Merge 46513ee into 408bf0e

doc/Examples/index.rst

@@ -37,8 +37,15 @@ HoloViews may be used:
   HoloViews.
 
 * `The Hipster Effect <HipsterDynamics.html>`_: Adapted version of `post from Jake Vanderplas
-<https://jakevdp.github.io/blog/2014/11/11/the-hipster-effect-interactive>`_
-about dynamic systems and modeling of conformity.
+  <https://jakevdp.github.io/blog/2014/11/11/the-hipster-effect-interactive>`_
+  about dynamic systems and modeling of conformity.
+
+* `t-SNE machine learning Tutorial
+  <http://philippjfr.com/work/work-in-progress/t-sne-tutorial/>`_:
+  Adapted version of a `tutorial
+  <https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm>`_
+  by Cyrille Rossant for O'Reilly on the t-SNE machine learning
+  visualization algorithm.
 
 
 Extensions
@@ -59,7 +66,7 @@ extended in new (and unexpected!) directions:
 
 * `Experimental Plotly backend
   <http://philippjfr.com/work/work-in-progress/plotly/>`_: A
-  prototype of a `Plotly`<https://plot.ly/python>`_-based backend for HoloViews,
+  prototype of a `Plotly-based backend <https://plot.ly/python>`_ for HoloViews,
   with progress summarized in an ongoing `pull request <https://github.com/ioam/holoviews/pull/398>`_.
   (Contributions welcome!)
 
@@ -70,9 +77,9 @@ Third party libraries, simulators and toolkits that make use of
 HoloViews for easier visualization and analysis:
 
 * `ImaGen library <http://ioam.github.io/imagen>`_: Generate
-  HoloViews `Image <https://ioam.github.io/holoviews/Tutorials/Elements.html#Image>`_
+  HoloViews `Image <https://holoviews/Tutorials/Elements.html#Image>`_
   and 
-  `RGB <https://ioam.github.io/holoviews/Tutorials/Elements.html#RGB>`_ 
+  `RGB <https://holoviews.org/Tutorials/Elements.html#RGB>`_ 
   patterns from mathematical functions.
 
 * `Topographica tutorials <http://topographica.org/Tutorials/>`_:

doc/FAQ.rst

@@ -20,7 +20,7 @@ directly to disk, with custom options, like this:
   renderer.save(my_object, 'example_I', style=dict(Image={'cmap':'jet'}))
 
 This process is described in detail in the 
-`Options tutorial <Tutorials/Options>`_.
+`Options tutorial <Tutorials/Options.html>`_.
 Of course, notebook-specific functionality like capturing the data in
 notebook cells or saving cleared notebooks is only for IPython/Jupyter.
 
@@ -57,7 +57,7 @@ see the structure of your object.
 
 In any Python session, you can look at ``print repr(obj)``. For
 an explanation of how this information helps you index into your
-object, see our `Composing Data tutorial <Tutorials/Composing_Data>`_.
+object, see our `Composing Data tutorial <Tutorials/Composing_Data.html>`_.
 
 
 **Q: Help! How do I find out the options for customizing the
@@ -70,7 +70,7 @@ present the available style and plotting options for that object.
 The same information is also available in any Python session using
 ``holoviews.help(obj)``. For more
 information on customizing the display of an object,
-see our `Options Tutorial <Tutorials/Options>`_.
+see our `Options Tutorial <Tutorials/Options.html>`_.
 
