TUBES_SKO_Agnesia_Indryany_Mangopo_1101184101_TT42_02.ipynb

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  "metadata": {
    "colab": {
      "name": "TUBES SKO_Agnesia Indryany Mangopo_1101184101_TT42-02.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyN7uG/5wY/vK++Osc2/0FtE",
      "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/indrianiim/Optik/blob/main/TUBES_SKO_Agnesia_Indryany_Mangopo_1101184101_TT42_02.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QlTxAkWcIhnD"
      },
      "source": [
        "Agnesia Indryany Mangopo\r\n",
        "\r\n",
        "1101184101\r\n",
        "\r\n",
        "TT-42-02"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7L3CSy2th6s"
      },
      "source": [
        "import math as m\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import numpy as np"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ml6XK17WQdfo"
      },
      "source": [
        "#Converter\r\n",
        "NtL = lambda x : 10 * np.log10(x)\r\n",
        "LtN = lambda x : 10**(x/10)\r\n",
        "\r\n",
        "#Parameter SNR\r\n",
        "R = 0.8\r\n",
        "SM = 6  \r\n",
        "M = -0.23\r\n",
        "q = 1.6*10**-19\r\n",
        "id = 90*10**-9 \r\n",
        "B = 9*10**9 \r\n",
        "F = 1 \r\n",
        "K = 1.38*10**-23\r\n",
        "T = 290 \r\n",
        "RL = 50\r\n",
        "\r\n",
        "#Parameter Pr\r\n",
        "#Jarak Sentral ke ONU = 20 km\r\n",
        "#Jarak ONU ke Roset = <500 m --> 5*19 = 95 m \r\n",
        "#Jarak Total = 20000 + 95 = 20095 m\r\n",
        "\r\n",
        "d = np.arange(0.95,20.096,1) #Jarak (km)\r\n",
        "L_Fiber = 0.28 #Berdasarkan jenis G652D (dB)  \r\n",
        "N_Splicer = 4 #Jumlah Splicer\r\n",
        "L_Splicer = 0.2 #Loss dari splicer (db)\r\n",
        "N_Connector = 2 #Jumlah Konektor\r\n",
        "L_Connector = 0.3 #Loss dari konektor (dB)\r\n",
        "L_Splitter = 16 + 17.5 \r\n",
        "Pt = 4 #Daya tx (dBm)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myuoHZqWQxuV",
        "outputId": "780a2ab3-411d-46db-980e-ff3965a31c5c"
      },
      "source": [
        "#Rumus Pr\r\n",
        "def P_DayaTerima():\r\n",
        "  hambatan_fiber = d * L_Fiber\r\n",
        "  hambatan_splicer = N_Splicer * L_Splicer\r\n",
