Scripts in all changes
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1Teselas/map/*
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2nc2image/img/*
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3image2video/ConCoralN/*
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3image2video/env/*
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3image2video/img/*
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3image2video/SinCoralN/*
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3image2video/Videos/*
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Data/*
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DataF/*
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DataOriginal/*
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DataVieja/*
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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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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 FusionNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",Map2="/home/mario/Documentos/Ocean/NetcdfToPng/MapsAModificar/",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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nc2b = xr.open_dataset(Map2+file,decode_times=False,decode_timedelta=False)
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#nc2b=nc2b.drop_sel({"time":1986})
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#nc2b=nc2b.drop_sel({"time":1985})
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nc1["DoY_DHW4cal"]=nc2b["DoY_DHW4"]
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nc1["DoY_DHW8cal"]=nc2b["DoY_DHW8"]
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nc1["nDays_DHW4cal"]=nc2b["nDays_DHW4"]
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nc1["nDays_DHW8cal"]=nc2b["nDays_DHW8"]
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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"]=1987
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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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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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df["nDays_DHW4"]=df["nDays_DHW4"].astype("float64")
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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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df["nDays_DHW8"]=df["nDays_DHW8"].astype("float64")
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if np.nanmax(df["nDays_DHW8"].values)>1000:
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df["nDays_DHW8"]=df["nDays_DHW8"]/1e9/60/60/24
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df["nDays_DHW4cal"]=df["nDays_DHW4cal"].astype("float64")
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if np.nanmax(df["nDays_DHW4cal"].values)>1000:
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df["nDays_DHW4cal"]=df["nDays_DHW4cal"]/1e9/60/60/24
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df["nDays_DHW8cal"]=df["nDays_DHW8cal"].astype("float64")
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if np.nanmax(df["nDays_DHW8cal"].values)>1000:
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df["nDays_DHW8cal"]=df["nDays_DHW8cal"]/1e9/60/60/24
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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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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_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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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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df.to_netcdf(path=HH, encoding=encoding)
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for i in glob.glob("/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/*"):
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A=i.split("/")[-1]
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#try:
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#SalidaDir="/home/mario/Documentos/Ocean/NetcdfToPng/MapsModificados/"
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#file=A
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print(A)
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#FusionNc(Map1="/home/mario/Documentos/Ocean/NetcdfToPng/NC2023Patron/",Map2="/home/mario/Documentos/Ocean/NetcdfToPng/MapsAModificar/",SalidaDir=SalidaDir,file=file)
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#print(1)
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#Corrector2(file,SalidaDir,"/home/mario/Documentos/Ocean/NetcdfToPng/MapCorregidoV2/")
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#print(2)
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#break
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0.01837109614206981
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0
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0
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-0.01837109614206981
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-180.36742192284140174
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89.99081445192896922
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Binary file not shown.
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After Width: | Height: | Size: 5.0 MiB |
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docker run -it --mount type=bind,src=/home/mario/Documentos/Ocean/ScuenciaOrganizadaPre/Teselas,dst=/temporal ghcr.io/osgeo/gdal:ubuntu-full-latest bash
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gdal2tiles.py -p geodetic -z 0-7 -r average -s EPSG:4326 --xyz -w mapml /temporal/P1_96P6.jpeg /temporal/map
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import xarray as xr
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import rioxarray as rio
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from matplotlib import cm
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import numpy as np
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import time
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import matplotlib
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import matplotlib.image as imgg
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import os
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from PIL import Image, ImageDraw, ImageFont
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def write_png(data,name, origin='upper', colormapD=None):
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"""
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Transform an array of data into a PNG string.
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This can be written to disk using binary I/O, or encoded using base64
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for an inline PNG like this:
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>>> png_str = write_png(array)
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>>> "data:image/png;base64,"+png_str.encode('base64')
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Inspired from
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https://stackoverflow.com/questions/902761/saving-a-numpy-array-as-an-image
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Parameters
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----------
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data: numpy array or equivalent list-like object.
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Must be NxM (mono), NxMx3 (RGB) or NxMx4 (RGBA)
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origin : ['upper' | 'lower'], optional, default 'upper'
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Place the [0,0] index of the array in the upper left or lower left
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corner of the axes.
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colormap : callable, used only for `mono` image.
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Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
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for transforming a mono image into RGB.
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It must output iterables of length 3 or 4, with values between
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0. and 1. Hint: you can use colormaps from `matplotlib.cm`.
