[Python チュートリアル] 地理視覚化

Matplotlib是Python常用的数据绘制包，其绘图功能强大；而Basemap则是Matplotlib的一个子包，负责地图绘制。本文简单介绍如何利用该程序包绘制风向图。具体操作如下：

导入命令

1）设置工作环境并导入程序包

%cd "F:\\Dropbox\\python"
import numpy as np
import matplotlib.pyplot as plt
import datetime
from mpl_toolkits.basemap import Basemap, shiftgrid
from netCDF4 import Dataset

3）设定时间并读取数据

yyyy=1993; mm=03; dd=14; hh=00
date = datetime.datetime(yyyy,mm,dd,hh)
URLbase="http://nomads.ncdc.noaa.gov/thredds/dodsC/modeldata/cmd_pgbh/"
URL=URLbase+"%04i/%04i%02i/%04i%02i%02i/pgbh00.gdas.%04i%02i%02i%02i.grb2" %\
(yyyy,yyyy,mm,yyyy,mm,dd,yyyy,mm,dd,hh)
data = Dataset(URL)

4)数据预处理

latitudes = data.variables['lat'][::-1]
longitudes = data.variables['lon'][:].tolist()
slpin = 0.01*data.variables['Pressure_msl'][:].squeeze()
slp[:,0:-1] = slpin[::-1]; slp[:,-1] = slpin[::-1,0]u = np.zeros((uin.shape[0],uin.shape[1]+1),np.float64)
u[:,0:-1] = uin[::-1]; u[:,-1] = uin[::-1,0]v = np.zeros((vin.shape[0],vin.shape[1]+1),np.float64)v[:,0:-1] = vin[::-1]; 
v[:,-1] = vin[::-1,0]longitudes.append(360.); longitudes = np.array(longitudes)lons, lats = np.meshgrid(longitudes,latitudes)

5)设定并绘制图示

m = Basemap(resolution='c',projection='ortho',lat_0=60.,lon_0=-60.)fig1 = plt.figure(figsize=(8,10))
ax = fig1.add_axes([0.1,0.1,0.8,0.8])clevs = np.arange(960,1061,5)x, y = m(lons, lats)parallels = np.arange(-80.,90,20.)
meridians = np.arange(0.,360.,20.)CS1 = m.contour(x,y,slp,clevs,linewidths=0.5,colors='k',animated=True)
CS2 = m.contourf(x,y,slp,clevs,cmap=plt.cm.RdBu_r,animated=True)ugrid,newlons = shiftgrid(180.,u,longitudes,start=False)
vgrid,newlons = shiftgrid(180.,v,longitudes,start=False)
uproj,vproj,xx,yy = \
m.transform_vector(ugrid,vgrid,newlons,latitudes,31,31,returnxy=True,masked=True)
Q = m.quiver(xx,yy,uproj,vproj,scale=700)qk = plt.quiverkey(Q, 0.1, 0.1, 20, '20 m/s', labelpos='W')m.drawcoastlines(linewidth=1.5)
m.drawparallels(parallels)
m.drawmeridians(meridians)
cb = m.colorbar(CS2,"bottom", size="5%", pad="2%")
cb.set_label('hPa')
ax.set_title('SLP and Wind Vectors '+str(date))
plt.show()

输出图像如下

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