Combine layers unstructured

nlmod/dims/attributes_encodings.py

@@ -167,9 +167,9 @@ def get_encodings(
         if np.issubdtype(da.dtype, np.character):
             continue
 
-        assert "_FillValue" not in da.attrs, (
-            f"Custom fillvalues are not supported. {varname} has a fillvalue set."
-        )
+        assert (
+            "_FillValue" not in da.attrs
+        ), f"Custom fillvalues are not supported. {varname} has a fillvalue set."
 
         encoding = {
             "zlib": True,

nlmod/dims/grid.py

@@ -36,11 +36,65 @@
     remove_inactive_layers,
 )
 from .rdp import rdp
-from .shared import get_area, get_delc, get_delr  # noqa: F401
+from .shared import GridTypeDims, get_area, get_delc, get_delr  # noqa: F401
 
 logger = logging.getLogger(__name__)
 
 
+def is_structured(ds):
+    """Check if a dataset is structured.
+
+    Parameters
+    ----------
+    ds : xr.Dataset or xr.Dataarray
+        dataset or dataarray
+
+    Returns
+    -------
+    bool
+        True if the dataset is structured.
+    """
+    return GridTypeDims.parse_dims(ds) in (
+        GridTypeDims.STRUCTURED,
+        GridTypeDims.STRUCTURED_LAYERED,
+    )
+
+
+def is_vertex(ds):
+    """Check if a dataset is vertex.
+
+    Parameters
+    ----------
+    ds : xr.Dataset or xr.Dataarray
+        dataset or dataarray
+
+    Returns
+    -------
+    bool
+        True if the dataset is structured.
+    """
+    return GridTypeDims.parse_dims(ds) in (
+        GridTypeDims.VERTEX,
+        GridTypeDims.VERTEX_LAYERED,
+    )
+
+
+def is_layered(ds):
+    """Check if a dataset is layered.
+
+    Parameters
+    ----------
+    ds : xr.Dataset or xr.Dataarray
+        dataset or dataarray
+
+    Returns
+    -------
+    bool
+        True if the dataset is layered.
+    """
+    return "layer" in ds.dims
+
+
 def snap_extent(extent, delr, delc):
     """Snap the extent in such a way that an integer number of columns and rows fit in
     the extent. The new extent is always equal to, or bigger than the original extent.
@@ -1513,9 +1567,9 @@ def aggregate_vector_per_cell(gdf, fields_methods, modelgrid=None):
         else:
             raise TypeError("cannot aggregate geometries of different types")
     if bool({"length_weighted", "max_length"} & set(fields_methods.values())):
-        assert geom_types[0] == "LineString", (
-            "can only use length methods with line geometries"
-        )
+        assert (
+            geom_types[0] == "LineString"
+        ), "can only use length methods with line geometries"
     if bool({"area_weighted", "max_area"} & set(fields_methods.values())):
         if ("Polygon" in geom_types) or ("MultiPolygon" in geom_types):
             pass

nlmod/dims/layers.py

@@ -6,6 +6,7 @@
 
 from ..util import LayerError, _get_value_from_ds_datavar
 from .resample import fillnan_da
+from .shared import GridTypeDims
 
 logger = logging.getLogger(__name__)
 
@@ -269,9 +270,9 @@ def split_layers_ds(
             split_dict[lay0] = [1 / split_dict[lay0]] * split_dict[lay0]
         elif hasattr(split_dict[lay0], "__iter__"):
             # make sure the fractions add up to 1
-            assert np.isclose(np.sum(split_dict[lay0]), 1), (
-                f"Fractions for splitting layer '{lay0}' do not add up to 1."
-            )
+            assert np.isclose(
+                np.sum(split_dict[lay0]), 1
+            ), f"Fractions for splitting layer '{lay0}' do not add up to 1."
             split_dict[lay0] = split_dict[lay0] / np.sum(split_dict[lay0])
         else:
             raise ValueError(
@@ -375,18 +376,9 @@ def layer_combine_top_bot(ds, combine_layers, layer="layer", top="top", bot="bot
     new_nlay = (
         ds[layer].size - sum((len(c) for c in combine_layers)) + len(combine_layers)
     )
-
     # create new DataArrays for storing new top/bot
-    new_bot = xr.DataArray(
-        data=np.nan,
-        dims=["layer", "y", "x"],
-        coords={"layer": np.arange(new_nlay), "y": ds.y.data, "x": ds.x.data},
-    )
-    new_top = xr.DataArray(
-        data=np.nan,
-        dims=["layer", "y", "x"],
-        coords={"layer": np.arange(new_nlay), "y": ds.y.data, "x": ds.x.data},
-    )
+    new_bot = _get_empty_layered_da(ds[bot], nlay=new_nlay)
+    new_top = _get_empty_layered_da(ds[top], nlay=new_nlay)
 
