GlobalWindPowerCumulativeCapacity.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GlobalWindPowerCumulativeCapacity.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "BXmeYCc3cjI5",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "b6962c55-c801-431a-a26a-d50162b83342"
      },
      "source": [
        "#1996 to 2018\n",
        "#Global Wind Energy Council\n",
        "#https://en.wikipedia.org/wiki/File:Global_Wind_Power_Cumulative_Capacity.svg\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM, Dense\n",
        "import os\n",
        "import random as rn\n",
        "\n",
        "os.environ['PYTHONHASHSEED'] = '0'\n",
        "\n",
        "# Setting the seed for numpy-generated random numbers\n",
        "\n",
        "\n",
        "# Setting the seed for python random numbers\n",
        "rn.seed(0)\n",
        "\n",
        "np.random.seed(0)\n",
        "tf.random.set_seed(0)\n",
        "seq=[6.1,7.6,10.2,13.6,17.4,23.9,31.1,39.4,47.6,59.1,74.0,93.9,120.7,159.1,198.0,238.1,282.9,318.7,369.9,432.7,487.3,539.1,591.0]\n",
        "log_data=np.log(seq)\n",
        "time=list(range(1996,2019,1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YXxbmgxLnjfN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "x=np.array(time)\n",
        "y=np.array(seq)\n",
        "import numpy\n",
        "mymodel = numpy.poly1d(numpy.polyfit(x, y, 3))\n",
        "\n",
        "myline = numpy.linspace(1995, 2025, 30)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "If48TgwZzvpa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred=mymodel(myline)\n",
        "z=list(y)+list(pred.flatten()[-7:])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mU5v2bgq0E23",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "outputId": "00698a44-843d-478e-9849-ff0a206cdbbd"
      },
      "source": [
        "\n",
        "x=np.array(list(range(1996,2026,1)))\n",
        "print(len(x))\n",
        "print(len(z))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "30\n",
            "30\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oyHye6qmullx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 563
        },
        "outputId": "f08edc5f-20d2-4ecc-d390-7b99a5efc924"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(30,15))\n",
        "plt.grid(False)\n",
        "plt.style.use('dark_background')\n",
        "#x=np.array(range(1995,2025,29))\n",
        "N=len(x)\n",
        "pred=mymodel(myline)\n",
        "\n",
        "\n",
        "ind = np.arange(N) \n",
        "width = 0.5\n",
        "colors=23*['c']+7*['r']\n",
        "bar1=plt.bar(x, z, width, label='50m',color=colors,alpha=0.5)\n",
        "\n",
        "\n",
        "\n",
        "#plt.scatter(x, y)\n",
        "plt.plot(myline,pred ,linewidth=5) #marker='o',markersize=10\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-OjPK1WN2KDY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 589
        },
        "outputId": "3dbe065f-4673-4c13-d4ed-1949a8a6e280"
      },
      "source": [
        "plt.figure(figsize=(30,15))\n",
        "plt.grid(False)\n",
        "plt.style.use('dark_background')\n",
        "#x=np.array(range(1995,2025,29))\n",
        "N=len(x)\n",
        "pred=mymodel(myline)\n",
        "\n",
        "\n",
        "ind = np.arange(N) \n",
        "width = 0.5\n",
        "colors=23*['c']+7*['r']\n",
        "bar1=plt.bar(x, z, width, label='50m',color=colors,alpha=1)\n",
        "\n",
        "for rect,val in zip(bar1,z):\n",
        "    height = rect.get_height()\n",
        "    #print(val)\n",
        "    if(val==0):\n",
        "      plt.text(rect.get_x() + rect.get_width()/2.0, height+0.01, str(\"-\"), ha='center', va='bottom',fontsize=20)\n",
        "    else:\n",
        "      plt.text(rect.get_x() + rect.get_width()/2.0, height+50, str(round(val,1)), ha='center', va='bottom',fontsize=20)\n",
        "\n",
        "\n",
        "import matplotlib.patches as mpatches\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "EX = mpatches.Patch(color='c', label='Existing Values')\n",
        "PR = mpatches.Patch(color='r', label='Predicted Values')\n",
        "#AP = mpatches.Patch(color='red', label='Asia/Pacific')\n",
        "#SA = mpatches.Patch(color='yellow', label='South America')\n",
        "#plt.legend(handles=[NA,EU,AP,SA], loc=2)\n",
        "\n",
        "#plt.scatter(x, y)\n",
        "plt.plot(myline,pred ,linewidth=5) #marker='o',markersize=10\n",
        "\n",
        "\n",
        "#plt.yticks([])\n",
        "plt.rcParams['xtick.labelsize']=20\n",
        "plt.rcParams['ytick.labelsize']=20\n",
        "\n",
        "plt.xlabel('Year', fontsize=20)\n",
        "plt.ylabel('Generated Wind Power (GW)', fontsize=20)\n",
        "\n",
        "plt.title('Cumulative Capacity of Global Wind Power (in GigaWatts)', fontsize=30)\n",
        "\n",
        "plt.ylim(0,1300)\n",
        "\n",
        "lg=plt.legend(loc='best',title='Type of Capacity', prop={'size': 20},handles=[EX,PR])\n",
        "lg.get_title().set_fontsize(20)\n",
        "lg._legend_box.align = \"center\"\n",
        "#plt.legend(loc='center left', bbox_to_anchor=(1, 1),fancybox = True, shadow = True)\n",
        "plt.savefig(\"cumulative_capacity.png\" ,dpi=100)\n",
        "plt.show()\n",
        "\n",
        "plt.show() "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y7GBzdHTM0GvJkiW1ePFilStXTmPGjFFSUlLOPoRcspbU3sFG75axk7N15l/7xKaatOBOklaOnazE+HiL9AUAAFCUPPDYxvPnz6tx48bZnrBx48bZ+k0uAAAAAACAzLRv316TJ0/Wjz/+qHPnzikmJkYuLi5q1aqVatasqcuXL+vtt99Oq4+Pj1fv3r21adMm7du3T2vXrlVUVJSaNGkiLy8vRUdH6x//+Ee6Z7Ru3Vrz5s1TSEiILl68KEdHR7300kuqVauWVq1apdGjR2foa8mSJfL29pa3t7f27NmTdv2tt97S0KFDtXv3bl2+fFnOzs7q1KmTnnjiCU2fPl2zZ88uuA/rL1ysDepZ2laVbMz/rvTJJKNWxiYrzmSRlgAAAIqkB4Zne/bs0eDBg9WkSRP9/PPPWdY2bdpUDRs21LRp0/KtQQAAAAAA8GgJCQlRrVq11LJlS3l4eKhs2bK6e/euIiIitHTpUs2cOTPtPWZ/vadx48YaPXq0XnjhBZUpU0ZXrlzRv/71L02YMEGXL19OVx8REaH9+/erVatWqlixouLj43Xs2DEFBQVp2bJlOer3yJEjOn36tF566SU5Ozvrzp07OnTokPr166dt27bl+fN4EIMkrxLWal/KRraZvJ9ekhJNJm2IS9HPicYC7wcAAKCoM0jK8neNatWqpdOnT+vChQvy9fXVb7/9lmld7dq1tW3bNlWuXFlPPfWUfv/994LoN98cPnw4w1EOAAAAAAAAhUJoaLbKylpJPRxt5W5nbbbmfHKqlsUmKyb1b18BZfIu+3yXzXXkWXFZC+vINkttnsw8js5fllgL68i+4rIOqfishXVkn6X+bhUXWeVED9x59vvvv2vChAkKCgpSeHi4Vq1apdDQUEVFRUm6/5JeHx8fdevWTfb29ho7dmyhD84AAAAAAACKOg97K71SylYlrTL/qsxoMumH+BSF3jMq1cK9AQAAFGUPDM8kafz48ZKkTz75RP7+/urZs2e6cYPBoJSUFI0dO1YTJkzI/y4BAAAAAAAgSSppkF5xtJWHvfndZldS7u82izLycjMAAICcylZ4Jt0P0JYsWaK+ffuqefPmcnV1lSRduXJF+/fv16JFi3Tu3LkCaxQAAAAAAOBRV9vWSj0cbVXG2vzBTHvvpWjz3RSlWLAvAACA4iTb4ZkknT9/XmPGjCmoXgAAAAAAAJAJW0kdStnIq6T5r3JuGU1aEZesM8kc0ggAAJAXOQrPAAAAAAAAYFmVrQ16o7StXGyszNaEJxq1Ji5Z9zilEQAAIM8IzwAAAAAAAAohK0ltSlqrnYONrA2ZH9MYn2rS2rhkhSex2wwAACC/EJ4BAAAAAAAUMs5W93ebVbM1v9ssIsmo7+KSdYvcDAAAIF8RngEAAAAAABQiTV7prC7l7GRvZrdZssmkzXdT9GOCUZzSCAAAkP8IzwAAAAAAAAoBR+dyenVsoJ72bmm25mJKqpbFJivaSGwGAABQUAjPAAAAAAAAHrJn2jyv7kEfy7F8uUzHU00m7bpn1Pb4FBkt3BsAAMCjhvAMAAAAAADgIbF3cFCXER+oiV8nszXXjalaHpus8ynsNgMAALAEwjMAAAAAAICHoGYjD7024RM5V65ktuZgQoq+v5uiRHIzAAAAi7F62A1kxcrKSmFhYdq4caMkqVq1ajp48KDOnDmjFStWyNbWVpJkZ2enFStW6MyZMzp48KDc3NweZtsAAAAAAABmlXQqre5BH+vdhf80G5zFppq04E6SVsURnAEAAFhaoQ7PBg8erNOnT6f9HBwcrGnTpsnd3V03b95Uv379JEn9+vXTzZs35e7urmnTpik4OPhhtQwAAAAAAGBWvRe89dH6ZWrarYvZmv+E7tXnNxN1MinVgp0BAADgT4U2PKtUqZI6dOigefPmpV1r06aNVq9eLUlavHixXn75ZUlSly5dtHjxYknS6tWr5ePjY/mGAQAAAAAAzHCqWEG9p09R72mT5VThsUxrEuPj9d2YSVoYMEJx7DYDAAB4aArtO8+mT5+ujz76SKVLl5YkOTs769atWzIajZKkixcvqlKl+0cbVKpUSRcuXJAkGY1G3b59W87OzoqJiUk359tvv60BAwZIkh57LPN/qAIAAAAAAOQXg8GgJt26qOOQQSpZ2tFs3dmjx7Ri1ETFXIyyYHcAAADITKEMzzp06KCrV68qLCxMrVq1yrd5586dq7lz50qSDh8+nG/zAgAAAAAA/F2FalXVPehj1WzkYbYmIe6uNn35tQ6uXi+Tie1mAAAAhUGhPLaxRYsW6ty5s86dO6cVK1aoTZs2mjFjhsqWLStra2tJUuXKlRUVdf+3saKiolSlShVJkrW1tcqUKZNh1xkAAAAAAI+CKVOmKCQkRJGRkYqPj1dMTIzCwsI0ZswYlS9fPl2tjY2NAgICtGDBAoWHhysxMVEmkyntHePmVKhQQcHBwTpx4oTu3Lmj69ev68iRIxo2bJgcHc3vrvq7c+fOyWQyZfln