Updates to matplotlib element references to use opts (#3270)

holoviz · Dec 11, 2018 · d21c1d7 · d21c1d7
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Showing 37 changed files with 228 additions and 142 deletions.
diff --git a/examples/reference/elements/matplotlib/Arrow.ipynb b/examples/reference/elements/matplotlib/Arrow.ipynb
@@ -37,10 +37,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts  Curve (color='#D3D3D3')\n",
     "xs = np.linspace(-5,5,100)\n",
     "ys = -(xs-2)**3\n",
-    "hv.Curve((xs,ys)) * hv.Arrow(0, 5, 'Inflection', 'v')"
+    "\n",
+    "curve = hv.Curve((xs,ys))\n",
+    "arrow = hv.Arrow(0, 5, 'Inflection', 'v')\n",
+    "\n",
+    "curve.opts(color='#D3D3D3') * arrow"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Bars.ipynb b/examples/reference/elements/matplotlib/Bars.ipynb
@@ -73,15 +73,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Bars.Grouped [category_index=0 group_index=1 stack_index=5] \n",
-    "%%opts Bars.Stacked [color_by=['stack'] stack_index=1 category_index=5]\n",
     "from itertools import product\n",
     "np.random.seed(3)\n",
     "index, groups = ['A', 'B'], ['a', 'b']\n",
     "keys = product(index, groups)\n",
     "bars = hv.Bars([k+(np.random.rand()*100.,) for k in keys],\n",
     "               ['Index', 'Group'], 'Count')\n",
-    "bars.relabel(group='Grouped') + bars.relabel(group='Stacked')"
+    "\n",
+    "grouped = bars.relabel('Grouped').opts(category_index=5, color_by=['stack'], stack_index=1)\n",
+    "stacked = bars.relabel('Stacked').opts(stack_index=5)\n",
+    "\n",
+    "grouped + stacked"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Bivariate.ipynb b/examples/reference/elements/matplotlib/Bivariate.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -58,8 +60,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Bivariate [filled=True colorbar=True] (cmap='Blues')\n",
-    "hv.Bivariate(normal)"
+    "hv.Bivariate(normal).opts(colorbar=True, cmap='Blues', filled=True)"
    ]
   },
   {
@@ -75,8 +76,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Bivariate {+axiswise}\n",
-    "hv.NdLayout({bw: hv.Bivariate(normal).options(bandwidth=bw)\n",
+    "hv.NdLayout({bw: hv.Bivariate(normal).options(axiswise=True, bandwidth=bw)\n",
     "             for bw in [0.05, 0.1, 0.5, 1]}, 'Bandwidth')"
    ]
   },

