YANG JIANKUAN
6dc89b504e
- 用量与 pace 配色由「红/黑」二档、黄「注意」档，改为绿（健康）/黑（正常）/红（危险）三档，新增 USAGE_OK／PACE_TOL 阈值，去掉黄色进度条 - Claude/ChatGPT 用量行标签改用 statusline 同款长标签（all 5h／fable 7d 等） - 今日实时区块改为指标名角标＋数值与环比同比整组居中；近7天区块改为真实数据表（取30天趋势缓存最后7天），不画折线 - renderer.py 抽出 _cmp_at／_pace_color 钩子＋新增 F36 字体供宽版复用；CLAUDE.md 同步版式与配置项说明 Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
298 lines
13 KiB
Python
298 lines
13 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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数据层 —— 汇总仪表盘所需的全部数据。
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橘喵今日经营 + 近30天趋势来自 jm_api（jm-devops 后端统计接口，免鉴权 GET）。
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天气来自 weather_api（和风天气，定位南京·秦淮）。日期/时间用真实系统时间。
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任何接口失败时相关数据以占位符 "--" 呈现，不回退 mock。
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"""
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import json
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from datetime import datetime
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import config
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import jm_api
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import weather_api
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import usage_local
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WEEK_CN = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
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TREND_DAYS = 30 # 近 N 天趋势
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FIVE_HOUR, SEVEN_DAY = 5 * 3600, 7 * 86400 # 两个滚动窗口长度（秒），用于算「时间已流逝%」
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def _read_weather_cache():
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try:
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with open(config.WEATHER_CACHE_PATH, encoding="utf-8") as f:
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return json.load(f) # {"ts": float, "data": {...}}
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except Exception:
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return None
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def _write_weather_cache(ts, data):
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try:
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with open(config.WEATHER_CACHE_PATH, "w", encoding="utf-8") as f:
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json.dump({"ts": ts, "data": data}, f, ensure_ascii=False)
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except Exception as e:
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print(f"[警告] 天气缓存写入失败：{e}")
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def _read_trend_cache():
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try:
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with open(config.TREND_CACHE_PATH, encoding="utf-8") as f:
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return json.load(f) # {"date": "YYYY-MM-DD", "data": {...}}
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except Exception:
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return None
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def _write_trend_cache(date_key, data):
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try:
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with open(config.TREND_CACHE_PATH, "w", encoding="utf-8") as f:
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json.dump({"date": date_key, "data": data}, f, ensure_ascii=False)
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except Exception as e:
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print(f"[警告] 趋势缓存写入失败：{e}")
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def _trend(now):
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"""近30天趋势，按日期缓存：recent-days 每天只更新一次。
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当天已有缓存（且有数据）→ 直接复用，不发请求；当天还没有 → 拉取并写缓存。
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拉取失败时沿用任意旧缓存（哪怕非当天），仍无缓存才回退空。
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API 降序 → 升序：左旧右新。
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"""
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date_key = now.strftime("%Y-%m-%d")
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cached = _read_trend_cache()
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if cached and cached.get("date") == date_key and cached.get("data", {}).get("series"):
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return cached["data"]
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try:
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asc = list(reversed(jm_api.get_recent_days(TREND_DAYS)))
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trend = {
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"dates": [d["date"][5:].replace("-", "/") for d in asc], # yyyy-MM-dd → MM/DD
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"series": [
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{"k": "单量", "style": "solid", "data": [int(d["orderCount"]) for d in asc]},
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{"k": "流水", "style": "dashed", "data": [float(d["payAmount"]) for d in asc]},
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{"k": "毛利", "style": "dotted", "data": [float(d["grossProfit"]) for d in asc]},
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],
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}
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_write_trend_cache(date_key, trend)
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return trend
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except Exception as e:
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print(f"[警告] 近30天趋势拉取失败：{e}")
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if cached and cached.get("data", {}).get("series"): # 失败沿用旧缓存，避免趋势图整片 "--"
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print("[警告] 沿用上次趋势缓存")
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return cached["data"]
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return {"dates": [], "series": []}
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def _weather(now):
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"""和风天气（南京·秦淮），带 WEATHER_TTL 文件缓存。
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缓存未过期 → 直接复用，不发请求（每分钟刷新时省掉 14/15 次天气调用）。
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过期 → 重新拉取并写缓存；全部接口失败时沿用上次缓存，仍无缓存才回退占位 "--"。
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"""
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ts = now.timestamp()
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cached = _read_weather_cache()
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if cached and ts - cached.get("ts", 0) < config.WEATHER_TTL:
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return cached["data"]
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out = {"loc": config.WEATHER_LOC_NAME, "cond": "--", "temp": "--", "range": "--", "icon": ""}
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ok = False
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try:
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w = weather_api.get_now()
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out["cond"] = w.get("text") or "--"
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out["temp"] = f"{w['temp']}°"
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out["icon"] = w.get("icon") or "" # 和风图标代码，供 renderer 映射图标
