TICKET-004: Day summary endpoint

- GET /api/log/summary?date= with totals vs historical target
- Nutrition math consolidated in services/nutrition.py (weight vs count)
- Null nutrition contributes 0; meals contribute 0 (TODO TICKET-007)
- Full suite green (104 passed)

backend/tests/test_summary.py

@@ -0,0 +1,343 @@
+"""Day summary tests — TICKET-004 (spec §2.1 quantity interpretation, §3.3 /api/log/summary, §8.1 rule 1).
+
+Covers:
+- weight-type scaling (150g of 380kcal/100g food = 570)
+- count-type scaling (2 × 70kcal egg = 140)
+- mixed entries summing
+- null nutrition fields contribute 0
+- correct target selected for historical dates
+- no-target case (target: null)
+- meal entries contribute 0 (TODO TICKET-007)
+"""
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+
+def _create_food(client, **overrides) -> dict:
+    """Create a food via POST and return the response JSON."""
+    payload = {
+        "name": "Test Food",
+        "calories_per_unit": 250.0,
+        "source": "manual",
+        "unit_type": "weight",
+    }
+    payload.update(overrides)
+    resp = client.post("/api/foods", json=payload)
+    assert resp.status_code == 201, f"food create failed: {resp.text}"
+    return resp.json()
+
+
+def _log_entry(client, food_id, quantity, date="2025-06-15", **overrides) -> dict:
+    """Create a log entry and return the parsed JSON (asserts 201)."""
+    payload = {"food_id": food_id, "quantity": quantity, "date": date}
+    payload.update(overrides)
+    resp = client.post("/api/log", json=payload)
+    assert resp.status_code == 201, f"log create failed: {resp.text}"
+    return resp.json()
+
+
+def _get_summary(client, date="2025-06-15") -> dict:
+    """Call the summary endpoint and return parsed JSON (asserts 200)."""
+    resp = client.get("/api/log/summary", params={"date": date})
+    assert resp.status_code == 200, f"summary failed: {resp.text}"
+    return resp.json()
+
+
+# ── Basic shape ──────────────────────────────────────────────────────────────
+
+
+def test_summary_empty_date_returns_zeros_and_null_target(client):
+    """A date with no log entries → all nutrition fields zero, target null."""
+    data = _get_summary(client, "2099-01-01")
+    assert data["date"] == "2099-01-01"
+    assert data["target"] is None
+    t = data["totals"]
+    assert t["calories"] == 0.0
+    assert t["protein_g"] == 0.0
+    assert t["carbs_g"] == 0.0
+    assert t["fat_g"] == 0.0
+    # Bonus fields
+    assert t["fiber_g"] == 0.0
+    assert t["saturated_fat_g"] == 0.0
+    assert t["sugars_g"] == 0.0
+    assert t["sodium_g"] == 0.0
+
+
+# ── Weight-type scaling (§2.1) ───────────────────────────────────────────────
+
+
+def test_weight_type_scaling_150g_of_380kcal_per_100g(client):
+    """150g of a 380kcal/100g food = 570 kcal. Math: 380 × 150/100 = 570."""
+    food = _create_food(client, name="Olive Oil", calories_per_unit=380.0, unit_type="weight")
+    _log_entry(client, food["id"], quantity=150.0, date="2025-07-01")
+    data = _get_summary(client, "2025-07-01")
+    assert data["totals"]["calories"] == 570.0
+
+
+def test_weight_type_scaling_macros(client):
+    """Macros also scale per 100g for weight-type foods."""
+    food = _create_food(
+        client,
+        unit_type="weight",
+        calories_per_unit=200.0,
+        protein_per_unit=10.0,
+        carbs_per_unit=20.0,
+        fat_per_unit=5.0,
+    )
+    _log_entry(client, food["id"], quantity=250.0, date="2025-07-02")
+    data = _get_summary(client, "2025-07-02")
+    t = data["totals"]
+    assert t["calories"] == 500.0   # 200 × 250/100
+    assert t["protein_g"] == 25.0   # 10 × 250/100
+    assert t["carbs_g"] == 50.0     # 20 × 250/100
+    assert t["fat_g"] == 12.5       # 5 × 250/100
+
+
+# ── Count-type scaling (§2.1) ────────────────────────────────────────────────
+
+
+def test_count_type_scaling_2_eggs(client):
+    """2 × 70kcal egg = 140 kcal. Math: 70 × 2 = 140."""
+    food = _create_food(client, name="Egg", calories_per_unit=70.0, unit_type="count")
+    _log_entry(client, food["id"], quantity=2.0, date="2025-07-03")
+    data = _get_summary(client, "2025-07-03")
+    assert data["totals"]["calories"] == 140.0
+
+
+def test_count_type_scaling_macros(client):
+    """Count-type macros scale per item."""
