Load exposure cache into memory

dataset.py

@@ -41,6 +41,12 @@ def _monthly_exposure_columns() -> list[str]:
     return cols
 
 
+def _load_readonly_npy(path: Path) -> np.ndarray:
+    arr = np.load(path)
+    arr.setflags(write=False)
+    return arr
+
+
 class ExposureCache:
     """Eid-sequence-aligned exposure windows from prepare_exposure_cache.py."""
 
@@ -86,17 +92,17 @@ class ExposureCache:
                 "Regenerate it with the current prepare_exposure_cache.py."
             )
 
-        self.eids = np.load(eid_path, mmap_mode="r")
+        self.eids = _load_readonly_npy(eid_path)
-        self.raw_tokens = np.load(token_path, mmap_mode="r")
+        self.raw_tokens = _load_readonly_npy(token_path)
-        self.age_days = np.load(age_path, mmap_mode="r")
+        self.age_days = _load_readonly_npy(age_path)
-        self.onset_dates = np.load(onset_date_path, mmap_mode="r")
+        self.onset_dates = _load_readonly_npy(onset_date_path)
-        self.row_index = np.load(row_index_path, mmap_mode="r")
+        self.row_index = _load_readonly_npy(row_index_path)
-        self.eid_index = np.load(eid_index_path, mmap_mode="r")
+        self.eid_index = _load_readonly_npy(eid_index_path)
-        self.eid_start = np.load(eid_start_path, mmap_mode="r")
+        self.eid_start = _load_readonly_npy(eid_start_path)
-        self.daily = np.load(daily_path, mmap_mode="r")
+        self.daily = _load_readonly_npy(daily_path)
-        self.monthly = np.load(monthly_path, mmap_mode="r")
+        self.monthly = _load_readonly_npy(monthly_path)
         quality_path = cache_dir / "exposure_quality.npy"
-        self.quality = np.load(quality_path, mmap_mode="r") if quality_path.is_file() else None
+        self.quality = _load_readonly_npy(quality_path) if quality_path.is_file() else None
 
         if self.daily.ndim != 3 or self.daily.shape[1:] != DAILY_EXPOSURE_SHAPE:
             raise ValueError(