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import math
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
from fnme.constants import SORT_KV
_PRICE_KEYS = ("e5_price", "e10_price", "diesel_price")
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["forecourts.location.latitude"].to_numpy(),
df["forecourts.location.longitude"].to_numpy(),
).round(1)
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"]),
)
records = df.rename(columns={"forecourts.trading_name": "station_name"})[
["station_name", *_PRICE_KEYS]
].to_dict(orient="records")
for record in records:
for key in _PRICE_KEYS:
v = record[key]
if isinstance(v, float) and math.isnan(v):
record[key] = None
return 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] is not None else float("inf")
)
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