diff --git a/Squeegee/README b/Squeegee/README
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+++ b/Squeegee/README
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+#centos
+source /modules/centos7/conda/Feb2021/etc/profile.d/conda.sh
+conda activate production
+python3 -m pip install cf_xarray==0.7.4 --user
+python3 -m pip install cartopy==0.21 --user
+
+#redhat (Helene sitt env)
+source /modules/rhel8/conda/install/etc/profile.d/conda.sh
+conda activate /lustre/storeB/project/nwp/H2O/wp4/conda/xesmf_envn
+python -c "import esmpy"
diff --git a/Squeegee/minimal_squeegee_runoff_drainage.py b/Squeegee/minimal_squeegee_runoff_drainage.py
new file mode 100644
index 0000000..a9bd3a3
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+++ b/Squeegee/minimal_squeegee_runoff_drainage.py
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+import sys, os, glob
+import os
+import numpy as np
+import xarray as xr
+import matplotlib
+import matplotlib.pyplot as plt
+#matplotlib.use('Agg')
+import pandas as pd
+from shapely.geometry import Polygon, MultiPolygon, shape, Point
+import geopandas as gp
+import xesmf as xe
+import pyproj
+import requests
+import cartopy.crs as ccrs
+from cartopy.io.img_tiles import Stamen
+import cartopy.feature as cfeature
+tiler = Stamen('terrain-background')
+
+
+def convert_3D_2D(geometry):
+    '''
+    Takes a GeoSeries of 3D Multi/Polygons (has_z) and returns a list of 2D Multi/Polygons
+    '''
+    new_geo = []
+    for p in geometry:
+        if p.has_z:
+            if p.geom_type == 'Polygon':
+                lines = [xy[:2] for xy in list(p.exterior.coords)]
+                new_p = Polygon(lines)
+                new_geo.append(new_p)
+            elif p.geom_type == 'MultiPolygon':
+                new_multi_p = []
+                for ap in p:
+                    lines = [xy[:2] for xy in list(ap.exterior.coords)]
+                    new_p = Polygon(lines)
+                    new_multi_p.append(new_p)
+                new_geo.append(MultiPolygon(new_multi_p))
+    return new_geo
+
+
+def getDomainData(expdirs):
+
+    # Assuming same forcing
+    #FORCING=xr.open_dataset(expdirs+'forcing/2023050100/FORCING.nc', cache=False,use_cftime=False)
+    FORCING=xr.open_dataset('/lustre/storeB/users/josteinbl/sfx_data/LDAS_NOR/archive/2022/12/12/06/raw.nc', cache=False,use_cftime=False)
+    print(FORCING.latitude.min(), FORCING.latitude.max())
+    print(FORCING.longitude.min(), FORCING.longitude.max())
+
+    fPREP=xr.open_dataset('/lustre/storeB/users/josteinbl/sfx_data/LDAS_NOR/'+'archive/2023/04/01/00/SURFOUT.nc', cache=False,use_cftime=False)
+    fPGD=xr.open_dataset('/lustre/storeB/users/josteinbl/sfx_data/LDAS_NOR/'+'climate/PGD.nc', cache=False,use_cftime=False)
+
+    # Add coords etc so that surfex files may be processed
+    R_pysurfex=6371000 # and met nordic
+    R_arome   =6371229
+    Rpy=6.371229e+06
+    proj_string = "+proj=lcc +lat_0=63 +lon_0=15 +lat_1=63 +lat_2=63 +units=m +no_defs +R=" + str(Rpy)
+    myP=pyproj.Proj(proj_string)
+
+    lcc = ccrs.LambertConformal(#globe=globe, # no datumshift in GCMs
+        central_longitude=15, central_latitude=63,
+        standard_parallels=(63,63))#,
+    geodetic=ccrs.Geodetic() #default WGS84
+
+    return myP, proj_string, fPREP, fPGD, FORCING, lcc
+
+
+def readStationList(stationfile):
+
+    #read a list of stations
+    stationst=stationfile
+    stations=pd.read_csv(stationst, header=0,index_col=0,sep=None,
+                         dtype={'GauID': 'str'})
+    stations=stations.drop_duplicates(subset=('GauID'), keep='last')
+
+    # Nr.	GauID	Lat	Long	Ara_km2	Group
+    daily = 1440
+    station='2.25.0'# stations.GauID[2]
+
+    stations['GauID']=stations.GauID+'.0'
+
+    return stations
+
+    
+def readShapeFiles(shapefiles, stations):
+
+    # or Outlets to sea defined for Europe CCM2
+#    ccm2_basins = gp.read_file(shapefiles,layer='SEAOUTLETS')
+    #ccm2sub = ccm2_basins.cx[FORCING.LON.min().values:FORCING.LON.max().values,FORCING.LAT.min().values:FORCING.LAT.max().values]    
+
+    # or cathements where NVE have measuring stations
