11 Imports and Exports.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        plt.show()\n",
    "    def internationalTrade(self,worldPrice):\n",
    "        self.mode = \"International\"\n",
    "        self.worldPrice = worldPrice\n",
    "        if worldPrice>self.startingP:\n",
    "            self.export = True\n",
    "            self.domesticQ = self.demandEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.supplyEquation.subs(p,worldPrice)-self.demandEquation.subs(p,worldPrice)\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        else:\n",
    "            self.export = False\n",
    "            self.domesticQ = self.supplyEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.demandEquation.subs(p,worldPrice)-self.domesticQ\n",
    "            self.totalQ = self.domesticQ+self.internationalQ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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QT+tRu49mVEp1B3oD1YGTWutf/rm4JKEQTnLt2jX69+9P8eLFOX36NKtXr2bl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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10904cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "True\n",
      "4\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "economy1 = supplyDemand(10-p,p)\n",
    "economy1.plot()\n",
    "economy1.internationalTrade(6)\n",
    "print(economy1.startingQ)\n",
    "print(economy1.export)\n",
    "print(economy1.domesticQ)\n",
    "print(economy1.totalQ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dOnXo2LEj//vf/1Ll/YV4UU8bPuqslOoLfAzUAX7VWv/y18UmCYWwkrt37zJk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\nvwF/1CPwB6uW1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10904ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    def internationalTrade(self,worldPrice):\n",
    "        self.mode = \"International\"\n",
    "        self.worldPrice = worldPrice\n",
    "        if worldPrice>self.startingP:\n",
    "            self.export = True\n",
    "            self.domesticQ = self.demandEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.supplyEquation.subs(p,worldPrice)-self.demandEquation.subs(p,worldPrice)\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        else:\n",
    "            self.export = False\n",
    "            self.domesticQ = self.supplyEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.demandEquation.subs(p,worldPrice)-self.domesticQ\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        if self.mode==\"International\":\n",
    "            plt.plot([self.totalQ,self.priceEnd],[self.worldPrice,self.worldPrice],'k')\n",
    "            if self.startingP!=self.worldPrice:\n",
    "                plt.plot(self.totalQ,self.worldPrice, 'bo')\n",
    "                plt.plot(self.domesticQ,self.worldPrice,'bo')\n",
    "                plt.plot([0,self.domesticQ],[self.worldPrice,self.worldPrice],label='Domestic Quantity')\n",
    "                plt.plot([self.domesticQ,self.totalQ],[self.worldPrice,self.worldPrice], label='International Quantity')\n",
    "                plt.legend()\n",
    "        plt.show()\n",
    "economy1 = supplyDemand(10-p,p)\n",
    "economy1.internationalTrade(6)\n",
    "economy1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The country exports during international trade, and the price moves from 5 to 6.\n",
      "The domestic producers go from producing 5 units to 6 units.\n",
      "The domestic consumers go from consuming 5 units to 4 units.\n",
      "Total domestic producer revenue is 36\n"
     ]
    },
    {
     "data": {
      "image/png": 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dOnXo2LEj//vf/1Ll/YV4UU8bPuqslOoLfAzUAX7VWv/y18UmCYWwkrt37zJk\nyBDKlCnDxYsX+fbbb1mzZg0FChSwyvIqVKhAREQEn376KUuXLsVkMvHtt99aZVlCPIunbREsBnyA\naKAuD88wFiLNO3DgAJ6enowbN4727dsTFxdHkyZNrL5cZ2dnxowZQ2hoKAULFkw+N+HixYtWX7YQ\n/+VphcCkH7acnsPDthKVbJBJCKu5efMmvXr1olKlSty/f5/t27cTGBhIrly5bJrDy8uLw4cPM27c\nOLZs2YLJZGLRokV/DdkWwqaeVgiST4/UWsdbOYsQVrVt2zbc3d35+uuv+fDDD4mOjuadd94xLE+m\nTJkYNGgQUVFRmM1mOnXqRO3atTlz5oxhmUTG9LRC4KmUupF0uQl4/HVdKXXDFgGFeFFXr16lQ4cO\n1KlTBxcXFw4cOMD06dN56aWXjI4GwJtvvsmePXuYOXMmwcHBuLu78+WXX5KQINOCC9t4YiHQWjtq\nrXMkXbJrrTP97XoOW4UU4nlorVm7di0mk4nly5czdOhQIiIiKF++vNHR/sXBwYGAgABiY2OpXLky\nffr0oXLlysTFydTgwvqkebpIly5evEjTpk1p3rw5r7/+OqGhoYwePRpnZ2ejoz1R4cKF2bJlC998\n8w3Hjx/Hy8uLzz//XJrYCasyrBAopRyVUhFKqc1GZRDpj9aahQsXYjKZ2Lp1K+PHjyckJARPT0+j\no6WYUoq2bdsSFxdH48aNGTp0KGXKlCE8PNzoaCKdMnKLoA8g270i1fz888/UqlWLzp07YzabiYyM\nZODAgWTKlNL5l+xLvnz5WLVqFevXr+fSpUuULVuWwYMHc+fOHaOjiXTGkEKglCoE1APmG7F8kb4k\nJCTw5Zdf4u7uTkhICF9//TV79uzhzTffNDpaqmjcuDEWi4WOHTsyfvx4PD092bdvn9GxRDpi1BbB\nNGAgkGjQ8kU6YbFYqFSpEn369KFKlSrExsbSo0ePZ24SZ+9efvll5s+fz86dO4mPj6dKlSr07NmT\nGzdk8J54cTb/bVFK1Qcuaa3DnvI8f6VUqFIq9PLlyzZKJ9KKBw8eMGbMGLy9vfnxxx/55ptv2LJl\nC4ULFzY6mlXVqFGD6Oho+vbty6xZs3B3d2fr1q1GxxJpnBFfmyoADZVSZ4CVQHWl1NJ/PklrPVdr\n7aO19smbN6+tMwo7FhYWho+PD8OGDeO9994jLi6Otm3bplqTOHuXLVs2pk6dyqFDh8iePTvvvvsu\n7dq148qJVeLOAAAgAElEQVSVK0ZHE2mUzQuB1nqI1rqQ1roo0BLYrbVua+scIu25c+cOgwYNomzZ\nsly+fJnvvvuOlStXki9fPqOjGcLPz4/w8HA+++wzVq5ciclkYvXq1dKmQjyz9LUjVaRb+/btw9PT\nkwkTJtC5c2csFguNGjUyOpbhsmTJwsiRIwkLC6NIkSK0aNGC9957jwsXLhgdTaQhhhYCrfUerXV9\nIzMI+3bjxg0CAgKoUqUK8fHx7Ny5k3nz5vHyyy8bHc2ueHh4EBwczMSJE9m2bRsmk4kFCxbI1oFI\nEdkiEHbr+++/x83NjTlz5tCvXz+io6OpUaOG0bHsVqZMmRgwYADR0dF4eXnRtWtXatasyenTp42O\nJuycFAJhd65cuULbtm2pV68eOXLk4NChQ0yePJls2bIZHS1NKFGiBLt372bOnDkcPXoUd3d3pk6d\nKk3sxH+SQiDshtaaVatWYTKZWLVqFcOHDyc8PBxfX1+jo6U5Dg4O+Pv7Y7FYqF69Ov369aNChQrE\nxsYaHU3YISkEwi5cuHCBxo0b07JlS4oUKUJ4eDgjRowgS5YsRkdL0woVKsSmTZtYvnw5p06dwtvb\nm1GjRnH//n2jowk7IoVAGEprzfz58zGZTGzfvp1JkyYRHByM2Ww2Olq6oZSiVatWWCwWmjVrxvDh\nwyldujRHjx41OpqwE1IIhGFOnz5NzZo16datG97e3kRHR9O/f/802yTO3uXNm5fly5ezceNG/vjj\nD/z8/BgwYAC3b982OpowmBQCYXMJCQlMnToVd3d3QkNDmTNnDrt27aJEiRJGR8sQGjRoQGxsLN26\ndWPy5Ml4eHiwZ88eo2MJA0khEDYVExND+fLl6devHzVq1CA2NhZ/f/901yTO3uXMmZPZs2eze/du\nAKpVq8YHH3zA9evXDU4mjCC/fcIm7t+/z8iRIylVqhSnT59O3kVRqFAho6NlaNWqVSMqKooBAwYw\nf/583Nzc2LxZ5orKaKQQCKs7evQopUuXZsSIETRv3hyLxUKrVq0yTJM4e+fi4sLEiRMJDg4mV65c\nNGjQgNatWyNdfzMOKQTCam7fvs2AAQPw8/Pjjz/+YOPGjSxbtgzpJmufypYtS1hYGCNHjmTt2rW4\nurqyfPlyaVORAUghEFaxZ88ePDw8mDx5Mt26dSM2NpYGDRoYHUs8hZOTE5999hkRERGUKFGCNm3a\n0LBhQ86fP290NGFFUghEqrp+/ToffPAB1apVAyAoKIjZs2eTM2dOg5OJZ+Hm5sbBgweZMmUKu3bt\nwmQyMWfOHBITZVLB9EgKgUg1mzdvxs3Njfnz5zNgwACioqKoWrWq0bHEc3J0dOSjjz4iJiaGMmXK\n0L17d2rUqMHJkyeNjiZSmRQC8cIuX75M69atadCgAa+88gohISFMnDgRFxcXo6OJVFC8ePHk9t/h\n4eGYzWYmTZpEfHy80dFEKpFCIJ6b1prly5fj6urK2rVrGTFiBKGhoZQpU8boaCKVKaXo2rUrFouF\nWrVq8fHHH1OuXDmioqKMjiZSgRQC8VzOnz9Pw4YNadOmDSVKlCAiIoLhw4fj5ORkdDRhRQULFkye\nIvSXX36hdOnSDB8+nHv37hkdTbwAKQTimSQmJjJnzhxMJhO7d+9m6tSpHDx4EDc3N6OjCRtRStGi\nRQssFgstW7Zk1KhRlCpVipCQEKOjieckhUCk2MmTJ6lRowbdu3enbNmyREdH07dvXxwdHY2OJgyQ\nJ08evvnmG7Zs2cKNGzeSW4f8+eefRkcTz0gKgXiq+Ph4Jk2ahNlsJiIigvnz57Njxw6KFy9udDRh\nB959911iY2Pp3r07U6dOxWw2s2vXLqNjiWcghUA8UVRUFOXKlePjjz+mdu3aWCwWunTpIu0hxCNy\n5MjB119/zd69e8mUKVNye/Fr164ZHU2kgBQC8Vj37t1LnsDkl19+YdWqVaxfv57XXnvN6GjCjlWu\nXJnIyEgGDhxIYGAgJpOJDRs2GB1LPIUUAvEvISEhlCpVilGjRtGyZUvi4uJ4//33ZStApEjWrFkZ\nP348hw8fJm/evMlTkF66dMnoaOI/SCEQyf7880/69etH+fLluXnzJlu2bOGbb74hd+7cRkcTaZCP\njw+hoaGMGTOG9evX4+rqytKlS6WJnR2SQiAA2LVrF2azmalTp9KjRw9iYmJ49913jY4l0rjMmTPz\n6aefcuzYMd566y3atWtHvXr1OHv2rNHRxN9IIcjgrl27Rrdu3ahZsyaZMmVi7969zJw5kxw5chgd\nTaQjrq6u7N+/n+nTp7N3717c3NyYNWuWNLGzE1IIMrANGzZgMplYuHAhgwYNIjIyksqVKxsdS6RT\njo6OfPjhh8TExODn50dAQABVq1blp59+MjpahieFIAP6/fffadGiBY0bNyZfvnwcPnyYcePGkTVr\nVqOjiQygWLFibN++ncDAQKKjo/H09GTChAnSxM5AUggyEK01S5cuxWQy8d133zFmzJjkaSSFsCWl\nFJ06dcJisVC3bl0GDRqEr68vkZGRRkfLkKQQZBBnz56lXr16tGvXjrfeeotjx47x6aefkjlzZqOj\niQzs1VdfZd26daxdu5Zff/0VHx8fhg0bJk3sbEwKQTqXmJjIrFmzcHNzY9++fXz55Zfs378fV1dX\no6MJkaxp06ZYLBbatGnDmDFj8Pb2Jjg42OhYGYbNC4FS6nWlVJBSyqKUilVK9bF1hozip59+omrV\nqgQEBFCuXDliYmLo3bu3NIkTdumVV15h0aJF/PDDD/z5559UqFCBvn37cuvWLaOjpXtGbBHEA/21\n1ibAD+iplDIZkCPdio+PZ8KECXh6ehIdHc3ChQvZtm0bRYsWNTqaEE9Vu3ZtYmJi6NmzJ9OnT8ds\nNrNjxw6jY6VrNi8EWuuLWuvwpOs3gTigoK1zpFeRkZH4+voyaNAg6tati8VioWPHjtIeQqQp2bNn\n56uvvmL//v1kyZKFWrVq0blzZ/744w+jo6VLhh4jUEoVBbyBw495zF8pFaqUCr18+bKto6U5d+/e\nZejQofj4+PDrr7+ydu1a1q1bx6uvvmp0NCGeW8WKFTl27BhDhgxhyZIlmEwm1q9fb3SsdMewQqCU\negn4Fuirtb7xz8e11nO11j5aa5+8efPaPmAacujQIby9vfn8889p06YNFouFpk2bGh1LiFTh7OzM\nF198wdGjRylQoABNmjShefPm/Pbbb0ZHSzcMKQRKqcw8LALLtNbrjMiQHty6dYs+ffpQsWJFbt++\nzQ8//MCiRYt45ZVXjI4mRKrz9vbmyJEjfPHFF2zatAmTycSSJUukiV0qMGLUkAIWAHFa6ym2Xn56\nsX37dtzd3fnqq6/o2bMnMTEx1K5d2+hYQlhV5syZGTJkCMeOHcNkMtGhQwfq1q3LL7/8YnS0NM2I\nLYIKQDugulLqWNJF2lym0B9//EGnTp2oXbs2zs7O7Nu3j6+++ors2bMbHU0Im3n77beTP/sHDhzA\n3d2dmTNnShO752TEqKEDWmultfbQWnslXb63dY60aN26dZhMJr755pvkb0UVK1Y0OpYQhnBwcKBX\nr17ExsZSoUIFevXqReXKlfnxxx+NjpbmyJnFacBvv/1Gs2bNaNq0KQUKFODo0aN88cUXODs7Gx1N\nCMMVKVKErVu3snjxYiwWC56enowdO5YHDx4YHS3NkEJgx7TWLFq0CJPJxObNmxk7dixHjhzB29vb\n6GhC2BWlFO3btycuLo4GDRrwySef4OvrS0REhNHR0gQpBHbqzJkz1KlTh06dOuHm5kZkZCSDBw+W\nJnFCPEH+/PlZs2YN3377LRcvXqRMmTJ88skn3L171+hodk0KgZ1JTEzkq6++wt3dnUOHDjFjxgz2\n7t3LW2+9ZXQ0IdKMJk2aYLFYaN++PWPHjsXLy4sDBw4YHctuSSGwI8ePH6dy5cp8+OGHVKxYMbnf\nioOD/DcJ8axy5cpFYGAg27dv5969e1SqVIlevXpx8+ZNo6PZHfkLYwcePHjAF198gaenJxaLhcWL\nF7N161aKFClidDQh0rx33nmH6Oho+vTpw9dff427uzvbtm0zOpZdUWnhrDwfHx8dGhpqdAyrCA8P\np0uXLhw7doxmzZoxY8YM8ufPb3Qs4GGBOn/+vOxfFc/N2dmZQoUK2c2xreDgYLp06UJcXBzt27dn\n6tSp6fpMfKVUmNba56lP1Frb/aV06dI6vbl9+7YePHiwdnR01AUKFNDr1q0zOtK/nD59Wl++fFkn\nJiYaHUWkQYmJifry5cv69OnTRkd5xN27d/XQoUN1pkyZdL58+fSaNWuMjmQ1QKhOwd9Y2TVkgAMH\nDuDl5cW4cePo0KEDFouF9957z+hY/3L37l1y584tLazFc1FKkTt3brvbosySJQujR48mNDSU119/\nnebNm9OkSRMuXrxodDTDSCGwoZs3b9KrVy8qVarE/fv32bFjBwsWLCBXrlxGR/tPUgTEi7Dnz4+n\npychISGMHz+erVu3YjKZWLhwYYZsYieFwEZ++OEH3N3d+frrr+nTpw/R0dHUrFnT6Fh2z9HRES8v\nL9zc3PD09GTy5MmG9JM5duwY33///51QNm7cyLhx41L8+uvXr9O+fXtKlCjBG2+8QZs2bawyycqT\ncn733XdYLJZUX2ZalilTJgYOHEhkZCQeHh507tyZWrVq8fPPPxsdzaakEFjZ1atXkzskZsuWjYMH\nDzJt2jReeuklo6OlCVmzZuXYsWPExsayY8cOtm7dysiRI22e459/YBs2bMjgwYNT/PouXbpQvHhx\nTp48yalTpyhRogQdO3a0aU4pBP/tzTffJCgoiFmzZnH48GHc3d358ssvSUhIMDqabaTkQILRl7R4\nsDgxMVGvXr1a58uXT2fKlEkPGzZM37171+hYz8RisRgdQWfLlu2R26dOndKvvPKKTkxM1Hfu3NEd\nO3bU7u7u2svLS+/evVtrrfXChQt1o0aNdM2aNXWRIkX0V199pSdPnqy9vLy0r6+vvnr1qtZa65Mn\nT+ratWvrUqVK6YoVK+q4uDittdarV6/Wbm5u2sPDQ1eqVEnfu3dPv/766zpPnjza09NTr1y5Ui9c\nuFD37NlTa631b7/9phs3bqw9PDy0h4eHPnjw4COZT5w4oYsWLarj4+OT74uPj9dFihTRx48f10FB\nQbpevXrJj/Xs2VMvXLhQa631yJEjtY+Pj3Zzc9PdunVLPnBfpUoVPXDgQF2mTBldsmRJvW/fvifm\nPHjwoM6VK5cuWrSo9vT01CdPntTe3t7Jy/zpp58euZ2a7OFz9CzOnj2r69atqwFdrly5NJf/70jh\nweJMRhei9OjixYsEBATw3XffUbp0abZv346np6fRsV7IyE2xWC78ayK5F2J6LQfDG7g902uKFy9O\nQkICly5dYunSpSiliI6O5vjx49SqVYuffvoJgJiYGCIiIrh79y4lSpRg/PjxRERE8NFHH7FkyRL6\n9u2Lv78/s2fPpmTJkhw+fJiAgAB2797NqFGj2LZtGwULFuTatWs4OTkxatQoQkNDmTFjBgCLFi1K\nzvThhx9SpUoV1q9fT0JCArdu3Xoks8ViwcvLC0dHx+T7HB0d8fb2Ji4ujpdffvk/17dXr1589tln\nALRr147NmzfToEEDAOLj4zly5Ajff/89I0eOZOfOnf+Zs3z58jRs2JD69evTrFkzAHLmzMmxY8fw\n8vJi4cKFdOrU6Zn+L9Kr119/nS1btrB8+XL69OmDl5cXw4YNY9CgQXYzDDa1ya6hVKS1JjAwEFdX\nV3744QcmTJhASEhImi8C9urAgQO0bdsWeNifvkiRIsmFoFq1amTPnp28efOSM2fO5D+eZrOZM2fO\ncOvWLQ4dOkTz5s3x8vLigw8+SB41UqFCBTp27Mi8efNStGtg9+7d9OjRA3j4Bz5nzpypto5BQUH4\n+vpiNpvZvXs3sbGxyY81adIEgNKlS3PmzJlnfu+uXbuycOFCEhISWLVqFa1bt06t2GmeUip52tf3\n3nuPYcOG4ePjQ1hYmNHRrEK2CFLJzz//jL+/Pzt37qRy5crMmzePN9980+hYqeZZv7lby+nTp3F0\ndCRfvnxPfF6WLFmSrzs4OCTfdnBwID4+nsTERF5++WWOHTv2r9fOnj2bw4cPs2XLFkqXLv3Cv/wm\nk4ljx46RmJiY3C4kMTGRyMhISpUqxdmzZx85AP7XcMu7d+8SEBCQPMxxxIgRjwzF/GudHB0diY+P\nf+ZcTZs2ZeTIkVSvXp3SpUuTO3fuF1nNdClfvnysXLmSVq1a0aNHD8qWLcuAAQMYMWIEWbNmNTpe\nqpEtgheUkJDA9OnTcXd35/Dhw8yaNYugoKB0VQTsxeXLl+nevTu9evVCKUWlSpVYtmwZAD/99BNn\nz55NcXO+HDlyUKxYMdasWQM83JqLjIwE4NSpU/j6+jJq1Cjy5s3LuXPnyJ49+3/2qKlRowazZs0C\nHn4erl+//sjjJUqUwNvbmzFjxiTfN2bMGGrUqEHhwoUpUqQIFouFe/fuce3aNXbt2gX8f0HIkycP\nt27dYu3atU9dryfl/Odjzs7O1K5dmx49eshuoado1KgRFouFLl26MGHCBDw9Pdm3b5/RsVKNFIIX\nYLFYqFixIn379qVKlSrExsbSvXt3aRKXiu7cuZM8fLRmzZrUqlWL4cOHAxAQEEBiYiJms5kWLVqw\naNGiR7YEnmbZsmUsWLAAT09P3Nzc2LBhAwAff/wxZrMZd3d3ypcvj6enJ9WqVUve179q1apH3mf6\n9OkEBQVhNpspXbr0Y0fmBAYGcuLECd544w3y5s1LSEgIs2fPBh7uk37//fdxd3fn/fffT55v4uWX\nX6Zbt264u7tTu3ZtypQp89R1elLOli1bMnHiRLy9vTl16hQAbdq0wcHBgVq1aqX43y2jevnll5k7\ndy67du0iISGBKlWqEBAQwI0bqXvszBApOaJs9MXeRg3du3dPjxo1Sjs5OencuXPrpUuXpss2DGl5\ntIQ9O378uH7jjTf0li1bjI6iJ06cqIcOHWrVZaTHz9GtW7d0v379tIODgy5UqJBd/F8+DikcNWT4\nH/mUXOypEBw9elR7eHhoQLds2VL//vvvRkeymvT4Cyz+X+PGjbXZbNaXL1+26nLS8+coJCREm0wm\nDeg2bdpY/d/yWaW0EMg+jBS6c+cOAwcOxNfXlytXrrBhwwZWrFjx1IOWQtir9evXExUVRZ48eYyO\nkmb5+voSHh7O8OHDWbVqFSaTiVWrVj38lp2GSCFIgb179+Lh4cHEiRPp0qULsbGxNGzY0OhYQgg7\nkCVLFkaMGEF4eDhFixalZcuWNG7cmAsXLhgdLcWkEDzBjRs36NGjB1WrViUxMZFdu3Yxd+7cJ54A\nJITImMxmM8HBwUyaNIkdO3ZgMpmYP39+mtg6kELwH7Zs2YKbmxtz586lX79+REVFUb16daNjCSHs\nmKOjI/379ycqKgpvb2+6detGzZo1k0dp2SspBP9w5coV2rZtS/369cmZMyeHDh1i8uTJZMuWzeho\nQog0okSJEuzatYs5c+YQGhqK2WxmypQpdtvETgpBEq01K1euxNXVldWrVzN8+HDCw8Px9fU1OlqG\nlpIurdOmTeP27dtWz3LmzBmWL1+efDs0NJQPP/ww1ZdTtGhRrly58q/7pZV12uLg4IC/vz+xsbHU\nqFGD/v37U758eWJiYoyO9i9SCIBff/2Vxo0b06pVK4oVK0ZYWBgjRozAycnJ6GgiBZ6nEDzPN7N/\nFgIfHx++/PLLZ36f5yWtrNOmQoUKsXHjRpYvX87p06cpVaoUI0eO5P79+0ZH+38pGWNq9MVa5xEk\nJibquXPn6hw5cuisWbPqSZMmPdIqOKOzh/Hff7WhDgoK0lWqVNFNmzbVb731lm7durVOTEzU06dP\n15kzZ9bu7u66atWqWmutt23bpv38/LS3t7du1qyZvnnzptZa6yJFiuiBAwdqb29vvWLFise2ctZa\n659//llXrFhRe3t7a29v7+S20r6+vjpHjhza09NTT5ky5ZH20VevXtWNGjXSZrNZ+/r66sjISK21\n1sOHD9edOnXSVapU0cWKFdPTp09PXrdGjRrpUqVKaZPJpOfMmZN8f5EiRf41Hj0tt7K2h8+Rvbh0\n6ZJu3bq1BrS7u7s+cuSIVZeHtKF+slOnTtGtWzeCgoKoWrUq8+