Final7.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final7.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/4en1x/diploma/blob/master/Final7.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "IuwhRQ9Q0tIg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "393cc0a7-75a7-4c7b-d8ad-f2db11823e6f"
      },
      "cell_type": "code",
      "source": [
        "!pip install git+git://github.com/andreinechaev/nvcc4jupyter.git\n",
        "%load_ext nvcc_plugin\n",
        "!cd /usr/src\n",
        "!ln -sfn linux-headers-2.6.35-28-generic linux"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+git://github.com/andreinechaev/nvcc4jupyter.git\n",
            "  Cloning git://github.com/andreinechaev/nvcc4jupyter.git to /tmp/pip-req-build-wdpr0o04\n",
            "Building wheels for collected packages: NVCCPlugin\n",
            "  Building wheel for NVCCPlugin (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /tmp/pip-ephem-wheel-cache-kajk1x8s/wheels/10/c2/05/ca241da37bff77d60d31a9174f988109c61ba989e4d4650516\n",
            "Successfully built NVCCPlugin\n",
            "Installing collected packages: NVCCPlugin\n",
            "Successfully installed NVCCPlugin-0.0.2\n",
            "created output directory at /content/src\n",
            "Out bin /content/result.out\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "RPdusjTB00-g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2021aa1a-f1d1-460e-f3b6-04283c1c691b"
      },
      "cell_type": "code",
      "source": [
        "%%cu\n",
        "#include <cuda_runtime.h>\n",
        "#include \"device_launch_parameters.h\"\n",
        "#include <stdlib.h>\n",
        "#include <stdio.h>\n",
        "#include <float.h>\n",
        "#include \"linux/kernel.h\"\n",
        "#include <iostream>\n",
        "#include <ctime>\n",
        "#include <iomanip>\n",
        "#include <fstream>\n",
        "#include <math.h>\n",
        "#include <cmath>\n",
        "\n",
        "using namespace std;\n",
        "\n",
        "#define ITERATIONS 100000000\n",
        "#define N 256\n",
        "\n",
        "#define e 2.7182818\n",
        "\n",
        "#define blockZSize 2\n",
        "\n",
        "\n",
        "#define extN (N + 2)\n",
        "#define Dx 1\n",
        "#define h ((float) Dx / (N + 1))\n",
        "#define h2 powf(h, 2.0)\n",
        "\n",
        "\n",
        "float f(int i, int j) {\n",
        "\treturn 2 * powf(e, i * h + j * h);\n",
        "}\n",
        "\n",
        "float u_t(float x) {\n",
        "\treturn powf(e, x);\n",
        "}\n",
        "\n",
        "float u_l(float x) {\n",
        "\treturn powf(e, x);\n",
