60 lines
1.7 KiB
Python
60 lines
1.7 KiB
Python
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from __future__ import annotations
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import math
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import numpy as np
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import pandas as pd
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INPUT_XLSX = "task1/01_clean.xlsx"
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OUTPUT_XLSX = "task1/02_neighbor.xlsx"
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RHO_MILES_LIST = [10.0, 20.0, 30.0]
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def haversine_miles(lat1: float, lon1: float, lat2: float, lon2: float) -> float:
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r_miles = 3958.7613
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phi1 = math.radians(lat1)
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phi2 = math.radians(lat2)
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dphi = math.radians(lat2 - lat1)
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dlambda = math.radians(lon2 - lon1)
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a = math.sin(dphi / 2.0) ** 2 + math.cos(phi1) * math.cos(phi2) * math.sin(dlambda / 2.0) ** 2
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c = 2.0 * math.atan2(math.sqrt(a), math.sqrt(1.0 - a))
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return r_miles * c
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def main() -> None:
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sites = pd.read_excel(INPUT_XLSX, sheet_name="sites")
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lat = sites["lat"].to_numpy(float)
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lon = sites["lon"].to_numpy(float)
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mu = sites["mu_clients_per_visit"].to_numpy(float)
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n = len(sites)
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dist = np.zeros((n, n), dtype=float)
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for i in range(n):
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for j in range(i + 1, n):
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d = haversine_miles(lat[i], lon[i], lat[j], lon[j])
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dist[i, j] = d
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dist[j, i] = d
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out = sites.copy()
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out["neighbor_demand_mu_self"] = mu.copy()
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for rho in RHO_MILES_LIST:
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w = np.exp(-(dist**2) / (2.0 * rho * rho))
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# D_i(rho) = sum_j mu_j * exp(-dist(i,j)^2 / (2 rho^2))
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out[f"neighbor_demand_mu_rho_{int(rho)}mi"] = w @ mu
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dist_df = pd.DataFrame(dist, columns=sites["site_id"].tolist())
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dist_df.insert(0, "site_id", sites["site_id"])
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with pd.ExcelWriter(OUTPUT_XLSX, engine="openpyxl") as writer:
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out.to_excel(writer, index=False, sheet_name="sites")
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dist_df.to_excel(writer, index=False, sheet_name="dist_miles")
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if __name__ == "__main__":
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main()
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