Minor tweaks to homepage

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 interactively or Python code in a 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, you can just type its name (``fractal``) to view it as an image in an [IPython/Jupyter Notebook](http://ipython.org/notebook/).  You can even easily view it annotated with a horizontal line and its histogram, next to a slice of it at the indicated cross-section, all 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:"
    ]
   },
   {
@@ -66,7 +66,7 @@
    "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, which are handled by HoloViews and the underlying plotting libraries.\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",

doc/index.rst

@@ -56,7 +56,8 @@ Python distributions::
   conda install -c ioam holoviews bokeh
 
 See our `installation page <install.html>`_ if you need other options,
-including ``pip`` installations, additional packages, development
+including `pip <https://pip.pypa.io/en/stable/installing>`_
+installations, additional packages, development
 versions, and minimal installations.  Minimal installations include only
 `Param <http://ioam.github.com/param/>`_ and `Numpy <http://numpy.org>`_ 
 as dependencies, neither of which has any required dependencies,