        "}\n",
        "\n",
        "float u_r(float x) {\n",
        "\treturn powf(e, x + 1);\n",
        "}\n",
        "\n",
        "float u_d(float x) {\n",
        "\treturn powf(e, x + 1);\n",
        "}\n",
        "\n",
        "\n",
        "\n",
        "float func(int i, int j) {\n",
        "\treturn powf(e, i * h + j * h);\n",
        "}\n",
        "\n",
        "float* createMatrix(int row, int col) {\n",
        "\tfloat* matrix = new float[row * col];\n",
        "\n",
        "\tfor(int i = 0; i < row; i++) {\n",
        "\t\tfor(int j = 0; j < col; j++) {\n",
        "\t\t\tmatrix[i * row + j] = 0;\n",
        "\t\t}\n",
        "\t}\n",
        "\n",
        "  for(int i = 0; i < row; i++) {\n",
        "      matrix[0 * row + i] = u_t((i) * h);\n",
        "      matrix[(row - 1) * row + i] = u_d((i) * h);\n",
        "  }\n",
        "\n",
        "  for(int j = 0; j < col; j++) {\n",
        "      matrix[j * col + 0] = u_l((j) * h);\n",
        "      matrix[j * col + row - 1] = u_r((j) * h);\n",
        "\t}\n",
        "    \n",
        "\treturn matrix;\n",
        "}\n",
        "\n",
        "float calculateDifference(float* matrix1) {\n",
        "\tfloat tolerance = 0.0f;\n",
        "\n",
        "  for (int i = 1; i < extN - 1; i++) {\n",
        "\t\tfor (int j = 1; j < extN - 1; j++) {\n",
        "      float fCurrent = func(i, j);\n",
        "      if(abs(matrix1[i * extN + j] - fCurrent) > tolerance) {\n",
        "          tolerance = abs(matrix1[i * extN + j] - fCurrent);\n",
        "      } \n",
        "\t\t}\n",
        "\t}\n",
        "\n",
        "\treturn tolerance;\n",
        "}\n",
        "\n",
        "int main() {\n",
        "\n",
        "\tfloat* matrixHost = createMatrix(extN, extN);\n",
        "\n",
        "\tlong startTime = clock();\n",
        "\n",
        "\tfor (size_t i = 0; i < ITERATIONS; i++) {\n",
        "\t\tfor (int i = 1; i < extN - 1; i++) {\n",
        "\t\t  for (int j = 1; j < extN - 1; j += 1) {\n",
        "        int b = extN * i + j;\n",
        "         matrixHost[b] = 0.25 * (\n",
        "                matrixHost[b + extN]\n",
        "              + matrixHost[b - extN]\n",
        "              + matrixHost[b + 1]\n",
        "              + matrixHost[b - 1] \n",
        "              - h2 * f(i, j)\n",
        "            );\n",
        "\t\t  }\n",
        "\t  }\n",
        "      \n",
        "\n",
        "\n",
        "    if(i%100 == 0) {\n",
        "        float diff = calculateDifference(matrixHost);\n",
        "        if(diff < 0.01) break;\n",
        "        cout << diff << \" \";\n",
        "    }  \n",
