anim.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [0, 1, 2 ,3 , 4, 5, 6,7 ,8]\n",
    "y = [0, 3, 2 ,4 , 3, 6, 5,8 ,7]\n",
    "c = [0,1, 2, 3, 4, 5, 6, 7, 8, ]\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(x,y)\n",
    "plt.scatter(x,y,c=c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "6AAAIygAAQAAAQAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAABAAAAA\n",
       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgAAAsB0cmFrAAAAXHRraGQAAAADAAAAAAAAAAAA\n",
       "AAABAAAAAAAAIygAAAAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAA\n",
       "AABAAAAAAoAAAAHgAAAAAAAkZWR0cwAAABxlbHN0AAAAAAAAAAEAACMoAACAAAABAAAAAAI4bWRp\n",
       "YQAAACBtZGhkAAAAAAAAAAAAAAAAAABAAAACQABVxAAAAAAALWhkbHIAAAAAAAAAAHZpZGUAAAAA\n",
       "AAAAAAAAAABWaWRlb0hhbmRsZXIAAAAB421pbmYAAAAUdm1oZAAAAAEAAAAAAAAAAAAAACRkaW5m\n",
       "AAAAHGRyZWYAAAAAAAAAAQAAAAx1cmwgAAAAAQAAAaNzdGJsAAAAt3N0c2QAAAAAAAAAAQAAAKdh\n",
       "dmMxAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAAAAoAB4ABIAAAASAAAAAAAAAABAAAAAAAAAAAAAAAA\n",
       "AAAAAAAAAAAAAAAAAAAAAAAAAAAAGP//AAAANWF2Y0MBZAAW/+EAGGdkABas2UCgPaEAAAMAAQAA\n",
       "AwACDxYtlgEABmjr48siwP34+AAAAAAcdXVpZGtoQPJfJE/FujmlG88DI/MAAAAAAAAAGHN0dHMA\n",
       "AAAAAAAAAQAAAAkAAEAAAAAAFHN0c3MAAAAAAAAAAQAAAAEAAABQY3R0cwAAAAAAAAAIAAAAAQAA\n",
       "gAAAAAABAAEAAAAAAAIAAEAAAAAAAQAAwAAAAAABAABAAAAAAAEAAMAAAAAAAQAAQAAAAAABAACA\n",
       "AAAAABxzdHNjAAAAAAAAAAEAAAABAAAACQAAAAEAAAA4c3RzegAAAAAAAAAAAAAACQAAEukAAASf\n",
       "AAAALwAAAEYAAAQ0AAAARQAABDQAAABgAAACgAAAABRzdGNvAAAAAAAAAAEAAAAwAAAAYnVkdGEA\n",
       "AABabWV0YQAAAAAAAAAhaGRscgAAAAAAAAAAbWRpcmFwcGwAAAAAAAAAAAAAAAAtaWxzdAAAACWp\n",
       "dG9vAAAAHWRhdGEAAAABAAAAAExhdmY1OS4yNy4xMDA=\n",
       "\">\n",
       "  Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "from IPython.display import HTML, display\n",
    "plt.ioff() #needed so the second time you run it you get only single plot\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "line = ax.plot(x, y, color='k')[0]\n",
    "scatter = ax.scatter(x, y, c=c)\n",
    "\n",
    "def update(num):\n",
    "    line.set_data(x[:num], y[:num])\n",
    "    return line,\n",
    "\n",
    "ani = animation.FuncAnimation(fig, update, len(x),\n",
    "                              interval=1000, blit=True)\n",
    "out = HTML(ani.to_html5_video())\n",
    "plt.close()\n",
    "plt.clf()\n",
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.scatter(x,y,c=c)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'TimedAnimation' object has no attribute '_framedata'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn [6], line 5\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmatplotlib\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01manimation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m TimedAnimation\n\u001b[0;32m      3\u001b[0m fig \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mfigure()\n\u001b[1;32m----> 5\u001b[0m anim \u001b[38;5;241m=\u001b[39m \u001b[43mTimedAnimation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minterval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1000\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\miniconda3\\envs\\pytorch311\\Lib\\site-packages\\matplotlib\\animation.py:1417\u001b[0m, in \u001b[0;36mTimedAnimation.__init__\u001b[1;34m(self, fig, interval, repeat_delay, repeat, event_source, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1415\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m event_source \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1416\u001b[0m     event_source \u001b[38;5;241m=\u001b[39m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39mnew_timer(interval\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_interval)\n\u001b[1;32m-> 1417\u001b[0m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__init__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mevent_source\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mevent_source\u001b[49m\u001b[43m,\u001b[49m\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\miniconda3\\envs\\pytorch311\\Lib\\site-packages\\matplotlib\\animation.py:876\u001b[0m, in \u001b[0;36mAnimation.__init__\u001b[1;34m(self, fig, event_source, blit)\u001b[0m\n\u001b[0;32m    870\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_blit \u001b[38;5;241m=\u001b[39m blit \u001b[38;5;129;01mand\u001b[39;00m fig\u001b[38;5;241m.\u001b[39mcanvas\u001b[38;5;241m.\u001b[39msupports_blit\n\u001b[0;32m    872\u001b[0m \u001b[38;5;66;03m# These are the basics of the animation.  The frame sequence represents\u001b[39;00m\n\u001b[0;32m    873\u001b[0m \u001b[38;5;66;03m# information for each frame of the animation and depends on how the\u001b[39;00m\n\u001b[0;32m    874\u001b[0m \u001b[38;5;66;03m# drawing is handled by the subclasses. The event source fires events\u001b[39;00m\n\u001b[0;32m    875\u001b[0m \u001b[38;5;66;03m# that cause the frame sequence to be iterated.\u001b[39;00m\n\u001b[1;32m--> 876\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mframe_seq \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnew_frame_seq\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    877\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mevent_source \u001b[38;5;241m=\u001b[39m event_source\n\u001b[0;32m    879\u001b[0m \u001b[38;5;66;03m# Instead of starting the event source now, we connect to the figure's\u001b[39;00m\n\u001b[0;32m    880\u001b[0m \u001b[38;5;66;03m# draw_event, so that we only start once the figure has been drawn.\u001b[39;00m\n",
      "File \u001b[1;32md:\\miniconda3\\envs\\pytorch311\\Lib\\site-packages\\matplotlib\\animation.py:1129\u001b[0m, in \u001b[0;36mAnimation.new_frame_seq\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1127\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return a new sequence of frame information.\"\"\"\u001b[39;00m\n\u001b[0;32m   1128\u001b[0m \u001b[38;5;66;03m# Default implementation is just an iterator over self._framedata\u001b[39;00m\n\u001b[1;32m-> 1129\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28miter\u001b[39m(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_framedata\u001b[49m)\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'TimedAnimation' object has no attribute '_framedata'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import celluloid"
   ]
  }
 ],
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  "kernelspec": {
   "display_name": "pytorch311",
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    "name": "ipython",
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