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Returns
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-------
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PNG formatted byte string
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"""
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if colormapD is None:
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def colormapD(x):
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return (x, x, x, 1)
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arr = np.atleast_3d(data)
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height, width, nblayers = arr.shape
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|
||||
if nblayers not in [1, 3, 4]:
|
||||
raise ValueError('Data must be NxM (mono), '
|
||||
'NxMx3 (RGB), or NxMx4 (RGBA)')
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
|
||||
|
||||
if nblayers == 1:
|
||||
arr = np.array(list(map(colormapD, arr.ravel())))
|
||||
nblayers = arr.shape[1]
|
||||
if nblayers not in [3, 4]:
|
||||
raise ValueError('colormap must provide colors of r'
|
||||
'length 3 (RGB) or 4 (RGBA)')
|
||||
arr = arr.reshape((height, width, nblayers))
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
|
||||
if nblayers == 3:
|
||||
arr = np.concatenate((arr, np.ones((height, width, 1))), axis=2)
|
||||
nblayers = 4
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
assert nblayers == 4
|
||||
|
||||
# Normalize to uint8 if it isn't already.
|
||||
if arr.dtype != 'uint8':
|
||||
with np.errstate(divide='ignore', invalid='ignore'):
|
||||
arr = arr * 255./np.array([1., 1., 1., 1.]).reshape((1, 1, 4))
|
||||
arr[~np.isfinite(arr)] = 0
|
||||
arr = arr.astype('uint8')
|
||||
|
||||
# Eventually flip the image.
|
||||
if origin == 'lower':
|
||||
arr = arr[::-1, :, :]
|
||||
r3 = arr.copy(order='C')
|
||||
matplotlib.image.imsave(name, r3)
|
||||
|
||||
|
||||
def image_to_url(image,name, colormapD=None, origin='upper'):
|
||||
"""
|
||||
Infers the type of an image argument and transforms it into a URL.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image: string, file or array-like object
|
||||
* If string, it will be written directly in the output file.
|
||||
* If file, it's content will be converted as embedded in the
|
||||
output file.
|
||||
* If array-like, it will be converted to PNG base64 string and
|
||||
embedded in the output.
|
||||
origin: ['upper' | 'lower'], optional, default 'upper'
|
||||
Place the [0, 0] index of the array in the upper left or
|
||||
lower left corner of the axes.
|
||||
colormap: callable, used only for `mono` image.
|
||||
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
|
||||
for transforming a mono image into RGB.
|
||||
It must output iterables of length 3 or 4, with values between
|
||||
0. and 1. You can use colormaps from `matplotlib.cm`.
|
||||
|
||||
"""
|
||||
|
||||
if 'ndarray' in image.__class__.__name__:
|
||||
img = write_png(image, origin=origin, colormapD=colormapD,name=name)
|
||||
|
||||
|
||||
def get_color(x,colormap,Min,Max):
|
||||
decimals = 2
|
||||
try:
|
||||
#print(x,Min,Max)
|
||||
x=(x*1.-Min)/(Max*1.-Min)
|
||||
#print(x)
|
||||
if x < 0:
|
||||
x=0.0
|
||||
if x > 1.0:
|
||||
x=1.0
|
||||
x = np.around(x, decimals=decimals)
|
||||
if colormap=="Spectral" or colormap=='ocean' or colormap=="RdYlBu":
|
||||
Tempcm=cm.get_cmap(colormap).reversed()
|
||||
else:
|
||||
Tempcm=cm.get_cmap(colormap)
|
||||
#print(x)
|
||||
ls = np.around(np.linspace(0,1,10**decimals+1),decimals=decimals)
|
||||
#print(ls)
|
||||
if 0 <= x <= 1:
|
||||
#print(cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)][0][0].shape,cm.get_cmap('viridis')(ls)[np.argwhere(ls==x)])
|
||||
#print(x,np.argwhere(ls==x))
|
||||
return Tempcm(ls)[np.argwhere(ls==x)][0][0]
|
||||
else:
|
||||
#print(np.array(np.zeros(4)).shape,np.array(np.zeros(4)))
|
||||
#print(x)
|
||||
return np.zeros(4)
|
||||
except:
|
||||
print(x,np.argwhere(ls==x))
|
||||
return np.zeros(4)
|
||||
|
||||
def ExtractMapImage(file,colormap,countyear,name,nc,geometry=""):
|
||||
|
||||
nc = nc.rio.write_crs(4326)
|
||||
if geometry=="":
|
||||
try:
|
||||
nc = nc.rio.clip(geometries)
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
pass
|
||||
bounds=[[float(nc.lat.min().values),float(nc.lon.min().values)],[float(nc.lat.max().values),float(nc.lon.max().values)]]
|
||||
i=countyear
|
||||
|
||||
year=int(nc.time[i].values)
|
||||
print(i,year)
|
||||
ncVar=nc.DHW_q99
|
||||
data = ncVar[i,:,:].values
|
||||
|
||||
|
||||
Min=np.around(np.nanquantile(ncVar[int(year)-1986,:,:].values, 0.01),2)
|
||||
Max=np.around(np.nanquantile(ncVar[int(year)-1986,:,:].values, 0.99),2)#np.around(np.nanmax(ncVar[year-1985,:,:].values),2)
|
||||
|
||||
image_to_url(data,name, colormapD=lambda x: get_color(x,colormap,Min,Max), origin="lower")
|
||||
|
||||
|
||||
def ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory):
|
||||
cc=0
|
||||
Var="DHW"
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
for CM in Colorpalete:
|
||||
for countyear in range(115):
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
year=int(nc.time[countyear].values)
|
||||
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
|
||||
if not os.path.isfile(Evaluado):
|
||||
cc=cc+1
|
||||
|
||||
Total=cc
|
||||
start=time.time()
|
||||
cc=0
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
for CM in Colorpalete:
|
||||
for countyear in range(115):
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
year=int(nc.time[countyear].values)
|
||||
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
|
||||
if not os.path.isfile(Evaluado):
|
||||
ExtractMapImage(ff,CM,countyear,Evaluado,nc)
|
||||
cc=cc+1
|
||||
now=time.time()
|
||||
print(cc,(start-now)/cc,(start-now)/cc*(Total-cc))
|
||||
|
||||
ssp=["ssp245","ssp370","ssp585"]
|
||||
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")#,"ensemble5","ensemble8")
|
||||
Colorpalete=["RdYlBu",'Spectral','ocean',"coolwarm",]
|
||||
ExportDirectory="img/"
|
||||
DataDirectory="../Data/"
|
||||
|
||||
ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory)
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,220 @@
|
|||
import xarray as xr
|
||||
import rioxarray as rio
|
||||
from matplotlib import cm
|
||||
import numpy as np
|
||||
import time
|
||||
import matplotlib
|
||||
import matplotlib.image as imgg
|
||||
import os
|
||||
|
||||
|
||||
def write_png(data,name, origin='upper', colormapD=None):
|
||||
"""
|
||||
Transform an array of data into a PNG string.
