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fix(dataProcessor): parse DD/M/YY dates from column C in sell-out CSV

Browse Source 
Add support for European day-first date format (e.g. "23/2/26" = 23 Feb 2026)
in the sell-out CSV pipeline so months and years are correctly extracted from
column C of Amazon Sell Out 2023-2025.csv.

- normalizeMonth: detect DD/MM/YY when first part > 12, return "Mon-YY"
- mapRowToRecord: add 'C', 'Date', 'Fecha', 'DATA' to month column aliases
- validateSellOutHeaders: accept date columns as substitute for YEAR + MONTH

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Christian Vidal Wolf
2026-03-03 13:07:59 +01:00
co-authored by Claude Sonnet 4.6
parent ad1695d360
commit 2eed94b94b
10 changed files with 112 additions and 747 deletions
Download Patch File Download Diff File Expand all files Collapse all files
CLAUDE.md
+54 -76
View File
@@ -12,64 +12,24 @@ npm run preview # Preview production build
No test or lint scripts are configured. ESLint config exists (`eslint.config.js`) but has no npm script.
## Environment
## Deployment
Requires Node.js >= 18. Set `GEMINI_API_KEY` in `.env.local` for the AI chat feature (Google Gemini API).
## Project Structure
Project: `craze-analytix2` under Vercel team `christians-projects-dd62b5fc`.
```bash
vercel --prod --scope christians-projects-dd62b5fc
```
├── App.tsx # Main app component, global state orchestrator
├── index.tsx # React entry point
├── index.html # HTML template (Tailwind CDN loaded here)
├── types.ts # All TypeScript interfaces
├── vite.config.ts # Vite config (port 3000, proxy routes, path alias)
├── vercel.json # Vercel deployment config (API rewrites, SPA routing)
├── firebase.json # Firebase hosting config (alternative deployment)
├── api/ # Vercel serverless functions
│ ├── ask-gemini.ts # Gemini AI chat proxy
│ ├── fetch-data.ts # Sales CSV from Dropbox
│ ├── fetch-ads.ts # Ads Excel from Dropbox
│ ├── fetch-traffic.ts # Traffic data
│ ├── fetch-stock.ts # Item availability
│ ├── fetch-paneu-stock.ts # PAN-EU vendor stock
│ ├── fetch-uk-inventory.ts# UK inventory
│ ├── fetch-buybox.ts # Buy Box tracker
│ ├── fetch-forecast.ts # Forecast data
│ └── fetch-vendor-stock.ts
├── components/ # React components
│ ├── Dashboard.tsx # KPI cards, charts, YoY comparisons
│ ├── DataGrid.tsx # Pivot table with Excel-style filters (~96KB)
│ ├── WeeklyGrid.tsx # Weekly time-series view
│ ├── TopMovers.tsx # Growth/decline analysis
│ ├── AdsPerformance.tsx # Ad spend, ROAS, ACOS metrics
│ ├── ForecastView.tsx # Product forecasts with seasonality
│ ├── FilterBar.tsx # Sticky filter controls
│ ├── AIChat.tsx # AI assistant sidebar
│ ├── FileUpload.tsx # Manual data upload modal
│ ├── ExcelFilter.tsx # Advanced column filtering UI
│ ├── MultiSelectDropdown.tsx # Reusable filter dropdown
│ ├── StockBadge.tsx # Stock status indicator
│ ├── VendorStockBadge.tsx # Vendor stock indicator
│ ├── BuyBoxWarningBadge.tsx # Buy Box loss warnings
│ ├── Top50Badge.tsx # Top 50 product badge
│ ├── InColumnStockFilter.tsx # In-column stock filter
│ ├── NumericColumnFilter.tsx # Numeric column filter
│ ├── CrazeLogo.tsx # Header branding
│ ├── ErrorBoundary.tsx # Error handling wrapper
│ └── Icons.tsx # SVG icon library
├── services/ # Business logic and data layer
│ ├── dataProcessor.ts # Core data engine (~2400 lines)
│ ├── storage.ts # IndexedDB + localStorage caching
│ ├── geminiService.ts # AI context builder + API calls
│ └── filterHelper.ts # Filter utilities
└── public/ # Static data files
├── fc 26.xlsx # EU forecast
├── fc UK 26.xlsx # UK forecast
├── Item Availability.xlsx
├── Buy_Box_tracker.xlsx
└── Vendor Stock.xlsx
```
The `.vercel/project.json` must point to `projectId: prj_TYI5xVvuZ0kPI5Ap8zmX7cqfjsCi` and `orgId: team_5AhzZHthpZINNw9vOrKxOINr`.
## Environment Variables
| Variable | Used in | Purpose |
|---|---|---|
| `GEMINI_API_KEY` | `.env.local` | AI chat (Google Gemini) |
| `SUPABASE_URL` | Vercel env / serverless | Supabase project URL |
| `SUPABASE_SERVICE_KEY` | `api/experiments.ts`, `api/upload-vendor-data.ts` | Server-side Supabase access |
| `SUPABASE_ANON_KEY` | `services/supabase.ts` | Client-side Supabase access |
## Architecture
@@ -87,41 +47,59 @@ Dropbox (CSV/Excel files)
Data is cached in IndexedDB via `services/storage.ts`. On load, cached data displays immediately while fresh data fetches in the background.
