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