3. attractor_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T10:18:50.621549Z",
     "start_time": "2023-05-24T10:18:50.595912Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "Notebook to generate Figure 3 from the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-05-24T16:59:07.597884Z",
     "start_time": "2023-05-24T16:59:06.955390Z"
    },
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'plots_new/3.png'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[2], line 25\u001b[0m\n\u001b[1;32m     23\u001b[0m ax2\u001b[38;5;241m.\u001b[39mset_ylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mAccuracy\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     24\u001b[0m fig\u001b[38;5;241m.\u001b[39mtight_layout()\n\u001b[0;32m---> 25\u001b[0m \u001b[43mfig\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msavefig\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mplots_new/3.png\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m400\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/figure.py:3390\u001b[0m, in \u001b[0;36mFigure.savefig\u001b[0;34m(self, fname, transparent, **kwargs)\u001b[0m\n\u001b[1;32m   3388\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m ax \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes:\n\u001b[1;32m   3389\u001b[0m         _recursively_make_axes_transparent(stack, ax)\n\u001b[0;32m-> 3390\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcanvas\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprint_figure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/backend_bases.py:2193\u001b[0m, in \u001b[0;36mFigureCanvasBase.print_figure\u001b[0;34m(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, pad_inches, bbox_extra_artists, backend, **kwargs)\u001b[0m\n\u001b[1;32m   2189\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2190\u001b[0m     \u001b[38;5;66;03m# _get_renderer may change the figure dpi (as vector formats\u001b[39;00m\n\u001b[1;32m   2191\u001b[0m     \u001b[38;5;66;03m# force the figure dpi to 72), so we need to set it again here.\u001b[39;00m\n\u001b[1;32m   2192\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m cbook\u001b[38;5;241m.\u001b[39m_setattr_cm(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfigure, dpi\u001b[38;5;241m=\u001b[39mdpi):\n\u001b[0;32m-> 2193\u001b[0m         result \u001b[38;5;241m=\u001b[39m \u001b[43mprint_method\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2194\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2195\u001b[0m \u001b[43m            \u001b[49m\u001b[43mfacecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfacecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2196\u001b[0m \u001b[43m            \u001b[49m\u001b[43medgecolor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43medgecolor\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2197\u001b[0m \u001b[43m            \u001b[49m\u001b[43morientation\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43morientation\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2198\u001b[0m \u001b[43m            \u001b[49m\u001b[43mbbox_inches_restore\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m_bbox_inches_restore\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2199\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2200\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m   2201\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bbox_inches \u001b[38;5;129;01mand\u001b[39;00m restore_bbox:\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/backend_bases.py:2043\u001b[0m, in \u001b[0;36mFigureCanvasBase._switch_canvas_and_return_print_method.<locals>.<lambda>\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   2039\u001b[0m     optional_kws \u001b[38;5;241m=\u001b[39m {  \u001b[38;5;66;03m# Passed by print_figure for other renderers.\u001b[39;00m\n\u001b[1;32m   2040\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfacecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124medgecolor\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124morientation\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m   2041\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mbbox_inches_restore\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n\u001b[1;32m   2042\u001b[0m     skip \u001b[38;5;241m=\u001b[39m optional_kws \u001b[38;5;241m-\u001b[39m {\u001b[38;5;241m*\u001b[39minspect\u001b[38;5;241m.\u001b[39msignature(meth)\u001b[38;5;241m.\u001b[39mparameters}\n\u001b[0;32m-> 2043\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mwraps(meth)(\u001b[38;5;28;01mlambda\u001b[39;00m \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs: \u001b[43mmeth\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2044\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m{\u001b[49m\u001b[43mk\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mk\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mskip\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m   2045\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:  \u001b[38;5;66;03m# Let third-parties do as they see fit.