Fix time indexing and dim ordering for regular datasets (#95)

* Fix time indexing and dim ordering for regular datasets

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---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

xpublish_wms/grids/regular.py

@@ -50,9 +50,15 @@ def project(
         # build new y coordinate
         coords["y"] = ("y", da.cf["latitude"].values, da.cf["latitude"].attrs)
         # build new data array
+        shape = da.shape
+        if shape == (len(da.cf["latitude"]), len(da.cf["longitude"])):
+            dims = ("y", "x")
+        else:
+            dims = ("x", "y")
+
         da = xr.DataArray(
             data=da,
-            dims=("y", "x"),
+            dims=dims,
             coords=coords,
             name=da.name,
             attrs=da.attrs,

xpublish_wms/wms/get_capabilities.py

@@ -246,8 +246,8 @@ def get_capabilities(ds: xr.Dataset, request: Request, query_params: dict) -> Re
 
         additonal_coords = ds.gridded.additional_coords(da)
         for coord in additonal_coords:
-            values = da.cf.coords[coord].values
-            units = da.cf.coords[coord].attrs.get("units", "")
+            values = da[coord].values
+            units = da[coord].attrs.get("units", "")
             coord_element = ET.SubElement(
                 layer,
                 "Dimension",

xpublish_wms/wms/get_map.py

@@ -233,11 +233,13 @@ def select_custom_dim(self, da: xr.DataArray) -> xr.DataArray:
         for dim, value in self.dim_selectors.items():
             if dim in da.coords:
                 dtype = da[dim].dtype
-                if np.issubdtype(dtype, np.integer):
+                if "timedelta" in str(dtype):
+                    value = pd.to_timedelta(value)
+                elif np.issubdtype(dtype, np.integer):
                     value = int(value)
                 elif np.issubdtype(dtype, np.floating):
                     value = float(value)
-                da = da.sel({dim: value})
+                da = da.sel({dim: value}, method="nearest")
 
         # Squeeze single value dimensions
         da = da.squeeze()