Python读取.mat数据
并绘制1950~2019年的nino3.4时间序列
作者：第八星系-向显均
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# 导入第三方库import matplotlib.pyplot as pltimport numpy as npimport scipy.io as sio
# 导入.mat数据SST = sio.loadmat("SST.mat")nino34 = sio.loadmat("nino34.mat")
'''计算nino3.4指数'''# 查看.mat数据print(SST.keys())print(nino34.keys())
# 读取数据LON = SST['lon']LAT = SST['lat']sst = SST['SST']ii = np.where((LON >= 190) & (LON <= 240))[0]jj = np.where((LAT >= -5) & (LAT <= 5))[0]i,j = np.meshgrid(ii,jj)
# 计算nino3.4指数nino_index = np.mean(sst[i,j])print(nino_index)
'''绘制1950~2019年的nino3.4时间序列'''# 读取所需数据NiNo34 = nino34['nino34']
# 数据处理Y_NiNo = NiNo34.reshape(840,1)Time_Year = np.linspace(1950,2020,840)Average_NiNo_Index = np.mean(NiNo34)El_nino = np.mean(NiNo34) + 0.5La_nina = np.mean(NiNo34) - 0.5
# 绘制图像h1 = plt.plot(Time_Year,Y_NiNo,color='blue',linewidth=1.5)h2 = plt.axhline(y=El_nino,color='red',linestyle='--',linewidth=1.5)h3=plt.axhline(y=Average_NiNo_Index,color='black',linestyle='--',linewidth=1.5)h4 = plt.axhline(y=La_nina,color='green',linestyle='--',linewidth=1.5)
# 图像优化plt.xlim([1950,2020])plt.xlabel("Time(year)")plt.ylabel("nio3.4 Index (℃)")plt.title("nino3.4 Index from 1950 to 2019")plt.legend(loc="upper left")plt.legend([h1,h2,h3,h4],['nino3.4 index','El nino','Average','La nina'])# 图像展示plt.show()
