{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA8kAAAJcCAYAAADKEObdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy86wFpkAAAACXBIWXMAAAsTAAALEwEAmpwYAABaQElEQVR4nO3dd3hUZf7+8XsmhXQCJCEJvZfQCQSpSlV6dwERBMT9rihgwYKuPxTBRVTE3RVRURGUoiiKiHQQlF4EQuihJaRAOglp5/dHjrPEJISWTID367q4zJzynM88OWbmnuc5ZyyGYRgCAAAAAACy2rsAAAAAAABKCkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgCgRAgKCtLGjRvtXUaBwsPDZbFYlJmZae9S7O7q39X/+3//T4888ogk6cyZM/Lw8FBWVpYdqwMA4NYQkgEA11S1alW5urrKw8PD9i8iIuK2H+fQoUO6//77b3u7V7s60N1NNm7cqIoVK+ZZfv/99+uTTz65pbZHjhypV155Jdeygn5XlStXVnJyshwcHG7L8ZcvX64mTZrIy8tLPj4+6tixo06dOnXT7QEAcD0c7V0AAKDk+/HHH9W5c2d7l4F7yPHjx/Xoo49q2bJl6tixo5KTk7V69WpbAL8dDMOQYRiyWhkzAAD8D68KAIAbFhcXp549e8rX11dlypRRz549de7cOdv6zz//XNWrV5enp6eqVaumhQsX2tZ9/PHHqlevnjw9PVW/fn3t2bNHUs6I9dq1ayVJO3bsUHBwsLy8vFS+fHk988wzkv435fmzzz5TpUqVVKZMGc2ZM0c7d+5Uo0aN5O3trXHjxl3387BYLJozZ45q1aolb29vPfnkkzIMQ5KUlZWl5557Tj4+Pqpevbp++umnXPsmJCRo9OjRCggIUIUKFfTKK68oKytL6enpatKkiT744ANbO23atNHrr7+e5/jbt2+Xv79/runJ3333nRo1anTNfrhZH3/8sWrWrKmyZcuqd+/ethkBhmFo4sSJ8vPzk5eXlxo2bKiDBw9q7ty5WrhwoWbMmCEPDw/16tVLUu7f1dWunpI+efJk/frrrxo3bpw8PDw0btw4Pfnkk3r22Wdz7dO7d2+99957edrat2+fqlWrpk6dOsliscjT01MDBgxQ5cqVJeX067Rp01SjRg15enqqefPmOnv2rCTpt99+U4sWLVS6dGm1aNFCv/32m63d+++/X5MnT1abNm3k5uamkydPKiwsTF26dFHZsmVVp04dLVmy5Jb6GQBwhzMAALiGKlWqGGvWrMm1LDY21vjmm2+MlJQUIzEx0Rg4cKDRp08fwzAMIzk52fD09DTCwsIMwzCMiIgI4+DBg4ZhGMaSJUuMwMBAY8eOHUZ2drZx7NgxIzw8PM9xWrVqZcyfP98wDMNISkoyfv/9d8MwDOPUqVOGJOOJJ54wUlNTjV9++cUoVaqU0adPHyMqKso4d+6c4evra2zcuDHf5/Laa68Zw4YNsz2WZPTo0cOIi4szTp8+bfj4+Bg///yzYRiG8eGHHxp16tQxzpw5Y1y8eNG4//77DUlGRkaGYRiG0bdvX2Ps2LFGcnKyERUVZbRo0cKYM2eOYRiGceDAAcPb29sIDQ01pk6daoSEhBiZmZn51lS9enVj9erVtscDBw40pk+ffs1++KsNGzYYFSpUyLO8Q4cOxscff2wYhmGsW7fOKFeunLF7924jLS3NGDdunNGuXTvDMAxj1apVRrNmzYy4uDgjOzvbCA0NNSIiIgzDMIwRI0YYkydPztXu1b+rq/v0z9/Pn3109fENwzC2b99uBAQEGFlZWYZhGEZMTIzh6upqXLhwIU/tJ06cMEqVKmVMmDDBWL9+vZGUlJRr/YwZM4wGDRoYYWFhRnZ2trFv3z4jNjbWuHjxouHt7W3Mnz/fyMjIML766ivD29vbiI2NtdVUqVIl4+DBg0ZGRoYRHx9vVKxY0Zg3b56RkZFh7NmzxyhXrpxx6NChfPsaAHD3YyQZAFCovn37ytvbW97e3urbt6/KlSunAQMGyM3NTZ6enpo8ebI2bdpk295qtergwYNKTU1VQECAgoKCJEmffPKJJk2apBYtWshisahmzZqqUqVKnuM5OTnp+PHjio2NlYeHh1q1apVr/auvvioXFxd17dpV7u7uGjJkiPz8/FShQgW1a9dOe/fuve7n9uKLL8rb21uVK1fWAw88oH379kmSlixZogkTJqhSpUoqW7asXnrpJds+UVFRWrlypWbNmiV3d3f5+flp4sSJWrRokSSpQYMGeuWVV9S3b1/NnDlTX375ZYHThIcMGaKvv/5akpSUlKSVK1dqyJAh19UPV4uIiLD9jv78t2XLFtv6hQsXatSoUWrWrJlKlSql6dOn6/fff1d4eLicnJyUlJSksLAwGYahevXqKSAg4Lr78Hq1bNlSpUuX1rp16yRJixYt0v3336/y5cvn2bZ69erauHGjzp8/r8GDB8vHx0cjR45UcnKypJxzaerUqapTp44sFosaN26scuXK6aefflKtWrU0fPhwOTo6asiQIapbt65+/PFHW9sjR45UUFCQHB0dtWrVKlWtWlWPPfaYHB0d1bRpUw0YMEBLly697c8fAHBnICQDAAr1/fffKz4+XvHx8fr+++91+fJlPfHEE6pSpYq8vLzUvn17xcfHKysrS+7u7lq8eLHmzJmjgIAA9ejRQ2FhYZKks2fPqkaNGoUe79NPP9XRo0dVt25dtWjRQitWrMi1/upQ5erqmufxn0Hqevj7+9t+dnNzs+0bERGhSpUq2dZdHeZPnz6tjIwMBQQE2ALpE088oejoaNs2I0aM0OnTp9W9e3fVqlWrwOMPHTpUy5Yt05UrV7Rs2TI1a9bMdqzC+uFqgYGBtt/Rn//atm1rWx8REZHrOXh4eKhcuXI6f/68OnbsaJsO7efnp7FjxyoxMfF6uu+GjRgxQgsWLJAkLViwQMOHDy9w21atWmnJkiWKiYnRr7/+qs2bN+vNN9+UVPC59NfnKeX87s6fP297fPXv9fTp09q+fXuuDxcWLlyoCxcu3NLzBADcuQjJAIAb9s477+jIkSPavn27EhMTtXnzZkmyXc/brVs3rVmzRpGRkapbt64ef/xxSTnh5MSJE4W2X6tWLX399deKjo7WCy+8oIEDByolJaXonlA+AgICbNe4Sjlfb/SnSpUqqVSpUoqNjbUF0sTERB06dMi2zT/+8Q/17NlTv/zyS64R3b+qX7++qlSpop9//llfffWVhg4dalt3O/shMDBQp0+ftj1OSUnRxYsXVaFCBUnS008/rd27dys0NFRHjx7V22+/LSnnuu2bld++jzzyiJYvX679+/fr8OHD6tu373W11aJFC/Xv318HDx6UVPC59NfnKeX87v58nn+tq1KlSurQoUOuDxeSk5P14YcfXlddAIC7DyEZAHDDkpKS5OrqKm9vb126dElTpkyxrYuKitLy5cuVkpKiUqVKycPDw3b34DFjxmjmzJnavXu3DMPQ8ePH8wQaKWeEMSYmRlarVd7e3pJU7HcgHjx4sGbPnq1z584pLi5Ob731lm1dQECAunbtqmeffVaJiYnKzs7WiRMnbFPOv/zyS+3evVuff/65Zs+erREjRlxzdHvo0KF6//33tXnzZg0aNMi2/Hb2w5AhQ/TZZ59p3759unLlil5++WWFhISoatWq2rlzp7Zv366MjAy5u7vLxcXFdpzy5cvr5MmTN3XM/PatWLGiWrRooeHDh2vAgAFydXXNd98tW7bo448/to3Oh4WF6YcffrBNOR8zZoxeffVVHTt2TIZh6I8//tDFixfVvXt3HT16VF999ZUyMzO1ePFihYaGqmfPnvkep2fPnjp69Ki+/PJLZWRkKCMjQzt37tThw4dv6jkDAO58hGQAwA2bMGGCUlNT5ePjo1atWunBBx+0rcvOzta7776rwMBAlS1bVps2bbKNyg0aNEiTJ0/W0KFD5enpqb59++rSpUt52l+1apWCgoLk4eGh8ePHa9GiRQWGqaLy+OOPq1u3bmrcuLGaNWum/v3751o/f/58paenq379+ipTpowGDhyoyMhInTlzRhMmTND8+fPl4eGhoUOHKjg4WBMnTizwWEOGDNGmTZvUsWNH+fj42Jbfzn7o3Lmz3njjDQ0YMEABAQE6ceKE7RrqxMREPf744ypTpoyqVKmicuXK6fnnn5ckjR49WqGhobbr0W/E+PHj9c0336hMmTJ6+umnbctHjBihAwcOXHOqtbe3t3744Qc1bNhQHh4eevDBB9WvXz9NmjRJkvTMM89o8ODB6tq1q7y8vDR69GilpqaqXLlyWrFihd555x2VK1dOM2bM0IoVK3L169U8PT21evVqLVq0SIGBgfL399cLL7ygK1eu3NBzBQDcPSzGn3PjAAAAisHmzZv1yCOP6PTp07c0nRsAgKLASDIAACg2GRkZev/99zVmzBgCMgCgRCIkAwCAYnH48GF5e3srMjJSEyZMsHc5AADki+nWAAAAAACYGEkGAAAAAMDkaO8CboSPj4+qVq1q7zIAAAAAAEUgPDxcsbGxdq3hjgrJVatW1a5du+xdBgAAAACgCAQHB9u7BKZbAwAAAADwJ0IyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmR3sXcCMizl7SPycstHcZAAAAAIC7FCPJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJwD1q6uzhmrvwZc1Z8KIW/fCO0q6k3La2f1z7sWIunr9t7eHu9dZ/R9u7hDven31oGNlatXG+5ix4UXMWvKhPvn5VcQnRkqTZ8ybocmpSru0BAMjPG+8/ojWbF9oe/777J23a9u0NtRF+LlRnI47aHi9f/ZFCj+0odL+rX6NWrlyp2rVr6/Tp0zd07NvBsdiPCKBEcHR01thh0yRJy1fP0c79a9WuZZ/b0navzo/flnZwb8rOzpLV6mDvMu44h45uU1JKnJ4YNk0Wi1WJSRfl5ORi77IAAHcYBwcnhZ3YpTYtesvN1fOG98/OztLpc4fl7OSiSoG1b6qGdevW6emnn9Yvv/yiKlWqFLq9YRgyDENW6+0ZAyYkA1BF/1qKij0jSZr/zVR1bjdUgeWr63Jqkj75+lU9PWqWoi+e049r5iorK1OGYWhgj/Hy9PDWtys/UGJynAwjW+1a9lVQ7Va52li5/jNFRJ1URma66tVsqfvvG2DnZ4uSKPxcqDb+/o1cSrnrYlyknhwxU4t/fE+JyReVmZmhkCbd1KxhR0k5nzK3bNJNx07tk5Ojkwb3fEYe7qXt/AzsLzklXp7u3rJYct4geHmWs3NFAIA7kdVqVdMGD2jb3p/VsfXgXOviE2P045qPdTk1SW6unurdZaxKe/lo+eqP5OjgpAsxp+XpUUbnIo/JarHqQNhWPXj/o5KkM+fDtH3vz0pOiVentkNUv1bLfI+flJSkxx9/XCtXrlSNGjUkSe+++67mzZsnSRozZowmTJig8PBwdevWTSEhIdq9e7dWrlypJUuWaMmSJbpy5Yr69eunKVOmSJL69u2rs2fPKi0tTePHj9fYsWOv2QeEZOAel52drVNnD6lJUIdrbrfnwDq1bNJNDeu2UVZWprKNbB0/tU+e7mU0pM/zkqS0K5fz7PdA60FydfFQdna2FiybpqiYMyrvW7lIngvubBeiT+uJR6arTGk/SVLvLo/L1cVDGZnp+nTRP1W3Zgu5uXoqI+OKKvrXVMfWg7V2y9fae2iD2rXsa9/iS4D6tUL0+dI3dOb8EVWrFKQGddsowK+qvcsCANyBWjTqrI8WvqzWzXvmWr5q43w1qtdWjeu3175Dm7Rq05d6uNdESVJS8iU9Nvg1Wa1Wbdr2rZydXHRf8x6SpL2HNik5JV4jB72q2EuRWvzjO/mG5KysTJ04cUJ79uxR3bp1JUm7d+/WZ599pu3bt8swDIWEhKhDhw4qU6aMjh07pi+++EKtWrXS6tWrdezYMe3YsUOGYah3797avHmz2rdvr3nz5qls2bJKTU1VixYtNGDAAJUrV/CHyXYLyWlpaWrfvr2uXLmizMxMDRw40Jb0AdycCwlpOhGTrLSMbLk4WVXD10P+pfOfbpmZma65C19WUkqcfMoEqnrlhtdsu4J/LW3ZuVyJyZdUt0YLlSvjLz+fSlrz61dau2WRaldrosoV6ubZL/Todu05uF7Z2dlKvhyvmEvnCcl3uRs5D68W6F/dFpAlace+XxR2YrckKTHpoi7FR8nN1VMODo6qVa2pJCnAr5pOnjlQNE/Ejm6mD708y+kfj76t8HOhCj97SAuWTdfA7k+pWuUGxVQ1AKCkutHXlVKl3NSoXlvt2PeLnBydbcvPRR7ToB7jJUkN67bR2i1f29bVq9XymtOd69RoLovFKt9yFZRyOTHfbaxWB7m7u+rTTz/V+++/L0nasmWL+vXrJ3d3d0lS//799euvv6p3796qUqWKWrVqJUlavXq1Vq9eraZNc94jJCcn69ixY2rfvr1mz56t7777TpJ09uxZHTt27Joh2W437ipVqpTWr1+v/fv3a9++fVq1apW2bdtmr3KAO96FhDSFXUhSWka2JCktI1thF5J0ISEt3+3/vCb56cdmyZChXX+skZTzx8kwDEk5QfpPDeu21sO9npGTo7MWLX9bp84eUrkyAXp86FT5+VTUht+/0ebt3+U6RlxCtH7f85Me6f+ynnhkumpWbaKsrIyiePooIW70PLyas2Mp28/h50J16swhjRr8mp4YNk3+vlWUmZVzPlqtDrJYLJIki8Wq7OzsIngm9nMrfejo6KSaVRurc7uhatuit46c3F3U5QIASribfV0Jafqg9h3apPSMK9d1nMLug+Hg4GT72ZCR7zYWi0XVq1fXjh07NG3atEKP+WdwlnKuS37ppZe0b98+7du3T8ePH9fo0aO1ceNGrV27Vr///rv279+vpk2bKi3t2s/dbiHZYrHIw8NDkpSRkaGMjAzbmx4AN+5ETLKysnP/wcnKNnQiJvma+zk5lVK3Do9q256Vys7OUmkvH0VGn5IkHT6+07ZdXEK0ypT2U8sm3VS7RnNFx55VUnKcnByd1ahuW93XrIcio8NztX0lPVVOTi5yKeWq5JQEnQjff3ueLEqsmz0P/+rKlVS5uLjLyamUYi9F6NyFE7ezzBLtZvswMvqUkpLjJOXc6Toq9qxKe/oUWZ0AgDvDzb6uuLp4qH7tEO07tMm2rGJALR06mjOwefDIb6pcoU6++zo7u+pKeuEf7ubHarXqp59+0sKFC/Xpp5+qXbt2+v7773X58mWlpKTou+++U7t27fLs161bN82bN0/JyTnP6/z584qOjlZCQoLKlCkjNzc3hYWFXdfArF2vSc7KylLz5s11/PhxPfnkkwoJCcmzzdy5czV37lxJ0uXU/IflAcj26eD1Lr9agF9V+flU1sEjv+u+Zj307coPtOfgBtWq2sS2TejRbfojbKscrA7ycC+tti16KyLqpNb++rUsFousVgd17/hYrnb9favI37eK/jt/krw8yqriTd7hEHeOWzkPr1ajSiPtPrBO/50/SeXKBKiif43bUd4d4Wb7MOVyolas+1RZWZmSpMDy1dWicZc822VkpGvWp0/ZHrdq+pBaNet+CxUDAEqyW3ltbtW0u3buX2N7/OD9I/Tjmrn6ffdPtht35ad2tab65qfZOnpyt+3GXTeibNmyWrVqldq3b6/3339fI0eOVMuWOdcwjxkzRk2bNlV4eHiufbp27arDhw/rvvvukyR5eHhowYIFevDBBzVnzhzVq1dPderUsU3PvhaL8ee8SjuKj49Xv3799MEHH6hBg4KvnQosX11jhrxRjJUBd46tx2Pz/WPn4mRVm5qMJqF4cB7eOvoQAHA73WmvKyu3vKddu3bZtQa7Tbe+mre3tx544AGtWrXK3qUAd6wavh5ysOa+ZMHBalENXw87VYR7EefhraMPAQC3E68rN85uITkmJkbx8fGSpNTUVK1Zs8Z2m28AN86/tIvq+nvKxSnnf2sXJ6vq+nte112FgduF8/DW0YcAgNuJ15UbZ7drkiMjIzVixAhlZWUpOztbgwcPVs+ePQvfEUCB/Eu78AcPdsd5eOvoQwDA7cTryo2xW0hu1KiR9u7da6/DAwAAAACQR4m4JhkAAAAAgJKAkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAydHeBdyIbGerUiq527sMAAAAAMBdipFkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATI72LuBe9N7zA+QTUFmGIVmtVnXs97gCq9a1Sy3R508pOfGSqtdrLkk6cWiHLkadVcuOA+xSDwAA9vDna/Ofeo98SaXL+t1SmxfOHlfo7o3q2HdMvusTLkUrIjxM9Zq1z3fd959O1YjnZ+d5rS7I2eMHtWvT9+o3+pVbqvtmvftcP9Vt1l7dh06UJGVnZemj10fJv3KtG6rpkzfHatiEmXJ197rtNZ46vFu//fK1MtKvyMHRSZVrNlSH3o9p1aLZql4vWLUbt861fXLCJW34/hP1GjHptteSn3ef66fm7XurQ+/HJEm7Nn6v9Ctpat3tbzfUzrXOvcgzR/XrivlKTrwk51Kucvcso7Y9hss3oMpteQ7A3YCQbAeOTs4a/sx7kqTwI3v168ov9fA/3iz2OrKzshQTcUoXzh63vfDWCGqpGkEti70WAADs6erX5tvFv1JN+VeqWeD6xEvRCtu7Od+QfLW/vlaXVE7OLrp44YwyMq7IyamUTh/dJ4/SZYv8uNnZWbJaHQrdLjbytNZ//7H6jX5FZf0qKjs7Swe2rbnmPh6lyxZbQJYkB0cnHTuwTS07DbilDwkKOvdSkuL105cz1X3YM7YBmvOnQpVw8cJ1h+TsrCxZHQrvb+BORki2s/S0y3Jx9ZAkGYahzSu+UHjYHsliUavOg1SnSVudPX5Qv69eJFd3L8VeOKPyFWvooaETZLFY9MmbY1W7cRudCtsjRydndR/2jMr4BOjEoZ3avnapsrIy5eruqYeGTpS7p7d++2WREi5eUMKlKHl6+ygiPEyZGemKCD+slh0HKDMjXRfOHlen/mN1dP9W/b56sSxWB5VycdPDT76pQzvX6/jB7cpIv6L42Ag179BX2VkZCt29SQ6OTuo35hW5unnauVcBALg16VdStfyz6bqSmqKsrEy1eXCoajYIUcaVNK34cqaSEi7KyM5Sqy6DVadJW104c0wbln9qjlA6atATryvq3AnbyO7ZEwe1cfmnZusWPfyPN/Xryi91Kfqcvnx3ouoHP6Dm7XvnqSMrM0O//fJ1rtdqr7J+2rj8U2VmZMjRyVndHn5KZf0q2PYxsrP12Yxx+tu46XLzKC0jO1vz/vWkhjz1ltw8Shdpv1Wr21ynQnerduPWCtu3RXWatNP5U6GSpNTLSVq9+N9KuBQlR6dS6jLw/+QbWFWpKYlaufBdJSdcUkCVOjKM/7UXunuj9m75SdlZmfKvXFud+o+V1eqgD14eokatuur0sT/Uqf/jWvbxG2rarodOhu6Wo5Oz+jz2ktw9vXPVtnPj9wrpNFBl/SpKkqxWBzVu/aBt/blTodq9+QddTopXux6Pqnbj1rlG9A/tXK8Th3YoIyNdCRcvqGaDELXvOUKStPbbOYo6e1yZGemq1eg+te425Kb6z2p1UKNWXbV78w9q+9AjudYV9N5u2SdvKCXxkqScGQgP9BktrzJ++c4q2Ld1peoHP5BrBmOFavVtP19OTtDab+coKS5WknR/n1GqUK1envePZXwrKPFSlBIuRSkxLlb393lMkaeP6lTYHnmULqe+o16Wg4Ojfl+9WCdDdykz44oCq9ZV54H/J4vFoiX/fUX+lWvr7IkDupKaoq6Dx6li9fpa/J/JeqDvGPlVqCZJWvTvl9Sp/1j5Bla7qf4EbhYh2Q4yM9L15bsTlZmRoZSkOA36++uSpGMHtikmIlzDn31PqSlJ+ur951Whes4fruiIUxrx3Pvy8CqrRf95SRHhh21/1Eq5uGnEc+8rdNcGbVz+qfqNfkUVqtXTkKf/JYvFogPb12jXhu9sU3cuRp3Vw+OmycmplA7tXG8LxZJ0aOd6W52/r1mi/mNfk2fpckpLTbEtj71wRsMnvqvMzHTNe+sfatd9uIY/8642Lp+nw7s2qln7XsXSjwAA3C5/vjZLklfZ8uo1/Hn1HvmiSrm4KTUlUV/NfkE1glrq1JG9ci9dRv3G5ISPK6kpysrM0E8L3lGPR56Vf+VaupJ2WY5Ozrna371xuTr2G6sK1eop/UqqHB2d1a778EKnRzs4Oql1tyG5XquvpF3Ww/+YJquDg04f3a8tPy9Q7xEv2PaxWK2q16yDwvZsVrP2vXT62B/yDaxa5AFZkuo0aatta5aoev1gxUaGq0GLTraQ/Psvi+RXobr6PPaSzhz7Q6sWva/hz7yn39csUWDVerqv68M6GbpLB3eslZTzfuXovq3627jpcnBw1LpvP1LYns2qH/yAMtLT5F+5tu29TUZ6mgIq11Hbhx7R5hVf6MD2NWrVeVCu2i5eOKPgDnk/iPhTSuIl/e3JaboUfV7ffzYtz9RrSYqJCNcjE9+Rg6OTPvvXODVt20Oe3j5q89Awubp5Kjs7S9/MeU0xEeHyDax6U33YuM1D+vKdCWpxf79cywt6b9d/zKuSpKhzJ/TL4g9Us0GIos+fyrftixfOqH7wAwUee8PyT9W8fS9VqFZfiXExWvbxFI2c9O+cfa96//jbL4sUf/GCBv3fG7oUdVZff/Ciej06