fixed bgi

Author: Sigrid Tofte Thiis

loop_to_python_adaptive/autotune_prep.py

@@ -3,77 +3,57 @@
 from dataclasses import dataclass
 from typing import Any, Optional
 
-import numpy as np
+import pandas as pd
 
-
-@dataclass(frozen=True)
-class AutotuneISFConfig:
-    min_points: int = 10
-    adjustment_fraction: float = 0.2  # oref0 uses 20%
-    autosens_max: float = 1.2         # oref0 default
-    autosens_min: float = 0.7         # oref0 default
-    min_bgi_abs: float = 1e-6         # avoid divide by tiny numbers
-    ratio_clip_low: float = 0.3       # hard clip to avoid insane outliers
-    ratio_clip_high: float = 3.0
+from loop_to_python_adaptive.loop_bgi import BGIConfig, prepare_isf_glucose_data
 
 
-def tune_isf_like_oref0(
-    *,
-    isf_current: float,
-    isf_glucose_data: list[dict[str, Any]],
-    pump_isf: Optional[float] = None,
-    config: AutotuneISFConfig | None = None,
-) -> dict[str, Any]:
+@dataclass(frozen=True)
+class AutotunePrepConfig:
     """
-    oref0-like ISF tuning:
-      ratio = 1 + deviation / BGI
-      fullNewISF = ISF * median(ratio)
-      newISF = (1-adjustment_fraction)*ISF + adjustment_fraction*adjustedISF
+    Configuration for preparing inputs to autotune_isf.tune_isf_like_oref0.
     """
-    cfg = config or AutotuneISFConfig()
-
-    ratios: list[float] = []
-    for p in isf_glucose_data:
-        dev = float(p["deviation"])
-        bgi = float(p["BGI"])
-        if abs(bgi) < cfg.min_bgi_abs:
-            continue
-        r = 1.0 + dev / bgi
-        if not np.isfinite(r):
-            continue
-        r = float(np.clip(r, cfg.ratio_clip_low, cfg.ratio_clip_high))
-        ratios.append(r)
+    action_duration_minutes: int = 360
+    peak_activity_minutes: int = 75
+    delay_minutes: int = 10
+    history_hours: int = 16
+    step_minutes: int = 5
 
-    if len(ratios) < cfg.min_points:
-        return {
-            "newISF": float(isf_current),
-            "p50_ratio": None,
-            "fullNewISF": None,
-            "adjustedISF": None,
-            "n_points": len(ratios),
-            "reason": f"Only {len(ratios)} usable ISF points (<{cfg.min_points}); leaving ISF unchanged.",
-        }
+    cgm_col: str = "CGM"
+    bgi_col: str = "BGI"
 
-    p50_ratio = float(np.median(ratios))
-    full_new_isf = float(isf_current * p50_ratio)
 
-    adjusted_isf = full_new_isf
-    if pump_isf is not None and pump_isf > 0:
-        # Match oref0 bounds:
-        # low autosens ratio = high ISF => maxISF = pumpISF / autosens_min
-        # high autosens ratio = low ISF => minISF = pumpISF / autosens_max
-        max_isf = pump_isf / cfg.autosens_min
-        min_isf = pump_isf / cfg.autosens_max
-        adjusted_isf = float(np.clip(adjusted_isf, min_isf, max_isf))
-
-    # Slow update like oref0
-    new_isf = (1.0 - cfg.adjustment_fraction) * float(isf_current) + cfg.adjustment_fraction * float(adjusted_isf)
+def prepare_for_autotune_isf(
+    df: pd.DataFrame,
+    *,
+    loop_algorithm_input: dict,
+    config: Optional[AutotunePrepConfig] = None,
+) -> dict[str, Any]:
+    """
+    Prepare the exact inputs autotune_isf.tune_isf_like_oref0 needs.
 
-    return {
-        "newISF": float(new_isf),
-        "p50_ratio": p50_ratio,
-        "fullNewISF": full_new_isf,
-        "adjustedISF": float(adjusted_isf),
-        "n_points": len(ratios),
-        "reason": "OK",
-    }
+    Returns:
+      {
+        "df": df_with_BGI,
+        "isf_glucose_data": list[{"date","avgDelta","BGI","deviation"}...]
+      }
+    """
+    cfg = config or AutotunePrepConfig()
+
+    bgi_cfg = BGIConfig(
+        action_duration_minutes=cfg.action_duration_minutes,
+        peak_activity_minutes=cfg.peak_activity_minutes,
+        delay_minutes=cfg.delay_minutes,
+        step_minutes=cfg.step_minutes,
+        history_hours=cfg.history_hours,
+    )
+
+    df2, isf_glucose_data = prepare_isf_glucose_data(
+        df,
+        loop_algorithm_input=loop_algorithm_input,
+        config=bgi_cfg,
+        cgm_col=cfg.cgm_col,
+        bgi_col=cfg.bgi_col,
+    )
+
+    return {"df": df2, "isf_glucose_data": isf_glucose_data}