docs: 更新方案文档

feat:更新rawData
docs: add maintainable PPMT financial workbook
2026-09-04 17:33:06 +08:00 · 2026-09-04 16:47:14 +08:00 · 2026-08-31 17:49:00 +08:00
29 changed files with 236 additions and 570 deletions
@@ -45,6 +45,10 @@ pnpm test                                              # 全量，只在改了 t

 ## 数据口径

+**底稿与图表数据分层。** `data/{company}/rawData/` 是唯一可维护的数据层。当前泡泡玛特的维护入口是其中的 `泡泡玛特_财报分析底稿.xlsx`：人工或 AI 从 PDF、截图、XLSX 提取的基础值先写入这份底稿，并保留来源文件、工作表和定位信息。`data/{company}/*.csv` 是图表运行时投影，只能由脚本生成，禁止手工编辑。
+
+**维护明细，不维护可推导结果。** 收入、门店数、财报科目等基础值应保存；占比、同比、利润率、H2 单半年和分析分类小计应由脚本生成。若财报披露了占比，可作为核对值保留，但不是图表的唯一输入。资产负债表的“类现金/经营类资产/有息负债”等分类以独立规则映射明细科目，不复制成第二套人工数字。
+
 **数值默认保留一位小数。** CSV 数据列与图表展示（柱/点标签、tooltip）统一保留一位小数（如 `71.1`、`311.4%`），四舍五入；确实需要其他精度时必须在 `footnote()` 或代码注释里说明原因。

 **CSV 存四位年份**（`2026H1`），简写只在显示层做。核对数据时无歧义。
@@ -1,17 +0,0 @@
-period,inventory,yoy
-2017FY,0.16,
-2018FY,0.29,87.0
-2019FY,0.96,231.4
-2020H1,2.24,
-2020FY,2.25,134.0
-2021H1,3.16,41.0
-2021FY,7.89,250.0
-2022H1,9.57,203.0
-2022FY,8.67,9.9
-2023H1,7.59,-20.8
-2023FY,9.05,4.4
-2024H1,9.17,20.8
-2024FY,15.25,68.5
-2025H1,22.74,148.0
-2025FY,54.73,259.0
-2026H1,61.02,168.4
@@ -1,10 +0,0 @@
-period,inventory,yoy
-2017FY,0.16,
-2018FY,0.29,87.0
-2019FY,0.96,231.4
-2020FY,2.25,134.0
-2021FY,7.89,250.0
-2022FY,8.67,9.9
-2023FY,9.05,4.4
-2024FY,15.25,68.5
-2025FY,54.73,259.0
@@ -1,8 +0,0 @@
-period,inventory,yoy
-2020H1,2.24,
-2021H1,3.16,41.0
-2022H1,9.57,203.0
-2023H1,7.59,-20.8
-2024H1,9.17,20.8
-2025H1,22.74,148.0
-2026H1,61.02,168.4
@@ -1,8 +0,0 @@
-period,china_retail_revenue,china_average_stores,china_per_store,apac_retail_revenue,apac_average_stores,apac_per_store,americas_retail_revenue,americas_average_stores,americas_per_store,europe_other_retail_revenue,europe_other_average_stores,europe_other_per_store
-2024H1,20.04,381.0,526.0,,,,,,,,,
-2024H2,25.97,415.0,625.8,11.83,46.5,2544.1,2.54,16.0,1587.5,1.16,11.5,1008.7
-2024FY,46.01,397.0,1158.9,,,,,,,,,
-2025H1,44.06,437.0,1008.2,14.93,61.5,2427.6,7.37,31.5,2339.7,2.72,16.0,1700.0
-2025H2,56.69,444.0,1276.8,28.26,77.0,3670.1,12.67,52.5,2413.3,5.84,27.0,2163.0
-2025FY,100.75,438.0,2300.2,43.19,69.5,6214.4,20.04,43.0,4660.5,8.56,25.0,3424.0
-2026H1,60.90,450.0,1353.3,17.16,87.5,1961.1,9.03,75.0,1204.0,4.03,40.5,995.1
@@ -1,3 +0,0 @@
-period,china_retail_revenue,china_average_stores,china_per_store,apac_retail_revenue,apac_average_stores,apac_per_store,americas_retail_revenue,americas_average_stores,americas_per_store,europe_other_retail_revenue,europe_other_average_stores,europe_other_per_store
-2024FY,46.01,397.0,1158.9,,,,,,,,,
-2025FY,100.75,438.0,2300.2,43.19,69.5,6214.4,20.04,43.0,4660.5,8.56,25.0,3424.0
@@ -1,6 +0,0 @@
-period,china_retail_revenue,china_average_stores,china_per_store,apac_retail_revenue,apac_average_stores,apac_per_store,americas_retail_revenue,americas_average_stores,americas_per_store,europe_other_retail_revenue,europe_other_average_stores,europe_other_per_store
-2024H1,20.04,381.0,526.0,,,,,,,,,
-2024H2,25.97,415.0,625.8,11.83,46.5,2544.1,2.54,16.0,1587.5,1.16,11.5,1008.7
-2025H1,44.06,437.0,1008.2,14.93,61.5,2427.6,7.37,31.5,2339.7,2.72,16.0,1700.0
-2025H2,56.69,444.0,1276.8,28.26,77.0,3670.1,12.67,52.5,2413.3,5.84,27.0,2163.0
-2026H1,60.90,450.0,1353.3,17.16,87.5,1961.1,9.03,75.0,1204.0,4.03,40.5,995.1
@@ -1,8 +0,0 @@
-period,china_robot_revenue,china_average_stores,china_per_store,overseas_robot_revenue,overseas_average_stores,overseas_per_store
-2024H1,3.38,2207.0,15.3,,,,
-2024H2,4.13,2281.5,18.1,0.49,,
-2024FY,7.51,2264.5,33.2,0.80,116.0,69.0
-2025H1,6.78,2388.0,28.4,1.42,146.5,96.9
-2025H2,6.68,2416.5,27.6,2.02,200.5,100.7
-2025FY,13.46,2367.5,56.9,3.44,187.0,184.0
-2026H1,7.79,2439.0,31.9,1.72,293.0,58.7
@@ -1,3 +0,0 @@
-period,china_robot_revenue,china_average_stores,china_per_store,overseas_robot_revenue,overseas_average_stores,overseas_per_store
-2024FY,7.51,2264.5,33.2,0.80,116.0,69.0
-2025FY,13.46,2367.5,56.9,3.44,187.0,184.0
@@ -1,6 +0,0 @@
-period,china_robot_revenue,china_average_stores,china_per_store,overseas_robot_revenue,overseas_average_stores,overseas_per_store
-2024H1,3.38,2207.0,15.3,,,,
-2024H2,4.13,2281.5,18.1,0.49,,
-2025H1,6.78,2388.0,28.4,1.42,146.5,96.9
-2025H2,6.68,2416.5,27.6,2.02,200.5,100.7
-2026H1,7.79,2439.0,31.9,1.72,293.0,58.7
@@ -1,6 +0,0 @@
-period,global,china,apac,americas,europe,other
-2024H1,457,87.3,8.5,2.2,2.0,0.0
-2024FY,521,82.7,10.4,4.2,2.7,0.0
-2025H1,571,77.6,12.1,7.2,3.2,0.0
-2025FY,630,70.6,13.5,10.2,5.7,0.0
-2026H1,676,67.3,13.3,12.7,6.7,0.0
@@ -1,3 +0,0 @@
-period,global,china,apac,americas,europe,other
-2024FY,521,82.7,10.4,4.2,2.7,0.0
-2025FY,630,70.6,13.5,10.2,5.7,0.0
@@ -1,4 +0,0 @@
-period,global,china,apac,americas,europe,other
-2024H1,457,87.3,8.5,2.2,2.0,0.0
-2025H1,571,77.6,12.1,7.2,3.2,0.0
-2026H1,676,67.3,13.3,12.7,6.7,0.0
@@ -1,14 +1,14 @@