        "  hambatan_connector = N_Connector * L_Connector\r\n",
        "\r\n",
        "  alfa_total = hambatan_fiber + hambatan_splicer + hambatan_connector + L_Splitter + SM\r\n",
        "  Pr = Pt - alfa_total\r\n",
        "  return Pr\r\n",
        "\r\n",
        "Pr = P_DayaTerima()\r\n",
        "print('Daya Terima = ', Pr)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Daya Terima =  [-37.166 -37.446 -37.726 -38.006 -38.286 -38.566 -38.846 -39.126 -39.406\n",
            " -39.686 -39.966 -40.246 -40.526 -40.806 -41.086 -41.366 -41.646 -41.926\n",
            " -42.206 -42.486]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I-e9AqTuWKZw",
        "outputId": "2197c440-1362-4505-c9f5-1643c4d00d8e"
      },
      "source": [
        "#Arus Primer\r\n",
        "def Arus_Primer():\r\n",
        "  Pr_num = LtN(Pr)\r\n",
        "  ip = Pr_num * R\r\n",
        "  return ip\r\n",
        "ip = Arus_Primer()\r\n",
        "print(\"Arus Primer = \", ip)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Arus Primer =  [1.53634937e-04 1.44042280e-04 1.35048569e-04 1.26616407e-04\n",
            " 1.18710733e-04 1.11298673e-04 1.04349407e-04 9.78340398e-05\n",
            " 9.17254787e-05 8.59983239e-05 8.06287612e-05 7.55944631e-05\n",
            " 7.08744966e-05 6.64492352e-05 6.23002783e-05 5.84103740e-05\n",
            " 5.47633475e-05 5.13440340e-05 4.81382155e-05 4.51325620e-05]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvCupxy4WLWo",
        "outputId": "a8b7e6e4-52f3-491a-b667-15d2ef83bcbf"
      },
      "source": [
        "#Signal Power\r\n",
        "def Signal_Power():\r\n",
        "  Is_kuadrat = (ip*M)**2\r\n",
        "  return Is_kuadrat\r\n",
        "Is_kuadrat = Signal_Power()\r\n",
        "print('Signal Power = ', Is_kuadrat)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Signal Power =  [1.24863541e-09 1.09757864e-09 9.64796337e-10 8.48077705e-10\n",
            " 7.45479398e-10 6.55293177e-10 5.76017458e-10 5.06332315e-10\n",
            " 4.45077506e-10 3.91233150e-10 3.43902748e-10 3.02298259e-10\n",
            " 2.65726976e-10 2.33579996e-10 2.05322076e-10 1.80482728e-10\n",
            " 1.58648382e-10 1.39455500e-10 1.22584524e-10 1.07754557e-10]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tzlg6vhyWQWQ",