        "\t}\n",
        "  \n",
        "\tstd::cout << \"Calculation take time: \" << clock() - startTime << std::endl;\n",
        "\n",
        "  cout << \" Difference: \" << calculateDifference(matrixHost);\n",
        "\n",
        " \n",
        "\treturn 0;\n",
        "}"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'7.27505 5.98953 5.53604 5.2255 4.98414 4.785 4.61546 4.46702 4.33514 4.21619 4.10775 4.00857 3.91658 3.83138 3.75166 3.67686 3.60643 3.53991 3.47685 3.41686 3.35958 3.30501 3.25258 3.20214 3.15358 3.10659 3.06102 3.01685 2.97376 2.93168 2.89053 2.8503 2.8108 2.77197 2.73376 2.69614 2.6591 2.62254 2.58642 2.5507 2.51549 2.48069 2.4462 2.41221 2.37855 2.34526 2.31238 2.27982 2.24768 2.21586 2.18443 2.15338 2.12262 2.09233 2.06232 2.03271 2.00349 1.97455 1.94604 1.91789 1.89005 1.86259 1.83552 1.80877 1.78233 1.75633 1.73066 1.7053 1.68026 1.65563 1.63133 1.60734 1.58367 1.56033 1.53737 1.51471 1.49236 1.47031 1.44857 1.42718 1.4061 1.38531 1.36481 1.34459 1.32466 1.30505 1.28574 1.26669 1.24792 1.22942 1.21117 1.1932 1.1755 1.15808 1.1409 1.12398 1.1073 1.09087 1.07467 1.05871 1.04298 1.0275 1.01226 0.997241 0.982441 0.967858 0.953488 0.93933 0.925379 0.911634 0.898091 0.884748 0.871611 0.858678 0.845935 0.83338 0.82101 0.808823 0.796815 0.784986 0.77333 0.761847 0.750535 0.739388 0.728408 0.71759 0.706932 0.696439 0.686106 0.675926 0.665896 0.656016 0.646281 0.63669 0.627241 0.617933 0.608762 0.599727 0.590826 0.582057 0.573418 0.564907 0.556522 0.548261 0.540123 0.532106 0.524207 0.516427 0.50876 0.501209 0.493773 0.486449 0.479233 0.472124 0.46512 0.45822 0.451423 0.444726 0.438128 0.431629 0.425225 0.418917 0.412702 0.406579 0.400548 0.394605 0.388751 0.382984 0.377302 0.371704 0.366189 0.360757 0.355404 0.350131 0.344937 0.339818 0.334778 0.32981 0.324918 0.320096 0.315347 0.310668 0.30606 0.301518 0.297045 0.292638 0.288296 0.284019 0.279804 0.275653 0.271563 0.267535 0.263564 0.259654 0.255802 0.252007 0.248268 0.244583 0.240955 0.23738 0.233859 0.23039 0.226972 0.223606 0.220288 0.21702 0.2138 0.210629 0.207504 0.204426 0.201392 0.198405 0.195461 0.192561 0.189704 0.186889 0.184117 0.181386 0.178696 0.176043 0.173431 0.170858 0.168323 0.165828 0.163366 0.160941 0.158553 0.156201 0.153884 0.1516 0.149351 0.147136 0.144951 0.1428 0.140684 0.138594 0.136537 0.134513 0.132515 0.13055 0.128612 0.126704 0.124824 0.12297 0.121149 0.119349 0.117578 0.115833 0.114115 0.112421 0.110751 0.109109 0.107489 0.105894 0.104321 0.102776 0.10125 0.0997469 0.0982659 0.0968072 0.0953736 0.0939553 0.0925624 0.0911865 0.0898371 0.0885017 0.0871882 0.0858953 0.0846176 0.0833652 0.0821254 0.0809071 