#!/usr/bin/env python3
# /// script
# dependencies = ["matplotlib>=3.9", "pandas>=2.2"]
# ///
"""
Bincio usage statistics — parses nginx access logs and produces a
multi-panel matplotlib figure saved as a PNG.

Run locally:  uv run scripts/usage_stats.py
On VPS cron:  0 3 * * 1  cd /opt/bincio && uv run scripts/usage_stats.py
Output:       /var/bincio/stats/latest.png  (served at /api/admin/stats)
"""
from __future__ import annotations

import argparse
import gzip
import re
import sys
from datetime import datetime
from pathlib import Path
from urllib.parse import urlparse

import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import numpy as np
import pandas as pd

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

LOG_DIR    = Path("/var/log/nginx")
OUTPUT_DIR = Path("/var/bincio/stats")
OUTPUT     = OUTPUT_DIR / "latest.png"

# ── Log parsing ───────────────────────────────────────────────────────────────

_LOG_RE = re.compile(
    r'(?P<ip>\S+) \S+ \S+ \[(?P<time>[^\]]+)\] '
    r'"(?P<method>\S+) (?P<path>\S+) [^"]+" '
    r'(?P<status>\d+) \S+ '
    r'"(?P<referer>[^"]*)" "(?P<ua>[^"]*)"'
)
_TS_FMT = "%d/%b/%Y:%H:%M:%S %z"

# ── Bot filtering ─────────────────────────────────────────────────────────────

_BOT_UA_RE = re.compile(
    r"bot|crawl|spider|scan|nmap|masscan|zgrab|python-requests|"
    r"curl|wget|nikto|nuclei|go-http|censys|shodan|paloalto|expanse|"
    r"dataforseo|semrush|ahrefs|mj12|dotbot|petalbot|fuzz|dirbuster",
    re.I,
)
_BOT_PATH_RE = re.compile(
    r"^/(wp-|phpmyadmin|xmlrpc|\.env|\.git|setup\.php|"
    r"SDK/|actuator/|cgi-bin/|PROPFIND|\.well-known/acme)",
    re.I,
)

def _is_bot(ua: str, path: str) -> bool:
    if not ua or ua == "-":
        return True
    if _BOT_UA_RE.search(ua):
        return True
    if _BOT_PATH_RE.search(path):
        return True
    return False

# ── Feature mapping (from Referer header) ─────────────────────────────────────

# Evaluated in order: first match wins. None label = exclude.
_FEATURE_MAP: list[tuple[str, str | None, str | None]] = [
    ("planner.bincio.org",  None,          "planner"),
    ("wiki.bincio.org",     None,          "wiki"),
    ("activity.bincio.org", "/admin/",     None),         # exclude admin polling
    ("activity.bincio.org", "/activity/",  "activity"),
    ("activity.bincio.org", "/segments/",  "segments"),
    ("activity.bincio.org", "/stats/",     "stats"),
    ("activity.bincio.org", "/explore/",   "explore"),
    ("activity.bincio.org", "/ideas/",     "ideas"),
    ("activity.bincio.org", "/u/",         "profile"),
    ("activity.bincio.org", None,          "feed"),
    ("bincio.org",          None,          "hub"),
]

FEATURE_COLORS = {
    "feed":     "#60a5fa",
    "activity": "#4ade80",
    "segments": "#facc15",
    "planner":  "#f97316",
    "wiki":     "#a855f7",
    "ideas":    "#f43f5e",
    "explore":  "#34d399",
    "profile":  "#94a3b8",
    "hub":      "#64748b",
    "stats":    "#e879a0",
}

def _feature(referer: str) -> str | None:
    if not referer or referer == "-":
        return None
    try:
        p = urlparse(referer)
        host = p.netloc.lower().lstrip("www.")
        path = p.path
    except Exception:
        return None
    for h, prefix, label in _FEATURE_MAP:
        if host == h:
            if prefix is None or path.startswith(prefix):
                return label
    return None