 period,inventory_days,receivable_days,payable_days,cash_conversion_cycle
-2020H1,102.0,9.6,27.8,83.8
-2020FY,63.9,9.0,28.8,44.1
-2021H1,74.7,10.9,41.1,44.6
-2021FY,106.9,10.1,30.4,86.6
-2022H1,159.9,11.7,44.9,126.6
-2022FY,154.0,14.5,47.0,121.5
-2023H1,131.9,13.0,54.7,90.2
-2023FY,132.7,14.9,51.9,95.7
-2024H1,100.6,11.6,54.8,57.4
-2024FY,102.4,11.2,53.6,59.9
-2025H1,83.5,9.5,49.1,43.9
-2025FY,123.3,6.9,36.6,93.6
-2026H1,201.2,8.2,42.9,166.5
+2020H1,101.4,9.6,27.6,83.3
+2020FY,63.0,8.9,28.4,43.5
+2021H1,74.3,10.9,40.9,44.3
+2021FY,105.4,10.0,29.9,85.5
+2022H1,159.0,11.6,44.7,125.9
+2022FY,151.8,14.3,46.3,119.8
+2023H1,131.2,12.9,54.4,89.7
+2023FY,130.9,14.7,51.2,94.4
+2024H1,100.0,11.6,54.5,57.1
+2024FY,101.0,11.0,52.9,59.1
+2025H1,83.1,9.4,48.8,43.7
+2025FY,121.6,6.8,36.1,92.3
+2026H1,200.0,8.1,42.6,165.5
@@ -0,0 +1,22 @@
+# 泡泡玛特财报分析底稿
+
+本目录是泡泡玛特的唯一维护数据层。日常只维护 [泡泡玛特_财报分析底稿.xlsx](泡泡玛特_财报分析底稿.xlsx)：图表使用的 `data/ppmt/*.csv` 由脚本生成，不直接编辑。
+
+## 工作簿结构
+
+- `说明`：维护入口、口径和颜色约定。
+- `资产负债表`：期末时点的财报明细。
+- `利润表`：半年累计与全年累计的财报明细。
+- `现金流量表`：现金流量表披露的原始明细。
+- `资产分类规则`：把财报明细映射为“类现金、投资资产、有息负债”等分析分类的规则；不维护分类金额。
+- `检查`：资产负债、现金流量的勾稽差额。出现红色警告时，应回到来源核对，不能为了消除警告而改数。
+
+## 更新流程
+
+1. 将新财报 PDF、截图或原始表格保留在 `data/ppmt/raw/`，作为证据文件。
+2. 由 AI 读取证据后，将直接披露的科目、期间和金额录入本工作簿的蓝色数字区域，并记录来源定位。
+3. 检查期间、单位和勾稽关系；确认后再生成图表 CSV 与图表。
+
+只维护基础值：收入、门店数、财报科目等。同比、占比、利润率、H2 单半年和分析分类小计都由后续脚本计算。若财报另有披露占比，可作为核对依据，不应成为第二套手工维护的数据。
+
+期间约定：`FY` 是全年累计/年末，`H1` 是上半年累计/六月末；不得把来源的 `H2` 直接当作下半年单期。金额单位为人民币亿元。
@@ -0,0 +1,172 @@
+# 财报图表项目数据架构改造方案
+
+> 状态：建议实施
+>
+> 目标：保持静态图表站点的低复杂度，同时建立可追溯、可重复生成的数据链路。
+
+## 1. 决策
+
+当前不建设在线数据库和后端 API。
+
+本项目目前是静态数据展示工具：浏览器按需读取 CSV，数据量很小，没有多人并发写入、权限控制、运行时查询或服务端计算的需求。此时引入 Postgres、API、鉴权和迁移机制会增加运维成本，但不能解决当前的数据正确性与可维护性问题。
+
+CSV 保留为图表的运行时交付格式；需要改造的是 CSV 之前的数据生产链路。
+
+## 2. 当前问题
+
+### 2.1 权威数据源不一致
+
+设计文档和 `data/ppmt/rawData/README.md` 将 `财报分析底稿.xlsx` 定义为唯一维护入口，但 `scripts/normalize_ppmt_financials.mjs` 实际读取的是 `data/ppmt/raw/*.csv`。底稿更新不会稳定地传递到图表。
+
+### 2.2 派生产物被当作输入
+
+归一化脚本的一部分通过 `readRuntimeRows()` 读取 `data/ppmt/*.csv`，再生成年度、半年等视图。IP、品类、资产等数据不能完全从上游重新构建；运行时 CSV 被删除、手改或过期时，会影响后续结果。
+
+### 2.3 数据更新不完整
+
+已出现底稿含有新期间、但相应图表 CSV 尚未更新的情况。问题不在某个数值，而在于链路没有可重复的全量构建保证。
+
+### 2.4 发布边界不清晰
+
+Vite 目前递归复制 `data/` 下的全部 CSV，因此原始披露 CSV 也会进入 `dist/`。原始证据、维护输入和公开运行时数据应有物理边界。
+
+### 2.5 现有测试只验证展示
+
+`pnpm test` 已能验证 manifest、CSV 可读性、图表渲染和部分视觉规范；还没有验证期间、单位、勾稽、同比口径、来源定位或派生逻辑。
+
+## 3. 目标架构
+
+```text
+原始披露文件（PDF / XLSX / 原始导出 CSV，只读证据）
+        │
+        ▼
+人工录入与复核的规范化基础事实层（唯一权威）
+        │  数据构建：映射、同比、期间拆分、勾稽、图表数据整形
+        ▼
+图表运行时 CSV（完全可重建，不允许手工修改）
+        │
+        ▼
+静态站点：manifest + Vanilla JS + ECharts
+```
+
+### 3.1 各层职责
+
+| 层 | 内容 | 是否人工编辑 | 发布到网站 |
+| --- | --- | --- | --- |
+| 原始证据 | 财报 PDF、公司 XLSX、原始导出 CSV、截图 | 否 | 否 |
+| 基础事实层 | 直接披露的数值、期间、单位、来源定位、科目映射 | 是，受控编辑 | 否 |
+| 派生层 | H2、同比、利润率、分类汇总、年度/半年视图 | 否，脚本生成 | 否 |
+| 图表 CSV | 仅包含图表需要的字段和布局 | 否，脚本生成 | 是 |
+
+## 4. 基础事实层如何维护
+
+基础事实层需要人工维护，但人工只编辑直接披露且可追溯的事实。
+
+允许人工编辑的内容：
+
+- 原始财报直接披露的金额、门店数等基础数值；
+- 期间、币种、单位、合并口径；
+- 来源文件、页码、表格、行号或单元格定位；
+- 科目映射与分类规则，例如“类现金”“投资资产”“经营资产”。
+
+禁止人工编辑的内容：
+
+- 同比、环比、占比、利润率；
+- `H2 = FY - H1` 等期间拆分；
+- 分析分类小计；
+- 任何图表运行时 CSV。
+
+现有 `财报分析底稿.xlsx` 可以继续作为人工录入界面。改造后的要求是：构建脚本必须从这份工作簿或其一次性导出的规范化输入文件读取，不再并行从其他位置读取同一事实。工作簿是编辑器，不是与其他 CSV 并列的第二权威来源。
+
+基础事实建议采用“长表”结构，便于复用、校验和未来接入多家公司：
+
+| company_id | metric_id | period_end | period_type | value | unit | source_file | source_locator | revision |
+| --- | --- | --- | --- | ---: | --- | --- | --- | --- |
+| ppmt | revenue | 2026-06-30 | H1_YTD | 171.73 | CNY_100M | 2026H1.pdf | p.12 / 利润表 / 营业收入 | 1 |
+
+运营类事实可使用相同结构，或按需要增加 `dimension_type`、`dimension_value`，例如地区、渠道、IP 和品类。
+
+## 5. 目录与构建边界
+
+建议将数据目录拆分，避免将证据文件误发布：
+
+```text
+data/
+  ppmt/
+    evidence/                 # 原始披露文件，只读，不发布
+    input/                    # 基础事实与映射规则，受控维护，不发布
+    generated/                # 构建中间产物，可删除并重建，不发布
+public/
+  data/
+    ppmt/                     # 仅图表运行时 CSV，发布
+scripts/
+  import_ppmt_facts.*         # 从 XLSX 或受控输入导入基础事实
+  build_ppmt_datasets.*       # 从基础事实完整生成图表 CSV
+  check_ppmt_data.*           # 质量与勾稽校验
+```
+
+Vite 仅复制 `public/`；不再以“复制整个 `data/` 再过滤扩展名”的方式发布文件。
+
+## 6. 数据构建与校验
+
+推荐提供三个明确命令：
+
+```bash
+pnpm data:import   # 可选：从人工维护的 XLSX 导出/读取基础事实
+pnpm data:build    # 从唯一权威源全量生成 generated 与 public/data
+pnpm data:check    # 校验输入、派生结果与勾稽关系
+pnpm test          # 图表渲染和展示回归
+pnpm build         # 静态站点构建
+```
+
+`data:build` 必须满足幂等性：在清空 `generated/` 和 `public/data/` 后，仍能只凭 `input/` 与 `evidence/` 完整生成相同的图表数据。构建脚本不得读取自身输出目录作为输入。
+
+`data:check` 至少覆盖：
+
+- 必填字段、数值类型、单位和币种；
+- 期间格式、排序、重复与缺失；
+- 流量类 `H2 = FY - H1`，时点类 H2/年末口径；
+- 同比仅比较同口径上一年期间；
+- 资产 = 负债 + 权益、现金流勾稽等适用检查；
+- 图表字段 schema、空值约束和合理数值范围；
+- 每条人工事实都有来源定位。
+
+## 7. 分阶段实施
+
+### P0：先恢复单一真相源
+
+1. 决定以现有底稿 XLSX 作为人工维护入口。
+2. 为底稿建立稳定导入器，输出规范化基础事实；或将其导出为版本化输入 CSV。
+3. 将现有脚本中读取运行时 CSV 的逻辑改为读取基础事实。