+    #regs = gp.read_file('/lustre/storeB/project/nwp/H2O/wp4/RRdata/shapefiles/utm33shp/NVEData/Hydrologi/Hydrologi_TotalNedborfeltMalestasjon.shp')
+    regs2 = gp.read_file(shapefiles)
+
+    print("prior")
+    print(regs2.columns)
+    regs2=regs2.loc[regs2['stID'].isin(stations.GauID)]
+
+    print("posterior")
+    #inspect the shape file
+    print(regs2.crs)
+    print(regs2.columns)
+
+    #remove extra dim in nve shape-files
+    regs2.geometry = convert_3D_2D(regs2.geometry)
+
+    plot_polygons=False
+    #make a plot of the polygons:
+    if plot_polygons==True:
+    
+        fig = plt.figure(figsize=(4,8))
+        ax1 = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree(), frameon=False)
+ 
+        ax1.coastlines(resolution='10m')
+        toplot= regs2.cx[FORCING.longitude.min().values:FORCING.longitude.max().values,FORCING.latitude.min().values:FORCING.latitude.max().values]
+
+        toplot.plot(column='stID',kind='geo',edgecolor="g",linewidth=0.2,
+                   ax=ax1,legend=True,cmap='Reds',transform=ccrs.Geodetic())
+        #,legend_kwds={"orientation": "horizontal", "pad": 0.01, "label": "VassOmrNr"})
+        #ccm2sub.plot(column='AREA_KM2',kind='geo',edgecolor="g",linewidth=0.2,ax=ax1,legend=True,cmap='Reds',legend_kwds={"orientation": "horizontal", "pad": 0.01})
+        ax1.axis('off')
+        plt.title("")
+        plt.savefig("polygons.png")
+        plt.show()
+
+    return regs2
+
+def readModelData(expdirs, expdirfs, fPREP, fPGD, ncstart, expnam, variables):
+
+    numruns=len(expnam)
+
+    #-----------------------------------------
+    # read data in this way because a large amount of data
+    def preprocess(ds):
+        return ds[variables]
+
+    dictds={}
+
+    for i in np.arange(0,numruns):
+        dictds[expnam[i]] = xr.open_mfdataset(expdirs+expdirfs[i]+ncstart[i]+
+                                          'sel2023??.nc',
+                                          cache=False,
+                                          preprocess=preprocess,
+                                          concat_dim="time",
+                                          combine="nested",
+                                          chunks={'time': 1})
+        print(expnam[i] + ' given to: ' +expdirs+expdirfs[i]+ncstart[i]+
+              'sel2023??.nc')
+
+    print(dictds)
+
+    return dictds
+
+def generateWeights(dictds, fPREP, fPGD, FORCING, regs2, weightfile):
+
+    # xesmf needs dataset to generate weights
+    TSr=dictds[expnam[-1]]['RUNOFFC_ISBA'].isel(time=0).to_dataset()
+    print('TSr')
+    print(TSr)
+
+    xfalseaR, yfalseaR = myP(fPREP.LONORI.data, fPREP.LATORI.data,inverse=False)
+    #Surfex makes all coords positive by setting the origin to the lower left corner
+
+    TSr=TSr.assign_coords(x= fPREP.XX[0,:].data+xfalseaR)
+    TSr=TSr.assign_coords(y= fPREP.YY[:,0].data+yfalseaR)
+    TSr = TSr.rename({'xx': 'x', 'yy': 'y'})
+
+    dx, dy = fPGD.DX.data[0,0], fPGD.DY.data[0,0]
+    Xcorners=np.arange(TSr['x'].data[0]-dx/2., TSr['x'].data[-1]+3*dx/2., dx)
+    Ycorners=np.arange(TSr['y'].data[0]-dy/2., TSr['y'].data[-1]+3*dy/2., dy)
+    #Lon2, Lat2 = myP(fPREP.XX.data+xfalseaR,fPREP.YY.data+yfalseaR,inverse=True)
+    Lon2b, Lat2b = myP(*np.meshgrid(Xcorners,Ycorners),inverse=True) 
+
+    TSr.coords['xb'] = (Xcorners)
+    TSr.coords['yb'] = (Ycorners)
+    TSr.coords['lat_b'] = (('yb','xb'),Lat2b)
+    TSr.coords['lon_b'] = (('yb','xb'),Lon2b)
+    TSr.set_coords(['lat_b','lon_b'])
+
+    print("Forcing lat data")
+    print(np.shape(FORCING.latitude.data))
+    #print(TSr.coords['lat_b'])
+
+    TSr.coords['lat'] = (('y','x'),FORCING.latitude.data)
+    TSr.coords['lon'] = (('y','x'),FORCING.longitude.data)
+    TSr.lon.attrs=FORCING.longitude.attrs
+    TSr.lat.attrs=FORCING.longitude.attrs
+    TSr.set_coords(['lat','lon'])
+
+    TSr['mask']=TSr.RUNOFFC_ISBA.isnull()
+    TSr['mask'].values=np.where(~TSr.RUNOFFC_ISBA.isnull(),1,0)
+
+    TSr.attrs['pyproj_srs']=proj_string
+
+    #make an outer domain polygon to crop the input shape file
+
+    dompoly=Polygon(zip([TSr['lon_b'][0,0].data,TSr['lon_b'][0,-1].data,TSr['lon_b'][-1,-1].data,