|
||||
This can be written to disk using binary I/O, or encoded using base64
|
||||
for an inline PNG like this:
|
||||
|
||||
>>> png_str = write_png(array)
|
||||
>>> "data:image/png;base64,"+png_str.encode('base64')
|
||||
|
||||
Inspired from
|
||||
https://stackoverflow.com/questions/902761/saving-a-numpy-array-as-an-image
|
||||
|
||||
Parameters
|
||||
----------
|
||||
data: numpy array or equivalent list-like object.
|
||||
Must be NxM (mono), NxMx3 (RGB) or NxMx4 (RGBA)
|
||||
|
||||
origin : ['upper' | 'lower'], optional, default 'upper'
|
||||
Place the [0,0] index of the array in the upper left or lower left
|
||||
corner of the axes.
|
||||
|
||||
colormap : callable, used only for `mono` image.
|
||||
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
|
||||
for transforming a mono image into RGB.
|
||||
It must output iterables of length 3 or 4, with values between
|
||||
0. and 1. Hint: you can use colormaps from `matplotlib.cm`.
|
||||
|
||||
Returns
|
||||
-------
|
||||
PNG formatted byte string
|
||||
|
||||
"""
|
||||
|
||||
if colormapD is None:
|
||||
def colormapD(x):
|
||||
return (x, x, x, 1)
|
||||
arr = np.atleast_3d(data)
|
||||
height, width, nblayers = arr.shape
|
||||
if nblayers not in [1, 3, 4]:
|
||||
raise ValueError('Data must be NxM (mono), '
|
||||
'NxMx3 (RGB), or NxMx4 (RGBA)')
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
if nblayers == 1:
|
||||
arr = np.array(list(map(colormapD, arr.ravel())))
|
||||
nblayers = arr.shape[1]
|
||||
if nblayers not in [3, 4]:
|
||||
raise ValueError('colormap must provide colors of r'
|
||||
'length 3 (RGB) or 4 (RGBA)')
|
||||
arr = arr.reshape((height, width, nblayers))
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
if nblayers == 3:
|
||||
arr = np.concatenate((arr, np.ones((height, width, 1))), axis=2)
|
||||
nblayers = 4
|
||||
assert arr.shape == (height, width, nblayers)
|
||||
assert nblayers == 4
|
||||
# Normalize to uint8 if it isn't already.
|
||||
if arr.dtype != 'uint8':
|
||||
with np.errstate(divide='ignore', invalid='ignore'):
|
||||
arr = arr * 255./np.array([1., 1., 1., 1.]).reshape((1, 1, 4))
|
||||
arr[~np.isfinite(arr)] = 0
|
||||
arr = arr.astype('uint8')
|
||||
# Eventually flip the image.
|
||||
if origin == 'upper':
|
||||
arr = arr[::-1, :, :]
|
||||
r3 = arr.copy(order='C')
|
||||
matplotlib.image.imsave(name, r3)
|
||||
|
||||
|
||||
def image_to_url(image,name, colormapD=None, origin='lower'):
|
||||
"""
|
||||
Infers the type of an image argument and transforms it into a URL.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
image: string, file or array-like object
|
||||
* If string, it will be written directly in the output file.
|
||||
* If file, it's content will be converted as embedded in the
|
||||
output file.
|
||||
* If array-like, it will be converted to PNG base64 string and
|
||||
embedded in the output.
|
||||
origin: ['upper' | 'lower'], optional, default 'upper'
|
||||
Place the [0, 0] index of the array in the upper left or
|
||||
lower left corner of the axes.
|
||||
colormap: callable, used only for `mono` image.
|
||||
Function of the form [x -> (r,g,b)] or [x -> (r,g,b,a)]
|
||||
for transforming a mono image into RGB.
|
||||
It must output iterables of length 3 or 4, with values between
|
||||
0. and 1. You can use colormaps from `matplotlib.cm`.
|
||||
|
||||
"""
|
||||
|
||||
if 'ndarray' in image.__class__.__name__:
|
||||
img = write_png(image, origin=origin, colormapD=colormapD,name=name)
|
||||
|
||||
def get_colorD(x,Tempcm,Min,Max,np):#*
|
||||
"""Calculate color of pixel
|
||||
Args:
|
||||
x (float): Value of pixel
|
||||
Tempcm (array): Colormap values
|
||||
Min (float): min value
|
||||
Max (float): max value
|
||||
ls (_type_): linear interpolation function
|
||||
decimals (_type_): number of decimals
|
||||
np (_type_): library numpy
|
||||
|
||||
Returns:
|
||||
array (4x1): array of color rbga
|
||||
"""
|
||||
import numpy as np
|
||||
try:
|
||||
if x <= 0:
|
||||
x = 0
|
||||
else:
|
||||
x = x+1
|
||||
if x > 21:
|
||||
x = 21
|
||||
x = int(np.fix(x))
|
||||
except:
|
||||
return Tempcm[-1]
|
||||
|
||||
return Tempcm[x]
|
||||
|
||||
def ExtractMapImage(file,colormap,countyear,name,nc,geometry=""):
|
||||
import matplotlib as mpl
|
||||
nc = nc.rio.write_crs(4326)
|
||||
if geometry=="":
|
||||
try:
|
||||
nc = nc.rio.clip(geometries)
|
||||
except:
|
||||
pass
|
||||
else:
|
||||
pass
|
||||
bounds=[[float(nc.lat.min().values),float(nc.lon.min().values)],[float(nc.lat.max().values),float(nc.lon.max().values)]]
|
||||
i=countyear
|
||||
|
||||
year=int(nc.time[i].values)
|
||||
print(i,year)
|
||||
ncVar=nc.DHW_q99
|
||||
data = ncVar[i,:,:].values
|
||||
|
||||
|
||||
if colormap=="noaa":
|
||||
Min=np.around(np.nanmin(data),0)
|
||||
Max=np.around(np.nanmax(data),0)
|
||||
cmap=np.array([[200, 250, 250, 255],
|
||||
[ 69, 49, 120, 255],
|
||||
[ 99, 79, 149, 255],
|
||||
[129, 110, 179, 255],
|
||||
[159, 140, 209, 255],
|
||||
[255, 252, 0, 255],
|
||||
[253, 220, 0, 255],
|
||||
[251, 185, 0, 255],
|
||||
[251, 149, 1, 255],
|
||||
[248, 2, 1, 255],
|
||||
[209, 1, 0, 255],
|
||||
[159, 1, 0, 255],
|
||||
[110, 0, 0, 255],
|
||||
[229, 125, 69, 255],
|
||||
[179, 90, 39, 255],
|
||||
[125, 60, 31, 255],
|
||||
[ 84, 45, 19, 255],
|
||||
[239, 33, 239, 255],
|
||||
[200, 25, 200, 255],
|
||||
[159, 18, 159, 255],
|
||||
[120, 10, 120, 255],
|
||||
[ 49, 2, 49, 255],
|
||||
[200, 250, 250, 255],
|
||||
[ 49, 2, 49, 255],
|
||||
[ 0, 0, 0, 0]], dtype=np.uint8)
|
||||
image_to_url(data, name,colormapD=lambda x: get_colorD(x,cmap,Min,Max,np), origin='upper')
|
||||
|
||||
|
||||
def ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory):
|
||||
cc=0
|
||||
Var="DHW"
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
for CM in Colorpalete:
|
||||
for countyear in range(115):
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
year=int(nc.time[countyear].values)
|
||||
Evaluado=ExportDirectory+"%s_%s"%(CM,ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
|
||||
if not os.path.isfile(Evaluado):
|
||||
cc=cc+1
|
||||
|
||||
Total=cc
|
||||
start=time.time()
|
||||
cc=0
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ff=DataDirectory+"%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
for CM in Colorpalete:
|
||||
for countyear in range(115):
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
year=int(nc.time[countyear].values)
|
||||
Evaluado=ExportDirectory+"%s_%s"%("noaa",ff.split("/")[-1].replace(".nc","_%s.png"%(year)))
|
||||
if not os.path.isfile(Evaluado):
|
||||
ExtractMapImage(ff,"noaa",countyear,Evaluado,nc)
|
||||
cc=cc+1
|
||||
now=time.time()
|
||||
print(cc,(start-now)/cc,(start-now)/cc*(Total-cc))
|
||||
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
|
||||
Colorpalete=['CRW-NOAA']
|
||||
ExportDirectory="img/"
|
||||
DataDirectory="../Data/"
|
||||
|
||||
ProcessAllImage(ssp,model,Colorpalete,ExportDirectory,DataDirectory)
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
affine==2.3.0
|
||||
attrs==21.4.0
|
||||
beautifulsoup4==4.10.0
|
||||
branca==0.4.2
|
||||
certifi==2021.10.8
|
||||
cftime==1.5.2
|
||||
charset-normalizer==2.0.10
|
||||
click==8.0.4
|
||||
click-plugins==1.1.1
|
||||
cligj==0.7.2
|
||||
contourpy==1.0.5
|
||||
cycler==0.11.0
|
||||
docopt==0.6.2
|
||||
folium==0.12.1.post1
|
||||
fonttools==4.37.4
|
||||
h5netcdf==0.14.0
|
||||
h5py==3.6.0
|
||||
idna==3.3
|
||||
imageio==2.16.1
|
||||
Jinja2==3.0.3
|
||||
joblib==1.1.0
|
||||
kiwisolver==1.3.2
|
||||
MarkupSafe==2.0.1
|
||||
matplotlib==3.6.0
|
||||
netCDF4==1.5.8
|
||||
networkx==2.7
|
||||
numpy==1.22.0
|
||||
packaging==21.3
|
||||
pandas==1.4.1
|
||||
Pillow==9.0.1
|
||||
plotly==5.6.0
|
||||
Pydap==3.2.2
|
||||
pyparsing==3.0.7
|
||||
pyproj==3.3.0
|
||||
python-dateutil==2.8.2
|
||||
pytz==2021.3
|
||||
PyWavelets==1.2.0
|
||||
rasterio==1.2.10
|
||||
requests==2.27.1
|
||||
rioxarray==0.12.2
|
||||
scikit-image==0.19.2
|
||||
scikit-learn==1.0.2
|
||||
scipy==1.8.0
|
||||
Shapely==1.8.4
|
||||
six==1.16.0
|
||||
sklearn==0.0
|
||||
snuggs==1.4.7
|
||||
soupsieve==2.3.1
|
||||
tenacity==8.0.1
|
||||
threadpoolctl==3.1.0
|
||||
tifffile==2022.2.9
|
||||
urllib3==1.26.8
|
||||
WebOb==1.8.7
|
||||
xarray==0.21.1
|
||||
|
|
@ -0,0 +1,134 @@
|
|||
import xarray as xr
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import os
|
||||
import os
|
||||
import moviepy.video.io.ImageSequenceClip
|
||||
|
||||
def calculategetValuesColormap(nc2,year):#*
|
||||
""" Calculate values of colormap
|
||||
|
||||
Args:
|
||||
nc (nc data): all layer of nc
|
||||
year (int): year
|
||||
|
||||
Returns:
|
||||
Min (float): Minimum value
|
||||
Max (float): Maximun value
|
||||
q1 (float): q1 value
|
||||
q50 (float): q50 value
|
||||
q25 (float): q25 value
|
||||
q75 (float): q75 value
|
||||
q99 (float): q99 value
|
||||
"""
|
||||
import numpy as np
|
||||
try:
|
||||
q1=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.01),2)
|
||||
Min=np.around(np.nanmin(nc2[int(year)-1986,:,:].values),2)
|
||||
Max=np.around(np.nanmax(nc2[int(year)-1986,:,:].values),2)
|
||||
q99=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.99),1)
|
||||
q50= np.around((q1+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
|
||||
q25= np.around((q1+q50)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
|
||||
q75= np.around((q50+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
|
||||
except Exception:
|
||||
pass
|
||||
return Min,Max,q1,q50,q25,q75,q99
|
||||
|
||||
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
|
||||
draw = ImageDraw.Draw(back_im)
|
||||
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
|
||||
textwidth, textheight = draw.textsize(str(text))
|
||||
#print(textwidth, textheight,text)
|
||||
if colormap:
|
||||
x=x-textheight
|
||||
draw.text((x, y), str(text),color,title_font)
|
||||
return back_im
|
||||
|
||||
def ProcessAllImage(ssp,model,Colormap):
|
||||
cc=0
|
||||
Var="DHW"
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ff="../Data/%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
print(ff)
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
for countyear in range(len(nc.time)):
|
||||
|
||||
|
||||
#try:
|
||||
year=int(nc.time[countyear].values)
|
||||
path='./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year)
|
||||
isFile = os.path.isfile(path)
|
||||
if isFile:
|
||||
print(" Ya existe "+ path)
|
||||
continue
|
||||
#print(ff,year)
|
||||
im1 = Image.open('./CapasJuntas_%s.png'%(Colormap))
|
||||
im2 = Image.open('../2nc2image/img/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
|
||||
back_im = im1.copy()
|
||||
back_im.paste(im2, (0, 27))
|
||||
#final2 = Image.new("RGBA", back_im.size)
|
||||
#final2 = Image.alpha_composite(final2, back_im)
|
||||
#im3 = Image.open("/home/mario/Documentos/Ocean/NetcdfToPng/CapasCoralJunta.png")
|
||||
#final2 = Image.alpha_composite(final2, im3)
|
||||
#back_im=final2
|
||||
im3 = Image.open("./FondoRosa.png")
|
||||
final2 = Image.alpha_composite(im3,back_im)
|
||||
back_im=final2
|
||||
x=10
|
||||
y=0
|
||||
back_im=textdraw(back_im,year,x,y+2,(0, 0, 0))
|
||||
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
|
||||
x=500
|
||||
y=0
|
||||
back_im=textdraw(back_im,i,x,y+2,(0, 0, 0))
|
||||
x=570
|
||||
y=0
|
||||
back_im=textdraw(back_im,j,x,y+2,(0, 0, 0))
|
||||
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
|
||||
|
||||
back_im.save('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year), quality=100)
|
||||
|
||||
ListY=[]
|
||||
path="Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm"
|
||||
isFile = os.path.isfile(path)
|
||||
if isFile:
|
||||
print(" Ya existe "+ path)
|
||||
continue
|
||||
for year in range(1986,2101):
|
||||
ListY.append('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
|
||||
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".avi",codec="rawvideo")
|
||||
|
||||
|
||||
|
||||
#except:
|
||||
# print(3432)
|
||||
#break
|
||||
#break
|
||||
#break
|
||||
|
||||
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
