Standardized constructor signature with dimensions as positional args

doc/Tutorials/Columnar_Data.ipynb

@@ -377,7 +377,7 @@
    "outputs": [],
    "source": [
     "obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=(0,0,3,3))\n",
-    "                   for i in range(3)}, key_dimensions=['Observation'])\n",
+    "                   for i in range(3)}, kdims=['Observation'])\n",
     "obs_hmap"
    ]
   },
@@ -415,7 +415,7 @@
     "extents = (0,0,3,3)\n",
     "error_hmap = hv.HoloMap({(i, j): hv.Image(j*np.random.randn(3, 3), bounds=extents)\n",
     "                         for i, j in product(range(3), np.linspace(0, 1, 3))},\n",
-    "                        key_dimensions=['Observation', 'noise'])\n",
+    "                        kdims=['Observation', 'noise'])\n",
     "noise_layout = error_hmap.layout('noise')\n",
     "noise_layout"
    ]

doc/Tutorials/Sampling_Data.ipynb

@@ -322,7 +322,7 @@
    "outputs": [],
    "source": [
     "obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=extents)\n",
-    "                       for i in range(3)}, key_dimensions=['Observation'])"
+    "                       for i in range(3)}, kdims=['Observation'])"
    ]
   },
   {
@@ -384,7 +384,7 @@
     "extents = (0, 0, 2, 10)\n",
     "curve = hv.HoloMap({(i) : hv.Curve(zip(xs, np.sin(xs)*i))\n",
     "                    for i in np.linspace(0.5, 1.5, 3)},\n",
-    "                   key_dimensions=['Observation'])\n",
+    "                   kdims=['Observation'])\n",
     "all_samples = curve.table().to.points()\n",
     "sampled = curve.sample([0, 2, 4, 6, 8])\n",
     "sampling = all_samples * sampled.to.points(extents=extents).opts(style=dict(color='r'))\n",

examples/topics/simulation/sri_model.ipynb

@@ -226,7 +226,7 @@
     "            raise Exception(\"Enable visualize option to get compute network visulizations.\")\n",
     "\n",
     "        # Declare HoloMap for network animation and counts array\n",
-    "        network_hmap = hv.HoloMap(key_dimensions=['Time'])\n",
+    "        network_hmap = hv.HoloMap(kdims=['Time'])\n",
     "        sird = np.zeros((steps, 5))\n",
     "        \n",
     "        # Declare dimensions and labels\n",
@@ -247,10 +247,10 @@
     "            \n",
     "            path_data = [list(el[()]) for el in paths]\n",
     "            # Create network path and node Elements\n",
-    "            network_paths = hv.Path(path_data, key_dimensions=spatial_dims)\n",
+    "            network_paths = hv.Path(path_data, kdims=spatial_dims)\n",
     "            network_nodes = hv.Points(np.hstack([points, state_array]),\n",
-    "                                      key_dimensions=spatial_dims,\n",
-    "                                      value_dimensions=['State'])\n",
+    "                                      kdims=spatial_dims,\n",
+    "                                      vdims=['State'])\n",
     "            \n",
     "            # Create overlay and accumulate in network HoloMap\n",
     "            network_hmap[i] = (network_paths * network_nodes * nlabel).relabel(group='Network', label='SRI')\n",
@@ -265,7 +265,7 @@
     "        \n",
     "        # Animate VLine on top of Curves\n",
     "        distribution = hv.HoloMap({i: (curves * hv.VLine(i)).relabel(group='Counts', label='SRI')\n",
-    "                                   for i in range(steps)}, key_dimensions=['Time'])\n",
+    "                                   for i in range(steps)}, kdims=['Time'])\n",
     "        \n",
     "        return network_hmap + distribution"
    ]
@@ -536,7 +536,7 @@
    "outputs": [],
    "source": [
     "exp2_dims = ['Connections', 'pVaccinated']\n",
-    "hmap = hv.HoloMap(key_dimensions=exp2_dims)\n",
+    "hmap = hv.HoloMap(kdims=exp2_dims)\n",
     "vacc_rates = np.linspace(0, 1, 21)\n",
     "mean_conns = [2**i for i in range(7)]\n",
     "for v, c in itertools.product(vacc_rates, mean_conns):\n",

