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最近需要根据有限的站位点绘制插值等值线图，在网上中文搜索一通，只发现了这货 Matplot Basemap 画湖北地图、插值、等值线 ，要么就是对这货的转载，这货不提供数据的形式，但是基本的代码思路还是不错的，于是继续转向google英文，搜到了如下的回答，我对代码做了注释，已备别人查询，关于文中提到的数据是txt格式的，我也直接将数据贴在下面了。总结一下：在地图上绘制等值线： 确定基本的绘图框架； 获取采集数据，与地图做映射，并根据映射数据插值；scipy.interpolate.griddata包插值比较快，常用的三种插值方法为liner(基于三角形的线性插补法)，cubic(基于三角形的三次插补法),nearest( 最近邻居插补法)，这些方法定义了匹配数据点的曲面类型，'cubic' 方法生成平滑曲面，而 'linear' 和 'nearest' 分别具有一阶导数和零阶导数不连续。 根据栅格插值数据绘图 # -*- coding: utf-8 -*- @author: Adwiy Wang import numpy as np import pandas as pd from matplotlib.mlab import griddata from mpl_toolkits.basemap import Basemap import matplotlib.pyplot as plt from matplotlib.colors import Normalize from scipy.interpolate import griddata as gd # 设置基本图片画板 fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, axisbg='w', frame_on=False) # 提取数据 data = pd.read_csv('datam.txt', delim_whitespace=True) norm = Normalize() #设置地图边界值 lllon = 21 lllat = -18 urlon = 34 urlat = -8 #初始化地图 m = Basemap( projection = 'merc', llcrnrlon = lllon, llcrnrlat = lllat, urcrnrlon = urlon, urcrnrlat = urlat, resolution='h') # 将经纬度点转换为地图映射点 data['projected_lon'], data['projected_lat'] = m(*(data.Lon.values, data.Lat.values)) # 生成经纬度的栅格数据 numcols, numrows = 1000, 1000 xi = np.linspace(data['projected_lon'].min(), data['projected_lon'].max(), numcols) yi = np.linspace(data['projected_lat'].min(), data['projected_lat'].max(), numrows) xi, yi = np.meshgrid(xi, yi) # 插值 x, y, z = data['projected_lon'].values, data['projected_lat'].values, data.Z.values zi = gd( (data[['projected_lon', 'projected_lat']]), data.Z.values, (xi, yi), method='cubic') # 设置地图细节 m.drawmapboundary(fill_color = 'white') m.fillcontinents(color='#C0C0C0', lake_color='#7093DB') m.drawcountries( linewidth=.75, linestyle='solid', color='#000073', antialiased=True, ax=ax, zorder=3) m.drawparallels( np.arange(lllat, urlat, 2.), color = 'black', linewidth = 0.5, labels=[True, False, False, False]) m.drawmeridians( np.arange(lllon, urlon, 2.), color = '0.25', linewidth = 0.5, labels=[False, False, False, True]) # 等值面图绘制 con = m.contourf(xi, yi, zi, zorder=4, alpha=0.6, cmap='jet') # 插入测绘点 m.scatter( data['projected_lon'], data['projected_lat'], color='#545454', edgecolor='#ffffff', alpha=.75, s=50 * norm(data['Z']), cmap='jet', ax=ax, vmin=zi.min(), vmax=zi.max(), zorder=4) # 插入色标、名称和范围 cbar = plt.colorbar(con,orientation='horizontal', fraction=.057, pad=0.05) cbar.set_label("Mean Rainfall - mm") m.drawmapscale( 24., -9., 28., -13, 100, units='km', fontsize=10, yoffset=None, barstyle='fancy', labelstyle='simple', fillcolor1='w', fillcolor2='#000000', fontcolor='#000000', zorder=5) plt.title("Mean Rainfall") plt.savefig("rainfall.png", format="png", dpi=300, transparent=True) plt.show()
数据文件：datam.txt on Lat Z 32.6 -13.6 41 27.1 -16.9 43 32.7 -10.2 46 24.2 -13.6 33 28.5 -14.4 43 28.1 -12.6 33 27.9 -15.8 46 24.8 -14.8 44 31.1 -10.2 35 25.9 -13.5 24 29.1 -9.8 10 25.8 -17.8 39 33.2 -12.3 44 28.3 -15.4 46 27.6 -16.1 47 28.9 -11.1 31 31.3 -8.9 39 31.9 -13.3 45 23.1 -15.3 31 31.4 -11.9 39 27.1 -15.0 42 24.4 -11.8 15 28.6 -13.0 39 31.3 -14.3 44 23.3 -16.1 39 30.2 -13.2 38 24.3 -17.5 32 26.4 -12.2 23 23.1 -13.5 27