\u001b[39;00m\n\u001b[1;32m   2046\u001b[0m     print_method \u001b[38;5;241m=\u001b[39m meth\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:497\u001b[0m, in \u001b[0;36mFigureCanvasAgg.print_png\u001b[0;34m(self, filename_or_obj, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mprint_png\u001b[39m(\u001b[38;5;28mself\u001b[39m, filename_or_obj, \u001b[38;5;241m*\u001b[39m, metadata\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, pil_kwargs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m    451\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    452\u001b[0m \u001b[38;5;124;03m    Write the figure to a PNG file.\u001b[39;00m\n\u001b[1;32m    453\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    495\u001b[0m \u001b[38;5;124;03m        *metadata*, including the default 'Software' key.\u001b[39;00m\n\u001b[1;32m    496\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 497\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_print_pil\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mpng\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/backends/backend_agg.py:446\u001b[0m, in \u001b[0;36mFigureCanvasAgg._print_pil\u001b[0;34m(self, filename_or_obj, fmt, pil_kwargs, metadata)\u001b[0m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;124;03mDraw the canvas, then save it using `.image.imsave` (to which\u001b[39;00m\n\u001b[1;32m    443\u001b[0m \u001b[38;5;124;03m*pil_kwargs* and *metadata* are forwarded).\u001b[39;00m\n\u001b[1;32m    444\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    445\u001b[0m FigureCanvasAgg\u001b[38;5;241m.\u001b[39mdraw(\u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m--> 446\u001b[0m \u001b[43mmpl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimsave\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    447\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuffer_rgba\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfmt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43morigin\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mupper\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m    448\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdpi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfigure\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdpi\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpil_kwargs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/matplotlib/image.py:1656\u001b[0m, in \u001b[0;36mimsave\u001b[0;34m(fname, arr, vmin, vmax, cmap, format, origin, dpi, metadata, pil_kwargs)\u001b[0m\n\u001b[1;32m   1654\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mformat\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mformat\u001b[39m)\n\u001b[1;32m   1655\u001b[0m pil_kwargs\u001b[38;5;241m.\u001b[39msetdefault(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdpi\u001b[39m\u001b[38;5;124m\"\u001b[39m, (dpi, dpi))\n\u001b[0;32m-> 1656\u001b[0m \u001b[43mimage\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfname\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mpil_kwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/nar_env/lib/python3.10/site-packages/PIL/Image.py:2436\u001b[0m, in \u001b[0;36mImage.save\u001b[0;34m(self, fp, format, **params)\u001b[0m\n\u001b[1;32m   2434\u001b[0m         fp \u001b[38;5;241m=\u001b[39m builtins\u001b[38;5;241m.\u001b[39mopen(filename, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr+b\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   2435\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2436\u001b[0m         fp \u001b[38;5;241m=\u001b[39m \u001b[43mbuiltins\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilename\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mw+b\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2438\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   2439\u001b[0m     save_handler(\u001b[38;5;28mself\u001b[39m, fp, filename)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'plots_new/3.png'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4))\n",
    "\n",
    "x = np.arange(6, 14)\n",
    "y = np.array([[0.9497, 0.9456, 0.9387, 0.9329, 0.9241, 0.9171, 0.9073, 0.8955],\n",
    "              [0.9545, 0.9490, 0.9403, 0.9318, 0.9226, 0.9110, 0.8955, 0.8909],\n",
    "              [0.9556, 0.9480, 0.9413, 0.9319, 0.9226, 0.9120, 0.8968, 0.8815]])\n",
    "# ax1.plot(x,y, '--')\n",
    "ax1.set_xticks(np.arange(6,14))\n",
    "ax1.scatter(x, y.mean(axis=0), marker='o', s=20)\n",
    "ax1.errorbar(x, y.mean(axis=0), yerr=y.std(axis=0), linestyle='--', capsize=4)\n",
    "ax1.set_xlabel('Step Count')\n",
    "ax1.set_ylabel('Accuracy')\n",
    "\n",
    "x = np.arange(-3, 5)\n",
    "y = np.array([[0.8198, 0.8949, 0.9308, 0.9368, 0.9286, 0.9200, 0.9147, 0.9129],\n",
    "              [0.8219, 0.8961, 0.9307, 0.9363, 0.9284, 0.9198, 0.9146, 0.9126],\n",
    "              [0.8204, 0.8951, 0.9303, 0.9364, 0.9282, 0.9197, 0.9144, 0.9126]])\n",
    "# ax2.plot(x,y, '--')\n",
    "ax2.set_xticks(np.arange(-3,5))\n",
    "ax2.scatter(x, y.mean(axis=0), marker='o', s=20)\n",
    "ax2.errorbar(x, y.mean(axis=0), yerr=3*y.std(axis=0), linestyle='--', capsize=4)\n",
    "ax2.set_xlabel('Step Offset')\n",
    "ax2.set_ylabel('Accuracy')\n",
    "fig.tight_layout()\n",
    "fig.savefig('plots_new/3.png', dpi=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}