All data sources are fetched automatically from Dropbox — there is no manual file upload.
### Key Files
- **App.tsx** — Main orchestrator. Holds all global state (rawData, adsData, trafficData, filters, etc.) and passes data/handlers as props to views.
- **services/dataProcessor.ts** (~2400 lines) — Core data engine. Handles CSV/Excel parsing, currency normalization (EU `1.234,56` and US `1,234.56` formats), Spanish/English month mapping, filtering (`filterData`, `filterAdsData`), aggregation (`aggregateData`), and pivot table generation (`pivotSalesData`).
- **types.ts** — All TypeScript interfaces: `SalesRecord`, `AdsRecord`, `TrafficRecord`, `ForecastRecord`, `FilterState`, `AggregatedData`, `PivotRow`, etc.
- **services/storage.ts** — IndexedDB + localStorage caching with schema versioning.
- **services/geminiService.ts** — Builds structured context from aggregated data and sends to Gemini API.
- **App.tsx** — Main orchestrator. Holds all global state and passes data/handlers as props to views. All data fetching (`handleDataFetch`, `handleAdsFetch`, `handleBSRFetch`, etc.) is initiated here.
- **services/dataProcessor.ts** (~2400 lines) — Core data engine. Handles CSV/Excel parsing, currency normalization (EU `1.234,56` and US `1,234.56` formats), Spanish/English month mapping, filtering (`filterData`, `filterAdsData`, `filterBsrData`), aggregation, and pivot table generation.
- **types.ts** — All TypeScript interfaces: `SalesRecord`, `AdsRecord`, `TrafficRecord`, `ForecastRecord`, `BSRRecord`, `FilterState`, `AggregatedData`, `PivotRow`, `Experiment`, etc.
- **services/storage.ts** — IndexedDB + localStorage caching with schema versioning. Increment `SCHEMA_VERSION` when changing cached data shapes.
- **services/experiments.ts** — CRUD operations for experiments via `/api/experiments` (Supabase-backed). Includes ASIN resolution logic for line-level experiments.
- **services/experimentAnalysis.ts** — Difference-in-Differences (DiD) statistical analysis engine. Uses ISO week boundaries (Monday start).
### Views (rendered conditionally by `view` state in App.tsx)
### Views
| View | Component | Purpose |
|------|-----------|---------|
| dashboard | Dashboard.tsx | KPI cards, charts, YoY comparisons |
| table | DataGrid.tsx | Pivot table with Excel-style column filters |
| weekly | WeeklyGrid.tsx | Weekly time-series breakdown |
| movers | TopMovers.tsx | Top growth/decline products |
| ads | AdsPerformance.tsx | Ad spend, ROAS, ACOS metrics |
| forecast | ForecastView.tsx | Product forecasts with velocity mapping |
| View key | Component | Purpose |
|----------|-----------|---------|
| `dashboard` | Dashboard.tsx | KPI cards, charts, YoY comparisons |
| `table` | DataGrid.tsx | Pivot table with Excel-style column filters |
| `weekly` | WeeklyGrid.tsx | Weekly time-series breakdown |
| `movers` | TopMovers.tsx | Top growth/decline products |
| `ads` | AdsPerformance.tsx | Ad spend, ROAS, ACOS metrics |
| `forecast` | ForecastView.tsx | Product forecasts with velocity mapping |
| `vendor` | VendorDataView.tsx | BSR trends, ratings per market. Shows product card (SKU/ASIN/title/BuyBox) when a single ASIN is filtered |
| `experiments` | ExperimentsView.tsx | A/B experiment tracking with DiD analysis and Bayesian verdicts |
### API Routes (`/api/`)
All serverless functions fetch data from Dropbox (direct download URLs with `dl=1`). Each returns the raw file content for client-side processing:
- `fetch-data.ts` (sales CSV), `fetch-ads.ts` (ads Excel), `fetch-traffic.ts`, `fetch-stock.ts`, `fetch-paneu-stock.ts`, `fetch-uk-inventory.ts`, `fetch-buybox.ts`, `fetch-forecast.ts`
- `ask-gemini.ts` — Proxies chat requests to Google Gemini API
All fetch routes proxy Dropbox direct-download URLs (`dl=1`) and return raw file content for client-side parsing:
- `fetch-data.ts` — Sales CSV
- `fetch-ads.ts` — Ads Excel (weekly)
- `fetch-traffic.ts` — Traffic/Glance Views Excel
- `fetch-stock.ts` — Item availability
- `fetch-paneu-stock.ts` — PAN-EU vendor stock
- `fetch-uk-inventory.ts` — UK inventory
- `fetch-buybox.ts` — Buy Box tracker Excel
- `fetch-forecast.ts` — Forecast Excel
- `fetch-bsr.ts` — BSR/ratings Excel (feeds Vendor tab)
- `ask-gemini.ts` — Proxies AI chat to Gemini API
- `experiments.ts` — CRUD for experiments stored in Supabase
- `upload-vendor-data.ts` — Batch upsert of vendor rows to Supabase
### Important Constants (in dataProcessor.ts)
- `PAN_EU_COUNTRIES = ['Amazon DE', 'Amazon IT', 'Amazon FR', 'Amazon ES']` — Default country filter when no customer is selected
- `MONTH_ORDER`, `MONTH_MAP` — Month normalization including Spanish names (Enero→Jan, etc.)
