"""智联招聘抓取数据分析 —— 读取 SQLite, 按城市/薪资/学历/经验/技能做统计。

纯标准库, 无需 pandas。

用法:
  python examples/analyze_zhaopin.py                      # 全量分析
  python examples/analyze_zhaopin.py --keyword AI         # 只看某关键词
  python examples/analyze_zhaopin.py --city 北京          # 只看某城市
  python examples/analyze_zhaopin.py --top 15             # 排行榜取前 15
  python examples/analyze_zhaopin.py --db zhaopin_jobs.db
"""
from __future__ import annotations

import argparse
import json
import re
import sqlite3
import statistics
from collections import Counter

WORKDAYS_PER_MONTH = 21.75  # 日薪 -> 月薪折算


def parse_salary(s: str) -> tuple[int, int] | None:
    """把薪资字符串解析成 (月薪下限, 月薪上限) 元。无法解析返回 None。"""
    if not s or "面议" in s:
        return None
    s = s.strip()
    is_daily = "/天" in s or "元/天" in s
    is_wan = "万" in s
    nums = re.findall(r"\d+(?:\.\d+)?", s)
    if not nums:
        return None
    vals = [float(x) for x in nums[:2]]
    if len(vals) == 1:
        vals = [vals[0], vals[0]]
    lo, hi = vals[0], vals[1]
    if is_wan:
        lo, hi = lo * 10000, hi * 10000
    if is_daily:
        lo, hi = lo * WORKDAYS_PER_MONTH, hi * WORKDAYS_PER_MONTH
    return int(lo), int(hi)


def city_of(location: str) -> str:
    return (location or "").split("·")[0].strip() or "未知"


SALARY_BUCKETS = [
    (0, 5000, "<5k"),
    (5000, 8000, "5-8k"),
    (8000, 12000, "8-12k"),
    (12000, 18000, "12-18k"),
    (18000, 25000, "18-25k"),
    (25000, 10**9, ">=25k"),
]


def bucket_of(mid: float) -> str:
    for lo, hi, label in SALARY_BUCKETS:
        if lo <= mid < hi:
            return label
    return "?"


def bar(n: int, maxn: int, width: int = 30) -> str:
    if maxn <= 0:
        return ""
    return "█" * max(1, round(n / maxn * width))


def section(title: str) -> None:
    print("\n" + "=" * 56)
    print(title)
    print("=" * 56)


def rank_table(counter: Counter, top: int, label: str) -> None:
    if not counter:
        print("  (无数据)")
        return
    maxn = counter.most_common(1)[0][1]
    for name, n in counter.most_common(top):
        print(f"  {name[:18]:<18} {n:>4}  {bar(n, maxn)}")


def main() -> None:
    ap = argparse.ArgumentParser()
    ap.add_argument("--db", default="zhaopin_jobs.db")
    ap.add_argument("--keyword", default=None, help="按关键词过滤")
    ap.add_argument("--city", default=None, help="按城市过滤")
    ap.add_argument("--top", type=int, default=12, help="排行榜条数")
    args = ap.parse_args()

    conn = sqlite3.connect(args.db)
    q = ("SELECT title,salary,experience,education,location,company,"
         "job_tags,company_tags,skills FROM jobs")
    params: list = []
    if args.keyword:
        q += " WHERE keyword=?"
        params.append(args.keyword)
    rows = conn.execute(q, params).fetchall()

    if args.city:
        rows = [r for r in rows if city_of(r[4]) == args.city]

    if not rows:
        print("没有匹配的数据。先用 scrape_zhaopin_full.py 抓一些, 或检查过滤条件。")
        return

    n_total = len(rows)
    print(f"分析样本: {n_total} 条"
          + (f" | 关键词={args.keyword}" if args.keyword else "")
          + (f" | 城市={args.city}" if args.city else ""))

    cities, edus, exps = Counter(), Counter(), Counter()
    skill_freq, salary_buckets = Counter(), Counter()
    mids: list[float] = []
    n_salary_parsed = 0

    for (title, salary, exp, edu, loc, comp, jtags, ctags, skills) in rows:
        cities[city_of(loc)] += 1
        edus[(edu or "未知").strip() or "未知"] += 1
        exps[(exp or "未知").strip() or "未知"] += 1
        # 技能: 合并 skills + job_tags
        for src in (skills, jtags):
            try:
                for t in json.loads(src or "[]"):
                    t = t.strip()
                    if t:
                        skill_freq[t] += 1
            except Exception:
                pass
        rng = parse_salary(salary)
        if rng:
            n_salary_parsed += 1
            mid = (rng[0] + rng[1]) / 2
            mids.append(mid)
            salary_buckets[bucket_of(mid)] += 1

    section("城市分布 (Top)")
    rank_table(cities, args.top, "城市")

    section("薪资区间分布 (按月薪中位点)")
    if mids:
        order = {label: i for i, (_, _, label) in enumerate(SALARY_BUCKETS)}
        maxn = max(salary_buckets.values())
        for label in sorted(salary_buckets, key=lambda x: order.get(x, 99)):
            n = salary_buckets[label]
            print(f"  {label:<8} {n:>4}  {bar(n, maxn)}")
        print(f"\n  可解析薪资: {n_salary_parsed}/{n_total} 条 (面议等已排除)")
        print(f"  月薪中位点  平均: {statistics.mean(mids):>8,.0f} 元")
        print(f"            中位数: {statistics.median(mids):>8,.0f} 元")
        print(f"          最低/最高: {min(mids):,.0f} / {max(mids):,.0f} 元")
    else:
        print("  (无可解析薪资)")

    section("学历要求")
    rank_table(edus, args.top, "学历")

    section("经验要求")
    rank_table(exps, args.top, "经验")

    section("技能/标签词频 (Top)")
    rank_table(skill_freq, args.top, "技能")


if __name__ == "__main__":
    main()