Removed all references to holocube and deleted interface

doc/Homepage.ipynb

@@ -4,13 +4,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "HoloCube is a [Python](http://python.org) library that makes it easy to explore and visualize geographical, meterological, oceanographic, and other multidimensional gridded datasets.  HoloCube interfaces between the  [HoloViews](http://holoviews.org) library for flexible visualizations of multidimensional data, the [Iris](http://scitools.org.uk/iris) library for storing and processing climate and weather data, and the [Cartopy](http://scitools.org.uk/cartopy) library for working with cartographic projections and visualizations in [Matplotlib](http://matplotlib.org/).  Specifically, HoloCube:\n",
+    "GeoViews is a [Python](http://python.org) library that makes it easy to explore and visualize geographical, meterological, oceanographic, and other multidimensional gridded datasets.  GeoViews interfaces between the  [HoloViews](http://holoviews.org) library for flexible visualizations of multidimensional data, the [Iris](http://scitools.org.uk/iris) library for storing and processing climate and weather data, and the [Cartopy](http://scitools.org.uk/cartopy) library for working with cartographic projections and visualizations in [Matplotlib](http://matplotlib.org/).  Specifically, GeoViews:\n",
     "\n",
-    "1. Extends HoloViews objects to allow them to use data stored in [Iris](http://scitools.org.uk/iris) [cubes](http://scitools.org.uk/iris/docs/latest/userguide/iris_cubes.html).  After `import holocube`, data can be provided to any Holoviews `Element` directly as a cube, without needing to first convert into one of the other supported formats (NumPy arrays, Pandas data frames, etc.). This support is independent of the other support below -- data from Iris cubes can be used even in non-geographic `Element`s, and most geographic Elements can accept data in any format.\n",
+    "1. Extends HoloViews objects to allow them to use data stored in [Iris](http://scitools.org.uk/iris) [cubes](http://scitools.org.uk/iris/docs/latest/userguide/iris_cubes.html).  After `import geoviews`, data can be provided to any Holoviews `Element` directly as a cube, without needing to first convert into one of the other supported formats (NumPy arrays, Pandas data frames, etc.). This support is independent of the other support below -- data from Iris cubes can be used even in non-geographic `Element`s, and most geographic Elements can accept data in any format.\n",
     "\n",
-    "2. Adds a set of new HoloViews `Element`s that have an associated geographic projection (`GeoElement`s), based on `cartopy.crs`. These currently include `GeoFeature`, `WMTS`, `GeoTiles`, `Points`, `Contours`, `Image`, and `Text` objects, each of which can easily be overlaid in the same plots.  E.g. an object with temperature data can be overlaid with coastline data using an expression like ``Image(temp_cube)*hc.GeoFeature(cartopy.feature.COASTLINE)``.  Each `GeoElement` can also be freely combined in layouts with any other HoloViews `Element`, making it simple to make even complex multi-figure layours.\n",
+    "2. Adds a set of new HoloViews `Element`s that have an associated geographic projection (`GeoElement`s), based on `cartopy.crs`. These currently include `Feature`, `WMTS`, `Tiles`, `Points`, `Contours`, `Image`, and `Text` objects, each of which can easily be overlaid in the same plots.  E.g. an object with temperature data can be overlaid with coastline data using an expression like ``Image(temp_cube)*gv.Feature(cartopy.feature.COASTLINE)``.  Each `GeoElement` can also be freely combined in layouts with any other HoloViews `Element`, making it simple to make even complex multi-figure layours.\n",
     "\n",
-    "With HoloCube, you can now work easily and naturally with large, multidimensional datasets, instantly visualizing any subset or combination of them, while always being able to access the raw data underlying any plot.  Here's a simple example:"
+    "With GeoViews, you can now work easily and naturally with large, multidimensional datasets, instantly visualizing any subset or combination of them, while always being able to access the raw data underlying any plot.  Here's a simple example:"
    ]
   },
   {
@@ -22,7 +22,7 @@
    "outputs": [],
    "source": [
     "import holoviews as hv\n",
-    "import holocube as hc\n",
+    "import geoviews as gv\n",
     "from cartopy import crs\n",
     "from cartopy import feature as cf\n",
     "\n",
@@ -37,11 +37,11 @@
    },
    "outputs": [],
    "source": [
-    "%%opts GeoFeature [projection=crs.Geostationary()]\n",
+    "%%opts Feature [projection=crs.Geostationary()]\n",
     "\n",
-    "coasts  = hc.GeoFeature(cf.COASTLINE)\n",
-    "borders = hc.GeoFeature(cf.BORDERS)\n",
-    "ocean   = hc.GeoFeature(cf.OCEAN)\n",
+    "coasts  = gv.Feature(cf.COASTLINE)\n",
+    "borders = gv.Feature(cf.BORDERS)\n",
+    "ocean   = gv.Feature(cf.OCEAN)\n",
     "\n",
     "ocean + borders + (ocean*borders).relabel(\"Overlay\")"
    ]
@@ -82,11 +82,12 @@
    "outputs": [],
    "source": [
     "%%opts GeoImage [colorbar=True] (cmap='viridis')\n",
-    "(hc.HoloCube(surface_temp).groupby(['time'], group_type=hc.Image) * hc.GeoFeature(cf.COASTLINE))"
+    "(hv.Dataset(surface_temp).groupby(['time'], group_type=gv.Image) * gv.Feature(cf.COASTLINE))"
    ]
   }
  ],
  "metadata": {
+  "hide_input": false,
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",