- `parseCurrency()` — Handles both EU and US number formats
- Country mapping normalizes various spellings (e.g., "Germany", "Deutschland", "DE", "Alemania" → "Amazon DE")
- `parseCurrency()` — Handles both EU (`1.234,56`) and US (`1,234.56`) number formats
- Country mapping normalizes various spellings → `Amazon DE`, `Amazon UK`, etc.
- `MONTH_MAP` — Normalizes Spanish month names (Enero→Jan, etc.)
### Filtering Architecture
Filters flow from `FilterBar.tsx` → `App.tsx` state → `filterData()`/`filterAdsData()` in dataProcessor.ts. When no customer filter is selected, ads data defaults to PAN_EU_COUNTRIES only. Sales data and ads data have separate filter functions.
Filters flow: `FilterBar.tsx` → `App.tsx` state → `filterData()` / `filterAdsData()` / `filterBsrData()` in dataProcessor.ts. When no customer filter is selected, ads data defaults to PAN_EU_COUNTRIES only. Sales and ads data have separate filter functions.
`globalAsinMetadata` (`Map<string, {sku, title, line}>`) is built from `rawData` in App.tsx and passed to views that need product metadata lookups.
### Path Alias
components/MarketGapReport.tsx
-279
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@@ -1,279 +0,0 @@
import React, { useState, useMemo, useCallback } from 'react';
import * as XLSX from 'xlsx';
import { SalesRecord } from '../types';
import { DownloadIcon } from './Icons';
interface MarketGapReportProps {
data: SalesRecord[];
}
interface GapProduct {
rank: number;
asin: string;
title: string;
sku: string;
line: string;
deSellOut: number;
deUnits: number;
}
const FlagDE = () => (
<span className="inline-flex items-center gap-1.5 px-2 py-0.5 rounded-full text-xs font-bold bg-yellow-500/10 border border-yellow-500/30 text-yellow-400">
🇩🇪 DE
</span>
);
const MarketGapReport: React.FC<MarketGapReportProps> = ({ data }) => {
const availableYears = useMemo(() => {
const years = Array.from(new Set(data.map(r => r.year))).sort((a, b) => b - a);
return years;
}, [data]);
const [selectedYear, setSelectedYear] = useState<number | null>(null);
const effectiveYear = selectedYear ?? availableYears[0] ?? new Date().getFullYear();
const top5 = useMemo((): GapProduct[] => {
if (data.length === 0) return [];
// 1. Build set of ASINs ever sold in Amazon ES (all years)
const esAsins = new Set<string>();
data.forEach(r => {
if (r.customer?.toLowerCase().includes('amazon es')) {
esAsins.add(r.asin.trim().toUpperCase());
}
});
// 2. Build metadata map (title, sku, line) from all years — prefer longest title
const metaMap = new Map<string, { title: string; sku: string; line: string }>();
data.forEach(r => {
const asin = r.asin.trim().toUpperCase();
const existing = metaMap.get(asin);
if (!existing || (r.title && r.title.length > (existing.title?.length || 0))) {
metaMap.set(asin, { title: r.title || r.articleName || asin, sku: r.sku || '', line: r.line || 'Unassigned' });
}
});
// 3. Aggregate DE sales for the selected year
const deMap = new Map<string, { sellOut: number; units: number }>();
data.forEach(r => {
if (r.year !== effectiveYear) return;
if (!r.customer?.toLowerCase().includes('amazon de')) return;
const asin = r.asin.trim().toUpperCase();
const entry = deMap.get(asin) || { sellOut: 0, units: 0 };
entry.sellOut += r.sellOut || 0;
entry.units += r.units || 0;
deMap.set(asin, entry);
});
// 4. Filter out ASINs sold in ES, sort desc by sellOut, top 5
const results: GapProduct[] = [];
deMap.forEach((val, asin) => {
if (esAsins.has(asin)) return;
if (val.sellOut <= 0) return;
const meta = metaMap.get(asin) || { title: asin, sku: '', line: 'Unassigned' };
results.push({
rank: 0,
asin,
title: meta.title,
sku: meta.sku,
line: meta.line,
deSellOut: val.sellOut,
deUnits: val.units,
});
});
results.sort((a, b) => b.deSellOut - a.deSellOut);
return results.slice(0, 5).map((r, i) => ({ ...r, rank: i + 1 }));
}, [data, effectiveYear]);
const handleExport = useCallback(() => {
if (top5.length === 0) return;
const exportData = top5.map(p => ({
Rank: p.rank,
ASIN: p.asin,
Title: p.title,
SKU: p.sku,
'Product Line': p.line,
[`DE Sell Out ${effectiveYear} (€)`]: Number(p.deSellOut.toFixed(2)),
[`DE Units ${effectiveYear}`]: p.deUnits,
'Amazon DE PDP': `https://www.amazon.de/dp/${p.asin}`,
}));
const ws = XLSX.utils.json_to_sheet(exportData);
const wb = XLSX.utils.book_new();
XLSX.utils.book_append_sheet(wb, ws, 'DE-Only Top 5');
XLSX.writeFile(wb, `DE_Only_Top5_${effectiveYear}.xlsx`);
}, [top5, effectiveYear]);
const fmt = (n: number) =>
`€${n.toLocaleString('de-DE', { minimumFractionDigits: 0, maximumFractionDigits: 0 })}`;
return (
