Replace flashing live animation with clean post-run version

Author: shuds13

examples/tutorials/aposmm/aposmm_tutorial_notebook.ipynb

@@ -226,28 +226,7 @@
    "source": [
     "## Run the Ensemble\n",
     "\n",
-    "Optionally run the next cell to set up a live graphic of the optimization progress during execution.\n",
-    "\n",
-    "**WARNING**: The graphic may flicker when the ensemble is running."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Configure to view live progress\n",
-    "from libensemble.tools.live_data.plot2n import Plot2N\n",
-    "\n",
-    "libE_specs[\"live_data\"] = Plot2N(plot_type=\"2d\")  # Alt: '3d'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, set `persis_info` (to provide random seeds to workers) and run the ensemble:"
+    "Finally, set persis_info (to provide random seeds to workers) and run the ensemble."
    ]
   },
   {
@@ -290,29 +269,109 @@
     "     \n",
     "The first six values correspond to the local minima for the Six-Hump Camel simulation function.\n",
     "\n",
-    "The 7th value is a repeat minimum, as APOSMM will continue to start local optimization runs.\n",
-    "\n",
-    "Please see the [API reference](https://libensemble.readthedocs.io/en/main/examples/aposmm.html) for more APOSMM configuration options and other information.\n",
+    "The 7th value is a repeat minimum, as APOSMM will continue to start local optimization runs."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Viewing Animation\n",
     "\n",
+    "The following cell produces a 3D animation showing the random sampling points, \n",
+    "and points produced by the optimization runs,  under the local Minima.  It may take\n",
+    "a few seconds to produce the animation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import matplotlib.animation as animation\n",
+    "from IPython.display import HTML\n",
+    "from matplotlib.lines import Line2D\n",
+    "\n",
+    "def animate_aposmm_3d(H, batch_size):\n",
+    "    x_vals = np.linspace(-2, 2, 50)\n",
+    "    y_vals = np.linspace(-1, 1.1, 50)\n",
+    "    X, Y = np.meshgrid(x_vals, y_vals)\n",
+    "    Z = np.array([six_hump_camel_func(np.array([x, y])) for x, y in zip(X.ravel(), Y.ravel())]).reshape(X.shape)\n",
+    "    fig = plt.figure(figsize=(10, 8))\n",
+    "    ax = fig.add_subplot(111, projection=\"3d\")\n",
+    "    ax.plot_surface(X, Y, Z, cmap=\"winter\", edgecolor=\"none\", alpha=0.3)\n",
+    "    sc_normal = ax.scatter3D([], [], [], s=6, color=\"black\", marker=\"o\", label=\"Point\")\n",
+    "    sc_localp = ax.scatter3D([], [], [], s=40, color=\"red\", marker=\"^\", label=\"Optimization point\")\n",
+    "    custom_M_marker = Line2D([0], [0], linestyle='None', marker='$\\\\mathrm{M}$',\n",
+    "                             markersize=8, markerfacecolor='black', markeredgecolor='black', color='white')\n",
+    "    ax.legend([sc_normal, sc_localp, custom_M_marker], [\"Point\", \"Optimization point\", \"Local minimum\"],loc=\"upper left\")\n",
+    "    fig.tight_layout()\n",
+    "    annotations = []\n",
+    "\n",
+    "    def update(frame):\n",
+    "        for ann in annotations:\n",
+    "            ann.remove()\n",
+    "        annotations.clear()\n",
+    "        end = min((frame + 1) * batch_size, len(H))\n",
+    "        H_sub = H[:end]\n",
+    "        masks = [~H_sub[\"local_pt\"] & ~H_sub[\"local_min\"], H_sub[\"local_pt\"], H_sub[\"local_min\"]]\n",
+    "        (x_n, y_n, f_n), (x_lp, y_lp, f_lp), (x_lm, y_lm, f_lm) = [\n",
+    "            (H_sub[\"x\"][m, 0], H_sub[\"x\"][m, 1], H_sub[\"f\"][m]) for m in masks\n",
+    "        ]\n",
+    "        sc_normal._offsets3d = (x_n, y_n, f_n)\n",
+    "        sc_localp._offsets3d = (x_lp, y_lp, f_lp)\n",
+    "        for i in range(len(x_lm)):\n",
+    "            annotations.append(ax.text(x_lm[i], y_lm[i], f_lm[i], \"M\", color=\"white\", fontsize=12,\n",
+    "                bbox=dict(facecolor=\"black\", alpha=0.7, pad=2), zorder=999\n",
+    "            ))\n",
+    "        return sc_normal, sc_localp\n",
+    "    total_frames = (len(H) + batch_size - 1) // batch_size\n",
+    "    ani = animation.FuncAnimation(fig, update, frames=total_frames, interval=500, blit=False, repeat=False)\n",
+    "    plt.close(fig)\n",
+    "    return HTML(ani.to_jshtml())\n",
+    "\n",
+    "# Reduce batch_size for more refined steps\n",
+    "animate_aposmm_3d(H, batch_size=50)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "## Applications\n",
     "\n",
     "APOSMM is not limited to evaluating minima from pure Python simulation functions.\n",
     "Many common libEnsemble use-cases involve using libEnsemble's Executor to launch user\n",
     "applications with parameters requested by APOSMM, then evaluate their output using\n",
     "APOSMM, and repeat until minima are identified. A currently supported example\n",
-    "can be found in libEnsemble's [WarpX Scaling Test](https://github.com/Libensemble/libensemble/tree/main/libensemble/tests/scaling_tests/warpx)"
+    "can be found in libEnsemble's [WarpX Scaling Test](https://github.com/Libensemble/libensemble/tree/main/libensemble/tests/scaling_tests/warpx)\n",
+    "\n",
+    "Please see the [API reference](https://libensemble.readthedocs.io/en/main/examples/aposmm.html) for more APOSMM configuration options and other information."
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
-   "name": "python"
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }