separate main logic, constants & helper functions

1 files changed, 98 insertions, 0 deletions

diff --git a/helpers.py b/helpers.py
new file mode 100644
index 0000000..64a3b0a
--- /dev/null
+++ b/helpers.py
@@ -0,0 +1,98 @@
+import argparse
+import math
+from io import StringIO
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+import pandas as pd
+import requests
+from geopy.geocoders import Nominatim
+from geopy.location import Location
+from tabulate import tabulate
+
+from constants import ENDPOINT, HEADERS, SORT_KV
+
+
+def get_location(address: str) -> tuple[float, float]:
+    geolocator = Nominatim(user_agent="FuelNearMe")
+    result = geolocator.geocode(address)
+    if not isinstance(result, Location):
+        raise ValueError(f"Failed to get location from address: '{address}'")
+    return (result.latitude, result.longitude)
+
+
+def get_latest_data() -> tuple[pd.DataFrame, Optional[str]]:
+    response = requests.get(ENDPOINT, headers=HEADERS, timeout=10)
+    response.raise_for_status()
+    return pd.read_csv(StringIO(response.text)), response.headers.get("Last-Modified")
+
+
+def filter_df(
+    dframe: pd.DataFrame, arguments: argparse.Namespace, loc: Tuple[float, float]
+) -> List[Dict[str, Any]]:
+
+    def bounding_box() -> pd.DataFrame:
+        lat, lon = loc
+        deg_lat = arguments.radius / 69.0
+        deg_lon = arguments.radius / (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).where(col.notna(), other="N/A")
+
+    df = bounding_box().copy()
+
+    df["distance"] = haversine_miles(
+        df["forecourts.location.latitude"].to_numpy(),
+        df["forecourts.location.longitude"].to_numpy(),
+    ).round(1)
+
+    df = df[df["distance"] < arguments.radius]
+
+    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"]),
+    )
+
+    return df.rename(columns={"forecourts.trading_name": "station_name"})[
+        ["station_name", "distance", "e5_price", "e10_price", "diesel_price"]
+    ].to_dict(orient="records")
+
+
+def sort_stations(stations: list[dict], sort: str) -> list[dict]:
+    sort_key = SORT_KV[sort]
+    return sorted(stations, key=lambda d: d[sort_key] if d[sort_key] != "N/A" else 999)
+
+
+def output_stations(stations: List[Dict[str, Any]]) -> None:
+    if not stations:
+        print("[*] No stations found.")
+        return
+    print(
+        tabulate(
+            stations,
+            headers={
+                "station_name": "Station Name",
+                "distance": "Distance (miles)",
+                "e5_price": "E5 (£/L)",
+                "e10_price": "E10 (£/L)",
+                "diesel_price": "B7S (£/L)",
+            },
+            floatfmt=".2f",
+        )
+    )