     # dict to keep track of old and new layer indices
     reindexer = {}
@@ -411,7 +403,7 @@ def layer_combine_top_bot(ds, combine_layers, layer="layer", top="top", bot="bot
                         "calculate new top/bot."
                     )
                 tops = ds[top].data[c, ...]
-                bots = ds[bot].data[c, :, :]
+                bots = ds[bot].data[c, ...]
                 new_top.data[j] = np.nanmax(tops, axis=0)
                 new_bot.data[j] = np.nanmin(bots, axis=0)
 
@@ -453,25 +445,49 @@ def sum_param_combined_layers(da, reindexer):
         data array containing new parameters for combined layers and old
         parameters for unmodified layers.
     """
-    da_new = xr.DataArray(
-        data=np.nan,
-        dims=["layer", "y", "x"],
-        coords={
-            "layer": np.arange(list(reindexer.keys())[-1] + 1),
-            "y": da["y"],
-            "x": da["x"],
-        },
-    )
-
+    da_new = _get_empty_layered_da(da, nlay=list(reindexer.keys())[-1] + 1)
     for k, v in reindexer.items():
         if isinstance(v, tuple):
-            psum = np.sum(da.data[v, :, :], axis=0)
+            psum = np.sum(da.data[v, ...], axis=0)
         else:
             psum = da.data[v]
         da_new.data[k] = psum
     return da_new
 
 
+def _get_empty_layered_da(da, nlay):
+    """Get empty DataArray with number of layer specified by nlay.
+
+    Parameters
+    ----------
+    da : xarray.DataArray
+        original data array
+    nlay : int
+        number of layers
+
+    Returns
+    -------
+    da_new : xarray.DataArray
+        new empty data array with updated number of layers
+    """
+    if set(GridTypeDims.STRUCTURED_LAYERED.value).issubset(da.dims):
+        dims = GridTypeDims.STRUCTURED_LAYERED.value
+        coords = {
+            "layer": np.arange(nlay),
+            "y": da["y"],
+            "x": da["x"],
+        }
+    elif set(GridTypeDims.VERTEX_LAYERED.value).issubset(da.dims):
+        dims = GridTypeDims.VERTEX_LAYERED.value
+        coords = {
+            "layer": np.arange(nlay),
+            "icell2d": da["icell2d"],
+        }
+    else:
+        raise TypeError("Cannot determine grid type of data array.")
+    return xr.DataArray(data=np.nan, dims=dims, coords=coords)
+
+
 def kheq_combined_layers(kh, thickness, reindexer):
     """Calculate equivalent horizontal hydraulic conductivity.
 
@@ -491,21 +507,12 @@ def kheq_combined_layers(kh, thickness, reindexer):
         for combined layers and original hydraulic conductivity in unmodified
         layers
     """
-    da_kh = xr.DataArray(
-        data=np.nan,
-        dims=["layer", "y", "x"],
-        coords={
-            "layer": np.arange(list(reindexer.keys())[-1] + 1),
-            "y": kh["y"],
-            "x": kh["x"],
-        },
-    )
-
+    da_kh = _get_empty_layered_da(kh, nlay=list(reindexer.keys())[-1] + 1)
     for k, v in reindexer.items():
         if isinstance(v, tuple):
             kheq = np.nansum(
-                thickness.data[v, :, :] * kh.data[v, :, :], axis=0
-            ) / np.nansum(thickness.data[v, :, :], axis=0)
+                thickness.data[v, ...] * kh.data[v, ...], axis=0
+            ) / np.nansum(thickness.data[v, ...], axis=0)
             kheq[np.isinf(kheq)] = np.nan
         else:
             kheq = kh.data[v]
@@ -532,20 +539,11 @@ def kveq_combined_layers(kv, thickness, reindexer):
         for combined layers and original hydraulic conductivity in unmodified
         layers
     """
-    da_kv = xr.DataArray(
-        data=np.nan,
-        dims=["layer", "y", "x"],
-        coords={
-            "layer": np.arange(list(reindexer.keys())[-1] + 1),
-            "y": kv["y"],
-            "x": kv["x"],
-        },
-    )
-
+    da_kv = _get_empty_layered_da(kv, nlay=list(reindexer.keys())[-1] + 1)
     for k, v in reindexer.items():
         if isinstance(v, tuple):
-            kveq = np.nansum(thickness.data[v, :, :], axis=0) / np.nansum(
-                thickness.data[v, :, :] / kv.data[v, :, :], axis=0
+            kveq = np.nansum(thickness.data[v, ...], axis=0) / np.nansum(
+                thickness.data[v, ...] / kv.data[v, ...], axis=0
             )
             kveq[np.isinf(kveq)] = np.nan
         else:
@@ -564,6 +562,7 @@ def combine_layers_ds(
     kv="kv",
     kD="kD",
     c="c",
+    return_reindexer=False,
 ):
     """Combine layers in Dataset.
 