bNo0SJEkbHsl9bB8Nv0an7ngXM\nUDfls3xFREQQGxvLa6+9RoUKFTh48CAffvghU6ZMISgoiDx58nDlyhXGjBnDzp07yZYtG+PHj2fK\nlCnJrZxz585NeHg48LC53ONaOefLl48dO3bg7OzMiRMnaNWqFaGhoYwbN45JkyaxefNmAPbs2ZOc\nbfjw4Xh7e/Pdd9+xe/du2rdvn9zQ7vjx4wQFBXHz5k3eeustevToQebMmQkMDOSVV17hzp07lClT\nhqZNm/5n4zdpZZ0+5M2bl2XLltGqVSu6d++On58fH330EaNGjcLFxcWwXBlu11BCQgJTpkzBbDYT\nFhbGnDlz2LVrlxSBNKBs2bIUKlQIBwcHvLy8Htt6OSQkBIvFQoUKFfDy8mLx4sX88ssvyY+3aNHi\nkec/rpXzgwcP6NatG2azmebNm6doV8iBAwdo164dANWrV+fq1avJPWjq1atHlixZyJMnD/ny5eP3\n338H4Msvv8TT0xM/Pz/OnTvHiRMnnvnfJCWklbX9qV+/PrGxsXTr1o3JkydjNpsJCgoyLI8hWwRK\nqTrAdMARmK+1TvnXwhcQExNDly5dOHLkCPXr12fWrFkUKlTIFotO+57hm7u1/L2h3H+1XtZa8847\n77BixYrHvsc/R389rpXz1KlTyZ8/P5GRkSQmJuLs7Jzquffs2cPOnTsJDg7GxcWFqlWrPtJi+p+k\nlXX6kzNnTmbPnk3Lli3p2rUr1atXx9/fnwkTJqTqnBYpYfMtAqWUIzATqAuYgFZKKZM1l3n//n1G\njhxJqVKlOH36NCtWrGDjxo1SBNKJv7dX9vPz4+DBg5w8eRKAP//8M3mympS6fv06r776Kg4ODnzz\nzTfJB5af1OL57y2x9+zZQ548eciRI8cTl5ErVy5cXFw4fvw4ISEhT8wkrazTr6pVqxIVFcWAAQOY\nP38+JpOJTZs22TSDEbuGygIntdantdb3gZVAI2st7MiRI5QuXZoRI0bQvHlz4uLiaNmyJUopay1S\n2Ji/vz916tShWrVq5M2bl0WLFtGqVSs8PDwoV64cx48ff6b3CwgIYPHixXh6enL8+PHkrQgPDw8c\nHR3x9PRk6tSpj7xmxIgRhIWF4eHhweDBg1m8ePETl1GnTh3i4+NxdXVl8ODB+Pn5PTWXtLJOv1xc\nXJg4cSIhISHkzp2bhg0b0rp1ay5fvmybACk5opyaF6AZD3cH/XW7HTDjSa953lFDo0eP1g4ODrpg\nwYJ606ZNz/UeGZmM9rBfaamVtXyOns1fbe4zZ86sc+fOrYOCgp77vUjro4aUUv6AP0DhwoWf6z3e\neOMNunXrxvjx422+z00Ia3rrrbeSd38Z6b333uPUqVPs3r3b6CjphpOTE8OGDaNJkyb069fPJgNZ\nlLZxQySlVDlghNa6dtLtIQBa67H/9RofHx8dGhpqo4TiL3Fxcbi6uhodQ6Rx8jkyjlIqTGvt87Tn\nGXGM4ChQUilVTCnlBLQENhqQQwghBAYMH9VaxyulegHbeDh8NFBrHfuUlwmDaK3lwLp4brbe4yCe\njyHHCLTW3wPfP/WJwlDOzs5cvXqV3LlzSzEQz0xrzdWrV1/4PAxhfXZ7sFgYr1ChQpw/f952Q9hE\nuuPs7Czn66QBUgjEf8qcOTPFihUzOoYQwsoyXK8hIYQQj5JCIIQQGZwUAiGEyOBsfkLZ81BKXQZ+\neeoTHy8P8O95/9I3WeeMQdY5Y3iRdS6itc77tCeliULwIpRSoSk5sy49kXXOGGSdMwZbrLPsGhJC\niAxOCoEQQmRwGaEQzDU6gAFknTMGWeeMwerrnO6PEQghhHiyjLBFIIQQ4gnSdSFQStVRSv2olDqp\nlBpsdB5rU0q9rpQKUkpZlFKxSqk+RmeyBaWUo1IqQim12egstqCUelkptVYpdVwpFZc0x0e6ppT6\nKOkzHaOUWqGUSned7JRSgUqpS0qpmL/d94pSaodS6kTSz1zWWHa6LQRKKUdgJlAXMAGtlFImY1NZ\nXTzQX2ttAvyAnhlgnQH6AHFGh7Ch6cAPWuu3AU/S+borpQoCHwI+Wmt3Hravb2lsKqtYBNT5x32D\ngV1a65LArqTbqS7dFgKgLHBSa31aa30fWAk0MjiTVWmtL2qtw5Ou3+ThH4iCxqayLqVUIaAeMN/o\nLLaglMoJVAYWAGit72utrxmbyiYyAVmVUpkAF+CCwXlSndZ6H/C/f9zdCFicdH0x0Ngay07PhaAg\ncO5vt8+Tzv8o/p1SqijgDRw2NonVTQMGAolGB7GRYsBlYGHS7rD5SqlsRoeyJq31r8Ak4CxwEbiu\ntd5ubCqbya+1vph0/TcgvzUWkp4LQYallHoJ+Bboq7W+YXQea1FK1Qcuaa3DjM5iQ5mAUsAsrbU3\n8CdW2l1gL5L2izfiYRF8DcimlGprbCrb0w+HeFplmGd6LgS/Aq//7XahpPvSNaVUZh4WgWVa63VG\n57GyCkBDpdQZHu76q66UWmpsJKs7D5zXWv+1pbeWh4UhPasJ/Ky1vqy1fgCsA8obnMlWfldKvQqQ\n9POSNRaSngvBUaCkUqqYUsqJhweXNhqcyarUw/kkFwBxWuspRuexNq31EK11Ia11UR7+/+7WWqfr\nb4pa69+Ac0qpt5LuqgFYDIxkC2cBP6WUS9JnvAbp/AD532wEOiRd7wBssMZC0u0MZVrreKVUL2Ab\nD0cZBGqtYw2OZW0VgHZAtFLqWNJ9nyTNES3Sj97AsqQvOKeBTgbnsSqt9WGl1FognIcj4yJIh2cY\nK4la4DMAAAY1SURBVKVWAFWBPEqp88BwYBywWinVhYcdmN+3yrLlzGIhhMjY0vOuISGEECkghUAI\nITI4KQRCCJHBSSEQQogMTgqBEEJkcFIIxBMppT5N6voYpZQ6ppTytcIyziil8qT2+z5hebf+4/6E\npHWMVUpFKqX6K6Xs4ndEKTVCKTXgPx7zT+pEelwpFaqUqmqlDJ/84/ahpJ9FlVKtrbFMYRt28SEX\n9impvXF9oJTW2oOHZ3iee/Kr0rQ7WmsvrbUb8A4PO9cONzjTEyW12fgAqJjUjdQfWJrUsTO1PVII\ntNZ/nd1bFJBCkIZJIRBP8ipwRWt9D0BrfUVrfQEe/RavlPJRSu1Juj5CKfWNUio4qYd6t6T7qyql\n9imltiTNETH7n9+2lVKjlFJ9/3b788fNqaCU+k4pFZb0zd3/b/ffSnpNpFL/1965hVhZRXH89zcj\nBXUojUiK0PBCERqWECT5ID4pGhY9WDpFGKEpQtKDQSMG2VR0k8iQJELEpEwpMlKcQmW860wZmJUv\nvZhmFzFNx9XDWof5PJ2ZOQes0c76wcfss2/f2nuf8619mW8ttUq6IeKHhTztkp6vpuFmdgx/qM6T\nc5WklyTtjtXRE4V2fSlpg6QfJC2TNFPSrrjfrZFvqqSdYShuc0G2Jrkd+pYoP7/QnsWSDkvaBoyq\nICbAM8AiMzsecu8DVgFzexin8dEn+yXtKL2pLKlR0keSNsX4NUf8Mtz65wFJq0v9HTIsAyZE2sIY\n57GFdmyTNKaafk96CTPLK6+KFzAAOAAcBt4C7iukHQWGRPguoCXCTcBBoD8wBF9BDMXfmDwDDMff\n9P4CeKBYFz6z3BdxfYDvgcEV5Lou/vYHvi7lwQ1yTY1wM/BshDcCsyI8FzjVRXv/EQ/8ilt8nFOo\n7xpgD24EbWLkuTHifwKWRL4FwGsRvpbOFzgfB14p9NeOKDsEOAFcDYwD2nGTy4OAI8DTFeT7BWgo\ni5sGfNzDOA0C+kZ4EvBhhBvxt5UbgH7426w3V+qf0ufog08K8bML7R4J7Ont73Je3V+5Iki6xMxO\n4Q+kObjp47WSGqsousHM/jSfpW7FfUMA7DL3D9EBrAHuLbvfUeCEpDuBycB+MztRof75kg4Crbhh\nwRER/xdQ8lK2F1cs4KY31kT4/Srkr8RkYFaY7tgJDC7cd7e5L4izuPIqmUhuL8hwE/C5pHZgEXB7\noe5Pzexs9NcxXPFMANab2WlzC7KX2k5WA7BO7g3r1TJ5tpjZb2Z2BrdjdEuNda8DpsgNID6GO1xJ\nLmNSESTdYmYdZtZiZs8B84AZkXSezu9PudvAcrsl1kN8kZX4rPRR4N3yxDgInQTcY2ZjcLszpfuf\ns5iGAh1cbEurZlsqkoZHPccAAU+ZnyGMNbNh1mkT/2yh2IXC5wsFGd4ElpvZHfiefrHPiuXL5e6J\nQ7iyLjIOX7FA1+O0FNhq7vFr6iWUBzM7ja/4puG2cVbXUj7570lFkHSJpFGSRhSixuJbBeBbDqUH\n0AwuZpqkfpIG49sGuyN+fOzX9wEeArZVuO163F3f3bjBwHIagJNmdlrSaNwlZ09sp9O14cwq8iPp\neuBt/OFtIcuTMctF0kjV5hCmgU4z6LO7yxh8BUyX1F/SQPxhXYlm4MXoa2Jv/n5gRaQfpfI4FeVp\nrEIegHOl9pfxBzCwLG4l8Aa+WjpZZf1JL5GKIOmOAcB7kg5JasN9PzdF2hLgdUl78FljkTZ8S6gV\nWGpxwIwrhOW4CeEf8Yf+RZi7Fd0KfBBbSOVsAvpK+hY/pGytoh0LcP/N7XTvpa50GPoNsBnf4lkS\naSvx2fe+2E5ZQW0z5SZ8K2YvcLynzOaHvmvx85bP6FSm5fk24qbHt0s6givX6Wb2c2TpapyagRck\n7a+hHe8AbaXD4gJtQEcc0i8MufYCv+MH18llTlofTS4pkprwQ8SXy+In4oedU3oo3wc3N/ygmX33\nb8n5f0Tuz3cVPsF72Hrxxy1pKNACjDazenEjesWSK4LkskHSbfh/x2xJJVA7ZnbezB4xs5m9rARm\n4Qfqi1MJXBnkiiBJkqTOyRVBkiRJnZOKIEmSpM5JRZAkSVLnpCJIkiSpc1IRJEmS1DmpCJIkSeqc\nvwF/1CPwB6uW1QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1138a6278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    def internationalTrade(self,worldPrice):\n",
    "        self.mode = \"International\"\n",
    "        self.worldPrice = worldPrice\n",
    "        if worldPrice>self.startingP:\n",
    "            self.export = True\n",
    "            self.domesticQ = self.demandEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.supplyEquation.subs(p,worldPrice)-self.demandEquation.subs(p,worldPrice)\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        else:\n",
    "            self.export = False\n",
    "            self.domesticQ = self.supplyEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.demandEquation.subs(p,worldPrice)-self.domesticQ\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        if self.mode==\"International\":\n",
    "            plt.plot([self.totalQ,self.priceEnd],[self.worldPrice,self.worldPrice],'k')\n",
    "            if self.startingP!=self.worldPrice:\n",
    "                plt.plot(self.totalQ,self.worldPrice, 'bo')\n",
    "                plt.plot(self.domesticQ,self.worldPrice,'bo')\n",
    "                plt.plot([0,self.domesticQ],[self.worldPrice,self.worldPrice],label='Domestic Quantity')\n",
    "                plt.plot([self.domesticQ,self.totalQ],[self.worldPrice,self.worldPrice], label='International Quantity')\n",
    "                plt.legend()\n",
    "                if self.export:\n",
    "                    print(\"The country exports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.totalQ*self.worldPrice))\n",
    "                else:\n",
