Mirrored website structure in examples/

doc/getting_started/Customization.rst

@@ -1,5 +1,5 @@
 Customization
 _____________
 
-.. notebook:: holoviews ../../guides/getting_started/2-Customization.ipynb
+.. notebook:: holoviews ../../examples/getting_started/2-Customization.ipynb
     :offset: 1

doc/getting_started/Gridded_Datasets.rst

@@ -1,5 +1,5 @@
 Gridded Datasets
 ________________
 
-.. notebook:: holoviews ../../guides/getting_started/4-Gridded_Datasets.ipynb
+.. notebook:: holoviews ../../examples/getting_started/4-Gridded_Datasets.ipynb
     :offset: 1

doc/getting_started/Introduction.rst

@@ -1,5 +1,5 @@
 Introduction
 ____________
 
-.. notebook:: holoviews ../../guides/getting_started/1-Introduction.ipynb
+.. notebook:: holoviews ../../examples/getting_started/1-Introduction.ipynb
     :offset: 1

doc/getting_started/Live_Data.rst

@@ -1,6 +1,6 @@
 Live Data
 _________
 
-.. notebook:: holoviews ../../guides/getting_started/5-Live_Data.ipynb
+.. notebook:: holoviews ../../examples/getting_started/5-Live_Data.ipynb
     :skip_output: When run live, this cell's output should match the behavior of the GIF below
     :offset: 1

doc/getting_started/Tabular_Datasets.rst

@@ -1,5 +1,5 @@
 Tabular Datasets
 ________________
 
-.. notebook:: holoviews ../../guides/getting_started/3-Tabular_Datasets.ipynb
+.. notebook:: holoviews ../../examples/getting_started/3-Tabular_Datasets.ipynb
     :offset: 1

doc/user_guide/Building_Composite_Objects.rst

@@ -1,5 +1,5 @@
 Building Composite Objects
 __________________________
 
-.. notebook:: holoviews ../../guides/user_guide/05-Building_Composite_Objects.ipynb
+.. notebook:: holoviews ../../examples/user_guide/05-Building_Composite_Objects.ipynb
     :offset: 1

doc/user_guide/Continuous_Coordinates.rst

@@ -1,5 +1,5 @@
 Continuous Coordinates
 ______________________
 
-.. notebook:: holoviews ../../guides/user_guide/Continuous_Coordinates.ipynb
+.. notebook:: holoviews ../../examples/user_guide/Continuous_Coordinates.ipynb
     :offset: 1

doc/user_guide/Custom_Interactivity.rst

@@ -1,5 +1,5 @@
 Custom Interactivity
 ____________________
 
-.. notebook:: holoviews ../../guides/user_guide/12-Custom_Interactivity.ipynb
+.. notebook:: holoviews ../../examples/user_guide/12-Custom_Interactivity.ipynb
     :offset: 1

doc/user_guide/Customizing_Plots.rst

@@ -1,5 +1,5 @@
 Customizing Plots
 _________________
 
-.. notebook:: holoviews ../../guides/user_guide/03-Customizing_Plots.ipynb
+.. notebook:: holoviews ../../examples/user_guide/03-Customizing_Plots.ipynb
     :offset: 1

doc/user_guide/Deploying_Bokeh_Apps.rst

@@ -1,6 +1,6 @@
 Deploying Bokeh Apps
 ____________________
 
-.. notebook:: holoviews ../../guides/user_guide/Deploying_Bokeh_Apps.ipynb
+.. notebook:: holoviews ../../examples/user_guide/Deploying_Bokeh_Apps.ipynb
     :skip_execute: True
     :offset: 1

doc/user_guide/Exporting_and_Archiving.rst

@@ -1,5 +1,5 @@
 Exporting and Archiving
 _______________________
 
-.. notebook:: holoviews ../../guides/user_guide/Exporting_and_Archiving.ipynb
+.. notebook:: holoviews ../../examples/user_guide/Exporting_and_Archiving.ipynb
     :offset: 1

doc/user_guide/Gridded_Datasets.rst

@@ -1,5 +1,5 @@
 Gridded Datasets
 ________________
 
-.. notebook:: holoviews ../../guides/user_guide/08-Gridded_Datasets.ipynb
+.. notebook:: holoviews ../../examples/user_guide/08-Gridded_Datasets.ipynb
     :offset: 1

doc/user_guide/Indexing_and_Selecting_Data.rst

@@ -1,5 +1,5 @@
 Indexing and Selecting Data
 ___________________________
 
-.. notebook:: holoviews ../../guides/user_guide/09-Indexing_and_Selecting_Data.ipynb
+.. notebook:: holoviews ../../examples/user_guide/09-Indexing_and_Selecting_Data.ipynb
     :offset: 1

doc/user_guide/Large_Data.rst

@@ -1,5 +1,5 @@
 Working with large data using datashader
 ________________________________________
 