examples/user_guide/07-Tabular_Datasets.ipynb

@@ -384,7 +384,7 @@
    "outputs": [],
    "source": [
     "obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=(0,0,3,3))\n",
-    "                   for i in range(3)}, key_dimensions=['Observation'])\n",
+    "                   for i in range(3)}, kdims=['Observation'])\n",
     "obs_hmap"
    ]
   },
@@ -424,7 +424,7 @@
     "extents = (0,0,3,3)\n",
     "error_hmap = hv.HoloMap({(i, j): hv.Image(j*np.random.randn(3, 3), bounds=extents)\n",
     "                         for i, j in product(range(3), np.linspace(0, 1, 3))},\n",
-    "                        key_dimensions=['Observation', 'noise'])\n",
+    "                        kdims=['Observation', 'noise'])\n",
     "noise_layout = error_hmap.layout('noise')\n",
     "noise_layout"
    ]

examples/user_guide/09-Indexing_and_Selecting_Data.ipynb

@@ -426,7 +426,7 @@
    "outputs": [],
    "source": [
     "obs_hmap = hv.HoloMap({i: hv.Image(np.random.randn(10, 10), bounds=extents)\n",
-    "                       for i in range(3)}, key_dimensions=['Observation'])"
+    "                       for i in range(3)}, kdims=['Observation'])"
    ]
   },
   {
@@ -490,7 +490,7 @@
     "extents = (0, 0, 2, 10)\n",
     "curve = hv.HoloMap({(i) : hv.Curve(zip(xs, np.sin(xs)*i))\n",
     "                    for i in np.linspace(0.5, 1.5, 3)},\n",
-    "                   key_dimensions=['Observation'])\n",
+    "                   kdims=['Observation'])\n",
     "all_samples = curve.table().to.points()\n",
     "sampled = curve.sample([0, 2, 4, 6, 8])\n",
     "sampling = all_samples * sampled.to.points(extents=extents).opts(style=dict(color='r'))\n",

holoviews/core/data/__init__.py

@@ -9,7 +9,7 @@
 import param
 
 from ..dimension import redim
-from ..util import dimension_range
+from ..util import dimension_range, basestring
 from .interface import Interface, iloc, ndloc
 from .array import ArrayInterface
 from .dictionary import DictInterface
@@ -170,19 +170,24 @@ class Dataset(Element):
     _vdim_reductions = {}
     _kdim_reductions = {}
 
-    def __init__(self, data, **kwargs):
+    def __init__(self, data, kdims=None, vdims=None, **kwargs):
         if isinstance(data, Element):
             pvals = util.get_param_values(data)
             kwargs.update([(l, pvals[l]) for l in ['group', 'label']
                            if l in pvals and l not in kwargs])
 
-        kdims, vdims = None, None
-        if 'kdims' in kwargs:
-            kdims = [kd if isinstance(kd, Dimension) else Dimension(kd)
-                     for kd in kwargs['kdims']]
-        if 'vdims' in kwargs:
-            vdims = [kd if isinstance(kd, Dimension) else Dimension(kd)
-                     for kd in kwargs['vdims']]
+        for group, dims in [('kdims', kdims), ('vdims', vdims)]:
+            if dims is None:
+                continue
+            elif isinstance(dims, (tuple, basestring, Dimension)):
+                dims = [dims]
+            elif not isinstance(dims, list):
+                raise ValueError("%s must be a Dimension or list of dimensions, "
+                                 "specified as tuples, string or Dimension instances, "
+                                 "not %s." % (group, dims))
+            kwargs[group] = [d if isinstance(d, Dimension) else Dimension(d)
+                             for d in dims]
+        kdims, vdims = kwargs.get('kdims'), kwargs.get('vdims')
 
         initialized = Interface.initialize(type(self), data, kdims, vdims,
                                            datatype=kwargs.get('datatype'))

holoviews/core/data/multipath.py

@@ -26,6 +26,10 @@ class MultiInterface(Interface):
     def init(cls, eltype, data, kdims, vdims):
         new_data = []
         dims = {'kdims': eltype.kdims, 'vdims': eltype.vdims}
+        if kdims is not None:
+            dims['kdims'] = kdims
+        if vdims is not None:
+            dims['vdims'] = vdims
         if not isinstance(data, list):
             raise ValueError('MultiInterface data must be a list tabular data types.')
         prev_interface, prev_dims = None, None

holoviews/core/dimension.py

@@ -301,6 +301,9 @@ def __init__(self, spec, **params):
 
         all_params = dict(existing_params, **params)
         if isinstance(spec, tuple):
+            if not all(isinstance(s, basestring) for s in spec) or len(spec) != 2:
+                raise ValueError("Dimensions specified as a tuple must be a tuple "
+                                 "consisting of the name and label not: %s" % spec) 
             name, label = spec
             all_params['name'] = name
             all_params['label'] = label
@@ -783,35 +786,21 @@ class to be associated with dimensions. The contents associated
     _dim_aliases = dict(key_dimensions='kdims', value_dimensions='vdims',
                         constant_dimensions='cdims', deep_dimensions='ddims')
 
