{
 "metadata": {
  "name": "loading-and-plotting-data"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# let's get started with plotting and loading real data"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = [ 5, 6, 7, 8 ]\n",
      "y = [ 10, 55, 22, 4 ]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x, y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 2,
       "text": [
        "[<matplotlib.lines.Line2D at 0x105f94d50>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD9CAYAAACsq4z3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAJf4fz+nD1CQQ7jlAAAAAElFTkSuQmCC\n"
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file numbers.dat\n",
      "3,20\n",
      "5,55\n",
      "7,22\n",
      "8,50\n",
      "9,20\n",
      "10,30\n",
      "15,45"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting numbers.dat\n"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file numbers2.dat\n",
      "1,2\n",
      "3,4\n",
      "5,6\n",
      "7,8"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Overwriting numbers2.dat\n"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import csv\n",
      "\n",
      "fp = open('numbers.dat')\n",
      "r = csv.reader(fp)\n",
      "x2 = []\n",
      "y2 = []\n",
      "for a, b in r:\n",
      "    a = float(a)\n",
      "    b = float(b)\n",
      "    x2.append(a)\n",
      "    y2.append(b)\n",
      "\n",
      "\n",
      "fp = open('numbers2.dat')\n",
      "r = csv.reader(fp)\n",
      "x3 = []\n",
      "y3 = []\n",
      "for a, b in r:\n",
      "    a = float(a)\n",
      "    b = float(b)\n",
      "    x3.append(a)\n",
      "    y3.append(b)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(x2, y2)\n",
      "plot(x3, y3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 21,
       "text": [
        "[<matplotlib.lines.Line2D at 0x10690de90>]"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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qw/zTT4Hhw/WuzDo6vUWf9Pfxx8DUqea4em+z3T4rT0oK7Hv7srq2PSNGAHv2+H+cYLdz\np5wDr66WaxwyMozdHjQrnpGbgDJ2aBbKattAt1eKi+UGQzV6rTwj9w9nwQOLQW5wZWVyCmT8eL0r\n8dzIkfLHwsLAvq8aFzoV3E3uG86C64NBbnB5efKRat27612J55q2VwJJjQudirvuAu65h7vJPcVZ\ncH0xyA3ObG0VxaxZgZ9eUfOMHGB7xROcBTcGBrmBXbggwyk9Xe9KvJeYKPvVu3cH5v2EkK0cBnlg\ncBbcWBjkBrZhg1yef9ddelfivUC3V06dAv7t3wA1NyozyFvjLLgxMcgNzCw3AbVn5kzgn/+Uf/m1\n5u/Gw7Y03U0e7DgLbmwMcoO6fBnYvh149FG9K/FdbCwQHi5nibWm1vx4U3a7vMgc7LvJW+4F/+IL\n4KGH9K6KmmKQG1RBgfzLY/aLRoFqr6h9oVMRzO0VzoKbB4PcoMzeVlHMnCknb+rrtXuPmzfVv9Cp\nCMYg5yy4+TDIDejaNXkhaepUvSvx39Chch572zbt3uPkSaBPH9nGUVswBTlnwc2LQW5AmzbJuyP7\n99e7EnVo3V5R80agloIhyDkLbn4McgOySltFkZ0t71CtrdXm+Fpc6FTcey/gdltzNzlnwa2DQW4w\ndXVyzGvaNL0rUc+gQXLueMsWbY6v1YVOwJq7yTkLbj0McoPZtg2IiQEGDtS7EnXNmiU3IqpNywud\nCqu0VzgLbl3cR24wVmurKGbMAF55Bbh+Xd2LZ999B4SGans9ISXF/LvJuRfc2nhGbiANDbKXbMUg\nHzBABuLGjeoeV8sLnQozn5FzFjw4MMgN5Msv5dPfhw3TuxJtKBsR1aRlf1xhxt3knAUPLgxyA1m7\n1ppn44rHHgM++0w+01MtgTgjN9Nucs6CBycGuUEIYd3+uKJ/f2DsWHmBTQ03bwL7999+IpGWjN5e\n4Sx4cGOQG0RhIXDHHUBCgt6VaEvNm4NOnADCwoB+/dQ5XkeMGuScBSeAQW4YypOArH4Rato0OU9+\n+bL/xwpEW0VhtCDnLDg11WGQX79+HWPHjkVKSgri4+OxaNEiAEBlZSUcDgdiYmKQnp6O6urqgBRr\nVUJYvz+uCA2VK1Dz8/0/ViAudCqMspucs+DUlg6DvEePHti2bRu++eYbHDx4ENu2bcPOnTuRm5sL\nh8OBoqIipKWlITc3N1D1WtLRo3JRVqDOLvWmVnslkGfkRthNzr3g1J5OWyt33XrOWG1tLRoaGhAa\nGor8/Hzk5OQAAHJycpCXl6dtlRanXOS0eltFkZkJ7NgBVFX5foxAXuhU6NVe4Sw4dabTOztv3ryJ\nkSNH4uTJk3juueeQkJAAt9sN+62rKXa7HW63u83XLl68uPHnqampSE1NVaVoq1m7Vl6wCha9e8v5\n5o8/BubO9e0YRUVybW1YmLq1dUQJ8oyMwLxfeTmwYIHsfy9aJB+bxzFC63G5XHC5XH4dwyaEZ12/\nS5cuYdKkSVi6dCmmT5+OqianU2FhYaisrGx+YJsNHh46qJ06Bdx/P1BWFlw3a6xZA6xaJUPKF++/\nD6xfL48TKB9+KN9v7Vrt30sI+eDt2Fh5Sz3HCIOHL9np8dRKnz59MGXKFOzbtw92ux0VFRUAgPLy\nckSo+ejyIPPxx0BWVnCFOABMmQJ89RVw/rxvrw/khU5FIFsrBQXyi/vrrzPEqXMdBvmFCxcaJ1Ku\nXbuGzZs3Y8SIEcjMzITT6QQAOJ1OZGVlaV+pRQXLtEpLPXsCkyf7fnYbyAudikDtJq+rA/7jP4A/\n/QkI4Vo78kCHQV5eXo4JEyYgJSUFY8eORUZGBtLS0rBw4UJs3rwZMTEx2Lp1KxYuXBioei2lrEze\n9j1+vN6V6MPX6ZWGhsBf6AQCt5v8f/9X7nCfPFnb9yHr8LhH7vWB2SPv1FtvyUVZq1frXYk+rl+X\nWxGPHJE/euroUXnB8bvvtKutPc89B8THA7/6lTbHr66+/RCOpCRt3oOMTdMeOalv3Tp5N2ew6tFD\nBvJHH3n3Oi0f7dYZrfvkr70mH7rNECdvMMh1cvGi3IuRnq53Jfry5clBe/fqd/OUlkF+8iTwzjvA\nq69qc3yyLga5TvLz5XjZrfutgtbEiXJXdmmp56/R84xcy93kCxcCv/mN3ElP5A0GuU6Cva2i6N5d\njl96Og/e0CDPiAN9oVOh1W7ynTvl4+ReeEHd41JwYJDr4IcfgO3b5Sw1eTe9cvy4PGPt21fbmjqi\ndnvl5k15Jr50KXDnneodl4IHg1wHn34qlx/xRg9p/Hjg++9lj7gzetwI1JLaQf6Pf8i9KbNmqXdM\nCi4Mch1Y/UlA3goJkW0mT9oretwI1JKaQV5TI/eo/Nd/AV34t5F8xD86AXbtmnwQwNSpeldiLJ62\nV/S80KlQczf5f/83MG4c8MAD/h+LgheDPMA2bZIX6vr317sSY3nwQbl3paOLiHpf6FSotZu8okIG\nOdf5k78Y5AHGtkrbunYFfv7zjs/Kjx0D7r4b6NMncHW1R432yssvyzW+fDwb+YtBHkB1dfIxXdOm\n6V2JMSntlfZaFka40KnwN8gPHAA2bAB+9zv1aqLgxSAPIJcLiIkBBg7UuxJjGjcOuHoVOHy47d83\nwoVOhT9BLgTw4ovAK6/oO0ZJ1sEgD6BgXVnrKZtNnpW3d8u+Vc7IlV3j//7v6tZEwYvbDwOkoUGe\niX/xBTBsmN7VGNe+fTLMT5xo/kzK+np59lpWZoz5+4YG2as/e9a7nn1dHZCcDKxYATzyiHb1kXlx\n+6GBffmlvCORId4xZSKlsLD5x48dk18IjRDigO+7yblrnLTAIA8QtlU80157Rc+Nh+3xtr1SVSU3\nG65Y0fy7DSJ/McgDQAiOHXpj1ix5l2fT7y6NcCNQS94GOXeNk1YY5AFQWCgfopCQoHcl5pCYKJ/p\nuXv37Y8Z6UKnwpsgP3kSePdd7honbTDIA0A5G+e3055R2ivKzUH19bIXPWKEvnW15M1u8gULuGuc\ntMMg15gQ7I/7YuZM2V5paJDP9Bw0yDgXOhWe7ib//HP5NCjuGietMMg1dvSo3HBntAt1RhcbC0RE\nyAcuGOlGoJY6a69w1zgFAoNcY2yr+E5prxjxQqeisyD/v/+T62m5a5y0xCDXGNsqvps5U/73273b\nnGfk3DVOgcI7OzV06hRw//3ybsSuXfWuxpx+/GM5sXL5MtCrl97VtOZ2A/HxwIULrb/r+s//lBdp\nPX0eKRHgW3aGaFQLAfj4Y/lgYYa472bOlM84NWKIA813kw8adPvj5eVy1/jXX+tXGwUPfsOnId4E\n5L9584C33tK7io611V55+WVZO3eNUyCwtaKRsjJ5Y0tFhTxjI+tatEiOIr78svz1gQNAejpw/DjX\n1JL3uDTLQPLygEcfZYgHg6Zn5ELIccPf/54hToHTYZCXlpZi/PjxSEhIQGJiIt58800AQGVlJRwO\nB2JiYpCeno7q6uqAFGsmbKsEj6ZB/umnsj/OXeMUSB22VioqKlBRUYGUlBRcuXIFo0aNQl5eHt55\n5x30798fv/3tb7Fs2TJUVVUht8UTZIO5tXLxouyNlpfLb7nJ2pTd5N9/DzzwgBw35K5x8pXqrZXI\nyEikpKQAAHr16oW4uDicPXsW+fn5yMnJAQDk5OQgLy/Px5KtKT8fcDgY4sFC2U0+fz4weDB3jVPg\neTx+WFJSgv3792Ps2LFwu92w2+0AALvdDrfb3eZrFi9e3Pjz1NRUpKam+lWsWaxbB8yerXcVFEgp\nKfKhEd98w7t4yTsulwsul8uvY3g0tXLlyhU8/PDDePnll5GVlYXQ0FBUVVU1/n5YWBgqKyubHzhI\nWyv19XJd7ddfG2/JE2lnwwb5FKglS/SuhMxOkxuC6urq8Nhjj+Gpp55CVlYWAHkWXlFRgcjISJSX\nlyMiIsK3ii0oJERuw+NZWXDJyJD/EOmhwx65EALz5s1DfHw8nn/++caPZ2Zmwul0AgCcTmdjwJPE\nECeiQOqwtbJz50489NBDSE5Ohu1WOi1duhQ//vGPkZ2djdOnTyM6Ohpr1qxB3xZDs8HaWiEi8ocv\n2ck7O4mIDIR3dhIRBSEGORGRyTHIiYhMjkFORGRyDHIiIpNjkBMRmRyDnIjI5BjkREQmxyAnIjI5\nBjkRkckxyImITI5BTkRkcgxyIiKTY5ATEZkcg5yIyOQY5EREJscgJyIyOQY5EZHJMciJiEyOQU5E\nZHIMciIik2OQExGZHIOciMjkGORERCbHICciMjkGORGRyTHIiYhMjkFORGRyDHIiIpPrMMjnzp0L\nu92OpKSkxo9VVlbC4XAgJiYG6enpqK6u1rxIIiJqX4dB/vTTT2Pjxo3NPpabmwuHw4GioiKkpaUh\nNzdX0wKJiKhjNiGE6OgTSkpKkJGRgUOHDgEAYmNjsX37dtjtdlRUVCA1NRXHjh1rfWCbDZ0cmoiI\nWvAlO0O8fRO32w273Q4AsNvtcLvd7X7u4sWLG3+empqK1NRUb9+OiMjSXC4XXC6XX8fw+ow8NDQU\nVVVVjb8fFhaGysrK1gfmGTkRkdd8yU6vp1aUlgoAlJeXIyIiwttDEBGRirwO8szMTDidTgCA0+lE\nVlaW6kUREZHnOmytPP7449i+fTsuXLgAu92OV199FVOnTkV2djZOnz6N6OhorFmzBn379m19YLZW\niIi85kt2dtojD2QxRETBLiA9ciIiMhavxw+JAqH+Zj1OXDyBg