move all logic into fnme folder

1 files changed, 0 insertions, 98 deletions

diff --git a/helpers.py b/helpers.py
deleted file mode 100644
index 64a3b0a..0000000
--- a/helpers.py
+++ /dev/null
@@ -1,98 +0,0 @@
-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",
-        )
-    )