+4. 将图表 CSV 移到仅发布目录，原始证据移出发布路径。
+5. 增加 `data:check` 的最小校验与全量重建测试。
+
+完成标准：删除全部图表 CSV 后，仍能从唯一输入源重建所有图表；底稿新增期间后，数据检查能明确指出哪些图表应更新。
+
+### P1：第二家公司接入后抽象共性
+
+1. 固化跨公司通用的基础事实 schema、期间模型、来源定位格式和校验规则。
+2. 仅在两个公司确实复用同一字段映射与口径时，抽取通用图表或数据模板。
+3. 为 manifest 增加可选的数据契约版本，避免图表代码默默依赖 CSV 列名。
+
+### P2：需要分析型查询时引入本地数据库
+
+当需要跨公司指标筛选、较多事实数据查询、复杂聚合或批量分析时，引入 DuckDB + Parquet 作为本地构建和分析层。它可以替代或补强 `generated/`，但前端仍然发布静态 CSV 或预聚合 JSON。
+
+### P3：满足以下条件才建设在线数据库
+
+仅在出现多人在线录入、权限分级、审计版本、自动数据抓取、实时更新或面向用户的任意组合查询时，再建设 Postgres + 后端 API。
+
+在线数据库只替换“基础事实层的存储与协作方式”；图表运行时仍应优先使用缓存后的静态数据集或受版本控制的 API 响应，不应让每张图直接查询底层事实表。
+
+## 8. 数据库选择原则
+
+“使用数据库”不是替代数据治理的方案。无论存储介质是 XLSX、CSV、DuckDB 还是 Postgres，都必须先满足：
+
+1. 只有一个权威输入源；
+2. 派生产物可从该输入源完整重建；
+3. 每个数值可回溯到披露来源；
+4. 口径、单位和期间语义经过自动校验；
+5. 前端只消费稳定的数据契约。
+
+在当前规模下，最优路线是“XLSX 受控人工录入 + 规范化事实 + 自动生成 CSV + 静态发布”，而不是立即迁移在线数据库。
@@ -23,10 +23,13 @@
 ## 2. 当前架构

 ```
-原始财报 / PDF / XLSX（仓库外保存）
-   │ 人工、AI 或脚本：抽取、归一化、口径确认
+原始财报 / PDF / XLSX / 截图（证据文件）
+   │ 人工或 AI 提取、复核并记录来源定位
   ▼
-data/{company}/*.csv                 ← 运行时数据
+data/{company}/rawData/财报分析底稿.xlsx ← 财报分析底稿：唯一可维护的数据层
+   │ 脚本：分类、同比/占比、年度/半年视图、勾稽
+   ▼
+data/{company}/*.csv                  ← 图表运行时数据，可随时重建，禁止手工编辑
   ▼
 manifest.json                        ← 展示编排与图表配置的唯一事实来源
   ├─ chartCatalog                   ← 每张图只定义一次
@@ -69,7 +72,8 @@ js/main.js + js/ui.js                ← 解析引用、渲染三栏、导出和
 - `js/charts/index.js`：注册 `key → build 函数`。
 - `js/charts/*.js`：只处理该图的业务字段与 ECharts 表达。
 - `js/theme.js`：字体、配色、图例、坐标轴、脚注和水印等统一视觉规范。
- `data/{company}/`：图表需要的规范化 CSV。
+- `data/{company}/rawData/`：财报分析底稿。每家公司以一份可读、可维护的工作簿作为入口；基础科目、期间、单位、来源文件/工作表/定位信息在此维护，分析分类通过独立规则映射，不复制维护分类金额。
+- `data/{company}/*.csv`：图表需要的规范化 CSV，由底稿生成，不是维护入口。

 ---

@@ -28,10 +28,6 @@ import { channelRevenueChart } from './ppmt_channel_revenue.js';
 import { storesRetailChart } from './ppmt_stores_retail.js';
 import { storesRobotChart } from './ppmt_stores_robot.js';
 import { regionShareChart } from './ppmt_region_share.js';
-import { inventoryBalanceChart } from './ppmt_inventory.js';
-import { storesRetailShareChart } from './ppmt_stores_retail_share.js';
-import { retailStoreRevenueChart } from './ppmt_retail_store_revenue.js';
-import { robotStoreRevenueChart } from './ppmt_robot_store_revenue.js';

 /** 图表构建函数注册表。manifest 只引用这里的 key，不直接引用 JS 函数。 */
 export const chartRegistry = {
@@ -65,10 +61,6 @@ export const chartRegistry = {
  'ppmt-stores-retail': storesRetailChart,
  'ppmt-stores-robot': storesRobotChart,
  'ppmt-region-share': regionShareChart,
-  'ppmt-inventory': inventoryBalanceChart,
-  'ppmt-stores-retail-share': storesRetailShareChart,
-  'ppmt-retail-store-revenue': retailStoreRevenueChart,
-  'ppmt-robot-store-revenue': robotStoreRevenueChart,
 };

 export function getChartBuilder(key) {
@@ -31,10 +31,6 @@ export function channelRevenueChart(rows, { width, height, view = 'all' }) {
  const totalMax = Math.max(...rows.map((row) => num(row.total_revenue) || 0));
  const axisMax = NICE_MAX.find((max) => max >= totalMax * 1.02)
    ?? Math.ceil((totalMax * 1.2) / 100) * 100;
-  const yoyValues = col(rows, 'revenue_yoy').filter(Number.isFinite);
-  // 全部／半年视图存在 2022H2 的负增长，右轴需包含负区间，避免折线被 0 轴裁切。
-  const yoyMin = Math.min(0, Math.floor(Math.min(...yoyValues) / 50) * 50);
-  const yoyMax = Math.max(50, Math.ceil(Math.max(...yoyValues) / 50) * 50);
  // 绘图区像素高：画布高 − grid top 145 − grid bottom 90
  const pxPerUnit = (height - LAYOUT.gridTop - 90) / axisMax;

@@ -115,8 +111,8 @@ export function channelRevenueChart(rows, { width, height, view = 'all' }) {
        nameGap: 60,
        nameRotate: -90,
        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: yoyMin,
-        max: yoyMax,
+        min: 0,
+        max: 250,
        interval: 50,
        axisLabel: { ...axisCommon.axisLabel, formatter: '{value}%' },
        splitLine: { show: false },
@@ -1,82 +0,0 @@
-import { col } from '../csv.js';
-import {
-  FINANCIAL_COLORS, PALETTE, FONT, axisCommon, standalonePeriodAxis, titleStyle, standardLegend,
-  financialViewLabel, barSeries, lineSeries, footnote, watermarks, LAYOUT,
-} from '../theme.js';
-
-/** 泡泡玛特存货余额与同报告期同比：存货为期末时点数。 */
-export function inventoryBalanceChart(rows, { width, height, view = 'all' }) {
-  const yoy = col(rows, 'yoy').filter(Number.isFinite);
-  const yoyMin = Math.min(0, Math.floor(Math.min(...yoy) / 20) * 20);
-  const yoyMax = Math.max(50, Math.ceil(Math.max(...yoy) / 50) * 50);
-  const barWidth = view === 'all' ? 28.8 : 60.8;
-
-  return {
-    backgroundColor: '#fff',
-    title: {
-      ...titleStyle,
-      text: `泡泡玛特存货余额及同比（${financialViewLabel(view)}）`,
-      subtext: '柱状为报告期末存货余额；折线为同报告期同比（H1 对 H1、全年对全年）',
-    },
-    legend: standardLegend(['存货余额', '存货同比']),
-    grid: { left: 78, right: 82, top: LAYOUT.gridTop, bottom: 90 },
-    tooltip: {
-      trigger: 'axis',