#model=("ensemble5","ensemble8")
|
||||
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
|
||||
Colormaps=["noaa"]
|
||||
for Colormap in Colormaps:
|
||||
ProcessAllImage(ssp,model,Colormap)
|
||||
|
||||
|
||||
import moviepy.video.io.ImageSequenceClip
|
||||
|
||||
Var="DHW"
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
model=("ensemble5","ensemble8","BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
|
||||
Colormaps=["noaa"]
|
||||
for Colormap in Colormaps:
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ListY=[]
|
||||
for year in range(1986,2101):
|
||||
ListY.append('ConCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
|
||||
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_ON"%(Colormap,Var,i,j)+".webm")
|
||||
|
||||
|
|
@ -0,0 +1,206 @@
|
|||
import xarray as xr
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import os
|
||||
import moviepy.video.io.ImageSequenceClip
|
||||
|
||||
def calculategetValuesColormap(nc2,year):#*
|
||||
""" Calculate values of colormap
|
||||
|
||||
Args:
|
||||
nc (nc data): all layer of nc
|
||||
year (int): year
|
||||
|
||||
Returns:
|
||||
Min (float): Minimum value
|
||||
Max (float): Maximun value
|
||||
q1 (float): q1 value
|
||||
q50 (float): q50 value
|
||||
q25 (float): q25 value
|
||||
q75 (float): q75 value
|
||||
q99 (float): q99 value
|
||||
"""
|
||||
import numpy as np
|
||||
try:
|
||||
q1=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.01),2)
|
||||
Min=np.around(np.nanmin(nc2[int(year)-1986,:,:].values),2)
|
||||
Max=np.around(np.nanmax(nc2[int(year)-1986,:,:].values),2)
|
||||
q99=np.around(np.nanquantile(nc2[int(year)-1986,:,:].values, 0.99),2)
|
||||
q50= int(np.around((q1+q99)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
|
||||
q25= int(np.around((q1+q50)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
|
||||
q75= int(np.around((q50+q99)/2,2))#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
|
||||
if q99<10:
|
||||
q99=8
|
||||
q1=0
|
||||
q25=2
|
||||
q50=4
|
||||
q75=6
|
||||
except Exception:
|
||||
pass
|
||||
return Min,Max,q1,q50,q25,q75,q99
|
||||
|
||||
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
|
||||
draw = ImageDraw.Draw(back_im)
|
||||
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
|
||||
textwidth, textheight = draw.textsize(str(text))
|
||||
#print(textwidth, textheight,text)
|
||||
if colormap:
|
||||
x=x-textheight
|
||||
draw.text((x, y), str(text),color,title_font)
|
||||
return back_im
|
||||
|
||||
def ProcessAllImage(ssp,model,Colormap):
|
||||
cc=0
|
||||
Var="DHW"
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
|
||||
ff="../Data/%s_%s_%s_DHW.nc"%(Var,i,j)
|
||||
|
||||
print(ff)
|
||||
try:
|
||||
print(ff)
|
||||
nc = xr.open_dataset(ff, decode_coords="all")
|
||||
except:
|
||||
pass
|
||||
for countyear in range(len(nc.time)):
|
||||
|
||||
|
||||
#try:
|
||||
nc2=nc.DHW_q99
|
||||
year=int(nc.time[countyear].values)
|
||||
path='SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year)
|
||||
isFile = os.path.isfile(path)
|
||||
if isFile:
|
||||
print(" Ya existe "+ path)
|
||||
continue
|
||||
|
||||
#print(ff,year)
|
||||
Min,Max,q1,q50,q25,q75,q99=calculategetValuesColormap(nc2,year)
|
||||
#print(Min,Max,q1,q50,q25,q75,q99)
|
||||
print(year)
|
||||
if q1<0.01:
|
||||
q1=0.01
|
||||
im1 = Image.open('./CapasJuntas_%s.png'%(Colormap))
|
||||
im2 = Image.open('../2nc2image/img/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
|
||||
back_im = im1.copy()
|
||||
back_im.paste(im2, (0, 27))
|
||||
final2 = Image.new("RGBA", back_im.size)
|
||||
final2 = Image.alpha_composite(final2, back_im)
|
||||
im3 = Image.open("./FondoRosa.png")
|
||||
final2 = Image.alpha_composite(im3,final2)
|
||||
back_im=final2
|
||||
x=10
|
||||
y=0
|
||||
back_im=textdraw(back_im,year,x,y+2,(0, 0, 0))
|
||||
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
|
||||
x=500
|
||||
y=0
|
||||
back_im=textdraw(back_im,i,x,y+2,(0, 0, 0))
|
||||
x=570
|
||||
y=0
|
||||
back_im=textdraw(back_im,j,x,y+2,(0, 0, 0))
|
||||
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
|
||||
x=230
|
||||
y=197
|
||||
back_im=textdraw(back_im,"<"+str(q1),x,y,(205, 205, 205),12,True)
|
||||
#back_im.save('rocket_pillow_paste_pos.png', quality=95)
|
||||
x1=470#91
|
||||
back_im=textdraw(back_im,">"+str(q99),x1,y,(205, 205, 205),12,True)