doc/Introductory_Tutorial.ipynb

@@ -19,7 +19,7 @@
     "import iris\n",
     "import numpy as np\n",
     "import holoviews as hv\n",
-    "import holocube as hc\n",
+    "import geoviews as gv\n",
     "from cartopy import crs\n",
     "from cartopy import feature as cf\n",
     "hv.notebook_extension()"
@@ -119,7 +119,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now we can wrap this Iris cube in a ``HoloCube``:"
+    "Now we can wrap this Iris cube in a ``Dataset``:"
    ]
   },
   {
@@ -130,7 +130,7 @@
    },
    "outputs": [],
    "source": [
-    "surface_temperature = hc.HoloCube(iris_cube)\n",
+    "surface_temperature = hv.Dataset(iris_cube)\n",
     "surface_temperature"
    ]
   },
@@ -156,7 +156,7 @@
    },
    "outputs": [],
    "source": [
-    "surface_temperature.to.image(['longitude', 'latitude'])"
+    "surface_temperature.to.image(['longitude', 'latitude'], datatype=['cube'])"
    ]
   },
   {
@@ -210,7 +210,7 @@
    },
    "outputs": [],
    "source": [
-    "air_temperature = hc.HoloCube(iris.load_cube(iris.sample_data_path('pre-industrial.pp')),\n",
+    "air_temperature = hv.Dataset(iris.load_cube(iris.sample_data_path('pre-industrial.pp')),\n",
     "                             group='Pre-industrial air temperature')\n",
     "air_temperature.data.coord('longitude').guess_bounds()\n",
     "air_temperature.data.coord('latitude').guess_bounds()\n",
@@ -258,7 +258,7 @@
     "# Sample the surface_temperature at (0,10)\n",
     "temp_curve = surface_temperature.to.curve('time', dynamic=True)[0, 10]\n",
     "# Show surface_temperature and air_temperature with Point (0,10) marked\n",
-    "temp_maps = [cb.to.image(['longitude', 'latitude']) * hc.Points([(0,10)]) \n",
+    "temp_maps = [cb.to.image(['longitude', 'latitude']) * gv.Points([(0,10)]) \n",
     "             for cb in [surface_temperature, air_temperature]]\n",
     "# Show everything in a two column layout\n",
     "hv.Layout(temp_maps + [temp_curve]).cols(2).display('all')"
@@ -298,7 +298,7 @@
    "outputs": [],
    "source": [
     "%%opts Image [projection=crs.Geostationary()] (cmap='Greens')\n",
-    "surface_temperature.to.image(['longitude', 'latitude']) * hc.GeoFeature(cf.COASTLINE)"
+    "surface_temperature.to.image(['longitude', 'latitude']) * gv.Feature(cf.COASTLINE)"
    ]
   },
   {
@@ -333,8 +333,8 @@
     "%%opts Image [projection=crs.Geostationary()] (cmap='Greens')\n",
     "# Declare a humidity dimension with a range from 0 to 0.01\n",
     "surface_temp_dim = hv.Dimension('surface_temperature', range=(300, max_surface_temp))\n",
-    "# Use it to declare the value dimension of a HoloCube\n",
-    "(hc.HoloCube(surface_temperature, vdims=[surface_temp_dim]).to.image(['longitude', 'latitude']) * hc.GeoFeature(cf.COASTLINE))"
+    "# Use it to declare the value dimension of a Dataset\n",
+    "(hv.Dataset(surface_temperature, vdims=[surface_temp_dim]).to.image(['longitude', 'latitude']) * gv.Feature(cf.COASTLINE))"
    ]
   },
   {
@@ -360,11 +360,12 @@
    "outputs": [],
    "source": [
     "%%opts Contours [levels=10]\n",
-    "(surface_temperature.to.contours(['longitude', 'latitude']) * hc.GeoFeature(cf.COASTLINE))"
+    "(surface_temperature.to.contours(['longitude', 'latitude']) * gv.Feature(cf.COASTLINE))"
    ]
   }
  ],
  "metadata": {
+  "hide_input": false,
   "kernelspec": {
    "display_name": "Python 2",
    "language": "python",