-      axisPointer: { type: 'shadow' },
-      textStyle: { fontFamily: FONT },
-      valueFormatter: (value) => `${value.toFixed(1)}`,
-    },
-    xAxis: { ...standalonePeriodAxis, data: col(rows, 'period') },
-    yAxis: [
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '存货余额（亿元）',
-        nameLocation: 'middle',
-        nameGap: 52,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: 0,
-      },
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '同比（%）',
-        nameLocation: 'middle',
-        nameGap: 58,
-        nameRotate: -90,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: yoyMin,
-        max: yoyMax,
-        interval: (yoyMax - yoyMin) / 5,
-        axisLabel: { ...axisCommon.axisLabel, formatter: '{value}%' },
-        splitLine: { show: false },
-      },
-    ],
-    series: [
-      {
-        ...barSeries({
-          name: '存货余额',
-          data: col(rows, 'inventory'),
-          color: FINANCIAL_COLORS.investingCashFlow,
-          barWidth,
-          formatter: (point) => point.value.toFixed(1),
-        }),
-        emphasis: { focus: 'series' },
-      },
-      {
-        ...lineSeries({
-          name: '存货同比',
-          data: col(rows, 'yoy'),
-          color: FINANCIAL_COLORS.cashConversion,
-          formatter: (point) => `${point.value.toFixed(1)}%`,
-        }),
-        yAxisIndex: 1,
-        z: 10,
-      },
-    ],
-    graphic: [
-      ...watermarks(width, height),
-      footnote('数据来源：泡泡玛特历年合并资产负债表；存货为报告期末时点数 | @思考的Joey'),
-    ],
-  };
-}
@@ -1,110 +0,0 @@
-import { col } from '../csv.js';
-import {
-  PALETTE, FONT, axisCommon, standalonePeriodAxis, titleStyle, standardLegend,
-  financialViewLabel, barSeries, lineSeries, footnote, watermarks, LAYOUT,
-} from '../theme.js';
-
-const REGIONS = [
-  { key: 'china', name: '中国（含港澳台）', color: '#3F7FBE' },
-  { key: 'apac', name: '亚太', color: '#36A28F' },
-  { key: 'americas', name: '美洲', color: '#E4A349' },
-  { key: 'europe_other', name: '欧洲及其他', color: PALETTE.brown },
-];
-
-function yoyByPeriod(rows, key) {
-  const values = new Map(rows.map((row) => [row.period, row[key]]));
-  return rows.map((row) => {
-    const year = Number(row.period.slice(0, 4));
-    const prior = values.get(`${year - 1}${row.period.slice(4)}`);
-    return Number.isFinite(row[key]) && Number.isFinite(prior) && prior !== 0
-      ? (row[key] / prior - 1) * 100
-      : null;
-  });
-}
-
-/** 各地区零售店单店收入：收入 ÷ 期初、期末平均零售店数。 */
-export function retailStoreRevenueChart(rows, { width, height, view = 'all' }) {
-  const values = REGIONS.flatMap((region) => col(rows, `${region.key}_per_store`)).filter(Number.isFinite);
-  const yMax = Math.ceil(Math.max(...values) / 1000) * 1000;
-  const yoySeries = REGIONS.map((region) => ({ ...region, data: yoyByPeriod(rows, `${region.key}_per_store`) }));
-  const yoyValues = yoySeries.flatMap((region) => region.data).filter(Number.isFinite);
-  const yoyMin = Math.min(0, Math.floor(Math.min(...yoyValues) / 50) * 50);
-  const yoyMax = Math.max(50, Math.ceil(Math.max(...yoyValues) / 50) * 50);
-  const legendItems = REGIONS.flatMap((region) => [region.name, `${region.name}同比`]);
-
-  return {
-    backgroundColor: '#fff',
-    title: {
-      ...titleStyle,
-      text: `泡泡玛特各地区零售店单店收入（${financialViewLabel(view)}）`,
-      subtext: '柱状为单店收入；同色虚线为单店收入同比；H2为下半年单期',
-    },
-    legend: standardLegend(legendItems, { itemGap: 18 }),
-    grid: { left: 78, right: 42, top: LAYOUT.gridTop, bottom: 90 },
-    tooltip: {
-      trigger: 'axis',
-      axisPointer: { type: 'shadow' },
-      textStyle: { fontFamily: FONT },
-      formatter: (params) => `<b>${params[0].axisValue}</b><br/>${params
-        .filter((item) => Number.isFinite(item.value))
-        .map((item) => `${item.marker}${item.seriesName}: ${Number(item.value).toFixed(1)}${item.seriesName.endsWith('同比') ? '%' : ' 万元/店'}`)
-        .join('<br/>')}`,
-    },
-    xAxis: { ...standalonePeriodAxis, data: col(rows, 'period') },
-    yAxis: [
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '单店收入（万元/店）',
-        nameLocation: 'middle',
-        nameGap: 58,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: 0,
-        max: yMax,
-        interval: yMax / 5,
-      },
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '单店收入同比（%）',
-        nameLocation: 'middle',
-        nameGap: 60,
-        nameRotate: -90,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: yoyMin,
-        max: yoyMax,
-        interval: 50,
-        axisLabel: { ...axisCommon.axisLabel, formatter: '{value}%' },
-        splitLine: { show: false },
-      },
-    ],
-    series: [
-      ...REGIONS.map((region) => ({
-        ...barSeries({
-          name: region.name,
-          data: col(rows, `${region.key}_per_store`),
-          color: region.color,