<div className="max-w-5xl mx-auto pb-24 px-4 animate-fade-in space-y-6">
{/* Header Card */}
<div className="bg-surface border border-border rounded-xl p-6 shadow-lg flex flex-col md:flex-row justify-between items-start md:items-center gap-4">
<div>
<h2 className="text-2xl font-bold text-indigo-400 flex items-center gap-2">
🌍 Market Gap Report
</h2>
<p className="text-sm text-slate-400 mt-1">
Top 5 productos vendidos en <span className="text-yellow-400 font-semibold">🇩🇪 Alemania</span> que{' '}
<span className="text-rose-400 font-semibold">nunca se han vendido</span> en{' '}
<span className="text-red-400 font-semibold">🇪🇸 España</span>
</p>
</div>
<div className="flex items-center gap-3 flex-wrap">
{/* Year selector */}
<div className="flex items-center gap-2">
<label className="text-xs text-slate-500 uppercase font-semibold tracking-wider">Año:</label>
<div className="flex bg-slate-900 rounded-lg p-0.5 border border-slate-800">
{availableYears.map(y => (
<button
key={y}
onClick={() => setSelectedYear(y)}
className={`px-3 py-1.5 rounded-md text-sm font-medium transition-all ${effectiveYear === y
? 'bg-indigo-600 text-white shadow'
: 'text-slate-400 hover:text-white'
}`}
>
{y}
</button>
))}
</div>
</div>
{/* Export */}
<button
onClick={handleExport}
disabled={top5.length === 0}
className="flex items-center gap-2 px-4 py-2 bg-slate-800 hover:bg-slate-700 text-slate-300 rounded-lg text-sm font-medium border border-slate-700 transition-colors disabled:opacity-40"
>
<DownloadIcon />
Exportar Excel
</button>
</div>
</div>
{/* Insight banner */}
{top5.length > 0 && (
<div className="bg-indigo-500/5 border border-indigo-500/20 rounded-xl px-5 py-3 text-sm text-indigo-300">
💡 <span className="font-semibold">Oportunidad de expansión:</span> Estos {top5.length} productos ya funcionan en DE y podrían tener potencial en el mercado español.
</div>
)}
{/* Table */}
<div className="bg-surface border border-border rounded-xl shadow-lg overflow-hidden">
<div className="px-6 py-4 border-b border-border bg-slate-900/50 flex justify-between items-center">
<h3 className="text-lg font-bold text-indigo-300 flex items-center gap-2">
📊 Top 5 — Solo Alemania ({effectiveYear})
</h3>
<span className="text-xs text-slate-500 uppercase font-semibold tracking-wider">
Ranking por Sell-Out (€)
</span>
</div>
<div className="overflow-x-auto">
<table className="w-full text-left text-sm border-collapse">
<thead>
<tr className="bg-slate-950 text-slate-400 uppercase text-xs font-semibold tracking-wider">
<th className="px-5 py-3 border-b border-border w-12 text-center">#</th>
<th className="px-5 py-3 border-b border-border">Producto</th>
<th className="px-5 py-3 border-b border-border">Línea</th>
<th className="px-5 py-3 border-b border-border text-right">Sell-Out DE</th>
<th className="px-5 py-3 border-b border-border text-right">Units DE</th>
<th className="px-5 py-3 border-b border-border text-center">Link</th>
</tr>
</thead>
<tbody className="divide-y divide-border">
{top5.map((product) => (
<tr key={product.asin} className="hover:bg-slate-800/50 transition-colors group">
{/* Rank */}
<td className="px-5 py-4 text-center">
<span className={`inline-flex items-center justify-center w-8 h-8 rounded-full font-black text-sm ${product.rank === 1
? 'bg-yellow-500/20 text-yellow-400 border border-yellow-500/40'
: product.rank === 2
? 'bg-slate-400/10 text-slate-300 border border-slate-500/30'
: product.rank === 3
? 'bg-orange-500/10 text-orange-400 border border-orange-500/30'
: 'bg-slate-800 text-slate-400 border border-slate-700'
}`}>
{product.rank}
</span>
</td>
{/* Product details */}
<td className="px-5 py-4">
<div className="flex flex-col gap-0.5">
<span className="text-white font-medium leading-snug max-w-sm truncate" title={product.title}>
{product.title || 'Unknown Title'}
</span>
<div className="flex items-center gap-2 mt-0.5">
{product.sku && (
<span className="text-xs text-slate-500 font-mono">SKU: {product.sku}</span>
)}
<span className="text-xs text-slate-600 font-mono">ASIN: {product.asin}</span>
</div>
</div>
</td>
{/* Line */}
<td className="px-5 py-4">
<span className="inline-flex items-center px-2.5 py-0.5 rounded-full text-xs font-medium bg-slate-800 text-slate-300 border border-slate-700">
{product.line}
</span>
</td>
{/* DE Sell Out */}
<td className="px-5 py-4 text-right">
<div className="flex flex-col items-end gap-0.5">
<span className="text-emerald-400 font-bold text-base">{fmt(product.deSellOut)}</span>
<FlagDE />
</div>
</td>
{/* DE Units */}
<td className="px-5 py-4 text-right text-slate-300 font-medium">
{product.deUnits.toLocaleString('de-DE')} uds.