    "                    print(\"The country imports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.domesticQ*self.worldPrice))\n",
    "        plt.show()\n",
    "economy1 = supplyDemand(10-p,p)\n",
    "economy1.internationalTrade(6)\n",
    "economy1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    def internationalTrade(self,worldPrice):\n",
    "        self.mode = \"International\"\n",
    "        self.worldPrice = worldPrice\n",
    "        if worldPrice>self.startingP:\n",
    "            self.export = True\n",
    "            self.domesticQ = self.demandEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.supplyEquation.subs(p,worldPrice)-self.demandEquation.subs(p,worldPrice)\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        else:\n",
    "            self.export = False\n",
    "            self.domesticQ = self.supplyEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.demandEquation.subs(p,worldPrice)-self.domesticQ\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        self.domesticQ\n",
    "    def drawSurplus1(self):\n",
    "        if self.export:\n",
    "            triangle1 = patches.Polygon([[self.domesticQ,self.worldPrice],[0,self.worldPrice],[0,self.priceEnd]],True,label=\"Consumer Surplus\")\n",
    "            triangle2 = patches.Polygon([[0,0],[0,self.startingP],[self.startingQ,self.startingP]],True,label=\"Original Producer Surplus\",color=\"red\")\n",
    "            rect1 = patches.Polygon([[0,self.startingP],[0,self.worldPrice],[self.domesticQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Transferred Surplus\",color=\"orange\")\n",
    "            triangle3 = patches.Polygon([[self.domesticQ,self.worldPrice],[self.totalQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Surplus from International\",color=\"yellow\")\n",
    "            currentAxis = plt.gca()\n",
    "            currentAxis.add_patch(triangle1)\n",
    "            currentAxis.add_patch(triangle2)\n",
    "            currentAxis.add_patch(rect1)\n",
    "            currentAxis.add_patch(triangle3)\n",
    "        else:\n",
    "            triangle1 = patches.Polygon([[self.startingQ,self.startingP],[0,self.startingP],[0,self.priceEnd]],True,label=\"Original Consumer Surplus\", color=\"red\")\n",
    "            triangle2 = patches.Polygon([[0,0],[0,self.worldPrice],[self.domesticQ,self.worldPrice]],True,label=\"Producer Surplus\")\n",
    "            rect1 = patches.Polygon([[0,self.startingP],[0,self.worldPrice],[self.domesticQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Transferred Surplus\",color=\"orange\")\n",
    "            triangle3 = patches.Polygon([[self.domesticQ,self.worldPrice],[self.totalQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Surplus from International\",color=\"yellow\")\n",
    "            currentAxis = plt.gca()\n",
    "            currentAxis.add_patch(triangle1)\n",
    "            currentAxis.add_patch(triangle2)\n",
    "            currentAxis.add_patch(rect1)\n",
    "            currentAxis.add_patch(triangle3)\n",
    "            \n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        if self.mode==\"International\":\n",
    "            plt.plot([0,self.priceEnd],[self.worldPrice,self.worldPrice],\"k\")\n",
    "            if self.startingP!=self.worldPrice:\n",
    "                plt.plot(self.totalQ,self.worldPrice, 'bo')\n",
    "                plt.plot(self.domesticQ,self.worldPrice,'bo')\n",
    "                plt.plot([0,self.domesticQ],[self.worldPrice,self.worldPrice],\"m\",label='Domestic Quantity')\n",
    "                plt.plot([self.domesticQ,self.totalQ],[self.worldPrice,self.worldPrice],\"g\", label='International Quantity')\n",
    "                \n",
    "                if self.export:\n",
    "                    print(\"The country exports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.totalQ*self.worldPrice))\n",
    "                else:\n",
    "                    print(\"The country imports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.domesticQ*self.worldPrice))\n",
    "                plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The country imports during international trade, and the price moves from 5 to 4.\n",
      "The domestic producers go from producing 5 units to 4 units.\n",
      "The domestic consumers go from consuming 5 units to 6 units.\n",
      "Total domestic producer revenue is 16\n"
     ]
    },
    {
     "data": {
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oQ+w+AX45fBh7S0t8ZYhdudGlSxciIiJyjTrevn07jo6OODg4MHfuXN0ymzZt\nonXr1rRv355jx47pHn9yUpSaNWvq/r5s2TIcHR1xdnZm3rx5gDbqoFevXrRt25YuXbroog68vb2Z\nNGkS7u7uzJkzJ0e98fHxNG/eXHff2tqaGzdu4Ofnx8CBA7GxscHCwgIfH5+nIqXd3NxYtWqVbv1u\nbm60bt061z2AvCKh79+/T58+fXB2dsbBwYEfZZz7U+ShoUqqCjAXGASMy8xk7KRJbFu3jrW7dmFh\nYaHf4qQ8ZWVlceDAAV365eXLl9m8eTMdOnQgLi6OuXPncvbsWerWrUuPHj3Yu3cv7u7uLFy4kLNn\nz2JqakrXrl1xcXF55nYOHDjATz/9xKlTp6hevbrunNKECRNYs2YNNjY2nDp1iilTpuDn5wdoZ1E7\nfvz4U3k/b7zxBj169GDXrl107tyZF154gaZNm1K/fn1MTEzyjLx48OCBbgyEt7c3UVFRnD59msjI\nSLp27aqL4M7Pb7/9RrNmzfjlF+0hveTk5AItV5nIPYJKrjVwWKPhK+DE2bM42Njw5aefPjVDk6Rf\naWlpqNVq3NzcaNmyJW+++SaQM+r4zJkzeHl50bBhQ6pUqcLIkSP5+++/OXXqlO5xY2NjXRTys/z1\n11+MHTtWdx6iXr163Lt3j+PHjzNs2DDdpC6PH1YcNmxYrqFvarWaS5cuMWbMGK5evcrIkSMBcuwl\n5ObJOocPH46BgQE2NjZYWVkVOHjN0dGRP//8k7lz5/LPP//oZca0sk42AgkDYAoQAnTJzmba3Lm8\n4OBAWJgc3lFWPDpHEBgYyJdffqmLiXjerJ3HY481Gk2e8yA/er5OnTq6OgIDA3P8jORVy+3bt4mK\niqJdu3asWrWK0aNHc+TIkWdGOOe2vievIHryfl7ra926NefOncPR0ZH58+fnmK1M0pKNQNJpCfwq\nBN8B4eHhqB0c+PC998jMzNR3aVIBtG/fniNHjnDr1i2ys7PZvn07np6euLu7c+TIERITE8nMzGTn\nzp26ZR6PPd63b5/u3/qll15i06ZNuqkpb9++Te3atbG0tNQtL4QgKCgoz3oePHhAREQEe/bsIS0t\nDXt7exo2bEhYWBjm5ua6SOnExEQyMjLyvfJn586daDQaIiMjuXLlCra2tjmezysSOi4ujurVqzNq\n1Chmz56te430/+Q5AikHBRiNdjDaNGD+xx+zc9s2Nu7ejWseUxBWNgW53FMfmjZtyieffELXrl0R\nQtCnTx/Gdl8ZAAAgAElEQVQGDBgAaE+kduzYkTp16uhmIgMYP348AwYMwNnZmV69eum+hffq1YvA\nwEDc3NwwNjamd+/efPTRR2zdupXJkyezdOlSMjMzefXVV3F2ds5Rx5Mhcffv32fixIm6K5v69OnD\nkCFDUBSFBQsW0L59e5o3b55vzHXLli1p3749d+/eZc2aNU9laA0ZMoTvvvsOlUqFu7u7LhL6woUL\nzJ49GwMDA4yMjPjmm2+e+7OuaEo9hrooZAy1/uwFJgMJisKsKVNYuHx5pct7Kc8x1KXtyZA4CwuL\nYgk99Pb2pm/fvgwdOrQYqqyYnieGWh4akp5pIBAKeAPLvvoKtaUl/zw216wkgXYv4MaNG0+FxMnk\n2/JBNgIpX3WB9ULwF5B58yYveHoydfRoUlJS9F2aVAakpaVx8eJFYmJiSiwkztfXV+4NlCDZCKQC\n6wZcEIIZwDfff4+qZUsOyKH9ldajkLjQ0FDS09OxtLSUIXHllGwEUqHUAFYCx4Faycn07tePMQMG\nkJiYqOfKpNJ0//59wsLCdCFxKpWK+vXry5C4cko2AqlIOgDnhOB9YPu+fdi1bMmObdtkTEUFp9Fo\niI2NJSwsjKysLFq1akWrVq3kHNnlnGwEUpFVBZYAZ4GW6em8MnIkg7t1Iy4uTs+VSSUhJSWFkJCQ\nfEPipPJHjiOQnpsTcFKjYSWw4NAh7K2s+MzHhzcmTqyYhwr+1wTSbxTf+kwaw+DreT6dmJhIt27d\nALh+/TqGhoY8mr719OnTJTIZ/TvvvMPvv