Notebook improvements (#2220)

holoviz · Jan 3, 2018 · fcdbca6 · fcdbca6
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diff --git a/examples/user_guide/07-Tabular_Datasets.ipynb b/examples/user_guide/07-Tabular_Datasets.ipynb
@@ -16,9 +16,29 @@
     "By default HoloViews will use one of these data storage formats for tabular data:\n",
     "\n",
     "* A pure Python dictionary containing 1D NumPy-arrays for each column.\n",
+    "```\n",
+    "{'x': np.array([0, 1, 2]), 'y': np.array([0, 1, 2])}\n",
+    "```\n",
     "* A purely NumPy array format for numeric data.\n",
+    "```\n",
+    "np.array([[0, 0], [1, 1], [2, 3]])\n",
+    "```\n",
     "* Pandas DataFrames\n",
-    "* Dask DataFrames"
+    "```\n",
+    "pd.DataFrame(np.array([[0, 0], [1, 1], [2, 3]]), columns=['x', 'y'])\n",
+    "```\n",
+    "* Dask DataFrames\n",
+    "\n",
+    "A number of additonal standard constructors are supported:\n",
+    "\n",
+    "* A tuple of array (or array-like) objects\n",
+    "```\n",
+    "([0, 1, 2], [0, 1, 2])\n",
+    "```\n",
+    "* A list of tuples:\n",
+    "```\n",
+    "[(0, 0), (1, 1), (2, 2)]\n",
+    "```"
    ]
   },
   {
@@ -37,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Simple Dataset"
+    "# A simple Dataset"
    ]
   },
   {
@@ -73,7 +93,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "hv.Scatter(table) + hv.Curve(table) + hv.Bars(table)"
+    "(hv.Scatter(table) + hv.Curve(table) + hv.Area(table) + hv.Bars(table)).cols(2)"
    ]
   },
   {
@@ -84,12 +104,10 @@
     "\n",
     "As a guide to which Elements can be converted to each other, those of the same dimensionality here should be interchangeable, because of the underlying similarity of their columnar representation:\n",
     "\n",
-    "* 0D: BoxWhisker, Spikes, Distribution*, \n",
-    "* 1D: Scatter, Curve, ErrorBars, Spread, Bars, BoxWhisker, Regression*\n",
-    "* 2D: Points, HeatMap, Bars, BoxWhisker, Bivariate*\n",
-    "* 3D: Scatter3D, TriSurface, VectorField, BoxWhisker, Bars\n",
-    "\n",
-    "\\* - requires Seaborn\n",
+    "* 0D: BoxWhisker, Spikes, Distribution\n",
+    "* 1D: Area, Bars, BoxWhisker, Curve, ErrorBars, Scatter, Spread\n",
+    "* 2D: Bars, Bivariate, BoxWhisker, HeatMap, Points, VectorField \n",
+    "* 3D: Scatter3D, TriSurface, VectorField\n",
     "\n",
     "This categorization is based only on the ``kdims``, which define the space in which the data has been sampled or defined. An Element can also have any number of value dimensions (``vdims``), which may be mapped onto various attributes of a plot such as the color, size, and orientation of the plotted items.  For a reference of how to use these various Element types, see the [Elements Reference](http://holoviews.org/reference/index.html#elements)."
    ]

diff --git a/examples/user_guide/Plotting_with_Bokeh.ipynb b/examples/user_guide/Plotting_with_Bokeh.ipynb
@@ -166,10 +166,10 @@
     "             ('None', {'yaxis': None}),\n",
     "             ('Rotate', {'xrotation': 90}),\n",
     "             ('N Ticks', {'xticks': 3}),\n",
-    "             ('List Ticks', {'xticks': [0, 100, 300, 500]})]\n",
+    "             ('List Ticks', {'xticks': [0, 20, 50, 90]})]\n",
     "\n",
     "hv.Layout([points.relabel(group=group).opts(plot=opts)\n",
-    "           for group, opts in axes_opts]).cols(3).display('all')"
+    "           for group, opts in axes_opts]).cols(3)"
    ]
   },
   {
@@ -244,15 +244,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Overlay [show_legend=False height=400 width=600] ErrorBars (line_width=5) Scatter(alpha=0.2 size=6)\n",
+    "%%opts Overlay [show_legend=False height=400 width=600] ErrorBars (line_width=5) Scatter [jitter=0.2] (alpha=0.2 size=6)\n",
     "\n",
     "overlay = hv.NdOverlay({group: hv.Scatter(([group]*100, np.random.randn(100)*(i+1)+i))\n",
     "                        for i, group in enumerate(['A', 'B', 'C', 'D', 'E'])})\n",
     "\n",
     "errorbars = hv.ErrorBars([(k, el.reduce(function=np.mean), el.reduce(function=np.std))\n",
     "                          for k, el in overlay.items()])\n",
     "\n",
-    "global_mean = hv.Text('A', 12, 'Global mean: %.3f' % overlay.dimension_values('y').mean())\n",
+    "global_mean = hv.Text('A', 12, 'Global mean: %.3f' % overlay.dimension_values('y').mean(), halign='left')\n",
     "\n",
     "errorbars * overlay * hv.Curve(errorbars) * global_mean"
    ]
@@ -298,6 +298,38 @@
     "Another reason to use ``tabs`` is that some Layout combinations may not be able to be displayed directly using HoloViews. For example, it is not currently possible to display a ``GridSpace`` as part of a ``Layout`` in any backend, and this combination will automatically switch to a ``tab`` representation for the bokeh backend."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Interactive Legends\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When using ``NdOverlay`` and ``Overlay`` containers each element will get a legend entry, which can be used to interactively toggle the visibility of the element. In this example we will create a number of ``Histogram`` elements each with a different mean. By setting a ``muted_fill_alpha`` we can define the style of the element when it is de-selected using the legend, simply try tapping on each legend entry to see the effect:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%opts Histogram [width=600] (alpha=0.8 muted_fill_alpha=0.1)\n",
+    "hv.NdOverlay({i: hv.Histogram(np.histogram(np.random.randn(100)+i*2)) for i in range(5)})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The other ``muted_`` options can be used to define other aspects of the Histogram style when it is unselected."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},