new script to assemble/stitch several downloaded ERA5 data files

ERA5_stitching.py

@@ -0,0 +1,100 @@
+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'][:])
+
+
+