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ok = True
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except Exception as e:
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print(f"[警告] 实时天气拉取失败：{e}")
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try:
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today = weather_api.get_today()
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out["range"] = f"{today['tempMin']}°~{today['tempMax']}°"
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ok = True
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except Exception as e:
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print(f"[警告] 今日温区拉取失败：{e}")
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if ok:
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_write_weather_cache(ts, out)
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return out
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if cached: # 全部失败：沿用上次缓存，避免天气区整片 "--"
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print("[警告] 天气全部拉取失败，沿用上次缓存")
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return cached["data"]
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return out
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# ---------- 格式化 ----------
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def _fmt_count(n):
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return f"{int(n):,}" # 千分位，如 1,284
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def _fmt_money(yuan):
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"""元金额 → 紧凑展示：≥1万显示「X.XX万」(两位小数)，否则取整元。"""
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v = float(yuan)
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return f"{v / 10000:.2f}万" if abs(v) >= 10000 else f"{v:.0f}"
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def _fmt_rate(rate):
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"""增长率字符串 → (方向, 文本)。null/空 → (None, '--')，方向 None 表示不画三角。
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数值统一两位小数；整数部分不足两位用前导 0 补齐(6.5→06.50)，便于环比/同比上下对齐；
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三/四位数(≥100%)原样完整输出不截断。"""
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if rate is None or rate == "":
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return (None, "--")
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v = float(rate)
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return ("down" if v < 0 else "up", f"{abs(v):05.2f}%")
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def _col(k, v, wow, yoy):
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return {"k": k, "v": v, "hb": _fmt_rate(wow), "tb": _fmt_rate(yoy)}
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def _placeholder_col(k):
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return {"k": k, "v": "--", "hb": (None, "--"), "tb": (None, "--")}
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# ---------- 橘喵业务数据 ----------
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def _mao(now):
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upd = now.strftime("%H:%M")
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# 今日经营（realtime）
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try:
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rt = jm_api.get_realtime()
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upd = (rt.get("dataTime") or "")[11:16] or upd
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cols = [
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_col("单量", _fmt_count(rt["todayOrderCount"]), rt["orderCountWowRate"], rt["orderCountYoyRate"]),
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_col("流水", _fmt_money(rt["todayPayAmount"]), rt["payAmountWowRate"], rt["payAmountYoyRate"]),
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_col("毛利", _fmt_money(rt["todayGrossProfit"]), rt["grossProfitWowRate"], rt["grossProfitYoyRate"]),
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]
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except Exception as e:
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print(f"[警告] 今日经营拉取失败：{e}")
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cols = [_placeholder_col(k) for k in ("单量", "流水", "毛利")]
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# 近30天趋势（按日期缓存，每天只拉一次）；宽版另用其最后 7 天做「近7天」数据表
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trend = _trend(now)
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return {"upd": upd, "cols": cols, "trend": trend, "week": _recent_days(trend, 7)}
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def _recent_days(trend, n):
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"""趋势的最后 n 天 → 表格数据 {dates:[MM/DD…], rows:[(名, [文本…], 合计文本)]}，供宽版「橘喵·近7天」表使用。
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recent-days 不含今日，故表头日期到昨天为止。单量按千分位、流水/毛利按紧凑金额格式；无数据返回空 dict。"""
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dates, series = trend.get("dates") or [], trend.get("series") or []
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if not dates or not series:
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return {}
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dates = dates[-n:]
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rows = []
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for s in series:
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data = (s.get("data") or [])[-n:]
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if len(data) != len(dates):
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continue
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fmt = _fmt_count if s["k"] == "单量" else _fmt_money
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rows.append((s["k"], [fmt(v) for v in data], fmt(sum(data))))
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return {"dates": dates, "rows": rows}
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# ---------- Claude Code 用量 ----------
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def _parse_reset_ts(s):
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"""resets_at → epoch 秒。兼容三种：epoch 数字（Claude Code stdin 给的就是 epoch）、
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ISO 时间串（含结尾 'Z'）、空。失败返回 None。"""
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if s is None or s == "":
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return None
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s = str(s)
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if s.replace(".", "", 1).isdigit(): # 纯数字 → 当作 epoch 秒
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return float(s)
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try:
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return datetime.fromisoformat(s.replace("Z", "+00:00")).timestamp()
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except Exception:
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return None
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def _window_time_pct(resets_at, window, now):
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"""该滚动窗口「已流逝时间百分比」= (window − 距重置剩余) / window ×100。失败返回 None。"""
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reset = _parse_reset_ts(resets_at)
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if reset is None:
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return None