/9Ov379+PDDD4mNjSUhIQFjY2Nat25N7dq1i21b8+fP\np0GDBsyYMSPH42FhYUyaNInk5GQyMjLw8vIqtjEAeW2zspKNQCoWVYDZaC83HZ+ZybjJk9m+fj1r\nd+zAyspKz9UVs+JsAgVYX/369QkMDAS0A8Nq1qzJrFmzcrxGCIEQAgOD59/JF0KwceNGbt++rdsL\nePDgAY0bN6ZZs2a55gllZWVRpUqVPO8XxVtvvcWcOXPo06cPQgiCg4MLtXx2dnautUpPk4eGpGJl\nA/hpNKwBTp89i6OtLT7LlskQuxIQERGBvb09I0eORKVSER8fz4QJE3Bzc0OlUuXI1DEzM2PRokW4\nuLjg5OTEpUuXAPDz88PZ2Rm1Wo2rq6susjklJQUHBwfWrFnD7du3WbJkCYMGDaJjx46cPHkS0H6r\nHjNmDB4eHnh7e7N+/XoGDhxI165d6dmzJwCffPIJ7du3x8nJKUc9S5YsoXXr1nTu3JnLly/n+v7i\n4+MxMzMDtLlCjo6OAKxfvz7HN/levXpx9OhRsrKyqFOnDjNmzMDJyYnTp09jZmbG3LlzcXR0xN3d\nnStXrjy1nc6dO+sa7fXr17G2tga0I5LbtWuHWq3Gyckp12UrCtkIpGJnAExEOxCtq0bDzHnz8HBw\nICQkRM+VVTzh4eHMnDmT0NBQmjdvzieffIK/vz9BQUH8+eefhIaG6l7buHFjAgICGDduHJ9//jkA\ny5cvZ+3atQQGBvL3339TtWpVfH19qV69Olu2bGHUqFF8/fXXvPfee/j7+7Njxw7GjRuXY/sHDx7k\n+++/ByAgIID//e9/HDx4kF9//ZXo6GhOnTpFYGAgx48f5/jx45w+fZrdu3cTFBTEL7/8wunTp3N9\nb++88w4vvPACvXv3xsfHp0Dx0cnJybzwwgucP3+ejh07Atrk1AsXLjBx4kTeeeedAn+2X3/9NbNm\nzSIwMJAzZ87QrFmzAi9b3shGIJUYM+BnjYatQER4OC5OTnzw3nvPTLiUCqdVq1a4uf1/gsD27dtx\ndXXF1dWVsLCwHI1g8ODBALRt25aoqCgAPDw8mD59Ol9++SW3bt0iKiqKK1euoCgKdnZ2NG/enIMH\nDzJp0iTUajUDBw7kzp07pKWlATBgwIAco4d79OihO4H8xx9/cODAAVxcXHB1dSUiIoJLly7x999/\nM2TIEKpVq4apqSn9+vXL9b2NGzeO0NBQhg4dysGDB+nYsWO+PzvGxsYMGjQox2MjRowAYOTIkRw/\nfrwgHysAnTp1YunSpXz66afExMRU6FHSshFIJUoBXgPCgCHAgo8/xs3GhjNnzui3sAri8ajmy5cv\ns2rVKvz8/Dh//jy9evXKEe38aKCXoaGhbvrH+fPn8+2333Ljxg06duzIhQsXMDMzw8DAQDcXgRCC\n06dP66Kn//33X13e1JNR0Y/fF0Iwf/583XIRERF4e3sX6v01b96cN954g59//hmNRkNYWNgz46ur\nVauWb1z1kx5f3+PrGj16NHv27KFq1ar06tWLvytwtIpsBFKpaAhs12j4CUiMiaFD+/bMmTpVF3Ms\nPb+7d+9Sq1YtateuTXx8PL///nu+y4SGhmJiYsLAgQNRqVQIIWjSpEmO13Tv3p2vvvpKd//R8fT8\n9OzZkw0bNnD//n1AO4PZrVu3eOGFF9izZw/p6encvXs3z/jp3377Tdew4uLiuHPnDs2aNcPCwoKA\ngACEEERFRelitPPyaGrK7du34+Hh8dTzj0dxPz5t55UrV7C2tmb69On07duX8+fPF+h9l0fyqiGp\nVPUHPIVgjoEBy7/+mj27d7P+hx/w9PLSd2kFZ9K4+C8fLQaurq7Y29vTpk0bzM3Nc/2l97gbN26w\nYMECAgMDMTY2xtXVlb59+z71uq+++orJkyezadMmsrKy6Nq1a47GkJfevXsTHh6um0GtVq1abNu2\njfbt2zNo0CCcnJxo3Lgx7du3z3X5AwcOMH36dExMTFAUBR8fHxo2bIinpyfNmzfHzs4OlUqVI1Y7\nN7du3cLJyYlq1aqxffv2p56fPXs2r7zyCt988w0vv/yy7vFt27axfft2jIyMaNasmW4+5YpIxlBL\nenMIGK8oRArBxJEjWfbVV2VyGsGKFkOdlpamm9Td1NQUc3PzEhmLUBaYmZkRHBxMnTp19F1KiZMx\n1FK51BU4LwTvKgrrtm5FZWHBLz//rO+yKiyNRkNcXByhoaFkZGRgaWmJtbV1hW0CUsHJRiDpVXVg\nhRCcAOomJ9O3f39G9u9PQkKCvkurUB6FxMXFxVG3bt1KExIXGxtbKfYGnpdsBFKZ0B44KwSLFIWd\nP/+MvaUlP2zdKkPsnlN2djYxMTG6kDhra2usrKxkSJyUg2wEUplhDCwUgnOAVVoaI0aNYkDXrvz7\n77/6Lq1cSklJITQ0lBs3btCwYUNUKpX8dizlSjYCqcxxAI5rNHwG/HXkCPatWrHum2/k3kEBZWVl\nce3aNS5evAhA69atMTc3f+7sH6niko1AKpMMgXeAC0DbzEwmTJlCt3btiIiI0HNlZVtSUhIhISEk\nJCTQuHFj7O3tizUpVKqYZCOQyrRWwEGNhnXA2bNncbKz47OPP9ZziF0TtGOmi+uWcwBXbj788ENU\nKhVOTk6o1WpOnTqV4/nMzEyuXLlCREQEVapUwc7OjhYtWhQofbNmzZoFetfPkpGRQffu3VGr1boB\nXCXFwsKCW7duPfM1vr6+pTIvRlJSEl9//bXuflxcXInMrezl5UWRL6EvAL00AkVR6iiKsktRlHBF\nUcIURemojzqk8kEBxqENseuu0TDrvffo6OBQ6Fji4lPMMdT5rO/EiRPs37+fc+fOcf78ef766y9a\ntGgBaGMcEhMTCQkJ0Y28tbOzyxH18Gh0bkkKCAgAtKOOX3nllRzP6aNpF6URFOVzerIRNGvWLMfo\n5PJCX3sEq4DfhBBtAGe0UTSS9EzNgZ80Gn4AosLDcXV2ZtF//lPhQ+zi4+Np0KCBLiuoQYMGNGvW\njAcPHtCiRQsCAgKoWrUqaWlpvPbaaxgYGLBo0SJGjx6Nh4cHo0ePxtfXlwEDBuDl5YWNjQ2LFy9+\najuHDx/OMbL4rbfewtfXF4B58+Zhb2+Pk5PTU3Mh3Lx5k1GjRnHmzBnUajWRkZFYWFgwd+5cXF1d\n2blzJ4GBgXTo0AEnJycGDRrEnTt3AO033ZkzZ+Lm5oadnR1nzpxh8ODB2NjYMH/+/Gd+LlFRUdjZ\n2TF+/HhUKhU9evQgLS2NXbt24e/vz8iRI1Gr1aSlpXH27Fk8PT1p27YtPXv2JD4+Xrf9GTNm4Obm\nxqpVq/D29mbatGl06tQJKysr3S/1e/fu0a1bN1xdXXF0dOSnn37SfS6RkZGo1Wpmz55NVFQUDg4O\ngDa3aOzYsTg6OuLi4sKhQ4cAbZMaPHgwvXr1wsbGhjlz5uje0+TJk3Ux4gsXLizYD0hxeDShRWnd\nAFPgKg9HNRfk1rZtW1FkIG8V8JYAYqSBgQCEqmVLcfLkyaL/jOQjNDT0yR+qErjlLSUlRTg7Owsb\nGxsxefJkcejQIXHz5k1x7tw50bRpUxESEiI0Go04c+aM8PT0FEIIsXDhQuHq6ipSU1OFEEJs2rRJ\nNGnSRNy6dUukpqYKlUolzpw5I4QQokaNGkIIIQ4dOiT69Omj2+7UqVPFpk2bxK1bt0Tr1q2FRqMR\nQghx586dp2p8cllzc3OxbNky3X1HR0dx+PBhIYQQ77//vpg+fboQQghPT08xZ84cIYQQPj4+omnT\npiIuLk6kp6eL5s2bi1u3bj21LXNzc5GQkCCuXr0qDA0NRUBAgBBCiGHDhoktW7bo1vvo/T148EB0\n7NhR3Lx5UwghxA8//CDGjh2re93kyZN163799dfF0KFDRXZ2tggJCRGtWrUSQgiRmZkpkpOThRBC\nJCQkiFatWgmNRiOuXr0qVCqVbvnH769YsUK3nbCwMNGiRQuRlpYmNm3aJCwtLUVSUpJIS0sTLVu2\nFNHR0UIIIRITE4UQQmRlZQlPT08RFBT01PvJy9M/p0IA/qIAP4D62COwBBKATYqiBCiKsl5RlBpP\nvkhRlAmKovgriuIvBxdJT2oAfK/RsB9IjomhU4cOvDtlSoUMsatZsyZnz55l7dq11K1bl2HDhrF6\n9WqqV6+OkZERjRo1ynVgWP/+/XUpoQAvvfQS9evXp1q1agwePJijR48WaPumpqaYmJjw5ptv8r//\n/U+XSpqfR4eIkpOTSUpKwtPTE4DXX389R5Jn//79AXB0dESlUtG0aVOqVq2KlZUVMTExz9yGpaWl\nLmvo8Xjtx128eJHg4GBeeukl1Go1S5cuJTY29qk6Hxk4cCAGBgbY29tz44b2sJ0Qgvfeew8nJye6\nd+/Ov//+q3suL0ePHmXUqFEAuvynRxMCdevWTfe52tvbc+3aNQB27NiBq6srLi4uhISE5IgRL0n6\naARVAFfgGyGEC3AfmPfki4QQa4UQbkIIt0fzs0rSk/oAIUIw0cCAz7/5BkdLS/wOHtR3WcXOwMCA\nNm3aMGTIEObMmcPJkydp3bo1RkZGuUYow9MR0fnFM+cV71ylShVOnz7N0KFD2b9/P7169SpQzU9u\nPy+PDnkZGBjo/v7ofn7H7R9//ePx2o8TQqBSqXRx2BcuXOCPP/7Is87H16n9Ug1bt24lISGBs2fP\nEhgYSOPGjZ/6vAsjt7qvXr3KihUrOHjwIOfPn6dPnz7PtY3C0EcjiAVihRCPLnvYhbYxSFKR1Aa+\n1mg4DBgkJNCte3fGjxxJUlKSnisrHoGBgfz222/ExsZSu3Ztbt++jY2NDYqi5IhQ3r179zPX8+ef\nf3L79m3S0tLYu3fvU+mk5ubmuhyipKQkDj5sqPfu3SM5OZnevXuzcuVKgoKCClW/qakpdevW5Z9/\n/gFgy5Ytur2DklKrVi1SUlIAsLW1JSEhgRMnTgDaK6wKO1tecnIyjRo1wsjIiEOHDum+wT++nSd1\n6dKFrVu3AnDp0iWio6OxtbXNcxt3796lRo0amJqacuPGDQ4cOFCoGp9HqY8wEUJcVxQlRlEUWyHE\nRaAb2gtCJOm5eKINsVukKKzYto1ff/2Vb3x96T9gQDFvqTHFe+VQ7jHUGo2G+Ph4goODWb58Oenp\n6VStWhVra2vWrl0LwMKFC3nzzTd5//338conyrt9+/YMGTKE2NhYRo0alWNmM4AWLVowfPhwHBwc\nsLS0xMXFBdCOUB4wYADp6ekIIXTTXBbG5s2bmTRpEqmpqVhZWbFp06ZCr6MwvL29mTRpEtWqVePE\niRPs2rWLadOmkZycTFZWFjNmzEClUhV4fSNHjqRfv344Ojri5uZGmzZtAKhfvz4eHh44ODjw8ssv\nM3XqVN0yU6ZMYfLkyTg6OlKlShV8fX1z7Ak8ydnZGRcXF9q0aUOLFi3yjREvTnqJoVYURQ2sR5sq\ncAUYK4S4k9frZQy1VFj+wBuKwgUheKVvX77YsIFGjRoVaV36iKG+d+8e165dIy0tjXr16tGiRYvn\nygfy9fXF39+f1atXF2OVUllS7mKohRCBD4//OwkhBj6rCUhSUbgB/kLwgaKwZ/9+7K2s2LplC/r4\n4lMYj0LiwsPDyc7OliFxUqmQI4ulCssYmC8EAYBNWhqjxoyhr5dXvlei6Mvdu3dLLCTO29tb7g1I\neZKNQKrw7IGjGg0+wOG//0ZlY8M3q1fnuEImPyW5J5GVlUVUVJTu0kJbW1vMzc0LFA8hSfD8P5+y\nEUiVgiEwHQgG3DMzmfL223Rt147Lly/nu6yJiQmJiYkl0gwehcTdunWLJk2aoFKpqFWrVrFvR6q4\nhNDGjJiYmBR5HXLOYqnSEcAmtOmmGVWqsHjRIt6ZOzfPmObMzExiY2OL9Zru7Oxsbt++TWpqKkZG\nRtSvX/+ZV5RI0rOYmJhgZmb21Lmkgp4sLtQ4eKB6YV5fXDcZMSFvJXH7F8RAQ0MBiLa2tiIwMLDo\nP2cFpNFoxJYtW0S9evWEsbGx+OCDD8SDBw9KfLtS5URxRkwoitJJUZRQIPzhfWdFUb7OZzFJKtOa\nAf/LzmYHEHPxIm4uLrw/dy4ZGRklsr3o6Gj69OnD6NGjad26NQEBAcyfP19eESTpXUHPEawEegKJ\nAEKIIOCFkipKkkqLAgxDO6LxNUVh6aef4tK6tW4UanHQaDR88803qFQqjhw5go+PD0ePHsXe3r7Y\ntiFJz6PAJ4uFEE9ec6fPmUEkqVjVBzZrNPwK3IuJwaNTJ2ZMnsy9e/eea72XLl3Cy8uLKVOm0KFD\nB4KDg5k+fbq8IkgqUwraCGIURekECEVRjBRFmYWcQ0CqgF5GG2I3xcCAVWvW4NiqFX/++Weh15OV\nlcWnn36Ks7MzFy5cYOPGjfzxxx9YWloWf9GS9JwK2ggmAVPRzg3yL6B+eF+SKpxawGqNhr8B44QE\nevTowRsjRugmU8lPUFAQ7u7uzJ07l5dffpnQ0FDGjh2ba1S0JJUFBWoEQohbQoiRQojGQohGQohR\nQojEki5OkvSpCxAkBPMMDPjuhx+wt7Rkz//+l+fr09PTmT9/Pm5ubsTGxrJz5052795N06ZNS69o\nSSqCgl41tFlRlDqP3a+rKMrGkitLksoGE+BjjYbTQJOUFAYPGcKwvn35+uskLCzAwAAsLGDRoku4\nuLjw4Ycf8tprrxEaGsrQoUPlXoBULhQ0htpJCKELdxdC3FEUxaWEapKkMscVOK3RsNzAgAW/1GbX\nL/9/yee1a7B4cXPq1XuJAwdWFnjiFkkqKwraCAwURakrHqaEKopSrxDL6k9Wmna2AxmkIRUDI+A9\nNHz190fEZTw5+1YNatTwoVcv+cMmlT8F/WX+GXBCUZSdaC+9Hgp8WGJVFZe4/TAaEPouRKpI4v9s\nmevjsbHyMJBUPhWoEQghvlMUxR948eFDg4UQ5WNWsWq1ICv3qeQkqbD+FwiKQTRCY/HUc8bG/3Lx\nYhK2tg6lX5gkPYdn7scqilL74Z/1gOvAtoe36w8fk6RK4XoSDF0HQ5aDmdl7mJjcz/G8sfF9DA3n\n4OzsxMcfv01mZqaeKpWkwsvvgOa2h3+eRTv736Pbo/uSVKEJAZtPgv1/Yf8J+OgjiIjYzvr14zE3\nj0JRNJibR7Fx43giI7fTt6/gvfdW4+5uRkDASX2XL0kFkm8MtaK9/q2FECK6dEp6WpFjqKN3wsk3\n5aEhqUiuJcDEH+D3k9CpE2zYAA/nLH+m3bth6lS4dQvmzBnNggVrnysrXpKKqtjmLH4YZfpLsVQl\nSeWARgOrj4Dqv3D0Anz5JfzzT8GaAMCQIRAaCmPGwMcfb0GtbsKxY4WPqZCk0lLQa93OKYrSrkQr\nkaQyIDwOXvgM3l4LnTtDSAi89ZZ24Fhh1KsHGzfC779DenoyXbr04O23B5CSIvdOpbKnoD/e7sBJ\nRVEiFUU5ryjKBUVRzpdkYZJUmjKz4OM/QD0fQq+Bry8cOADm5s+33h49IDgY3n4bvvpqHw4OTfj9\n913FUrMkFZeCNoKegBXay0f7AX0f/ilJ5V7ANWj/Mby3Gfr10x7Wef314pvltGZNWLVKe3ipevU0\nevUahre3F7dv3y6eDUjSc8rv8lETRVFmALOBXsC/Qohrj26lUqEklZD0B/CffdDufYi/oz3Ju3Mn\nNGlSMtvz8ICAAMF//wvff38Ee/um7N69vmQ2JkmFkN8ewWbADbiANqr9sxKvSJJKwdEIcF4Cn/yo\nPakbFgaDB5f8dk1MYOlS8PeH5s0fMHToeIYMaUt8fFzJb1yS8pBfI7B/GDn9LdpYiS6lUJMklZiU\nNHhrB3RZCA8E/PGH9qRu3bqlW4daDadOwSefwC+/nMPeviW+vivI73JuSSoJ+TUC3fBIIURWCdci\nSSXq91BwWAhf74Np0+DCBXjpJf3VU6UKzJ0L58+Do2M2Y8fOpmfPNkRFReqvKKlSyq8ROCuKcvfh\nLQVwevR3RVHulkaBkvS8ElPg9e+g14dQvRYcPao9eVuzpr4r02rdGg4fhq++ghMnLuHg0JovvniP\n7Gw5LbhUOp7ZCIQQhkKI2g9vtYQQVR77e+3SKlKSikII2BUA9vNh20GYPx8CArSjhMsaAwOYMkU7\nbuGFFzRMn/4xL7xgSViYvEpbKnkyPF2qkOLvwJB1MGwFtDDXnpz94APtydqyrGVL+OUX2LIFwsNj\nUKud+fDDKTLETipRemsEiqIYKooSoCjKfn3VIFU8QsCmhyFxB07BsmVw8iQ4O+u7soJTFBg1Snsl\n08CBMH/+N7Rr15xz547ruzSpgtLnHsF0IEyP25cqgK3HRmAx/SoGI7Np/tZVHBeM4I0vwdEJgoJg\nzhztSdnyqFEj+PFH2LMHbt68Rfv2HsybN5JNmx7kmC9561Z9VyqVd3r5L6IoihnQB+0sZ+/oowap\n/Nt6bAQT1q8j9YF22si4OxbE3VmHtzds2LC90PlAZdXAgeDlJZg1C5YtEyhKFkIYA9r5kidM0L5u\n5Eg9FimVa/nGUJfIRhVlF/AxUAuYJYTo+6zXFzWGeoPqMwyjDNHOrilVNHdT3dFQlT29lhPRJJI4\nqgGgKOnUrn1Kz9WVjLt33RHi4YmO62r4zQeAqlWv06HDq3qsTCoparUaHx+fIi1b0BjqUt8jUBSl\nL3BTCHFWURSvZ7xuAjABoGXL3OeIzZexKamaTNkGKiAhQEPVPJ4zAaqXbkGlRNcEnpCR0aiUK5Eq\nklLfI1AU5WO0U8pnASZAbeB/QohReS1T5IlpAIt5ciqFiibjegSJv/qQmXAIsHjqeXNziIoq7apK\nh4WF9nDQk2rUuEVUFDRo0KC0S5LKsGKbmKa4CSH+I4QwE0JYAK8Cfs9qApL0iCYzgzuHN3H9u3eo\nraQxc+Ytqj/xxb96dfjwQ/3UVxo+/JCn3rOR0QNSU2dib2/Pjh07ZEyFVGgV5HSaVNGlxwQTv+lt\n7p7ajfdYbyIuhvP5526sXavdA1AU7Z9r11bsk6YjR/LUe960yZjAwNmYm5vzyiuvMGjQIOLiZIid\nVHB6OVlcWPLQUOWlyUjlzhFf7gX8SvMW5mzetIFu3brpu6wyKSsrCx8fH95//32qVq3KZ599xhtv\nvIFSXBMrSOVOmT00JEkFlRZ5hvgNU7gf9BvTZ8zgYliIbALPUKVKFWbNmsWFCxdQq9WMGzeO7t27\nc+XKFX2XJpVxshFIZU52ajK3fl7BzV2LMW/agBPHj+OzciU1atTQd2nlgrW1NX5+fnz77becOXMG\nBwcHVq5cKUPspDzJRiCVGUII7of9TfyGKaRfPMr7CxYQcj4Qd3d3fZdW7hgYGDBhwgRCQ0N58cUX\neeedd/Dw8CAkJETfpUllkGwEUpmQlZJIwv+Wcmvfp6hatyIg4BxLFi+matXcxwpIBWNmZsbPP//M\ntm3biIyMxMXFhSVLlvDgwQN9lyaVIbIRSHolhCAl6HfiN0whKyaQ5cuXc/bMKRwdHfVdWoWhKAoj\nRowgNDSUoUOHsnDhQtq2bcuZM2f0XZpURshGIOlNZtJ1bv74X27/9iUd2rclNDiYWbNmUaW8psSV\ncQ0bNmTbtm3s27ePO3fu0KFDB2bNmkVqaqq+S5P0TDYCqdQJTTZ3z+wlfuNUDBKvsmbNGv45fAhr\na2t9l1Yp9OvXj5CQEMaPH89nn32Gk5MThw8f1ndZkh7JRiCVqgcJUVz/fjZ3/NbTo3s3LoaFMnHi\nRAwqSlRoOWFqasqaNWvw8/MDoGvXrkycOJHk5GQ9Vybpg/zfJ5UKkZ1J0tFtXN88g2rpiWzbto0D\nv+zHzMxM36VVal27duX8+fPMmjWL9evXo1Kp2L9fzhVV2chGIJW4jPhLxG+eQfKxbQwbOpTLF8MY\nMWKEHPFaRlSvXp3ly5dz4sQJ6tatS79+/XjttddISEjQd2lSKZGNQCoxmsx07vht4PqWWdQ1fMC+\nffv48YftNGzYUN+lSblo3749Z8+eZfHixezatQs7Ozu2bdsmQ+wqAdkIpBKRHn2e+I1vc/fMHsaN\nG8el8DD69eun77KkfBgbG7NgwQICAgKwtrZm5MiR9O/fn9jYWH2XJpUg2QikYqXJuE/ib6u5sf09\nmtYx4dChQ6xb+y2mpqb6Lk0qBJVKxbFjx/j88885ePAg9vb2fPvtt2g0Gn2XJpUA2QikYpMacVob\nEppdiXEAABksSURBVHfhD955513CQ4Lx8vLSd1lSERkaGjJz5kyCg4Np164dkyZNolu3bkREROi7\nNKmYyUYgPbfs1GQS9i0nYfcSWrVowqmTJ/nssxVUf3IGFalcsrKy4q+//mLdunWcO3cOR0dHVqxY\nQVZWlr5Lk4qJbARSkQkhuB96mPj1k8m4fJyFCxdyPuAc7dq103dpUjFTFIVx48YRGhpKjx49mD17\nNh07duT8+fP6Lk0qBrIRSEWSdfcWCbuXcOvnFTirbAkKDGDRokUYGxvruzSpBDVv3py9e/fyww8/\ncO3aNdq2bcvChQvJyMjQd2nSc5CNQCoUITSkBB4gfuMUNP8G8/nnn3P65AlUKpW+S5NKiaIovPLK\nK4SGhvLqq6+yZMkSXF1dOXnypL5Lk4pINgKpwP6vvXuPs7Fe/z/+uhinHHPIVvyiXWQyDSEk8pCU\nUpSfvtqKyo98EUWJypd2voloRyLFOJOaSLPl1OxBzgZjxqxBJZWaE8b5PK7fH/dtN812rFnrnlnr\nej4e6+Fe9zrc73utsa77sNb1OZP1K+mfvMaBpR/QrEkjfMnbefHFFylcuLDX0YwHKlasyMyZM1m0\naBGHDx/mrrvuon///hw7dszraOYqWSEwl6Xnsjm0YT5pUX0Iy/qRyZMnE/evWG666Savo5l84MEH\nHyQ5OZmePXvyj3/8g4iICGJjY72OZa6CFQJzSaczfiBt5kscXBFFmzYPsGtHCt26dbP2EOZ3ypQp\nw4QJE1i5ciVhYWG0atWK7t27c/DgQa+jmStghcBckJ49w8FvZpM2/QVKnsli3rx5xCz8guuvv97r\naCYfa968Odu2bWPgwIFERUURHh7OwoULvY5lLsMKgfkPp37ZQer0fhxaO5dOnTrx7c4dPP7447YX\nYK5IiRIlGDlyJBs2bKBSpUq0b9+eTp06kZGR4XU0cxFWCMy/nTt9kgOxH5M2+2UqFD3HokWLmDN7\nFhUqVPA6mimAGjRoQHx8PMOHD2fBggXUrl2bWbNmWRO7fMgKgQHgxJ4E0qb24Uj8Qnr27MnOlGQe\nfPBBr2OZAq5IkSK89tprJCQkUKtWLZ566ikeeughfvrpJ6+jmRysEIS4cyePsn/xODLmvc4N5Uux\ncuVKJk6YQJkyZbyOZoJI7dq1+eabbxg7diwrV67ktttuY+LEidbELp+wQhDCjn+7ntQpvTieHMvA\ngQPxbU+kefPmXscyQapw4cL07duX7du307hxY3r16kWLFi3YtWuX19FCnhWCEJR9LIvMhSPJnD+c\nmtVvYOOGDYwcOZISJUp4Hc2EgBo1arBs2TKioqJISkoiMjKSUaNGWRM7D1khCCGqytHkOFKn9OL0\n9xt48803Sdiymfr163sdzYQYEeGZZ57B5/PRpk0bXnnlFRo1asS2bdu8jhaSrBCEiLOHM8iIHsb+\nf47hjttvI3FbAq+//jpFihTxOpoJYVWqVGH+/PlER0fzyy+/0KBBA4YMGWJN7ALMCkGQUz3Hka1f\nkTqlN6SlMG7cONatWU3t2rW9jmbMv3Xo0AGfz0fnzp0ZPnw49erVY926dV7HChkBLwQiUk1E4kTE\nJyLJItIv0BlCxZkDv5A+ZzAHlk3gnmZNSUlO5vnnn7cmcSZfKl++PNOmTWPJkiUcO3aMpk2b8sIL\nL3D06FGvowU9L/YIzgIDVDUcaAz0FpFwD3IELadJXDRpU5+n6OG9TJ06ldjly6hevbrX0Yy5rPvv\nv5/t27fTu3dvxo4dS0REBMuXL/c6VlALeCFQ1VRV3eJOHwFSgBsCnSNYnc7YTdrM/hxcMY22Dz3I\nzh0pPP3009YewhQopUuX5v333+ebb76hWLFitG7dmmeffZasrCyvowUlT88RiEh1oB6w4QK39RCR\neBGJz8zMDHS0AkfPniZr1UzSpr9I6ewjREdHs/CLBVSpUsXraMb8YXfffTcJCQkMHjyYGTNmEB4e\nzoIFC7yOFXQ8KwQiUgr4HHhBVQ/nvl1VP1LVBqraoFKlSoEPWICc3JtC6rR+HF43jyef7MyuHSl0\n6NDB61jG5InixYvz1ltvsWnTJv7yl7/w2GOP0bFjR9LS0ryOFjQ8KQQiUgSnCMxW1fleZAgG506f\n4MDXk0ifM5BKJWDJkiXMmD6d8uXLex3NmDxXr149Nm7cyFtvvUVMTAzh4eHMmDHDmtjlAS++NSTA\nFCBFVd8N9PKDxYkftpAW1ZujW/5J71692OFL5v777/c6ljF+VaRIEQYPHkxCQgLh4eF07dqVNm3a\n8OOPP3odrUDzYo+gKfAU0FJEEtyLtbm8Qtknj7Jv0XtkfPo//J/ryrFq1SrGjx9P6dKlvY5mTMDc\neuutrFq1ivfff5/Vq1dTp04dPvjgA2ti9wd58a2h1aoqqnq7qtZ1L18FOkdBdHznWtKm9OJEShyD\nBg1ie+I27r77bq9jGeOJQoUK0adPH5KTk2natCl9+vShefPm7Ny50+toBY79srgAyD6aReaCt8j8\n4i1q31SN+E2bGDFiBMWLF/c6mjGeu/HGG1m8eDHTp0/H5/MRGRnJiBEjOHPmjNfRCgwrBPmYqnI0\n6WtSo3pxZs9mRowYweb4TdSrV8/raMbkKyJCly5dSElJ4eGHH+bVV1+lUaNGbN261etoBYIVgnzq\n7KF0Mj79H/Z/9R531rudpMRtDBo0yJrEGXMJlStX5rPPPuPzzz8nNTWVhg0b8uqrr3Ly5Emvo+Vr\nVgjyGdVzHN4cQ2pUbwpl7mL8+PGs/mYVtWrV8jqaMQXGY489hs/no0uXLowYMYK6deuyevVqr2Pl\nW1YI8pEz+38mfc4gsr6exL0tmuNLTqZ3794UKmRvkzFX69prryUqKoply5Zx6