Fix bolus/basal insulin categorization: split SMB from manual corrections, add basal calc

AndroidAPS records an automatic SMB micro-bolus as eventType "Correction
Bolus" with type "SMB", not as its own eventType. bolus_stats() was
grouping by eventType alone, so every SMB was silently counted as a manual
correction bolus. Verified against production data: 100% of "Correction
Bolus" treatments over 90 days were actually type=SMB.

Also adds basal_stats(), computing delivered basal directly from Temp
Basal treatments (rate x duration) without needing the pump profile.

Closes gitea #20.

tests/test_nightscout_client.py

@@ -90,6 +90,38 @@ def test_fetch_treatments_fills_missing_columns(monkeypatch):
     assert result.iloc[0]["eventType"] == "Site Change"
     assert pd_isna(result.iloc[0]["insulin"])
     assert pd_isna(result.iloc[0]["carbs"])
+    assert result.iloc[0]["type"] == ""
+    assert pd_isna(result.iloc[0]["absolute"])
+    assert pd_isna(result.iloc[0]["duration"])
+
+
+def test_fetch_treatments_passes_through_type_and_temp_basal_fields(monkeypatch):
+    """SMB-classificatie (reporting.bolus_stats) en basaalberekening
+    (reporting.basal_stats) hebben deze velden nodig — zie gitea #20."""
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    now = datetime.now(timezone.utc)
+    payload = [
+        {
+            "created_at": now.isoformat(),
+            "eventType": "Correction Bolus",
+            "insulin": 0.5,
+            "type": "SMB",
+        },
+        {
+            "created_at": now.isoformat(),
+            "eventType": "Temp Basal",
+            "absolute": 1.2,
+            "duration": 30,
+        },
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_treatments(hours=24)
+    smb_row = result[result["eventType"] == "Correction Bolus"].iloc[0]
+    basal_row = result[result["eventType"] == "Temp Basal"].iloc[0]
+    assert smb_row["type"] == "SMB"
+    assert basal_row["absolute"] == pytest.approx(1.2)
+    assert basal_row["duration"] == pytest.approx(30)
 
 
 def pd_isna(value):

tests/test_reporting.py

@@ -5,6 +5,7 @@ import pytest
 
 from reporting import (
     agp_percentile_bands,
+    basal_stats,
     bolus_stats,
     completeness_warning,
     day_of_week_pattern,
@@ -339,6 +340,7 @@ def test_bolus_stats_splits_by_event_type():
             "eventType": ["Meal Bolus", "Correction Bolus", "Site Change"],
             "insulin": [4.0, 1.5, None],
             "carbs": [45.0, None, None],
+            "type": ["NORMAL", "NORMAL", ""],
         }
     )
     result = bolus_stats(treatments)
@@ -347,12 +349,76 @@ def test_bolus_stats_splits_by_event_type():
     assert result["by_type"] == {"Meal Bolus": 4.0, "Correction Bolus": 1.5}
 
 
+def test_bolus_stats_reclassifies_smb_correction_boluses_separately():
+    """AndroidAPS registreert een automatische SMB-microbolus als
+    eventType == "Correction Bolus" met type == "SMB" — dit moet apart van
+    een handmatige correctiebolus (type != "SMB") geteld worden (gitea #20)."""
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
+            "eventType": ["Correction Bolus", "Correction Bolus"],
+            "insulin": [0.3, 0.9],
+            "carbs": [None, None],
+            "type": ["SMB", "NORMAL"],
+        }
+    )
+    result = bolus_stats(treatments)
+    assert result["by_type"] == {"SMB": 0.3, "Correction Bolus": 0.9}
+
+
+def test_bolus_stats_treats_missing_type_column_as_non_smb():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00"]),
+            "eventType": ["Correction Bolus"],
+            "insulin": [0.9],
+            "carbs": [None],
+        }
+    )
+    result = bolus_stats(treatments)
+    assert result["by_type"] == {"Correction Bolus": 0.9}
+
+
 def test_bolus_stats_handles_empty_input():
-    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs", "type"])
     result = bolus_stats(empty)
     assert result == {"total_units": 0.0, "count": 0, "by_type": {}}
 
 
+def test_basal_stats_sums_temp_basal_rate_times_duration():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 08:30"]),
+            "eventType": ["Temp Basal", "Temp Basal"],
+            "absolute": [1.2, 0.6],
+            "duration": [30, 60],
+        }
+    )
+    # 1.2 E/u * 0.5u + 0.6 E/u * 1u = 0.6 + 0.6 = 1.2 E over 1 dag
+    result = basal_stats(treatments, period_days=1)
+    assert result["total_units"] == pytest.approx(1.2)
+    assert result["avg_units_per_day"] == pytest.approx(1.2)
+
+
+def test_basal_stats_ignores_non_temp_basal_rows_and_missing_fields():
+    treatments = pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 08:30"]),
+            "eventType": ["Meal Bolus", "Temp Basal"],
+            "absolute": [None, None],
+            "duration": [None, 30],
+        }
+    )
+    result = basal_stats(treatments, period_days=1)
+    assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+
+def test_basal_stats_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
+    result = basal_stats(empty, period_days=1)
+    assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
+
+
 def test_site_interval_stats_splits_by_site_change():
     glucose = _series(
         [