+    food = _create_food(
+        client,
+        unit_type="count",
+        calories_per_unit=90.0,
+        protein_per_unit=6.0,
+        carbs_per_unit=1.0,
+        fat_per_unit=7.0,
+    )
+    _log_entry(client, food["id"], quantity=3.0, date="2025-07-04")
+    data = _get_summary(client, "2025-07-04")
+    t = data["totals"]
+    assert t["calories"] == 270.0  # 90 × 3
+    assert t["protein_g"] == 18.0  # 6 × 3
+    assert t["carbs_g"] == 3.0     # 1 × 3
+    assert t["fat_g"] == 21.0      # 7 × 3
+
+
+# ── Mixed entries summing ────────────────────────────────────────────────────
+
+
+def test_multiple_entries_summed(client):
+    """Two weight-type foods on the same date → totals are summed."""
+    f1 = _create_food(client, name="Rice", calories_per_unit=130.0, unit_type="weight")
+    f2 = _create_food(client, name="Chicken", calories_per_unit=165.0, unit_type="weight")
+
+    _log_entry(client, f1["id"], quantity=200.0, date="2025-07-05")  # 260 kcal
+    _log_entry(client, f2["id"], quantity=150.0, date="2025-07-05")  # 247.5 kcal
+
+    data = _get_summary(client, "2025-07-05")
+    assert data["totals"]["calories"] == 507.5  # 260 + 247.5
+
+
+def test_mixed_unit_types_summed(client):
+    """Weight + count entries on the same date sum correctly."""
+    weight_food = _create_food(
+        client, name="Pasta", calories_per_unit=350.0, unit_type="weight",
+        protein_per_unit=12.0, carbs_per_unit=70.0, fat_per_unit=2.0,
+    )
+    count_food = _create_food(
+        client, name="Meatball", calories_per_unit=50.0, unit_type="count",
+        protein_per_unit=4.0, carbs_per_unit=1.0, fat_per_unit=3.0,
+    )
+
+    _log_entry(client, weight_food["id"], quantity=200.0, date="2025-07-06")  # 700 kcal
+    _log_entry(client, count_food["id"], quantity=4.0, date="2025-07-06")      # 200 kcal
+
+    data = _get_summary(client, "2025-07-06")
+    t = data["totals"]
+    assert t["calories"] == 900.0   # 700 + 200
+    assert t["protein_g"] == 40.0   # 24 + 16
+    assert t["carbs_g"] == 144.0    # 140 + 4
+    assert t["fat_g"] == 16.0       # 4 + 12
+
+
+# ── Null nutrition fields contribute 0 ───────────────────────────────────────
+
+
+def test_null_calories_contributes_zero(client):
+    """A food with null calories_per_unit contributes 0 (not an error)."""
+    # Per the CHECK constraint, non-meal foods MUST have calories_per_unit,
+    # so we use is_meal=True to get null calories.
+    food = _create_food(
+        client, name="Null Cal Food", is_meal=True, calories_per_unit=None,
+        source="meal",
+    )
+    _log_entry(client, food["id"], quantity=100.0, date="2025-07-07")
+    data = _get_summary(client, "2025-07-07")
+    assert data["totals"]["calories"] == 0.0  # meals → 0 for now
+
+
+def test_null_macros_contribute_zero(client):
+    """Foods with null macro fields contribute 0 for those fields."""
+    # Create a food with calories but no macros (all null by default)
+    food = _create_food(
+        client, name="Sugar Water", calories_per_unit=40.0,
+        protein_per_unit=None, carbs_per_unit=10.0, fat_per_unit=None,
+        unit_type="weight",
+    )
+    _log_entry(client, food["id"], quantity=200.0, date="2025-07-08")
+    data = _get_summary(client, "2025-07-08")
+    t = data["totals"]
+    assert t["calories"] == 80.0    # 40 × 200/100
+    assert t["carbs_g"] == 20.0     # 10 × 200/100
+    assert t["protein_g"] == 0.0    # null → 0
+    assert t["fat_g"] == 0.0        # null → 0
+
+
+# ── Bonus nutrition fields included ──────────────────────────────────────────
+
+
+def test_bonus_fields_fiber_satfat_sugars_sodium(client):
+    """fiber, saturated_fat, sugars, sodium are included in summary."""
+    food = _create_food(
+        client,
+        calories_per_unit=200.0, unit_type="weight",
+        fiber_per_unit=3.0,
+        saturated_fat_per_unit=2.0,
+        sugars_per_unit=5.0,
+        sodium_per_unit=0.4,
+    )
+    _log_entry(client, food["id"], quantity=100.0, date="2025-07-09")
+    data = _get_summary(client, "2025-07-09")
+    t = data["totals"]
+    assert t["fiber_g"] == 3.0
+    assert t["saturated_fat_g"] == 2.0
+    assert t["sugars_g"] == 5.0
+    assert t["sodium_g"] == 0.4
+
+
+# ── Target selection (historical lookup from TICKET-002) ─────────────────────
+
+
+def test_summary_includes_correct_target_for_date(client):
+    """Summary returns the target whose half-open date range covers the log date."""
