import cmaps

import maskout

import pandas as pd

import numpy as np

import matplotlib.pyplot as plt

from scipy.interpolate import Rbf

from mpl_toolkits.basemap import Basemap

plt.rcParams['font.sans-serif']=['SimHei'] #用来正常显示中文

plt.rcParams['axes.unicode_minus']=False #用来正常显示负号

data = pd.read_csv('../rain.dat', header=None,

names=['站名','站号','lon','lat','降水','气温'] )

# 插值

lon = data['lon']

lat = data['lat']

rain_data = data['降水']

olon = np.linspace(78,100,88)

olat = np.linspace(26,38,88)

olon,olat = np.meshgrid(olon,olat)

# 插值处理

func = Rbf(lon, lat, rain_data,function='linear')

rain_data_new = func(olon, olat)

# 画图

fig = plt.figure(figsize=(16,9))

plt.rc('font',size=15,weight='bold')

ax = fig.add_subplot(111)

m = Basemap(projection='cyl',llcrnrlat=26,llcrnrlon=78,urcrnrlat=38,urcrnrlon=100)

m.readshapefile('../tibet_shp/xizang_all','xizang_all.shp', linewidth=1, color='k')

m.readshapefile('../tibet_shp/river_1','river_1.shp',linewidth=1,color ='b')

m.readshapefile('../tibet_shp/river_2','river_2.shp',linewidth=0.8,color ='b')

m.readshapefile('../tibet_shp/river_3','river_3.shp',linewidth=0.6,color ='b')

m.readshapefile('../tibet_shp/xzlake','xzlake.shp',linewidth=1,color ='b')

x,y = m(olon,olat)

xx,yy = m(lon,lat)

levels = np.linspace(0,np.max(rain_data_new),50)

cf = m.contourf(x,y,rain_data_new, levels=levels, cmap=cmaps.CBR_wet)

cbar = m.colorbar(cf,location='right',format='%d',size=0.3,

ticks=np.linspace(0,np.max(rain_data_new),10),label='毫米')

st = m.scatter(xx-0.1,yy,c='k',s=10,marker='o')

for i in range(0,len(xx)):

plt.text(xx[i],yy[i],data['站名'][i],va='center',fontsize=10)

lon_num = np.arange(78,101,2)

lon_label = ['78°','80°','82°','84°','86°','88°','90°','92°','94°','96°','98°','100°E']

lat_num =  np.arange(26,39,2)

lat_label = ['26°','28°','30°','32°','34°','36°','38°N']

plt.yticks(lat_num,lat_label)

plt.xticks(lon_num,lon_label)

plt.title('测试图')

# 白化

clip = maskout.shp2clip(cf,ax,m,'shapefile/bou2_4p',[540000])

plt.savefig('test.png', bbox_inches='tight',dpi=300)