+6DsNlsyE7I1rskIsNikJOuhBBw\nX3XjoPsgDrkP4eA5+eOxC8cwsPdAJEUkIX1Yut5lEhkae+QUMDV1Nfj23Lc4dO6QDO5bP94UN5Fs\nT0ayPRlJEUlIticjITwBPbv31LtkooDjxU4yhJviJk5VnZJn2E0C+8zlM4jpF9MssJPsSRjQawBs\nNpveZRMZAoOcAu5izcVWZ9jfnvsW/e7q1zywI5IQ0y8G3bp207tkIkNjkJNmbtTfwLELx5oF9qFz\nh3Cl9gqSIpKQZE9CcoQ8w06MSETfHq3vLSCizjHIyW9CCJReLm3VFjlZdRJDQ4c2O8NOtidjcJ/B\nbIsQqYhBTl65fOMyDp873Pws230Id3a7s9XFx9j+segR0kPvkoksj0FObWo6k920LXLu6jkkhCc0\na4skRSQhvGe43iUTBS0GeZDzZCa76Vn20NCh6Nqlq95lE1ETDPIgwplsImtikFuQMpPd8iy79HIp\nftTvR5zJJrIYBrnJXay52KqPzZlsouDCIDeJG/U3cPTC0WZn2AfdB3G17ioSIxKbhTZnsomCC4Pc\nYIQQOH3pdKs+9qmqUxjSd0irtsg9fe5hW4QoyDHIdXTp+qVWM9mHzx1unMlu2haJC4/jTDYRtYlB\nHgD1N+tRdLGoVVvkfM35xpnspqHNmWwi8gaDXEVCCFRcqWjVFjl+4ThnsolIMwxyHykz2S0nRpSZ\n7KahnRCRgF7de+ldMhFZFIO8E+3tyW45k51kl8HNmWwiCjQGeRPck01EZhSUQa7syW4Z2ldqr3Am\nm4hMx/JBXnqpFAfdB1vtyeZMNhFZheWD/JH3H0GDaOBMNhFZluWDnIjI6nzJzi4a1UJERAES9EHu\ncrn0LqFTZqgRYJ1qY53qMkudvvA5yDdu3IjY2Fjcd999WLZsmZo1BZQZ/ueaoUaAdaqNdarLLHX6\nwqcgb2howC9/+Uts3LgRR44cwT/+8Q8cPXpU7dqIiMgDPgX5nj17cO+99yI6OhrdunXDrFmzsH79\nerVrIyIiD/g0tfLRRx/hX//6F/72t78BAP7+97/jq6++wsqVK28fmDPcREQ+8TaWQ3x5E09CmqOH\nRESB4VNrZeDAgSgtLW38dWlpKaKiolQrioiIPOdTkI8ePRonTpxASUkJamtr8eGHHyIzM1Pt2oiI\nyAM+tVZCQkLw5z//GZMmTUJDQwPmzZuHuLg4tWsjIiIP+DxHPnnyZBw/fhzfffcdFi1a1Oz3zDBj\nXlpaivHjxyMhIQGJiYl488039S6pXQ0NDRgxYgQyMjL0LqVd1dXVmDFjBuLi4hAfH4/du3frXVKb\nli5dioSEBCQlJWH27Nm4ceOG3iUBAObOnQu73Y6kpKTGj1VWVsLhcCAmJgbp6emorq7WsUKprTpf\neuklxMXXhqRXAAAFRUlEQVTFYfjw4Zg+fTouXbqkY4VSW3UqVqxYgS5duqCyslKHypprr86VK1ci\nLi4OiYmJWLBgQecHEiqrr68Xw4YNE8XFxaK2tlYMHz5cHDlyRO238Vt5ebnYv3+/EEKIH374QcTE\nxBiyTiGEWLFihZg9e7bIyMjQu5R2zZkzR6xatUoIIURdXZ2orq7WuaLWiouLxZAhQ8T169eFEEJk\nZ2eLd999V+eqpB07dojCwkKRmJjY+LGXXnpJLFu2TAghRG5urliwYIFe5TVqq85NmzaJhoYGIYQQ\nCxYsMGydQghx+vRpMWnSJBEdHS0uXryoU3W3tVXn1q1bxcSJE0Vtba0QQohz5851ehzVb9E3y4x5\nZGQkUlJSAAC9evVCXFwcysrKdK6qtTNnzqCgoADPPPOMYSeBLl26hM8//xxz584FIFtvffr00bmq\n1nr37o1u3bqhpqYG9fX1qKmpwcCBA/UuCwDw05/+FKGhoc0+lp+fj5ycHABATk4O8vLy9Citmbbq\ndDgc6NJFRsnYsWNx5swZPUprpq06AeA3v/kN/vjHP+pQUdvaqvMvf/kLFi1ahG7d5MNuwsM7f4C7\n6kF+9uxZDBo0qPHXUVFROHv2rNpvo6qSkhLs378fY8eO1buUVl544QUsX7688S+KERUXFyM8PBxP\nP/00Ro4ciWeffRY1NTV6l9VKWFgYXnzxRQwePBh33303+vbti4kTJ+pdVrvcbjfsdjsAwG63w+12\n61xR595++2088sgjepfRpvXr1yMqKgrJycl6l9KhEydOYMeOHRg3bhxSU1Oxd+/eTl+jejqY7Uag\nK1euYMaMGXjjjTfQq5exHqr8ySefICIiAiNGjDDs2TgA1NfXo7CwEPPnz0dhYSF69uyJ3Nxcvctq\n5eTJk3j99ddRUlKCsrIyXLlyBe+//77eZXnEZrMZ/u/Wa6+9hu7du2P27Nl6l9JKTU0NlixZgj/8\n4Q+NHzPq36n6+npUVVVh9+7dWL58ObKzszt9jepBbqYZ87q6Ojz22GN48sknkZWVpXc5rezatQv5\n+fkYMmQIHn/8cWzduhVz5szRu6xWoqKiEBUVhTFjxgAAZsyYgcLCQp2ram3v3r34yU9+gn79+iEk\nJATTp0/Hrl279C6rXXa7HRUVFQCA8vJyRERE6FxR+959910UFBQY9gvjyZMnUVJSguHDh2PIkCE4\nc+YMRo0ahXPnzuldWitRUVGYPn06AGDMmDHo0qULLl682OFrVA9ys8yYCyEwb948xMfH4/nnn9e7\nnDYtWbIEpaWlKC4uxgcffIAJEybgvffe07usViIjIzFo0CAUFRUBALZs2YKEhASdq2otNjYWu3fv\nxrVr1yCEwJYtWxAfH693We3KzMyE0+kEADidTkOebABySm358uVYv349evQw5tO6kpKS4Ha7UVxc\njOLiYkRFRaGwsNCQXxyzsrKwdetWAEBRURFqa2vRr1+/jl+kxZXYgoICERMTI4YNGyaWLFmixVv4\n7fPPPxc2m00MHz5cpKSkiJSUFPHZZ5/pXVa7XC6XoadWvvnmGzF69GiRnJwspk2bZsipFSGEWLZs\nmYiPjxeJiYlizpw5jZMBeps1a5YYMGCA6Natm4iKihJvv/22uHjxokhLSxP33XefcDgcoqqqSu8y\nW9W5atUqce+994rBgwc3/j167rnn9C6zsc7u3bs3/vdsasiQIYaYWmmrztraWvHkk0+KxMREMXLk\nSLFt27ZOj6PZo96IiCgwjDsKQUREHmGQExGZHIOciMjkGORERCbHICciMjkGORGRyf0/tOQXpA0h\nXZMAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num1 = numpy.loadtxt?"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%%file numbers3.dat\n",
      "10,20,30\n",
      "30,40,50\n",
      "50,60,50\n",
      "70,80,70"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Writing numbers3.dat\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "num1 = numpy.loadtxt('numbers.dat', delimiter=',')\n",
      "num2 = numpy.loadtxt('numbers2.dat', delimiter=',')\n",
      "num3 = numpy.loadtxt('numbers3.dat', delimiter=',')\n",
      "num4 = num3*2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(num1[:,0], num1[:,1], label='num1')\n",
      "plot(num2[:,0], num2[:,1], label='num2')\n",
      "plot(num3[:,0], num3[:,2], label='num3')\n",
      "plot(num4[:,0], num4[:,2], label='num4')\n",
      "legend()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "<matplotlib.legend.Legend at 0x106f39710>"
       ]
      },
      {
       "output_type": "display_data",
       "png": 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M7HPdtYa1uH307Ypo8kRESidpsj9y5AgmTJiAmJgYBAYG4sknn0RJSUm/19WM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\nsyci8gNs9kREfoDNnojID/x/YZGiDiqrkVIAAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}