Eliminated all references to key_dimensions

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/layout.py

@@ -381,20 +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):
         """