fixes

dfm_tools/hydrolib_helpers.py

@@ -41,12 +41,12 @@ def get_ncbnd_construct():
                    "axis":"Z",
                     }
 
-    ncbnd_construct = {"varn_depth":"depth",
-                       "dimn_depth":"depth",
+    ncbnd_construct = {"varn_depth":"z",
+                       "dimn_depth":"z",
                        "varn_pointx":"lon",
                        "varn_pointy":"lat",
                        "varn_pointname":"station_id",
-                       "dimn_point":"location",
+                       "dimn_point":"node",
                        "attrs_pointx":attrs_pointx,
                        "attrs_pointy":attrs_pointy,
                        "attrs_depth":attrs_depth,
@@ -270,7 +270,7 @@ def DataFrame_to_TimModel(tim_pd, refdate:(dt.datetime, pd.Timestamp, str)):
     return timmodel
 
 
-def ForcingModel_to_plipointsDataset(forcingmodel:hcdfm.ForcingModel, npoints=None) -> xr.Dataset:
+def ForcingModel_to_plipointsDataset(forcingmodel:hcdfm.ForcingModel, npoints=None, convertnan=False) -> xr.Dataset:
     if not isinstance(forcingmodel, hcdfm.ForcingModel):
         raise TypeError('ForcingModel_to_plipointsDataset expects type hcdfm.ForcingModel, not type {type(forcingobj)}')
 
@@ -279,7 +279,7 @@ def ForcingModel_to_plipointsDataset(forcingmodel:hcdfm.ForcingModel, npoints=No
     varn_pointname = ncbnd_construct['varn_pointname']
     plipointsDataset_list = []
     for forcinglike in forcingmodel.forcing[:npoints]:
-        ds_onepoint = forcinglike_to_Dataset(forcinglike)
+        ds_onepoint = forcinglike_to_Dataset(forcinglike, convertnan=convertnan)
         ds_onepoint = ds_onepoint.expand_dims(dimn_point)
         for datavar in ds_onepoint.data_vars:
             longname = datavar

dfm_tools/interpolate_grid2bnd.py

@@ -545,36 +545,28 @@ def maybe_convert_fews_to_dfmt(ds):
     ncbnd_construct = get_ncbnd_construct()
     dimn_point = ncbnd_construct['dimn_point']
     varn_pointname = ncbnd_construct['varn_pointname']
-    dimn_depth = ncbnd_construct['dimn_depth']
-    varn_depth = ncbnd_construct['varn_depth']
 
-    # potential FEWS converts
+    # convert station data_vars to coords to avoid dfmt issues
     for var_to_coord in [varn_pointname,'station_names']:
         if var_to_coord in ds.data_vars:
             ds = ds.set_coords(var_to_coord)
+    # assign timeseries_id cf_role to let FM read the station names
     ds[varn_pointname] = ds[varn_pointname].assign_attrs({'cf_role': 'timeseries_id'})
 
-    # rename data_vars to long_name (e.g. renames so to salinitybnd)
+    # rename data_vars to long_name (e.g. renames FEWS so to salinitybnd)
     for datavar in ds.data_vars:
         if datavar in ['ux','uy']: #TODO: keeping these is consistent with hardcoded behaviour in dfm_tools elsewhere, but not desireable
             continue
         if hasattr(ds[datavar],'long_name'):
             longname = ds[datavar].attrs['long_name']
             ds = ds.rename_vars({datavar:longname})
 
-    # rename dims/vars
-    if 'node' in ds.dims:
-        ds = ds.rename_dims({'node':dimn_point})
-    if 'z' in ds.dims:
-        ds = ds.rename_dims({'z':dimn_depth})
-    if 'z' in ds.variables:
-        ds = ds.rename_vars({'z':varn_depth})
-
     # transpose dims #TODO: the order impacts the model results: https://issuetracker.deltares.nl/browse/UNST-7402
     # dfmt (arbitrary) dimension ordering is node/time/z
     # required to reorder to FEWS time/node/z order for comparable results
     # also time/z/node will result in unexpected results
-    ds = ds.transpose("time", dimn_point, ...)
+    if "time" in ds.dims: # check if time dimension is present (astronomic does not have time)
+        ds = ds.transpose("time", dimn_point, ...)
 
     # convert station names to string format (keep attrs and encoding)
     # also needed to properly export, since we cannot encode it at dtype S1 properly otherwise

tests/examples/preprocess_hydrolib_readbc.py

@@ -44,6 +44,8 @@
     fig, ax = plt.subplots(figsize=(12, 6))
     forcingobj = m.forcing[1]
     forcing_xr = dfmt.forcinglike_to_Dataset(forcingobj, convertnan=True)
+    forcing_xr_all = dfmt.ForcingModel_to_plipointsDataset(m, convertnan=True)
+    forcing_xr2 = forcing_xr_all.sel(node=1)
     data_vars = list(forcing_xr.data_vars)
     if forcingobj.function=='t3d':
         forcing_ts = dfmt.Dataset_to_T3D(forcing_xr)