140 lines
6.2 KiB
Python
140 lines
6.2 KiB
Python
import xarray as xr
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import glob
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import numpy as np
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def ModAttrs(DictOri,site,label,data):
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keys=list(DictOri.keys())
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NewDict={}
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for key in range(len(keys)):
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if site==key:
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NewDict[label]=data
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NewDict[keys[key]]=DictOri[keys[key]]
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return NewDict
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def date2float(df,var):
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V1=df[var].astype("float64")
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return V1
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def cleanAttr(var,T):
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A=T.attrs
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if var in A.keys():
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A.pop(var)
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return A
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def Corrector2(file,DataEntry,DataSal):
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T=xr.open_dataset(DataEntry+file)
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T.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
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'units': 'day of the year',
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'comment': 'considering January 1st the first day of the year'}
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T.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
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'units': 'day of the year',
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'comment': 'considering January 1st the first day of the year'}
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data =T.DHW_q99[0,:,:].values
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T["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
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T["nCellsValid"]=np.count_nonzero(~np.isnan(data))
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T.nCellsTotal.attrs={"long_name": "number of data cells in selected area"}
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T.nCellsValid.attrs={ "long_name": "number of valid data cells in selected area"}
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T.to_netcdf(path=DataSal+file)
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def CorrectNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",SalidaDir="/home/mario/Documentos/Ocean/NetcdfToPng/MapsModificados/",file="DHW_ssp245_BCC-CSM2-MR_DHW.nc"):
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nc1 = xr.open_dataset(Map1+file)#,decode_times=False,decode_timedelta=False
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#nc1["DoY_DHW4cal"]=nc1["DoY_DHW4"]
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#nc1["DoY_DHW8cal"]=nc1["DoY_DHW8"]
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#nc1["nDays_DHW4cal"]=nc1["nDays_DHW4"]
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#nc1["nDays_DHW8cal"]=nc1["nDays_DHW8"]
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#nc1=nc1.drop("DoY_DHW4")
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#nc1=nc1.drop("DoY_DHW8")
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#nc1=nc1.drop("nDays_DHW4")
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#nc1=nc1.drop("nDays_DHW8")
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#nc1["DoY_DHW4"]=nc1["DoYrel_DHW4"]
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#nc1["DoY_DHW8"]=nc1["DoYrel_DHW8"]
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#nc1=nc1.drop("DoYrel_DHW4")
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#nc1=nc1.drop("DoYrel_DHW8")
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#nc1=nc1.drop_sel({"time":1986})
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df=nc1
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try:
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df=df.drop("quantile")
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except:
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print("noq")
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df.attrs.keys()
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#df.attrs=ModAttrs(df.attrs,5,"label","data")
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df.attrs["time_coverage_start"]=1986
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data=df.DHW_q99[0,:,:].values
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df.attrs=ModAttrs(df.attrs,5,"region_name","Global")
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#df["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
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#df["nCellsValid"]=np.count_nonzero(~np.isnan(data))
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file=file.replace("_start1986","").replace("_dec22","")
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HH=SalidaDir+file
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# df.DoY_DHW4.attrs={'long_name': "first day of the year when DHW exceeds 4 degree-weeks, relative to the climatological coldest DOY",
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# 'units': 'day of the year',
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# 'comment': "considering the coldest climatological DoY as the first day of the year"}
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# df.DoY_DHW8.attrs={'long_name': "first day of the year when DHW exceeds 8 degree-weeks, relative to the climatological coldest DOY",
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# 'units': 'day of the year',
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# 'comment': "considering the coldest climatological DoY as the first day of the year"}
