
    files.sort(reverse=True)
    return os.path.join(report_dir, files[0])


# ─── Aggregation ──────────────────────────────────────────────────────────────

def aggregate_monthly(rows: List[dict]) -> Dict[str, dict]:
    """
    Aggregate by year_month:
    {
      "2025-01": {
        "total_visits":   12345,
        "countries":      {"United States": 5000, "Taiwan": 3000, ...},
        "unique_countries": 42
      }
    }
    """
    monthly: Dict[str, dict] = defaultdict(lambda: {
        "total_visits": 0,
        "countries":    defaultdict(int)
    })

    for row in rows:
        ym = row["year_month"]
        monthly[ym]["total_visits"]          += row["count"]
        monthly[ym]["countries"][row["country"]] += row["count"]

    # Compute unique country count per month
    result = {}
    for ym in sorted(monthly.keys()):
        data = monthly[ym]
        result[ym] = {
            "total_visits":     data["total_visits"],
            "countries":        dict(data["countries"]),
            "unique_countries": len(data["countries"]),
        }

    return result


# ─── MoM Calculation ──────────────────────────────────────────────────────────

def calc_mom(current: int, previous: int) -> Tuple[float, str]:
    """
    Returns (pct_change, formatted_string)
    e.g. (12.5, '+12.5%') or (-3.2, '-3.2%') or (0.0, 'N/A')
    """
    if previous == 0:
        return 0.0, "N/A"
    pct = (current - previous) / previous * 100
    sign = "+" if pct >= 0 else ""
    return pct, f"{sign}{pct:.1f}%"


def build_monthly_stats(monthly: Dict[str, dict]) -> List[dict]:
    """
    Build a flat list of monthly stats with MoM columns.
    """
    months  = sorted(monthly.keys())
    records = []

    for i, ym in enumerate(months):
        prev_ym   = months[i - 1] if i > 0 else None
        curr_data = monthly[ym]
        prev_data = monthly[prev_ym] if prev_ym else None

        curr_visits    = curr_data["total_visits"]
        curr_countries = curr_data["unique_countries"]
        prev_visits    = prev_data["total_visits"]    if prev_data else 0
        prev_countries = prev_data["unique_countries"] if prev_data else 0

        visits_mom_pct, visits_mom_str       = calc_mom(curr_visits,    prev_visits)
        countries_mom_pct, countries_mom_str = calc_mom(curr_countries, prev_countries)

        # Top N countries this month
        top = sorted(
            curr_data["countries"].items(),
            key=lambda x: -x[1]
        )[:TOP_N_COUNTRIES]

        records.append({
            "year_month":            ym,
            "total_visits":          curr_visits,
            "visits_prev_month":     prev_visits,
            "visits_mom":            visits_mom_str,
            "visits_mom_pct":        visits_mom_pct,
            "unique_countries":      curr_countries,
            "countries_prev_month":  prev_countries,
            "countries_mom":         countries_mom_str,
            "countries_mom_pct":     countries_mom_pct,
            "top_countries":         top,
        })

    return records


# ─── Export ───────────────────────────────────────────────────────────────────

def export_monthly_summary(records: List[dict], output_dir: str) -> str:
    """Export monthly summary CSV."""
    os.makedirs(output_dir, exist_ok=True)
    path = os.path.join(output_dir, f"monthly_summary_{datetime.today().strftime('%Y%m%d')}.csv")

    with open(path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow([
            "Year-Month",
            "Total Visits",
            "Prev Month Visits",
            "Visits MoM",
            "Unique Countries",
            "Prev Month Countries",
            "Countries MoM",
            "Top 1 Country",
            "Top 1 Visits",
            "Top 2 Country",
            "Top 2 Visits",
            "Top 3 Country",
            "Top 3 Visits",
        ])
        for r in records:
            top = r["top_countries"]
            def tc(n, field):
                return top[n][field] if len(top) > n else ""
            writer.writerow([
                r["year_month"],
                r["total_visits"],
                r["visits_prev_month"],
                r["visits_mom"],
                r["unique_countries"],
                r["countries_prev_month"],
                r["countries_mom"],
                tc(0, 0), tc(0, 1),
                tc(1, 0), tc(1, 1),
                tc(2, 0), tc(2, 1),
            ])

    print(f"Monthly summary saved: {path}")
    return path


def export_top_countries(records: List[dict], output_dir: str) -> str:
    """Export per-month top countries CSV."""
    path = os.path.join(output_dir, f"top_countries_{datetime.today().strftime('%Y%m%d')}.csv")

