Finalize post-refactor layout and remove old pipeline files

README.md

@@ -6,21 +6,15 @@ Run each script step-by-step from the terminal.
 
 ## What It Does
 
-1. `scrape_giant.py`: download Giant orders and items
-2. `enrich_giant.py`: normalize Giant line items
-3. `scrape_costco.py`: download Costco orders and items
-4. `enrich_costco.py`: normalize Costco line items
+1. `collect_giant_web.py`: download Giant orders and items
+2. `normalize_giant_web.py`: normalize Giant line items
+3. `collect_costco_web.py`: download Costco orders and items
+4. `normalize_costco_web.py`: normalize Costco line items
 5. `build_purchases.py`: combine retailer outputs into one purchase table
 6. `review_products.py`: review unresolved product matches in the terminal
 7. `report_pipeline_status.py`: show how many rows survive each stage
 8. `analyze_purchases.py`: write chart-ready analysis CSVs from the purchase table
 
-Active refactor entrypoints:
-- `collect_giant_web.py`
-- `collect_costco_web.py`
-- `normalize_giant_web.py`
-- `normalize_costco_web.py`
-
 ## Requirements
 
 - Python 3.10+
@@ -65,13 +59,20 @@ data/
     collected_items.csv
     normalized_items.csv
   review/
+    catalog.csv
     review_queue.csv
     review_resolutions.csv
     product_links.csv
-    purchases.csv
     pipeline_status.csv
     pipeline_status.json
-  catalog.csv
+  analysis/
+    purchases.csv
+    comparison_examples.csv
+    item_price_over_time.csv
+    spend_by_visit.csv
+    items_per_visit.csv
+    category_spend_over_time.csv
+    retailer_store_breakdown.csv
 ```
 
 ## Run Order
@@ -122,21 +123,21 @@ Costco:
 - `data/costco-web/normalized_items.csv` preserves raw totals and matched net discount fields
 
 Combined:
-- `data/review/purchases.csv`
-- `data/review/analysis/item_price_over_time.csv`
-- `data/review/analysis/spend_by_visit.csv`
-- `data/review/analysis/items_per_visit.csv`
-- `data/review/analysis/category_spend_over_time.csv`
-- `data/review/analysis/retailer_store_breakdown.csv`
+- `data/analysis/purchases.csv`
+- `data/analysis/comparison_examples.csv`
+- `data/analysis/item_price_over_time.csv`
+- `data/analysis/spend_by_visit.csv`
+- `data/analysis/items_per_visit.csv`
+- `data/analysis/category_spend_over_time.csv`
+- `data/analysis/retailer_store_breakdown.csv`
 - `data/review/review_queue.csv`
 - `data/review/review_resolutions.csv`
 - `data/review/product_links.csv`
-- `data/review/comparison_examples.csv`
 - `data/review/pipeline_status.csv`
 - `data/review/pipeline_status.json`
-- `data/catalog.csv`
+- `data/review/catalog.csv`
 
-`data/review/purchases.csv` is the main analysis artifact. It is designed to support both:
+`data/analysis/purchases.csv` is the main analysis artifact. It is designed to support both:
 - item-level price analysis
 - visit-level analysis such as spend by visit, items per visit, category spend by visit, and retailer/store breakdown
 
@@ -164,9 +165,7 @@ The review step is intentionally conservative:
 
 ## Notes
 - This project is designed around fragile retailer scraping flows, so the code favors explicit retailer-specific steps over heavy abstraction.
-- `scrape_giant.py`, `scrape_costco.py`, `enrich_giant.py`, and `enrich_costco.py` are now legacy-compatible entrypoints; prefer the `collect_*` and `normalize_*` scripts for active work.
 - Costco discount rows are preserved for auditability and also matched back to purchased items during enrichment.
-- `validate_cross_retailer_flow.py` is a proof/check script, not a required production step.
 
 ## Test