利用Python画中国地图外加站点分布

小小悟空

很好，学习了

nubist

谢谢楼主分享

guoaxing

{:5_213:}

明明~~

谢谢楼主分享

呆萌的梨花

给力，萌新表示受益匪浅

guoaxing

多谢楼主分享

Jeaven

全全很给力的一个贴，感谢楼主分享！

schliezer

你好，我用spyder画图时变量场总是被地图遮挡，无论先画哪个，contourf，scatter都是这样，然而contour却是正常的，能帮忙看下这是怎么回事吗？

1. import pandas as pd
   
2. import numpy as np
   
3. from sklearn.metrics import classification_report
   
4. from sklearn.metrics import confusion_matrix
   
5. from sklearn.metrics import accuracy_score
   
6. from sklearn.metrics import precision_score
   
7. from sklearn.metrics import recall_score
   
8. from sklearn.metrics import f1_score
   
9. import  matplotlib.pyplot as plt
   
10. from scipy.io import loadmat
    
11. from matplotlib.patches import Polygon
    
12. from scipy.interpolate import griddata
    
13. from mpl_toolkits.basemap import Basemap
    
14. from matplotlib.font_manager import FontProperties
    
15. # read in data, drop nan columns and convert floats into integers
    
16. def Readdata():
    
17. #    file1='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\DiscreOutput(0430-0608).xlsx'
    
18. #    data1=pd.read_excel(file1,header=None)
    
19. #    file2='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\DiscreOutput(0610-0715).xlsx'
    
20. #    data2=pd.read_excel(file2,header=None)
    
21. #    file3='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\DiscreOutput(0716-0815).xlsx'
    
22. #    data3=pd.read_excel(file3,header=None)
    
23. #    file4='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\DiscreOutput(0816-0901).xlsx'
    
24. #    data4=pd.read_excel(file4,header=None)
    
25. #    file5='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\DiscreOutput(0902-0910).xlsx'
    
26. #    data5=pd.read_excel(file5,header=None)
    
27. #    data=[data1,data2,data3,data4,data5]
    
28.     file1='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\Output62720.xlsx'
    
29.     data1=pd.read_excel(file1,header=None)
    
30.     file2='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\Output80520.xlsx'
    
31.     data2=pd.read_excel(file2,header=None)
    
32.     file3='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\Output91220.xlsx'
    
33.     data3=pd.read_excel(file3,header=None)
    
34. #    file1='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\OriginalResult\Output62720.xlsx'
    
35. #    data1=pd.read_excel(file1,header=None)
    
36. #    file2='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\OriginalResult\Output80520.xlsx'
    
37. #    data2=pd.read_excel(file2,header=None)
    
38. #    file3='C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone_1214temp\OriginalResult\Output91220.xlsx'
    
39. #    data3=pd.read_excel(file3,header=None)
    
40.     data=[data1,data2,data3]
    
41.     Output=pd.concat(data,ignore_index=True)
    
42.     Output=Output.dropna(axis=1,how='all')
    
43.     Output.rename(columns={1:'date',2:'site',3:'fcst',4:'obs'},inplace=True)
    
44.     Output['fcst']=Output['fcst'].apply(np.round)
    
45.     return Output
    
46. 
    
47. # prepare forecast and observation data
    
48. def confusion(Output):
    
49.     y_true=Output['obs']
    
50.     y_pred=Output['fcst']
    
51.     y_true=y_true[y_pred!=0.0]
    
52.     y_pred=y_pred[y_pred!=0.0]
    
53.     labels=list(set(y_true))
    
54.     conf_mat=confusion_matrix(y_true,y_pred,labels=labels)
    
55.     result=classification_report(y_true,y_pred)
    
56.     accuracy=accuracy_score(y_true,y_pred)
    
57.     print "classification_report(left:labels):"
    
58.     print result
    
59.     print "Accuracy(left:labels):"
    
60.     print accuracy   
    
61.     return conf_mat,result,accuracy
    
62. 
    
63. Output=Readdata()
    
64. P=[]
    
65. R=[]
    
66. f1=[]
    
67. Ar=[]
    
68. for i in enumerate(set(Output['site'])):
    
69.     y_true=Output[Output['site']==i[1]]['obs']
    
70.     y_pred=Output[Output['site']==i[1]]['fcst']
    
71.     y_true=y_true[y_pred!=0.0]
    
72.     y_pred=y_pred[y_pred!=0.0]
    
73.     labels=[1,2,3]
    
74.     P.append(precision_score(y_true,y_pred,labels=labels,average=None))
    
75.     R.append(recall_score(y_true,y_pred,labels=labels,average=None))
    
76.     f1.append(f1_score(y_true,y_pred,labels=labels,average=None))
    
77.     Ar.append(accuracy_score(y_true,y_pred))
    
78. P=np.array(P)
    
79. R=np.array(R)
    
80. f1=np.array(f1)
    
81. Ar=np.array(Ar)
    
82. # Plot contour of result
    
83. font=FontProperties(fname=r'c:\windows\fonts\simsun.ttc',size=14)
    
84. lonlat=loadmat('C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone\lonlat.mat')
    
85. station=loadmat('C:\Users\CQQXT\Documents\MATLAB\BP_forecast_onebyone\station.mat')
    
86. grid_lon,grid_lat=np.meshgrid(np.arange(105,110.5,0.1),np.arange(28,32.5,0.1))
    
87. lon_lat=np.transpose(np.array([station['station'][:,0],lonlat['lonlat'][:,0],lonlat['lonlat'][:,1]]))
    
88. STN=pd.DataFrame(lon_lat,columns=list('abc'))
    
89. lon_lat=lon_lat[STN.isin(set(Output['site']))['a'],1:3]
    
90. fig=plt.figure()
    
91. ax1=fig.add_axes([0.1,0.1,0.8,0.8])
    
92. #    ax0=fig.add_axes([0.85,0.10,0.04,0.80])
    
93. map=Basemap(projection='cyl',resolution='l',llcrnrlon=105,llcrnrlat=28,urcrnrlon=111,urcrnrlat=33)
    
94. shapeinfo=map.readshapefile('F:\\2016\\Youth_Foundation\\CHN_adm_shp\\CHN_adm3','states',drawbounds=False)
    
95. for info, shp in zip(map.states_info,map.states):
    
96.     proid=info['NAME_1']
    
97.     if proid=='Chongqing':
    
98.         poly=Polygon(shp,facecolor='w',edgecolor='k',lw=0.5)
    
99.         ax1.add_patch(poly)
    
100. map.drawparallels(np.arange(28,33,2),color='k',labels=[1,0,0,0])
     
101. map.drawmeridians(np.arange(105,110,2),color='k',labels=[0,0,0,1])  
     
102. agency=griddata(lon_lat,P[:,1],(grid_lon,grid_lat),method='cubic')
     
103. cf=map.scatter(grid_lon,grid_lat,agency)

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bluelilyfly

谢谢楼主分享，正好需要读取站点数据

mockings

感谢楼主这么详细的分享