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.

nightscout_client.py

@@ -176,29 +176,39 @@ def _entries_since(since: datetime, until: datetime | None = None) -> pd.DataFra
     return df[["timestamp", "mmol"]].sort_values("timestamp").reset_index(drop=True)
 
 
+_TREATMENT_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "type", "absolute", "duration"]
+
+
 def _treatments_since(since: datetime, until: datetime | None = None) -> pd.DataFrame:
-    """Haal treatments op sinds `since` (en optioneel tot `until`), lokaal gefilterd."""
+    """Haal treatments op sinds `since` (en optioneel tot `until`), lokaal gefilterd.
+
+    `type` en `absolute`/`duration` komen erbij naast de oorspronkelijke
+    kolommen: AndroidAPS registreert een SMB-microbolus niet als eigen
+    `eventType`, maar als `eventType == "Correction Bolus"` met
+    `type == "SMB"` (zie reporting.bolus_stats() / gitea #20) — en
+    `absolute` (E/uur) + `duration` (minuten) op Temp Basal-treatments maken
+    het mogelijk afgeleverde basaalinsuline te berekenen zonder het
+    pompprofiel te hoeven ophalen (zie reporting.basal_stats()).
+    """
     data = _paginated_get("/api/v1/treatments.json", since, until, TREATMENTS_CURSOR_FIELD)
 
     if not data:
-        return pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+        return pd.DataFrame(columns=_TREATMENT_COLUMNS)
 
     df = pd.DataFrame(data)
     df["timestamp"] = pd.to_datetime(df["created_at"], utc=True).dt.tz_localize(None)
-    for col in ("insulin", "carbs"):
+    for col in ("insulin", "carbs", "absolute", "duration"):
         if col not in df.columns:
             df[col] = pd.NA
         df[col] = pd.to_numeric(df[col], errors="coerce")
-    if "eventType" not in df.columns:
-        df["eventType"] = ""
+    for col in ("eventType", "type"):
+        if col not in df.columns:
+            df[col] = ""
+        df[col] = df[col].fillna("")
     df = df[df["timestamp"] >= since.replace(tzinfo=None)]
     if until is not None:
         df = df[df["timestamp"] <= until.replace(tzinfo=None)]
-    return (
-        df[["timestamp", "eventType", "insulin", "carbs"]]
-        .sort_values("timestamp")
-        .reset_index(drop=True)
-    )
+    return df[_TREATMENT_COLUMNS].sort_values("timestamp").reset_index(drop=True)
 
 
 def fetch_entries(hours: int = 24) -> pd.DataFrame: