Fixed bugs introduced by irregular data handling

holoviews/core/data/grid.py

@@ -273,7 +273,7 @@ def ndloc(cls, dataset, indices):
                 selected[kd.name] = coords
                 all_scalar = False
         for d in dataset.dimensions():
-            if d in dataset.kdims and cls.irregular(dataset, d):
+            if d in dataset.kdims and not cls.irregular(dataset, d):
                 continue
             arr = dataset.dimension_values(d, flat=False)
             if all_scalar and len(dataset.vdims) == 1:

holoviews/core/data/iris.py

@@ -132,6 +132,12 @@ def validate(cls, dataset, vdims=True):
             raise DataError("Iris cubes do not support more than one value dimension", cls)
 
 
+    @classmethod
+    def irregular(cls, dataset, dim):
+        "CubeInterface does not support irregular data"
+        return False
+
+
     @classmethod
     def shape(cls, dataset, gridded=False):
         if gridded:

holoviews/core/data/xarray.py

@@ -191,7 +191,7 @@ def coords(cls, dataset, dim, ordered=False, expanded=False, edges=False):
     def values(cls, dataset, dim, expanded=True, flat=True):
         dim = dataset.get_dimension(dim, strict=True)
         data = dataset.data[dim.name].data
-        irregular = cls.irregular(dataset, dim)
+        irregular = cls.irregular(dataset, dim) if dim in dataset.kdims else False
         if dim in dataset.vdims or irregular:
             coord_dims = list(dataset.data[dim.name].dims)
             if dask and isinstance(data, dask.array.Array):
@@ -226,18 +226,18 @@ def unpack_scalar(cls, dataset, data):
 
     @classmethod
     def ndloc(cls, dataset, indices):
-        kdims = [d.name for d in dataset.kdims[::-1]]
+        kdims = [d for d in dataset.kdims[::-1]]
         adjusted_indices = []
         slice_dims = []
         for kd, ind in zip(kdims, indices):
-            if dataset.data[kd].ndim > 1:
+            if cls.irregular(dataset, kd):
                 coords = [c for c in dataset.data.coords if c not in dataset.data.dims]
-                dim = dataset.data[kd].dims[coords.index(kd)]
-                shape = dataset.data[kd].shape[coords.index(kd)]
+                dim = dataset.data[kd.name].dims[coords.index(kd.name)]
+                shape = dataset.data[kd.name].shape[coords.index(kd.name)]
                 coords = np.arange(shape)
             else:
                 coords = cls.coords(dataset, kd, False)
-                dim = kd
+                dim = kd.name
             slice_dims.append(dim)
             ncoords = len(coords)
             if np.all(coords[1:] < coords[:-1]):