import numpy as np import math import xarray as xr import matplotlib.pyplot as plt from scipy.spatial import cKDTree from scipy import interpolate import cartopy.crs as ccrs from datetime import datetime from netCDF4 import Dataset begin_time = datetime.now() import pandas as pd name = ["testneu1.nc", "testneu2.nc", "testneu3.nc", "testneu4.nc"] ds = xr.open_mfdataset(name, combine='by_coords', concat_dim="time") #res = Dataset(name) res1 = Dataset('testneu1.nc') # res2 = Dataset('testneu2.nc') # res3 = Dataset('testneu3.nc') res4 = Dataset('testneu4.nc') #print(res1.variables['time'][0]) #print(res1.variables['time'][-1]) start = int(res1.variables['time'][0]) end = int(res4.variables['time'][-1]) a = np.linspace(start, end, (end - start) + 1) ds = ds.assign_coords(time=a) res_comp = Dataset("testfull.nc") #ERAtime1 = res1.variables['time'][:] #ERAtime2 = res2.variables['time'][:] #ERAtime3 = res3.variables['time'][:] #ERAtime4 = res4.variables['time'][:] #ERAtemp1 = res1.variables['t'][:] #ERAtemp2 = res2.variables['t'][:] #ERAtemp3 = res3.variables['t'][:] #ERAtemp4 = res4.variables['t'][:] ERAtemp = ds.variables['z'][:].values ERAtime_sel = ds.variables['time'][:] ERAtemp_sel = ERAtemp[:, 6, 82, 21] ERAtime0 = res_comp.variables['time'][:] ERAtemp0 = res_comp.variables['z'][:] #ERAtemp1_sel = ERAtemp1[:, 6, 82, 21] #ERAtemp2_sel = ERAtemp2[:, 6, 82, 21] #ERAtemp3_sel = ERAtemp3[:, 6, 82, 21] #ERAtemp4_sel = ERAtemp4[:, 6, 82, 21] ERAtemp_sel = ERAtemp[:, 6, 82, 21] ERAtemp0_sel = ERAtemp0[:, 6, 82, 21] #print(ERAtime2[0]) #ERAtime_sel = ds.variables['time'][:] ERAtimecomp_sel = res_comp.variables['time'][:] plt.plot(ERAtime_sel, ERAtemp_sel, 'rx') plt.plot(ERAtimecomp_sel, ERAtemp0_sel, 'k-') plt.show() #print("hier") #print(int(ds['time'][0])) #print(int(ds['time'][1])) #print(type(ds['time'][0])) #print(type(datetime(1900, 1, 1))) #print(ds['time']) # print(ds['time']) ds.to_netcdf('single_new2.nc') df = Dataset('single_new2.nc') #print(df.variables['z'][:]) #print(df.variables['time'][:])