+                     TSr['lon_b'][-1,0].data],[TSr['lat_b'][0,0].data,TSr['lat_b'][0,-1].data,
+                                               TSr['lat_b'][-1,-1].data,TSr['lat_b'][-1,0].data]))
+
+    gdom = gp.GeoSeries([dompoly])
+
+    # assing defined polygon to a new dataframe
+    pol_gpd= gp.GeoDataFrame()
+    pol_gpd['geometry'] = None
+    pol_gpd.loc[0,'geometry'] = dompoly
+    pol_gpd.crs=regs2.crs #hope okay
+
+    # crop shape file to domain, remove basins > 2 km2 
+    result = gp.sjoin(regs2, pol_gpd, how='inner')#, op='within')
+    resultn=result[result.areal_km2 > 2]
+    resultn=resultn[resultn.stID!='2.11.0'] #only missing data 
+    resultn.to_csv('basins_used.csv')
+
+    # This generates the weights, takes time the first time only
+    # ...
+    savg = xe.SpatialAverager(TSr, resultn.geometry, geom_dim_name="stID",
+                          filename=weightfile,reuse_weights=True)
+
+    plotweights=False
+
+    # this takes a lot of memory for all these basins, not recommended
+    if plotweights:
+        w = xr.DataArray(
+            savg.weights.toarray().reshape(resultn.geometry.size, *TSr.lat.shape),
+            dims=("stID", *TSr.lat.dims),
+            coords=dict(stID=out.stID, **TSr.lon.coords),
+        )
+
+        plt.subplots_adjust(top=0.9)
+        facets = w.plot(col="stID", col_wrap=6, aspect=2, vmin=0, vmax=0.05)
+        facets.cbar.set_label("Averaging weights")
+        plt.savefig('weights.png')
+
+    return TSr, savg, resultn
+
+#store the dfs in dict
+
+# here the sparse matrix with grid cell weights for each ploygon is generated
+# see xesmf doc: 
+# https://pangeo-xesmf.readthedocs.io/en/latest/notebooks/Spatial_Averaging.html
+# the matrix can be stored as a nc-file and re-used, so that the program runs 
+# very fast once it is generated. 
+# The weights are specific to the domain, projection, resolution, and to 
+# options like 
+#&nam_pgd_arrange_cover 
+#  lwater_to_nature = .true. 
+#  ltown_to_rock = .true.
+#  which alters where RUNOFFC etc is defined 
+
+
+def run(dictds, expname, TSr, savg, resultn):
+
+    numruns=len(expnam)
+    cachrunoff={}
+
+    for i in np.arange(0,numruns):
+
+        tmp1=dictds[expnam[i]]['RUNOFFC_ISBA'] +dictds[expnam[i]]['DRAINC_ISBA']
+    
+        #some experiments have accumlated runoff (i.e. do not use option
+        # LRESETCUMUL = .true. in NAM_WRITE_DIAG_SURFn 
+        # see https://www.umr-cnrm.fr/surfex/spip.php?article406 )
+        # so need to deaccumulate
+        #if expnam[i] in ['LDAS_FC']:
+        #    tmp1=tmp1.diff(dim='time')
+        tmp1=tmp1.to_dataset(name=expnam[i])
+        tmp1['mask']=TSr.RUNOFFC_ISBA.isnull()
+        tmp1['mask'].values=np.where(~TSr.RUNOFFC_ISBA.isnull(),1,0)
+        out=savg(tmp1[expnam[i]])
+        #out = out.assign_coords(roms_id=xr.DataArray(resultn.index.values, dims=("roms_id",)))
+        out = out.assign_coords(stID=xr.DataArray(resultn["stID"], dims=("stID",)))
+        print("out")
+        print(out)
+        cachrunoff[expnam[i]]=out.to_pandas()
+        rainfRun= cachrunoff[expnam[i]]=out.to_pandas()
+        print("rainfRun")
+        print(rainfRun.keys())
+
+        rainfRun[:].plot()
+        #rainfRun.plot()
+        plt.title(expnam[i], fontsize=12)
+        plt.show()
+        cachrunoff[expnam[i]].to_csv(expnam[i]+'.csv')
+
+
+if __name__ == "__main__":
+
+    expdirs='/lustre/storeB/users/josteinbl/sfx_data/'
+    #forcingfile = '/lustre/storeB/project/nwp/H2O/wp4/FORCING/527_450_2020/FORCING_527_450_202010.nc'
+    stationfile = '/lustre/storeB/project/nwp/H2O/wp4/RRdata/stations_Huang20.txt'
+#    shapefiles = '/lustre/storeB/project/nwp/H2O/wp4/RRdata/shapefiles/CCM2/ccm21/WGS84_W2008.gdb'
+    shapefiles = '/lustre/storeB/project/nwp/H2O/wp4/RRdata/shapefiles/latlonHyd/latlonhydorder/NVEData/Hydrologi/Hydrologi_TotalNedborfeltMalestasjon.shp'
+    #shapefiles = '/lustre/storeB/project/nwp/H2O/wp4/RRdata/shapefiles/latlonREG/Nedborfelt/Nedborfelt_Vassdragsomr.shp'
+
+    weightfile = '/lustre/storeB/users/josteinbl/sfx_data/LDAS_NOR/spatial_avg_4catchments_527nature2lim.nc'
+
+    # folder where runs are, this could have been a long list of different exps.