1KhRufocHiYrG2v59H9LQ1cvyTI4+0/oXk19+XX9tGodwRkAAEAhUih3ngUGBiowMFCS1KpVKw0bNkz+/v5auXKlunXrpu+++05vvfWWNmzYIEn6/vvv9dZbb+ngwYPq1q2bdu3a9TDbBwAAAADgoRkyZIjCwsK0Y8cOXb16VaVKlVLTpk01btw4DRgwQE2bNtXFixclSaVKldKMGTMkSVeuXNGVK1dUtWrVLOd3c3PTzz//LBcXF4WGhmrr1q0qUaKE2rVrp88++0z+/v5q2rSpEhISHtjr9OnTVbZs2QzXDQaDAgMDZWtrq61bt+biU3h4qjz9pF4dN1JP1HE3WxMbc0NrJ32hX7bz/QUAAEBhVCjDM3NGjBihFStWaOLEiQoPD9f8+fMlSfPnz9fSpUt15swZ3bhxQz169HjInQIAAAAA8HA4OTkpMTExw/WJEyfqk08+0ciRIzVo0CBJUnx8vHx9fXXs2DFduXJFQUFBGjt2bJbzDx8+XC4uLgoKCtL48ePTrltZWWn79u3y8fFR9+7dtXTp0gf2+mdw93ft2rWTra2twsLCdPTo0QfOUxjYlSyhFwe9ref9X5PV/07NyczPazdq4xezdO/OHQt2BwAAgJwo9OHZnj17tGfPHkn3j3No0qRJhprExES9+uqrlm4NAAAAAIBCJ7PgTJJWrlypTz75RO7u/78jKjk5Wdu2bcvR/DVq1JB0/xSYv0pNTdXmzZvl4+OjChUq5LDr9AYMGCBJmj17dp7msZTazRqr25gRcq78hNma6xcuavW4YJ35+YgFOwMAAEBuFMp3ngEAAAAAgPzVqVMnSdIvv/ySp3lOnjwpSerQoUO66waDQb6+vjIajXl6nULFihXVqVMnxcbGatmyZXnqtaA5lHFSj4mj9Y85M8wGZ6lGo0IX/Fuf+/kTnAEAABQRhX7nGQAAAAAAyLmhQ4fK0dFRZcqUUaNGjeTl5aXjx49rypQpeZp36tSp6tixoyZOnKjWrVsrLCxMdnZ2ateunVxdXdW/f38dO3Ys1/P37dtXdnZ2WrRokeLi4vLUa0Hy8G2rLiM+UGnn8mZrok5H6LugTxV1OsKCnQEAACCvCM8AAAAAACiGhg0yD6NsAAAgAElEQVQbJldX17Sft27dqt69e+v69et5mvfatWtq2rSpFixYID8/P/n4+Ei6f2zj3LlzFRISkqf5+/fvL0maM2dOnuYpKGVdXfTKqOF6qlULszXJCYn64Z9ztWfpCqWmGC3YHQAAAPIDxzYCAAAAAFAMPf744zIYDHJxcVHXrl1Vo0YNhYeHy8PDI0/zurm5ae/evapXr558fX3l5OQkV1dXvfPOO+rZs6cOHz6satWq5WruF154QTVr1tTRo0d19OjRPPWZ3wwGg1q83k3D13+bZXD2+6Gj+vwVf4Uu/JbgDAAAoIhi5xkAAAAAAMXY1atXtX79eoWFhSkiIkJLlixRvXr1cj3fokWL9Oyzz+rZZ5/ViRMnJEmxsbGaM2eOSpQooRkzZigoKEh9+vTJ8dwDBgyQVPh2nbnUrK5Xx45UtfrmP7d7d2L1/edf6dC6jRbsDAAAAAWB8AwAAAAAgEdAZGSkTp06JQ8PDzk7OysmJibHczg6Osrb21sxMTFpwdlfhYaGSpIaNmyY47krVKigLl26KDY2VsuWLcvx/QXB2tZWPv17yeftt2Rja2u27vj2XVo3+UvFXs/5ZwoAAIDCh/AMAAAAAIBHxBNPPCFJMhpzd5ygnZ2dJMnJyUm2trZKTk5ON16hQgVJUlJSUo7n7tOnj+zs7LRo0SLFxcXlqr/85PbcM3p17Ei51qphtub21Wta++kX+s+uPRbsDAAAAAWNd54BAAAAAFBMuLu7y8nJKcN1g8GgiRMnysXFRfv379etW7dyNf+NGzd06tQp2draavTo0enG7O3tNWrUKEnSzp070425urqqTp06mfb2p/79+0uSZs+enave8ou9g4O6jvxQ7y2ZnWVwdmDlOk19+Q2CMwAAgGKInWcAAAAAABQT7du31+TJk/Xjjz/q3LlziomJkYuLi1q1aqWaNWvq8uXLevvtt9PdM2LECNWtW1eSVL9+fUn3d4G1bNlSkvTjjz9q/vz5afUBAQHavHmzRo8erbZt2+rAgQMqWbKkfH19Va1aNZ05c0bBwcHpnjF58mT17t1bvXv31uLFizP03aZNG7m7u+vo0aMKCwvL188kJ570aq5XRg9XucddzdZcPfdfrRo3RWePHrNgZwAAALAkwjMAAAAAAIqJkJAQ1apVSy1btpSHh4fKli2ru3fvKiIiQkuXLtXMmTN18+bNdPe89NJL8vb2TnetRYsWatGiRdrPfw3Pdu7cKU9PTw0fPlytWrXSe++9J6PRqLNnz2rSpEmaOnWqbt++naO+BwwYIEmaM2dODlecPxzLl1OXER+oQft2ZmuMySnatXCpQmYvUkoujqUEAABA0WGQZHrYTTwMhw8flqen58NuAwAAAAAAPEQNO/mqy0eDVapsGbM1kf85pZVBk3U54nfLNRYaWvDPaN264J9hiXVIxWctrCPbLPWFpsECz7DEWlhH9hWXdUjFZy2sI/ss9XeruMgqJ2LnGQAAAAAAeOSUr/S4uo0ZoTrNm5itSYy/p22z5mjftytlSk21YHcAAAB4mAjPAAAAAADAI8NgZSUv/1f10qABsncoabbutwM/a/X4YN2IumzB7gAAAFAYEJ4BAAAAAIDiI4sj6R63NuhVR1tVtbUyW3M31aQNd5N1tPZz0r+XZV5kiaP1AAAA8NAQngEAAAAAgGLNRlJbBxu1Lmkta4P5t4GEJRi14W6y4h7Jt8MDAADgT4RnAAAAAACg2KphY1B3R1tVtDG/2+ym0aQ1cck6ncx7zQAAAEB4BgAAAAAAiqESBqmDg42alzT/1UeqyaQDCUZtiU9RIrvNAAAA8D+EZwAAAAAAoFh5xs5KfqVsVcba/BGNV1JStSouWedTSM0AAACQHuEZAAAAAAAoFko/5qyupW31nL212ZoUk0k741O0855RRgv2BgAAgKLD/IHfAAAAAAA8IsqXL69+/fpp7dq1OnPmjOLj43Xr1i3t27dPffv2lcGQcQeTnZ2d3n33Xf3888+6du2aYmNjderUKc2YMUNVq1bNUN+8eXMFBwfr0KFDunr1qhISEnT27FnNnTtXNWvWzHHPNWvW1IIFC3ThwgUlJibq0qVLWrJkiWrUqJGrz6Coa9y1kz7asCzL4Ox8cqq+vJWk7QRnAAAAyAI7zwAAAAAAj7zu3bvrm2++0aVLlxQaGqrIyEi5uLjIz89P8+fPl6+vr7p3755Wb21trZ07d6ply5Y6ffq0li9frsTERHl6eiogIEC9evVS8+bNdfr06bR71qxZowoVKujAgQP69ttvlZKSombNmql///7q0aOH2rZtq4MHD2ar34YNG2rXrl1ycnJSSEiIli9fLjc3N/Xo0UOdO3eWt7e3jh07lu+fU2HkXKWyugeNkHuTRmZrElJN2hKfogMJRnFIIwAAAB6E8AwAAAAA8MiLiIhQp06dtHnzZplM/x+vBAYG6tChQ+rWrZv8/Py0du1aSVLXrl3VsmVLhYSEqF27dunuGTt2rIKCgjRs2DD169cv7fq0adO0dOlSXb58Od2zR44cqUmTJmnOnDl69tlns9Xv/Pnz5eTkpCFDhmj69Olp11u0aKHdu3dr4cKF8vDwyNVnUVRYWVur1Vuv68V3+su2hL3ZulNJRq2JS9atVAs2BwAAgCKNYxsBAAAAAI+80NBQbdq0KV0IJknR0dH65ptvJEne3t5p1/88GvHvYZskbdiwQZJUoUKFdNenTp2aITiTpODgYMXHx6tevXoqX778A3utXr26nnvuOUVHR2vGjBnpxvbv369Nmzapfv368vLyeuBcRVWlJ2tr8LL56jhkkNngLDbVpKV3kjT/DsEZAAAAcobwDAAAAACALCQnJ0uSUlJS0q6dPHlSkuTr65vhfWgdO3aUJIWEhGRrfpPJlDa30fjgN3G5urpKks6fP58huJOks2fPSpJ8fHyy9fyixK5kCXUcMkgfLF+gyk/VMVt3OMGoqTcTdSyJ1AwAAAA5x7GNAAAAAACYYW1trV69ekmStm3blnZ98+bNWrNmjV555RWdOHFCISEhSkpKUsOGDdWyZUvNnDlTX3/9dbae0b17dzk5Oemnn37S7du3H1h//fp1SZKbm1um43/uiqtTx3y4VBTVf9FHnYa9r7KuLmZrYi5e0urSjykimdAMAAAAuUd4BgAAAACAGVOmTFG9evW0efNmbd++Pd1Yt27dFBQUpFGjRunpp59Oux4SEqJly5ZlaxdZtWrV9NVXXyk5OVkffvhhtno6c+aMIiIiVLt2bQUEBGjmzJlpY82aNUvb+VauXLlszVfYubrXVNePh6hW44Zma1KNRu3993f64eu5Stqy1YLdAQAAoDji2EYAAAAAADLx/vvva9iwYTp9+rTefPPNdGP29vb67rvvNHToUA0aNEiurq5ycnKSr6+v3NzctHfvXnXu3DnL+StUqKCtW7eqYsWKGjx4sA4ePJjt3gYOHKjExETNmDFD27dv19SpU7V8+XLt3r1bJ06ckCSlphbt3VclnUqr68gPNXTV4iyDs0sRv2um/wBt/PwrJd1LsGCHAAAAKK7YeQYAAAAAwN8MGjRIM2fO1MmTJ+Xj46ObN2+mG//444/16quvKiAgQHPmzEm7vm3bNnXr1k3Hjx/XjBkz9P3332c6f4UKFbRr1y7VrVtXAQEB+te//pWj/kJDQ9W0aVONGjVKzz//vFq1aqWzZ89qxIgRioqK0sqVK3X16tWcL7wQMFhZqXHXjmofMFCO5c3vnktOTNSObxYqdNG/lZry4F1+AAAAQHYRngEAAAAA8BeDBw/W9OnTdeLECfn4+OjatWsZav48GjE0NDTD2C+//KIbN26oWrVqKl++vG7cuJFu3NXVVTt37lTdunX17rvv5jg4+9OxY8fUrVu3DNfHjRsnSTp8+HCu5n2Y3J57Rl1HfqgqTz+ZZd3J3T/q+89m6HrkRQt1BgAAgEcJ4RkAAAAAAP/z0UcfKTg4WOHh4Wrbtq1iYmIyrbO3t5d0fwfZ39nZ2al06dKSpKSkpHRjlSpV0q5du1SrVi0NHDhQc+fOzdf+bWxs9PrrryspKUmrV6/O17kLUunHnNXhg3fl2aV9lnXX/ntB64On6dd9P1moMwAAADyKCM8AAAAAAJA0atQoTZgwQUeOHFG7du0yHNX4V/v27VO9evUUGBio/fv3pwvJxo4dK1tbWx06dEhxcXFp16tWrarQ0FC5ubmpb9++Wrx48QN7cnV1VZkyZXT58mXduXMn7bqDg4MSEhLSvdfM2tpaM2fOlLu7u6ZMmaLo6OicfgQWZ21jI6+er6rtwL4q4VjKbF1ifLx2zF6ovUu/kzE52YIdAgAA4FFEeAYAAAAAeOT16tVLEyZMUEpKivbt26eAgIAMNefPn08LvD799FN16tRJL7zwgn799Vdt27ZN9+7dU4sWLdSkSRPFx8dr8ODB6e7fvXu3qlevriNHjqhatWoKCgrK8IxFixbpv//9b9rPkydPVu/evdW7d+90YVvr1q01b948hYSE6OLFi3J0dNRLL72kWrVqadWqVRo9enR+fTQFpk7zJnr54yGqWN0ty7qwzT9o45df687VjMdnAgAAAAWB8AwAAAAA8MirXr26pPvHHg4ZMiTTmt27d6cFWJcuXVKDBg00YsQIdejQQX369JGVlZUuX76shQsXKjg4WL/99lumz2jUqJEaNWpk9hl/Dc/MiYiI0P79+9WqVStVrFhR8fHxOnbsmIKCgrRs2bJsr/thKF/5CXUZHqBn2rTKsi7qdITWTf5C58J/sVBnAAAAwH0GSaaH3cTDcPjwYXl6ej7sNgAAAAAAeCTYlrCXT/+35N37Ddn+751xmbl767a2fjVbB1dvkOkvx1JmW2hoHrrMptatC/4ZrCNnistaWEe2WeoLTYMFnmGJtbCO7Csu65CKz1pYR/ZZ6u9WcZFVTsTOMwAAAAAAUKCee9FHnYa+p3KPu5qtSTUa9dOq9do2a47ib98xWwcAAAAUNMIzAAAAAABQIFxr1dDLHw+Re5PMj6n809mjx7Ru8pe69NsZC3UGAAAAmEd4BgAAAAAA8lVJp9J68d3+av6an6xtzH/1cDv6mjZ+OUvhW7ZbsDsAAAAga4RnAAAAAAAgXxisrNT45Q5qP/gdOZYvZ7YuJTlZexYvV8icRUq6d8+CHQIAAAAPRngGAAAAAADyrOqzT8svcKiqPP1klnWn9u7XhuDpuh550UKdAQAAADlDeAYAAAAAAHKttHN5dRjyrjy7dMiy7tp/L2hD8HSd3nfAQp0BAAAAuUN4BgAAAAAAcszaxkYt3+iudu/0UwnHUmbrEuPjFTJnkfYsWSFjcrIFOwQAAAByh/AMAAAAAADkSO1mjfXyx0PkUqNalnVhW7Zr05ezdDv6mmUaAwAAAPIB4RkAAAAAAMiW8pUeV+fhg1XPp1WWdVG/Rmjd5C91Luy4hToDAAAA8g/hGQAAAAAAyJJtCXu16ddLrfv0lK29vdm6+Nt3tPWr2Tq4eoNSjUYLdggAAADkH8IzAAAAAABg1rNtW6vTsPdV/onHzdakpqbq4Kr12vrVbMXfvmPB7gAAAID8R3gGAAAAAACk0NB0P7pYG9S1lI3c7ayzvO1ccqrWxSUryqe95NM+62e0bp3XLgEAAIACR3gGAAAAAADSlDBILzrYqEUJa1kbDGbrbhtN2hSfrLDEVAt2BwAAABQ8q4fdAAAAAACgaCpfvrz69euntWvX6syZM4qPj9etW7e0b98+9e3bV4a/BS+VK1fW119/rYMHD+ry5ctKSEhQVFSU9u7dq969e8vGJvPf76xQoYKCg4N14sQJ3blzR9evX9eRI0c0bNgwOTo6ZrvfSpUqKTAwUCtXrtSZM2dkNBplMplUs2bNPH0OxYVBUmN7a31czl7Pl7QxG5ylmEzaFZ+i4FuJBGcAAAAolth5BgAAAADIle7du+ubb77RpUuXFBoaqsjISLm4uMjPz0/z58+Xr6+vunfvnlZfs2ZN9ezZUz///LPWr1+vGzduyNnZWb6+vlq4cKHefPNNtWvXTkajMe0eNzc3/fzzz3JxcVFoaKi2bt2qEiVKqF27dvrss8/k7++vpk2bKiEh4YH9NmrUSJ9++qlSU1N17tw53b59W+XKlSuQz6aoqVrvKXUtY6eqtln/ju3pJKPWx6XoeqrJQp0BAAAAlkd4BgAAAADIlYiICHXq1EmbN2+WyfT/YUpgYKAOHTqkbt26yc/PT2vXrpUkHThwQOXKlUtXK0k2Njbavn272rRpIz8/P61atSptbPjw4XJxcVFQUJDGjx+fdt3Kykrbt2+Xj4+PunfvrqVLlz6w3yNHjsjLy0vHjx9XbGysQkND5e3tncdPoWhzdC6nDh+8q8Yvd8yy7roxVevjUnQ6mZ1mAAAAKP44thEAAAAAkCuhoaHatGlThjAsOjpa33zzjSSlC6eSk5Mz1EpSSkqK1q9fL0lyd3dPN1ajRg1J0vfff5/uempqqjZv3izp/rGO2REVFaUff/xRsbGx2aovzqxsrPV8rx76eOPKLIOzRJNJW+4m67ObSQRnAAAAeGQQngEAAAAA8l1ycrKk+8HYg1hZWal9+/aSpF9++SXd2MmTJyVJHTp0SHfdYDDI19dXRqNRu3btyo+WHxm1m3lq6Oql6jJ8sEqWNv/OuPBEo4JvJmrnPaMe/L8iAAAAUHxwbCMAAAAAIF9ZW1urV69ekqRt27ZlGHd2dtZ7770ng8GgChUqqG3btnJ3d9e3336rTZs2paudOnWqOnbsqIkTJ6p169YKCwuTnZ2d2rVrJ1dXV/Xv31/Hjh2zyLqKunJPuKrz8MF69gXvLOsupaRqXVyyzqbwXjMAAAA8mgjPAAAAAAD5asqUKapXr542b96s7du3Zxh/7LHHNHbs2LSfU1NT9dlnnykwMDBD7bVr19S0aVMtWLBAfn5+8vHxSbtn7ty5CgkJKbB1FBe2JezVpu+bat3HX7Yl7M3WxaeatC0+RT8lGMUBjQAAAHiUcWwjAAAAACDfvP/++xo2bJhOnz6tN998M9Oa3377TQaDQdbW1qpataqGDBmiAQMGaO/evSpXrly6Wjc3N+3du1f16tWTr6+vnJyc5OrqqnfeeUc9e/bU4cOHVa1aNQusrOgxGAzyaN9OH61frnbv9DMbnKWmpurAynWacjNR+wnOAAAAAHaeAQAAAADyx6BBgzRz5kydPHlSPj4+unnzZpb1qampunDhgmbOnKno6GitWLFC48eP1/vvv59Ws2jRIj377LN69tlndeLECUlSbGys5syZoxIlSmjGjBkKCgpSnz59CnRtRU2d5k3U4YN3VenJ2lnWnQv/Resmf6Go0xFSG18LdQcAAAAUboRnAAAAAIA8Gzx4sKZPn64TJ07Ix8dH165dy9H9W7dulSR5e3unXXN0dJS3t7diYmLSgrO/Cg0NlSQ1bNgw940XM1WeflIdhrwr9yaNsqy7ffWaNk37WmGbfrBQZwAAAEDRQXgGAAAAAMiTjz76SMHBwQoPD1fbtm0VExOT4zkqVaokSUpJSUm7ZmdnJ0lycnKSra2tkpOT091ToUIFSVJSUlJuWy82HnOrovYBA/VcuzZZ1qUkJ2vf0u+0Y/ZCJcbHW6g7AAAAoGjhnWcAAAAAgFwbNWqUgoODdeTIEfn4+GQZnHl4eMjKKuP/DS1VqpRmzJghSdq8eXPa9Rs3bujUqVOytbXV6NGj091jb2+vUaNGSZJ27tyZbszV1VV16tSRk5NTrtdVVJR+zFmvjP5IH61f9sDg7PS+A/rcz1+bpn1NcAYAAABkgZ1nAAAAAIBc6dWrlyZMmKCUlBTt27dPAQEBGWrOnz+vxYsXS5LGjBmjFi1a6MCBA4qMjFR8fLyqVKkiX19flStXTvv379fkyZPT3R8QEKDNmzdr9OjRatu2rQ4cOKCSJUvK19dX1apV05kzZxQcHJzunsmTJ6t3797q3bt32rP/tHDhwrT/rlu3riQpODhYsbGxkqR58+Zp//79ef9wClgJx1Jq3cdfXv6vyd6hZJa1kSdOadO0r/XH4TALdQcAAAAUbYRnAAAAAIBcqV69uiTJxsZGQ4YMybRm9+7daQHW3LlzFRcXp8aNG8vb21sODg66efOmjh49qpUrV2rBggUyGo3p7t+5c6c8PT01fPhwtWrVSu+9956MRqPOnj2rSZMmaerUqbp9+3a2e+7du3eGa6+88kq6fgtzeGZjZ6fmPfz0wtu9VapsmSxrr577r7Z+NVu/7Ai1UHcAAABA8UB4BgAAAADIlXHjxmncuHHZrt+yZYu2bNmS4+ecOHFCvXr1ynZ9nz591KdPn0zHDAZDjp9fGBisrNSw40t6cVB/lX/i8Sxrb1+9pu3/mq9D6zcpNcWYZS0AAACAjAjPAAAAAAAoxJ70aq4OQ97V4+41s6y7Fxun0AX/1r5vv1PSvQQLdQcAAAAUP4RnAAAAAAAUQm7PPaMOQ95VzYYeWdalJCXpx+WrtXPuYsXfvmOh7gAAAIDii/AMAAAAAIBCpGJ1N7Uf/I7q+bTKsi41NVVHN27VD1/P083LVyzUHQAAAFD8EZ4BAAAAAFAIlHGpoBff6S/PlzvIyto6y9qTu3/Ulpnf6MqZPyzUHQAAAPDoIDwDAAAAAOAhKulUWm36vSmvN16VbQn7LGvPHzuhTdO+1rmw4xbqDgAAAHj0EJ4BAAAAAJBboaG5vtVGkldJa7UpaSMHK0OWtVf+OKctM/6lk6H7cv08AAAAANlDeAYAAAAAgAVZSWpkb60XHWxU1jrr0OyW0aQfxk3Wke+3KNVotEyDAAAAwCOO8AwAAAAAAAt5xs5Kvg42crWxyrIuPtWknfdS9OM9o1LWbbRQdwAAAAAkwjMAAAAAAApcdRuDOpayVTXbrEOzZJNJ++4Zteteiu6ZLNQcAAAAgHQIzwAAAAAAKCCu1ga1L2Wjp+2ss6xLNZl0KNGo7f/H3p2GZV3m/R9/s8siLqDivhDiaAuk5i7gkvuK25SimTozif1z0hkzMyinsbRGy7mzXMilXErFckdEBXcNNJdxww0KERRFWS8u/g+85Y4B5bpQEPTzOo6OI8/1ezI+yPl4nr80AzeNpVSciIiIiIgU6sF/5U1ERERERERKhL+/P59//jm7d+/m5s2b5ObmsmzZsvuOd3JyYsaMGZw6dYr09HSuX7/Oli1b6NSp033nNGjQgC+//JJTp05x584dEhIS2Lt3L2PHjsXGxsasei0tLXnrrbc4evQoaWlpJCcns3HjRtq0aWPWOk+LKpYwzMmGtyvbFhmcHcvMYVZKFt/fVnAmIiIiIlIW6OaZiIiIiIjIYzBt2jS8vLxITU0lLi4OZ2fn+46tXLkyUVFRNGvWjOPHjzN//nycnJzo168f4eHhvP766yxevDjfnBYtWhAREYG9vT1btmxh/fr1ODs706dPH77++mv8/f3p3r27yfWuXLmSwYMH85///Id58+ZRtWpVhg4dyu7du/H39+fHH38s9s/iSeJgAZ0drGlfwQprC4sHjj2fbWTjnWwuGfQ+o4iIiIhIWaLwTERERERE5DGYOHEicXFxnDt3Dh8fH3bu3HnfsUFBQTRr1ow1a9YwdOhQcnJyAJg6dSqHDx/miy++YOvWrcTHx+eb4+TkxMiRI1m6dGle+6RJkzh48CDdunWjQ4cOREZGFlnrsGHDGDx4MHv27KFz585kZmYCMH/+fKKioliwYAE7duzg9u3bxfxplH+2QAd7K/zsrbG3fHBo9pvByMY7Bk5l65qZiIiIiEhZpGcbRUREREREHoOdO3dy7tw5k8YOGDAAgOnTp+cFZwDXrl3js88+w8HBgdGjR+eb06hRI4ACN8LS0tIIDw8HoFq1aibt/5e//AW4e1vuXnAGcPjwYVatWkX16tUZNGiQSWs9aSyB1hWseKeqHT0dbR4YnF3PyeW71Cw+TclScCYiIiIiUoYpPBMRERERESnj3NzcAIiNjS3Qd6+tc+fO+dpPnDgBQK9evfK129vb06lTJ+7cucO+ffuK3NvOzo62bdty586dQm+pbd68GeCB3157Uj3f1Y+/VbFlsJMNzg8Ize4Yc1l/O5uPb2RyJNOIHmkUERERESnb9GyjiIiIiIhIGZeUlEStWrVo2LAhp06dytd374aZp6dnvvZp06bRtm1bvvnmG4YMGcLJkydxdnamd+/eWFtbM2jQIH777bci93Z3d8fa2ppTp07lu/V2z9mzZwFo3LhxcY9X7ri3fJHeE8dT77mmDxyXlZvLrvQcdqYbyFBiJiIiIiJSbig8ExERERERKeM2btzI2LFjCQ4OZtiwYRiNd5/8c3V1ZeLEiQBUqVIl35zTp0/TsmVLVqxYQd++fenbty8AWVlZzJkzh/3795u0d6VKlQC4efNmof332itXrmz+wcqZWp4e9HrrDZq0b/3AcTm5uRzIyGFbmoFUhWYiIiIiIuWOwjMREREREZEybvr06XTr1o3BgwfTpEkTwsPDcXR0pF+/fsTHx1O/fv28QO0eLy8vQkNDSUxMpH379sTExFC5cmWGDx/OjBkz6N+/Py1btuTWrVuP6VTlR9XaNekeOI7mvbsXOTYmM4fNdwwkGZWaiYiIiIiUVwrPREREREREyriEhARatmzJe++9R+/evXnjjTdISkpi1apVzJ07l3PnzpGYmJg33srKitWrV1OtWjVatWrF1atXAbhz5w4ff/wxNWrUYOLEiUycOJHg4OAH7n3vZtm9G2j/7V57SkrKozhqmeJYpTJdxo2i7dCBWNvYPHDs2awcNqYZuGJQaCYiIiIiUt4pPBMRERERESkHEhMTmTBhAhMmTMjX7ufnB8ChQ4fy2po0aYKHhwdHjhzJC85+LyIigokTJ9K8efMi9z1//jwGg4FGjRphZWVV4LtnHh4eAJw5c80qYlAAACAASURBVMbsM5VVtvb2+Iz8I76jXqGCo+MDx8YbjGy8Y+B0tvGB40REREREpPywfNwFiIiIiIiISPEFBAQA8N133+W12dnZAXe/iVaYatWqAXe/f1aUzMxM9u7di6OjIx06dCjQ36NHDwB27NhhXuFlkK19BXwC/sjUzT/QffzYBwZnyXHxLP/7+/wrJUvBmYiIiIjIE0bhmYiIiIiISBlnYWGBYyFBzvDhwwkICGDPnj2EhobmtR8/fpwbN25Qv359Xn/99XxzKlWqxKRJkwAIDw/P11e3bl08PT2xt7fP1/7ll18CMGPGjLxgDqBFixYMHTqUxMRE1qxZ83CHfIzsHB3o9HoA725ZS9/Jb1LRpep9x6YmX2fdPz/l4z7DiN60DT3SKCIiIiLy5NGzjSIiIiIiIo9Bv3796N+/PwBubm4AtGnThpCQEACSkpKYPHkyAA4ODly9epWwsDDOnz+P0WikXbt2tG3blpMnTzJ48GByc/8vxsnKyuKtt94iJCSEhQsXMmzYMKKjo6lSpQp9+/alevXq7Nu3j0WLFuWraenSpfj6+uLr68uuXbvy2leuXMnAgQMZPHgw0dHR/PTTT7i4uDB06FCsrKwYO3YsqampJfrzKgn2zhXp8OoQOgwfgoOz8wPHZqalsfOb79i1ZAWZaWmlVKGIiIiIiDwOCs9EREREREQeAy8vL0aNGpWvzd3dHXd3dwAuXryYF55lZmaycuVK2rdvT9euXQE4e/YsU6dOZc6cOaSnpxdYf+nSpVy4cIG33nqLNm3a4OPjQ2ZmJqdPn+azzz5jzpw5Jj3beM8f//hH9u7dy+jRo5kwYQIZGRns3r2bGTNmsG/fvmL+FB4PxyqV8Qn4I+2G+VPB6cHfNMvJNrDv+3WEfR3C7eQbpVShiIiIiIg8TgrPREREREREHoPg4GCCg4NNGmswGBgzZozZe0RGRhIZGWnyeD8/v/v25eTkMGfOHObMmWN2HWVFRVcXfEe9QpvBA7BzsH/gWKPRSMyW7Wz54muS4+JLqUIRERERESkLFJ6JiIiIiIjIE61yjer4jR5OK/++2Pzum22FyTEYiN4URvjCJSReuFRKFYqIiIiISFmi8ExERERERESeSFXr1KLT6yNo2a8X1jY2DxxryM7m8PpN7Fi0TDfNRERERESecgrPRERERERE5IlSrUE9Oo8J4MVe3bCyfvAfe7MzMzm4bgM7Fi0jJeFqKVUoIiIiIiJlmcIzEREREREReSK4PdOILmNH8kK3zlhaWT1wbFZ6Bvu+X8fOb77j1rWkUqpQRERERETKA4VnIiIiIiIiUq7VbtKYLuNG8XxXvyLHZty5w96Va9i1dCW3r98ohepERERERKS8UXgmIiIiIiIi5VK955rS9U+jaerTrsix6bdSifx2NbuXryb91q1SqE5ERERERMorhWciIiIiIiJSrjR88QW6/uk1PNu2KnLsnRsp7Fq2kj0rfiDj9p1SqE5ERERERMo7hWciIiIiIiJSLni0bknXP72GewvvIsfeSkpm1zffsXf1OrLS00uhOhEREREReVIoPBMREREREZHSFxFh8tAmNpZ0dbCmgY1lkWNTcnKJSDewH0cMS757mApFREREROQpVfSfPERERERERMoIf39/Pv/8c3bv3s3NmzfJzc1l2bJlhY6tX78+ubm59/1nxYoVhc5zd3dn8eLFXLlyhczMTH799VeWLl1Ko0aNzKq1SZMmBAUFERoayqVLl/L2tbKyMvvcTyML4FlbS96qbMvYSrZFBmfXc3L5/nY2H93IJCojB0PplCkiIiIiIk8g3TwTEREREZFyY9q0aXh5eZGamkpcXBzOzs5FzomJiSE0NLRA+/Hjxwu0NW/enB07duDs7Mz27dtZsWIF9evXZ9iwYfTt2xdfX19iYmJMqrVbt268//77GAwGzp49S3p6Ovb29ibNfZpZAC/YWtLFwZqa1kX/fc9rOUbC03I4kpmDseTLExERERGRp4DCMxERERERKTcmTpxIXFwc586dw8fHh507dxY5JyYmhuDgYJPWX7RoEc7OzkycOJE5c+bktbdr146dO3cSEhKCt3fR39sC2Lx5M/v27ePYsWNkZGRw4cIFGjRoYNLcp5El4G1nSRd7a6qbEJolGIxsTzdwNNOo0ExERERERB4phWciIiIiIlJumBKWFVfDhg154YUXuHr1KnPnzs3Xt2fPHjZs2ED//v3p0KEDkZGRRa535syZkir1iWIFtLCzopODFa5WRYdm8QYj29MM/JJlJLfkyxMRERERkaeQwjMREREREXmi1apVi3HjxuHi4kJycjL79u3jl19+KTDOzc0NgIsXL5KbWzCWiY2NBaBz584mhWfyYNZAqwpW+NlbU8XKosjxl7ONhKUbOJmle2YiIiIiIlKyFJ6JiIiIiMgT7eWXX+bll1/O1xYREcHIkSO5cuVKXltSUhIA9evXL3SdRo0aAeDp6VlClT4dbCrY0WbwAHyr2FHJhNDsQraRsDQDp7MVmomIiIiISOko+k0MERERERGRcigtLY0PPviAF198kcqVK1O5cmU6duzIjh078PPzIzw8HAcHh7zxZ8+e5cyZM7i5ufHmm2/mW6tNmzb07t0bgCpVqpTqOZ4Udg4OdHp9BO9uWUu/v/2/IoOzs1k5/M/NLObdzFJwJiIiIiIipUo3z0RERERE5Il07do13n///XxtkZGRvPzyy0RFRdG6dWvGjBnD559/ntf/5z//mc2bNzN37lx69+5NTEwMdevWZeDAgfzyyy94e3tjNCrIMUeFik50eHUIHYcPxaGSc5HjT2XlsD3NwEWDvmgmIiIiIiKPh8IzERERERF5quTk5LBw4UJat25Nx44d84VnERERtG7dmmnTptGxY0d8fHyIjY3l73//O/Hx8axevZrExMTHWH354Vi5Eh1HDKPdHwdhX9GpyPHHM3MISzcQp9BMREREREQeM4VnIiIiIiLy1Ll27RoAjo6OBfpiYmIYNGhQgfbg4GAADh06VLLFlXMVXariM/IV2g4dgN3vnsUsjDE3l2NZRranGfgtR6GZiIiIiIiUDQrPRERERETkqdO6dWsAYmNjTRpvbW3NH//4R7Kysvjhhx9KsrRyq1KNaviOepU2g/pjU8HugWONOTn8nA3h6QYSFZqJiIiIiEgZo/BMRERERESeSN7e3sTExJCbmz+c6dSpExMnTgRg+fLl+focHBzIyMjI910zKysrPv/8czw8PJg5cyZXr17NN6du3bo4ODhw+fJl0tPTS+g0ZZdrvTr4jHyFl/r3wtrW9oFjc7INHP5pM+ELl5K8dFkpVSgiIiIiImIehWciIiIiIlJu9OvXj/79+wPg5uYGQJs2bQgJCQEgKSmJyZMnA/DZZ5/h4eHB3r17iYuLA+D555+nc+fOAEybNo19+/blW9/Pz4+FCxeyfft24uLicHJyonv37jzzzDN8//33vPfeewVqWrp0Kb6+vvj6+rJr1668dhcXF2bPnp33a1dXVwAWLVqUF+jNnDmT06dPP/wP5jF45qXmdBwxjD90bIulpeUDxxqysjiw9iciFi/nxm8JpVShiIiIiIhI8Sg8ExERERGRcsPLy4tRo0bla3N3d8fd3R2Aixcv5oVny5YtY8CAAbRs2ZIePXpgY2PD1atXWbVqFfPmzSMqKqrA+mfOnGHPnj34+PhQvXp10tLSiImJ4f333+e7774zq1YnJ6cCtQKMHDky79+/+eabchWeWdva4t2zKx2HD6WWp0eR47MzMtn3fSgR33zLrcRrpVChiIiIiIjIw1N4JiIiIiIi5UZwcDDBwcEmjV28eDGLFy82a/2zZ88yaNAgs+b4+fkV2n7p0iUsLCzMWquscnKpQtshA2k7dCAVXaoWOT4zLY09K9ewa+kKbiffKIUKRUREREREHh2FZyIiIiIiIlKoWp4edBwxFO8eXYv8nhlAeuptor77nt3LVpJ281YpVCgiIiIiIvLoKTwTERERERGRPBaWljT1aUfH4UN55qXmJs25Hv8bUd99z4F1P5GReruEKxQRERERESlZCs9EREREREQEOwcHWvbvRYdXh+Bar45Jcy78fJTdy1dxfMdujDk5JVyhiIiIiIhI6VB4JiIiIiIi8hSrUsuN9q8MptXAvthXdCpyfE62gaPbwtm9bBVXTpwqhQpFRERERERKl8IzERERERGRp1BD7+fpMHwoz3X2wdLKqsjxd1Jusu/7UPasXMOtxGulUKGIiIiIiMjjUSbDszp16rB06VJq1KhBbm4uX3/9NZ9//jlVqlRh1apVNGjQgIsXLzJkyBBSUlIAmDt3Lj179iQtLY1Ro0YRHR39mE8hIiIiIiJStlhZW/NCt050GD6Ues82NWlOwvkLRC5fxZENW8jOyCzhCkVERERERB6/MhmeGQwG3n77baKjo3FycuLIkSOEhYUxatQowsPD+fjjj/n73//OlClTmDJlCj169MDDwwMPDw9atWrFl19+SevWrR/3MURERERERMoEx8qVaD24P+2G+VOpejWT5vwnaj+7l63kzL6D5ObmlnCFIiIiIiIiZUeZDM8SEhJISEgA4Pbt25w6dYratWvTr18/fH19AViyZAk7d+5kypQp9OvXj6VLlwJw4MABKleujJubW94aIiIiIiIiT6Ma7g3pMHwILXr3wKaCXZHjs9IzOPzTZqK+Xc3V2IslX6CIiIiIiEgZVCbDs9+rX78+3t7eHDhwgBo1auQFYgkJCdSoUQOA2rVrc+XKlbw5cXFx1K5du0B4NnbsWMaNGweAq6trKZ1ARERERESk9FhYWODZrhUdhw/Fs51pL3LcvHqNqBU/sP+HUNJu3irhCkVERERERMq2Mh2eOTo6smbNGt566y1SU1ML9Jv7dMiCBQtYsGABAIcOHXokNYqIiIiIiJQFNhXsaNGnJx2GD6FGowYmzbl8/CS7l63i6LZwjIacki1QRERERESknCiz4Zm1tTVr1qzh22+/Zd26dQBcvXo17zlGNzc3EhMTAYiPj6du3bp5c+vUqUN8fPxjqVtEREREpCzy9/fHx8cHLy8vXnjhBZydnVm+fDkjRowoMPaZZ55h4MCBdOvWDQ8PD2rUqMGNGzfYv38/c+bMYefOnffdJyAggPHjx9O0aVNycnKIjo5m9uzZbNy40eRaIyIi8p5rv59FixYxZswYk9d8okRE5PtlJUtoV8GaNhWscLC0KHK6MTeXX7KM7E43cNGtEUx+5+4/v+fn9ygrFhERERERKVfKbHi2aNEiTp06xb/+9a+8th9//JGRI0fy8ccfM3LkSNavX5/XHhgYyMqVK2nVqhU3b97U985ERERERH5n2rRpeHl5kZqaSlxcHM7Ozvcd++GHHzJs2DBOnDjBpk2buH79Op6envTt25d+/frx5ptv8sUXXxSYN2vWLCZNmsSVK1dYsGABtra2DBs2jA0bNhAYGMi///1vk2r95ptv7hvQTZgwARcXFzZv3mzSWk+yutYW+Nhb87ytJVYWRYdm6cZcDmTkEJVh4IaxFAoUEREREREpp8pkeNauXTsCAgI4duwY0dHRAEydOpWZM2eyevVqXn/9dS5dusSQIUMA2LRpEz179uTcuXOkpaXx2muvPc7yRURERETKnIkTJxIXF8e5c+fw8fF54O2xLVu28PHHHxMTE5OvvWPHjoSFhTFr1iy+//77fH9hrU2bNkyaNIlz587RsmVLUlJSgLuB2pEjR5g9ezYbNmzg0qVLRda6ZMmSQtsbN25MUFAQCQkJeX+R7mljaWXFs7aW+Nhb08DG0qQ5STlGItNzOJSZQ6Z5L9+LiIiIiIg8lcpkeLZnzx4s7vM3J7t06VJoe2BgYEmWJCIiIiJSrj0oLPtv9wuvdu/ezc6dO3n55Zdp27Yta9euzev785//DMA//vGPvOAM4NKlS/z73/9m+vTpvPbaawQFBRWrfoBx48YBEBISgsFgKPY65VGFik609u9H+1cGUcXZ1qQ5Z7Ny2J2Rw6ksI8rMRERERERETGfaX1UUEREREREBsrOzAQqEV506dQLu3lr7b/eeWLw3pjhsbW0JCAjAaDSyYMGCYq9T3rjWr8uAqW8zfft6+rwdSJWabg8cb8jN5WCGgdk3Mpl/K5uTCs5ERERERETMViZvnomIiIiISNlTr149OnfuzJ07d9i9e3deu4ODA3Xq1CE1NbXQbw+fPXsWuPvsYnENHDiQatWqsW3bNi5cuFDsdcoLj1Yt6DB8KM1825s0PtWYy950A3szcrittExEREREROShKDwTEREREZEi2dra8u2331KhQgUmT56c72nGSpUqAXDz5s1C595rr1y5crH3v/dk49dff13sNco6a1tbXuz5Mh1GDKVW42dMmvOrwcju9Bx+zswhp4TrExEREREReVooPBMRERERkQeytLRk2bJltG/fnpUrVzJ79uxS3f+ZZ57Bz8+PhIQE1q9fX6p7l4aKLlVpO3QgbYYMoKJL1SLHG3NzOZllZHdGDuezjaVQoYiIiIiIyNNF4ZmIiIiIiNyXpaUly5cvZ8iQIaxatYrhw4cXGHPvZtm9G2j/7V7772+rmePerbOQkJAC31orz2p5etBxxDC8e3bF2samyPGZaWkcXLeByB79SDbqbUYREREREZGSovBMREREREQKZW1tzbfffsuQIUP49ttvCQgIwGgseNMpLS2NuLg46tSpg5ubW4Hvnnl4eABw5swZs2uwsbFh5MiRGI1GFixYULyDlCFWNjY816kjbYYO5JmWL5o053r8b0R+t5qD6zaQkXobuvUt4SpFRERERESebgrPRERERESkABsbG1avXk3//v1ZsmQJr732Grm597/ttGPHDgICAujevTvffPNNvr4ePXrkjTHXgAEDqF69Otu2bePChQtmzy8rarg3pJV/X1r06YFj5cJv6P23Cz8fZdeylZyIiMSYoy+aiYiIiIiIlBaFZyIiIiIiko+trS1r166lV69eLFy4kHHjxj0wOAOYP38+AQEBvPvuu4SGhuY90Vi/fn3Gjx9PRkYGISEh+ea4uLjg6upKUlISycnJha5778nGr7766hGcrHTZ2tvj1a0zrfz70sDrOZPm5GQbiNm6ncjlq7ly4lQJVygiIiIiIiKFUXgmIiIiIvIU6NevH/379wfAzc0NgDZt2uQFWklJSUyePBm4G4T16tWLa9euER8fz/Tp0wust3PnTnbt2pX363379vHpp5/y9ttvc+zYMX744QdsbW0ZOnQoLi4uBAYGcunSpXxrBAYGEhQURFBQEMHBwQX2cHd3x8/Pj4SEBH788cdH84MoBXWb/YFW/n3x7tGVCk6OJs25k3KTfavXsWfVWm4lXivhCkVERERERORBFJ6JiIiIiDwFvLy8GDVqVL42d3d33N3dAbh48WJeeNawYUMAqlWrxvvvv1/oekFBQfnCM4BJkybxyy+/MH78eMaNG4fRaOTnn39m1qxZbNy40eyax44di6WlJSEhIRgMBrPnlyZ754q82KsbrQb2oXaTxibPSzh/gcjlqziyYQvZGZklWKGIiIiIiIiYygJ48PsrT6hDhw7RsmXLx12GiIiIiIiUY41aeNNqYB9e6NoJmwp2Js3JzszkWFgEB9b8yPnD0eZvGhFh/hxz+fmV/B46h+lK4xzw5JxF5zDdk3IOeHLOonOYrLT+D02LUtijNM6ic5juSTkHPDln0TlMV1q/t54UD8qJdPNMRERERETEDE4uVWjZtyetBvalWoN6Js/79cw5DqxZz5ENW0m/lVqCFYqIiIiIiMjDsHzcBYiIiIiIlFdjxoxh//79pKamcvv2bQ4dOsSf/vQnLCzy/32/Z599lgULFvDzzz+TmJhIRkYGly9fJiwsjAEDBpi9b/Xq1fn888+JjY0lIyODxMRE1q5di7e396M6mvwXC0tLmrRvzch//ZPpYT/S+6+BJgVnGXfusO+HUOYMG82n/iOI+u4HBWciIiIiIiJlnG6eiYiIiIgUw/Lly3n11Ve5evUqK1asIC0tja5duzJ//nzatm3LyJEj88Y2b96c/v37s3//fvbu3cvNmzdxc3OjT58+rF27lqVLl+Yb/yD169dn79691KpViwMHDrB27VqqVavGwIED6dWrF3369GHbtm0ldeynTpWabrTs34uXBvSmSk03k+ddPPoLB374kZit4WSlp5dghSIiIiIiIvKoKTwTERERETFT//79efXVV4mNjeWll14iOTkZABsbG9asWUNAQAChoaGsW7cOgBUrVrBkyZIC61SsWJH9+/cTEBDAvHnzOHToUJF7z507l1q1ajF37lzeeuutvPYZM2Zw+PBhQkJC8PDwIC0t7RGd9uljZW1NU9/2tPbvR+O2L2FpadqDHXdSbnLkpy0cWPsjCediS7hKERERERERKSl6tlFERERExEz3nlr89NNP84IzgOzsbN577z0AAgMD89qzsrIKXSc1NZWtW7cC4OHhUeS+dnZ29OjRg5ycHKZNm5av7+zZsyxevJhatWrh7+9v3oEEgGoN6tH7r4G8t309o/71T5q0b21ScHZ2/2GWTX6PDzr3Zf0ncxSciYiIiIiIlHO6eSYiIiIiYiY3t7vP98XGFgxJ7rV16NABGxsbsrOz77uOvb09nTp1AuCXX34pct+qVatia2vL1atXuX379n337ty5M8uWLSv6IIJNBTue7+JHq0F9cW9u+jfjbiZe41DoRg6u20ByXHwJVigiIiIiIiKlTeGZiIiIiIiZkpKSAGjYsGGBvkaNGgF3n3Bs1KgRp0+fzutzd3dn+PDhWFlZUaNGDXr16kXt2rX56KOPTArPbty4gcFgwNXVFUdHR+7cuVPo3p6ensU+29OilqcHrfz70rxXN+ydK5o0x5iTw6nIfRxYs55Tkfsw5uSUcJUiIiIiIiLyOCg8ExEREREx08aNG3nllVf461//ysqVK7lx4wYA1tbWBAcH542rUqVKvnnPPPMMQUFBeb/OzMxk0qRJfPrppybtm5GRQUREBF27duWDDz7g7bffzutzd3dn9OjRhe4rd9k5OuDd82Va+/elbrM/mDwvOe5XDq77iYOhG7mVeK0EKxQREREREZGyQOGZiIiIiIiZVq5cyYgRI+jevTsnT55k/fr1ZGRk0KVLF2rWrMmlS5eoX78+RqMx37ytW7diYWGBtbU19erV49VXX+Wjjz7Cx8cHf3//Bz7xeM9bb73Fnj17+Otf/0qbNm3Ys2cP1apVw9/fn7Nnz+Lt7V1g36ddgxeeo9WgvrzwcmfsHOxNmmPIzuZ4+C72r/mRcwcOk5ubW8JVioiIiIiISFmh8ExERERExExGo5E+ffrw17/+leHDhzNy5EgyMjLYuXMn/v7+/PDDDwAkJiYWOt9gMBAbG8uHH35IVlYWM2fO5M033zTpBtrJkydp3rw506dPp2vXrjRv3pxff/2Vf/3rX2zdupWoqKj77vs0caxcieZ9e9BqYF/c3As+r3k/CQYjBzJyOJKZw50W7aFFe9M39fMrRqUiIiIiIiJS1ig8ExEREREpBoPBwCeffMInn3ySr93Ozg4PDw+uXbvGxYsXi1xn8+bNzJw5E19fX5Ofb4yNjWXUqFEF2l977TUADh06ZNI6TxoLCws8Wreg1cC+PNvZB2sbG5PmZebmcjQzhwMZOVw06IaZiIiIiIjI007hmYiIiIjIIzRs2DDs7OxYsWKFSeNr164N3A3jHtaIESMA+O677x56rfLEuXo1Xurfi5cG9MGlTi2T513JNrI/M4eYzBwylJmJiIiIiIjI/1J4JiIiIiJSDBUrViQ1NTVf2wsvvMCsWbO4fv06M2fOzGtv3rw5R44cKbCGq6tr3riNGzfm63NxccHV1ZWkpCSSk5Pz2m1tbQHIysrKN37q1Kn4+fmxcuVKoqOjH+5w5YCllRV/6NiWVgP78ocObbC0sjJpXroxlyOZORzMyCE+R4mZiIiIiIiIFKTwTERERESkGMLCwkhPT+f48eOkpqbyhz/8gV69epGenk6fPn347bff8sYuXLgQFxcXDh48yOXLl8nJyaFBgwb07NkTBwcH1q1bx+LFi/OtHxgYSFBQEEFBQQQHB+e1e3h4EBkZSVhYGBcvXsTGxobOnTvz/PPPExkZybhx40rtZ/A4uNSpzUsD+9CyX08qVa9m8rzz2UYOZBg4lmkkuwTrExERERERkfJP4ZmIiIiIlJqRI0fyzTffPHBMTk4O1tb3/8/UBQsWMGbMGACeeeYZzp8/b1YNvXr1YtKkSXh7e2NlZcWJEyf4n//5H5YuXWrWOj/88APDhg1j+PDh2NvbEx8fz9dff80///lP4uPj842dPXs2/fv358UXX6Rbt27Y2tqSlJTEjh07WLZsGatXrzZ536tXr7Jp0ybatGlDnz59yM7O5uTJk4wfP56vvvqKnJwcs85RHthUsONZv460GtgXj9YtTJ6Xmnydw+s3cWDdT1wLWVKCFYqIiIiIiMiTROGZiIiIiJSamJgYgoKCCu3r0KEDnTt3ZvPmzfed37t3b8aMGUNqaioVK1Y0e//x48czb948kpKSWL58OVlZWQwaNIglS5bw3HPPMXnyZJPXmj17NrNnzzZp7Lfffsu3335rVq3BwcH5bpzdk5SUxPDhw81aqzyysramcdtWePfoQjO/DlRwdDRpntFo5Mzeg+xfs56TO6PIeQTfkhMREREREZGni8IzERERESk1R48e5ejRo4X27d27F4Cvv/660H5XV1cWLFjAypUrcXNzw9fX16y969evz+zZs0lOTqZFixZcunQJgA8++IBDhw4xadIk1qxZw/79+81aVx4dC0tLGjX3wrtnV57v4odj5Uomz73xWwIH123gUOhGbvyWUIJVioiIiIiIyJNO4ZmIiIiIPHbPPvssbdq0ojQXTAAAIABJREFUIS4ujo0bNxY65l6oNn78eNasWWP2HqNHj6ZChQp8/PHHecEZQEpKCh999BGLFy/mz3/+s8Kzx6Dec03x7vEyL3TrZNZ3zHKyDZzYGcmBtT9yeu9Bco3GEqxSREREREREnhYKz0RERETksRs3bhwAixYtwlhIADJy5EgGDBhAv379uH79erH26NSpEwBbtmwp0Hfvqch7Y6TkuXm4492jK949uuBSp7ZZc69dvMyBtT9y+MfNpCYX7/eDiIiIiIiIyP0oPBMRERGRx6pChQoMHz4cg8HAwoULC/TXq1ePuXPnsmzZMn788cdi7+Pp6QnAmTNnCvQlJCRw+/Zt6tati729Penp6cXeR+7PpU5tvHt2xat7F2p6uJs1N/1WKr+E7+LQj5uIPRxdQhWKiIiIiIiIKDwTERERkcdsyJAhVKlShQ0bNhAXF5evz8LCgiVLlnD79m3efPPNh9qnUqW738+6efNmof03b97EycmJSpUqKTx7hJyrV8OrW2e8e3Sl3nNNzZqblZ7BiZ2RRG8O4z9R+8nJzi6hKkVERERERET+j8IzEREREXms7j3Z+NVXXxXomzhxIr6+vvTs2ZOUlJTSLk2KyaGSM8+/3AnvHl1p1NwLS0tLk+casrM5HbWf6C3bORERSZaCTBERERERESllCs9ERERE5LFp2rQp7dq148qVK2zatClfn4eHB//4xz9YvHhx3jfJHsbNmzepVq0alSpVKvS7aUXdTJMHs3Nw4NlOHfHu2ZXGrV/Cysb0P2oYjUbOH/yZ6M3bOLZ9F+m3bpVgpSIiIiIiIiIPpvBMREREpJy4cOECDRo0KLQvISGBmjVr5mtzcnJiypQp+Pv706BBA9LT0zl48CCffPIJO3bsMHnfSpUqMXbsWLy8vPD29qZx48ZYW1vTpUsXwsPDH+ZIebfOFi1ahNFozNfXtGlTKlSowOjRoxk9enSh88+dOwdA//79Wb9+/QP3On36NNWqVaNx48bs378/X5+bmxtOTk5cuXJFTzaawdrOjj90aIN3j6407dgOmwp2Zs2/ePQXojeFcXTbDlKTkkuoShERERERERHzKDwTERERKUdSUlKYM2dOgfbbt2/n+3XlypWJioqiWbNmHD9+nPnz5+Pk5ES/fv0IDw/n9ddfZ/HixSbt2aBBA2bNmgXAlStXSEpKws3N7aHPYmdnx4gRIzAYDCxatKhA/8WLF1m4cGGhc3v16kXNmjVZvXo1t27d4uLFi0Xut2PHDtq3b0/37t0LhGc9evTIGyMPZmltRePWLfHq3pXnOvtQwcnRrPm/nj5L9OYwYrZs53r8byVUpYiIiIiIiEjxKTwTERERKUdSUlIIDg4uclxQUBDNmjVjzZo1DB06lJycHACmTp3K4cOH+eKLL9i6dSvx8fFFrnXp0iU6d+5MdHQ0N27cICQkhFGjRj3sURg8eDBVq1blp59+Ii4urkD/0aNHGTt2bKFzIyIiqFmzJlOnTuX8+fP5+lxcXHB1dSUpKYnk5P+7zRQSEsLf/vY3AgMDCQkJ4dKlS8DdoHHq1KkAzJ8//6HP9SSysLCg4Ysv4N2jK8939cOpahWz5l+7dOVuYLY5jKuxF0umSBEREREREZFHROGZiIiIyBNowIABAEyfPj0vOAO4du0an332GXPmzGH06NF8+OGHRa6VkpJSIjey7j3Z+PXXXz/SdQMDAwkKCiIoKChf0Hjx4kUmT57MF198weHDh1m1ahVZWVkMGjSIunXrMnv27AI30p52dZo2wbtnV7y6d6FyjepmzU25mkjM5u1Ebw4j7uR/SqhCERERERERkUdP4ZmIiIhIOWJnZ8err75KvXr1uHPnDseOHWP37t0Fvhd271nF2NjYAmvca+vcubNJ4VlJaNKkCR06dODKlSts2rSp1PadN28eFy9eZNKkSQQEBGBpacnJkyeZNm0aS5cuLbU6yrIajRrg3fNlvLp3oVr9umbNvXMjhaPbdhC9OYwLPx8lNze3hKoUERERERERKTkKz0RERETKkZo1a7J8+fJ8bbGxsbz22mvs3r07ry0pKYlatWrRsGFDTp06lW98o0aNAPD09Cz5gu/jP//5DxYWFsWe7+fnd9++4ODgBz5tuWHDBjZs2FDsvZ9EVWvXxKt7F7x7dKWWp4dZczNu3+GX8F1Ebw7j7IFDGA05RU8SERERERERKcMUnomIiIiUEyEhIURGRnLixAlSU1Np1KgRgYGBjBs3js2bN9OmTRuOHTsGwMaNGxk7dizBwcEMGzYs72aaq6srEydOBKBKFfO+WyVPloouVXmhW2e8e3alwQvPmTU3OyOTk7v3EL1pG6ei9mPIzCyhKkVERERERERKn8IzEREReeq8+uqrebe3xowZw6JFi/L6IiIi8PX1feD8RYsWMWbMGJP3s7W1JTAwkGHDhuHp6YmVlRXx8fHs27ePSZMmkZSUZNI6H3zwQb5fnzhxgr/85S/cvn2bSZMmERQUxMCBA4G73zrr1q0bgwcPpkmTJoSHh+Po6Ei/fv2Ij4+nfv36BZ56lCefvXNFnu/ii3ePl3Fv6Y2llZXJc3OyDZzed4CYzds5HrGbzDtpJVipiIiIiIiIyOOj8ExERESeKnXq1GHevHmkpqZSsWLFAv3ffPMNO3fuLHTuhAkTcHFxYfPmzSbvV6NGDbZt28bzzz9PVFQUCxYsICcnh3r16tGtWzdmzZplcnh2P/Pnz2fSpEl07Ngxry0hIYGWLVvy3nvv0bt3b9544w2SkpJYtWoVc+fO5dy5cyQmJj7UvlI+2Nrb08yvA949uuLZrhXWNjYmzzUajcQejiZ6y3Z+CYvgTsrNEqxUREREREREpGxQeCYiIiJPlZCQEJKTk1m7di2TJ08u0L9kyZJC5zVu3JigoCASEhJYv369SXtZWFiwevVqPD096dOnT6Hf2bK0tDTvAIW4du0aAI6OjvnaExMTmTBhAhMmTMjXfu97YYcOHXrovaVssqlgh2fbVnh170JTn/bYOdibNf/SsRPEbNlOzNZwbiVeK6EqRURERERERMomhWciIiLy1HjzzTfp1KkTvr6+dOrUyay548aNA+6GbwaDwaQ5/fv3p2PHjsycObPQ4Ax4JE8ntm7dGoDY2FiTxgcEBADw3XffPfTeUnZUdKlKU592NPPtgEfrltjaVzBr/m9nzxO9OYyYzdtJjov/v46IiEdcaSH+N9AVERERERERKQsUnomIiMhToUmTJsycOZO5c+cSGRlpVnhma2tLQEAARqORBQsWmDzvlVdeAWDFihVUr16d3r17U716dRISEti2bRu//vqrWfVfvnyZtLT835mqX78+8+bNA8j7jhvcvfXm4ODAnTt38o0fPnw4AQEB7Nmzh9DQ0Hx9Li4uuLq6kpSURHJyssm1yeNTs7E7zXw70NSnPfWfb2b2/OS4eKI3hRG9ZTsJZ8+XQIUiIiIiIiIi5Y/CMxEREXniWVlZsWzZMi5fvszUqVPNnj9w4ECqVavGtm3buHDhgsnzWrZsCcBLL73EnDlz8j2rmJWVxQcffMA//vEPk9YaOnQob7/9Nrt37+bSpUukpqbi7u5Or169sLe3Z+PGjcyePTtvvIODA1evXiUsLIzz589jNBpp164dbdu25eTJkwwePJjc3Nx8ewQGBhIUFERQUBDBwcH5+mbNmoWrqysA7du3B2Dy5MkMHz4cgNDQUJOfs5Tis7Kxwb2FN838OtDUpx1Va9U0e42bidc4unUH0Zu3cfmXkyVQpYiIiIiIiEj5pvBMRERECjVz5kxatGhB48aNcXV1JT09nUuXLhEaGsq8efO4fv16vvFOTk5MmTIFf39/GjRoQHp6OgcPHuSTTz5hx44dD1XLtm3b6Nq1KwDW1tbk5OSYNX/69Ol4e3vTvn17MjIyzN7/3pONX3/9tVnzqlevDsCXX37JV199xezZs7l+/TqdO3fmyy+/ZMaMGcTFxd33O2u/FxERgaenJ97e3rRr1w5HR0dSUlKIiopi2bJlLFu2LN/4zMxMVq5cSfv27fN+dmfPnmXq1KnMmTOH9PR0s84yaNAgGjRokK+tW7duef9+8eJFhWclxLFyJZp0aEsz3/Z4tmtFhf/6tp0p0m7e4mjYDqI3hRF7JIbcR/BcqIiIiIiIiMiTSuGZiIiIFGrixIn8/PPPhIWFkZiYiKOjI61btyY4OJhx48bRunVr4uLiAKhcuTJRUVE0a9aM48ePM3/+fJycnOjXrx/h4eG8/vrrLF68uFh1BAYG4ufnR3p6Ovb29mbPf+mll5g6dSqffvop+/fvN3v+M888g5+fHwkJCWaHQ5aWlgBs376dwMDAvPZ169aRnZ3NTz/9xDvvvGNSeLZ79252795t8t4Gg4ExY8aYVW9wcHCBG2f3NGzY0Ky15OFUa1CPZr4daObbngZez2FpZWX2GndupHBy916Obg3nzL6D5Jj4rT4RERERERGRp53CMxERESmUs7MzmZmZBdpnzJjBu+++yzvvvMP48eMBCAoKolmzZqxZs4ahQ4fm3QybOnUqhw8f5osvvmDr1q3Ex8ebVUPjxo35+OOPmT17NsOGDStw86koVlZWLF26lDNnzvDee++ZNfeee7fOQkJCMJgZPqSkpFCjRg3WrVtXoG/Tpk1kZmbi6emJs7Mzt27dKlZ98mSwtLKigffzNPNtTzOf9lRrUK9Y6yScv8DJXVGciIji0rHjumEmIiIiIiIiUgwKz0RERKRQhQVnAKtXr+bdd9/Fw8Mjr23AgAHA3ecRf/+k4rVr1/jss8+YM2cOo0eP5sMPPzR5/3vfKYuNjeX9999n2LBhZp/ByckJT0/PB55n4cKFLFy4kDlz5jBx4sR8fTY2NowcORKj0ciCBQvM3v/06dPUqFGDlJSUAn1Go5Fbt25RrVo17O3tFZ49hSo4OdKkXWua+rbnDx3a4lDJ2ew1cgwGYo/EcHLXHk7sjCL5SlwJVCoiIiIiIiLydFF4JiIiImbp06cPAMeOHctrc3NzAyA2NrbA+HttnTt3Nis8mzZtGt7e3rRp04asrKxi1ZqZmcnChQsL7XvxxRd58cUXiYyM5PTp0+zbt6/AmAEDBlC9enW2bdvGhQsXzN5/+/btdOzYkWeffZbVq1fn66tevTrVqlUjNTWVpKQks9eW8qlq7Zo09WlPM78OuDf3xsrG/P8cT7+Vyn+i9nFiZxT/2bOf9FupJVCpiIiIiIiIyNNL4ZmIiMgjNnPmTFq0aEHjxo1xdXUlPT2dS5cuERoayrx587h+/XreWGtra9544w28vLzw9vamadOm2NraMmbMGBYtWmTWvo9yrd97++23cXJyolKlSrRo0YIOHTpw9OhRZs6cmTcmKSmJWrVq0bBhQ06dOpVvfqNGjQDyboCZokWLFrz77rvMnDmTI0eOFLv2jIwMxo4dW2jf+++/z4svvsiSJUvu+/O592TjV1999cB9XFxccHV1JSkpieTk5Lz2xYsX8/e//53x48cTEhKSF8BZWloya9YsAL7//vt8t/XkyWJhYUHd55rmfb+spod7sdZJuhLHiZ1RnNwZRezPMRgN+j0jIiIiIiIiUlIUnomIiDxiEydO5OeffyYsLIzExEQcHR1p3bo1wcHBjBs3jtatWxMXd/dpNUdHR+bOnQtAQkICCQkJ1KtXvG8dPcq1fm/SpEl5N8sANm/ezKhRo/Ldltq4cSNjx44lODiYYcOGYfzf7yy5urrmPYVYpUoVk/arUKECy5Yt48SJE3zwwQcPXX9xubu74+fnR0JCAj/++OMDxwYGBhIUFERQUBDBwcF57fHx8bzxxhuEhIQQExPDunXruH79Or6+vnh7e3P69Gn+9re/lfRRpJTZ2legcZuXaOrTnqY+7ajoUtXsNYxGI5eOHv/f75dFcjX24qMvVEREREREREQKpfBMRETkEXN2di70+1ozZszg3Xff5Z133mH8+PEApKWl0aNHD2JiYkhISOD9998nKCioWPs+yrV+r2bNmsDdZwbbtm3LzJkziY6Opnfv3kRHRwN3v3XWrVs3Bg8eTJMmTQgPD8fR0ZF+/foRHx9P/fr18wK1onzyySc0atSIli1bYjAYHrr+4ho7diyWlpaEhIQ8VB1Lly7l0qVLTJkyhb59++Lo6Mjly5f55JNP+Oijj7h58+YjrFoeF+fq1Wjq045mvu3xaNUCGzs7s9fITEvj9N6DnNwZyanIfdy+fqMEKhURERERERGRoig8ExERecQKC84AVq9ezbvvvouHh0deW3Z2Nlu2bHkk+z7KtQqTmJhIaGgoP//8M2fOnGHp0qU899xzwN2bbi1btuS9996jd+/evPHGGyQlJbFq1Srmzp3LuXPnSExMLHKPjh07Mn78eIKCgvJ9U60kBAcH57sl9t+mTJnClClTHslau3btYteuXWbXKGVb7SaNaebbnqa+7anb7A/FWiPlaiInIiI5uSuKcwd/xlDM7/uJiIiIiIiIyKOj8ExERMqEqlWrMmDAAHr16sVzzz1H7dq1ycrK4pdffiEkJISQkBByc3PzxtepU4d33nmH5s2bU79+fapUqUJycjLnz59n8eLFLF++3KzbQk5OTkyZMgV/f38aNGhAeno6Bw8e5JNPPmHHjh2P5Ix9+vQBKPFQqKRdvnyZkydP4u3tjYuLS943vhITE5kwYQITJkzIN97Pzw+AQ4cOFbm2t7c3lpaWfPDBB/d9svHe/65eXl4cPXr0YY4iYhZrW1ueeelFmvl2oKlPOyq71SjWOldOnOLkzihO7Iwi/j9nHnGVIiIiIiIiIvKwFJ6JiEiZMHjwYObPn8+vv/5KREQEly9fpkaNGgwcOJBFixbRo0cPBg8enDfe3d2dV199lQMHDhAaGsr169dxcXGhR48ehISEMGLECF5++WVycnKK3Lty5cpERUXRrFkzjh8/zvz583FycqJfv36Eh4fz+uuvs3jxYrPP9Pbbb+Pk5ESlSpVo0aIFHTp04OjRo8ycOdPstcqaWrVqAZj08w0ICADgu+++K3Ls8ePHWbhwYaF9Q4cOpWLFiixatIjc3Ny80E6kJDlWqUzTjm1p6tsBz7YvYefgYPYa2ZmZnN1/mBO7oji5aw+3Eq+VQKUiIiIiIiIi8qgoPBMRkTLhzJkz9OnTh40bN+a7YTZ16lQOHjzIoEGDGDhwIGvXrgVg7969VKlSJd9YAGtra7Zt20anTp0YOHAg33//fZF7BwUF0axZM9asWcPQoUPzAqGpU6dy+PBhvvjiC7Zu3Up8fLxZZ5o0aRJubm55v968eTOjRo0iKSnJrHUeBw8PD65evcqtW7fytVtYWPDhhx9So0YN9uzZQ0pKSl67g4MDd+7cyTd++PDhBAQEsGfPHkJDQ/P1ubi44OrqSlJSUl4QFh4eTnh4eKE1denShYoVK/KnP/3JpNBOpLhquDe8+xyjT3vqv/AslpaWZq+Rmnydk7v2cGJnJGf3HyIrPaMEKhURERERERGRkqDwTEREyoSIiIhC269evcr8+fP56KOP8PX1zQvPsrOzCx1vMBgIDQ3Fz88v37fFHmTAgAEATJ8+PV8oc+3aNT777DPmzJnD6NGj+fDDD805EjVr1gSgevXqtG3blpkzZxIdHU3v3r2Jjo42a63S1rNnT/75z38SFRXFhQsXSE5OpkaNGvj4+ODu7s5vv/3G2LFj88Y7ODhw9epVwsLCOH/+PEajkXbt2tG2bVtOnjzJ4MGDCwSdgYGBBAUFERQU9MDvhYmUNDsHBxo196Jxm5do6tsO17p1irXOb2fPc2JnFCd2RnLll5MFfs+LiIiIiIiISPmg8ExEpJzz9/fHx8cHLy8vXnjhBZydnVm+fDkjRowoMDYkJIRRo0Y9cL3w8HC6dOlSrFoWLFjAmDFjAPj/7d15fJTlvf//90z2PSGEPYRIgqIspmxasEARFVBwl18FBCv2WIUe9LgcCtatB/21VUF79Iii1KKgoiyuSAEXqBIgJARISEKAhEAgJCRkT2bu7x8zGRJIYCaZbOT1fDw+j5m5l+u+7vi4MsDb67pjYmKUkZHRqHbOVROUOfMMM7PZrIkTJ0py/tliNbPDDh48eN6+mm3jxo1zOTyrceLECa1Zs0a7du3SgQMH9I9//EMDBw5sVFstZePGjYqJidGoUaMUFxen0NBQlZSU6MCBA3r//fe1ZMkSFRQUOI6vqKjQypUrNWrUKI0fP16SlJaWpvnz5+vVV19VWVlZa90KcB5Pb29FDR6g2BFDFTtiqCIH9JeHp+t/LLZUVStjxy7t3fKj9n33o/KPHmuG3gIAAAAAgJZGeAYA7dyCBQt09dVX68yZM8rOzlZwcHCDx65Zs0aHDh2qd9/06dPVt29fffXVV43qx80336wHHnhAZ86cUVBQUKPaqI+Hh4fjmVlff/31efvDw8P1yCOPyGQyKSIiQuPHj1dsbKxWrFihzz//3Klr5OXlqUePHoqOjtb+/fvr7LvsssskSZdffnkT70Q6cuSI9u3bp7i4OIWHh7fpZ3bt3btXc+bMcfr46upqR3DqrGeffdalGWfR0dEutQ/UMHt4qGf/y21h2TVDFX31IHn5+jSqrdLCIu3/YZv2bvlRqVt/UnlxScMHNzCj1q3Gjm3+awAAAAAA0MEQngFAOzdv3jxlZ2crPT1do0eP1pYtWxo8du3atVq7du1520NCQvTEE0+ooqJC7733nst96Ny5s5YuXaqVK1eqW7duGjNmjMttNOTFF1/UwIED9cUXX2jDhg31XvuZZ55xfLZarfrLX/6i+fPnO32NL774QrNnz9azzz6rqVOnymq1OtqeN2+eJCksLKxpN2LXo0cPSeKZXUAz6xZzmWJHDFXMiCHqO/QX8gsKbHRbJw8dsS3H+N2POpSQJCvjFwAAAACASxrhGYAOy5XlDnv16qX//u//1pAhQxQVFaWwsDCdOnVKGRkZWrZsmf75z386taRgbWazWbNmzdKMGTM0cOBA+fr66tixY4qPj9fChQuVlpbmVDsXCsucNX36dPn7++vDDz9s1Gyot956S5L08MMPa/Xq1U3uT405c+bov/7rv7R///56/7tIUmpqqkwmk8xms3r27KnbbrtNzz33nEaNGqVJkybVWVqwIU8//bRuvPFG3XXXXbriiiv0r3/9SwEBAZoyZYqOHj2qqKgoR6B2MbGxscrNzVVRUVGd7SaTSc8//7y6du2qrVu36vTp0061V5/g4GB1795dhYWFOn78eKPbAS4lnXr1UOzwIfbAbKiCwjs1ui2rxaLM3Unat2Wr9m75QScPHXFjTwEAAAAAQFtHeAagw3JlucO+ffvq3nvv1c8//6w1a9YoPz9f4eHhmjBhgt59911Nnz5dN9xwg9OziQICArR27VqNGzdOCQkJWr58ucrLy9WzZ09dd9116tevn9PhmTvMnj1b0tkQzBX33XefbrvtNk2ZMkX5+flu69PDDz+sJUuWaO/evRo3btxFQzCr1aqsrCwtWbJEubm5WrlypZ577jmnlh48fvy4hg0bpoULF+rmm2/W73//e+Xl5WnVqlVavHix0tPTdeLECaf6PXHiRC1atEg//vijMjMzderUKXXt2lWjR49W3759dezYMcfPu8aTTz6pK664QpJ09dVXS5JmzZqlUaNGSZJ+/PFHvfPOO47jb7vtNr333nt67733NGvWrCa1BbRXQeGdFDNiqGKHD1HMiKEK79WjSe3l5xxT+s87lfZzvFK3/qyS04Vu6ikAAAAAAGhvCM8AuMSV2Vo1rr32Wi1YsEDXXHON/Pz8lJaWpmXLlum1115zejZPjx49dPvtt2vixInq37+/unfvruLiYu3atUtvvPGGPvvsM5fvxZXlDrdt26awsDAZhlFnu6enpzZs2KBf//rXuv322/Xxxx87de3/+7//07hx4/S73/2u3sDK07Plfj1fc801GjRokFJTU12exda7d28tXrxY77//vtatW+e2Pv3hD3/Qq6++qj179mjcuHE6efKkS+fXPLfNleUjT5w4oTlz5pwXto21P08oPj7eqXY2btyomJgYjRo1SnFxcQoNDVVJSYkOHDig999/X0uWLDkvCLzpppvO6+vIkSM1cuRIx2dnAy93tgW0Jb5Bgeo7NM723LIRQ9Ut5rImtXfmVL7St+9U+vadSvtph05lH3VTTwEAAAAAQHtHeAa0svvuu++iz5iyWCwuhSm//vWv9cgjj+jaa691LC+4Z88eLV682BEqNJYrs7UkafLkyVq9erXKy8u1atUq5efn65ZbbtGrr76qkSNH6u6773bqunPmzNFTTz2lgwcPavPmzTp+/LiioqJ0++23a/z48Xr55Zf12GOPuXQvrgRFVVVV9W6vrq7WmjVrNHbsWMXGxjrVVlxcnO69916tXLmywZleri4B2RQPPvigJGnp0qUunWcymbR8+XIVFxdr7ty5buvPE088oZdeekkJCQkaP358o5aR7NmzpyT3/BxnzJghSfrggw+cOn7v3r1OzXarrSagc9by5cu1fPlyt7QFtFVevj6KjhtkW4Zx+FD1uvJymT08Gt1eeXGJMnYkKO3nHUr7eYdy0w+e9z9EAAAAAAAASIRnuAQ0NShqzEwqd9q9e7eeeeaZevddd911GjdunEuB10svvaQnnnhCWVlZWrdunfLy8hQREaEhQ4ZozJgxTQ7PXJmtFRQUpKVLl8pisWjMmDHauXOnJGnhwoXatGmT7rrrLt1zzz1atWrVRa+7fft2jR49Wt9//32d7VdccYV++uknPfroo1qxYoV27drVpPtzldls1sSJEyVJSUlJTp3zm9/8RpL04YcfKjg4WLfccosiIyN16tQpbdq0SRkZGc3W33MFBwfr7rvvVkVFxUVD3HPNmzdPY8aM0cSJE5v0/K7aFixYoOeff147duzQDTfO1iCWAAAgAElEQVTccMGlGuPi4pSYmHje7MWAgAAtXrxYkvTFF1/U2dfQs8JMJpP8/f1VUlJS5/hp06ZpxowZ2rp1q9asWdPU2wNwAWZPD/UecJViRtieW9Zn8AB5ens3ur2qigodSthjC8u271D23hRZnVxaFwAAAAAAdGyEZ2jX3BEUuTqTyt0SExOVmJhY775t27ZJcv45VA888ICeeOIJvffee3rwwQfPmy3ljqUAXZmtdeedd6pLly5avny5IziTpIqKCi1YsECbNm3SQw895FR41tCyjCkpKVq1apUefPBBjRkzptnDs/DwcD3yyCMymUyKiIjQ+PHjFRsbqxUrVujzzz93qo1hw4ZJkqKiopSRkaHOnTs79lmtVr3xxhuaO3eu00taNsW0adMUEBCgDz/80KUZXrGxsfrzn/+sZcuWNTmQrTFjxgw9//zzqq6u1g8//FDvbLZDhw45Zlw9/fTTGjlypLZt26YjR46otLRUkZGRmjBhgsLCwrR161YtWrSozvkNPSvM399fubm5+vbbb5WRkSGr1aqRI0fql7/8pfbt26e77rqLGSqAm5lMJnXvF2ObWTZiiC4bcrV8AwIa3Z6lulpZe/fbn1u2Q4cSk1VdUeHGHgMAAAAAgI6C8AztlruCIldmUrWkAQMG6Nprr1V2dvZ5s2fq4+3trT//+c86fPhwvT8PqWWXApRsswIl6euvvz5v3/fff6+SkhL98pe/lLe3tyorKxt9nZp7bYn769y5c52ZglarVX/5y180f/58p9vo0qWLJOnll1/WmjVrtGDBAmVnZ2vEiBF688039fDDD+vkyZN69tln3d3988yePVuS7Rlsrrjyyivl6+ur+++/X/fff3+9x6Snp0uSbr31Vq1du/aibUZHR0uyjd158+bVe8yWLVsc4dnSpUtVXFys4cOHa8yYMfL391dBQYF27typjz76SMuWLZPFyVkmFRUVWrlypUaNGqXx48dLktLS0jR//ny9+uqrKisrc6odABfWOSpSscOHKvaaoYoZ9gsFhIU2qb2cA+lK+3mH0n/eqYM7E1ReXHLxkwAAAAAAAC6C8AztkjuDorYSlp2r5jlU77zzjlMzkMaPH68uXbrolVdekdVq1cSJEzVgwACVl5dr+/bt+umnn5q7y+e5/PLLJUkHDhw4b5/FYlFmZqYGDBigyy67TCkpKY26RlBQkO644w5ZrVZt2LChSf11Rmpqqkwmk8xms3r27KnbbrtNzz33nEaNGqVJkyZdcJnBGmazWZJt1tw999zj+O+7adMm3Xnnndq1a5ceffRR/c///E+Dz1pzh+HDh+vqq69WamqqvvvuO5fOPXTokN5+++16902aNEndu3fXRx99pKKiIh06dMipNp999lmXAsMvv/xSX375pdPHSw0/K6y6uloPPPCAS20BuLjgLhGKHTHUXkMU2q1rk9rLO5JtD8t2KD1+l4rzL/47FwAAAAAAwFWEZ2iX2mJQ5E6+vr6aNm2aqqurGwwozlWzFGB5ebkSEhI0cODAOvu/++473XnnncrLy3N7fxsSEhIiSSosLKx3f8320NDGzzx4++231a1bN/39739vdADXGFarVVlZWVqyZIlyc3O1cuVKPffcc5ozZ85Fz615Ptj69evPC0aTkpKUmZmpmJgY9e/f3+nnqDVGTUB7sWVB63tOWGJiomPW2rk2b96s7t27a/78+S36/DYArc8/JFgxw4coZrjtuWVdoqOa1F7hiZNK377TMbus4Njxi58EAAAAAADQRIRnaJfaYlDkTnfffbfCwsL0+eefKzs726lzapYCfPzxx7Vv3z6NGjVKu3fvVnR0tP7617/qxhtv1Mcff6yxY8c2Z9db1N/+9jfdfffd+v777/Xoo4+2Wj9qnvk1ZswYp45PTU3ViBEjHCHauWpmr/n5+TnV3pQpU3TrrbdKkrp16yZJuvbaa/Xuu+9KkvLy8vT444/XOScoKEj33HOPysvL652JVVtDzwkDAG8/P102ZLBiRwxTzPAh6nFFrGN2bWOUFhUpffsuW2D2U7xOZB52Y28BAAAAAACcQ3iGdulSD4pqZgS58hyqmn+srK6u1uTJk3X4sO0fHJOTk3XbbbcpNTVVY8aM0TXXXNNiM/NqZpbVzEA7V832hkKkC3nppZf06KOP6rvvvtOkSZOa9My0purZs6ck55cK3bhxo2bMmKEBAwact8/b21uxsbGS5PRyh1dffbVmzpxZZ1vfvn3Vt29fRzvnhmf33nuvAgMD9eGHH+rUqVNOXQcAwiN7qc/gAYoaPEC9B12lHv1i5OHkM0brU1lWrsxdu5X28w6l/bxDR1PSZPzrX9LV10gPurHj9Wmnf0YAAAAAAADNj/AM7VJbDIrc5corr9TIkSOVlZXl0vOcagKohIQEx8+jRllZmb755hs98MADGj58eIv9TFJTUzVs2DD169dPu3btqrPPw8ND0dHRqqqq0sGDB11q9+WXX9a8efO0adMm3XzzzSorK3Nnt+sVFxenxMTE85ZZDAgI0OLFiyVJX3zxRZ199S13KEmrV6/WokWLdM899+i1115TfHy8Y9/ChQsVGhqqTZs2KTc316m+ufqsMEl688039eabbzp1bEPPCWtIew2tAdTlGxigyAFXKmrQVYoaNEBRg65SQFjjl9mVJEtVtQ4nJduWYdy+U4eT9srSjM92BAAAAAAAaAzCM7RLbTEocpeaWWfvvPPOeUHNhaSmpkpqeBaXq0sBusOmTZs0bdo03XTTTVq5cmWdfb/61a8UEBCg7777zqVZY6+//roefvhhbdiwQVOmTFF5eXmj++fKcodPP/20Ro4cqW3btunIkSMqLS1VZGSkJkyYoLCwMG3dulWLFi2q035Dyx2WlpZq5syZ+vzzz/XDDz/o008/1dGjRzVixAhdd911ys3N1e9+97tG3xcAuMpkMqlr32hHUNZ70FXq2je6SUswSrbnQx5NOaD0n23PLcvclajKFvgfHgAAAAAAAJqC8AztUlsMitzBx8dH06dPV3V1td555x2Xzv3Xv/4lq9WqK6+8UiaTSYZh1Nlfs0RgZmam2/p7MZ988oleeuklTZ06Va+99pp27twpyXafL7zwgiTpjTfeqHOOn5+fevfurdLSUmVlZdXZ99Zbb2n27Nn68ssvdfvtt6uioqJJ/XNlucOlS5equLhYw4cP15gxY+Tv76+CggLt3LlTH330kZYtWyaLxeL0tTdu3Kjhw4dr4cKFuv766xUSEqLjx4/rjTfe0PPPP69jx4416d4A4EICQkPUe+BVihpsm1EWOeBK+QUFuqXtE5mHHcswZsTvUmlhkVvaBQAAAAAAaCmEZ2iX2mJQ5A533XWXOnXqpPXr1ys7O7veYzw9PdW3b9/zljs8cuSI1q9frylTpugPf/iDXn31Vce+8ePH68Ybb1RBQYG+/vrrJvXRldlaZ86c0ezZs/XJJ59oy5YtWrlypfLz8zV58mRdccUV+vjjj7Vq1ao67Q8fPlxbtmzRli1b6iz/9/TTT2v27NkqLS3V7t279dR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AAAAAAAAAAGBHeAYAAAAAAAAAAADYEZ4BAAAAAAAAAAAAdoRnAAAAAAAAAAAAgB3hGQAAAAAAAAAAAGBHeAYAAAAAAAAAAADYEZ4BAAAAAAAAAAAAdoRnAAAAAAAAAAAAgB3hGQAAAAAAAAAAAGD3/wCmIDUCXlu3FwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dZ2e6jNt0DFo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "La7WhWRAtDfD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "mymodel(myline)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PuqwOenItDjn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cDQ0KyttX5y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EkcbdHx8tX-M",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLpfIMR_tYD6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFoXxcOQtDrL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gUD8SkH9tDpQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fq0k6Ua3qH4H",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd  \n",
        "import numpy as np  \n",
        "import matplotlib.pyplot as plt  \n",
        "import seaborn as seabornInstance \n",
        "from sklearn.model_selection import train_test_split \n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import metrics\n",
        "%matplotlib inline"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wDeVyim_q-in",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rZXjdNA5q-n9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kirlRm5zq-vN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rUcVHDQDq-tA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8z9Hk6yFp-sq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# single exponential smoothing\n",
        "\n",
        "from statsmodels.tsa.holtwinters import SimpleExpSmoothing\n",
        "# prepare data\n",
        "data = np.array(seq)\n",
        "# create class\n",
        "model = SimpleExpSmoothing(data)\n",
