promote nested functions in process_stations to private helpers

1 files changed, 35 insertions, 29 deletions

diff --git a/fnme/station.py b/fnme/station.py
index 109b927..94d965a 100644
--- a/fnme/station.py
+++ b/fnme/station.py
@@ -9,36 +9,42 @@ from fnme.constants import SORT_KV
 _PRICE_KEYS = ("e5_price", "e10_price", "diesel_price")
 
 
+def _bounding_box(
+    dframe: pd.DataFrame, loc: Tuple[float, float], rad: int
+) -> pd.DataFrame:
+    lat, lon = loc
+    deg_lat = rad / 69.0
+    deg_lon = rad / (69.0 * math.cos(math.radians(lat)))
+    return dframe[
+        dframe["forecourts.location.latitude"].between(lat - deg_lat, lat + deg_lat)
+        & dframe["forecourts.location.longitude"].between(lon - deg_lon, lon + deg_lon)
+    ]
+
+
+def _haversine_miles(
+    loc: Tuple[float, float], lat2: np.ndarray, lon2: np.ndarray
+) -> np.ndarray:
+    R = 3958.8
+    lat1, lon1 = np.radians(loc[0]), np.radians(loc[1])
+    lat2, lon2 = np.radians(lat2), np.radians(lon2)
+    dlat = lat2 - lat1
+    dlon = lon2 - lon1
+    a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
+    return R * 2 * np.arcsin(np.sqrt(a))
+
+
+def _pence_to_pounds(col: pd.Series) -> pd.Series:
+    return (col / 100).round(2)
+
+
 def process_stations(
     dframe: pd.DataFrame, rad: int, loc: Tuple[float, float]
 ) -> List[Dict[str, Any]]:
 
-    def bounding_box() -> pd.DataFrame:
-        lat, lon = loc
-        deg_lat = rad / 69.0
-        deg_lon = rad / (69.0 * math.cos(math.radians(lat)))
-        return dframe[
-            dframe["forecourts.location.latitude"].between(lat - deg_lat, lat + deg_lat)
-            & dframe["forecourts.location.longitude"].between(
-                lon - deg_lon, lon + deg_lon
-            )
-        ]
-
-    def haversine_miles(lat2: np.ndarray, lon2: np.ndarray) -> np.ndarray:
-        R = 3958.8
-        lat1, lon1 = np.radians(loc[0]), np.radians(loc[1])
-        lat2, lon2 = np.radians(lat2), np.radians(lon2)
-        dlat = lat2 - lat1
-        dlon = lon2 - lon1
-        a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2
-        return R * 2 * np.arcsin(np.sqrt(a))
-
-    def pence_to_pounds(col: pd.Series) -> pd.Series:
-        return (col / 100).round(2)
-
-    df = bounding_box().copy()
-
-    df["distance"] = haversine_miles(
+    df = _bounding_box(dframe, loc, rad).copy()
+
+    df["distance"] = _haversine_miles(
+        loc,
         df["forecourts.location.latitude"].to_numpy(),
         df["forecourts.location.longitude"].to_numpy(),
     ).round(1)
@@ -46,9 +52,9 @@ def process_stations(
     df = df[df["distance"] < rad]
 
     df = df.assign(
-        e5_price=pence_to_pounds(df["forecourts.fuel_price.E5"]),
-        e10_price=pence_to_pounds(df["forecourts.fuel_price.E10"]),
-        diesel_price=pence_to_pounds(df["forecourts.fuel_price.B7S"]),
+        e5_price=_pence_to_pounds(df["forecourts.fuel_price.E5"]),
+        e10_price=_pence_to_pounds(df["forecourts.fuel_price.E10"]),
+        diesel_price=_pence_to_pounds(df["forecourts.fuel_price.B7S"]),
     )
 
     records = df.rename(columns={"forecourts.trading_name": "station_name"})[