@@ -102,7 +102,7 @@

    "source": [

     "# The end condition is when the \"loss\" is less than 0.1. In the context of the\n",

     "# 1D learner this means that we will resolve features in 'func' with width 0.1 or wider.\n",

-    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.1)"

+    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01)"

    ]

   },

   {

@@ -177,6 +177,9 @@

    "source": [

     "def func(xy):\n",

     "    import numpy as np\n",

+    "    from time import sleep\n",

+    "    from random import random\n",

+    "    sleep(random())\n",

     "    x, y = xy\n",

     "    a = 0.2\n",

     "    return x + np.exp(-(x**2 + y**2 - 0.75**2)**2/a**4)\n",

@@ -190,7 +193,7 @@

    "metadata": {},

    "outputs": [],

    "source": [

-    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() > 1500)"

+    "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.01, log=True)"

    ]

   },

   {

@@ -199,7 +202,22 @@

    "metadata": {},

    "outputs": [],

    "source": [

-    "adaptive.live_plot(runner)"

+    "%%output size=100\n",

+    "%%opts Contours (alpha=0.3)\n",

+    "from adaptive.learner import *\n",

+    "\n",

+    "def plot(self):\n",

+    "    tri = self.ip.tri\n",

+    "    return hv.Contours([p for p in tri.points[tri.vertices]])\n",

+    "\n",

+    "def plot_poly(self):\n",

+    "    tri = self.ip.tri\n",

+    "    return hv.Polygons([p for p in tri.points[tri.vertices]])\n",

+    "\n",

+    "\n",

+    "(adaptive.live_plot(runner) +\n",

+    " adaptive.live_plot(runner, plotter=plot_poly) +\n",

+    " adaptive.live_plot(runner) * adaptive.live_plot(runner, plotter=plot))"

    ]

   },

   {