-          barWidth: view === 'all' ? 17 : 34,
-          formatter: (point) => point.value.toFixed(0),
-        }),
-        emphasis: { focus: 'series' },
-      })),
-      ...yoySeries.map((region) => ({
-        ...lineSeries({
-          name: `${region.name}同比`,
-          data: region.data,
-          color: region.color,
-          width: 2,
-          formatter: (point) => `${point.value.toFixed(0)}%`,
-        }),
-        yAxisIndex: 1,
-        lineStyle: { color: region.color, width: 2, type: 'dashed' },
-        z: 10,
-      })),
-    ],
-    graphic: [
-      ...watermarks(width, height),
-      footnote('数据来源：泡泡玛特历年公告；收入仅含“零售店”渠道；H2收入=全年−H1\n2024年海外各区域缺少期初店数，相关单店收入留空 | @思考的Joey'),
-    ],
-  };
-}
@@ -1,108 +0,0 @@
-import { col } from '../csv.js';
-import {
-  PALETTE, FONT, axisCommon, standalonePeriodAxis, titleStyle, standardLegend,
-  financialViewLabel, barSeries, lineSeries, footnote, watermarks, LAYOUT,
-} from '../theme.js';
-
-const REGIONS = [
-  { key: 'china', name: '中国（含港澳台）', color: '#71AD81' },
-  { key: 'overseas', name: '海外', color: PALETTE.brown },
-];
-
-function yoyByPeriod(rows, key) {
-  const values = new Map(rows.map((row) => [row.period, row[key]]));
-  return rows.map((row) => {
-    const year = Number(row.period.slice(0, 4));
-    const prior = values.get(`${year - 1}${row.period.slice(4)}`);
-    return Number.isFinite(row[key]) && Number.isFinite(prior) && prior !== 0
-      ? (row[key] / prior - 1) * 100
-      : null;
-  });
-}
-
-/** 中国与海外机器人商店单店收入：收入 ÷ 期初、期末平均机器人商店数。 */
-export function robotStoreRevenueChart(rows, { width, height, view = 'all' }) {
-  const values = REGIONS.flatMap((region) => col(rows, `${region.key}_per_store`)).filter(Number.isFinite);
-  const yMax = Math.ceil(Math.max(...values) / 50) * 50;
-  const yoySeries = REGIONS.map((region) => ({ ...region, data: yoyByPeriod(rows, `${region.key}_per_store`) }));
-  const yoyValues = yoySeries.flatMap((region) => region.data).filter(Number.isFinite);
-  const yoyMin = Math.min(0, Math.floor(Math.min(...yoyValues) / 50) * 50);
-  const yoyMax = Math.max(50, Math.ceil(Math.max(...yoyValues) / 50) * 50);
-  const legendItems = REGIONS.flatMap((region) => [region.name, `${region.name}同比`]);
-
-  return {
-    backgroundColor: '#fff',
-    title: {
-      ...titleStyle,
-      text: `泡泡玛特机器人商店单店收入（${financialViewLabel(view)}）`,
-      subtext: '柱状为单店收入；同色虚线为单店收入同比；H2为下半年单期',
-    },
-    legend: standardLegend(legendItems, { itemGap: 18 }),
-    grid: { left: 78, right: 42, top: LAYOUT.gridTop, bottom: 90 },
-    tooltip: {
-      trigger: 'axis',
-      axisPointer: { type: 'shadow' },
-      textStyle: { fontFamily: FONT },
-      formatter: (params) => `<b>${params[0].axisValue}</b><br/>${params
-        .filter((item) => Number.isFinite(item.value))
-        .map((item) => `${item.marker}${item.seriesName}: ${Number(item.value).toFixed(1)}${item.seriesName.endsWith('同比') ? '%' : ' 万元/店'}`)
-        .join('<br/>')}`,
-    },
-    xAxis: { ...standalonePeriodAxis, data: col(rows, 'period') },
-    yAxis: [
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '单店收入（万元/店）',
-        nameLocation: 'middle',
-        nameGap: 58,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: 0,
-        max: yMax,
-        interval: yMax / 5,
-      },
-      {
-        ...axisCommon,
-        type: 'value',
-        name: '单店收入同比（%）',
-        nameLocation: 'middle',
-        nameGap: 60,
-        nameRotate: -90,
-        nameTextStyle: { color: PALETTE.grey, fontSize: 13, fontFamily: FONT },
-        min: yoyMin,
-        max: yoyMax,
-        interval: 50,
-        axisLabel: { ...axisCommon.axisLabel, formatter: '{value}%' },
-        splitLine: { show: false },
-      },
-    ],
-    series: [
-      ...REGIONS.map((region) => ({
-        ...barSeries({
-          name: region.name,
-          data: col(rows, `${region.key}_per_store`),
-          color: region.color,
-          barWidth: view === 'all' ? 28 : 52,
-          formatter: (point) => point.value.toFixed(0),
-        }),
-        emphasis: { focus: 'series' },
-      })),
-      ...yoySeries.map((region) => ({
-        ...lineSeries({
-          name: `${region.name}同比`,
-          data: region.data,
-          color: region.color,
-          width: 2,
-          formatter: (point) => `${point.value.toFixed(0)}%`,
-        }),
-        yAxisIndex: 1,
-        lineStyle: { color: region.color, width: 2, type: 'dashed' },
-        z: 10,
-      })),
-    ],
-    graphic: [
-      ...watermarks(width, height),
-      footnote('数据来源：泡泡玛特历年公告；海外收入=亚太+美洲+欧洲及其他的机器人商店收入；H2收入=全年−H1\n2024H1海外期末机器人商店数未披露，相关单店收入留空 | @思考的Joey'),
-    ],
-  };
-}
@@ -1,83 +0,0 @@
-import { col } from '../csv.js';
-import {
-  PALETTE, FONT, axisCommon, standalonePeriodAxis, titleStyle, standardLegend,