Se17jtDyz9/SqcO7VbNBiJq07a77uj4sSfr5q1k6GbpLNYJaSJKM7CwNG/+2Th7erW1rFmvgE1PUoGUnHdq1Xn4VRisu5ryyMjMIyLALu4Xks2fP6tFHH1VUVJQsFovGjh2r8ePH26ucW3YhMU0nopOVlpklF0cH1fDzkL+XS77bXj2lKyI8TKu+fl+PPve+Ik4dVt2mbWW1Osjd01sVqwcp6uxxOZdyk3+lWvL09pEk+QZWU8KlaFtIrtO0ne2/G3+YJ0lKSrionxbMVHJinLKzMuVVtrzt+DWCWsrJqVShz6lC1br6ZdEHqt24tWo1bGVbXqlmAzm7uMpZrirl4qbq5h87n4DKiok8faNdBwBAkbjZ12ZJysrK1JaVC3T+VKgsFouSEy7pclK8fAMqa/OPn2nzivmqXj9YFavXV0zkabl7lZF/5VqScj68/qvAanW16YfPVLdZe9Vq2ErO3q43/bzS0y5r1aLZio+JkCwWZWdl5dmmQctOWv7ZdDVr30uHdq5TgxYdb+pYN9KHkuQbWFWJcdEK2/urqtXNPT38/KnDtqnLlWs1UmpKkq6kXdb5k4fUywz51esHq5Q5w+7MsQOKOn9CX73/vKScDzJczaBvsVpVq9H/3ps4ODiqev1gSVL5ijV0+uj+G36uNRuEyGK1qpx/JV1Ojs93m0q1GqqUq7skqVz5ikqMi5ant4+O7tuqA9vXKDs7SymJcboYddYWkm+0D0u5uKle8/u1d8tPuT5sudZ7u9SURP381Sz1HP6crb7r8dX7k5R+5bKq1G6iB/qO0Zmj+3Up6qxt/ZW0VKVfSZWU9/1jtbrN5ODgKB//KjKys1W1bjNJko9/ZSVeipZkXie/4TtlZFxR2uVklfOvZAvJNc33luUr1lCCuX3txm20be1Ste85Qgd3rFP94Js7b4FbZbeQ7OjoqHfeeUfNmjVTUlKSmjdvri5duqh+/fqF71zCXEhM0+HIRGWb84PSMrN0ODJRkq75R1CSAqvWVWpKolKTE665nYPj/35VFotVRnb2/x7LctWWOT9v+O5jNe/QWzWCWtqma//JybnwgCxJnQf+nyJPH9XJw7u0cNZzGjZhZk4tDk5X1WKRo6OTra78XqgBAChut/LaLElhezYrNSVRwybMlIODoz55c6wyMzNUxreChk18R6cO79bWVQtVuWYj25v9a2nZcYCq1QvWqcO7tejfL2vA2H/e9HPbuuorVarRQH1GvqiES9Fa+mHekWhPbx+5eZTWmWN/6MKZY3po6IQbPs7N9mH1+i20ecXnGvR/U5WWknTDx/0fQ/WDH1C77sPzrHF0dM51HbLVwVEWS857IIvFquzsvO9HypWvpKhzJwocmbz6/Y2MfDfJ/R7IalV2VrYSLkZp16blGjb+bbm4eWjVotnKysyQdPN92Kx9Ly1871kFtehkW1bQe7vs7Cz9tOAdteoyWD6FXFdczr+yos+fVM0GIZKkoeNn6Oj+33Ty8K6cp20YGvLUv/LMhJDyvn90+PP9n9Wab/9nZqRr/bKPNGzCTHl6++i3XxYpMyMjz/5Wq1WG+ftyci6lKrUb68TBHTq6/zfbe0+guNntK6ACAgLUrFnOJ06enp6qV6+ezp8/b69ybsmJ6GTbH78/ZRuGTkQnF7rvpehzys7Olou7pypUr6cj+7YqOztLl5MTdO5kqPwr1Sq0jSP7t+T8d98WBVapIylnKpZH6XKSpNBdGwrc16mUqzLMTwj/Kj42UgFVaqvNg0Pl6l5aSfEXC60FAICS4FZemyXpSlqK3DxKy8HBUWeOH1BiXIyknLsdOzmVUv3m96vF/X0Vff6kyvoGKiUxThfOHJMkpael5vnQOD42Ur4BVdSyY3/5V6qpS9Hn5eziahulu5a/vlZf/Rp/9WVSf9UgpIt+/nqWajVqfV03tvqrm+3DBi07qVWXh/PcCKpC9foK27NZUs4Io6u7l0q5uKlC9SCF7f1VUs7dp6+k5rRfuWYjHfvjd11OipeUc03znyOUNyP4/r7avu5bxcXkvN80srO1/7dVN93en9KvXJaTs4tKubgpJSk+594yppvtQ1c3T9Vu3MY29Vwq+L3drz99KZ+AKqprziy8liatH9KhnRsUER5mW5aZccX2c5U6TbR3y0+2xwVN274emeYHBS7unkq/kqpjf/x2Xfs1DOmiDcs/UflKNeXi5nHTxwduRYm4Jjk8PFx79+5VSEhInnVz587V3LlzJUmXUxKLu7TrkpaZ/+hpQcuvvu7JMKQH//a0rFYH1WzQShHhR/TlOxMli0Xtez4qd68yuhR97Q8P0i6naP47E+Tg4KTujzwjSbqv68NaMf9tlXJ1V6WaDZVwKSrffSvVbKCd65fpy3cn5vnap80rvlBcbKRkGKpcq5F8A6sqJuLm/1gCAFBcbvS1+a/qNeug7+e9qS9mjpd/xZq2G2PFXjitzSu+kMVikdXqqE4DnpCDo5N6PPKs1n//iTIz0uXo5KyBT/y/XO3t+XWFzh4/kDOdt3wlVa3bLKcNi1Xz35mooBb537hLyvta3eL+vlq1aLa2r116zTte1whqodWLP1CDljc3ZfVm+9DT20fN2vXMs/y+rg9r9eJ/a/47E+ToVEoP/u3pnOVdBmvlwnf1xdu/KqBqHXl6+0qSyvlXUpsHh+rbj6fIMAxZrQ7q2H+svG7yq7l8A6vqgT6j9NOCd81gaLFN0b4VvoHV5Fehmj6bMU6e3j65bop1K+dh8w59tG/rStvjgt7b7d60XOXKV7K9t2zdbYicS+Wd8i9J7l5l1GP4s/r1p/lKTrgkN4/ScnX3UqsugyVJHfuO0bplczX/nQnKzspSxer11Xng/11fR/yFi6u7GoZ00fyZ4+XuWea6Bn6knOnXzqXcFHSTlwgAt4PFMIwCJpQUj+TkZHXo0EGTJ09W//79r7mtf6WaJXLaxdbjsfn+sXNxdFCbmj5Feuyi/C5BAADuVPZ8bS4pLpw9rk0/zNPDT067qf3pw1tHH9645IRLWvLhK3ps0r9lsdpt0ivsaNPXU7Vr1y671mDXMy8jI0MDBgzQsGHDCg3IJVkNPw9ZLZZcy6wWi2r4MUUEAAB7uNdfm3es/1Y/fjFDbbs/UvjGBbjX+/B2oA9vTOiuDfpq9iS1fWgYARl2ZbeRZMMwNGLECJUtW1azZs26rn1K6kiydON3LgQAAEWL1+ZbRx/eOvoQuDElYSTZbtckb926VV9++aUaNmyoJk2aSJKmTZum7t2726ukW+Lv5cIfPAAAShBem28dfXjr6EPgzmO3kNy2bVvZ+XJoAAAAAAByYbI/AAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZHexdwI7KcpJQAe1cBAAAAALhbMZIMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAO55l+PjtPaDd/T1xL/r28nPauWMNxS6frV+fntqvttv+vg/ijt3tpirLNn2fL9USyY9raUvTtA3L01U1PGjN9XOyhlv6EpKyrWPtfybAtd9OmpIrsdHNq3Xls/nXrO9lLhLWj1rxvUXCeCu5mjvAgAAAOzJMAz98t5bqtPuAXV+6llJ0sXTpxS+Z2eB+3R4/MniKu+OcOFYmE7v3aUBb74jBycnpSYlKjsz86ba6j7p1UK32bv8WzXrM/Cm2s+Pe5my6jph0m1rD8CdjZAMAADuaRGhB2R1cFT9zg/alpWrUk1XUlJ0/tAfWj1rhuLOnZFPtRrq+I8Jslgs+mHqK7pv6Ej5Vq+pT0cNUcMHe+j03t1ydHJWt2dfkltpb4Xv2am93y9VVmamXDw81fHJiXIr7W2/J1qELsfHycXTSw5OTpIkV08v27pzB//Qtq8+l5GVJd8aNdXusb/rfOgBHdm4Vl3G5wTTiNCD2v/T93ro+Ve0cPxY9Z86U66eXjq6ZaMO/vKTsjMz5Veztto+NlY7lyxUVnq6vnlpospUrKxOT0687jo3zJktZ1c3xZw6rsvx8Wo15FFVD2mtpJho/Txzqgb/a7Yy09P162cfKfbkcVkcHHTfsMdUIaihjmxar/A9O5SZnq7EqAuqFhyiVkNH3N6OBFAiMN0aAADc0y6dPSPfajXyXXcx/JRaDx+lwTNmKzH6gi4cPZxnm8wrafKrWUeDpr+ngLr1FbZ+jSQpoE499Z3yLw2c9q5q3NdW+3/8rkifhz1VathEKRdjtejZf+jXzz5SxOGDkqTM9HRt/Gi2Oj/1rAb9631lZ2Xr0NpVqtigsaJPHFNGWpok6cS2LapxX9tcbcadP6sT27aqz2vTNXD6e7JYrTq+dbNC/vaoHJydNXD6ezcUkP90Of6S+vxzmh56brK2L/4yz/pDa36WxSIN+tf76jTuGW38KCc4S9LF0+E5z+WtWTqxbauSL8be8PEBlHyMJAMAABTAt0YteZTzkST5VKmmpJhoBdSpn2sbq6OjqjQNztmmWg2dP7hfkpR88aK2zZ6py/FxysrKlKdv+eItvhg5ubiq/5szdSHssM6HHtDaD95RyMPD5VO1mjx9y8s7oIIkqXa7B3RozUo1eqiXKjZqqtN7d6p6y9Y6s2+3QobkHpU9f+iAYk+d0HevPi9JysxIl6tX6Zsr0GKx/Vg1OEQWq1VlKlZSakJ8nk0vHD2sBl27S5LKBFaUh4+vEi5ESJIqBDVUKTf3nHUVKiopNtp2fgC4exCSAQDAPa1Mxco6ueP3fNc5OP7vrZLFapWRlZ1nG6uDoyxmCLNarcrOypIkbZ3/sRo91FtVm7dUROhB7Vq2qAiqLzmsVgcF1m+gwPoNVK5SFR35dYN8qlYrcPua97XVwdUrVcrdUz7VasjZ1TX3Boah2u0eUMjfht9QHY7OzsrKzJCDY87U7yspSXLx+N/07z+Xm4e4IX9OJ5cKPh8A3PmYbg0AAO5pFYIaKjszQ6HrV9uWXTwTrsgjobfUbvrly3IvW06SdOTXDbfUVkkXH3HeNtoqSbGnT8nTx1elAyooOTZaCRciJUnHtmxUYL0gSVJAvSDFhp9U2IbVqnlfuzxtVghqpJM7freN9qYlJykpJlqSZHVwUFYBNwYLqBukY1s2SZIy06/oxLbfFFi/wXU/l4A69XRs6+ac5xV5XsmxsbaRcAD3BkaSAQDAPc1isajrxBf125efav+Py+Tg5CxPXz9Vbd7yltoN7v+w1rz/tkq5uyswqKGSYqJuU8UlT8aVNG394mNdSUmR1cFBXuX91X7MP+To7KwOY5/Smtlv227cVb9Tzg3SrFYHVWkarCObN+j+v4/P02aZipXUYtBQ/fTWFBmGIauDg9qOHCtPXz/V69hV37w0QT5Va+S5LrnNo6O1+dMPdfCXn2RIqt32flswvx71Oz+kXz/7SEtfGC+Lg4Puf+KpXCPIAO5+FsO40Ykm9uNbvaYGTJ1p7zIAAAAAAEVg17tTtWvXLrvWwHRrAAAAAABMhGQAAAAAAExckwwAAO55l+Pj9NuX8xRz8pic3dzlWtpbrYePKpE3bEqJu6StX3yirhMm2buUXOhDAHcLQjIAALinGYahX957S3XaPaDOTz0rSbp4+pRSExJKZMBzL1O2xIU7+hDA3YSQDAAA7mkRoQdkdXBU/c4P2paVq1JNhmHo968+19n9e2SRRU37DlLN+9rmfOfxt1/L2c1dl86eUY1WrVW2UhUdWLVCWenp6vrMiypdPkAb5syWo7OzYsNPKTUxQfePHaejWzYo6tgR+dWorQf+/rQk6dNRQzR63teSpJPbf9Ppvbv0wN+f1oY5s+Xs6qaYU8d1OT5erYY8quohrZUUE62fZ07V4H/N1qVzZ7Txow+UnZkpwzDUdcIkWR0ctfJfr8uvZm1FHQuTb/VaqtOho3Z9s0ipiQnq9OQE+dWoTR+WsD4EUHJwTTIAALinXTp7Rr7VauRZfmrnNl08Ha6B099Tj5enaPvXXygl7pKknO9Rbjfq73r47Q90dMsmxUdGqP8bb6vuA511cPVKWxtXUlLUd8pbav3IY1r1zjQ1fLB3TjA7e1qx4acKre1y/CX1+ec0PfTcZG1f/GWe9aHrflHDB3tq4PT31H/q27bvZU6IilSj7n308Nv/VnzEOR3/bbP6vDZN9w0dob3Lv73ZrioQfQjgbmLXkDxq1Cj5+fmpQYPr/4J3AACA63EhIU1bj1/UusPR2nr8oi4kpN3Y/kcOq+Z9bWW1OsittLcC6gYp5uRxSZJv9VpyL1NWDk5O8vLzV6WGTSRJZStVUXJMtK2NKs2CZbFYVLZyFbmW9la5ylVksVpVpmJlJcVG53fYXKoGh5jbV1JqQnye9eVr1tHe5d9q34/LlBwbI0fnUpIkT9/yuY5VIaiRrY6kmMKPa+sD+vCW+xDAnceuIXnkyJFatWqVPUsAAAB3oQsJaQq7kKS0jCxJUlpGlsIuJOUb8spUrKyYUyduqH0Hx/9dsWaxWuTg5JTzs8Wq7Kysq7b733IHp6v2sVhkmNtZLP9rNzMj/S/HcbL9bBh566jVpr26PfuSHJyctXLGVJ0/9EfOfn851tV1ZGdn5W0oH/ThrfchgDuTXUNy+/btVbZsWXuWAAAA7kInYlKUlZ07EWVlGzoRk5Jn2wpBDZWdmaHQ9attyy6eCZezm7tObNuq7OwspSYmKDIsVH41at32Wl1Leyvu/FkZ2dkK37X9hvZNjL4gLz9/NXywp6o2b6mLZ07ftrroQwD3qhJ/4665c+dq7ty5kqS0pEQ7VwMAAO4Ef45+Xs9yi8WirhNf1G9ffqr9Py6Tg5OzPH391Hr4KGVcSdU3L02URRaFDHlUbt5lFB9x/rbWGvLwcK2a+aZcPEvLt3oNZaRd/5TmE9u26tiWTbI6OMjVu4ya9hmgjNTU21IXfQjgXmUxjPwmnhSf8PBw9ezZUwcPHix0W9/qNTVg6sxiqAoAANzJth6/mG+Yc3FyUJua5exQ0Z2HPgRgD7venapdu3bZtQbubg0AAO46NXzd5WC15FrmYLWohq+7nSq689CHAO5VJX66NQAAwI3yL+0iKee62rSMLLk4OaiGr7ttOQpHHwK4V9k1JA8ZMkQbN25UbGysKlasqClTpmj06NH2LAkAANwl/Eu7EOhuEX0I4F5k15D89ddf2/PwAAAAAADkwjXJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmAjJAAAAAACYCMkAAAAAAJgIyQAAAAAAmBztXcCNMJwMZfhn2LsMAAAAAMBd6rpGkk+fPq21a9dKklJTU5WUlFSkRQEAAAAAYA+FhuSPP/5YAwcO1BNPPCFJOnfunPr27VvUdQEAAAAAUOwKDcn/+c9/tHXrVnl5eUmSatWqpejo6CIvDAAAAACA4lZoSC5VqpScnZ1tjzMzM2WxWIq0KAAAAAAA7KHQkNyhQwdNmzZNqampWrNmjQYNGqRevXoVR20AAAAAABSrQkPyv/71L/n6+qphw4b66KOP1L17d02dOrU4agMAAAAAoFhd8yugsrKyFBQUpLCwMD3++OPFVRMAAAAAAHZxzZFkBwcH1alTR2fOnCmuegAAAAAAsJtrjiRLUlxcnIKCgtSyZUu5u7vblv/www9FWhgAAAAAAMWt0JD8xhtvFEcdAAAAAADYXaEhuUOHDoqKitLOnTslSS1btpSfn1+RFwYAAAAAQHEr9O7WS5YsUcuWLbV06VItWbJEISEh+uabb4qjNgAAAAAAilWhI8lvvvmmdu7caRs9jomJUefOnTVw4MAiLw4AAAAAgOJU6EhydnZ2runV5cqVU3Z2dpEWBQAAAACAPRQ6kvzggw+qW7duGjJkiCRp8eLFeuihh4q8MAAAAAAAiluhIfntt9/WsmXLtGXLFknS2LFj1a9fvyIvDAAAAACA4lZoSD516pS6d++u/v37S5JSU1MVHh6uqlWrFnVtAAAAAAAUq0KvSR40aJCs1v9t5uDgoEGDBhVpUQAAAAAA2EOhITkzM1POzs62x87OzkpPTy/SogAAAAAAsIdCQ7Kvr69++OEH2+Ply5fLx8enSIsCAAAAAMAeCr0mec6cORo2bJjGjRsnwzBUqVIlzZ8/vzhqAwAAAACgWBUakmvUqKFt27YpOTlZkuTh4VHkRQEAAAAAYA8FTrf+8ccfdfr0advjd999V23atFHv3r116tSpYikOAAAAAIDiVGBInjx5snx9fSVJK1as0IIFCzRv3jz17t1bf//734utQAAAAAAAikuBIdliscjNzU2StGzZMo0ePVrNmzfXmDFjFBMTU2wFAgAAAABQXAoMyYZhKDk5WdnZ2Vq3bp06depkW5eWllYsxQEAAAAAUJwKvHHXhAkT1KRJE3l5ealevXoKDg6WJO3du1cBAQHFViAAAAAAAMWlwJA8atQodevWTdHR0WrcuLFtub+/vz777LNiKQ4AAAAAgOJ0za+AqlChgipUqJBrGaPIAAAAAIC7VYHXJAMAAAAAcK8hJAMAAAAAYCo0JD/77LM6dOhQkRx81apVqlOnjmrWrKm33nqrSI4BAAAAAMD1KjQk16tXT2PHjlVISIjmzJmjhISE23LgrKwsPfnkk/r5558VGhqqr7/+WqGhobelbQAAAAAAbkahIXnMmDHaunWr5s+fr/DwcDVq1EhDhw7Vhg0bbunAO3bsUM2aNVW9enU5Ozvrb3/7m5YvX35LbQIAAAAAcCuu65rkrKwshYWFKSwsTD4+PmrcuLHeffdd/e1vf7vpA58/f16VKlWyPa5YsaLOnz+fZ7u5c+cqODhYwcHBSotPvOnjAQAAAABQmGt+BZQkTZw4UStWrFDHjh318ssvq2XLlpKkF154QXXq1CnyAseOHauxY8dKknzq1Cjy4wEAAAAA7l2FhuRGjRpp6tSpcnd3z7Nux44dN33gChUq6OzZs7bH586dy/OdzAAAAAAAFKdCp1svWLAgT0Du1KmTJKl06dI3feAWLVro2LFjOnXqlNLT07Vo0SL17t37ptsDAAAAAOBWFTiSnJaWpsuXLys2NlZxcXEyDEOSlJiYmO+1wzd8YEdH/fvf/1a3bt2UlZWlUaNGKSgo6JbbBQAAAADgZhUYkj/66CPNmjVLERERatasmW25l5eXxo0bd1sO3r17d3Xv3v22tAUAAAAAwK0qMCSPHz9e48eP1wcffKCnnnqqOGsCAAAAAMAuCgzJ69evV8eOHVWhQgUtW7Ysz/r+/fsXaWEAAAAAABS3AkPypk2b1LFjR/3444951lksFkIyAAAAAOCuU2BInjJliiTps88+K7ZiAAAAAACwpwJD8rvvvnvNHZ955pnbXgwAAAAAAPZUYEhOSkoqzjoAAAAAALC7AkPya6+9Vpx1AAAAAABgdwWG5BkzZmjSpEl66qmnZLFY8qyfPXt2kRYGAAAAAEBxKzAk16tXT5IUHBxcbMUAAAAAAGBPBYbkXr16SZLc3Nw0aNCgXOuWLl1atFUBAAAAAGAH1sI2mD59+nUtAwAAAADgTlfgSPLPP/+slStX6vz583r66adtyxMTE+XoWOBuAAAAAADcsQpMu4GBgQoODtYPP/yg5s2b25Z7enrqvffeK5biAAAAAAAoTgWG5MaNG6tx48YaOnSonJycJElxcXE6e/asypQpU2wFAgAAAABQXAq9JrlLly5KTEzUpUuX1KxZMz3++OOaOHFicdQGAAAAAECxKjQkJyQkyMvLS8uWLdOjjz6q7du3a926dcVRGwAAAAAAxarQkJyZmanIyEgtWbJEPXv2LI6aAAAAAACwi0JD8j//+U9169ZNNWrUUIsWLXTy5EnVqlWrOGoDAAAAAKBYWQzDMOxdxPXyqVNDfT58y95lAAAAAACKwP5J/9KuXbvsWkOhI8nnzp1Tv3795OfnJz8/Pw0YMEDnzp0rjtoAAAAAAChWhYbkxx57TL1791ZERIQiIiLUq1cvPfbYY8VRGwAAAAAAxarQkBwTE6PHHntMjo6OcnR01MiRIxUTE1MctQEAAAAAUKwKDcnlypXTggULlJWVpaysLC1YsEDlypUrjtoAAAAAAChWhYbkefPmacmSJfL391dAQIC++eYbffbZZ8VRGwAAAAAAxcqxsA2qVKmiH374oThqAQAAAADArgoMyU899ZQsFkuBO86ePbtICgIAAAAAwF4KDMnBwcG2n1977TVNmTKlWAoCAAAAAMBeCgzJI0aMsP08a9asXI8BAAAAALgbFXrjLknXnHYNAAAAAMDdotAbd5Ukzo6Zqux3yd5lAAAAAACKwH57F6BrhGRPT0/bCPLly5fl5eUlSTIMQxaLRYmJicVTIQAAAAAAxaTAkJyUlFScdQAAAAAAYHfXdU0yAAAAAAD3AkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACZCMgAAAAAAJkIyAAAAAAAmQjIAAAAAACa7hOSlS5cqKChIVqtVu3btskcJAAAAAADkYZeQ3KBBAy1btkzt27e3x+EBAAAAAMiXoz0OWq9ePXscFgAAAACAa7JLSL4Rc+fO1dy5cyVJl+OS7VwNAAAAAOBuVmQhuXPnzrpw4UKe5W+++ab69Olz3e2MHTtWY8eOlSQFBlW5bfUBAAAAAPBXRRaS165dW1RNAwAAAABQJPgKKAAAAAAATHYJyd99950qVqyo33//XT169FC3bt3sUQYAAAAAALnY5cZd/fr1U79+/exxaAAAAAAACsR0awAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMjvYu4EZ4Ol5R27LH7V0GAAAAAKAI/GjvAsRIMgAAAAAANoRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATHYJyc8//7zq1q2rRo0aqV+/foqPj7dHGQAAAAAA5GKXkNylSxcdPHhQf/zxh2rXrq3p06fbowwAAAAAAHKxS0ju2rWrHB0dJUmtWrXSuXPn7FEGAAAAAAC52P2a5Hnz5umhhx4qcP3cuXMVHBys4OBgxV+8XIyVAQAAAADuNY5F1XDnzp114cKFPMvffPNN9enTx/azo6Ojhg0bVmA7Y8eO1dixYyVJdRr7F02xAAAAAACoCEPy2rVrr7n+888/14oVK7Ru3TpZLJaiKgMAAAAAgOtWZCH5WlatWqUZM2Zo06ZNcnNzs0cJAAAAAADkYZdrkseNG6ekpCR16dJFTZo00d///nd7lAEAAAAAQC52GUk+fvy4PQ4LAAAAAMA12f3u1gAAAAAAlBSEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATI72LuBGeFqz9IBror3LAAAAAADcpRhJBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREgGAAAAAMBESAYAAAAAwERIBgAAAADAREjGHSkq+rKGPb5ONZt+rRYPLFObrt/ruxWnCt2veuOvFHsxTZL0wUcHFRSyRI+MXZ9rm8uXM/XI2PVq3GapGrVeqvYPLVdycobiE67ow08PFXqM690OAAAAQMnjaO8CgBtlGIb6P7Jaw4fU1sKPO0mSTp9N0o8/n76hdj6cd0irl/VQxQoeuZbP/uiAyvu6asHWQZKkI8fi5eRkVeylNH34aaj+b3TQNduNT0i/ru0AAAAAlDyMJOOOs35zhJydHfT3x+rbllWp5KlxYxtIkj7/6oiemrTFtq7X31Zp45aIXG383zO/6mR4knoM/lmz/vtHrnWRUZdVIcDd9rhOLW+VKuWgl6Zs14nwRDVr/60m/XObkpMz1KXvCgXf/60at1mq5SvDJSnPdpI0c/Z+hXT6Tk3afqP/N32XJCklJUM9H/5ZTdt9o0atl2rxshO3r5MAAAAA3BRGknHHCQ2LU9NG5W6pjQ/fbadf1p3Vuh96yaecS651jw2rowcHrNS3P5xUxw4V9OjfaqtWjdKa/lqIDh2O057NAyRJmZnZ+nZ+V3l5OSv2Yppad/1evR+qkme71evP6djJBG1b21eGIfUZ+os2/xapmNhUBfq7a8XihyRJCYnpt/ScAAAAANw6u4TkV199VcuXL5fVapWfn58+//xzBQYG2qMUlCAXEtJ0IiZZaRlZcnFyUA1fD/mXdil0v3HPb9HWbRfk7Oyg7ev63XIdTRr66PieIVq94ZzWbTqvkE7faesvfeTqmvt/F8OQJk/doV9/uyCr1aLzkSmKik7N096aDee0ZsM5Ne+wTJKUnJKhYycS1O4+fz3/6ja9+P+2q0e3ymp3X8At1w4AAADg1tglJD///PN64403JEmzZ8/W66+/rjlz5tijFJQQFxLSFBaZqCzDkCSlZWQpLDJRkvIE5fp1y2jZj/+7Sde/326r2ItpatkxJ4Q6OlqVnf2/7a9cybrhejw8nNS/VzX171VNVqv085qz6t+7Wq5tFi49ppjYNO3c0F9OTlZVb/yV0vI5lmEYemFiEz0xsn6edbs29tfKNWf1zzd3qWP7QL06qfkN1woAAADg9rHLNcleXl62n1NSUmSxWOxRBkqQEzHJtoD8pyzD0ImY5DzbdmwfqLS0TH04L9S27HJqpu3nqpU9tf/ARWVnGzp7Llk7dkffUC1bt11QXPwVSVJ6epYOH4lX5Uoe8vRwUlJyhm27xMR0+fm6ysnJqg2/Ruj02Zxa/7pd146V9PmCI0o2l52PSFF0TKoiIlPk5uqoRwbX0rNPNdKeP2JvqE4AAAAAt5/drkmePHmy5s+fr9KlS2vDhg0Fbjd37lzNnTtXkhQTm1Zc5aGYpWXkP9qb33KLxaJlC7rpmcm/aebs/fL1cZG7m6OmvxYiSWoTUl5Vq3iqwX1LVLd2GTVr7HNDtZwIT9STz22RYRjKzpa6d62kAb2ryWKxqHVIeTVqvVQPdq6kSeObqM+QVWrcZqmaN/FV3VrekqRyZV1ybTfj9VYKOxqnNt2+lyR5uDtp/kcddfxkgl54bbusVoucnKz6z8y2N1QnAAAAgNvPYhh/Gb67TTp37qwLFy7kWf7mm2+qT58+tsfTp09XWlqapkyZUmibwU19tWN9/9taJ0qGrcdj8w3ELk4OalPzxkIuAAAAgDtTyy67tWvXLrvWUGQjyWvXrr2u7YYNG6bu3btfV0jG3auGr0eua5IlycFiUQ1fj2vsBQAAAAC3l12uST527Jjt5+XLl6tu3br2KAMliH9pF9UN8JKLk4OknBHkugFe13V3awAAAAC4XexyTfKLL76oI0eOyGq1qkqVKtzZGpJygjKhGAAAAIA92SUkf/vtt/Y4LAAAAAAA12SX6dYAAAAAAJREhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyEZAAAAAAATIRkAAAAAABMhGQAAAAAAEyO9i7ghjhUkbXMR/auAgAAAABQJILtXQAjyQAAAAAA/ImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAIDJYhiGYe8irpePj4+qVq1q7zKuKSYmRr6+vvYuA/c4zkOUBJyHKCk4F1EScB6iJLgTzsPw8HDFxsbatYY7KiTfCYKDg7Vr1y57l4F7HOchSgLOQ5QUnIsoCTgPURJwHl4fplsDAAAAAGAiJAMAAAAAYCIk32Zjx461dwkA5yFKBM5DlBSciygJOA9REnAeXh+uSQYAAAAAwMRIMgAAAAAAJkIyAAAAAAAmQvJNGjVqlPz8/NSgQQPbskuXLqlLly6qVauWunTpori4ODtWiHtFfufi888/r7p166pRo0bq16+f4uPj7Vcg7gn5nYd/euedd2SxWOz+nYe4+xV0Hn7wwQeqW7eugoKCNGnSJDtVh3tJfufivn371KpVKzVp0kTBwcHasWOHHSvEveDs2bN64IEHVL9+fQUFBen999+XRGa5HoTkmzRy5EitWrUq17K33npLnTp10rFjx9SpUye99dZbdqoO95L8zsUuXbro4MGD+uOPP1S7dm1Nnz7dTtXhXpHfeSjlvECvXr1alStXtkNVuNfkdx5u2LBBy5cv1/79+3Xo0CE999xzdqoO95L8zsVJkybptdde0759+/T666/zgQ2KnKOjo9555x2FhoZq27Zt+s9//qPQ0FAyy3UgJN+k9u3bq2zZsrmWLV++XCNGjJAkjRgxQt9//70dKsO9Jr9zsWvXrnJ0dJQktWrVSufOnbNHabiH5HceStLEiRM1Y8YMWSwWO1SFe01+5+GHH36oF198UaVKlZIk+fn52aM03GPyOxctFosSExMlSQkJCQoMDLRHabiHBAQEqFmzZpIkT09P1atXT+fPnyezXAdC8m0UFRWlgIAASZK/v7+ioqLsXBEgzZs3Tw899JC9y8A9aPny5apQoYIaN25s71JwDzt69Kh+/fVXhYSEqEOHDtq5c6e9S8I9atasWXr++edVqVIlPffcc8zyQrEKDw/X3r17FRISQma