# ── Loading ───────────────────────────────────────────────────────────────────

def load_logs(log_dir: Path) -> pd.DataFrame:
    rows = []
    files = sorted(log_dir.glob("access.log*"), reverse=True)
    if not files:
        print(f"No log files found in {log_dir}", file=sys.stderr)
        sys.exit(1)

    for f in files:
        opener = gzip.open if f.suffix == ".gz" else open
        try:
            with opener(f, "rt", errors="replace") as fh:
                for line in fh:
                    m = _LOG_RE.match(line)
                    if not m:
                        continue
                    ua   = m.group("ua")
                    path = m.group("path")
                    if _is_bot(ua, path):
                        continue
                    try:
                        ts = datetime.strptime(m.group("time"), _TS_FMT)
                    except ValueError:
                        continue
                    rows.append({
                        "ts":      ts,
                        "ip":      m.group("ip"),
                        "method":  m.group("method"),
                        "path":    path,
                        "status":  int(m.group("status")),
                        "referer": m.group("referer"),
                    })
        except Exception as exc:
            print(f"Warning: skipping {f.name}: {exc}", file=sys.stderr)

    if not rows:
        print("No usable log entries found after bot filtering.", file=sys.stderr)
        sys.exit(1)

    df = pd.DataFrame(rows)
    df["ts"]      = pd.to_datetime(df["ts"], utc=True)
    df["hour"]    = df["ts"].dt.hour
    df["dow"]     = df["ts"].dt.dayofweek          # 0 = Monday
    df["feature"] = df["referer"].map(_feature)
    return df

# ── Figure ────────────────────────────────────────────────────────────────────

BG    = "#09090b"
FG    = "#e4e4e7"
GRID  = "#27272a"
BLUE  = "#60a5fa"

def _style_ax(ax: plt.Axes) -> None:
    ax.set_facecolor(BG)
    ax.tick_params(colors=FG, labelsize=9)
    for spine in ax.spines.values():
        spine.set_edgecolor(GRID)

def make_figure(df: pd.DataFrame, output: Path) -> None:
    # ── daily logins ──────────────────────────────────────────────────────────
    login_mask  = (df["method"] == "POST") & (df["path"] == "/api/auth/login") & (df["status"] == 200)
    full_range  = pd.date_range(df["ts"].min(), df["ts"].max(), freq="D", tz="UTC")
    daily_logins = df[login_mask].set_index("ts").resample("D").size().reindex(full_range, fill_value=0)
    rolling7     = daily_logins.rolling(7, center=True, min_periods=1).mean()

    # ── feature usage (weekly) ────────────────────────────────────────────────
    feat_df = df[df["feature"].notna()].copy()
    feat_weekly = (
        feat_df.set_index("ts")
        .groupby([pd.Grouper(freq="W-MON"), "feature"])
        .size()
        .unstack(fill_value=0)
    )
    feat_order = [f for f in FEATURE_COLORS if f in feat_weekly.columns]
    feat_weekly = feat_weekly[feat_order]

    # ── heatmap: hour × weekday (API requests, no admin) ─────────────────────
    api_df = df[
        df["path"].str.startswith("/api/") &
        ~df["path"].str.startswith("/api/admin")
    ]
    heat = (
        api_df.groupby(["dow", "hour"]).size()
        .unstack(fill_value=0)
        .reindex(index=range(7), columns=range(24), fill_value=0)
    )

    # ── layout ────────────────────────────────────────────────────────────────
    plt.style.use("dark_background")
    fig = plt.figure(figsize=(15, 10), facecolor=BG)
    gs  = fig.add_gridspec(2, 2, hspace=0.42, wspace=0.30,
                           left=0.07, right=0.97, top=0.92, bottom=0.08)
    ax_log  = fig.add_subplot(gs[0, 0])
    ax_heat = fig.add_subplot(gs[0, 1])
    ax_feat = fig.add_subplot(gs[1, :])

    for ax in (ax_log, ax_heat, ax_feat):
        _style_ax(ax)

