exclude.json gains an "adpings" map (18 ids, value = package for
reference) alongside the existing package list. build_data.py drops those
Adpingids from both the 投手 ownership counts and the spend rows.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Package now renders as pkg_GEO, where GEO is the region with the highest
total spend for that package. The new Adpingid column shows the
top-spending Adping within that region, so label and id stay consistent.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
build_data.py now emits active_ids (Adpingids with spend in the window)
and owned_ids (Adpingids assigned to the buyer, exclusions removed).
active_ids sums to the 覆盖 Adpingid 数 card.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
- 各投手本周期内投放花费: draw value labels at each bar end via a small
inline Chart.js plugin (no extra CDN dependency); right padding added
so labels stay inside the panel.
- Prefix all spend figures with $: summary card, both chart axes and
tooltips, buyer subtotals, and package detail tables.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Add exclude.json (12 package names, permanent) and build_data.py so the
report is reproducible. Exclusion is exact-match on package name, applied
both to 平台消耗统计 rows and to Adpingids whose 投手 pkg is excluded.
Exact match avoids false positives (e.g. in.swiggy.android vs "igg").
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Refresh xlsx source and regenerate data.json from
平台消耗统计 (Date/Adpingid/花费) x 投手 (Adpingid/Media Buyer).
@某某 merged with 某某; Adpingid without a buyer excluded.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