</td>
{/* Amazon PDP Link */}
<td className="px-5 py-4 text-center">
<a
href={`https://www.amazon.de/dp/${product.asin}`}
target="_blank"
rel="noopener noreferrer"
className="inline-flex items-center gap-1 px-3 py-1.5 bg-orange-500/10 hover:bg-orange-500/20 border border-orange-500/30 text-orange-400 text-xs font-medium rounded-lg transition-colors"
title={`Ver ${product.asin} en Amazon.de`}
>
Amazon.de ↗
</a>
</td>
</tr>
))}
{top5.length === 0 && (
<tr>
<td colSpan={6} className="px-6 py-16 text-center text-slate-500 italic">
No se encontraron productos exclusivos de DE en {effectiveYear}.<br />
<span className="text-xs mt-1 block">Verifica que los datos contienen registros de Amazon DE y Amazon ES.</span>
</td>
</tr>
)}
</tbody>
</table>
</div>
{/* Footer note */}
{top5.length > 0 && (
<div className="px-6 py-3 bg-slate-900/40 border-t border-border text-xs text-slate-500">
* Se excluyen todos los ASINs con <span className="text-rose-400 font-medium">cualquier venta histórica</span> en Amazon ES, independientemente del año seleccionado.
</div>
)}
</div>
</div>
);
};
export default MarketGapReport;
services/dataProcessor.ts
+28 -5
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@@ -193,9 +193,30 @@ const normalizeMonth = (rawMonth: string): string => {
}
// 2. Handle numeric months "01", "1", "01-2023"
// If it's a full date string like "2023-04-01" or "01/04/2023"
// If it's a full date string like "2023-04-01", "01/04/2023", or "23/2/26" (DD/M/YY)
if (m.includes('/') || m.includes('-')) {
// Try parsing standard date
// Handle DD/M/YY or DD/MM/YY (European day-first format, e.g. "23/2/26" = 23 Feb 2026)
const parts = m.split(m.includes('/') ? '/' : '-');
if (parts.length === 3) {
const [a, b, c] = parts.map(p => parseInt(p, 10));
if (!isNaN(a) && !isNaN(b) && !isNaN(c)) {
// If first part > 12: definitely day-first → DD/MM/YY
if (a > 12 && b >= 1 && b <= 12) {
const yearShort = c < 100 ? String(c).padStart(2, '0') : String(c).slice(2);
const result = `${MONTH_ORDER[b - 1]}-${yearShort}`;
monthCache[rawMonth] = result;
return result;
}
// YYYY/MM/DD or YYYY-MM-DD (ISO-like, year is 4 digits in first position)
if (a > 31 && b >= 1 && b <= 12) {
const yearShort = String(a).slice(2);
const result = `${MONTH_ORDER[b - 1]}-${yearShort}`;
monthCache[rawMonth] = result;
return result;
}
}
}
// Try parsing standard date as fallback
const date = new Date(m);
if (!isNaN(date.getTime())) {
const monthIdx = date.getMonth();
@@ -289,8 +310,10 @@ const isAllowedCustomer = (customer: string): boolean => {
export const validateSellOutHeaders = (headers: string[]) => {
const normHeaders = headers.map(h => String(h).trim().toLowerCase());
const hasYear = normHeaders.includes('year');
const hasTime = normHeaders.includes('month') || normHeaders.includes('week');
// A date column (e.g. column named "C", "Date", "Fecha") can provide both year and month
const hasDateCol = ['c', 'date', 'fecha', 'data'].some(d => normHeaders.includes(d));
const hasYear = normHeaders.includes('year') || hasDateCol;
const hasTime = normHeaders.includes('month') || normHeaders.includes('week') || hasDateCol;
const hasCustomerRef = normHeaders.includes('customer reference') || normHeaders.includes('asin');
const hasEan = normHeaders.includes('ean');
const hasUnits = normHeaders.includes('units');
@@ -315,7 +338,7 @@ const mapRowToRecord = (row: any, index: number): SalesRecord => {
const yearStr = getColumnValue(row, ['YEAR', 'Year', 'D']);
// Sanitize year string before parsing (remove commas/dots e.g. "2,023")
let year = parseInt(yearStr.replace(/[,.]/g, '')) || 0;
const monthStr = getColumnValue(row, ['MONTH', 'Month', 'Period']);
const monthStr = getColumnValue(row, ['MONTH', 'Month', 'Period', 'C', 'Date', 'DATE', 'Fecha', 'FECHA', 'Data', 'DATA']);
const month = normalizeMonth(monthStr);
// BACKFILL YEAR if missing but present in Month (e.g. "Apr-23")
test-calc-2.js
-106
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@@ -1,106 +0,0 @@
// Script to deeply test the calculation logic
import fs from 'fs';
const mockExperiment = {
id: 'test-1',
name: 'Test Exp',
type: 'advertising',
status: 'active',
start_date: '2026-01-01',
end_date: '2026-01-14',
asins: ['LINE:HAIRCARE'],
primary_metric: 'revenue'
};
const mockSalesData = [
// Before experiment (Baseline)