diff --git a/examples/reference/elements/matplotlib/Bounds.ipynb b/examples/reference/elements/matplotlib/Bounds.ipynb
@@ -36,9 +36,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Bounds (color='orange' linewidth=6)\n",
     "penguins = hv.RGB.load_image('../assets/penguins.png')\n",
-    "penguins * hv.Bounds((-0.15, -0.4, 0.2, 0))"
+    "bounds = hv.Bounds((-0.15, -0.4, 0.2, 0))\n",
+    "penguins * bounds.opts(color='orange', linewidth=6)"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Box.ipynb b/examples/reference/elements/matplotlib/Box.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -37,11 +39,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Box (linewidth=5 color='red') Image (cmap='gray')\n",
     "data = np.sin(np.mgrid[0:100,0:100][1]/10.0)\n",
     "data[np.arange(40, 60), np.arange(20, 40)] = -1\n",
     "data[np.arange(40, 50), np.arange(70, 80)] = -3    \n",
-    "hv.Image(data) * hv.Box(-0.2, 0, 0.25 ) * hv.Box(-0, 0, (0.4,0.9) )"
+    "(hv.Image(data) * hv.Box(-0.2, 0, 0.25 ) * hv.Box(-0, 0, (0.4,0.9))).opts(\n",
+    "    opts.Box(color='red', linewidth=5),\n",
+    "    opts.Image(cmap='gray'))"
    ]
   },
   {
@@ -59,10 +62,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Box (linewidth=5 color='purple') Image (cmap='gray')\n",
     "data = np.sin(np.mgrid[0:100,0:100][1]/10.0)\n",
     "data[np.arange(30, 70), np.arange(30, 70)] = -3\n",
-    "hv.Image(data) * hv.Box(-0, 0, 0.25, aspect=3, orientation=-np.pi/4)"
+    "box = hv.Box(-0, 0, 0.25, aspect=3, orientation=-np.pi/4)\n",
+    "\n",
+    "(hv.Image(data) * box).opts(\n",
+    "    opts.Box(color='purple', linewidth=5),\n",
+    "    opts.Image(cmap='gray'))"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/BoxWhisker.ipynb b/examples/reference/elements/matplotlib/BoxWhisker.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -62,10 +64,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts BoxWhisker [aspect=2 fig_size=200 show_legend=False] (whiskerprops={'color': 'gray'})\n",
     "groups = [chr(65+g) for g in np.random.randint(0, 3, 200)]\n",
-    "hv.BoxWhisker((groups, np.random.randint(0, 5, 200), np.random.randn(200)),\n",
-    "              ['Group', 'Category'], 'Value').sort()"
+    "box = hv.BoxWhisker((groups, np.random.randint(0, 5, 200), np.random.randn(200)),\n",
+    "                    ['Group', 'Category'], 'Value').sort()\n",
+    "\n",
+    "box.opts(opts.BoxWhisker(aspect=2, fig_size=200, whiskerprops={'color': 'gray'}))"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Chord.ipynb b/examples/reference/elements/matplotlib/Chord.ipynb
@@ -23,10 +23,12 @@
    "source": [
     "import pandas as pd\n",
     "import holoviews as hv\n",
+    "\n",
+    "from holoviews import dim, opts\n",
     "from bokeh.sampledata.les_mis import data\n",
     "\n",
     "hv.extension('matplotlib')\n",
-    "%output size=200 fig='svg'"
+    "hv.output(fig='svg', size=200)"
    ]
   },
   {
@@ -96,8 +98,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Chord [labels='name'] (node_color=dim('index').astype(str) edge_color=dim('source').astype(str) cmap='Category20' edge_cmap='Category20')\n",
-    "hv.Chord((links, nodes)).select(value=(5, None))"
+    "hv.Chord((links, nodes)).select(value=(5, None)).opts(\n",
+    "    opts.Chord(cmap='Category20', edge_color=dim('source').astype(str), labels='name', node_color=dim('index').astype(str)))"
    ]
   }
  ],

diff --git a/examples/reference/elements/matplotlib/Contours.ipynb b/examples/reference/elements/matplotlib/Contours.ipynb
@@ -60,12 +60,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Contours [colorbar=True] (cmap='fire')\n",
     "x,y = np.mgrid[-50:51, -50:51] * 0.05\n",
     "img = hv.Image(np.sin(x**2+y**3))\n",
+    "contours = hv.operation.contours(img, levels=5)\n",
     "\n",
-    "z0, z1 = img.range('z')\n",
-    "img + hv.operation.contours(img, levels=5)"
+    "img + contours.opts(colorbar=True, cmap='fire')"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Curve.ipynb b/examples/reference/elements/matplotlib/Curve.ipynb
@@ -70,9 +70,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts NdOverlay [legend_position='right']\n",
-    "hv.NdOverlay({interp: hv.Curve(points[::8]).options(interpolation=interp)\n",
-    "              for interp in ['linear', 'steps-mid', 'steps-pre', 'steps-post']})"
+    "overlay = hv.NdOverlay({\n",
+    "    interp: hv.Curve(points[::8]).options(interpolation=interp)\n",
+    "    for interp in ['linear', 'steps-mid', 'steps-pre', 'steps-post']})\n",
+    "\n",
+    "overlay.opts(legend_position='right')"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/Distribution.ipynb b/examples/reference/elements/matplotlib/Distribution.ipynb
@@ -62,8 +62,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Distribution [filled=False] (alpha=1)\n",
-    "hv.NdOverlay({bw: hv.Distribution(normal).options(bandwidth=bw)\n",
+    "hv.NdOverlay({bw: hv.Distribution(normal).opts(alpha=1, bandwidth=bw, filled=False)\n",
     "              for bw in [0.05, 0.1, 0.5, 1]}, 'Bandwidth')"
    ]
   },