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remaining = max(0, min(window, reset - now.timestamp()))
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return int(round((window - remaining) / window * 100))
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def _fmt_reset(resets_at, window):
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"""重置时刻 → 文本：≤1 天的窗口只给 HH:MM，更长的窗口带 MM/DD。解析失败返回 None。"""
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ts = _parse_reset_ts(resets_at)
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if ts is None:
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return None
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dt = datetime.fromtimestamp(ts)
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return dt.strftime("%H:%M") if window <= 86400 else dt.strftime("%m/%d %H:%M")
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# Claude 用量窗口：（快照键，短标签，长标签，窗口长度秒，scoped）。
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# 短标签 k 给 400x300 小屏（标签列只有 26px）；长标签 label 给 800x480 宽版，沿用 statusline 的写法
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# （all 5h / all 7d / fable 7d，小写开头）。scoped=True 为「按模型限定」的额度（Fable 7d，来自
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# /api/oauth/usage 的 limits[]，由 statusline 写入快照 seven_day_fable），小屏放不下第三行，只在宽版展示。
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USAGE_WINDOWS = (
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("five_hour", "5h", "all 5h", FIVE_HOUR, False),
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("seven_day", "7d", "all 7d", SEVEN_DAY, False),
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("seven_day_fable", "Fable 7d", "fable 7d", SEVEN_DAY, True),
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)
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def _usage(now):
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"""Claude Code 用量（5h / 7d / Fable 7d）——只读本地 statusline 生产的快照（见 usage_local.py），**不发任何请求**。
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utilization 来自 statusline 从 Claude Code stdin 落盘的权威实时值（Fable 行来自其转存的 API limits）；
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time_pct(时间已流逝%) 与 pace 每次按当前时间**现算**（resets_at 为绝对时间）。
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文件缺失/损坏 → 回退占位 pct=None（renderer 显示 "--"）；快照里没有某窗口（如旧版 statusline 无 Fable）同样 "--"。
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结构契约（renderer 依赖）：{title, warn, ok, pace_tol, updated?, bars:[{k, label, pct(0~100 或 None), time_pct?, reset?, scoped}]}；
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pct ≥ warn 时该条进度条与百分比转红（宽版另有 pct ≤ ok 绿档）；pace = pct − time_pct（>0 超前↑ / <0 节余↓，宽版按 ±pace_tol 分三色）；
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reset 为重置时刻文本；updated 为快照落盘时刻 HH:MM（活跃使用时持续刷新，空闲时停在最后一次）。
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"""
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raw = {}
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try:
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raw = usage_local.get_usage()
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except Exception as e:
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print(f"[警告] Claude 用量读取失败：{e}")
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bars = []
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for key, k, label, win, scoped in USAGE_WINDOWS:
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seg = raw.get(key) or {}
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util = seg.get("utilization")
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pct = int(round(float(util))) if util is not None else None
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bar = {"k": k, "label": label, "pct": pct, "scoped": scoped}
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tp = _window_time_pct(seg.get("resets_at"), win, now)
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if pct is not None and tp is not None:
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bar["time_pct"] = tp
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rs = _fmt_reset(seg.get("resets_at"), win)
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if rs:
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bar["reset"] = rs
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bars.append(bar)
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out = {"title": "Claude Usage", "warn": config.USAGE_WARN, "ok": config.USAGE_OK, "pace_tol": config.PACE_TOL, "bars": bars}
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upd = _parse_reset_ts(raw.get("updated_at"))
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if upd:
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out["updated"] = datetime.fromtimestamp(upd).strftime("%H:%M")
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return out
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def _usage_gpt(now):
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"""ChatGPT 用量——**目前为 mock 占位**（mock=True，renderer 在标题右侧标「示例数据」）。
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结构与 _usage 完全一致，后续接入真实数据源时只需替换本函数、保持返回结构不变。
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mock 取值刻意覆盖宽版三档配色：5h 88%（≥warn 红）、7d 71%（黑）、Codex 7d 22%（≤ok 绿，scoped，与左侧 fable 行对称）。"""
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bars = []
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for k, label, pct, win, remain, scoped in (("5h", "all 5h", 88, FIVE_HOUR, 2 * 3600, False),
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("7d", "all 7d", 71, SEVEN_DAY, 3 * 86400, False),
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("Codex 7d", "codex 7d", 22, SEVEN_DAY, 3 * 86400, True)):
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reset_ts = now.timestamp() + remain
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bars.append({"k": k, "label": label, "pct": pct, "scoped": scoped,
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"time_pct": _window_time_pct(reset_ts, win, now),
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"reset": _fmt_reset(reset_ts, win)})
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return {"title": "ChatGPT Usage", "warn": config.USAGE_WARN, "ok": config.USAGE_OK, "pace_tol": config.PACE_TOL,
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"bars": bars, "mock": True}
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def get_dashboard_data(now=None):
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"""返回渲染所需的完整数据字典。"""
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now = now or datetime.now()
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return {
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"date": {
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"greg": now.strftime("%Y/%m/%d"),
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"week": WEEK_CN[now.weekday()],
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"time": now.strftime("%H:%M"),
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},
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"weather": _weather(now),
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"mao": _mao(now),
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"usage": _usage(now),
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"usage_gpt": _usage_gpt(now),
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}
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