        "# fit model\n",
        "model_fit = model.fit()\n",
        "# make prediction\n",
        "yhat = model_fit.predict()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lESJMX-ZrV3R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_fit.forecast(12)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTGWstywqNJ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "yhat"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xRNiO-XOnjZo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq=[6.1,7.6,10.2,13.6,17.4,23.9,31.1,39.4,47.6,59.1,74.0,93.9,120.7,159.1,198.0,238.1,282.9,318.7,369.9,432.7,487.3,539.1,591.0]\n",
        "time=list(range(1996,2019,1))\n",
        "\n",
        "x_data = np.array(time)\n",
        "\n",
        "y_data = np.array(seq)\n",
        "\n",
        "\n",
        "log_x_data = np.log(x_data)\n",
        "\n",
        "log_y_data = np.log(y_data)\n",
        "\n",
        "\n",
        "curve_fit = np.polyfit(x_data, log_y_data, 1)\n",
        "\n",
        "print(curve_fit)\n",
        "\n",
        "\n",
        "y = np.exp(curve_fit[0]) * np.exp(curve_fit[1]*x_data)\n",
        "\n",
        "plt.plot(x_data, y_data, \"o\")\n",
        "\n",
        "plt.plot(x_data, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UfgNy9_ylA9m",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.bar(time,seq)\n",
        "#plt.plot(time,seq)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "37P9sanAldrd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "\n",
        "\n",
        "curve_fit = np.polyfit(np.array(time), log_data, 1)\n",
        "\n",
        "print(curve_fit)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sejkIr6-nKvD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "curve_fit[0]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CZkVMGNmnQBc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "curve_fit[1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7WBzBBXgnXnq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "time"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EIJ6zGll2ao",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y = np.exp(curve_fit[0]) * np.exp(curve_fit[1]*np.array(time))\n",
        "\n",
        "plt.plot(np.array(time), np.array(seq), \"o\")\n",
        "\n",
        "plt.plot(np.array(time), y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZVnwfd-cl2gK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wkxw7p_Xl2kO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "687jNiUCl2vt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pIXUlrtHl2uS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rOW0zoXdl2ew",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5EEgz2wl2Y_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s7VRMPwjl1KI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWIhuPPDl1OV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L0stELUgl1U_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3g8ZjRKTl1aY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VBZEeP5ql1gT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xeBnR9eel1ex",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PB-0JbHvl1ZB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OwOKZGJil1Sp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "78hlKY7blyyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MEa-WIx5ly7l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IQBTPpn3lzF9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JfzzEUOYlzEB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9MN0QG43lzBp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6dApCqMKly47",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S7UxCrP7qIFN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "time=np.array(time)-1996\n",
        "X = time.reshape(-1,1)\n",
        "y = np.array(log_data).reshape(-1,1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKF38z_isN6R",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yqVk3dcLsPlB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g2vX6jzLqINR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pNxXgtI0sSvZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wNRF3_tBqILb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "regressor = LinearRegression()  \n",
        "regressor.fit(X_train, y_train) #training the algorithm"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UwHoLexNqIJ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#To retrieve the intercept:\n",
        "print(regressor.intercept_)#For retrieving the slope:\n",
        "print(regressor.coef_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSoXRT_YqID-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDEeqQblr-gS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_test"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIRtFrxptyGr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qy8NeUR5r5fM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "pred_time=list(range(2018,2026))\n",
        "pred_arr=np.array(pred_time)-1996\n",
        "pred_arr=pred_arr.reshape(-1,1)\n",
        "print(pred_arr)\n",
        "regressor.predict(pred_arr)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "umcfF51muJLa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.exp(regressor.predict(pred_arr))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PsJaE4U6qICF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
        "df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7fEQRQPPqH_v",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df1 = df.head(25)\n",
        "df1.plot(kind='bar',figsize=(16,10))\n",
        "plt.grid(which='major', linestyle='-', linewidth='0.5', color='green')\n",
        "plt.grid(which='minor', linestyle=':', linewidth='0.5', color='black')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3Lg7HiLoqH9C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.scatter(X_test, y_test,  color='gray')\n",
        "plt.plot(X_test, y_pred, color='red', linewidth=2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-vCMrjfraBi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))  \n",
        "print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))  \n",
        "print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h2LFBERaraMM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hhEA1NRue6W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XxGK5OhZufJ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JGtpr0qmufNL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HwcgXDATufGo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QfIJZ5qoufE0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rpgvel5kufCM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kIE226c_ufAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SGfbQolMue-g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Hb4UAvRraKl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2z-7RTE6raJA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yWgvibdkraGg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DzTkpoG4rZ-0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8g_mPVEifo0g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close_data = np.array(seq).reshape((-1,1))\n",
        "\n",
        "split_percent = 0.70\n",
        "split = int(split_percent*len(close_data))\n",
        "\n",
        "close_train = close_data[:split]\n",
        "close_test = close_data[split:]\n",
        "\n",
        "\n",
        "print(len(close_train))\n",
        "print(len(close_test))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2R1V3NR3eANe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "look_back = 6\n",
        "\n",
        "train_generator = TimeseriesGenerator(close_train, close_train, length=look_back, batch_size=1)     \n",
        "test_generator = TimeseriesGenerator(close_test, close_test, length=look_back, batch_size=3)\n",