tQpmjVrRp8+fThy\n5IjX0fId+4TJBzT7LIfWfUrq1L4UO/Ir06dPZ+mSJdx4441eRzOmwLvvvvtISkqiX79+TJgwgTp1\n6rB06VKvY+UrVgg8dirtO9Jn9ufgqhk82v4Rdu3cQZcuXaxJnDF5qFSpUrz33nusWbOGkiVL8sAD\nD9C1a1cOHDjgdbR8wQqBR86dOUXWymmkzxxAGT3G/Pnz+Tw6msqVK3sdzZig1aRJE7Zu3crrr7/O\nnDlzqF27NtHR0V7H8pwVAg+c3JtM+rS+HF4fzdNdu7BzRwqPPvqo17GMCQnFihXjzTffJD4+nmrV\nqtGxY0cee+wxUlNTvY7mGSsEAXTu1HEOLJ9I+uxXqFwqjOXLlxMVFcW1117rdTRjQk5kZCTr169n\n5MiRLF68mPDwcKZOnRqSTeysEATIid2bSZvah6Nbv6Jv3774krfTqlUrr2MZE9LCwsIYOHAg27Zt\n4/bbb+fZZ5+ldevW/PDDD15HCygrBH6WfeIw+xa9S8ZnQ6nxl/KsWbOGsWPHUqpUKa+jGWNcNWvW\nJC4ujokTJ7Jhwwbq1KnDuHHjyM7O9jpaQFgh8BNV5diO1aRN6cXJHasYMmQIidsSaNKkidfRjDEX\nUKhQIXr27ElycjL33HMP/fr1o1mzZqSkpHgdze+sEPjB2aMHyFzwv+xb+DZ1at7E5vh4/v73v1Os\nWDGvoxljLqNatWosWrSIWbNmsWvXLurWrcvw4cODuomdFYI8pKocTVxG2pT/JvunBEaNGsWmjRuI\njIz0Opox5iqICJ07d8bn8/Hoo48yZMgQGjRowObNm72O5hdWCPLImYNpZHw6hP2Lx9Gk4R0kJW7j\n5ZdfJiwszOtoxpg/6LrrruOTTz7hiy++IDMzkzvvvJNXXnmFEydOeB0tT1kh+JP0XDaH4xeSPrUP\nhfd9z8SJE1m5YgU1a9b0OpoxJo+0a9cOn89Ht27dGDVqFJGRkaxatcrrWHnGCsGfcHrfT2TMeYWs\n2I9p1bIFKb5kevbsaU3ijAlC5cqV46OPPiI2Npbs7GzuueceevXqxeHDh72O9qfZJ9YfoNlnOLhm\nLunT+1H8RAazZs1i8VdfUa1aNa+jGWP8rGXLliQmJtK/f38mTZrEbbfdxldfFezRdq0QXKVTqd+S\nPqM/h1bPpmOHDuxMSaFz587WJM6YEFKyZEnGjBnD2rVrKVOmDA899BBPPvkk+/bt8zraH2KF4Aqd\nO3OKrLgo0mcOoFyhkyxcuJBPPpnLdddd53U0Y4xHGjVqxJYtWxg6dCjz5s0jPDycefPmFbg2FVYI\nrsDJn5JIn/Y8hzfOp1u3Z9mR4uORRx7xOpYxJh8oVqwYw4YNY8uWLVSvXp1OnTrRvn17fv31V6+j\nXTErBJdw7tRx9i/9gPS5g6lSphixsbF8/PHHlCtXzutoxph8JiIignXr1jF69GiWL19OeHg4kydP\nLhB7B1YILuL495tIi+rN8cSl9O/fn+TtSbRs2dLrWMaYfKxw4cIMGDCAxMRE6tWrR/fu3WnVqhXf\nf/+919EuyQpBLtnHD7EvZjSZ0W9w8w2VWLt2LWPGjKFkyZJeRzPGFBA333wzsbGxTJo0ifj4eCIi\nInj33XfzbRM7KwQuVeWYbyVpUb049e0ahg4dSkLCVho1auR1NGNMAVSoUCF69OhBcnIy9957LwMG\nDOCuu+5i+/btXkf7D1YIgLNH9rFv/nD2xbxDZO1b2LJ5M8OGDaNo0aJeRzPGFHBVq1blyy+/ZM6c\nOezevZs77riDN954g9OnT3sd7d9CuhCoKkcSlpAe1ZtzexMZPXo0G9avJyIiwutoxpggIiI88cQT\n+Hw+OnbsyLBhw6hfvz6bNm3yOhoQwoXgTFYqmfNe48DS8TRt1JCkpEQGDBhA4cKFvY5mjAlSlSpV\nYvbs2cTExJCVlUXjxo156aWXOH78uKe5Qq4Q6LlsDm9c4DSJO7CHSZMm8a9/xXLzzTd7Hc0YEyLa\ntm1LcnIy3bt3Z8yYMURERBAXF+dZHk8KgYg8ICI7ReQ7ERkUqOWeztxDxuyBZMVN4YHW95HiS6ZH\njx7WJM4YE3Bly5blww8/JC4uDhGhZcuWPPfccxw6dCjgWQL+CSgihYEPgDZAOPCEiIT7c5mafYaD\nq+eQPv0Frjm1n7lz5xIT8yVVq1b152KNMeayWrRoQWJiIi+99BKTJ08mPDycmJiYgGbwYlP4TuA7\nVd2tqqeBT4B2/lrYqV93kj7jRQ6tmUOn/3qcnTtS6NSpkzWJM8bkG9dccw3vvPMO69evp0KFCjzy\nyCP87W9/IzMzMyDL96IQ3AD8nOP6Xndenhs+fDjps16mfNhpYmJimD17NhUrVvTHoowx5k9r2LAh\n8e4Y59HR0dSuXZsVK1b4fbn59uC4iPQQkXgRif+jVfGvf/0rPXp0J8Xno23btnmc0Bhj8l7RokUZ\nMmQIW7dupX79+gH5IosEuiGSiDQBhqnq/e71wQCqOuJij2nQoIHGx8cHKKExxgQHEdmsqg0udz8v\n9gg2AbeISA0RKQp0Ar70IIcxxhggLNALVNWzItIHWAoUBqJUNTnQOYwxxjgCXggAVPUroGAP8mmM\nMUEi354sNsYYExhWCIwxJsRZITDGmBBnhcAYY0KcFQJjjAlxAf9B2R8hIpnAj3/w4RWBfXkYpyCw\ndQ4Nts6h4c+s842qWulydyoQheDPEJH4K/llXTCxdQ4Nts6hIRDrbIeGjDEmxFkhMMaYEBcKheAj\nrwN4wNY5NNg6hwa/r3PQnyMwxhhzaaGwR2CMMeYSgroQiMgDIrJTRL4TkUFe5/E3EakmInEi4hOR\nZBHp53WmQBCRwiKyVUT+6XWWQBCRciISLSI7RCTFHeMjqInIi+7f9HYRmSsixb3OlNdEJEpEMkRk\ne4555UVkuYh86/57rT+WHbSFQEQKAx8AbYBw4AkRCfc2ld+dBQaoajjQGOgdAusM0A9I8TpEAI0F\nlqjqrUAkQb7uInID0BdooKp1cNrXd/I2lV9MAx7INW8QEKuqtwCx7vU8F7SFALgT+E5Vd6vqaeAT\noJ3HmfxKVVNVdYs7fQTnA8Iv40HnFyJSFXgImOx1lkAQkbJAc2AKgKqeVtWD3qYKiDCghIiEAdcA\nv3qcJ8+p6irgQK7Z7YDp7vR0oL0/lh3MheAG4Occ1/cS5B+KOYlIdaAesMHbJH73HjAQOOd1kACp\nAWQCU93DYZNFpKTXofxJVX8BRgM/AanAIVVd5m2qgKmsqqnudBpQ2R8LCeZCELJEpBTwOfCCqh72\nOo+/iEhbIENVN3udJYDCgDuAiapaDziGnw4X5BfucfF2OEXweqCkiDzpbarAU+crnn75mmcwF4Jf\ngGo5rld15wU1ESmCUwRmq+p8r/P4WVPgERHZg3Por6WIzPI2kt/tBfaq6vk9vWicwhDMWgE/qGqm\nqp4B5gN3eZwpUNJFpAqA+2+GPxYSzIVgE3CLiNQQkaI4J5e+9DiTX4mI4Bw7TlHVd73O42+qOlhV\nq6pqdZz391+qGtRbiqqaBvwsIrXcWfcCPg8jBcJPQGMRucb9G7+XID9BnsOXQFd3uiuw0B8L8WTM\n4kBQ1bMi0gdYivMtgyhVTfY4lr81BZ4CkkQkwZ33qjtGtAkezwOz3Q2c3cAzHufxK1XdICLRwBac\nb8ZtJQh/YSwic4EWQEUR2QsMBd4GPhWRbjgdmB/3y7Ltl8XGGBPagvnQkDHGmCtghcAYY0KcFQJj\njAlxVgiMMSbEWSEwxpgQZ4XAXJKIvOZ2fUwUkQQRaeSHZewRkYp5/byXWN7Ri8zPdtcxWUS2icgA\nEckX/0dEZJiIvHSR23q4nUh3iEi8iLTwU4ZXc11f6/5bXUT+5o9lmsDIF3/kJn9y2xu3Be5Q1dtx\nfuH586UfVaCdUNW6qnobcB9O59qhHme6JLfNxnPA3W430h7ALLdjZ177XSFQ1fO/7q0OWCEowKwQ\nmEupAuxT1VMAqrpPVX+F32/Fi0gDEVnhTg8TkZkiss7tod7dnd9CRFaJyCJ3jIgPc29ti8jfReSF\nHNf/90JjKojIFyKy2d1y75Fj/lH3MdtEZL2IVHbn13DzJInI8CtZcVXNwPlQ7SOOwiLyjohscveO\nnsuxXitFZKGI7BaRt0Wks4hsdJf3V/d+D4vIBrdR3Nc5sg0Tpw/9CvfxfXOsz2sisktEVgO1LhAT\n4BXgZVXd5+beAkwFel/mfbrTfU22isja879UFpGnRWS+iCxx379R7vy3cbp/JojI7POvt5vhbaCZ\ne9uL7vtcN8d6rBaRyCt53Y1HVNUudrngBSgFJAC7gAnAPTlu2wNUdKcbACvc6WHANqAEUBFnD+J6\nnF9MngRuwvml93Lg/+Z8Lpwtyy3uvELA90CFC+Qq7/5bAth+/j44DbkedqdHAa+7018CXdzp3sDR\ni6zvf8wHDuJ0fOyR4/mKAfE4TdBauPep4s7/BXjDvV8/4D13+lp++wHn/wPG5Hi91rqPrQjsB4oA\n9YEknJbLZYDvgJcukO8AUDbXvHbAF5d5n8oAYe50K+Bzd/ppnF8rlwWK4/yatdqFXp/z193X4J85\n5nfNsd41gXiv/5btcumL7RGYi1LVozgfSD1wWh/PE5Gnr+ChC1X1hDpbqXE4Y0MAbFRnfIhsYC5w\nd67l7QH2i0g9oDWwVVX3X+D5+4rINmA9TmPBW9z5p4Hzo5Rtxiks4LTemOtOz7yC/BfSGujitu7Y\nAFTIsdxN6owFcQqneJ1vkZyUI0NVYKmIJAEvA7fleO5FqnrKfb0ycApPM2CBqh5Xp4NsXvfJKgt8\nJs5oWP/IlSdWVQ+p6kmcPkY3XuVzfwa0FacB4rM4A66YfMwKgbkkVc1W1RWqOhToA3RwbzrLb38/\nuYcNzN23RC8zP6fJOFulzwBRuW90T4S2ApqoaiRO35nzyz+j7mYokM3ve2lddS8VEbnJfZ4MQIDn\n1TmHUFdVa+hvPfFP5XjYuRzXz+XI8D4wXlUjcI7p53zNcj4+d+7L8eEU65zq4+yxwMXfpzeBOHVG\n/Ho4D/Ogqsdx9vja4fTGmX01jzeBZ4XAXJSI1BKRW3LMqotzqACcQw7nP4A68HvtRKS4iFTAOWyw\nyZ1/p3u8vhDwX8DqCyx2Ac5wfQ1xGgbmVhbIUtXjInIrzpCcl7OG34Y27HwF90dEKgEf4nx4q5vl\nv92tXESkplzdgDBl+a0NetdL3dG1CmgvIiVEpDTOh/WFjAJGuq817rH5R4FJ7u17uPD7lDPP01eQ\nB+DM+fXP5QhQOte8ycA4nL2lrCt8fuMRKwTmUkoB00XEJyKJOGM/D3NvewMYKyLxOFuNOSXiHBJa\nD7yp7glmnIIwHqeF8A84H/q/o86wonHAp+4hpNyWAGEikoJzknL9FaxHP5zxm5O49Ch150+GJgNf\n4xziecO9bTLO1vcW93DKJK5uS3kYzqGYzcC+y91ZnZO+83DOtyzmt2Ka+35f4rQeXyMi3+EU1/aq\nmune5WLv0yhghIhsvYr1+AhIPH+yOIdEINs9Sf+im2szcBjnxLXJ56z7qMlTIjIM5yTi6FzzW+Cc\n7Gx7mccXwmk33FFVv/VXzmAkzni+U3E28J5UD/9zi8j1wArgVlUNlWFECyzbIzD5hoiE43w7JtaK\nwNVT1bOq+pSqdva4CHTBOaH+mhWBgsH2CIwxJsTZHoExxoQ4KwTGGBPirBAYY0yIs0JgjDEhzgqB\nMcaEOCsExhgT4v4/GCWh0JoIzucAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1137c0208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "economy1 = supplyDemand(10-p,p)\n",
    "economy1.internationalTrade(4)\n",
    "economy1.drawSurplus1()\n",
    "economy1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import sympy\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as patches\n",
    "p = sympy.Symbol(\"p\")\n",
    "class supplyDemand:\n",
    "    def __init__(self,demandEquation,supplyEquation):\n",
    "        self.demandEquation = demandEquation\n",
    "        self.supplyEquation = supplyEquation\n",
    "        self.priceEnd = sympy.solve(demandEquation)[0]\n",
    "        self.prices = []\n",
    "        self.demandQ = []\n",
    "        self.supplyQ = []\n",