        "outputId": "5bb239ad-2f9d-4627-c97b-0c2db9589638"
      },
      "source": [
        "#Rumus SNR\r\n",
        "def SNR():\r\n",
        "  BDC = 2*q*id*B*(M**2)*F #Dark Current (Numerik)\r\n",
        "  SN = 2*q*ip*B*(M**2)*F #Shot Noise (Numerik)\r\n",
        "  NT = (4*K*T*B)/RL #Noise Thermal (Numerik)\r\n",
        "  Signal = Is_kuadrat # (Numerik)\r\n",
        "  Noise = BDC + SN + NT # (Numerik)\r\n",
        "\r\n",
        "  SNR = Signal / Noise\r\n",
        "  SNR_dBm = NtL(SNR)\r\n",
        "  return SNR_dBm\r\n",
        "\r\n",
        "SNR = SNR()\r\n",
        "print(SNR)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[26.33310395 25.77528947 25.21733953 24.65926247 24.10106612 23.54275784\n",
            " 22.98434452 22.42583266 21.86722835 21.30853731 20.74976489 20.19091614\n",
            " 19.63199579 19.07300826 18.51395774 17.95484812 17.39568308 16.83646604\n",
            " 16.27720025 15.71788873]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M7CUwqCHWUD5",
        "outputId": "d2904810-fbb1-403c-9d3a-05da435fa65b"
      },
      "source": [
        "#Rumus Q\r\n",
        "def Q_rumus():\r\n",
        "  Q_rumus = 0.5*(10**(SNR/20))\r\n",
        "  return Q_rumus\r\n",
        "Q = Q_rumus()\r\n",
        "print(Q)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[10.3663341   9.7215268   9.11668566  8.54935061  8.01721094  7.5180965\n",
            "  7.04996937  6.61091598  6.19913968  5.81295378  5.45077492  5.11111687\n",
            "  4.79258466  4.4938691   4.21374152  3.95104895  3.70470949  3.47370798\n",
            "  3.25709193  3.05396771]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BGqOOkJ_WX03",
        "outputId": "b9ac5491-81d4-4e5d-d8e0-347c8452848d"
      },
      "source": [
        "#Rumus BER\r\n",
        "pengkali_list = []\r\n",
        "def BER_rumus():\r\n",
        "  akar = 1/(Q * m.sqrt(2 * m.pi))\r\n",
        "  for i in range(len(Q)):\r\n",
        "    pengkali = m.exp(-(Q[i]**2)/2)\r\n",
        "    pengkali_list.append(pengkali)\r\n",
        "  BER = (1/akar)*(pengkali_list)\r\n",
        "  return BER\r\n",
        "BER = BER_rumus()\r\n",