    # Panel 1 — daily logins + rolling mean
    ax_log.bar(daily_logins.index, daily_logins.values,
               color=BLUE, alpha=0.30, width=pd.Timedelta(hours=20))
    ax_log.plot(daily_logins.index, rolling7.values,
                color=BLUE, linewidth=2, label="7-day avg")
    ax_log.set_title("Daily logins", color=FG, fontsize=11, pad=8)
    ax_log.set_ylabel("count", color=FG, fontsize=9)
    ax_log.yaxis.set_major_locator(ticker.MaxNLocator(integer=True, nbins=5))
    ax_log.tick_params(axis="x", rotation=25)
    ax_log.legend(fontsize=8, framealpha=0.15, facecolor=BG, edgecolor=GRID)
    ax_log.grid(axis="y", color=GRID, linewidth=0.5)

    # Panel 2 — heatmap
    days = ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"]
    im = ax_heat.imshow(heat.values, aspect="auto", cmap="Blues",
                        interpolation="nearest", origin="upper")
    ax_heat.set_xticks(range(0, 24, 3))
    ax_heat.set_xticklabels([f"{h:02d}h" for h in range(0, 24, 3)], color=FG, fontsize=8)
    ax_heat.set_yticks(range(7))
    ax_heat.set_yticklabels(days, color=FG, fontsize=9)
    ax_heat.set_title("API requests: hour × weekday (UTC)", color=FG, fontsize=11, pad=8)
    cb = fig.colorbar(im, ax=ax_heat, fraction=0.046, pad=0.04)
    cb.ax.tick_params(labelcolor=FG, labelsize=8)

    # Panel 3 — feature usage stacked area
    if not feat_weekly.empty:
        n = len(feat_weekly)
        x = np.arange(n)
        bottom = np.zeros(n)
        for feat in feat_order:
            vals = feat_weekly[feat].values.astype(float)
            ax_feat.fill_between(x, bottom, bottom + vals,
                                 color=FEATURE_COLORS[feat], alpha=0.80, label=feat)
            bottom += vals

        week_labels = [str(w.date()) for w in feat_weekly.index]
        step = max(1, n // 12)
        ax_feat.set_xticks(x[::step])
        ax_feat.set_xticklabels(week_labels[::step], rotation=30, ha="right",
                                fontsize=8, color=FG)
        ax_feat.set_xlim(0, n - 1)
        ax_feat.set_title("Feature usage per week  (from Referer)", color=FG, fontsize=11, pad=8)
        ax_feat.set_ylabel("API requests", color=FG, fontsize=9)
        ax_feat.legend(loc="upper left", fontsize=8, framealpha=0.15,
                       facecolor=BG, edgecolor=GRID, ncol=len(feat_order))
        ax_feat.grid(axis="y", color=GRID, linewidth=0.5)
    else:
        ax_feat.text(0.5, 0.5, "No feature data (no Referer headers yet)",
                     ha="center", va="center", color=FG, transform=ax_feat.transAxes)

    total_logins = int(login_mask.sum())
    span_days    = (df["ts"].max() - df["ts"].min()).days + 1
    fig.suptitle(
        f"bincio — {total_logins} logins over {span_days} days "
        f"· generated {datetime.utcnow().strftime('%Y-%m-%d')}",
        color=FG, fontsize=12, y=0.97,
    )

    output.parent.mkdir(parents=True, exist_ok=True)
    fig.savefig(output, dpi=150, facecolor=BG, bbox_inches="tight")
    print(f"Saved → {output}")
    plt.close(fig)

# ── Entry point ───────────────────────────────────────────────────────────────

def main() -> None:
    ap = argparse.ArgumentParser(description="Generate bincio usage stats figure.")
    ap.add_argument("--log-dir", type=Path, default=LOG_DIR,  metavar="DIR")
    ap.add_argument("--output",  type=Path, default=OUTPUT,   metavar="FILE")
    args = ap.parse_args()

    print("Loading logs…", file=sys.stderr)
    df = load_logs(args.log_dir)
    span = (df["ts"].max() - df["ts"].min()).days + 1
    print(f"  {len(df):,} non-bot requests over {span} days", file=sys.stderr)
    make_figure(df, args.output)


if __name__ == "__main__":
    main()