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# df.nDays_DHW8.attrs={"long_name" : "number of days above 8 degrees-week",
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# "units" : "days"}
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# df.nDays_DHW4.attrs={"long_name" : "number of days above 4 degrees-week",
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# "units" : "days"}
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mask_land = 1 * np.ones((df.dims['lat'], df.dims['lon'])) * np.isnan(df.DHW_q99.isel(time=0))
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df["mask_land"]=mask_land
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df["nDays_DHW4"]=df["nDays_DHW4"].astype(np.float64)/1e9/60/60/24
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#if np.nanmax(df["nDays_DHW4"].values)>1000:
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#df["nDays_DHW4"]=df["nDays_DHW4"]/1e9/60/60/24
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#print("NDais")
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df["nDays_DHW8"]=df["nDays_DHW8"].astype(np.float64)/1e9/60/60/24
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df["nDays_DHW8"]=df["nDays_DHW8"].where(df.mask_land != 1)
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df["nDays_DHW4"]=df["nDays_DHW4"].where(df.mask_land != 1)
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#if np.nanmax(df["nDays_DHW8"].values)>1000:
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#df["nDays_DHW8"]=df["nDays_DHW8"]
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#print("NDais")
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# df["nDays_DHW4"]=df["nDays_DHW4"].astype("float64")
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# if np.nanmax(df["nDaysrel_DHW4"].values)>1000:
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# df["nDaysrel_DHW4"]=df["nDaysrel_DHW4"]/1e9/60/60/24
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# print("NDais")
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# df["nDaysrel_DHW8"]=df["nDaysrel_DHW8"].astype("float64")
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# if np.nanmax(df["nDaysrel_DHW8cal"].values)>1000:
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# df["nDaysrel_DHW8"]=df["nDaysrel_DHW8"]/1e9/60/60/24
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# print("NDais")
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#df["nDays_DHW8"].attrs={'long_name': 'number of days above 8 degrees-week, relative to the climatological coldest DOY'}
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#df["nDays_DHW4"].attrs={'long_name': 'number of days above 4 degrees-week, relative to the climatological coldest DOY'}
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#df["nDays_DHW8cal"].attrs={'long_name': 'number of days above 8 degrees-week, considering January 1st the first day of the year'}
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#df["nDays_DHW4cal"].attrs={'long_name': 'number of days above 4 degrees-week, considering January 1st the first day of the year'}
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#df["nDays_DHW8cal"]=df["nDays_DHW8cal"].where(df.mask_land != 1)
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#df["nDays_DHW4cal"]=df["nDays_DHW4cal"].where(df.mask_land != 1)
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df=df.drop("mask_land")
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Tempp=list(df.var().keys())
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for i in Tempp:
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df[i].attrs=cleanAttr("coordinates",df[i])
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# df.DoY_DHW8cal.attrs={'long_name': 'first day of the year when DHW exceeds 8 degree-weeks',
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# 'units': 'day of the year',
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# 'comment': 'considering January 1st the first day of the year'}
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# df.DoY_DHW4cal.attrs={'long_name': 'first day of the year when DHW exceeds 4 degree-weeks',
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# 'units': 'day of the year',
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# 'comment': 'considering January 1st the first day of the year'}
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comp = dict(zlib=True, complevel=5)
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encoding = {var: comp for var in df.data_vars}
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data =df.DHW_q99[0,:,:].values
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df["nCellsTotal"]=np.count_nonzero(np.isnan(data))+np.count_nonzero(~np.isnan(data))
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df["nCellsValid"]=np.count_nonzero(~np.isnan(data))
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df.nCellsTotal.attrs={"long_name": "number of data cells in selected area"}
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df.nCellsValid.attrs={ "long_name": "number of valid data cells in selected area"}
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print(df)
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df.to_netcdf(path=HH, encoding=encoding)
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nn=0
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for i in glob.glob("../DataOriginal/*"):
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A=i.split("/")[-1]
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SalidaDir="../Data/"
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nn+=1
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print(A)
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CorrectNc(Map1="../DataOriginal/",SalidaDir=SalidaDir,file=A)
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print(1)
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#if nn>1:
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#break
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#SalidaDir2="../DataF/"
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#Corrector2(A,SalidaDir,SalidaDir2)
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#print(2)
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#break
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