5DoTkImKxWBg5gd29+eabcnR01LBhw+xdCu4xly9f1rRp0/T666/buxTc4zIzM3Xp0iVt27ZNb7/9tgYPHiy+/RL28OGHH+q9997T2bNn9d5772n06NH2Lgn3iOTkZA0YMECzZs2Sl5dXrnVklvwRkm+j8uXLKzIyUpIUGRnJlC7Y1eeff64VK1Zo4cKF/PFDsTtx4oROnTqlxo0bq2rVqjp37pyaNWumCxcu2Ls03GMqVqyo/v37y2KxqGXLlrJardxEDnbxxRdfqH///pKkQYMGceMuFIuMjAwNGDBAw4YNs51/ZJbCEZJvo969e+uLL76QlPOHsE+fPnauCPeqVatWacaMGfrhhx/k5uZm73JwD2rYsKGio6MVHh6u8PBwVaxYUXv27JG/v7+9S8M9pm/fvtqwYYOknKnX6enp8vHxsXNVuBcFBgZq06ZNkqT169erVq1adq4IdzvDMDR69GjVq1dPzzzzjG05maVwFoM5RzdlyJAh2rhxo2JjY1W+fHlNmTJFffv21eDBg3XmzBlVqVJFS5YsyfdGNsDtlN+5OH36dF25ckXlypWTlHPzrjlz5ti5UtzN8jsPr55KWLVqVe3atYtwgiKV33k4fPhwjRo1Svv27ZOzs7Nmzpypjh072rtU3OXyOxfr1Kmj8ePHKzMzUy4uLvrvf/+r5s2b27tU3MW2bNmidu3aqWHDhrJac8ZGp02bppCQEDJLIQjJAAAAAACYmG4NAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDADAXzg4OKhJkya2f+Hh4bfc5q5du/T000/fenFX+fzzzzVu3Lgb2mfkyJH65ptvbmsdAADcTRztXQAAACWNq6ur9u3bd1vbDA4OVnBw8G1tEwAA3H6MJAMAUIjk5GR16tRJzZo1U8OGDbV8+XJJUkpKinr06KHGjRurQYMGWrx4sSRp586dat26tRo3bqyWLVsqKSlJGzduVM+ePSVJmzZtso1SN23a1La+Q4cO6tOnj6pXr64XX3xRCxcuVMuWLdWwYUOdOHHimjWOHDlSTz/9tFq3bq3q1avbRosNw9C4ceNUp04dde7cWdHR0bZ9du/erQ4dOqh58+bq1q2bIiMjlZCQoDp16ujIkSOSpCFDhujjjz++7X0KAEBJxUgyAAB/kZqaqiZNmkiSqlWrpqVLl+q7776Tl5eXYmNj1apVK/Xu3VurVq1SYGCgfvrpJ0lSQkKC0tPT9fDDD2vx4sVq0aKFEhMT5erqmqv9mTNn6j//+Y/atGmj5ORkubi4SJL279+vw4cPq2zZsqpevbrGjBmjHTt26P3339cHH3ygWbNmXbPuyMhIbdmyRWFhYerdu7cGDhyo7777TkeOHFFoaKiioqJUv359jRo1ShkZGXrqqae0fPly+fr6avHixZo8ebLmzZunf//73xo5cqTGjx+vuLg4Pf7447e9jwEAKKkIyQAA/MVfp1tnZGTo5Zdf1ubNm2W1WnX+/HlFRUWpYcOGevbZZ/XCCy+oZ8+eateunQ4cOKCAgAC1aNFCkuTl5ZWn/TZt2uiZZ57RsGHD1L9/f1WsWFGS1KJFCwUEBEiSatSooa5du0qSGjZsqA0bNhRad9++fWW1WlW/fn1FRUVJkjZv3qwhQ4bIwcFBgYGB6tixoyTpyJEjOnjwoLp06SJJysrKsh27S5cuWrp0qZ588knt37//ZroQAIA7FiEZAIBCLFy4UDExMdq9e7ecnJxUtWpVpaWlqXbt2tqzZ49WrlypV155RZ06dVK/fv0Kbe/FF19Ujx49tHLlSrVp00a//PKLJKlUqVK2baxWq+2x1WpVZmZmoe1evb9hGNfc1jAMBQUF6ffff8+zLjs7W4cPH5abm5vi4uJsIR4AgHsB1yQDAFCIhIQE+fn5ycnJSRs2bNDp06clSREREXJzc9Mjjzyi559/Xnv27FGdOnUUGRmpnTt3SpKSkpLyBNwTJ06oYcOGeuGFF9SiRQuFhYUVWe3t27fX4sWLlZWVpcjISNuIdJ06dRQTE2MLyRkZGTp06JAk6b333lO9evX01Vdf6bHHHlNGRkaR1QcAQEnDSDIAAIUYNmyYevXqpYYNGyo4OFh169aVJB04cEDPP/+8rFarnJyc9OGHH8rZ2VmLFy/WU089pdTUVLm6umrt2rW52ps1a5Y2bNggq9WqoKAgPfTQQ/mO6N4O/fr10/r161W/fn1VrlxZ9913nyTJ2dlZ33zzjZ5++mklJCQoMzNTEyZMkKOjoz755BPt2LFDnp6eat++vaZOnaopU6YUSX0AAJQ0FqOw+VgAAAAAANwjmG4NAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAICJkAwAAAAAgImQDAAAAACAiZAMAAAAAIDp/wMLhJiLbqTQyQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import cm\n",
    "\n",
    "x = [18, 16, 17, 12, 16, 17, 20, 14, 11, 15, 10]\n",
    "y = [2, 2, 1, 3, 1, 2, 3, 3, -3, 3, 2]\n",
    "s = [\"Nazi Germany\", \"Fascist Italy\", \"Soviet Union\\nCommunism\", \"Russia\", \"China\\nCommunism\", \"Modern China\", \"North Korea\", \"Iran\", \"Gulf States\", \"ISIL\", \"Bonpartism\"]\n",
    "\n",
    "plt.rcParams['figure.facecolor'] = 'white'\n",
    "plt.rcParams[\"figure.figsize\"] = (16,10)\n",
    "fig, ax = plt.subplots()\n",
    "ax.scatter(x, y)\n",
    "\n",
    "viridis = cm.get_cmap('viridis', 8)\n",
    "for i in range(-3, 4):\n",
    "    plt.axhspan(i-0.5, i+0.5, facecolor=viridis(-i+4), alpha=0.7)\n",
    " \n",
    "for i, txt in enumerate(s):\n",
    "    ax.annotate(txt, (x[i]+0.1, y[i]))\n",
    "    \n",
    "ax.set_title(\"Fascism Index vs Hostility Score\")\n",
    "ax.set_xlabel(\"Fascism Index\")\n",
    "ax.set_ylabel(\"Hostility Score\")\n",
    "\n",
    "fig.savefig(\"fascism_index.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "matplotlib",
   "language": "python",
   "name": "matplotlib"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}