    with open(path, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["Year-Month", "Rank", "Country", "Visits", "% of Month"])
        for r in records:
            total = r["total_visits"]
            for rank, (country, visits) in enumerate(r["top_countries"], 1):
                pct = f"{visits/total*100:.1f}%" if total else "0%"
                writer.writerow([r["year_month"], rank, country, visits, pct])

    print(f"Top countries saved: {path}")
    return path


# ─── Console Report ───────────────────────────────────────────────────────────

def _mom_arrow(pct: float) -> str:
    if pct > 0:   return "↑"
    if pct < 0:   return "↓"
    return "→"

def _bar(value: int, max_value: int, width: int = 20) -> str:
    if max_value == 0:
        return ""
    filled = int(value / max_value * width)
    return "█" * filled + "░" * (width - filled)

def print_console_report(records: List[dict]):
    """Print a formatted report to the terminal."""

    max_visits    = max(r["total_visits"]     for r in records) if records else 1
    max_countries = max(r["unique_countries"] for r in records) if records else 1

    # ── Monthly visits ────────────────────────────────────────────────────────
    print()
    print("═" * 72)
    print("  月份訪問量統計 Monthly Visit Count")
    print("═" * 72)
    print(f"  {'月份':<10} {'訪問量':>12} {'MoM':>10}  {'趨勢'}")
    print("  " + "─" * 68)

    for r in records:
        arrow = _mom_arrow(r["visits_mom_pct"])
        bar   = _bar(r["total_visits"], max_visits)
        print(
            f"  {r['year_month']:<10} "
            f"{r['total_visits']:>12,} "
            f"{r['visits_mom']:>10}  "
            f"{arrow} {bar}"
        )

    print()

    # ── Monthly unique countries ───────────────────────────────────────────────
    print("═" * 72)
    print("  月份不重複國家數 Monthly Unique Country Count")
    print("═" * 72)
    print(f"  {'月份':<10} {'國家數':>10} {'MoM':>10}  {'趨勢'}")
    print("  " + "─" * 68)

    for r in records:
        arrow = _mom_arrow(r["countries_mom_pct"])
        bar   = _bar(r["unique_countries"], max_countries)
        print(
            f"  {r['year_month']:<10} "
            f"{r['unique_countries']:>10,} "
            f"{r['countries_mom']:>10}  "
            f"{arrow} {bar}"
        )

    print()

    # ── Top countries per month ────────────────────────────────────────────────
    print("═" * 72)
    print(f"  各月 Top {TOP_N_COUNTRIES} 國家 Top Countries per Month")
    print("═" * 72)

    for r in records:
        total = r["total_visits"]
        print(f"\n  {r['year_month']}  (共 {total:,} 次訪問)")
        print(f"  {'排名':>4}  {'國家':<28} {'訪問量':>10}  {'佔比':>6}")
        print("  " + "─" * 56)
        for rank, (country, visits) in enumerate(r["top_countries"], 1):
            pct = f"{visits/total*100:.1f}%" if total else "0%"
            print(f"  {rank:>4}.  {country:<28} {visits:>10,}  {pct:>6}")

    print()

    # ── MoM Summary ───────────────────────────────────────────────────────────
    if len(records) >= 2:
        latest = records[-1]
        prev   = records[-2]
        print("═" * 72)
        print("  最新月份 MoM 摘要 Latest Month MoM Summary")
        print("═" * 72)
        print(
            f"  {prev['year_month']} → {latest['year_month']}\n"
            f"  訪問量：{prev['total_visits']:,} → {latest['total_visits']:,}  "
            f"({latest['visits_mom']})\n"
            f"  國家數：{prev['unique_countries']} → {latest['unique_countries']}  "
            f"({latest['countries_mom']})"
        )
        print()


# ─── Visualization ────────────────────────────────────────────────────────────

def generate_charts(records: List[dict], output_dir: str) -> str:
    """
    Generate a 4-panel chart:
      1. Monthly total visits (bar + MoM line)
      2. Monthly unique countries (bar + MoM line)
      3. MoM % change comparison (line chart)
      4. Top 5 countries stacked bar (latest 6 months)
    """
    if not HAS_MATPLOTLIB:
        print("matplotlib not installed — skipping charts.")
        print("Install with: pip install matplotlib")
        return ""

    months  = [r["year_month"]      for r in records]
    visits  = [r["total_visits"]    for r in records]
    ctries  = [r["unique_countries"] for r in records]
    v_mom   = [r["visits_mom_pct"]  for r in records]
    c_mom   = [r["countries_mom_pct"] for r in records]