-.. notebook:: holoviews ../../guides/user_guide/14-Large_Data.ipynb
+.. notebook:: holoviews ../../examples/user_guide/14-Large_Data.ipynb
     :offset: 1

doc/user_guide/Live_Data.rst

@@ -1,5 +1,5 @@
 Live Data
 _________
 
-.. notebook:: holoviews ../../guides/user_guide/06-Live_Data.ipynb
+.. notebook:: holoviews ../../examples/user_guide/06-Live_Data.ipynb
     :offset: 1

doc/user_guide/Plotting_with_Bokeh.rst

@@ -1,5 +1,5 @@
 Plotting with Bokeh
 ___________________
 
-.. notebook:: holoviews ../../guides/user_guide/Plotting_with_Bokeh.ipynb
+.. notebook:: holoviews ../../examples/user_guide/Plotting_with_Bokeh.ipynb
     :offset: 1

doc/user_guide/Responding_to_Events.rst

@@ -1,5 +1,5 @@
 Responding to Events
 ____________________
 
-.. notebook:: holoviews ../../guides/user_guide/11-Responding_to_Events.ipynb
+.. notebook:: holoviews ../../examples/user_guide/11-Responding_to_Events.ipynb
     :offset: 1

doc/user_guide/Tabular_Datasets.rst

@@ -1,5 +1,5 @@
 Tabular Datasets
 ________________
 
-.. notebook:: holoviews ../../guides/user_guide/07-Tabular_Datasets.ipynb
+.. notebook:: holoviews ../../examples/user_guide/07-Tabular_Datasets.ipynb
     :offset: 1

doc/user_guide/Transforming_Elements.rst

@@ -1,5 +1,5 @@
 Transforming Elements
 _____________________
 
-.. notebook:: holoviews ../../guides/user_guide/10-Transforming_Elements.ipynb
+.. notebook:: holoviews ../../examples/user_guide/10-Transforming_Elements.ipynb
     :offset: 1

guides/getting_started/1-Introduction.ipynb → examples/getting_started/1-Introduction.ipynb