        "\n",
        "\n",
        "model = Sequential()\n",
        "model.add(\n",
        "    LSTM(10,\n",
        "        activation='relu',\n",
        "        input_shape=(look_back,1))\n",
        ")\n",
        "model.add(Dense(1))\n",
        "model.compile(optimizer='adam', loss='mse')\n",
        "\n",
        "num_epochs = 30\n",
        "model.fit_generator(train_generator, epochs=num_epochs, verbose=1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QSGkfBjSf8D6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction = model.predict_generator(test_generator)\n",
        "\n",
        "close_train = close_train.reshape((-1))\n",
        "close_test = close_test.reshape((-1))\n",
        "prediction = prediction.reshape((-1))\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qf1cCW-ngQ9N",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "prediction"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nnx-IkZqke4C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dir(test_generator)\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cfpxlcmOghug",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "close_data = close_data.reshape((-1))\n",
        "print(close_data)\n",
        "\n",
        "def predict(num_prediction, model):\n",
        "    prediction_list = close_data[-look_back:]\n",
        "    \n",
        "    for _ in range(num_prediction):\n",
        "        x = prediction_list[-look_back:]\n",
        "        x = x.reshape((1, look_back, 1))\n",
        "        print(x)\n",
        "        out = model.predict(x)[0][0]\n",
        "        print(out)\n",
        "        prediction_list = np.append(prediction_list, out)\n",
        "    prediction_list = prediction_list[look_back-1:]\n",
        "        \n",
        "    return prediction_list\n",
        "    \n",
        "\n",
        "\n",
        "num_prediction = 8\n",
        "forecast = predict(num_prediction, model)\n",
        "print(forecast)\n",
        "#forecast_dates = predict_dates(num_prediction)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y0Wgy4-6g0PW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#[ 591.          828.46136475 1023.57403564 1205.86376953 1439.38427734 1764.12072754 2207.29370117 2669.37963867 3241.99511719]\n",
        "seq"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JB3Hp_S2is1P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "seq"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nf6qsvQTmdVk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "log_data=np.log(seq)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xEX-_OeUmdZG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "time=list(range(1996,2019,1))\n",
        "time=np.array(time)-1996"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1tW8aS17nO96",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(log_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oiGOOsNsnT5z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(time)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZJcqogAnVku",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "len(seq)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ECzkjoawndO4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(log_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q8pdsEgJoSFo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np \n",
        "import matplotlib.pyplot as plt \n",
        "  \n",
        "def estimate_coef(x, y): \n",
        "    # number of observations/points \n",
        "    n = np.size(x) \n",
        "  \n",
        "    # mean of x and y vector \n",
        "    m_x, m_y = np.mean(x), np.mean(y) \n",
        "  \n",
        "    # calculating cross-deviation and deviation about x \n",
        "    SS_xy = np.sum(y*x) - n*m_y*m_x \n",
        "    SS_xx = np.sum(x*x) - n*m_x*m_x \n",
        "  \n",
        "    # calculating regression coefficients \n",
        "    b_1 = SS_xy / SS_xx \n",
        "    b_0 = m_y - b_1*m_x \n",
        "  \n",
        "    return(b_0, b_1) \n",
        "  \n",
        "def plot_regression_line(x, y, b): \n",
        "    # plotting the actual points as scatter plot \n",
        "    plt.scatter(x, y, color = \"m\", \n",
        "               marker = \"o\", s = 30) \n",
        "  \n",
        "    # predicted response vector \n",
        "    y_pred = b[0] + b[1]*x \n",
        "  \n",
        "    # plotting the regression line \n",
        "    plt.plot(x, y_pred, color = \"g\") \n",
        "  \n",
        "    # putting labels \n",
        "    plt.xlabel('x') \n",
        "    plt.ylabel('y') \n",
        "  \n",
        "    # function to show plot \n",
        "    plt.show() \n",
        "  \n",
        "def main(): \n",
        "    # observations \n",
        "    #x = np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]) \n",
        "    #y = np.array([1, 3, 2, 5, 7, 8, 8, 9, 10, 12]) \n",
        "    x=log_data\n",
        "    y=np.array(seq)\n",
        "  \n",
        "    # estimating coefficients \n",
        "    b = estimate_coef(x, y) \n",
        "    print(\"Estimated coefficients:\\nb_0 = {}  \\ \\nb_1 = {}\".format(b[0], b[1])) \n",
        "  \n",
        "    # plotting regression line \n",
        "    plot_regression_line(x, y, b) \n",
        "main()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PfKkk8y7o1Pc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import matplotlib.pyplot as plt \n",
        "import numpy as np \n",
        "from sklearn import datasets, linear_model, metrics \n",
        "\n",
        "# load the boston dataset \n",
        "#boston = datasets.load_boston(return_X_y=False) \n",
        "\n",
        "# defining feature matrix(X) and response vector(y) \n",
        "X = np.array(seq)\n",
        "y = log_data \n",
        "\n",
        "\n",
        "# splitting X and y into training and testing sets \n",
        "from sklearn.model_selection import train_test_split \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, \n",
        "\t\t\t\t\t\t\t\t\t\t\t\t\trandom_state=1) \n",
        "\n",
        "X_train= X_train.reshape(-1, 1)\n",
        "y_train= y_train.reshape(-1, 1)\n",
        "X_test = X_test.reshape(-1, 1)\n",
        "# create linear regression object \n",
        "reg = linear_model.LinearRegression() \n",
        "\n",
        "# train the model using the training sets \n",
        "reg.fit(X_train, y_train) \n",
        "\n",
        "# regression coefficients \n",
        "print('Coefficients: \\n', reg.coef_) \n",
        "\n",
        "# variance score: 1 means perfect prediction \n",
        "print('Variance score: {}'.format(reg.score(X_test, y_test))) \n",
        "\n",
        "# plot for residual error \n",
        "\n",
        "## setting plot style \n",
        "plt.style.use('fivethirtyeight') \n",
        "\n",
        "## plotting residual errors in training data \n",
        "plt.scatter(reg.predict(X_train), reg.predict(X_train) - y_train, \n",
        "\t\t\tcolor = \"green\", s = 10, label = 'Train data') \n",
        "\n",
        "## plotting residual errors in test data \n",
        "plt.scatter(reg.predict(X_test), reg.predict(X_test) - y_test, \n",
        "\t\t\tcolor = \"blue\", s = 10, label = 'Test data') \n",
        "\n",
        "## plotting line for zero residual error \n",
        "plt.hlines(y = 0, xmin = 0, xmax = 50, linewidth = 2) \n",
        "\n",
        "## plotting legend \n",
        "plt.legend(loc = 'upper right') \n",
        "\n",
        "## plot title \n",
        "plt.title(\"Residual errors\") \n",
        "\n",
        "## function to show plot \n",
        "plt.show() \n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCRrhM1eplsp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}