diff --git a/examples/reference/elements/matplotlib/Ellipse.ipynb b/examples/reference/elements/matplotlib/Ellipse.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -37,7 +39,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Ellipse (linewidth=6)\n",
+    "opts.defaults(opts.Ellipse(linewidth=6))\n",
+    "\n",
     "# Generate some data\n",
     "c1 = np.random.normal(loc=2, scale=0.2, size=(200,200))\n",
     "c2x = np.random.normal(loc=-2, scale=0.6, size=200)\n",
@@ -61,7 +64,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Ellipse (linewidth=6)\n",
     "clusters = hv.Points(c1) * hv.Points((c2x, c2y)) * hv.Points(c3)\n",
     "clusters *  hv.Ellipse(0,0, 4, orientation=np.pi/5, aspect=2) "
    ]

diff --git a/examples/reference/elements/matplotlib/Graph.ipynb b/examples/reference/elements/matplotlib/Graph.ipynb
@@ -23,6 +23,7 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
     "\n",
     "hv.extension('matplotlib')"
    ]
@@ -60,8 +61,8 @@
     "\n",
     "padding = dict(x=(-1.2, 1.2), y=(-1.2, 1.2))\n",
     "\n",
-    "simple_graph = hv.Graph(((source, target),)).options(padding=0.1)\n",
-    "simple_graph"
+    "simple_graph = hv.Graph(((source, target),))\n",
+    "simple_graph.opts(padding=0.1)"
    ]
   },
   {
@@ -79,7 +80,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "simple_graph.options(directed=True, arrowhead_length=0.08)"
+    "simple_graph.opts(directed=True, arrowhead_length=0.08)"
    ]
   },
   {
@@ -107,7 +108,7 @@
     "#### Additional features\n",
     "\n",
     "\n",
-    "Next we will extend this example by supplying explicit edges, node information and edge weights. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color_index``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself, which we can map to a color using the ``edge_color_index``."
+    "Next we will extend this example by supplying explicit edges, node information and edge weights. By constructing the ``Nodes`` explicitly we can declare an additional value dimensions, which are revealed when hovering and/or can be mapped to the color by specifying the ``color``. We can also associate additional information with each edge by supplying a value dimension to the ``Graph`` itself, which we can map to a color using the ``edge_color``."
    ]
   },
   {
@@ -135,8 +136,8 @@
     "nodes = hv.Nodes((x, y, node_indices, node_labels), vdims='Type')\n",
     "graph = hv.Graph(((source, target, edge_weights), nodes, paths), vdims='Weight')\n",
     "\n",
-    "graph.redim.range(**padding).options(color_index='Type', edge_color_index='Weight',\n",
-    "                                     cmap=['blue', 'red'], edge_cmap='viridis')"
+    "graph.opts(\n",
+    "    opts.Graph(cmap=['blue', 'red'], edge_cmap='viridis', edge_color='Weight', node_color='Type', padding=0.1))"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/HLine.ipynb b/examples/reference/elements/matplotlib/HLine.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -37,10 +39,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts HLine (color='blue' linewidth=6) Points (color='#D3D3D3')\n",
     "xs = np.random.normal(size=100)\n",
     "ys = np.random.normal(size=100) * xs\n",
-    "hv.Points((xs,ys)) * hv.HLine(ys.mean())"
+    "points = hv.Points((xs,ys))\n",
+    "\n",
+    "(points * hv.HLine(ys.mean())).opts(\n",
+    "    opts.HLine(color='#D3D3D3')\n",
+    "    opts.Points(color='blue', linewidth=6))"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/HSV.ipynb b/examples/reference/elements/matplotlib/HSV.ipynb
@@ -21,6 +21,8 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')"
    ]
   },
@@ -67,7 +69,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%opts Image (cmap='gray')\n",
+    "opts.defaults(opts.Image(cmap='gray'))\n",
+    "\n",
     "hsv[..., 'H'].relabel('H') + hsv[..., 'S'].relabel('S') + hsv[..., 'V'].relabel('V')"
    ]
   },