    "        for price in range(0,self.priceEnd+1):\n",
    "            self.prices += [price]\n",
    "            self.demandQ += [demandEquation.subs(p,price)]\n",
    "            self.supplyQ += [supplyEquation.subs(p,price)]\n",
    "        self.startingQ = sympy.solve(demandEquation-supplyEquation)[0]\n",
    "        self.startingP = demandEquation.subs(p,self.startingQ)\n",
    "        self.mode = \"Normal\"\n",
    "    def internationalTrade(self,worldPrice):\n",
    "        self.mode = \"International\"\n",
    "        self.worldPrice = worldPrice\n",
    "        if worldPrice>self.startingP:\n",
    "            self.export = True\n",
    "            self.domesticQ = self.demandEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.supplyEquation.subs(p,worldPrice)-self.demandEquation.subs(p,worldPrice)\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        else:\n",
    "            self.export = False\n",
    "            self.domesticQ = self.supplyEquation.subs(p,worldPrice)\n",
    "            self.internationalQ = self.demandEquation.subs(p,worldPrice)-self.domesticQ\n",
    "            self.totalQ = self.domesticQ+self.internationalQ\n",
    "        self.domesticQ\n",
    "    def drawSurplus1(self):\n",
    "        if self.export:\n",
    "            triangle1 = patches.Polygon([[self.domesticQ,self.worldPrice],[0,self.worldPrice],[0,self.priceEnd]],True,label=\"Consumer Surplus\")\n",
    "            triangle2 = patches.Polygon([[0,0],[0,self.startingP],[self.startingQ,self.startingP]],True,label=\"Original Producer Surplus\",color=\"red\")\n",
    "            rect1 = patches.Polygon([[0,self.startingP],[0,self.worldPrice],[self.domesticQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Transferred Surplus\",color=\"orange\")\n",
    "            triangle3 = patches.Polygon([[self.domesticQ,self.worldPrice],[self.totalQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Surplus from International\",color=\"yellow\")\n",
    "            currentAxis = plt.gca()\n",
    "            currentAxis.add_patch(triangle1)\n",
    "            currentAxis.add_patch(triangle2)\n",
    "            currentAxis.add_patch(rect1)\n",
    "            currentAxis.add_patch(triangle3)\n",
    "            print(\"The consumer surplus goes from \"+str((self.priceEnd-self.startingP)*self.startingQ*.5)+\" to \"+str((self.priceEnd-self.worldPrice)*self.domesticQ*.5))\n",
    "            print(\"The producer surplus goes from \"+str(self.startingP*self.startingQ*.5)+\" to \"+str(self.worldPrice*self.totalQ*.5))\n",
    "        else:\n",
    "            triangle1 = patches.Polygon([[self.startingQ,self.startingP],[0,self.startingP],[0,self.priceEnd]],True,label=\"Original Consumer Surplus\", color=\"red\")\n",
    "            triangle2 = patches.Polygon([[0,0],[0,self.worldPrice],[self.domesticQ,self.worldPrice]],True,label=\"Producer Surplus\")\n",
    "            rect1 = patches.Polygon([[0,self.startingP],[0,self.worldPrice],[self.domesticQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Transferred Surplus\",color=\"orange\")\n",
    "            triangle3 = patches.Polygon([[self.domesticQ,self.worldPrice],[self.totalQ,self.worldPrice],[self.startingQ,self.startingP]],True,label=\"Surplus from International\",color=\"yellow\")\n",
    "            currentAxis = plt.gca()\n",
    "            currentAxis.add_patch(triangle1)\n",
    "            currentAxis.add_patch(triangle2)\n",
    "            currentAxis.add_patch(rect1)\n",
    "            currentAxis.add_patch(triangle3)\n",
    "            print(\"The producer surplus goes from \"+str(self.startingP*self.startingQ*.5)+\" to \"+str(self.worldPrice*self.domesticQ*.5))\n",
    "            print(\"The consumer surplus goes from \"+str((self.priceEnd-self.startingP)*self.startingQ*.5)+\" to \"+str((self.priceEnd-self.worldPrice)*self.domesticQ*.5))\n",
    "    \n",
    "    def plot(self):\n",
    "        plt.plot(self.demandQ,self.prices,'k')\n",
    "        plt.plot(self.supplyQ,self.prices,'k')\n",
    "        plt.plot(self.startingQ,self.startingP, 'bo')\n",
    "        plt.xlabel(\"Supply and Demand Quantity\")\n",
    "        plt.ylabel(\"Price\")\n",
    "        if self.mode==\"International\":\n",
    "            plt.plot([0,self.priceEnd],[self.worldPrice,self.worldPrice],\"k\")\n",
    "            if self.startingP!=self.worldPrice:\n",
    "                plt.plot(self.totalQ,self.worldPrice, 'bo')\n",
    "                plt.plot(self.domesticQ,self.worldPrice,'bo')\n",
    "                plt.plot([0,self.domesticQ],[self.worldPrice,self.worldPrice],\"m\",label='Domestic Quantity')\n",
    "                plt.plot([self.domesticQ,self.totalQ],[self.worldPrice,self.worldPrice],\"g\", label='International Quantity')\n",
    "                \n",
    "                if self.export:\n",
    "                    print(\"The country exports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.totalQ*self.worldPrice))\n",
    "                else:\n",
    "                    print(\"The country imports during international trade, and the price moves from \"+str(self.startingP)+\" to \"+str(self.worldPrice)+\".\")\n",
    "                    print(\"The domestic producers go from producing \"+str(self.startingQ)+\" units to \"+str(self.domesticQ)+\" units.\")\n",
    "                    print(\"The domestic consumers go from consuming \"+str(self.startingQ)+\" units to \"+str(self.totalQ)+\" units.\")\n",
    "                    print(\"Total domestic producer revenue is \"+str(self.domesticQ*self.worldPrice))\n",
    "                plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The consumer surplus goes from 12.5000000000000 to 8.00000000000000\n",
      "The producer surplus goes from 12.5000000000000 to 18.0000000000000\n",
      "The country exports during international trade, and the price moves from 5 to 6.\n",
      "The domestic producers go from producing 5 units to 6 units.\n",
      "The domestic consumers go from consuming 5 units to 4 units.\n",
      "Total domestic producer revenue is 36\n"
     ]
    },
    {
     "data": {
      "image/png": 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JnVGFhhTzmYswt6F/v764uLkTHS0v75A+37p167CxsWHFihV8//33HD9+HHd3d7VjSW+Q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\n8noAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113bf5208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "economy1 = supplyDemand(10-p,p)\n",
    "economy1.internationalTrade(6)\n",
    "economy1.drawSurplus1()\n",
    "economy1.plot()"
   ]
  }
 ],
 "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}