@@ -253,13 +253,13 @@

    "metadata": {},

    "outputs": [],

    "source": [

-    "def func(arg):\n",

+    "def func(xy):\n",

     "    import numpy as np\n",

-    "    x, y = arg\n",

+    "    x, y = xy\n",

     "    a = 0.2\n",

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

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

     "\n",

-    "learner = adaptive.learner.AdaptiveTriSampling(func, bounds=[(-1, 1), (-1, 1)])"

+    "learner = adaptive.learner.Learner2D(func, bounds=[(-1, 1), (-1, 1)])"

    ]

   },

   {

@@ -269,8 +269,8 @@

    "outputs": [],

    "source": [

     "from concurrent.futures import ProcessPoolExecutor\n",

-    "executor = ProcessPoolExecutor(max_workers=2)\n",

-    "runner = adaptive.Runner(learner, executor, goal=lambda l: l.loss() > 2000)"

+    "executor = ProcessPoolExecutor(max_workers=48)\n",

+    "runner = adaptive.Runner(learner, executor, goal=lambda l: l.loss() > 1500, log=True)"

    ]

   },

   {

@@ -282,6 +282,20 @@

     "adaptive.live_plot(runner)"

    ]

   },

+  {

+   "cell_type": "code",

+   "execution_count": null,

+   "metadata": {},

+   "outputs": [],

+   "source": [

+    "import numpy as np\n",

+    "learner2 = adaptive.learner.Learner2D(func, bounds=[(-1, 1), (-1, 1)])\n",

+    "lin = np.linspace(-1, 1, len(learner.points)**0.5)\n",

+    "xy = [(x, y) for x in lin for y in lin]\n",

+    "learner2.add_data(xy, map(func, xy))\n",

+    "learner2.plot().relabel('Homogeneous grid') + learner.plot().relabel('With adaptive')"

+   ]

+  },

   {

    "cell_type": "markdown",

    "metadata": {