|
||||
x2=(x+x1)/2#53
|
||||
back_im=textdraw(back_im,str(q50),x2,y,(205, 205, 205),12,True)
|
||||
x3=(x2+x1)/2
|
||||
back_im=textdraw(back_im,str(q75),x3,y,(205, 205, 205),12,True)
|
||||
x4=(x+x2)/2
|
||||
back_im=textdraw(back_im,str(q25),x4,y,(205, 205, 205),12,True)
|
||||
back_im.save('SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year), quality=95)
|
||||
|
||||
|
||||
ListY=[]
|
||||
path="Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm"
|
||||
isFile = os.path.isfile(path)
|
||||
if isFile:
|
||||
print(" Ya existe "+ path)
|
||||
continue
|
||||
for year in range(1986,2101):
|
||||
ListY.append('./SinCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
|
||||
movie_clip.write_videofile("./Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm")
|
||||
|
||||
#except:
|
||||
# print(3432)
|
||||
#break
|
||||
#break
|
||||
#break
|
||||
|
||||
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM","ensemble5","ensemble8",)
|
||||
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
|
||||
for Colormap in Colormaps:
|
||||
ProcessAllImage(ssp,model,Colormap)
|
||||
|
||||
|
||||
import xarray as xr
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import os
|
||||
|
||||
|
||||
# for e in os.walk('./SinCoralN'):
|
||||
# pass
|
||||
# for file in e[2]:
|
||||
# #print(file)
|
||||
|
||||
# back_im = Image.open("./SinCoralN/"+file)
|
||||
|
||||
# final2 = Image.new("RGBA", back_im.size)
|
||||
# #print(back_im.size,final2.size)
|
||||
# im1 = Image.open("./3CoralesOrig.png")
|
||||
# final2.paste(im1, (0, 27))
|
||||
# #print(back_im.size,final2.size)
|
||||
|
||||
# final2 = Image.alpha_composite(back_im,final2,)
|
||||
# final2.save("./ConcoralN/"+file, quality=95)
|
||||
|
||||
for e in os.walk('./SinCoralN'):
|
||||
pass
|
||||
for file in e[2]:
|
||||
print(file)
|
||||
isFile = os.path.isfile("./ConCoralN/"+file)
|
||||
if isFile:
|
||||
print(" Ya existe "+ "./ConCoralN/"+file)
|
||||
continue
|
||||
|
||||
back_im = Image.open("./SinCoralN/"+file)
|
||||
|
||||
final2 = Image.new("RGBA", back_im.size)
|
||||
#print(back_im.size,final2.size)
|
||||
im1 = Image.open("./3CoralesOrig.png")
|
||||
final2.paste(im1, (0, 27))
|
||||
#print(back_im.size,final2.size)
|
||||
|
||||
final2 = Image.alpha_composite(back_im,final2,)
|
||||
final2.save("./ConCoralN/"+file, quality=95)
|
||||
|
||||
|
||||
import moviepy.video.io.ImageSequenceClip
|
||||
Var="DHW"
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
model=("BCC-CSM2-MR","CESM2","CanESM5","IPSL-CM6A-LR","MIROC6","NorESM2-MM","MRI-ESM2-0","EC-Earth3")
|
||||
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
|
||||
for Colormap in Colormaps:
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ListY=[]
|
||||
path="Videos/Animated_%s_%s_%s_%s_ON"%(Colormap,Var,i,j)+".webm"
|
||||
isFile = os.path.isfile(path)
|
||||
if isFile:
|
||||
print(" Ya existe "+ path)
|
||||
continue
|
||||
for year in range(1986,2101):
|
||||
ListY.append('./ConCoralN/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
print("%s_%s_%s_%s_ON"%(Colormap,Var,i,j))
|
||||
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
|
||||
movie_clip.write_videofile(path)
|
||||
|
||||
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 7.6 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.7 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 2.7 KiB |
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|
After Width: | Height: | Size: 2.6 KiB |
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|
After Width: | Height: | Size: 3.3 KiB |
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|
After Width: | Height: | Size: 7.0 KiB |
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|
After Width: | Height: | Size: 3.0 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 6.2 KiB |
|
|
@ -0,0 +1,202 @@
|
|||
|
||||
Apache License
|
||||
Version 2.0, January 2004
|
||||
http://www.apache.org/licenses/
|
||||
|
||||
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
|
||||
|
||||
1. Definitions.
|
||||
|
||||
"License" shall mean the terms and conditions for use, reproduction,
|
||||
and distribution as defined by Sections 1 through 9 of this document.
|
||||
|
||||
"Licensor" shall mean the copyright owner or entity authorized by
|
||||
the copyright owner that is granting the License.