        "print(BER)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1.20193496e-22 7.32242394e-20 2.04625786e-17 2.88023626e-15\n",
            " 2.21732100e-13 1.00380724e-11 2.84846042e-10 5.35923666e-09\n",
            " 7.02421465e-08 6.69865800e-07 4.82945331e-06 2.72242800e-05\n",
            " 1.23604268e-04 4.63929874e-04 1.47290405e-03 4.03610857e-03\n",
            " 9.71685928e-03 2.08758471e-02 4.05771312e-02 7.22242644e-02]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XPfg653gqXak",
        "outputId": "8e1fab08-c301-4e72-be2f-f1fe203a1f07"
      },
      "source": [
        "#GRAFIK\r\n",
        "def Grafik_SNR():\r\n",
        "  plt.figure()\r\n",
        "  plt.plot(d,SNR,'b')\r\n",
        "  plt.title('Perbandingan Jarak dengan SNR')\r\n",
        "  plt.xlabel('Jarak (Km)')\r\n",
        "  plt.ylabel('SNR (dB)')\r\n",
        "  plt.show()\r\n",
        "\r\n",
        "def Grafik_Q():\r\n",
        "  plt.figure()\r\n",
        "  plt.plot(d,Q,'r')\r\n",
        "  plt.title('Perbandingan Jarak dengan Q')\r\n",
        "  plt.xlabel('Jarak (Km)')\r\n",
        "  plt.ylabel('Q')\r\n",
        "  plt.show()\r\n",
        "\r\n",
        "def Grafik_BER():\r\n",
        "  plt.figure()\r\n",
        "  plt.plot(d,BER,'g')\r\n",
        "  plt.title('Perbandingan Jarak dengan BER')\r\n",
        "  plt.xlabel('Jarak (Km)')\r\n",
        "  plt.ylabel('BER')\r\n",
        "  plt.show()\r\n",
        "\r\n",
        "def Grafik_Perbandingan_BER_SNR():\r\n",
        "    plt.figure()\r\n",
        "    plt.plot(SNR,BER,'--')\r\n",
        "    plt.title('Perbandingan BER dan SNR')\r\n",
        "    plt.xlabel('SNR')\r\n",
        "    plt.ylabel('BER')\r\n",
        "    plt.show()\r\n",
        "\r\n",
        "Grafik_SNR()\r\n",
        "Grafik_Q()\r\n",
        "Grafik_BER()\r\n",
        "Grafik_Perbandingan_BER_SNR()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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BPfeEOXNiRyQitYwSf23UpUsY6OUeSv4vvRQ7IhGpRZT4a6u2beGVV2CzzcJSjk88ETsiEakllPhrs1at4OWXw0Cvo46Cu+6KHZGI1AJK/LXdJpuEBV0OOQTOOAMuuUQDvUSkUkr8haBRo1DVc9JJ8Oc/h9+rV8eOSkTyVJQBXGb2f8BJgANvAr939//EiKVglJTAkCFhoNdVV8GyZaHrZ+PGsSMTkTyT8xK/mW0BnA2UufvOQF2gT67jKEhmcOWVcPfdYTH3rl1hxYrYUYlInolV1VMCNDSzEqAR8HGkOArTqafC44/DrFnwm9/AvHmxIxKRPJLzxO/uHwE3AB8CS4GV7v5s+n5mNtDMppvZ9OXLl+c6zNrv8MNh0iT44os1i7qLiBCnqqcZcBjwK2BzoLGZHZe+n7sPcfcydy8rLS3NdZiFoXNnmDZtTV9/zesvIsSp6jkAeN/dl7v7D8ATwG8ixFEc2rSBf/0rLOJ+8skwaJCmdhYpcjES/4fAHmbWyMwM6ArMjRBH8WjSBMaNgzPPhBtvhN694auvYkclIpHEqOOfBjwOzCR05awDDMl1HEWnpARuvx3uuCP0+NlrL/jww9hRiUgEUXr1uPvl7r69u+/s7r9z9+9ixFGUzjgjlP4XLYJOnUIbgIgUFY3cLUbduoV6/0aNwkyfjz4aOyIRySEl/mK1446htF9WBn36hNG+muNHpCgo8Rez0tIwwdvxx8Pll0O/fvAfzZwhUuiU+Itd/fowfHhYyP2RR2C//cI8PyJSsJT4JczxM3gwjB4Ns2eHRt8334wdlYhkiRK/rHHEEWFJx9Wrwxw/48bFjkhEskCJX/5Xx47w6quw3XbQqxfcdJMafUUKjBK//NwWW4QF3Hv3hvPOg+OOg2+/jR2ViNQQJX7JrHFjeOwxuPrq0Oi7556wcGHsqESkBijxS8Xq1IE//hHGj4fFi0M10IQJsaMSkfWkxC9V694dpk+HVq2gZ8/wLeCnn2JHJSLrSIlfqqdNG3jlFejbFy69NPQAWrkydlQisg6U+KX6GjWCBx+EW2+Fp58O/f3ffjt2VCKylpT4Ze2Ywdlnw+TJ8OWXIfk//njsqERkLSjxy7rZZx+YORN22QWOPhouvDAM/BKRvKfEL+tuiy1gyhQ49VT461/h4INhxYrYUYlIFZT4Zf3Urw933w333Reme+jYEWbMiB2ViFRCiV9qxoAB8PLLYXqHzp3hgQdiRyQiFch54jezX5vZrJSfVWZ2bq7jkCwoKwul/c6d4YQTwjKP338fOyoRSRNjsfV33b29u7cHOgLfAk/mOg7JktJSeOYZOP98uOsu2HdfLeoukmdiV/V0Bd5z9w8ixyE1qaQkNPaOGgVvvQXt28OYMbGjEpFE7MTfB3gk0wYzG2hm081s+vLly3McltSIo48OXT7btAkzfZ57Lnz3XeyoRIpetMRvZhsAvYDHMm139yHuXubuZaWlpbkNTmrONtvAP/8Zkv6tt4YFXhYsiB2VSFGLWeI/GJjp7lrgtdDVrw833wz/+Ae8/z506AAjR8aOSqRoxUz8famgmkcK1GGHwaxZYbRv374wcKAWeBGJIEriN7PGwIHAEzGOLxG1bBlG+150EQwdCrvvroneRHIsSuJ392/cfRN317y+xahePbjmGpg4EZYtg912g2HDtLavSI5UK/GbWSMza5v81M92UFIkunWD2bNDqX/AADj+ePjqq9hRiRS8ShO/mdUzs1uAJcAwYDiw0MwGJ9vbZz1CKWwtWsBzz8GVV8LDD4fRv7NmxY5KpKBVVeK/EdgQaOXuHd29A7AD0MbM7kYjbqUm1K0Ll10W5vj/+mvYY48w6ldVPyJZUVXi7wGc7O7//f7t7quA0wiDr/pmMTYpNvvuG0r7++8f5vk5+uiw2IuI1KiqEv9P7j8vdrn7j8Byd5+anbCkaJWWhmUdr78+TPPQvj289FLsqEQKSlWJ/20zOz79TjM7DpibnZCk6NWpA4MGhWmeS0qgSxe44AJN9yBSQ0qq2H4G8ISZDQDKV9coAxoCh2czMBF23z1U/QwaFL4BPPNMWOx9l11iRyZSq1Va4nf3j9x9d+AqYFHyc5W7d3L3j7IfnhS9DTeEe+4J1T/LloVePzfcAD/+GDsykVqrWv343X2yu9+e/EzKdlAiP9OzJ7z5Zvh9/vnQtSt8oNm8RdZF7GmZRaqvtBRGjw6jfGfOhLZtYcQIdfsUWUtK/FK7mIVlHWfPhnbtoH//0O1zxYrYkYnUGkr8Ujv96lfwwgtw3XUwdmxo8J0wIXZUIrWCEr/UXnXrhm6er70GzZtDjx5w+unwzTexIxPJa0r8Uvu1axeS/6BBoQfQrrvCtGmxoxLJW0r8UhgaNAh9/SdPDgO9OneGK66AH36IHZlI3lHil8LSpQu88Qb06xdm/PzNb0I3UBH5LyV+KTxNmsADD8Djj4e+/h07htL/99/HjkwkLyjxS+E68siwrOOxx4bSf8eOoS1ApMjFWnO3qZk9bmbvmNlcM9szRhxSBJo3h7//PUz58MUXYa7/QYO0yLsUtVgl/luBie6+PdAOzfQp2dazJ7z1Fpx8Mtx4Y+gJ9OKLsaMSiSLnid/MmgD7APcBuPv37q7VNiT7mjQJ3T0nTw7TPHTpAqedBqtWxY5MJKdilPh/BSwHhpnZ62Z2r5k1Tt/JzAaa2XQzm758+fLcRymFa7/9Qs+fP/wBhgyBnXaC8eNjRyWSMzESfwnQAbjb3XcFvgEGp+/k7kPcvczdy0pLS3MdoxS6Ro1Clc8rr4RvAj17wu9+B599FjsykayLkfiXAEvcvXxo5eOEC4FI7u2+O8yYERZ7HzkSdtgBRo3SjJ9S0HKe+N39E2Cxmf06uasr8Hau4xD5r/r1Q3fPGTOgZcvQ/fOII2Dp0tiRiWRFrF49ZwEPmdkbQHvgmkhxiKzRti1MnRpm/Jw4EXbcEe6/X6V/KThREr+7z0rq79u6e293/yJGHCI/U1ISZvycPTtM9XziibDvvjBnTuzIRGqMRu6KZLLddjBlCtx7b+j/v+uu4YLw9dexIxNZb0r8IhWpUyeU+N99N6z0df31ofrnySdV/SO1mhK/SFWaNw8l/5dfhqZNQ8PvIYfAwoWxIxNZJ0r8ItXVuXNY5P2mm+Cll8LAr6uvDvP/i9QiSvwia6OkBP7v/2DuXDj0ULj00tAbaNKk2JGJVJsSv8i62HLLMNBrwgT48Uc44AD47W/V919qBSV+kfXRvXvo6nn55TB6NGy/Pdx+O6xeHTsykQop8YusrwYNwgpfc+aE+f7PPhs6dYJXX40dmUhGSvwiNWXbbcOI31GjYNmycBE45RTQ7LKSZ5T4RWqSGRx9dGj8PfdcuO++cEG4+Wat+St5Q4lfJBt+8YvQ7fPNN2HPPcPc/7vsEpaA1OAviUyJXySbdtgh9PwZNy58Gzj00NAg/NZbsSOTIqbEL5ILPXqE0v8tt4RG33bt4MwztfCLRKHEL5Ir9erBOefA/Pmh0ffuu2GbbeDWW+GHH2JHJ0VEiV8k15o3hzvvDFM/77ZbaARu2zZUCYnkgBK/SCw77wzPPANjx4bRvz16hJ+5c2NHJgVOiV8kpvIG3zlz1iz+vssuoUro889jRycFSolfJB9ssEHo8jl/Ppx0EtxxR+j/f/vt6v8vNS5K4jezRWb2ppnNMrPpMWIQyUulpXDPPfD669C+fZj+Yccd4dFH4aefYkcnBSJmiX8/d2/v7mURYxDJT23bwvPPh/7/jRpBnz6hIfj552NHJgVAVT0i+cosNPa+/jqMGAErVsCBB4afGTNiRye1WKzE78CzZjbDzAZm2sHMBprZdDObvlyTXEkxq1sXfve7sPbvTTeFC0FZGfTtC++9Fzs6qYViJf693L0DcDBwhpntk76Duw9x9zJ3LystLc19hCL5pkGDsPrXe+/BJZeEbqDbbx9GAC9bFjs6qUWiJH53/yj5/SnwJNApRhwitVKTJvCnP8GCBaEH0D33wNZbh8VgVq2KHZ3UAjlP/GbW2Mw2Kr8NHATMyXUcIrVeixZh2oe33w5tAVddFS4At92mBeClUjFK/JsCL5vZbOBVYJy7T4wQh0hh2G67sPjLtGlrBn/tsAM8/LC6gEpGOU/87r7Q3dslPzu5+59zHYNIQerUCSZNCquA/eIX0K8fdOgAY8ZoDQD5H+rOKVJIzKBbN5g5Ex58EL7+Gnr3Dr2AnnpKFwABlPhFClOdOqHE/847MGwYfPkl9OoVvhWMG6cLQJFT4hcpZCUlcMIJ4QJw331hENghh4SF4CdM0AWgSCnxixSDevVgwACYNw+GDg39/nv0COsBP/OMLgBFRolfpJjUqxf6/s+bB3/7G3z8cVgDeK+94LnndAEoEkr8IsVogw1g4MAwDfRdd8GHH8JBB8E++4SeQboAFDQlfpFiVr8+nHZaGAV8xx2wcCEccAB06QJTpsSOTrJEiV9EwgXgjDPCPEC33Ra+Cey3X7gAPPusvgEUGCV+EVmjQQM466xwAbjllnAB6NYtrAUwerRGAhcIJX4R+bmGDcPUDwsXwpAhsHIlHHVUWA1s2DAtB1nLKfGLSMXq14eTTw7jAB59NFwQBgwIk8Hdeit8803sCGUdKPGLSNXq1oVjjglTQUyYAG3awLnnQqtWYYroL76IHaGsBSV+Eak+s9Dv/8UX4eWXwwCwyy6Dli3h/PNh6dLYEUo1KPGLyLrp3DlM/DZ7Nhx6aFgWsnVrOOUULQmZ55T4RWT9tG0b5v6fNw9+/3sYPjysEdC3b7goSN5R4heRmrH11mEZyEWL4Lzz4OmnoX17OPBAGD9eXUHziBK/iNSsFi3gr38N00D85S9haciePWGnncL8QN9+GzvCoqfELyLZ0awZDB4M778fFoVp1AhOPTU0BF9yiRqCI4qW+M2srpm9bmZPx4pBRHJggw3CojDTp4feQHvvDddcE7qC9u8Ps2bFjrDoxCzxnwPMjXh8EcklszD755NPhobgU08N00Dsuivsv39oE1A7QE5ESfxmtiXQE7g3xvFFJLJttgmTwS1eHNoD5s8PXUJ32AHuvlsjgrMsVon/FuACoMLLu5kNNLPpZjZ9+fLluYtMRHKnWbMw8GvhQnjkEWjSBE4/HbbaCi6+GD76KHaEBSnnid/MDgE+dfcZle3n7kPcvczdy0pLS3MUnYhEUa8e9OkD06aFEcH77QfXXRcGhB17bGgb0NTQNSZGib8z0MvMFgEjgf3N7MEIcYhIvjELI4JHjw7VP2efHZaE7NIFdtklrBa2alXsKGu9nCd+d7/I3bd099ZAH2Cyux+X6zhEJM+1aQM33ghLlsD994e1As44A7bYIvyeMyd2hLWW+vGLSH5r1ChMBTF9eqgKOvJIuO++8A1g331h1Cj44YfYUdYqURO/u09x90NixiAitUinTmEuoCVLQm+gxYtDG0DLlnD55WoMriaV+EWk9mnePPQGmj8fxo2Djh3DugCtWoWVwiZPVmNwJZT4RaT2qlsXevQIg78WLAiTw02ZAl27hmUib7tNi8RkoMQvIoWhTZvQBXTJEnjggTAm4JxzwqRx/frBCy9oZHBCiV9ECkuDBnD88TB1Krz+elgzePz4MC3EdtuFGUM//jh2lFEp8YtI4WrfHm6/PST6Bx9cMyK4ZUvo1QvGjoXVq2NHmXNK/CJS+Bo2XFPdM38+XHABvPYaHHZYuAhcfHFoIygSSvwiUly22SZMC714MYwZA2VloWvottuGqSIeegj+/e/YUWaVEr+IFKeSkjXVPR9+uOZicNxxsPnmcOaZBbtWgBK/iMjmm8NFF4V1AiZPDl1E7703rBXQrh3ccENBNQgr8YuIlKtTZ011z8cfw513hikjzj8/NAx36xa21fL1ApT4RUQy2XjjsDbAv/4F774bGoDnzQtVQZttBiecEL4d1MKxAUr8IiJV2W67MCXEe++FtQH69AlLSHbtGqaJuOgiePvt2FFWmxK/iEh11akT1g0eOhQ++QRGjgxtANdfDzvtFHoI3XYbfPpp7EgrpcQvIrIuGjYMM4M+/XSYFfTmm8PEcOecE9YMOPTQMGX0t9/GjvRnlPhFRNbXppvCuefCjBlhgciSo28AAAl1SURBVJjzzgvTRRx7LPzyl2Hw2FNPwXffxY4UUOIXEalZO+0E114LH3wAkyaFpD9xYhgzsOmmMGAAPPts1KkilPhFRLKhbt0wMdzf/hbaA8aPh969w3rC3bqFWUNPOy00Fv/4Y05DU+IXEcm2evXg4IPD6mHLlq3pETRiRFhIvmXLUFU0dWpOFpDJeeI3swZm9qqZzTazt8zsylzHICISTYMGoeQ/cmTo/TNyZFhS8p57YM89w7oCgweHNoIsXQRilPi/A/Z393ZAe6C7me0RIQ4RkbgaNw4NwE8+Gb4JPPAA7LAD3HgjdOgA228Pb71V44fNeeL34Ovkz3rJjxbHFJHi1qRJWEBm/PjQJjBkSCj9t25d44cyj7AgsZnVBWYA2wB3uvuFGfYZCAwEaNmyZccPPvggt0GKiNRyZjbD3cvS74/SuOvuP7p7e2BLoJOZ7ZxhnyHuXubuZaWlpbkPUkSkQEXt1ePuXwIvAN1jxiEiUkxi9OopNbOmye2GwIHAO7mOQ0SkWJVEOGYL4IGknr8OMMrdn44Qh4hIUcp54nf3N4Bdc31cEREJNHJXRKTIKPGLiBQZJX4RkSITZQDX2jKzr4B3Y8dRiebAithBVELxrR/Ft34U3/pZn/haufvPBkLF6NWzLt7NNPosX5jZdMW37hTf+lF866cY41NVj4hIkVHiFxEpMrUl8Q+JHUAVFN/6UXzrR/Gtn6KLr1Y07oqISM2pLSV+ERGpIUr8IiJFJq8Sv5l1N7N3zWyBmQ3OsL2+mT2abJ9mZq1zGNtWZvaCmb2drBV8ToZ9upjZSjOblfxclqv4kuMvMrM3k2NPz7DdzOy25Py9YWYdchjbr1POyywzW2Vm56btk9PzZ2b3m9mnZjYn5b6Nzew5M5uf/G5WwWP7J/vMN7P+OYzvejN7J3n/niyf6TbDYyv9LGQxvivM7KOU97BHBY+t9H89i/E9mhLbIjObVcFjc3H+MuaUnHwG3T0vfoC6wHtAG2ADYDawY9o+pwP3JLf7AI/mML4WQIfk9kbAvAzxdQGejngOFwHNK9neA5gAGLAHMC3ie/0JYXBJtPMH7AN0AOak3PdXYHByezBwXYbHbQwsTH43S243y1F8BwElye3rMsVXnc9CFuO7AhhUjfe/0v/1bMWXtv1G4LKI5y9jTsnFZzCfSvydgAXuvtDdvwdGAoel7XMY8EBy+3Ggq5lZLoJz96XuPjO5/RUwF9giF8euQYcBIzyYCjQ1sxYR4ugKvOfuUdfTdPeXgM/T7k79jD0A9M7w0G7Ac+7+ubt/ATxHFhYTyhSfuz/r7quTP6cSVrGLooLzVx3V+V9fb5XFl+SNY4BHavq41VVJTsn6ZzCfEv8WwOKUv5fw88T6332SD/9KYJOcRJciqWLaFZiWYfOeZjbbzCaY2U45DSwsWv+smc2wsGZxuuqc41zoQ8X/cDHPH8Cm7r40uf0JsGmGffLlPA4gfIPLpKrPQjadmVRF3V9BNUU+nL+9gWXuPr+C7Tk9f2k5JeufwXxK/LWCmW0IjAbOdfdVaZtnEqov2gG3A//IcXh7uXsH4GDgDDPbJ8fHr5KZbQD0Ah7LsDn2+fsfHr5T52V/ZzP7I7AaeKiCXWJ9Fu4GtgbaA0sJ1Sn5qC+Vl/Zzdv4qyynZ+gzmU+L/CNgq5e8tk/sy7mNmJUAT4LOcRBeOWY/wBj3k7k+kb3f3Ve7+dXJ7PFDPzJrnKj53/yj5/SnwJOErdarqnONsOxiY6e7L0jfEPn+JZeXVX8nvTzPsE/U8mtkJwCFAvyQx/Ew1PgtZ4e7L3P1Hd/8JGFrBcWOfvxLgCODRivbJ1fmrIKdk/TOYT4n/NWBbM/tVUirsA4xN22csUN56fRQwuaIPfk1L6gTvA+a6+00V7LNZeZuDmXUinN+cXJjMrLGZbVR+m9AIOCdtt7HA8RbsAaxM+UqZKxWWtGKevxSpn7H+wJgM+zwDHGRmzZKqjIOS+7LOzLoDFwC93P3bCvapzmchW/GlthkdXsFxq/O/nk0HAO+4+5JMG3N1/irJKdn/DGaz1XodWrl7EFq23wP+mNx3FeFDDtCAUEWwAHgVaJPD2PYifOV6A5iV/PQATgVOTfY5E3iL0EthKvCbHMbXJjnu7CSG8vOXGp8Bdybn902gLMfvb2NCIm+Scl+080e4AC0FfiDUkZ5IaDOaBMwHngc2TvYtA+5NeeyA5HO4APh9DuNbQKjbLf8Mlvdy2xwYX9lnIUfx/T35bL1BSGAt0uNL/v7Z/3ou4kvuH17+mUvZN8b5qyinZP0zqCkbRESKTD5V9YiISA4o8YuIFBklfhGRIqPELyJSZJT4RUSKjBK/FDQz+7qGnmdRVYPJkvERk83sF+nHNrMeZjbPzFqtxTGfr2hmRpH1ocQvwn+T9vr+P/QAZnvasHsz6wrcBhzsazcx3d8JM9KK1Cglfil4ZrahmU0ys5nJHOuHJfe3tjAn/AjCyMytzOxuM5uezI9+ZYbnaphMIHdyhkP1I22UZTLHy1DgEHd/L7lveHKcqWa20MI6BPeb2VwzG57y8LGEkc4iNaokdgAiOfAf4HB3X5VU10w1s/IpArYF+nuYphoz+6O7f25mdYFJZtbW3d9I9t2QMIXwCHcfkeE4nYFTUv6uT5horou7v5O2bzNgT8KEdWOTx54EvGZm7d19lrt/YWHxoU3cPddTV0gBU4lfioEB15jZG4Qh8FuwZqrbD8qTfuIYM5sJvA7sRFgYo9wYYFgFSR/C0PqvUv7+AXiFMJVBuqc8DJt/kzA98JseJjZ7C2idst+nhOkERGqMEr8Ug35AKdDR3dsDywjzPgF8U76Tmf0KGAR0dfe2wLiU/QD+CXQvn0gug9Vp7QQ/ERb76GRmF6ft+13KPt+lPSb1m3gD4N+VvzyRtaPEL8WgCfCpu/9gZvsBFfWs+QXhQrDSzDYlTCGd6jLgC8JEd5m8S5jg6788zKDZE+hnZplK/hVKLjCbEZYBFKkxSvxSsJJ5178jLFZSZmZvAscD6fXtALj7bEIVzzvAw4QSfrpzgIZm9tcM28YR1g1Of97PCcviXWJmvdbiJXQEpvqapRZFaoRm55SCZWbtgKHunpNFSJK56Ee4+4E19Hy3AmPdfVJNPJ9IOZX4pSCZ2amE+dgvydUxPSxqM7R8AFcNmKOkL9mgEr+ISJFRiV9EpMgo8YuIFBklfhGRIqPELyJSZJT4RUSKzP8HKInzM2YO3dkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OTOY-UCEisC_"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}