209 lines
6.7 KiB
Python
209 lines
6.7 KiB
Python
#!/usr/bin/env python3
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"""
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Fix Procurement Data Structure and Add Realistic Price Trends
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Issues to fix:
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1. Remove nested 'items' arrays from purchase_orders (wrong structure)
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2. Use existing purchase_order_items table structure at root level
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3. Add price trends to existing PO items
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4. Align PO items with actual inventory stock conditions
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"""
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import json
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from pathlib import Path
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import random
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# Set seed for reproducibility
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random.seed(42)
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# Price trend data (realistic price movements over time)
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# These match the 6 ingredients we track in inventory
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PRICE_TRENDS = {
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"10000000-0000-0000-0000-000000000001": { # Harina T55
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"name": "Harina de Trigo T55",
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"base_price": 0.85,
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"current_price": 0.92, # +8% over 90 days
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"trend": 0.08
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},
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"10000000-0000-0000-0000-000000000002": { # Harina T65
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"name": "Harina de Trigo T65",
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"base_price": 0.95,
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"current_price": 1.01, # +6%
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"trend": 0.06
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},
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"10000000-0000-0000-0000-000000000011": { # Mantequilla
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"name": "Mantequilla sin Sal",
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"base_price": 6.50,
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"current_price": 7.28, # +12% (highest increase)
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"trend": 0.12
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},
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"10000000-0000-0000-0000-000000000012": { # Leche
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"name": "Leche Entera Fresca",
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"base_price": 0.95,
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"current_price": 0.92, # -3% (seasonal surplus)
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"trend": -0.03
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},
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"10000000-0000-0000-0000-000000000021": { # Levadura
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"name": "Levadura Fresca",
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"base_price": 4.20,
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"current_price": 4.37, # +4%
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"trend": 0.04
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},
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"10000000-0000-0000-0000-000000000032": { # Azúcar
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"name": "Azúcar Blanco",
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"base_price": 1.10,
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"current_price": 1.12, # +2% (stable)
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"trend": 0.02
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}
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}
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def calculate_price_for_date(ingredient_id: str, days_ago: int) -> float:
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"""Calculate historical price based on trend"""
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if ingredient_id not in PRICE_TRENDS:
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return None
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trend_data = PRICE_TRENDS[ingredient_id]
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base = trend_data["base_price"]
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total_trend = trend_data["trend"]
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# Apply trend proportionally
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# If 90 days trend is +8%, then 45 days ago had +4% from base
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trend_factor = 1 + (total_trend * (90 - days_ago) / 90)
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# Add small variability (±2%)
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variability = random.uniform(-0.02, 0.02)
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price = base * trend_factor * (1 + variability)
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return round(price, 2)
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def parse_days_ago(date_str: str) -> int:
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"""Parse BASE_TS marker to extract days ago"""
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if not date_str or 'BASE_TS' not in date_str:
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return 30
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if '- ' in date_str:
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parts = date_str.split('- ')[1].strip()
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if 'd' in parts:
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try:
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return int(parts.split('d')[0])
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except:
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pass
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elif 'h' in parts:
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return 0 # Same day
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elif '+ ' in date_str:
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return 0 # Future order, use current price
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return 0 # BASE_TS alone = today
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def main():
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fixture_path = Path(__file__).parent / "07-procurement.json"
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print("🔧 Fixing Procurement Data Structure...")
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print()
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# Load existing data
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with open(fixture_path, 'r', encoding='utf-8') as f:
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data = json.load(f)
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purchase_orders = data.get('purchase_orders', [])
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po_items = data.get('purchase_order_items', [])
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print(f"📦 Found {len(purchase_orders)} purchase orders")
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print(f"📋 Found {len(po_items)} PO items")
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print()
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# Step 1: Remove nested 'items' arrays from POs (wrong structure)
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items_removed = 0
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for po in purchase_orders:
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if 'items' in po:
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items_removed += len(po['items'])
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del po['items']
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if items_removed > 0:
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print(f"✓ Removed {items_removed} nested items arrays (wrong structure)")
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print()
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# Step 2: Update existing PO items with realistic price trends
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items_updated = 0
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for item in po_items:
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ingredient_id = item.get('inventory_product_id')
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if ingredient_id in PRICE_TRENDS:
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# Find the PO to get order date
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po_id = item.get('purchase_order_id')
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po = next((p for p in purchase_orders if p['id'] == po_id), None)
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if po:
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order_date = po.get('order_date', 'BASE_TS')
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days_ago = parse_days_ago(order_date)
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# Calculate price for that date
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historical_price = calculate_price_for_date(ingredient_id, days_ago)
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if historical_price:
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# Update item with historical price
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ordered_qty = float(item.get('ordered_quantity', 0))
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item['unit_price'] = historical_price
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item['line_total'] = round(ordered_qty * historical_price, 2)
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items_updated += 1
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print(f"✓ Updated {items_updated} PO items with price trends")
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print()
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# Step 3: Recalculate PO totals based on updated items
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for po in purchase_orders:
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po_id = po['id']
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po_items_for_this_po = [item for item in po_items if item.get('purchase_order_id') == po_id]
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if po_items_for_this_po:
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# Calculate subtotal from items
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subtotal = sum(float(item.get('line_total', 0)) for item in po_items_for_this_po)
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tax_rate = 0.21 # 21% IVA in Spain
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tax = subtotal * tax_rate
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# Keep existing shipping cost or default
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shipping = float(po.get('shipping_cost', 15.0 if subtotal < 500 else 20.0))
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discount = float(po.get('discount_amount', 0.0))
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total = subtotal + tax + shipping - discount
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po['subtotal'] = round(subtotal, 2)
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po['tax_amount'] = round(tax, 2)
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po['shipping_cost'] = round(shipping, 2)
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po['discount_amount'] = round(discount, 2)
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po['total_amount'] = round(total, 2)
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print(f"✓ Recalculated totals for {len(purchase_orders)} purchase orders")
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print()
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# Save fixed data
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with open(fixture_path, 'w', encoding='utf-8') as f:
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json.dump(data, f, indent=2, ensure_ascii=False)
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print("=" * 60)
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print("✅ PROCUREMENT STRUCTURE FIXED")
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print("=" * 60)
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print()
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print("🎯 Changes Applied:")
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print(f" • Removed {items_removed} incorrectly nested items")
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print(f" • Updated {items_updated} PO items with price trends")
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print(f" • Recalculated {len(purchase_orders)} PO totals")
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print()
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print("📊 Price Trends Applied:")
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for ing_id, data in PRICE_TRENDS.items():
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direction = "↑" if data["trend"] > 0 else "↓"
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print(f" {direction} {data['name']}: {data['trend']*100:+.1f}%")
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print()
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print("✅ Data structure now matches PurchaseOrderItem model")
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print("✅ Price trends enable procurement AI insights")
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print()
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if __name__ == "__main__":
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main()
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