-  financialViewLabel, barSeries, footnote, watermarks, LAYOUT,
-} from '../theme.js';
-
-/** 泡泡玛特分地区零售门店占比：每根柱标准化为各期末全球门店数的 100%。 */
-const REGIONS = [
-  { key: 'china', name: '中国（含港澳台）', color: '#3F7FBE', labelColor: '#fff' },
-  { key: 'apac', name: '亚太', color: '#36A28F', labelColor: '#fff' },
-  { key: 'americas', name: '美洲', color: '#E4A349', labelColor: PALETTE.ink },
-  { key: 'europe', name: '欧洲', color: PALETTE.brown, labelColor: '#fff' },
-  { key: 'other', name: '其他', color: PALETTE.sand, labelColor: PALETTE.ink },
-];
-
-function sharePoint(value, labelColor) {
-  if (!Number.isFinite(value) || value === 0) return null;
-  return {
-    value,
-    label: value >= 5
-      ? { position: 'inside', color: labelColor }
-      : { show: false },
-  };
-}
-
-export function storesRetailShareChart(rows, { width, height, view = 'all' }) {
-  const periods = col(rows, 'period');
-  const barWidth = view === 'all' ? 48 : 80;
-
-  return {
-    backgroundColor: '#fff',
-    title: {
-      ...titleStyle,
-      text: `泡泡玛特分地区零售门店数量占比（${financialViewLabel(view)}）`,
-      subtext: '归一化堆叠：每根柱为当期末全球零售门店数的 100%',
-    },
-    legend: standardLegend(REGIONS.map((item) => item.name), { itemGap: 20 }),
-    tooltip: {
-      trigger: 'axis',
-      axisPointer: { type: 'shadow' },
-      textStyle: { fontFamily: FONT },
-      formatter: (params) => {
-        const row = rows[periods.indexOf(params[0].axisValue)];
-        return `<b>${params[0].axisValue}</b>　全球合计 ${row.global} 家<br/>${params
-          .filter((item) => item.value !== null)
-          .slice()
-          .reverse()
-          .map((item) => `${item.marker}${item.seriesName}: ${item.value.toFixed(1)}%`)
-          .join('<br/>')}`;
-      },
-    },
-    grid: { left: 78, right: 42, top: LAYOUT.gridTop, bottom: 100 },
-    xAxis: { ...standalonePeriodAxis, data: periods },
-    yAxis: {
-      ...axisCommon,
-      type: 'value',
-      min: 0,
-      max: 100,
-      interval: 20,
-      axisLabel: { ...axisCommon.axisLabel, formatter: '{value}%' },
-    },
-    series: REGIONS.map((item) => barSeries({
-      name: item.name,
-      data: rows.map((row) => sharePoint(row[item.key], item.labelColor)),
-      color: item.color,
-      barWidth,
-      stack: 'stores-retail-share',
-      label: {
-        show: true,
-        position: 'inside',
-        fontSize: 11,
-        fontFamily: FONT,
-        fontWeight: 500,
-        formatter: (point) => `${point.value.toFixed(1)}%`,
-      },
-      emphasis: { focus: 'series' },
-    })),
-    graphic: [
-      ...watermarks(width, height),
-      footnote('数据来源：泡泡玛特历年公告；门店数为各报告期末时点数\n中国=中国内地+港澳台；其他=全球合计−中国−亚太−美洲−欧洲 | @思考的Joey'),
-    ],
-  };
-}
@@ -36,7 +36,7 @@ export function workingCapitalDaysChart(rows, { width, height }) {
    title: {
      ...titleStyle,
      text: '泡泡玛特营运资本效率',
-      subtext: '现金转换周期 = 存货周转天数 + 应收周转天数 − 应付周转天数（中报按181天、年报按365天）',
+      subtext: '现金转换周期 = 存货周转天数 + 应收周转天数 − 应付周转天数',
    },
    legend: { ...legendStyle, top: LAYOUT.legendTop, data: SERIES.map((item) => item.name) },
    grid: { left: 78, right: 42, top: LAYOUT.gridTop, bottom: 90 },
@@ -78,7 +78,7 @@ export function workingCapitalDaysChart(rows, { width, height }) {
    })),
    graphic: [
      ...watermarks(width, height),
-      footnote('数据来源：泡泡玛特财务指标（合并报表）；原始180/360天口径已换算为181/365天 | @思考的Joey'),
+      footnote('数据来源：泡泡玛特财务指标（合并报表）| @思考的Joey'),
    ],
  };
 }
@@ -12,7 +12,6 @@
        "ppmt-bridge": { "key": "ppmt-bridge", "title": "净利率同比拆解", "csv": "data/ppmt/ppmt_margin_bridge.csv", "height": 700, "filename": "泡泡玛特净利率同比拆解" },
        "ppmt-margins": { "key": "ppmt-margins", "title": "利润率与费用率拆解", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_margins.csv", "annual": "data/ppmt/ppmt_margins_annual.csv", "halfyear": "data/ppmt/ppmt_margins_halfyear.csv" }, "height": 680, "filename": "泡泡玛特利润率与费用率拆解" },
        "ppmt-assets": { "key": "ppmt-assets", "title": "资产结构演变", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_assets.csv", "annual": "data/ppmt/ppmt_assets_annual.csv", "halfyear": "data/ppmt/ppmt_assets_halfyear.csv" }, "height": 680, "filename": "泡泡玛特资产结构" },
-        "ppmt-inventory": { "key": "ppmt-inventory", "title": "存货余额及同比", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_inventory.csv", "annual": "data/ppmt/ppmt_inventory_annual.csv", "halfyear": "data/ppmt/ppmt_inventory_halfyear.csv" }, "height": 680, "filename": "泡泡玛特存货余额及同比" },
        "ppmt-ip": { "key": "ppmt-ip", "title": "主要 IP 收入趋势", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_ip.csv", "annual": "data/ppmt/ppmt_ip_annual.csv", "halfyear": "data/ppmt/ppmt_ip_halfyear.csv" }, "height": 720, "filename": "泡泡玛特IP收入趋势" },
        "ppmt-ip-share": { "key": "ppmt-ip-share", "title": "主要 IP 收入占比", "csv": "data/ppmt/ppmt_ip_share_2026h1.csv", "height": 720, "filename": "泡泡玛特IP收入占比2026H1" },
