利用Python插值绘制等值线图

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：

1. 确定基本的绘图框架；
2. 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。
3. 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