Various fixes for website linking

doc/index.rst

@@ -13,7 +13,7 @@ The `Getting-Started <getting_started>`_ guide explains the basic concepts and h
 
 The `User Guide <user_guide>`_ goes more deeply into key concepts from HoloViews, when you are ready for further study.
 
-The `API <api>`_ is the definitive guide to each HoloViews object, but the same information is available more conveniently via the `hv.help()` command and tab completion in the Jupyter notebook.
+The `API <Reference_Manual>`_ is the definitive guide to each HoloViews object, but the same information is available more conveniently via the `hv.help()` command and tab completion in the Jupyter notebook.
 
 If you have any `issues <https://github.com/ioam/holoviews/issues>`_ or wish to `contribute code <https://help.github.com/articles/about-pull-requests>`_, you can visit our `GitHub site <https://github.com/ioam/holoviews>`_ or chat with the developers on `gitter <https://gitter.im/ioam/holoviews>`_.
 

examples/getting_started/1-Introduction.ipynb

@@ -46,7 +46,7 @@
    "source": [
     "This is the standard way to make the numpy and pandas libraries available in the namespace. We recommend always importing HoloViews as ``hv`` and if you haven't already installed HoloViews, check out our [installation page].\n",
     "\n",
-    "Note that after importing HoloViews as ``hv`` we run ``hv.extension('bokeh')`` to load the bokeh plotting extension, allowing us to generate visualizations with [Bokeh](http://bokeh.pydata.org/). In the next section we will see how you can use other plotting libraries such as [matplotlib](matplotlib.org) and even how you can mix and match between them.\n",
+    "Note that after importing HoloViews as ``hv`` we run ``hv.extension('bokeh')`` to load the bokeh plotting extension, allowing us to generate visualizations with [Bokeh](http://bokeh.pydata.org/). In the next section we will see how you can use other plotting libraries such as [matplotlib](http://matplotlib.org) and even how you can mix and match between them.\n",
     "\n",
     "Now let's load our subway data using pandas:"
    ]
@@ -218,7 +218,7 @@
     "\n",
     "On the left, we have the visual representation of the ``image`` object we declared. Using ``+`` we put it into a ``Layout`` together with a new compositional object created with the ``*`` operator called an ``Overlay``. This particular overlay displays the station positions on top of our image which works correctly as both elements contain data that exist in the same space, namely New York City.\n",
     "\n",
-    "This overlay on the right lets us see the location of all the subway stations in relation to our midnight taxi dropoffs. Of course,  HoloViews allows you to visually express more of the available information with our points.  For instance, you could represent the ridership of each subway by point color or point size. For more information see [Customizing Plots](../user_guide/Customizing_Plots.ipynb)."
+    "This overlay on the right lets us see the location of all the subway stations in relation to our midnight taxi dropoffs. Of course,  HoloViews allows you to visually express more of the available information with our points.  For instance, you could represent the ridership of each subway by point color or point size. For more information see [Customizing Plots](../user_guide/03-Customizing_Plots.ipynb)."
    ]
   },
   {
@@ -353,7 +353,7 @@
    "source": [
     "## Onwards\n",
     "\n",
-    "The next getting-started section shows how to do [Customization](../Customization.ipynb) of the visual appearance of your data, allowing you highlight the most important features and change the look and feel. Other related topics for deeper study:\n",
+    "The next getting-started section shows how to do [Customization](2-Customization.ipynb) of the visual appearance of your data, allowing you highlight the most important features and change the look and feel. Other related topics for deeper study:\n",
     " \n",
     "* The above plots did not require any special geographic-data support, but when working with larger areas of the Earth's surface (for which curvature becomes significant) or when overlaying data with geographic features, the separate [GeoViews](http://geo.holoviews.org) library provides convenient geo-specific extensions to HoloViews.\n",
     "* The taxi array data was derived from a very large tabular dataset and rasterized using [datashader](http://https://github.com/bokeh/datashader), an optional add-on to HoloViews and Bokeh that makes it feasible to work with very large datasets in a web browser."

examples/getting_started/2-Customization.ipynb

@@ -257,7 +257,7 @@
    "source": [
     "As you can see, we can set long descriptive labels on our dimensions (including unicode) while still making use of the short dimension name in methods such as ``select``.  \n",
     "\n",
-    "Now that you know how to set up and customize basic visualizations, the next [Getting-Started sections](../3-Tabular_Datasets.ipynb) show how to work with various common types of data in HoloViews."
+    "Now that you know how to set up and customize basic visualizations, the next [Getting-Started sections](./3-Tabular_Datasets.ipynb) show how to work with various common types of data in HoloViews."
    ]
   }
  ],

examples/getting_started/3-Tabular_Datasets.ipynb

@@ -96,7 +96,7 @@
     "\n",
     "The ``repr`` shows us both the ``kdims`` (in square brackets) and the ``vdims`` (in parentheses) of the ``Dataset``.  Because it can hold arbitrary combinations of dimensions, a ``Dataset`` is *not* immediately visualizable. There's no single clear mapping from these four dimensions onto a two-dimensional page, hence the textual representation shown above.\n",
     "\n",
-    "To make this data visualizable, we'll need to provide a bit more metadata, by selecting one of the large library of [Elements](../reference/elements/) that can help answer the questions we want to ask about the data. Perhaps the most obvious representation of this dataset is as a Curve displaying the incidence for each year, for each state. We could pull out individual columns one by one from the original dataset, but now that we have declared information about the dimensions, the cleanest approach is to map the dimensions of our ``Dataset`` onto the dimensions of an Element using ``.to``:"
+    "To make this data visualizable, we'll need to provide a bit more metadata, by selecting one of the large library of Elements that can help answer the questions we want to ask about the data. Perhaps the most obvious representation of this dataset is as a ``Curve`` displaying the incidence for each year, for each state. We could pull out individual columns one by one from the original dataset, but now that we have declared information about the dimensions, the cleanest approach is to map the dimensions of our ``Dataset`` onto the dimensions of an Element using ``.to``:"
    ]
   },
   {

examples/getting_started/4-Gridded_Datasets.ipynb

@@ -258,7 +258,7 @@
    "source": [
     "Of course, we could combine all of these approaches and aggregate each ROI, faceting the entire dataset by ROI to show how the activity of the various neurons differs.\n",
     "\n",
-    "As you can see, HoloViews makes it simple for you to select and display data from a large gridded dataset, allowing you to focus on whatever aspects of the data are important to answer a given question.  The final getting-started section covers how you can provide [Live Data](05-Live_Data.ipynb) visualizations to let users dynamically choose what to display interactively."
+    "As you can see, HoloViews makes it simple for you to select and display data from a large gridded dataset, allowing you to focus on whatever aspects of the data are important to answer a given question.  The final getting-started section covers how you can provide [Live Data](5-Live_Data.ipynb) visualizations to let users dynamically choose what to display interactively."
    ]
   }
  ],

examples/user_guide/01-Annotating_Data.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Annotating your Data"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -11,13 +18,6 @@
     "hv.extension('bokeh')"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Annotating your Data"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},

examples/user_guide/02-Composing_Elements.ipynb

@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Composing Elements"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -15,8 +22,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Composing Elements\n",
-    "\n",
     "HoloViews supports two primary composition operators that apply to (nearly) all visual HoloViews objects in order to create heterogeous collections: ``+`` to generate [``Layout``](../reference/containers/bokeh/Layout.ipynb) objects and ``*`` to create [``Overlay``](../reference/containers/bokeh/Overlay.ipynb) objects.\n",
     "\n",
     "* **Layout:** A collection of HoloViews objects to be  grouped and displayed side-by-side \n",