import xarray as xr
import glob,os

domains={"dom1":[17.5,19,-34.5,-32]}

dsets={"CPC":"../data/observed/global/gridded/CPC/Global-Daily-Temperature/day/wcape/{}_day_CPC_Global-Daily-Temperature_merged.nc",
"MSWX":"../data/observed/global/gridded/GloH2O/MSWX/day/wcape/{}_day_GloH2O_MSWX_merged.nc",
"ERA5-Land":"../data/reanalysis/global/reanalysis/ECMWF/ERA5-Land/day/wcape/{}_day_ECMWF_ERA5-Land_merged.nc"}

outdir="../data/timeseries/"


var="tasmin"
dset="CPC"
domain="dom1"

lonmin,lonmax,latmin,latmax=domains[domain]

for var in ["tasmin","tasmax","tas","uas","vas","tpds"]:
    for dset in dsets.keys():
        file=dsets[dset].format(var)

        outfile="{}/{}_day_{}.csv".format(outdir,var,dset)


        if os.path.exists(file):
            ds=xr.open_dataset(file)
            if "lat" in ds.dims:
               ds=ds.rename({"lat":"latitude","lon":"longitude"})

            da=ds[var].sortby("latitude")
            data=da.sel(latitude=slice(latmin,latmax),longitude=slice(lonmin,lonmax)).mean(["latitude","longitude"])
            data=data.to_pandas()
            data.to_csv(outfile)