0.0797069 0.0785215 0.0773594 0.0762138 0.075078 0.0739648 0.0728662 0.0717831 0.0707209 0.0696719 0.0686347 0.0676172 0.066613 0.0656221 0.0646515 0.0636971 0.0627453 0.0618136 0.0608971 0.0599906 0.0590994 0.058224 0.0573637 0.0565064 0.0556681 0.0548427 0.0540285 0.0532231 0.0524337 0.0516608 0.0508974 0.0501344 0.0493891 0.0486569 0.0479362 0.0472212 0.0465186 0.0458291 0.0451534 0.0444849 0.0438178 0.0431664 0.0425274 0.0418963 0.0412734 0.0406575 0.0400538 0.0394614 0.0388815 0.0383093 0.0377371 0.0371687 0.036617 0.0360739 0.0355394 0.035013 0.0344889 0.0339754 0.0334699 0.0329747 0.0324888 0.0320106 0.0315337 0.0310581 0.0305963 0.0301425 0.0296972 0.0292554 0.0288212 0.0283911 0.0279653 0.02755 0.0271413 0.0267408 0.0263493 0.025965 0.0255835 0.025202 0.0248206 0.0244439 0.0240805 0.0237236 0.0233719 0.0230255 0.0226848 0.0223486 0.0220149 0.021683 0.0213592 0.0210412 0.0207279 0.020421 0.0201204 0.0198257 0.0195365 0.0192504 0.0189643 0.0186782 0.0183921 0.0181158 0.0178463 0.0175815 0.0173218 0.0170662 0.0168126 0.0165629 0.0163167 0.0160749 0.0158365 0.0155981 0.0153606 0.0151303 0.0149052 0.014684 0.0144665 0.0142534 0.0140445 0.01384 0.0136411 0.0134461 0.0132539 0.0130632 0.0128725 0.0126817 0.012491 0.0123003 0.0121095 0.0119188 0.0117354 0.01156 0.0113878 0.0112193 0.0110533 0.0108907 0.0107305 0.0105715 0.0104158 0.0102623 0.0101101 Calculation take time: 84929946\\n Difference: 0.00996137'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "LjUE5NPs4Y-W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "outputId": "6b1df361-4867-45bf-d1c8-0c88833f54f7"
      },
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "objects = (\n",
        "    'Параллельный',\n",
        "    'Последовательный',\n",
        "    \n",
        ")\n",
        "y_pos = np.arange(len(objects))\n",
        "performance = [3.53603, 84.929946]\n",
        "\n",
        "plt.style.use('seaborn-darkgrid')\n",
        "plt.barh(y_pos, performance, align='center', alpha=0.5, height=0.2)\n",
        "plt.yticks(y_pos, objects)\n",
        "plt.title(\"Время работы последовательного и параллельного алгоритма, размер матрицы = 216\", loc='left', fontsize=12, fontweight=0, color='orange')\n",
        "plt.ylabel('Тип Алгоритма')\n",
        "plt.xlabel('Время')\n",
        "\n",
        "for i, v in enumerate(performance):\n",
        "    plt.text(performance[i] + 0.1, \n",
        "             i-.02,\n",
        "              performance[i], \n",
        "              fontsize=11, \n",
