Made dimension_values unique argument supported throughout

holoviews/core/data.py

@@ -27,7 +27,7 @@
 from .ndmapping import NdMapping, item_check, sorted_context
 from .spaces import HoloMap
 from . import util
-from .util import wrap_tuple, basestring
+from .util import wrap_tuple, basestring, unique_array
 
 
 class Columns(Element):
@@ -343,11 +343,8 @@ def dimension_values(self, dim, unique=False):
         """
         dim = self.get_dimension(dim).name
         dim_vals = self.interface.values(self, dim)
-        if unique and pd:
-            return pd.unique(dim_vals)
-        elif unique:
-            _, uniq_inds = np.unique(dim_vals, return_index=True)
-            return dim_vals[np.sort(uniq_inds)]
+        if unique:
+            return unique_array(dim_vals)
         else:
             return dim_vals
 

holoviews/core/dimension.py

@@ -736,7 +736,7 @@ def select(self, selection_specs=None, **kwargs):
         return selection
 
 
-    def dimension_values(self, dimension):
+    def dimension_values(self, dimension, unique=False):
         """
         Returns the values along the specified dimension. This method
         must be implemented for all Dimensioned type.

holoviews/core/element.py

@@ -10,7 +10,8 @@
 from .overlay import Overlayable, NdOverlay, CompositeOverlay
 from .spaces import HoloMap, GridSpace
 from .tree import AttrTree
-from .util import dimension_sort, get_param_values, dimension_sanitizer
+from .util import (dimension_sort, get_param_values, dimension_sanitizer,
+                   unique_array)
 
 
 class Element(ViewableElement, Composable, Overlayable):
@@ -481,14 +482,15 @@ def aggregate(self, dimensions, function, **kwargs):
         return self.clone(rows, kdims=grouped.kdims)
 
 
-    def dimension_values(self, dim):
+    def dimension_values(self, dim, unique=False):
         dim = self.get_dimension(dim)
         value_dims = self.dimensions('value', label=True)
         if dim.name in value_dims:
             index = value_dims.index(dim.name)
-            return np.array([v[index] for v in self.data.values()])
+            vals = np.array([v[index] for v in self.data.values()])
+            return unique_array(vals) if unique else vals
         else:
-            return NdMapping.dimension_values(self, dim.name)
+            return NdMapping.dimension_values(self, dim.name, unique)
 
 
     def values(self):

holoviews/core/layout.py

@@ -15,7 +15,8 @@
 from .dimension import Dimension, Dimensioned, ViewableElement
 from .ndmapping import OrderedDict, NdMapping, UniformNdMapping
 from .tree import AttrTree
-from .util import int_to_roman, sanitize_identifier, group_sanitizer, label_sanitizer
+from .util import (int_to_roman, sanitize_identifier, group_sanitizer,
+                   label_sanitizer, unique_array)
 from . import traversal
 
 
@@ -125,12 +126,9 @@ def get(self, key, default=None):
         return self.data[key] if key in self.data else default
 
 
-    def dimension_values(self, dimension):
+    def dimension_values(self, dimension, unique=False):
         dimension = self.get_dimension(dimension).name
-        if dimension in self.kdims:
-            return self.layout_order[:len(self.data)]
-        else:
-            return self.main.dimension_values(dimension)
+        return self.main.dimension_values(dimension, unique)
 
 
     def __getitem__(self, key):
@@ -433,16 +431,17 @@ def clone(self, *args, **overrides):
         return clone
 
 
-    def dimension_values(self, dimension):
+    def dimension_values(self, dimension, unique=False):
         "Returns the values along the specified dimension."
         dimension = self.get_dimension(dimension).name
         all_dims = self.traverse(lambda x: [d.name for d in x.dimensions()])
         if dimension in chain.from_iterable(all_dims):
             values = [el.dimension_values(dimension) for el in self
                       if dimension in el.dimensions(label=True)]
-            return np.concatenate(values)
+            vals = np.concatenate(values)
+            return unique_array(vals) if unique else vals
         else:
-            return super(Layout, self).dimension_values(dimension)
+            return super(Layout, self).dimension_values(dimension, unique)
 
 
     def cols(self, ncols):

holoviews/core/ndmapping.py

@@ -343,17 +343,18 @@ def drop_dimension(self, dimensions):
                           kdims=dims)
 
 
-    def dimension_values(self, dimension):
+    def dimension_values(self, dimension, unique=False):
         "Returns the values along the specified dimension."
         dimension = self.get_dimension(dimension).name
         if dimension in self.kdims:
             return np.array([k[self.get_dimension_index(dimension)] for k in self.data.keys()])
         if dimension in self.dimensions(label=True):
             values = [el.dimension_values(dimension) for el in self
                       if dimension in el.dimensions()]
-            return np.concatenate(values)
+            vals = np.concatenate(values)
+            return unique_array(vals) if unique else vals
         else:
-            return super(MultiDimensionalMapping, self).dimension_values(dimension)
+            return super(MultiDimensionalMapping, self).dimension_values(dimension, unique)
 
 
     def reindex(self, kdims=[], force=False):

holoviews/core/overlay.py

@@ -65,18 +65,20 @@ def hist(self, index=0, adjoin=True, dimension=None, **kwargs):
         return layout
 
 
-    def dimension_values(self, dimension):
+    def dimension_values(self, dimension, unique=False):
         values = []
         found = False
         for el in self:
             if dimension in el.dimensions(label=True):
                 values.append(el.dimension_values(dimension))
                 found = True
         if not found:
-            return super(CompositeOverlay, self).dimension_values(dimension)
+            return super(CompositeOverlay, self).dimension_values(dimension, unique)
         values = [v for v in values if v is not None and len(v)]
-        return np.concatenate(values) if len(values) else np.array()
-
+        if not values:
+            return np.array()
+        vals = np.concatenate(values)
+        return unique_array(vals) if unique else vals
 
 
 class Overlay(Layout, CompositeOverlay):

holoviews/core/spaces.py

@@ -261,7 +261,7 @@ def sample(self, samples=[], bounds=None, **sample_values):
                 raise NotImplementedError("Regular sampling not implemented "
                                           "for high-dimensional Views.")
 
-            samples = list(util.unique_iterator(self.last.closest(linsamples)))
+            samples = util.unique_array(self.last.closest(linsamples))
 
         sampled = self.clone([(k, view.sample(samples, **sample_values))
                               for k, view in self.data.items()])

holoviews/core/util.py

@@ -470,6 +470,17 @@ def unique_iterator(seq):
             yield item
 
 
+def unique_array(arr):
+    """
+    Returns an array of unique values in the input order
+    """
+    if pd:
+        return pd.unique(arr)
+    else:
+        _, uniq_inds = np.unique(arr, return_index=True)
+        return arr[np.sort(uniq_inds)]
+
+
 def match_spec(element, specification):
     """
     Matches the group.label specification of the supplied
@@ -829,3 +840,5 @@ def groupby_python(self_or_cls, ndmapping, dimensions, container_type,
                                   else [((), (v,))]), **kwargs))
                   for k, v in iterative_select(ndmapping, dim_names, selects)]
         return container_type(groups, kdims=dimensions)
+
+