 
 **Q: Why don't you let me pass** *matplotlib_option* **as a style

doc/Homepage.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible.  Without HoloViews, there are typically many steps required before you can see your data, whether you use a GUI to build up a plot interactively or you write plotting code in Python to put together that specific type of plot.  HoloViews instead lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization.  For instance, if you wrap a two-dimensional dataset like the fractal below ([mandelbrot.npy](https://github.com/ioam/holoviews/raw/master/doc/mandelbrot.npy)) in a HoloViews ``Image`` object, you can view the data as an image (here annotated with a horizontal line), its histogram, and a slice of it at the indicated cross-section, without writing any plotting code:"
+    "HoloViews is a [Python](http://python.org) library that makes analyzing and visualizing scientific or engineering data much simpler, more intuitive, and more easily reproducible.  Without HoloViews, there are typically many steps required before you can see your data, whether you use a GUI interactively or write a function or script to build up a plot.  HoloViews instead lets you store your data in an annotated format that is instantly visualizable, with immediate access to both the numeric data *and* its visualization.  For instance, if you wrap a two-dimensional dataset like the fractal below ([mandelbrot.npy](https://github.com/ioam/holoviews/raw/master/doc/mandelbrot.npy)) in a HoloViews ``Image`` object named ``fractal``,  you can just type ``fractal`` to view it as an image in an [IPython/Jupyter Notebook](http://ipython.org/notebook/).  Most importantly, combining it with other objects is now easy -- you can e.g. view it annotated with a horizontal line and a histogram, next to a slice of it from the indicated cross-section, all without writing any plotting code:"
    ]
   },
   {
@@ -27,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The original data always remains available in its native format (accessible via ``fractal.data`` in this case), but accessing it via the HoloViews object instead lets the data display itself, either alone (just type ``fractal`` in an IPython/Jupyter notebook) or alongside or overlaid with other HoloViews objects as shown above. The actual plotting is done using a separate library like [matplotlib](http://matplotlib.org) or [bokeh](http://bokeh.pydata.org), but all of the HoloViews objects can be used without any plotting library available, so that you can easily create, save, load, and manipulate HoloViews objects from within your own programs.  HoloViews objects support arbitrarily high dimensions, using continuous or discrete indexes and values, with flat or hierarchical organizations, and sparse or dense data formats.  The objects can then be flexibly combined, selected, sliced, sorted, sampled, or animated, all by specifying what data you want to see rather than by writing plotting code. The goal is to put the plotting code into the background, as an implementation detail to be written once and reused often, letting you focus clearly on your data in daily work."
+    "The original data always remains available in its native format (accessible via ``fractal.data`` in this case), but working with the HoloViews object instead lets the data display itself, either alone or alongside or overlaid with other HoloViews objects as shown above. The actual plotting is done using a separate library like [matplotlib](http://matplotlib.org) or [bokeh](http://bokeh.pydata.org), but all of the HoloViews objects can be used without any plotting library available, so that you can easily create, save, load, and manipulate HoloViews objects from within your own programs for later analysis.  HoloViews objects support arbitrarily high dimensions, using continuous, discrete, or categorical indexes and values, with flat or hierarchical organizations, and sparse or dense data formats.  The objects can then be flexibly combined, selected, sliced, sorted, sampled, or animated, all by specifying what data you want to see rather than by writing plotting code. The goal is to put the plotting code into the background, as an implementation detail to be written once and reused often, letting you focus clearly on your data in daily work."
    ]
   },
   {
@@ -66,11 +66,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Here in **A** we have taken the same fractal data and indicated a horizontal cross section using a set of dots with sizes proportional to the underlying data values, illustrating how even a simple annotation can be used to reflect other data of interest.  We then add a cross-section curve **B**, a thresholded version of the data **C**, and a version of the data with a contour outline overlaid **D**.  The threshold and contour levels used are not fixed, but are calculated as the 90th or 60th percentile of the data values along the selected cross section, using standard Python/Numpy functions.  All of this data is then packaged into a single ``HoloMap`` data structure for a range of cross sections, allowing the data for a particular cross section to be revealed by moving the Y-value slider at right.  Even with these complicated interrelationships between data elements, the code still only needs to focus on the data that you want to see, not on the details of the plotting or interactive controls.\n",
+    "Here in **A** we have taken the same fractal data and indicated a horizontal cross section using a set of dots with sizes proportional to the underlying data values, illustrating how even a simple annotation can be used to reflect other data of interest.  We then add a cross-section curve **B**, a thresholded version of the data **C**, and a version of the data with a contour outline overlaid **D**.  The threshold and contour levels used are not fixed, but are calculated as the 90th or 60th percentile of the data values along the selected cross section, using standard Python/NumPy functions.  All of this data is then packaged into a single ``HoloMap`` data structure for a range of cross sections, allowing the data for a particular cross section to be revealed by moving the Y-value slider at right.  Even with these complicated interrelationships between data elements, the code still only needs to focus on the data that you want to see, not on the details of the plotting or interactive controls, which are handled by HoloViews and the underlying plotting libraries.\n",
     "\n",
     "Note that just as the 2D array became a 1D curve automatically by sampling to get the cross section, this entire figure would become a single static frame with no slider bar if you chose a specific ``Y`` value by re-running with ``.select(Y=0.3)`` before ``.cols(2)``.  In fact, there is nothing in the code above that adds the slider bar explicitly -- it appears automatically, just because there is an additional dimension of data (``Y`` in this case) that has not been laid out spatially.  Additional sliders would appear if there were other dimensions being varied as well, e.g. for parameter-space explorations.\n",
     "\n",
-    "This functionality is designed to complement the [IPython/Jupyter Notebook](http://ipython.org/notebook/) interface, though it can be used just as well separately.  This web page and all the [HoloViews Tutorials](Tutorials/) are runnable notebooks, which allow you to interleave text, Python code, and graphical results easily.  With HoloViews, you can put a minimum of code in the notebook (typically one or two lines per subfigure), specifying what you would like to see rather than the details of how it should be plotted.  HoloViews makes the IPython Notebook a practical solution for both exploratory research (since viewing nearly any chunk of data just takes a line or two of code) and for long-term [reproducibility](Tutorials/Exporting) of the work (because both the code and the visualizations are preserved in the notebook file forever, and the data and publishable figures can both easily be exported to an archive on disk).  See the [Tutorials](Tutorials/) for detailed examples, and then start enjoying working with your data!"
+    "This functionality is designed to complement the [IPython/Jupyter Notebook](http://ipython.org/notebook/) interface, though it can be used just as well separately.  This web page and all the [HoloViews Tutorials](Tutorials/) are runnable notebooks, which allow you to interleave text, Python code, and graphical results easily.  With HoloViews, you can put a minimum of code in the notebook (typically one or two lines per subfigure), specifying what you would like to see rather than the details of how it should be plotted.  HoloViews makes the IPython Notebook a practical solution for both exploratory research (since viewing nearly any chunk of data just takes a line or two of code) and for long-term [reproducibility](Tutorials/Exporting.html) of the work (because both the code and the visualizations are preserved in the notebook file forever, and the data and publishable figures can both easily be exported to an archive on disk).  See the [Tutorials](Tutorials/) for detailed examples, and then start enjoying working with your data!"
    ]
   }
  ],