        "              color='red')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9q7Q2sjis7Of/XFhISEiu0555DStNFm88eE1cByFxTYfd0A57oR32QjsK794O\n9a/6PHPdTOCi+E8CAAAANnPNBLS8espKc+8ZAABwXddMQAMAALhWENAAAABshoAGAABgMwQ0AAAA\nmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABshoAGAABgMwQ0AAAAmyGgAQAA\n2AwBDQAAwGYIaAAAADbjsCzLKukiUHQiI2NLuoSrIijI/5poC+2wF9phL7TDXly1HUFB/iVdwlVB\nDxoAAIDNENAAAABshoAGAABgMwQ0AAAAmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDN\nENAAAABshoAGAABgMwQ0AAAAmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABs\nhoAGAABgMwQ0AAAAmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABshoAGAABg\nMwQ0AAAAmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABshoAGAABgMwQ0AAAA\nmyGgAQAA2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABshoAGAABgMwQ0AAAAmyGgAQAA\n2AwBDQAAwGYIaAAAADZDQAMAALAZAhoAAIDNENAAAABshoAGAABgMwQ0AAAAm/lTAW3hwoVXuw4A\nAACk8yhohAMHDujtt9/WpUuXJElJSUk6c+aMBgwYUOTFAQAAlEYF9qBNmjRJ3bp1U3R0tB577DHV\nrVtX06dPL47aAAAASqUCA1qZMmXUs2dP+fv7q2PHjpo6darmzZtXHLUBAACUSgUGtMTERP3666/y\n9vbWjz/+qOjoaJ08ebI4agMAACiVCrwGbeTIkTp+/LieffZZjRo1SlFRUXriiSeKozYAAIBSqcCA\n1rp168zHGzduLNJiAAAAUIiA5uxuTf7UBgAAQNEoMKBdunRJ3t7e6tatmzp27KiyZcsWR10AAACl\nVoEBbdWqVTp69Kg2bNigF198URUrVlTXrl1Vo0aN4qgPAACg1CnUfxKoW7euQkJC9MgjjygpKYnT\nmwAAAEWowB60zz//XJs2bVJMTIw6deqkCRMmqFatWsVRGwAAQKlUYA/ahAkTFB8fr9q1a+vw4cP6\nz3/+o7FjxxZHbQAAAKVSgT1oCxYsKI46AAAAkK7AgPbpp5/qtddeK45aAAAAoEIEtDNnzsiyLFmW\nlWO4m1uh7i8AAADAH1RgQNu+fbuaNGmS+dyyLDkcDh04cKBICwMAACitCgxobdq0UWhoaHHUAgAA\nABXiLs7q1asXRx0AAABIV2BsSKswAAAZs0lEQVQPWr9+/bR9+/Zcw9u0aVMkBQEAAJR2BQa0IUOG\nqGrVqmrZsmWO4QQ0AACAolFgQNuwYYNmzpyppKQkPf/886patWpx1AUAAFBqFRjQqlSpomnTpmn/\n/v164YUX1KpVKz3xxBPy8vIqjvoAAABKnQID2ltvvZX5uEWLFtq0aZOWLVumr776qkgLAwAAKK0K\nDGg1atTI8XzgwIFFVgwAAAAKEdDuu+8+/frrrwoPD5fD4VCjRo1Uv3794qgNAACgVCowoE2bNk1f\nfvmlmjVrprS0NL3++uvq1auX/vGPfxRHfQAAAKVOgQEtLCxMa9eulaenpyQpKSlJDz30EAENAACg\niBT4nwQqVaokD4+sHOfp6ZnrujQAAABcPQX2oJUvX17333+/br31VlmWpe3bt6tWrVqaNWuWJGn4\n8OFFXiQAAEBpUmBAq1WrlmrVqpX5vGPHjkVZDwAAQKlXYEAbNmyYEhISdOTIETkcDtWrV09ly5Yt\njtoAAABKpQID2ubNmzVx4kRVrVpVaWlpOn/+vCZPnqw77rijOOoDAAAodQoMaHPnztWqVatUoUIF\nSdLZs2c1fPhwAhoAAEARKfAuTk9Pz8xwJpn/zZnxJzcAAABw9RXYg+br66v58+erbdu2kqTvvvtO\nvr6+RV4YAABAaVVgQJs6dapmzZqlVatWyeFwqGXLlnrllVeKozYAAIBSqcCAFh4erpdffrk4agEA\nAIAKcQ3anDlziqMOAAAApCuwB+3cuXNatmxZruH9+vUrkoIAAABKuwIDWsuWLbVz585cwwloAAAA\nRaPAgPavf/2rOOoAAABAugIDmrP/vblly5arXAoAAACkQgS0KlWqaObMmcVRCwAAAFSIgObt7a0a\nNWoURy0AAABQIQLa3r17FRISorp166phw4Zq2bKlmjdvXhy1AQAAlEoFBrQvv/xSEREROn78uH77\n7TfNmDFDPj4+euedd4qjPgAAgFKnwIBWoUIFVahQIUev2bhx44q0KAAAgNKswICW3c6dO/XZZ5/p\nq6++Kqp6AAAASr0CA9rZs2e1YsUKLV++XGfOnNHTTz+ttWvXFkdtAAAApZLT/8W5bt06PfHEE+re\nvbvCw8M1ceJE1a1bV0OGDFHFihWLs0YAAIBSxWkP2nPPPadGjRppyZIlCg4OliS5uRX4v9UBAADw\nFzkNaJ988ok+++wzhYSEqHHjxrr//vuVmppanLUBAACUSk67xJo3b65Jkybp22+/Vd++fbVs2TL9\n9ttvmjx5ssLDw4uzRgAAgFKlwHOWZcqU0b333qvQ0FCtXbtWPj4+euyxx4qjNgAAgFLpD11UVqdO\nHY0YMYJ/lA4AAFCE/tRV/9wsAAAAUHRIWgAAADZT4B+qPXHiRJ7Da9WqddWLAQAAQCECWs+ePRUU\nFCTLsjKHORwOffnll0VaGAAAQGlVYEBr0aKFQkNDi6MWAAAAqBDXoDkcjuKoAwAAAOkK7EE7fPiw\nRo0alWv49OnTi6QgAACA0q7AgDZy5MjiqAMAAADpCgxot9xyS3HUAQAAgHQFBrQuXbqoevXqql69\neuadnA6HQwsXLizy4gAAAEqjAgPap59+qpkzZ6pJkyZ66qmn5OPjUxx1AQAAlFoF3sV5ww03aN68\neWrevLmGDBmilStXFkddAAAApVaBPWgrVqzIfNy7d28tWLBAixcv1tKlS4u0MAAAgNKqwIAWERGR\n43mXLl2KrBgAAADkE9BWrVql3r17a9iwYcVZDwAAQKnn9Bq0ZcuWFWcdAAAASFfgTQIAAAAoXk5P\ncf7vf/9Tx44dcw23LEsOh0NbtmwpwrIAAABKL6cBrUmTJpo5c2Zx1gIAAADlE9C8vLxUo0aN4qwF\nAAAAyucatObNmxdnHQAAAEjnNKA9//zzxVkHAAAA0nEXJwAAgM0Q0AAAAGyGgAYAAGAzBDQAAACb\nIaABAADYDAENAADAZghoAAAANkNAAwAAsBkCGgAAgM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADY\nDAENAADAZghoAAAANkNAAwAAsBkCGgAAgM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADYDAENAADA\nZghoAAAANkNAAwAAsBkCGgAAgM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADYDAENAADAZghoAAAA\nNkNAAwAAsBkCGgAAgM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADYDAENAADAZghoAAAANkNAAwAA\nsBkCGgAAgM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADYDAENAADAZghoAAAANkNAAwAAsBkCGgAA\ngM0Q0AAAAGzGo6QLQNFZvPGg4uMT//J87u1Q/ypUAwAACoseNAAAAJshoAEAANgMAQ0AAMBmCGgA\nAAA2Q0ADAACwGQIaAACAzRDQAAAAbIaABgAAYDMENAAAAJshoAEAANgMAQ0AAMBmCGgAAAA2Q0AD\nAACwGQIaAACAzRDQAAAAbIaABgAAYDMENAAAAJshoLmQxYsXS5KioqK0YcOGEq4GAAAUFY+SLqCw\nwsLCNHz4cDVs2DBz2G+//aYpU6aoU6dOJVhZ8Vm3bp06dOigXbt26fDhw+revXvxLTwpSX5jR8rj\nwM+SZSmlSVPFTZ8pubtLktyOH1P5zu2V0rRZ5iTxY15Qyq23qczcd1Rm2VJZZcpK3t6KfWOO0qpV\nlyMqSv7DhsjtQpRkWYqd875SG14vt98Oy/+5Z6S0NMnhUOy/35KCWsp9/z75jXtecnOT43KCEp7+\nh5Lu6VN86wAAgGLiMgFNkm6++WbNnj078/mYMWNKsJriN3LkSE2YMEFly5bVlClTinXZXl9tlry8\ndGndZklSuT53y2vjeiX16JU5TkrTZopesS7HdI6zZ+W9aoUurdwgeXvL519TVHbOLMVPmSa/ieOV\nfFs7XX72OXltWi+vdat1efgI+Y97Xlf+7wkl9ukrrzWr5D/6OWnLV/L5z2wljB6v5Lbt5Xb0iMp3\nuV1RBDQAwDXIpQKaMyNGjNDZs2eVkJCgZ555Rp06dVJISIiaNm2qffv2KTExUf/+979VpUoVjR49\nOte4ktSnTx/5+/srKSlJKSkpWr58uSSpbdu2atCggWJiYlSnTh3Nnj1bmzZt0vz58+Xh4aGmTZtm\nBsUPPvhAS5YsUeXKlXXgwAEtXrxY+/bt06FDhzR69OjMeiMiIvTss89mLqNz585avXq15s+fr/Ll\ny6t///6Z444ZM0bdunWTj4+PPv/8c3344YfaunWr2rVrp19++aW4VrGSuvdQUvce5klcnNyio5VW\nvXqB01lVqih6Vfrp2LQ0uZ05rdRGjSXLktf6tYqb/KqZf9e7pa53S8nJ8tz2g6JDl5rhd/dUwLAh\nUmKiYue8lzlf91MnlVaj5tVtJAAANuHy16BFR0erffv2WrRokWbNmqU333wz87Xy5csrNDRU99xz\njz788MN8x42JidGiRYs0c+bMzGGxsbGqWLGiQkNDNW7cOElSfHy83n77bS1cuFCLFi3S6dOntXPn\nTknSqVOnNGLECIWGhqpx48ZF1uaFCxcqMDCwyOafH/9nnlTFm1voSr8HldLyxhyvuUdEKOD/+iuw\nRxf5jnteSkjIfK3se/9RhZuayS0mRpcHPylHZKTkcKjMJ0tUrs/dCnj4frkf+lVuUedl+fpJnp7p\nM3VXWrlA6exZ8zT8kAK7dZT/s0MVky2wAQBwLXH5HrSAgAB99913Wrp0qdzc3HTp0qXM12677TZJ\nUsuWLfXNN98oICBAe/fuzTVuTEyMAgICcs07IiJCtWrVyjEsPDxcp06d0uOPPy7JhLhTp06pdevW\nioiI0H333ZdrPuvWrdPevXvl7e2tsWPHqkyZMjpy5IhCQkIkSZGRkZnjLly4UOvXr1flypU1efLk\nXPPavn27mjRpUujeM19f70KNl5+gIP+sJx9/JMXFye+ee+TXLFjq188ML1NHenmi3B96SHJ3l+fA\ngfKZ+5aU0Ybxo6UxI+U+bpyCXnlRmjBBiomW321tzGuhofIe+Yy0bJnk5si5TIckR/qwoBulXTul\nHTtU4YEHpL17JT+/v9zG4pSjbS6MdtgL7bAX2oG/yuUDWnR0tKKjo7V48WJdunRJ/TICgyTLsjJ/\nOxwOrVmzJs9xjx8/rrp16+aad17DPT091bRpU82bNy/X+CdOnFCdOnVyDe/Ro4dGjx6tTZs2ac6c\nORoxYoTq1aun0NBQSeYUZ4YBAwaof//+mjJlilatWpVrXqGhoZo6dao+//zzgleOpPj4xEKNl5/I\nyFh5/LRblo+vUq8zN2mU7dJd7us2Ku6Oblkj9ugrxSRJkry63aMyiz5Q3J6Dcj9+TMm3tpUkuXfv\no4CnHtfFCVNUydtb529oLUXGytH+TlV88kmdd5RVpdg4nT8ZJXl5ScnJqnQpWo4qVRQz90Ml9upj\nbkyo00iBfgGK+2GHUlq1/sttLC5BQf6KjIwt6TL+MtphL7TDXmhHybpWQqXLn+I8efKkatasKTc3\nN33xxRdKSkrKfG3Hjh2SpN27d6tBgwa6ePFinuPu2bNHTZs2zTXvvIbXq1dPhw8fVlRUlCRp9uzZ\nOnv2rGJiYuRwOOTj4+O01oCAAKWmphaqXQEBAUpLS8sx7KefflKjRo3k71/8O5/Hzh3ynTJRSg+9\nHtt/VEqjrNO4nv/9Wn7Dh2a+7vXN10pp1lyOmBj5D/u7HLExZrwft5np3NyUdGdXeX2x0czvx21K\nCW4seXgo6fY75L3KBFDvlcuV1L6D5OWlsm+/Ka+N6yVJjnPn5H7mlFLr1iumNQAAQPFx+R60wMBA\nLV26VLt379b999+vqlWr6q233pKkzFORsbGxevPNN5WcnKynnnoq17gbN27U3Llzc8x33bp12r59\nu5555pkcw8uWLatx48Zp8ODB8vLyUpMmTVS5cmU99NBDmacsf+/LL7/U0aNHFR0drbFjx+bbnk8+\n+UTfffedEhMT9cYbb2jfvn2Zr50/f14DBw78M6vpL7sSMkgeB39WYM+7JCtNKdcH60r/gfIfMkjx\nk15RcrsO8l69UoFdO0peXkqtXUcJ4ydKfn5KePY5levXW5Z3GcnLW7FvmO0T968Z8h86RD5v/luW\nm5tiZ5g7dOOmTpf/8KEq+8E8Wel/lsNbUuzsd+Q3ZoTKvvcfOeLiFPfKa7LKVyiR9QEAQFFyWBnn\nAa8xISEheuGFF3T99deXdCklZvHGg1flFOe9HepfhWr+Glftav892mEvtMNeaIe9uGo7OMUJAACA\nIuHypzidybgAHwAAwNXQgwYAAGAzBDQAAACbIaABAADYDAENAADAZghoAAAANkNAAwAAsBkCGgAA\ngM0Q0AAAAGyGgAYAAGAzBDQAAACbIaABAADYDAENAADAZghoAAAANkNAAwAAsBkCGgAAgM14lHQB\nKDqPdAtWZGRsSZcBAAD+IHrQAAAAbIaABgAAYDMENAAAAJshoAEAANgMAQ0AAMBmCGgAAAA2Q0AD\nAACwGQIaAACAzRDQAAAAbIaABgAAYDMENAAAAJshoAEAANgMAQ0AAMBmHJZlWSVdBAAAALLQgwYA\nAGAzBDQAAACbIaABAADYDAENAADAZghoAAAANkNAAwAAsBmPki4AReOVV17Rnj175HA4NG7cODVv\n3rykS/pDfv31Vw0dOlSDBg1S//79dfr0aY0aNUqpqakKCgrSa6+9Ji8vr5Ius0DTp0/Xzp07lZKS\nor///e9q1qyZS7Xj8uXLGjNmjKKiopSYmKihQ4cqODjYpdqQ3ZUrV9SrVy8NHTpUt912m0u2Iyws\nTMOHD1fDhg0lSddff72eeOIJl2zLqlWrNHfuXHl4eOjZZ59Vo0aNXK4dn376qVatWpX5fN++fVqy\nZIkmTpwoSWrUqJEmTZpUQtUVXnx8vEaPHq3o6GglJyfr6aefVlBQkMu145pi4ZoTFhZmDRkyxLIs\nywoPD7f+9re/lXBFf0x8fLzVv39/a8KECVZoaKhlWZY1ZswYa926dZZlWdbrr79uffTRRyVZYqFs\n3brVeuKJJyzLsqwLFy5Yd9xxh8u1Y+3atdZ7771nWZZlRUREWF27dnW5NmQ3c+ZMq2/fvtZnn33m\nsu3Ytm2b9cwzz+QY5optuXDhgtW1a1crNjbWOnv2rDVhwgSXbEd2YWFh1sSJE63+/ftbe/bssSzL\nsp577jlry5YtJVxZwUJDQ60ZM2ZYlmVZZ86csbp16+aS7biWcIrzGrR161Z16dJFktSgQQNFR0cr\nLi6uhKsqPC8vL73//vuqXLly5rCwsDDdeeedkqROnTpp69atJVVeobVp00azZs2SJAUEBOjy5csu\n144ePXpo8ODBkqTTp0+rSpUqLteGDIcPH1Z4eLg6duwoyTX3KWdcsS1bt27VbbfdJj8/P1WuXFmT\nJ092yXZkN2fOHA0ePFgnT57MPGvhKu0oX768Ll26JEmKiYlRYGCgS7bjWkJAuwadP39e5cuXz3xe\noUIFRUZGlmBFf4yHh4fKlCmTY9jly5czT3VUrFjRJdrj7u4uHx8fSdKyZct0++23u2Q7JOmhhx7S\nyJEjNW7cOJdtw7Rp0zRmzJjM567aDkkKDw/Xk08+qYcffljff/+9S7YlIiJCV65c0ZNPPqlHHnlE\nW7dudcl2ZPjpp59UrVo1ubu7KyAgIHO4q7SjZ8+eOnXqlO666y71799fo0aNcsl2XEu4Bq0UsK6x\n/+blau3ZvHmzli1bpvnz56tr166Zw12pHR9//LEOHDig559/PkfdrtKGFStWqGXLlqpVq1aer7tK\nOySpbt26GjZsmO6++26dOHFCAwYMUGpqaubrrtSWS5cu6a233tKpU6c0YMAAl9y3Mixbtkz33Xdf\nruGu0o6VK1eqevXqmjdvng4ePKinn35a/v7+ma+7SjuuJQS0a1DlypV1/vz5zOfnzp1TUFBQCVb0\n1/n4+OjKlSsqU6aMzp49m+P0p519++23eueddzR37lz5+/u7XDv27dunihUrqlq1amrcuLFSU1Pl\n6+vrUm2QpC1btujEiRPasmWLzpw5Iy8vL5fbFhmqVKmiHj16SJJq166tSpUqae/evS7XlooVK6pV\nq1by8PBQ7dq15evrK3d3d5drR4awsDBNmDBBDocj81ShJJdpx65du9S+fXtJUnBwsBITE5WSkpL5\nuqu041rCKc5rULt27bRx40ZJ0v79+1W5cmX5+fmVcFV/Tdu2bTPbtGnTJnXo0KGEKypYbGyspk+f\nrnfffVeBgYGSXK8dO3bs0Pz58yWZU+cJCQku1wZJeuONN/TZZ5/pk08+0QMPPKChQ4e6ZDskc+fj\nvHnzJEmRkZGKiopS3759Xa4t7du317Zt25SWlqaLFy+67L4lmfDi6+srLy8veXp6qn79+tqxY4ck\n12lHnTp1tGfPHknSyZMn5evrqwYNGrhcO64lDot+y2vSjBkztGPHDjkcDr300ksKDg4u6ZIKbd++\nfZo2bZpOnjwpDw8PValSRTNmzNCYMWOUmJio6tWr69VXX5Wnp2dJl5qvpUuX6s0331S9evUyh/3r\nX//ShAkTXKYdV65c0fjx43X69GlduXJFw4YNU9OmTTV69GiXacPvvfnmm6pRo4bat2/vku2Ii4vT\nyJEjFRMTo+TkZA0bNkyNGzd2ybZ8/PHHWrZsmSTpqaeeUrNmzVyyHfv27dMbb7yhuXPnSjLXCL74\n4otKS0tTixYtNHbs2BKusGDx8fEaN26coqKilJKSouHDhysoKMjl2nEtIaABAADYDKc4AQAAbIaA\nBgAAYDMENAAAAJshoAEAANgMAQ0AAMBm+EO1AJCHiIgIde/eXa1atZIkJSQk6LbbbtOIESPkcDhK\nuDoA1zoCGgA4UaFCBYWGhkqSUlJS1KNHD/Xs2VONGzcu4coAXOs4xQkAhRAdHa2UlBRVrFhRnTt3\n1syZMzVw4ED17t1b27ZtkySdOnVKf//73zVgwAD169dPP/zwgyRpzJgxatWqlRISEiSZ//ARHBys\n5cuXS5KmTJmivn37KiQkRG3bts0cDqD0ogcNAJy4cOGCQkJClJaWpvDwcA0aNCjz/xEGBgbqww8/\n1NatWzVt2jR9/vnnmjhxoh577DHdeuutioyM1IMPPqhNmzZJkpo2bapvvvlG3bt314YNG9S0aVNJ\n0sGDB7VlyxZt2rRJbm5uGjlyZIm1F4B9ENAAwInspziTkpI0btw4LVq0SJIy/7H0jTfeqPDwcEnm\nH2bHx8drzpw5kiQPDw9FRUVJkrp3765Nmzape/fu+uGHH9SuXTtJkre3t1JSUpScnCxvb+9ibR8A\n+yKgAUAheHl5qXv37pn/OzItLU2SZFlW5k0DXl5eevPNN1WhQoVc01933XVasWKFfvrpJ9WvXz/z\nf0zWq1dP/fr1U/v27RUcHKzDhw+rbdu2xdQqAHbFNWgAUEg7duxQw4YNJSnzurOdO3eqUaNGkqTW\nrVtr/fr1kszp0alTp+aYvl27dpozZ466deuWY3hKSooeeeQRhYaGEs4ASKIHDQCcyrgGTZKSk5NV\ns2ZNvfzyy1q7dq3Onj2rIUOG6MyZM3rppZckSePHj9eLL76otWvXKikpSU899VSO+XXv3l2hoaGa\nNWuWDhw4IElas2aN9u/fr3fffbd4GwfA1hyWZVklXQQAuJLOnTtrwYIFqlOnTkmXAuAaxSlOAAAA\nm6EHDQAAwGboQQMAALAZAhoAAIDNENAAAABshoAGAABgMwQ0AAAAmyGgAQAA2Mz/A0mU6glGANr7\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x396 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}