    # replace 0.0 (N/A first month) with nan for cleaner line
    # fill_between requires float nan, not None
    v_mom_plot = [v if i > 0 else float("nan") for i, v in enumerate(v_mom)]
    c_mom_plot = [v if i > 0 else float("nan") for i, v in enumerate(c_mom)]

    fig = plt.figure(figsize=(16, 12))
    fig.suptitle("IP Geo Report — Monthly Analysis", fontsize=16, fontweight="bold", y=0.98)
    gs  = GridSpec(2, 2, figure=fig, hspace=0.45, wspace=0.35)

    colors = {
        "visits":    "#4C6EF5",
        "countries": "#20C997",
        "mom_pos":   "#51CF66",
        "mom_neg":   "#FF6B6B",
        "neutral":   "#ADB5BD",
    }

    x = range(len(months))

    # ── Panel 1: Monthly visits bar ───────────────────────────────────────────
    ax1 = fig.add_subplot(gs[0, 0])
    bars = ax1.bar(x, visits, color=colors["visits"], alpha=0.85, zorder=2)
    ax1.set_title("Monthly Total Visits", fontweight="bold")
    ax1.set_xticks(list(x))
    ax1.set_xticklabels(months, rotation=45, ha="right", fontsize=8)
    ax1.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{v/1000:.0f}K" if v >= 1000 else str(int(v))))
    ax1.set_ylabel("Visits")
    ax1.grid(axis="y", linestyle="--", alpha=0.4, zorder=1)
    ax1.spines[["top","right"]].set_visible(False)

    # MoM % on secondary axis
    ax1b = ax1.twinx()
    ax1b.plot(list(x), v_mom_plot, color="#FF6B6B", marker="o", linewidth=1.8,
              markersize=5, linestyle="--", label="MoM %", zorder=3)
    ax1b.axhline(0, color="#FF6B6B", linewidth=0.6, linestyle=":")
    ax1b.set_ylabel("MoM %", color="#FF6B6B", fontsize=9)
    ax1b.tick_params(axis="y", labelcolor="#FF6B6B")
    ax1b.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{v:+.0f}%"))

    # value labels on bars
    for bar, val in zip(bars, visits):
        ax1.text(bar.get_x() + bar.get_width()/2, bar.get_height() + max(visits)*0.01,
                 f"{val:,}", ha="center", va="bottom", fontsize=7, color="#333")

    # ── Panel 2: Monthly unique countries bar ─────────────────────────────────
    ax2 = fig.add_subplot(gs[0, 1])
    bars2 = ax2.bar(x, ctries, color=colors["countries"], alpha=0.85, zorder=2)
    ax2.set_title("Monthly Unique Countries", fontweight="bold")
    ax2.set_xticks(list(x))
    ax2.set_xticklabels(months, rotation=45, ha="right", fontsize=8)
    ax2.set_ylabel("Countries")
    ax2.grid(axis="y", linestyle="--", alpha=0.4, zorder=1)
    ax2.spines[["top","right"]].set_visible(False)

    ax2b = ax2.twinx()
    ax2b.plot(list(x), c_mom_plot, color="#FF922B", marker="s", linewidth=1.8,
              markersize=5, linestyle="--", label="MoM %", zorder=3)
    ax2b.axhline(0, color="#FF922B", linewidth=0.6, linestyle=":")
    ax2b.set_ylabel("MoM %", color="#FF922B", fontsize=9)
    ax2b.tick_params(axis="y", labelcolor="#FF922B")
    ax2b.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{v:+.0f}%"))

    for bar, val in zip(bars2, ctries):
        ax2.text(bar.get_x() + bar.get_width()/2, bar.get_height() + max(ctries)*0.02,
                 str(val), ha="center", va="bottom", fontsize=7, color="#333")

    # ── Panel 3: MoM % comparison line chart ─────────────────────────────────
    ax3 = fig.add_subplot(gs[1, 0])
    ax3.plot(list(x), v_mom_plot, color=colors["visits"],    marker="o", linewidth=2,
             markersize=6, label="Visits MoM %")
    ax3.plot(list(x), c_mom_plot, color=colors["countries"], marker="s", linewidth=2,
             markersize=6, label="Countries MoM %", linestyle="--")
    ax3.axhline(0, color=colors["neutral"], linewidth=1, linestyle=":")