{ year: 2025, week: 52, asin: 'ASIN1', line: 'HAIRCARE', salesTotal: 500, unitsTotal: 50, cost: 50, glanceViews: 100 },
{ year: 2025, week: 52, asin: 'ASIN2', line: 'HAIRCARE', salesTotal: 200, unitsTotal: 20, cost: 20, glanceViews: 50 },
// During experiment
{ year: 2026, week: 1, asin: 'ASIN1', line: 'HAIRCARE', salesTotal: 1000, unitsTotal: 100, cost: 100, glanceViews: 200 },
{ year: 2026, week: 2, asin: 'ASIN2', line: 'HAIRCARE', salesTotal: 400, unitsTotal: 40, cost: 40, glanceViews: 100 },
// Outside date range
{ year: 2026, week: 3, asin: 'ASIN1', line: 'HAIRCARE', salesTotal: 100, unitsTotal: 10, cost: 10, glanceViews: 20 },
// Different product line
{ year: 2026, week: 1, asin: 'ASIN3', line: 'SKINCARE', salesTotal: 1000, unitsTotal: 100, cost: 100, glanceViews: 200 }
];
const getExperimentAsins = (experimentAsins, salesData) => {
const explicitAsins = new Set();
const lines = new Set();
(experimentAsins || []).forEach(a => {
const val = (a || '').trim().toUpperCase();
if (val.startsWith('LINE:')) {
lines.add(val.substring(5).trim());
} else if (val) {
explicitAsins.add(val);
}
});
const asinSet = new Set(explicitAsins);
if (lines.size > 0 && salesData) {
salesData.forEach(r => {
const line = (r.line || '').trim().toUpperCase();
if (line && lines.has(line)) {
if (r.asin) asinSet.add(r.asin.toUpperCase());
}
});
}
return asinSet;
};
const parseLocalDate = (dateStr) => {
if (!dateStr) return new Date();
const [y, m, d] = dateStr.split('T')[0].split('-');
return new Date(Number(y), Number(m) - 1, Number(d));
};
const calculate = (experiment, salesData) => {
const startDate = parseLocalDate(experiment.start_date);
const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date();
console.log('Parsed start date:', startDate);
console.log('Parsed end date:', endDate);
const durationMs = endDate.getTime() - startDate.getTime();
const baselineStart = new Date(startDate.getTime() - durationMs);
const baselineEnd = startDate;
console.log('Parsed baseline start:', baselineStart);
console.log('Parsed baseline end:', baselineEnd);
const asinSet = getExperimentAsins(experiment.asins, salesData);
console.log('Resolved ASINs:', Array.from(asinSet));
const baselineData = salesData.filter(r => {
const recordDate = new Date(r.year, 0, 1 + (r.week - 1) * 7);
const inRange = recordDate >= baselineStart && recordDate < baselineEnd;
const isAsin = asinSet.has(r.asin.toUpperCase());
if (isAsin) console.log(`[Baseline] Week ${r.year}-${r.week} date: ${recordDate} (inRange: ${inRange})`);
return isAsin && inRange;
});
const experimentData = salesData.filter(r => {
const recordDate = new Date(r.year, 0, 1 + (r.week - 1) * 7);
const inRange = recordDate >= startDate && recordDate <= endDate;
const isAsin = asinSet.has(r.asin.toUpperCase());
if (isAsin) console.log(`[Experiment] Week ${r.year}-${r.week} date: ${recordDate} (inRange: ${inRange})`);
return isAsin && inRange;
});
console.log('Baseline records matched:', baselineData.length);
console.log('Experiment records matched:', experimentData.length);
const baseline_units = baselineData.reduce((sum, r) => sum + r.unitsTotal, 0);
const experiment_units = experimentData.reduce((sum, r) => sum + r.unitsTotal, 0);
console.log('Units baseline:', baseline_units, 'Units experiment:', experiment_units);
};
calculate(mockExperiment, mockSalesData);
test-calc-es.js
-83
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@@ -1,83 +0,0 @@
import fs from 'fs';
const experiment = {
start_date: "2026-01-18",
end_date: null,
asins: ["LINE:LEGENDS"],
marketplace: "ES"
};
const salesData = [
{ year: 2026, week: 7, asin: "B0CHW338VR", line: "LEGENDS", unitsTotal: 4, salesTotal: 24.40, marketplace: "ES" },
{ year: 2026, week: 7, asin: "B0CHW338VR", line: "LEGENDS", unitsTotal: 4, salesTotal: 24.40, marketplace: "GB" }
];
const getExperimentAsins = (experimentAsins, salesData) => {
const explicitAsins = new Set();
const lines = new Set();
(experimentAsins || []).forEach(a => {
const val = (a || '').trim().toUpperCase();
if (val.startsWith('LINE:')) {
lines.add(val.substring(5).trim());
} else if (val) {
explicitAsins.add(val);
}
});
const asinSet = new Set(explicitAsins);
if (lines.size > 0 && salesData) {
salesData.forEach(r => {
const line = (r.line || '').trim().toUpperCase();
if (line && lines.has(line)) {
if (r.asin) asinSet.add(r.asin.toUpperCase());
}
});
}
return asinSet;
};