        "ppmt-ip-share-history": { "key": "ppmt-ip-share-history", "title": "各 IP 半年收入占比", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_ip_share_history.csv", "annual": "data/ppmt/ppmt_ip_share_history_annual.csv", "halfyear": "data/ppmt/ppmt_ip_share_history_halfyear.csv" }, "height": 700, "filename": "泡泡玛特各IP半年收入占比" },
@@ -28,20 +27,16 @@
        "ppmt-expense-structure": { "key": "ppmt-expense-structure", "title": "费用结构与费用率", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_expense_structure.csv", "annual": "data/ppmt/ppmt_expense_structure_annual.csv", "halfyear": "data/ppmt/ppmt_expense_structure_halfyear.csv" }, "height": 700, "filename": "泡泡玛特费用结构与费用率" },
        "ppmt-channel-revenue": { "key": "ppmt-channel-revenue", "title": "渠道收入结构与增速", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_channel_revenue.csv", "annual": "data/ppmt/ppmt_channel_revenue_annual.csv", "halfyear": "data/ppmt/ppmt_channel_revenue_halfyear.csv" }, "height": 720, "filename": "泡泡玛特渠道收入结构与增速" },
        "ppmt-stores-retail": { "key": "ppmt-stores-retail", "title": "分地区零售门店数量与同比增速", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_stores_retail.csv", "annual": "data/ppmt/ppmt_stores_retail_annual.csv", "halfyear": "data/ppmt/ppmt_stores_retail_halfyear.csv" }, "height": 680, "filename": "泡泡玛特分地区零售门店数量与同比增速" },
-        "ppmt-stores-retail-share": { "key": "ppmt-stores-retail-share", "title": "分地区零售门店数量占比", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_stores_retail_share.csv", "annual": "data/ppmt/ppmt_stores_retail_share_annual.csv", "halfyear": "data/ppmt/ppmt_stores_retail_share_halfyear.csv" }, "height": 680, "filename": "泡泡玛特分地区零售门店数量占比" },
-        "ppmt-retail-store-revenue": { "key": "ppmt-retail-store-revenue", "title": "各地区零售店单店收入", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_retail_store_revenue.csv", "annual": "data/ppmt/ppmt_retail_store_revenue_annual.csv", "halfyear": "data/ppmt/ppmt_retail_store_revenue_halfyear.csv" }, "height": 680, "filename": "泡泡玛特各地区零售店单店收入" },
        "ppmt-stores-robot": { "key": "ppmt-stores-robot", "title": "分地区机器人商店数量与同比增速", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_stores_robot.csv", "annual": "data/ppmt/ppmt_stores_robot_annual.csv", "halfyear": "data/ppmt/ppmt_stores_robot_halfyear.csv" }, "height": 680, "filename": "泡泡玛特分地区机器人商店数量与同比增速" },
-        "ppmt-robot-store-revenue": { "key": "ppmt-robot-store-revenue", "title": "机器人商店单店收入", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_robot_store_revenue.csv", "annual": "data/ppmt/ppmt_robot_store_revenue_annual.csv", "halfyear": "data/ppmt/ppmt_robot_store_revenue_halfyear.csv" }, "height": 680, "filename": "泡泡玛特机器人商店单店收入" },
        "ppmt-region-share": { "key": "ppmt-region-share", "title": "分地区营收占比", "defaultView": "all", "views": { "all": "data/ppmt/ppmt_region_share.csv", "annual": "data/ppmt/ppmt_region_share_annual.csv", "halfyear": "data/ppmt/ppmt_region_share_halfyear.csv" }, "height": 680, "filename": "泡泡玛特分地区营收占比" }
      },
      "tabs": [
        { "id": "overview", "name": "概览", "chartRefs": ["ppmt-growth", "ppmt-profit", "ppmt-ip-share", "ppmt-region-channel"] },
        { "id": "profitability", "name": "盈利能力", "chartRefs": ["ppmt-growth", "ppmt-core-profit", "ppmt-core-margin", "ppmt-bridge", "ppmt-sankey", "ppmt-cash-conversion", "ppmt-cashflow-composition"] },
        { "id": "expense", "name": "费用率", "chartRefs": ["ppmt-margins", "ppmt-expense-structure"] },
-        { "id": "balance-sheet", "name": "资产负债", "chartRefs": ["ppmt-assets", "ppmt-inventory", "ppmt-liquidity-working-capital", "ppmt-working-capital-days"] },
+        { "id": "balance-sheet", "name": "资产负债", "chartRefs": ["ppmt-assets", "ppmt-liquidity-working-capital", "ppmt-working-capital-days"] },
        { "id": "ip-category", "name": "IP 与品类", "chartRefs": ["ppmt-ip", "ppmt-ip-share", "ppmt-ip-share-history", "ppmt-ip-ex-monsters", "ppmt-category"] },
-        { "id": "region-channel", "name": "区域渠道", "chartRefs": ["ppmt-region-channel", "ppmt-region-yoy", "ppmt-region-yoy-timeseries", "ppmt-channel-revenue", "ppmt-stores-retail", "ppmt-stores-retail-share", "ppmt-stores-robot", "ppmt-region-share"] },
-        { "id": "store-revenue", "name": "单店收入", "chartRefs": ["ppmt-retail-store-revenue", "ppmt-robot-store-revenue"] }
+        { "id": "region-channel", "name": "区域渠道", "chartRefs": ["ppmt-region-channel", "ppmt-region-yoy", "ppmt-region-yoy-timeseries", "ppmt-channel-revenue", "ppmt-stores-retail", "ppmt-stores-robot", "ppmt-region-share"] }
      ]
    },
    {
@@ -184,21 +184,13 @@ const workingCapitalDaysRows = indicators.dates.map((date, index) => ({
 })).filter((item) => (
  item.date >= '2020-01-01'
  && item.inventory !== null && item.receivable !== null && item.payable !== null