-
-    # Long-name aliases
-
-    @property
-    def key_dimensions(self): return self.kdims
-
-    @property
-    def value_dimensions(self): return self.vdims
-
-    @property
-    def constant_dimensions(self): return self.cdims
-
-    @property
-    def deep_dimensions(self): return self.ddims
-
-    def __init__(self, data, **params):
-        for group in self._dim_groups+list(self._dim_aliases.keys()):
-            if group in ['deep_dimensions', 'ddims']: continue
-            if group in params:
-                if group in self._dim_aliases:
-                    params[self._dim_aliases[group]] = params.pop(group)
-                    group = self._dim_aliases[group]
-                if group == 'cdims':
-                    dimensions = {d if isinstance(d, Dimension) else Dimension(d): val
-                                  for d, val in params.pop(group).items()}
-                else:
-                    dimensions = [d if isinstance(d, Dimension) else Dimension(d)
-                                  for d in params.pop(group)]
-                params[group] = dimensions
+    def __init__(self, data, kdims=None, vdims=None, **params):
+        for group, dims in [('kdims', kdims), ('vdims', vdims)]:
+            if dims is None:
+                continue
+            elif isinstance(dims, (tuple, basestring, Dimension)):
+                dims = [dims]
+            elif not isinstance(dims, list):
+                raise ValueError("%s must be a Dimension or list of dimensions, "
+                                 "specified as tuples, string or Dimension instances, "
+                                 "not %s." % (group, dims))
+            params[group] = [d if isinstance(d, Dimension) else Dimension(d)
+                             for d in dims]
+        if 'cdims' in params:
+            params['cdims'] = {d if isinstance(d, Dimension) else Dimension(d): val
+                               for d, val in params['cdims'].items()}
         super(Dimensioned, self).__init__(data, **params)
         self.ndims = len(self.kdims)
         cdims = [(d.name, val) for d, val in self.cdims.items()]

holoviews/core/element.py

@@ -390,7 +390,7 @@ def _add_dimensions(self, item, dims, constant_keys):
         elif isinstance(item, self._nest_order[self.merge_type]):
             if len(dim_vals):
                 dimensions, key = zip(*dim_vals)
-                new_item = self.merge_type({key: item}, kdims=dimensions,
+                new_item = self.merge_type({key: item}, kdims=list(dimensions),
                                            cdims=constant_keys)
             else:
                 new_item = item

holoviews/core/layout.py

@@ -280,10 +280,11 @@ class NdLayout(UniformNdMapping):
 
     data_type = (ViewableElement, AdjointLayout, UniformNdMapping)
 
-    def __init__(self, initial_items=None, **params):
+    def __init__(self, initial_items=None, kdims=None, **params):
         self._max_cols = 4
         self._style = None
-        super(NdLayout, self).__init__(initial_items=initial_items, **params)
+        super(NdLayout, self).__init__(initial_items=initial_items, kdims=kdims,
+                                       **params)
 
 
     @property
@@ -380,20 +381,6 @@ def __init__(self, items=None, identifier=None, parent=None, **kwargs):
         Dimensioned.__init__(self, self.data, **params)
 
 
-    @classmethod
-    def collate(cls, data, kdims=None, key_dimensions=None):
-        kdims = key_dimensions if (kdims is None) else kdims
-        if kdims is None:
-            raise Exception("Please specify the key dimensions.")
-
-        collate_deprecation.warning("Layout.collate will be deprecated after version 1.3.0."
-                                    "\nUse HoloMap.collate instead (see HoloViews homepage for example usage)")
-        from .element import Collator
-        layouts = {k:(v if isinstance(v, Layout) else Layout.from_values([v]))
-                      for k,v in data.items()}
-        return Collator(layouts, kdims=kdims)()
-
-
     @classmethod
     def from_values(cls, vals):
         """

holoviews/core/ndmapping.py

@@ -95,11 +95,13 @@ class MultiDimensionalMapping(Dimensioned):
     _deep_indexable = False
     _check_items = True
 
-    def __init__(self, initial_items=None, **params):
+    def __init__(self, initial_items=None, kdims=None, **params):
         if isinstance(initial_items, MultiDimensionalMapping):
             params = dict(util.get_param_values(initial_items),
                           **dict({'sort': self.sort}, **params))
-        super(MultiDimensionalMapping, self).__init__(OrderedDict(), **params)
+        if kdims is not None:
+            params['kdims'] = kdims
+        super(MultiDimensionalMapping, self).__init__(OrderedDict(), **dict(params))
         if type(initial_items) is dict and not self.sort:
             raise ValueError('If sort=False the data must define a fixed '
                              'ordering, please supply a list of items or '
@@ -733,11 +735,11 @@ class UniformNdMapping(NdMapping):
     _deep_indexable = True
     _auxiliary_component = False
 