diff --git a/examples/reference/elements/matplotlib/HeatMap.ipynb b/examples/reference/elements/matplotlib/HeatMap.ipynb
@@ -75,9 +75,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts HeatMap [ colorbar=True fig_size=250]\n",
-    "hv.HeatMap((np.random.randint(0, 10, 100), np.random.randint(0, 10, 100),\n",
-    "            np.random.randn(100), np.random.randn(100)), vdims=['z', 'z2']).redim.range(z=(-2, 2))"
+    "heatmap = hv.HeatMap((np.random.randint(0, 10, 100), np.random.randint(0, 10, 100),\n",
+    "                      np.random.randn(100), np.random.randn(100)), vdims=['z', 'z2']).redim.range(z=(-2, 2))\n",
+    "\n",
+    "heatmap.opts(colorbar=True, fig_size=250)"
    ]
   },
   {

diff --git a/examples/reference/elements/matplotlib/HexTiles.ipynb b/examples/reference/elements/matplotlib/HexTiles.ipynb
@@ -19,8 +19,10 @@
    "source": [
     "import numpy as np\n",
     "import holoviews as hv\n",
+    "from holoviews import opts\n",
+    "\n",
     "hv.extension('matplotlib')\n",
-    "%output fig='svg' size=200"
+    "hv.output(fig='svg', size=200)"
    ]
   },
   {
@@ -40,7 +42,7 @@
    "source": [
     "np.random.seed(44)\n",
     "hex_tiles = hv.HexTiles(np.random.randn(100000, 2))\n",
-    "hex_tiles.options(colorbar=True)"
+    "hex_tiles.opts(colorbar=True)"
    ]
   },
   {
@@ -60,9 +62,11 @@
    "source": [
     "xs, ys = np.random.randn(2, 1000)\n",
     "hex_with_values = hv.HexTiles((xs, ys, xs*(ys/2.), (xs**2)*ys), vdims=['Values', 'Hover Values'])\n",
+    "points = hv.Points(hex_with_values)\n",
     "\n",
-    "hex_with_values.options(aggregator=np.sum) *\\\n",
-    "hv.Points(hex_with_values).options(s=3, color='black')"
+    "(hex_with_values * points).opts(\n",
+    "    opts.HexTiles(aggregator=np.sum),\n",
+    "    opts.Points(s=3, color='black'))"
    ]
   },
   {
@@ -82,8 +86,11 @@
    "source": [
     "x, y = np.hstack([np.random.randn(2, 10000), np.random.randn(2, 10000)*0.8+2])\n",
     "hex_two_distributions = hv.HexTiles((x, y))\n",
-    "hex_two_distributions.options(min_count=0) *\\\n",
-    "hv.Bivariate(hex_two_distributions).options(show_legend=False, linewidth=3)"
+    "bivariate = hv.Bivariate((x, y))\n",
+    "\n",
+    "(hex_two_distributions * bivariate).opts(\n",
+    "    opts.Bivariate(linewidth=3, show_legend=False),\n",
+    "    opts.HexTiles(min_count=0))"
    ]
   }
  ],