-)).map((item) => {
-  const period = periodFor(item.date);
-  // 原始指标表按半年 180 天、全年 360 天计算；此处统一换算为自然日 181/365 天。
-  const factor = period.endsWith('H1') ? 181 / 180 : 365 / 360;
-  const inventory = item.inventory * factor;
-  const receivable = item.receivable * factor;
-  const payable = item.payable * factor;
-  return [
-    period,
-    inventory.toFixed(1),
-    receivable.toFixed(1),
-    payable.toFixed(1),
-    (inventory + receivable - payable).toFixed(1),
-  ];
-}).reverse();
+)).map((item) => [
+  periodFor(item.date),
+  item.inventory.toFixed(1),
+  item.receivable.toFixed(1),
+  item.payable.toFixed(1),
+  (item.inventory + item.receivable - item.payable).toFixed(1),
+]).reverse();

 writeCSV('ppmt_working_capital_days.csv', [
  'period', 'inventory_days', 'receivable_days', 'payable_days', 'cash_conversion_cycle',
@@ -349,9 +341,9 @@ function yoyFor(rows, key, outputKey = key) {
  });
 }

-function toOutputRows(rows, headers, digits = {}, startYear = OUTPUT_START_YEAR) {
+function toOutputRows(rows, headers, digits = {}) {
  return rows
-    .filter((row) => Number(row.period.slice(0, 4)) >= startYear)
+    .filter((row) => Number(row.period.slice(0, 4)) >= OUTPUT_START_YEAR)
    .map((row) => headers.map((header) => {
      if (header === 'period') return row.period;
      const value = row[header];
@@ -359,14 +351,14 @@ function toOutputRows(rows, headers, digits = {}, startYear = OUTPUT_START_YEAR)
    }));
 }

-function writeViewFiles(base, headers, views, digits, startYear = OUTPUT_START_YEAR) {
+function writeViewFiles(base, headers, views, digits) {
  const filenames = {
    all: `${base}.csv`,
    annual: `${base}_annual.csv`,
    halfyear: `${base}_halfyear.csv`,
  };
  Object.entries(filenames).forEach(([view, filename]) => {
-    writeCSV(filename, headers, toOutputRows(views[view], headers, digits, startYear));
+    writeCSV(filename, headers, toOutputRows(views[view], headers, digits));
  });
 }

@@ -622,31 +614,6 @@ writeRuntimeViews('ppmt_category', ['period', 'plush', 'figure', 'mega', 'other'
  plush: 2, figure: 2, mega: 2, other: 2,
 });

-// 2024H1 起门店披露可将中国内地与港澳台合并，并按全球总数归一化为地区占比。
-const retailStoreRows = readRuntimeRows('ppmt_stores_retail.csv');
-const retailStoreShareViews = { all: [], annual: [], halfyear: [] };
-retailStoreRows.filter((row) => (
-  Number(row.period.slice(0, 4)) >= 2024
-  && ['mainland', 'hkmtw', 'apac', 'americas', 'europe', 'global'].every((key) => Number.isFinite(row[key]))
-)).forEach((row) => {
-  const china = row.mainland + row.hkmtw;
-  const other = row.global - china - row.apac - row.americas - row.europe;
-  const shareRow = {
-    period: row.period,
-    global: row.global,
-    china: china / row.global * 100,
-    apac: row.apac / row.global * 100,
-    americas: row.americas / row.global * 100,
-    europe: row.europe / row.global * 100,
-    other: other / row.global * 100,
-  };
-  retailStoreShareViews.all.push(shareRow);
-  retailStoreShareViews[row.period.endsWith('FY') ? 'annual' : 'halfyear'].push(shareRow);
-});
-writeRuntimeViews('ppmt_stores_retail_share', ['period', 'global', 'china', 'apac', 'americas', 'europe', 'other'], retailStoreShareViews, {
-  global: 0, china: 1, apac: 1, americas: 1, europe: 1, other: 1,
-});
-
 // 资产负债表为时点指标：H2 是年末时点，而非“全年累计”。
 function buildSnapshotViews(base, headers, digits) {
  const source = readRuntimeRows(`${base}.csv`);
@@ -676,25 +643,4 @@ buildSnapshotViews('ppmt_liquidity_working_capital', ['period', 'cash', 'total_l
  cash: 2, total_liabilities: 2, net_working_capital: 2,
 });

-// 存货为资产负债表期末时点数：半年仅取中报期末，年度仅取年报期末。
-// 同比严格按相同报告期（H1 对 H1、FY 对 FY）计算，不把中报与年报交叉比较。
-const inventoryBalances = row(balance, '存货');
-const inventoryViews = { all: [], annual: [], halfyear: [] };
-balance.dates.map((date, index) => ({
-  date,
-  inventory: inventoryBalances[index],
-})).filter(({ date, inventory }) => (
-  date >= '2017-01-01' && inventory > 0 && (date.endsWith('-06-30') || date.endsWith('-12-31'))
-)).sort((a, b) => a.date.localeCompare(b.date)).forEach(({ date, inventory }) => {
-  const row = { period: periodFor(date), inventory: inventory / HUNDRED_MILLION };
-  inventoryViews.all.push(row);
-  inventoryViews[row.period.endsWith('FY') ? 'annual' : 'halfyear'].push(row);
-});
-Object.keys(inventoryViews).forEach((view) => {
-  inventoryViews[view] = yoyFor(inventoryViews[view], 'inventory', 'yoy');
-});
-writeViewFiles('ppmt_inventory', ['period', 'inventory', 'yoy'], inventoryViews, {
-  inventory: 2, yoy: 1,
-}, 2017);
-
 console.log('已生成第二批连续图表的全部、年度、半年 CSV。');
Author	SHA1	Message	Date
R524809	d093a322d3	docs: 更新方案文档	2026-09-04 17:33:06 +08:00
R524809	04105ad6eb	feat:更新rawData	2026-09-04 16:47:14 +08:00
R524809	143cd1fbf8	docs: add maintainable PPMT financial workbook	2026-08-31 17:49:00 +08:00