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