1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
|
import argparse
import math
from typing import Any, Dict, List, Tuple
import numpy as np
import pandas as pd
from constants import SORT_KV
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)
|
