m.drawparallels(np.arange(-1., 45., 9.), labels=[1,0,0,0], fontsize=10) #纬线
#为地图添加上海岸线和经纬网格
m.drawmeridians(np.arange(102., 134., 6.), labels=[0,0,0,1], fontsize=10) #经线
m.drawcoastlines()
m.fillcontinents(color='gray')#为陆地填色

cs=m.pcolor(lon,lat,chl,cmap="jet") #绘制浓度分布
cbar = m.colorbar(cs ,ticks=[-4,-3,-2,-1, 0, 1, 2, 3, 4, 5],location='right', label='Log Chla(\mug/L)',pad = '10%')
#显示颜色图例
plt.title('Global distribution of chlorophyll concentration ',fontsize='10')
#添加标题
plt.savefig('C:\Auseful/china.png',dpi=300,bbox_inches='tight') #保存图片,去掉图片周围空白

plt.show()

import netCDF4 as nc
import numpy as np
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import datetime
file=nc.Dataset("sst.wkmean.1990-present.nc")
lon = file.variables['lon'][:] #经度
lat = file.variables['lat'][:] #纬度
sst = file.variables['sst'][:] #海表温度
time = file.variables['time'][:] #时间
time_bnds = file.variables['time_bnds'][:] #时间段
stdate = [1800,1,1]#days since 1800-1-1 00:00:00，起始时间
time1 = datetime.date(1998,1,1) - datetime.date(1800,1,1) #1998.1.1 距起始时间的天数
time2 = datetime.date(1998,1,31) - datetime.date(1800,1,1) #1998.1.31 距起始时间的天数
trange = np.where((time >= time1.days)&(time <= time2.days),1,0)#找出所有一月的数据
time9019 = time[trange==1]
sstmean9019 = np.mean(sst[trange==1,:,:],0)#90到19的平均值
lons, lats = np.meshgrid(lon, lat)
m=Basemap()
m=Basemap(lat_0=21,lon_0=117,llcrnrlon=102,llcrnrlat=0,urcrnrlon=132,urcrnrlat=42,projection='lcc',resolution='l')
m.drawparallels(np.arange(-90.,91.,9.),labels=[1,0,0,0],fontsize=10)
m.drawmeridians(np.arange(-180.,181.,6.),labels=[0,0,0,1],fontsize=10)
m.drawcoastlines()
m.fillcontinents(color='grey')
lons,lats=m(lons,lats)
cs=m.contourf(lons,lats,sstmean9019,range(0,35,1),cmap='jet')
cbar = m.colorbar(cs,pad="10%",label='Sea Surface Temperature(degC)') #显示颜色图例
plt.title('Distribution of SST near China',fontsize=10)
plt.show()
plt.close()

time1 = datetime.date(1998,7,1) - datetime.date(1800,1,1) #1998.1.1 距起始时间的天数
time2 = datetime.date(1998,7,31) - datetime.date(1800,1,1) #1998.1.31 距起始时间的天数
trange = np.where((time >= time1.days)&(time <= time2.days),1,0)#找出所有一月的数据
time9019 = time[trange==1]
sstmean9019 = np.mean(sst[trange==1,:,:],0)#90到19的平均值
lons, lats = np.meshgrid(lon, lat)
m=Basemap()
m=Basemap(lat_0=21,lon_0=117,llcrnrlon=102,llcrnrlat=0,urcrnrlon=132,urcrnrlat=42,projection='lcc',resolution='l')
m.drawparallels(np.arange(-90.,91.,9.),labels=[1,0,0,0],fontsize=10)
m.drawmeridians(np.arange(-180.,181.,6.),labels=[0,0,0,1],fontsize=10)
m.drawcoastlines()
m.fillcontinents(color='grey')
lons,lats=m(lons,lats)
cs=m.contourf(lons,lats,sstmean9019,range(0,35,1),cmap='jet')
cbar = m.colorbar(cs,pad="10%",label='Sea Surface Temperature(degC)') #显示颜色图例
plt.title('Distribution of SST near China',fontsize=10)
plt.show()
plt.close()