120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
"""
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Task 3 - Step 1: 距离矩阵计算
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=============================
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输入: task1/03_allocate.xlsx (70个站点的坐标信息)
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输出: task3/01_distance.xlsx (70×70距离矩阵 + 站点信息)
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距离计算公式 (Haversine简化):
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l_ij = 69.0 * sqrt((lat_i - lat_j)^2 + cos^2(lat_avg * pi/180) * (lon_i - lon_j)^2)
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单位: 英里
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"""
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import pandas as pd
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import numpy as np
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# ============================================
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# 参数设置
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# ============================================
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INPUT_FILE = '../task1/03_allocate.xlsx'
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OUTPUT_FILE = '01_distance.xlsx'
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# ============================================
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# 读取数据
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# ============================================
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print("=" * 60)
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print("Task 3 - Step 1: 距离矩阵计算")
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print("=" * 60)
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df = pd.read_excel(INPUT_FILE)
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print(f"\n读取站点数据: {len(df)} 个站点")
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print(f"列名: {df.columns.tolist()}")
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# 提取关键列
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sites = df[['site_id', 'site_name', 'lat', 'lon', 'mu', 'sigma', 'mu_tilde', 'k']].copy()
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print(f"\n站点数据概览:")
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print(sites.head())
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# ============================================
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# 距离计算函数
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# ============================================
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def calc_distance(lat1, lon1, lat2, lon2):
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"""
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计算两点间的近似距离（英里）
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使用Haversine公式的简化版本，适用于小范围地域
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"""
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lat_avg = (lat1 + lat2) / 2
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lat_avg_rad = np.radians(lat_avg)
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delta_lat = lat1 - lat2
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delta_lon = lon1 - lon2
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# 69.0 miles per degree of latitude
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# cos(lat) correction for longitude
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distance = 69.0 * np.sqrt(delta_lat**2 + (np.cos(lat_avg_rad) * delta_lon)**2)
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return distance
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# ============================================
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# 构建距离矩阵
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# ============================================
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n = len(sites)
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distance_matrix = np.zeros((n, n))
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for i in range(n):
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for j in range(n):
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if i != j:
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distance_matrix[i, j] = calc_distance(
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sites.iloc[i]['lat'], sites.iloc[i]['lon'],
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sites.iloc[j]['lat'], sites.iloc[j]['lon']
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)
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# 转换为DataFrame
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site_ids = sites['site_id'].values
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df_distance = pd.DataFrame(distance_matrix, index=site_ids, columns=site_ids)
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# ============================================
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# 统计信息
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# ============================================
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# 提取上三角（排除对角线）
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upper_tri = distance_matrix[np.triu_indices(n, k=1)]
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print(f"\n距离矩阵统计:")
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print(f" - 站点对总数: {len(upper_tri)}")
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print(f" - 最小距离: {upper_tri.min():.2f} 英里")
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print(f" - 最大距离: {upper_tri.max():.2f} 英里")
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print(f" - 平均距离: {upper_tri.mean():.2f} 英里")
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print(f" - 中位数距离: {np.median(upper_tri):.2f} 英里")
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# 按阈值统计
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thresholds = [30, 40, 50, 60, 70]
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print(f"\n距离阈值统计:")
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for th in thresholds:
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count = np.sum(upper_tri <= th)
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print(f" - ≤ {th} 英里: {count} 对 ({count/len(upper_tri)*100:.1f}%)")
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# ============================================
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# 保存结果
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# ============================================
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with pd.ExcelWriter(OUTPUT_FILE, engine='openpyxl') as writer:
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# Sheet 1: 站点信息
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sites.to_excel(writer, sheet_name='sites', index=False)
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# Sheet 2: 距离矩阵
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df_distance.to_excel(writer, sheet_name='distance_matrix')
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# Sheet 3: 距离统计
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stats = pd.DataFrame({
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'metric': ['min', 'max', 'mean', 'median', 'std', 'total_pairs'],
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'value': [upper_tri.min(), upper_tri.max(), upper_tri.mean(),
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np.median(upper_tri), upper_tri.std(), len(upper_tri)]
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})
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stats.to_excel(writer, sheet_name='statistics', index=False)
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print(f"\n结果已保存至: {OUTPUT_FILE}")
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print(" - Sheet 'sites': 站点信息")
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print(" - Sheet 'distance_matrix': 70×70距离矩阵")
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print(" - Sheet 'statistics': 距离统计")
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print("\n" + "=" * 60)
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