geoviews/__init__.py

@@ -1,4 +1,4 @@
-from .element import (_Element, HoloCube, Feature,  # noqa (API import)
-                      Tiles, WMTS, Contours, Text,
-                      Image, Points)
+from .element import (_Element, Feature, Tiles,     # noqa (API import)
+                      WMTS, Contours, Text, Image,
+                      Points)
 from . import plotting                              # noqa (API import)

geoviews/element/__init__.py

@@ -1,3 +1,50 @@
-from .cube import HoloCube                      # noqa (API import)
+import iris
+from holoviews.core.data import Dataset
+from holoviews.element import ElementConversion, Points as HvPoints
+
 from .geo import (_Element, Feature, Tiles,     # noqa (API import)
                   WMTS, Points, Image, Text, Contours)
+
+
+def is_geographic(dataset, kdims):
+    """
+    Small utility that determines whether the supplied dataset
+    and kdims represent a geographic coordinate system.
+    """
+
+    kdims = [dataset.get_dimension(d) for d in kdims]
+    if (len(kdims) == 2 and
+        ((isinstance(GeoElement) and kdims == dataset.kdims) or
+        (isinstance(dataset, iris.cube.Cube) and all(dataset.data.coord(
+            kd.name).coord_system for kd in kdims)))):
+        return True
+    return False
+
+
+class GeoConversion(ElementConversion):
+    """
+    GeoConversion is a very simple container object which can
+    be given an existing Dataset and provides methods to convert
+    the Dataset into most other Element types. If the requested
+    key dimensions correspond to geographical coordinates the
+    conversion interface will automatically use a geographical
+    Element type while all other plot will use regular HoloViews
+    Elements.
+    """
+
+    def __init__(self, cube):
+        self._element = cube
+
+    def contours(self, kdims=None, vdims=None, mdims=None, **kwargs):
+        return self(Contours, kdims, vdims, mdims, **kwargs)
+
+    def image(self, kdims=None, vdims=None, mdims=None, **kwargs):
+        return self(Image, kdims, vdims, mdims, **kwargs)
+
+    def points(self, kdims=None, vdims=None, mdims=None, **kwargs):
+        if kdims is None: kdims = self._element.kdims
+        el_type = Points if is_geographic(self._element, kdims) else HvPoints
+        return self(el_type, kdims, vdims, mdims, **kwargs)
+
+
+Dataset._conversion_interface = GeoConversion