update plots in example-notebook.ipynb

Author: basnijholt

example-notebook.ipynb

@@ -215,8 +215,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts EdgePaths (color='w')\n",
-    "\n",
     "import itertools\n",
     "\n",
     "# Create a learner and add data on homogeneous grid, so that we can plot it\n",
@@ -232,7 +230,7 @@
     "    + learner.plot().relabel(\"With adaptive\")\n",
     "    + learner2.plot(n, tri_alpha=0.4)\n",
     "    + learner.plot(tri_alpha=0.4)\n",
-    ").cols(2)"
+    ").cols(2).opts({\"EdgePaths\": dict(color=\"w\")})"
    ]
   },
   {
@@ -287,7 +285,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "runner.live_plot(update_interval=0.1)"
+    "runner.live_plot(update_interval=0.3)"
    ]
   },
   {
@@ -392,7 +390,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "runner.live_plot(update_interval=0.1)"
+    "runner.live_plot(update_interval=1)"
    ]
   },
   {
@@ -538,7 +536,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "runner.live_plot(update_interval=0.1)"
+    "runner.live_plot(update_interval=0.3)"
    ]
   },
   {
@@ -602,15 +600,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts Path {+framewise}\n",
     "def plot_cut(x1, x2, directions, learner=learner):\n",
-    "    cut_mapping = {'xyz'.index(d): x for d, x in zip(directions, [x1, x2])}\n",
-    "    return learner.plot_slice(cut_mapping)\n",
+    "    cut_mapping = {\"xyz\".index(d): x for d, x in zip(directions, [x1, x2])}\n",
+    "    return learner.plot_slice(cut_mapping).opts({\"Path\": dict(framewise=True)})\n",
     "\n",
-    "dm = hv.DynamicMap(plot_cut, kdims=['v1', 'v2', 'directions'])\n",
-    "dm.redim.values(v1=np.linspace(-1, 1),\n",
-    "                v2=np.linspace(-1, 1),\n",
-    "                directions=['xy', 'xz', 'yz'])"
+    "\n",
+    "dm = hv.DynamicMap(plot_cut, kdims=[\"v1\", \"v2\", \"directions\"])\n",
+    "dm.redim.values(\n",
+    "    v1=np.linspace(-1, 1),\n",
+    "    v2=np.linspace(-1, 1),\n",
+    "    directions=[\"xy\", \"xz\", \"yz\"],\n",
+    ")"
    ]
   },
   {
@@ -676,26 +676,36 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts EdgePaths (color='w') Image [logz=True]\n",
-    "\n",
     "from adaptive.runner import SequentialExecutor\n",
     "\n",
+    "\n",
     "def uniform_sampling_2d(ip):\n",
     "    from adaptive.learner.learner2D import areas\n",
+    "\n",
     "    A = areas(ip)\n",
     "    return np.sqrt(A)\n",
     "\n",
+    "\n",
     "def f_divergent_2d(xy):\n",
     "    x, y = xy\n",
-    "    return 1 / (x**2 + y**2)\n",
+    "    return 1 / (x ** 2 + y ** 2)\n",
+    "\n",
     "\n",
-    "learner = adaptive.Learner2D(f_divergent_2d, [(-1, 1), (-1, 1)], loss_per_triangle=uniform_sampling_2d)\n",
+    "def plot_logz(learner):\n",
+    "    p = learner.plot(tri_alpha=0.3).relabel(\"1 / (x^2 + y^2) in log scale\")\n",
+    "    return p.opts({\"Image\": dict(logz=True), \"EdgePaths\": dict(color=\"w\")})\n",
+    "\n",
+    "\n",
+    "learner = adaptive.Learner2D(\n",
+    "    f_divergent_2d,\n",
+    "    bounds=[(-1, 1), (-1, 1)],\n",
+    "    loss_per_triangle=uniform_sampling_2d,\n",
+    ")\n",
     "\n",
     "# this takes a while, so use the async Runner so we know *something* is happening\n",
     "runner = adaptive.Runner(learner, goal=lambda l: l.loss() < 0.02)\n",
     "runner.live_info()\n",
-    "runner.live_plot(update_interval=0.2,\n",
-    "                 plotter=lambda l: l.plot(tri_alpha=0.3).relabel('1 / (x^2 + y^2) in log scale'))"
+    "runner.live_plot(update_interval=0.2, plotter=plot_logz)"
    ]
   },
   {
@@ -724,8 +734,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%opts EdgePaths (color='w') Image [logz=True]\n",
-    "\n",
     "def resolution_loss(ip, min_distance=0, max_distance=1):\n",
     "    \"\"\"min_distance and max_distance should be in between 0 and 1\n",
     "    because the total area is normalized to 1.\"\"\"\n",
@@ -757,7 +765,7 @@
     "\n",
     "learner = adaptive.Learner2D(f_divergent_2d, [(-1, 1), (-1, 1)], loss_per_triangle=loss)\n",
     "runner = adaptive.BlockingRunner(learner, goal=lambda l: l.loss() < 0.02)\n",
-    "learner.plot(tri_alpha=0.3).relabel('1 / (x^2 + y^2) in log scale')"
+    "plot_logz(learner)"
    ]
   },
   {