-    def __init__(self, initial_items=None, group=None, label=None, **params):
+    def __init__(self, initial_items=None, kdims=None, group=None, label=None, **params):
         self._type = None
         self._group_check, self.group = None, group
         self._label_check, self.label = None, label
-        super(UniformNdMapping, self).__init__(initial_items, **params)
+        super(UniformNdMapping, self).__init__(initial_items, kdims=kdims, **params)
 
 
     def clone(self, data=None, shared_data=True, new_type=None, *args, **overrides):

holoviews/core/overlay.py

@@ -245,8 +245,8 @@ class NdOverlay(UniformNdMapping, CompositeOverlay, Overlayable):
 
     _deep_indexable = True
 
-    def __init__(self, overlays=None, **params):
-        super(NdOverlay, self).__init__(overlays, **params)
+    def __init__(self, overlays=None, kdims=None, **params):
+        super(NdOverlay, self).__init__(overlays, kdims=kdims, **params)
 
 
 __all__ = list(set([_k for _k, _v in locals().items()

holoviews/core/spaces.py

@@ -1341,8 +1341,8 @@ class GridSpace(UniformNdMapping):
 
     kdims = param.List(default=[Dimension("X"), Dimension("Y")], bounds=(1,2))
 
-    def __init__(self, initial_items=None, **params):
-        super(GridSpace, self).__init__(initial_items, **params)
+    def __init__(self, initial_items=None, kdims=None, **params):
+        super(GridSpace, self).__init__(initial_items, kdims=kdims, **params)
         if self.ndims > 2:
             raise Exception('Grids can have no more than two dimensions.')
 

holoviews/element/chart.py

@@ -165,6 +165,10 @@ class Histogram(Element2D):
     vdims = param.List(default=[Dimension('Frequency')], bounds=(1,1))
 
     def __init__(self, values, edges=None, **params):
+        if edges is not None:
+            self.warning("Histogram edges should be supplied as a tuple "
+                         "along with the values, passing the edges will "
+                         "be deprecated in holoviews 2.0.")
         self.values, self.edges, settings = self._process_data(values, edges)
         settings.update(params)
         super(Histogram, self).__init__((self.values, self.edges), **settings)
@@ -340,10 +344,10 @@ class VectorField(Points):
     _null_value = np.array([[], [], [], []]).T # For when data is None
     _min_dims = 3                              # Minimum number of columns
 
-    def __init__(self, data, **params):
-        if isinstance(data, list) and all(isinstance(d, np.ndarray) for d in data):
+    def __init__(self, data, kdims=None, vdims=None, **params):
+        if isinstance(data, list) and data and all(isinstance(d, np.ndarray) for d in data):
             data = np.column_stack([d.flat if d.ndim > 1 else d for d in data])
-        super(VectorField, self).__init__(data, **params)
+        super(VectorField, self).__init__(data, kdims=kdims, vdims=vdims, **params)
 
 
 

holoviews/element/chart3d.py

@@ -27,9 +27,9 @@ class Surface(Image, Element3D):
 
     group = param.String(default='Surface', constant=True)
 
-    def __init__(self, data, extents=None, **params):
+    def __init__(self, data, kdims=None, vdims=None, extents=None, **params):
         extents = extents if extents else (None, None, None, None, None, None)
-        Image.__init__(self, data, extents=extents, **params)
+        Image.__init__(self, data, kdims=kdims, vdims=vdims, extents=extents, **params)
 
 
 

holoviews/element/graphs.py

@@ -101,7 +101,7 @@ class Graph(Dataset, Element2D):
     kdims = param.List(default=[Dimension('start'), Dimension('end')],
                        bounds=(2, 2))
 
-    def __init__(self, data, **params):
+    def __init__(self, data, kdims=None, vdims=None, **params):
         if isinstance(data, tuple):
             data = data + (None,)* (3-len(data))
             edges, nodes, edgepaths = data
@@ -122,7 +122,7 @@ def __init__(self, data, **params):
             edgepaths = EdgePaths(edgepaths)
         self._nodes = nodes
         self._edgepaths = edgepaths
-        super(Graph, self).__init__(edges, **params)
+        super(Graph, self).__init__(edges, kdims=kdims, vdims=vdims, **params)
         if self._nodes is None and node_info:
             nodes = self.nodes.clone(datatype=['pandas', 'dictionary'])
             for d in node_info.dimensions():