Pure-Python module + 14 unit tests proving the centerpiece works:
test_rice_mixed:
in: [(2 cup, rice), (1.25 lb, rice)]
out: 2.25 lb rice (one line, properly mass+volume combined via density)
test_butter_mixed:
in: [(0.5 cup, butter), (4 oz, butter)]
out: ~227g butter (~8oz / 0.5 lb)
test_three_recipes:
feeds 9 ingredients across 3 recipes through the aggregator;
rice (cup + lb) collapses, garlic (cloves) sums, eggs count, salt as 'pinch'
bucketed as to-taste. All on one shopping list.
Algorithm in cauldron/aggregator.py:
1. Bucket ingredients by canonical food (foods_lookup callable injected — no DB coupling)
2. Within each food, classify each unit (mass / volume / count / vague / unknown)
3. CASE 1: only one unit class present → simple sum, display in canonical store-friendly unit
4. CASE 2: mass + volume (the killer) → use density_g_per_ml to combine to grams
5. CASE 3: count + (mass | volume) → use common_size_g to convert count to grams
6. CASE 4: anything that can't reconcile (no density, mixed unknown) → split into 1 line per class with is_split=True
7. vague (pinch, dash, to taste) → annotate as 'plus to-taste'
8. unknown units → emit verbatim with the original text
Display: store-friendly unit picker:
<30g → grams
<500g → ounces (nearest 0.5)
<2kg → pounds (nearest 0.25)
>2kg → big pounds
The aggregator is dependency-injection-friendly — foods_lookup(name) is
the only external call. Tests pass a stub dict; production will pass
foods.search_food(db, name). Decouples math from data quality.
Tests run via:
python3 -m unittest discover -s tests -v