const parseLocalDate = (dateStr) => {
if (!dateStr) return new Date();
const [y, m, d] = dateStr.split('T')[0].split('-');
return new Date(Number(y), Number(m) - 1, Number(d));
};
const calculate = (experiment, salesData) => {
const startDate = parseLocalDate(experiment.start_date);
const endDate = experiment.end_date ? parseLocalDate(experiment.end_date) : new Date();
console.log('Parsed start date:', startDate);
console.log('Parsed end date:', endDate);
const durationMs = endDate.getTime() - startDate.getTime();
const baselineStart = new Date(startDate.getTime() - durationMs);
const baselineEnd = startDate;
console.log('Parsed baseline start:', baselineStart);
console.log('Parsed baseline end:', baselineEnd);
const asinSet = getExperimentAsins(experiment.asins, salesData);
console.log('Resolved ASINs:', Array.from(asinSet));
const baselineData = salesData.filter(r => {
const recordDate = new Date(r.year, 0, 1 + (r.week - 1) * 7);
const inRange = recordDate >= baselineStart && recordDate < baselineEnd;
const isAsin = asinSet.has(r.asin.toUpperCase());
return isAsin && inRange && (experiment.marketplace === 'All' || r.marketplace === experiment.marketplace);
});
const experimentData = salesData.filter(r => {
const recordDate = new Date(r.year, 0, 1 + (r.week - 1) * 7);
const inRange = recordDate >= startDate && recordDate <= endDate;
const isAsin = asinSet.has(r.asin.toUpperCase());
return isAsin && inRange && (experiment.marketplace === 'All' || r.marketplace === experiment.marketplace);
});
console.log('Baseline records matched:', baselineData.length);
console.log('Experiment records matched:', experimentData.length);
};
calculate(experiment, salesData);
test-calc.js
-69
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@@ -1,69 +0,0 @@
import fs from 'fs';
import path from 'path';
// read dummy data or mock
const mockSalesData = [
{
asin: 'B08F2J8S1Y',
line: 'LEGENDS',
year: 2026,
week: 1,
unitsTotal: 10,
salesTotal: 100,
},
{
asin: 'B08F2J8S1Y',
line: 'LEGENDS',
year: 2026,
week: 2,
unitsTotal: 15,
salesTotal: 150,
}
];
const experimentAsins = ['LINE:LEGENDS'];
const lines = new Set();
const explicitAsins = new Set();
experimentAsins.forEach(a => {
const val = (a || '').trim().toUpperCase();
if (val.startsWith('LINE:')) {
lines.add(val.substring(5).trim());
} else if (val) {
explicitAsins.add(val);
}
});
const asinSet = new Set(explicitAsins);
if (lines.size > 0 && mockSalesData) {
mockSalesData.forEach(r => {
const line = (r.line || '').trim().toUpperCase();
if (line && lines.has(line)) {
if (r.asin) asinSet.add(r.asin.toUpperCase());
}
});
}
console.log('Resolved ASINs:', Array.from(asinSet));
// Date test
const startDateStr = '2026-01-01';
const endDateStr = '2026-01-14';
const startDate = new Date(startDateStr);
const endDate = new Date(endDateStr);
console.log('Start Date UTC:', startDate.toISOString(), 'Local:', startDate.toString());
console.log('End Date UTC:', endDate.toISOString(), 'Local:', endDate.toString());
const recordDate1 = new Date(2026, 0, 1); // week 1
const recordDate2 = new Date(2026, 0, 8); // week 2
const recordDate3 = new Date(2026, 0, 15); // week 3
console.log('Week 1 Record Date Local:', recordDate1.toString());
console.log('Week 1 included in experiment?', recordDate1 >= startDate && recordDate1 <= endDate);
console.log('Week 2 Record Date Local:', recordDate2.toString());
console.log('Week 2 included in experiment?', recordDate2 >= startDate && recordDate2 <= endDate);
console.log('Week 3 Record Date Local:', recordDate3.toString());
console.log('Week 3 included in experiment?', recordDate3 >= startDate && recordDate3 <= endDate);
test-csv.js
-66
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@@ -1,66 +0,0 @@
import fs from 'fs';
import https from 'https';
const url = "https://www.dropbox.com/scl/fi/b9zxn4z5i7sxwfakk5g5y/Amazon-Sell-Out-2023-2025.csv?rlkey=uoto6v0mm99py8nszy8ldtez8&dl=1";
https.get(url, (res) => {
if (res.statusCode >= 300 && res.statusCode < 400 && res.headers.location) {
https.get(res.headers.location, (res2) => {
processData(res2);
});
} else {
processData(res);
}
}).on('error', (e) => {
console.error(e);
});
function processData(stream) {
let data = '';
stream.on('data', chunk => {
data += chunk.toString('utf8');
});
stream.on('end', () => {
analyze(data);
});
}
function analyze(data) {
const lines = data.split('\n');