@@ -61,7 +61,7 @@
    },
    "outputs": [],
    "source": [
-    "station_info = pd.read_csv('../datasets/station_info.csv')\n",
+    "station_info = pd.read_csv('../assets/station_info.csv')\n",
     "station_info.head()"
    ]
   },
@@ -97,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here we passed our dataframe to [``hv.Scatter``](../../examples/elements/bokeh/Scatter.ipynb) to create an *object* called `scatter`, which is independent of any plotting library.  `scatter` is a simple wrapper around our dataframe that knows that the 'services' column is the independent variable, normally plotted along the x-axis, and that the 'ridership' column is a dependent variable, plotted on the y-axis. These are our *dimensions* which we will describe in more detail a little later.\n",
+    "Here we passed our dataframe to [``hv.Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) to create an *object* called `scatter`, which is independent of any plotting library.  `scatter` is a simple wrapper around our dataframe that knows that the 'services' column is the independent variable, normally plotted along the x-axis, and that the 'ridership' column is a dependent variable, plotted on the y-axis. These are our *dimensions* which we will describe in more detail a little later.\n",
     "\n",
     "Given that we have the handle ``scatter`` on our ``Scatter`` object, we can show that it is indeed an object and not a plot by printing it:"
    ]
@@ -126,7 +126,7 @@
    "source": [
     "## Compositional ``Layouts``\n",
     "\n",
-    "The class [``Scatter``](../../examples/elements/bokeh/Scatter.ipynb) is a subclass of ``Element``.  As shown in our [element gallery], Elements are the simplest viewable components in HoloViews. Now that we have a handle on ``scatter``, we can demonstrate the compositionality of these objects:"
+    "The class [``Scatter``](../reference/elements/elements/bokeh/Scatter.ipynb) is a subclass of ``Element``.  As shown in our [element gallery], Elements are the simplest viewable components in HoloViews. Now that we have a handle on ``scatter``, we can demonstrate the compositionality of these objects:"
    ]
   },
   {
@@ -176,7 +176,7 @@
    },
    "outputs": [],
    "source": [
-    "taxi_dropoffs = {hour:arr for hour, arr in np.load('../datasets/hourly_taxi_data.npz').items()}\n",
+    "taxi_dropoffs = {hour:arr for hour, arr in np.load('../assets/hourly_taxi_data.npz').items()}\n",
     "#print('Hours: {hours}'.format(hours=', '.join(taxi_dropoffs.keys())))\n",
     "print('Taxi data contains {num} arrays (one per hour).\\nDescription of the first array:\\n'.format(num=len(taxi_dropoffs)))\n",
     "np.info(taxi_dropoffs['0'])"
@@ -195,7 +195,7 @@
    "source": [
     "## Compositional  ``Overlays``\n",
     "\n",
-    "Once again, we can easily visualize this data with HoloViews by passing our array to [``hv.Image``](../../examples/elements/bokeh/Image.ipynb) to create the ``image`` object.  This object has the spatial extent of the data declared as the ``bounds``, in terms of the corresponding range of latitudes and longitudes."
+    "Once again, we can easily visualize this data with HoloViews by passing our array to [``hv.Image``](../reference/elements/elements/bokeh/Image.ipynb) to create the ``image`` object.  This object has the spatial extent of the data declared as the ``bounds``, in terms of the corresponding range of latitudes and longitudes."
    ]
   },
   {
@@ -214,7 +214,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "HoloViews supports ``numpy``, ``xarray``, ``iris``, and ``dask`` arrays when working with array data (see [Gridded Datasets](../user_guide/Gridded_Datasets.ipynb)).  We can also compose elements containing array data with those containing tabular data. To illustrate, let's pass our tabular station data to a [``Points``](../../examples/elements/bokeh/Points.ipynb) element which is used to mark positions in two-dimensional space:"
+    "HoloViews supports ``numpy``, ``xarray``, ``iris``, and ``dask`` arrays when working with array data (see [Gridded Datasets](../user_guide/Gridded_Datasets.ipynb)).  We can also compose elements containing array data with those containing tabular data. To illustrate, let's pass our tabular station data to a [``Points``](../reference/elements/elements/bokeh/Points.ipynb) element which is used to mark positions in two-dimensional space:"
    ]
   },
   {
@@ -245,7 +245,7 @@
    "source": [
     "## Effortlessly exploring data\n",
     "\n",
-    "You can keep composing datastructures together until there are more dimensions than can fit on simultaneously on your screen. For instance, you can visualize a dictionary of [``Images``](../../examples/elements/bokeh/Image.ipynb) (one for every hour of the day) by declaring a ``HoloMap``: "
+    "You can keep composing datastructures together until there are more dimensions than can fit on simultaneously on your screen. For instance, you can visualize a dictionary of [``Images``](../reference/elements/elements/bokeh/Image.ipynb) (one for every hour of the day) by declaring a ``HoloMap``: "
    ]
   },
   {
@@ -313,7 +313,7 @@
    "source": [
     "## Data as visualization\n",
     "\n",
-    "Holomaps are able to compose with elements and other holomaps into overlay and layouts just as easily as you compose two elements together. Here is one such composition where we select a range of longitudes and latitudes from our [``Points``](../../examples/elements/bokeh/Points.ipynb) before we overlay them:"
+    "Holomaps are able to compose with elements and other holomaps into overlay and layouts just as easily as you compose two elements together. Here is one such composition where we select a range of longitudes and latitudes from our [``Points``](../reference/elements/elements/bokeh/Points.ipynb) before we overlay them:"
    ]
   },
   {
@@ -374,7 +374,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We see that slicing the HoloViews [``Points``](../../examples/elements/bokeh/Points.ipynb) object in the visualization sliced the underlying data, with the structure of the table left intact. We can see that the Times Square 42nd Street station is indeed one of the subway stations surrounding our taxi dropoff hotspot. This seamless interplay and exchange between the raw data and easy-to-generate visualizations of it is crucial to how HoloViews helps you understand your data."
+    "We see that slicing the HoloViews [``Points``](../reference/elements/elements/bokeh/Points.ipynb) object in the visualization sliced the underlying data, with the structure of the table left intact. We can see that the Times Square 42nd Street station is indeed one of the subway stations surrounding our taxi dropoff hotspot. This seamless interplay and exchange between the raw data and easy-to-generate visualizations of it is crucial to how HoloViews helps you understand your data."
    ]
   },
   {

guides/getting_started/2-Customization.ipynb → examples/getting_started/2-Customization.ipynb

@@ -36,7 +36,7 @@
    "source": [
     "import pandas as pd\n",
     "import holoviews as hv\n",
-    "spike_train = pd.read_csv('../datasets/spike_train.csv.gz')\n",
+    "spike_train = pd.read_csv('../assets/spike_train.csv.gz')\n",
     "spike_train.head(n=3)"
    ]
   },

guides/getting_started/3-Tabular_Datasets.ipynb → examples/getting_started/3-Tabular_Datasets.ipynb

@@ -51,7 +51,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "diseases = pd.read_csv('../datasets/diseases.csv.gz')\n",
+    "diseases = pd.read_csv('../assets/diseases.csv.gz')\n",
     "diseases.head()"
    ]
   },

guides/getting_started/4-Gridded_Datasets.ipynb → examples/getting_started/4-Gridded_Datasets.ipynb

@@ -48,7 +48,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data = np.load('../datasets/twophoton.npz')\n",
+    "data = np.load('../assets/twophoton.npz')\n",
     "calcium_array = data['Calcium']\n",
     "calcium_array.shape"
    ]