|
||||
|
||||
"Legal Entity" shall mean the union of the acting entity and all
|
||||
other entities that control, are controlled by, or are under common
|
||||
control with that entity. For the purposes of this definition,
|
||||
"control" means (i) the power, direct or indirect, to cause the
|
||||
direction or management of such entity, whether by contract or
|
||||
otherwise, or (ii) ownership of fifty percent (50%) or more of the
|
||||
outstanding shares, or (iii) beneficial ownership of such entity.
|
||||
|
||||
"You" (or "Your") shall mean an individual or Legal Entity
|
||||
exercising permissions granted by this License.
|
||||
|
||||
"Source" form shall mean the preferred form for making modifications,
|
||||
including but not limited to software source code, documentation
|
||||
source, and configuration files.
|
||||
|
||||
"Object" form shall mean any form resulting from mechanical
|
||||
transformation or translation of a Source form, including but
|
||||
not limited to compiled object code, generated documentation,
|
||||
and conversions to other media types.
|
||||
|
||||
"Work" shall mean the work of authorship, whether in Source or
|
||||
Object form, made available under the License, as indicated by a
|
||||
copyright notice that is included in or attached to the work
|
||||
(an example is provided in the Appendix below).
|
||||
|
||||
"Derivative Works" shall mean any work, whether in Source or Object
|
||||
form, that is based on (or derived from) the Work and for which the
|
||||
editorial revisions, annotations, elaborations, or other modifications
|
||||
represent, as a whole, an original work of authorship. For the purposes
|
||||
of this License, Derivative Works shall not include works that remain
|
||||
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APPENDIX: How to apply the Apache License to your work.
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|
|
@ -0,0 +1,73 @@
|
|||
import xarray as xr
|
||||
from PIL import Image, ImageDraw, ImageFont
|
||||
import os
|
||||
import moviepy.video.io.ImageSequenceClip
|
||||
|
||||
def calculategetValuesColormap(nc2,year):#*
|
||||
""" Calculate values of colormap
|
||||
|
||||
Args:
|
||||
nc (nc data): all layer of nc
|
||||
year (int): year
|
||||
|
||||
Returns:
|
||||
Min (float): Minimum value
|
||||
Max (float): Maximun value
|
||||
q1 (float): q1 value
|
||||
q50 (float): q50 value
|
||||
q25 (float): q25 value
|
||||
q75 (float): q75 value
|
||||
q99 (float): q99 value
|
||||
"""
|
||||
import numpy as np
|
||||
try:
|
||||
q1=np.around(np.nanquantile(nc2[int(year)-1987,:,:].values, 0.01),2)
|
||||
Min=np.around(np.nanmin(nc2[int(year)-1987,:,:].values),2)
|
||||
Max=np.around(np.nanmax(nc2[int(year)-1987,:,:].values),2)
|
||||
q99=np.around(np.nanquantile(nc2[int(year)-1987,:,:].values, 0.99),2)
|
||||
q50= np.around((q1+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.50),1)
|
||||
q25= np.around((q1+q50)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.25),1)
|
||||
q75= np.around((q50+q99)/2,2)#np.around(np.nanquantile(nc.DHW[int(year)-1987,:,:].values, 0.75),1)
|
||||
if q99<10:
|
||||
q99=8
|
||||
q1=0
|
||||
q25=2
|
||||
q50=4
|
||||
q75=6
|
||||
except Exception:
|
||||
pass
|
||||
return Min,Max,q1,q50,q25,q75,q99
|
||||
|
||||
def textdraw(back_im,text,x,y,color,size=18,colormap=False):
|
||||
draw = ImageDraw.Draw(back_im)
|
||||
title_font = ImageFont.truetype('Roboto/Roboto-Regular.ttf', size)
|
||||
textwidth, textheight = draw.textsize(str(text))
|
||||
#print(textwidth, textheight,text)
|
||||
if colormap:
|
||||
x=x-textheight
|
||||
draw.text((x, y), str(text),color,title_font)
|
||||
return back_im
|
||||
|
||||
def ProcessAllImage(ssp,model,Colormap):
|
||||
cc=0
|
||||
Var="DHW"
|
||||
for i in ssp:
|
||||
for j in model:
|
||||
ListY=[]
|
||||
for year in range(1987,2101):
|
||||
ListY.append('/home/mario/Documentos/Ocean/NetcdfToPng/SinCoral/%s_%s_%s_%s_DHW_%s.png'%(Colormap,Var,i,j,year))
|
||||
movie_clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip(ListY, 2)
|
||||
movie_clip.write_videofile("Videos/Animated_%s_%s_%s_%s_OFF"%(Colormap,Var,i,j)+".webm")
|
||||
|
||||
#except:
|
||||
# print(3432)
|
||||
#break
|
||||
#break
|
||||
#break
|
||||
|
||||
|
||||
ssp=("ssp245","ssp370","ssp585")
|
||||
model=("BCC-CSM2-MR","CESM2","CanESM5","EC-Earth3","IPSL-CM6A-LR","MIROC6","MRI-ESM2-0","NorESM2-MM")
|
||||
Colormaps=['Spectral','ocean',"coolwarm","RdYlBu"]
|
||||
for Colormap in Colormaps:
|
||||
ProcessAllImage(ssp,model,Colormap)
|
||||
Loading…
Reference in New Issue