console.log("Total Lines in CSV:", lines.length);
let maxYear = 0;
let maxWeek = 0;
let minYear = 2030;
let minWeek = 52;
let legendsRows = 0;
let legendsMaxYear = 0;
let legendsMaxWeek = 0;
for (let i = 1; i < lines.length; i++) {
const row = lines[i].split(',');
if (row.length < 5) continue;
const year = parseInt(row[3]); // Based on app processor logic it's mostly col 3 or 1
const week = parseInt(row[4]); // Based on app processor
// Use regex fallback if parsing fails
const matchedYear = parseInt(row.find(r => r.startsWith('202')) || '0');
const matchedWeek = parseInt(row.find(r => r.match(/^[0-9]{1,2}$/)) || '0');
const y = year || matchedYear || 0;
const w = week || matchedWeek || 0;
if (y > maxYear) { maxYear = y; maxWeek = w; }
else if (y === maxYear && w > maxWeek) { maxWeek = w; }
if (y < minYear && y > 2000) { minYear = y; minWeek = w; }
if (lines[i].toLowerCase().includes('legends')) {
legendsRows++;
if (y > legendsMaxYear) { legendsMaxYear = y; legendsMaxWeek = w; }
else if (y === legendsMaxYear && w > legendsMaxWeek) { legendsMaxWeek = w; }
}
}
console.log(`Global -> Min Date: Year ${minYear}, Week ${minWeek} | Max Date: Year ${maxYear}, Week ${maxWeek}`);
console.log(`LEGENDS stats -> Total rows: ${legendsRows}, Max Date: Year ${legendsMaxYear}, Week ${legendsMaxWeek}`);
}
test-fetch.js
-22
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@@ -1,22 +0,0 @@
import { createClient } from '@supabase/supabase-js';
import dotenv from 'dotenv';
dotenv.config({ path: '.env.local' });
const supabase = createClient(
process.env.SUPABASE_URL || '',
process.env.SUPABASE_SERVICE_KEY || process.env.SUPABASE_ANON_KEY || ''
);
async function testFetch() {
const { data: exps, error: err1 } = await supabase.from('experiments').select('*').limit(10);
if (err1 || !exps?.length) {
console.log('No experiments found or error:', err1);
return;
}
for (const exp of exps) {
console.log(`[${exp.id}] Name: ${exp.name} | Start: ${exp.start_date} | ASINs: ${JSON.stringify(exp.asins)}`);
}
}
testFetch();
test-supabase.js
-40
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@@ -1,40 +0,0 @@
import { createClient } from '@supabase/supabase-js';
import dotenv from 'dotenv';
dotenv.config({ path: '.env.local' });
const supabase = createClient(
process.env.VITE_SUPABASE_URL || '',
process.env.VITE_SUPABASE_SERVICE_KEY || process.env.VITE_SUPABASE_ANON_KEY || ''
);
async function testUpdate() {
// Grab the first active experiment
const { data: exps, error: err1 } = await supabase.from('experiments').select('*').limit(1);
if (err1 || !exps?.length) {
console.log('No experiments found or error:', err1);
return;
}
const exp = exps[0];
console.log('Testing update on:', exp.id, exp.name);
// Try to push a dummy update just for the new columns
const { data, error } = await supabase
.from('experiments')
.update({
experiment_acos: 15.5,
experiment_cvr: 10.2,
updated_at: new Date().toISOString()
})
.eq('id', exp.id)
.select()
.single();
if (error) {
console.error('Supabase Error:', error);
} else {
console.log('Success!', data);
}
}
testUpdate();
vite.config.ts
+30 -1
View File
@@ -1,4 +1,5 @@
import path from 'path';
import fs from 'fs';
import { defineConfig, loadEnv } from 'vite';
import react from '@vitejs/plugin-react';
@@ -53,7 +54,35 @@ export default defineConfig(({ mode }) => {
}
}
},
plugins: [react()],
// To support fetching local 'BSR.xlsx' in Vite dev mode, we configure a custom middleware
// since we use a local file instead of a dropbox link for BSR:
plugins: [
react(),
{
name: 'serve-local-bsr',
configureServer(server) {
server.middlewares.use('/api/fetch-bsr', (req, res, next) => {
const filePath = path.join(process.cwd(), 'BSR.xlsx');
try {
if (fs.existsSync(filePath)) {
res.setHeader('Content-Type', 'application/vnd.openxmlformats-officedocument.spreadsheetml.sheet');
res.setHeader('Content-Disposition', 'attachment; filename="BSR.xlsx"');
res.setHeader('Cache-Control', 'public, max-age=3600');
const fileStream = fs.createReadStream(filePath);
fileStream.pipe(res);
} else {
res.statusCode = 404;
res.end(JSON.stringify({ error: 'BSR data file not found' }));
}
} catch (err) {
res.statusCode = 500;
res.end(JSON.stringify({ error: 'Failed' }));
}
});
}
}
],
define: {
// loadEnv reads .env files; process.env has Vercel/system env vars at build time
'process.env.API_KEY': JSON.stringify(