    # shade positive / negative regions
    import math
    x_num = list(x)
    ax3.fill_between(x_num, v_mom_plot, 0,
                     where=[not math.isnan(v) and v > 0 for v in v_mom_plot],
                     alpha=0.12, color=colors["mom_pos"], interpolate=True)
    ax3.fill_between(x_num, v_mom_plot, 0,
                     where=[not math.isnan(v) and v < 0 for v in v_mom_plot],
                     alpha=0.12, color=colors["mom_neg"], interpolate=True)

    ax3.set_title("MoM % Change Comparison", fontweight="bold")
    ax3.set_xticks(list(x))
    ax3.set_xticklabels(months, rotation=45, ha="right", fontsize=8)
    ax3.set_ylabel("MoM %")
    ax3.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{v:+.0f}%"))
    ax3.legend(fontsize=8)
    ax3.grid(linestyle="--", alpha=0.4)
    ax3.spines[["top","right"]].set_visible(False)

    # ── Panel 4: Top 5 countries stacked bar (latest 6 months) ───────────────
    ax4  = fig.add_subplot(gs[1, 1])
    last = records[-6:] if len(records) >= 6 else records
    top_months = [r["year_month"] for r in last]

    # Collect global top 5 countries across shown months
    country_totals: Dict[str, int] = defaultdict(int)
    for r in last:
        for country, cnt in r["top_countries"]:
            country_totals[country] += cnt
    top5 = [c for c, _ in sorted(country_totals.items(), key=lambda x: -x[1])[:5]]

    palette = ["#4C6EF5","#20C997","#FF922B","#CC5DE8","#FF6B6B"]
    bottoms = [0] * len(last)

    for i, country in enumerate(top5):
        vals = []
        for r in last:
            cmap = dict(r["top_countries"])
            vals.append(cmap.get(country, 0))
        ax4.bar(range(len(last)), vals, bottom=bottoms,
                label=country, color=palette[i % len(palette)], alpha=0.88)
        bottoms = [b + v for b, v in zip(bottoms, vals)]

    ax4.set_title(f"Top 5 Countries — Last {len(last)} Months", fontweight="bold")
    ax4.set_xticks(range(len(last)))
    ax4.set_xticklabels(top_months, rotation=45, ha="right", fontsize=8)
    ax4.yaxis.set_major_formatter(mticker.FuncFormatter(lambda v, _: f"{v/1000:.0f}K" if v >= 1000 else str(int(v))))
    ax4.set_ylabel("Visits")
    ax4.legend(fontsize=7, loc="upper left")
    ax4.grid(axis="y", linestyle="--", alpha=0.4, zorder=1)
    ax4.spines[["top","right"]].set_visible(False)

    # ── Save ─────────────────────────────────────────────────────────────────
    os.makedirs(output_dir, exist_ok=True)
    path = os.path.join(output_dir, f"analysis_chart_{datetime.today().strftime('%Y%m%d')}.png")
    fig.savefig(path, dpi=150, bbox_inches="tight")
    plt.close(fig)
    print(f"Chart saved: {path}")
    return path


# ─── Entry Point ──────────────────────────────────────────────────────────────

def main():
    global TOP_N_COUNTRIES
    parser = argparse.ArgumentParser(description="IP Geo Report Analyzer")
    parser.add_argument(
        "--input", "-i",
        help="Path to report CSV (default: latest in ./report/)",
        default=None
    )
    parser.add_argument(
        "--output", "-o",
        help="Output directory (default: ./analysis/)",
        default=OUTPUT_DIR
    )
    parser.add_argument(
        "--top", "-n",
        help=f"Top N countries per month (default: {TOP_N_COUNTRIES})",
        type=int,
        default=TOP_N_COUNTRIES
    )
    args = parser.parse_args()

    # Find input file
    input_path = args.input
    if not input_path:
        input_path = find_latest_report(REPORT_DIR)
        if not input_path:
            print(f"No report_*.csv found in {REPORT_DIR}")
            print("Run ip_geo_report.py first, or specify --input path")
            return

    print(f"Analyzing: {input_path}")

    # Load → aggregate → build stats
    rows    = load_report(input_path)
    monthly = aggregate_monthly(rows)
    records = build_monthly_stats(monthly)

    if not records:
        print("No data found in report.")
        return

    # Print to console
    print_console_report(records)

    # Export CSVs
    export_monthly_summary(records, args.output)
    export_top_countries(records, args.output)

    # Generate chart
    generate_charts(records, args.output)

    print(f"\nAnalysis complete. Files saved to: {args.output}/")


if __name__ == "__main__":
    main()