+    from database import SessionLocal
+    from models import Target
+    from sqlalchemy import delete as sa_delete
+
+    # Create targets with sequential start dates; each auto-closes the previous
+    resp1 = client.post("/api/targets", json={"start_date": "2025-01-01", "calories": 2000})
+    assert resp1.status_code == 201
+    tid1 = resp1.json()["id"]
+
+    resp2 = client.post("/api/targets", json={"start_date": "2025-04-01", "calories": 2200})
+    assert resp2.status_code == 201
+    tid2 = resp2.json()["id"]
+
+    resp3 = client.post("/api/targets", json={"start_date": "2025-07-01", "calories": 2500})
+    assert resp3.status_code == 201
+    tid3 = resp3.json()["id"]
+
+    # Close the last active target to clean up after ourselves
+    client.put(f"/api/targets/{tid3}", json={"end_date": "2025-12-31"})
+
+    try:
+        # Feb 2025 → target 1 (2000 kcal)
+        data = _get_summary(client, "2025-02-15")
+        assert data["target"] is not None
+        assert data["target"]["id"] == tid1
+        assert data["target"]["calories"] == 2000
+
+        # May 2025 → target 2 (2200 kcal)
+        data = _get_summary(client, "2025-05-15")
+        assert data["target"] is not None
+        assert data["target"]["id"] == tid2
+        assert data["target"]["calories"] == 2200
+
+        # Sep 2025 → target 3 (2500 kcal)
+        data = _get_summary(client, "2025-09-15")
+        assert data["target"] is not None
+        assert data["target"]["id"] == tid3
+        assert data["target"]["calories"] == 2500
+    finally:
+        # Hard-delete these targets so they don't leak into other tests
+        db = SessionLocal()
+        try:
+            db.execute(sa_delete(Target).where(Target.id.in_([tid1, tid2, tid3])))
+            db.commit()
+        finally:
+            db.close()
+
+
+def test_summary_no_target_returns_null(client):
+    """When no target covers the requested date, target is null."""
+    # Use a date far in the past before any target was created
+    data = _get_summary(client, "2000-01-01")
+    assert data["target"] is None
+
+
+# ── Meal entries contribute 0 (TODO TICKET-007) ──────────────────────────────
+
+
+def test_meal_entry_contributes_zero(client):
+    """Meal foods have null calories_per_unit per the CHECK constraint,
+    so they temporarily contribute 0 to the day's totals."""
+    meal = _create_food(
+        client, name="My Meal", is_meal=True, calories_per_unit=None,
+        source="meal",
+    )
+    _log_entry(client, meal["id"], quantity=1.0, date="2025-07-10")
+    data = _get_summary(client, "2025-07-10")
+    assert data["totals"]["calories"] == 0.0
+    assert data["totals"]["protein_g"] == 0.0
+    assert data["totals"]["carbs_g"] == 0.0
+    assert data["totals"]["fat_g"] == 0.0
+
+
+def test_meal_mixed_with_regular_foods(client):
+    """Meal entries contribute 0 while regular foods contribute normally."""
+    regular = _create_food(client, name="Rice", calories_per_unit=130.0, unit_type="weight")
+    meal = _create_food(
+        client, name="Lunch Meal", is_meal=True, calories_per_unit=None,
+        source="meal",
+    )
+
+    _log_entry(client, regular["id"], quantity=200.0, date="2025-07-11")  # 260 kcal
+    _log_entry(client, meal["id"], quantity=1.0, date="2025-07-11")       # 0 kcal
+
+    data = _get_summary(client, "2025-07-11")
+    assert data["totals"]["calories"] == 260.0  # only the regular food
+
+
+# ── Past/future dates ───────────────────────────────────────────────────────
+
+
+def test_arbitrary_past_date(client):
+    """Summary works for any past date with correct historical data."""
+    food = _create_food(client, name="Old Food", calories_per_unit=100.0)
+    _log_entry(client, food["id"], quantity=50.0, date="2023-12-25")
+    data = _get_summary(client, "2023-12-25")
+    assert data["totals"]["calories"] == 50.0  # 100 × 50/100
+
+
+def test_arbitrary_future_date(client):
+    """Summary works for future dates (no entries → zeros)."""
+    data = _get_summary(client, "2030-06-15")
+    assert data["date"] == "2030-06-15"
+    assert data["totals"]["calories"] == 0.0
+    # target may be null or a still-active target — just verify totals are correct
+
+
+# ── Date isolation ───────────────────────────────────────────────────────────
+
+
+def test_summary_isolated_by_date(client):
+    """Entries on different dates don't cross-contaminate summaries."""
+    f1 = _create_food(client, name="Monday Food", calories_per_unit=100.0)
+    f2 = _create_food(client, name="Tuesday Food", calories_per_unit=200.0)
+
+    _log_entry(client, f1["id"], quantity=100.0, date="2025-07-12")  # 100 kcal
+    _log_entry(client, f2["id"], quantity=100.0, date="2025-07-13")  # 200 kcal
+
+    assert _get_summary(client, "2025-07-12")["totals"]["calories"] == 100.0
+    assert _get_summary(client, "2025-07-13")["totals"]["calories"] == 200.0