@@ -595,6 +594,9 @@ def combine_layers_ds(
     c : str, optional
         name of data variable containg resistance or c,
         by default 'c'. Not parsed if set to None.
+    return_reindexer : bool, optional
+        Return a dictionary that can be used to reindex variables from the original
+        layer-dimension to the new layer-dimension when True. The default is False.
 
     Returns
     -------
@@ -625,7 +627,7 @@ def combine_layers_ds(
     ----
     When passing integers to combine_layers, these are always intepreted as the
     layer index (i.e. starting at 0 and numbered consecutively), and not the
-    layer "name". If the dataset layer index is integer, only the layer index
+    layer "name". If the dataset layer names are integers, only the layer index
     can be used to specify which layers to merge.
     """
     data_vars = []
@@ -634,10 +636,14 @@ def combine_layers_ds(
             data_vars.append(dv)
     parsed_dv = set([top, bot] + data_vars)
 
-    dropped_dv = set(ds.data_vars.keys()) - parsed_dv
-    if len(dropped_dv) > 0:
-        msg = f"Following data variables will be dropped: {dropped_dv}"
-        logger.warning(msg)
+    check_remaining_dv = set(ds.data_vars.keys()) - parsed_dv
+    keep_dv = []
+    for dv in check_remaining_dv:
+        if "layer" in ds[dv].dims:
+            msg = f"Data variable has 'layer' dimension and will be dropped: {dv}"
+            logger.warning(msg)
+        else:
+            keep_dv.append(dv)
 
     # calculate new tops/bots
     logger.info("Calculating new layer tops and bottoms...")
@@ -700,16 +706,16 @@ def combine_layers_ds(
 
     # calculate equivalent kh/kv
     if kh is not None:
-        logger.info(f"Calculate equivalent '{kh}' for combined layers.")
+        logger.info("Calculate equivalent '%s' for combined layers.", kh)
         da_dict[kh] = kheq_combined_layers(ds[kh], thickness, reindexer)
     if kv is not None:
-        logger.info(f"Calculate equivalent '{kv}' for combined layers.")
+        logger.info("Calculate equivalent '%s' for combined layers.", kv)
         da_dict[kv] = kveq_combined_layers(ds[kv], thickness, reindexer)
     if kD is not None and kD in ds:
-        logger.info(f"Calculate value '{kD}' for combined layers with sum.")
+        logger.info("Calculate value '%s' for combined layers with sum.", kD)
         da_dict[kD] = sum_param_combined_layers(ds[kD], reindexer)
     if c is not None and c in ds:
-        logger.info(f"Calculate value '{c}' for combined layers with sum.")
+        logger.info("Calculate value '%s' for combined layers with sum.", c)
         da_dict[c] = sum_param_combined_layers(ds[c], reindexer)
 
     # get new layer names, based on first sub-layer from each combined layer
@@ -725,17 +731,19 @@ def combine_layers_ds(
     for k, da in da_dict.items():
         da_dict[k] = da.assign_coords(layer=layer_names)
 
-    # add reindexer to attributes
-    attrs = ds.attrs.copy()
-    attrs["combine_reindexer"] = reindexer
+    # add original data variables to new dataset
+    for dv in keep_dv:
+        da_dict[dv] = ds[dv]
 
     # create new dataset
     logger.info("Done! Created new dataset with combined layers!")
-    ds_combine = xr.Dataset(da_dict, attrs=attrs)
+    ds_combine = xr.Dataset(da_dict, attrs=ds.attrs)
 
     # remove layer dimension from top
     ds_combine = remove_layer_dim_from_top(ds_combine, inconsistency_threshold=1e-5)
 
+    if return_reindexer:
+        return ds_combine, reindexer
     return ds_combine
 
 

nlmod/dims/shared.py

@@ -1,11 +1,46 @@
+from enum import Enum
+
 import numpy as np
 import xarray as xr
 
 
+class GridTypeDims(Enum):
+    """Enum for grid dimensions."""
+
+    STRUCTURED_LAYERED = ("layer", "y", "x")
+    VERTEX_LAYERED = ("layer", "icell2d")
+    STRUCTURED = ("y", "x")
+    VERTEX = ("icell2d",)
+
+    @classmethod
+    def parse_dims(cls, ds):
+        """Get GridTypeDim from dataset or dataarray.
+
+        Parameters
+        ----------
+        ds : xr.Dataset or xr.DataArray
+            Dataset or DataArray to parse.
+
+        Returns
+        -------
+        gridtype : GridTypeDims
+            type of grid
+
+        Raises
+        ------
+        ValueError
+            If no partially matching gridtype is found.
+        """
+        for gridtype in GridTypeDims:
+            if set(gridtype.value).issubset(ds.dims):
+                return gridtype
+        # raises ValueError if no gridtype is found
+        return cls(ds.dims)
+
+
 def get_delr(ds):
     """
-    Get the distance along rows (delr) from the x-coordinate of a structured model
-    dataset.
+    Get the distance along rows (delr) from the x-coordinate of a structured model ds.
 
     Parameters
     ----------