+    #expdirfs=['LDAS_NOR/', 'LDAS_NOR_eps_05/']
+    expdirfs=['LDAS_FC/']
+    # start of nc-file-output from runs
+    #openoflsel201903.nc
+    
+    # Analysis mode:
+    #ncstart=  ['openofl', 'openofl']
+    ncstart=  ['openofl']
+    # choose a name for legend in plots
+    #expnam=['LDAS_NOR', 'LDAS_NOR_eps_05']
+    expnam=['LDAS_FC']
+
+    variables = ['RUNOFFC_ISBA', 'DRAINC_ISBA']
+
+    myP, proj_string, fPREP, fPGD, FORCING, lcc = getDomainData(expdirs)
+    stations = readStationList(stationfile)
+
+    regs2 = readShapeFiles(shapefiles, stations)
+
+    dictds = readModelData(expdirs, expdirfs, fPREP, fPGD, ncstart, expnam, variables)
+    
+    TSr, savg, resultn = generateWeights(dictds, fPREP, fPGD, FORCING, regs2, weightfile)
+
+    run(dictds, expnam, TSr, savg, resultn)
+
+    plot_catchments = False
+
+    if plot_catchments == True:
+
+        #for sid in regs2.stID:
+        #for sid in stations["GauID"]:
+
+        fig = plt.figure(figsize=(12,7))
+        #gs = fig.add_gridspec(1, 1)
+        #ax0=fig.add_subplot(gs[0,0], projection=ccrs.PlateCarree())
+        #ax0.set_extent([FORCING.longitude.data.min()-15, FORCING.longitude.data.max()+15, FORCING.latitude.data.min()-15, FORCING.latitude.data.max()+15], crs=ccrs.PlateCarree())
+        #ax0.add_feature(cfeature.LAND)
+        #ax0.add_feature(cfeature.COASTLINE)
+        #ax0.add_image(tiler, 7)
+        #gl = ax0.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
+        #              linewidth=2, color='gray', alpha=0.5, linestyle='--')
+        #ax0.plot(FORCING.longitude[[0,0,-1,-1,0],[0,-1,-1,0,0]].values.diagonal(),FORCING.latitude[[0,0,-1,-1,0],[0,-1,-1,0,0]].values.diagonal(),color='blue', linewidth=2,transform=ccrs.Geodetic())
+        #gl.top_labels = gl.right_labels = False
+        ax0b = fig.add_subplot(projection=lcc)
+        ax0b.set_extent([TSr.x.min(), TSr.x.max(), TSr.y.min(), TSr.y.max()], crs=lcc)
+        gl = ax0b.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
+                  linewidth=0.8, color='gray', alpha=0.5, linestyle='--')
+        gl.top_labels = gl.right_labels = False
+        ax0b.add_image(tiler, 7)
+        #regs2[regs2.stID==sid].boundary.plot(ax=ax0b,transform=ccrs.Geodetic(),color='r')            
+        regs2.boundary.plot(ax=ax0b,transform=ccrs.Geodetic(),color='r')            
+        #plt.title(sid+' '+str(resultn.areal_km2[resultn.stID==sid])+' km2')
+        #regs2.boundary.plot(ax=ax0b,transform=ccrs.Geodetic(),color='r')
+        plt.tight_layout()
+        #plt.savefig("/lustre/storeB/users/josteinbl/TOPD/Figures/"+sid+'_snowDA_test.png')
+        #plt.savefig('/lustre/storeB/users/josteinbl/TOPD/Figures/All_catchments_snowDA_test.png')
+        #plt.close()
+        #plt.show()